Secure private shard infrastructure lets you protect your data while leveraging all the business benefits of open-source blockchains. Calimero offers its Console as a product. Console is a shard configuration interface that enables you to develop, test and deploy your private shard in a matter of minutes. A perfect way to accelerate your business with this out-of-the-box solution. But highly customizable at the same time. The private shard tech stack offers drastical improvements to a business by moving part (or the entirety) of operations to a private shard. Offering an impressive tech stack of console (interface), bridge (to communicate between private shards and a public network), privacy features to protect the data on the permissioned private network, and a drastic gas cost reduction by essentially making the private shard gas fees ZERO. Connect to private or public blockchain applications like DeFi, NFTs, on-chain KYC, and more without any compromise on confidentiality and privacy. You control who has the access to which data. Other benefits: - Giving you all the benefits of NEAR in terms of technology and security, but on your own subnetwork, independent from public validators. - Interoperable with other enterprises, shards, and public blockchain. - Designed to support confidential native NEAR WASM and Aurora EVM smart contracts. - Customizable block times, contract sizes and gas limits. - Bridging the data between the shard and the mainnet is extremely fast (5sec). - Predictable costs. Plan out your budget ahead of time by having access to the pricing tiers.

Calimero

calimero

(Scroll down to the hack) ‘Lying’ by Sam Harris explores why we lie to one another. Is someone with makeup lying to us about their appearance? We’ve all been Seinfeld & Aziz at the restaurant. And we do it because lying is easier. The social scientist will point out ☝️the effort vs benefit of ethical responsibility is too high☝️ And so, we shouldn’t be surprised that our users lie to us. It’s what makes listening to users an underrated skill. Most times, we don’t even intend to lie. We just forget to think about the truth. We fall prey to biases and tricks that the mind plays on us. Like the availability bias. Someone asks us “What’s keeping you up at night?” and we blurt out the first thing our minds make available to us. Not a great question to ask. By the way, congratulations if you’re already talking to users. Having spent hundreds of hours doing it at multiple ventures, I can tell you’re already. Most people don’t even do it. People don’t talk to users for a variety of reasons. It’s uncomfortable. You’re troubling someone. You need to say please and thank you. It’s scary. You may mess it up. You’re thinking about what to ask next. And not listening to what they’re saying. As such, many organizations and individuals hide behind surveys, or just read support tickets and data. Others justify different priorities, or.. just look busy. Hack Question (HQ) Get started on a call today by simply asking the user this again and again “And what did you do next?” Some theory and nuance follow, but if you want to skip it and hop on a call, just go on try it! It’s a Hack question for a reason It helps recall vs reimagine: We’re terrible at reimagining others’ worlds. “If I cooked up some tapioca balls and put them in tea would you drink it?” Err… sure! It’s open vs close-ended: Asking close-ended questions isn’t useful. It allows the easy way out. “How is everything?” Great! It’s non-leading vs leading: Leading questions make it easy for someone to lie. “This recipe is from my grandmother, isn’t it amazing” Yes! It forces detail: Building products is a high detail activity. Simply knowing “world hunger is a problem” doesn’t help you decide what to cook next. It uncovers jobs to be done: Every successful product solves some job. This question forces the user to recall jobs they need to do to accomplish a goal. Meeting bookends You naturally don’t want to start and finish the call with a Hack question. So, what do bookends look like? Smile, thank, and welcome your guest. Let them know it isn’t an inquisition. You’re just trying to understand better. Ok to record? Intro: Ask exactly what your product intends to solve. Remember we’re going for detail. Kickoff: “Tell me about the last time you bought steak at a food truck”. Depending on their response, adjust, but . They may be further up the buyer journey. That’s ok, you’ll get different information from this audience. Like how to acquire them. settle into HQ asap Speak from the heart. Big thank you, it was a valuable conversation and meant a lot. Finish: a BANTer s you get better at it You’ve overcome the initial fear. Now learn to BANTer between HQs as they describe pain points. Budget: Have you spent money on solving that? Authority: Did you need to get an ok from someone else first? Need: Do you really need to solve it, how many hrs/week do you get back? Timing: When was the last time you faced this, how often do you face it? It’s a sales discovery technique but applies to feature discovery too. Not every pain point is worth solving. Uncover the one to focus on. Don’t panic, and good luck! 😅

Hack Calmer Conversations and Get Reliable Feedback from Users

I am a 🇵🇹 PhD researcher @ Técnico Lisboa, where I teach User Centered Design. I am being a mentor at the Hyperledger Fabric Based Access Control project, supported and funded by Hyperledger and the Linux Foundation 🔥. 🚀 💪 Please make sure to check my blockchain projects on my Github sponsors  page ⛓️ Testing blockchain applications can be cumbersome, mainly because they are complex, resource-demanding distributed systems. Testing your blockchain app on the cloud might improve the efficiency of the testing process, as well as achieving more realistic test environments. 302665) applies , , , and more to come) This article provides a 101 guide on how to use Hyperledger Caliper (commit on Google Cloud , tackling the scenario of testing a Hyperledger Composer application, a blockchain framework which facilitates the development of Hyperledger Fabric applications. Nonetheless, this testing process to all blockchains supported by Hyperledger Caliper ( Hyperledger Burrow Hyperledger Fabric Hyperledger Iroha Hyperledger Sawtooth . Setting Up The Testing Environment Create a private repository (e.g., on Github).Fork the Hyperledger Caliper project to your private repo: 1. Create a private repository (e.g., on Github). 2.Fork the Hyperledger Caliper project to your private repo: git clone --bare https: cd caliper.git
git push --mirror https: cd ..
rm -rf caliper.git //github.com/hyperledger/caliper.git //github.com/USERNAME/CALIPER-PRIVATE-REPO.git 3. Clone your private repo: git clone https: //github.com/USERNAME/CALIPER-PRIVATE-REPO.git 4. On , create a folder with the name of your application, e.g. . CALIPER-PRIVATE-REPO/packages/caliper-samples/benchmark/composer my-network 5. On the folder, paste the contents of the . On a future article, I will explain how to write tests ( ) and benchmark configurations ( ). my-network configuration and test files from this repository my-network.js config.yaml 6. You should have a developed business network using Composer (the BNA archive). If you have not developed a business network, you can still configure Caliper to test default business networks. On , create a folder named . Paste your business network archive files ( BNA file) inside the folder. You should have a lib folder, a models folder, and the files package.json, permission.acl and optionally, a README.md. CALIPER-PRIVATE-REPO-justicechain/packages/caliper-samples/src/contract/composer my-network decompressed jsonNote that on package.json, the name and version of the network have to coincide with the one on composer.json. Example package.json: { :{ : }, : , : ,
 
 ... ETC "engines" "composer" "^0.20.0" "name" "my-network" "version" "0.2.6-deploy.1" 7. You have the necessary files to test your app. Push them to your repository: git add *
git commit -m git push origin master "add benchmark files" Setting Up Google Cloud 1. Create an account on Google Cloud. New accounts can apply for a free trial grant. Create a project, and activate the Compute Instance API. 2. At Compute Engine -> VM Instances, click on . Create a new instance 3. Start your instance. 4. Click on Connect — SSH. 5. Install the prerequisites for Hyperledger Caliper (thanks ): Daz Wilkin sudo apt-get update
sudo apt-get install \
    linux-image-extra-$(uname -r) \
    linux-image-extra-virtual
sudo apt-get update
sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    software-properties-common
curl -fsSL https: sudo add-apt-repository \ sudo apt-get update
sudo apt-get install docker-ce
sudo usermod -aG docker $USER
sudo systemctl enable docker
docker --version //download.docker.com/linux/ubuntu/gpg | sudo apt-key add - "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable" sudo curl -L https: sudo chmod +x /usr/local/bin/docker-compose
Golang (optional)
VERSION= OS=linux
ARCH=amd64
sudo curl \
--location https: --output go$VERSION.$OS-$ARCH.tar.gz
sudo tar -C /usr/local -xzf go$VERSION.$OS-$ARCH.tar.gz PATH=$PATH: local/go/bin
go version //github.com/docker/compose/releases/download/1.16.1/docker-compose-`uname -s`-`uname -m` -o /usr/local/bin/docker-compose 1.9 .1 //golang.org/dl/go$VERSION.$OS-$ARCH.tar.gz \ export /usr/ 6. Clone your private repository with Caliper and your blockchain app to your Google Compute instance. Testing your Application To run your benchmark, navigate to caliper/packages/caliper-samples. To benchmark your application with run: caliper benchmark run -w ./ -c benchmark/composer/justice/config.yaml -n benchmark/composer/justice/composer.json CouchDB To benchmark your application with run: caliper benchmark run -w ./ -c benchmark/composer/justice/config.yaml -n benchmark/composer/justice/composer-level.json LevelDB The results of the benchmark will be reported on the terminal. Do not forget to stop your machines in Google Cloud after finishing the experiments or else 💸. Next Steps Thanks very much for reading. You Rock 💪. 🚀 🙏 If you like blockchain and open source, be sure to support my work on Github sponsors  page

Test Your Blockchain On Google Cloud Using Hyperledger Caliper [A How To Guide]

Episode 27 of the : An interview with , Chairman and Co-Founder of , and co-creator of Kodak Coin. Hacker Noon Podcast Cameron Chell ICOx Innovations Today’s show would not be possible without Digital Ocean . Listen to the interview on , or , or watch on . iTunes Google Podcast YouTube In this episode and discuss bringing blockchain technology to enterprise companies we already know and trust, and what it is going to take to move from centralized systems to decentralized systems. Trent Lapinski Cameron Chell “Our thinking, yet to be proven, is that we go to these established brands who are already transacting in tens or hundreds of millions of dollars in revenue and they can utilize a coin or the crypto infrastructure that can either reduce their costs or increase customer attraction.” “Ultimately, society and certainly all economics run on trust, there is no other currency other than trust. When you have a system that can replace trust or create an environment where you can transact in a trustless way, the impact globally to me is beyond measure.” — Cameron Chell _Episode 27 of the Hacker Noon Podcast: An interview with Cameron Chell, Chairman and Co-Founder of ICOx Innovations…_www.podbean.com Building The Impossible via Business Acceleration with Cameron Chell P.S. If you dig the new Hacker Noon Podcast , consider giving us a 5 star review on iTunes. Today’s show would not be possible without Digital Ocean . Learn more at do.co/hackernoon . Also check out , and . February top stories the latest stories today’s homepage

Building The Impossible via Business Acceleration with Cameron Chell

I’m 32 and spent $200k on biohacking. Became calmer, thinner, extroverted, healthier & happier.

Tengo 32 años y gasté $200k en biohacking. Se volvió más tranquilo, más delgado, extrovertido, más saludable y más feliz.

Object-oriented programming (OOP) is a programming model that organizes software around objects(data) and object manipulation. OOP’s use of objects helps to break complex problems into smaller manageable parts, thus making code more straightforward to comprehend and manage, providing developers with a sense of ease.  Over the next few minutes, we will go over the following core concepts as they relate to OOP: Class


Object


Encapsulation


Inheritance


Polymorphism


Association 📓 All upcoming examples will use python code Class a class is a template used to create objects. Classes define the attributes of objects and the methods that the class instances will share. class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") The above snippet creates a class named Parent, which contains attributes, name and hair_color and a method, description(). Every instance of the class will have the same attributes and methods. Object Regarding OOP, an object is an instance of a class. It has attributes and can perform actions defined by its class. The following example creates instances of the Parent class; once they have been created, we can access its publicly available attributes. For example, father.hair_color returns a value of Red. father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown Encapsulation Encapsulation bundles attributes and methods within a class and can restrict access to some of the object’s components. For example, the code below defines a Parent class. The class has three attributes: name, hair_color, and __age. The name and hair_color attributes are publicly accessible; however, calling the __age attribute will result in an AttributeError. The double underscore causes an attribute to become private. To get the value of the __age attribute, the get_age() method must be called, as it will return the age attribute given to the Parent. class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! Inheritance 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism Inheritance occurs when a class inherits the attributes and methods of another class. In the following example, the Child class inherits the same attributes as the Parent class while creating its own unrelated attributes. class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science Polymorphism Polymorphism  allows you to override or modify class methods in subclasses. As seen in the previous snippet on Inheritance, the Child class adapts and modifies the Parent method description(). By including the super().description() within the description() method, you can adapt the inclusion of the Parent output into the Child’s description() method output. In the next snippet, the Child class inherits the attributes of the Parent class but overrides the description() method, resulting in a different output. class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! Association Association in programming is versatile, as it can be one-to-one, one-to-many, or many-to-many. It refers to the relationship between two classes where one class uses another. In the following example, we will use a one-to-many relation between classes. The Parent class can have many children, and we can add multiple Child instances via the add_child() method. class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 Conclusion Object-oriented programming (OOP) offers a robust framework for developing modular, reusable, and maintainable code. Its foundational concepts, such as classes, objects, encapsulation, inheritance, polymorphism, and association, enable developers to create scalable and efficient software solutions. This efficiency makes OOP an indispensable paradigm in modern programming. Embracing OOP enhances code organization and facilitates easier debugging and future-proofing of applications, making developers more productive and effective. Object-oriented programming (OOP) is a programming model that organizes software around objects(data) and object manipulation. OOP’s use of objects helps to break complex problems into smaller manageable parts, thus making code more straightforward to comprehend and manage, providing developers with a sense of ease.  Over the next few minutes, we will go over the following core concepts as they relate to OOP: Class Object Encapsulation Inheritance Polymorphism Association Class Class Object Object Encapsulation Encapsulation Inheritance Inheritance Polymorphism Polymorphism Association Association 📓 All upcoming examples will use python code 📓 All upcoming examples will use python code Class a class is a template used to create objects. Classes define the attributes of objects and the methods that the class instances will share. class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") class Parent:
    def __init__(self, name, hair_color):
        self.name = name
        self.hair_color= hair_color

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") The above snippet creates a class named Parent , which contains attributes, name and hair_color and a method, description() . Every instance of the class will have the same attributes and methods. Parent name hair_color description() Object Regarding OOP, an object is an instance of a class. It has attributes and can perform actions defined by its class. The following example creates instances of the Parent class; once they have been created, we can access its publicly available attributes. For example, father.hair_color returns a value of Red . Parent father.hair_color Red father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown father= Parent("Dylan", "Red")
mother= Parent("Grace", "Brown")

father.hair_color # Red
mother.hair_color # Brown Encapsulation Encapsulation bundles attributes and methods within a class and can restrict access to some of the object’s components. For example, the code below defines a Parent class. The class has three attributes: name , hair_color , and __age . The name and hair_color attributes are publicly accessible; however, calling the __age attribute will result in an AttributeError. The double underscore causes an attribute to become private. To get the value of the __age attribute, the get_age() method must be called, as it will return the age attribute given to the Parent . Parent name hair_color __age name hair_color __age __age get_age() Parent class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! class Parent:
    def __init__(self, name, hair_color, age):
        self.name = name
        self.hair_color= hair_color
        self.__age= age

    def description(self):
        print(f"I'm {self.name}, my hair is {self.hair_color}!") 

    def get_age(self):
        return self.__age

father= Parent("Dylan", "Red", 45)

father.name # Dylan
father.__age# will result in an AttributeError
father.get_age() # 45
father.description() # I'm Dylan, my hair color is Red! Inheritance 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism 👀 ignore super().description() in the following example. We’ll go over this once we get to Polymorphism super().description() Inheritance occurs when a class inherits the attributes and methods of another class. In the following example, the Child class inherits the same attributes as the Parent class while creating its own unrelated attributes. Child Parent class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science class Child(Parent):
    def __init__( self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        super().description()
        print(f"Name: {self.name}")
        print(f"Hair Color: {self.get_hair_color()")
        print(f"Age: {self.get_age()}")
        print(f"Major: {self.major}")

child = Child("Dwight", "Auburn", 20, "Computer Science")
child.description()
#Output
# I'm Dwight, my hair color is Auburn!
# Name: Dwight
# Hair Color: Auburn
# Age: 20
# Major: "Computer Science Polymorphism Polymorphism  allows you to override or modify class methods in subclasses. As seen in the previous snippet on Inheritance, the Child class adapts and modifies the Parent method description() . By including the super().description() within the description() method, you can adapt the inclusion of the Parent output into the Child ’s description() method output. In the next snippet, the Child class inherits the attributes of the Parent class but overrides the description() method, resulting in a different output. Child Parent description() super().description() description() Parent Child description() Child Parent description() class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! class Child(Parent):
    def __init__(self, name, hair_color, age, major):
        super().__init__(name, hair_color, age)
        self.major = major

    def description(self):
        print(f"Hello my name is {self.name}, I'm {self.get_age()} and I major in {self.major}!")

parent = Parent("Charles", "Grey", 45)
child = Child("Dwight", "Auburn", 20, "Computer Science")
parent.description()
#Output
# I'm Charles, my hair color is Grey!

child.description()
# Hello my name is Dwight, I'm 20 and I major in Computer Science! Association Association in programming is versatile, as it can be one-to-one, one-to-many, or many-to-many. It refers to the relationship between two classes where one class uses another. In the following example, we will use a one-to-many relation between classes. The Parent class can have many children, and we can add multiple Child instances via the add_child() method. Parent Child add_child() class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 class Parent:
    def __init__(self, name):
        self.name = name
        self.children = []

    def add_child(self, child):
        self.children.append(child)

    def list_children(self):
        return [child.name for child in self.children]

    def number_of_children(self):
        if not self.children:
            return "No children"
        return len(self.children)

class Child:
    def __init__(self, name, age):
        self.name = name
        self.age = age

parent = Parent("James")
child1 = Child("James Jr.", 12)
child2 = Child("Cameron", 16)

parent.add_child(child1)
parent.add_child(child2)

# List Children
for child in parent.list_children():
    print(child)

# James Jr.
# Cameron

parent.number_of_children()

# 2 Conclusion Object-oriented programming (OOP) offers a robust framework for developing modular, reusable, and maintainable code. Its foundational concepts, such as classes, objects, encapsulation, inheritance, polymorphism, and association, enable developers to create scalable and efficient software solutions. This efficiency makes OOP an indispensable paradigm in modern programming. Embracing OOP enhances code organization and facilitates easier debugging and future-proofing of applications, making developers more productive and effective.

Object-oriented Programming by Example

Full Guide to Hiring a Salesforce Pardot Consultant

সেলসফোর্স পারডট কনসালটেন্ট নিয়োগের সম্পূর্ণ নির্দেশিকা

Vollständiger Leitfaden zur Einstellung eines Salesforce Pardot-Beraters

Guía completa para contratar un consultor de Salesforce Pardot

Guide complet pour embaucher un consultant Salesforce Pardot

Salesforce Pardot सलाहकार को नियुक्त करने के लिए पूर्ण गाइड

Salesforce Pardot コンサルタントを雇うための完全ガイド

Salesforce Pardot 컨설턴트 채용에 대한 전체 가이드

Guia completo para contratar um consultor Salesforce Pardot

Полное руководство по найму консультанта Salesforce Pardot

Salesforce Pardot Danışmanı Kiralamaya İlişkin Tam Kılavuz

Hướng dẫn đầy đủ về việc thuê tư vấn viên Pardot của Salesforce

聘请 Salesforce Pardot 顾问的完整指南

Paramount/Skydance: The Birth of a New Hollywood Mogul?

Authors:
(1) Moritz Jasper, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (moritz.jasperl@barkhauseninstitut.org);
(2) Stefan Kopsell, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (stefan.koepsell@barkhauseninstitut.org). Table of Links Abstract and Introduction Related Work Description of LCM Attacker Model and Security Goals LCMSec: The Proposed Protocol Implementation and Evaluation Conclusion Appendix and References APPENDIX A. Two attacks on the Dutta-Barua group key agreement Zhang et al. present two attacks on the DBGKA protocol [23]. To fully understand them and this section, some understanding of the Dutta-Barua protocol [17] is required. While a full review of the protocol is out of scope for this work, for the purposes of this section, the most important thing is to understand that each KeyAgree() and Join() operation is associated with an instance id d. This instance id is incremented for each of those operations and can never be reused. Note that d can be regarded as a nonce: while it is not random, it is never reused. Another example of a protocol that uses non-random nonces is Wireguard [24]. Both attacks described by Zhang et al. are carried out by one or multiple malicious users who are part of the Dutta-Barua group, that is they have successfully participated in the DuttaBarua key agreement in the past. In this sense, the premise of the DBGKA is already violated: The DBGKA protocol provides no security against malicious insiders. Nevertheless, one should take this form of attack seriously: An honest user - representing, for instance, an IoT device - might at some point be compromised and become dishonest. Alternatively, he might have been dishonest all along, but his certificate is only revoked at a later stage. We will therefore discuss both attacks and show why they pose no threat to the LCMsec protocol. 1) First Attack: The first attack is carried out by a malicious leaving user who has been part of a previous successful DuttaBarua KeyAgreement() operation during which he has made some preparation for the attack by storing some of the protocol messages. When the Leave() operation is executed to expel this user from the group, Zhang et. al. show that the attacker can compute the new session key using the values he stored earlier. However, as we understand the DBGKA, the purpose of the Leave() operation is not to expel dishonest users, but as a way for honest users to leave. When an honest user leaves in this way, it is possible for the remaining users to efficiently agree on a new key. If an honest user, on the other hand, does not execute the Leave() operation, a new KeyAgreement() operation has to performed, which is a lot less efficient for large groups. To expel a malicious user, the remaining users instead execute the KeyAgree() operation amongst themselves – this way, the attack is bypassed entirely. Note that in the current version of LCMsec, we do not include a mechanism for certificate revocation or expelling users from the group and make no use of the Leave() operation, so this attack does not concern us. Still, the ability to add such a feature in the future is important. As we discussed, this can be done safely by using the KeyAgree() operation whenever a certificate is revoked. REFERENCES [1] P. T. Eugster, P. A. Felber, R. Guerraoui, and A.-M. Kermarrec, “The many faces of publish/subscribe,” ACM Computing Surveys, vol. 35, no. 2, pp. 114–131, Jun. 2003. [2] A. S. Huang, E. Olson, and D. C. Moore, “LCM: Lightweight Communications and Marshalling,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4057–4062. [3] R. Canetti, J. Garay, G. Itkis, D. Micciancio, M. Naor, and B. Pinkas, “Multicast security: A taxonomy and some efficient constructions,” in IEEE INFOCOM ’99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future Is Now (Cat. No.99CH36320), pp. 708–716 vol.2. [4] “Message Queuing Telemetry Transport.” [Online]. Available: https: //mqtt.org/ [5] G. Pardo-Castellote, “OMG Data-Distribution Service: Architectural overview,” in 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings., May 2003, pp. 200–206. [6] S. E. Deering, “Host extensions for IP multicasting,” Internet Engineering Task Force, Request for Comments RFC 1112, Aug. 1989. [7] E. Onica, P. Felber, H. Mercier, and E. Riviere, “Towards Scalable ` and Dependable Privacy-Preserving Publish/Subscribe Services,” in Fast Abstract in the 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, M. Roy, J. A. Lopez, and A. Casimiro, Eds., Toulouse, France, Jun. 2016. [8] S. Bernard, M. G. Potop-Butucaru, and S. Tixeuil, “A Framework for Secure and Private P2P Publish/Subscribe,” in Stabilization, Safety, and Security of Distributed Systems, ser. Lecture Notes in Computer Science, S. Dolev, J. Cobb, M. Fischer, and M. Yung, Eds., pp. 531–545. [9] L. Malina, G. Srivastava, P. Dzurenda, J. Hajny, and R. Fujdiak, “A Secure Publish/Subscribe Protocol for Internet of Things,” in Proceedings of the 14th International Conference on Availability, Reliability and Security, ser. ARES ’19, pp. 1–10. [10] M. Ion, G. Russello, and B. Crispo, “Supporting Publication and Subscription Confidentiality in Pub/Sub Networks,” in Security and Privacy in Communication Networks, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, S. Jajodia and J. Zhou, Eds., pp. 272–289. [11] M. Hamad, E. Regnath, J. Lauinger, V. Prevelakis, and S. Steinhorst, “SPPS: Secure Policy-based Publish/Subscribe System for V2C Communication,” in 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), Feb. 2021, pp. 529–534. [12] M. Dahlmanns, J. Pennekamp, I. B. Fink, B. Schoolmann, K. Wehrle, and M. Henze, “Transparent End-to-End Security for Publish/Subscribe Communication in Cyber-Physical Systems,” in Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, ser. SAT-CPS ’21, pp. 78–87. [13] iMatix Corporation, “ZeroMQ Broker vs . Brokerless Messaging - Whitepaper.” [Online]. Available: http://wiki.zeromq.org/whitepapers: brokerless [14] iMatix Corporation, “CurveZMQ - a protocol for secure messaging across the Internet,” Jan. 2023. [Online]. Available: http://rfc.zeromq. org/spec/26/ [15] Object Management Group, “DDS Security Specification,” Jun. 2018. [Online]. Available: https://www.omg.org/spec/DDS-SECURITY/1.1/ PDF [16] J. Kim, J. M. Smereka, C. Cheung, S. Nepal, and M. Grobler, “Security and Performance Considerations in ROS 2: A Balancing Act,” Sep. 2018. [Online]. Available: http://arxiv.org/abs/1809.09566 [17] R. Dutta and R. Barua, “Provably Secure Constant Round Contributory Group Key Agreement in Dynamic Setting,” IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 2007–2025, May 2008. [18] D. Ongaro and J. Ousterhout, “In search of an understandable consensus algorithm,” in Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference, ser. USENIX ATC’14, pp. 305–320. [19] M. J. Dworkin, “Recommendation for Block Cipher Modes of Operation: Galois/Counter Mode (GCM) and GMAC,” NIST, Nov. 2007. [20] R. Atkinson and S. Kent, “Security Architecture for the Internet Protocol,” Internet Engineering Task Force, Request for Comments RFC 2401, Nov. 1998. [21] T. T. T. ZOU) and X. Zhang, “IPsec Anti-Replay Algorithm without Bit Shifting,” Internet Engineering Task Force, Request for Comments RFC 6479, Jan. 2012. [22] S. Boeyen, S. Santesson, T. Polk, R. Housley, S. Farrell, and D. Cooper, “Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile,” Internet Engineering Task Force, Request for Comments RFC 5280, May 2008. [23] H. Zhang, C. Xu, C. Li, and A. R. Sangi, “Two Attacks on Dutta’s Dynamic Group Key Agreement Protocol,” in Wireless Communications and Applications, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, P. Senac, M. Ott, and A. Seneviratne, Eds., pp. 419–425. ´ [24] J. A. Donenfeld, “WireGuard: Next Generation Kernel Network Tunnel,” in Proceedings 2017 Network and Distributed System Security Symposium. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Moritz Jasper, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (moritz.jasperl@barkhauseninstitut.org); (2) Stefan Kopsell, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (stefan.koepsell@barkhauseninstitut.org). Authors: Authors: (1) Moritz Jasper, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (moritz.jasperl@barkhauseninstitut.org); (2) Stefan Kopsell, Barkhausen Institut gGmbH, Wurzburger Straße 46, Dresden, Germany (stefan.koepsell@barkhauseninstitut.org). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Description of LCM Description of LCM Attacker Model and Security Goals Attacker Model and Security Goals LCMSec: The Proposed Protocol LCMSec: The Proposed Protocol Implementation and Evaluation Implementation and Evaluation Conclusion Conclusion Appendix and References Appendix and References APPENDIX A. Two attacks on the Dutta-Barua group key agreement A. Two attacks on the Dutta-Barua group key agreement Zhang et al. present two attacks on the DBGKA protocol [23]. To fully understand them and this section, some understanding of the Dutta-Barua protocol [17] is required. While a full review of the protocol is out of scope for this work, for the purposes of this section, the most important thing is to understand that each KeyAgree() and Join() operation is associated with an instance id d. This instance id is incremented for each of those operations and can never be reused. Note that d can be regarded as a nonce: while it is not random, it is never reused. Another example of a protocol that uses non-random nonces is Wireguard [24]. Both attacks described by Zhang et al. are carried out by one or multiple malicious users who are part of the Dutta-Barua group, that is they have successfully participated in the DuttaBarua key agreement in the past. In this sense, the premise of the DBGKA is already violated: The DBGKA protocol provides no security against malicious insiders. Nevertheless, one should take this form of attack seriously: An honest user - representing, for instance, an IoT device - might at some point be compromised and become dishonest. Alternatively, he might have been dishonest all along, but his certificate is only revoked at a later stage. We will therefore discuss both attacks and show why they pose no threat to the LCMsec protocol. 1) First Attack : The first attack is carried out by a malicious leaving user who has been part of a previous successful DuttaBarua KeyAgreement() operation during which he has made some preparation for the attack by storing some of the protocol messages. When the Leave() operation is executed to expel this user from the group, Zhang et. al. show that the attacker can compute the new session key using the values he stored earlier. 1) First Attack DuttaBarua KeyAgreement() Leave() However, as we understand the DBGKA, the purpose of the Leave() operation is not to expel dishonest users, but as a way for honest users to leave. When an honest user leaves in this way, it is possible for the remaining users to efficiently agree on a new key. If an honest user, on the other hand, does not execute the Leave() operation, a new KeyAgreement() operation has to performed, which is a lot less efficient for large groups. To expel a malicious user, the remaining users instead execute the KeyAgree() operation amongst themselves – this way, the attack is bypassed entirely. Leave() Leave() KeyAgreement() KeyAgree() Note that in the current version of LCMsec, we do not include a mechanism for certificate revocation or expelling users from the group and make no use of the Leave() operation, so this attack does not concern us. Still, the ability to add such a feature in the future is important. As we discussed, this can be done safely by using the KeyAgree() operation whenever a certificate is revoked. Leave() KeyAgree() REFERENCES [1] P. T. Eugster, P. A. Felber, R. Guerraoui, and A.-M. Kermarrec, “The many faces of publish/subscribe,” ACM Computing Surveys, vol. 35, no. 2, pp. 114–131, Jun. 2003. [2] A. S. Huang, E. Olson, and D. C. Moore, “LCM: Lightweight Communications and Marshalling,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4057–4062. [3] R. Canetti, J. Garay, G. Itkis, D. Micciancio, M. Naor, and B. Pinkas, “Multicast security: A taxonomy and some efficient constructions,” in IEEE INFOCOM ’99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future Is Now (Cat. No.99CH36320), pp. 708–716 vol.2. [4] “Message Queuing Telemetry Transport.” [Online]. Available: https: //mqtt.org/ [5] G. Pardo-Castellote, “OMG Data-Distribution Service: Architectural overview,” in 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings., May 2003, pp. 200–206. [6] S. E. Deering, “Host extensions for IP multicasting,” Internet Engineering Task Force, Request for Comments RFC 1112, Aug. 1989. [7] E. Onica, P. Felber, H. Mercier, and E. Riviere, “Towards Scalable ` and Dependable Privacy-Preserving Publish/Subscribe Services,” in Fast Abstract in the 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, M. Roy, J. A. Lopez, and A. Casimiro, Eds., Toulouse, France, Jun. 2016. [8] S. Bernard, M. G. Potop-Butucaru, and S. Tixeuil, “A Framework for Secure and Private P2P Publish/Subscribe,” in Stabilization, Safety, and Security of Distributed Systems, ser. Lecture Notes in Computer Science, S. Dolev, J. Cobb, M. Fischer, and M. Yung, Eds., pp. 531–545. [9] L. Malina, G. Srivastava, P. Dzurenda, J. Hajny, and R. Fujdiak, “A Secure Publish/Subscribe Protocol for Internet of Things,” in Proceedings of the 14th International Conference on Availability, Reliability and Security, ser. ARES ’19, pp. 1–10. [10] M. Ion, G. Russello, and B. Crispo, “Supporting Publication and Subscription Confidentiality in Pub/Sub Networks,” in Security and Privacy in Communication Networks, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, S. Jajodia and J. Zhou, Eds., pp. 272–289. [11] M. Hamad, E. Regnath, J. Lauinger, V. Prevelakis, and S. Steinhorst, “SPPS: Secure Policy-based Publish/Subscribe System for V2C Communication,” in 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE), Feb. 2021, pp. 529–534. [12] M. Dahlmanns, J. Pennekamp, I. B. Fink, B. Schoolmann, K. Wehrle, and M. Henze, “Transparent End-to-End Security for Publish/Subscribe Communication in Cyber-Physical Systems,” in Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems, ser. SAT-CPS ’21, pp. 78–87. [13] iMatix Corporation, “ZeroMQ Broker vs . Brokerless Messaging - Whitepaper.” [Online]. Available: http://wiki.zeromq.org/whitepapers: brokerless [14] iMatix Corporation, “CurveZMQ - a protocol for secure messaging across the Internet,” Jan. 2023. [Online]. Available: http://rfc.zeromq. org/spec/26/ [15] Object Management Group, “DDS Security Specification,” Jun. 2018. [Online]. Available: https://www.omg.org/spec/DDS-SECURITY/1.1/ PDF [16] J. Kim, J. M. Smereka, C. Cheung, S. Nepal, and M. Grobler, “Security and Performance Considerations in ROS 2: A Balancing Act,” Sep. 2018. [Online]. Available: http://arxiv.org/abs/1809.09566 [17] R. Dutta and R. Barua, “Provably Secure Constant Round Contributory Group Key Agreement in Dynamic Setting,” IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 2007–2025, May 2008. [18] D. Ongaro and J. Ousterhout, “In search of an understandable consensus algorithm,” in Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference, ser. USENIX ATC’14, pp. 305–320. [19] M. J. Dworkin, “Recommendation for Block Cipher Modes of Operation: Galois/Counter Mode (GCM) and GMAC,” NIST, Nov. 2007. [20] R. Atkinson and S. Kent, “Security Architecture for the Internet Protocol,” Internet Engineering Task Force, Request for Comments RFC 2401, Nov. 1998. [21] T. T. T. ZOU) and X. Zhang, “IPsec Anti-Replay Algorithm without Bit Shifting,” Internet Engineering Task Force, Request for Comments RFC 6479, Jan. 2012. [22] S. Boeyen, S. Santesson, T. Polk, R. Housley, S. Farrell, and D. Cooper, “Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile,” Internet Engineering Task Force, Request for Comments RFC 5280, May 2008. [23] H. Zhang, C. Xu, C. Li, and A. R. Sangi, “Two Attacks on Dutta’s Dynamic Group Key Agreement Protocol,” in Wireless Communications and Applications, ser. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, P. Senac, M. Ott, and A. Seneviratne, Eds., pp. 419–425. ´ [24] J. A. Donenfeld, “WireGuard: Next Generation Kernel Network Tunnel,” in Proceedings 2017 Network and Distributed System Security Symposium. 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

Secure and Dynamic Publish/Subscribe: LCMsec: Appendix and References

The Rise of Blockchain-based Video Streaming: Exclusive Interview With Replay CEO Michael Jelen

WildlifeDatasets: an Open-source Toolkit for Animal Re-identification: Conclusion and References

Why Chainwire is the Best Crypto PR Distribution Tool for Web3 Marketing Professionals

কেন চেইনওয়্যার ওয়েব3 মার্কেটিং পেশাদারদের জন্য সেরা ক্রিপ্টো পিআর ডিস্ট্রিবিউশন টুল

Warum Chainwire das beste Krypto-PR-Verteilungstool für Web3-Marketingprofis ist

Por qué Chainwire es la mejor herramienta de distribución de relaciones públicas criptográficas para los profesionales del marketing Web3

Pourquoi Chainwire est le meilleur outil de distribution Crypto PR pour les professionnels du marketing Web3

वेब3 मार्केटिंग पेशेवरों के लिए चेनवायर सर्वश्रेष्ठ क्रिप्टो पीआर वितरण उपकरण क्यों है

Chainwire が Web3 マーケティング専門家にとって最高の暗号 PR 配信ツールである理由

Chainwire가 Web3 마케팅 전문가를 위한 최고의 암호화 PR 배포 도구인 이유

Por que Chainwire é a melhor ferramenta de distribuição de criptografia para profissionais de marketing Web3

Почему Chainwire — лучший инструмент распространения крипто-PR для специалистов по маркетингу Web3

Chainwire Neden Web3 Pazarlama Uzmanları için En İyi Kripto PR Dağıtım Aracıdır?

Tại sao Chainwire là Công cụ phân phối PR tiền điện tử tốt nhất dành cho các chuyên gia tiếp thị Web3

为什么 Chainwire 是 Web3 营销专业人士的最佳加密 PR 分发工具

If you are an app developer, unfortunately, the weight of uncertainty surrounding an influx in layoffs and talks of layoffs has you living life on the edge of indecision, but don't worry, you're not the only one. Just mentioning downsizing or restructuring within a company can quickly result in severe anxiety for anyone—even the most seasoned professionals. When your position feels tenuous, it's crucial to have strategies to manage these challenges. "Layoffs have threatened the app development industry recently. Of course, reports showing a noticeable spike in job losses around different sectors do nothing to alleviate concerns. Among the industries hit the hardest, the tech and government industries have likely experienced the most significant threat of downsizing. Veteran app developer and mind behind the anti-anxiety app Calmer, Daniel Leimanis, understands the struggles. Drawing from his own experiences and expertise, Leimanis has opened up to share his top six most invaluable tips for app developers who might be contending with anxiety in 2024. 1. Embrace Mindfulness and Meditation Incorporate mindful practices into your daily routine to promote a sense of remaining grounded and evident during times of uncertainty. Utilize deep breathing exercises, guided meditation sessions, or any other technique that resonates with your core being. This essential practice offers respite from the whirlwind of stress accompanying intense coding sessions and debugging challenges. 2. Prioritize Physical Well-being As most know, maintaining physical health is a principal necessity. However, many might need to realize how crucial physical health is for strengthening mental resilience. Exercise regularly, maintain a balanced diet, and ensure adequate sleep to safeguard your overall well-being. Take brief breaks for a stroll or whatever you need to do to relax and recharge your batteries. Additionally, paying attention to ergonomics and frequent stretch breaks can help prevent physical strain during prolonged coding stints. 3. Set Realistic Goals and Boundaries Avoid falling into unrealistic expectations by breaking down larger projects into manageable tasks with achievable deadlines. Utilize project management tools to track progress and outline unmistakable boundaries between work and personal life. Burnout is a typical result of over-commitment, so prioritize self-care and know when to step away from the desk. 4. Cultivate a Supportive Work Environment Build connections with supportive colleagues and promote open communication channels within your workplace. Collaborative efforts, like peer programming and regular check-ins, can lead to camaraderie and lessen feelings of isolation. A positive work culture can make all the difference. 5. Incorporate Stress Relief Techniques Incorporate effective stress-relief practices into your routine to diminish the daily pressures. Yoga, journaling, or listening to your favourite music can relieve stress. Find whatever works best for you, but periodically taking breaks to engage in hobbies or non-work-related activities can be an immensely vital reset button for the brain. 6. Seek Professional Support When Needed Lastly, remember that there is no shame in obtaining some help when anxiety becomes overwhelming. Therapy, counseling, or consulting with a mental health professional can give you invaluable insights and coping strategies. Numerous tech companies now offer employee assistance programs that include crucial mental health services. Use these resources if you have access to them. As Leimanis emphasizes, managing anxiety is an obstacle-filled journey, but there is no inherent reason why it must be traversed alone. By implementing the above-noted six strategies and being bold in reaching out for support, app developers can navigate the uncertain days ahead with greater resilience and grander fortitude. If you are an app developer, unfortunately, the weight of uncertainty surrounding an influx in layoffs and talks of layoffs has you living life on the edge of indecision, but don't worry, you're not the only one. Just mentioning downsizing or restructuring within a company can quickly result in severe anxiety for anyone—even the most seasoned professionals. When your position feels tenuous, it's crucial to have strategies to manage these challenges. "Layoffs have threatened the app development industry recently. Of course, reports showing a noticeable spike in job losses around different sectors do nothing to alleviate concerns. Among the industries hit the hardest, the tech and government industries have likely experienced the most significant threat of downsizing. Veteran app developer and mind behind the anti-anxiety app Calmer , Daniel Leimanis, understands the struggles. Drawing from his own experiences and expertise, Leimanis has opened up to share his top six most invaluable tips for app developers who might be contending with anxiety in 2024. Calmer Calmer 1. Embrace Mindfulness and Meditation Incorporate mindful practices into your daily routine to promote a sense of remaining grounded and evident during times of uncertainty. Utilize deep breathing exercises, guided meditation sessions, or any other technique that resonates with your core being. This essential practice offers respite from the whirlwind of stress accompanying intense coding sessions and debugging challenges. 2. Prioritize Physical Well-being As most know, maintaining physical health is a principal necessity. However, many might need to realize how crucial physical health is for strengthening mental resilience. Exercise regularly, maintain a balanced diet, and ensure adequate sleep to safeguard your overall well-being. Take brief breaks for a stroll or whatever you need to do to relax and recharge your batteries. Additionally, paying attention to ergonomics and frequent stretch breaks can help prevent physical strain during prolonged coding stints. 3. Set Realistic Goals and Boundaries Avoid falling into unrealistic expectations by breaking down larger projects into manageable tasks with achievable deadlines. Utilize project management tools to track progress and outline unmistakable boundaries between work and personal life. Burnout is a typical result of over-commitment, so prioritize self-care and know when to step away from the desk. 4. Cultivate a Supportive Work Environment Build connections with supportive colleagues and promote open communication channels within your workplace. Collaborative efforts, like peer programming and regular check-ins, can lead to camaraderie and lessen feelings of isolation. A positive work culture can make all the difference. 5. Incorporate Stress Relief Techniques Incorporate effective stress-relief practices into your routine to diminish the daily pressures. Yoga, journaling, or listening to your favourite music can relieve stress. Find whatever works best for you, but periodically taking breaks to engage in hobbies or non-work-related activities can be an immensely vital reset button for the brain. 6. Seek Professional Support When Needed Lastly, remember that there is no shame in obtaining some help when anxiety becomes overwhelming. Therapy, counseling, or consulting with a mental health professional can give you invaluable insights and coping strategies. Numerous tech companies now offer employee assistance programs that include crucial mental health services. Use these resources if you have access to them. As Leimanis emphasizes, managing anxiety is an obstacle-filled journey, but there is no inherent reason why it must be traversed alone. By implementing the above-noted six strategies and being bold in reaching out for support, app developers can navigate the uncertain days ahead with greater resilience and grander fortitude.

6 Strategies for App Developers Dealing With Anxiety During Layoffs

ছাঁটাইয়ের সময় উদ্বেগ মোকাবেলা করার জন্য অ্যাপ বিকাশকারীদের জন্য 6 কৌশল

6 Strategien für App-Entwickler zum Umgang mit Ängsten bei Entlassungen

Seis estrategias para desarrolladores de aplicaciones que enfrentan la ansiedad durante los despidos

6 stratégies pour les développeurs d'applications face à l'anxiété lors des licenciements

छंटनी के दौरान चिंता से निपटने वाले ऐप डेवलपर्स के लिए 6 रणनीतियाँ

解雇時の不安に対処するアプリ開発者のための 6 つの戦略

해고 중 불안에 대처하는 앱 개발자를 위한 6가지 전략

6 estratégias para desenvolvedores de aplicativos que lidam com a ansiedade durante as dispensas

6 стратегий для разработчиков приложений, которые помогут справиться с тревогой во время увольнений

İşten Çıkarılma Sırasında Kaygıyla Başa Çıkan Uygulama Geliştiricileri için 6 Strateji

6 chiến lược dành cho nhà phát triển ứng dụng đối phó với nỗi lo lắng khi bị sa thải

应用程序开发人员应对裁员焦虑的 6 个策略

NEW YORK, USA, May 23rd, 2024/Chainwire/-- Orderly Network has partnered with Quickswap, the leading DEX on all Polygon chains, to launch QuickPerps: Falkor, a next-generation decentralized Perpetual Exchange. This integration unlocks a new level of functionality for DeFi traders on Polygon PoS, with key benefits powered by Orderly's innovative omnichain vaults: Enhanced Liquidity: Orderly's robust liquidity layer combines with Quickswap's, enabling leveraged trading with tighter spreads and lower slippage.
Effortless Cross-Chain Trading: Deposit, withdraw, and trade assets seamlessly across all major EVM chains supported by Orderly, directly from the Falkor vault. No asset bridging required.
Zero-Gas Leveraged Trading: Enjoy Polygon PoS's fast network and low fees for vault deposits. Leveraged trading within the vault happens entirely gas-free thanks to Orderly Network.
CEX-Like Experience: A familiar orderbook interface powered by Orderly's CLOB infrastructure simplifies trading for both new and experienced users.
Advanced Order Controls: Manage risk effectively with limit orders – all available within the user-friendly interface. This collaboration positions Quickswap to become the go-to DEX for leveraged trading on Polygon PoS. By solving the re-org issues that plagued previous perpetual protocols, Falkor has the potential to ignite a surge in trading volume on the platform. Said Arjun Arora, COO of Orderly Network: “Partnering with QuickSwap aligns with Orderly’s mission to empower DEXs with superior infrastructure that enables them to rival traditional CEXs in functionality and performance. This integration is also a testament to Orderly Network’s versatility, as it enables an Automated Market Maker like QuickSwap to widen their product offering and offer perpetual futures for the first time on Polygon PoS.” Said Sameep Singhania, Core Contributor at QuickSwap: “Our partnership with Orderly Network marks a significant leap forward for QuickSwap and the DragonFi ecosystem. By integrating Falkor with Orderly Network’s omnichain vault system, QuickSwap can empower its users with the best-in-class leveraged trading experience, complete with zero-gas fees, deep liquidity, and a user-friendly interface.” Orderly Network has come a long way in a short amount of time. Since launching in 2022, Orderly has already surpassed US$38 billion in trading volume, and integrated some of the world’s highest-caliber decentralized exchanges into its novel permissionless liquidity layer. Backed by major investors like Sequoia China, Pantera Capital, Primitive Ventures, Dragonfly Capital, Jump Crypto, and GSR Ventures, Orderly has rapidly gained traction with DeFi builders and traders alike. A Perfect Match for DeFi Growth Polygon PoS's fast network, cheap fees, and large user base (over 400k daily active users) create the perfect environment for DeFi to thrive. Orderly's robust infrastructure, combined with Quickswap's reach and Polygon PoS's scalability, positions this collaboration as a major step forward for omnichain trading and DeFi on Polygon PoS. "The integration of Orderly Network's advanced perpetuals trading system into Quickswap marks a significant advancement in DeFi on Polygon PoS," said Jack Melnick, Head of DeFi, Polygon Labs. "This development, supported by Orderly's innovative omnichain vaults, underscores a vision of fostering a scalable, efficient, and user-friendly DeFi ecosystem." About Orderly Network Orderly Network is a combination of an orderbook-based trading infrastructure and a robust liquidity layer offering spot and perpetual futures orderbooks. Unlike traditional platforms, Orderly doesn’t have a front end; instead, it operates at the core of the ecosystem, providing essential services to projects built on top of it. Orderly Network's DEX white-label solution is carefully crafted to save builders time and capital while granting access to our bootstrapped liquidity. Picture having the best features of CEXs while keeping settlements on-chain and maintaining full self-custody. With Orderly, anyone can create a trading application thanks to our seamless plug-and-play experience leveraging our liquidity and composability. Looking ahead, Orderly Network's grand vision is to create an omnichain protocol, connecting traders from both EVM and non-EVM chains within the same orderbook. For more information, users can visit Orderly Network's: Official Website | Twitter | Telegram | Discord | Linkedin Contact Chief Vibes Officer Drew Pierson Orderly Network pr@orderly.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. NEW YORK, USA, May 23rd, 2024/Chainwire/-- Orderly Network has partnered with Quickswap, the leading DEX on all Polygon chains, to launch QuickPerps: Falkor, a next-generation decentralized Perpetual Exchange. This integration unlocks a new level of functionality for DeFi traders on Polygon PoS, with key benefits powered by Orderly's innovative omnichain vaults: Enhanced Liquidity: Orderly's robust liquidity layer combines with Quickswap's, enabling leveraged trading with tighter spreads and lower slippage. Effortless Cross-Chain Trading: Deposit, withdraw, and trade assets seamlessly across all major EVM chains supported by Orderly, directly from the Falkor vault. No asset bridging required. Zero-Gas Leveraged Trading: Enjoy Polygon PoS's fast network and low fees for vault deposits. Leveraged trading within the vault happens entirely gas-free thanks to Orderly Network. CEX-Like Experience: A familiar orderbook interface powered by Orderly's CLOB infrastructure simplifies trading for both new and experienced users. Advanced Order Controls: Manage risk effectively with limit orders – all available within the user-friendly interface. Enhanced Liquidity: Orderly's robust liquidity layer combines with Quickswap's, enabling leveraged trading with tighter spreads and lower slippage. Effortless Cross-Chain Trading: Deposit, withdraw, and trade assets seamlessly across all major EVM chains supported by Orderly, directly from the Falkor vault. No asset bridging required. Zero-Gas Leveraged Trading: Enjoy Polygon PoS's fast network and low fees for vault deposits. Leveraged trading within the vault happens entirely gas-free thanks to Orderly Network. CEX-Like Experience: A familiar orderbook interface powered by Orderly's CLOB infrastructure simplifies trading for both new and experienced users. Advanced Order Controls: Manage risk effectively with limit orders – all available within the user-friendly interface. This collaboration positions Quickswap to become the go-to DEX for leveraged trading on Polygon PoS. By solving the re-org issues that plagued previous perpetual protocols, Falkor has the potential to ignite a surge in trading volume on the platform. Said Arjun Arora, COO of Orderly Network: “Partnering with QuickSwap aligns with Orderly’s mission to empower DEXs with superior infrastructure that enables them to rival traditional CEXs in functionality and performance. This integration is also a testament to Orderly Network’s versatility, as it enables an Automated Market Maker like QuickSwap to widen their product offering and offer perpetual futures for the first time on Polygon PoS.” Said Arjun Arora, COO of Orderly Network: “Partnering with QuickSwap aligns with Orderly’s mission to empower DEXs with superior infrastructure that enables them to rival traditional CEXs in functionality and performance. This integration is also a testament to Orderly Network’s versatility, as it enables an Automated Market Maker like QuickSwap to widen their product offering and offer perpetual futures for the first time on Polygon PoS.” Said Sameep Singhania, Core Contributor at QuickSwap: “Our partnership with Orderly Network marks a significant leap forward for QuickSwap and the DragonFi ecosystem. By integrating Falkor with Orderly Network’s omnichain vault system, QuickSwap can empower its users with the best-in-class leveraged trading experience, complete with zero-gas fees, deep liquidity, and a user-friendly interface.” Said Sameep Singhania, Core Contributor at QuickSwap: “Our partnership with Orderly Network marks a significant leap forward for QuickSwap and the DragonFi ecosystem. By integrating Falkor with Orderly Network’s omnichain vault system, QuickSwap can empower its users with the best-in-class leveraged trading experience, complete with zero-gas fees, deep liquidity, and a user-friendly interface.” Orderly Network has come a long way in a short amount of time. Since launching in 2022, Orderly has already surpassed US$38 billion in trading volume, and integrated some of the world’s highest-caliber decentralized exchanges into its novel permissionless liquidity layer. Backed by major investors like Sequoia China, Pantera Capital, Primitive Ventures, Dragonfly Capital, Jump Crypto, and GSR Ventures, Orderly has rapidly gained traction with DeFi builders and traders alike. A Perfect Match for DeFi Growth Polygon PoS's fast network, cheap fees, and large user base (over 400k daily active users) create the perfect environment for DeFi to thrive. Orderly's robust infrastructure, combined with Quickswap's reach and Polygon PoS's scalability, positions this collaboration as a major step forward for omnichain trading and DeFi on Polygon PoS. "The integration of Orderly Network's advanced perpetuals trading system into Quickswap marks a significant advancement in DeFi on Polygon PoS," said Jack Melnick, Head of DeFi, Polygon Labs. "This development, supported by Orderly's innovative omnichain vaults, underscores a vision of fostering a scalable, efficient, and user-friendly DeFi ecosystem." About Orderly Network Orderly Network is a combination of an orderbook-based trading infrastructure and a robust liquidity layer offering spot and perpetual futures orderbooks. Unlike traditional platforms, Orderly doesn’t have a front end; instead, it operates at the core of the ecosystem, providing essential services to projects built on top of it. Orderly Network's DEX white-label solution is carefully crafted to save builders time and capital while granting access to our bootstrapped liquidity. Picture having the best features of CEXs while keeping settlements on-chain and maintaining full self-custody. With Orderly, anyone can create a trading application thanks to our seamless plug-and-play experience leveraging our liquidity and composability. Looking ahead, Orderly Network's grand vision is to create an omnichain protocol, connecting traders from both EVM and non-EVM chains within the same orderbook. For more information, users can visit Orderly Network's: Official Website | Twitter | Telegram | Discord | Linkedin Official Website Official Website Twitter Twitter Telegram Telegram Discord Discord Linkedin Linkedin Contact Chief Vibes Officer Drew Pierson Orderly Network pr@orderly.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Orderly Network Expands To Polygon PoS, Bringing Advanced Perpetuals Trading To Quickswap

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 REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934 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 REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934

Magnetic Wind From a Massive White Dwarf Merger: References

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 3. RESULT Table. 3 shows a summary of our simulation. A model BxΩy corresponds to the case with B∗ = x and Ω∗ = y in the cgs unit. When the rotating magnetic wind becomes quasi-steady, it can be characterized by the mass loss rate wind luminosity and spindown torque estimated at the outer boundary. As we show later, the strength of rotating magnetic winds can be characterized by a dimensionless parameter Hereafter we take B1.5e6Ω0.23 with σ = 34.3 as the fiducial model, and first show the multi-dimensional structure of the rotating magnetic wind in Sec. 3.1. We then investigate the time variability of the system primarily focusing on the impacts of quasi-periodic eruption along with the equatorial plane in Sec. 3.2. Finally, we show how the time-averaged spin-down torque scales with system parameters in Sec. 3.3. 3.1. Anisotropic wind structure In the equatorial direction (80◦ ≲ θ ≲ 100◦), magnetic fields are closed at small radii, forming a corotating magnetosphere. Beyond the last closed loop, the magnetic field lines are open with a predominant toroidal component, having opposite polarities with respect to the equatorial plane. The transition of the magnetic field configuration is mediated by reconnection occurring at around the tip of the last closed loop, or the Y point. As can be observed from the top-right panel of Fig. 1, the plasma beta in this transition region is higher than those along the open magnetic fields, implying that the gas is trapped mainly by magnetic tensions. In this high plasma-beta region sandwiched by low plasma beta regions, gases are pinched and radially accelerated in the reconnection region, eventually become supersonic at around r ∼ 5 R∗. The latitudinal angle dependence of the wind at the outer boundary is illustrated in Fig. 2, where the gray shaded regions represent the dynamical range of radial velocity (top-left), luminosity (top-right), torque (bottom-left), and mass loss rate (bottom-right). As described in the previous paragraphs, radial velocities in the near-equatorial direction can reach and transiently exceed the Michel velocity (represented by the horizontal dotted line) during eruptions caused by reconnection at the Y-point. Consequently, the wind luminosity, dominated by the radial kinetic term, also exhibits a sharp peak in the equatorial direction. On the other hand, the torque is primarily exerted by corotation with the magnetic fields and reaches its peak slightly off the equatorial plane (θ ∼ 85◦), corresponding to the edge of the concave shape of the Alfven radius. 3.2. Time variability The acceleration of the rotating magnetic wind in the equatorial direction occurs in a time-variable manner, associated with magnetic reconnection at the Y-point. Consequently, the overall flux of mass, energy, and angular momentum from the central WD can also vary with time. Fig. 3 displays the time evolution of the mass loss rate (M˙ ), luminosity (L), and torque (T) in our fiducial model. All quantities are normalized by their time-averaged values. The lower panel of Fig. 3, which provides a long-term perspective, reveals a recurrent eruptive behavior. A recurrent cycle consists of pre-eruption, reconnection, post-eruption phases: In the pre-eruption phase, gases injected into the near-equatorial plane become trapped within the closed field lines. Due to the centrifugal force, the gases accumulate at the tip of the last closed loop, resulting in a continuous decrease in plasma beta in that region. When the centrifugal force acting on the accumulated gases exceeds the tension of the closed magnetic fields, the tip of the closed zone starts expanding radially and is subsequently ejected as a plasmoid through reconnection. Such a plasmoid can be observed at r ∼ 4 - 5 R∗ in Fig. 1. Afterwards, the cycle returns to the pre-eruption phase and restores the gases within the closed magnetic field lines. This type of recurrent eruptions has been known as slingshot prominence in the context of magnetically-active rapidly-rotating stars (e.g., Ferreira 2000; Townsend & Owocki 2005; Jardine & Collier Cameron 2019). The upper panel of Fig. 3 focuses on the fluxes during a rotation period (t = 15-16 [2π/Ω∗]), as also depicted in Fig. 2. In comparison to the pre- and post-eruption phases, represented by the purple and red lines, respectively, the observed fluxes of mass, energy, and angular momentum consistently increase as the erupted plasmoids reach the outer boundary, indicated by the green lines. Notably, the magnetic torque significantly contributes to the overall torque increase during the eruption phase and plays a dominant role in the central WD’s spin-down. We note that the specifics of reconnection dynamics, such as the frequency of recurrent eruptions and the resulting time evolution of mass, energy, and angular momentum fluxes, may be influenced by our numerical parameters, including spatial resolution (which governs numerical resistivity) and the width of the wind launching region. However, we have verified that the time-averaged values of wind velocities, mass loss rate, luminosity, and torque have all reached convergence concerning the spatial resolution in our simulations and the width of the wind launching region (see Appendix B). 3.3. Scaling relation of the spin-down torque Here we consider how the spin-down torque of rotating magnetic wind depends on the system parameters based on our numerical results. As we show in the previous sub-sections, the time-averaged torque is essentially determined by the magnetic torque exerted on the gases at the tip of the last closed field lines, or the Y-point, where the field configuration is still roughly compatible with the rotating dipole. In this case, the (electro)magnetic torque at the Y point can be estimated as [3] In relativistic MHD regime, speed of light c is conventionally used as the characteristic speed of the system (e.g., see the definition of σ0 in Bucciantini et al. 2006). 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 3. RESULT Table. 3 shows a summary of our simulation. A model BxΩy corresponds to the case with B∗ = x and Ω∗ = y in the cgs unit. When the rotating magnetic wind becomes quasi-steady, it can be characterized by the mass loss rate wind luminosity and spindown torque estimated at the outer boundary. As we show later, the strength of rotating magnetic winds can be characterized by a dimensionless parameter Hereafter we take B1.5e6Ω0.23 with σ = 34.3 as the fiducial model, and first show the multi-dimensional structure of the rotating magnetic wind in Sec. 3.1. We then investigate the time variability of the system primarily focusing on the impacts of quasi-periodic eruption along with the equatorial plane in Sec. 3.2. Finally, we show how the time-averaged spin-down torque scales with system parameters in Sec. 3.3. 3.1. Anisotropic wind structure Anisotropic wind structure In the equatorial direction (80◦ ≲ θ ≲ 100◦), magnetic fields are closed at small radii, forming a corotating magnetosphere. Beyond the last closed loop, the magnetic field lines are open with a predominant toroidal component, having opposite polarities with respect to the equatorial plane. The transition of the magnetic field configuration is mediated by reconnection occurring at around the tip of the last closed loop, or the Y point. As can be observed from the top-right panel of Fig. 1, the plasma beta in this transition region is higher than those along the open magnetic fields, implying that the gas is trapped mainly by magnetic tensions. In this high plasma-beta region sandwiched by low plasma beta regions, gases are pinched and radially accelerated in the reconnection region, eventually become supersonic at around r ∼ 5 R∗. The latitudinal angle dependence of the wind at the outer boundary is illustrated in Fig. 2, where the gray shaded regions represent the dynamical range of radial velocity (top-left), luminosity (top-right), torque (bottom-left), and mass loss rate (bottom-right). As described in the previous paragraphs, radial velocities in the near-equatorial direction can reach and transiently exceed the Michel velocity (represented by the horizontal dotted line) during eruptions caused by reconnection at the Y-point. Consequently, the wind luminosity, dominated by the radial kinetic term, also exhibits a sharp peak in the equatorial direction. On the other hand, the torque is primarily exerted by corotation with the magnetic fields and reaches its peak slightly off the equatorial plane (θ ∼ 85◦), corresponding to the edge of the concave shape of the Alfven radius. 3.2. Time variability Time variability The acceleration of the rotating magnetic wind in the equatorial direction occurs in a time-variable manner, associated with magnetic reconnection at the Y-point. Consequently, the overall flux of mass, energy, and angular momentum from the central WD can also vary with time. Fig. 3 displays the time evolution of the mass loss rate (M˙ ), luminosity (L), and torque (T) in our fiducial model. All quantities are normalized by their time-averaged values. The lower panel of Fig. 3, which provides a long-term perspective, reveals a recurrent eruptive behavior. A recurrent cycle consists of pre-eruption, reconnection, post-eruption phases: In the pre-eruption phase, gases injected into the near-equatorial plane become trapped within the closed field lines. Due to the centrifugal force, the gases accumulate at the tip of the last closed loop, resulting in a continuous decrease in plasma beta in that region. When the centrifugal force acting on the accumulated gases exceeds the tension of the closed magnetic fields, the tip of the closed zone starts expanding radially and is subsequently ejected as a plasmoid through reconnection. Such a plasmoid can be observed at r ∼ 4 - 5 R∗ in Fig. 1. Afterwards, the cycle returns to the pre-eruption phase and restores the gases within the closed magnetic field lines. This type of recurrent eruptions has been known as slingshot prominence in the context of magnetically-active rapidly-rotating stars (e.g., Ferreira 2000; Townsend & Owocki 2005; Jardine & Collier Cameron 2019). The upper panel of Fig. 3 focuses on the fluxes during a rotation period (t = 15-16 [2π/Ω∗]), as also depicted in Fig. 2. In comparison to the pre- and post-eruption phases, represented by the purple and red lines, respectively, the observed fluxes of mass, energy, and angular momentum consistently increase as the erupted plasmoids reach the outer boundary, indicated by the green lines. Notably, the magnetic torque significantly contributes to the overall torque increase during the eruption phase and plays a dominant role in the central WD’s spin-down. We note that the specifics of reconnection dynamics, such as the frequency of recurrent eruptions and the resulting time evolution of mass, energy, and angular momentum fluxes, may be influenced by our numerical parameters, including spatial resolution (which governs numerical resistivity) and the width of the wind launching region. However, we have verified that the time-averaged values of wind velocities, mass loss rate, luminosity, and torque have all reached convergence concerning the spatial resolution in our simulations and the width of the wind launching region (see Appendix B). 3.3. Scaling relation of the spin-down torque Scaling relation of the spin-down torque Here we consider how the spin-down torque of rotating magnetic wind depends on the system parameters based on our numerical results. As we show in the previous sub-sections, the time-averaged torque is essentially determined by the magnetic torque exerted on the gases at the tip of the last closed field lines, or the Y-point, where the field configuration is still roughly compatible with the rotating dipole. In this case, the (electro)magnetic torque at the Y point can be estimated as [3] In relativistic MHD regime, speed of light c is conventionally used as the characteristic speed of the system (e.g., see the definition of σ0 in Bucciantini et al. 2006).

Magnetic Wind From a Massive White Dwarf Merger: Result

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com];
(2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za];
(3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za];
(4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction
Methodology
Neural Network Methodology
Numerical results, implementation and discussion
Conclusion, Acknowledgments, and Funding
Availability of data, code and materials, Contributions and Declarations
References 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding. This paper is available on arxiv under CC 4.0 license. Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Edson Pindza, Tshwane University of Technology; Department of Mathematics and Statistics; 175 Nelson Mandela Drive OR Private Bag X680 and Pretoria 0001; South Africa [edsonpindza@gmail.com]; (2) Jules Clement Mba, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [jmba@uj.ac.za]; (3) Sutene Mwambi, University of Johannesburg; School of Economics, College of Business and Economics and P. O. Box 524, Auckland Park 2006; South Africa [sutenem@uj.ac.za]; (4) Nneka Umeorah, Cardiff University; School of Mathematics; Cardiff CF24 4AG; United Kingdom [umeorahn@cardiff.ac.uk]. Table of Links Abstract and Introduction Methodology Neural Network Methodology Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations References Abstract and Introduction Abstract and Introduction Methodology Methodology Neural Network Methodology Neural Network Methodology Numerical results, implementation and discussion Numerical results, implementation and discussion Conclusion, Acknowledgments, and Funding Conclusion, Acknowledgments, and Funding Availability of data, code and materials, Contributions and Declarations Availability of data, code and materials, Contributions and Declarations References References 5. Conclusion This paper has considered the valuation of the bitcoin call option when the bitcoin price dynamics follow a bivariate Merton jump-diffusion model. This research generally provided a novel pricing framework that described the behaviour of bitcoin prices and the model parameter estimation with the intent of pricing the corresponding derivatives. Since bitcoin is normally affected by investors’ attention and sentiment, we used the continuous-time stochastic jump-diffusion process to capture the dynamics of the bitcoin prices and the transaction volumes affecting the price. In the methodological aspect of the research, we extended the classical Black-Scholes model in order to obtain the extended Black-Scholes equation for the bitcoin options. By introducing the artificial NN, we proposed a trial solution that solves the associated BlackScholes PDE for the bitcoin call options with European features. As a result, the original constrained optimization problem was transformed into an unconstrained one. The numerical results considered both the normal Black-Scholes model and the Merton jump-diffusion model, and it was observed that the latter resulted in a more efficient valuation process. Hence, we can conclude that the NN can be employed efficiently in solving complex PDE-related problems and can be applied to pricing certain financial derivatives without analytical forms. For the Model I and II comparison, we noted that the Black-Scholes used in connection with Model II showed the out-of-the-money feature of the call option. The option absolutely pays off when the underlying price falls below the strike price. The JMD overprices the out-of-the-money options for Model II, whereas the BlackScholes overprices the call options in Model I. Hence, one of the limitations of this research lies in finding the optimal neural network configuration which ensures the fair pricing of bitcoin options using the proposed two models. This optimal feature will be incorporated to avoid over-pricing or under-pricing of these option values, and future research will focus on this. One of the limitations of the approach used in this paper is that it may be prone to artificial Google searches affecting sentiment-based data analysis and decisions. Even though search-based measures appear to be more transparent than other social media-driven measures, they seem limited as the volume of data is concerned. Social media like Twitter provides high-frequency data (second, minute, hour, daily, . . . ), which can provide meaningful and instantaneous insights into the price dynamics of cryptocurrencies and bitcoin in particular. Our future work will first examine the correlation between search-based data and tweets data, then compare their performance when included in the valuation process. The tweets sentiment scores will be computed using the stateof-the-art Lexicon-based sentiment analyzer Valence Aware Dictionary and sEntiment Reasoner (VADER). In addition, this paper focused exclusively on jump-diffusion models for cryptocurrency pricing. Levy processes allow the modelling of heavy-tailed return distributions and large deviations from the mean. Expanding the set of stochastic processes considered would provide a more thorough treatment of cryptocurrency dynamics. Processes like variance gamma, normal inverse Gaussian, and generalized hyperbolic Levy motions have shown promise in modelling assets with frequent extreme moves. Given Bitcoin’s volatility clustering and significant outliers, applying Levy processes could potentially improve model fitting. We acknowledge the limitations of only exploring a jumpdiffusion framework presently. Incorporating alternatives like Levy processes would strengthen the generalizability and robustness of the modelling approach. Building on the initial foundation proposed here, researchers could examine a wider set of stochastic processes for capturing empirically observed features. Comparative testing using historical data would elucidate the relative effectiveness of diffusions, Levy processes, and other probabilistic models. Extending this work to Levy processes represents a valuable progression for future research. The flexibility of Levy’s motions shows potential for modelling emerging cryptocurrency returns. We hope this paper provides a starting point that can be incrementally improved by incorporating innovations like heavy-tailed processes. Evaluating a range of stochastic models will ultimately enhance financial engineering techniques tailored specifically to cryptocurrencies. Acknowledgement The second author thanks the Research Centre of AIMS-Cameroon for hosting him during the preparation of this manuscript. Funding This research received no external funding.

Neural Network for Valuing Bitcoin: Conclusion, Acknowledgments, and Funding

By Mostofa Adib Shakib In the rapidly evolving landscape of technology, I find myself pondering a significant question as a software engineer: Will Devin AI take my job or merely serve as another tool in our expansive arsenal? For those unfamiliar with the name, Devin is not your average newcomer to the tech scene. It's the moniker given to the world's first AI Software Engineer, developed by the visionary minds at Cognition Labs. Since its inception in November 2023, Cognition Labs has secured**$21 million** in funding, notably fromPeter Thiel's Founders Fund and former Twitter executive Elad Gil. Thiel, celebrated for co-founding PayPal and being the first outside investor in Facebook, marks the caliber of faith vested in this endeavor. On March 12, 2024, Cognition Labs unveiled Devin through a series of videos, showcasing the capabilities of this fully autonomous AI, which has sent ripples through the tech community, sparking debates on job security across all levels of software engineering. Behind this groundbreaking innovation is Scott Wu, leading a small yet extraordinarily talented team at Cognition Labs. Comprising 10 individuals, each a gold medalist from the International Olympiads in Informatics (IOI), their team's pedigree is unmatched. Wu, a Harvard-educated math prodigy, boasts an impressive resume, including three IOI gold medals with perfect scores in 2014, and holds the title of Legendary Grandmaster on Codeforces. His journey from a math enthusiast to the co-founder and CTO of Lunchclub, an AI-powered networking platform, alongside his brother Neal Wu, a fellow prodigy and co-founder of Cognition Labs, lays the foundation for Devin's creation. The Wu brothers' unique combination of mathematical brilliance and entrepreneurial spirit is the driving force behind the development of Devin, positioning Cognition Labs at the forefront of AI innovation in software engineering. So, what exactly can Devin do? According to Cognition Labs' demonstrations, Devin is 9% better at solving real Software Engineering tasks compared to all the existing models and capable of self-training, identifying bugs in code, and resolving issues on platforms like GitHub and Upwork. Today I will gauge the impact of this AI on the industry, and analyze the claims made by Cognition Lab on Devin’s capabilities. While Devin is impressive, I see it as something other than replacing me or being able to perform the tasks I did during my time as a Software Engineer at Snapchat. The problems solved by Devin in one of the 5 videos released by Cognition Labs had clear instructions and guidelines. However, in the real world, problems are often ambiguous, and sometimes we need to first figure out what exactly we are solving, and to do that cross-functional communication between several teams is needed. At Snapchat, one time I worked on a bug that blacklisted Snapchat in Samsung’s multi-hierarchy window for a few months. To solve this issue and unblock Snapchat, I had to take an iterative approach and talk back and forth with different cross-functional teams until a solution had been found something AI could not do. I also spent the majority of my time identifying deep performance bottlenecks, scalability issues, and memory corruption as part of Snapchat’s platform & performance team, challenges that AI cannot yet tackle. Moreover, software engineers play a crucial role in challenging technical designs and validating the code generated by AI. At first glance, the claims made by Cognition Labs regarding Devin look very ambitious and scary, but if you dive deep, it’s actually a bit misleading and not as scary as people think. For example: In one of the videos released by Cognition Labs, they claimed that Devin can teach himself by learning from blog articles and other resources. However, if you visit the blog article that was used in the video, you will notice that it links to a GitHub page where all the code and the exact steps to take are already provided. So essentially, Devin is just following the instructions instead of writing custom code and figuring things out on the fly. Another key point to note is that, unlike ChatGPT, Devin currently takes 2-3 hours to perform basic tasks that a Junior Software Engineer will be able to accomplish much faster, and another human Software Engineer has to guide Devin through the entire process to make sure Devin is going down the right path. As an AI2030 Global Fellow and drawing from my extensive experience in AI research, Devin is impressive and will change the way Software Development is currently done, much like how calculators made mathematicians far more productive. AI will serve as a productivity tool for software engineers, automating basic tasks and allowing them to focus on more creative endeavors. It won't replace them, just as calculators didn't replace mathematicians. This comparison between calculators and mathematicians, software engineers, and Devin, is particularly apt. When calculators were first introduced, there was a fear that they would render mathematicians obsolete. However, calculators became tools that enhanced the productivity of mathematicians, allowing them to perform calculations more quickly and accurately, and freeing up their time to focus on more complex and creative mathematical problems. Similarly, AI tools like Devin are not meant to replace software engineers but to augment their capabilities. By automating routine tasks such as bug identification and code optimization, Devin can enable software engineers to concentrate on higher-level design, architecture, and problem-solving aspects of software development. Moreover, the human element in software engineering cannot be understated. Software engineers bring creativity, critical thinking, and the ability to understand and respond to user needs—qualities that AI, in its current state, cannot replicate. While AI can follow instructions and learn from patterns, it lacks the intuition and emotional intelligence that humans possess. Just as mathematicians use calculators as a tool to aid their work, software engineers can use AI like Devin as a tool to enhance their productivity and creativity. In conclusion, the relationship between software engineers and AI tools like Devin is symbiotic. AI can automate certain aspects of software development, but it is the human software engineer who provides the creative vision, strategic direction, and critical oversight that are essential to successful software projects. As AI continues to evolve, it will undoubtedly transform the landscape of software engineering, but it will do so in a way that complements and enhances the work of human engineers rather than replacing them. By Mostofa Adib Shakib In the rapidly evolving landscape of technology, I find myself pondering a significant question as a software engineer: Will Devin AI take my job or merely serve as another tool in our expansive arsenal? For those unfamiliar with the name, Devin is not your average newcomer to the tech scene. It's the moniker given to the world's first AI Software Engineer, developed by the visionary minds at Cognition Labs. Since its inception in November 2023, Cognition Labs has secured**$21 million** in funding, notably from Peter Thiel 's Founders Fund and former Twitter executive Elad Gil . Thiel, celebrated for co-founding PayPal and being the first outside investor in Facebook, marks the caliber of faith vested in this endeavor. On March 12, 2024, Cognition Labs unveiled Devin through a series of videos, showcasing the capabilities of this fully autonomous AI, which has sent ripples through the tech community, sparking debates on job security across all levels of software engineering. Peter Thiel Elad Gil Behind this groundbreaking innovation is Scott Wu, leading a small yet extraordinarily talented team at Cognition Labs. Comprising 10 individuals, each a gold medalist from the International Olympiads in Informatics (IOI), their team's pedigree is unmatched. Wu, a Harvard-educated math prodigy, boasts an impressive resume, including three IOI gold medals with perfect scores in 2014, and holds the title of Legendary Grandmaster on Codeforces. His journey from a math enthusiast to the co-founder and CTO of Lunchclub, an AI-powered networking platform, alongside his brother Neal Wu, a fellow prodigy and co-founder of Cognition Labs, lays the foundation for Devin's creation. The Wu brothers' unique combination of mathematical brilliance and entrepreneurial spirit is the driving force behind the development of Devin, positioning Cognition Labs at the forefront of AI innovation in software engineering. So, what exactly can Devin do? According to Cognition Labs' demonstrations, Devin is 9% better at solving real Software Engineering tasks compared to all the existing models and capable of self-training, identifying bugs in code, and resolving issues on platforms like GitHub and Upwork. Today I will gauge the impact of this AI on the industry, and analyze the claims made by Cognition Lab on Devin’s capabilities. While Devin is impressive, I see it as something other than replacing me or being able to perform the tasks I did during my time as a Software Engineer at Snapchat. The problems solved by Devin in one of the 5 videos released by Cognition Labs had clear instructions and guidelines. However, in the real world, problems are often ambiguous, and sometimes we need to first figure out what exactly we are solving, and to do that cross-functional communication between several teams is needed. At Snapchat, one time I worked on a bug that blacklisted Snapchat in Samsung’s multi-hierarchy window for a few months. To solve this issue and unblock Snapchat, I had to take an iterative approach and talk back and forth with different cross-functional teams until a solution had been found something AI could not do. I also spent the majority of my time identifying deep performance bottlenecks, scalability issues, and memory corruption as part of Snapchat’s platform & performance team, challenges that AI cannot yet tackle. Moreover, software engineers play a crucial role in challenging technical designs and validating the code generated by AI. At first glance, the claims made by Cognition Labs regarding Devin look very ambitious and scary, but if you dive deep, it’s actually a bit misleading and not as scary as people think. For example: In one of the videos released by Cognition Labs, they claimed that Devin can teach himself by learning from blog articles and other resources. However, if you visit the blog article that was used in the video, you will notice that it links to a GitHub page where all the code and the exact steps to take are already provided. So essentially, Devin is just following the instructions instead of writing custom code and figuring things out on the fly. Another key point to note is that, unlike ChatGPT, Devin currently takes 2-3 hours to perform basic tasks that a Junior Software Engineer will be able to accomplish much faster, and another human Software Engineer has to guide Devin through the entire process to make sure Devin is going down the right path. As an AI2030 Global Fellow and drawing from my extensive experience in AI research, Devin is impressive and will change the way Software Development is currently done, much like how calculators made mathematicians far more productive. AI will serve as a productivity tool for software engineers, automating basic tasks and allowing them to focus on more creative endeavors. It won't replace them, just as calculators didn't replace mathematicians. This comparison between calculators and mathematicians, software engineers, and Devin, is particularly apt. When calculators were first introduced, there was a fear that they would render mathematicians obsolete. However, calculators became tools that enhanced the productivity of mathematicians, allowing them to perform calculations more quickly and accurately, and freeing up their time to focus on more complex and creative mathematical problems. Similarly, AI tools like Devin are not meant to replace software engineers but to augment their capabilities. By automating routine tasks such as bug identification and code optimization, Devin can enable software engineers to concentrate on higher-level design, architecture, and problem-solving aspects of software development. Moreover, the human element in software engineering cannot be understated. Software engineers bring creativity, critical thinking, and the ability to understand and respond to user needs—qualities that AI, in its current state, cannot replicate. While AI can follow instructions and learn from patterns, it lacks the intuition and emotional intelligence that humans possess. Just as mathematicians use calculators as a tool to aid their work, software engineers can use AI like Devin as a tool to enhance their productivity and creativity. In conclusion, the relationship between software engineers and AI tools like Devin is symbiotic. AI can automate certain aspects of software development, but it is the human software engineer who provides the creative vision, strategic direction, and critical oversight that are essential to successful software projects. As AI continues to evolve, it will undoubtedly transform the landscape of software engineering, but it will do so in a way that complements and enhances the work of human engineers rather than replacing them.

Devin AI: End of Software Engineers or Just A Tool?

Learning how to use AI tools using plots and examples from science fiction movies. 77% of people fear they could lose their jobs to artificial intelligence in the next 12 months. These are the results of a July 2023 study published by Forbes. It is a great demonstration of people's propensity for irrational fears. Particularly, when people have been scared for a long time with stories about Skynet, TechnoCore, and similar soulless, but invariably bloodthirsty, AI. In reality, there are no streams of ex-employees leaving their offices with boxes of personal belongings in their hands. Among our colleagues, acquaintances, and clients, there is not a single person who has fired an employee or lost his or her job because artificial intelligence has replaced them. On the contrary, there is a consistent shortage of qualified personnel. This includes those who are able to effectively search for and use various AI applications in their work. There is a very real, factual problem that we want to dedicate this article to. While working on our Pitch Avatar, we have more than once encountered complaints about the difficulties of working with AI tools. To summarize, they boil down to the fact that many users have found their expectations of AI to be unrealistic. "I thought it would be smarter," one of our acquaintances irritatedly remarked, sharing his experience with one popular AI chatbot that he used as a text generator and editor. And he is, to repeat, not alone. Notorious "hallucinations", errors, and multiple repetitions or banalities are only part of the problem. Much worse is the fact that people often fail to find a "common language" with AI. The application, which purports to be trained to perceive natural speech, nevertheless struggles to understand what users want from it, interpreting its requests in its own way time after time. In turn, the human user is repeatedly frustrated in their attempts to tell the AI what it wants... Have you experienced any of the above? If so, then welcome aboard. In search of answers and solutions to these problems, we embark on a journey through the plots of sci-fi movies. The Brute Force Method To begin with, while seemingly similar, AI-based tools, even those designed to accomplish the same goals, are still different. You shouldn't conclude that AI can't handle your tasks based on one or two solutions. Even if they all look the same to you. Try to act like Detective Del Spooner from Alex Proyas' movie I, Robot. He believed in the existence of a unique robot among serial models, persistently searched for it, and managed to find it. Moreover, in the end, this robot turned out to be the tool that helped the detective to fulfill a supremely difficult task - to defeat an out-of-control artificial intelligence, V.I.K.I. So be persistent. If you must - explore the myriad possibilities and try dozens, if necessary, of different AI tools in your work. Almost certainly you will find one to your liking. Exclusion Method Imagine that you have formulated a task for your AI (for example, to find some article or create some content), and it "mined" or generated something that technically meets your request, but is not what you wanted. At the same time, for various reasons, you can't clarify your query or reformulate it. This may be because you lack sufficient information. What to do? Skynet and the Terminator in James Cameron's movie were in the same situation. Remember, they didn't know exactly which Sarah Connor from Los Angeles they wanted. So the Terminator decided to "visit" all the Sarah Connors in Los Angeles by simply moving through all of the Sarah Connors listed in the phone book. Makes sense, doesn't it? This is not a bad solution in situations where the AI responding to your query is not giving you the result you want. If you have time, you can simply be patient and methodically repeat your query, specifying that the previous results are not suitable. Sooner or later, the AI will hit the target. A Method of Checking for Complexity and Contradictions Anyone who has worked with AI has encountered the proverbial machine hallucinations and delusions. This includes all of those situations where text generators write incoherent nonsense, AI analytics show calendar data from a century ago, universal AI assistants create empty references and invent quotes from non-existent people, and image generators offer a maze of disfigured people, skewed buildings, and insane landscapes. However, before you rage at your AI assistants, consider why this is happening. Let's reveal a terrible secret: Artificial intelligence isn't trying to piss you off. It's trying to please you by producing the most satisfying result possible. But all AI is limited in terms of resources, time, and skill. That is why AI always tries to follow the path of least resistance. And if a task exceeds its capabilities for one reason or another, it starts to "rave", simplifying the task to a level that allows it to fit into the Procrustean bed of its skills and resources. Think of the classic example from Stanley Kubrick's movie 2001: A Space Odyssey, made in 1968. In it, HAL 9000, a very smart AI that is installed in a spaceship was given a task that contradicted its basic programming. As a result, it "went mad" and decided to eliminate the contradiction by destroying the crew... When faced with signs of "madness" in your AI tools, think about whether your task is ambiguous or contradictory. Can it be formulated more clearly, specifically, and as a result, more simply? Keep in mind - "simpler" does not always mean "shorter". Sometimes, in order to make the task unambiguous, you have to spend not less, but more words for clarification. A Method for Identifying Major and Minor Another common problem is that AI focuses on secondary tasks rather than primary ones. Let's say you need a text about otters living in a lake. And you instruct the AI writer to generate it. But as a result, it produces a narrative 90% is dedicated to the lake itself and only 10% to otters. And the story about the lake, in its turn, is full of details that have nothing to do with nature at all. For example, human settlements located on its shores and their history. Something similar is demonstrated by such movie characters as the protocol robot C3PO from the epic Star Wars or the android Data from the Star Trek: The Next Generation series. They would, occasionally, start sharing information not directly related to the solution for which they had been approached. Other characters were often forced to either clarify the task at hand or simply interrupt the stream of consciousness from the AI. From these examples, it is clear that when formulating tasks for AI, one should clearly prioritize them. A good example is the straightforward order given to the android Ash in Ridley Scott's movie, Alien. Ash was tasked with ensuring the delivery of an alien organism to Earth. It was explicitly specified that the survival of the crew of the ship Nostromo, which included him, could be neglected. Though Ash himself even admitted to sympathizing with the humans he served with, orders were orders and he followed them diligently. Human-AI System, or the Inevitable "42" It is time for the main conclusions of this text. Let's ask ourselves - why, for all their logic, did Skynet and Terminator, HAL 9000 and Ash, and many other movie-villain AIs fail? It goes without saying that "Because the script said so" is not an acceptable answer. The correct answer is because all of these AIs opposed humans, rather than cooperating with them. And this is the point of any artificial intelligence’s basic philosophy. It is not designed to work independently but rather to interact with humans. If you will, any artificial intelligence is a part of a "Human-AI" system. Outside of this system, it is incomplete. For AI to work effectively, it will always need people to give it tasks, as well as to edit, correct, and refine the results of its work. As a consequence, as we develop artificial intelligence we will constantly need to develop our skills in working with it. AI mistakes are inevitable because they are inevitable in humans. It is important to keep in mind that AI is learning and developing. And, as we know, it is impossible to travel this path without mistakes and failures. That's why we should always be ready to hear this answer from AI: "42" The article was created in collaboration with Andriy Tkachenko. Feature (C) Image created with Bing Learning how to use AI tools using plots and examples from science fiction movies. Learning how to use AI tools using plots and examples from science fiction movies. 77% of people fear they could lose their jobs to artificial intelligence in the next 12 months. These are the results of a July 2023 study published by Forbes . It is a great demonstration of people's propensity for irrational fears. Particularly, when people have been scared for a long time with stories about Skynet, TechnoCore, and similar soulless, but invariably bloodthirsty, AI. published by Forbes published by Forbes In reality, there are no streams of ex-employees leaving their offices with boxes of personal belongings in their hands. Among our colleagues, acquaintances, and clients, there is not a single person who has fired an employee or lost his or her job because artificial intelligence has replaced them. On the contrary, there is a consistent shortage of qualified personnel. This includes those who are able to effectively search for and use various AI applications in their work. There is a very real, factual problem that we want to dedicate this article to. While working on our Pitch Avatar , we have more than once encountered complaints about the difficulties of working with AI tools. To summarize, they boil down to the fact that many users have found their expectations of AI to be unrealistic. Pitch Avatar Pitch Avatar "I thought it would be smarter," one of our acquaintances irritatedly remarked, sharing his experience with one popular AI chatbot that he used as a text generator and editor. And he is, to repeat, not alone. Notorious "hallucinations", errors, and multiple repetitions or banalities are only part of the problem. Much worse is the fact that people often fail to find a "common language" with AI. The application, which purports to be trained to perceive natural speech, nevertheless struggles to understand what users want from it, interpreting its requests in its own way time after time. In turn, the human user is repeatedly frustrated in their attempts to tell the AI what it wants... Have you experienced any of the above? If so, then welcome aboard. In search of answers and solutions to these problems, we embark on a journey through the plots of sci-fi movies. The Brute Force Method To begin with, while seemingly similar, AI-based tools, even those designed to accomplish the same goals, are still different. You shouldn't conclude that AI can't handle your tasks based on one or two solutions. Even if they all look the same to you. Try to act like Detective Del Spooner from Alex Proyas' movie I, Robot. He believed in the existence of a unique robot among serial models, persistently searched for it, and managed to find it. Moreover, in the end, this robot turned out to be the tool that helped the detective to fulfill a supremely difficult task - to defeat an out-of-control artificial intelligence, V.I.K.I. So be persistent. If you must - explore the myriad possibilities and try dozens, if necessary, of different AI tools in your work. Almost certainly you will find one to your liking. Exclusion Method Imagine that you have formulated a task for your AI (for example, to find some article or create some content), and it "mined" or generated something that technically meets your request, but is not what you wanted. At the same time, for various reasons, you can't clarify your query or reformulate it. This may be because you lack sufficient information. What to do? Skynet and the Terminator in James Cameron's movie were in the same situation. Remember, they didn't know exactly which Sarah Connor from Los Angeles they wanted. So the Terminator decided to "visit" all the Sarah Connors in Los Angeles by simply moving through all of the Sarah Connors listed in the phone book. Makes sense, doesn't it? This is not a bad solution in situations where the AI responding to your query is not giving you the result you want. If you have time, you can simply be patient and methodically repeat your query, specifying that the previous results are not suitable. Sooner or later, the AI will hit the target. A Method of Checking for Complexity and Contradictions Anyone who has worked with AI has encountered the proverbial machine hallucinations and delusions. This includes all of those situations where text generators write incoherent nonsense, AI analytics show calendar data from a century ago, universal AI assistants create empty references and invent quotes from non-existent people, and image generators offer a maze of disfigured people, skewed buildings, and insane landscapes. However, before you rage at your AI assistants, consider why this is happening. Let's reveal a terrible secret: Artificial intelligence isn't trying to piss you off. It's trying to please you by producing the most satisfying result possible. But all AI is limited in terms of resources, time, and skill. That is why AI always tries to follow the path of least resistance. And if a task exceeds its capabilities for one reason or another, it starts to "rave", simplifying the task to a level that allows it to fit into the Procrustean bed of its skills and resources. Think of the classic example from Stanley Kubrick's movie 2001: A Space Odyssey, made in 1968. In it, HAL 9000, a very smart AI that is installed in a spaceship was given a task that contradicted its basic programming. As a result, it "went mad" and decided to eliminate the contradiction by destroying the crew... When faced with signs of "madness" in your AI tools, think about whether your task is ambiguous or contradictory. Can it be formulated more clearly, specifically, and as a result, more simply? Keep in mind - "simpler" does not always mean "shorter". Sometimes, in order to make the task unambiguous, you have to spend not less, but more words for clarification. A Method for Identifying Major and Minor Another common problem is that AI focuses on secondary tasks rather than primary ones. Let's say you need a text about otters living in a lake. And you instruct the AI writer to generate it. But as a result, it produces a narrative 90% is dedicated to the lake itself and only 10% to otters. And the story about the lake, in its turn, is full of details that have nothing to do with nature at all. For example, human settlements located on its shores and their history. Something similar is demonstrated by such movie characters as the protocol robot C3PO from the epic Star Wars or the android Data from the Star Trek: The Next Generation series. They would, occasionally, start sharing information not directly related to the solution for which they had been approached. Other characters were often forced to either clarify the task at hand or simply interrupt the stream of consciousness from the AI. From these examples, it is clear that when formulating tasks for AI, one should clearly prioritize them. A good example is the straightforward order given to the android Ash in Ridley Scott's movie, Alien. Ash was tasked with ensuring the delivery of an alien organism to Earth. It was explicitly specified that the survival of the crew of the ship Nostromo, which included him, could be neglected. Though Ash himself even admitted to sympathizing with the humans he served with, orders were orders and he followed them diligently. Human-AI System, or the Inevitable "42" It is time for the main conclusions of this text. Let's ask ourselves - why, for all their logic, did Skynet and Terminator, HAL 9000 and Ash, and many other movie-villain AIs fail? It goes without saying that "Because the script said so" is not an acceptable answer. The correct answer is because all of these AIs opposed humans, rather than cooperating with them. And this is the point of any artificial intelligence’s basic philosophy. It is not designed to work independently but rather to interact with humans. If you will, any artificial intelligence is a part of a "Human-AI" system. Outside of this system, it is incomplete. For AI to work effectively, it will always need people to give it tasks, as well as to edit, correct, and refine the results of its work. As a consequence, as we develop artificial intelligence we will constantly need to develop our skills in working with it. AI mistakes are inevitable because they are inevitable in humans. It is important to keep in mind that AI is learning and developing. And, as we know, it is impossible to travel this path without mistakes and failures. That's why we should always be ready to hear this answer from AI: "42" The article was created in collaboration with Andriy Tkachenko. The article was created in collaboration with Andriy Tkachenko. Feature (C) Image created with Bing Feature (C) Image created with Bing

A Cinematic Tutorial on How to Work With Artificial Intelligence

Stories, News & Mentions about CALIMERO

Calimero’s Stories on HackerNoon