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关键词： Computer science   Machine learning  

摘要： New metric assesses how AI is getting better at completing long tasks — but some researchers are wary of long-term predictions.

关键词： Computer science   Condensed-matter physics   Quantum information  

摘要： Some attendees at a packed presentation were curious about the prospect of the first ‘topological’ qubits, but left with questions unanswered.

关键词： Nonhomogeneous media   Vectors   Computer science   Complex networks   Social networking (online)   Petroleum   Tensors   Software engineering   Public healthcare   Object recognition   Coupled weighting   influential nodes   inter-layer influence factors   intra-layer influence factors   multilayer network  

摘要： The identification of influential nodes in multilayer networks is a rapidly growing area in network science. However, insufficient consideration of both inter- and intra-layer weights in existing research has limited the effectiveness of node identification methods. To address this gap, we propose a novel algorithm, coupling weighted intra-layer and inter-layer influence factors (CWIIIF), which accurately identifies nodes that exert significant influence in multilayer networks. The algorithm integrates weighted intra- and inter-layer influence factors, taking into account the unique properties of multilayer network structures. First, we define a set of layer weight influence parameters, including active nodes, active paths, and communication intersections between layers, to determine the weight of each network layer. We then calculate the intra-layer influence of each node using a combination of K-shell and betweenness centrality methods. Finally, we introduce a set of coupled equations that convert the intra-layer influence vectors into scalar values by incorporating the weights of each layer, producing a final influence score for each node. To validate the effectiveness of our algorithm, we conducted four comparative experiments across nine real-world and one synthetic multilayer networks. The results demonstrate that our algorithm significantly outperforms nine classical and state-of-the-art methods for identifying influential nodes.

摘要： The use of water as a weapon in highly industrialized areas in the Russo-Ukrainian war has resulted in catastrophic economic and environmental damages. We analyze environmental effects caused by the military destruction of the Kakhovka Dam. We link field, remote sensing, and modeling data to demarcate the disaster's spatial-temporal scales and outline trends in reestablishment of damaged ecosystems. Although media attention has focused on the immediate impacts of flooding on society, politics, and the economy, our results show that toxic contamination within newly exposed sediments of the former reservoir bed poses a largely overlooked long-term threat to freshwater, estuarine, and marine ecosystems. The continued use of water as a weapon may lead to even greater risks for people and the environment.

关键词： Reinforcement learning  

摘要： The aim of this paper is to discuss the potential of using methods from Reinforcement Learning for Life Cycle Assessment in a circular economy. To give some context, we explain how Reinforcement Learning was successfully applied in computer chess (and beyond). As computer chess was historically called the "drosophila of AI", we start by describing a method for the board representation called’rotated bitboards’ that can potentially also be applied in the context of sustainability. There exist several techniques for representing the chess board inside the computer. In the first part of this paper, the concepts of the bitboard-representation and the advantages of (rotated) bitboards in move generation are explained. In order to illustrate those ideas practice, the concrete implementation of the move-generator in FUSc# (a chess engine developed at FU Berlin in C# some years ago) is described. In addition, rotated binary neural networks are discussed briefly. The second part deals with reinforcement learning in computer chess (and beyond). We exemplify the progress that has been made in this field in the last 15-20 years by comparing the "state of the art" from 2002-2008, when FUSc# was developed, with the groundbreaking innovations connected to "AlphaZero". We discuss how a "FUSc#-Zero" can be implemented and what is necessary to reduce the number of training games necessary to achieve a good performance. This can be seen as a test case to the general problem of improving "sample efficiency" in reinforcement learning. We then move beyond computer chess, as the importance of sample efficiency extends far beyond board games into a wide range of applications where data is costly, difficult to obtain, or time consuming to generate. We review some application of the ideas developed in AlphaZero in other domains, e.g. the "other Alphas" like AlphaFold, AlphaTensor, AlphaGeometry and AlphaProof. In the final part of the paper, we discuss the computer-science related challenges

关键词： Computer science   Quantum information   Information technology  

摘要： Quantum computers have gained the powerful abstractions that allow programmers of classical computers to design and integrate new apps and hardware, and connect devices into networks with ease.

关键词： Educational technology  

摘要： In this paper, we present an educational project aimed to introduce students to the technology behind Captive Portals infrastructures. For doing this, we developed a series of modules to emphasize each of the different aspects and features of this technology. The project is based on an open source implementation which is widely used in many computer network courses, making it well-suited and very appealing for instructors and practitioners in this field. Copyright © 2025, The Authors. All rights reserved.

关键词： serious games   digital games   digital game-based learning   gamification   computational thinking   programming   computer science   computer education   primary education   secondary education   k-12 education   review  

摘要： Computational thinking and problem-solving skills have become vital for students to develop. Digital games and serious games are increasingly being used in educational settings and present great potential to aid students' learning. This study aims to explore the role and impact of serious games and digital games on students' computational thinking and programming skills in primary, secondary, and K-12 education through a systematic review of the existing literature. Four research questions were set to be examined. Following the PRISMA framework, 78 studies deriving from IEEE, Scopus, and Web of Science over the period of 2011-2024 are examined. The studies are categorized into Theoretical and Review studies, Proposal and Showcase studies, and Experimental and Case studies. Based on the results, serious games and digital games arose as meaningful educational tools that are positively viewed by education stakeholders and that can effectively support and improve K-12 education students' computational thinking and programming skills. Among the benefits identified, it was revealed that serious games offer enjoyable and interactive learning experiences that can improve students' learning performance, engagement, and motivation, enhance students' confidence and focus, and promote self-regulated learning and personalized learning. Additionally, serious games emerged as an educational means that can effectively support social learning and provide real-time feedback. The challenges identified were related to the selection of games and the game-related design elements, decisions, and approaches. Hence, the study highlights the significance of the design of serious games and the need to cultivate students' computational thinking, problem-solving, and social skills from a young age. Finally, the study reveals key design principles and aspects to consider when developing serious games and digital games and highlights the need to involve education stakeholders throughout the desig

关键词： Number theory  

摘要： We survey large value problems, including the large value problem forDirichlet polynomials, the restriction problem, and problems from computerscience. We describe known techniques and open problems, drawing onperspectives from all three *** Codes 42B15, 11M26 © 2025, CC BY-NC-ND.

摘要： Existing long-text generation methods primarily concentrate on producing lengthy texts from short inputs, neglecting the long-input and long-output tasks. Such tasks have numerous practical applications while lacking available benchmarks. Moreover, as the input grows in length, existing methods inevitably encounter the "lost-in-the-middle" phenomenon. In this paper, we first introduce a Long Input and Output Benchmark (LONGINOUTBENCH), including a synthetic dataset and a comprehensive evaluation framework, addressing the challenge of the missing benchmark. We then develop the Retrieval-Augmented Long-Text Writer (RAL-WRITER), which retrieves and restates important yet overlooked content, mitigating the "lost-in-the-middle" issue by constructing explicit prompts. We finally employ the proposed LONGINOUTBENCH to evaluate our RAL-WRITER against comparable baselines, and the results demonstrate the effectiveness of our approach. Our code has been released at https://***/OnlyAR/RAL-Writer © 2025, CC BY.