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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： Medical applications

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Change detection in synthetic aperture radar images using a dual-domain network

arXiv

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arXiv 2021年

作者： Qu, Xiaofan Gao, Feng Dong, Junyu Du, Qian Li, Heng-Chao The School of Information Science and Engineering Ocean University of China Qingdao266100 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China

Change detection from synthetic aperture radar (SAR) imagery is a critical yet challenging task. Existing methods mainly focus on feature extraction in spatial domain, and little attention has been paid to frequency domain. Furthermore, in patch-wise feature analysis, some noisy features in the marginal region may be introduced. To tackle the above two challenges, we propose a Dual-Domain Network. Specifically, we take features from the discrete cosine transform domain into consideration and the reshaped DCT coefficients are integrated into the proposed model as the frequency domain branch. Feature representations from both frequency and spatial domain are exploited to alleviate the speckle noise. In addition, we further propose a multi-region convolution module, which emphasizes the central region of each patch. The contextual information and central region features are modeled adaptively. The experimental results on three SAR datasets demonstrate the effectiveness of the proposed model. Our codes are available at https://***/summitgao/SAR_CD_DDNet. © 2021, CC BY.

关键词： Frequency domain analysis

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Adaptive Bias Correction for Improved Subseasonal Forecasting

arXiv

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arXiv 2022年

作者： Mouatadid, Soukayna Orenstein, Paulo Flaspohler, Genevieve Cohen, Judah Oprescu, Miruna Fraenkel, Ernest Mackey, Lester Department of Computer Science University of Toronto TorontoON Canada Instituto de Matemática Pura e Aplicada Rio de Janeiro Brazil nLine Inc. BerkeleyCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Applied Ocean Science and Engineering Woods Hole Oceanographic Institution FalmouthMA United States Atmospheric and Environmental Research LexingtonMA United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA United States Department of Computer Science Cornell University IthacaNY United States Department of Biological Engineering Massachusetts Institute of Technology CambridgeMA United States Microsoft Research New England CambridgeMA United States

Subseasonal forecasting—predicting temperature and precipitation 2 to 6 weeks ahead—is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over baseline skills of 0.11-0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions. © 2022, CC BY.

关键词： Weather forecasting

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Automating the Practice of science – Opportunities, Challenges, and Implications

arXiv

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arXiv 2024年

作者： Musslick, Sebastian Bartlett, Laura K. Chandramouli, Suyog H. Dubova, Marina Gobet, Fernand Griffiths, Thomas L. Hullman, Jessica King, Ross D. Nathan Kutz, J. Lucas, Christopher G. Mahesh, Suhas Pestilli, Franco Sloman, Sabina J. Holmes, William R. Institute of Cognitive Science Osnabrück University Osnabrück49090 Germany Department of Cognitive Linguistic & Psychological Sciences Brown University ProvidenceRI02912 United States Centre for Philosophy of Natural and Social Science London School of Economics Lakatos Building Houghton Street LondonWC2A 2AE United Kingdom AaltoFI-00076 Finland Department of Computing Science University of Alberta 8900 114 St NW EdmontonABT6G 2S4 Canada Cognitive Science Program Indiana University 1101 E 10th St BloomingtonIN47405 United States School of Psychology University of Roehampton LondonSW15 4JD United Kingdom Departments of Psychology and Computer Science Princeton University PrincetonNJ United States Department of Computer Science Northwestern University IL United States Department of Chemical Engineering and Biotechnology University of Cambridge CambridgeCB3 0AS United Kingdom Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden Department of Applied Mathematics and Electrical and Computer Engineering University of Washington Seattle98195 United States School of Informatics University of Edinburgh 10 Crichton St. EH8 9AB United Kingdom Department of Materials Science and Engineering University of Toronto Canada Department of Psychology Department of Neuroscience The University of Texas AustinTX United States Department of Computer Science University of Manchester M13 9PL United Kingdom

Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?;What are the current bottlenecks of automating scientific practice?;and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. © 2024, CC BY.

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Multitask Deep Learning for Accurate Risk Stratification and Prediction of Next Steps for Coronary CT Angiography Patients

arXiv

引用

arXiv 2023年

作者： Lu, Juan Bennamoun, Mohammed Stewart, Jonathon Eshraghian, Jason K. Liu, Yanbin Chow, Benjamin Sanfilippo, Frank M. Dwivedi, Girish The Medical school and Harry Perkins Institute of Medical Research School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The Medical School Harry Perkins Institute of Medical Research The University of Western Australia PerthWA6009 Australia School of Computer Science and Software Engineering The University of Western Australia PerthWA6009 Australia The Department of Electrical and Computer Engineering University of California Santa CruzCA95064 United States The School of Computing Australian National University CanberraACT2601 Australia The Division of Cardiology Departments of Medicine University of Ottawa Heart Institute OttawaONK1Y 4W7 Canada The School of Population and Global Health The University of Western Australia PerthWA6009 Australia The Medical School Harry Perkins Institute of Medical Research The University of Western Australia Australia The Department of Cardiology Fiona Stanley Hospital PerthWA6154 Australia

Diagnostic investigation has an important role in risk stratification and clinical decision making of patients with suspected and documented Coronary Artery Disease (CAD). However, the majority of existing tools are primarily focused on the selection of gatekeeper tests, whereas only a handful of systems contain information regarding the downstream testing or treatment. We propose a multi-task deep learning model to support risk stratification and down-stream test selection for patients undergoing Coronary Computed Tomography Angiography (CCTA). The analysis included 14,021 patients who underwent CCTA between 2006 and 2017. Our novel multitask deep learning framework extends the state-of-the art Perceiver model to deal with real-world CCTA report data. Our model achieved an Area Under the receiver operating characteristic Curve (AUC) of 0.76 in CAD risk stratification, and 0.72 AUC in predicting downstream tests. Our proposed deep learning model can accurately estimate the likelihood of CAD and provide recommended downstream tests based on prior CCTA data. In clinical practice, the utilization of such an approach could bring a paradigm shift in risk stratification and downstream management. Despite significant progress using deep learning models for tabular data, they do not outperform gradient boosting decision trees, and further research is required in this area. However, neural networks appear to benefit more readily from multi-task learning than tree-based models. This could offset the shortcomings of using single task learning approach when working with tabular data. © 2023, CC BY.

关键词： Decision trees

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Decoding the Functional Interactome of Non-model Organisms with PHILHARMONIC 29th

Decoding the Functional Interactome of Non-model Organisms...

引用

29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Sledzieski, Sam Versavel, Charlotte Singh, Rohit Ocitti, Faith Devkota, Kapil Kumar, Lokender Shpilker, Polina Roger, Liza Yang, Jinkyu Lewinski, Nastassja Putnam, Hollie Berger, Bonnie Klein-Seetharaman, Judith Cowen, Lenore Center for Computational Biology Flatiron Institute New YorkNY United States Department of Computer Science Tufts University MedfordMA United States Departments of Biostatistics and Bioinformatics Duke University DurhamNC United States School of Biotechnology Faculty of Applied Sciences and Biotechnology Shoolini University Himachal Pradesh Solan173229 India School of Molecular Sciences and School of Ocean Futures Arizona State University TempeAZ United States Department of Mechanical Engineering Seoul National University Seoul Korea Republic of Department of Chemical and Life Science Engineering Virginia Commonwealth University RichmondVA United States Department of Biological Sciences University of Rhode Island KingstonRI United States Computer Science and Artificial Intelligence Laboratory and Department of Mathematics MIT CambridgeMA United States School of Molecular Sciences and College of Health Solutions Arizona State University PhoenixAZ United States

ISBN: (纸本)9783031902512

We introduce PHILHARMONIC, a computational framework that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our novel clustering approach produces highly informative functional modules by de-noising the predicted network. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We initially perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances;our clusters then enable us to newly assign functions to uncharacterized proteins through "function by association." We validate the ability of PHILHARMONIC to recover gold-standard functional enrichments in the well-annotated fruit fly D. melanogaster, and apply it to investigate stress response in the reef-building coral P. damicornis and its algal symbiont C. goreaui. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)

引用

Smart Agricultural Technology 2023年 5卷

作者： Naomi A. Ubina Hsun-Yu Lan Shyi-Chyi Cheng Chin-Chun Chang Shih-Syun Lin Kai-Xiang Zhang Hoang-Yang Lu Chih-Yung Cheng Yi-Zeng Hsieh Department of Computer Science and Engineering National Taiwan Ocean University Taiwan College of Computing Studies Information and Communications Technology Isabela State University Isabela Philippines Department of Aquaculture National Taiwan Ocean University Taiwan Department of Electrical Engineering National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

This paper focuses on designing a Digital Twin infrastructure that supports an agile-based Artificial Intelligence Internet of Things (AIoT) system for intelligent fish farming in aquaculture. Our infrastructure includes the Internet of Things, cloud technology, and Artificial Intelligence (AI) as its building blocks. Our physical entity is equipped with smart devices such as sensors and actuators embedded in smart machines (fish feeding and sorting machines) that collect and transmits big data to the cloud using wireless communication networks for real-time and remote monitoring. We have four major digital twin services: fish feeding to automate the feeding process, metric estimation (fish count, size, and weight), environmental monitoring (water condition, net hole, and green algae), and health monitoring (vitality, mortality, and diseases). Each digital twin service is equipped with multiple AI services (or the digital twin objects) capable of performing complex and other functions such as optimizations, predictions, and analyses for intelligent decision-making to optimize farm profits and production. We integrated a prototype that represents the virtual entity accessible using the web and mobile devices where users can perform fish farm monitoring using the various digital twin services and their related AI services.

关键词： Big data Big data analytics Digital twins Smart aquaculture AIoT system

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Wedge-lifted codes

arXiv

引用

arXiv 2020年

作者： Hastings, Jabari Kanne, Amy Li, Ray Wootters, Mary Department of Mathematics Stanford University Department of Computer Science Stanford University Departments of Computer Science and Electrical Engineering Stanford University

We define wedge-lifted codes, a variant of lifted codes, and we study their locality properties. We show that (taking the trace of) wedge-lifted codes yields binary codes with the t-disjoint repair property (t-DRGP). When t = N1/2d, where N is the block length of the code and d ≥ 2 is any integer, our codes give improved trade-offs between redundancy and locality among binary codes. © 2020, CC BY.

关键词： Binary codes

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Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications

arXiv

引用

arXiv 2024年

作者： Liu, Yuan Singh, Shraddha Smith, Kevin C. Crane, Eleanor Martyn, John M. Eickbusch, Alec Schuckert, Alexander Li, Richard D. Sinanan-Singh, Jasmine Soley, Micheline B. Tsunoda, Takahiro Chuang, Isaac L. Wiebe, Nathan Girvin, Steven M. Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States Department of Computer Science North Carolina State University RaleighNC27606 United States Yale Quantum Institute Yale University New HavenCT06511 United States Departments of Applied Physics and Physics Yale University New HavenCT06520 United States Brookhaven National Laboratory UptonNY11973 United States Joint Quantum Institute QuICS NIST University of Maryland College ParkMD20742 United States Department of Chemistry University of Wisconsin-Madison MadisonWI53706 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Computer Science University of Toronto TorontoONM5S1J7 Canada Canadian Institute for Advanced Research TorontoON Canada Pacific Northwest National Laboratory RichlandWA United States

Quantum computing with discrete variable (DV, qubit) hardware is approaching the large scales necessary for computations beyond the reach of classical computers. However, important use cases such as quantum simulations of physical models containing bosonic modes, and quantum error correction are challenging for DV-only systems. Separately, hardware containing native continuous-variable (CV, oscillator) systems has received attention as an alternative approach, yet the universal control of such systems is non-trivial. In this work, we show that hybrid CV-DV hardware offers a great advantage in meeting these challenges, offering a powerful computational paradigm that inherits the strengths of both DV and CV processors. We provide a pedagogical introduction to CV-DV systems and the multiple abstraction layers needed to produce a full software stack connecting applications to hardware. We present a variety of new hybrid CV-DV compilation techniques, algorithms, and applications, including the extension of quantum signal processing concepts to CV-DV systems and strategies to simulate systems of interacting spins, fermions, and bosons. To facilitate the development of hybrid CV-DV processor systems, we introduce formal Abstract Machine Models and Instruction Set Architectures – essential abstractions that enable developers to formulate applications, compile algorithms, and explore the potential of current and future hardware for realizing fault-tolerant circuits, modules, and processors. Hybrid CV-DV quantum computations are beginning to be performed in superconducting, trapped ion, and neutral atom platforms, and large-scale experiments are set to be demonstrated in the near future. We present a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and provide an architectural backbone to guide future development. © 2024, CC BY.

关键词： Trapped ions

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On-Chip Monolithically Integrated Ultraviolet Low-Threshold Plasmonic Metal‒Semiconductor Heterojunction Nanolasers

引用

Advanced science 2023年第28期10卷 e2301493页

作者： Sun, Jia-Yuan Nguyen, Duc Huy Liu, Jia-Ming Lo, Chia-Yao Ma, Yuan-Ron Chen, Yi-Jia Yi, Jui-Yun Huang, Jian-Zhi Giap, Hien Nguyen, Hai Yen Thi Liao, Chun-Da Lin, Ming-Yi Lai, Chien-Chih Department of Physics National Dong Hwa University Hualien974301 Taiwan Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Institute of Photonics National Yang Ming Chiao Tung University Tainan711010 Taiwan Institute of Optoelectronics National Chung Hsing University Taichung402202 Taiwan Department of Optoelectronics and Materials Technology National Taiwan Ocean University Keelung202301 Taiwan Department of Materials Science and Engineering National Dong Hwa University Hualien974301 Taiwan Department of Electrical Engineering National Kaohsiung Normal University Kaohsiung824004 Taiwan Department of Opto-Electronic Engineering National Dong Hwa University Hualien974301 Taiwan R&D Center Taiwan Semiconductor Manufacturing Company Hsinchu300091 Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine National Taiwan University Taipei100229 Taiwan

The metal‒semiconductor heterojunction is imperative for the realization of electrically driven nanolasers for chip-level platforms. Progress in developing such nanolasers has hitherto rarely been realized, however, because of their complexity in heterojunction fabrication and the need to use noble metals that are incompatible with microelectronic manufacturing. Most plasmonic nanolasers lase either above a high threshold (101‒103 MW cm−2) or at a cryogenic temperature, and lasing is possible only after they are removed from the substrate to avoid the large ohmic loss and the low modal reflectivity, making monolithic fabrication impossible. Here, for the first time, record-low-threshold, room-temperature ultraviolet (UV) lasing of plasmon-coupled core‒shell nanowires that are directly grown on silicon is demonstrated. The naturally formed core‒shell metal‒semiconductor heterostructure of the nanowires leads to a 100-fold improvement in growth density over previous results. This unprecedentedly high nanowire density creates intense plasmonic resonance, which is outcoupled to the resonant Fabry‒Pérot microcavity. By boosting the emission strength by a factor of 100, the hybrid photonic‒plasmonic system successfully facilitates a record-low laser threshold of 12 kW cm−2 with a spontaneous emission coupling factor as high as ≈0.32 in the 340‒360 nm range. Such architecture is simple and cost-competitive for future UV sources in silicon integration. © 2023 The Authors. Advanced science published by Wiley-VCH GmbH.

关键词： Nanowires

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