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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Learning to Prevent Monocular SLAM Failure using Reinforcement Learning 18

Learning to Prevent Monocular SLAM Failure using Reinforceme...

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Proceedings of the 11th Indian Conference on computer Vision, Graphics and Image Processing

作者： Vignesh Prasad Karmesh Yadav Rohitashva Singh Saurabh Swapnil Daga Nahas Pareekutty K. Madhava Krishna Balaraman Ravindran Brojeshwar Bhowmick Embedded Systems & Robotics TCS Research & Innovation Kolkata India Robotics Institute Carnegie Mellon University Pittsburgh USA Dept. of Robotics Engineering John Hopkins University Baltimore USA Robotics Research Center KCIS IIIT Hyderabad India Dept. of Computer Science Indian Institute of Technology Madras India

ISBN: (纸本)9781450366151

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

关键词： Monocular SLAM

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Blended Multi-modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

arXiv

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arXiv 2020年

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Shareef, Shaik Nagur Hakak, Saqib Bilal, Muhammad Reddy, Praveen Kumar M. Jo, Ohyun Department of CSE Vignan’s Foundation for Science Technology and Research Guntur522213 India Department of IT Vignan’s Foundation for Science Technology and Research Guntur522213 India Faculty of Computer Science University of Northern British Columbia Canada Dept. of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si17035 Korea Republic of School of Information Technology and Engineering VIT Vellore632014 India Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. Major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. In order to extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extract different features, fusing them using 1-D pooling, and cross pooling lead to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that, DNN with dropout at input layer converges faster when trained using blended features, than compared to the same model trained using uni-modal deep *** Codes 68T10, 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Eye protection

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Unsupervised Ensemble Classification with Dependent Data

arXiv

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arXiv 2019年

作者： Traganitis, Panagiotis A. Giannakis, Georgios B. IEEE Dept. of Electrical and Computer Engineering and the Digital Technology Center University of Minnesota MinneapolisMN55455 United States

Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most prior works on unsupervised ensemble classification are designed for independent and identically distributed (i.i.d.) data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies. Two types of data dependencies are considered: sequential data and networked data whose dependencies are captured by a graph. Moment matching and Expectation Maximization algorithms are developed for the aforementioned cases, and their performance is evaluated on synthetic and real datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Crowdsourcing

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Broadband Elliptical Slotted Patch Antenna for 5G Communications

Broadband Elliptical Slotted Patch Antenna for 5G Communicat...

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2018 International Conference on computer, Communication, Chemical, Material and Electronic engineering, IC4ME2 2018

作者： Das, S. Samsuzzaman, M. Rahman, M.N. Islam, M.T. Islam, M. Tarikul Dept. of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Dept. of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia

ISBN: (纸本)9781538647752

In this paper, a broadband elliptical slotted patch antenna has been proposed for 5G wireless communications. The proposed antenna apparatus has a minimal size of 0.308λ0 × 0.308λ0 × 0.009λ0 at 22 GHz. The antenna bandwidth is enhanced for an elliptical ground plane. Microstrip feed line has been connected by a stub, to achieve better impedance. Array design has been designed for better gain, therefore array design has been made. The array has a high gain of about 18 dBi at 28 GHz and 21dBi at 38 GHz. The proposed antenna covers a wide band area from 22 GHz to 48 GHz. The proposed antenna covers 28/38 GHz. The antenna additionally gives omnidirectional to required 5G bands. © 2018 IEEE.

关键词： Millimeter waves

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Polygonal Shaped Patch with Circular Slotted Ground Antenna for Ultra-Wideband Applications

Polygonal Shaped Patch with Circular Slotted Ground Antenna ...

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2018 International Conference on computer, Communication, Chemical, Material and Electronic engineering, IC4ME2 2018

作者： Syeed, M.A.A. Samsuzzaman, M. Islam, M. Tarikul Azim, R. Islam, M.T. Dept. of Computer and Communication Engineering Patuakhali Science and Technology University Patuakhali Bangladesh Dept. of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia

ISBN: (纸本)9781538647752

In this paper, a compact Ultra-Wideband (UWB) antenna is presented which has a polygonal shaped patch with the circular slotted ground. Rectangular shaped modification is also done at the side of the ground plane in this UWB antenna. Both HFSS and CST software have been used for simulation purpose of this proposed antenna which has an electrical dimension of 0.30λ × 0.285λ × 0.012λ at the lowest frequency 2.25GHz. Operating bandwidth of this proposed antenna is of 8.8GHz (2.25-11.05GHZ) which is observed in both simulation and fabrication result with a constant increase in gain and stable radiation pattern. This proposed antenna is both simulated and fabricated on FR4 substrate which makes it inexpensive, high current density, low profile, omnidirectional. It shows an average gain of 4.68 dBi, the peak gain of 5.05dBi and maximum efficiency of 97%. After exhibiting these simulated and measured results, it is proved that this proposed antenna is suitable for working in Ultra-Wideband (UWB) applications. © 2018 IEEE.

关键词： Antenna grounds

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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI

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Neuroimage: Reports 2022年第2期2卷 100091-100091页

作者： DiGregorio, J. Gibicar, A. Khosravani, H. Maralani, P. Jabehdar Tardif, J.-C. Tyrrell, P.N. Moody, A.R. Khademi, A. Electrical Computer and Biomedical Engineering Dept. Ryerson University Toronto ON Canada Division of Neurology Department of Medicine University of Toronto Toronto ON Canada Department of Medical Imaging University of Toronto Toronto ON Canada Montreal Heart Institute Montreal QU Canada Department of Medicine Université de Montréal QU Canada Department of Statistical Science University of Toronto Toronto ON Canada Keenan Research Center for Biomedical Science St. Michael's Hospital Unity Health Network Toronto ON Canada Institute for Biomedical Engineering Science and Technology (iBEST) A Partnership Between St. Michael's Hospital and Ryerson University Canada

Fluid-attenuated inversion recovery (FLAIR) MRI has emerged as an important sequence for the analysis of cerebrovascular (CVD) and Alzheimer's disease (AD). Large-scale, automated cross-sectional and longitudinal cerebral biomarker extraction from FLAIR datasets could progress disease characterization, improve disease monitoring, and help to determine optimal intervention times. Despite this, most automated biomarker extraction algorithms are designed for T1-weighted or multi-modal inputs. In this work, automated tools were used to extract biomarkers from large, FLAIR-only datasets to evaluate the feasibility of this sequence to characterize healthy, AD, and CVD subjects in a similar manner to traditional approaches. Total brain volume (TBV), cerebrospinal fluid (CSF) volume, and white matter lesion (WML) volume was measured for the cross-sectional biomarkers and the corresponding annual rates of change over multiple scans represented the longitudinal biomarkers. Biomarkers were extracted from two dementia cohorts (4356 vol, 162 233 images) and one vascular disease cohort (869 vol, 42 850 images) using deep learning-based segmentation algorithms designed specifically for FLAIR. Biomarkers from all cohorts were summarized using descriptive statistics, correlation analysis, and ANCOVA to assess differences in diagnostic labels while accounting for demographic and acquisition factors. Biomarkers from FLAIR MRI had similar trends with those extracted from traditional modalities in the literature for characterizing healthy, AD, and CVD subjects. This demonstrates that FLAIR MRI can be used for end-to-end analysis of large AD and CVD datasets, which can lower acquisition costs, simplify clinical translation, and reduce measurement error associated with multi-modal approaches. © 2022 The Author(s)

关键词： Alzheimer's disease Cerebral biomarkers Cerebrovascular disease FLAIR MRI

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Impact of DC bias on weak optical-field-driven electron emission in nano-vacuum-gap detectors

arXiv

引用

arXiv 2020年

作者： Turchetti, Marco Bionta, Mina R. Yang, Yujia Ritzkowsky, Felix Candido, Denis Ricardo Flatté, Michael Berggren, Karl K. Keathley, Phillip D. Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Center for Free-Electron Laser Science Notkestraße 85 Hamburg22607 Germany Dept. Electrical and Computer Engineering University of Iowa Iowa CityIA52242 United States

In this work, we investigate multiphoton and optical-field tunneling emission from metallic surfaces with nanoscale vacuum gaps. Using time-dependent Schrödinger equation (TDSE) simulations, we find that the properties of the emitted photocurrent in such systems can be greatly altered by the application of only a few-volt DC bias. We find that when coupled with expected plasmonic enhancements within the nanometer-scale metallic gaps, the application of this DC bias significantly reduces the threshold for the transition to optical-field-driven tunneling from the metal surface, and could sufficiently enhance the emitted photocurrents, to make it feasible to electronically tag fJ ultrafast pulses at room temperature. Given the petahertz-scale instantaneous response of the photocurrents, and the low effective capacitance of thin-film nanoantenna devices that enables ¡ 1 fs response time, detectors that exploit this bias-enhanced surface emission from nanoscale vacuum gaps could prove to be useful for communication, petahertz electronics, and ultrafast optical-field-resolved metrology. Copyright © 2020, The Authors. All rights reserved.

关键词： Electron emission

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An Enhanced Time-Domain Simulator of Transient Stability in Power systems

An Enhanced Time-Domain Simulator of Transient Stability in ...

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IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Matthew Overlin Marc Barbar Krishan Kant James Kirtley Christopher Smith Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA MIT Lincoln Laboratory Energy Systems Group Lexington MA

Time-domain simulations can be used to study and verify the operation of an electrical network, whether large or small, fast or slow. In this study a classic 9-bus system is modeled in detail. The quantities of interest in this simulation are generator states, generator currents and node voltages. A differential-algebraic system of equations (DAE) is formulated. The generator states are dynamic while all other variables are algebraic. Important numerical techniques are highlighted: an implicit integration method with a technique to re-use prior information to speed up the simulation. A detailed analysis of several key state variables follows. The time evolution is discussed for different variables of interest in the synchronous machine models and network. This detailed model, coupled with an enhanced implicit integration method, yields numerical stability and stability in the model.

关键词： Mathematical model Generators Load modeling Numerical models Time-domain analysis Numerical stability Power system dynamics

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Disentangling factors of variation using few labels

arXiv

引用

arXiv 2019年

作者： Locatello, Francesco Tschannen, Michael Bauer, Stefan Rätsch, Gunnar Schölkopf, Bernhard Bachem, Olivier Google Research Brain Team Dept. of Computer Science ETH Zurich Switzerland Dept. of Information Technology and Electrical Engineering ETH Zurich Switzerland Max-Planck Institute for Intelligent Systems

Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a very limited amount of supervision, for example through manual labeling of training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 29 000 models under well-defined and reproducible experimental conditions. We first observe that a very limited number of labeled examples (0.01-0.5% of the data set) is sufficient to perform model selection on state-of-the-art unsupervised models. Yet, if one has access to labels for supervised model selection, this raises the natural question of whether they should also be incorporated into the training process. As a case-study, we test the benefit of introducing (very limited) supervision into existing state-of-the-art unsupervised disentanglement methods exploiting both the values of the labels and the ordinal information that can be deduced from them. Overall, we empirically validate that with very little and potentially imprecise supervision it is possible to reliably learn disentangled representations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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