检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Gao, Minbo Ji, Zhengfeng Wang, Qisheng Yu, Wenjun Zhao, Qi Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University Beijing China School of Informatics University of Edinburgh United Kingdom QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

We formalize and study the Hamiltonian certification problem, a fundamental task in quantum physics, crucial for verifying the accuracy of quantum simulations and quantum-enhanced technologies. Given access to e−iHt for an unknown Hamiltonian H, the goal of the problem is to determine whether H is Ε1-close to or Ε2-far from a target Hamiltonian H0. While Hamiltonian learning methods have been extensively studied, they often require restrictive assumptions and suffer from inefficiencies when adapted for certification tasks. This work introduces a direct and efficient framework for Hamiltonian certification, which distinguishes whether an unknown Hamiltonian matches a target specification within given precision bounds. Our approach achieves optimal total evolution time Θ((Ε2 − Ε1)−1) for certification under the normalized Frobenius norm, without prior structural assumptions. This approach also extends to certify Hamiltonians with respect to all Pauli norms and normalized Schatten p-norms for 1 ≤ p ≤ 2 in the one-sided error setting (Ε1 = 0), where the optimality is consistently maintained. Notably, the result in Pauli 1-norm suggests a quadratic advantage of our approach over all possible Hamiltonian learning approaches. We also establish matching lower bounds to show the optimality of our approach across all the above settings. We complement our result by showing that the certification problem with respect to normalized Schatten ∞-norm is coQMA-hard, and therefore unlikely to have efficient solutions. This hardness result provides strong evidence that our focus on above metrics is not merely a technical choice but a requirement for efficient certification. To enhance practical applicability, we develop an ancilla-free certification method that maintains the inverse precision scaling while eliminating the need for auxiliary qubits, making our approach immediately accessible for near-term quantum devices with limited resources. Copyright © 2025, The Authors. All rights

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

Real-Time Decoding for Fault-Tolerant Quantum computing: Progress, Challenges and Outlook

arXiv

引用

arXiv 2023年

作者： Battistel, Francesco Chamberland, Christopher Johar, Kauser Overwater, Ramon W.J. Sebastiano, Fabio Skoric, Luka Ueno, Yosuke Usman, Muhammad Qblox Elektronicaweg 10 Delft2628 XG Netherlands AWS Center for Quantum Computing PasadenaCA91125 United States IQIM California Institute of Technology PasadenaCA91125 United States Riverlane Ltd Cambridge United Kingdom QuTech Department of Quantum and Computer Engineering Delft University of Technology Delft2600 GA Netherlands Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Department of Computer Engineering Technical University of Munich Garching Germany School of Physics The University of Melbourne Parkville3010 Australia Data61 CSIRO Clayton3168 Australia

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g., on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum and computer science. Copyright © 2023, The Authors. All rights reserved.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

A Survey on Deep Learning Event Extraction: Approaches and Applications

arXiv

引用

arXiv 2021年

作者： Li, Qian Li, Jianxin Sheng, Jiawei Cui, Shiyao Wu, Jia Hei, Yiming Peng, Hao Guo, Shu Wang, Lihong Beheshti, Amin Yu, Philip S. The School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The Institute of Information Engineering Chinese Academy of Sciences Beijing100083 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The School of Computing Macquarie University Sydney Australia The Department of Computer Science University of Illinois at Chicago Chicago60607 United States

Event extraction is a crucial research task for promptly apprehending event information from massive textual data. With the rapid development of deep learning, event extraction based on deep learning technology has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the research gap by reviewing the state-of-the-art approaches, especially focusing on the general domain event extraction based on deep learning models. We introduce a new literature classification of current general domain event extraction research according to the task definition. Afterwards, we summarize the paradigm and models of event extraction approaches, and then discuss each of them in detail. As an important aspect, we summarize the benchmarks that support tests of predictions and evaluation metrics. A comprehensive comparison among different approaches is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

来源：评论

学校读者我要写书评

暂无评论

Two classes of narrow-sense BCH codes and their duals

arXiv

引用

arXiv 2022年

作者： Wang, Xiaoqiang Wang, Jiaojiao Li, Chengju Wu, Yansheng Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan430062 China Data Science and Information Technology Research Center Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen Inter national Graduate School Shenzhen China The Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China

BCH codes and their dual codes are two special subclasses of cyclic codes and are the best linear codes in many cases. A lot of progress on the study of BCH cyclic codes has been made, but little is known about the minimum distances of the duals of BCH codes. Recently, a new concept called dually-BCH code was introduced to investigate the duals of BCH codes and the lower bounds on their minimum distances in [12]. For a prime power q and an integer (formula presented), or (formula presented). In this paper, some sufficient and necessary conditions in terms of the designed distance will be given to ensure that the narrow-sense BCH codes of length n are dually-BCH codes, which extended the results in [12]. Lower bounds on the minimum distances of their dual codes are developed for (formula presented). As byproducts, we present the largest coset leader δ1 modulo n being of two types, which proves a conjecture in [28] and partially solves an open problem in [21]. We also investigate the parameters of the narrow-sense BCH codes of length n with design distance δ1. The BCH codes presented in this paper have good parameters in general. © 2022, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization

arXiv

引用

arXiv 2024年

作者： Deng, Xiumei Li, Jun Wei, Kang Shi, Long Xiong, Zeihui Ding, Ming Chen, Wen Jin, Shi Poor, H. Vincent School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Data61 CSIRO SydneyNSW2015 Australia Department of Electronics Engineering Shanghai Jiao Tong University Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electrical and Computer Engineering Princeton University NJ08544 United States

Adaptive moment estimation (Adam), as a Stochastic Gradient Descent (SGD) variant, has gained widespread popularity in federated learning (FL) due to its fast convergence. However, federated Adam (FedAdam) algorithms suffer from a threefold increase in uplink communication overhead compared to federated SGD (FedSGD) algorithms, which arises from the necessity to transmit both local model updates and first and second moment estimates from distributed devices to the centralized server for aggregation. Driven by this issue, we propose a novel sparse FedAdam algorithm called FedAdam-SSM, wherein distributed devices sparsify the updates of local model parameters and moment estimates and subsequently upload the sparse representations to the centralized server. To further reduce the communication overhead, the updates of local model parameters and moment estimates incorporate a shared sparse mask (SSM) into the sparsification process, eliminating the need for three separate sparse masks. Theoretically, we develop an upper bound on the divergence between the local model trained by FedAdam-SSM and the desired model trained by centralized Adam, which is related to sparsification error and imbalanced data distribution. By minimizing the divergence bound between the model trained by FedAdam-SSM and centralized Adam, we optimize the SSM to mitigate the learning performance degradation caused by sparsification error. Additionally, we provide convergence bounds for FedAdam-SSM in both convex and non-convex objective function settings, and investigate the impact of local epoch, learning rate and sparsification ratio on the convergence rate of FedAdam-SSM. Experimental results show that FedAdam-SSM outperforms baselines in terms of convergence rate (over 1.1× faster than the sparse FedAdam baselines) and test accuracy (over 14.5% ahead of the quantized FedAdam baselines). Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Size-fixed group discovery via multi-constrained graph pattern matching

引用

information sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Beware of "Explanations" of AI

arXiv

引用

arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

引用

Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive information Bottleneck Guided Joint Source and Channel Coding for Image Transmission

arXiv

引用

arXiv 2022年

作者： Sun, Lunan Yang, Yang Chen, Mingzhe Guo, Caili Saad, Walid Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Beijing Laboratory of Advanced Information Net works School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Wireless@VT Group Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Joint source and channel coding (JSCC) for image transmission has attracted increasing attention due to its robustness and high efficiency. However, the existing deep JSCC research mainly focuses on minimizing the distortion between the transmitted and received information under a fixed number of available channels. Therefore, the transmitted rate may be far more than its required minimum value. In this paper, an adaptive information bottleneck (IB) guided joint source and channel coding (AIB-JSCC) method is proposed for image transmission. The goal of AIB-JSCC is to reduce the transmission rate while improving the image reconstruction quality. In particular, a new IB objective for image transmission is proposed so as to minimize the distortion and the transmission rate. A mathematically tractable lower bound on the proposed objective is derived, and then, adopted as the loss function of AIB-JSCC. To trade off compression and reconstruction quality, an adaptive algorithm is proposed to adjust the hyperparameter of the proposed loss function dynamically according to the distortion during the training. Experimental results show that AIB-JSCC can significantly reduce the required amount of transmitted data and improve the reconstruction quality and downstream task accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR Deep Learning Approach for NLP ...

引用

2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020

作者： Amin, Kareem Kapetanakis, Stelios Polatidis, Nikolaos Althoff, Klaus-Dieter Dengel, Andreas Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781728167992

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture. © 2020 IEEE.

关键词： Case based reasoning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：