IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2024-11-15 DOI: 10.1109/tie.2024.3488320

Xiaosheng Wang, C. Q. Jiang, Jiayu Zhou, Weisheng Guo, Yuanshuang Fan, Liping Mo

引用次数: 0

Theoretical understanding of gradients of spike functions as boolean functions 对作为布尔函数的尖峰函数梯度的理论理解

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2024-11-15 DOI: 10.1007/s40747-024-01607-9

DongHyung Yoo, Doo Seok Jeong

{"title":"Theoretical understanding of gradients of spike functions as boolean functions","authors":"DongHyung Yoo, Doo Seok Jeong","doi":"10.1007/s40747-024-01607-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01607-9","url":null,"abstract":"<p>Applying an error-backpropagation algorithm to spiking neural networks frequently needs to employ fictive derivatives of spike functions (popularly referred to as surrogate gradients) because the spike function is considered non-differentiable. The non-differentiability comes into play given that the spike function is viewed as a numeric function, most popularly, the Heaviside step function of membrane potential. To get back to basics, the spike function is not a numeric but a Boolean function that outputs <i>True</i> or <i>False</i> upon the comparison of the current potential and threshold. In this regard, we propose a method to evaluate the gradient of spike function viewed as a Boolean function for fixed- and floating-point data formats. For both formats, the gradient is considerably similar to a delta function that peaks at the threshold for spiking, which justifies the approximation of the spike function to the Heaviside step function. Unfortunately, the error-backpropagation algorithm with this gradient function fails to outperform popularly employed surrogate gradients, which may arise from the narrow peak of the gradient function and consequent potential undershoot and overshoot around the spiking threshold with coarse timesteps. We provide theoretical grounds of this hypothesis.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Sparsity Knowledge Transfer-Based Evolutionary Algorithm for Large-Scale Multitasking Multi-Objective Optimization 用于大规模多任务多目标优化的基于稀疏性知识转移的进化算法

IF 14.3 1区计算机科学

IEEE Transactions on Evolutionary Computation Pub Date : 2024-11-15 DOI: 10.1109/tevc.2024.3498952

Chengming Wu, Ye Tian, Limiao Zhang, Xiaoshu Xiang, Xingyi Zhang

引用次数: 0

On the Capacity Region of Optical Mobile Communication Systems With Spatial Light Modulation 论采用空间光调制的光移动通信系统的容量区域

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2024-11-15 DOI: 10.1109/tvt.2024.3499321

Shuo Shao, Yuxuan Shi, Jian Dang, Zaichen Zhang

引用次数: 0

Information Age-Based Task Splitting Scheme for Edge Computing-Enabled Networks 基于信息时代的边缘计算网络任务分工方案

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2024-11-15 DOI: 10.1109/lwc.2024.3492095

Jinwoong Kim, Jemin Lee

引用次数: 0

A Novel Multimode AC-DC LLC Resonant Converter for Wide Output Voltage Range Applications 适用于宽输出电压范围应用的新型多模交直流 LLC 谐振转换器

IF 7.7 1区工程技术

IEEE Transactions on Industrial Electronics Pub Date : 2024-11-15 DOI: 10.1109/tie.2024.3493165

Haolei Meng, Hanjing Dong, Xiaogao Xie

引用次数: 0

Feature Matching via Graph Clustering with Local Affine Consensus 通过图聚类与局部仿射共识进行特征匹配

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2024-11-15 DOI: 10.1007/s11263-024-02291-5

Yifan Lu, Jiayi Ma

{"title":"Feature Matching via Graph Clustering with Local Affine Consensus","authors":"Yifan Lu, Jiayi Ma","doi":"10.1007/s11263-024-02291-5","DOIUrl":"https://doi.org/10.1007/s11263-024-02291-5","url":null,"abstract":"<p>This paper studies graph clustering with application to feature matching and proposes an effective method, termed as GC-LAC, that can establish reliable feature correspondences and simultaneously discover all potential visual patterns. In particular, we regard each putative match as a node and encode the geometric relationships into edges where a visual pattern sharing similar motion behaviors corresponds to a strongly connected subgraph. In this setting, it is natural to formulate the feature matching task as a graph clustering problem. To construct a geometric meaningful graph, based on the best practices, we adopt a local affine strategy. By investigating the motion coherence prior, we further propose an efficient and deterministic geometric solver (MCDG) to extract the local geometric information that helps construct the graph. The graph is sparse and general for various image transformations. Subsequently, a novel robust graph clustering algorithm (D2SCAN) is introduced, which defines the notion of density-reachable on the graph by replicator dynamics optimization. Extensive experiments focusing on both the local and the whole of our GC-LAC with various practical vision tasks including relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multimodel fitting, demonstrate that our GC-LAC is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code for this work is publicly available at: https://github.com/YifanLu2000/GCLAC.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"75 1","pages":""},"PeriodicalIF":19.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient generation of protein pockets with PocketGen 利用 PocketGen 高效生成蛋白质口袋

IF 23.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2024-11-15 DOI: 10.1038/s42256-024-00920-9

Zaixi Zhang, Wan Xiang Shen, Qi Liu, Marinka Zitnik

{"title":"Efficient generation of protein pockets with PocketGen","authors":"Zaixi Zhang, Wan Xiang Shen, Qi Liu, Marinka Zitnik","doi":"10.1038/s42256-024-00920-9","DOIUrl":"https://doi.org/10.1038/s42256-024-00920-9","url":null,"abstract":"<p>Designing protein-binding proteins is critical for drug discovery. However, artificial-intelligence-based design of such proteins is challenging due to the complexity of protein–ligand interactions, the flexibility of ligand molecules and amino acid side chains, and sequence–structure dependencies. We introduce PocketGen, a deep generative model that produces residue sequence and atomic structure of the protein regions in which ligand interactions occur. PocketGen promotes consistency between protein sequence and structure by using a graph transformer for structural encoding and a sequence refinement module based on a protein language model. The graph transformer captures interactions at multiple scales, including atom, residue and ligand levels. For sequence refinement, PocketGen integrates a structural adapter into the protein language model, ensuring that structure-based predictions align with sequence-based predictions. PocketGen can generate high-fidelity protein pockets with enhanced binding affinity and structural validity. It operates ten times faster than physics-based methods and achieves a 97% success rate, defined as the percentage of generated pockets with higher binding affinity than reference pockets. Additionally, it attains an amino acid recovery rate exceeding 63%.</p>","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"21 1","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the performance of graphene and LCP 1x2 rectangular microstrip antenna arrays for terahertz applications using photonic band gap structures 利用光子带隙结构提高太赫兹应用中石墨烯和 LCP 1x2 矩形微带天线阵列的性能

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2024-11-15 DOI: 10.1016/j.compeleceng.2024.109858

Hamed Abderrahime Bouzid , Mohammed Belkheir , Allel Mokaddem , Mehdi Rouissat , Djamila Ziani

{"title":"Enhancing the performance of graphene and LCP 1x2 rectangular microstrip antenna arrays for terahertz applications using photonic band gap structures","authors":"Hamed Abderrahime Bouzid , Mohammed Belkheir , Allel Mokaddem , Mehdi Rouissat , Djamila Ziani","doi":"10.1016/j.compeleceng.2024.109858","DOIUrl":"10.1016/j.compeleceng.2024.109858","url":null,"abstract":"<div><div>The terahertz (THz) frequency band (0.1–10 THz) has drawn a lot of attention due to the growing demand for greater resolutions, lower latency, faster data rates, and wider bandwidth in 6 G technologies. This range provides data speeds exceeding tens of gigabits per second, large bandwidth, great spectral resolution, and non-ionizing characteristics. THz signals have potential; however they are affected by attenuation, route losses, and atmospheric conditions, necessitating the use of specialised antenna designs. This work presents a 300 GHz rectangular microstrip patch antenna with Graphene as the patch material and Liquid Crystal Polymer (LCP) as the substrate. Photonic band gap (PBG) substrates are used to incorporate cuboid and cylindrical air gaps in square and triangular lattices, hence improving performance. The highest performance is found with cylindrical air gaps in a triangular lattice PBG substrate, which has a bandwidth of 29.56 GHz, a return loss of –48.12 dB, a gain of 10.4 dBi, a directivity of 10.8 dBi, and a radiation efficiency of 91 %. These results establish the proposed antennas as highly effective for broadband and high-speed THz applications, particularly in 6 G systems like advanced sensing applications, ultra-fast device-to-device (D2D) communications, potential beam steering applications, and non-invasive imaging solutions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"120 ","pages":"Article 109858"},"PeriodicalIF":4.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142655442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-tuning language model embeddings to reveal domain knowledge: An explainable artificial intelligence perspective on medical decision making 微调语言模型嵌入以揭示领域知识：从可解释的人工智能角度看医疗决策

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2024-11-15 DOI: 10.1016/j.engappai.2024.109561

Ceca Kraišniković , Robert Harb , Markus Plass , Wael Al Zoughbi , Andreas Holzinger , Heimo Müller

{"title":"Fine-tuning language model embeddings to reveal domain knowledge: An explainable artificial intelligence perspective on medical decision making","authors":"Ceca Kraišniković , Robert Harb , Markus Plass , Wael Al Zoughbi , Andreas Holzinger , Heimo Müller","doi":"10.1016/j.engappai.2024.109561","DOIUrl":"10.1016/j.engappai.2024.109561","url":null,"abstract":"<div><div>Integrating large language models (LLMs) to retrieve targeted medical knowledge from electronic health records enables significant advancements in medical research. However, recognizing the challenges associated with using LLMs in healthcare is essential for successful implementation. One challenge is that medical records combine unstructured textual information with highly sensitive personal data. This, in turn, highlights the need for explainable Artificial Intelligence (XAI) methods to understand better how LLMs function in the medical domain. In this study, we propose a novel XAI tool to accelerate data-driven cancer research. We apply the Bidirectional Encoder Representations from Transformers (BERT) model to German language pathology reports examining the effects of domain-specific language adaptation and fine-tuning. We demonstrate our model on a real-world pathology dataset, analyzing the contextual representations of diagnostic reports. By illustrating decisions made by fine-tuned models, we provide decision values that can be applied in medical research. To address interpretability, we conduct a performance evaluation of the classifications generated by our fine-tuned model, as assessed by an expert pathologist. In domains such as medicine, inspection of the medical knowledge map in conjunction with expert evaluation reveals valuable information about how contextual representations of key disease features are categorized. This ultimately benefits data structuring and labeling and paves the way for even more advanced approaches to XAI, combining text with other input modalities, such as images which are then applicable to various engineering problems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109561"},"PeriodicalIF":7.5,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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