For what purpose was the dataset created?Was therea specific task in mind? Was there aspecific gap that needed to be filled? Please provide a description.The Multi-LexSum dataset was curated to facilitate the development of automaticsummarization methods for civil rights lawsuits.Recent advances in document summarization have led to impressive results in generating ashort description for passages typically in hundreds of words. However, the source inputs forsummarizing civil right lawsuits are considerably longer: they can contain up to 70k words onaverage.

With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy document collections. One such setting is the Civil Rights Litigation Clearinghouse (CRLC) (https://clearinghouse.net),which posts information about large-scale civil rights lawsuits, serving lawyers, scholars, and the general public. Today, summarization in the CRLC requires extensive training of lawyers and law students who spend hours per case understanding multiple relevant documents in order to produce high-quality summaries of key events and outcomes. Motivated by this ongoing real-world summarization effort, we introduce Multi-LexSum, a collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing. Multi-LexSum presents a challenging multi-document summarization task given the length of the source documents, often exceeding two hundred pages per case. Furthermore, Multi-LexSum is distinct from other datasets in its multiple target summaries, each at a different granularity (ranging from one-sentence "extreme" summaries to multi-paragraph narrations of over five hundred words). We present extensive analysis demonstrating that despite the high-quality summaries in the training data (adhering to strict content and style guidelines), state-of-the-art summarization models perform poorly on this task. We release Multi-LexSum for further research in summarization methods as well as to facilitate development of applications to assist in the CRLC's mission at https://multilexsum.github.io.

We have reached a stage where Artificial Intelligence (AI) has moved out of the pages of science fiction and has become areality of life. With an immense potential to streamline business operations and enhancedecision-making, this powerful technology has made its way into numerous sectors including manufacturing, education real estate, banking, and healthcare, just to name a few. AI's contribution to healthcare and banking iswhat makes it the most remarkable technology of this era, as these industries are active in modernizing systems with the objectives of reducing costs, improving accuracy andupdating infrastructures. Today, companies in the healthcare and banking sectors are paying significant attention to solutions based on AI, machine learning and deep learning. Among those, OrboGraph has become a brand that customers trust for its quintessential solutions.

Barclays and JPMorgan Chase have been experimenting with IBM's quantum computing technology since December, when they joined the tech company's Q network. Salvatore Cucchiara at Morgan Stanley last week articulated the bank's hope of speeding up portfolio optimizations like Monte Carlo simulations with the help of quantum computing. True Positive Technologies, which creates investment strategies for institutional investors with the use of machine learning, has been working with quantum computers since 2014 for portfolio optimization and scenario simulations. Dr. Marcos Lopez de Prado, who founded Guggenheim Partners' Quantitative Investment Strategies business and is now CEO of True Positive, argues that quantum computing will solve financial firms' need for increased computing capacity in the future, while requiring less energy than traditional computers suck up. "Quantum computing will become increasingly important over time," he said.

If they haven't done so already, cyber attackers may soon be arming themselves with artificial intelligence and machine learning (ML) strategies and algorithms. Before long, it may not be a fair fight if defenders remain naive to what AI and ML can do on both sides of the battle. So suggests a new report by IEEE and the Canadian tech consulting firm Syntegrity. The report -- stemming from a three-day intensive meeting last October of cybersecurity experts from government, the military, and industry -- aggregates the group's findings into what it calls the six "dimensions" at the intersection of AI, ML, and cybersecurity. First, the report advocates ways to keep cybersecurity regulations and laws up to speed with the latest developments in the field.

If they haven't done so already, cyber attackers may soon be arming themselves with artificial intelligence and machine learning (ML) strategies and algorithms. Before long, it may not be a fair fight if defenders remain naive to what AI and ML can do on both sides of the battle. So suggests a new report by IEEE and the Canadian tech consulting firm Syntegrity. The report--stemming from a three-day intensive last October of cybersecurity experts from government, the military, and industry--aggregates the group's findings into what it calls the six "dimensions" at the intersection of AI, ML, and cybersecurity. First, the report advocates ways to keep cybersecurity regulations and laws up to speed with the latest developments in the field.

We present a credit-based matching mechanism for dynamic barter markets — and kidney exchange in particular — that is both strategy proof and efficient, that is, it guarantees truthful disclosure of donor-patient pairs from the transplant centers and results in the maximum global matching. Furthermore, the mechanism is individually rational in the sense that, in the long run, it guarantees each transplant center more matches than the center could have achieved alone. The mechanism does not require assumptions about the underlying distribution of compatibility graphs — a nuance that has previously produced conflicting results in other aspects of theoretical kidney exchange. Our results apply not only to matching via 2-cycles: the matchings can also include cycles of any length and altruist-initiated chains, which is important at least in kidney exchanges. The mechanism can also be adjusted to guarantee immediate individual rationality at the expense of economic efficiency, while preserving strategy proofness via the credits. This circumvents a well-known impossibility result in static kidney exchange concerning the existence of an individually rational, strategy-proof, and maximal mechanism. We show empirically that the mechanism results in significant gains on data from a national kidney exchange that includes 59% of all US transplant centers.