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arXiv 2024年

作者： Peters, Benjamin DiCarlo, James J. Gureckis, Todd Haefner, Ralf Isik, Leyla Tenenbaum, Joshua Konkle, Talia Naselaris, Thomas Stachenfeld, Kimberly Tavares, Zenna Tsao, Doris Yildirim, Ilker Kriegeskorte, Nikolaus Zuckerman Mind Brain Behavior Institute Columbia University United States School of Psychology & Neuroscience University of Glasgow United Kingdom Department of Brain and Cognitive Sciences MIT United States McGovern Institute for Brain Research MIT United States NSF Center for Brains Minds and Machines MIT United States Quest for Intelligence Schwarzman College of Computing MIT United States Department of Psychology New York University United States Brain and Cognitive Sciences University of Rochester United States Center for Visual Science University of Rochester United States Department of Cognitive Science Johns Hopkins University United States Computer Science and Artificial Intelligence Laboratory MIT United States Department of Psychology Harvard University United States Center for Brain Science Harvard University United States Kempner Institute for Natural and Artificial Intelligence Harvard University United States Department of Neuroscience University of Minnesota United States DeepMind United Kingdom Data Science Institute Columbia University United States Dept of Molecular & Cell Biology University of California Berkeley United States Howard Hughes Medical Institute United States Department of Psychology Yale University United States Department of Statistics and Data Science Yale University United States Department of Psychology Columbia University United States Department of Neuroscience Columbia University United States Department of Electrical Engineering Columbia University United States

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision. © 2024, CC BY.

关键词： Feedforward neural networks

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Roof greening in major Chinese cities possibly afford a large potential carbon sink

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science Bulletin 2024年第20期69卷 3216-3220页

作者： Chao Yang Yinghui Zhang Min Chen Song Zhu Yuzhi Tang Zhixin Zhang Wei Ma Huizeng Liu Junyi Chen Bohui Tang Dejin Zhang Zhengdong Huang Xuqing Wang Wei Tu Cuiling Liu Tiezhu Shi Haiying Xu aihong Cui Fanyi Meng Tianhong Zhao Kai Guo Wenhao Guo Bi Fan Quande Qin Wuyang Hong Haishan Wu Bing Wang Jinrong Zeng Ka Wai Wu Qingquan Li Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Ministry of Natural Resources&Guangdong Key Laboratory of Urban InformaticsShenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Shenzhen Zhiyuan Space Innovation Technology Co. LtdShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China School of Geography and Ocean Science Nanjing UniversityNanjing 210008China School of Geography Nanjing Normal UniversityNanjing 210023China School of Civil Engineering Chongqing Jiaotong UniversityChongqing 400074China Institute for Advanced Study&Shenzhen University Shenzhen 518060China Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Landscape Architecture and Urban Planning Texas A&M UniversityTX 77840USA Department of Geography Hong Kong Baptist UniversityHong Kong 999077China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China College of Management Shenzhen UniversityShenzhen 518060China WeBank(Shenzhen)Co. LtdShenzhen 518060China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong 100872China Esri C

China is experiencing accelerated urbanisation,with a large number of people moving from rural to urban areas[1].It has resulted in large losses in the net primary production(NPP),biodiversity and carbon stocks and an increase in environmental pollution and CO_(2)emissions[2–4].In 2015,196 countries signed the Paris Agreement and committed to setting long-term goals to jointly manage climate change and reduce their individual emissions,aiming to control the increase in global average temperature from the pre-industrial level to below 2℃and to curtail the temperature rise within 1.5℃till the end of the 21st century[5].China is bolstering its efforts to achieve the climate change mitigation goals and has announced a plan for achieving carbon neutrality by 2060[6].The carbon neutrality goal poses a challenge to the current policies promoting rapid urbanisation across China.

关键词： jointly carbon goals

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Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach

arXiv

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arXiv 2021年

作者： Wang, Xiong Ye, Jiancheng Lui, John C.S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Network Technology Lab Hong Kong Research Center Huawei Technologies Co. Ltd. Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

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End-to-end ai Framework for Interpretable Prediction of Molecular and Crystal Properties

arXiv

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arXiv 2022年

作者： Park, Hyun Zhu, Ruijie Huerta, E.A. Chaudhuri, Santanu Tajkhorshid, Emad Cooper, Donny Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States

We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable ai inference. The framework is based on state-of-the-art ai models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these ai models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National center for Supercomputing applications to provide researchers with modern tools to conduct accelerated ai-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab. Copyright © 2022, The Authors. All rights reserved.

关键词： Organometallics

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Computational pathology: A survey review and the way forward

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Journal of Pathology Informatics 2024年 15卷 100357-100357页

作者： Hosseini, Mahdi S. Bejnordi, Babak Ehteshami Trinh, Vincent Quoc-Huy Chan, Lyndon Hasan, Danial Li, Xingwen Yang, Stephen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, Sina Zhang, Ryan Zhu, Jiadai Khaki, Samir Buin, Andrei Chaji, Fatemeh Salehi, Ala Nguyen, Bich Ngoc Samaras, Dimitris Plataniotis, Konstantinos N. Department of Computer Science and Software Engineering (CSSE) Concordia Univeristy Montreal H3H 2R9 QC Canada Qualcomm AI Research Qualcomm Technologies Netherlands B.V. Amsterdam Netherlands Institute for Research in Immunology and Cancer of the University of Montreal Montreal H3T 1J4 QC Canada The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) University of Toronto Toronto M5S 3G4 ON Canada Huron Digitial Pathology St. Jacobs N0B 2N0 ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton E3B 5A3 NB Canada University of Montreal Hospital Center Montreal H2X 0C2 QC Canada Department of Computer Science Stony Brook University Stony Brook 11794 NY United States

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

关键词： Clinical pathology computer aided diagnosis (CAD) Deep learning Digital pathology Survey Whole slide image (WSI)

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Practical Iterative Quantum Consensus Protocol with Sharding Construction

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IEEE Journal on Selected Areas in Communications 2025年

作者： Ying, Chenhao Du, Yuxuan Zhang, Weiting Jiang, Xikun Wang, Gang Jin, Haiming Li, Jie Luo, Yuan Tao, Dacheng Shanghai Jiao Tong University Department of Computer Science Shanghai China Zhejiang University Shanghai Jiao Tong University State Key Laboratory of Blockchain and Data Security Blockchain Advanced Research Center Jiangsu Wuxi214101 China Nanyang Technological University College of Computing&Data Science Singapore Beijing Jiaotong University School of Electronic and Information Engineering Beijing China University of Copenhagen Department of Computer Science Denmark Northeastern University School of Computer Science and Engineering Shenyang China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 China

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols. © 1983-2012 IEEE.

关键词： Quantum entanglement

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Provably Secure and Efficient Identity-Based Medical data Sharing Framework Using Cloud-Assisted Blockchain

SSRN

引用

SSRN 2025年

作者： Ghosh, Sudeep Islam, S.K. Hafizul Vasilakos, Athanasios V. Department of Information Technology Guru Nanak Institute of Technology Kolkata700114 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Center for AI Research University of Agder Grimstad4879 Norway Fuzhou University China

Cloud computing has become a popular paradigm in healthcare industries for ubiquitously sharing and accessing patient's electronic medical records (EMRs). However, for security reasons, all EMRs are stored in an encrypted format on a cloud server. However, searching encrypted EMR using the traditional plaintext search method is computationally infeasible. Public key encryption with keywords (PEKS) is used to search encrypted EMRs over an extensive database. The current EMR storage systems have problems with data leakage, improper user authorization, untrustworthy service providers, etc. Blockchain has exciting features like decentralization, transparency, and immutability. We design a blockchain-assisted identity-based medical data-sharing (BCT-IMDS) scheme to share EMRs among the cloud servers maintained at different hospitals. BCT-IMDS uses private and consortium blockchains to share and access the EMRs of patients. All participating hospitals keep the encrypted keywords and verification index in the consortium blockchain. BCT-IMDS scheme provides ciphertext, trapdoor indistinguishability, and designated-server testability in the random oracle model. We verified BCT-IMDS using Scyther verification tools and implemented it using the Python programming language, PBC, and the Charm crypto library. We considered 80, 112, 128, 192, and 256-bit security, estimated the computation, communication, and storage overheads of BCT-IMDS, and compared them with the existing PEKS schemes. © 2025, The Authors. All rights reserved.

关键词： Authorization

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Pointgs: Bridging and Fusing Geometric and Semantic Space for 3d Point Cloud Analysis

SSRN

引用

SSRN 2022年

作者： Jiang, Chenru Huang, Kaizhu Wu, Junwei Wang, Xinheng Xiao, Jimin Hussain, Amir Department of Computer Science University of Liverpool LiverpoolL69 7ZX United Kingdom Data Science Research Center Duke Kunshan University No. 8 Duke Avenue Kunshan215316 China Department of Electrical and Electronic Engineering Xi’an Jiaotong-Liverpool University Suzhou215123 China School of Computing Edinburgh Napier University EdinburghEH11 4BN United Kingdom

Directly processing 3D point cloud data becomes dominant in classification and segmentation tasks. Present mainstream point based methods usually focus on learning in either geometric space ( i.e. PointNet++) or semantic space ( i.e. DGCNN). Owing to the irregular and unordered data property of point cloud, these methods still suffer from drawbacks of either ambiguous local features aggregation in geometric space or poor global features extraction in semantic space. While few prior works address these two defects simultaneously by fusing information from the dual spaces, we make a first attempt to develop a synergistic framework, called PointGS. Leveraging both the strength of geometric structure and semantic representation, PointGS establishes a mutual supervision mechanism that can bridge the two spaces and fuse complementary information for better analyzing 3D point cloud data. Compared with existing popular networks, our work attains obvious performance improvement on all three mainstream tasks without any sophisticated operations. © 2022, The Authors. All rights reserved.

关键词： Semantics

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FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Cheng, Bingyang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Street Scene Semantic Understanding (denoted as S3U) is a crucial but complex task for autonomous driving (AD) vehicles. Their inference models typically face poor generalization due to domain-shift. Federated Learning (FL) has emerged as a promising paradigm for enhancing the generalization of AD models through privacy-preserving distributed learning. However, these FL AD models face significant temporal catastrophic forgetting when deployed in dynamically evolving environments, where continuous adaptation causes abrupt erosion of historical knowledge. This paper proposes Federated Exponential Moving Average (FedEMA), a novel framework that addresses this challenge through two integral innovations: (I) Server-side model’s historical fitting capability preservation via fusing current FL round’s aggregation model and a proposed previous FL round’s exponential moving average (EMA) model;(II) Vehicle-side negative entropy regularization to prevent FL models’ possible overfitting to EMA-introduced temporal patterns. Above two strategies empower FedEMA a dual-objective optimization that balances model generalization and adaptability. In addition, we conduct theoretical convergence analysis for the proposed FedEMA. Extensive experiments both on Cityscapes dataset and Camvid dataset demonstrate FedEMA’s superiority over existing approaches, showing 7.12% higher mean Intersection-over-Union (mIoU). © 2025, CC BY-NC-ND.

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Dose-finding design based on level set estimation in phase I cancer clinical trials

arXiv

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arXiv 2025年

作者： Seno, Keiichiro Matsui, Kota Iwazaki, Shogo Inatsu, Yu Takeno, Shion Matsui, Shigeyuki Department of Biostatistics Nagoya University Japan Department of Biostatistics Kyoto University Japan MI-6 Ltd. Department of Computer Science Nagoya Institute of Technology Japan Department of Mechanical Systems Engineering Nagoya University Japan Center for Advanced Intelligence Project RIKEN Japan Research Center for Medical and Health Data Science The Institute of Statistical Mathematics Japan

The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD estimation problem can be regarded as a level set estimation (LSE) problem whose objective is to determine the regions where an unknown function value is above or below a given threshold. Then, we propose a novel dose-finding design in the framework of LSE. The proposed design determines the next dose on the basis of an acquisition function incorporating uncertainty in the posterior distribution of the dose-toxicity curve as well as overdose control. Simulation experiments show that the proposed LSE design achieves a higher accuracy in estimating the MTD and involves a lower risk of overdosing allocation compared to existing designs, thereby indicating that it provides an effective methodology for phase I cancer clinical trial design. © 2025, CC BY.

关键词： Intellectual property core

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