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arXiv 2020年

作者： van Viegen, Tara Akrami, Athena Bonnen, Kate DeWitt, Eric Hyafil, Alexandre Ledmyr, Helena Lindsay, Grace W. Mineault, Patrick Murray, John D. Pitkow, Xaq Puce, Aina Sedigh-Sarvestani, Madineh Stringer, Carsen Achakulvisut, Titipat Alikarami, Elnaz Atay, Melvin Selim Batty, Eleanor Erlich, Jeffrey C. Galbraith, Byron V. Guo, Yueqi Juavinett, Ashley L. Krause, Matthew R. Li, Songting Pachitariu, Marius Straley, Elizabeth Valeriani, Davide Vaughan, Emma Vaziri-Pashkam, Maryam Waskom, Michael L. Blohm, Gunnar Kording, Konrad Schrater, Paul Wyble, Brad Escola, Sean Peters, Megan A.K. Princeton Neuroscience Institute Princeton University PrincetonNJ United States Sainsbury Wellcome Centre University College London London United Kingdom Center for Neural Science New York University New York CityNY United States Champalimaud Research Champalimaud Foundation Lisbon Portugal Centre de Recerca Matemàtica Bellaterra Universitat Autònoma de Barcelona Barcelona Spain International Neuroinformatics Coordinating Facility Sweden Gatsby Computational Neuroscience Unit Sainsbury Wellcome Centre University College London London United Kingdom Department of Psychiatry Yale University New HavenCT United States Baylor College of Medicine Dept. of Neuroscience HoustonTX United States Baylor College of Medicine Center for Neuroscience and Artificial Intelligence HoustonTX United States Rice University Dept of Electrical and Computer Engineering HoustonTX United States Psychological & Brain Sciences Indiana University BloomingtonIN United States Max Planck Florida Institute for Neuroscience JupiterFL United States HHMI Janelia Research Campus AshburnVA United States University of Pennsylvania Dept of Neuroscience and Bioengineering PhiladelphiaPA United States Faculty of dentistry McGill University MontrealQB Canada Middle East Technical University Ankara Turkey Department of Neurobiology Harvard University CambridgeMA United States New York University Shanghai Shanghai China Talla Inc BostonMA United States Johns Hopkins University Department of Biomedical Engineering BaltimoreMD United States Division of Biological Sciences UC San Diego San diegoCA United States Montreal Neurological Institute McGill University MontrealQB Canada School of Mathematical Sciences MOE-LSC Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China Department of Otolaryngology - Head & Neck Surgery Massachusetts Eye and Ear BostonMA United States Department of Otolaryngology - Head & Neck Surgery Harvard Medical School BostonM

Neuromatch Academy designed and ran a fully online 3-week computational Neuroscience summer school for 1757 students with 191 teaching assistants working in virtual inverted (or flipped) classrooms and on small group projects. Fourteen languages, active community management, and low cost allowed for an unprecedented level of inclusivity and universal accessibility. © 2020, CC BY.

关键词： Python

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Time-series and phasecurve photometry of episodically-active asteroid (6478) gault in a quiescent state using APO, GROWTH, P200 and ZTF

arXiv

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arXiv 2021年

作者： Purdum, Josiah N. Lin, Zhong-Yi Bolin, Bryce T. Sharma, Kritti Choi, Philip I. Bhalerao, Varun Hanuš, Josef Kumar, Harsh Quimby, Robert van Roestel, Joannes C. Zhai, Chengxing Fernandez, Yanga R. Lisse, Carey M. Bodewits, Dennis Fremling, Christoffer Golovich, Nathan Ryan Hsu, Chen-Yen Ip, Wing-Huen Ngeow, Chow-Choong Saini, Navtej S. Shao, Michael Yao, Yuhan Ahumada, Tomás Anand, Shreya Andreoni, Igor Burdge, Kevin B. Burruss, Rick Chang, Chan-Kao Copperwheat, Chris M. Coughlin, Michael De, Kishalay Dekany, Richard Delacroix, Alexandre Drake, Andrew Duev, Dmitry Graham, Matthew Hale, David Kool, Erik C. Kasliwal, Mansi M. Kostadinova, Iva S. Kulkarni, Shrinivas R. Laher, Russ R. Mahabal, Ashish Masci, Frank J. Mróz, Przemyslaw J. Neill, James D. Riddle, Reed Rodriguez, Hector Smith, Roger M. Walters, Richard Yan, Lin Zolkower, Jeffry Department of Astronomy San Diego State University 5500 Campanile Dr San DiegoCA92182 United States Institute of Astronomy National Central University Taoyuan32001 Taiwan IPAC Mail Code 100-22 Caltech 1200 E. California Blvd. PasadenaCA91125 United States Department of Mechanical Engineering Indian Institute of Technology Bombay Powai Mumbai400076 India Physics and Astronomy Department Pomona College 333 N. College Way ClaremontCA91711 United States Department of Physics Indian Institute of Technology Bombay Powai Mumbai400076 India Institute of Astronomy Faculty of Mathematics and Physics Charles University V Holešovičkách 2 Prague18000 Czech Republic LSSTC Data Science The University of Tokyo Institutes for Advanced Study The University of Tokyo Kashiwa Chiba277-8583 Japan Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Dept. of Physics and Florida Space Inst. Univ. of Central Florida 4000 Central Florida Boulevard OrlandoFL32816-2385 United States Johns Hopkins University Applied Physics Laboratory LaurelMD20723 United States Physics Department Leach Science Center Auburn University AuburnAL36832 United States Lawrence Livermore National Laboratory 7000 East Avenue LivermoreCA94550 United States Graduate Institute of Astronomy National Central University 32001 Taiwan Institute of Astronomy National Central University 32001 Taiwan Department of Astronomy University of Maryland College ParkMD20742 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States Astrophysics Research Institute Liverpool John Moores University 146 Brownlow Hill LiverpoolL3 5RF United Kingdom School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics

We observed Episodically Active Asteroid (6478) Gault in 2020 with multiple telescopes in Asia and North America and have found that it is no longer active after its recent outbursts at the end of 2018 and start of 2019. The inactivity during this apparation allowed us to measure the absolute magnitude of Gault of Hr = 14.63±0.02, Gr = 0.21±0.02 from our secular phasecurve observations. In addition, we were able to constrain Gault's rotation period using time-series photometric lightcurves taken over 17 h on multiple days in 2020 August, September and October. The photometric lightcurves have a repeating.0.05 magnitude feature suggesting that (6478) Gault has a rotation period of ∼2.5 hours and may have a semi-spherical or top-like shape, much like Near-Earth Asteroids Ryugu and Bennu. The rotation period of ∼2.5 hours is near to the expected critical rotation period for an asteroid with the physical properties of (6478) Gault suggesting that its activity observed over multiple epochs is due to surface mass shedding from its fast rotation spun up by the Yarkovsky-O'Keefe-Radzievskii-Paddack effect. © 2021, CC BY.

关键词： Asteroids

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Integrated particle swarm and evolutionary algorithm approaches to the quadratic assignment problem

Integrated particle swarm and evolutionary algorithm approac...

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IEEE Symposium Series on computational Intelligence (SSCI)

作者： Ayah M. Helal Enas Jawdat Islam Elnabarawy Donald C. Wunsch Ashraf M. Abdelbar School of Computing University of Kent United Kingdom Dept. of Computer Science & Engineering American University in Cairo Cairo Egypt Applied Computational Intelligence Lab Missouri University of Science and Technology Rolla Missouri USA Dept. of Mathematics & Computer Science Brandon University Brandon Manitoba Canada

This paper introduces three integrated hybrid approaches that apply a combination of Hierarchical Particle Swarm Optimization (HPSO) and Evolutionary Algorithms (EA) to the Quadratic Assignment Problem (QAP). The approaches maintain a single population. In the first approach, Alternating HPSO-EA (AHE), the population alternates between applying HPSO and EA in successive generations. In the second, more integrated approach, Integrated HPSO-EA (IHE), each population element chooses to apply one of the two algorithms in each generation with some probability. An element applying HPSO in a given generation can be influenced by an element applying EA in that generation, and vice versa. Thus, within the same generation, some elements act as HPSO particles and others as EA population members, and yet the entire population still cooperates. In the third approach, we present a Social Evolutionary Algorithm (SEA), in which the population applies EA, and each population element can choose to apply the PSO-style social mutation operator in each generation with some probability. The three approaches are compared to HPSO and EA using 31 instances of varying size from the QAP instance library.

关键词： Sociology Statistics Vegetation Biological cells Particle swarm optimization Probabilistic logic

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A Parallel Algorithm for Finding All Pairs κ-Mismatch Maximal Common Substrings

A Parallel Algorithm for Finding All Pairs κ-Mismatch Maxim...

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2016 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2016

作者： Chockalingam, Sriram P. Thankachan, Sharma V. Aluru, Srinivas Dept. of Computer Science and Engineering Indian Institute of Technology Mumbai India School of Computational Science and Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781467388153

We present an efficient parallel algorithm for the following problem: Given an input collection D of n sequences of total length N, a length threshold f and a mismatch threshold κ, report all κ-mismatch maximal common substrings of length at least f over all pairs of strings in D. This problem is motivated by clustering and assembly applications in computational biology, where D is a collection of millions of short DNA sequences. Sequencing errors and massive size of these datasets necessitate efficient parallel approximate sequence matching algorithms. We present a novel distributed memory parallel algorithm that solves this approximate sequence matching problem in O ((N/p log N + occ)logk N) expected time and takes only O(logk+1 N) expected rounds of global communications, under some realistic assumptions, where p is the number of processors and occ is the output size. To our knowledge, this is the first provably sub-quadratic time algorithm for solving this problem. We demonstrate the performance and scalability of our algorithm using large high throughput sequencing data sets. © 2016 IEEE.

关键词： Parallel algorithms

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Exemplar or matching: Modeling DCJ problems with unequal content genome data

arXiv

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arXiv 2017年

作者： Yin, Zhaoming Tang, Jijun Schaeffer, Stephen W. Bader, David A. School of Computer Science and Technology Tianjin University China School of Computational Science and Engineering Georgia Institute of Technology United States Dept. of Computer Science and Engineering University of South Carolina United States Huck Institutes of Life Sciences Pennsylvania State University United States

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance;versus the other by gene matching, namely DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of optimization methods to compute exact distances for both. Furthermore, median problems are discussed in alignment with both of these distance methods, which are to find a median genome that minimizes distances between itself and three given genomes. Lin-Kernighan (LK) heuristic is leveraged and powered up by sub-graph decomposition and search space reduction technologies to handle median computation. A wide range of experiments are conducted on synthetic data sets and real data sets to show pros and cons of these two distance metrics per se, as well as putting them in the median computation scenario. Copyright © 2017, The Authors. All rights reserved.

关键词： Genes

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An Open Smart City IoT Test Bed: Street Light Poles as Smart City Spines: Poster Abstract 17

An Open Smart City IoT Test Bed: Street Light Poles as Smart...

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Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

作者： Bharadwaj Amrutur Vasanth Rajaraman Srikrishna Acharya Rakshit Ramesh Ashish Joglekar Abhay Sharma Yogesh Simmhan Abhijit Lele Ashwin Mahesh Sathya Sankaran RBCCPS IISc Bangalore Dept. of Computation and Data Science IISc Bangalore Robert Bosch Engineering and Business Solutions Bangalore India Mapunity Inc. Bangalore India

Street light poles will be a key enabler for a smart city's hardware infrastructure, thanks to their ubiquity throughout the city as well as access to power. We propose an IoT test bed around light poles for the c... 详细信息

ISBN: (纸本)9781450349666

关键词： LoRa Smart City Test Bed Streetlight Poles Semantic Interoperability GPON Distributed Analytics IoT Middleware

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Informative data projections: A framework and two examples 24

Informative data projections: A framework and two examples

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24th European Symposium on Artificial Neural Networks, computational Intelligence and Machine Learning, ESANN 2016

作者： De Bie, Tijl Lijffijt, Jefrey Santos-Rodríguez, Raúl Kang, Bo Data Science Lab Ghent University Belgium Dept. of Engineering Mathematics University of Bristol United Kingdom

ISBN: (纸本)9782875870278

Projection Pursuit aims to facilitate visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge in Projection Pursuit is the design of a projection index-a suitable quality measure to maximise. We introduce a strategy for tackling this problem based on quantifying the amount of information a projection conveys, given a user's prior beliefs about the data. The resulting projection index is a subjective quantity, explicitly dependent on the intended user. As an illustration, we developed this principle for two kinds of prior beliefs;the first leads to PCA, the second leads to a novel projection index, which we call t-PCA, that can be regarded as a robust PCA-variant. We demonstrate t-PCA's usefulness in comparative experiments against PCA and FastICA, a popular PP method.

关键词： Clustering algorithms

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DESI Strong Lens Foundry I: HST Observations and Modeling with GIGA-Lens

arXiv

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arXiv 2025年

作者： Huang, X. Baltasar, S. Ratier-Werbin, N. Storfer, C. Sheu, W. Agarwal, S. Tamargo-Arizmendi, M. Schlegel, D.J. Aguilar, J. Ahlen, S. Aldering, G. Banka, S. BenZvi, S. Bianchi, D. Bolton, A. Brooks, D. Cikota, A. Claybaugh, T. de la Macorra, A. Dey, A. Doel, P. Edelstein, J. Filipp, A. Forero-Romero, J.E. Gaztañaga, E. Gontcho, S.A. Gontcho Gu, A. Gutierrez, G. Honscheid, K. Jullo, E. Juneau, S. Kehoe, R. Kirkby, D. Kisner, T. Kremin, A. Kwon, K.J. Lambert, A. Landriau, M. Lang, D. Le Guillou, L. Liu, J. Meisner, A. Miquel, R. Moustakas, J. Myers, A.D. Perlmutter, S. Pérez-Ràfols, I. Prada, F. Rossi, G. Rubin, D. Sanchez, E. Schubnell, M. Shu, Y. Silver, E. Sprayberry, D. Suzuki, N. Tarlé, G. Weaver, B.A. Zou, H. Department of Physics & Astronomy University of San Francisco San FranciscoCA94117 United States Physics Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Department of Physics Complutense University of Madrid Madrid28040 Spain Department of Mathematics Complutense University of Madrid Madrid28040 Spain Institute for Astronomy University of Hawai’i HonoluluHI96822-1897 United States Department of Physics & Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Chicago Department of Astronomy ChicagoIL60615 United States Department of Physics & Astronomy University of Pittsburgh PittsburghPA15260 United States Physics Dept. Boston University 590 Commonwealth Avenue BostonMA02215 United States Department of Electrical Engineering & Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Physics & Astronomy University of Rochester 206 Bausch and Lomb Hall P.O. Box 270171 RochesterNY14627-0171 United States Dipartimento di Fisica "Aldo Pontremoli" Università degli Studi di Milano Via Celoria 16 MilanoI-20133 Italy NSF’s National Optical-Infrared Astronomy Research Laboratory TucsonAZ85719 United States Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Gemini Observatory NSF’s NOIRLab Casilla 603 La Serena Chile Instituto de Física Universidad Nacional Autónoma de México Circuito de la Investigación Científica Ciudad Universitaria Cd. de MéxicoC. P. 04510 Mexico Space Sciences Laboratory University of California Berkeley 7 Gauss Way BerkeleyCA94720 United States Université de Montréal Physics Department 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Ciela – Montreal Institute for Astrophysical Data Analysis and Machine Learning 1375 Av. Thérèse-Lavoie-Roux MontréalQCH2V 0B3 Canada Technical University Munich TUM School of Natural Sciences

We present the Dark Energy Spectroscopic Instrument (DESI) Strong Lens Foundry. We discovered ∼ 3500 new strong gravitational lens candidates in the DESI Legacy Imaging Surveys using residual neural networks (ResNet). We observed a subset (51) of our candidates using the Hubble Space Telescope (HST). All of them were confirmed to be strong lenses. We also briefly describe spectroscopic follow-up observations by DESI and Keck NIRES programs. From this very rich dataset, a number of studies will be carried out, including evaluating the quality of the ResNet search candidates and lens modeling. In this paper, we present our initial effort in these directions. In particular, as a demonstration, we present the lens model for DESI-165.4754-06.0423, with imaging data from HST, and lens and source redshifts from DESI and Keck NIRES, respectively. In this effort, we have applied a fully forward-modeling Bayesian approach (GIGA-Lens), using multiple GPUs, for the first time in both regards, to a strong lens with HST data, or any high resolution imaging. © 2025, CC BY.

关键词： Dark energy

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on Big data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules

arXiv

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arXiv 2017年

作者： Feng, Xinyang Yang, Jie Laine, Andrew F. Angelini, Elsa D. Dept. of Biomedical Engineering Columbia University New YorkNY United States NIHR Imperial BRC ITMAT Data Science Group Imperial College London United Kingdom

Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method. Copyright © 2017, The Authors. All rights reserved.

关键词： Image segmentation

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