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

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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Real-time Digital RF Emulation – I: The Direct Path Computational Model

arXiv

引用

arXiv 2024年

作者： DeLude, Coleman Driscoll, Joe Mukherjee, Mandovi Rahman, Nael Kamal, Uday Mao, Xiangyu Khan, Sharjeel Sivaraman, Hariharan Huang, Eric McHarg, Jeffrey Swaminathan, Madhavan Pande, Santosh Mukhopadhyay, Saibal Romberg, Justin The School of Electrical and Computer Engineering The Georgia Institute of Technology AtlantaGA United States The Pennsylvania State University State CollegePA United States Apple Inc. CupertinoCA United States Nvidia Corp. Santa ClaraCA United States The Advanced Concepts and Technologies Group The MIT Lincoln Lab LexingtonMA United States

In this paper we consider the problem of developing a computational model for emulating an RF channel. The motivation for this is that an accurate and scalable emulator has the potential to minimize the need for field testing, which is expensive, slow, and difficult to replicate. Traditionally, emulators are built using a tapped delay line model where long filters modeling the physical interactions of objects are implemented directly. For an emulation scenario consisting of M objects all interacting with one another, the tapped delay line model’s computational requirements scale as O(M3) per sample: there are O(M2) channels, each with O(M) complexity. In this paper, we develop a new "direct path" model that, while remaining physically faithful, allows us to carefully factor the emulator operations, resulting in an O(M2) per sample scaling of the computational requirements. The impact of this is drastic, a 200 object scenario sees about a 100× reduction in the number of per sample computations. Furthermore, the direct path model gives us a natural way to distribute the computations for an emulation: each object is mapped to a computational node, and these nodes are networked in a fully connected communication graph. Alongside a discussion of the model and the physical phenomena it emulates, we show how to efficiently parameterize antenna responses and scattering profiles within this direct path framework. To verify the model and demonstrate its viability in hardware, we provide several numerical experiments produced using a cycle level C++ simulator of a hardware implementation of the model. © 2024, CC BY.

关键词： Computation theory

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SIOD: Single Instance Annotated Per Category Per Image for Object Detection

arXiv

引用

arXiv 2022年

作者： Li, Hanjun Pan, Xingjia Yan, Ke Tang, Fan Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-Sen University China Youtu Lab Tencent China Jilin University China Peng Cheng Laboratory China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled and unlabeled data. In this study, we propose the Single Instance annotated Object Detection (SIOD), requiring only one instance annotation for each existing category in an image. Degraded from inter-task (WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD provides more reliable and rich prior knowledge for mining the rest of unlabeled instances and trades off the annotation cost and performance. Under the SIOD setting, we propose a simple yet effective framework, termed Dual-Mining (DMiner), which consists of a Similarity-based Pseudo label Generating module (SPLG) and a Pixel-level Group Contrastive Learning module (PGCL). SPLG firstly mines latent instances from feature representation space to alleviate the annotation missing problem. To avoid being misled by inaccurate pseudo labels, we propose PGCL to boost the tolerance to false pseudo labels. Extensive experiments on MS COCO verify the feasibility of the SIOD setting and the superiority of the proposed method, which obtains consistent and significant improvements compared to baseline methods and achieves comparable results with fully supervised object detection (FSOD) methods with only 40% instances annotated. Code is available at https://***/solicucu/SIOD. Copyright © 2022, The Authors. All rights reserved.

关键词： Object recognition

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Predictive analysis of the effects of personality traits on an academic program 2

Predictive analysis of the effects of personality traits on ...

引用

2nd International Conference on advanced Information and Communication Technology, ICAICT 2020

作者： Kamal, Nabila Sarker, Farhana Mukta, Md. Saddam Hossain Mamun, Khondaker A. United International University Advanced Intelligent Multidisciplinary Systems Lab Department of Computer Science and Enginnering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh United International University Department of Computer Science and Enginnering Dhaka Bangladesh

ISBN: (纸本)9780738123233

The choice of academic program is a crucial selection as this decision plays a major role in enhancing opportunities in career path. Young adolescents often experience indecisions while choosing their academic major, which may lead to uncertainty in future. Therefore, to assist students to find the right field of study, several researches related to personality dynamics have been conducted in recent years to determine correlation between academic fields and personalities of students and the result showed evident association. Consequently, this increases the productivity and success rate in comparison. This study determines the differences amongst the students of three popular academic programs in Bangladesh and envisage the appropriate academic choice for the prospective students by analyzing their personality traits. For this study, we have collected data from a total of 135 participants. We used students' Big5 Personality Factors (Extraversion, Openness, Agreeableness, Emotional Stability, and Conscientiousness), and academic information to develop the prediction model. Using Gaussian Classifier along with 10-fold cross-validation method, we have achieved an accuracy of 91.1%. The findings of this study demonstrate that the proposed framework has a great potential in assisting prospective students to make an informed decision to determine the suitable higher study options while increasing academic motivation ratio. © 2020 IEEE.

关键词： Students

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FLaaS6G: Federated Learning as a Service in 6G Using Distributed Data Management Architecture

FLaaS6G: Federated Learning as a Service in 6G Using Distrib...

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wenxuan Ye Xueli Ant Xueqiang Yan Mohammad Hamad Sebastian Steinhorst Department of Electrical and Computer Engineering Technical University of Munich Advanced Wireless Technology Laboratory Munich Research Center Huawei Technologies Duesseldorf GmbH Wireless Technology Lab 2012 Laboratories Huawei Technologies Co. Ltd

ISBN: (纸本)9781665435413

AI/ML is envisioned to play an essential role in 6G mobile communication systems. The privacy-preserving capabil-ities of Federated Learning (FL) make it promising in vertical applications; however, the central server-based system and lack of trusted data management limit its widespread use. To effectively support FL as a service from a network architecture perspective, this work provides a comprehensive design including three key features: First, the network architecture enables transparent and traceable data management based on Distributed Ledger Technology (DLT) platform, and realizes distributed and off-chain data storage by adopting Distributed Data Storage Entity (DDSE). Second, the central aggregator of an FL service is decoupled from the data management scheme mentioned above, and is decentralized through smart contracts for aggregator selection among a set of aggregator candidates, with the selected aggregator subsequently responsible for client selection and model aggregation. Third, a completed set of procedures for FL services operations is defined. A simulation system is developed to verify the feasibility of the proposed architecture and to study the impact of introducing the data management mechanisms on the overall performance overhead. The results show that the impact is related to the FL settings, with a worst-case time overhead of 15% observed in selected test cases, i.e., 15% of the total time spent on the interactions with the DLT platform and DDSE.

关键词： 6G mobile communication Distributed ledger Federated learning Smart contracts Memory Distributed databases Network architecture

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ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans

arXiv

引用

arXiv 2022年

作者： Chai, Zhizhong Lin, Huangjing Luo, Luyang Heng, Pheng-Ann Chen, Hao Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotation is very labor-intensive and time-consuming because radiologists need to investigate and annotate the rib fractures on a slice-by-slice basis. Although a few studies have proposed weakly-supervised methods or semi-supervised methods, they could not handle different forms of supervision simultaneously. In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance. Specifically, the proposed network contains an omni-supervised detection head, in which each form of annotation data corresponds to a unique classification branch. Furthermore, we proposed a dynamic label assignment strategy for different annotated forms of data to facilitate better learning for each branch. Moreover, we also design a confidence-aware classification loss to emphasize the samples with high confidence and further improve the model’s performance. Extensive experiments conducted on the testing dataset show our proposed method outperforms other state-of-the-art approaches consistently, demonstrating the efficacy of deep omni-supervised learning on improving rib fracture detection performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Modified U-Net Architeture based Ischemic Stroke Lesions Segmentation

Modified U-Net Architeture based Ischemic Stroke Lesions Seg...

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International Conference on Sciences and Techniques of Automatic Control and computer engineering (STA)

作者： Nabil Abdmouleh Amira Echtioui Fathi Kallel Ahmed Ben Hamida ATMS Lab Advanced Technologies for Medicine and Signals ENISSfax University Sfax Tunisia National Engineering School of Electronics and Communications Sfax University Tunisia Department IS College of Computer Science King Khaled University ‘KKU’ KSA

ISBN: (纸本)9781665482622

An ischemic stroke transpires when part of the brain tissue is unable to get oxygen and nutrients due to the interruption or the reduction of blood supply. A stroke is a medical emergency that can lead to serious complications if left untreated. Early action can reduce potential brain damage and other consequences. Therefore, quantitative assessment of ischemic stroke lesions using magnetic resonance imaging (MRI) is paramount for an accurate and decisive diagnosis. However, a diagnosis of hundreds of MRI-generated slices by trained specialists is time-consuming and requires painstaking attention to details. Automating this process using advanced image segmentation methods allows an exact localization of the tissue presenting lesions and provide an approximation of its volume. Automatic detection of lesions provides faster answers and better assessments to assist medical practitioners. In this paper, we propose a novel method for ischemic stroke lesions detection based on a modified UNet architecture. We use the data from 2015 Ischemic Stroke Lesion Segmentation Challenge (ISLES 2015) for performance evaluation of our proposed method. Experiments have shown that the latter outperforms the state-of-the-art methods.

关键词： Performance evaluation Location awareness Image segmentation Costs Magnetic resonance imaging Architecture computer architecture

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Uncertainty Quantification of Signal Integrity Analysis for Neuromorphic Chips

IEEE Transactions on Signal and Power Integrity

引用

IEEE Transactions on Signal and Power Integrity 2022年 1卷 160-169页

作者： Hanzhi Ma Da Li Tuomin Tao Xingjian Shangguan En-Xiao Liu Jose Schutt-Aine Andreas C. Cangellaris Er-Ping Li College of Information Science and Electronic Engineering ZJU-UIUC Institute Zhejiang University Hangzhou China Key Lab of Advanced Micro/Nano Electronic Devices and Smart Systems of Zhejiang University Hangzhou China Zhejiang-Singapore Innovation and AI Joint Research Lab China Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA A*STAR Institute of High Performance Computing Singapore

A dimensionality reduction based neural network framework is introduced for uncertainty quantification of time-domain response based on system uncertain design parameters for neuromorphic chips. The proposed method firstly makes use of the singular value decomposition (SVD) method to find the basis functions and corresponding coefficients of time-domain response, of which coefficients are used as a lower dimensional target outputs in neural network model compared with time sampling points prediction. This newly proposed method then develops an integrated neural network structure to simultaneously find the mean and variance of target coefficients with a combined definition of loss function, which can be utilized together with basis functions to construct the prediction interval of time-domain response. A memrisor-based crossbar array is applied in this work to verify the performance of the proposed method with the comparison of Monte Carlo method.

关键词： Biological neural networks Uncertainty Memristors Time-domain analysis Integrated circuit modeling Neuromorphics Signal integrity Neural networks Neuromorphics

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Observation of charge to spin conversion in p-orbital InBi alloy

arXiv

引用

arXiv 2025年

作者： Li, Gen Zhang, Ying Xu, Xiaoguang Shen, Lei Feng, Zheng Meng, Kangkang Li, Ang Cheng, Lu He, Kang Tan, Wei Wu, Yong Wu, Yihong Jiang, Yong Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Research Institute Chongqing Liang Jiang New Area Chongqing401123 China Department of Mechanical Engineering National University of Singapore 117575 Singapore Microsystem & Terahertz Research Center CAEP Chengdu610200 China Faculty of Materials and Manufacturing Beijing Key Lab of Microstructure and Properties of Advanced Materials Beijing University of Technology Beijing100124 China Institute of Quantum Materials and Devices School of Electronic and Information Engineering State Key Laboratory of Separation Membranes and Membrane Processes Tiangong University Tianjin300387 China

High density data storage and spin-logic devices require highly efficient all-electric control of spin moment. So far, charge-to-spin conversion through the spin Hall effect limits to d-orbital materials associated with strong spin-orbit coupling, especially heavy metals. However, d-orbital heavy metals with strong spin-orbit coupling results in a short spin diffusion length, which restricts the spin transport and accumulation in spintronic devices. Therefore, the conflict between charge-to-spin conversion efficiency and spin transport ability is irreconcilable in d-orbital materials. Here, we report a large charge to spin conversion experimentally observed in the p-orbital In2Bi alloy, exhibiting the coexistence of a large spin Hall angle comparable to heave metal Pt and a long spin diffusion length (4 times that of Pt). First-principles calculations reveal that topological symmetry-protected gap opening near the Fermi level results in large Berry curvature-related spin Hall conductivity. Due to the delocalized nature of p-orbitals and semimetal properties of In2Bi, its spin current can overcome the physical barriers between spin Hall angle and spin diffusion length in d-orbital metals, thereby advancing the development of high-performance spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Heavy metals

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

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

引用

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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