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arXiv 2023年

作者： Cao, Yiwen Su, Yukun Wang, Wenjun Liu, Yanxia Wu, Qingyao The School of Software Engineering South China University of Technology Guangzhou510640 China The Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou China The School of Computer Science and Engineering Nanyang Technological University Singapore The Pazhou Lab Guangzhou China

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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New Quantum Algorithms for Computing Quantum Entropies and Distances

arXiv

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arXiv 2022年

作者： Wang, Qisheng Guan, Ji Liu, Junyi Zhang, Zhicheng Ying, Mingsheng The Graduate School of Mathematics Nagoya University Japan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China The Joint Center for Quantum Information and Computer Science University of Maryland United States The Centre for Quantum Software and Information University of Technology Sydney Australia

We propose a series of quantum algorithms for computing a wide range of quantum entropies and distances, including the von Neumann entropy, quantum Rényi entropy, trace distance, and fidelity. The proposed algorithms significantly outperform the prior best (and even quantum) ones in the low-rank case, some of which achieve exponential speedups. In particular, for N-dimensional quantum states of rank r, our proposed quantum algorithms for computing the von Neumann entropy, trace distance and fidelity within additive error Ε have time complexity of Õ(r/Ε2), Õ(r5/Ε6) and Õ(r6.5/Ε7.5), respectively. By contrast, prior quantum algorithms for the von Neumann entropy and trace distance usually have time complexity Ω(N), and the prior best one for fidelity has time complexity Õ(r12.5/Ε13.5). The key idea of our quantum algorithms is to extend block-encoding from unitary operators in previous work to quantum states (i.e., density operators). It is realized by developing several convenient techniques to manipulate quantum states and extract information from them. The advantage of our techniques over the existing methods is that no restrictions on density operators are required;in sharp contrast, the previous methods usually require a lower bound on the minimal non-zero eigenvalue of density operators. Copyright © 2022, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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ESM all-atom: multi-scale protein language model for unified molecular modeling 24

ESM all-atom: multi-scale protein language model for unified...

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Proceedings of the 41st International Conference on Machine Learning

作者： Kangjie Zheng Siyu Long Tianyu Lu Junwei Yang Xinyu Dai Ming Zhang Zaiqing Nie Wei-Ying Ma Hao Zhou School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University Beijing China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science Tsinghua University Institute for AI Industry Research (AIR) Tsinghua University and PharMolix Inc. Institute for AI Industry Research (AIR) Tsinghua University

Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESMAA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins.

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Lessons Learned from Optimizing the Sunway Storage System for Higher Application I/O Performance

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Journal of computer science & technology 2020年第1期35卷 47-60页

作者： Qi Chen Kang Chen Zuo-Ning Chen Fellow CCF Wei Xue Xu Ji Bin Yang Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijing 100084China Chinese Academy of Engineering Beijing 100088China National Supercomputing Center in Wuxi Wuxi 214100China School of Software Shandong UniversityJinan 250101China

It is hard for applications to make full utilization of the peak bandwidth of the storage system in highperformance computers because of I/O interferences,storage resource misallocations and complex long I/O *** performed several studies to bridge this gap in the Sunway storage system,which serves the supercomputer Sunway *** locate these issues and connections between them,an end-to-end performance monitoring and diagnosis tool was developed to understand I/O behaviors of applications and the *** the help of the tool,we were about to find out the root causes of such performance barriers at the I/O forwarding layer and the parallel file system *** application-aware I/O forwarding allocation framework was used to address the I/O interferences and resource misallocations at the I/O forwarding layer.A performance-aware data placement mechanism was proposed to mitigate the impact of I/O interferences and performance variations of storage devices in the ***,applications obtained much better I/O *** the process,we also proposed a lightweight storage stack to shorten the I/O path of applications with N-N I/O *** paper summarizes these studies and presents the lessons learned from the process.

关键词： high performance computing I/O interference parallel file system performance optimization resource misallocation

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities

A Machine Learning Approach for Predicting Air Quality Index...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： S. Swamynathan N. Sneha S. P. Ramesh R. Niranjana D. David Neels Ponkumar R. Saravanakumar Department of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions; however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction.

关键词： Support vector machines Adaptation models Atmospheric modeling Biological system modeling Computational modeling Artificial neural networks Predictive models Air quality Data models Indexes

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Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT

arXiv

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arXiv 2023年

作者： Liu, Wentao Tian, Tong Xu, Weijin Wang, Lemeng Li, Haoyuan Yang, Huihua School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manual assessment is inherently subjective with considerable inter- and intra-expert variability. In the paper, we propose a hybrid supervised framework, StMt, that integrates self-training and mean teacher for the segmentation of abdominal organs and tumors using partially labeled and unlabeled data. We introduce a two-stage segmentation pipeline and whole-volume-based input strategy to maximize segmentation accuracy while meeting the requirements of inference time and GPU memory usage. Experiments on the testing set of FLARE2023 demonstrate that our method achieves excellent segmentation performance as well as fast and low-resource model inference. Our method achieved an average DSC score of 90.48% and 50.00% for the organs and lesions on the testing set and the average running time and area under GPU memory-time cure are 11.1 seconds and 8979 MB, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Welcome from the Chairs

MSR4P and S 2022 - Proceedings of the 1st International Work...

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MSR4P and S 2022 - Proceedings of the 1st International Workshop on Mining software Repositories Applications for Privacy and Security, co-located with ESEC/FSE 2022 2022年 III-IV页

作者： Vidoni, Melina Ferreyra, Nicolás E. Díaz Codabux, Zadia Australian National University School of Computing Australia Hamburg University of Technology Institute of Software Security Germany University of Saskatchewan Dept. of Computer Science Canada

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A Mobile-Based Screening and Refinement System to Identify the Risk of Dyscalculia and Dysgraphia Learning Disabilities in Primary school Students 10

A Mobile-Based Screening and Refinement System to Identify t...

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10th International Conference on Information and Automation for Sustainability, ICIAfS 2021

作者： Hewapathirana, Chamil Abeysinghe, Kalpani Maheshani, Pamodya Liyanage, Prabath Krishara, Jenny Thelijjagoda, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology SLIIT Business School Malabe Sri Lanka

ISBN: (纸本)9781665444293

Learning Disability is a condition that has a direct effect on the brain and there is no cure or any identified medical treatments. Most of these cases remain undiagnosed due to the lack of awareness from their parents and teachers in underdeveloped countries like Sri Lanka. Mobile application-based solution 'Nana Shilpa' was developed for the screening and intervention processes for the specific Learning Disabilities which are Verbal and Lexical Dyscalculia, Operational and Practognostic Dyscalculia, Letter Level Dysgraphia and Numeric Dysgraphia. Deep Learning with Machine Learning techniques is used in the screening process to provide a better solution. To detect the written letters/numbers, trained Convolutional Neural Networks (CNN) achieved the accuracy of 92%, 99%, 99% for Verbal and Lexical Dyscalculia, Letter Level Dysgraphia and Number Dysgraphia respectively. The Machine Learning algorithms used for screening processes are Support Vector Machine (SVM) and Random Forest (RF). In the machine learning models, it is achieved the accuracy of 98%, 97% for Operational and Practognostic Dyscalculia and Number Dysgraphia respectively. In Sri Lanka, this has been recognized as an acceptable solution for screening and intervention via a mobile-based application for above mentioned four variants of learning disability conditions which are developed based on the gaming environment. © 2021 IEEE.

关键词： Convolutional neural networks

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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