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IEEE Spoken Language Technology Workshop

作者： Xinhu Zheng Anbai Jiang Bing Han Yanmin Qian Pingyi Fan Jia Liu Wei-Qiang Zhang Department of Electronic Engineering Tsinghua University Beijing China Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artifcial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350392258

ISBN: (纸本)9798350392265

Anomalous Sound Detection (ASD) has gained significant interest through the application of various Artificial Intelligence (AI) technologies in industrial settings. Though possessing great potential, ASD systems can hardly be readily deployed in real production sites due to the generalization problem, which is primarily caused by the difficulty of data collection and the complexity of environmental factors. This paper introduces a robust ASD model that leverages audio pre-trained models. Specifically, we fine-tune these models using machine operation data, employing SpecAug as a data augmentation strategy. Additionally, we investigate the impact of utilizing Low-Rank Adaptation (LoRA) tuning instead of full fine-tuning to address the problem of limited data for fine-tuning. Our experiments on the DCASE2023 Task 2 dataset establish a new benchmark of 77.75% on the evaluation set, with a significant improvement of 6.48% compared with previous state-of-the-art (SOTA) models, including top-tier traditional convolutional networks and speech pre-trained models, which demonstrates the effectiveness of audio pre-trained models with LoRA tuning. Ablation studies are also conducted to showcase the efficacy of the proposed scheme.

关键词： Adaptation models Production Data collection Data augmentation Harmonic analysis Data models Environmental factors Convolutional neural networks Artificial intelligence Tuning

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Combining Numerical Linear Algebra with Simulation to Compute Stationary Distributions 22

Combining Numerical Linear Algebra with Simulation to Comput...

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Proceedings of the Winter Simulation Conference

作者： Zeyu Zheng Alex Infanger Peter W. Glynn Department of Industrial Engineering and Operations Research University of California Berkeley Berkeley CA USA Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Department of Management Science and Engineering Stanford University Stanford CA USA

This paper introduces the first fully integrated algorithm for combining simulation with numerical linear algebra, as a means of computing stationary distributions for Markov chains and Markov jump processes. We use linear algebra to analyze the "center" of the state space, while simulation is used to estimate contributions to the steady-state from path excursions outside the "center". The method yields consistent estimators for stationary expectations, and can be viewed as an application of the variance reduction technique known as conditional Monte Carlo.

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Sex-specific cardiometabolic multimorbidity, metabolic syndrome and left ventricular function in heart failure with preserved ejection fraction in the UK Biobank

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Cardiovascular Diabetology 2025年第1期24卷 1-16页

作者： Bertrand, Ambre Zhou, Xin Lewis, Andrew Monfeuga, Thomas Gupta, Ramneek Grau, Vicente Rodriguez, Blanca Computational Cardiovascular Science Group Department of Computer Science University of Oxford Oxford OX1 3QD United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford Oxford OX3 9DU United Kingdom Novo Nordisk Research Centre Oxford Ltd Roosevelt Drive Headington Oxford OX3 7FZ United Kingdom Disease Intelligence Pte Ltd. 10 Anson Road 33-10C Singapore 079903 Singapore Department of Engineering Science Institute of Biomedical Engineering University of Oxford Oxford OX3 7DQ United Kingdom

Background: Cardiometabolic disturbances play a central role in the pathogenesis of heart failure with preserved ejection fraction (HFpEF). Due to its complexity, HFpEF is a challenging condition to treat, making phenotype-specific disease management a promising approach. However, HFpEF phenotypes are heterogenous and there is a lack of detailed evidence on the different, sex-specific profiles of cardiometabolic multimorbidity and metabolic syndrome present in HFpEF. Methods: We performed a retrospective, modified cross-sectional study examining a subset of participants in the UK Biobank, an ongoing multi-centre prospective cohort study in the United Kingdom. We defined HFpEF as a record of a heart failure diagnosis using ICD-10 code I50, coupled with a left ventricular ejection fraction (LVEF) ≥ 50% derived from cardiac magnetic resonance (CMR) imaging. We examined sex-specific differences in cardiometabolic comorbidity burden and metabolic syndrome, performed latent class analysis (LCA) to identify distinct clusters of patients based on their cardiometabolic profile, and compared CMR imaging-derived parameters of left ventricular function at rest in the different clusters identified to reflect possible differences in adverse cardiac remodelling. Results: We ascertained HFpEF in 445 participants, of which 299 (67%) were men and 146 (33%) women. The median age was 70 years old (interquartile range: [66.0–74.0]). A combination of hypertension and obesity was the most prevalent cardiometabolic pattern both in men and women with HFpEF. Most men had 2–3 clinical cardiometabolic comorbidities while most women had 1–2, despite a similar metabolic syndrome profile (p = 0.05). LCA revealed three distinct, clinically relevant phenogroups, namely (1) a most male and multimorbid group (n = 117);(2) a group with a high prevalence of severe obesity, abnormal waist circumference and with the highest relative proportion of females (n = 116);and finally (3) a group with an apparent

关键词： cardiac magnetic resonance imaging cardiometabolic diseases HFpEF machine learning, phenomapping metabolic syndrome UK Biobank

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Experimenting on Markov Decision Processes with Local Treatments

arXiv

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arXiv 2024年

作者： Chen, Shuze Simchi-Levi, David Wang, Chonghuan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China Institute for Data Systems and Society Department of Civil and Environmental Engineering Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Utilizing randomized experiments to evaluate the effect of short-term treatments on the short-term outcomes has been well understood and become the golden standard in industrial practice. However, as service systems become increasingly dynamical and personalized, much focus is shifting toward maximizing long-term cumulative outcomes, such as customer lifetime value, through lifetime exposure to interventions. To bridge this gap, we investigate the randomized experiments within dynamical systems modeled as Markov Decision Processes (MDPs). Our goal is to assess the impact of treatment and control policies on long-term cumulative rewards from relatively short-term observations. We first develop optimal inference techniques for assessing the effects of general treatment patterns. Furthermore, recognizing that many real-world treatments tend to be fine-grained and localized for practical efficiency and operational convenience, we then propose methods to harness this localized structure by sharing information on the non-targeted states. Our new estimator effectively overcomes the variance lower bound for general treatments while matching the more stringent lower bound incorporating the local treatment structure. Furthermore, our estimator can optimally achieve a linear reduction with the number of test arms for a major part of the variance. Finally, we explore scenarios with perfect knowledge of the control arm and design estimators that further improve inference efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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ViPRA-Haplo: De Novo Reconstruction of Viral Populations Using Paired End Sequencing Data

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IEEE/ACM Transactions on computational Biology and Bioinformatics 2024年第3期21卷 492-500页

作者： Li, Weiling Malhotra, Raunaq Wu, Steven Jha, Manjari Rodrigo, Allen Poss, Mary Acharya, Raj Weill Cornell Medical College Institute for Computational Biomedicine New YorkNY10021 United States ScienceIO New YorkNY10100 United States National Taiwan University Department of Agronomy Taipei10617 Taiwan Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States University of Auckland School of Biological Sciences Private Bag 92019 New Zealand University of Virginia Department of Hematology and Oncology School of Medicine CharlottesvilleVA22908 United States Indiana University Bloomington Luddy School of Informatics Computing and Engineering BloomingtonIN47405 United States

We present ViPRA-Haplo, a de novo strain-specific assembly workflow for reconstructing viral haplotypes in a viral population from paired-end next generation sequencing (NGS) data. The proposed Viral Path Reconstruction Algorithm (ViPRA) generates a subset of paths from a De Bruijn graph of reads using the pairing information of reads. The paths generated by ViPRA are an over-estimation of the true contigs. We propose two refinement methods to obtain an optimal set of contigs representing viral haplotypes. The first method clusters paths reconstructed by ViPRA using VSEARCH Deorowicz et al. 2015 based on sequence similarity, while the second method, MLEHaplo, generates a maximum likelihood estimate of viral populations. We evaluated our pipeline on both simulated and real viral quasispecies data from HIV (and real data from SARS-COV-2). Experimental results show that ViPRA-Haplo, although still an overestimation in the number of true contigs, outperforms the existing tool, PEHaplo, providing up to 9% better genome coverage on HIV real data. In addition, ViPRA-Haplo also retains higher diversity of the viral population as demonstrated by the presence of a higher percentage of contigs less than 1000 base pairs (bps), which also contain k-mers with counts less than 100 (representing rarer sequences), which are absent in PEHaplo. For SARS-CoV-2 sequencing data, ViPRA-Haplo reconstructs contigs that cover more than 90% of the reference genome and were able to validate known SARS-CoV-2 strains in the sequencing data. © 2004-2012 IEEE.

关键词： Coronavirus

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DPA-2:a large atomic model as a multitask learner

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npj computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Generalising Violence Detection with a New Near-Real-World Violence Dataset

Generalising Violence Detection with a New Near-Real-World V...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Mahmudul Haque Hussain Nyeem Tareque Bashar Ovi Al Nahid Md. Sabbir Hossain Molla Md. Tanjim Mahmud Tuhin Fardin Shahab Ayat Subah Alam Saadia Binte Alam Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Electrical Electronic and Communication Engineering (EECE) Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Detecting and classifying violence is crucial for public safety and addressing societal violence. DL models have greatly improved the automation of violence detection systems by effectively capturing intricate visual patterns. However, the quality and diversity of the training data greatly impact the effectiveness of these models. Existing datasets may be biased towards specific situations, limiting their practical use. To address this limitation, we introduce the Movie Clip (MC) dataset to enhance the generalisability of Automated Violence Detection and Classification (AVDC) systems. The MC dataset encompasses a broad spectrum of near-real-world violent actions, incorporating diverse demographics, environmental circumstances, and cultural elements extracted from movies. Consequently, it accurately reflects the complexity and diversity of real-world violent scenarios. The potential of the new dataset is investigated against its existing counterparts, like Hockey Fight (HF) and AIRTLab datasets. These datasets are used to train the ConvLSTM models based on the VGG16 and VGG19 architectures. The proposed dataset significantly improves AVDC model generalisation, outperforming the generalisability of current datasets, thereby advancing violence detection and facilitating the development of more robust and efficient AVDC systems.

关键词： Training Visualization Atmospheric modeling Training data Hafnium Motion pictures Public security

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Streamlining Attendance with Fast Face Recognition Model

Streamlining Attendance with Fast Face Recognition Model

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Koushik Sundar Ravikumar S Jeyalakshmi J Eugene Berna I Prithi Samuel Citibank NA Jersey City NJ USA Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment.

关键词： Training Accuracy Databases Face recognition Manuals Feature extraction User experience Security Usability System implementation

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Identifying Novel Drug Targets by iDTPnd:A Case Study of Kinase Inhibitors

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Genomics, Proteomics & Bioinformatics 2021年第6期19卷 986-997页

作者： Hammad Naveed Corinna Reglin Thomas Schubert Xin Gao Stefan T.Arold Michael L.Maitland Toyota Technological Institute at Chicago ChicagoIL 60637USA Department of Computer Science National University of Computer and Emerging SciencesIslamabad 44000Pakistan 2bind GmbH D-93053 RegensburgGermany King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955Saudi Arabia King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)Biological and Environmental Sciences and Engineering(BESE)DivisionThuwal 23955Saudi Arabia Inova Center for Personalized Health and Schar Cancer Institute Falls ChurchVA 22042USA University of Virginia Cancer Center AnnandaleVA 22003USA

Current FDA-approved kinase inhibitors cause diverse adverse effects,some of which are due to the me-chanism-independent effects of these *** these mechanism-independent interactions could improve drug safety and support drug ***,we develop iDTPnd(integrated Drug Target Predictor with negative dataset),a computational approach for large-scale discovery of novel targets for known *** a given drug,we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding *** facilitate assessment of unintended targets,iDTPnd also provides a docking-based interaction score and its statistical *** confirm the interactions of sorafenib,imatinib,dasatinib,sunitinib,and pazopanib with their known targets at a sensitivity of 52%and a specificity of 55%.We also validate 10 predicted novel targets by using in vitro *** results suggest that proteins other than kinases,such as nuclear receptors,cytochrome P450,and MHC class I molecules,can also be physiologically relevant targets of kinase *** method is general and broadly applicable for the identification of protein–small molecule interactions,when sufficient drug–target 3D data are *** code for constructing the structural signatures is available at https://***/Documents/***.

关键词： Protein-drug interaction iDTPnd Kinase inhibitor Drug-binding site signature Drug repurposing

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Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification

arXiv

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arXiv 2023年

作者： Prakash, Ashwin Thejaswin, S. Nambiar, Athira Bernardino, Alexandre Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Electrical and Computer Engineering Instituto Superior Técnico Univ. of Lisbon Portugal

Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to changes in individual users and in the external environment. To this end, we propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks. Various external factors such as viewpoint and distance to the camera, are investigated in this study. Extensive experiments have shown superior performance of the proposed model compared with the state-of-the-art model. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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