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arXiv 2022年

作者： Amiri, Mandana Bandura, Kevin Boskovic, Anja Chen, Tianyue Cliche, Jean-François Deng, Meiling Denman, Nolan Dobbs, Matt Fandino, Mateus Foreman, Simon Halpern, Mark Hanna, David Hill, Alex S. Hinshaw, Gary Höfer, Carolin Kania, Joseph Klages, Peter Landecker, T.L. Maceachern, Joshua Masui, Kiyoshi Mena-Parra, Juan Milutinovic, Nikola Mirhosseini, Arash Newburgh, Laura Nitsche, Rick Ordog, Anna Pen, Ue-Li Pinsonneault-Marotte, Tristan Polzin, Ava Reda, Alex Renard, Andre Richard Shaw, J. Siegel, Seth R. Singh, Saurabh Smegal, Rick Tretyakov, Ian van Gassen, Kwinten Vanderlinde, Keith Wang, Haochen Wiebe, Donald V. Willis, James S. Wulf, Dallas Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States Center for Gravitational Waves and Cosmology West Virginia University MorgantownWV United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States Department of Physics McGill University MontrealQC Canada Dominion Radio Astrophysical Observatory Herzberg Astronomy & Astrophysics Research Centre National Research Council Canada PentictonBC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoON Canada Department of Physical Sciences Thompson Rivers University KamloopsBC Canada Department of Computer Science Math Physics and Statistics University of British Columbia-Okanagan KelownaBC Canada Department of Physics and Astronomy West Virginia University MorgantownWV United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Physics Yale University New HavenCT United States Canadian Institute for Theoretical Astrophysics TorontoON Canada Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoON Canada Institute of Astronomy and Astrophysics Academia Sinica Taipei Taiwan Department of Astronomy Yale University New HavenCT United States Raman Research Institute Sadashivanagar Bengaluru India Department of Physics University of Toronto TorontoON Canada

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a drift scan radio telescope operating across the 400 MHz to 800 MHz band. CHIME is located at the Dominion Radio Astrophysical Observatory near Penticton, BC Canada. The instrument is designed to map neutral hydrogen over the redshift range 0.8 to 2.5 to constrain the expansion history of the Universe. This goal drives the design features of the instrument. CHIME consists of four parallel cylindrical reflectors, oriented north-south, each 100 m × 20 m and outfitted with a 256 element dual-polarization linear feed array. CHIME observes a two degree wide stripe covering the entire meridian at any given moment, observing 3/4 of the sky every day due to Earth rotation. An FX correlator utilizes FPGAs and GPUs to digitize and correlate the signals, with different correlation products generated for cosmological, fast radio burst, pulsar, VLBI, and 21 cm absorber backends. For the cosmology backend, the Nfeed2 correlation matrix is formed for 1024 frequency channels across the band every 31 ms. A data receiver system applies calibration and flagging and, for our primary cosmological data product, stacks redundant baselines and integrates for 10 s. We present an overview of the instrument, its performance metrics based on the first three years of science data, and we describe the current progress in characterizing CHIME's primary beam response. We also present maps of the sky derived from CHIME data;we are using versions of these maps for a cosmological stacking analysis as well as for investigation of Galactic foregrounds. © 2022, CC BY.

关键词： Interferometers

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Detection of Cosmological 21 cm Emission with the Canadian Hydrogen Intensity Mapping Experiment

arXiv

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arXiv 2022年

作者： Amiri, Mandana Bandura, Kevin Chen, Tianyue Deng, Meiling Dobbs, Matt Fandino, Mateus Foreman, Simon Halpern, Mark Hill, Alex S. Hinshaw, Gary Höfer, Carolin Kania, Joseph Landecker, T.L. Maceachern, Joshua Masui, Kiyoshi Mena-Parra, Juan Milutinovic, Nikola Mirhosseini, Arash Newburgh, Laura Ordog, Anna Pen, U.E.-L.I. Pinsonneault-Marotte, Tristan Polzin, Ava Reda, Alex Renard, Andre Richard Shaw, J. Siegel, Seth R. Singh, Saurabh Vanderlinde, Keith Wang, Haochen Wiebe, Donald V. Wulf, Dallas Department of Physics and Astronomy University of British Columbia VancouverBC Canada Department of Computer Science and Electrical Engineering West Virginia University MorgantownWV United States Center for Gravitational Waves and Cosmology West Virginia University MorgantownWV United States MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA United States Dominion Radio Astrophysical Observatory Herzberg Astronomy & Astrophysics Research Centre National Research Council Canada PentictonBC Canada Perimeter Institute for Theoretical Physics WaterlooON Canada Department of Physics McGill University MontrealQC Canada Department of Physical Sciences Thompson Rivers University KamloopsBC Canada Department of Computer Science Math Physics and Statistics University of British Columbia-Okanagan KelownaBC Canada Department of Physics and Astronomy West Virginia University MorgantownWV United States Department of Physics Massachusetts Institute of Technology CambridgeMA United States Department of Physics Yale University New HavenCT United States Canadian Institute for Theoretical Astrophysics TorontoON Canada Institute of Astronomy and Astrophysics Academia Sinica Taipei Taiwan David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoON Canada Department of Astronomy Yale University New HavenCT United States Dunlap Institute for Astronomy and Astrophysics University of Toronto TorontoON Canada Raman Research Institute Sadashivanagar Bengaluru India

We present a detection of 21 cm emission from large-scale structure (LSS) between redshift 0.78 and 1.43 made with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). Radio observations acquired over 102 nights are used to construct maps which are foreground filtered and stacked on the angular and spectral locations of luminous red galaxies (LRG), emission line galaxies (ELG), and quasars (QSO) from the eBOSS clustering catalogs. We find decisive evidence for a detection when stacking on all three tracers of LSS, with the logarithm of the Bayes Factor equal to 18.9 (LRG), 10.8 (ELG), and 56.3 (QSO). An alternative frequentist interpretation, based on the likelihood-ratio test, yields a detection significance of 7.1σ (LRG), 5.7σ (ELG), and 11.1σ (QSO). These are the first 21 cm intensity mapping measurements made with an interferometer. We constrain the effective clustering amplitude of neutral hydrogen (HI), defined as AHI ≡ 103 ΩHI (bHI + hfµ2i), where ΩHI is the cosmic abundance of HI, bHI is the linear bias of HI, and hfµ2i = 0.552 encodes the effect of redshift-space distortions at linear order. We find AHI = 1.51+3.60−0.97 for LRGs (z = 0.84), AHI = 6.76+9.04−3.79 for ELGs (z = 0.96), and AHI = 1.68+1.10−0.67 for QSOs (z = 1.20), with constraints limited by modeling uncertainties at nonlinear scales. We are also sensitive to bias in the spectroscopic redshifts of each tracer, and find a non-zero bias ∆ v = −66 ±20 km/s for the QSOs. We split the QSO catalog into three redshift bins and have a decisive detection in each, with the upper bin at z = 1.30 producing the highest redshift 21 cm intensity mapping measurement thus far. © 2022, CC BY.

关键词： Galaxies

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Extended Drude Model for Intraband-Transition-Induced Optical Nonlinearity

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Physical Review Applied 2019年第6期11卷 064062-064062页

作者： Heng Wang Kang Du Chuhao Jiang Zhiqiang Yang Lixia Ren Wending Zhang Soo Jin Chua Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information Technology School of Science Northwestern Polytechnical University Xi’an 710072 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 LEES Program Singapore-MIT Alliance for Research & Technology (SMART) 1 CREATE Way #10-01 CREATE Tower Singapore 138602

No existing model has completely explained the physics of the optical nonlinearity for epsilon-near-zero materials, which have recently emerged as candidates for design and fabrication of active nanophotonic devices. We propose to extend the Drude model by adopting a weighted electron effective mass, which takes into account the electron distribution in the nonparabolic conduction band. The extended Drude model is able to interpret the cause of the nonlinearity and provide a functional relationship between the refractive index and wavelength. Both the band nonparabolicity and the temperature-dependent mobility are responsible for the intraband-transition-induced optical nonlinearity. The spectrally resolved nonlinear refractive index and nonlinear susceptibilities are obtained from this model. The results can be applied to other transparent conducting oxides and will help in the design and modeling of spectral responses of nonlinear plasmonic devices.

关键词： Electronic transitions Nonlinear optical susceptibility Optoelectronics Plasmonics Ultrafast optics Thin films Free-electron model Ultrafast pump-probe spectroscopy

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Thwarting adversarial examples: an L0-robust sparse fourier transform 18

Thwarting adversarial examples: an L0-robust sparse fourier ...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： mitali Bafna Jack Murtagh Nikhil Vyas School of Engineering & Applied Sciences Harvard University Cambridge MA Department of Electrical Engineering and Computer Science MIT Cambridge MA

We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that is robust to worst-case L0 corruptions, namely that a bounded number of coordinates of the signal can be corrupted arbitrarily. Our techniques generalize to a wide range of linear transformations that are used in data analysis such as the Discrete Cosine and Sine transforms, the Hadamard transform, and their high-dimensional analogs. We use our algorithm to successfully defend against worst-case L0 adversaries in the setting of image classification. We give experimental results on the Jacobian-based Saliency Map Attack (JSMA) and the Carlini Wagner (CW) L0 attack on the MNIST and Fashion-MNIST datasets as well as the Adversarial Patch on the ImageNet dataset.

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Familiar strangers: The collective regularity in human behaviors

arXiv

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arXiv 2018年

作者： Leng, Yan Santistevan, Dominiquo Pentland, Alex Sandy Mit Media Lab CambridgeMA United States Electrical Engineering and Computer Science CambridgeMA United States

The social phenomenon of familiar strangers was identified by Stanley Milgram in 1972 with a small-scale experiment. However, there has been limited research focusing on uncovering the phenomenon at a societal scale and simultaneously investigating the social relationships between familiar strangers. With the help of the large-scale mobile phone records, we empirically show the existence of the relationship in the country of Andorra. Built upon the temporal and spatial distributions, we investigate the mechanisms, especially collective temporal regularity and spatial structure that trigger this phenomenon. Moreover, we explore the relationship between social distances on the communication network and the number of encounters and show that larger number of encounters indicates shorter social distances in a social network. The understanding of the physical encounter network could have important implications to understand the phenomena such as epidemics spreading and information diffusion. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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Slip Detection with Combined Tactile and Visual Information

Slip Detection with Combined Tactile and Visual Information

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jianhua Li Siyuan Dong Edward Adelson Computer Science and Artificial Intelligence Laboratory (CSAIL) MIT Cambridge MA USA Department of Electrical Engineering & Computer Science and CSAIL MIT Cambridge MA USA Faculty of Department of Brain and Cognitive Sciences and CSAIL MIT Cambridge MA USA

Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03 % is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine manipulation.

关键词： Grippers Grasping Cameras Tactile sensors Force

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Efficient Binomial Channel Capacity Computation with an Application to Molecular Communication

Efficient Binomial Channel Capacity Computation with an Appl...

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Information Theory and Applications Workshop, ITA

作者： Richard D. Wesel Emily E. Wesel Lieven Vandenberghe Christos Komninakis Muriel Medard Department of Electrical and Computer Engineering UCLA Los Angeles CA Harvard-Westlake Studio City CA Qualcomm San Diego CA Department of Electrical Engineering and Computer Science MIT Cambridge MA

This paper develops an efficient method to compute the binomial channel capacity and applies it to the molecular channel. The binomial channel (with parameter n) takes the success probability for a Bernoulli trial as input and produces the number of successes in trials as output. The input alphabet is the unit interval and the output alphabet is the set of integers from zero to n. Despite the fact that the input alphabet is uncountably infinite the capacity-achieving input distributions turn out to have a small finite support that evolves gracefully as n increases. The ellipsoid algorithm was previously used to compute the binomial channel capacity, but convergence is rather slow even with a well-chosen initial condition. The Dynamic Assignment Blahut-Arimoto (DAB) algorithm starts with the capacity-achieving mass point locations for the n-1 case and exploits Csiszàr's Min-Max Capacity Theorem to check convergence and adjust mass point locations to achieve a much faster convergence rate, unlocking the potential for the capacity and corresponding input distribution to be computed for larger values of n.

关键词： Ellipsoids Heuristic algorithms Channel capacity Convex functions Electronic mail Convergence Molecular communication

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A VLBI Calibrator Grid at 600 MHz for Fast Radio Transient Localizations with CHIME/FRB Outriggers

arXiv

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arXiv 2024年

作者： Andrew, Shion Leung, Calvin Li, Alexander Masui, Kiyoshi W. Andersen, Bridget C. Bandura, Kevin Curtin, Alice P. Kaczmarek, Jane Lanman, Adam E. Lazda, Mattias Mena-Parra, Juan Michilli, Daniele Nimmo, Kenzie Pearlman, Aaron B. Rahman, Mubdi Shah, Vishwangi Shin, Kaitlyn Wang, Haochen Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Department of Astronomy University of California Berkeley BerkeleyCA94720 United States Department of Physics McGill University 3600 rue University MontréalQCH3A 2T8 Canada Trottier Space Institute McGill University 3550 rue University MontréalQCH3A 2A7 Canada Lane Department of Computer Science and Electrical Engineering 1220 Evansdale Drive PO Box 6109 MorgantownWV26506 United States Center for Gravitational Waves and Cosmology West Virginia University Chestnut Ridge Research Building MorgantownWV26505 United States CSIRO Space & Astronomy Parkes Observatory P.O. Box 276 ParkesNSW2870 Australia Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada David A. Dunlap Department of Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada MIT Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Sidrat Research 124 Merton Street Suite 507 TorontoONM4S 2Z2 Canada

The Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Project has a new VLBI Outrigger at the Green Bank Observatory (GBO), which forms a 3300 km baseline with CHIME operating at 400–800 MHz. Using 100 ms long full-array baseband "snapshots" collected commensally during FRB and pulsar triggers, we perform a shallow, wide-area VLBI survey covering a significant fraction of the Northern sky targeted at the positions of compact sources from the Radio Fundamental Catalog. In addition, our survey contains calibrators detected from two 1s long trial baseband snapshots for a deeper survey with CHIME and GBO. In this paper, we present the largest catalog of compact calibrators suitable for 30-milliarcsecond-scale VLBI observations at sub-GHz frequencies to date. Our catalog consists of 200 total calibrators in the Northern Hemisphere that are compact on 30-milliarcsecond scales with fluxes above 100 mJy. This calibrator grid will enable the precise localization of hundreds of FRBs a year with CHIME/FRB-Outriggers. © 2024, CC BY.

关键词： Pulsars

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VoxelMorph: A learning framework for deformable medical image registration

arXiv

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arXiv 2018年

作者： Balakrishnan, Guha Zhao, Amy Sabuncu, Mert R. Guttag, John Dalca, Adrian V. Computer Science and Artificial Intelligence Lab MIT School of Electrical and Computer Engineering Meinig School of Biomedical Engineering Cornell University Martinos Center for Biomedical Imaging MGH HMS

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this work, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to state-of-the-art methods, while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time, and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is freely available at http://***. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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