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

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

19 Variational analysis of equilibrium and stability is sho

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

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

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

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

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

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

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

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

28 We use variational approaches and numerical simulations to addr

29 A variational approximation is used for efficient paramete

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

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

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

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

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

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

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

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

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

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

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

41 -package, including an implementation of our variational Bayes spike regression (vBsr) algorithm, is

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

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

44 A variational Bayesian Expectation Maximization (EM) with

45 exploiting an approximation technique termed variational Bayesian expectation maximization.

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

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

48 The variational Bayesian independent component analysis mixt

49 POP uses variational Bayesian independent component mixture model

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

51 du/wild/index.htm and Matlab source code for variational Bayesian learning of SSMs is available at ht

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

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

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

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

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

57 Variational crystallization focuses on protein modificat

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

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

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

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

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

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

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

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

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

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

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

69 The resulting variational expression, deltaVmax = GudeltaPel + Peldelt

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

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

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

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

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

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

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

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

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

79 We use variational inference techniques to learn the model para

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

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

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

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

84 The variational method directly yields predicted chevron plo

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

86 The variational methods are typically derived from the early

87 Energetic variational methods can deal with these characteristic p

88 Using variational methods, we demonstrate that feedbacks can i

89 ussler, loopy belief propagation and several variational methods.

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

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

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

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

94 ng morphological traits and individuation of variational modules.

95 A variational multiscale approach is needed to deal with i

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

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

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

99 dered eigenvectors of orthogonal hydrophobic variational patterns.

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

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

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

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

104 Variational principle is utilized to derive the nonlinea

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

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

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

108 Using variational principles and simulations, we use active in

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

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

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

112 Certain variational problems in this setting are considered, inc

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

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

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

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

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

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

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

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

121 election are inefficient at creating modular variational structures.

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

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

124 Conventional and variational transition state theories can predict neithe

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

146 We employ canonical variational transition-state theory with multidimensiona

147 Variational transition-state theory with semiclassical g

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

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

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

151 Using variational wavefunctions, gauge theoretic arguments, an