Oblique parameter constraints on large extra dimensions [PDF]
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Han, Tao +2 more
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Carleman Estimates with a Second Large Parameter
Unique continuation of solutions to linear partial differential equations is of importance in many branches of applied mathematics, in particular, in control theory and inverse problems. The Carleman estimates are an important tool for proving the unique continuation for linear operators with non-analytic coefficients.
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Two-parameter sample path large deviations for infinite-server queues
Let Qλ(t,y) be the number of people present at time t with y units of remaining service time in an infinite server system with arrival rate equal to λ>0.
Jose H. Blanchet, Xinyun Chen, Henry Lam
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Asymptotics for Laguerre polynomials with large order and parameters
Consider the Laguerre polynomials \(L_n^{\alpha_n}(x)\) that are orthogonal on the positive real axis with respect to the varying weight function \[ w_n(x)=x^{\alpha_n}e^{-x}. \] The aim of this paper is to study the asymptotics of this polynomials when \(\alpha_n\) satisfies the condition \[ \lim_{n\rightarrow \infty}\frac{\alpha_n}{n}=a \in (0 ...
Dan Dai, Mourad E. H. Ismail, Jun Wang
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Flight Trajectory Simulation and Aerodynamic Parameter Identification of Large-Scale Parachute
In this paper, the multibody parachute-payload system is simplified and analyzed. A six-degree-of-freedom rigid body flight dynamic model is established to calculate the flight trajectory, attitude, velocity, and drop point of the parachute-payload ...
Yihua Cao, Ning Wei
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Order parameter fluctuations in natural time and b-value variation before large earthquakes [PDF]
Self-similarity may stem from two origins: the process increments infinite variance and/or process memory. The b-value of the Gutenberg-Richter law comes from the first origin.
E. S. Skordas +2 more
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Reply to determining structural identifiability of parameter learning machines [PDF]
The paper Ran and Hu (2014, Neurocomputing) examines identifiability and parameter redundancy in classes of models used in machine learning. This note discusses the results on global identifiability and also clarifies that the paper's results on ...
Cole, Diana J.
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter
We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be huge or infinite.
Andrew Lowy, Meisam Razaviyayn
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High-Efficient Parameter-Pruning Algorithm of Decision Tree for Large Dataset [PDF]
Decision tree(DT) have a good effect on data classification but easily develop overfitting. The solution to this problem is to prune the DT. However, the pruning algorithm has shortcomings; for example, prepruning is prone to underfitting, the ...
Zhaoxian XIE, Xingmin ZOU, Wenjing ZHANG
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Correction: Exponential random graph model parameter estimation for very large directed networks.
[This corrects the article DOI: 10.1371/journal.pone.0227804.].
PLOS ONE Staff
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