10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of MichiganUS1.5k papers
- Michigan State UniversityUS1.5k papers
- Centre National de la Recherche ScientifiqueFR1.4k papers
- Lawrence Berkeley National LaboratoryUS1.4k papers
- McGill UniversityCA1.4k papers
- Institut National de Physique Nucléaire et de Physique des ParticulesFR1.3k papers
- University of ManchesterGB1.3k papers
- University of LiverpoolGB1.3k papers
- University of TorontoCA1.3k papers
- University of OxfordGB1.3k papers
- TRIUMFCA1.3k papers
- Argonne National LaboratoryUS1.3k papers
243 papers · 1 filter
Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks
Mohammad Akbari, Jie Liang, Jingning Han +1
Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rat…
Discrete phase space and continuous time relativistic quantum mechanics I: Planck oscillators and closed string-like circular orbits
Anadijiban Das, Rupak Chatterjee
The discrete phase space continuous time representation of relativistic quantum mechanics involving a characteristic length is investigated. Fundamental physical constants such…
Maximum-likelihood parameter estimation in terahertz time-domain spectroscopy
Laleh Mohtashemi, Paul Westlund, Derek G. Sahota +4
We present a maximum-likelihood method for parameter estimation in terahertz time-domain spectroscopy. We derive the likelihood function for a parameterized frequency response func…
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
Arash Khoeini, Saman Haratizadeh, Ehsan Hoseinzade
Many Deep Learning approaches solve complicated classification and regression problems by hierarchically constructing complex features from the raw input data. Although a few works…
Pattern Morphing for Efficient Graph Mining
Kasra Jamshidi, Keval Vora
Graph mining applications analyze the structural properties of large graphs, and they do so by finding subgraph isomorphisms, which makes them computationally intensive. Existing g…
Real-Time Formal Verification of Autonomous Systems With An FPGA
Minh Bui, Michael Lu, Reza Hojabr +2
Hamilton-Jacobi reachability analysis is a powerful technique used to verify the safety of autonomous systems. This method is very good at handling non-linear system dynamics with…
On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions
Karen Leung, Edward Schmerling, Mengxuan Zhang +4
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is en…
Bogoliubov-Fermi surface with inversion symmetry and electron-electron interactions: relativistic analogies and lattice theory
Igor F. Herbut, Julia M. Link
We show that the general low-energy Bogoliubov-de Genness Hamiltonian in a multiband superconductor with broken time reversal and preserved inversion symmetry is a generator of rea…
Simple and statistically sound recommendations for analysing physical theories
Shehu S. AbdusSalam, Fruzsina J. Agocs, Benjamin C. Allanach +72
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle…
Are We Ready For Learned Cardinality Estimation?
Xiaoying Wang, Changbo Qu, Weiyuan Wu +2
Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data
Ben Adcock, Simone Brugiapaglia, Nick Dexter +1
Accurate approximation of scalar-valued functions from sample points is a key task in computational science. Recently, machine learning with Deep Neural Networks (DNNs) has emerged…
Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in collisions at = 13 TeV with the ATLAS detector
ATLAS Collaboration
A search for new phenomena with top quark pairs in final states with one isolated electron or muon, multiple jets, and large missing transverse momentum is performed. Signal region…
Muon reconstruction and identification efficiency in ATLAS using the full Run 2 collision data set at TeV
ATLAS Collaboration
This article documents the muon reconstruction and identification efficiency obtained by the ATLAS experiment for 139 fb of collision data at TeV collecte…
Generating Unobserved Alternatives
Shichong Peng, Ke Li
We consider problems where multiple predictions can be considered correct, but only one of them is given as supervision. This setting differs from both the regression and class-con…
High-Precision Branching Ratio Measurement and Spin Assignment Implications for Ga Superallowed Decay
A. D. MacLean, A. T. Laffoley, C. E. Svensson +48
A high-precision branching ratio measurement for the superallowed Fermi emitter Ga was performed with the Gamma-Ray Infrastructure for Fundamental Investigations of…
A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions
Javier Fernandez, Luke Bornn, Daniel Cervone
The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or receiving the next goal at any time instance. By decomposing the EPV into…
CoSam: An Efficient Collaborative Adaptive Sampler for Recommendation
Jiawei Chen, Chengquan Jiang, Can Wang +5
Sampling strategies have been widely applied in many recommendation systems to accelerate model learning from implicit feedback data. A typical strategy is to draw negative instanc…
CalibreNet: Calibration Networks for Multilingual Sequence Labeling
Shining Liang, Linjun Shou, Jian Pei +3
Lack of training data in low-resource languages presents huge challenges to sequence labeling tasks such as named entity recognition (NER) and machine reading comprehension (MRC).…
Maximizing power and velocity of an information engine
Tushar K. Saha, Joseph N. E. Lucero, Jannik Ehrich +2
Information-driven engines that rectify thermal fluctuations are a modern realization of the Maxwell-demon thought experiment. We introduce a simple design based on a heavy colloid…
LBGP: Learning Based Goal Planning for Autonomous Following in Front
Payam Nikdel, Richard Vaughan, Mo Chen
This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the following in front application. Here, an autonom…
Two-Gap Time Reversal Symmetry Breaking Superconductivity in Non-Centrosymmetric LaNiC2
Shyam Sundar, S. R. Dunsiger, S. Gheidi +13
We report a SR investigation of a non-centrosymmetric superconductor (LaNiC) in single crystal form. Compared to previous SR studies of non-centrosymmetric superconductin…
Recombination-independent determination of the sound horizon and the Hubble constant from BAO
Levon Pogosian, Gong-Bo Zhao, Karsten Jedamzik
The Hubble tension and attempts to resolve it by modifying the physics of (or at) recombination motivate finding ways to determine and the sound horizon at the epoch of baryo…
Exclusive dimuon production in ultraperipheral Pb+Pb collisions at TeV with ATLAS
ATLAS Collaboration
Exclusive dimuon production in ultraperipheral collisions (UPC), resulting from photon-photon interactions in the strong electromagnetic fields of colliding high-energy lead nuclei…