检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Scherbela, Michael Gerard, Leon Grohs, Philipp Faculty of Mathematics University of Vienna Austria Research Network Data Science University of Vienna Austria Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Austria

Deep neural networks have become a highly accurate and powerful wavefunction ansatz in combination with variational Monte Carlo methods for solving the electronic Schrödinger equation. However, despite their success and favorable scaling, these methods are still computationally too costly for wide adoption. A significant obstacle is the requirement to optimize the wavefunction from scratch for each new system, thus requiring long optimization. In this work, we propose a novel neural network ansatz, which effectively maps uncorrelated, computationally cheap Hartree-Fock orbitals, to correlated, high-accuracy neural network orbitals. This ansatz is inherently capable of learning a single wavefunction across multiple compounds and geometries, as we demonstrate by successfully transferring a wavefunction model pre-trained on smaller fragments to larger compounds. Furthermore, we provide ample experimental evidence to support the idea that extensive pre-training of a such a generalized wavefunction model across different compounds and geometries could lead to a foundation wavefunction model. Such a model could yield high-accuracy ab-initio energies using only minimal computational effort for fine-tuning and evaluation of observables. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on Parkinson's Disease Detection Through Machine Learning Techniques

A Comprehensive Study on Parkinson's Disease Detection Throu...

引用

Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Rushikesh Burle Sanskruti Gaurkhede Bhushan Fulkar Swapnil Gundewar Utkarsha Pacharaney Prateek Verma Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

One of the biggest issues with worldwide public health is Parkinson's disease (PD). It is usually known that about are around one million Americans who have Parkinson's disease, whereas there are approximately five million cases of the condition globally. As a result, it's critical to identify Parkinson's disease early on and make the appropriate treatment plans. These days, machine learning is essential for analysing problems in real time and offering answers thanks to its widely used algorithms. These days, machine learning algorithms are used in the healthcare industry to identify patients' health problems. This research went into great detail about the methods used using algorithms that use machine learning to classify Parkinson's disease. A fault in the brain cells that generate dopamine, a necessary chemical for synapses to connect with one another, is the cause of Parkinson's disease. The brain's dopamine-producing neurons are in charge of control, flexibility, and event familiarity. When 60–80% of these cells disappear, inadequate dopamine is produced, which causes Parkinson's disease symptoms to manifest. In this case, the Random Forest, Decision Tree, K-Nearest Neighbours, and Support Vector Machine techniques were used to identify the illness. Compared to Decision Tree and Support Vector Machine, Random Forest and K-Nearest Neighbours offer the best performance.

关键词： Support vector machines Machine learning algorithms Parkinson's disease Vectors Real-time systems Classification algorithms Decision trees

来源：评论

学校读者我要写书评

暂无评论

The necessity of depth for artificial neural networks to approximate certain classes of smooth and bounded functions without the curse of dimensionality

arXiv

引用

arXiv 2023年

作者： Gonon, Lukas Graeber, Robin Jentzen, Arnulf Faculty of Natural Sciences Department of Mathematics Imperial College London United Kingdom Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

In this article we study high-dimensional approximation capacities of shallow and deep artificial neural networks (ANNs) with the rectified linear unit (ReLU) activation. In particular, it is a key contribution of this work to reveal that for all a, b ∈ with b−a ≥ 7 we have that the functions (Formula Presented) as well as the functions (formula Presented) can neither be approximated without the curse of dimensionality by means of shallow ANNs nor insufficiently deep ANNs with ReLU activation but can be approximated without the curse of dimensionality by sufficiently deep ANNs with ReLU activation. More specifically, we prove that in the case of shallow ReLU ANNs or deep ReLU ANNs with a fixed number of hidden layers and with the size of scalar real parameters of the approximating ANNs growing at most polynomially in the dimension d ∈ N we have that the number of ANN parameters must grow at least exponentially in the dimension d ∈ N while in the case of deep ReLU ANNs with the number of hidden layers growing in the dimension d ∈ N we have that the number of scalar real parameters of the approximating ANNs grows at most polynomially in both the inverse of the prescribed approximation accuracy Ε > 0 and the dimension d ∈ , even if the absolute values of the ANN parameters are assumed to be uniformly bounded by one. We thus show that the product functions and the sine of the product functions are polynomially tractable approximation problems among the approximating class of deep ReLU ANNs with the number of hidden layers being allowed to grow in the dimension d ∈ . We establish the above outlined statement not only for the product functions and the sine of the product functions but also for other classes of target functions, in particular, for classes of uniformly globally bounded C∞-functions with compact support on any [a, b]d with a ∈ , b ∈ (a, ∞). Roughly speaking, in this work we lay open that simple approximation problems such as approximating the sine or cosine

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Efficient Learning Using Spiking Neural networks Equipped with Affine Encoders and Decoders

SSRN

引用

SSRN 2024年

作者： Neuman, Anh Martina Petersen, Philipp Christian University of Vienna Faculty of Mathematics Kolingasse 14-16 Wien1090 Austria University of Vienna Faculty of Mathematics and Research Network Data Science @ Uni Vienna Kolingasse 14-16 Wien1090 Austria Michigan State University United States

We study the learning problem associated with spiking neural networks. Specifically, we consider hypothesis sets of spiking neural networks with affine temporal encoders and decoders and simple spiking neurons having only positive synaptic weights. We demonstrate that the positivity of the weights continues to enable a wide range of expressivity results, including rate-optimal approximation of smooth functions or approximation without the curse of dimensionality. Moreover, positive-weight spiking neural networks are shown to depend continuously on their parameters which facilitates classical covering number-based generalization statements. Finally, we observe that from a generalization perspective, contrary to feedforward neural networks or previous results for general spiking neural networks, the depth has little to no adverse effect on the generalization capabilities. © 2024, The Authors. All rights reserved.

关键词： Feedforward neural networks

来源：评论

学校读者我要写书评

暂无评论

Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

引用

IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

引用

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

A Multimodal Medical Image Fusion Method Incorporating an Adaptive Attention Mechanism

A Multimodal Medical Image Fusion Method Incorporating an Ad...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Yingxian Zhang Yushui Geng Hu Liang Hai Zhong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China Department of Radiology The Second Hospital of Shandong University Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Multimodal medical image fusion technology provides more comprehensive and accurate image support for clinical diagnosis and treatment by integrating complementary information from different imaging modalities. Aiming at the problem that existing methods are still insufficient in detail feature extraction and inter-modal information fusion, this paper proposes a multimodal medical image fusion method combined with an adaptive attention mechanism. First, we design the Grouped Receptive Field Attentional Convolution (GRFAConv) to solve the problem of insufficient detail feature extraction capability. With the multi-head receptive field adaptive weighting strategy of grouped convolution, the range and weight of the receptive field of the convolution kernel can be adaptively adjusted according to the different demands of local and global features of the image to improve the effect of detail retention. Second, for the problem of information fusion between different modalities, we introduce an improved CBAM attention module in the feature fusion process, which adaptively selects and enhances the features in the key regions through the channel attention and spatial attention mechanisms, which greatly improves the clarity of the fused image details and the accuracy of the information expression in the key regions. Furthermore, experimental results on several medical image datasets show that the algorithm proposed in this paper can generate relatively high-quality fused images. It not only enriches the detailed features of the image, but also achieves significant advantages in several evaluation metrics.

关键词： Measurement Attention mechanisms Accuracy Convolution Federated learning Feature extraction Clinical diagnosis Kernel Medical diagnostic imaging Image fusion

来源：评论

学校读者我要写书评

暂无评论

Seat Occupancy Detection in Electric Vehicles: Unveiling Passenger Presence through Overlap Area Analysis

Seat Occupancy Detection in Electric Vehicles: Unveiling Pas...

引用

International Electronics Symposium (IES)

作者： Alivia Haris Alfan Rizaldy Pratama Bima Sena Bayu Dewantara Dadet Pramadihanto Center for Research and Innovation on Advanced Transportation Electrification Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Data Science Department Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

The high number of bus passengers often leads to situations where some passengers are unable to secure a seat, resulting in the inconvenience of standing for the whole ride or wasting time waiting at the bus stop. To address this issue, this paper aims to develop a system capable of detecting bus seat occupancy, differentiating between occupied and unoccupied seats by passengers to inform potential passengers if seats are available before they take the next bus and help the government monitor its citizen and improve its public transportation services. The system uses a YOLO algorithm to perform object detection, enabling it to detect passengers, and an overlap percentage formula between passenger and seat bounding box to determine if the detected passenger is occupying the seat. The system provides real-time video output with texts indicating the total number of current passengers, total boarded passengers, also occupied and unoccupied seats. The system achieves accuracy of 95% for the front seat section and 92% for the back seat section.

关键词： YOLO Accuracy Government Streaming media Electric vehicles Real-time systems Distance measurement

来源：评论

学校读者我要写书评

暂无评论

THE SAMPLING COMPLEXITY OF LEARNING INVERTIBLE RESIDUAL NEURAL networkS

arXiv

引用

arXiv 2024年

作者： Li, Yuanyuan Grohs, Philipp Petersen, Philipp Fudan University School of Mathematical Sciences 220 Handan Road Shanghai200433 China University of Vienna Faculty of Mathematics and Research Network Data Science @ Uni Vienna Kolingasse 14-16 Wien1090 Austria

In recent work it has been shown that determining a feedforward ReLU neural network to within high uniform accuracy from point samples suffers from the curse of dimensionality in terms of the number of samples needed. As a consequence, feedforward ReLU neural networks are of limited use for applications where guaranteed high uniform accuracy is required. We consider the question of whether the sampling complexity can be improved by restricting the specific neural network architecture. To this end, we investigate invertible residual neural networks which are foundational architectures in deep learning and are widely employed in models that power modern generative methods. Our main result shows that the residual neural network architecture and invertibility do not help overcome the complexity barriers encountered with simpler feedforward architectures. Specifically, we demonstrate that the computational complexity of approximating invertible residual neural networks from point samples in the uniform norm suffers from the curse of dimensionality. Similar results are established for invertible convolutional Residual neural networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedforward neural networks

来源：评论

学校读者我要写书评

暂无评论

Resilient Routing Table Computation Based on Connectivity Preserving Graph Sequences 42

Resilient Routing Table Computation Based on Connectivity Pr...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tapolcai, Janos Babarczi, Peter Ho, Pin-Han Ronyai, Lajos Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary University of Waterloo Deptartment of Electrical and Computer Engineering Canada Institute for Computer Science and Control Eötvös Loránd Research Networkand Department of Algebra Hungary Institute of Mathematics Budapest University of Technology and Economics BudapestH-1111 Hungary

ISBN: (纸本)9798350334142

Fast reroute (FRR) mechanisms that can instantly handle network failures in the data plane are gaining attention in packet-switched networks. In FRR no notification messages are required as the nodes adjacent to the failure are prepared with a routing table such that the packets are re-routed only based on local information. However, designing the routing algorithm for FRR is challenging because the number of possible sets of failed network links and nodes can be extremely high, while the algorithm should keep track of which nodes are aware of the failure. In this paper, we propose a generic algorithmic framework that combines the benefits of Integer Linear Programming (ILP) and an effective approach from graph theory related to constructive graph characterization of k-connected graphs, i.e., edge splitting-off. We illustrate these benefits through arborescence design for FRR and show that (i) due to the ILP we have great flexibility in defining the routing problem, while (ii) the problem can still be solved very fast. We demonstrate through simulations that our framework outperforms state-of-the-art FRR mechanisms and provides better resilience with shorter paths in the arborescences. © 2023 IEEE.

关键词： Graph theory

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：