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Abstract

abstract

Introduction Last chapter of this series. If you have followed up along these blog posts you are now fully capable of creating projects using the Object Oriented paradigm. I am glad you have followed all the sections and in this part, you are going to end up what you have started. In fact, now you will learn how to use abstract classes and several other less common feature of PHP such as reflection API and the singleton design pattern. Keep reading and you will see that as per a lot of other web developers, myself included, these resources will let you do that breakthrough you are looking for. Follow the series … This blog post is the sixth part of “ The complete guide to Object-Oriented Programming: Go from procedural programming to OOP expert in ” PHP If you did not read the other parts yet You can check the other blog posts following the links below , , , , and Introduction to Object-Oriented Programming Inheritance and Interfaces in PHP More Interfaces and Polymorphism Visibility and Static keyword Constructor and Magic methods Abstract Classes and Extra bits Table of Content Abstract classes Method chaining Autoloading Anonymous classes Reflection API Singleton Abstract Classes Abstract classes are very similar to interfaces but they have different rules that a web developer need to be aware of. The definition is quite straightforward, basically, an abstract class is a class that contains abstract methods, Abstract methods are methods that have been declared but not implemented in the code. This kind of classes cannot be constructed as an object, in fact, they need to be extended. A ‘normal’ class that extends an abstract one must implement all methods that are inside the parent. Something to notice is that a child class must define the abstract methods with the same or less restrictive visibility, This means that if in the abstract class there is a protected method it can be implemented only as a public or protected. Another rule is that the number and type or required arguments need to be the same as the parameters. abstract

    abstract

    public

    {

        echo

    }

}

class

    public

    {

        return

    }

}

$myOffice

// This command will return “Building cannot be constructed”

$myOffice->getWindowsCount();

// This command will return 12 I have defined the Office class that extends the abstract class . Building I have implemented and changed its visibility from protected to public. getWindowsCount() There are no abstract methods within the class thus, I can construct an object from it. Office I have added a constructor inside the abstract class so that you will be aware of the fact the parent’s constructor is called when the object is instantiated. What is the difference between an interface and an abstract class? To sum up, Consider an interface as an empty shell. It has only signatures of the methods, which means the methods do not have a body. An interface does not do anything is just a pattern, a set of methods that you need to replicate. On the other hands, abstract classes are proper classes. Even though they look like interfaces the have some specific characteristics. You can specify their use by what they do by a given method name. Here is an example that highlights the differences between the two: interface

{

     public

     public

}

class

{

    public

    public

    {

        echo

    }

    public

    {

        return

    }

} In here we are saying all the Building should look like this than is up to the class to change the functionality. Office abstract class Building{public $windows;public function getWindowsCount(){return $this->windows;}abstract public function build();} class Office extends Building{public function build(){echo “Office built”;}} In an abstract class instead, we are saying that all the Building must have . getWindowsCount() Then we say that the method can be different, so we obligate the class to provide the implementation according to its needs. build() Office Extra / Bonus We have almost finished here with this introduction to Object Oriented in PHP, programming Lot’s of information has been shared and I hope you understood all of it (if not just write your question below). However, there are a few things that have been just quickly mentioned and I would love to go deeper and give you a more wide comprehension on this topic. Method chaining Method chaining is an OOP technique that permits the programmer to perform several different but connected actions in a sequential way. Let’s break down this sentence,“Perform different actions“ means calling different methods or functions; “ ” can be translated into actions related to the same object or instance of the class; Perform connected actions “ ” simply suggest that the methods are called one after another. In a sequential way Here is an example that will clarify the concept: class

{

    public

    function

    {

        $this->message = "Foundation poured - ";

    }

    function

    {

        $this->message .= "framing, plumbing, electrical and HVAC completed -";

        return

    }

    function

    {

        $this->message .= "interior and exterior completed";

        return

    }

    function

    {

        return

    }

}

$myHome

echo Here you go, All the method of the class have one trait in common all of them return the object itself via the use of the keyword , Building $this If you need to brush up your memory I have explained what the keyword does in the . $this introduction to Object-Oriented Programming The last command is chaining down all the methods of the class necessary in order to build up our sentence. The outcome of the last command will look like this: “Foundation poured — framing, plumbing, electrical and HVAC completed — interior and exterior completed”. This is so easy that you can implement this to your code straight away without any problem, just be careful to return the object. Autoloading Any class must be defined before you need to use it, forget to do so will result in an error thrown. Autoload alongside PSR-4 standards allows you to define them when a class is required. I am sure that I do not need to say that this is an incredibly important feature. In PHP autoloading is done by the that accept a function as a parameter, spl_autoload_register() This is quite out-topic and just to explain how it works in detail I will need to write another article, so I will just give you an understandable example: Given that the class you want to autoload is within the “classes/” path, here is what you would do. This is more or less what happens behind the scene of most of the PHP frameworks out there. function

    include

}

spl_autoload_register(myAutoloader); spl_autoload_register has two more parameters, the second is a boolean and defines if the need to throw exceptions when the autoload_function cannot be registered, spl_autoload_register the third parameter is boolean as well and if true it will prepend the autoloader rather then attaching it. If an error occurred and the function was not able to find the specified class, it will throw a fatal error. Anonymous classes Wouldn’t be great if PHP allowed the web developer to create and instantiate objects on the fly? The release of PHP 7 included this present to the list of its features. $myAnonymousBuilding

{

    public

    {

        echo

    }

} Cool, right? Notice how easy is to implement this class inline and that it can even accept parameters like a normal class. The code in the example will output the string “a new class has been constructed in Main Street”. The main use of an anonymous class is the case you need to extend a named class. If, for instance, you will need to override properties or methods, instead of declaring it you can just create an in-line implementation in the same workflow you are currently having. Reflection API Reflection is a very useful feature that has been introduced with the release of PHP 5.0 and became a standard later on 5.3. This API permits you to examine PHP elements and fetch information about them at runtime. For me, one of the most useful places to use reflection API is during automated testing, in specific during PHP Unit. For example, when testing the values of private property in classes you can entree the properties via the API and then make assertion according to the test. There are different reflection classes, each of them allows you to inspect different types of variables. Here are some classes: ReflectionClass ReflectionObject ReflectionMethod ReflectionFunction ReflectionProperty Below a quick example of how the ReflectionClass is used: $ReflectionObject

print_r($ReflectionObject->getMethods()); This example will output an array of all the methods available within the Building class. The parameter passed to the ReflectionClass can be of two types, either a string with the name of the class or a true instance of the class itself. Each of the reflection classes has few methods that allow you to retrieve different information about the element you are inspecting. Singleton Any self-respecting web developer will encounter design patterns in its way to master the programming art sooner or later. Those patterns are nothing more than established methods that solve problems in a predefined way. 99% of PHP framework use them in order to increase the performances and the quality of the code. If you have never heard the words design pattern here is your shot. I want to introduce you the easiest among them all. We are going to use again our imaginary city, we have had several etc, Buildings, Offices, Schools What about the city hall? the mayor needs to keep on eye on all these classes and instantiation and method and properties. In a city, there is only one mayor and he works in only one building the city hall. There cannot be more that one city hall in a city. Well… How do we add this rule in programming? Singleton class cityHall{private static $instance = null;private function __construct(){...}public static function getInstance(){if (self::$instance == null){self::$instance = new Singleton();}return self::$instance;}} Here is it, this is an implementation of the Singleton design pattern. How does it actually work? As you can see the visibility of the constructor is set to private it blocks the creation of the object. In order to create an instance of the class, you need to invoke the static method . getInstance() It checks if the instance has been already created if not, it creates a new instance with the command self::$instance = new Singleton(); Otherwise, it just returns the object already created. The result is that in this case, you are always going to have only one single cityHall in all your program. Where to use the Singleton design pattern in a real case? Singleton is very easy but it can also become problematic to deal with, think at this design pattern whenever you must have a single instance of a class. and it has to be one in a really long term. An easy implementation could be when creating a database class to connect a DB to the code, in the majority of the cases you are going to have only one database per project. This is the perfect use for it. Conclusion The full version of “The complete guide to Object-Oriented Programming” in an all-in-one edition is available in a Kindle format on Amazon. If you have followed all the part of this article this has been quite a journey. You have realized the Object Oriented Programming or OOP is less difficult than it may seem from an outsider. The reality is that as web developers we love to use jargon. Properties, paramethers, interfaces, abstract classes, dependency injection. Those words can provide a headache very quickly. However, just keeping on going taking a little bit at a time you will find that apply these new knowledge is not only possible but easier than you thought. What’s the next step? If you understood the basic concept of this article you are ready to learn about PHP frameworks, There are plenty of them out there, and choose one of them can be overwhelming. Below there is a really deep comparison that I wrote that compare 24 of them, you will not find anything similar elsewhere. Guide to PHP frameworks. If you like this content and you are hungry for some more join the Facebook’s community in which we share info and news just like this one!

Abstract Classes and OOP extras

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro
Corpora Compilation
Political Stance Classification
Summary and Conclusions
Limitations and Ethics Statement
Acknowledgments and References
A. Newspapers in OSCAR 22.01
B. Topics
C. Distribution of Topics per Newspaper
D. Subjects for the ChatGPT and Bard Article Generation
E. Stance Classification at Article Level
F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com 2. https://mediabiasfactcheck.com 3. https://adfontesmedia.com This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro Corpora Compilation Political Stance Classification Summary and Conclusions Limitations and Ethics Statement Acknowledgments and References A. Newspapers in OSCAR 22.01 B. Topics C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level F. Training Details Abstract and Intro Abstract and Intro Corpora Compilation Corpora Compilation Political Stance Classification Political Stance Classification Summary and Conclusions Summary and Conclusions Limitations and Ethics Statement Limitations and Ethics Statement Acknowledgments and References Acknowledgments and References A. Newspapers in OSCAR 22.01 A. Newspapers in OSCAR 22.01 B. Topics B. Topics C. Distribution of Topics per Newspaper C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level E. Stance Classification at Article Level F. Training Details F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com https://www.allsides.com 2. https://mediabiasfactcheck.com https://mediabiasfactcheck.com 3. https://adfontesmedia.com https://adfontesmedia.com

Multilingual Coarse Political Stance Classification of Media: Abstract and Intro

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: বিমূর্ত এবং ভূমিকা

Mehrsprachige grobe politische Standpunktklassifizierung von Medien: Zusammenfassung und Einführung

Clasificación multilingüe de posturas políticas generales de los medios: resumen e introducción

Classification multilingue grossière des positions politiques des médias : résumé et introduction

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: सार और परिचय

多言語メディアの粗い政治的立場の分類：概要と序論

미디어의 다국어 거친 정치적 입장 분류: 개요 및 소개

Classificação Multilíngue da Posição Política Grosseira da Mídia: Resumo e Introdução

Многоязычная грубая политическая классификация СМИ: Аннотация и введение

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Özet ve Giriş

Phân loại lập trường chính trị thô sơ đa ngôn ngữ của truyền thông: Tóm tắt và giới thiệu

多语言媒体粗略政治立场分类：摘要和简介

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;
(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro
Related Work
Approach
Experiments
Conclusion
Limitations, Ethics Statement and References
A. Experimental Details
B. Generation Results Abstract Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report). 1. Introduction Framing bias has become a pervasive problem in modern media, misleading the understanding of what really happened via a skewed selection of information and language (Entman, 2007, 2010; Gentzkow and Shapiro, 2006). The most notable impact of framing bias is the amplified polarity between conflicting political parties and media outlets. Mitigating framing bias is critical to promote accurate and objective delivery of information. One promising mitigation paradigm is to generate a neutralized version of a news article by synthesizing multiple views from biased source articles (Sides, 2018; Lee et al., 2022). To more effectively achieve news neutralization, we introduce a polarity minimization loss that leverages inductive bias that encourages the model to prefer generation with minimized polarity difference. Our proposed loss trains the model to be simultaneously good at mapping articles from one end of the polarity spectrum to another end of the spectrum and vice versa as illustrated in Fig. 1. Intuitively, the model is forced to learn and focus on the shared aspect between contrasting polarities from two opposite ends. In this work, we demonstrate the effectiveness of our proposed loss function by minimizing polarity in different dimensions of framing bias – lexical and informational (Entman, 2002). Lexical polarization results from the choice of words with different valence and arousal to explain the same information (e.g., "protest" vs "riot"). Informational polarization results from a differing selection of information to cover, often including unnecessary or unrelated information related to the issue being covered. Our investigation suggests that learning the opposite polarities that are distinct in the informational dimension enables the model to acquire a better ability to focus on common ground and minimize biases in the polarized input articles. Ultimately, our proposed loss enables the removal of bias-inducing information and the generation of more neutral language choices. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology; (3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology. Table of Links Abstract and Intro Related Work Approach Experiments Conclusion Limitations, Ethics Statement and References A. Experimental Details B. Generation Results Abstract and Intro Abstract and Intro Related Work Related Work Approach Approach Experiments Experiments Conclusion Conclusion Limitations, Ethics Statement and References Limitations, Ethics Statement and References A. Experimental Details A. Experimental Details B. Generation Results B. Generation Results Abstract Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report). 1. Introduction Framing bias has become a pervasive problem in modern media, misleading the understanding of what really happened via a skewed selection of information and language (Entman, 2007, 2010; Gentzkow and Shapiro, 2006). The most notable impact of framing bias is the amplified polarity between conflicting political parties and media outlets. Mitigating framing bias is critical to promote accurate and objective delivery of information. One promising mitigation paradigm is to generate a neutralized version of a news article by synthesizing multiple views from biased source articles (Sides, 2018; Lee et al., 2022). To more effectively achieve news neutralization, we introduce a polarity minimization loss that leverages inductive bias that encourages the model to prefer generation with minimized polarity difference. Our proposed loss trains the model to be simultaneously good at mapping articles from one end of the polarity spectrum to another end of the spectrum and vice versa as illustrated in Fig. 1. Intuitively, the model is forced to learn and focus on the shared aspect between contrasting polarities from two opposite ends. In this work, we demonstrate the effectiveness of our proposed loss function by minimizing polarity in different dimensions of framing bias – lexical and informational (Entman, 2002). Lexical polarization results from the choice of words with different valence and arousal to explain the same information (e.g., "protest" vs "riot"). Informational polarization results from a differing selection of information to cover, often including unnecessary or unrelated information related to the issue being covered. Our investigation suggests that learning the opposite polarities that are distinct in the informational dimension enables the model to acquire a better ability to focus on common ground and minimize biases in the polarized input articles. Ultimately, our proposed loss enables the removal of bias-inducing information and the generation of more neutral language choices.

Mitigating Framing Bias with Polarity Minimization Loss: Abstract and Intro

পোলারিটি মিনিমাইজেশন লস সহ ফ্রেমিং বায়াস প্রশমিত করা: বিমূর্ত এবং ভূমিকা

Abmilderung des Framing Bias durch Polarity Minimization Loss: Zusammenfassung und Einführung

Mitigar el sesgo de encuadre con pérdida de minimización de polaridad: resumen e introducción

Atténuer le biais de cadrage grâce à la perte de minimisation de polarité : résumé et introduction

पोलारिटी मिनिमाइजेशन लॉस के साथ फ्रेमिंग पूर्वाग्रह को कम करना: सार और परिचय

極性最小化損失によるフレーミングバイアスの緩和: 概要と概要

극성 최소화 손실로 프레이밍 편향 완화: 요약 및 소개

Mitigando o viés de enquadramento com perda de minimização de polaridade: resumo e introdução

Смягчение смещения кадра с помощью минимизации потерь полярности: Краткое содержание и введение

Polarite Minimizasyon Kaybı ile Çerçeveleme Önyargısını Azaltma: Özet ve Giriş

Giảm thiểu xu hướng đóng khung với sự mất mát tối thiểu hóa phân cực: Tóm tắt và giới thiệu

利用极性最小化损失减轻框架偏差：摘要和简介

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Abstract & Intro

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: বিমূর্ত এবং ভূমিকা

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Zusammenfassung und Einführung

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resumen e introducción

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résumé et introduction

भारतीय ट्विटर पर पत्रकार-राजनेता के बीच बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: सार और परिचय

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：概要と序文

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 요약 및 소개

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resumo e introdução

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: Аннотация и введение

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Özet ve Giriş

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tóm tắt & Giới thiệu

揭示印度推特上记者与政客互动中的性别偏见：摘要与简介

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro
Diverse cross-document coreference and media bias analysis
Annotation tool
Annotation guidelines
Conclusion and future work
Acknowledgements
Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords: coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” (4) “The AfD is circulating a photo of Angela Merkel with a Hijab, although Merkel never wore Muslim clothes.” In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ”the White House” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Jakob Vogel, M.A. Digital Humanities, Institute for Digital Humanities, Faculty of Philosophy, Georg August University of G¨ottingen. Table of Links Abstract and Intro Diverse cross-document coreference and media bias analysis Annotation tool Annotation guidelines Conclusion and future work Acknowledgements Bibliographical References Abstract and Intro Abstract and Intro Diverse cross-document coreference and media bias analysis Diverse cross-document coreference and media bias analysis Annotation tool Annotation tool Annotation guidelines Annotation guidelines Conclusion and future work Conclusion and future work Acknowledgements Acknowledgements Bibliographical References Bibliographical References Abstract This paper presents a scheme for annotating coreference across news articles, extending beyond traditional identity relations by also considering near-identity and bridging relations. It includes a precise description of how to set up Inception, a respective annotation tool, how to annotate entities in news articles, connect them with diverse coreferential relations, and link them across documents to Wikidata’s global knowledge graph. This multi-layered annotation approach is discussed in the context of the problem of media bias. Our main contribution lies in providing a methodology for creating a diverse cross-document coreference corpus which can be applied to the analysis of media bias by word-choice and labelling. Keywords : coreference resolution, diverse coreference annotation, entity annotation, entity linking, media bias analysis, natural language processing Keywords 1. Introduction Coreference is the phenomenon of several expressions in a text all referring to the same person, object, or other entity or event as their referent. Thus, in a narrow sense, analyzing a document with regards to coreference means detecting relations of identity between phrases. The following example (1) illustrates such an identity relation, where coreferential expressions are printed in italics: (1) “Joe Biden arrived in Berlin yesterday, but the president did not come alone.” In (1), the noun phrase “Joe Biden” introduces a new entity while “the president” relates back to that introducing phrase. Within this relation, the introducing phrase “Joe Biden” is called the antecedent while the back-relating phrase “the president” is called an anaphor. Both expressions are coreferential in the way that they refer to the same non-textual entity, namely to the actual ‘realworld’ Joe Biden or at least to a corresponding mental concept. We can think of an antecedent and its anaphora as forming a cluster of mentions that as a whole represents its extra-textual referent within a textual document, as shown in Figure 1. As a task of natural language processing (NLP), coreference resolution has become quite efficient in detecting identity relations between phrases. However, reflecting on how we use language to refer to something, we are forced to realize that coreference in a broader sense is actually far more complex. We can address an entity or event by using a variety of expressions that are in fact not strictly identical to each other. Consider the following examples: (2) “ President Biden was clearly not satisfied with today’s outcome. As the White House stated this afternoon, efforts will be made to . . . ” President Biden the White House (3) “Even if the young Erdogan used to be pro-Western, Turkey’s president nowadays often acts against Western interests.” the young Erdogan Turkey’s president (4) “The AfD is circulating a photo of Angela Merkel with a Hijab , although Merkel never wore Muslim clothes.” a photo of Angela Merkel with a Hijab In these given examples, the highlighted mentions mean ‘almost’ the same, but not completely. In (2), we are aware by world-knowledge that ” the White House ” is often used as a substitute expression for the current US president, although the former is a place which in strict terms cannot be identical to the president, who is a person. In (3), on the other hand, both mentions refer to the ’real-world’ person Erdogan, but at different time steps. Finally, in (4), a mention representing the person Merkel is juxtaposed with a mention representing a picture of Merkel. While these two mentions could refer to separate entities, the juxtaposition indicates a connection between both where the attributes of the first mention do influence the perception of the second mention. Hence, we would miss essential semantic connections if we chose not to mark them as coreferential. Having said that, the simple classification of two mentions into coreferential (identical) or noncoreferential (non-identical) does not seem to suffice the complexity of common text data. Instead, we need to allow for diverse coreference clusters that include finer-grained relations lying between identity and non-identity. We need to allow for near-identity relations to mark two mentions that are partially, but not totally, identical (Recasens et al., 2010). the White House diverse coreference near-identity In news coverage, identity and near-identity references are extensively used to report on persons, organizations, and other entities of public interest. It is our goal to build up a corpus that contains annotated examples of such diverse forms of coreference. While diverse coreference occurs in all sorts of news media, we focus on digital print media, only. Furthermore, although in practice both entities and events can act as referent, we ignore references to events for now, as their annotation would go beyond the limits of our present scheme.[1] The ordinary business of journalism is to write about current political affairs and other happenings of public interest. These happenings are normally reported by several newspapers at the same time. All of these news articles are considered documents that contain references to the same entities and together form a discourse about them. To include the whole picture of such intradiscursive references, we want our corpus to link document-level clusters with corresponding clusters of other documents of the same discourse. Hence, our corpus is to depict cross-document coreference data. On a discourse level, corresponding clusters form discourse entities that themselves can be linked to their non-textual referents by some knowledge graph identifier. For this project, we use Wikidata’s Uniform Resource Identifiers (URIs) for entity linking. By doing so, world knowledge is included into the data. This allows for drawing connections even between different discourse entities that refer to a common referent, yet at a different time step or rather in the context of a different happening. Figure 2 illustrates the multiple layers of this annotation model. In building a corpus for diverse cross-document coreference in news articles, we hope to provide a valuable resource for the evaluation of automated coreference resolution tasks. The contribution of this paper mainly lies in providing an answer to the question of how to create such a corpus. How can diverse coreference relations be annotated in a cross-document setup? We believe our scheme as we present it here tackles this problem efficiently, extensively, and unambiguously. Additionally, we would like to use the data resulting from our own annotations for further research in the area of media bias. Even if it plays no direct part in the outlined scheme, a lot of our choices how to annotate references were made because of this requirement to make the data usable for later media bias analysis. Eventually, we hope to contribute to the wider research question of how to identify media bias by word choice and labelling based on the usage of diverse coreference relations in news articles. The following section 2 will further elaborate on this connection between diverse coreference and the problem of media bias analysis. Despite its only subtle impact on our practical annotation instructions, that section means to highlight the theoretical background and motivation behind our project. The sections thereafter will then deal with the actual annotation process. Section 3 will guide coders through the setup and controls of Inception, our selected annotation software. Finally, section 4 will define annotation instructions in three passes while also outlining our typology of diverse coreference. The data we use for our own annotations consists of the text bodies of articles that report on the same happenings. All articles are in English and were published by one of the following US-American newspapers: HuffPost (categorized as ”Left” by AllSides (2023) or ”Skews Left” by Ad Fontes Media (2023), abbreviated in our data as ”LL”), The New York Times (categorized as ”Lean Left” by AllSides or ”Skews Left” by Ad Fontes Media, abbreviated as ”L”), USA Today (categorized as ”Lean Left” or ”Middle or Balanced Bias”, abbreviated as ”M”), Fox News (categorized as ”Right” or ”Skews Right”, abbreviated as ”R”), Breitbart News Network (categoized as ”Right” or ”Strong Right”, abbreviated as ”RR”).[2] [1] Though at a later point, this scheme could be extended to also include the annotation of events (Linguistic Data Consortium, 2005; O’Gorman et al., 2016). [2] Looking at the political orientation of these newspapers, the data is unbalanced with an underrepresentation of politically centered media. However, at the current state of this project, the imbalance is unlikely to influence our analysis which does not yet target political orientation or media bias itself. Therefore, we will ignore this issue for now.

Code Book for Annotation of Diverse Cross-Document Coreference: Abstract and Intro

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan;
(2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan;
(3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan;
(4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
(5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Setup Result Summary and Discussion Appendix A. Dual Energy Formalism B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime References ABSTRACT Keywords: white dwarfs — stars: winds, outflows — stars: rotation 1. INTRODUCTION Consequences of a merger of massive white dwarfs (WDs) are of great astrophysical importance. It may explode as a Type Ia supernova in particular when the binary constitutes of carbon-oxygen WDs with a total mass exceeding the Chandrasekhar limit (Webbink 1984; Iben & Tutukov 1984). Instead, if a super-Chandrasekhar oxygen-neon core is synthesized after the merger, it may collapse into a neutron star (NS) (Nomoto & Iben 1985; Saio & Nomoto 2004). Such a merger induced collapse has gotten attention as a scenario for the formation of peculiar type of neutron stars, e.g., sources of fast radio bursts (e.g., Kashiyama & Murase 2017; Kremer et al. 2021; Kirsten et al. 2022; Lu et al. 2022). On the other hand, the expansion velocity of the wind observed in WD J005311 significantly surpasses the escape velocity of a WD with a typical mass. This suggests that the wind is either thermally driven, originating from a superor near-Chandrasekhar mass WD, or magnetically driven due to the rapid rotation and strong magnetic field of the WD. In the former case, the wind velocity will be (Parker 1965): while in the latter case, the maximum wind velocity along the equatorial plane is (Weber & Davis 1967; Michel 1969): The wind is so fast that it catches up and clashes into the surrounding supernova ejecta, forming a wind termination shock, which is observed as an inner X-ray nebula (Oskinova et al. 2020; Ko et al. 2023). The X-ray nebula is still in its infancy; given the observed angular size, it is only a few tens of years old (Ko et al. 2023). Subsequent observations may reveal the time variability and anisotropy of the wind, which is generally expected for a rotating magnetic wind but has not been explored in this context. These properties of the wind can also be linked to the mass-loss and spindown rates of the central WD, which are important in determining the fate of the central WD: whether it eventually collapses into a neutron star, and if so, how rapidly rotating and strongly magnetized the neutron star would be. Here we model a system like WD J005311 by numerically constructing a 2D axisymmetric wind solution driven by rotating dipole, with implementing a wind launching region that mimics the near-surface carbon burning region. We investigate the wind structure together with its time evolution (i.e., how the mass, energy and angular momentum loss rate from the system evolves with time), and the scaling of the spin-down torque with respect to system parameters such as surface magnetic field, rotation frequency and mass loss rate. This paper is organized as follows. We introduce our setup in Sec. 2, including numerical details. In Sec. 3, we show our results on wind structure, time evolution and scaling of spin-down torque. Finally, we discuss several implications and applications on observational results in Sec. 4. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Abstract and Intro Setup Setup Result Result Summary and Discussion Summary and Discussion Appendix Appendix A. Dual Energy Formalism A. Dual Energy Formalism B. Convergence of Results B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime C. Change of the Mass Loss Rate in MHD Regime References References ABSTRACT Keywords : white dwarfs — stars: winds, outflows — stars: rotation Keywords 1. INTRODUCTION Consequences of a merger of massive white dwarfs (WDs) are of great astrophysical importance. It may explode as a Type Ia supernova in particular when the binary constitutes of carbon-oxygen WDs with a total mass exceeding the Chandrasekhar limit (Webbink 1984; Iben & Tutukov 1984). Instead, if a super-Chandrasekhar oxygen-neon core is synthesized after the merger, it may collapse into a neutron star (NS) (Nomoto & Iben 1985; Saio & Nomoto 2004). Such a merger induced collapse has gotten attention as a scenario for the formation of peculiar type of neutron stars, e.g., sources of fast radio bursts (e.g., Kashiyama & Murase 2017; Kremer et al. 2021; Kirsten et al. 2022; Lu et al. 2022). On the other hand, the expansion velocity of the wind observed in WD J005311 significantly surpasses the escape velocity of a WD with a typical mass. This suggests that the wind is either thermally driven, originating from a superor near-Chandrasekhar mass WD, or magnetically driven due to the rapid rotation and strong magnetic field of the WD. In the former case, the wind velocity will be (Parker 1965): while in the latter case, the maximum wind velocity along the equatorial plane is (Weber & Davis 1967; Michel 1969): The wind is so fast that it catches up and clashes into the surrounding supernova ejecta, forming a wind termination shock, which is observed as an inner X-ray nebula (Oskinova et al. 2020; Ko et al. 2023). The X-ray nebula is still in its infancy; given the observed angular size, it is only a few tens of years old (Ko et al. 2023). Subsequent observations may reveal the time variability and anisotropy of the wind, which is generally expected for a rotating magnetic wind but has not been explored in this context. These properties of the wind can also be linked to the mass-loss and spindown rates of the central WD, which are important in determining the fate of the central WD: whether it eventually collapses into a neutron star, and if so, how rapidly rotating and strongly magnetized the neutron star would be. Here we model a system like WD J005311 by numerically constructing a 2D axisymmetric wind solution driven by rotating dipole, with implementing a wind launching region that mimics the near-surface carbon burning region. We investigate the wind structure together with its time evolution (i.e., how the mass, energy and angular momentum loss rate from the system evolves with time), and the scaling of the spin-down torque with respect to system parameters such as surface magnetic field, rotation frequency and mass loss rate. This paper is organized as follows. We introduce our setup in Sec. 2, including numerical details. In Sec. 3, we show our results on wind structure, time evolution and scaling of spin-down torque. Finally, we discuss several implications and applications on observational results in Sec. 4.

Magnetic Wind From a Massive White Dwarf Merger: Abstract and Intro

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction
Formal Model - Rollups with Decentralized Common Pool (DCP)
Atomic composability & ZK-proofs
Incorporating Zk-proofs
Application of the Formal Model
Conclusion and References Abstract In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. In this paper, we introduce a comprehensive formal model to address atomic composability across multiple rollups on Ethereum. The proposed model incorporates mechanisms like buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs. Moreover, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. The application of the proposed model to shared sequencers and other existing solutions accentuates its versatility and universality. 1 Introduction In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. Ethereum, recognized as a pioneering smart contract platform, has initiated a myriad of advancements, with rollups being a significant answer to the blockchain trilemma: balancing scalability, security, and decentralization [5]. However, as rollups appear promising for scalability, they might unintentionally lead to fragmented composability. Given the intertwining nature of systems and applications, ensuring atomicity in transactions across systems is vital. Atomic composability is predicated on the principle that a transaction (A) can only be finalized if another transaction (B) is likewise finalized [8]. For decentralized applications that operate over multiple rollups, this assurance is critical. Yet, actualizing this atomicity with disconnected rollups on Ethereum presents major obstacles. This paper offers a thorough formal model that addresses atomic composability across multiple rollups on Ethereum. Incorporating insights from established distributed system solutions and contemporary cryptographic methodologies, the proposed model encompasses buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs [1]. Beyond proposing the model, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. Our intent extends beyond presenting a solution; we seek to stimulate a wider discourse on the future trajectory of interconnected blockchains. With a surge in applications shifting to a multi-rollup framework on Ethereum and elsewhere, a robust system guaranteeing atomic composability becomes indispensable. Through our model and ensuing discussions, we aim to make substantial contributions to this burgeoning field of blockchain study. 1.1 What are Rollups? Rollups, in the Ethereum context, are scaling mechanisms that bolster network throughput. They operate by conducting transactions off-chain and subsequently submitting a transaction summary to the primary Ethereum chain, thus enhancing transaction capacity without overloading the main Ethereum network [5]. 1.2 What is Composability? Within blockchain and Ethereum paradigms, composability pertains to the capability of decentralized applications (dApps) and smart contracts to effortlessly integrate and leverage one another’s features [11]. This can be analogized to “money Legos”, where each protocol or dApp represents an individual Lego piece, capable of diverse combinations. 1.3 Limits of Composability Between Rollups With the deployment of multiple rollups on Ethereum, challenges arise in ensuring seamless interaction of dApps and contracts across these rollups. This dilemma is intensified if rollups operate in isolation or lack an effective bridging mechanism. Atomic Transactions Across Rollups: Ensuring atomic composability between rollups necessitates that a transaction in one rollup is only finalized if its counterpart in another rollup is as well. This is intricate because each rollup might possess unique consensus algorithms, validation methodologies, and operational latency. Data Availability: For a contract in one rollup to interface with data or another contract on a distinct rollup, the requisite data from the latter may not be readily accessible or may be costly to retrieve. Differing Rules and Standards: Distinct rollups with divergent standards or rules regarding transaction processing can further impede cross-rollup interactions. This paper is available on arxiv under CC 4.0 license. Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Dipankar Sarkar, Cryptuon Research and me@dipankar.name Table of Links Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Incorporating Zk-proofs Application of the Formal Model Conclusion and References Abstract and Introduction Abstract and Introduction Formal Model - Rollups with Decentralized Common Pool (DCP) Formal Model - Rollups with Decentralized Common Pool (DCP) Atomic composability & ZK-proofs Atomic composability & ZK-proofs Incorporating Zk-proofs Incorporating Zk-proofs Application of the Formal Model Application of the Formal Model Conclusion and References Conclusion and References Abstract In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. In this paper, we introduce a comprehensive formal model to address atomic composability across multiple rollups on Ethereum. The proposed model incorporates mechanisms like buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs. Moreover, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. The application of the proposed model to shared sequencers and other existing solutions accentuates its versatility and universality. 1 Introduction In the rapidly evolving domain of distributed ledger technology, scalability and interoperability have become paramount challenges for both academic and industry sectors. Ethereum, recognized as a pioneering smart contract platform, has initiated a myriad of advancements, with rollups being a significant answer to the blockchain trilemma: balancing scalability, security, and decentralization [5]. However, as rollups appear promising for scalability, they might unintentionally lead to fragmented composability. Given the intertwining nature of systems and applications, ensuring atomicity in transactions across systems is vital. Atomic composability is predicated on the principle that a transaction (A) can only be finalized if another transaction (B) is likewise finalized [8]. For decentralized applications that operate over multiple rollups, this assurance is critical. Yet, actualizing this atomicity with disconnected rollups on Ethereum presents major obstacles. This paper offers a thorough formal model that addresses atomic composability across multiple rollups on Ethereum. Incorporating insights from established distributed system solutions and contemporary cryptographic methodologies, the proposed model encompasses buffering, dependency management, concurrency control, and the groundbreaking zero-knowledge proofs [1]. Beyond proposing the model, we evaluate its practical repercussions, strengths, and weaknesses, ensuring resilience against manipulative or erroneous actions. Our intent extends beyond presenting a solution; we seek to stimulate a wider discourse on the future trajectory of interconnected blockchains. With a surge in applications shifting to a multi-rollup framework on Ethereum and elsewhere, a robust system guaranteeing atomic composability becomes indispensable. Through our model and ensuing discussions, we aim to make substantial contributions to this burgeoning field of blockchain study. 1.1 What are Rollups? 1.1 What are Rollups? Rollups, in the Ethereum context, are scaling mechanisms that bolster network throughput. They operate by conducting transactions off-chain and subsequently submitting a transaction summary to the primary Ethereum chain, thus enhancing transaction capacity without overloading the main Ethereum network [5]. 1.2 What is Composability? 1.2 What is Composability? Within blockchain and Ethereum paradigms, composability pertains to the capability of decentralized applications (dApps) and smart contracts to effortlessly integrate and leverage one another’s features [11]. This can be analogized to “money Legos”, where each protocol or dApp represents an individual Lego piece, capable of diverse combinations. 1.3 Limits of Composability Between Rollups 1.3 Limits of Composability Between Rollups With the deployment of multiple rollups on Ethereum, challenges arise in ensuring seamless interaction of dApps and contracts across these rollups. This dilemma is intensified if rollups operate in isolation or lack an effective bridging mechanism. Atomic Transactions Across Rollups: Ensuring atomic composability between rollups necessitates that a transaction in one rollup is only finalized if its counterpart in another rollup is as well. This is intricate because each rollup might possess unique consensus algorithms, validation methodologies, and operational latency. Atomic Transactions Across Rollups: Data Availability: For a contract in one rollup to interface with data or another contract on a distinct rollup, the requisite data from the latter may not be readily accessible or may be costly to retrieve. Data Availability: Differing Rules and Standards: Distinct rollups with divergent standards or rules regarding transaction processing can further impede cross-rollup interactions. Differing Rules and Standards:

Towards Universal Atomic Composability: Abstract and Introduction

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References ABSTRACT In recent years, the integration of gamification into educational settings has garnered significant attention as a means to enhance student engagement and learning outcomes. By leveraging gamified elements such as points and leaderboards, educators aim to promote active participation, motivation, and deeper understanding among students. This study investigates the effects of gamification on student engagement in a flipped classroom environment. The findings suggest that gamification strategies, when effectively implemented, can have a positive impact on student motivation and engagement. This paper concludes with recommendations for educators, potential challenges such as superficial engagement and demotivation, and future directions for research to address these challenges and further explore the potential of gamification in fostering student success. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References ABSTRACT In recent years, the integration of gamification into educational settings has garnered significant attention as a means to enhance student engagement and learning outcomes. By leveraging gamified elements such as points and leaderboards, educators aim to promote active participation, motivation, and deeper understanding among students. This study investigates the effects of gamification on student engagement in a flipped classroom environment. The findings suggest that gamification strategies, when effectively implemented, can have a positive impact on student motivation and engagement. This paper concludes with recommendations for educators, potential challenges such as superficial engagement and demotivation, and future directions for research to address these challenges and further explore the potential of gamification in fostering student success. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Exploring the Impact of Gamification in Flipped Classrooms: Abstract and Introduction

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Muhammed Yusuf Kocyigit, Boston University;
(2) Anietie Andy, University of Pennsylvania;
(3) Derry Wijaya, Boston University. Table of Links Abstract and Intro
Related Works
Data
Method
Analysis and Results
Conclusion
Limitations
Ethics Statement and References
Appendix: Toxicity Measurement
Appendix: Correlation Over Time
Appendix: Wikidata
Appendix: Hyperparameter Sensitivity Abstract Long-term exposure to biased content in literature or media can significantly influence people’s perceptions of reality, leading to the development of implicit biases that are difficult to detect and address (Gerbner 1998). In this study, we propose a novel method to analyze the differences in representation between two groups and use it examine the representation of African Americans and White Americans in books between 1850 to 2000 with the Google Books dataset (Goldberg and Orwant 2013). By developing better tools to understand differences in representation, we aim to contribute to the ongoing efforts to recognize and mitigate biases. To improve upon the more common phrase-based (men, women, white, black, etc) methods to differentiate context (Tripodi et al. 2019; Lucy, Tadimeti, and Bamman 2022), we propose collecting a comprehensive list of historically significant figures and using their names to select relevant context. This novel approach offers a more accurate and nuanced method for detecting implicit biases through reducing the risk of selection bias. We create group representations for each decade and analyze them in an aligned semantic space (Hamilton, Leskovec, and Jurafsky 2016). We further support our results by assessing the time-adjusted toxicity (Bassignana, Basile, and Patti 2018) in the context for each group and identifying the semantic axes (Lucy, Tadimeti, and Bamman 2022) that exhibit the most significant differences between the groups across decades. We support our method by showing that our proposed method can capture known socio-political changes accurately and our findings indicate that while the relative number of African American names mentioned in books have increased over time, the context surrounding them remains more toxic than white Americans. Introduction In the last two hundred years, perhaps with the exception of the last decade, books have been one of the most important medium for the dissemination and perpetuation of knowledge and culture across generations. Analyzing the content of books through time resembles examining the rings in the cross section of a tree. It can tell us about the past and help us understand the present. By studying how different groups of people are portrayed in books throughout history, we aim to better understand the ways in which racial bias has manifested and developed through time. Recent advances in text representation and content analysis has enabled us to process large corpora and create meaningful representations that we can analyze, enabling large scale, longitudinal text based studies. In this work we aim to integrate and enhance a diverse array of methods to create a toolbox for analyzing such content. Our method[1] as summarized in Figure 1 consists of 4 stages. First we collect a large sample of individuals that represent the groups we will analyze. In our case we want to examine the context surrounding African-Americans and White Americans in books published in the United States between 1850-2000. So we collect the name and ethnicity of every person who has lived in the United States from Wikipedia. Then we take the Google Books dataset and filter the n-grams that contain any of the names that we have collected. We use these n-grams to train embeddings that represent each ethnic group and finally use these embeddings to study the differences in the context in which each group is talked about and how it has changed over 15 decades. The longitudinal nature is a vital aspect of this study as it allows us to study biases that are cultivated through extended exposure. These biases can affect the base frame that people ground their notion of normalcy in. Thus, understanding them presents a critical issue since gaining awareness of one’s sense of normalcy is a challenging task without the aid of explicit external information and intentional inquiry. While similar work exists at the intersection of Natural Language Processing and social sciences, our work has some important differences. Researchers have used word representations in combination with other text processing tools to represent changes in the meanings of words (Martinc, Kralj Novak, and Pollak 2020), analyze online communities to detect gender bias (Lucy, Tadimeti, and Bamman 2022), analyze literary work to detect racial bias (Tripodi et al. 2019), or quantify ethnic stereotypes in the news (Sorato, Zavala-Rojas, and del Carme Colominas Ventura 2021). However, these methods predominantly use referring words (woman, man, African American, Jewish, etc.) or gendered terms (he, she, lady, gentleman, etc.) to differentiate each group’s context. Instead of referring phrases, in this work, we directly employ a list of names of African American and White American figures (politician, athlete, musician, and social leader categories) for each decade from Wikipedia. While any representation method has downsides, we propose that this personal-based representation may better capture implicit biases such as coded language (covert expression of racism that avoids any mention of race)[2] particularly as names do not have explicit references to a larger group. Although individuals may possess knowledge of the race of the person they are writing about, the statements they make are not inherently generalizations. As a result, authors may express their implicit biases openly in their writing. Consequently, if we analyze a substantial corpus of text and find that these instances of bias occur frequently, we can identify a systematic pattern of implicit bias. Hence, we use how individuals from a racial group are presented to analyze biases for the general group. In addition, investigating personal-based racial bias holds significance as studies have shown its association with more detrimental effects on mental health than group-level bias (Hagiwara, Alderson, and Mezuk 2016). Additionally our proposed method gets around the problem of selecting referring phrases. Selecting unbiased, phrases that will return a large enough context for analysis becomes a challenge, if not a roadblock since social-context itself is ever changing and the tools for referring change as well. We present the number of words returned from the complete Google n-grams data with the referring phraseand person-based filtering methods for 15 decades in Figure 2. The number of n-grams containing referring phrases for the decades before 1970 is around 10K, which is too few considering these n-grams contain a lot of stop words and part-of-speech tags. Similar to (Lucy, Tadimeti, and Bamman 2022) we use learned group representations as our unit of analysis. Unlike previous work, we propose using a contrastive objective to learn the representations to better capture implicit biases since the absence of a positive context should be just as strong a signal toward bias as the existence of a negative one. Due to its subtle language, implicit biases may be hard to identify when doing a one-sided analysis alone but may be visible when we look at the differences in the context used when referring to people from different racial groups. Contrastive learning allows us to represent positive and negative distributional differences in the context of two groups. Additionally, we enhance our analysis by analyzing multiple aspects of the text surrounding each group. We look at the frequency of toxic words in each decade per racial group. We also employ semantic axes (Lucy, Tadimeti, and Bamman 2022) to analyze the difference in the embeddings of the racial groups with respect to a variety of semantic axes and how this changes over the 15 decades. In summary, our contributions in this work are: • We propose a novel method of analyzing bias between two groups with person-based filtering that is based on the names of people in a group instead of referringphrases or group terms. • We use contrastive learning to capture both the presence and absence of relevant information to better represent the difference in context. • We conduct, to the best of our knowledge, the first largescale longitudinal study of the bias between AfricanAmericans and White Americans in books published in the United States between 1850-2000. • We orchestrate multiple analysis methods and integrate them with our representation learning method, showing easy integration of our representation learning method with existing literature. In our integration, we also take into account the semantic changes that occur through long time periods. [1] We will release the data and code to encourage further development and application of our method. [2] https://www.r2hub.org/library/overt-and-covert-racism This paper is available on arxiv under CC 4.0 license. Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Muhammed Yusuf Kocyigit, Boston University; (2) Anietie Andy, University of Pennsylvania; (3) Derry Wijaya, Boston University. Table of Links Abstract and Intro Related Works Data Method Analysis and Results Conclusion Limitations Ethics Statement and References Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Wikidata Appendix: Hyperparameter Sensitivity Abstract and Intro Abstract and Intro Related Works Related Works Data Data Method Method Analysis and Results Analysis and Results Conclusion Conclusion Limitations Limitations Ethics Statement and References Ethics Statement and References Appendix: Toxicity Measurement Appendix: Toxicity Measurement Appendix: Correlation Over Time Appendix: Correlation Over Time Appendix: Wikidata Appendix: Wikidata Appendix: Hyperparameter Sensitivity Appendix: Hyperparameter Sensitivity Abstract Long-term exposure to biased content in literature or media can significantly influence people’s perceptions of reality, leading to the development of implicit biases that are difficult to detect and address (Gerbner 1998). In this study, we propose a novel method to analyze the differences in representation between two groups and use it examine the representation of African Americans and White Americans in books between 1850 to 2000 with the Google Books dataset (Goldberg and Orwant 2013). By developing better tools to understand differences in representation, we aim to contribute to the ongoing efforts to recognize and mitigate biases. To improve upon the more common phrase-based (men, women, white, black, etc) methods to differentiate context (Tripodi et al. 2019; Lucy, Tadimeti, and Bamman 2022), we propose collecting a comprehensive list of historically significant figures and using their names to select relevant context. This novel approach offers a more accurate and nuanced method for detecting implicit biases through reducing the risk of selection bias. We create group representations for each decade and analyze them in an aligned semantic space (Hamilton, Leskovec, and Jurafsky 2016). We further support our results by assessing the time-adjusted toxicity (Bassignana, Basile, and Patti 2018) in the context for each group and identifying the semantic axes (Lucy, Tadimeti, and Bamman 2022) that exhibit the most significant differences between the groups across decades. We support our method by showing that our proposed method can capture known socio-political changes accurately and our findings indicate that while the relative number of African American names mentioned in books have increased over time, the context surrounding them remains more toxic than white Americans. Introduction In the last two hundred years, perhaps with the exception of the last decade, books have been one of the most important medium for the dissemination and perpetuation of knowledge and culture across generations. Analyzing the content of books through time resembles examining the rings in the cross section of a tree. It can tell us about the past and help us understand the present. By studying how different groups of people are portrayed in books throughout history, we aim to better understand the ways in which racial bias has manifested and developed through time. Recent advances in text representation and content analysis has enabled us to process large corpora and create meaningful representations that we can analyze, enabling large scale, longitudinal text based studies. In this work we aim to integrate and enhance a diverse array of methods to create a toolbox for analyzing such content. Our method[1] as summarized in Figure 1 consists of 4 stages. First we collect a large sample of individuals that represent the groups we will analyze. In our case we want to examine the context surrounding African-Americans and White Americans in books published in the United States between 1850-2000. So we collect the name and ethnicity of every person who has lived in the United States from Wikipedia. Then we take the Google Books dataset and filter the n-grams that contain any of the names that we have collected. We use these n-grams to train embeddings that represent each ethnic group and finally use these embeddings to study the differences in the context in which each group is talked about and how it has changed over 15 decades. The longitudinal nature is a vital aspect of this study as it allows us to study biases that are cultivated through extended exposure. These biases can affect the base frame that people ground their notion of normalcy in. Thus, understanding them presents a critical issue since gaining awareness of one’s sense of normalcy is a challenging task without the aid of explicit external information and intentional inquiry. While similar work exists at the intersection of Natural Language Processing and social sciences, our work has some important differences. Researchers have used word representations in combination with other text processing tools to represent changes in the meanings of words (Martinc, Kralj Novak, and Pollak 2020), analyze online communities to detect gender bias (Lucy, Tadimeti, and Bamman 2022), analyze literary work to detect racial bias (Tripodi et al. 2019), or quantify ethnic stereotypes in the news (Sorato, Zavala-Rojas, and del Carme Colominas Ventura 2021). However, these methods predominantly use referring words (woman, man, African American, Jewish, etc.) or gendered terms (he, she, lady, gentleman, etc.) to differentiate each group’s context. Instead of referring phrases, in this work, we directly employ a list of names of African American and White American figures (politician, athlete, musician, and social leader categories) for each decade from Wikipedia. While any representation method has downsides, we propose that this personal-based representation may better capture implicit biases such as coded language (covert expression of racism that avoids any mention of race)[2] particularly as names do not have explicit references to a larger group. Although individuals may possess knowledge of the race of the person they are writing about, the statements they make are not inherently generalizations. As a result, authors may express their implicit biases openly in their writing. Consequently, if we analyze a substantial corpus of text and find that these instances of bias occur frequently, we can identify a systematic pattern of implicit bias. Hence, we use how individuals from a racial group are presented to analyze biases for the general group. In addition, investigating personal-based racial bias holds significance as studies have shown its association with more detrimental effects on mental health than group-level bias (Hagiwara, Alderson, and Mezuk 2016). Additionally our proposed method gets around the problem of selecting referring phrases. Selecting unbiased, phrases that will return a large enough context for analysis becomes a challenge, if not a roadblock since social-context itself is ever changing and the tools for referring change as well. We present the number of words returned from the complete Google n-grams data with the referring phraseand person-based filtering methods for 15 decades in Figure 2. The number of n-grams containing referring phrases for the decades before 1970 is around 10K, which is too few considering these n-grams contain a lot of stop words and part-of-speech tags. Similar to (Lucy, Tadimeti, and Bamman 2022) we use learned group representations as our unit of analysis. Unlike previous work, we propose using a contrastive objective to learn the representations to better capture implicit biases since the absence of a positive context should be just as strong a signal toward bias as the existence of a negative one. Due to its subtle language, implicit biases may be hard to identify when doing a one-sided analysis alone but may be visible when we look at the differences in the context used when referring to people from different racial groups. Contrastive learning allows us to represent positive and negative distributional differences in the context of two groups. Additionally, we enhance our analysis by analyzing multiple aspects of the text surrounding each group. We look at the frequency of toxic words in each decade per racial group. We also employ semantic axes (Lucy, Tadimeti, and Bamman 2022) to analyze the difference in the embeddings of the racial groups with respect to a variety of semantic axes and how this changes over the 15 decades. In summary, our contributions in this work are: In summary, our contributions in this work are: • We propose a novel method of analyzing bias between two groups with person-based filtering that is based on the names of people in a group instead of referringphrases or group terms. • We use contrastive learning to capture both the presence and absence of relevant information to better represent the difference in context. • We conduct, to the best of our knowledge, the first largescale longitudinal study of the bias between AfricanAmericans and White Americans in books published in the United States between 1850-2000. • We orchestrate multiple analysis methods and integrate them with our representation learning method, showing easy integration of our representation learning method with existing literature. In our integration, we also take into account the semantic changes that occur through long time periods. [1] We will release the data and code to encourage further development and application of our method. [2] https://www.r2hub.org/library/overt-and-covert-racism

A Novel Method for Analysing Racial Bias: Collection of Person Level References: Abstract and Intro

Grassroots Distributed Systems for Digital Sovereignty: Abstract and Introduction

Neural Network for Valuing Bitcoin: Abstract and Introduction

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) D.Sinclair, Imense Ltd, and email: david@imense.com;
(2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction
Interrogating the LLM with an Emotion Eliciting Tail Prompt
PCA analysis of the Emotion of Amazon reviews
Future Work
Conclusions
Acknowledgments and References Abstract This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotion analysis of Amazon product reviews we demonstrate emotion descriptors can be mapped into a PCA type space. It was hoped that text descriptions of actions to improve a current text described state could also be elicited through a tail prompt. Experiment seemed to indicate that this is not straightforward to make work. This failure put our hoped for selection of action via choosing the best predicted outcome via comparing emotional responses out of reach for the moment. Keywords: synthetic consciousness, emotion vector, emotion dictionary, emotion probability vector 1. Introduction Human behaviour is necessarily governed by emotion [3]. Sensed information about the world around us has to be reconciled with our internal state and any action to be taken is chosen so as to lead to future state that seems preferable to our current state [4], where preferable means ‘my feeling is I would like to try the new state or the action possibly leading to a new state’. If we are hungry we will often choose to eat. If we are very hungry we will take greater risk to acquire food. If we are cold we will try to get warm etc. Advertising aims to convince us a course of action will lead to more happiness. Sugary carbonated drinks do not objectively lead to long term happiness but the known short term emotional response to eating sugar is desirable. Sensed data about the world is tremendously diverse, often inaccurate and incomplete and required responses have varying degrees of urgency. The arbitration engine that processes these inputs needs to naturally cope with vagueness while appearing to provide certainty internally. Emotions are the term we use to describe our experience of using this apparatus to make decisions. The phrase computers do not have emotions is often wrongly used to assert that interactive computer software running on a machine cannot ever exhibit or experience emotion. Large Language Models (LLMs) [5, 1, 2] offer a ready means of linking a chunk of text with an estimated emotional state, bridging the gap between the world of text and the realm of human emotion. LLMs have been used in focused sentiment analysis and are reported to perform adequately [6] but at the time of writing we are unaware of other researchers using probabilistic emotion dictionaries. This paper explores the intersection of LLMs and emotions, demonstrating how these models can be harnessed to estimate the emotional content of a piece of text. We present a novel approach to summarizing emotional states by constructing a dictionary of emotion-related words and calculating the probabilities of these words appearing following a prompt that includes both the original text and an emotion-eliciting tail. This methodology allows us to quantitatively assess the emotional landscape of text. To demonstrate our approach we choose a dictionary of 271 emotion describing words and estimate their probability of being associated with a sections Amazon product reviews. Limited computational resources and time means we are only in a position to publish a cursory study. It is likely that many emotion are correlated and an estimate of the dimension of emotional space may be derivable via PCA analysis on a large sample of emotion vectors. We discuss some of the limitations we encountered during experiment and some of the obstacles to producing and regulating the behaviour of emotion based synthetic consciousness. This paper is layed out as follows, section 2 details the LLM and hardware used to run it, section 2.1 details our choices of words to make up our emotion dictionary, section 2.1.1 covers estimating emotion probabilities from an LLM using a tail prompt. Section 2.1.2 shows results on Amazon reviews. A hint at the PCA structure with in emotion vectors is given in 3. Finally future directions are considered and conclusion given. This paper is available on arxiv under CC 4.0 license. Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews Future Work Conclusions Acknowledgments and References Abstract and Introduction Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews PCA analysis of the Emotion of Amazon reviews Future Work Future Work Conclusions Conclusions Acknowledgments and References Acknowledgments and References Abstract This paper shows how LLMs (Large Language Models) [5, 2] may be used to estimate a summary of the emotional state associated with piece of text. The summary of emotional state is a dictionary of words used to describe emotion together with the probability of the word appearing after a prompt comprising the original text and an emotion eliciting tail. Through emotion analysis of Amazon product reviews we demonstrate emotion descriptors can be mapped into a PCA type space. It was hoped that text descriptions of actions to improve a current text described state could also be elicited through a tail prompt. Experiment seemed to indicate that this is not straightforward to make work. This failure put our hoped for selection of action via choosing the best predicted outcome via comparing emotional responses out of reach for the moment. Keywords: synthetic consciousness, emotion vector, emotion dictionary, emotion probability vector Keywords: synthetic consciousness, emotion vector, emotion dictionary, emotion probability vector 1. Introduction Human behaviour is necessarily governed by emotion [3]. Sensed information about the world around us has to be reconciled with our internal state and any action to be taken is chosen so as to lead to future state that seems preferable to our current state [4], where preferable means ‘my feeling is I would like to try the new state or the action possibly leading to a new state’. If we are hungry we will often choose to eat. If we are very hungry we will take greater risk to acquire food. If we are cold we will try to get warm etc. Advertising aims to convince us a course of action will lead to more happiness. Sugary carbonated drinks do not objectively lead to long term happiness but the known short term emotional response to eating sugar is desirable. Sensed data about the world is tremendously diverse, often inaccurate and incomplete and required responses have varying degrees of urgency. The arbitration engine that processes these inputs needs to naturally cope with vagueness while appearing to provide certainty internally. Emotions are the term we use to describe our experience of using this apparatus to make decisions. The phrase computers do not have emotions is often wrongly used to assert that interactive computer software running on a machine cannot ever exhibit or experience emotion. Large Language Models (LLMs) [5, 1, 2] offer a ready means of linking a chunk of text with an estimated emotional state, bridging the gap between the world of text and the realm of human emotion. LLMs have been used in focused sentiment analysis and are reported to perform adequately [6] but at the time of writing we are unaware of other researchers using probabilistic emotion dictionaries. This paper explores the intersection of LLMs and emotions, demonstrating how these models can be harnessed to estimate the emotional content of a piece of text. We present a novel approach to summarizing emotional states by constructing a dictionary of emotion-related words and calculating the probabilities of these words appearing following a prompt that includes both the original text and an emotion-eliciting tail. This methodology allows us to quantitatively assess the emotional landscape of text. To demonstrate our approach we choose a dictionary of 271 emotion describing words and estimate their probability of being associated with a sections Amazon product reviews. Limited computational resources and time means we are only in a position to publish a cursory study. It is likely that many emotion are correlated and an estimate of the dimension of emotional space may be derivable via PCA analysis on a large sample of emotion vectors. We discuss some of the limitations we encountered during experiment and some of the obstacles to producing and regulating the behaviour of emotion based synthetic consciousness. This paper is layed out as follows, section 2 details the LLM and hardware used to run it, section 2.1 details our choices of words to make up our emotion dictionary, section 2.1.1 covers estimating emotion probabilities from an LLM using a tail prompt. Section 2.1.2 shows results on Amazon reviews. A hint at the PCA structure with in emotion vectors is given in 3. Finally future directions are considered and conclusion given.

Estimate Emotion Probability Vectors Using LLMs:  Abstract and Introduction

LLMs ব্যবহার করে আবেগ সম্ভাব্যতা ভেক্টর অনুমান করুন: বিমূর্ত এবং ভূমিকা

Schätzen Sie Emotionswahrscheinlichkeitsvektoren mit LLMs: Zusammenfassung und Einführung

Estimar vectores de probabilidad de emociones mediante LLM: resumen e introducción

Estimer les vecteurs de probabilité d'émotion à l'aide de LLM : résumé et introduction

एलएलएम का उपयोग करके भावना संभाव्यता वैक्टर का अनुमान लगाएं: सार और परिचय

LLM を使用した感情確率ベクトルの推定: 概要と概要

LLM을 사용한 감정 확률 벡터 추정: 요약 및 소개

Estimar vetores de probabilidade de emoção usando LLMs: Resumo e introdução

Оцените векторы вероятности эмоций с помощью LLM: Аннотация и введение

Yüksek Lisans'ı Kullanarak Duygu Olasılığı Vektörlerini Tahmin Edin: Özet ve Giriş

Ước tính các vectơ xác suất cảm xúc bằng cách sử dụng LLM: Tóm tắt và giới thiệu

使用 LLM 估计情绪概率向量：摘要和简介

Authors:
(1) Philipp Straubinger, University of Passau, Passau, Germany;
(2) Gordon Fraser, University of Passau, Passau, Germany. Table of Links Abstract and Introduction
Gamification Elements of Gamekins
Intellij Plugin for Gamekins
Conclusions
Acknowledgement and References ABSTRACT Interruptions and context switches resulting from meetings, urgent tasks, emails, and queries from colleagues contribute to productivity losses in developers’ daily routines. This is particularly challenging for tasks like software testing, which are already perceived as less enjoyable, prompting developers to seek distractions. To mitigate this, applying gamification to testing activities can enhance motivation for test writing. One such gamification tool is Gamekins, which integrates challenges, quests, achievements, and leaderboards into the Jenkins CI (continuous integration) platform. However, as Gamekins is typically accessed through a browser, it introduces a context switch. This paper presents an IntelliJ plugin designed to seamlessly integrate Gamekins’ gamification elements into the IDE, aiming to minimize context switches and boost developer motivation for test writing. CCS CONCEPTS • Software and its engineering → Software testing and debugging; Integrated and visual development environments. KEYWORDS Gamification, IDE, IntelliJ, Software Testing, Continuous Integration ACM Reference Format: Philipp Straubinger and Gordon Fraser. 2024. An IDE Plugin for Gamified Continuous Integration. In 2024 First IDE Workshop (IDE ’24), April 20, 2024, Lisbon, Portugal. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/ 3643796.3648462 1 INTRODUCTION Gamekins [3] is a tool that integrates gamification into the software testing process within the Jenkins continuous integration platform, to encourage developers to improve their testing practices. Addressing the challenge of achieving high software quality and recognizing the common lack of motivation for testing among developers, Gamekins incorporates gamification elements like challenges, quests, leaderboards, and achievements. Developers earn points by completing test-related challenges and quests, engaging in leaderboard competition, and seeking achievements, with the integration into Jenkins aiming to make gamification easily accessible to developers without additional training or resources. In a recent study [4] we integrated Gamekins into an undergraduate software testing course to motivate students to engage more with testing. This study revealed a positive correlation between students’ testing behavior and the use of Gamekins, resulting in a significant improvement in correct results compared to a nongamified cohort. Additionally, students expressed satisfaction with the tool, confirming its effectiveness as a teaching tool. However, a notable issue reported by the students in this experiment was the necessity to switch from their Integrated Development Environment (IDE) to a browser with Gamekins open, causing a productivity dip and time loss navigating to Jenkins. Such context switches, which represent a known productivity challenge [2], are a concern not only for students but also for developers, who spend approximately 50% of their work time in the IDE [1]. In other work, we established that gamification of testing is possible directly in the IDE. Our IntelliGame plugin for the IntelliJ Java IDE[1] implements a multi-level achievement system rewarding positive testing behavior [5]. In a controlled experiment with 49 participants IntelliGame demonstrated clear improvements in testing behavior, for example, causing users to write more tests, achieve higher coverage and mutation scores, run tests more frequently, and achieve functionality earlier. In this paper, we aim to use the experience of gamifying testing directly in the IDE to address the problem of context switches caused by gamification in the CI. We therefore developed a new plugin for the IntelliJ Java IDE that seamlessly integrates the functionality of Gamekins directly into the IDE, offering enhanced features to improve developers’ workflow. This paper is available on arxiv under CC BY-SA 4.0 DEED license. [1] https://www.jetbrains.com/idea/ Authors: (1) Philipp Straubinger, University of Passau, Passau, Germany; (2) Gordon Fraser, University of Passau, Passau, Germany. Authors: Authors: (1) Philipp Straubinger, University of Passau, Passau, Germany; (2) Gordon Fraser, University of Passau, Passau, Germany. Table of Links Abstract and Introduction Gamification Elements of Gamekins Intellij Plugin for Gamekins Conclusions Acknowledgement and References Abstract and Introduction Abstract and Introduction Gamification Elements of Gamekins Gamification Elements of Gamekins Intellij Plugin for Gamekins Intellij Plugin for Gamekins Conclusions Conclusions Acknowledgement and References Acknowledgement and References ABSTRACT Interruptions and context switches resulting from meetings, urgent tasks, emails, and queries from colleagues contribute to productivity losses in developers’ daily routines. This is particularly challenging for tasks like software testing, which are already perceived as less enjoyable, prompting developers to seek distractions. To mitigate this, applying gamification to testing activities can enhance motivation for test writing. One such gamification tool is Gamekins, which integrates challenges, quests, achievements, and leaderboards into the Jenkins CI (continuous integration) platform. However, as Gamekins is typically accessed through a browser, it introduces a context switch. This paper presents an IntelliJ plugin designed to seamlessly integrate Gamekins’ gamification elements into the IDE, aiming to minimize context switches and boost developer motivation for test writing. CCS CONCEPTS CCS CONCEPTS • Software and its engineering → Software testing and debugging; Integrated and visual development environments. • Software and its engineering → Software testing and debugging; Integrated and visual development environments. KEYWORDS KEYWORDS Gamification, IDE, IntelliJ, Software Testing, Continuous Integration ACM Reference Format: ACM Reference Format: Philipp Straubinger and Gordon Fraser. 2024. An IDE Plugin for Gamified Continuous Integration. In 2024 First IDE Workshop (IDE ’24), April 20, 2024, Lisbon, Portugal. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/ 3643796.3648462 1 INTRODUCTION Gamekins [3] is a tool that integrates gamification into the software testing process within the Jenkins continuous integration platform, to encourage developers to improve their testing practices. Addressing the challenge of achieving high software quality and recognizing the common lack of motivation for testing among developers, Gamekins incorporates gamification elements like challenges, quests, leaderboards, and achievements. Developers earn points by completing test-related challenges and quests, engaging in leaderboard competition, and seeking achievements, with the integration into Jenkins aiming to make gamification easily accessible to developers without additional training or resources. In a recent study [4] we integrated Gamekins into an undergraduate software testing course to motivate students to engage more with testing. This study revealed a positive correlation between students’ testing behavior and the use of Gamekins, resulting in a significant improvement in correct results compared to a nongamified cohort. Additionally, students expressed satisfaction with the tool, confirming its effectiveness as a teaching tool. However, a notable issue reported by the students in this experiment was the necessity to switch from their Integrated Development Environment (IDE) to a browser with Gamekins open, causing a productivity dip and time loss navigating to Jenkins. Such context switches, which represent a known productivity challenge [2], are a concern not only for students but also for developers, who spend approximately 50% of their work time in the IDE [1]. In other work, we established that gamification of testing is possible directly in the IDE. Our IntelliGame plugin for the IntelliJ Java IDE[1] implements a multi-level achievement system rewarding positive testing behavior [5]. In a controlled experiment with 49 participants IntelliGame demonstrated clear improvements in testing behavior, for example, causing users to write more tests, achieve higher coverage and mutation scores, run tests more frequently, and achieve functionality earlier. In this paper, we aim to use the experience of gamifying testing directly in the IDE to address the problem of context switches caused by gamification in the CI. We therefore developed a new plugin for the IntelliJ Java IDE that seamlessly integrates the functionality of Gamekins directly into the IDE, offering enhanced features to improve developers’ workflow. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv [1] https://www.jetbrains.com/idea/

An IDE Plugin for Gamified Continuous Integration: Abstract and Introduction

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University;
(2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of
Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics;
(3) Nicole Arulanantham, Space Telescope Science Institute;
(4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences;
(6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro
X-ray Observations and Data Extraction
Flare Analyses
Comparison with X-ray Flares from Young Stars
Discussion
Conclusions
Acknowledgments and References ABSTRACT DQ Tau is a unique young high-eccentricity binary system that exhibits regular magnetic reconnection flares and pulsed accretion near periastron. We conducted NuSTAR, Swift, and Chandra observations during the July 30, 2022 periastron to characterize X-ray, near-ultraviolet (NUV), and optical flaring emissions. Our findings confirm the presence of X-ray super-flares accompanied by substantial NUV and optical flares, consistent with previous discoveries of periastron flares in 2010 and 2021. These observations, supported by new evidence, strongly establish the magnetosphere collision mechanism as the primary driver of magnetic energy release during DQ Tau’s periastron flares. The energetics of the observed X-ray super-flares remain consistent across the three periastrons, indicating recurring energy sources during each passage, surpassing the capabilities of single stars. The observed flaring across multiple bands supports the Adams et al. model for magnetosphere interaction in eccentric binaries. Evidence from modeling and past and current observations suggests that both the mm/X-ray periastron flares and tentatively, the magnetic reconnection-related components of the optical/NUV emissions, conform to the classical solar/stellar non-thermal thick-target model, except for the distinctive magnetic energy source. However, our NuSTAR observations suffered from high background levels, hindering the detection of anticipated non-thermal hard X-rays. Furthermore, we report serendipitous discovery of X-ray super-flares occurring away from periastron, potentially associated with interacting magnetospheres. The current study is part of a broader multi-wavelength campaign, which is planned to investigate the influence of DQ Tau’s stellar radiation on gas-phase ion chemistry within its circumbinary disk. Keywords: Pre-main sequence stars (1290) — Spectroscopic binary stars (1557) — X-ray stars (1823) — Stellar magnetic fields (1610) — Optical flares (1166) — Ultraviolet transient sources (1854) —- Stellar x-ray flares (1637) — Solar x-ray flares (1816) — Solar flares (1496) — Stellar flares (1603) — Protoplanetary disks (1300) 1. INTRODUCTION DQ Tau is a nearby (D = 195 pc; Gaia Collaboration et al. 2023), non-eclipsing, double-lined spectro scopic binary system, consisting of two pre-main sequence (PMS) stars of equal mass (0.6 M⊙) and equal radius (2 R⊙) (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). These stars exhibit spectral types within the range of M0 to K7. The rotational periods of the primary and secondary components are 3 days (K´osp´al et al. 2018) and 4.5 days (Pouilly et al. in prep.), respectively. The orbital period measures 15.8 days. DQ Tau boasts a highly eccentric orbit (e ∼ 0.6) and displays an exceptionally small periastron separation, measuring only about 8 −10 stellar radii (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). Furthermore, the binary components of DQ Tau harbor relatively strong surface magnetic fields, estimated at around 2.5 kG, which give rise to formidable magnetospheres (Pouilly et al. 2023, Pouilly et al. in prep.). Surrounding DQ Tau is a protoplanetary disk of average size (≤ 100 au), complete with a small 0.3 au cavity (Czekala et al. 2016; K´osp´al et al. 2018; Ballering & Eisner 2019). Large optical and UV brightenings primarily occur at orbital phase (Φ = 0.8 − 1.2), and they are mainly attributed to the pulsed accretion of disk material onto the binary components (Tofflemire et al. 2017; K´osp´al et al. 2018; Muzerolle et al. 2019; Fiorellino et al. 2022). However, far-UV (FUV) observations of DQ Tau with the Cosmic Origins Spectrograph onboard HST (HST-COS) showed no correlation between the orbital phase of the binary and the C IV flux, a tracer of mass accretion rate, indicating that some component of the behavior is stochastic (Ardila et al. 2015). The system exhibits powerful mm/X-ray flares coinciding with periastron passage, attributed to collisions between the magnetospheres of the binary components. The evidence supporting the magnetsophere collision hypothesis includes the recurrence of synchrotron mmband flaring during 4 periastron encounters (Salter et al. 2008, 2010), the recurrence of soft X-ray flaring in 2 periastron encounters (Getman et al. 2011, 2022a), the timing and energy relationships between the mm and X-ray flares, and the consistency observed between the flare loop size and binary separation (Salter et al. 2010; Getman et al. 2011). Several other young binary systems with high eccentricities have been reported to exhibit enhanced levels of either X-ray, optical/mm, or radio emissions near their periastron passages. Notable examples include ϵ Lupi (Das et al. 2023), a collective study of four binaries (Parenago 523, RX J1622.7-2325 Nw, UZ Tau E, and HD 152404) conducted by Getman et al. (2016), UZ Tau E (K´osp´al et al. 2011), and V773 Tau A (Massi et al. 2008; Adams et al. 2011). In these systems, magnetosphere collision has been proposed as a primary mechanism responsible for generating the magnetic reconnection energy that drives these events. Modelling studies have shown that PMS X-rays have strong impact on disk ionization and chemistry (Glassgold et al. 2000; Alexander et al. 2014). But most studies assume continuous irradiation without considering the high-amplitude variations in flux and spectrum due to super-flares. A few time-dependent calculations show that disk ionization may respond to sudden large Xray flares (Rab et al. 2017; Waggoner & Cleeves 2022). This may already have been seen. One empirical report of ionization variability has emerged: the H13CO+ abundance of IM Lup’s disk jumped up and down by a factor of 2.5 over months (Cleeves et al. 2017). However, since no concurrent X-ray observations were conducted, the exact cause cannot be definitively ascribed to X-ray flaring. The occurrence of large X-ray flares in young stars is relatively rare and unpredictable, displaying a stochastic nature (Getman & Feigelson 2021). However, the presence of predictable X-ray super-flares and accretion outbursts in close proximity to periastron passage makes DQ Tau an extraordinary laboratory for investigating the influence of stellar radiation on the gas-phase ion chemistry within its disk. To achieve this, we conducted a single X-ray observation using the NuSTAR telescope, along with multiple short-duration observations using the Swift telescope in the X-ray, UV, and optical wavelengths. These observations were strategically timed near a specific periastron passage of DQ Tau, taking place in July-August 2022. Additionally, we captured multiple snapshots of the non-periastron portion of DQ Tau’s orbit using the Chandra X-ray telescope. However, we encountered unfavorable weather conditions that limited our ALMA observations to a single short session near periastron. The results from this ALMA observation will be presented in a forthcoming paper. Meanwhile, building upon the X-ray/UV/optical data acquired in 2022 and the previously obtained X-ray/mm data (Salter et al. 2010; Getman et al. 2011, 2022a), our current study is dedicated to further investigating the origins and energetic properties of X-ray flares, along with their corresponding near-ultraviolet (NUV) and optical flare counterparts, observed within the remarkable young binary system DQ Tau. The structure of the paper is outlined as follows: Section 2 provides a detailed description of the data reduction and reduction procedures employed for X-ray, UV, and optical analyses. In Section 3, we present the detection of flares and examine their spectral properties. Section 4 offers a comparison between the X-ray periastron flares observed in DQ Tau and super-flares observed in numerous other PMS stars. Finally, Section 5 delves into a discussion surrounding the origin and energetics of the X-ray flares, as well as their associated NUV and optical counterparts. This paper is available on arxiv under CC 4.0 license. Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Konstantin V. Getman, Department of Astronomy & Astrophysics, Pennsylvania State University; (2) Agnes Kospal, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, E¨otv¨os Lor´and Research Network (ELKH), MTA Centre of Excellence, Max Planck Institute for Astronomy, and ELTE E¨otv¨os Lor´and University, Institute of Physics; (3) Nicole Arulanantham, Space Telescope Science Institute; (4) Dmitry A. Semenov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (5) Grigorii V. Smirnov-Pinchukov, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences; (6) Sierk E. van Terwisga, Konkoly Observatory, Research Centre for Astronomy and Earth Sciences. Table of Links Abstract and Intro X-ray Observations and Data Extraction Flare Analyses Comparison with X-ray Flares from Young Stars Discussion Conclusions Acknowledgments and References Abstract and Intro Abstract and Intro X-ray Observations and Data Extraction X-ray Observations and Data Extraction Flare Analyses Flare Analyses Comparison with X-ray Flares from Young Stars Comparison with X-ray Flares from Young Stars Discussion Discussion Conclusions Conclusions Acknowledgments and References Acknowledgments and References ABSTRACT DQ Tau is a unique young high-eccentricity binary system that exhibits regular magnetic reconnection flares and pulsed accretion near periastron. We conducted NuSTAR, Swift, and Chandra observations during the July 30, 2022 periastron to characterize X-ray, near-ultraviolet (NUV), and optical flaring emissions. Our findings confirm the presence of X-ray super-flares accompanied by substantial NUV and optical flares, consistent with previous discoveries of periastron flares in 2010 and 2021. These observations, supported by new evidence, strongly establish the magnetosphere collision mechanism as the primary driver of magnetic energy release during DQ Tau’s periastron flares. The energetics of the observed X-ray super-flares remain consistent across the three periastrons, indicating recurring energy sources during each passage, surpassing the capabilities of single stars. The observed flaring across multiple bands supports the Adams et al. model for magnetosphere interaction in eccentric binaries. Evidence from modeling and past and current observations suggests that both the mm/X-ray periastron flares and tentatively, the magnetic reconnection-related components of the optical/NUV emissions, conform to the classical solar/stellar non-thermal thick-target model, except for the distinctive magnetic energy source. However, our N u STAR observations suffered from high background levels, hindering the detection of anticipated non-thermal hard X-rays. Furthermore, we report serendipitous discovery of X-ray super-flares occurring away from periastron, potentially associated with interacting magnetospheres. The current study is part of a broader multi-wavelength campaign, which is planned to investigate the influence of DQ Tau’s stellar radiation on gas-phase ion chemistry within its circumbinary disk. u Keywords : Pre-main sequence stars (1290) — Spectroscopic binary stars (1557) — X-ray stars (1823) — Stellar magnetic fields (1610) — Optical flares (1166) — Ultraviolet transient sources (1854) —- Stellar x-ray flares (1637) — Solar x-ray flares (1816) — Solar flares (1496) — Stellar flares (1603) — Protoplanetary disks (1300) Keywords 1. INTRODUCTION DQ Tau is a nearby (D = 195 pc; Gaia Collaboration et al. 2023), non-eclipsing, double-lined spectro scopic binary system, consisting of two pre-main sequence (PMS) stars of equal mass (0.6 M⊙) and equal radius (2 R⊙) (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). These stars exhibit spectral types within the range of M0 to K7. The rotational periods of the primary and secondary components are 3 days (K´osp´al et al. 2018) and 4.5 days (Pouilly et al. in prep.), respectively. The orbital period measures 15.8 days. DQ Tau boasts a highly eccentric orbit (e ∼ 0.6) and displays an exceptionally small periastron separation, measuring only about 8 −10 stellar radii (Mathieu et al. 1997; Czekala et al. 2016; Pouilly et al. 2023). Furthermore, the binary components of DQ Tau harbor relatively strong surface magnetic fields, estimated at around 2.5 kG, which give rise to formidable magnetospheres (Pouilly et al. 2023, Pouilly et al. in prep.). Surrounding DQ Tau is a protoplanetary disk of average size (≤ 100 au), complete with a small 0.3 au cavity (Czekala et al. 2016; K´osp´al et al. 2018; Ballering & Eisner 2019). Large optical and UV brightenings primarily occur at orbital phase (Φ = 0.8 − 1.2), and they are mainly attributed to the pulsed accretion of disk material onto the binary components (Tofflemire et al. 2017; K´osp´al et al. 2018; Muzerolle et al. 2019; Fiorellino et al. 2022). However, far-UV (FUV) observations of DQ Tau with the Cosmic Origins Spectrograph onboard HST (HST-COS) showed no correlation between the orbital phase of the binary and the C IV flux, a tracer of mass accretion rate, indicating that some component of the behavior is stochastic (Ardila et al. 2015). The system exhibits powerful mm/X-ray flares coinciding with periastron passage, attributed to collisions between the magnetospheres of the binary components. The evidence supporting the magnetsophere collision hypothesis includes the recurrence of synchrotron mmband flaring during 4 periastron encounters (Salter et al. 2008, 2010), the recurrence of soft X-ray flaring in 2 periastron encounters (Getman et al. 2011, 2022a), the timing and energy relationships between the mm and X-ray flares, and the consistency observed between the flare loop size and binary separation (Salter et al. 2010; Getman et al. 2011). Several other young binary systems with high eccentricities have been reported to exhibit enhanced levels of either X-ray, optical/mm, or radio emissions near their periastron passages. Notable examples include ϵ Lupi (Das et al. 2023), a collective study of four binaries (Parenago 523, RX J1622.7-2325 Nw, UZ Tau E, and HD 152404) conducted by Getman et al. (2016), UZ Tau E (K´osp´al et al. 2011), and V773 Tau A (Massi et al. 2008; Adams et al. 2011). In these systems, magnetosphere collision has been proposed as a primary mechanism responsible for generating the magnetic reconnection energy that drives these events. Modelling studies have shown that PMS X-rays have strong impact on disk ionization and chemistry (Glassgold et al. 2000; Alexander et al. 2014). But most studies assume continuous irradiation without considering the high-amplitude variations in flux and spectrum due to super-flares. A few time-dependent calculations show that disk ionization may respond to sudden large Xray flares (Rab et al. 2017; Waggoner & Cleeves 2022). This may already have been seen. One empirical report of ionization variability has emerged: the H13CO+ abundance of IM Lup’s disk jumped up and down by a factor of 2.5 over months (Cleeves et al. 2017). However, since no concurrent X-ray observations were conducted, the exact cause cannot be definitively ascribed to X-ray flaring. The occurrence of large X-ray flares in young stars is relatively rare and unpredictable, displaying a stochastic nature (Getman & Feigelson 2021). However, the presence of predictable X-ray super-flares and accretion outbursts in close proximity to periastron passage makes DQ Tau an extraordinary laboratory for investigating the influence of stellar radiation on the gas-phase ion chemistry within its disk. To achieve this, we conducted a single X-ray observation using the N u STAR telescope, along with multiple short-duration observations using the Swift telescope in the X-ray, UV, and optical wavelengths. These observations were strategically timed near a specific periastron passage of DQ Tau, taking place in July-August 2022. Additionally, we captured multiple snapshots of the non-periastron portion of DQ Tau’s orbit using the Chandra X-ray telescope. However, we encountered unfavorable weather conditions that limited our ALMA observations to a single short session near periastron. The results from this ALMA observation will be presented in a forthcoming paper. u Meanwhile, building upon the X-ray/UV/optical data acquired in 2022 and the previously obtained X-ray/mm data (Salter et al. 2010; Getman et al. 2011, 2022a), our current study is dedicated to further investigating the origins and energetic properties of X-ray flares, along with their corresponding near-ultraviolet (NUV) and optical flare counterparts, observed within the remarkable young binary system DQ Tau. The structure of the paper is outlined as follows: Section 2 provides a detailed description of the data reduction and reduction procedures employed for X-ray, UV, and optical analyses. In Section 3, we present the detection of flares and examine their spectral properties. Section 4 offers a comparison between the X-ray periastron flares observed in DQ Tau and super-flares observed in numerous other PMS stars. Finally, Section 5 delves into a discussion surrounding the origin and energetics of the X-ray flares, as well as their associated NUV and optical counterparts.

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Abstract and Intro

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrea Roncoli, Department of Computer, Science (University of Pisa);
(2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago);
(3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory);
(4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute);
(5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Data and Methods Results Conclusions Acknowledgments and Disclosure of Funding, and References Additional Plots Abstract 1 Introduction Accurate determination of cosmological parameters using big data from astronomical surveys is a task of paramount importance in modern science. Historically, the extraction of valuable cosmological information has relied on computing summary statistics [31, 16, 15]. More recently, deep learning methods, such as 2D and 3D Convolutional Neural Networks (CNNs), showed great promise in extracting rich non-linear information that summary statistics struggle to capture [32, 39, 29]. However, CNNs lack scale-invariance, as their analysis is firmly anchored to the grid size of the convolutional kernels, while any information on scales below that is lost. Choosing a superfine grid to avoid information loss, though, would simply yield almost entirely zeros in case of sparse and irregular data, such as galaxy clusterings. Thus, CNNs result in an inadequate method for structured sparse data. In contrast, Graph Neural Networks (GNNs) [23, 4, 49, 46] can handle structured cosmic web data in a scale-free manner [41, 14]. As with any other model, the typical procedure is to train GNNs on labeled data (like simulations) and then infer cosmological parameters from unlabeled data (like observations). However, there is a significant risk of these models not generalizing in the presence of the domain shift between simulations and observations. Systematic biases have been demonstrated even in experiments that train and test on simulations with different subgrid physics [41]. Domain adaptation (DA) techniques [11, 43, 18, 27] can be used to increase model robustness to this type of domain shift. Here we propose the use of Domain Adaptive Graph Neural Networks (DA-GNNs) and investigate the utility of distance-based DA losses i.e., Maximum Mean Discrepancy (MMD) [6]. MMD is an unsupervised DA technique because it does not require labeled data, which is paramount for future applications on observations. We show that our domain-adaptive models achieve stronger generalization across datasets than regular GNN models. Our work is a significant step towards building future models trained on simulations, yet robust enough to work on observational data. Related Work GNNs have shown great potential for extracting information from large sparse datasets, such as the distribution of galaxies, galaxy clusters, and cosmic large-scale structures [25, 28, 41, 33, 42, 14]. Unfortunately, due to the complexity of most deep learning models, they often learn dataset-specific features, which renders them useless when testing on a different dataset (different simulations or astronomical observations). In astronomy, it has been shown that DA techniques applied to different types of CNNs can substantially improve model performance in cross-dataset applications [8, 10, 9, 37, 21, 2]. Recently, it has been shown that DA can be used on other types of deep learning algorithms such as GNNs [12, 24, 45, 47, 7, 44, 17]. However, DA on GNNs has not been used for any astrophysics or cosmology applications. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). This paper is available on arxiv under CC 4.0 license. available on arxiv Authors: Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Abstract and Intro Data and Methods Data and Methods Results Results Conclusions Conclusions Acknowledgments and Disclosure of Funding, and References Acknowledgments and Disclosure of Funding, and References Additional Plots Additional Plots Abstract 1 Introduction Accurate determination of cosmological parameters using big data from astronomical surveys is a task of paramount importance in modern science. Historically, the extraction of valuable cosmological information has relied on computing summary statistics [31, 16, 15]. More recently, deep learning methods, such as 2D and 3D Convolutional Neural Networks (CNNs), showed great promise in extracting rich non-linear information that summary statistics struggle to capture [32, 39, 29]. However, CNNs lack scale-invariance, as their analysis is firmly anchored to the grid size of the convolutional kernels, while any information on scales below that is lost. Choosing a superfine grid to avoid information loss, though, would simply yield almost entirely zeros in case of sparse and irregular data, such as galaxy clusterings. Thus, CNNs result in an inadequate method for structured sparse data. In contrast, Graph Neural Networks (GNNs) [23, 4, 49, 46] can handle structured cosmic web data in a scale-free manner [41, 14]. As with any other model, the typical procedure is to train GNNs on labeled data (like simulations) and then infer cosmological parameters from unlabeled data (like observations). However, there is a significant risk of these models not generalizing in the presence of the domain shift between simulations and observations. Systematic biases have been demonstrated even in experiments that train and test on simulations with different subgrid physics [41]. Domain adaptation (DA) techniques [11, 43, 18, 27] can be used to increase model robustness to this type of domain shift. Here we propose the use of Domain Adaptive Graph Neural Networks (DA-GNNs) and investigate the utility of distance-based DA losses i.e., Maximum Mean Discrepancy (MMD) [6]. MMD is an unsupervised DA technique because it does not require labeled data, which is paramount for future applications on observations. We show that our domain-adaptive models achieve stronger generalization across datasets than regular GNN models. Our work is a significant step towards building future models trained on simulations, yet robust enough to work on observational data. Related Work GNNs have shown great potential for extracting information from large sparse datasets, such as the distribution of galaxies, galaxy clusters, and cosmic large-scale structures [25, 28, 41, 33, 42, 14]. Unfortunately, due to the complexity of most deep learning models, they often learn dataset-specific features, which renders them useless when testing on a different dataset (different simulations or astronomical observations). In astronomy, it has been shown that DA techniques applied to different types of CNNs can substantially improve model performance in cross-dataset applications [8, 10, 9, 37, 21, 2]. Recently, it has been shown that DA can be used on other types of deep learning algorithms such as GNNs [12, 24, 45, 47, 7, 44, 17]. However, DA on GNNs has not been used for any astrophysics or cosmology applications. Related Work

Adaptive Graph Neural Networks for Cosmological Data Generalization: Abstract and Intro

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords: Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release. Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords : Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) Keywords 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release.

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Abstract and Intro

Authors:
(1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;
(2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;
(3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;
(4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Table of Links Abstract and Intro Related Works DIALSTORY Dataset Proposed Tasks Methodology Experiments Discussion Future Work Conclusion Limitations and References Abstract Many classical fairy tales, fictions, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which require capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DIALSTORY, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DIALSTORY. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines. 1 Introduction Dialogue plays an important role in various types of literary works such as short stories, novels, and screenplays by advancing plots, establishing characters, and providing expositions using natural and lifelike words (Kennedy et al., 1983). Compared to dialogue in conversational scenarios such as chitchat bots (Shang et al., 2015) or task-oriented dialogue systems (Deng et al., 2012), dialogue in stories is mainly used to exhibit emotions, motivations, or personalities of characters following the authors’ design, which further serve for the coherence, informativeness, engagingness, and plot development of whole stories. Dialogue is also revealed to be essential for user-agent interaction in many text adventure games (Xi et al., 2021;Li et al., 2022). It has not been widely explored for machines to understand and generate dialogue in stories despite the broad recognition of the importance of this ability. To spur research in this field, we present a new story dataset named DIALSTORY, which consists of 105k Chinese short stories with automatic annotations of dialogue turns and corresponding speakers. Furthermore, we formulate two new tasks including: (1) Masked Dialogue Generation (DialGen): completing a story with several dialogue turns masked with placeholders; and (2) Dialogue Speaker Recognition (DialSpk): choosing correct speakers from given candidates for several specified dialogue turns. We construct standardized datasets for these tasks through automatic or manual annotation based on DIALSTORY. As exemplified in Figure 1, these tasks comprehensively investigate the ability to capture characters’ emotions (e.g., the mother squirrel is worried about baby squirrel’s catching cold), motivations (e.g., the mother squirrel intends to call her baby to come back home or get medicine), and relationship between each other (e.g. mother rabbit knows the kind of flower as a friend of mother squirrel when they were kids). Furthermore, we found in massive stories that dialogue had a strong connection to different characters, reflected in their emotional states, speaking styles, and plot development. To provide more insights to tackle these tasks, we propose to learn character representations for modeling dependencies between characters and dialogue explicitly. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Authors: Authors: (1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology; (4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Table of Links Abstract and Intro Abstract and Intro Related Works Related Works DIALSTORY Dataset DIALSTORY Dataset Proposed Tasks Proposed Tasks Methodology Methodology Experiments Experiments Discussion Discussion Future Work Future Work Conclusion Conclusion Limitations and References Limitations and References Abstract Many classical fairy tales, fictions, and screenplays leverage dialogue to advance story plots and establish characters. We present the first study to explore whether machines can understand and generate dialogue in stories, which require capturing traits of different characters and the relationships between them. To this end, we propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition, i.e., generating missing dialogue turns and predicting speakers for specified dialogue turns, respectively. We build a new dataset DIALSTORY, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DIALSTORY. Furthermore, we propose to learn explicit character representations to improve performance on these tasks. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines. 1 Introduction Dialogue plays an important role in various types of literary works such as short stories, novels, and screenplays by advancing plots, establishing characters, and providing expositions using natural and lifelike words (Kennedy et al., 1983). Compared to dialogue in conversational scenarios such as chitchat bots (Shang et al., 2015) or task-oriented dialogue systems (Deng et al., 2012), dialogue in stories is mainly used to exhibit emotions, motivations, or personalities of characters following the authors’ design, which further serve for the coherence, informativeness, engagingness, and plot development of whole stories. Dialogue is also revealed to be essential for user-agent interaction in many text adventure games (Xi et al., 2021;Li et al., 2022). It has not been widely explored for machines to understand and generate dialogue in stories despite the broad recognition of the importance of this ability. To spur research in this field, we present a new story dataset named DIALSTORY, which consists of 105k Chinese short stories with automatic annotations of dialogue turns and corresponding speakers. Furthermore, we formulate two new tasks including: (1) Masked Dialogue Generation (DialGen): completing a story with several dialogue turns masked with placeholders; and (2) Dialogue Speaker Recognition (DialSpk): choosing correct speakers from given candidates for several specified dialogue turns. We construct standardized datasets for these tasks through automatic or manual annotation based on DIALSTORY. As exemplified in Figure 1, these tasks comprehensively investigate the ability to capture characters’ emotions (e.g., the mother squirrel is worried about baby squirrel’s catching cold), motivations (e.g., the mother squirrel intends to call her baby to come back home or get medicine), and relationship between each other (e.g. mother rabbit knows the kind of flower as a friend of mother squirrel when they were kids). Furthermore, we found in massive stories that dialogue had a strong connection to different characters, reflected in their emotional states, speaking styles, and plot development. To provide more insights to tackle these tasks, we propose to learn character representations for modeling dependencies between characters and dialogue explicitly. Extensive experiments and case studies show that our approach can generate more coherent and informative dialogue, and achieve higher speaker recognition accuracy than strong baselines. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Understanding and Generating Dialogue between Characters in Stories: Abstract and Intro

Authors:
(1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu;
(2) Eric Bier, Palo Alto Research Center, USA bier@parc.com;
(3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com;
(4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov;
(5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Related Work Methodology Experiments Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords: Clustering based methods · Floor plans · Augmented Reality· 3D Models. 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Authors: Authors: (1) Ritesh Sharma, University Of California, Merced, USA rsharma39@ucmerced.edu; (2) Eric Bier, Palo Alto Research Center, USA bier@parc.com; (3) Lester Nelson, Palo Alto Research Center, USA lnelson@parc.com; (4) Mahabir Bhandari, Oak Ridge National Laboratory, USA bhandarims@ornl.gov; (5) Niraj Kunwar, Oak Ridge National Laboratory, USA kunwarn1@ornl.gov. Table of Links Abstract and Intro Abstract and Intro Related Work Related Work Methodology Methodology Experiments Experiments Conclusion & Future work and References Conclusion & Future work and References Abstract This paper describes a novel approach for generating accurate floor plans and 3D models of building interiors using scanned mesh data. Unlike previous methods, which begin with a high resolution point cloud from a laser range-finder, our approach begins with triangle mesh data, as from a Microsoft HoloLens. It generates two types of floor plans, a "pen-and-ink" style that preserves details and a drafting-style that reduces clutter. It processes the 3D model for use in applications by aligning it with coordinate axes, annotating important objects, dividing it into stories, and removing the ceiling. Its performance is evaluated on commercial and residential buildings, with experiments to assess quality and dimensional accuracy. Our approach demonstrates promising potential for automatic digitization and orientation of scanned mesh data, enabling floor plan and 3D model generation in various applications such as navigation, interior design, furniture placement, facilities management, building construction, and HVAC design. Keywords : Clustering based methods · Floor plans · Augmented Reality· 3D Models. Keywords 1 Introduction Floor plans are useful for many applications including navigating in building interiors; remodeling; efficient placement of furniture; placement of pipes; heating, ventilation, and air conditioning (HVAC) design; and preparing an emergency evacuation plan. Depending on the application, different kinds of floor plan are appropriate. For remodeling building interiors or designing HVAC systems, users may prefer a drafting-style floor plan that focuses on planar walls and removes furniture and other clutter. For furniture placement, navigation, or evacuation planning, users may prefer a more detailed floor plan that shows the positions of furniture, cabinets, counter tops, etc. In either case, producing a floor plan can be time consuming, requiring expert skills, such as measuring distances and angles or entering data into a CAD program. Furthermore, it may need to be done more than once because a building changes when walls and furniture are moved, added, or removed. So it is valuable to be able to generate floor plans automatically with little or no training. To generate floor plans, it helps to begin with accurate data that can be collected automatically. Laser range finders, smartphones, tablets, and augmented reality (AR) headsets are some of the devices that have made it easier to collect high-resolution building data in the form of RGBD images, point clouds, and triangle meshes. In this paper, we describe a method for generating drafting style and pen-and-ink-style floor plans by leveraging incomplete and imperfect triangle mesh data. This approach efficiently generates both types of floor plans accurately, supporting a wide range of applications. Main Contribution We describe a new method for generating accurate floor plans using poorly captured triangle mesh data from the Microsoft HoloLens 2. The main contributions are: Main Contribution – A modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, using blocks to capture wall height and thickness. – An orientation-based clustering method that finds walls at arbitrary angles. – The use of k-means clustering to rotate the mesh to the principal axes and to identify the floor and ceiling. – Generating two kinds of precise floor plans from incomplete mesh data. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Abstract and Introduction

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