Now showing items 17321-17340 of 55971

    • Fast & accurate interatomic potentials for describing thermal vibrations 

      Rohskopf, Andrew; Wyant, Spencer; Gordiz, Kiarash; Reza Seyf, Hamid; Gopal Muraleedharan, Murali; e.a. (Elsevier BV, 2020)
      © 2020 The Authors Molecular dynamics (MD) is a powerful technique that can be used to study thermal vibrations/phonons and properly account for their role in different phenomena that are important in mechanical engineering, ...
    • Fast Algorithm for N-2 Contingency Problem 

      Turitsyn, Konstantin; Kaplunovich, P. A. (Institute of Electrical and Electronics Engineers (IEEE), 2013-01)
      We present a novel selection algorithm for N-2 contingency analysis problem. The algorithm is based on the iterative bounding of line outage distribution factors and successive pruning of the set of contingency pair ...
    • A fast algorithm for separated sparsity via perturbed lagrangians 

      Madry, A; Mitrović, S; Schmidt, L (2018)
      Copyright 2018 by the author(s). Sparsity-based methods are widely used in machine learning, statistics, and signal processing. There is now a rich class of structured sparsity approaches that expand the modeling power of ...
    • Fast algorithms for the quantile regression process 

      Chernozhukov, Victor; Fernández-Val, Iván; Melly, Blaise (Springer Science and Business Media LLC, 2020)
      © 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The widespread use of quantile regression methods depends crucially on the existence of fast algorithms. Despite numerous algorithmic improvements, the computation ...
    • A Fast Analysis-Based Discrete Hankel Transform Using Asymptotic Expansions 

      Townsend, Alex John (Society for Industrial and Applied Mathematics, 2015-08)
      A fast and numerically stable algorithm is described for computing the discrete Hankel transform of order 0 as well as evaluating Schlömilch and Fourier--Bessel expansions in O(N(log N)[superscript 2]/loglog N) operations. ...
    • Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trains 

      Ko, Ching-Yun; Batselier, Kim; Yu, Wenjian; Wong, Ngai (Institute of Electrical and Electronics Engineers (IEEE), 2015-08)
      We propose a new tensor completion method based on tensor trains. The to-be-completed tensor is modeled as a low-rank tensor train, where we use the known tensor entries and their coordinates to update the tensor train. A ...
    • A fast and accurate universal Kepler solver without Stumpff series 

      Wisdom, Jack; Hernandez, David Michael (Oxford University Press, 2015-09)
      We derive and present a fast and accurate solution of the initial value problem for Keplerian motion in universal variables that does not use the Stumpff series. We find that it performs better than methods based on the ...
    • Fast and accurate variant identification tool for sequencing-based studies 

      Gaston, Jeffry M.; Alm, Eric J.; Zhang, An-Ni (Springer Science and Business Media LLC, 2024-04-22)
      Background Accurate identification of genetic variants, such as point mutations and insertions/deletions (indels), is crucial for various genetic studies into epidemic tracking, population genetics, and ...
    • Fast and flexible inference of joint distributions from their marginals 

      Frogner, C; Poggio, T (2019-01-01)
      Copyright 2019 by the author(s). Across the social sciences and elsewhere, practitioners frequently have to reason about relationships between random variables, despite lacking joint observations of the variables. This is ...
    • Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks 

      Oviedo, Felipe; Ren, Zekun; Sun, Shijing; Settens, Charles; Liu, Zhe; e.a. (Springer Science and Business Media LLC, 2019)
      © 2019, The Author(s). X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to ...
    • Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks 

      Oviedo, Felipe; Ren, Zekun; Sun, Shijing; Settens, Charles M; Liu, Zhe; e.a. (Springer Science and Business Media LLC, 2019)
      © 2019, The Author(s). X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to ...
    • A Fast and Selective Near-Infrared Fluorescent Sensor for Multicolor Imaging of Biological Nitroxyl (HNO) 

      Wrobel, Alexandra T.; Lippard, Stephen J.; Rivera-Fuentes, Pablo; Johnstone, Timothy; Liang, Alexandria D. (American Chemical Society (ACS), 2014-02)
      The first near-infrared fluorescent turn-on sensor for the detection of nitroxyl (HNO), the one-electron reduced form of nitric oxide (NO), is reported. The new copper-based probe, CuDHX1, contains a dihydroxanthene (DHX) ...
    • Fast and slow responses of Southern Ocean sea surface temperature to SAM in coupled climate models 

      Ferreira, David; Armour, Kyle C.; Holland, Marika M.; Kostov, Yavor Krasimirov; Marshall, John C; e.a. (Springer Berlin Heidelberg, 2016-05)
      We investigate how sea surface temperatures (SSTs) around Antarctica respond to the Southern Annular Mode (SAM) on multiple timescales. To that end we examine the relationship between SAM and SST within unperturbed ...
    • Fast and Smooth Interpolation on Wasserstein Space 

      Chewi, Sinho; Clancy, Julien; Le Gouic, Thibaut; Rigollet, Philippe; Stepaniants, George; e.a. (2021)
    • Fast Augmenting Paths by Random Sampling from Residual Graphs 

      Karger, David R.; Levine, Matthew S. (Society for Industrial and Applied Mathematics, 2015-03)
      Consider an n-vertex, m-edge, undirected graph with integral capacities and max-flow value v. We give a new [~ over O](m + nv)-time maximum flow algorithm. After assigning certain special sampling probabilities to edges ...
    • Fast Averaging 

      Bodas, Shreeshankar; Shah, Devavrat (Institute of Electrical and Electronics Engineers (IEEE), 2011-10)
      We are interested in the following question: given n numbers x[subscript 1], ..., x[subscript n], what sorts of approximation of average x[subscript ave] = 1overn (x[subscript 1] + ... + x[subscript n]) can be achieved by ...
    • Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms 

      Hazimeh, Hussein; Mazumder, Rahul (Institute for Operations Research and the Management Sciences (INFORMS), 2020-08)
      The L₀-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. ...
    • A fast butterfly algorithm for generalized Radon transforms 

      Hu, Jingwei; Fomel, Sergey; Demanet, Laurent; Ying, Lexing (Society of Exploration Geophysicists, 2013-06)
      Generalized Radon transforms, such as the hyperbolic Radon transform, cannot be implemented as efficiently in the frequency domain as convolutions, thus limiting their use in seismic data processing. We have devised a fast ...
    • Fast characterization of segmental duplications in genome assemblies 

      Numanagic, Ibrahim; Gokkaya, Alim S.; Zhang, Lillian; Berger Leighton, Bonnie; Alkan, Can; e.a. (Oxford University Press, 2018-09-08)
      Segmental duplications (SDs) or low-copy repeats, are segments of DNA > 1 Kbp with high sequence identity that are copied to other regions of the genome. SDs are among the most important sources of evolution, a common cause ...
    • Fast concurrent object localization and recognition 

      Yeh, Tom; Lee, John J.; Darrell, Trevor J. (Institute of Electrical and Electronics Engineers (IEEE), 2009-08)
      Object localization and recognition are important problems in computer vision. However, in many applications, exhaustive search over all object models and image locations is computationally prohibitive. While several methods ...