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Reducing the Number of Qubits from n2 to nlog2(n) to Solve the Traveling Salesman Problem with Quantum computers: A Proposal for Demonstrating Quantum Supremacy in the NISQ Era

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arXiv 2024年

作者： Ramezani, Mehdi Salami, Sadegh Shokhmkar, Mehdi Moradi, Morteza Bahrampour, Alireza Department of Physics Sharif University of Technology Tehran14588 Iran Centre for Quantum Engineering and Photonics Technology Sharif University of Technology Tehran14588 Iran Department of Computer Engineering Sharif University of Technology Tehran14588 Iran Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Banacha 2 Warsaw02-097 Poland

In our pursuit of quantum supremacy during the NISQ era, this research introduces a novel approach rooted in the Quantum Approximate Optimization Algorithm (QAOA) framework to address the Traveling Salesman Problem (TSP). By strategically reducing the requisite qubit count from n2 to n log2 (n), our QAOA-based algorithm not only contributes to the ongoing discourse on qubit efficiency but also demonstrates improved performance based on established metrics, underscoring its potential for achieving NISQ-era supremacy in solving real-world optimization challenges. © 2024, CC BY-NC-ND.

关键词： Traveling salesman problem

Weighted Sum of Segmented Correlation: an Efficient Method for Spectra Matching in Hyperspectral Images

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Weighted Sum of Segmented Correlation: an Efficient Method f...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sampriti Soor Priyanka Kumari B. S. Daya Sagar Amba Shetty School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Department of Water Resources and Ocean Engineering NIT Karnataka India Systems Science and Informatics Unit Indian Statistical Institute Bangalore India

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Matching a target spectrum with known spectra in a spectral library is a common method for material identification in hyperspectral imaging research. Hyperspectral spectra exhibit precise absorption features across different wavelength segments, and the unique shapes and positions of these absorptions create distinct spectral signatures for each material, aiding in their identification. Therefore, only the specific positions can be considered for material identification. This study introduces the Weighted Sum of Segmented Correlation method, which calculates correlation indices between various segments of a library and a test spectrum, and derives a matching index, favoring positive correlations and penalizing negative correlations using assigned weights. The effectiveness of this approach is evaluated for mineral identification in hyperspectral images from both Earth and Martian surfaces.

关键词： Image segmentation Correlation Absorption Shape Geoscience and remote sensing Libraries Iron

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可信联邦学习进化优化算法综述

Engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization Data-driven optimization Bayesian optimization

Transforming Experimental Cobot Cell to Industrial Realization – an Ethical AI Approach

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IFAC-PapersOnLine 2023年第2期56卷 7335-7341页

作者： Istvan Mezgár József Váncza Imre Paniti József Tóth EPIC Center of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Technology Budapest University of Technology and Economics Budapest Hungary Hepenix Ltd. Diósd Hungary

Collaborative Robot (cobot) cells are getting more and more integrative building blocks of Cyber Physical Enterprises. These cells integrate the advantages of human workers with the special capabilities of robots in a safe manner. Cobots need advanced, in many cases artificial intelligence (AI) based control systems to harmonize the collaborative activities. When transferring/transforming experimental setups into industrial application, not only technological and business related, but also ethical aspects have to be taken into consideration. The paper introduces a novel workflow supporting this transformation and presents its application in a case-study of a cobot cell which uses advanced sensing, symbolic AI planning and mixed reality techniques for planning and explaining visually the operation of the cell. The work which takes the responsible artificial intelligence (RAI) approach combines the actual relevant AI standards with the explicit requirements of industrial practitioners.

关键词： Collaborative robotics Artificial intelligence Ethics Design methodology

A SEMI-AUTOMATIC CRANIAL IMPLANT DESIGN TOOL BASED ON RIGID ICP TEMPLATE ALIGNMENT AND VOXEL SPACE RECONSTRUCTION

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arXiv 2024年

作者： Lackner, Michael Puladi, Behrus Kleesiek, Jens Egger, Jan Li, Jianning Germany Institute of Computer Graphics and Vision Graz University of Technology Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Germany

In traumatic medical emergencies, the patients heavily depend on cranioplasty - the craft of neurocranial repair using cranial implants. Despite the improvements made in recent years, the design of a patient-specific implant (PSI) is among the most complex, expensive, and least automated tasks in cranioplasty. Further research in this area is needed. Therefore, we created a prototype application with a graphical user interface (UI) specifically tailored for semi-automatic implant generation, where the users only need to perform high-level actions. A general outline of the proposed implant generation process involves setting an area of interest, aligning the templates, and then creating the implant in voxel space. Furthermore, we show that the alignment can be improved significantly, by only considering clipped geometry in the vicinity of the defect border. The software prototype will be open-sourced at https://***/3Descape/Cranial_Implant_Design. © 2024, CC BY.

关键词： Graphical user interfaces

Perfect Sampling for Hard Spheres from Strong Spatial Mixing

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arXiv 2023年

作者： Anand, Konrad Göbel, Andreas Pappik, Marcus Perkins, Will School of Informatics University of Edinburgh United Kingdom Hasso Plattner Institute University of Potsdam United States School of Computer Science Georgia Institute of Technology United States

We provide a perfect sampling algorithm for the hard-sphere model on subsets of d with expected running time linear in the volume under the assumption of strong spatial mixing. A large number of perfect and approximate sampling algorithms have been devised to sample from the hard-sphere model, and our perfect sampling algorithm is efficient for a range of parameters for which only efficient approximate samplers were previously known and is faster than these known approximate approaches. Our methods also extend to the more general setting of Gibbs point processes interacting via finite-range, repulsive potentials. © 2023, CC BY.

关键词： Spheres

A Comparative Analysis of Transformer Models in Zero-Shot Text Classification

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A Comparative Analysis of Transformer Models in Zero-Shot Te...

International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Konstantinos Kyritsis Charalampos M. Liapis Nikolaos Spatiotis Isidoros Perikos Michael Paraskevas Electrical & Computer Engineering Dept. University of Peloponnese Patras Greece Mathematics Dept. University of Patras Patras Greece Computer Technology Institute & Press “Diophantus” Patras Greece Computer Engineering & Informatics Dept. University of Patras Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification.

关键词： Training Analytical models Text categorization Transformers

Bridging the Domain Gaps in Context Representations for k-Nearest Neighbor Neural Machine Translation

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arXiv 2023年

作者： Cao, Zhiwei Yang, Baosong Lin, Huan Wu, Suhang Wei, Xiangpeng Liu, Dayiheng Xie, Jun Zhang, Min Su, Jinsong School of Informatics Xiamen University China Language Technology Lab Alibaba DAMO Academy China Institute of Artificial Intelligence Xiamen University China Institute of Computer Science and Technology Soochow University China

k-Nearest neighbor machine translation (kNNMT) has attracted increasing attention due to its ability to non-parametrically adapt to new translation domains. By using an upstream NMT model to traverse the downstream training corpus, it is equipped with a datastore containing vectorized key-value pairs, which are retrieved during inference to benefit translation. However, there often exists a significant gap between upstream and downstream domains, which hurts the retrieval accuracy and the final translation quality. To deal with this issue, we propose a novel approach to boost the datastore retrieval of kNN-MT by reconstructing the original datastore. Concretely, we design a reviser to revise the key representations, making them better fit for the downstream domain. The reviser is trained using the collected semantically-related key-queries pairs, and optimized by two proposed losses: one is the key-queries semantic distance ensuring each revised key representation is semantically related to its corresponding queries, and the other is an L2-norm loss encouraging revised key representations to effectively retain the knowledge learned by the upstream NMT model. Extensive experiments on domain adaptation tasks demonstrate that our method can effectively boost the datastore retrieval and translation quality of kNN-MT. © 2023, CC BY.

关键词： Nearest neighbor search

Spatio-temporal Network Analysis for Detecting Traffic Accident Clusters 14

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Spatio-temporal Network Analysis for Detecting Traffic Accid...

14th IIAI International Congress on Advanced Applied informatics, IIAI-AAI 2023

作者： Ando, Keisuke Kuniyoshi, Yusuke Shimizu, Kazuhiko Uchitane, Takeshi Mukai, Naoto Iwata, Kazunori Ito, Nobuhiro Jiang, Yong Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Faculty of Information Science Aichi Institute of Technology Toyota Japan Sugiyama Jogakuen University Faculty of Culture-Information Studies Nagoya Japan Aichi University Faculty of Business Administration Nagoya Japan Aichi University Faculty of Regional Policy Toyohashi Japan

ISBN: (纸本)9798350324228

Various methods have been proposed for analyzing traffic accident hotspots. One of these methods is to detect traffic accident hotspots on the road network using a hypothesis testing method. However, this method does not consider the time of occurrence of a traffic accident. In other words, this method provides information on high-risk locations for traffic accidents but not on the corresponding time. This paper proposes a new method for detecting traffic accident hotspots considering not only location but also time. We therefore extended the previous hypothesis testing method to consider the time of occurrence of a traffic accident. First, we check for changes in spatial properties over time in the target area using local indicators of spatial autocorrelation (LISA) cluster maps. Next, we estimate the probability density function of traffic accidents in the spatio-temporal network using the spatio-temporal network kernel density estimation (STNKDE) method. Finally, we detect clusters where the probability density of traffic accidents is significantly higher through hypothesis testing based on the estimation results. We detect clusters under four conditions while testing different hypotheses and parameters. The results show that the proposed method can detect traffic accident clusters. Therefore, when considering traffic safety measures, the method can be adopted to detect the location and time that require attention and allocate the necessary resources. © 2023 IEEE.

关键词： Roads and streets