Results 61 to 70 of about 425,331 (287)
Stochastic search for semiparametric linear regression models
, 2013 Technical report 75, IMSV, University of ...
Dümbgen Lutz +2 more
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Advanced Materials, EarlyView.Detecting proteins secreted by a single cell while retaining its viability remains challenging. A particles‐in‐particle (PiPs) system made by co‐encapsulating barcoded microparticles (BMPs) with a single cell inside an alginate hydrogel particle is introduced.
Félix Lussier +10 more
wiley +1 more source
MathematicsThe stochastic structural plane of a rock mass is the key factor controlling the stability of rock mass. Obtaining the distribution of stochastic structural planes within a rock mass is crucial for analyzing rock mass stability and supporting rock slopes
Han Meng, Xiaoyu Qi, Gang Mei
doaj +1 more source
Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics
Advanced Materials, EarlyView.An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang +8 more
wiley +1 more source
Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies
, 2017 We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new framework for the convergence analysis, we prove improved convergence rates and computational complexities of the stochastic L-BFGS algorithms compared to previous works.
Haskell, William B. +2 more
core +1 more source
Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization
Advanced Materials, EarlyView.The search for advanced battery materials is pushing us into highly complex composition spaces. Here, a space with about 14 million unique combinations is efficiently explored using high‐throughput experimentation guided by Bayesian optimization with a deep kernel trained on both the Materials Project database and our data.
Nooshin Zeinali Galabi +6 more
wiley +1 more source
AIP Advances, 2021 The dynamic modeling of structures in a thermal environment has become a new research topic in structural dynamics. Uncertainties caused by noise or material variability increase the difficulty in structural dynamic modeling when considering thermal ...
Zhe Chen, Huan He, Qi-jun Zhao
doaj +1 more source
Strategies for Enhancing Thermal Conductivity of PDMS in Electronic Applications
Advanced Materials Technologies, EarlyView.This review explores effective strategies for enhancing heat dissipation in Polydimethylsiloxane (PDMS)‐based composites, focusing on particle optimization, 3D network design, and multifunctional integration. It offers key insights into cutting‐edge methods and simulations that are advancing thermal management in modern electronic devices.
Xiang Yan, Marisol Martin‐Gonzalez
wiley +1 more source
A general asymptotic scheme for inference under order restrictions
, 2006 Limit distributions for the greatest convex minorant and its derivative are considered for a general class of stochastic processes including partial sum processes and empirical processes, for independent, weakly dependent and long range dependent data ...
Anevski, D., Hössjer, O.
core +1 more source
On regression representations of stochastic processes
Stochastic Processes and their Applications, 1993 Two types of so-called regression representations of a discrete time stochastic process \(X=(X_ n)_{n\in N}\) are developed. The one has the form \(X_ n=f_ n(X_ 1,\ldots,X_{n-1},U_ n)\), \(X_ 1=f_ 1(U_ 1)\), and is called Markov regression on \(X\) and the other is of the form \(X_ n=g_ n(U_ 1,\ldots,U_ n)\) and is called standard representation of \(X\
Rüschendor, L, de Valk, V
openaire +2 more sources