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A multi-branch hybrid transformer network for corneal endothelial cell segmentation

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arXiv 2021年

作者： Zhang, Yinglin Higashita, Risa Fu, Huazhu Xu, Yanwu Zhang, Yang Liu, Haofeng Zhang, Jian Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Tomey Corporation Nagoya451-0051 Japan Global Big Data Technologies Centre University of Technology SydneyNSW Australia Inception Institute of Artificial Intelligence United Arab Emirates Intelligent Healthcare Unit Baidu Beijing100085 China

Corneal endothelial cell segmentation plays a vital role in quantifying clinical indicators such as cell density, coefficient of variation, and hexagonality. However, the corneal endothelium’s uneven reflection and the subject’s tremor and movement cause blurred cell edges in the image, which is difficult to segment, and need more details and context information to release this problem. Due to the limited receptive field of local convolution and continuous downsampling, the existing deep learning segmentation methods cannot make full use of global context and miss many details. This paper proposes a Multi-Branch hybrid Transformer Network (MBT-Net) based on the transformer and body-edge branch. Firstly, We use the convolutional block to focus on local texture feature extraction and establish long-range dependencies over space, channel, and layer by the transformer and residual connection. Besides, We use the body-edge branch to promote local consistency and to provide edge position information. On the self-collected dataset TM-EM3000 and public Alisarine dataset, compared with other State-Of-The-Art (SOTA) methods, the proposed method achieves an improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Endothelial cells

Large language models surpass human experts in predicting neuroscience results

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

A silicon integrated microwave photonic beamformer

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arXiv 2020年

作者： Zhu, Chen Lu, Liangjun Shan, Wensheng Xu, Weihan Zhou, Gangqiang Zhou, Linjie Chen, Jianping State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data China Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Optical beamforming networks (OBFNs) based on optical true time delay lines (OTTDLs) are well-known as the promising candidate to solve the bandwidth limitation of traditional electronic phased array antennas (PAAs) due to beam squinting. Here we report the first monolithic 1×8 microwave photonic beamformer based on switchable OTTDLs on the silicon-on-insulator platform. The chip consists of a modulator, an eight-channel OBFN, and 8 photodetectors, which includes hundreds of active and passive components in total. It has a wide operating bandwidth from 8 to 18 GHz, which is almost two orders larger than that of electronic PAAs. The beam can be steered to 31 distinguishable angles in the range of -75.51° to 75.64° based on the beam pattern calculation with the measured RF response. The response time for beam steering is 56 μs. These results represent a significant step towards the realization of integrated microwave photonic beamformers that can satisfy compact size and low power consumption requirements for the future radar and wireless communication systems. © 2020 Optical Society of America Copyright © 2020, The Authors. All rights reserved.

关键词： Antenna phased arrays

Fabrication of amino acid conjugated polymeric micelles for controlled anticancer drug delivery using radiation and pH-stimuli-triggering systems

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Journal of Drug Delivery science and Technology 2023年 80卷

作者： Addisu, Kefyalew Dagnew Lee, Wen-Yi Tsai, Hsieh-Chih Wu, Szu-Yuan Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei 106 Taiwan Faculty of Chemical and Food Engineering Bahir Dar University P. O. Box 26 Bahir Dar Ethiopia Advance Membrane Materials Center National Taiwan University of Science and Technology Taipei 106 Taiwan R&D Center for Membrane Technology Chung Yuan Christian University Chungli Taoyuan 320 Taiwan Department of Food Nutrition and Health Biotechnology College of Medical and Health Science Asia University Taichung 413 Taiwan Big Data Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Division of Radiation Oncology Department of Medicine Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Department of Healthcare Administration College of Medical and Health Science Asia University Taichung 413 Taiwan Cancer Center Lo-Hsu Medical Foundation Lotung Poh-Ai Hospital Yilan 256 Taiwan Graduate Institute of Business Administration Fu Jen Catholic University Taipei 242 Taiwan Centers for Regional Anesthesia and Pain Medicine Taipei Municipal Wan Fang Hospital Taipei Medical University Taipei 110 Taiwan

Drug delivery to the tumor site and the minimization of adverse effects on surrounding tissues remain major issues in pharmaceutical research. In this study, pH- and radiation-responsive PLGA-PEG-PLGA amphiphilic copolymers were terminally conjugated with tryptophan (Try), tyrosine (Tyr), or histidine (His), utilizing PEG as an initiator and Sn (Oct) 2 as a catalyst. Micelles having an inner core of hydrophobic blocks (PLGA and Try/Tyr/His) and a corona of hydrophilic PEG were developed to improve stability. The structural destabilization of the micelles caused by radiation and pH was characterized by an increase in DOX fluorescence intensities and a shift in particle size. Both Try-PLGA-PEG-PLGA-Try and Tyr-PLGA-PEG-PLGA-Tyr micelles displayed low cytotoxicity in MTT experiments with NIH-3T3 and HeLa cell lines at 100 μg/mL concentrations, with cell viabilities of more than 95.33% and 92.33%, respectively. Samples containing 10 μg/mL DOX, on the other hand, demonstrated significant anticancer efficacy against HeLa cells (28.54% and 29.97% of the cells remained viable after treatment with Tyr and Try, respectively). The cellular uptake experiment also revealed that DOX-loaded micelles were up taken by the cell and that irradiation and pH-sensitive stimuli induced drug release in the cytoplasm and nucleus of the cancer cell. Finally, these findings imply that the synthesized copolymers could be used as stimuli-responsive carriers for anti-cancer drug delivery. © 2023

关键词： Dual-responsive PLGA-PEG-PLGA Radiation-sensitive Tryptophan Tyrosine

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

Explicit Distributed and Localized Model Predictive Control via System Level Synthesis

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arXiv 2020年

作者： Alonso, Carmen Amo Matni, Nikolai Anderson, James Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Department of Electrical Engineering Data Science Institute Columbia University New YorkNY United States

An explicit Model Predictive Control algorithm for large-scale structured linear systems is presented. We base our results on Distributed and Localized Model Predictive Control (DLMPC), a closed-loop model predictive control scheme based on the System Level Synthesis (SLS) framework wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We provide an explicit solution for each of the subproblems resulting from the distributed MPC scheme. We show that given the separability of the problem, the explicit solution is only divided into three regions per state and input instantiation, making the point location problem very efficient. Moreover, given the locality constraints, the subproblems are of much smaller dimension than the full problem, which significantly reduces the computational overhead of explicit solutions. We conclude with numerical simulations to demonstrate the computational advantages of our method, in which we show a large improvement in runtime per MPC iteration as compared with the results of computing the optimization with a solver online. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

A new index for information gain in the Bayesian framework ⁎

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IFAC-PapersOnLine 2020年第1期53卷 634-639页

作者： Prem Jagadeesan Karthik Raman Arun K. Tangirala Department of Chemical Engineering Indian Institute of Technology (IIT) Madras Department of Biotechnology Bhupat and Jyoti Mehta School of Biosciences IIT Madras Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) IIT Madras Initiative for Biological Systems Engineering IIT Madras Chennai — 600 036 India

In data-driven dynamical modeling, precise estimation of the parameters of large models from limited data has been considered a challenging task. The precision of the parameter estimates is highly dependent upon the information contained in the data; Loss of practical identifiability and sloppiness in the model structure are major challenges in estimating parameters precisely and closely related to the information contained in the data. Therefore, quantifying information is an important step in data-driven modeling. Quantifying information is a well-studied problem in the frequentist approach, where Fisher Information is one of the widely used metrics. However, Fisher Information computed via maximum likelihood estimation cannot accommodate any known prior knowledge about the parameters. Prior knowledge of the parameters along with informative experiments will improve the precision of the estimates. Bayesian estimation accommodates prior information in the form of a p.d.f. There has been very little work in the literature for quantifying information in the Bayesian framework. In this work, we introduce a new method for estimating information gain in the Bayesian framework using what is known as the Bhattacharyya coefficient. It is seen that the bounds of the coefficient have an insightful interpretation naturally in terms of information gain on the parameter of interest. We also demonstrate using case studies that the information gain of each parameter is an indication of loss of practical identifiability and sloppy parameters. It is also shown that the proposed information gain can be used as a model selection tool in black-box identification.

关键词： Information Gain Bhattacharyya Coefficient Approximate Bayesian Computation (ABC) Model Sloppiness Practical Identifiability Model selection

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

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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

Ethical Aspects of Computational Modelling in science, Decision Support and Communication 15th

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Ethical Aspects of Computational Modelling in Science, Decis...

15th International Workshop on Artificial Life and Evolutionary Computation, WIVACE 2021

作者： Füchslin, Rudolf Marcel Ambühl, Jacques Faggian, Alessia Fellermann, Harold M. Flumini, Dandolo Geller, Armando Hanczyc, Martin M. Klinkert, Andreas Krütli, Pius Matuttis, Hans-Georg Ott, Thomas Scheidegger, Stephan Schmid, Gary Bruno Smieszek, Timo Schneider, Johannes J. Steiner, Albert Weyland, Mathias S. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Zurich Technikumstrasse 9 Winterthur8400 Switzerland European Centre for Living Technology Ca’ Bottacin Dorsoduro 3911 Calle Crosera Venice30123 Italy World Bank Group Geneva Switzerland University of Trento Trento38123 Italy School of Computing Newcastle University Science Square 1 Newcastle Upon TyneNE4 5TG United Kingdom GmbH Fraumünsterstrasse 11 Zurich8001 Switzerland Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8400 Switzerland Transdisciplinarity Lab ETH Zurich Zurich8092 Switzerland Department of Mechanical Engineering and Intelligent Systems The University of Electrocommunications Tokyo182-8585 Japan Institute of Computational Life Sciences Zurich University of Applied Sciences Schloss 1 Wädenswil8820 Switzerland Swiss NeuroCreations GmbH Hambergersteig 25 Zurich8008 Switzerland Muesmattweg 70 Allschwil4123 Switzerland

ISBN: (纸本)9783031239281

The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential;however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a "narrative." We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making