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computer science, Engineering and Applications (ICCSEA), 2020 International Conference on

作者： M. Kannan C. Priya Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India Department of Information Technology Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9781728158303

ISBN: (纸本)9781728158310

Viral disease can occur through direct contact with immunopathogenesis. The consecutive reappearance of the Nipah virus is one of the human-zoonotic viruses relevant to the Hendra virus (HeV) which spreads due to the impact of pteropus bats/flying foxes or unprotected pets and causes a serious attack and encephalitis. First, the NiV was reported in Malaysia in 1998. Subsequently, lung disease was recently discovered in the Kerala district. According to WHO statistics, the identification of zoonotic disease, one of the main challenges and cause of numerous outbreaks, coincide with the season of the sap harvest, that is, from winter to spring. The huge family of Henipavirus extends new species of deadly disease called Nipah by the novel Paramyxovirus pathogen. The goal of this work is to predict and diagnose virus by knowing the effectiveness of machine learning as soon as possible due to lethality. In current medical care, PCR or serology works effectively to diagnose the virulent, in which the body is rapidly transformed through the blood immunity cells, which it infects with asymptomatic symptoms. This document aims to summarize the machine learning technique, which is one of the main areas in the field of data analysis, although it plays a more important role in many real-time applications; the healthcare sector analyses the multiple machine learning algorithms for disease prediction and/or solution analysis. The minor inconvenience of the healthcare sector may take some time to clarify the test result. From a medical point of view, there are no syringes and there are no validated drugs available against the Nipah virus. So far, the result reaches a high mortality. Here, the summary of Nipah reports to stimulate researchers to observe the disease in the first phase with high precision and also tracks the drugs associated with Nipah. Machine learning algorithms are very essential in the medical society to isolate the virus of suspicious and emergency cases using ML

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Distributed Feedback Laser (DFB) for Signal Power Amplitude Level Improvement in Long Spectral Band

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Journal of Optical Communications 2023年第s1期44卷 S125-S130页

作者： Samanta, Debabrata Sivaram, M. Rashed, Ahmed Nabih Zaki Boopathi, C.S. Amiri, I.S. Yupapin, P. Computational Optics Research Group Advanced Institute of Materials Science Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam Bangalore Bangalore India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of EEE SRM Institute of Science and Technology Chennai India

This study outlines the distributed feedback laser for signal power amplitude level improvement in the long spectral band of 1550 nm wavelength within supporting pumped wavelength of 1480 nm. The bias and modulation peak currents based distributed feedback laser are varied in order to test the signal power level, peak signal amplitude variations after the fiber-optic channel and light detectors. The signal power level, peak signal amplitude is measured against spectral wavelength and time bit period variations. The study emphasis the signal power level, peak signal amplitude are enhanced for the best selection values of both a bias current at 45 mA and modulation peak current at 0.5 mA. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Distributed feedback lasers

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Disentangling new physics in K → πνν¯ and B → K(K∗)νν¯ observables

arXiv

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arXiv 2024年

作者： Buras, Andrzej J. Harz, Julia Mojahed, Martin A. TUM Institute for Advanced Study Lichtenbergstr. 2a GarchingD-85747 Germany Physik Department TUM School of Natural Sciences TU München James-Franck-Straße GarchingD-85748 Germany PRISMA+ Cluster of Excellence Mainz Institute for Theoretical Physics FB 08 - Physics Mathematics and Computer Science Johannes Gutenberg-Universität Mainz Mainz55099 Germany

We investigate the possibility of disentangling different new physics contributions to the rare meson decays K → π + E/ and B → K(K∗) + E/ through kinematic distributions in the missing energy E/. We employ dimension-6 operators within the Low-Energy Effective Field Theory (LEFT), identifying the invisible part of the final state as either active or sterile neutrinos. Special emphasis is given to lepton-number violating (LNV) operators with scalar and tensor currents. We show analytically that contributions from vector, scalar, and tensor quark currents can be uniquely determined from experimental data of kinematic distributions. In addition, we present new correlations of branching ratios for K and B-decays involving scalar and tensor currents. As there could a priori also be new invisible particles in the final states, we include dark-sector operators giving rise to two dark scalars, fermions, or vectors in the final state. In this context, we present new calculations of the inclusive decay rate B → Xs + E/ for dark operators. We show that careful measurements of kinematic distributions make it theoretically possible to disentangle the contribution from LEFT operators from most of the dark-sector operators, even when multiple operators are contributing. We revisit sum rules for vector currents in LEFT and show that the latter are also satisfied in some new dark-physics scenarios that could mimic LEFT. Finally, we point out that an excess in rare meson decays consistent with a LNV hypothesis would point towards highly flavor non-democratic physics in the UV, and could put high-scale leptogenesis under tension. Copyright © 2024, The Authors. All rights reserved.

关键词： Kinematics

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Matched filtering for gravitational wave detection without template bank driven by deep learning template prediction model bank

arXiv

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arXiv 2023年

作者： Ma, CunLiang Wang, Sen Wang, Wei Cao, Zhoujian School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China School of Computer Science Fudan University Shanghai201203 China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study UCAS Hangzhou310024 China

The existing matched filtering method for gravitational wave (GW) search relies on a template bank. The computational efficiency of this method scales with the size of the templates within the bank. Higher-order modes and eccentricity will play an important role when third-generation detectors operate in the future. In this case, traditional GW search methods will hit computational limits. To speed up the computational efficiency of GW search, we propose the utilization of a deep learning (DL) model bank as a substitute for the template bank. This model bank predicts the latent templates embedded in the strain data. Combining an envelope extraction network and an astrophysical origin discrimination network, we realize a novel GW search framework. The framework can predict the GW signal’s matched filtering signal-to-noise ratio (SNR). Unlike the end-to-end DL-based GW search method, our statistical SNR holds greater physical interpretability than the pscore metric. Moreover, the intermediate results generated by our approach, including the predicted template, offer valuable assistance in subsequent GW data processing tasks such as parameter estimation and source localization. Compared to the traditional matched filtering method, the proposed method can realize real-time analysis. The minor improvements in the future, the proposed method may expand to other scopes of GW search, such as GW emitted by the supernova explosion. Copyright © 2023, The Authors. All rights reserved.

关键词： Gravity waves

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Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

arXiv

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arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

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Learning Disentangled Representations of Negation and Uncertainty

arXiv

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arXiv 2022年

作者： Vasilakes, Jake Zerva, Chrysoula Miwa, Makoto Ananiadou, Sophia National Centre for Text Mining Department of Computer Science The University of Manchester United Kingdom Instituto Superior Técnico Portugal Instituto de Telecomunicações Portugal Toyota Technological Institute Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Japan

Negation and uncertainty modeling are longstanding tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, previous works on representation learning do not explicitly model this independence. We therefore attempt to disentangle the representations of negation, uncertainty, and content using a Variational Autoencoder. We find that simply supervising the latent representations results in good disentanglement, but auxiliary objectives based on adversarial learning and mutual information minimization can provide additional disentanglement gains. © 2022, CC BY-NC-SA.

关键词： Natural language processing systems

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Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures

arXiv

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arXiv 2024年

作者： Shin, Myeongjin Lee, Seungwoo Lee, Junseo Lee, Mingyu Ji, Donghwa Yeo, Hyeonjun Jeong, Kabgyun School of Computing KAIST Daejeon34141 Korea Republic of Quantum AI Team Norma Inc. Seoul04799 Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of College of Liberal Studies Seoul National University Seoul08826 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Research Institute of Mathematics Seoul National University Seoul08826 Korea Republic of School of Computational Sciences Korea Institute for Advanced Study Seoul02455 Korea Republic of

The estimation of quantum entropies and distance measures, such as von Neumann entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the Bures distance, has been a key area of research in quantum information science. In our study, we introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Estimation algorithms for these quantities are generally tied to the size of the Hilbert space of the quantum state to be estimated. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities. We provide an in-depth discussion of the physical scenarios and limitations in which our algorithm is applicable, as well as the learnability of the proposed quantum neural network. Copyright © 2024, The Authors. All rights reserved.

关键词： Hilbert spaces

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Selection for Cheaper Amino Acids Drives Nucleotide Usage at the Start of Translation in Eukaryotic Genes

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 949-957页

作者： Na L.Gao Zilong He Qianhui Zhu Puzi Jiang Songnian Hu Wei-Hua Chen Key Laboratory of Molecular Biophysics of the Ministry of Education Hubei Key Laboratory of Bioinformatics and Molecular-imagingDepartment of Bioinformatics and Systems BiologyCollege of Life Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Institute for Computer Science and Cluster of Excellence on Plant Sciences Heinrich Heine UniversityD-40225 DuesseldorfGermany CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of SciencesBeijing 100101China State Key Laboratory of Microbial Resources Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Interdisciplinary Innovation Institute of Medicine and EngineeringBeihang UniversityBeijing 100191China University of Chinese Academy of Sciences Beijing 100049China

Coding regions have complex interactions among multiple selective forces,which are manifested as biases in nucleotide *** studies have revealed a decreasing GC gradient from the 5′-end to 3′-end of coding regions in various *** confirmed that this gradient is universal in eukaryotic genes,but the decrease only starts from the~25th *** trend is mostly found in nonsynonymous(ns)sites at which the GC gradient is universal across the eukaryotic *** GC contents at ns sites result in cheaper amino acids,indicating a universal selection for energy efficiency toward the N-termini of encoded *** a genome,the decreasing GC gradient is intensified from lowly to highly expressed genes(more and more protein products),further supporting this *** reveals a conserved selective constraint for cheaper amino acids at the translation start that drives the increased GC contents at ns *** GC contents can facilitate transcription but result in a more stable local secondary structure around the start codon and subsequently impede translation ***,the GC gradients at four-fold and two-fold synonymous sites vary across *** could decrease or increase,suggesting different constraints acting at the GC contents of different codon sites in different *** study reveals that the overall GC contents at the translation start are consequences of complex interactions among several major biological processes that shape the nucleotide sequences,especially efficient energy usage.

关键词： Macroevolution Prioritization of selective forces Energy efficiency Transcription Translation initiation

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Differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval 1

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3rd International Symposium on Signal and Image Processing, ISSIP 2020

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Department of Computer Science Amity School of Engineering and Technology Amity University Noida U.P India Department of EEE Amity School of Engineering and Technology Amity University Noida U.P India Department of Management Studies Rajiv Gandhi Institute of Petroleum Technology Raebareli U.P. India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

With the increase of multimedia devices on internet, enormous number of videos are being added as part of digital content. There is a huge challenge in retrieval of these videos as mostly the videos are kept in unstructured form. Intended users try to retrieve video content as per the relevancy and need. Shot boundary detection is a significant and critical approach in the domain of digital video processing. It is the foremost critical job of content-based video retrieval and indexing. In this paper, a novel approach for shot boundary detection algorithm based on Differential evolution algorithm (DE) with SVM classifier is proposed. In this method we first calculate the curves difference of U-component histograms as the feature of difference between video frames. In the next step, Slide-Window Mean Filter to filter difference curves and SVM Classifier applying DE to detect and classify the shot transitions. The well-known TRECVID 2005, 2006, and 2007 datasets are used to test the performance of our proposed approach. The result shows the superior performance of our proposed approach, and this method can achieve high recall, precision rate, accuracy, and better computation time. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow

Dynamic Lane Management with IoT for Real-time Lane Configur...

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computer, Communication and Control (IC4), International Conference on

作者： Mohammed Rehaman Pasha S. Latha R. Usha V. Bini Marin M.V Ishwarya M. Muthulekshmi Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Department of Biotechnology Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation(DU) Salem Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Ramapuram Chennai Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Thazhambur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology’s potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control.

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