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arXiv 2023年

作者： Teramoto, Kosei Raymond, Rudy Wakakuwa, Eyuri Imai, Hiroshi Department of Computer Science The University of Tokyo Japan IBM Quantum IBM Japan Department of Computer Science The University of Tokyo Quantum Computing Center Keio University Japan

Quantum Random Access Optimizer (QRAO) is a quantum-relaxation based optimization algorithm proposed by Fuller et al. that utilizes Quantum Random Access Code (QRAC) to encode multiple variables of binary optimization in a single qubit. The approximation ratio bound of QRAO for the maximum cut problem is 0.555 if the bit-to-qubit compression ratio is 3x, while it is 0.625 if the compression ratio is 2x, thus demonstrating a trade-off between space efficiency and approximability. In this research, we extend the quantum-relaxation by using another QRAC which encodes three classical bits into two qubits (the bit-to-qubit compression ratio is 1.5x) and obtain its approximation ratio for the maximum cut problem as 0.722. Also, we design a novel quantum relaxation that always guarantees a 2x bit-to-qubit compression ratio which is unlike the original quantum relaxation of Fuller et al. We analyze the condition when it has a non-trivial approximation ratio bound (> 1/2). We hope that our results lead to the analysis of the quantum approximability and practical efficiency of the quantum-relaxation based approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Qubits

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

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Intelligent Automation & Soft computing 2023年第10期38卷 57-81页

作者： Emmanuel Lule Chomora Mikeka Alexander Ngenzi Didacienne Mukanyiligira African Center of Excellence in Internet of Things(ACEIoT) College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda Department of Computer Science College of Computing&Information SciencesMakerere UniversityKampala7062Uganda Directorate of Science Technology&Innovation(DSTI)Ministry of EducationMalawiLilongwe328Malawi Department of Computer&Software Engineering College of Science&TechnologyUniversity of RwandaNyarugenge-Kigali3900Rwanda National Council for Science&Technology(NCST) Government of RwandaKigali2285Rwanda

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby *** circuits,arson,and neglected charcoal stoves are the major causes of these *** methods,i.e.,satellites,are expensive to maintain and cause unnecessary ***,unit-smoke detectors are highly prone to false *** this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy *** crisp input parameters,namely:FIT and FIG��are *** show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression *** study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire *** a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false *** informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

关键词： Interval type-2(IT2)fuzzy systems mean absolute error(MAE) mean square error(MSE) root mean square

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware Key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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APP: Enabling Soft Real-Time Execution on Densely-Populated Hybrid Memory System 23

APP: Enabling Soft Real-Time Execution on Densely-Populated ...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Zheng-Wei Wu Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University and Mohamed bin Zayed University of Artificial Intelligence Department of Computer Science and NTU High Performance and Scientific Computing Center National Taiwan University

ISBN: (纸本)9798350323481

Memory swapping was considered slow and evil, but swapping to Ultra Low-Latency storage like Optane has become a promising solution to save power and cost, helping densely-populated edge server to overcome its DRAM capacity bottleneck. However, the lack of integration between CPU scheduling and memory paging causes soft real-time tasks running on edge servers to miss deadlines under heavy memory multiplexing. We propose APP (Adaptive Page Pinning), lightweight protection of working set memory to ensure meeting soft real-time task deadlines without starving other non-real-time tasks. Experiments show that APP alleviates thrashing in memory-intensive tasks and upholds soft real-time task deadlines.

关键词：

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CLINICAL INSPIRED MRI LESION SEGMENTATION

arXiv

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arXiv 2025年

作者： Yan, Lijun Wang, Churan Zhong, Fangwei Wang, Yizhou School of Software and Microelectronics Peking University China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology Peking University China School of Artificial Intelligence Beijing Normal University China

Magnetic resonance imaging (MRI) is a potent diagnostic tool for detecting pathological tissues in various diseases. Different MRI sequences have different contrast mechanisms and sensitivities for different types of lesions, which pose challenges to accurate and consistent lesion segmentation. In clinical practice, radiologists commonly use the subsequence feature, i.e. the difference between post contrast-enhanced T1-weighted (post) and pre-contrast-enhanced (pre) sequences, to locate lesions. Inspired by this, we propose a residual fusion method to learn subsequence representation for MRI lesion segmentation. Specifically, we iteratively and adaptively fuse features from pre- and post-contrast sequences at multiple resolutions, using dynamic weights to achieve optimal fusion and address diverse lesion enhancement patterns. Our method achieves state-of-the-art performances on BraTS2023 dataset for brain tumor segmentation and our in-house breast MRI dataset for breast lesion segmentation. Our method is clinically inspired and has the potential to facilitate lesion segmentation in various applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Dynamic contrast enhanced MRI

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LARGE LANGUAGE MODELS ASSUME PEOPLE ARE MORE RATIONAL THAN WE REALLY ARE

arXiv

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arXiv 2024年

作者： Liu, Ryan Geng, Jiayi Peterson, Joshua C. Sucholutsky, Ilia Griffiths, Thomas L. Department of Computer Science Princeton University United States Computing & Data Sciences Boston University United States Center for Data Science New York University United States Department of Psychology Department of Computer Science Princeton University United States

In order for AI systems to communicate effectively with people, they must understand how we make decisions. However, people’s decisions are not always rational, so the implicit internal models of human decision-making in Large Language Models (LLMs) must account for this. Previous empirical evidence seems to suggest that these implicit models are accurate — LLMs offer believable proxies of human behavior, acting how we expect humans would in everyday interactions. However, by comparing LLM behavior and predictions to a large dataset of human decisions, we find that this is actually not the case: when both simulating and predicting people’s choices, a suite of cutting-edge LLMs (GPT-4o & 4-Turbo, Llama-3-8B & 70B, Claude 3 Opus) assume that people are more rational than we really are. Specifically, these models deviate from human behavior and align more closely with a classic model of rational choice — expected value theory. Interestingly, people also tend to assume that other people are rational when interpreting their behavior. As a consequence, when we compare the inferences that LLMs and people draw from the decisions of others using another psychological dataset, we find that these inferences are highly correlated. Thus, the implicit decision-making models of LLMs appear to be aligned with the human expectation that other people will act rationally, rather than with how people actually act. © 2024, CC BY-NC-SA.

关键词： Human form models

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What Affects the Adoption of Metaverse in Education? A SEM-based Approach 20

What Affects the Adoption of Metaverse in Education? A SEM-b...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Rohan, Rohani Roy, Pranab Vanijja, Vajirasak Funilkul, Suree Mukherjee, Subhodeep Pal, Debajyoti School of Information Technology King Mongkut's University of Technology Thonburi Bangkok Thailand Jk Laxmipat University Department of Computer Science and Engineering Jaipur India Department of Operations Management Visakhapatnam India Kmutt Innovative Cognitive Computing Research Center Bangkok Thailand

ISBN: (纸本)9798350300505

Metaverse is an emerging 3D digital space that augments the real world and the virtual world. It has been envisioned to be the trendsetter for future education having tremendous potential. However, the success of any new technology depends on its mass adoption and widespread societal diffusion. Therefore, it becomes important to understand the factors that can lead to its success by ensuring widespread adoption. In this work, we investigate the different psychological gratifications of the educational usage of Metaverse under the lens of the Uses and Gratification (UGT) theory. Particularly, we propose and test a research model by using the Structural Equation Modelling (SEM) approach by considering four fundamental psychological gratifications of autonomy, achievement, affiliation, and dominance, together with two additional factors of hedonic motivation and student personality. Data is collected from two Asian universities, and results show the importance of autonomy, hedonic motivation, and personality in predicting Metaverse adoption. © 2023 IEEE.

关键词： Motivation

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Quantum State Tomography via Nonconvex Riemannian Gradient Descent

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Physical Review Letters 2024年第24期132卷 240804-240804页

作者： Ming-Chien Hsu En-Jui Kuo Wei-Hsuan Yu Jian-Feng Cai Min-Hsiu Hsieh Hon Hai Quantum Computing Research Center Taipei Taiwan Joint Center for Quantum Information and Computer Science NIST and University of Maryland College Park Maryland USA Department of Mathematics National Central University Taoyuan Taiwan Department of Mathematics Hong Kong University of Science and Technology Hong Kong

The recovery of an unknown density matrix of large size requires huge computational resources. State-of-the-art performance has recently been achieved with the factored gradient descent (FGD) algorithm and its variants since they are able to mitigate the dimensionality barrier by utilizing some of the underlying structures of the density matrix. Despite the theoretical guarantee of a linear convergence rate, convergence in practical scenarios is still slow because the contracting factor of the FGD algorithms depends on the condition number κ of the ground truth state. Consequently, the total number of iterations needed to achieve the estimation error ϵ can be as large as O(κln(1/ϵ)). In this Letter, we derive a quantum state tomography scheme that improves the dependence on κ to the logarithmic scale. Thus, our algorithm can achieve the approximation error ϵ in O(ln(1/κϵ)) steps. The improvement comes from the application of nonconvex Riemannian gradient descent (RGD). The contracting factor in our approach is thus a universal constant that is independent of the given state. Our theoretical results of extremely fast convergence and nearly optimal error bounds are corroborated by the numerical results.

关键词： Quantum measurements Quantum metrology Quantum parameter estimation Quantum protocols Quantum tomography

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