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arXiv 2022年

作者： Shen, Tao Hu, Zhihang Sun, Siqi Liu, Di Wong, Felix Wang, Jiuming Chen, Jiayang Wang, Yixuan Hong, Liang Xiao, Jin Zheng, Liangzhen Krishnamoorthi, Tejas King, Irwin Wang, Sheng Yin, Peng Collins, James J. Li, Yu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Zelixir Biotech Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Wyss Institute for Biologically Inspired Engineering Harvard University BostonMA United States Department of Systems Biology Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Broad Institute of MIT and Harvard CambridgeMA United States Synthetic Biology Center Massachusetts Institute of Technology CambridgeMA United States Integrated Biosciences Redwood CityCA United States OneAIM Ltd. Hong Kong Shanghai AI Laboratory Shanghai China The CUHK Shenzhen Research Institute Hi-Tech Park Shenzhen Nanshan China Shenzhen Institute of Advanced Technology Guangdong Shenzhen China School of Molecular Sciences Center for Molecular Design and Biomimetics the Biodesign Institute Arizona State University TempeAZ United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States

Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The structural flexibility of RNA, which leads to scarcity of experimentally determined data, complicates computational prediction efforts. Here, we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. By integrating an RNA language model pre-trained on ∼23.7 million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction. Retrospective evaluations on RNA-Puzzles and CASP15 natural RNA targets demonstrate RhoFold+’s superiority over existing methods, including human expert groups. Its efficacy and generalizability are further validated through cross-family and cross-type assessments, as well as time-censored benchmarks. Additionally, RhoFold+ predicts RNA secondary structures and inter-helical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Route Coverage Testing for Autonomous Vehicles via Map Modeling

Route Coverage Testing for Autonomous Vehicles via Map Model...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yun Tang Yuan Zhou Fenghua Wu Yang Liu Jun Sun Wuling Huang Gang Wang Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Alibaba Group Alibaba DAMO Academy China

Autonomous vehicles (AVs) play an important role in transforming our transportation systems and relieving traffic congestion. To guarantee their safety, AVs must be sufficiently tested before they are deployed to public roads. Existing testing often focuses on AVs’ collision avoidance on a given route. There is little work on the systematic testing for AVs’ route planning and tracking on a map. In this paper, we propose CROUTE, a novel testing method based on a new AV testing criterion called route coverage. First, the map is modeled as a labeled Petri net, where roads, junctions, and traffic signs are modeled as places, transitions, and labels, respectively. Second, based on the Petri net, we define junctions’ topology features and route features for junction classification. The topology feature describes the topology of roads forming the junction, and the route feature identifies the actions that a vehicle can take to follow a route. They can characterize route types on a map. Hence, route coverage measures how many route types are covered. We then propose a systematic method that aims to cover all route types for a well-designed AV system with a small number of test cases. We implement and evaluate CROUTE on Baidu Apollo running with the LGSVL simulator. We carry out testing on the map from a section of San Francisco and find six different types of issues in Apollo. The experiment results show the validity of route coverage and the efficiency of CROUTE.

关键词： Systematics Roads Petri nets Web and internet services Transportation Topology Planning

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Patient contrastive learning: A performant, expressive, and practical approach to ECG modeling

arXiv

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arXiv 2021年

作者： Diamant, Nathaniel Reinertsen, Erik Song, Steven Aguirre, Aaron Stultz, Collin Batra, Puneet Research Laboratory of Electronics MIT Data Sciences Platform Broad Institute of MIT and Harvard Division of Cardiology Massachusetts General Hospital Center for Systems Biology Massachusetts General Hospital Research Institute Harvard Medical School Wellman Center for Photomedicine Massachusetts General Hospital Research Institute Harvard Medical School Department of Electrical Engineering and Computer Science MIT Harvard-MIT Division of Health Sciences and Technology

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of ECGs from a large number of unlabeled examples. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs, and demonstrate substantial improvements across multiple new tasks when there are fewer than 5,000 labels. We release our model to extract ECG representations at https://***/broadinstitute/ml4h/tree/master/model_zoo/PCLR. Copyright © 2021, The Authors. All rights reserved.

关键词： Supervised learning

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

arXiv

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arXiv 2021年

作者： Kung, Pou-Chun Wang, Chieh-Chih Lin, Wen-Chieh The Graduate Degree Program of Robotics National Yang Ming Chiao Tung University Hsinchu Taiwan The Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan The Mechanical and Mechatronics Systems Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the two sensors. For example, some objects visible to lidar are invisible to radar, and some objects occluded in lidar scans are visible in radar images because of the radar's penetrating capability. These sensing differences cause false positive and penetrating capability degeneration, respectively. In this paper, we propose training data preprocessing and polar sliding window inference to solve the issues. The data preprocessing aims to reduce the effect caused by radar-invisible measurements in lidar scans. The polar sliding window inference aims to solve the limited sensing range issue by applying a near-range trained network to the long-range region. Instead of using common Cartesian representation, we propose to use polar representation to reduce the shape dissimilarity between long-range and near-range data. We find that extending a near-range trained network to long-range region inference in the polar space has 4.2 times better IoU than in Cartesian space. Besides, the polar sliding window inference can preserve the radar penetrating capability by changing the viewpoint of the inference region, which makes some occluded measurements seem non-occluded for a pretrained network. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical radar

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TraCemop: Towards Federated Learning with Traceable Contribution Evaluation and Model Ownership Protection

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Jingwei Zhou, Zihan Sun, Rong Liu, Lei Lu, Rongxing Dustdar, Schahram Niyato, Dusit Ministry of Education Engineering Research Center of Blockchain Technology Application and Evaluation China Xidian University Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510555 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Queen's University School of Computing KingstonONK7L 2N8 Canada TU Wien Distributed Systems Group Austria UPF ICREA Barcelona Spain Nanyang Technological University School of Computer Science and Engineering Singapore

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership. © 2002-2012 IEEE.

关键词： Federated learning

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Advanced Thermal Optimization of Solar Stills Using Encapsulated Phase Change Materials and Graphene Oxide Nanoparticles for Enhanced Energy Efficiency

Engineering Reports

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engineering Reports 2025年第6期7卷

作者： T. Sathish R. Saravanan Jayant Giri Ahmad O. Hourani Nidhal Becheikh Boutheyna Belhaj Bettaieb A. Johnson Santhosh A. Anderson Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Mining Research Center Northern Border University Arar Saudi Arabia Engineering School (ENIG) RL Processes Energetic Environment and Electric Systems (PEESE) University of Gabes Gabes Tunisia Faculty of Mechanical Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia Department of Aeronautical Engineering Sathyabama Institute of Science and Technology Chennai India

Solar desalination is a widely employed system that harnesses solar energy to produce drinking water. The key advantages of solar stills include that the generation of freshwater is a renewable energy-based, eco-friendly, and cost-effective process. This sustainable approach addresses the critical need for clean water while minimizing ecological impact and offering economic benefits. In the solar still system, the configuration of the absorber plays a crucial role, as an ineffective absorber can lead to lower thermal performance and reduced water productivity. This investigation focuses on an absorber design that incorporates a tube container containing Phase Change Material (PCM) of paraffin wax. The encapsulation of PCM within the still enhances heat transfer and provides heat energy, especially during radiation fluctuations. Moreover, the thermal properties of the PCM were improved by introducing graphene oxide nanoparticles dispersed within. Three different concentrations of graphene oxide (0.3 wt%, 0.6 wt%, and 0.9 wt%) were investigated. It was explored that paraffin with 0.9 wt% graphene oxide nanoparticle demonstrates superior thermal performance compared to paraffin alone. Significantly, at a concentration of 0.9 wt%, the paraffin/graphene oxide nanoparticles showed increased water productivity, temperature, and still thermal efficiency by around 33.9%, 41.2%, and 68.7%, respectively.

关键词： desalination encapsulated PCM graphene oxide nanoparticles water productivity and thermal efficiency

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Slow Retrial Asymptotics for a Single Server Queue with Two-Way Communication and Markov Modulated Poisson

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Journal of systems Science and systems engineering 2019年第2期28卷 181-193页

作者： Anatoly Nazarov Tuan Phung-Duc Svetlana Paul Institute of Applied Mathematics and Computer Science National Research Tomsk State University 36 Lenina ave. 634050 Tomsk Russian Federation Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 305-8573 Japan

In this paper, we consider an MMPP/M/1/1 retrial queue where incoming fresh calls arrive at the server according to a Markov modulated Poisson process (MMPP). Upon arrival, an incoming call either occupies the server if it is idle or joins a virtual waiting room called orbit if the server is busy. From the orbit, incoming calls retry to occupy the server in an exponentially distributed time and behave the same as a fresh incoming call. After an exponentially distributed idle time, the server makes an outgoing call whose duration is also exponentially distributed but with a different parameter from that of incoming calls. Our contribution is to derive the first order (law of large numbers) and the second order (central lim让 theorem) asymptotics for the distribution of the number of calls in the orb计 under the condition that the retrial rate is extremely low. The asymptotic results are used to obtain the Gaussian approximation for the distribution of the number of calls in the orbit. Our result generalizes earlier results where Poisson input was assumed.

关键词： Retrial queueing system incoming calls and outgoing calls MMPP-process asymptotic analysismethod gaussian approximation

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Electromagnetic Exposure Aware RSMA for Multiuser Massive MIMO Uplink

Electromagnetic Exposure Aware RSMA for Multiuser Massive MI...

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International Conference on Signal Processing systems, ICSPS

作者： Hanyu Jiang Li You Ahmed Elzanaty Jue Wang Wenjin Wang Xiqi Gao Mohamed-Slim Alouini National Mobile Communications Research Laboratory Southeast University Nanjing China 5GIC & 6GIC Institute for Communication Systems University of Surrey Guildford United Kingdom School of Information Science and Technology Nantong University Nantong China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Makkah Province Saudi Arabia

This paper investigates the rate-splitting multiple access (RSMA) transmission design for multiuser multiple-input multiple-output (MIMO) uplink with EM exposure constraints. Specifically, the transmit covariance matrices and decoding order are optimized at the users and BS, respectively, via utilizing statistical channel state information (CSI) to maximize the energy efficiency (EE). The problem is formulated as non-convex mixed integer program, which is divided into the equivalent two subproblems. We first handle the inner problem by adopting the minorization-maximization (MM) and Dinkelbachs methods. Then, a modified water-filling scheme is proposed to obtain the transmit covariance matrices with fixed decoding permutation. For the outer problem, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure aware EE maximization scheme for uplink RSMA.

关键词：

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Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication 28

Keep it Consistent: Topic-Aware Storytelling from an Image S...

引用

28th International Conference on Computational Linguistics, COLING 2020

作者： Wang, Ruize Wei, Zhongyu Cheng, Ying Li, Piji Shan, Haijun Zhang, Ji Zhang, Qi Huang, Xuanjing Academy for Engineering and Technology Fudan University China School of Data Science Fudan University China Research Institute of Intelligent and Complex Systems Fudan University China Tencent AI Lab. China Zhejiang Lab. China School of Computer Science Fudan University China

ISBN: (纸本)9781952148279

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method’s good ability in generating stories with higher quality compared to state-of-the-art methods. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Iterative methods

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Chance Constrained Economic Dispatch Considering the Capability of Network Flexibility Against Renewable Uncertainties

arXiv

引用

arXiv 2021年

作者： Song, Yue Liu, Tao Hill, David J. The Department of Control Science and Engineering Tongji University Shanghai201804 China The National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China The Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai200120 China Hong Kong The HKU Shenzhen Institute of Research and Innovation Shenzhen China The Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3000 Australia The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong

This paper incorporates a continuous-type network flexibility into chance constrained economic dispatch (CCED). In the proposed model, both power generations and line susceptances are continuous variables to minimize the expected generation cost and guarantee a low probability of constraint violation in terms of generations and line flows under renewable uncertainties. From the analytical form of CCED, we figure out the mechanism of network flexibility against uncertainties-while renewable uncertainties shrink the usable line capacities and aggravate transmission congestion, network flexibility mitigates congestion by re-routing the base-case line flows and reducing the line capacity shrinkage caused by uncertainties. Further, we propose an alternate iteration solver for this problem. By duality theory, we set up a master problem in the form of second-order cone programming to optimize generation dispatch scheme and a subproblem in the form of linear programming to optimize line susceptances. A satisfactory solution can be obtained efficiently by alternately solving these two problems. The proposed method applies to both Gaussian uncertainty and non-Gaussian uncertainty by means of Gaussian mixture model. The case studies on the IEEE 14-bus system and IEEE 118-bus system suggest that network flexibility can significantly improve operational economy while ensuring security under uncertainties. © 2021, CC BY.

关键词： Second-order cone programming

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