Andersen is an international custom software development company that specializes in building high-quality, bespoke software solutions for our clients

Andersen

Application Programming Interfaces are an integral part of the emerging digital space. Without them, benefiting from many of today’s habitual financial services would be at least difficult if not impossible. In this piece, Andersen’s experts in FinTech software development will tell how API-based IT solutions contribute to the success of banks and financial organizations. A brief outline of API technology APIs allow FinTech companies to make data and features of their applications available to external custom software development companies, business partners, and internal departments. Thus, services and products interact with each other using third-party data and features via dedicated interfaces. The implementation of API protocols is required for looking up the weather forecast in real-time, making a bank transfer, reading text messages, and more. attests to the popularity of this tool. Namely, 61,3% of software development companies said that in 2020 they had used APIs in their work more frequently than in the year before that, while 71,1% of respondents were planning to increasingly use them in 2021. Over 58% of organizations consider implementing the API economy model to be their highest priority. In their view, APIs are mostly used in Telecom, Healthcare, and Finance. RapidAPI Developer Survey 2020-2021 Rest APIs remain a dominant technology, and there is also a growing interest in Websockets, Serverless & FaaS, gRPC, etc. For example, in 2020, the usage of AsyncAPIs and GraphQL increased by several times. The larger a company is the more internal services and communication protocols it uses. For example, 40% of major companies employing ten thousand people and more reported that they simultaneously use over 250 internal APIs. Three in four companies are inclined to rely on their own solutions while reducing their dependence on external products. The following infographics represent API market segmenting by industries based on the above survey. The API market dynamics According to , the global API management market will reach $5.1 billion by 2023 growing by nearly five times compared to 2018 and at an average annual rate of 32,9%. The forecast by , in turn, states that the global API market growth will surpass $21 billion by 2028. MarketsandMarkets Adroit Market Research Tools for API platforms, analytics, and security are increasingly used thanks to the advancement of mobile technologies. This motivates companies to develop their solutions for internal and external use. The above two pieces of research underscore the advantages of cloud technologies compared to the on-premises approach when a company uses its own data centers. Cloud technologies reduce companies’ expenses and speed up product development. North America is the main region for the development of API technologies. There, API-related technologies are commonly used, vendors supply API management solutions on a massive scale, technology is constantly evolving, and there are skilled and knowledgeable experts. The rapid API market advancement is projected in the Asia-Pacific region with its technological leap and increased number of users. APIs are most commonly used in Telecom, the IT sector, Banking, Finance, Security and Insurance, and public administration. The Banking and Finance industries are constantly looking for cutting-edge, flexible, and efficient tools for providing their clients with digital services. Numerous are developed by large organizations at a faster rate than by small and medium businesses. The main global market players are Adobe, IBM, Oracle, Google, Red Hat, Software AG, Microsoft, SAP SE, and others. These are mostly US and European companies. The API economy structure Some companies following the global don't fully understand how the API economy structure is organized. Thus, their API implementation strategies are inconsistent and fragmented. API trend The API economy is based on the following pillars: the right approach to API adoption; the choice of the right technology; API development management; the establishment of an ecosystem. Let’s consider how the above stages are implemented within Banking, Finance, Security, and Insurance. The right approach to API adoption Companies implement a business-driven approach to API management based on the adoption of Payment Services Directive 2 (PSD2) and Open Banking. These regulatory policies allow external financial service providers access to banking data, transactions, and other financial data of banks’ clients and financial institutions via APIs. The above approach is based on the and the top-down approach. The former suggests shifting a company’s business and IT processes to applying key API management principles. The latter requires that any solutions are made and the main channels of communication with clients are established via APIs. API-first mindset API reusability allows companies to develop their products based on already existing solutions. This principle substantially speeds up product launches and allows companies to get ROI faster. To implement the right approach to API adoption, financial institutions must change their organizational policies, ensuring communication with their clients and within their organizations mainly via APIs. Financial institutions can develop them in-house, order them from software development companies, or use open-source solutions. The choice of the right API technology The choice and comprehensive implementation of a suitable technological solution is another crucial factor for establishing the API economy. This stage is required to build the production environment. As a rule, companies consider the following tools and technologies: API management platforms are the core of an entire API management system allowing organizations to establish data security policies, choose communication protocols and repositories, and get data analytics; Microservice architecture is a decentralized system for data processing and storing. Thanks to it, companies perform the needed operations and establish communication protocols within the IT architecture of their solutions. Thus, each API is functioning independently, is easy to develop, and is testable and secure; API management catalog is a cataloging system that stores information about APIs for their subsequent reuse or for the development of new APIs; API monitoring technologies are used for efficient platform and service management, collection of statistics, and managing systems’ performance and user access. The following infographics represent API architecture. Companies face the challenge of choosing the right platforms for ensuring their production and communication processes, organizing efficient and harmonious microservice structure, and establishing effective API cataloging and monitoring. To do so, businesses look for reliable finance software development companies that specialize in API development and implementation. API development management This stage of the API economy implementation involves the adoption of Agile practices for API development management. The following development teams are responsible for API development: manage API software architecture and processes on a platform level, establish use standards and patterns, and work with API-related tools, defining API development strategy. Central teams coordinate the interaction of API processes and standards within departments. They manage API development based on legislation and company rules within their industries, e.g. Finance, Insurance, or Banking. For these purposes, API capability centers are established. Business unit design teams Finally, whose work is based on DevOps principles finalize a product. They rely on strategic principles and standards outlined by central teams, and on developments achieved by business unit design teams. delivery teams Successful API development and implementation lie in the efficient distribution of tasks between different teams, as well as in the interaction of customers with the key FinTech development specialists on a project. Building of API ecosystem To ensure the stable functioning of the API economy within an organization it needs to establish an ecosystem where API solutions are included in its production and marketing strategies as a key element of the infrastructure. The API ecosystem establishment implies using of developer portals for efficient support and transparent document flow. It's recommended that an organization’s specialists would participate in technical conferences, meetups, and hackathons where new ideas for the development of API banking solutions are generated. After all, businesses should implement advanced, user-friendly, and innovative software products to stay competitive in fast-evolving markets. A consumer-first approach is also crucial for building a productive ecosystem. It implies paying attention primarily to the development and improvement of services that are used by an organization’s clients and business partners most. Finally, companies successfully monetize their API ecosystems by leasing their tools, adopting new payment methods, and enhancing client support. To sum up The API economy of companies in the Banking and Finance industries. API solutions help businesses to interact with each other at a new level establishing efficient and convenient communication. is the future At each of the API economy levels specified above, companies face the challenge of choosing the right API development and management strategies. Cooperation with top-rated finance software development companies will help companies solve this problem.

API Economy as a Key for the Success of Banking and Finance in the Future

According to , no more than one-third of Scrum Masters also work as Project Managers or have held this position. A Scrum Master and a PM are not the same sort of thing. In order to become a Scrum Master, you need to change your mindset, learn particular methods and techniques, and successfully apply them in your work. Knowledgehut Certification is part of the process of learning and becoming a Scrum Master. And this part, although not mandatory, is highly desirable. We will analyze in this article how to choose a certificate, how to prepare for the exam, and how much it will cost. Why you need certification at all There are several reasons that can motivate both established Scrum Masters and candidates for this position to get certified. Otherwise, there is a high risk of a selective approach; the “adjustment” of Scrum to the preferences of the customer (or manager, or team members) may begin. Sometimes what is passed off as Scrum is something that is not Scrum at all. 1. Courses and certification from a reputable resource provide insight into what Scrum really is. . The opinion of a certified specialist is more often considered. 2. The certificate increases the credibility of the Scrum Master in the eyes of the customer 83% of Scrum Masters are certified, so this is not just a useful achievement but rather a must-have. 3. Having a certificate makes a specialist more competitive in the market. The inverse dependence works as well. 4. Among highly-paid Scrum Masters, those with certificates prevail. US Scrum Master salary data Popular Scrum and Agile certifications Let’s consider the two most popular and reputable certificate providers that the majority of Scrum Masters and candidates choose. In addition, we will consider two Agile certificates that are equally useful for Scrum Masters, PMs, POs, and others. Scrum Alliance The founders of Scrum Alliance stood at the origins of Scrum as a concept, on par with the founders of Scrum.org. Scrum Alliance is the best option for those who need certification as soon as possible. There are several levels of certification – not only for Scrum Masters but also for POs, developers, and other professionals. Pros: a quick way to get a certificate; short but high-quality training; a relatively easy exam, but also a high percentage pass it. Cons: a rather high cost; renewal of the certificate every 2 years. Scrum.org Another popular certificate provider founded by the creators of the Scrum movement. Its main difference from Scrum Alliance is the absence of mandatory training. If candidates need the training, they can find courses on Scrum.org, just like on Scrum Alliance. And those who like to prepare on their own or already possess the necessary knowledge have the opportunity to go directly to the exam, saving almost $1,000. Pros: preliminary training is optional, so one can save money; independent preparation planning (at a convenient time and pace); a smaller pool of those who passed the exam; the certificate is termless, so no need to renew it. Cons: a relatively difficult exam; retaking costs the same as a full exam. ICAgile A global Agile certification system for POs, BAs, DevOps Engineers, and other professionals. ICAgile poses itself as an educational training provider. There are no exams, and the client pays only for tuition. PMI Agile Certified Professional Agile certification with the highest requirements for participants and a challenging exam. It includes elements of various frameworks (Scrum, Kanban, etc.). The certificate needs to be renewed every 3 years. How to prepare for the scrum.org certification exam I chose a certificate from Scrum.org for myself. For me, the issue of cost was quite important – here PSM I from Scrum.org is the undisputed leader. At the same time, I don’t think self-preparation necessarily means loss of quality. The exam itself is quite difficult, the passing percentage is high, so one needs to prepare for it thoroughly. In addition, I was impressed by the fact that the pool of people who passed this exam is smaller than that of its competitors. This fact only increased my motivation. This may sound trite, but preparation should start with reading. There are several resources you should start exploring first. . In the Scrum Guide, every sentence is full of meaning, so read it as attentively as possible. The first time, you can read in your native language for familiarization; the English version is important for passing the exam (read at least twice in English).Materials from scrum.org, especially the . I believe everyone understands what a Scrum Board or Burn-down Chart is, but the glossary is worth mastering because even literal quotations from there may come up in the exam. The Scrum Guide glossary It is important to be able to recognize some concepts in the Scrum Guide language.Books. For example, by Gunther Verheyen. Reading books will help you to once again perceive the information from the Scrum Guide, but in a more lively and free form, with examples. Scrum: A Pocket Guide: A Smart Travel Companion Having mastered the theory, we move on to practice. There are no less than five qualitative simulators of a Scrum Master Certificate exam: A mock exam from . 30 questions in 30 minutes. The questions there seem simpler than on the exam itself, but practice helps to become familiar with both the interface and the general style of the exam. scrum.org Free simulator . 20 questions in 15 minutes. These paraphrased questions from the scrum.org exam prevent you from getting used to specific phrasing and order of answers. The Scrum Master A simulator by . 80 questions in 60 minutes; there is an option to try it without a timer. Here, it is possible to get comments on each answer with an analysis of your mistakes. Mikhail Lapshin Agile and Scrum Practice Test by as an example of a large number of small mock tests on the English-speaking Internet. KnowledgeHut A simulator from the “accredited” provider . 80 questions in 60 minutes. Costs around 30 euros. Management Plaza In order to pass the exam with flying colors, you need to have a good level of English. A candidate needs to be able to quickly read and understand the questions and answer options – there will be no time to look up unknown words in the dictionary. Here are some more obvious but important tips. Firstly, set aside a place and time for the exam so that no one distracts you. You will have 60 minutes and 80 questions – quite a lot of tasks in a relatively short period of time. Secondly, the exam interface doesn’t have the function of “scrap paper”, so the numbers of the questions to which you want to return later should be written down on a piece of paper. Thirdly, it is better to answer the maximum number of questions; wrong answers don’t decrease your score. Fourthly, you probably won’t have time to google questions, so prepare responsibly. As a result of preparing and passing, you not only receive a certificate that appears on your CV and is added to your LinkedIn profile but also get systematic knowledge of what the perfect Scrum looks like. Get ready for the fact that reality is often far from this standard, but you will have a guide and answers to numerous questions like “Why?” and “What for?” Previously published at https://blog.andersenlab.com/en/scrum-master-certification

Best Courses and Certification Exams  to Help You Become a Scrum Master

Just five years ago, remote work was considered to be the way of the future, but only the distant future. Most specialized web publications predicted a rise in the popularity of telecommuting, but only in ten years’ time. However, this future arrived much earlier. says that 98% of employees would like to have the possibility to work remotely, and 97% consider this to be objectively optimal. The global quarantine at the beginning of 2020 accelerated the process of transferring to the remote work, and many companies continue to work in this way even after the lockdown was canceled. Buffer’s research This situation has become somewhat of a challenge for managers: although the team and the project remained the same, many processes had to be radically changed or even set up from scratch. In this article, we are going to analyze the main problems of managing remote personnel and consider options for solving them. Why This Is Important Fact №1: The number of remote employees is growing with each year. According to FlexJob for 2019, the number of remote workers over the past 12 years. Although some companies decided to bring their employees back into the office, it is likely that a full or partial transition to remote work will still happen, even if it takes a while. The market is expanding, new startups are appearing, companies are opening representative offices in other regions, new teams are coming into existence. When creating each new company or division, managers consider the pros and cons of both onsite and remote formats, and preference is being given to telecommuting with increasing frequency. has grown by 2.5 times Fact №2: If applying the principles, which were used when working at the office, in personnel management on a remote site, a manager will not be able to fully control a number of processes. Simply said, if a manager doesn’t take the transition to remote work seriously, they risk losing touch with their team and noticing a decrease in productivity, which will only worsen over time. On the other hand, when they understand the problems that the team members are facing and implement the right practices, a PM will be able to reap all the benefits of remote work. Problems that Remote Team Members May Face The 2020 survey showed that remote employees are most concerned about the following consequences: Due to the absence of non-verbal signals, employees may sometimes misunderstand each other. Misunderstandings most often arise when communication is conducted only via messages, without voice calls. Communication problems. , a sense of being abandoned. For many people, most of their social activity takes place at work, and colleagues are their close friends. The absence of such communication can lead to social isolation and cause a feeling of loneliness. Loneliness If you work in the same place you live in, life often turns into non-stop work. When there is no clear office/home division, as well as no intermediate stage like commuting, boundaries are blurred. Problems with “disconnecting” from work issues. When there is no need to go out every day, many people stop taking care of their health and wellbeing as much as they used to. Also, motivation to develop intellectually and physically reduces. Personal degradation. The above-mentioned problems, as well as many other, not obvious ones, lead to a loss of motivation. People stop being passionate about their work: it turns into a routine, sometimes a very painful one. To avoid this effect, a PM should pay special attention to personnel management. What a Manager Should Do There are many ways to increase the motivation of your team members, improve communication with them, and tune in to common goals. Good managers already know many of these methods; some managers apply them intuitively. Still, it will be useful for every PM to refresh this list in their memory from time to time. So, here is what a manager can do for their remote-working team: Daily Skype calls with the team will allow you to keep in touch and be aware of each other’s tasks. More communication. Although some team members are more comfortable with voice-only conversations, a video call best imitates a face-to-face meeting and eliminates the shortage of social interactions. Video calls as frequently as possible. A manager should communicate with each team member in private; once every two to three days would be perfect. Together with daily calls, this will allow you to always be aware of current problems and solve them in a timely fashion. The opportunity to talk one-on-one. It’s not that comfortable, of course, but thanks to this approach, you can gain maximum loyalty. When employees know they can get advice and support at any time, they will try not to let you down. On the contrary, the contact-with-questions-only-during-working-hours policy makes team members less loyal. Why would they care about the projects and the company’s interests if the manager doesn’t? Availability 24/7. These tools help to portray emotions, thus compensating for the absence of non-verbal communication. But it’s important to know when enough is enough. Emoticons, stickers, and gifs in messages. If the team members are distributed around the world, it is necessary to organize several regular calls per day, so that each employee can conveniently communicate during their working hours. A work schedule that is convenient for everyone. It is necessary to provide all team members with corporate equipment, if possible. This will eliminate downtime due to the fact that the employee’s personal laptop broke down, for example. Necessary equipment. Remote employees, like onsite ones, are interested in advancement. It is necessary to help them improve their skills and offer them new opportunities if their abilities are growing. Opportunities for personal and career growth. Taking the initiative is valuable because it shows that the employee cares for the company and the project. Moreover, a person will implement their own idea with greater enthusiasm. Team members should be encouraged to take the initiative as, even if their idea is not currently relevant, it shows a willingness to go above and beyond, and their idea may even be useful further down the road. Encouragement of initiative. It’s easier to evaluate the work of employees by activity, but the results are objective. A manager who understands their team members’ work will be able to evaluate the effectiveness of each member and give fair feedback. Focus on the result, not the activity. For efficient work, not only internal communication between team members is important but also communication with colleagues from the company’s other departments. Inviting an employee to a group call is much more effective than constantly mediating between team members and other employees. Intensify communication by inviting people to other calls. It is useful to engage the team in solving common problems. Organization of brainstorming sessions. This advice is relevant not only for remote work, but also for personnel management in general. Most people are uncomfortable with working on several tasks simultaneously, so it is recommended to avoid multitasking, if possible. Proper allocation of tasks. People often miss communicating informally with colleagues, especially if they used to work together at the office. Virtual coffee breaks during lunch or casual Friday calls will help brighten up your leisure time. Informal communication on Skype/Zoom. At least once or twice a year, you need to get together. The easiest and most effective way is corporate parties. People who previously only communicated virtually can get to know each other better and bond, which will help them to work more efficiently in the future. Offline events. Corporate merch may seem trivial at first glance, but it reflects and reinforces the company’s values. Small gifts and bonuses, including some for employees’ families. Perhaps not all of these methods will suit every single company and team. But each PM will probably learn something useful from this list. Managers' Personal Traits Let’s not forget that, along with the right actions, the manager should also demonstrate certain traits. These will not only help motivate employees but also help to earn their respect. By taking a person into the team, the manager has already shown confidence in them. Checking up on the employee constantly and doubting their effectiveness will make them feel disrespected. Any checks and reports should be reasonable – such as, for example, a daily team meeting. You need to understand the difference between jointly analysing the team’s work and requiring an employee to report on each and every action. Trust. If the manager does not love what they do, motivating the team will be much more difficult. Passion for work. Don’t forget to show that you value the efforts of your colleagues and the results they achieve. The more specific and personalized the gratitude, the better. Gratitude. The manager should be able to stand up for their team in front of both the customer and top managers. Few will follow a leader who does not take responsibility for the actions of their people and does not believe in their ideas. Willingness to take risks for the sake of employees. Sometimes even loyal employees disagree with the manager. You need to be able to objectively evaluate your arguments and those of others. If the manager is confident about being right, they should be able to convince others of it, even if the team members do not like this position. Self-confidence and perseverance. You need to understand the difference between perseverance and stubbornness and be able to quickly see when you are in the wrong. If the team was right to disagree with the manager’s position, the manager’s ego should not interfere with acknowledging this and making the necessary changes. Ability to admit to mistakes. To reinforce these theories with practical experience and real cases, we interviewed our PMs about what methods they use to organize the remote work of their team, and what they consider useful or overrated. Olga Volkova , a PM on the project of an international platform for taxi sharing (NDA) As practice shows, remote work is good for introverts who are so disciplined that they are able to work almost without interacting with colleagues at the office. However, discipline is such a thing that even the most exemplary worker may weasel out of it. It’s important for me to keep in touch with the team. Jokes, discussion of weekends and plans for the evening, and turned-on web cameras help to establish this connection in a team. We worked with cameras even before the transfer to remote mode, because my teams are very diverse in terms of geography. But now this is another reason to put each other in a better mood: we set stupid backgrounds on Skype, laugh, discuss the news – so it’s like we talked about work and at the same time maintained friendly relationships. And work becomes more fun for everyone. On my Russian-language project, I organized a small English club twice a week. And half of the team’s 15 members attend it regularly. We improve our English and get to know each other better. There is somehow more mutual trust because of these meetings. I do this not because it is necessary, but because the folks in the teams are really cool; it’s interesting to spend time with them. Sometimes you learn such amazing things about people that you get really surprised and motivated. About availability 24/7: my team understand that I’m a human being too, not a robot on the other side of the monitor. They won’t bother me after working hours with little things that can surely wait till the next morning. But I also have a realistic attitude to my position and understand that I may be needed at any time. We once discussed , and army recruitment, and even decided what to give to a beloved girl as a birthday present. But this does not get on my nerves, on the contrary, it’s great that the team is so open and positive. jobs by distribution Yaroslav Gritsyna , a PM at the Andersen CRM project Communication one-on-one with each member of the team is necessary, and once every 2-3 weeks is more than enough. A daily call is a must, so the whole team will be aware of the entire project, as the slightest deviations from the original plan will be visible at this stage. Reports are a very good practice. Customers who are not involved in the development process will be very grateful to see summaries of the development status. 98% (referring to the statistics at the beginning of the article – author’s note) is a bit surprising – is the remote work format really so popular and desired? Leonid Vysochin , a PM at the EuroFunk project I don’t agree that one-on-one communication should take place every 2-3 days. It should be done depending on the need, when the PM sees that something is wrong with the person or the person behaves in a nontypical manner. Such frequent one-on-ones will lead to the opposite effect. I recommend doing one-on-ones every month or when necessary. Also, everyone should understand that there are working hours, and all the tasks assigned to the day should be solved during these working hours. A PM who is available 24/7 pampers both the team and the customer. They will just use you to solve issues whether you are necessary or not. There can be emergencies, of course, but they happen extremely rarely, and you need to be ready for them, but no more than that. And here is a small life hack that will help improve relations within the team, boost fighting spirit, and make the team even more holistic.  You should organize events with prizes for the winners, namely: a competition for the best Skype background during a daily meeting, a competition for the best clothes that a team member will wear for a daily meeting, and other competitions like that. The main thing is to have fun so that everyone participates with the cameras turned on, speaks, and votes (they can do it anonymously by messaging the PM directly). And of course, some tasty prize for the winner – pizza, sushi, delivery of some other tasty food, followed by a photo from the winner in a chat with the team. Sergey Krugley , a PM at Paxful.com project Along with the morning daily call, we recommend making another regular but shorter group call in the afternoon. This will allow keeping in touch and being aware of each other’s tasks, as well as give a greater sense of involvement. If the PM can be a career manager for the most promising employees, everyone will benefit from this. Employees will develop, the company will grow rich, and the manager himself will be able to become, like a famous poet once said, “a servant of the king, a father to the soldiers”, i.e. enjoy credibility among the company’s managers and employees. It is important to maintain communication in the group chat besides working topics and outside of working hours so that relations in the team are not that formal. If there is a human relationship, many issues are resolved more easily and things go more quickly. Pay attention to the development of your soft skills and the soft skills of your key employees. These skills are very important for remote management. It is important to understand that not all employees enjoy the remote mode. As Joel Spolski recommends, include in your team only those people who are smart and get things done, i.e. the intelligent ones and those who don’t need a nanny. And improve your PM professional skills. The skills that are especially important are the skills of conflict resolution, making presentations, thinking one step ahead, and holding distributed meetings – preparation, facilitation, and listening. Among the books about teams, I recommend the book The Wisdom of Teams: Creating the High-Performance Organization, as well as books by Joel Spolsky. Conclusion We hope this selection of techniques for working with employees remotely, as well as the advice of our PMs, will be useful and will help you in your relationship with your team. If you have something to add, write about it in the comments! Previously published at https://blog.andersenlab.com/en/problems-of-remote/#scroll-block

How Project Managers Can Build the Most Productive Remote Teams 

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. Where should the person stop and the information search interface start? Information Processing & Management 26, 5 (1990), 575–591. [3] Nicholas J Belkin. 1980. Anomalous states of knowledge as a basis for information retrieval. Canadian journal of information science 5, 1 (1980), 133–143. [4] Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 185–194. [5] Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2022. Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue 20, 6 (2022), 35–57. [6] Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). [7] Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 141–159. [8] Andrei Broder. 2002. A taxonomy of web search. In ACM Sigir forum, Vol. 36. ACM New York, NY, USA, 3–10. [9] Andrei Z Broder and Preston McAfee. 2023. Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis. arXiv preprint arXiv:2308.07525 (2023). [10] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023). [11] Katriina Byström and Kalervo Järvelin. 1995. Task complexity affects information seeking and use. Information processing & management 31, 2 (1995), 191–213. [12] Robert Capra and Jaime Arguello. 2023. How does AI chat change search behaviors? arXiv preprint arXiv:2307.03826 (2023). [13] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023) (2023). [14] Antonia Creswell and Murray Shanahan. 2022. Faithful reasoning using large language models. arXiv preprint arXiv:2208.14271 (2022). [15] Brenda Dervin. 1998. Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of knowledge management 2, 2 (1998), 36–46. [16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). [17] Karl Duncker and Lynne S Lees. 1945. On problem-solving. Psychological monographs 58, 5 (1945), i. [18] Brad Everman, Trevor Villwock, Dayuan Chen, Noe Soto, Oliver Zhang, and Ziliang Zong. 2023. Evaluating the Carbon Impact of Large Language Models at the Inference Stage. In 2023 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 150–157. [19] Guglielmo Faggioli, Laura Dietz, Charles LA Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, et al. 2023. Perspectives on Large Language Models for Relevance Judgment. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval. 39–50. [20] Jianfeng Gao, Chenyan Xiong, Paul Bennett, and Nick Craswell. 2023. Neural Approaches to Conversational Information Retrieval. Vol. 44. Springer Nature. [21] Ahmed Hassan Awadallah, Ryen W White, Patrick Pantel, Susan T Dumais, and Yi-Min Wang. 2014. Supporting complex search tasks. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 829–838. [22] Tom Hope, Doug Downey, Daniel S Weld, Oren Etzioni, and Eric Horvitz. 2023. A computational inflection for scientific discovery. Commun. ACM 66, 8 (2023), 62–73. [23] Peter Ingwersen and Kalervo Järvelin. 2005. The turn: Integration of information seeking and retrieval in context. Vol. 18. Springer Science & Business Media. [24] Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. Comput. Surveys 55, 12 (2023), 1–38. [25] Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 133–142. [26] Jeonghyun Kim. 2006. Task difficulty as a predictor and indicator of web searching interaction. In CHI’06 extended abstracts on human factors in computing systems. 959–964. [27] David R Krathwohl. 2002. A revision of Bloom’s taxonomy: An overview. Theory into practice 41, 4 (2002), 212–218. [28] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474. [29] Yuelin Li and Nicholas J Belkin. 2008. A faceted approach to conceptualizing tasks in information seeking. Information processing & management 44, 6 (2008), 1822–1837. [30] Yuanchun Li and Oriana Riva. 2021. Glider: A reinforcement learning approach to extract UI scripts from websites. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1420–1430. [31] Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning. PMLR, 6565–6576. [32] Gary Marchionini. 2006. Exploratory search: from finding to understanding. Commun. ACM 49, 4 (2006), 41–46. [33] James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, and Scott Miller. 2023. Synthetic Cross-language Information Retrieval Training Data. arXiv preprint arXiv:2305.00331 (2023). [34] Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. 2023. Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707 (2023). [35] Marc Najork. 2023. Generative Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1–1. [36] Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Alexandra Olteanu, Ana Lucic, AnaAndreea Stoica, et al. 2021. FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. In ACM SIGIR Forum, Vol. 53. ACM New York, NY, USA, 20–43. [37] Soo Young Rieh, Kevyn Collins-Thompson, Preben Hansen, and Hye-Jung Lee. 2016. Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science 42, 1 (2016), 19–34. [38] Shawon Sarkar and Chirag Shah. 2021. An integrated model of task, information needs, sources and uncertainty to design task-aware search systems. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 83–92. [39] Reijo Savolainen. 2012. Expectancy-value beliefs and information needs as motivators for task-based information seeking. Journal of Documentation 68, 4 (2012), 492–511. [40] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 (2023). [41] Chirag Shah. 2023. Generative AI and the Future of Information Access. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (Birmingham, United Kingdom) (CIKM ’23). Association for Computing Machinery, New York, NY, USA, 3. https://doi.org/10.1145/3583780.3615317 [42] Chirag Shah, Ryen White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, and Nicholas Belkin. 2023. Taking search to task. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. 1–13. [43] Chirag Shah, Ryen W White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, et al. 2023. Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies. arXiv preprint arXiv:2309.13063 (2023). [44] Ben Shneiderman. 2022. Human-centered AI. Oxford University Press. [45] Adish Singla, Ryen White, and Jeff Huang. 2010. Studying trailfinding algorithms for enhanced web search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 443–450. [46] Jaime Teevan, Kevyn Collins-Thompson, Ryen W White, and Susan Dumais. 2014. Slow search. Commun. ACM 57, 8 (2014), 36–38. [47] Jaime Teevan, Susan T Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 449–456. [48] Jaime Teevan, Meredith Ringel Morris, and Steve Bush. 2009. Discovering and using groups to improve personalized search. In Proceedings of the second acm international conference on web search and data mining. 15–24. [49] Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, and Ryen W White. 2020. Conversations with documents: An exploration of document-centered assistance. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 43–52. [50] Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023. Large language models can accurately predict searcher preferences. arXiv preprint arXiv:2309.10621 (2023). [51] Randall H Trigg. 1988. Guided tours and tabletops: Tools for communicating in a hypertext environment. ACM Transactions on Information Systems (TOIS) 6, 4 (1988), 398–414. [52] Sarah K Tyler and Jaime Teevan. 2010. Large scale query log analysis of re-finding. In Proceedings of the third ACM international conference on Web search and data mining. 191–200. [53] Pertti Vakkari. 2001. A theory of the task-based information retrieval process: A summary and generalisation of a longitudinal study. Journal of documentation 57, 1 (2001), 44–60. [54] Pertti Vakkari. 2016. Searching as learning: A systematization based on literature. Journal of Information Science 42, 1 (2016), 7–18. [55] Nicholas Vincent. 2022. The Paradox of Reuse, Language Models Edition.https://nmvg.mataroa.blog/blog/the-paradox-of-reuse-language-modelsedition/. Accessed: 2023-09-12. [56] Yu Wang, Xiao Huang, and Ryen W White. 2013. Characterizing and supporting cross-device search tasks. In Proceedings of the sixth ACM international conference on Web search and data mining. 707–716. [57] Ryen W White. 2016. Interactions with search systems. Cambridge University Press. [58] Ryen W White. 2018. Opportunities and challenges in search interaction. Commun. ACM 61,12 (2018), 36–38. [59] Ryen W White. 2018. Skill discovery in virtual assistants. Commun. ACM 61, 11 (2018), 106–113. [60] Ryen W White. 2022. Intelligent futures in task assistance. Commun. ACM 65, 11 (2022), 35–39. [61] Ryen W. White. 2023. Tasks, Copilots, and the Future of Search. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 5–6. https://doi.org/10.1145/3539618.3593069 [62] Ryen W White, Mikhail Bilenko, and Silviu Cucerzan. 2007. Studying the use of popular destinations to enhance web search interaction. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 159–166. [63] Ryen W White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang. 2013. Enhancing personalized search by mining and modeling task behavior. In Proceedings of the 22nd international conference on World Wide Web. 1411–1420. [64] Ryen W White, Adam Fourney, Allen Herring, Paul N Bennett, Nirupama Chandrasekaran, Robert Sim, Elnaz Nouri, and Mark J Encarnación. 2019. Multi-device digital assistance. Commun. ACM 62, 10 (2019), 28–31. [65] Ryen W White, Ian Ruthven, and Joemon M Jose. 2005. A study of factors affecting the utility of implicit relevance feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 35–42. [66] Ryen W White, Ian Ruthven, Joemon M Jose, and CJ Van Rijsbergen. 2005. Evaluating implicit feedback models using searcher simulations. ACM Transactions on Information Systems (TOIS) 23, 3 (2005), 325–361. [67] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. AutoGen: Enabling nextgen LLM applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023). [68] Iris Xie. 2008. Interactive information retrieval in digital environments. IGI global. [69] Jinyun Yan, Wei Chu, and Ryen W White. 2014. Cohort modeling for enhanced personalized search. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 505–514. [70] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. 2021. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500 (2021). [71] Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020. Generating clarifying questions for information retrieval. In Proceedings of the web conference 2020. 418–428. [72] Jieyu Zhang, Ranjay Krishna, Ahmed H Awadallah, and Chi Wang. 2023. EcoAssistant: Using LLM Assistant More Affordably and Accurately. arXiv preprint arXiv:2310.03046 (2023). [73] Yi Zhang, Sujay Kumar Jauhar, Julia Kiseleva, Ryen White, and Dan Roth. 2021. Learning to decompose and organize complex tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2726–2735. [74] Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Jiajun Chen, Lei Li, and Shujian Huang. 2023. Multilingual machine translation with large language models: Empirical results and analysis. arXiv preprint arXiv:2304.04675 (2023). [75] Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019).

Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VII. CONCLUSION In this work, we have carried out a full-blown analysis and experimental implementation of a Slice Reconciliation scheme applied to a specific CV QKD protocol (with post-selection) under simulated channel conditions anticipated for satelliteto-Earth channels. We have provided the optimal solution for the classical reconciliation process for this CV-QKD protocol in the context of massive parallelisation under the finite key regime. More specifically, we have identified the optimal block length when a large-code block is to be subdivided so as to improve the final secure key rate in bits per second. Although our results were based on a specific CV-QKD protocol and a specific GPU architecture, the type of analysis we have introduced here will apply in general terms a large suite of CV-QKD protocols run over any form of architecture that offers massive parallelisation. Our results, therefore, pave the way to optimal reconciliation system design for a wide range of practical CV-QKD systems that operate in the finite key regime. As the demand on the finite key size grows (better security thresholds), and technology advances lead to larger quantum signalling rates, the importance of optimised multithreaded CV-QKD reconciliation will grow. REFERENCES [1] N. Hosseinidehaj, Z. Babar, R. Malaney, S. X. Ng, and L. Hanzo, “Satellite-Based Continuous-Variable Quantum Communications: Stateof-the-Art and a Predictive Outlook,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, 881–919, 2018. [2] C. H. Bennett and G. Brassard, “Quantum Cryptography: Public Key Distribution and Coin Tossing,” in Proceedings of IEEE International Conference on Computers, Systems and Signal Processing,, vol. 175, 8, 1984. [3] H. Weinfurter, “Quantum Communication Experiments with Discrete Variables,” Quantum Information: From Foundations to Quantum Technology Applications, 369–381, 2016. [4] L. Gy ¨ongy ¨osi, L. Bacsardi, and S. Imre, “A Survey on Quantum Key Distribution,” Infocommunications Journal, vol. 11, no. 2, 14–21, 2019. [5] S. Pirandola, U. L. Andersen, L. Banchi, M. Berta, D. Bunandar, R. Colbeck, D. Englund, T. Gehring, C. Lupo, C. Ottaviani, et al., “Advances in Quantum Cryptography,” Advances in Optics and Photonics, vol. 12, no. 4, 1012–1236, 2020. [6] B. Korzh, C. C. W. Lim, R. Houlmann, N. Gisin, M. J. Li, D. Nolan, B. Sanguinetti, R. Thew, and H. Zbinden, “Provably Secure and Practical Quantum Key Distribution over 307 km of Optical Fibre,” Nature Photonics, vol. 9, no. 3, 163–168, 2015. [7] T. A. Eriksson, T. Hirano, B. J. Puttnam, G. Rademacher, R. S. Lu´ıs, M. Fujiwara, R. Namiki, Y. Awaji, M. Takeoka, N. Wada, et al., “Wavelength Division Multiplexing of Continuous Variable Quantum Key Distribution and 18.3 Tbit/s Data Channels,” Communications Physics, vol. 2, no. 1, 1–8, 2019. [8] S.-Y. Shen, M.-W. Dai, X.-T. Zheng, Q.-Y. Sun, G.-C. Guo, and Z.- F. Han, “Free-Space Continuous-Variable Quantum Key Distribution of Unidimensional Gaussian Modulation Using Polarized Coherent States in an Urban Environment,” Physical Review A, vol. 100, 012325, 2019. [9] L. Gyongyosi and S. Imre, “Secret Key Rates of Free-Space Optical Continuous-Variable Quantum Key Distribution,” International Journal of Communication Systems, vol. 32, no. 18, e4152, 2019. [10] P. Jouguet, S. Kunz-Jacques, and A. Leverrier, “Long-Distance Continuous-Variable Quantum Key Distribution with a Gaussian Modulation,” Physical Review A, vol. 84, no. 6, 062317, 2011. [11] P. Jouguet, S. Kunz-Jacques, T. Debuisschert, S. Fossier, E. Diamanti, R. All´eaume, R. Tualle-Brouri, P. Grangier, A. Leverrier, P. Pache, et al., “Field Test of Classical Symmetric Encryption with Continuous Variables Quantum Key Distribution,” Optics Express, vol. 20, no. 13, 14030–14041, 2012. [12] Q. Liao, Y. Guo, D. Huang, P. Huang, and G. Zeng, “Long-Distance Continuous-Variable Quantum Key Distribution Using Non-Gaussian State-Discrimination Detection,” New Journal of Physics, vol. 20, no. 2, 023015, 2018. [13] D. Guo, C. He, T. Guo, Z. Xue, Q. Feng, and J. Mu, “Comprehensive High-Speed Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information Processing, vol. 19, no. 9, 1–19, 2020. [14] Y. Zhang, Z. Chen, S. Pirandola, X. Wang, C. Zhou, B. Chu, Y. Zhao, B. Xu, S. Yu, and H. Guo, “Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber,” Physical Review Letters, vol. 125, no. 1, 010502, 2020. [15] A. Leverrier, F. Grosshans, and P. Grangier, “Finite-Size Analysis of a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 81, no. 6, 062343, 2010. [16] F. Furrer, T. Franz, M. Berta, A. Leverrier, V. B. Scholz, M. Tomamichel, and R. F. Werner, “Continuous Variable Quantum Key Distribution: Finite-Key Analysis of Composable Security against Coherent Attacks,” Physical Review Letters, vol. 109, no. 10, 100502, 2012. [17] S. Kish, E. Villase˜nor, R. Malaney, K. Mudge, and K. Grant, “Feasibility Assessment for Practical Continuous Variable Quantum Key Distribution over the Satellite-to-Earth Channel,” Quantum Engineering, vol. 2, no. 3, e50, 2020. [18] M. Tomamichel, C. C. W. Lim, N. Gisin, and R. Renner, “Tight FiniteKey Analysis for Quantum Cryptography,” Nature Communications, vol. 3, no. 1, 1–6, 2012. [19] Z. Yuan, A. Plews, R. Takahashi, K. Doi, W. Tam, A. Sharpe, A. Dixon, E. Lavelle, J. Dynes, A. Murakami, et al., “10-Mb/s Quantum Key Distribution,” Journal of Lightwave Technology, vol. 36, no. 16, 3427– 3433, 2018. [20] S.-K. Liao, W.-Q. Cai, W.-Y. Liu, L. Zhang, Y. Li, J.-G. Ren, J. Yin, Q. Shen, Y. Cao, Z.-P. Li, et al., “Satellite-to-Ground Quantum Key Distribution,” Nature, vol. 549, no. 7670, 43–47, 2017. [21] D. Lin, D. Huang, P. Huang, J. Peng, and G. Zeng, “High Performance Reconciliation for Continuous-Variable Quantum Key Distribution with LDPC Code,” International Journal of Quantum Information, vol. 13, no. 02, 1550010, 2015. [22] X. Wang, Y. Zhang, S. Yu, B. Xu, Z. Li, and H. Guo, “Efficient Rate-Adaptive Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information & Computation, vol. 17, no. 13– 14, 1123–1134, 2017. [23] C. Zhou, X. Wang, Y. Zhang, Z. Zhang, S. Yu, and H. Guo, “ContinuousVariable Quantum Key Distribution with Rateless Reconciliation Protocol,” Physical Review Applied, vol. 12, no. 5, 054013, 2019. [24] M. Milicevic, C. Feng, L. M. Zhang, and P. G. Gulak, “Quasi-Cyclic Multi-Edge LDPC Codes for Long-Distance Quantum Cryptography,” NPJ Quantum Information, vol. 4, no. 1, 1–9, 2018. [25] S.-S. Yang, Z.-G. Lu, and Y.-M. Li, “High-Speed Post-Processing in Continuous-Variable Quantum Key Distribution Based on FPGA Implementation,” Journal of Lightwave Technology, vol. 38, no. 15, 3935–3941, 2020. [26] H. Li and Y. Pang, “FPGA-Accelerated Quantum Computing Emulation and Quantum Key Distillation,” IEEE Micro, vol. 41, no. 4, 49–57, 2021. [27] C. Weedbrook, A. M. Lance, W. P. Bowen, T. Symul, T. C. Ralph, and P. K. Lam, “Quantum Cryptography without Switching,” Physical Review Letters, vol. 93, no. 17, 170504, 2004. [28] N. Hosseinidehaj, A. M. Lance, T. Symul, N. Walk, and T. C. Ralph, “Finite-Size Effects in Continuous-Variable Quantum Key Distribution with Gaussian Postselection,” Physical Review A, vol. 101, no. 5, 052335, 2020. [29] D. Dequal, L. T. Vidarte, V. R. Rodriguez, G. Vallone, P. Villoresi, A. Leverrier, and E. Diamanti, “Feasibility of Satellite-to-Ground Continuous-Variable Quantum Key Distribution,” NPJ Quantum Information, vol. 7, no. 1, 1–10, 2020. [30] M. Bloch, A. Thangaraj, and S. W. McLaughlin, “Efficient Reconciliation of Correlated Continuous Random Variables Using LDPC Codes,” arXiv preprint cs/0509041, 2005. [31] P. Jouguet, D. Elkouss, and S. Kunz-Jacques, “High-Bit-Rate Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 90, no. 4, 042329, 2014. [32] H. Mani, T. Gehring, P. Grabenweger, B. Omer, C. Pacher, and ¨ U. L. Andersen, “Multiedge-Type Low-Density Parity-Check Codes for Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 103, no. 6, 062419, 2021. [33] H. Imai and S. Hirakawa, “A New Multilevel Coding Method Using Error-Correcting Codes,” IEEE Transactions on Information Theory, vol. 23, no. 3, 371–377, 1977. [34] U. Wachsmann, R. F. Fischer, and J. B. Huber, “Multilevel codes: Theoretical concepts and practical design rules,” IEEE Transactions on Information Theory, vol. 45, no. 5, 1361–1391, 1999. [35] R. D. Wesel, X. Liu, J. M. Cioffi, and C. Komninakis, “Constellation Labeling for Linear Encoders,” IEEE Transactions on Information Theory, vol. 47, no. 6, 2417–2431, 2001. [36] M. Bloch, A. Thangaraj, S. W. McLaughlin, and J.-M. Merolla, “LDPCBased Gaussian Key Reconciliation,” in IEEE Information Theory Workshop-ITW’06 Punta del Este, 116–120, IEEE, 2006. [37] A. Leverrier, R. All´eaume, J. Boutros, G. Z´emor, and P. Grangier, “Multidimensional Reconciliation for a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 77, no. 4, 042325, 2008. [38] J. Fiur´aˇsek and N. J. Cerf, “Gaussian Postselection and Virtual Noiseless Amplification in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 6, 060302, 2012. [39] F. Darema, “The SPMD Model: Past, Present and Future,” in European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, 1–1, Springer, 2001. [40] M. Pharr and W. R. Mark, “A SPMD Compiler for High-Performance CPU Programming,” in Innovative Parallel Computing (InPar), 1–13, IEEE, 2012. [41] J. Lodewyck, M. Bloch, R. Garc´ıa-Patr ´on, S. Fossier, E. Karpov, E. Diamanti, T. Debuisschert, N. J. Cerf, R. Tualle-Brouri, S. W. McLaughlin, et al., “Quantum Key Distribution over 25 km with an AllFiber Continuous-Variable System,” Physical Review A, vol. 76, no. 4, 042305, 2007. [42] F. Laudenbach, C. Pacher, C.-H. F. Fung, A. Poppe, M. Peev, B. Schrenk, M. Hentschel, P. Walther, and H. H ¨ubel, “Continuous-Variable Quantum Key Distribution with Gaussian Modulation—The Theory of Practical Implementations,” Advanced Quantum Technologies, vol. 1, no. 1, 1800011, 2018. [43] Y. Polyanskiy, H. V. Poor, and S. Verd ´u, “Channel Coding Rate in the Finite Blocklength Regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, 2307–2359, 2010. [44] D. Slepian and J. Wolf, “Noiseless Coding of Correlated Information Sources,” IEEE Transactions on Information Theory, vol. 19, no. 4, 471–480, 1973. [45] T. Richardson and R. Urbanke, Modern Coding Theory. Cambridge University Press, 2008. [46] X. Ai, R. Malaney, and S. X. Ng, “A Reconciliation Strategy for RealTime Satellite-Based QKD,” IEEE Communications Letters, vol. 24, no. 5, 1062–1066, 2020. [47] S.-Y. Chung, T. J. Richardson, and R. L. Urbanke, “Analysis of SumProduct Decoding of Low-Density Parity-Check Codes Using a Gaussian Approximation,” IEEE Transactions on Information Theory, vol. 47, no. 2, 657–670, 2001. [48] V. A. Chandrasetty and S. M. Aziz, “FPGA Implementation of an LDPC Decoder Using a Reduced Complexity Message Passing Algorithm,” Journal of Networks, vol. 6, no. 1, 36, 2011. [49] A. Leverrier, “Composable Security Proof for Continuous-Variable Quantum Key Distribution with Coherent States,” Physical Review Letters, vol. 114, no. 7, 070501, 2015. [50] C. Lupo, C. Ottaviani, P. Papanastasiou, and S. Pirandola, “ContinuousVariable Measurement-Device-Independent Quantum Key Distribution: Composable Security against Coherent Attacks,” Physical Review A, vol. 97, no. 5, 052327, 2018. [51] N. Hosseinidehaj, N. Walk, and T. C. Ralph, “Optimal Realistic Attacks in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 99, no. 5, 052336, 2019. [52] S. Pirandola, “Limits and Security of Free-Space Quantum Communications,” Physical Review Research, vol. 3, no. 1, 013279, 2021. [53] S. Pirandola, “Satellite Quantum Communications: Fundamental Bounds and Practical Security,” Physical Review Research, vol. 3, no. 2, 023130, 2021. [54] J. H. Mathews and K. D. Fink, Numerical Methods Using MATLAB, vol. 4. Pearson Prentice Hall Upper Saddle River, NJ, 2004. [55] A. Leverrier, “Security of Continuous-Variable Quantum Key Distribution via a Gaussian de Finetti Reduction,” Physical Review Letters, vol. 118, no. 20, 200501, 2017. [56] E. Villase˜nor, R. Malaney, K. A. Mudge, and K. J. Grant, “Atmospheric Effects on Satellite-to-Ground Quantum Key Distribution Using Coherent States,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 1–6, IEEE, 2020. [57] J. Mateo, Efficient Information Reconciliation for Quantum Key Distribution. PhD thesis, Universidad Politecnica de Madrid, 2011. [58] T. Tian, C. R. Jones, J. D. Villasenor, and R. D. Wesel, “Selective Avoidance of Cycles in Irregular LDPC Code Construction,” IEEE Transactions on Communications, vol. 52, no. 8, 1242–1247, 2004. [59] D. V. Nguyen, S. K. Chilappagari, M. W. Marcellin, and B. Vasic, “On the Construction of Structured LDPC Codes Free of Small Trapping Sets,” IEEE Transactions on Information Theory, vol. 58, no. 4, 2280– 2302, 2012. [60] S. Landner and O. Milenkovic, “Algorithmic and Combinatorial Analysis of Trapping Sets in Structured LDPC Codes,” in International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, 630–635, IEEE, 2005. [61] X. Wang, Y. Zhang, S. Yu, and H. Guo, “High Speed Error Correction for Continuous-Variable Quantum Key Distribution with Multi-Edge Type LDPC Code,” Scientific Reports, vol. 8, no. 1, 1–7, 2018. [62] Y. Li, X. Zhang, Y. Li, B. Xu, L. Ma, J. Yang, and W. Huang, “HighThroughput GPU Layered Decoder of Quasi-Cyclic Multi-Edge Type Low Density Parity Check Codes in Continuous-Variable Quantum Key Distribution Systems,” Scientific Reports, vol. 10, no. 1, 1–11, 2020. [63] F. Grosshans, “Collective Attacks and Unconditional Security in Continuous Variable Quantum Key Distribution,” Physical Review Letters, vol. 94, 020504, 2005. [64] M. Navascu´es, F. Grosshans, and A. Acin, “Optimality of Gaussian Attacks in Continuous-Variable Quantum Cryptography,” Physical Review Letters, vol. 97, no. 19, 190502, 2006. [65] S. Fossier, E. Diamanti, T. Debuisschert, R. Tualle-Brouri, and P. Grangier, “Improvement of Continuous-Variable Quantum Key Distribution Systems by Using Optical Preamplifiers,” Journal of Physics B: Atomic, Molecular and Optical Physics, vol. 42, no. 11, 114014, 2009. [66] P. Jouguet, S. Kunz-Jacques, E. Diamanti, and A. Leverrier, “Analysis of Imperfections in Practical Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 3, 032309, 2012.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Conclusion & References

The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 13 of 15. HackerNoon’s Legal PDF Series here C. The DMCA Claim Fails for Multiple Independent Reasons Count V is a claim for violation of Section 1202(b) of the Copyright Act, which prohibits the “[r]emoval or [a]lteration” of copyright management information or “CMI.” 17 U.S.C. § 1202(b). Congress passed that provision in the early days of the internet in recognition of the ease with which unauthorized copies of images and other works might proliferate in cyberspace. The provision encourages rightsholders to affix CMI to their works (and prohibits its removal) so that, if their works do proliferate on the internet, the public will be able to trace those works back to their owner. S. Rep. No. 105-190, at 16–17 (1998) (CMI intended to “track[] and monitor[]”). But Congress limited the statute with a “double-scienter requirement” that prevents its application when the CMI removal occurs as an unintended result of an “automatic [] process.” Zuma Press, Inc. v. Getty Images (US), Inc., 845 F. App’x 54, 57–58 (2d Cir. 2021). A typical CMI case might involve the surreptitious removal of a photograph’s “gutter credit” to conceal a failure to seek a license from the rightsholder. Mango v. BuzzFeed, Inc., 970 F.3d 167, 169–70, 173 (2d Cir. 2020). 1. The Times Did Not Specify the CMI at Issue Count V should be dismissed at the outset for failure to specify the CMI at issue. The Complaint’s relevant paragraph fails to state what CMI is included in what work, and simply repeats the statutory text. Compl. ¶ 182 (alleging “one or more forms of [CMI]” and parroting language of Section 1202(c)).[36] The only firm allegation states that the Times placed “copyright notices” and “terms of service” links on “every page of its websites.” Compl. ¶ 125. But, at least for some articles, it did not.[37] And when it did, the information was not “conveyed in connection with” the works, 17 U.S.C. § 1202(c) (defining CMI), but hidden in small text at the bottom of the page.[38] Judge Orrick of the Northern District of California rejected similar allegations as deficient in another recent AI case. Andersen v. Stability AI Ltd., No. 23-cv-00201, 2023 WL 7132064, at *11 (N.D. Cal. Oct. 30, 2023) (must plead “exact type of CMI included in [each] work”).[39] 2. The Training-Based Section 1202 Claim Fails The first Section 1202 violation alleged in the Complaint asserts that OpenAI “removed” CMI “in building the training datasets” in violation of Section 1202(b)(1) of the DMCA. Compl. ¶ 184. As a preliminary matter, to the extent this claim is based on the “building [of] training datasets” that occurred more than three years ago, it is time-barred. 17 U.S.C. § 507(b). The Complaint also fails to plausibly allege that any CMI was removed. The Times advances three theories of removal: (1) removal of CMI when OpenAI allegedly “scraped” articles from the Times’s website; (2) removal of CMI “from third-party datasets,” i.e., Common Crawl; and (3) removal of CMI during “the training process,” which the Times alleges “does not preserve any [CMI]” “by design.” Compl. ¶¶ 184, 187. As a preliminary matter, each theory fails for failure to allege what CMI was removed or “not preserve[d],” which is particularly damning as the Times concedes that some CMI was preserved.[40] Judge Martínez-Olguín of the Northern District of California recently held an identical set of allegations to be insufficient. See Tremblay v. OpenAI, Inc., No. 3:23-cv-03416, 2024 WL 557720, at *4 (N.D. Cal. Feb. 12, 2024) (allegation that “training process does not preserve any CMI” “by design” was “conclusory”). Each theory fails separately as well. The first theory fails because, while the Times states that OpenAI “scraped” its articles “directly from [its] websites,” none of the specific allegations actually suggest OpenAI designed its alleged “scrap[ing]” process to omit CMI. Compl. ¶ 184.[41] And the only allegations about OpenAI “scraping” articles from Times websites relate to the creation of WebText, which occurred over three years before this lawsuit. Supra Section IV.A.[42] The second theory fails because the Complaint lacks allegations about the inclusion (or exclusion) of the Times’s CMI in any “third-party datasets” like Common Crawl,[43] much less about OpenAI scrubbing any CMI from those datasets. First Nationwide Bank v. Gelt Funding Corp., 27 F.3d 763, 771 (2d Cir. 1994) (courts ignore “unwarranted deductions of fact”). And the third theory fails because there is no allegation in the Complaint supporting the conclusion that the “training process” excludes CMI “[b]y design.” Compl. ¶ 187; see also Tremblay, 2024 WL 557720, at *4. Moreover, the Times fails to allege facts that could show how the alleged CMI removal could “induce, enable, facilitate, or conceal an infringement” of copyright—much less how OpenAI could have “reasonable grounds to know” it would. 17 U.S.C. § 1202(b). The “point of CMI” is to provide information to “the public,” not to govern purely internal databases. Roberts v. BroadwayHD LLC, 518 F. Supp. 3d 719, 737 (S.D.N.Y. 2021); Compl. ¶ 59. As Judge Martínez-Olguín explained, it is far from obvious how “the alleged removal of CMI in an internal database [could] enable infringement.” Tremblay, 2024 WL 557720, at *4 (dismissing claim).[44] 3. The Output-Based Section 1202 Claim Fails The second category of Section 1202 violation in the Complaint alleges that (1) OpenAI violated Section 1202(b)(1)’s removal prohibition by failing to include the Times’s CMI in model outputs, Compl. ¶¶ 185–86; and (2) by displaying those outputs via ChatGPT or its API, OpenAI violated Section 1202(b)(3)’s prohibition on “distribut[ing]” works “knowing that [CMI] has been removed,” Compl. ¶ 189. Neither theory states a claim for relief. As a preliminary matter, the Times’s Section 1202(b)(3) claim fails because the Complaint does not allege that OpenAI “distribute[d]” any outputs. In this context, “distribution” requires a “sale or transfer of ownership extending beyond that of a mere public display.” Wright v. Miah, No. 22-cv-4132, 2023 WL 6219435, at *7 (E.D.N.Y. Sept. 7, 2023) (emphasis added).[45] But “mere public display” of outputs is all the Complaint alleges. See, e.g., Compl. ¶ 102. Regardless, this “output” theory fails because the outputs alleged in the Complaint are not wholesale copies of entire Times articles. They are, at best, reproductions of excerpts of those articles, some of which are little more than collections of scattered sentences. Supra 12. If the absence of CMI from such excerpts constituted a “removal” of that CMI, then DMCA liability would attach to any journalist who used a block quote in a book review without also including extensive information about the book’s publisher, terms and conditions, and original copyright notice. See supra note 22 (example of the Times including 200-word block quote in book review). To avoid such anomalous results, courts have cabined applications of Section 1202(b)(1) and (3) to circumstances in which the works in question were “substantially or entirely reproduced.” Fischer v. Forrest, 286 F. Supp. 3d 590, 609 (S.D.N.Y. 2018). As such, failure to include original CMI in anything less than an identical reproduction of all (or almost all) of the work does not qualify as CMI removal. Tremblay, 2024 WL 557720, at *5 (dismissing claim because “Plaintiffs have not alleged that [OpenAI] distributed their books or copies of [them]”).[46] As the Times has not alleged that OpenAI reproduced entire articles, the output-based claim fails. Even setting that aside, the Times’s output-based CMI claim fails for the independent reason that there was no CMI to remove from the relevant text. The Exhibit J outputs, for example, feature text from the middle of articles. Ex. J. at 2–126. As shown in the exhibit, the “Actual text from NYTimes” contains no information that could qualify as CMI. See, e.g., id. at 3; 17 U.S.C. § 1202(c) (defining CMI). So too for the ChatGPT outputs featured in the Complaint, which request the “first [and subsequent] paragraph[s]” from Times articles. See, e.g., Compl. ¶¶ 104, 106, 118, 121. None of those “paragraphs” contains any CMI that OpenAI could have “removed.” 4. The Times Fails to Allege a CMI-Based Injury Count V separately fails for lack of standing. “[T]o have standing” to sue for a DMCA violation, the Times “must show that [it] was injured by that violation.” Steele v. Bongiovi, 784 F. Supp. 2d 94, 97–98 (D. Mass. 2011); see also 17 U.S.C. § 1203(a). Here, the Complaint’s “Harm to the Times” section relates entirely to its inability to receive speculative licensing revenue, see Compl. ¶¶ 155–56, and the possibility that ChatGPT will “divert readers,” see id. ¶ 157. Neither injury has any nexus to CMI. Nor is there any imaginable harm here: because all of the Complaint’s outputs were either generated using the original Times article itself, see Ex. J, or referenced the Times by name, see, e.g., Compl. ¶ 104, any user who encountered those outputs would have no doubt as to the provenance of the text and could easily find it on the Times’s website (as ChatGPT often invites them to do, see id. ¶¶ 106, 134). Cf. Kelly, 77 F. Supp. 2d at 1122 (DMCA claim failed because users who encounter images are “given the name of the Web site from which Defendant obtained the image, where any associated [CMI] would be available”). Continue Reading . Here [36] Design Pics Inc. v. PBH Network, Inc., No. 20-cv-1096, 2020 WL 8413512, at *4 (E.D.N.Y. Oct. 27, 2020) (dismissing CMI claim based on “conclusory, boilerplate parroting of the statutory text”). [37] See, e.g., John Branch, Snow Fall: The Avalanche at Tunnel Creek, N.Y. Times, https://www.nytimes.com/projects/2012/snow-fall/index.html#/?part=tunnel-creek (last accessed Feb. 11, 2024); see also Compl. ¶¶ 104–05 & n.28 (citing this article). [38] See, e.g., Pete Wells, As Not Seen on TV, N.Y. Times (Nov. 13, 2012), https://www.nytimes.com/2012/11/14/dining/reviews/restaurant-review-guys-american-kitchen-bar-in-timessquare.html; see also Compl. ¶¶ 106–07 & n.29 (citing and quoting this article). [39] See also Wood v. Observer Holdings, LLC, No. 20-cv-07878, 2021 WL 2874100, at *8 (S.D.N.Y., July 8, 2021) (terms on “separate website” not CMI); GC2 Incorporated v. Int’l Game Tech. PLC, 255 F. Supp. 3d 812, 821–22 (N.D. Ill. 2017) (“terms of use notice near a copyrighted work” not “conveyed in connection with” work). [40] See, e.g., Compl. ¶ 106 (ChatGPT responding to query naming “Pete Wells” with a completion correctly identifying the publication date of Wells’ article); see also id. ¶ 104 (same, for title). [41] Kelly v. Arriba Soft Corp., 77 F. Supp. 2d 1116, 1122 (C.D. Cal. 1999), rev'd on other grounds by 336 F.3d 811 (9th Cir. 2003) (no Section 1202 liability vs. search engine “crawler [that] did not include [CMI]”). [42] The Times suggests that OpenAI’s “Browse with Bing” feature “scrap[es] Times Works from The Times’s websites,” Compl. ¶ 185, but the Complaint does not include a single allegation supporting that conclusion, see supra 13 & n.32 (noting that Browse with Bing fetched content from third-party sites, not the Times’s website). [43] OpenAI cannot have removed CMI from datasets that “contained no such [CMI]” in the first place. McGucken v. Shutterstock, Inc., No. 22-cv-00905, 2023 WL 6390530, at 11 (S.D.N.Y. Oct. 2, 2023) (rejecting DMCA claim). [44] See Victor Elias Photography, LLC v. Ice Portal, Inc., 43 F.4th 1313, 1325 (11th Cir. 2022) (requiring “some identifiable connection between the defendant’s actions and the infringement or the likelihood of infringement.”). [45] Id. at *10 (endorsing Section 1202(b)(3) claim where defendant distributed artwork on Etsy); MyPlayCity, Inc. v. Conduit Ltd., No. 10-cv-1615, 2012 WL 1107648, at *12 (S.D.N.Y. Mar. 30, 2012) (“distribution” means “actual dissemination of copies”); FurnitureDealer.Net, Inc v. Amazon.com, Inc, No. 18-cv-232, 2022 WL 891473, at *23 (D. Minn. Mar. 25, 2022) (“[P]ublic display does not constitute distribution, and thus is not a [DMCA] violation.”). [46] See also Doe 1 v. GitHub, Inc., No. 22-cv-06823, 2024 WL 235217, *9 (N.D. Cal. Jan. 22, 2024) (dismissing Section 1202(b) claim against OpenAI because outputs were “not identical” to originals); A’Lor Int’l, Ltd. v. Tapper Fine Jewelry, Inc., No. 12-cv-02215, 2012 WL 12921035, at *10 (C.D. Cal. Aug. 8, 2012) (“the plain language of the statute encompasses only removal and alteration;” does not “include [mere] omissions”); Faulkner Press, L.L.C. v. Class Notes, L.L.C., 756 F. Supp. 2d 1352, 1358–59 (N.D. Fla. 2010) (rejecting claim where “word for word” text was “copied into a different form and [] incorporated into” commercial materials); Kelly, 77 F. Supp. 2d at 1121–22 (“displaying thumbnails of Plaintiffs’ images without [] the corresponding [CMI]” was not CMI “removal”). About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. fingfx.thomsonreuters.com

ChatGPT Never Produced "Wholesale Copies" of NYT's Articles

Aspects of Thermal QCD Phenomenology at Intermediate Gauge/'t Hooft Coupling: Bibliography

Authors: (1) JunJie Wee, Department of Mathematics, Michigan State University; (2) Jiahui Chen, Department of Mathematical Sciences, University of Arkansas; (3) Kelin Xia, Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University & xiakelin@ntu.edu.sg; (4)Guo-Wei Wei, Department of Mathematics, Michigan State University, Department of Biochemistry and Molecular Biology, Michigan State University, Department of Electrical and Computer Engineering, Michigan State University & xiakelin@ntu.edu.sg. Table of Links Abstract & Introduction Results Discussion Conclusion Materials and Methods Software and resources, Code and Data Availability Supporting Information, Acknowledgments & References Supporting Information Supporting Information is available for supplementary tables, figures, and methods. Acknowledgments This work was supported in part by NIH grants R01GM126189, R01AI164266, and R01AI146210, NSF grants DMS-2052983, DMS-1761320, and IIS-1900473, NASA grant 80NSSC21M0023, MSU Foundation, Bristol-Myers Squibb 65109, and Pfizer. It was supported in part by Nanyang Technological University Startup Grant M4081842.110, Singapore Ministry of Education Academic Research fund Tier 1 RG109/19 and Tier 2 MOE-T2EP20120-0013, MOE-T2EP20220- 0010, and MOE-T2EP20221-0003. References [1] Y. Qiu and G.-W. Wei, “Persistent spectral theory-guided protein engineering,” Nature Computational Science, vol. 3, no. 2, pp. 149–163, 2023. [2] R. Guerois, J. E. Nielsen, and L. Serrano, “Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations,” Journal of molecular biology, vol. 320, no. 2, pp. 369–387, 2002. [3] P. Sormanni, F. A. Aprile, and M. Vendruscolo, “The CamSol method of rational design of protein mutants with enhanced solubility,” Journal of molecular biology, vol. 427, no. 2, pp. 478–490, 2015. [4] Y. Tian, C. Deutsch, and B. Krishnamoorthy, “Scoring function to predict solubility mutagenesis,” Algorithms for Molecular Biology, vol. 5, no. 1, pp. 1–11, 2010. [5] Y. Yang, A. Niroula, B. Shen, and M. Vihinen, “PON-Sol: Prediction of effects of amino acid substitutions on protein solubility,” Bioinformatics, vol. 32, no. 13, pp. 2032–2034, 2016. [6] L. Paladin, D. Piovesan, and S. C. Tosatto, “SODA: Prediction of protein solubility from disorder and aggregation propensity,” Nucleic acids research, vol. 45, no. W1, pp. W236– W240, 2017. [7] J. Van Durme, G. De Baets, R. Van Der Kant, M. Ramakers, A. Ganesan, H. Wilkinson, R. Gallardo, F. Rousseau, and J. Schymkowitz, “Solubis: a webserver to reduce protein aggregation through mutation,” Protein Engineering, Design and Selection, vol. 29, no. 8, pp. 285–289, 2016. [8] M. Vihinen, “Solubility of proteins,” ADMET and DMPK, vol. 8, no. 4, pp. 391–399, 2020. [9] A.-M. Fernandez-Escamilla, F. Rousseau, J. Schymkowitz, and L. Serrano, “Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins,” Nature Biotechnology, vol. 22, no. 10, pp. 1302–1306, 2004. [10] H. Land and M. S. Humble, “YASARA: A tool to obtain structural guidance in biocatalytic investigations,” Protein engineering: methods and protocols, pp. 43–67, 2018. [11] Y. Yang, L. Zeng, and M. Vihinen, “PON-Sol2: Prediction of effects of variants on protein solubility,” International Journal of Molecular Sciences, vol. 22, no. 15, p. 8027, 2021. [12] H. Edelsbrunner and J. L. Harer, Computational Topology: An Introduction. American Mathematical Society, 2022. [13] A. Zomorodian and G. Carlsson, “Computing Persistent Homology,” in Proceedings of the twentieth annual symposium on Computational geometry, pp. 347–356, 2004. [14] K. Xia and G.-W. Wei, “Persistent homology analysis of protein structure, flexibility, and folding,” International journal for numerical methods in biomedical engineering, vol. 30, no. 8, pp. 814–844, 2014. [15] Z. Cang, L. Mu, K. Wu, K. Opron, K. Xia, and G.-W. Wei, “A topological approach for protein classification,” Computational and Mathematical Biophysics, vol. 3, no. 1, 2015. [16] Z. Cang and G.-W. Wei, “Topologynet: Topology based deep convolutional and multitask neural networks for biomolecular property predictions,” PLoS computational biology, vol. 13, no. 7, p. e1005690, 2017. [17] Z. X. Cang and G. W. Wei, “Integration of element specific persistent homology and machine learning for protein-ligand binding affinity prediction,” International journal for numerical methods in biomedical engineering, vol. 34, no. 2, p. e2914, 2018. [18] M. Wang, Z. Cang, and G.-W. Wei, “A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation,” Nature Machine Intelligence, vol. 2, no. 2, pp. 116–123, 2020. [19] J. Chen, R. Wang, M. Wang, and G.-W. Wei, “Mutations strengthened SARS-CoV-2 infectivity,” Journal of molecular biology, vol. 432, no. 19, pp. 5212–5226, 2020. [20] D. D. Nguyen, Z. Cang, K. Wu, M. Wang, Y. Cao, and G.-W. Wei, “Mathematical deep learning for pose and binding affinity prediction and ranking in D3R grand challenges,” Journal of computer-aided molecular design, vol. 33, pp. 71–82, 2019. [21] D. D. Nguyen, K. Gao, M. Wang, and G.-W. Wei, “MathDL: mathematical deep learning for d3r grand challenge 4,” Journal of computer-aided molecular design, vol. 34, pp. 131– 147, 2020. [22] D. D. Nguyen, Z. Cang, and G.-W. Wei, “A review of mathematical representations of biomolecular data,” Physical Chemistry Chemical Physics, vol. 22, no. 8, pp. 4343–4367, 2020. [23] R. Wang, D. D. Nguyen, and G.-W. Wei, “Persistent spectral graph,” arXiv preprint arXiv:1912.04135, 2019. [24] J. Chen, R. Zhao, Y. Tong, and G.-W. Wei, “Evolutionary de Rham-Hodge method,” arXiv preprint arXiv:1912.12388, 2019. [25] R. Wang, D. D. Nguyen, and G.-W. Wei, “Persistent spectral graph,” International Journal for Numerical Methods in Biomedical Engineering, p. e3376, 2020. [26] Z. Meng and K. Xia, “Persistent spectral–based machine learning (PerSpect ML) for protein-ligand binding affinity prediction,” Science Advances, vol. 7, no. 19, p. eabc5329, 2021. [27] J. Wee and K. Xia, “Persistent spectral based ensemble learning (PerSpect-EL) for protein–protein binding affinity prediction,” Briefings in Bioinformatics, p. bbac024, 2022. [28] J. Bi, J. Wee, X. Liu, C. Qu, G. Wang, and K. Xia, “Multiscale Topological Indices for the Quantitative Prediction of SARS CoV-2 Binding Affinity Change upon Mutations,” Journal of Chemical Information and Modeling, vol. 63, no. 13, pp. 4216–4227, 2023. [29] J. Chen, Y. Qiu, R. Wang, and G.-W. Wei, “Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants,” Computers in Biology and Medicine, vol. 151, p. 106262, 2022. [30] R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, P. Chen, J. Canny, P. Abbeel, and Y. Song, “Evaluating protein transfer learning with TAPE,” Advances in neural information processing systems, vol. 32, 2019. [31] T. Bepler and B. Berger, “Learning protein sequence embeddings using information from structure,” in International Conference on Learning Representations, 2018. [32] E. C. Alley, G. Khimulya, S. Biswas, M. AlQuraishi, and G. M. Church, “Unified rational protein engineering with sequence-based deep representation learning,” Nature methods, vol. 16, no. 12, pp. 1315–1322, 2019. [33] A. Rives, J. Meier, T. Sercu, S. Goyal, Z. Lin, J. Liu, D. Guo, M. Ott, C. L. Zitnick, J. Ma, et al., “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences,” Proceedings of the National Academy of Sciences, vol. 118, no. 15, p. e2016239118, 2021. [34] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, vol. 30, 2017. [35] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in North American Chapter of the Association for Computational Linguistics, 2019. [36] J. Meier, R. Rao, R. Verkuil, J. Liu, T. Sercu, and A. Rives, “Language models enable zero-shot prediction of the effects of mutations on protein function,” in Advances in Neural Information Processing Systems (M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, eds.), vol. 34, pp. 29287–29303, Curran Associates, Inc., 2021. [37] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Zˇ´ıdek, A. Potapenko, et al., “Highly accurate protein structure prediction with alphafold,” Nature, vol. 596, no. 7873, pp. 583–589, 2021. [38] J. Z. Xiang and B. Honig, “Jackal: A protein structure modeling package,” Columbia University and Howard Hughes Medical Institute, New York, 2002. [39] R. M. Rao, J. Liu, R. Verkuil, J. Meier, J. Canny, P. Abbeel, T. Sercu, and A. Rives, “MSA Transformer,” in International Conference on Machine Learning, pp. 8844–8856, PMLR, 2021. [40] P. Baldi, S. Brunak, Y. Chauvin, C. A. Andersen, and H. Nielsen, “Assessing the accuracy of prediction algorithms for classification: An Overview,” Bioinformatics, vol. 16, no. 5, pp. 412–424, 2000. [41] E. D. Levy, “A simple definition of structural regions in proteins and its use in analyzing interface evolution,” Journal of molecular biology, vol. 403, no. 4, pp. 660–670, 2010. [42] D. S. Goodsell, L. Autin, and A. J. Olson, “Illustrate: Software for biomolecular illustration,” Structure, vol. 27, no. 11, pp. 1716–1720, 2019. [43] Y. Hozumi, K. A. Tanemura, and G.-W. Wei, “Preprocessing of Single Cell RNA Sequencing Data Using Correlated Clustering and Projection,” Journal of Chemical Information and Modeling, 2023. [44] J. R. Munkres, Elements of algebraic topology. CRC Press, 2018. [45] A. J. Zomorodian, Topology for computing, vol. 16. Cambridge university press, 2005. [46] H. Edelsbrunner and J. Harer, Computational topology: an introduction. American Mathematical Soc., 2010. [47] K. Mischaikow and V. Nanda, “Morse theory for filtrations and efficient computation of persistent homology,” Discrete and Computational Geometry, vol. 50, no. 2, pp. 330–353, 2013. [48] D. Horak and J. Jost, “Spectra of combinatorial Laplace operators on simplicial complexes,” Advances in Mathematics, vol. 244, pp. 303–336, 2013. [49] B. Eckmann, “Harmonische funktionen und randwertaufgaben in einem komplex,” Commentarii Mathematici Helvetici, vol. 17, no. 1, pp. 240–255, 1944. [50] A. Zomorodian, “Topological data analysis,” Advances in applied and computational topology, vol. 70, pp. 1–39, 2012. [51] T. G. Project, GUDHI User and Reference Manual. GUDHI Editorial Board, 2015. [52] M. Mirdita, K. Schutze, Y. Moriwaki, L. Heo, S. Ovchinnikov, and M. Steinegger, “Colab- ¨ Fold: Making protein folding accessible to all,” Nature methods, vol. 19, no. 6, pp. 679– 682, 2022. [53] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [54] T. J. Dolinsky, J. E. Nielsen, J. A. McCammon, and N. A. Baker, “PDB2PQR: An automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations,” Nucleic acids research, vol. 32, no. suppl 2, pp. W665–W667, 2004. [55] B. Liu, B. Wang, R. Zhao, Y. Tong, and G.-W. Wei, “ESES: Software for eulerian solvent excluded surface,” 2017. [56] D. Chen, Z. Chen, C. Chen, W. Geng, and G.-W. Wei, “MIBPB: A software package for electrostatic analysis,” Journal of computational chemistry, vol. 32, no. 4, pp. 756–770, 2011. [57] H. Li, A. D. Robertson, and J. H. Jensen, “Very fast empirical prediction and rationalization of protein pKa values,” Proteins: Structure, Function, and Bioinformatics, vol. 61, no. 4, pp. 704–721, 2005. [58] M. Johnson, I. Zaretskaya, Y. Raytselis, Y. Merezhuk, S. McGinnis, and T. L. Madden, “NCBI BLAST: A better web interface,” Nucleic acids research, vol. 36, no. suppl 2, pp. W5–W9, 2008. [59] R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma, J. Wang, A. Sattar, Y. Yang, and Y. Zhou, “Improving prediction of secondary structure, local backbone angles and solvent accessible surface area of proteins by iterative deep learning,” Scientific reports, vol. 5, no. 1, p. 11476, 2015. [60] C. Maria, J.-D. Boissonnat, M. Glisse, and M. Yvinec, “The GUDHI library: Simplicial complexes and persistent homology,” in Mathematical Software–ICMS 2014: 4th International Congress, Seoul, South Korea, August 5-9, 2014. Proceedings 4, pp. 167–174, Springer, 2014. This paper is under CC 4.0 license. available on arxiv Lead image by Cytonn Photography on Unsplash

ANDERSEN

andersenlab.com

-- employees

Since n.d.

COMPANY RANKING

ANDERSEN

EVERGREEN INDEX #1616

Representing this company? Click here to claim and customize this page!

Andersen WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

ANDERSEN

andersenlab.com

-- employees

Since n.d.

COMPANY RANKING

ANDERSEN

EVERGREEN INDEX #1616

RELATED COMPANIESRANK

STORIES ABOUT ANDERSEN

Representing this company? Click here to claim and customize this page!

Andersen WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!