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arXiv 2020年

作者： Li, You Zhuang, Yuan Hu, Xin Gao, Zhouzheng Hu, Jia Chen, Long He, Zhe Pei, Ling Chen, Kejie Wang, Maosong Niu, Xiaoji Chen, Ruizhi Thompson, John Ghannouchi, Fadhel El-Sheimy, Naser Department of Geomatics Engineering University of Calgary State Key Laboratory of Surveying Mapping and Remote Sensing Wuhan University School of Electronic Engineering Beijing University of Posts and Telecommunications Department of Land Sciences China University of Geosciences Beijing China Department of Computer Science University of Exeter School of Data and Computer Science Sun Yat-sen University Shanghai Key Laboratory of Navigation and Location Based Services School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Earth and Space Sciences Southern University of Science and Technology College of Intelligence Science and Technology National University of Defense Technology NSS Research Center Wuhan University School of Engineering University of Edinburgh

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for massmarket localization applications. However, there are various error sources that have limited the localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review-localization data sources-localization algorithms-localization error sources and mitigation-localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors. Copyright © 2020, The Authors. All rights reserved.

关键词： Wide area networks

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Quantum advantage in learning from experiments

arXiv

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arXiv 2021年

作者： Huang, Hsin-Yuan Broughton, Michael Cotler, Jordan Chen, Sitan Li, Jerry Mohseni, Masoud Neven, Hartmut Babbush, Ryan Kueng, Richard Preskill, John McClean, Jarrod R. Institute for Quantum Information and Matter Caltech PasadenaCA United States Department of Computing and Mathematical Sciences Caltech PasadenaCA United States Google Quantum AI 340 Main Street VeniceCA90291 United States Cambridge MA02138 United States Black Hole Initiative CambridgeMA02138 United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Simons Institute for the Theory of Computing BerkeleyCA United States Microsoft Research AI RedmondWA98052 United States Institute for Integrated Circuits Johannes Kepler University Linz Austria AWS Center for Quantum Computing PasadenaCA91125 United States

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have significant advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer. We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments. The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. In some tasks, the quantum processing needed to achieve the exponential advantage can be modest;for example, one can simultaneously learn about many noncommuting observables by processing only two copies of the system. Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today's relatively noisy quantum processors. Our results highlight how quantum technology can enable powerful new strategies to learn about nature. © 2021, CC BY.

关键词： Principal component analysis

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Sharpening semantic gradient in a planarized sentence representation

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Neural networks : the official journal of the International Neural Network Society 2025年 190卷 107687页

作者： Caiwei Yang Yanping Chen Shuai Yu Bo Dong Jiwei Qin Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang 550025 Guizhou PR China State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.cwyang21@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: ypench@***. State Key Laboratory of Public Big Data Guizhou university Guiyang 550025 Guizhou PR China. Electronic address: gs.syu21@***. Department of Computer Science and Technology Xi'an Jiaotong University Xi'an 710049 Shanxi PR China. Electronic address: dong.bo@***. College of Information Science and Engineering Xinjiang University Urumqi 830046 Xinjiang PR China. Electronic address: jwqin_xju@***.

Mapping a sentence into a two-dimensional representation has the advantage of unfolding nested semantic structures in a sentence and encoding the interaction between tokens. In the planarized sentence representation, neighboring elements denote overlapped linguistic units in a sentence. An important phenomenon is that the semantic information of a true linguistic unit may penetrate neighboring elements, which blurs the semantic edge of a linguistic unit and disturbs the planarized sentence representation. Therefore, sharpening the semantic gradient helps aggravate semantic information from neighborhoods and depressing noises in neighboring elements. This paper reveals the mechanism of sharpening semantic gradient in the planarized sentence representation. Our method is evaluated on six evaluation datasets. The results show impressive improvement on three information extraction tasks. The success indicates that representing and processing sentences in a two-dimensional representation has a great potential to decode the sentential semantic structure and support sentence-level information extraction. Our code to implement the model is available at: https://***/caiwyang/Semantic_Gradient.

关键词： Information extraction Planarized sentence representation Semantic gradient

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Molecular and Serologic Diagnostic Technologies for SARS-CoV-2

arXiv

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arXiv 2022年

作者： Rando, Halie M. Brueffer, Christian Lordan, Ronan Dattoli, Anna Ada Manheim, David Meyer, Jesse G. Mundo, Ariel I. Perrin, Dimitri Mai, David Wellhausen, Nils Gitter, Anthony Greene, Casey S. Department of SystemsPharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and MolecularGenetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Clinical Sciences Lund University Lund Sweden Institute for TranslationalMedicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Department of Pathology Laboratory Medicine The Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Systems Pharmacology & Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States 1DaySooner Delaware United States Communication Research Center School of Public Health University of Haifa Haifa Israel Technion Israel Institute of Technology Haifa Israel Department of Biochemistry Medical College of Wisconsin MilwaukeeWI United States Department of Biomedical Engineering University of Arkansas FayettevilleAR United States School of Computer Science Queensland University of Technology Brisbane Australia Centre for Data Science Queensland University of Technology Brisbane Australia Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Center for Cellular Immunotherapies Perelman School of Medicine Parker Institute for Cancer Immunotherapy University of Pennsylvania PhiladelphiaPA United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Medical

The COVID-19 pandemic has presented many challenges that have spurred biotechnological research to address specific problems. Diagnostics is one area where biotechnology has been critical. Diagnostic tests play a vital role in managing a viral threat by facilitating the detection of infected and/or recovered individuals. From the perspective of what information is provided, these tests fall into two major categories, molecular and serological. Molecular diagnostic techniques assay whether a virus is present in a biological sample, thus making it possible to identify individuals who are currently infected. Additionally, when the immune system is exposed to a virus, it responds by producing antibodies specific to the virus. Serological tests make it possible to identify individuals who have mounted an immune response to a virus of interest and therefore facilitate the identification of individuals who have previously encountered the virus. These two categories of tests provide different perspectives valuable to understanding the spread of SARS-CoV-2. Within these categories, different biotechnological approaches offer specific advantages and disadvantages. Here we review the categories of tests developed for the detection of the SARS-CoV-2 virus or antibodies against SARS-CoV-2 and discuss the role of diagnostics in the COVID-19 pandemic. © 2022, CC BY.

关键词： SARS

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A Movie Trailer Recommendation System Based on Pre-trained Vector of Relationship and Scenario Content Discovered from Plot Summaries and Social Media

A Movie Trailer Recommendation System Based on Pre-trained V...

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International Conference on Technologies and applications of Artificial Intelligence (TAai)

作者： Chun-Yu Chien Guo-Hao Qiu Wen-Hsiang Lu Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Data Research & Development Center CTBC Bank Taipei Taiwan

Posting articles on the social platform is the favorite activity of young people. With the potential of digital movie and tv series industry, developing an automatic movie recommendation engine becomes a popular issue. Traditionally movie recommendation is based on structured information like director, players, rough class, etc. Recently, there are more and more researches trying to make a recommendation based on context information like music recommendation based on lyrics with the word vector representation. However, in the long text scenario, the recommendation based on all context vector makes the inference very *** this paper, we propose effective features types, relationships, and scenarios, to extract important information then improve the recommendation. Furthermore, comparing different pre-training model, we try to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between social media articles and movie plot.

关键词： Motion pictures Social networking (online) Semantics Feature extraction Bit error rate Task analysis Training

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Game theory of pandemic control: Can collaborative vaccine allocation strategies lead to better outcomes?

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IJID Regions 2025年 14卷 100535-100535页

作者： Yiwen Wang Yang Ye Xiaoyi Su Qingpeng Zhang Hsiang-Yu Yuan Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Center for Infectious Disease Modeling and Analysis Yale School of Public Health New Haven CT 06520 Department of Biostatistics City University of Hong Kong Hong Kong China HKU Musketeers Foundation Institute of Data Science and Department of Pharmacology and Pharmacy The University of Hong Kong Hong Kong China Department of Biomedical Sciences Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong China Centre for Applied One Health Research and Policy Advice Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong China

Introduction Increasing vaccine uptake is an effective way to prevent COVID-19 infection in individual countries after the relaxation of social distancing. However, under the limited vaccine resources, vaccine inequality between high- and low-income countries can also produce more infection and cost. Reducing such inequality relies on a more cooperative (unselfish) strategy, such as vaccine donation from high-income countries, which may initially increase their cost. The study aims to analyze the impact of vaccine inequality on cooperative decision-making between two populations. The results provide important insights on achieving global control of infectious disease outbreaks. Material and Methods We developed a transmission model incorporating virus evolution under different vaccine-induced natural selection and the migration to allow a long-term repeated outbreak. Vaccine allocation strategies were implemented between two populations the high vaccine coverage population (HC) and the low coverage population (LC). A game theory approach was employed to explore whether pursuing minimum cost for both sides (cooperative strategy) or itself (non-cooperative strategy) for each population. We estimated the effect of each strategy on the total cost (i.e. the sum of vaccine, hospitalization and labor costs). The Nash equilibrium was obtained by minimizing their own cost for individual countries under different vaccine inequality scenarios (from low to high inequality). Results Under low inequality, both populations achieved the same optimal cost with both cooperative and non-cooperative strategies. Under medium or high inequality, both populations reached a Nash equilibrium with non-cooperative strategies and could not reach global optimum. When inequality was high, HC tended towards vaccine donation even though non-cooperative strategies were adopted. Discussion When inequality is high, although cooperative decisions can obtain global optimal outcomes, populations tend to

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The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCai BraTS-PEDs)

arXiv

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arXiv 2024年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Gandhi, Deep Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Franson, Andrea Gottipati, Anurag Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Khalili, Neda Kofler, Florian LaBella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Sheth, Nakul Tu, Wenxin Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Zapaishchykova, Anna Bornhorst, Miriam Deutsch, Michelle Fouladi, Maryam Lazow, Margot Mikael, Leonie Hummel, Trent Kann, Benjamin de Blank, Peter Hoffman, Lindsey Aboian, Mariam Nabavizadeh, Ali Packer, Roger Bakas, Spyridon Resnick, Adam Rood, Brian Vossough, Arastoo Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center DurhamNC United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States C. S. Mott Children’s Hospital University of Michigan MI United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center OH United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center DurhamNC United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of De

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCai BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCai BraTS-PEDs challenge brings together clinicians and ai/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2024, CC BY.

关键词： Deep learning

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Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues (vol 3, e883, 2023)

CURRENT PROTOCOLS

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CURRENT PROTOCOLS 2023年第10期3卷 e883页

作者： Yen, Allen Mateusiak, Chase Sarafinovska, Simona Gachechiladze, Mariam A. Guo, Juanru Chen, Xuhua Moudgil, Arnav Cammack, Alexander J. Hoisington-Lopez, Jessica Crosby, MariaLynn Brent, Michael R. Mitra, Robi D. Dougherty, Joseph D. Department of Genetics Washington University School of Medicine Saint Louis Missouri Department of Psychiatry Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Software Supervision Validation Visualization Writing - original draft Writing - review & editing Edison Family Center for Genome Sciences and Systems Biology Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Methodology Software Visualization Writing - original draft Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - original draft Writing - review & editing Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - review & editing Contribution: Data curation Methodology Software Writing - review & editing Contribution: Methodology Writing - review & editing Current address: Department of Ophthalmology Stanford University School of Medicine Stanford California Contribution: Conceptualization Methodology Software Validation Department of Neurology Washington University School of Medicine Saint Louis Missouri Current address: Department of Neurodegenerative Diseases University College London London United Kingdom Contribution: Conceptualization Methodology Validation Contribution: Investigation Writing - review & editing Department of Computer Science and Engineering Washington University Saint Louis Missouri Contribution: Funding acquisition Contribution: Conceptualization Funding acquisition Project administration Resources Software Supervision Writing - review & editing Intellectual and Developmental Disabilities Research Center Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Funding acquisition Project administration Resources Supervision Writing - origin

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. This article was corrected on 16 October 2023. See the end of the full text for details. Basic Protocol 1 : Preparation and delivery of Calling Cards reagents Support Protocol 1 : Next-generation sequencing quantification of barcode

关键词： calling cards epigenetics enhancer usage gene regulation transcription factor binding

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Robust self-supervised denoising of voltage imaging data using CellMincer

Npj imaging

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Npj imaging 2024年第1期2卷 51页

作者： Brice Wang Tianle Ma Theresa Chen Trinh Nguyen Ethan Crouse Stephen J Fleming Alison S Walker Vera Valakh Ralda Nehme Evan W Miller Samouil L Farhi Mehrtash Babadi Data Sciences Platform (DSP) Broad Institute of MIT and Harvard Cambridge MA USA. Department of Computer Science and Engineering Oakland University Rochester MI USA. Spatial Technology Platform (STP) Broad Institute of MIT and Harvard Cambridge MA USA. Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard Cambridge MA USA. Departments of Molecular & Cell Biology and Chemistry and Helen Wills Neuroscience Institute UC Berkeley Berkeley CA USA.

Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such as matrix factorization, impose rigid assumptions about noise and signal structures, while existing deep learning approaches fail to fully capture the rapid dynamics and complex dependencies inherent in voltage imaging data. Here, we introduce CellMincer, a novel self-supervised deep learning method specifically developed for denoising voltage imaging datasets. CellMincer operates by masking and predicting sparse pixel sets across short temporal windows and conditions the denoiser on precomputed spatiotemporal auto-correlations to effectively model long-range dependencies without large temporal contexts. We developed and utilized a physics-based simulation framework to generate realistic synthetic datasets, enabling rigorous hyperparameter optimization and ablation studies. This approach highlighted the critical role of conditioning on spatiotemporal auto-correlations, resulting in an additional 3-fold SNR gain. Comprehensive benchmarking on both simulated and real datasets, including those validated with patch-clamp electrophysiology (EP), demonstrates CellMincer's state-of-the-art performance, with substantial noise reduction across the frequency spectrum, enhanced subthreshold event detection, and high-fidelity recovery of EP signals. CellMincer consistently outperforms existing methods in SNR gain (0.5-2.9 dB) and reduces SNR variability by 17-55%. Incorporating CellMincer into standard workflows significantly improves neuronal segmentation, peak detection, and functional phenotype identification, consistently surpassing current methods in both SNR gain and consistency.

关键词： Cellular imaging Fluorescence imaging Image processing

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A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

arXiv

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arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

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