At that time, Angelina, a Swiss pharmaceutical company owner and activist, tries to make generic medicines for life-threatening diseases available in India at low cost affordable by poor people. Siddharth&#x27;s assassin Abbas kills Angelina before she can sign the agreement document with the government and shoots Dhruva ...

Dhruva (2016 film) - Wikipedia

Druva Inc. is an American privately-held software company based in Santa Clara, California. The company provides SaaS-based data protection and management products. The company was founded in 2008, raised several rounds of funding, and grew to more than 800 employees.

Druva - Wikipedia

The Sanskrit term dhruva nakshatra (ध्रुव नक्षत्र, &quot;polar star&quot;) has been used for Pole Star in the Mahabharata, personified as son of Uttānapāda and grandson of Manu, even though Polaris at the likely period of the recension of the text of the Mahabharata was still ...

Dhruva - Wikipedia

400 Druva reviews. A free inside look at company reviews and salaries posted anonymously by employees.

Druva Reviews: What Is It Like to Work At Druva? | Glassdoor

Druva offers a fresh perspective on data resilience with a fully managed SaaS platform to protect your data wherever it lives – backed by a $10M guarantee!

Data Resiliency Cloud | Fully Managed SaaS Platform | Druva

Dhruva Interactive was an Indian video game developer based in Bangalore. Rajesh Rao founded the company in March 1997 out of the multimedia company he had established two years earlier. Starbreeze Studios acquired a majority holding in Dhruva Interactive in 2017, which it sold to Rockstar ...

Dhruva Interactive - Wikipedia

The team at Dhruva has deep industry experience across virtually all sectors including Aerospace &amp; Defence, Automobile &amp; Ancillary, Agro &amp; Chemicals, Conglomerates, Energy &amp; Resources, Education, Financial Services, IT &amp; ITes, Manufacturing &amp; Real Estate, Pharma, Life Sciences &amp; Healthcare, ...

Dhruva

Druva | 78,348 followers on LinkedIn. The industry’s leading SaaS platform for data resilience. | Druva is the industry’s leading SaaS platform for data resiliency, and the only vendor to ensure data protection across the most common data risks backed by a $10 million guarantee.

Druva | LinkedIn

The movie story deals with a committed police officer who is on a very dangerous mission to destroy the most corrupt person in the country, Siddharth Abhiman...

Dhruva Full Action Hindi Dubbed Movie In HD Quality | Ram Charan, ...

Founded in 2008, Druva is a fast-growing provider of data protection solutions for the enterprise. The company offers a full suite of data protection and management solutions for enterprise laptops, PCs, smartphones, and tablets. Its flagship product, inSync, is a highly scalable solution designed ...

AWS Case Study: Druva

dhruva

The (IN-SPACe) announced the Integrated Launch Manifesto for space launches utilizing launch infrastructure. This manifesto details the launches planned by ISRO, (NSIL) and various Non-Governmental Entities (NGEs) for the fiscal years 2023-24 (Q4) and 2024-25. As is evident there is a notable increase in launches and in private sector involvement in the Indian space sector. Indian National Space Promotion and Authorization Centre ISRO’s New Space India Limited This initiative aligns with the directives of the recently ratified Indian Space Policy, entrusting IN-SPACe with authorization of launch manifesto for launch-infrastructure created through public expenditure. The launch manifesto comprehensively covers user-funded, scientific missions & other technology demonstration launches by ISRO along with the commercial launches and associated primary and co-sharing passengers. The year 2023 stood as a milestone with the new Space Policy fostering rapid advancements in the sector. The Integrated Launch Manifesto is in line with Dept of Space’s vision on optimizing resources and encouraging innovation to establish India as a Global Manufacturing Hub for space activities, aligning with the vision of ‘Aatmnirbhar Bharat’ (Self-Reliant India). About 30 launch are planned for 2023-24 (Q4) & 2024-25 from SDSC, SHAR, half of which are catering to the commercial space sector of India with rest being user-funded, scientific missions or technology test launches. Out of the 14 commercial missions identified, 7 launches are being undertaken by M/s NSIL including 2 numbers of PSLVs being realized through an industry consortium.  This marks a substantial increase in launch activity compared to previous years and is a positive indicator of the expanding space ecosystem in the country. The key NGE highlights for this period include planned Sub-Orbital and Orbital launches by M/s Agnikul Cosmos and M/s Skyroot Aerospace. Few of the NGEs facilitated for their satellite launch requirements include M/s Digantara Research & Technology, M/s Dhruvaspace, M/s Space Kidz India and academic institutes like IIT-Madras, Manipal Institute of Technology and C.V. Raman Global University Odisha. However, IN-SPACe will make every effort to accommodate any additional requirement of NGEs during this period. The detailed Launch Manifesto is accessible on IN-SPACe’s digital portal. The summary of Launch Manifesto for the said period is provided in Annexure – A. Summary of Launch Manifesto for 2023-24 (Q4) and 2024-25 Annexure -A: Launch by ISRO NSIL Other NGE (Q4) 2023-24 3 (PSLV, GSLV, SSLV) Nil 1 (AGNIBAAN-SORTeD) 2024-25 13 (PSLV: 3, GSLV: 3,  Gaganyaan & Test vehicle :7nos.) 7 (PSLV:4 + LVM3:1 +  SSLV:2) 6 (4 VIKRAM-1 +2 AGNIBAAN) IN-SPACe is an autonomous nodal agency under Department of Space, Government of India formed on 24 June 2020 to promote, enable, authorize and supervise NGE to undertake space activities. These activities include manufacturing of launch vehicles and satellites, providing space-based services, establishing a ground station, sharing of space infrastructure & facilities; and establishing new facilities under DOS. IN-SPACe is currently functioning with three directorates viz., Promotion Directorate (PD), Technical Directorate (TD) and Program Management and Authorization Directorate (PMAD) and Legal, Finance and Administration Wing with its headquarters at Ahmedabad. This article was originally published by on Navanwita Bora Sachdev The Tech Panda.

IN-SPACe's Launch Manifesto: Mapping the Future of Space Exploration

After the successful touchdown of ’s lander in the south of the Moon, all eyes are on India’s space program. Already, ISRO and the Japan Aerospace Exploration Agency (JAXA) launch LUPEX, a successor to Chandrayaan-3, in 2026. Chandrayaan-3 have decided to jointly In the limelight is also startup ecosystem, which has secured its position as a formidable contender on the global stage. According to recent insights by on August 11, 2023, India ranks seventh in terms of funding within the international SpaceTech landscape for the year. India’s SpaceTech Tracxn The Indian SpaceTech sector’s journey has been one of transformation, driven by the sector’s privatization in 2020. Previously dominated by governmental players, the sector has witnessed a significant surge in private sector participation post-privatization. Private entities are now actively involved in crucial aspects of research, manufacturing, and fabrication of rockets and satellites, fostering a vibrant ecosystem of innovation. The funding trajectory of the Indian SpaceTech ecosystem tells a compelling story of growth and resilience. From a modest $35 million in funds raised between 2010 and 2019, the sector experienced a meteoric rise in 2020, securing a remarkable $28 million in funding. This trend continued with exponential growth, reaching $96 million in 2021 and an impressive $112 million in 2022, marking a 17% increase from the previous year. This steady expansion in funding sets the Indian SpaceTech sector apart from other industries affected by funding challenges and macroeconomic fluctuations. Building on its upward trajectory, the Indian SpaceTech sector has already attracted $62 million in funding for the year 2023, reflecting a substantial 60% increase compared to the same period last year. This exceptional growth trend is expected to continue in the upcoming months. India’s emergence as a potent player in SpaceTech is further highlighted by its impressive satellite deployment. With 381 satellites placed in low orbit, India has positioned itself as a key player in this arena. Notably, the successful launch of 36 satellites into orbit by ISRO for UK-based OneWeb illustrates India’s contribution to global satellite ownership. The funding trajectory of the Indian SpaceTech ecosystem tells a compelling story of growth and resilience The Indian private sector has taken the lead in for practical applications, including satellite-based communication services like phone signals, broadband, OTT, and 5G. The government’s Budget 2023 initiatives further support the private enterprises’ role in this sector, from launching satellites and rockets to operating Earth stations with a 0% GST regime. Plans for a Production Linked Incentive (PLI) scheme for satellite manufacturing are also in the pipeline. launching satellites India’s space agency, ISRO, boasts a series of remarkable milestones, including the successful landing of Chandrayaan-3 on the moon’s south pole, paving the way for groundbreaking studies in the region. Also, India achieved a major achievement by becoming the first country to successfully land a spacecraft near the moon’s south pole. Additionally, ISRO’s feat of deploying a record-breaking 104 satellites in sun-synchronous orbits during a single mission in 2017 stands as a testament to India’s prowess. Among the thriving business models within the Indian SpaceTech sector, Small Payload-based Launch Vehicles have garnered significant attention, securing $75.6 million in funding over the last two years. Key players such as Skyroot, AgniKul, and Pixxel are spearheading innovative endeavors that promise to reshape the industry landscape. Skyroot, for instance, has launched India’s first privately built rocket, Vikram-S, into space, with plans to revolutionize satellite launches. Satellite-based Imaging Solutions have also witnessed substantial support, with $84.2 million in funding secured over the past two years. Companies like Pixxel have introduced pioneering technologies for earth observation, while Dhruva Space and Bellatrix Aerospace are contributing to the sector through their unique offerings. The global space industry is undergoing a phase of rapid innovation, spanning 3D-printed rocket components, commercial space tourism, reusable launch vehicles, asteroid mining, and novel propulsion technologies. In this ever-evolving landscape, India stands poised to capitalize on emerging opportunities, underlined by its proven capabilities in manufacturing and satellite/launch vehicle development. The accomplishments of the Indian Space industry, bolstered by government support, are expected to fuel entrepreneurship and investments, ushering in a new era of innovation and growth in the years ahead. This article was originally published by on Navanwita Bora Sachdev The Tech Panda.

India's SpaceTech Startup Ecosystem Spotlighted After Chandrayaan-3 Triumph

চন্দ্রযান-৩ জয়ের পর ভারতের স্পেসটেক স্টার্টআপ ইকোসিস্টেম স্পটলাইট হয়েছে

Indiens SpaceTech-Startup-Ökosystem rückt nach Chandrayaan-3-Triumph ins Rampenlicht

El ecosistema de startups de SpaceTech de la India se destaca después del triunfo de Chandrayaan-3

L'écosystème indien des startups SpaceTech mis en lumière après le triomphe de Chandrayaan-3

चंद्रयान-3 की जीत के बाद भारत का स्पेसटेक स्टार्टअप इकोसिस्टम सुर्खियों में आया

チャンドラヤーン3号の成功を受けて注目されるインドの宇宙技術スタートアップエコシステム

찬드라얀 3호 성공 이후 인도의 SpaceTech 스타트업 생태계가 주목받다

Ecossistema de startups SpaceTech da Índia destacado após o triunfo do Chandrayaan-3

Индийская экосистема стартапов SpaceTech оказалась в центре внимания после триумфа Chandrayaan-3

Hindistan'ın SpaceTech Startup Ekosistemi Chandrayaan-3 Zaferinden Sonra Dikkate Alındı

Hệ sinh thái khởi nghiệp công nghệ vũ trụ của Ấn Độ nổi bật sau chiến thắng Chandrayaan-3

“月船 3 号”胜利后，印度航天科技初创企业生态系统成为众人瞩目的焦点

Technophiles and bug hunters of all ages - the results of the #MobileDebugging Writing Contest Round 3 are here! If you're reading about this contest for the first time, you still stand a chance to win $$$. Do you have a knack for unraveling complex coding conundrums? Well then, Sentry and HackerNoon present you with the Mobile Debugging Writing Contest! Entering the contest is super easy - submit a story related to mobile app testing and debugging! #MobileDebugging: Some ideas to help you get started Do you remember those support tickets from customers writing in to report their application crashing or freezing?
Was it right after a new release?
Were you notified by a monitoring tool?
Did a problem arise from a 3rd party library?
Did a problem occur when receiving an unexpected HTTP response? ​ Enter the Mobile Debugging Writing Contest using this template. The Mobile Debugging Writing Contest: Round 3 Nominations 🔥 We followed our tradition of selecting the winners by selecting the top ten story submissions that had the highest number of views and votes. And to add some extra spice, HackerNoon's editorial team cast their votes to determine the crème de la crème, the top three winners. #MobileDebugging Writing Contest: Top 10 nominations: Debugging Rust Cargo Issues in Gitlab by @maksimmuravev
The Red Death of Flutter by @sipping
Implementing 'UICollectionView Compositional' Layout with Pinterest Section by @vadimchistiako
Cocoapod as XCFramework With Dependencies by @maxkalik
Running Parallel Code in Flutter With Isolates by @dhruvam
How to Upload Source Maps to Sentry with Next.js by @chilledcowfan
An Intro to the Playwright Testing Framework by @puppo
The Mighty App Developer vs a Pesky Bug by @thedebuggingdetective
Improve Your Code Quality With These Useful Kotlin Extensions for Android by @victorbrndls
How to Generate Test Code with Playwright by @puppo #MobileDebugging: Round 3 Winners 🔥🔥🔥 The story that got the highest number of votes is: https://hackernoon.com/the-red-death-of-flutter?embedable=true Flutter is beautiful. Why such a statement? It is because Flutter takes the hardships out of mobile development... for the most part. Congratulations,@sipping, on winning first place. You have won $500. The second place goes to: https://hackernoon.com/how-to-upload-source-maps-to-sentry-with-nextjs?embedable=true A little explanation: When Sentry is reacting to any error or event, it tries to get the source maps from the release archive that it has uploaded. According to the GitHub issue conversation, the framework's code does not get uploaded by default anymore and Sentry doesn’t have access to all files for resolving stack trace. Way to go@chilledcowfan! You have won $300! Third place and the most-read story award goes to: https://hackernoon.com/debugging-rust-cargo-issues-in-gitlab?embedable=true That’s a double-win,  @maksimmuravev! You won $200! Once again, congratulations to all the winners! We will be in touch with y’all shortly. In the meantime, keep calm and continue submitting your stories on #mobiledebugging. To find out more about our ongoing contests, please visit contests.hackernoon.com. You can enter the Mobile Debugging Writing Contest using this template. Technophiles and bug hunters of all ages - the results of the #MobileDebugging Writing Contest Round 3 are here! #MobileDebugging If you're reading about this contest for the first time, you still stand a chance to win $$$. Do you have a knack for unraveling complex coding conundrums? Well then, Sentry and HackerNoon present you with the Mobile Debugging Writing Contest! Sentry Entering the contest is super easy - submit a story related to mobile app testing and debugging! #MobileDebugging: Some ideas to help you get started Do you remember those support tickets from customers writing in to report their application crashing or freezing? Was it right after a new release? Were you notified by a monitoring tool? Did a problem arise from a 3rd party library? Did a problem occur when receiving an unexpected HTTP response? ​ Do you remember those support tickets from customers writing in to report their application crashing or freezing? Was it right after a new release? Were you notified by a monitoring tool? Did a problem arise from a 3rd party library? Did a problem occur when receiving an unexpected HTTP response? ​ Enter the Mobile Debugging Writing Contest using this template. Enter the Mobile Debugging Writing Contest using this template . this template this template The Mobile Debugging Writing Contest: Round 3 Nominations 🔥 The Mobile Debugging Writing Contest: Round 3 Nominations 🔥 We followed our tradition of selecting the winners by selecting the top ten story submissions that had the highest number of views and votes. And to add some extra spice, HackerNoon's editorial team cast their votes to determine the crème de la crème, the top three winners. #MobileDebugging Writing Contest: Top 10 nominations: #MobileDebugging Writing Contest: Debugging Rust Cargo Issues in Gitlab by @maksimmuravev The Red Death of Flutter by @sipping Implementing 'UICollectionView Compositional' Layout with Pinterest Section by @vadimchistiako Cocoapod as XCFramework With Dependencies by @maxkalik Running Parallel Code in Flutter With Isolates by @dhruvam How to Upload Source Maps to Sentry with Next.js by @chilledcowfan An Intro to the Playwright Testing Framework by @puppo The Mighty App Developer vs a Pesky Bug by @thedebuggingdetective Improve Your Code Quality With These Useful Kotlin Extensions for Android by @victorbrndls How to Generate Test Code with Playwright by @puppo Debugging Rust Cargo Issues in Gitlab by @maksimmuravev Debugging Rust Cargo Issues in Gitlab Debugging Rust Cargo Issues in Gitlab @maksimmuravev @maksimmuravev The Red Death of Flutter by @sipping The Red Death of Flutter The Red Death of Flutter @sipping @sipping Implementing 'UICollectionView Compositional' Layout with Pinterest Section by @vadimchistiako Implementing 'UICollectionView Compositional' Layout with Pinterest Section Implementing 'UICollectionView Compositional' Layout with Pinterest Section @vadimchistiako Cocoapod as XCFramework With Dependencies by @maxkalik Cocoapod as XCFramework With Dependencies Cocoapod as XCFramework With Dependencies @maxkalik @maxkalik Running Parallel Code in Flutter With Isolates by @dhruvam Running Parallel Code in Flutter With Isolates Running Parallel Code in Flutter With Isolates @dhruvam @dhruvam How to Upload Source Maps to Sentry with Next.js by @chilledcowfan How to Upload Source Maps to Sentry with Next.js How to Upload Source Maps to Sentry with Next.js @chilledcowfan @chilledcowfan An Intro to the Playwright Testing Framework by @puppo An Intro to the Playwright Testing Framework An Intro to the Playwright Testing Framework @puppo @puppo The Mighty App Developer vs a Pesky Bug by @thedebuggingdetective The Mighty App Developer vs a Pesky Bug The Mighty App Developer vs a Pesky Bug @thedebuggingdetective @thedebuggingdetective Improve Your Code Quality With These Useful Kotlin Extensions for Android by @victorbrndls Improve Your Code Quality With These Useful Kotlin Extensions for Android Improve Your Code Quality With These Useful Kotlin Extensions for Android @victorbrndls @victorbrndls How to Generate Test Code with Playwrigh t by @puppo How to Generate Test Code with Playwrigh @puppo @puppo #MobileDebugging: Round 3 Winners 🔥🔥🔥 #MobileDebugging: Round 3 Winners 🔥🔥🔥 The story that got the highest number of votes is: https://hackernoon.com/the-red-death-of-flutter?embedable=true https://hackernoon.com/the-red-death-of-flutter?embedable=true Flutter is beautiful. Why such a statement? It is because Flutter takes the hardships out of mobile development... for the most part. Flutter is beautiful. Why such a statement? It is because Flutter takes the hardships out of mobile development... for the most part. Congratulations, @sipping, on winning first place. You have won $500. @sipping , @sipping The second place goes to: https://hackernoon.com/how-to-upload-source-maps-to-sentry-with-nextjs?embedable=true https://hackernoon.com/how-to-upload-source-maps-to-sentry-with-nextjs?embedable=true A little explanation: When Sentry is reacting to any error or event, it tries to get the source maps from the release archive that it has uploaded. According to the GitHub issue conversation, the framework's code does not get uploaded by default anymore and Sentry doesn’t have access to all files for resolving stack trace. A little explanation: When Sentry is reacting to any error or event, it tries to get the source maps from the release archive that it has uploaded. According to the GitHub issue conversation, the framework's code does not get uploaded by default anymore and Sentry doesn’t have access to all files for resolving stack trace. release Way to go @chilledcowfan ! You have won $300! @chilledcowfan @chilledcowfan Third place and the most-read story award goes to: https://hackernoon.com/debugging-rust-cargo-issues-in-gitlab?embedable=true https://hackernoon.com/debugging-rust-cargo-issues-in-gitlab?embedable=true That’s a double-win, @maksimmuravev ! You won $200! @maksimmuravev @maksimmuravev Once again, congratulations to all the winners! We will be in touch with y’all shortly. In the meantime, keep calm and continue submitting your stories on #mobiledebugging . To find out more about our ongoing contests, please visit contests.hackernoon.com . #mobiledebugging contests.hackernoon.com You can enter the Mobile Debugging Writing Contest using this template. You can enter the Mobile Debugging Writing Contest using this template . this template this template

The #MobileDebugging Writing Contest: Round 3 Results Announced!

#MobileDebugging রচনা প্রতিযোগিতা: রাউন্ড 3 ফলাফল ঘোষণা করা হয়েছে!

Der #MobileDebugging-Schreibwettbewerb: Ergebnisse der dritten Runde bekannt gegeben!

El concurso de escritura #MobileDebugging: ¡Resultados de la ronda 3 anunciados!

Concours d'écriture #MobileDebugging : résultats de la 3e étape annoncés !

#MobileDebugging लेखन प्रतियोगिता: तीसरे दौर के परिणाम घोषित!

#MobileDebugging ライティング コンテスト: ラウンド 3 の結果が発表されました!

#MobileDebugging 글쓰기 콘테스트: 3차 결과가 발표되었습니다!

O Concurso de Redação #MobileDebugging: Resultados da 3ª Rodada Anunciados!

#MobilHata Ayıklama Yazma Yarışması: 3. Tur Sonuçları Açıklandı!

Cuộc thi viết #MobileDebugging: Đã công bố kết quả vòng 3!

#MobileDebugging 写作比赛：第 3 轮结果公布！

I have always wondered what set of code to run in the background to make my app powerful and responsive, but I don’t really know how. Some time ago, I got to know about isolates and tried implementing them. And I should tell you, it was painful. But I recently discovered how easy it has become. So here it is. You might have heard about isolates but never really understood them. Or maybe you might have implemented isolates, but the code was always messy and tedious to write. In any case, this blog will guide you through the ups and downs of the Isolate history and the current and better implementation that you should follow. You might want to use the latest method, or you might want to use the old method after all, it’s all up to you. The basics This is how the defines Isolates Flutter Documentation An isolated Dart execution context. Did you understand anything? At least I did not. So let’s begin understanding Isolates and then we’ll write our own definition. So what do we need for Isolates Recipe? What are Isolates? Why do we need them? What is Event Handling? How to implement Isolates? And finally, what are Isolate Groups? We’ll start with the bigger picture of Isolates and see what it really means and go deep down and piece all the parts together to see how each part of it works together so that we can understand what Isolates really do and why we need them. What are Isolates? To really understand isolates, first we need to go further back and make sure we know the answer to these two questions: What is the difference between Processor Cores and Threads? Core is a physical hardware component whereas thread is the virtual component that manages the tasks of the core. Cores enable completion of more work at a time, while threads enhance computational speed and throughput. Cores use content switching but threads use multiple processors for executing different processes. What is the difference between concurrent and parallel processing? Concurrency is when two or more tasks can start, run, and complete in overlapping time periods. It doesn’t necessarily mean they’ll ever both be running at the same instant. For example, multitasking on a single-core machine. Parallelism is when tasks literally run at the same time, e.g., on a multicore processor. Let’s get back to Isolates. Dart uses the Isolate model for concurrency. Isolate is nothing but a wrapper around the thread. But threads, by definition, can share memory which might be easy for the developer but makes code prone to race conditions and locks. Isolates, on the other hand, cannot share memory and instead rely on message-passing mechanisms to talk with each other. If anything is difficult to comprehend, keep reading. I am sure, you’ll get it. Using isolates, Dart code can perform multiple independent tasks at once, using additional cores if they’re available. Each Isolate has its own memory and a single thread running an event loop. We’ll get to the event loop in a minute. Why do we need Isolates? Before we get into more detail, we first need to understand how async-await really works. void main() async {
 // Read some data.
 final fileData = await _readFileAsync();
 final jsonData = jsonDecode(fileData);

 // Use that data.
 print('Number of JSON keys: ${jsonData.length}');
}

Future<String> _readFileAsync() async {
 final file = File(filename);
 final contents = await file.readAsString();
 return contents.trim();
} We want to read some data from a file, decode that JSON, and print the JSON Keys length. We don’t need to go into the implementation details here but can take the help of the image below to understand how it works. When we click on this button , it sends a request to _readFileAsync, all of which is dart code that we wrote. But this function _readFileAsync, executes code using Dart Virtual Machine/OS to perform the I/O operation which in itself is a different thread, the I/O thread. This means, the code in the main function runs inside the main isolate. When the code reaches the _readFileAsync, it transfers the code execution to I/O thread and the Main Isolate waits until the code is completely executed or an error occurs. This is what await keyword does. Place Bid Now, once the contents of the files are read, the control returns back to the main isolate and we start parsing the String data as JSON and print the number of keys. This is pretty straight forward. But let’s suppose, the JSON parsing was a very big operation, considering a very huge JSON and we start manipulating the data to conform to our needs. Then this work is happening on the Main Isolate. At this point of time, the UI could hang, making our users fustrated. What is Event Handling? As we discussed, Isolate is a wrapper around thread and each Isolate has an event loop executing events. These events are nothing but what happens when we use the application. These events are added in a queue which then the Event loop takes in and processes. These events are processed in the first-in-first-out fashion. The image below is just an example. Let’s use this code again for understanding event handlers. We already know what is happening in this block. void main() async {
 // Read some data.
 final fileData = await _readFileAsync();
 final jsonData = jsonDecode(fileData);

 // Use that data.
 print('Number of JSON keys: ${jsonData.length}');
}

Future<String> _readFileAsync() async {
 final file = File(filename);
 final contents = await file.readAsString();
 return contents.trim();
} Our apps start and it draws the UI (Paint Event) is pushed on to the queue. We click on the button and and file handling code starts. So the Tap Event is pushed in the queue. After it is complete, let’s suppose the UI is updated, so again the Paint Event is pushed in the queue. Place Bid Now because the our logic for handling the file and JSON was very small, the UI doesn’t stutter or jank. But let’s, for a while, imagine again that out code for handling file was huge and it takes a lot of time. Now the event queue and the event loop looks similar to this image below. Now that the main isolate takes a lot of time to actually process that event, our animation or UI might hang and irritate your users, causing huge dropoffs. This is where spawning a new isolate or a worker Isolate comes in. How to implement Isolates? All our dart code in the flutter app runs in isolate. Whether it is a main isolate or a worker isolate is up to you. The main isolate is created for you and you don’t have to do anything else here. The main function starts on the Main Isolate. Once we have our main function running, we can start spawning new isolates. So there’s 2 ways of implementing Isolates, the short and new method or the long and old method. We can use either, depending on the use case. Let’s start with the already existing method. As we had already discussed, Isolates, unlike threads, don’t share memory. This is done so as to prevent race conditions and locks. But the communication between Isolates is done using message passing. These messages are primitives and you can check out the whole list of objects that can be passed between isolates . here To pass messages, Dart provides us with Ports. SendPort and ReceivePort. Since we’re discussing the old method for spawning Isolates, we need to know that isolate methods need to be top-level or static functions. Here is the to the code if you want to follow along. link Future<String> startDownloadUsingOldIsolateMethod() async {
 const String imageDownloadLink = 'this is a link';
 // create the port to receive data from
 final resultPort = ReceivePort();
 // spawn a new isolate and pass down a function that will be used in a new isolate
 // and pass down the result port that will send back the result.
 // you can send any number of arguments.
 await Isolate.spawn(
 _readAndParseJson,
 [resultPort.sendPort, imageDownloadLink],
 );
 return await (resultPort.first) as String;
} What does this code do: Here we create an instance of RecievePort to receive data. Remember this is the old method for spawning Isolates. It can be a little long but it is necessary to know the details. We create a Worker Isolate on the Main Isolate using Isolate.spawn and pass down a top-level function that runs the blocking code. We also pass down a list of arguments, the first one, SendPort which will be used to send the data from the worker Isolate, and the second is the download link. We wait till the new Isolate has been spawned. We then wait for the result, which is some form of a String and use it however we want. This data can be anything from list of object. this ResultPort.first uses a stream subscription behind the screen and waits for the data from the worker isolate to be pushed onto it. As soon as the first item arrives, we return the result. This is the _readAndParseJson function that receives the argument and runs the worker isolate code. This is a dummy function that does nothing but delays the control for 2 seconds and then exits. The exit function terminates the current isolate synchronously. Certain checks are performed before sending the data back to the calling isolate and the data is sent back using the SendPort. // we create a top-level function that specifically uses the args
// which contain the send port. This send port will actually be used to
// communicate the result back to the main isolate

// This function should have been isolate-agnostic
Future<void> _readAndParseJson(List<dynamic> args) async {
 SendPort resultPort = args[0];
 String fileLink = args[1];

 String newImageData = fileLink;

 await Future.delayed(const Duration(seconds: 2));

 Isolate.exit(resultPort, newImageData);
} Although this functions correctly but we have not handled any errors which can be thrown from the worker isolate or any error which can occur while spawning a new isolate. // Error Handling
Future<String> startDownloadUsingOldIsolateMethodWithErrorHandling() async {
 const String imageDownloadLink = 'this is a link';
 // create the port to receive data from
 final resultPort = ReceivePort();
 // Adding errorsAreFatal makes sure that the main isolates receives a message
 // that something has gone wrong
 try {
 await Isolate.spawn(
 _readAndParseJson,
 [resultPort.sendPort, imageDownloadLink],
 errorsAreFatal: true,
 onExit: resultPort.sendPort,
 onError: resultPort.sendPort,
 );
 } on Object {
 // check if sending the entrypoint to the new isolate failed.
 // If it did, the result port won’t get any message, and needs to be closed
 resultPort.close();
 }

 final response = await resultPort.first;

 if (response == null) {
 // this means the isolate exited without sending any results
 // TODO throw error
 return 'No message';
 } else if (response is List) {
 // if the response is a list, this means an uncaught error occurred
 final errorAsString = response[0];
 final stackTraceAsString = response[1];
 // TODO throw error
 return 'Uncaught Error';
 } else {
 return response as String;
 }
} Everything is pretty same here, we just have added error handling here. What this code does is: We add errorsAreFatal to true while spawning a new isolate to make sure that the Main Isolate is aware of any errors. We assign the SendPort for onExit and onError handlers to make sure that if any errors that occur during exiting or spawning. We also add a try-catch block while spawning a new isolate to make sure that if any error occurs during spawning, we catch that and stop this operation, altogether. If the spawning is successful and some data do come from the worker isolate, we need to check if it’s error or not. If the message sent back is null, this means the isolate exited without any message, and an error has occurred. If the response is a list, this means the worker Isolate has sent back an error and a stacktrace. Else this is a successful transaction. This does seem like an overkill if we wanted to do just one-off message passing. One message and close the Isolate. Everytime you wanted to spawn a new isolate you’ll have to write the same code again. Since the Isolate logic is pretty custom. Every time you might want to pass in some different arguments and it would be very tedious. This is why a new method was devised for one-off transactions. The new method: Isolate.run // Isolates with run function
Future<String> startDownloadUsingRunMethod() async {
 final imageData = await Isolate.run(_readAndParseJsonWithoutIsolateLogic);
 return imageData;
}

Future<String> _readAndParseJsonWithoutIsolateLogic() async {
 await Future.delayed(const Duration(seconds: 2));
 return 'this is downloaded data';
} This is all there is for the new method. We spawn a new Isolate using the run method which abstracts out all the granular details and the error handling and saving you a lot of time. This helps in spawning, error handling, message passing and terminating the Isolate all using these few little lines of code. One thing to note here is that the function _readAndParseJsonWithoutIsolateLogic, does not contain any custom logic for the Isolate. No ports, no arguments. When to use the new Run method and when to use the old spawn method? These examples above shows message passing that happens only 1 time. So run method should be used. It greatly reduces the code lines and test cases. But if you want to create something that needs multiple messages to be passed between the Isolates, we need to use the old method. An example of this could be when you start downloading a file on a worker isolate and want to show the progress of the download on the UI. This means the progress count needs to be passed again and again. Isolate.spawn() With this, we need to implement the whole SendPort and ReceivePort for message passing and the custom logic for receiving the arguments and sending the progress back to the main Isolate. What are Isolate Groups? So, we already know how Isolates passes messages to each other. But let’s assume, the message we are passing is a huge JSON. Before Dart 2.15, this huge object passing, could involve stutter in UI. This is because, we already know that Isolate has some memory, and when one Isolate passes an object to the other, that object had to be deep copied. This meant, a lot of time for copying the object to the main Isolate which can cause a jank. To avoid this circumstance, Isolates were reworked and Isolate Groups were invented. Isolate Groups, meaning a group of isolates, which share some common internal data structures representing the running application. This means each time a new Isolate is spawned, new internal data structures don’t need to be constructed again. Because they share them together. Don’t confuse these internal data structures with the mutable objects. The Isolates still can’t share this memory with each other. Message passing is still needed. But, because Isolates in the same Isolate group, share the same heap, this means spawning a new Isolate is 100 times faster and consume 10–100 times less memory. An example is a worker isolate that makes a network call to get data, parses that data into a large JSON object graph, and then returns that JSON graph to the main isolate. Before Dart 2.15, that result needed to be deep-copied, which could itself cause UI jank if the copy took longer than the frame budget. This means that the main Isolate can receive this JSON in almost constant time. And sending messages is now approximately 8 times faster. The good news is that, if you’re using Flutter version greater than 2.8, you don’t need to do anything to use these advancements. Hope you liked the understanding of Isolates. If you have any doubts, please comment. Reference for this code: https://github.com/DhruvamSharma/NFT-Material3?embedable=true Also published . here

Running Parallel Code in Flutter With Isolates

Bitcoin Security Brief Volume 1 with Dhruv Bansal

The Only Automated Step Your Business Will Ever Need to Handle Ransomware Attacks

Author:
(1) CALLA TSCHANZ. Table of Links Abstract and Intro
Background on tropical perspective
The expanded construction
GIT stability
Stack perspective
The canonical moduli stack
References Abstract The aim of this paper is to extend the expanded degeneration construction of Li and Wu to obtain good degenerations of Hilbert schemes of points on semistable families of surfaces, as well as to discuss alternative stability conditions and parallels to the GIT construction of Gulbrandsen, Halle and Hulek and logarithmic Hilbert scheme constructions of Maulik and Ranganathan. We construct a good degeneration of Hilbert schemes of points as a proper Deligne-Mumford stack and show that it provides a geometrically meaningful example of a construction arising from the work of Maulik and Ranganathan. 1. Introduction The study of moduli spaces is a central topic in algebraic geometry; among moduli spaces, Hilbert schemes form an important class of examples. They have been widely studied in geometric representation theory, enumerative and combinatorial geometry and as the two main examples of hyperk¨ahler manifolds, namely Hilbert schemes of points on K3 surfaces and generalised Kummer varieties. A prominent direction in this area is to understand the local moduli space of such objects and, in particular, the ways in which a degeneration of smooth Hilbert schemes may be given a modular compactification. For example, we may consider the geometry of relative Hilbert schemes on a degeneration whose central fibre has normal crossing singularities. We may then ask how the singularities of such a Hilbert scheme may be resolved while preserving certain of its properties or how it may be expressed as a good moduli space. This then becomes a compactification problem with respect to the boundary given by the singular locus. Historically, an important method used in moduli and compactification problems has been Geometric Invariant Theory (GIT). More recently, the work of Maulik and Ranganthan [MR20] has explored how methods of tropical and logarithmic geometry can be used to address such questions for Hilbert schemes. This builds upon previous work of Li [Li13] and Li and Wu [LW15] on expanded degenerations for Quot schemes and work of Ranganathan [Ran22b] on logarithmic Gromov-Witten theory with expansions. Briefly stated, the aim of this paper is to provide explicit examples of such compactifications and explore the connections between these methods. 1.1 Basic setup As is mentioned in Section 1.3, this type of construction can be applied to construct type III degenerations of Hilbert schemes of points on K3 surfaces. This will be described in future work. 1.2 Previous work in this area Following on from [LW15], Gulbrandsen, Halle and Hulek [GHH19] present a GIT version of the above construction in the case of Hilbert schemes of points. They construct an explicit expanded degeneration, i.e. a modified family over a larger base, whose fibres correspond to blow-ups of components of X0 in the family. They present a linearised line bundle on this space for the natural torus action and they are able to show that in this case the Hilbert-Mumford criterion simplifies down to a purely combinatorial criterion. Using this, they impose a GIT stability condition which recovers the transverse zero-dimensional subschemes of Li and Wu and prove that the corresponding stack quotient is isomorphic to that of Li and Wu. A motivation for this work was to construct type II degenerations of Hilbert schemes of points on K3 surfaces. Indeed, type II good degenerations of K3 surfaces present these types of singularities in the special fibre, which is a chain of surfaces intersecting along smooth curves. There is more recent work of Maulik and Ranganathan [MR20], building upon earlier ideas of Ranganathan [Ran22b] and results of Tevelev [Tev07], in which they use techniques of logarithmic and tropical geometry to construct appropriate expansions of X ! C. This allows them to define moduli stacks of transverse subschemes starting from the case where X0 is any simple normal crossing variety. They show that the stacks thus constructed are proper and Deligne-Mumford. For more details on this, see Section 2.2. 1.3 Main results Let X ! C be a semistable degeneration of surfaces. In the following sections, we propose explicit constructions of expanded degenerations and stacks of stable length m zero-dimensional subschemes on these expanded families, which we show to have good properties. Allowing for different choices of expansions. In this paper, we discuss only a specific choice of model for the Hilbert scheme of points which we call the canonical moduli stack. In upcoming work, we will investigate how these methods can be extended to describe other choices of models. We will consider an approach which parallels work of Kennedy-Hunt on logarithmic Quot schemes [Ken23], as well as recover certain geometrically meaningful choices of moduli stacks arising from the methods of Maulik and Ranganathan [MR20]. In particular, we will discuss how tube components and DonaldsonThomas stability enter the picture in these more general cases (see Section 2.2 for definitions). 1.4 Organisation We start, in Section 2, by giving some background on logarithmic and tropical geometry, and an overview of the work of Maulik and Ranganathan from [MR20] which we will want to refer to in later sections. Then, in Section 3, we set out an expanded construction on schemes and, in 4, we discuss how various GIT stability conditions can be defined on this construction. In Section 5, we describe a corresponding stack of expansions and family over it, building on the expanded degenerations we constructed as schemes. In Section 6, we extend our stability conditions to this setting. We then show that the stacks of stable objects defined have the desired Deligne-Mumford and properness properties. Acknowledgements. I would like to thank Gregory Sankaran for all his support throughout this project. Thank you also to my PhD examiners, Alastair Craw and Dhruv Ranganathan, for their many helpful comments. This work was undertaken while funded by the University of Bath Research Studentship Award. I am also grateful to Patrick Kennedy-Hunt and Thibault Poiret for many interesting conversations. This paper is available on arxiv under CC 4.0 DEED license. Author: (1) CALLA TSCHANZ. Author: Author: (1) CALLA TSCHANZ. Table of Links Abstract and Intro Background on tropical perspective The expanded construction GIT stability Stack perspective The canonical moduli stack References Abstract and Intro Abstract and Intro Background on tropical perspective Background on tropical perspective The expanded construction The expanded construction GIT stability GIT stability Stack perspective Stack perspective The canonical moduli stack The canonical moduli stack References References Abstract The aim of this paper is to extend the expanded degeneration construction of Li and Wu to obtain good degenerations of Hilbert schemes of points on semistable families of surfaces, as well as to discuss alternative stability conditions and parallels to the GIT construction of Gulbrandsen, Halle and Hulek and logarithmic Hilbert scheme constructions of Maulik and Ranganathan. We construct a good degeneration of Hilbert schemes of points as a proper Deligne-Mumford stack and show that it provides a geometrically meaningful example of a construction arising from the work of Maulik and Ranganathan. 1. Introduction The study of moduli spaces is a central topic in algebraic geometry; among moduli spaces, Hilbert schemes form an important class of examples. They have been widely studied in geometric representation theory, enumerative and combinatorial geometry and as the two main examples of hyperk¨ahler manifolds, namely Hilbert schemes of points on K3 surfaces and generalised Kummer varieties. A prominent direction in this area is to understand the local moduli space of such objects and, in particular, the ways in which a degeneration of smooth Hilbert schemes may be given a modular compactification. For example, we may consider the geometry of relative Hilbert schemes on a degeneration whose central fibre has normal crossing singularities. We may then ask how the singularities of such a Hilbert scheme may be resolved while preserving certain of its properties or how it may be expressed as a good moduli space. This then becomes a compactification problem with respect to the boundary given by the singular locus. Historically, an important method used in moduli and compactification problems has been Geometric Invariant Theory (GIT). More recently, the work of Maulik and Ranganthan [MR20] has explored how methods of tropical and logarithmic geometry can be used to address such questions for Hilbert schemes. This builds upon previous work of Li [Li13] and Li and Wu [LW15] on expanded degenerations for Quot schemes and work of Ranganathan [Ran22b] on logarithmic Gromov-Witten theory with expansions. Briefly stated, the aim of this paper is to provide explicit examples of such compactifications and explore the connections between these methods. 1.1 Basic setup As is mentioned in Section 1.3, this type of construction can be applied to construct type III degenerations of Hilbert schemes of points on K3 surfaces. This will be described in future work. 1.2 Previous work in this area Following on from [LW15], Gulbrandsen, Halle and Hulek [GHH19] present a GIT version of the above construction in the case of Hilbert schemes of points. They construct an explicit expanded degeneration, i.e. a modified family over a larger base, whose fibres correspond to blow-ups of components of X0 in the family. They present a linearised line bundle on this space for the natural torus action and they are able to show that in this case the Hilbert-Mumford criterion simplifies down to a purely combinatorial criterion. Using this, they impose a GIT stability condition which recovers the transverse zero-dimensional subschemes of Li and Wu and prove that the corresponding stack quotient is isomorphic to that of Li and Wu. A motivation for this work was to construct type II degenerations of Hilbert schemes of points on K3 surfaces. Indeed, type II good degenerations of K3 surfaces present these types of singularities in the special fibre, which is a chain of surfaces intersecting along smooth curves. There is more recent work of Maulik and Ranganathan [MR20], building upon earlier ideas of Ranganathan [Ran22b] and results of Tevelev [Tev07], in which they use techniques of logarithmic and tropical geometry to construct appropriate expansions of X ! C. This allows them to define moduli stacks of transverse subschemes starting from the case where X0 is any simple normal crossing variety. They show that the stacks thus constructed are proper and Deligne-Mumford. For more details on this, see Section 2.2. 1.3 Main results Let X ! C be a semistable degeneration of surfaces. In the following sections, we propose explicit constructions of expanded degenerations and stacks of stable length m zero-dimensional subschemes on these expanded families, which we show to have good properties. Allowing for different choices of expansions. In this paper, we discuss only a specific choice of model for the Hilbert scheme of points which we call the canonical moduli stack. In upcoming work, we will investigate how these methods can be extended to describe other choices of models. We will consider an approach which parallels work of Kennedy-Hunt on logarithmic Quot schemes [Ken23], as well as recover certain geometrically meaningful choices of moduli stacks arising from the methods of Maulik and Ranganathan [MR20]. In particular, we will discuss how tube components and DonaldsonThomas stability enter the picture in these more general cases (see Section 2.2 for definitions). Allowing for different choices of expansions. 1.4 Organisation We start, in Section 2, by giving some background on logarithmic and tropical geometry, and an overview of the work of Maulik and Ranganathan from [MR20] which we will want to refer to in later sections. Then, in Section 3, we set out an expanded construction on schemes and, in 4, we discuss how various GIT stability conditions can be defined on this construction. In Section 5, we describe a corresponding stack of expansions and family over it, building on the expanded degenerations we constructed as schemes. In Section 6, we extend our stability conditions to this setting. We then show that the stacks of stable objects defined have the desired Deligne-Mumford and properness properties. Acknowledgements . I would like to thank Gregory Sankaran for all his support throughout this project. Thank you also to my PhD examiners, Alastair Craw and Dhruv Ranganathan, for their many helpful comments. This work was undertaken while funded by the University of Bath Research Studentship Award. I am also grateful to Patrick Kennedy-Hunt and Thibault Poiret for many interesting conversations. Acknowledgements 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

Expansions for Hilbert Schemes: Abstract and Intro

হিলবার্ট স্কিমগুলির সম্প্রসারণ: বিমূর্ত এবং ভূমিকা

Erweiterungen für Hilbert-Schemata: Zusammenfassung und Einführung

Ampliaciones para esquemas de Hilbert: resumen e introducción

Extensions pour les schémas Hilbert : résumé et introduction

हिल्बर्ट योजनाओं के लिए विस्तार: सार और परिचय

ヒルベルトスキームの展開: 概要と導入

Hilbert 체계의 확장: 개요 및 소개

Expansões para esquemas de Hilbert: resumo e introdução

Разложения схем Гильберта: Аннотация и введение

Hilbert Şemaları için Genişletmeler: Özet ve Giriş

Mở rộng cho các sơ đồ Hilbert: Tóm tắt và giới thiệu

希尔伯特方案的扩展：摘要和简介

Author:
(1) CALLA TSCHANZ. Table of Links Abstract and Intro
Background on tropical perspective
The expanded construction
GIT stability
Stack perspective
The canonical moduli stack
References References [AK16] Jarod Alper and Andrew Kresch. “Equivariant versal deformations of semistable curves”. In: Michigan Math. J. 65.2 (2016), pp. 227–250. [EH00] David Eisenbud and Joe Harris. The geometry of schemes. Vol. 197. Graduate Texts in Mathematics. Springer-Verlag, New York, 2000, pp. x+294. [FKX17] Tommaso de Fernex, J´anos Koll´ar, and Chenyang Xu. “The dual complex of singularities”. In: Higher dimensional algebraic geometry—in honour of Professor Yujiro Kawamata’s sixtieth birthday. Vol. 74. Adv. Stud. Pure Math. Math. Soc. Japan, Tokyo, 2017, pp. 103–129. [GHH15] Martin G. Gulbrandsen, Lars H. Halle, and Klaus Hulek. “A relative HilbertMumford criterion”. In: Manuscripta Math. 148.3-4 (2015), pp. 283–301. [GHH19] Martin G. Gulbrandsen, Lars H. Halle, and Klaus Hulek. “A GIT construction of degenerations of Hilbert schemes of points”. In: Doc. Math. 24 (2019), pp. 421–472. [Ken23] Patrick Kennedy-Hunt. The Logarithmic Quot space: foundations and tropicalisation. 2023. arXiv: 2308.14470 [math.AG]. [KLSV18] J´anos Koll´ar, Radu Laza, Giulia Sacc`a, and Claire Voisin. “Remarks on degenerations of hyper-K¨ahler manifolds”. In: Ann. Inst. Fourier (Grenoble) 68.7 (2018), pp. 2837–2882. [Li13] Jun Li. “Good degenerations of moduli spaces”. In: Handbook of moduli. Vol. II. Vol. 25. Adv. Lect. Math. (ALM). Int. Press, Somerville, MA, 2013, pp. 299–351. [Log] Dan Abramovich, Qile Chen, Danny Gillam, Yuhao Huang, Martin Olsson, Matthew Satriano, and Shenghao Sun. “Logarithmic geometry and moduli”. In: Handbook of moduli. Vol. I. Vol. 24. Adv. Lect. Math. (ALM). Int. Press, Somerville, MA, 2013, pp. 1–61. [LW15] Jun Li and Baosen Wu. “Good degeneration of Quot-schemes and coherent systems”. In: Comm. Anal. Geom. 23.4 (2015), pp. 841–921. [MFK94] David Mumford, John Fogarty, and Frances Kirwan. Geometric invariant theory. Vol. 34. Ergebnisse der Mathematik und ihrer Grenzgebiete (2). Berlin: Springer Berlin, Heidelberg, 1994. [MR20] Davesh Maulik and Dhruv Ranganathan. Logarithmic Donaldson–Thomas theory. 2020. url: arXiv:2006.06603v2. [Nag08] Yasunari Nagai. “On monodromies of a degeneration of irreducible symplectic K¨ahler manifolds”. In: Math. Z. 258.2 (2008), pp. 407–426. [Ran22a] Dhruv Ranganathan. “Logarithmic and tropical moduli theory”. Notes for GAeL lecture course. June 2022. url: https://www.dpmms.cam.ac.uk/~dr508/GAeL2022.p [Ran22b] Dhruv Ranganathan. “Logarithmic Gromov-Witten theory with expansions”. In: Algebr. Geom. 9.6 (2022), pp. 714–761. 4 [Tev07] Jenia Tevelev. “Compactifications of subvarieties of tori”. In: Amer. J. Math. 129.4 (2007), pp. 1087–1104. This paper is available on arxiv under CC 4.0 license. Author: (1) CALLA TSCHANZ. Author: Author: (1) CALLA TSCHANZ. Table of Links Abstract and Intro Background on tropical perspective The expanded construction GIT stability Stack perspective The canonical moduli stack References Abstract and Intro Abstract and Intro Background on tropical perspective Background on tropical perspective The expanded construction The expanded construction GIT stability GIT stability Stack perspective Stack perspective The canonical moduli stack The canonical moduli stack References References References [AK16] Jarod Alper and Andrew Kresch. “Equivariant versal deformations of semistable curves”. In: Michigan Math. J. 65.2 (2016), pp. 227–250. [EH00] David Eisenbud and Joe Harris. The geometry of schemes. Vol. 197. Graduate Texts in Mathematics. Springer-Verlag, New York, 2000, pp. x+294. [FKX17] Tommaso de Fernex, J´anos Koll´ar, and Chenyang Xu. “The dual complex of singularities”. In: Higher dimensional algebraic geometry—in honour of Professor Yujiro Kawamata’s sixtieth birthday. Vol. 74. Adv. Stud. Pure Math. Math. Soc. Japan, Tokyo, 2017, pp. 103–129. [GHH15] Martin G. Gulbrandsen, Lars H. Halle, and Klaus Hulek. “A relative HilbertMumford criterion”. In: Manuscripta Math. 148.3-4 (2015), pp. 283–301. [GHH19] Martin G. Gulbrandsen, Lars H. Halle, and Klaus Hulek. “A GIT construction of degenerations of Hilbert schemes of points”. In: Doc. Math. 24 (2019), pp. 421–472. [Ken23] Patrick Kennedy-Hunt. The Logarithmic Quot space: foundations and tropicalisation. 2023. arXiv: 2308.14470 [math.AG]. [KLSV18] J´anos Koll´ar, Radu Laza, Giulia Sacc`a, and Claire Voisin. “Remarks on degenerations of hyper-K¨ahler manifolds”. In: Ann. Inst. Fourier (Grenoble) 68.7 (2018), pp. 2837–2882. [Li13] Jun Li. “Good degenerations of moduli spaces”. In: Handbook of moduli. Vol. II. Vol. 25. Adv. Lect. Math. (ALM). Int. Press, Somerville, MA, 2013, pp. 299–351. [Log] Dan Abramovich, Qile Chen, Danny Gillam, Yuhao Huang, Martin Olsson, Matthew Satriano, and Shenghao Sun. “Logarithmic geometry and moduli”. In: Handbook of moduli. Vol. I. Vol. 24. Adv. Lect. Math. (ALM). Int. Press, Somerville, MA, 2013, pp. 1–61. [LW15] Jun Li and Baosen Wu. “Good degeneration of Quot-schemes and coherent systems”. In: Comm. Anal. Geom. 23.4 (2015), pp. 841–921. [MFK94] David Mumford, John Fogarty, and Frances Kirwan. Geometric invariant theory. Vol. 34. Ergebnisse der Mathematik und ihrer Grenzgebiete (2). Berlin: Springer Berlin, Heidelberg, 1994. [MR20] Davesh Maulik and Dhruv Ranganathan. Logarithmic Donaldson–Thomas theory. 2020. url: arXiv:2006.06603v2. [Nag08] Yasunari Nagai. “On monodromies of a degeneration of irreducible symplectic K¨ahler manifolds”. In: Math. Z. 258.2 (2008), pp. 407–426. [Ran22a] Dhruv Ranganathan. “Logarithmic and tropical moduli theory”. Notes for GAeL lecture course. June 2022. url: https://www.dpmms.cam.ac.uk/~dr508/GAeL2022.p [Ran22b] Dhruv Ranganathan. “Logarithmic Gromov-Witten theory with expansions”. In: Algebr. Geom. 9.6 (2022), pp. 714–761. 4 [Tev07] Jenia Tevelev. “Compactifications of subvarieties of tori”. In: Amer. J. Math. 129.4 (2007), pp. 1087–1104. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Expansions for Hilbert Schemes: References

হিলবার্ট স্কিমগুলির সম্প্রসারণ: রেফারেন্স

Erweiterungen für Hilbert-Schemata: Referenzen

Ampliaciones para esquemas de Hilbert: referencias

Extensions pour les schémas Hilbert : références

हिल्बर्ट योजनाओं का विस्तार: संदर्भ

ヒルベルトスキームの展開: 参考文献

힐베르트 체계의 확장: 참고자료

Expansões para esquemas de Hilbert: referências

Разложения схем Гильберта: ссылки.

Hilbert Şemaları için Genişletmeler: Referanslar

Mở rộng cho Lược đồ Hilbert: Tài liệu tham khảo

希尔伯特方案的扩展：参考文献

Orca 2: Enhancing Reasoning in Smaller Language Models - Conclusions and References

Authors:
(1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work;
(2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work;
(3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro
Motivation
Related Work
Dataset
Experiments and Evaluation
Results and Analysis
Conclusion and Future Work
Limitations and References
A. Appendix 8. Limitations • In the plot generation dataset, the Wikipedia plots are sometimes not written by professional content writers from the film industry. Therefore these plots may fail to include the main events of the movie. • In a few cases, the model fails to generate coherent events along with the abrupt introduction of characters in the plots and scenes. • Although it has been noticed only a few times, the plot or scene generated contains repeated clauses or phrases. • The model hallucinates and generates factually incorrect things, making it incapable of generating biographies or documentaries. • The plot or scene may not abide by the theme of the input or genre mentioned along with the prompt. References Selmer Bringsjord and David Ferrucci. 1999. Artificial intelligence and literary creativity: Inside the mind of brutus, a storytelling machine. Psychology Press. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833. Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for structuring story generation. arXiv preprint arXiv:1902.01109. S. Field. 1979. Screenplay: The Foundations of Screenwriting. A Delta book. Dell Publishing Company. Chenyang Huang, Osmar R Zaiane, Amine Trabelsi, and Nouha Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 49–54. Ting-Hao Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, et al. 2016. Visual storytelling. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 1233–1239. Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017. Story generation from sequence of independent short descriptions. arXiv preprint arXiv:1707.05501. Frederick Jelinek, Robert L. Mercer, Lalit R. Bahl, and J. Baker. 1977. Perplexity—a measure of the difficulty of speech recognition tasks. Journal of the Acoustical Society of America, 62. Mihir Kale and Abhinav Rastogi. 2020. Template guided text generation for task-oriented dialogue. arXiv preprint arXiv:2004.15006. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541. Rensis Likert. 1932. A technique for the measurement of attitudes / by Rensis Likert. Archives of psychology ; no. 140. [s.n.], New York. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Rafael Pérez Ý Pérez and Mike Sharples. 2001. Mexica: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence, 13(2):119–139. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap between neural text and human text using divergence frontiers. Advances in Neural Information Processing Systems, 34:4816–4828. Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outline conditioned generation with dynamic plot state tracking. arXiv preprint arXiv:2004.14967. Mark O Riedl and Robert Michael Young. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research, 39:217–268. Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019. Targetguided open-domain conversation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5624–5634, Florence, Italy. Association for Computational Linguistics. Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016. Chinese poetry generation with planning based neural network. arXiv preprint arXiv:1610.09889. Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. Proactive human-machine conversation with explicit conversation goal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3794–3804, Florence, Italy. Association for Computational Linguistics. Rui Yan. 2016. i, poet: Automatic poetry composition through recurrent neural networks with iterative polishing schema. In IJCAI, volume 2238, page 2244. Zheng Zhao, Shay B Cohen, and Bonnie Webber. 2020. Reducing quantity hallucinations in abstractive summarization. arXiv preprint arXiv:2009.13312. Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, and Jin Zhou. 2020. ScriptWriter: Narrative-guided script generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8647–8657, Online. Association for Computational Linguistics. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Authors: Authors: (1) Prerak Gandhi, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, prerakgandhi@cse.iitb.ac.in, and these authors contributed equally to this work; (2) Vishal Pramanik, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, vishalpramanik,pb@cse.iitb.ac.in, and these authors contributed equally to this work; (3) Pushpak Bhattacharyya, Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai. Table of Links Abstract and Intro Motivation Related Work Dataset Experiments and Evaluation Results and Analysis Conclusion and Future Work Limitations and References A. Appendix Abstract and Intro Abstract and Intro Motivation Motivation Related Work Related Work Dataset Dataset Experiments and Evaluation Experiments and Evaluation Results and Analysis Results and Analysis Conclusion and Future Work Conclusion and Future Work Limitations and References Limitations and References A. Appendix A. Appendix 8. Limitations • In the plot generation dataset, the Wikipedia plots are sometimes not written by professional content writers from the film industry. Therefore these plots may fail to include the main events of the movie. • In a few cases, the model fails to generate coherent events along with the abrupt introduction of characters in the plots and scenes. • Although it has been noticed only a few times, the plot or scene generated contains repeated clauses or phrases. • The model hallucinates and generates factually incorrect things, making it incapable of generating biographies or documentaries. • The plot or scene may not abide by the theme of the input or genre mentioned along with the prompt. References Selmer Bringsjord and David Ferrucci. 1999. Artificial intelligence and literary creativity: Inside the mind of brutus, a storytelling machine. Psychology Press. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. Angela Fan, Mike Lewis, and Yann Dauphin. 2018. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833. Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for structuring story generation. arXiv preprint arXiv:1902.01109. S. Field. 1979. Screenplay: The Foundations of Screenwriting. A Delta book. Dell Publishing Company. Chenyang Huang, Osmar R Zaiane, Amine Trabelsi, and Nouha Dziri. 2018. Automatic dialogue generation with expressed emotions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 49–54. Ting-Hao Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, et al. 2016. Visual storytelling. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: Human language technologies, pages 1233–1239. Parag Jain, Priyanka Agrawal, Abhijit Mishra, Mohak Sukhwani, Anirban Laha, and Karthik Sankaranarayanan. 2017. Story generation from sequence of independent short descriptions. arXiv preprint arXiv:1707.05501. Frederick Jelinek, Robert L. Mercer, Lalit R. Bahl, and J. Baker. 1977. Perplexity—a measure of the difficulty of speech recognition tasks. Journal of the Acoustical Society of America, 62. Mihir Kale and Abhinav Rastogi. 2020. Template guided text generation for task-oriented dialogue. arXiv preprint arXiv:2004.15006. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, and Dan Jurafsky. 2016. Deep reinforcement learning for dialogue generation. arXiv preprint arXiv:1606.01541. Rensis Likert. 1932. A technique for the measurement of attitudes / by Rensis Likert. Archives of psychology ; no. 140. [s.n.], New York. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics. Kishore Papineni, Salim Roukos, Todd Ward, and WeiJing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311–318. Rafael Pérez Ý Pérez and Mike Sharples. 2001. Mexica: A computer model of a cognitive account of creative writing. Journal of Experimental & Theoretical Artificial Intelligence, 13(2):119–139. Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021. Mauve: Measuring the gap between neural text and human text using divergence frontiers. Advances in Neural Information Processing Systems, 34:4816–4828. Hannah Rashkin, Asli Celikyilmaz, Yejin Choi, and Jianfeng Gao. 2020. Plotmachines: Outline conditioned generation with dynamic plot state tracking. arXiv preprint arXiv:2004.14967. Mark O Riedl and Robert Michael Young. 2010. Narrative planning: Balancing plot and character. Journal of Artificial Intelligence Research, 39:217–268. Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric Xing, and Zhiting Hu. 2019. Targetguided open-domain conversation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5624–5634, Florence, Italy. Association for Computational Linguistics. Zhe Wang, Wei He, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, and Enhong Chen. 2016. Chinese poetry generation with planning based neural network. arXiv preprint arXiv:1610.09889. Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, Xiyuan Zhang, Rongzhong Lian, and Haifeng Wang. 2019. Proactive human-machine conversation with explicit conversation goal. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3794–3804, Florence, Italy. Association for Computational Linguistics. Rui Yan. 2016. i, poet: Automatic poetry composition through recurrent neural networks with iterative polishing schema. In IJCAI, volume 2238, page 2244. Zheng Zhao, Shay B Cohen, and Bonnie Webber. 2020. Reducing quantity hallucinations in abstractive summarization. arXiv preprint arXiv:2009.13312. Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, and Jin Zhou. 2020. ScriptWriter: Narrative-guided script generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8647–8657, Online. Association for Computational Linguistics. 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

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