Results 11 to 20 of about 24,109 (217)
Jitter: Random Jittering Loss Function [PDF]
Regularization plays a vital role in machine learning optimization. One novel regularization method called flooding makes the training loss fluctuate around the flooding level. It intends to make the model continue to random walk until it comes to a flat loss landscape to enhance generalization.
Zhicheng Cai, Chenglei Peng, Sidan Du
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In a small scale turbulent medium, when the nonrelativistic Larmor radius $R_{\rm L}=mc^2/eB$ exceeds the correlation length $λ$ of the magnetic field, the magnetic bremsstrahlung of charged relativistic particles unavoidably proceeds in the so-called jitter radiation regime.
Kelner, S. +2 more
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A Jitter Education: An Introduction To Jitter For The Freshman [PDF]
Comment: 8 ...
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Correlated jitter sampling for jitter cancellation in pipelined TDC [PDF]
In this paper, the Correlated Jitter Sampling (CJS) technique, which alleviates the jitter induced error from the time reference in pipelined Time-to-Digital Converter (TDC), is proposed. The auxiliary pipelined TDC is employed to remove the jitter induced error of the main pipelined TDC in the CJS technique.
Taehwan Oh +3 more
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On the discrepancy of jittered sampling
We study the discrepancy of jittered sampling sets: such a set $\mathcal{P} \subset [0,1]^d$ is generated for fixed $m \in \mathbb{N}$ by partitioning $[0,1]^d$ into $m^d$ axis aligned cubes of equal measure and placing a random point inside each of the $N = m^d$ cubes. We prove that, for $N$ sufficiently large, $$ \frac{1}{10}\frac{d}{N^{\frac{1}{2} +
Florian Pausinger, Stefan Steinerberger
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Calculation of the electromyographic jitter [PDF]
The electromyographic jitter is the variability at consecutive discharges in the time interval between two action potentials from two muscle fibres from the same motor unit. This paper deals with different methods of expressing the jitter. The method of choice seems to be Mean Consecutive Difference (MCD)[FORMULA: see text]where D(1), D(2) etc. are the
J, Ekstedt, G, Nilsson, E, Stalberg
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Hardware acceleration of number theoretic transform for zk‐SNARK
An FPGA‐based hardware accelerator with a multi‐level pipeline is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. It can be flexibly scaled to different scales of FPGAs and has been equipped in the heterogeneous acceleration system with the help of HLS and OpenCL.
Haixu Zhao +6 more
wiley +1 more source
Endothelial Cell Proteins as Biomarkers in Susac Syndrome
ABSTRACT Objective Susac syndrome (SS) is a rare CD8+ T cell–mediated microangiopathy affecting the brain, retina, and auditory labyrinth. Endothelial injury is thought to be a central mechanism; however, no circulating disease biomarkers are known. We performed targeted proteomic profiling to identify circulating endothelial‐associated proteins as ...
Rohit Benjamin +11 more
wiley +1 more source
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
We propose a method for implicitly generating blue noise point sets. Our method is based on the observations that curl noise vector fields are volume-preserving and that jittering can be construed as moving points along the streamlines of a vector field.
Jakob Andreas Bærentzen +2 more
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