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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Junqi Yin Siming Liang Siyan Liu Feng Bao Hristo G. Chipilski Dan Lu Guannan Zhang National Center for Computational Science Oak Ridge National Laboratory Oak Ridge TN USA Department of Mathematics Florida State University Tallahassee FL USA Computational Science and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA Department of Scientific Computing Florida State University Tallahassee FL USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF); and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework’s exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations.

关键词： Earth Foundation models Computational modeling High performance computing Scalability Atmospheric modeling Supercomputers Real-time systems Numerical models Data assimilation

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Foundation Model for Advancing Healthcare: Challenges, Opportunities and Future Directions

arXiv

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arXiv 2024年

作者： He, Yuting Huang, Fuxiang Jiang, Xinrui Nie, Yuxiang Wang, Minghao Wang, Jiguang Chen, Hao The Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science The Hong Kong University of Science and Technology Hong Kong The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong The Department of Chemical and Biological Engineering Division of Life Science State Key Laboratory of Molecular Neuroscience Hong Kong University of Science and Technology Hong Kong SIATHKUST Joint Laboratory of Cell Evolution and Digital Health Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Guangdong Shenzhen518045 China Hong Kong Center for Neurodegenerative Diseases InnoHK Hong Kong

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial intelligence (AI) models, breaking the contradiction between limited AI models and diverse healthcare practices. Much more widespread healthcare scenarios will benefit from the development of a healthcare foundation model (HFM), improving their advanced intelligent healthcare services. Despite the impending widespread deployment of HFMs, there is currently a lack of clear understanding about how they work in the healthcare field, their current challenges, and where they are headed in the future. To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey. It first conducted a comprehensive overview of the HFM including the methods, data, and applications for a quick grasp of the current progress. Then, it made an in-depth exploration of the challenges present in data, algorithms, and computing infrastructures for constructing and widespread application of foundation models in healthcare. This survey also identifies emerging and promising directions in this field for future development. We believe that this survey will enhance the community's comprehension of the current progress of HFM and serve as a valuable source of guidance for future development in this field. The latest HFM papers and related resources are maintained on our website. © 2024, CC BY-NC-ND.

关键词： Artificial intelligence

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chen, Caijuan Zhang, Chaofeng Li, Gaolei Liu, Yuchen National Institute of Informatics The Information Systems Architecture Science Research Division Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Nc State University Department of Computer Science NC United States

ISBN: (纸本)9798350368369

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints. © 2025 IEEE.

关键词： Multiobjective optimization

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Navigating the future:Embracing resilience and interoperability in the industrial internet

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Fundamental research 2024年第1期4卷 1-2页

作者： Chaojie Gu Chunxiao Jiang Jiming Chen Yang Cong Bin Xiao College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Division II Department of Information SciencesNational Natural Science Foundation of ChinaBeijing 100085China Chongqing Key Laboratory of Image Cognition School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing 400065China

In the midst of the fourth industrial revolution,the convergence of the digital,physical,and biological realms is propelling industrial innovation to new *** the heart of this transformative era lies the Industrial In... 详细信息

关键词： internet network shaping

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Revealing intrinsic and extrinsic piezoelectric contributions in phase coexistence system of PbTiO_(3)-BiScO_(3)

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science China Materials 2022年第1期65卷 170-178页

作者： Shengdong Sun Hui Liu Yang Ren Manuel Hinterstein Jun Chen Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Department of Physical Chemistry University of Science and Technology BeijingBeijing 100083China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Institute of Applied Materials Ceramic Materials and TechnologiesKarlsruhe Institute of Technology 76131KarlsruheGermany X-Ray Science Division Advanced Photon SourceArgonne National LaboratoryArgonneIllinois 60439US

PbTiO_(3)-BiScO_(3) ceramics possess high piezoelectricity and high Curie temperature simultaneously,presenting a very promising candidate for high-temperature actuators and *** out the underlying piezoelectric mechanisms is of great importance for its ***,we reveal the quantified intrinsic and extrinsic contributions to the high electrostrain of the morphotropic composition with phase coexistence by employing the advanced in situ electrical biasing high-energy synchrotron X-ray diffraction combined with the STRAP(strain,texture,and Rietveld analysis for piezoceramics from diffraction)*** electric field-induced phase transformation is observed between the coexisting tetragonal and rhombohedral *** is found that the tetragonal phase contributes most of the domain switching strain,while the predominant lattice strain is from the rhombohedral *** phase fraction changes,the quantitative results demonstrate that the fieldinduced electrostrain is mainly contributed by the intrinsic lattice *** contrast,the high irreversible domain switching strain dominates the large remanent *** implies that transforming the large remanent strain into reversible strain would be a strategic direction to improve the piezoelectric *** present results provide a further understanding of the high piezoelectricity and could help to advance the application of PbTiO_(3)-BiScO_(3).

关键词： PbTiO_(3)-BiScO_(3) piezoelectricity in situ synchrotron diffraction electrostrain

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

引用

IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Kalman Filtering for Precise Indoor Position and Orientation Estimation Using IMU and Acoustics on Riemannian Manifolds

arXiv

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arXiv 2024年

作者： AlSharif, Mohammed H. Ahmed, Mohanad Siala, Mohamed Al-Naffouri, Tareq Y. The Research and Development Department Aramco Research Center Thuwal23955 Saudi Arabia The Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Ariana2083 Tunisia

Indoor tracking and pose estimation, i.e., determining the position and orientation of a moving target, are increasingly important due to their numerous applications. While Inertial Navigation Systems (INS) provide high update rates, their positioning errors can accumulate rapidly over time. To mitigate this, it is common to integrate INS with complementary systems to correct drift and improve accuracy. This paper presents a novel approach that combines INS with an acoustic Riemannian-based localization system to enhance indoor positioning and orientation tracking. The proposed method employs both the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) for fusing data from the two systems. The Riemannian-based localization system delivers high-accuracy estimates of the target’s position and orientation, which are then used to correct the INS data. A new projection algorithm is introduced to map the EKF or UKF output onto the Riemannian manifold, further improving estimation accuracy. Our results show that the proposed methods significantly outperform benchmark algorithms in both position and orientation estimation. The effectiveness of the proposed methods was evaluated through extensive numerical simulations and testing using our in-house experimental setup. These evaluations confirm the superior performance of our approach in practical scenarios. © 2024, CC BY-NC-ND.

关键词： Extended Kalman filters

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Prediction of ultimate tensile strength of Al-Si alloys based on multimodal fusion learning

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Materials Genome engineering Advances 2024年第1期2卷 104-119页

作者： Longfei Zhu Qun Luo Qiaochuan Chen Yu Zhang Lijun Zhang Bin Hu Yuexing Han Qian Li State Key Laboratory of Advanced Special Steels&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai UniversityShanghaiChina School of Computer Engineering and Science Shanghai UniversityShanghaiChina College of Materials Science and Engineering Chongqing UniversityChongqingChina State Key Laboratory of Powder Metallurgy Central South UniversityChangshaChina CSMET Group Co. Ltd.ShanghaiChina Zhejiang Laboratory HangzhouChina National Engineering Research Center for Magnesium Alloy Chongqing UniversityChongqingChina National Key Laboratory of Advanced Casting Technologies Chongqing UniversityChongqingChina

Exploring the“composition-microstructure-property”relationship is a long-standing theme in materials ***,complex interactions make this area of research *** on the image processing and machine learning techniques,this paper proposes a multimodal fusion learning framework that comprehensively considers both composition and microstructure in prediction of the ultimate tensile strength(UTS)of Al-Si ***,the composition and image information are collected from the literature and supplementary experi-ments,followed by the image segmentation and quantitative analysis of eutectic Si ***,the quantitative analysis results are combined with other features for three-step feature screening,and 12 key features are ***,four machine-learning models(i.e.,decision tree,random forest,adaptive boosting,and extreme gradient boosting[XGBoost])are used to predict the UTS of Al-Si *** results show that the quantitative analysis method proposed in this paper is superior to Image-Pro Plus(IPP)software in some *** XGBoost model has the best prediction performance with R^(2)=***,five mixed features and their critical values that significantly affect UTS are *** study provides enlightenment for the prediction of UTS of Al-Si alloys from composition and microstructure,and would be applicable to other alloys.

关键词： Al-Si alloys image processing machine learning multimodal property prediction

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Physique- Based Human Activity Recognition Using Deep Learning Approaches and Smartphone Sensors 8

Physique- Based Human Activity Recognition Using Deep Learni...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Pravesjit, Sakkayaphop Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Information Technology University of Phayao Phayao Thailand School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

Understanding human actions via the analysis of sensor data captured by wearable sensors is the goal of the complex subject of study known as sensor-based human activity recognition (S-HAR). Human participants' characteristics are only periodically included in deep learning (DL) approaches to S-HAR. Recognizing people was challenging for these DL methods because of the variety of physical characteristics people have. To address this challenge, we introduce a physique-based S-HAR architecture that could support deep learning networks to achieve higher identification a ccuracies a nd F1-scores. The HARSense dataset, a publicly available benchmark S-HAR dataset that compiles raw sensor data acquired from smartphones, was employed to build and evaluate five DL networks. A ccording to the experiments, the five models' detection performance improves dramatically when given access to biological data. © 2023 IEEE.

关键词： Classification (of information)

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28-4: Positive Aging Resulted in Highly Efficient Blue Quantum Rod Light Emitting Diodes

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SID Symposium Digest of Technical Papers 2024年第1期55卷 360-363页

作者： Kumar Mallem Zebing Liao Maksym F. Prodanov Jianxin Song Debjyoti Bhadra Roja Ramani Gavara Abhishek K. Srivastava State Key Laboratory on Advanced Displays and Optoelectronics Technologies and Centre for Display Research Department of Electronics and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China 999077 IAS Center for Quantum Technologies The Hong Kong University of Science and Technology Hong Kong China

The synthesis of blue quantum rods (QRs) is challenging due to fast kinetics and a significant shift in color during the synthesis of w-CdSe seeds. Herein, we synthesized highly bright CdSe/ZnxCd1-xS/ZnS core/shell blue QRs with an emission wavelength of 480 nm and fabricated blue QRLEDs. We found that the aging process of the QRLEDs significantly improved their performance by slowly reacting Al with ZnMgO and forming an AlOx interlayer among the ZnMgO/Al, which increased electron injection by over 3x times. The AlOx layer minimized the electron injection barrier and produced highly conductive ZnMgO by forming oxidation deficiency. Additionally, the exciton quenching suppressed at the QRs/ZnMgO/Al interface, and film morphology also changed among the QRs/ZnMgO stacks with the time. As a result, the aged QRLEDs showed an EQE of 9.4% and luminance of 61500 cd/m 2 , which is 2.6x and 3x times higher than the freshly made QRLEDs. These are the highest reported numbers in EQE and luminance of blue QRLEDs. Additionally, the durability of the blue QRLEDs significantly improved from 50 to 300 hours at 100 cd/m 2 initial luminance. Overall, the use of QRs in LEDs greatly improves device efficiency and brightness.

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