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关键词： Computer science   Engineering   Energy engineering  

摘要： This study addresses the challenge of virtual machine (VM) placement in cloud computing to improve resource utilization and energy efficiency. We propose a mixed integer linear programming (MILP) model incorporating G-robustness theory to handle uncertainties in VM usage, optimizing both performance and energy consumption. A heuristic algorithm is developed for large-scale VM allocation. Experiments with Huawei Cloud data demonstrate significant improvements in resource utilization and energy efficiency.

关键词： Computer science   Social sciences   Psychology  

摘要： Social status, which represents the relative dominance structure in societies, forms the backdrop against which most social decisions are made. Effective social decision-making demands flexible integration of decision attribute weight (importance of an attribute) and attribute latency (when attributes start to affect decisions). However, current understanding of how attribute weight and latency are influenced by relative social status is limited. In three experiments, we dynamically manipulated subjects' relative social status before they engaged in an altruistic decision task and found that their altruistic behavior was better explained by a time-varying drift diffusion model, in which relative social status selectively modulated attribute latency but not attribute weights. Furthermore, prosocial subjects exhibited higher sensitivity to attribute latency in response to changes in relative social status compared with individualistic subjects. Our results introduce a new dimension to the computational mechanisms underlying the intricate interplay between relative social status and attribute integration.

关键词： Computer science   Condensed-matter physics   Quantum information  

摘要： Analysis pokes holes in protocol that underpins Microsoft’s claim to have created the first topological qubits.

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.

关键词： Hypercubes   Multiprocessing systems   Fault diagnosis   Computational modeling   Computer science   Time measurement   Testing   Terminology   Stars   Training   Interconnection networks   conditional diagnosability   PMC model   enhanced hypercubes   fault tolerance   multiprocessor systems  

摘要： In recent years, due to the continuous expansion of the scale of multi-processor systems, processor fault diagnosis has become increasingly important in measuring system reliability. Diagnosability of many well-known multiprocessor systems has been extensively studied. Conditional diagnosability is a new system diagnostic measure that inserts an additional condition that all neighbors (adjacent nodes) of any node in the system cannot all fail at the same time. In this report, we evaluate the conditional diagnosability for enhanced hypercubes under the PMC model. We first give several properties about hypercubes and enhanced hypercubes, and then based on these properties, the conditional diagnosability of an (n,k)-enhanced hypercube Q(n,k) is proved to be 4n-7 for n >= 5 and k=3, and to be 4n-3 for n >= 6 and 5 <= k <= n.

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