ライフサイエンスコーパス: variational

1 conclusion, resistive loading changed total variational activity according to the size of the load:

2 When partitioned, the increase in total variational activity during isocapnic hypoxia was found

3 We speculate that the observed changes in variational activity may reflect an attempt by the contr

4 ce periodic breathing and increase the total variational activity of breath components.

5 effect of hyperoxic hypercapnia (CO2) on the variational activity of breathing in 14 volunteers befor

6 To examine the effect of elastic loading on variational activity of breathing, we studied 11 healthy

7 o examine the effect of resistive loading on variational activity of breathing, we studied 18 healthy

8 ive load of 3 cm H2O/L/s decreased the total variational activity of expiratory time (TE) and minute

9 s a load of 6 cm H2O/L/s increased the total variational activity of inspiratory time (TI).

10 /s, the load of 6 cm H2O/L/s increased total variational activity of tidal volume (VT), TI, TE, and V

11 f 18 cm H2O/L decreased only the fraction of variational activity of VT and TE due to uncorrelated, r

12 H2O/L/s increased the correlated fraction of variational activity of VT.

13 Partitioning of the total variational activity revealed that these alterations wer

14 emory," and the correlated fraction of total variational activity- increased with loading.

15 component by altering the random fraction of variational activity; it had no significant effect on th

16 ugh a hierarchical Bayesian framework, and a variational algorithm for inference.

17 ational ANeuploidy analysis (SCEVAN), a fast variational algorithm for the deconvolution of the clona

18 Here we present an arbitrarily accurate variational algorithm that, instead of fixing an ansatz

19 Utilizing deletion, mutation and co-variational analyses, we have identified three regions i

20 briefly introduce epi-convergence theory of variational analysis and transform the physical mapping

21 Variational analysis of equilibrium and stability is sho

22 , organismic and hierarchical selection, and variational and essentialist thinking.

23 nergies-similar to recent calculations using variational and Monte Carlo methods.

24 interaction energy have been demonstrated by variational and perturbation based energy decomposition

25 mine the theory behind the currently popular variational and perturbation based methods from the poin

26 Variational and tunneling effects are treated by canonic

27 cular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy f

28 Here we report Single CEll Variational ANeuploidy analysis (SCEVAN), a fast variati

29 uliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a

30 In this work, we develop a simple variational approach allowing one to find the best possi

31 The method is based on a variational approach and consists of using air concentra

32 lve the inference problem using an efficient variational approach and demonstrate our method on simul

33 A second method is based on the variational approach and involves a global minimization

34 By implementing a recently developed variational approach based on the exact fractionalized e

35 Here, we develop an analytical and numerical variational approach that combines continuum mechanics a

36 The recently developed energetic variational approach to dissipative systems allows mathe

37 We integrate 3D matching and variational approach to handle a diverse range of motion

38 ed rings (e.g., polygons) based on an energy variational approach.

39 an are the Feynman diagram technique and his variational approach.

40 We use variational approaches and numerical simulations to addr

41 Alternatives to variational approaches are needed for large-scale experi

42 m and software program, hFRET, that uses the variational approximation for Bayesian inference to esti

43 A variational approximation is used for efficient paramete

44 n exact inference algorithm and an efficient variational approximation that allows scalable inference

45 In addition, we identify a particular variational approximation to be best-one in which the po

46 abilistic PCA (ZIPPCA) model, mbDenoise uses variational approximation to learn the latent structure

47 he scale of the datasets, we develop several variational approximations and explore their accuracy.

48 In this article, variational approximations are used to perform the analo

49 It is shown with the aid of H+/- and variational arguments that, in fact, there is a much ric

50 A generalized variational auto-encoder (VAE) was trained to learn a lo

51 tigate how latent space models trained using variational auto-encoders can infer these properties fro

52 e that the latent space models learned using variational auto-encoders provide a mechanism for explor

53 ere, we introduce SpatialMETA, a conditional variational autoencoder (CVAE)-based framework for cross

54 oregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the math

55 ng independent latent spaces within a single variational autoencoder (VAE) encompassing at least four

56 A variational autoencoder (VAE) is a machine learning algo

57 paper we take a different approach, fitting variational autoencoder (VAE) models parameterized by de

58 total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precord

59 To this end, we developed a variational autoencoder (VAE) to learn a continuous nume

60 Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning

61 imensionality reduction technique, including variational autoencoder (VAE), is a potential solution t

62 We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuatio

63 Here we present XOmiVAE, a variational autoencoder (VAE)-based interpretable deep l

64 o approaches: a semisupervised model using a variational autoencoder and a pretrained supervised lear

65 ed scVital, a computational tool that uses a variational autoencoder and discriminator to embed scRNA

66 ural networks (CNNs), but neither CNNs nor a variational autoencoder captured the human geometric reg

67 Here we present biVI, which combines the variational autoencoder framework of scVI with biophysic

68 tion of a recently published highly scalable variational autoencoder framework that provides interpre

69 egrates the factorization principle into the variational autoencoder framework, ensuring the preserva

70 Deep-learning models like Variational AutoEncoder have enabled low dimensional cel

71 l on a square lattice is investigated with a variational autoencoder in the non-vanishing field case

72 Here we describe 'Dhaka', a variational autoencoder method which transforms single c

73 Here, SeATAC uses a conditional variational autoencoder model to learn the latent repres

74 including the long-short term memory model, variational autoencoder model, and generative adversaria

75 By contrast, we propose a probabilistic variational autoencoder model, scVAEIT, to integrate and

76 aterial dynamics, we construct Convolutional Variational Autoencoder models to track structural phase

77 dual dimensions in denoising autoencoder and variational autoencoder models trained using an intermed

78 coder model, exmiR or mRNA-based models, and variational autoencoder models.

79 scPDA, a probabilistic model that employs a variational autoencoder to achieve high computational ef

80 s are encoded and decoded collectively via a variational autoencoder to infer candidates for approved

81 CLARM consists of a Conditional Variational Autoencoder transforming six-dimensional pha

82 A residual variational autoencoder was developed to extract physiol

83 A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulf

84 First, using a variational autoencoder, it generates complex latent rep

85 learning framework based on Vector Quantized Variational AutoEncoder, tailored for comprehensive CRE

86 ised generative neural methods, based on the variational autoencoder, that can model cell differentia

87 n optional data augmentation procedure via a variational autoencoder, which improves the method's rob

88 A variational autoencoder-based approach, neural relationa

89 DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of ge

90 CLigOpt is a variational autoencoder-based model which utilizes co-em

91 Here we propose CODAL, a variational autoencoder-based statistical model which us

92 s, and laboratory methods with a conditional variational autoencoder.

93 l datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training meth

94 , a method using Nearest Neighbours (NN) and Variational Autoencoders (VAE), which we apply to k-mers

95 Deep generative models such as variational autoencoders (VAEs) and generative adversari

96 Deep learning models, such as variational autoencoders (VAEs), can enhance clustering

97 ative filtering, denoising autoencoders, and variational autoencoders can impute missing values in th

98 Deep learning architectures such as variational autoencoders have revolutionized the analysi

99 rative analysis-by-synthesis model (based on variational autoencoders) for MNIST and a hybrid discrim

100 sociative network) trains generative models (variational autoencoders) to (re)create sensory experien

101 etworks, Deep Neural Networks, Autoencoders, Variational Autoencoders, and Gated Recurrent Units.

102 eneration, each using genetic algorithms and variational autoencoders.

103 VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variatio

104 methods such as Markov chain Monte Carlo and Variational Bayes (VB) are typically used.

105 Enricherator uses the variational Bayes algorithm to fit a generalized linear

106 We develop a variational Bayes approach (GEMINI) that jointly analyze

107 thin a Bayesian hierarchical framework and a variational Bayes approximation is derived which allows

108 ergence of the algorithm beyond the standard Variational Bayes Expectation Maximization algorithm.

109 n Annotation Guided eQTL Analysis (BAGEA), a variational Bayes framework to model cis-eQTLs using dir

110 ning of factor models with the auto-encoding variational Bayes framework, is not domain specific and

111 dicted by a sparse set of regulators using a variational Bayes method, and then building a bipartite

112 We propose a novel variational Bayes network reconstruction algorithm to ex

113 Our method employs an efficient variational Bayes scheme for model inference enabling it

114 ker within the GWAS, based on results from a variational Bayes spike regression algorithm.

115 ed model and use a computationally efficient variational Bayesian algorithm to fit the model.

116 A recently developed variational Bayesian analysis using pattern recognition

117 A variational Bayesian Expectation Maximization (EM) with

118 exploiting an approximation technique termed variational Bayesian expectation maximization.

119 n for model selection that is derived from a variational Bayesian framework with a popular alternativ

120 Here we develop a novel Variational Bayesian Hidden Markov Model (VB-HMM) to inv

121 The variational Bayesian independent component analysis mixt

122 POP uses variational Bayesian independent component mixture model

123 We derive a fast variational Bayesian inference algorithm and show that i

124 al efficiency is achieved through the use of variational Bayesian inference.

125 Using simulated data, we show that the variational Bayesian method is more accurate in finding

126 tumour microarray datasets and show that the variational Bayesian method is more sensitive to capturi

127 red with an unsupervised machine classifier, variational Bayesian mixture of factor analysis (vbMFA).

128 ers of an underlying state space model using variational Bayesian procedures.

129 ore, we highlight the challenges in studying variational bias and propose potential approaches to ide

130 Here we use the invariant variational bicomplex formalism to derive the first equi

131 erefore obey a maximum-entropy path-integral variational calculus ("the principle of least exertion",

132 ms can be formulated within the framework of variational calculus, their solution for complex systems

133 he inherent difficulties of the conventional variational-calculus approach prevents the numerical cal

134 One method, the quantum variational classifier, uses a variational quantum circu

135 Variational crystallization focuses on protein modificat

136 ns which show that a strategy, which we term variational crystallization, substantially enhances the

137 try, distribute material efficiently and for variational cutting of orthosis padding material.

138 Here, a technique called variational data assimilation is introduced as a means o

139 crystallographic theory, implemented in the variational deep learning framework Careless.

140 roposal parameters informed by approximating variational densities via auxiliary parameters, is used

141 We present theoretical and variational details of the formulation and verify our im

142 We also show that the variational effect is important in computing the energy-

143 ants have been analyzed in detail, including variational effects, tunneling contributions, the effect

144 The design sensitivity for the mixed variational eigenvalue problem is derived using the adjo

145 ccomplished by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Pr

146 We developed a variational EM algorithm for a hierarchical Bayesian mod

147 We demonstrate that our variational EM algorithm has comparable sensitivity and

148 Furthermore, we show that our model with a variational EM inference algorithm has higher specificit

149 iational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has

150 tor, indicating the presence/absence of each variational event compared to a "reference" sequence.

151 e propose a Bayesian statistical model and a variational expectation maximization (EM) algorithm to e

152 The resulting variational expression, deltaVmax = GudeltaPel + Peldelt

153 Here, we consider a variant of variational filtering for static inputs, to which we ref

154 e selection of a reaction coordinate and the variational formulation of the reaction probability prob

155 This paper describes an L1 regularized variational framework for developing a spatially localiz

156 ropose a model called PRotein Engineering by Variational frEe eNergy approximaTion (PREVENT), which g

157 Here, we used a variational free energy functional to calculate the char

158 brain hypothesis, predictive processing, and variational free energy minimisation are typically used

159 d molecular dynamics, umbrella sampling, and variational free energy profile methodologies.

160 s in effective synaptic connectivity reduced variational free energy, where the connection strengths

161 s to a single principle--the minimisation of variational free energy--to provide Bayes optimal soluti

162 per, we integrate these candidates using the variational (free energy) approach to human cognition an

163 Adopting a variational Gaussian approximation for the posterior of

164 ent framework for parameter estimation using variational Gaussian approximations (VGA).

165 pulse retrieval method based on conditional variational generative network (CVGN) that can address b

166 Here, we introduce signed variational graph auto-encoder (S-VGAE), an improved gra

167 he-art graph embedding methods, hierarchical variational graph auto-encoder learns more informative a

168 First, we have developed hierarchical variational graph auto-encoders trained end-to-end to jo

169 We present a variational hybrid quantum-classical algorithm for findi

170 n that uses well-established techniques from variational imaginary time evolution.

171 We present a variational independent component analysis (ICA) method

172 e present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep l

173 Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to impr

174 of the data using computationally efficient variational inference and supports flexible sparsity con

175 We apply a variational inference approach to the learning of Gaussi

176 The MAP approximation and variational inference described in this paper have been

177 Furthermore, using modern variational inference methods based on automatic differe

178 multilayered Dirichlet model for stochastic variational inference of pixel-level spatial factors, an

179 We use variational inference techniques to learn the model para

180 e an expectation-maximization algorithm with variational inference that maximizes the likelihood of t

181 Here, we introduce contrastive variational inference, a framework for deconvolving vari

182 Here we present Total Variational Inference, a framework for end-to-end joint

183 these two components, coupled with efficient variational inference, enables the selection of networks

184 andard application in Bayesian networks, via variational inference, to their use in the literature on

185 nce algorithm for our model using stochastic variational inference.

186 Variational interaction energies (E(i)) of side chain-li

187 cation of this sort of model by illustrating variational inversion (using simulated data) of this mod

188 methods have been largely restricted to the Variational Laplace (VL) algorithm which assumes that th

189 The parameters are determined in a variational learning scheme aimed at minimizing an appro

190 nearity is to project the dynamics onto some variational manifold.

191 In particular, we characterize the models of variational, maxmin, constant absolute risk aversion, an

192 These are variational message passing and belief propagation - eac

193 While variational message passing offers a simple and neuronal

194 e expense of the architectural simplicity of variational message passing.

195 The variational method directly yields predicted chevron plo

196 Here, we use the maximum caliber variational method to build a minimal model of cell beha

197 trate that, unlike the other approximations, variational methods are accurate and are guaranteed to l

198 The variational methods are typically derived from the early

199 Energetic variational methods can deal with these characteristic p

200 for the S4 segment are combined using energy variational methods in which all densities and movements

201 Using variational methods, we demonstrate that feedbacks can i

202 ussler, loopy belief propagation and several variational methods.

203 Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified si

204 We proposed a residue-level coarse-grained variational model for the investigation of the aggregati

205 We analyze folding routes predicted by a variational model in terms of a generalized formalism of

206 ontrolling allosteric transitions by using a variational model inspired from work in protein folding.

207 A coarse-grained variational model is used to investigate the polymer dyn

208 ng morphological traits and individuation of variational modules.

209 A variational multiscale approach is needed to deal with i

210 T(1rho) maps obtained by deep learning-based variational network (VN) and compressed sensing (CS).

211 Here we implement variational optical flow analysis as a new approach to a

212 e replace the actual dynamic simulation with variational optimization of a reaction path connecting k

213 mathematical physics, which can be recast as variational optimization problems, such as the important

214 ments and then extend its application to the variational optimization(6-8) of Hamiltonians for quantu

215 e search and closed-loop optimization of the variational parameters, approximating the ground-state e

216 Using a variational path sampling algorithm, we simulated the en

217 dered eigenvectors of orthogonal hydrophobic variational patterns.

218 l atomic interactions into the semi-discrete variational Peierls framework.

219 Here, we develop a nonlocal semi-discrete variational Peierls-Nabarro (SVPN) model by incorporatin

220 Calculations within a semidiscrete variational Peierls-Nabarro model informed by first-prin

221 een computed using the second-order Kleinert variational perturbation theory (KP2) in the framework o

222 we include nuclear quantum effects through a variational polaron transformation of the high-frequency

223 ata through univariate approximations of the variational posterior probability of inclusion, with pro

224 ocumented human transmission biases on their variational preferences.

225 First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and

226 By discretizing the variational principle in a natural way we obtain discret

227 We introduce a discrete-time variational principle inspired by the quantum clock orig

228 Variational principle is utilized to derive the nonlinea

229 variational principles and the time-embedded variational principle presented.

230 tion State identification via Dispersion and vAriational principle Regularized neural networks (TS-DA

231 advantage of the unique perspective of this variational principle to examine the error of basis appr

232 variational states using the time-dependent variational principle, results in classical chaotic Hami

233 eved by adding an regularization term to the variational principle, which is shown to yield solutions

234 o called Minimum Entropy Generation; and the Variational Principle.

235 n this case the Riemann mapping has a linear variational principle: It is the minimizer of the Dirich

236 Using variational principles and simulations, we use active in

237 tion between previously known time-dependent variational principles and the time-embedded variational

238 In a recent paper, the authors explored variational principles that help one understand chemical

239 We formulated a variational problem using the geodesic shortest path, wh

240 of then-available numerical solvers, the NRT variational problem was recently shown to be amenable to

241 Certain variational problems in this setting are considered, inc

242 We also introduce a variational procedure to optimize reaction coordinates.

243 cs Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversar

244 s proposed and its transmission genetic, and variational properties are analysed.

245 our understanding of the organizational and variational properties of complex phenotypes.

246 Collectively, these results reveal the variational properties of murine molar development that

247 experimentally that an efficient, low-depth variational quantum algorithm with few parameters can re

248 , the quantum variational classifier, uses a variational quantum circuit(1,2) to classify the data in

249 Moreover, we find that the variational quantum circuits exhibit noise thresholds ab

250 e the capabilities of near-term hardware for variational quantum eigensolver or more broad applicatio

251 Compared with the popular variational quantum eigensolver(7,8), our hybrid quantum

252 The variational quantum eigensolver, a leading algorithm for

253 We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with

254 of valid wavefunctions and is trained using variational quantum Monte Carlo.

255 ich to mitigate quantum back action, such as variational readout and squeezed light injection(7), wit

256 Among these, variational relativistic multiconfigurational multirefer

257 We introduce a variational representation of quantum states based on ar

258 Bayesian variational representational similarity analyses on fMRI

259 actions using DFT and ab initio theories and variational RRKM/master equation (vRRKM/ME) formalism.

260 Causality is assessed by a variational scheme based on the Information Imbalance of

261 From the variational solutions of the many-body master equation f

262 Instead, accurate variational solutions of the vibration-rotation Schrodin

263 erwise inaccessible level of accuracy in the variational solutions using our noisy processor.

264 Here we introduce a variational solver for MaxCut problems over m = O(nk) bi

265 Projecting quantum dynamics onto variational states using the time-dependent variational

266 oupled cluster methods, as well as many-body variational states.

267 election are inefficient at creating modular variational structures.

268 Solar magnetism displays a host of variational timescales of which the enigmatic 11-year su

269 ion coordinate, and it is used to locate the variational transition state that defines a transition s

270 Conventional and variational transition state theories can predict neithe

271 basis set, have been combined with canonical variational transition state theory (CVT) and small-curv

272 te constants were calculated using canonical variational transition state theory (CVT) as well as wit

273 e reaction have been computed with canonical variational transition state theory (CVT), both with and

274 tunneling to multistructural microcanonical variational transition state theory (MS-muVT) rate const

275 This article reviews the fundamentals of variational transition state theory (VTST), its recent t

276 ined using the G3B3 theory coupled with both variational transition state theory and Rice-Ramsperger-

277 K parameters, and it eliminates the need for variational transition state theory calculations as a fu

278 -mechanical electronic structure methods and variational transition state theory kinetic calculations

279 s of the rate constants by ensemble-averaged variational transition state theory with multidimensiona

280 d tunneling effects are treated by canonical variational transition state theory with multidimensiona

281 ters to agree with multistructural canonical variational transition state theory with multidimensiona

282 l with multidimensional tunneling (canonical variational transition state theory with small curvature

283 based on coupling density functional theory, variational transition state theory, and a microscale ma

284 ransition state implied by ensemble-averaged variational transition state theory.

285 ) molecule(-)(1) s(-)(1) using the canonical variational transition state theory.

286 ein isotope labeling in the framework of the Variational Transition State Theory.

287 Then, the key interactions at the reactant, variational transition state, and product are analyzed i

288 Thermal rate coefficients are computed using variational transition-state theory (VTST) calculations

289 ing QM/MM molecular dynamics simulations and variational transition-state theory calculations.

290 miclassical calculations employing canonical variational transition-state theory drastically underpre

291 for the direct component of a reaction with variational transition-state theory for an indirect comp

292 results were reproduced in the framework of variational transition-state theory that includes a dyna

293 he dynamics of this reaction by means of the variational transition-state theory with multidimensiona

294 We employ canonical variational transition-state theory with multidimensiona

295 Variational transition-state theory with semiclassical g

296 adical have been calculated using multi-path variational transition-state theory with small-curvature

297 Direct dynamics calculation using canonical variational transtition state theory (CVT) inclusive of

298 Based on a rigorous variational treatment, we present both numerical as well

299 Crucially, this formal (variational) treatment seeks to resolve key debates in c

300 Using variational wavefunctions, gauge theoretic arguments, an