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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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Deep learning-based pixel interpolation in spatial-domain phase shifting digital holography

Deep learning-based pixel interpolation in spatial-domain ph...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Kajitani, Shu Takabayashi, Masanori Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2-4 Hibikino Wakamatsu-ku Kitakyushu Fukuoka808-0196 Japan

We propose to apply deep learning to interpolate missing pixels in interferograms acquired by the spatial-domain phase-shifting interferometry (PSI), and numerically evaluate the quality of object light reconstructed ...

ISBN: (纸本)9798350350012

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Software Susceptibility Detection Using Deep Learning Techniques

Software Susceptibility Detection Using Deep Learning Techni...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： K. Sudheer Kumar Dillip Narayan Sahu Tamirat Tagesse Takore Navdeep Singh CSL Vijaya Durga Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of MCA Gangadhar Meher University Odisha India Department of Electrical and Computer Engineering Wachemo University Ethiopia Lovely Professional University Phagwara Institute of Aeronautical Engineering Hyderabad Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

Software susceptibilities are flaws in the source code that could be used to do havoc or collect information. A variety of automated machine learning methods have been suggested in prior research to find them. The majority of these methods operate at the release level, which means they try to foresee which files might be vulnerable in a future version. Manually detecting insecure code is both challenging and costly in both time and money. Therefore, to reduce costs, developers must use automatic vulnerability prediction (AVP) techniques. Recent AVP research has started to employ deep learning (DL) methods. All of the suggested methods are built on feature extraction techniques that were influenced by earlier DL uses such as autonomous language processing. When developing AVP models based on traditional machine learning, code metrics were frequently employed as features. The following piece provides a method centered on deep neural networks over leveraging code metrics to detect vulnerabilities in code, bridging the difference between machine learning parameters and deep learning. Results obtained indicate that while code metrics are excellent, they weren't the best to use as DL-based AVP characteristics.

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Chatbot and its Impact on the Retail Industry

Chatbot and its Impact on the Retail Industry

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Reshmi Tatikonda M. Kempanna Ramya Thatikonda A. Bhuvanesh Ragupathi Thota R. Keerthanadevi Department of Computer Information Systems University of the Central Missouri Warrensburg USA Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including ai-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences.

关键词： Industries Training Reviews Psychology Transforms Inventory management Chatbots Multilingual Resource management Sustainable development

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Matching Game for Optimized Association in Quantum Communication Networks

Matching Game for Optimized Association in Quantum Communica...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mahdi Chehimi Bernd Simon Walid Saad Anja Klein Don Towsley Mérouane Debbah Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Arlington VA USA Communication Engineering Lab Technische Universität Darmstadt Darmstadt Germany Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA USA Electrical Engineering and Computer Science Department Khalifa University of Science and Technology Abu Dhabi UAE

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

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QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 998-1011页

作者： Fatima Zohra Smaili Shuye Tian Ambrish Roy Meshari Alazmi Stefan T.Arold Srayanta Mukherjee P.Scott Hefty Wei Chen Xin Gao Computational Bioscience Research Center(CBRC) ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Biology Southern University of Science and Technology of China(SUSTC)Shenzhen 518055China Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48109USA College of Computer Science and Engineering University of Ha’ilHa'il 55476Saudi Arabia Biological and Environmental Sciences and Engineering(BESE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia Department of Molecular Bioscience University of KansasLawrenceKS 66047USA

The number of available protein sequences in public databases is increasing ***,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems ***,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)*** uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence *** three pieces of information are combined by consensus averaging to make the final *** approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark *** results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or *** further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

关键词： Protein function prediction GO term EC number Protein structure similarity Functionally discriminative motif

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Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games

arXiv

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arXiv 2022年

作者： Cen, Shicong Chi, Yuejie Du, Simon S. Xiao, Lin Department of Electrical and Computer Engineering Carnegie Mellon University United States Paul G. Allen School of Computer Science and Engineering University of Washington United States Meta AI Research United States

Multi-Agent Reinforcement Learning (MARL)-where multiple agents learn to interact in a shared dynamic environment-permeates across a wide range of critical applications. While there has been substantial progress on understanding the global convergence of policy optimization methods in single-agent RL, designing and analysis of efficient policy optimization algorithms in the MARL setting present significant challenges, which unfortunately, remain highly inadequately addressed by existing theory. In this paper, we focus on the most basic setting of competitive multi-agent RL, namely two-player zero-sum Markov games, and study equilibrium finding algorithms in both the infinite-horizon discounted setting and the finite-horizon episodic setting. We propose a single-loop policy optimization method with symmetric updates from both agents, where the policy is updated via the entropy-regularized optimistic multiplicative weights update (OMWU) method and the value is updated on a slower timescale. We show that, in the full-information tabular setting, the proposed method achieves a finite-time last-iterate linear convergence to the quantal response equilibrium of the regularized problem, which translates to a sublinear last-iterate convergence to the Nash equilibrium by controlling the amount of regularization. Our convergence results improve upon the best known iteration complexities, and lead to a better understanding of policy optimization in competitive Markov games. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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A Facile Approach for Fabricating Ordered Submicrometer-Wide Surface Patterns by Imprinting Polydimethylsiloxane Cracks

A Facile Approach for Fabricating Ordered Submicrometer-Wide...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Yang Bu Sheng Ni Levent Yobas Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR Division of Bioengineering The Hong Kong University of Science and Technology Hong Kong SAR

A novel and cost-effective method for creating ordered submicrometer-wide surface patterns using PDMS cracks is presented in this study. Cracks are first formed on a flat PDMS substrate by stretching an oxidized PDMS slab, and then the cracks are imprinted onto a target substrate to create uniform and ordered surface patterns. The resulting submicrometer-wide patterns are difficult to achieve using conventional fabrication methods limited by photolithography's resolution. Applications of the imprinted patterns, such as selective particle capture and DNA combing, are demonstrated. This approach offers a new way to utilize PDMS cracks and provides a simple and low-cost method for creating submicrometer-wide surface patterns with potential applications in various fields.

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Deep Learning Methods on Recognizing Autism Spectrum Disorder

Deep Learning Methods on Recognizing Autism Spectrum Disorde...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Vengidusamy S Kottaimalai R Pallikonda Rajasekaran M Arunprasath T Vishnuvarthanan Govindaraj Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Neurodevelopmental disorders (NDD) are conditions that impact brain function and interfere with neurological development, resulting in difficulties with social, cognitive, and emotional skills. The most common NDD include autism spectrum disorder (ASD). It is a complicated NDD marked by ongoing difficulties in social communication and interaction, as well as restricted or repetitive behaviors. The origins of ASD are intricate, encompassing a combination of environmental, genetic, and neurobiological factors. This research delves into the latest insights regarding ASD, stressing the critical role of early diagnosis and intervention. To improve long-term outcomes. The study also examines neuroimaging methods, genetic markers, and behavioural assessments that contribute to more precise diagnoses. Furthermore, the paper addresses the variability within ASD, highlighting the need for tailored treatment approaches and the vital role of individualized educational and therapeutic strategies. Despite significant progress, there remain gaps in our comprehension of the underlying mechanisms of ASD, and this research emphasizes the necessity for continued interdisciplinary collaboration. The findings aim to enhance diagnostic accuracy, inform treatment strategies, and improve the overall and improve the overall well-being of life for individuals with ASD and their families.

关键词： Neuroimaging Deep learning Autism Green communications Neural networks Collaboration Feature extraction Genetics Classification algorithms Facial features

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BRaiNTEASER: Lateral Thinking Puzzles for Large Language Models

arXiv

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arXiv 2023年

作者： Jiang, Yifan Ilievski, Filip Ma, Kaixin Sourati, Zhivar Information Sciences Institute Viterbi School of Engineering University of Southern California United States Department of Computer Science Faculty of Science Vrije Universiteit Amsterdam Netherlands Tencent AI Lab BellevueWA United States

The success of language models has inspired the NLP community to attend to tasks that require implicit and complex reasoning, relying on human-like commonsense mechanisms. While such vertical thinking tasks have been relatively popular, lateral thinking puzzles have received little attention. To bridge this gap, we devise BRaiNTEASER: a multiple-choice Question Answering task designed to test the model’s ability to exhibit lateral thinking and defy default commonsense associations. We design a three-step procedure for creating the first lateral thinking benchmark, consisting of data collection, distractor generation, and generation of reconstruction examples, leading to 1,100 puzzles with high-quality annotations. To assess the consistency of lateral reasoning by models, we enrich BRaiNTEASER based on a semantic and contextual reconstruction of its questions. Our experiments with state-of-the-art instruction- and commonsense language models reveal a significant gap between human and model performance, which is further widened when consistency across reconstruction formats is considered. We make all of our code and data available to stimulate work on developing and evaluating lateral thinking models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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