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arXiv 2022年

作者： Sánchez, Julia Docampo Computer Applications in Science and Engineering Barcelona Supercomputing Center Barcelona08015 Spain

We present an efficient, parallel, constrained optimization technique for approximating CAD curves with super-convergent rates. The optimization function is a disparity measure in terms of a piece-wise polynomial approximation and a curve re-parametrization. The constrained problem solves the disparity functional fixing the mesh element interfaces. We have numerical evidence that the constrained disparity preserves the original super-convergence: 2p order for planar curves and [3/2 (p − 1) ] + 2 for 3D curves, p being the mesh polynomial degree. Our optimization scheme consists of a globalized Newton method with a nonmonotone line search, and a log barrier function preventing element inversion in the curve re-parameterization. Moreover, we introduce a Julia interface to the EGADS geometry kernel and a parallel optimization algorithm. We test the potential of our curve mesh generation tool on a computer cluster using several aircraft CAD models. We conclude that the solver is well-suited for parallel computing, producing superconvergent approximations to CAD curves. © 2022, CC BY-NC-SA.

关键词： Polynomial approximation

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Search-in-Memory (SiM): Reliable, Versatile, and Efficient Data Matching in SSD's NAND Flash Memory Chip for Data Indexing Acceleration

arXiv

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arXiv 2024年

作者： Chen, Yun-Chih Chang, Yuan-Hao Kuo, Tei-Wei The Department of Computer Science TU Dortmund Germany The Institute of Information Science Academia Sinica Taipei115 Taiwan Department of Computer Science and Information Engineering High Performance and Scientific Computing Center Center of Data Intelligence: Technologies Applications and Systems of National Taiwan University Taiwan

To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. We introduce the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy utilization

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Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

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Journal of Integrative Agriculture 2023年第9期22卷 2865-2881页

作者： ZHANG Sha YANG Shan-shan WANG Jing-wen WU Xi-fang Malak HENCHIRI Tehseen JAVED ZHANG Jia-hua BAI Yun Research Center for Space Information and Big Earth Data College of Computer Science and TechnologyQingdao UniversityQingdao 266071P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000P.R.China Department of Environmental Sciences Kohat University of Science and TechnologyKohat 26000Pakistan

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited ***,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing *** approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and ***,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the *** IPPs were determined using statistical yield data of reference years(2010–2015)over the *** findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),*** data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–*** addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city ***,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

关键词： approximating irrigations process-based model remote sensing winter wheat yield North China Plain

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yihuan Huang Yuanzhe Li Yanzhen Ren Weiping Tu Yuhong Yang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds.

关键词： Time-frequency analysis Accuracy Noise Speech recognition Feature extraction Acoustics Speaker recognition Low latency communication Speech processing

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Stress compensation based on interfacial nanostructures for stable perovskite solar cells

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Interdisciplinary Materials 2023年第2期2卷 348-359页

作者： Cheng Zhu Xi Wang Hangxuan Li Chenyue Wang Ziyan Gao Pengxiang Zhang Xiuxiu Niu Nengxu Li Zipeng Xu Zhenhuang Su Yihua Chen Huachao Zai Haipeng Xie Yizhou Zhao Ning Yang Guilin Liu Xueyun Wang Huanping Zhou Jiawang Hong Xingyu Gao Yang Bai Qi Chen Beijing Key Laboratory of Construction Tailorable Advanced Functional Materials and Green Applications MIIT Key Laboratory for Lowdimensional Quantum Structure and DevicesExperimental Center of Advanced MaterialsSchool of Materials Science and EngineeringBeijing Institute of TechnologyBeijingChina School of Aerospace Engineering Beijing Institute of TechnologyBeijingChina Department of Materials Science and Engineering College of EngineeringPeking UniversityBeijingChina Shanghai Synchrotron Radiation Facility Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghaiChina Hunan Key Laboratory for Super-microstructure and Ultrafast Process School of Physics and ElectronicsCentral South UniversityChangshaChina School of Science Jiangnan University WuxiWuxiJiangsuChina

The long-term stability issue of halide perovskite solar cells hinders their *** residual stress-strain affects device stability,which is derived from the mismatched thermophysical and mechanical properties between adjacent *** this work,we introduced the Rb_(2)CO_(3)layer at the interface of SnO_(2)/perovskite with the hierarchy morphology of snowflake-like microislands and dendritic *** a suitable thermal expansion coefficient,the Rb_(2)CO_(3)layer benefits the interfacial stress relaxation and results in a compressive stress-strain in the perovskite ***,reduced nonradiative recombination losses and optimized band alignment were *** enhancement of open-circuit voltage from 1.087 to 1.153 V in the resultant device was witnessed,which led to power conversion efficiency(PCE)of 22.7%(active area of 0.08313 cm^(2))and 20.6%(1 cm2).Moreover,these devices retained 95%of its initial PCE under the maximum power point tracking(MPPT)after 2700 *** suggests inorganic materials with high thermal expansion coefficients and specific nanostructures are promising candidates to optimize interfacial mechanics,which improves the operational stability of perovskite cells.

关键词： interfacial nanostructures long-term stability perovskite solar cells strain engineering thermal expansion coefficient

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Analysis and study of IDSs for cybersecurity vulnerability detection and prevention: A Comprehensive Review

Analysis and study of IDSs for cybersecurity vulnerability d...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Lijin Shaji Suji Pramila R Computer Applications Noorul Islam Center for Higher Education Thucklay Tamil Nadu India Computer Science and Engineering Mar Baselios Institute of Technology and Science Kothamangalam Kerala India

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated; thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used.

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Causal discovery with endogenous context variables 24

Causal discovery with endogenous context variables

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wiebke Günther Oana-Iuliana Popescu Martin Rabel Urmi Ninad Andreas Gerhardus Jakob Runge German Aerospace Center (DLR) Institute of Data Science Jena Germany and Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany and Technische Universität Dresden Faculty of Computer Science Dresden Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany German Aerospace Center (DLR) Institute of Data Science Jena Germany and Technische Universität Berlin Institute of Computer Engineering and Microelectronics Berlin Germany and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany and Technische Universität Dresden Faculty of Computer Science Dresden Germany

ISBN: (纸本)9798331314385

Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables - precipitation can make a dry soil wet - leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method.

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FCGSM: Fast Conjugate Gradient Sign Method for Adversarial Attack on Image Classification 6th

FCGSM: Fast Conjugate Gradient Sign Method for Adversarial ...

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6th International Conference on Innovative Computing, IC 2023

作者： Xia, Xiaoyan Xue, Wei Wan, Pengcheng Zhang, Hui Wang, Xinyu Zhang, Zhiting School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Maanshan243032 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819922864

Deep neural network is sensitive to adversarial samples that crafted by adding imperceptible perturbations to original images, and many methods of generating adversarial samples have emerged. Although existing methods based on gradient direction have good attack performance, some ill-conditioned issues may reduce their performance on occasion. In this paper, we propose a novel attack method based on three-terms conjugate gradient direction, which is effectively for improving this limitation, and its is named as fast conjugate gradient sign method (FCGSM). The proposed method FCGSM can jump from the local maximum during the process of finding the maximum value of loss function, thus generating more adversarial samples than the SOTA methods APGD and ACG. Experiments conducted on two benchmark datasets show that the FCGSM works well in attacking deep neural network-based classification models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Revolutionizing Prostate Cancer Diagnosis: Harnessing the Potential of Transfer Learning for MRI-Based Classification 4

Revolutionizing Prostate Cancer Diagnosis: Harnessing the Po...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Trivedi, Sanjay Dave, Harsh Nilaykumar, Patel Khush Dalal, Pankaj Sigma University Department of Computer Engineering Gujarat India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gjpiasr Surgeon Department of Surgery Gujrat Anand India Smt.B.K.Shah Medical Institute and Research Center Gujarat Vadodara India Charotar University of Science and Technology Changa India Sigma University Computer Science and Applications Gujrat Vadodara India

ISBN: (纸本)9798350300888

This study introduces a novel framework that harnesses the potential of transfer learning for MRI -based classification in prostate cancer diagnosis. The study explores the challenges of accurately classifying prostate cancer from MRI images and proposes a methodology that integrates pre-trained deep neural networks with a large, annotated dataset. The framework demonstrates superior performance compared to conventional methods, achieving higher Accuracy, Precision, Recall, Fl-Score. The approach exhibits robustness across different MRI scanners and patient populations, highlighting its potential for real-world clinical applications. Additionally, this study emphasizes the interpretability of the model through visualization techniques, aiding clinicians in understanding the underlying pathology and making informed treatment decisions. Overall, this research contributes to the revolutionization of prostate cancer diagnosis, offering improved accuracy and efficiency in clinical settings and paving the way for advanced computer-aided diagnostic systems. © 2023 IEEE.

关键词： Magnetic resonance imaging

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