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High-speed vision sensing is essential for real-time perception in applications such as robotics, autonomous vehicles, and industrial automation. Traditional frame-based vision systems suffer from motion blur, high latency, and redundant…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Riadul Islam , Joey Mulé , Dhandeep Challagundla , Shahmir Rizvi , Sean Carson

Large classes of new physics theories predict the existence of new scalar states, commonly dubbed sgluons, lying in the adjoint representation of the QCD gauge group. Since these new fields are expected to decay into colored Standard Model…

High Energy Physics - Phenomenology · Physics 2013-04-15 Samuel Calvet , Benjamin Fuks , Philippe Gris , Loic Valery

The results of a search for supersymmetry in events with large missing transverse momentum and heavy flavour jets using an integrated luminosity corresponding to 2.05 fb^-1 of pp collisions at sqrt(s) = 7 TeV recorded with the ATLAS…

High Energy Physics - Experiment · Physics 2012-08-27 ATLAS Collaboration

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray…

Computer Vision and Pattern Recognition · Computer Science 2022-02-16 Simon B. Jensen , Thomas B. Moeslund , Søren J. Andreasen

We calculate the SUSY-QCD corrections to the inclusive total cross sections of the associated production processes $pp\to W^{\pm}H^{\mp}+X$ in the Minimal Supersymmetric Standard Model(MSSM) at the CERN Large Hadron Collider(LHC). The…

High Energy Physics - Phenomenology · Physics 2009-11-11 Jun Zhao , Chong Sheng Li , Qiang Li

This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly…

High Energy Physics - Phenomenology · Physics 2022-09-26 S. V. Chekanov , W. Hopkins

The Cryogenic Dark Matter Search (CDMS-II) employs low-temperature Ge and Si detectors to seek Weakly Interacting Massive Particles (WIMPs) via their elastic scattering interactions with nuclei. Simultaneous measurements of both ionization…

Astrophysics · Physics 2012-08-27 CDMS Collaboration

High-energy hadronic collisions are dominated by soft interactions with low momentum transfers. The description of these processes typically relies on phenomenological models. The soft QCD measurements, such as characterisation of the…

High Energy Physics - Experiment · Physics 2021-09-21 Sushanta Tripathy

Heavy quarks provide a new dimension to QCD, allowing tests of fundamental theory, the nature of color confinement, and the production of new exotic multiquark states. I also discuss novel explanations for several apparently anomalous…

High Energy Physics - Phenomenology · Physics 2014-01-29 Stanley J. Brodsky

Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly…

Quantum Physics · Physics 2024-09-19 Hyeondo Oh , Daniel K. Park

The production of jets in low $Q^2$ $ep$ scattering (photoproduction) and in low $Q^2$ $e^+e^-$ scattering ($\gamma\gamma$ scattering) allows for testing perturbative QCD and for measuring the proton and photon structure functions. This…

High Energy Physics - Phenomenology · Physics 2014-11-17 Michael Klasen , Thorsten Kleinwort , Gustav Kramer

In the autoencoder based anomaly detection paradigm, implementing the autoencoder in edge devices capable of learning in real-time is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these…

Mesoscale and Nanoscale Physics · Physics 2025-08-27 Muhammad Sabbir Alam , Walid Al Misba , Jayasimha Atulasimha

As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud…

Computer Vision and Pattern Recognition · Computer Science 2022-03-29 Yatian Pang , Wenxiao Wang , Francis E. H. Tay , Wei Liu , Yonghong Tian , Li Yuan

Machine learning has become a powerful tool in high-energy collider experiments, which enables the studies based on data-driven approaches to complex reconstruction and regression tasks. The study of identified hadron spectra in…

High Energy Physics - Phenomenology · Physics 2026-05-12 Rishabh Gupta , Kangkan Goswami , Suraj Prasad , Raghunath Sahoo

The underlying dynamics and patterns of 3D surface meshes deforming over time can be discovered by unsupervised learning, especially autoencoders, which calculate low-dimensional embeddings of the surfaces. To study the deformation patterns…

Computer Vision and Pattern Recognition · Computer Science 2022-12-13 Sara Hahner , Felix Kerkhoff , Jochen Garcke

Measurements of inclusive, differential cross-sections for the production of events with missing transverse momentum in association with jets in proton-proton collisions at $\sqrt{s}=13~$TeV are presented. The measurements are made with the…

High Energy Physics - Experiment · Physics 2024-09-04 ATLAS Collaboration

Measurements of proton and nuclear collisions at the Large Hadron Collider at nucleon-nucleon c.m. energies up to $\sqrt{s_{NN}}=$ 13 TeV, have improved our understanding of hadronic interactions at the highest energies reached in…

High Energy Astrophysical Phenomena · Physics 2019-06-12 David d'Enterria

A search for supersymmetry with R-parity conservation in proton-proton collisions at a centre-of-mass energy of 7 TeV is presented. The data correspond to an integrated luminosity of 35 inverse picobarns collected by the CMS experiment at…

High Energy Physics - Experiment · Physics 2015-03-17 CMS Collaboration

Neutron irradiation produces, within a few picoseconds, displacement cascades that are sequences of atomic collisions generating point and extended defects which subsequently affects the long-term evolution of materials. The diversity of…

An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Computational experiments confirm that an…

Machine Learning · Computer Science 2022-10-14 Tommi Kärkkäinen , Jan Hänninen
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