Results 61 to 70 of about 109,420 (245)
Adaptive Importance Sampling for Deep Ritz
Communications on Applied Mathematics and ComputationWe introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed to approximate the solution of PDEs, while the other one is a deep generative model used to generate new collocation points to refine the training set. The adaptive sampling
Xiaoliang Wan, Tao Zhou, Yuancheng Zhou
openaire +3 more sources
Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic kidney disease is a growing public health problem worldwide, and the number of patients requiring renal replacement therapy is steadily increasing. Türkiye has experienced a similar rise in both the incidence and prevalence of renal replacement therapy over the past decades; however, national‐level projections of future ...
Arzu Akgül +2 more
wiley +1 more source
Statistical Theory and Related Fields, 2023 This paper deals with the Monte Carlo Simulation in a Bayesian framework. It shows the importance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model $ X_{t}=\rho X_{t-1}+Y_{t} $ , where $ 0 \lt \rho
Djoweyda Ghouil, Megdouda Ourbih-Tari
doaj +1 more source
Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM
FEBS Letters, EarlyView.Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley +1 more source
Improvement of low level bark beetle damage estimates with adaptive cluster sampling
Silva Fennica, 2010 Detection of low level infestation in forest stands is of principle importance to determine effective control strategies before the attack spread to large areas.
Coggins, Sam +2 more
doaj +1 more source
Self-Adaptive Priority Correction for Prioritized Experience Replay
Applied Sciences, 2020 Deep Reinforcement Learning (DRL) is a promising approach for general artificial intelligence. However, most DRL methods suffer from the problem of data inefficiency.
Hongjie Zhang +3 more
doaj +1 more source
Tree pyramidal adaptive importance sampling
, 2019 20 pages + 13 pages of additional result plots and evaluation ...
Felip, Javier +2 more
openaire +2 more sources
Representativity for Robust and Adaptive Multiple Importance Sampling [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2011 We present a general method enhancing the robustness of estimators based on multiple importance sampling (MIS) in a numerical integration context. MIS minimizes variance of estimators for a given sampling configuration, but when this configuration is less adapted to the integrand, the resulting estimator suffers from extra variance.
Pajot, Anthony +3 more
openaire +3 more sources
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source