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10th International Conference on Learning Representations, ICLR 2022

作者： Chen, Yixuan Shi, Yubin Li, Dongsheng Wang, Yujiang Dong, Mingzhi Zhao, Yingying Dick, Robert Lv, Qin Yang, Fan Shang, Li China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Microsoft Research Asia Shanghai China Department of Computing Imperial College London London United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Michigan United States Department of Computer Science University of Colorado Boulder Boulder United States School of Microelectronics Fudan University Shanghai China

Disentangled feature representation is essential for data-efficient learning. The feature space of deep models is inherently compositional. Existing β-VAE-based methods, which only apply disentanglement regularization to the resulting embedding space of deep models, cannot effectively regularize such compositional feature space, resulting in unsatisfactory disentangled results. In this paper, we formulate the compositional disentanglement learning problem from an information-theoretic perspective and propose a recursive disentanglement network (RecurD) that propagates regulatory inductive bias recursively across the compositional feature space during disentangled representation learning. Experimental studies demonstrate that RecurD outperforms β-VAE and several of its state-of-the-art variants on disentangled representation learning and enables more data-efficient downstream machine learning tasks. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Information theory

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Multi-Agent Coordination of Autonomous Vehicles for Smart Logistics in Industrial Environments

Multi-Agent Coordination of Autonomous Vehicles for Smart Lo...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Kirubhakaran D. Karthikeswaran M. Arun R. Arun Chakravarthy J. Akshya M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Dynamic effects in industrial logistics demand more creative solutions towards the pursuit of efficiency and agility in evolving environments. Though static path-planning and taskallocation algorithms have provided the basis for how autonomous vehicles are guided within structured environments, those approaches usually fail to contend with indeterminacy outside of their modelled world, where there is dynamic obstacle appearance and changing task requirements, thus raising inefficiencies and costs of operations. To address these issues, the paper introduces a new multi-agent coordination system that uses a hybrid A* algorithm, integrating path planning with sophisticated detection methods for dynamic obstacles and improving superior task allocation. The system is supposed to provide improvements in real-time coordination in industries of autonomous vehicles to achieve efficient management of tasks and paths in changing environments. We integrate the latest in machine learning techniques for improved and real-time updates within the changes of the vehicle routing process based on both immediate sensory input and environmental interactions. Simulations show that our system performs the task much faster than classic systems: up to 30% improvement in completing tasks compared with classic models, and an average improvement of 25% in allround energy efficiency. It intelligently integrates machine learning techniques with real-time data processing. With the ability to scale up or down, or to adapt itself, this system addresses the chief challenges existing methods face and could revolutionize logistics operations within any industry.

关键词： Industries Adaptation models Heuristic algorithms Real-time systems Path planning Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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From transparent to opaque: rethinking neural implicit surfaces with α-NeuS 24

From transparent to opaque: rethinking neural implicit surfa...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoran Zhang Junkai Deng Xuhui Chen Fei Hou Wencheng Wang Hong Qin Chen Qian Ying He Key Laboratory of System Software (CAS) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences and University of Chinese Academy of Sciences College of Computing and Data Science Nanyang Technological University Department of Computer Science Stony Brook University SenseTime Research

ISBN: (纸本)9798331314385

Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque objects simultaneously. This paper introduces a-NeuS—an extension of NeuS—that proves NeuS is unbiased for materials from fully transparent to fully opaque. We find that transparent and opaque surfaces align with the non-negative local minima and the zero iso-surface, respectively, in the learned distance field of NeuS. Traditional iso-surfacing extraction algorithms, such as marching cubes, which rely on fixed iso-values, are ill-suited for such data. We develop a method to extract the transparent and opaque surface simultaneously based on DCUDF. To validate our approach, we construct a benchmark that includes both real-world and synthetic scenes, demonstrating its practical utility and effectiveness. Our data and code are publicly available at https://***/728388808/alpha-NeuS.

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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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Investigation of Traffic Radar Coverage Efficiency under Different Placement Strategies

Investigation of Traffic Radar Coverage Efficiency under Dif...

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International Conference on Internet of Things 2024, ICIoT 2024

作者： Yao, Shun Zhou, Jin Chieng, David Kwong, Chiew Foong Lee, Boon Giin Li, Jianqiang Chen, Yusen Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computer Science University of Nottingham Ningbo China Ningbo China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Zhejiang Communication Investment Group Co. Ltd. Zhejiang China

This paper presents a comprehensive investigation of traffic radar coverage efficiency under different placement strategies in collaboration with Zhejiang Communications Investment Group Company Limited (CICO). The overall goal is to maximise coverage while minimising blind spots and the number of radars, given the constraints of the road. The NSGA-II multi-objective optimisation algorithm is found to be capable of generating a set of optimal solutions for the proposed radar placement configurations. When aiming for 100% coverage, the results indicate that a multi-antenna radar configuration requires the lowest radar density. Overall, this study provides valuable insights to transportation infrastructure owners in determining the best radar placement strategies for road traffic monitoring. © The Institution of Engineering & Technology 2024.

关键词： Multiobjective optimization

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images

Generative Adversarial Networks for Spatio-Spectral Compress...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Martin Hermann Paul Fuchs Akshara Preethy Byju Alisa Walda Behnood Rasti Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCs E ); and ii) HiFiC with 3D convolutions (denoted as HiFiC 3D ) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCs E and HiFiC 3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC.

关键词： Convolutional codes Solid modeling Image coding Three-dimensional displays Convolution Redundancy Bit rate Transform coding Image reconstruction Hyperspectral imaging

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Income Tax Fraud Deduction Using Machine Learning

Income Tax Fraud Deduction Using Machine Learning

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Pradeep Sudhakaran Prudhvi Kolamudi Preetha M K Pragash Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Career Development Centre SRM Institute of Science and Technology Potheri Kattankulathur Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering College Madagadipet Puducherry India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Specifically, the program is based on boosting methods in the context of the used machine learning, to reveal potential cases of evasion of income tax. The procedure consists of obtaining and using datasets of income tax for the learning and training purpose. The first and crucial goal is to enhance the accuracy of predicting fraud cases through learning boosting algorithms and graphical representation of the most significant factors leading to fraud detection. Depending on accuracy and sensitivity as well as specificity and precision for that matter, the said assessment will be conducted. The first goal entails a mechanization of the process of identifying foraging activities, eliminating possible errors, and improving efficiency of and security in income tax assessments with particular emphasis in distinguishing between probable real and potentially fraudulent tax returns [10]. The boosting techniques alongside other futuristic ways of identifying the suspicious transactions can help the tax authorities to improve the risk management processes of their organization. In other words, this approach of fraud detection makes it possible to proceed to more efficient work in terms of both accuracy and the reliability of certain results. Also, the <|reserved_special_token_265|> channels of tax avoidance can be closed by concentrating efforts on cases of high risk as the authorities are more likely to lose a lot of revenues. Machine learning technologies can easily address these requirements, giving the necessary flexibility and precision to prevent and identify fraudulent activities and strategies. These strategies are critical in protecting the Tax system a longer term game which can effectively counter financial fraudsters. The goal of the project are to gather information on the corporate taxation; to apply boosting algorithms for fraudulent activity detection, and to evaluate the efficiency of boosting algorithms. The main focus is primarily aimed at the increased app

关键词： Training Accuracy Machine learning algorithms Sensitivity Finance Boosting Prediction algorithms Fraud Stakeholders Security

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent Systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy data analysis Computational modeling Predictive models Feature extraction data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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Prior-guided longitudinal metabolomic analysis

Prior-guided longitudinal metabolomic analysis

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kowshika Sarker Ruoqing Zhu Hannah Diane Holscher ChengXiang Zhai Siebel School of Computing and Data Science University of Illinois at Urbana-Champaign Champaign USA Department of Statistics University of Illinois at Urbana-Champaign Champaign USA Food Science and Human Nutrition University of Illinois at Urbana-Champaign Urbana USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Longitudinal metabolomics frequently benefits biomarker discovery and other applications. Existing studies ignore prior knowledge of holistic metabolic systems, which can yield interpretable features and causal insights. We integrate longitudinal metabolome with prior knowledge from a genome-scale metabolic model and propose a graph recurrent neural network-based novel representation learning framework to infer interpretable heuristic features for metabolic reactions and metabolic subsystems. In downstream clustering and classification, our proposed features often outperform and sometimes match metabolome-based performances. Our novel features can also reveal new hypotheses on causal mechanisms, such as potential reactions and subsystems involved in underlying biomechanism. Novel causal understanding and improved downstream performances affirm the impact of domain knowledge in time-series metabolome analyses.

关键词： Representation learning Knowledge engineering Biomechanics Metabolomics Recurrent neural networks Biological system modeling Genomics Bioinformatics

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Error Interference in Quantum Simulation

arXiv

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arXiv 2024年

作者： Chen, Boyang Xu, Jue Zhao, Qi Yuan, Xiao Department of Computer Science and Technology Tsinghua University Beijing100084 China QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Understanding algorithmic error accumulation in quantum simulation is crucial due to its fundamental significance and practical applications in simulating quantum many-body system dynamics. Conventional theories typically apply the triangle inequality to provide an upper bound for the error. However, these often yield overly conservative and inaccurate estimates as they neglect error interference — a phenomenon where errors in different segments can destructively interfere. Here, we introduce a novel method that directly estimates the long-time algorithmic errors with multiple segments, thereby establishing a comprehensive framework for characterizing algorithmic error interference. We identify the sufficient and necessary condition for strict error interference and introduce the concept of approximate error interference, which is more broadly applicable to scenarios such as power-law interaction models, the Fermi-Hubbard model, and higher-order Trotter formulas. Our work demonstrates significant improvements over prior ones and opens new avenues for error analysis in quantum simulation, offering potential advancements in both theoretical algorithm design and experimental implementation of Hamiltonian simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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