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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Duan, Xiaohui Wang, Jin Gao, Ping Ma, Ming Gan, Lin Liu, Xin Fu, Haohuan Xue, Wei Chen, Dexun Yang, Guangwen Liu, Weiguo School of Software Shandong University China International School for Advanced Studies Trieste Italy Tsinghua University Department of Computer Science and Technology Beijing China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China Ministry of Education Key Lab for Earth System Modeling Department of Earth System Science Tsinghua University Beijing China

ISBN: (纸本)9798400701092

Molecular dynamics (MD) simulation can provide an affordable way for inspecting microscopic phenomena, which is a powerful complement to real-world experiments. But the spatial scale of MD simulations is usually magnitudes smaller than experiment systems. In this paper, we present our work, redesigning the widely used inter-layer potential in structural superlubricity. By carrying out a specialized neighbor list for inter-layer potential computation, the total memory access amount is reduced significantly. Besides, a simple but efficient vectorization strategy is implemented based on the new neighbor list. In the extreme case, our work can scale to 38 million cores to achieve a sustainable performance of 61 PFLOPS, enabling a simulation of a superlubricity system of 32 μm2 with 7.2 billion atoms at 4.75 ns/day, which is 11,834 times of reported largest scale simulation in superlubricity systems in contact area and almost ten times faster in time-to-solution. Furthermore, we have done a simulation at 9 μm2 which results in consistency with real-world experiments and verified some theoretical predictions in the mesoscopic scale. © 2023 ACM.

关键词： Molecular dynamics

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Model learning:a survey of foundations,tools and applications

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Frontiers of computer Science 2021年第5期15卷 71-92页

作者： Shahbaz ALI Hailong SUN Yongwang ZHAO Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191China SKLSDE School of Computer Science and EngineeringBeihang UniversityBeijing 100191China School of Software Beihang UniversityBeijing 100191China School of Cyber Science and Technology College of Computer ScienceZhejiang UniversityHangzhou 310058China

software systems are present all around us and playing their vital roles in our daily *** correct functioning of these systems is of prime *** addition to classical testing techniques,formal techniques like model checking are used to reinforce the quality and reliability of software ***,obtaining of behavior model,which is essential for model-based techniques,of unknown software systems is a challenging *** mitigate this problem,an emerging black-box analysis technique,called Model Learning,can be *** complements existing model-based testing and verification approaches by providing behavior models of blackbox systems fully *** paper surveys the model learning technique,which recently has attracted much attention from researchers,especially from the domains of testing and ***,we review the background and foundations of model learning,which form the basis of subsequent ***,we present some well-known model learning tools and provide their merits and shortcomings in the form of a comparison ***,we describe the successful applications of model learning in multidisciplinary fields,current challenges along with possible future works,and concluding remarks.

关键词： model learning active automata learning automata learning libraries/tools inferring behavior models testing and formal verification

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Information-Theoretic Analysis of Multimodal Image Translation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Liu, Ruihao Li, Yudu Li, Yao Du, Yiping P. Liang, Zhi-Pei Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States University of Illinois at Urbana-Champaign National Center for Supercomputing Applications Department of Bioengineering Grainger College of Engineering Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States Shanghai Jiao Tong University School of Biomedical Engineering Institute for Medical Imaging Technology Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology Department of Electrical and Computer Engineering Grainger College of Engineering UrbanaIL61801 United States

Multimodal image translation has found useful applications in solving several medical imaging problems. In this paper, we presented a systematic analysis of multimodal images and machine learning-based image translation from an information-theoretic perspective. Specifically, we analyzed the amount of mutual information that exists in some commonly used multimodal images. This analysis revealed varying structural correlation across modalities and tissue-dependence of mutual information. We also analyzed the amount of information transferred and gained in multimodal image translation and provided an upper bound on the information gain. Information-theoretic measures were also proposed to assess the effectiveness of an image translator, and the uncertainty associated with image translation. Numerical results were presented to demonstrate the information gain in practical multimodal image translation, and to validate the proposed upper bound on information gain and the translation error predictor. Finally, several potential applications of our analysis results were discussed, including the image denoising and reconstruction using side information generated by image translation. The findings from this study may prove useful for guiding the further development and application of multimodal image translation. © 1982-2012 IEEE.

关键词： Machine translation

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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International Conference on Emerging Technologies, ICET

作者： Omar Kashif Majeed Zain ul Abideen Usama Arshad Raja Hashim Ali Abdullah Habib Rafay Mustafa Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty.

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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International Conference on Emerging Technologies, ICET

作者： Usama Arshad Hamza Hasan Ellahie Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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Exploring Depth-based Perception Conflicts in Virtual Reality through Error-Related Potentials

Exploring Depth-based Perception Conflicts in Virtual Realit...

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IEEE Annual International Symposium Virtual Reality

作者： Haolin Gao Kang Yue Songyue Yang Yu Liu Mei Guo Yue Liu Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences Beijing China

Virtual Reality (VR) offers a valuable platform for real-life skills training. However, previous research has indicated that human’s perception of depth in VR differs from that of the real world. Such perceptual conflicts can impact immersion and the learning of skills, thus attracting widespread attention. Various methods have been proposed to enhance users’ depth perception, yet the underlying mechanisms of depth perception conflicts still require further research. In this paper, we used Error-Related Potentials (ErrPs) from electroencephalography (EEG) data to investigate the differences in participants’ perceptions at varying depths within the near-field. We designed a within-subjects experiment to successfully introduce depth perception conflicts. From participants exposed to three distinct depths, we collected questionnaire results, performance data, and EEG data. Our findings showed that EEG can effectively detect depth perception conflicts and, following each conflict, participants’ behavioral patterns showed significant changes. In situations with shallower depths, participants exhibited stronger responses to the designed conflicts. This increased sensitivity correlates with their accuracy in depth estimation. This study represents a novel approach to depth perception in VR using ErrPs, setting the stage for further use of physiological signals to measure the granularity of depth perception in VR/AR environments.

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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International Conference on Emerging Technologies, ICET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

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Entirety of Quantum Uncertainty and Its Experimental Verification

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Chinese Physics Letters 2021年第7期38卷 11-16页

作者： Jie Xie Li Zhou Aonan Zhang Huichao Xu Man-Hong Yung Ping Xu Nengkun Yu Lijian Zhang National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences and School of PhysicsNanjing UniversityNanjing 210093China Collaborative Innovation Center of Advanced Microstructures Nanjing UniversityNanjing 210093China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Centre for Quantum Software and Information School of SoftwareFaculty of Engineering and Information TechnologyUniversity of Technology SydneyNSWAustralia

As a foundation of quantum physics,uncertainty relations describe ultimate limit for the measurement uncertainty of incompatible ***,uncertainty relations are formulated by mathematical bounds for a specific *** we present a method for geometrically characterizing uncertainty relations as an entire area of variances of the observables,ranging over all possible input *** find that for the pair of position and momentum operators,Heisenberg's uncertainty principle points exactly to the attainable area of the variances of position and ***,for finite-dimensional systems,we prove that the corresponding area is necessarily semialgebraic;in other words,this set can be represented via finite polynomial equations and inequalities,or any finite union of such *** particular,we give the analytical characterization of the areas of variances of(a)a pair of one-qubit observables and(b)a pair of projective observables for arbitrary dimension,and give the first experimental observation of such areas in a photonic system.

关键词： observable union operators

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