ライフサイエンスコーパス: Gaussian

1 genetic association statistics with mixture Gaussians.

2 mixture distributions, including mixtures of Gaussians.

3 mance calculations were carried out by using Gaussian 03 (Revision D.01).

4 tonation properties were calculated by using Gaussian 03 and EXPLO5 v6.01 programs, respectively.

5 Heats of formation (HOF) were calculated (Gaussian 03) and combined with experimental densities to

6 ividual chromophores that make up melanin as Gaussian absorbers with bandwidth related via Frenkel ex

7 Using the Gaussian accelerated molecular dynamics (GaMD) method th

8 ions in an enhanced sampling regime, using a Gaussian-accelerated molecular dynamics (GaMD) methodolo

9 The Gaussian allele model (GAM) involves a finite number of

10 exponents change and approach an apparently Gaussian (alpha = 0) model.

11 h assumes that the posterior distribution is Gaussian and finds model parameters that are only locall

12 Here, we report restructuring the Gaussian and mean curvatures of smectic A films with fre

13 There are optimal proportions of Gaussian and non-Gaussian noises, which maximise the qua

14 mial approach has the ability to capture non-Gaussian and nonlinear features that might be present in

15 state-of-the-art approximate methods such as Gaussian and pseudo-likelihood inference.

16 ations, compared to those obtained by faster Gaussian and pseudo-likelihood methods.

17 atial-mode basis - exemplified using Hermite-Gaussian and radial Laguerre-Gaussian modes.

18 We show that the popular Gaussian approximation tends to perform poorly under ext

19 ation trees from ancestry components using a Gaussian approximation.

20 to fitting STICS correlation functions to a Gaussian approximation.

21 The distribution of binding affinity is Gaussian around the mean and becomes exponential near th

22 equacy of the STRUCTURE-style models and the Gaussian assumption for identifying ancestry components

23 general eavesdropping to a two-mode coherent Gaussian attack, and then showing that the superadditivi

24 escribed that combines an upward propagating Gaussian beam and a downward propagating Bessel beam.

25 the field of view over the use of a standard Gaussian beam by a factor of six is demonstrated.

26 When a Gaussian beam carries linear chirp, the 1D beam deflects

27 ens-based nanofocusing device can compress a Gaussian beam down to tens-of-nanometers of spot size in

28 ion-limited performance, generating a nearly Gaussian beam profile with a Strehl ratio above 0.8.

29 that includes scattering losses of a focused Gaussian beam reliably predicted several experimental ob

30 Without chirp, a 1D Gaussian beam splits into two nondiffracting Gaussian be

31 aussian beams during propagation, while a 2D Gaussian beam undergoes conical diffraction.

32 In the case of 2D Gaussian beam, the propagation is also deflected, but th

33 both a Bessel beam and photons in a focused Gaussian beam.

34 patial resolution at a higher speed than the Gaussian-beam-based stimulated Raman scattering sectioni

35 te this with the decomposition into Laguerre-Gaussian beams and introduce a distribution over the int

36 Both 1D and 2D Gaussian beams are diffractionless and display uniform p

37 the potential of using 'self-healing' Bessel-Gaussian beams carrying orbital-angular-momentum to over

38 Gaussian beam splits into two nondiffracting Gaussian beams during propagation, while a 2D Gaussian b

39 one-dimensional and two-dimensional (1D, 2D) Gaussian beams in the fractional Schrodinger equation (F

40 s are in agreement with predictions based on Gaussian beams propagation.

41 m centre, optical and millimetre-wave Bessel-Gaussian beams show ~6 dB and ~8 dB reduction in crossta

42 We show that the non-Gaussian behavior is a consequence of significant hetero

43 This ensemble non-Gaussian behavior is caused by a combination of an expon

44 n in the time-averaged diffusivities and non-Gaussian behavior of individual trajectories.

45 Stimuli consisted of colored Gaussian-blobs arranged either mirror-symmetrically or q

46 ss distribution relative to that of the near-Gaussian broad-band mass distribution.

47 h small noise intensities in contrast to the Gaussian case which requires large intensities for this.

48 re well modeled by an AF having an enhancing Gaussian center with a suppressive surround.

49 siders a biologically relevant question of a Gaussian chain (such as an unfolded protein) binding to

50 istic motion to generate continuous variable Gaussian cluster states within cavity modes.

51 ed against a generative model, which assumes Gaussian clusters overlaid on a completely spatially ran

52 The "Gaussian" component was a consequence of background subt

53 three-dimensional structure, with isotropic Gaussian components as moveable pseudo-atoms.

54 h based on a finite mixture model (FMM) with Gaussian components is proposed.

55 rest for hemodynamics, to a superposition of Gaussian components, easily amenable to the analysis of

56 librium smectic films with negative and zero Gaussian curvature are transformed into structures with

57 nt decrease in the deformation energy due to Gaussian curvature associated with scalloped edges, demo

58 ument predicts the sign and magnitude of the Gaussian curvature modulus that is in agreement with exp

59 alized shells containing regions of negative Gaussian curvature naturally develop anomalously large f

60 6 +/- 11)x more concentrated on the negative Gaussian curvature neck of the nanoscale membrane buds.

61 med into structures with pronounced positive Gaussian curvature of layers packing, which are rare in

62 e disclinations are associated with positive Gaussian curvature, whereas negative disclinations give

63 onforming materials to rigid substrates with Gaussian curvature-positive for spheres and negative for

64 enoid-shaped particles that exhibit negative Gaussian curvature.

65 th periodic minimal surfaces having negative Gaussian curvatures and can possess unusual mechanical a

66 geometric cues including the local mean and Gaussian curvatures.

67 ansient dwell occurrence to the sum of three Gaussian curves suggests that the asymmetry of the three

68 trate that network inference methods for non-Gaussian data can help in accurate modeling of the data

69 h, based on a finite mixture of multivariate Gaussian densities.

70 ariations on a single underlying function: a Gaussian density function truncated at roughly two SDs.

71 The analysis confirms that a Gaussian density, estimated on past forecasting errors,

72 teria, wild-type cells exhibit anomalous non-Gaussian deviations and are able to move in trajectories

73 server is released, which implements DelPhi Gaussian dielectric function to calculate electrostatic

74 Using a Gaussian diffusion model, annual iPCB emissions of 110-1

75 These non-Gaussian diffusion white matter metrics are promising su

76 shape corresponds to the expected effect of Gaussian disorder of the pigment transition energies.

77 affic-origin PM2.5 and black carbon followed Gaussian dispersion in the near road area in the daytime

78 nd landfills were estimated using an Inverse Gaussian Dispersion Model and the Environmental Protecti

79 ning a chemical transport model (CTM) with a Gaussian dispersion model, both reactive and nonreactive

80 Gaussian dispersion simulations of these methane plumes,

81 new primary model has been developed, using Gaussian distributed populations and Eyrings rate consta

82 eter recovery from data generated from an ex-Gaussian distribution and from a Ratcliff Diffusion Mode

83 e a robust yield of 90% and percentage obeys Gaussian distribution at various stages.

84 This method utilizes a grayscale Gaussian distribution effect to model inaccuracies inher

85 ImpG-Summary/LD), that assume a multivariate Gaussian distribution for the association summary statis

86 led as two different independent generalized Gaussian distribution functions, representing, respectiv

87 The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential d

88 ons of a protein described as a multivariate Gaussian distribution of atomic displacements and compar

89 imes, which is responsible for the canonical Gaussian distribution of timing behavior.

90 was processed using a He(2+) ion-beam with a Gaussian distribution or by exposure to ultraviolet/O3,

91 n at all observed timescales rather than the Gaussian distribution predicted by the central limit the

92 T, background signals are modeled by a local Gaussian distribution that is accurately estimated from

93 alculated for each scan by fitting a bimodal gaussian distribution to the voxel-intensity histogram w

94 th, the centroid distribution converges to a Gaussian distribution whose mean and variance are determ

95 t in data that can be well-approximated by a Gaussian distribution whose mean and variance are determ

96 Assuming the varphi angles follow a Gaussian distribution, the width of this distribution ca

97 m that models LD using a simple multivariate Gaussian distribution.

98 narrow size distribution and agree with the gaussian distribution.

99 cal analyses because the data did not follow Gaussian distribution.

100 Highly symmetric non-Gaussian distributions of CF support zero-sum dynamics.

101 were often best fit with several single non-Gaussian distributions or mixtures of Gaussian distribut

102 s describing the two independent generalized Gaussian distributions that underlie the WBQ chromatogra

103 ipal orientations drawn from two categories: Gaussian distributions with different means and equal va

104 le non-Gaussian distributions or mixtures of Gaussian distributions, rather than the more frequently

105 ow that it also performs excellently for non-Gaussian distributions.

106 nalog models to the discrete and continuous (Gaussian) distributions of a single proton binding-disso

107 or assessing the template-free Difference-of-Gaussian (DoG) particle-picking method to detect complex

108 Non-Gaussian entangled states have been produced in small en

109 at each cortical site using a difference of Gaussian envelopes and derived estimates of the strength

110 The new fitting Gaussian equation only depends on two parameters: The ap

111 We modified a Gaussian equation, which achieves the deconvolution of A

112 istributions (exponential, multiexponential, Gaussian, etc.), as well as user-specified PDFs without

113 ic soliton and discover that is surprisingly Gaussian, exhibiting excellent agreement with our experi

114 thms to efficiently capture the distinct non-Gaussian features at different locations in a [Formula:

115 The clustered particle filter captures non-Gaussian features of the true signal, which are typical

116 an structures, which contain many strong non-Gaussian features such as intermittency and fat-tailed p

117 onal data from complex systems including non-Gaussian features.

118 vement was encoded within a multidimensional Gaussian field (MGF).

119 Iteration number and FWHM of the gaussian filter have a similar impact on the image featu

120 Data were analyzed within a hierarchical Gaussian filter model that captures interindividual vari

121 ionally, the influence of postreconstruction gaussian filtering was investigated.

122 action in combination with a two-dimensional Gaussian fit algorithm, performing the best.

123 nction variance instead of using the typical Gaussian fitting procedure.

124 ion accuracy by ~60% with compare to regular Gaussian fitting, and improves minimal resolvable distan

125 increase in computation time as compared to Gaussian fitting.

126 of syngas release were clearly identified by Gaussian fittings, i.e., volatile release, char transfor

127 In the rod [Formula: see text], Gaussian [Formula: see text] and excluded volume chain [

128 op-down module selection is implemented by a Gaussian function tuned for the visual orientation of th

129 Performance was well described by a Gaussian function with a bandwidth around 25 degrees .

130 velocity fluctuations were well described by Gaussian functions and the velocity gradient was uniform

131 Gaussian graphical model (GGM), a probability model that

132 atistically sound procedure for constructing Gaussian graphical model and making inference with high-

133 ted that LDGM consistently outperforms other Gaussian graphical model based methods.

134 s article, we introduce a covariate-adjusted Gaussian graphical model to estimate the Markov equivale

135 Gaussian Graphical Models (GGMs) were used to identify c

136 Sparse Gaussian graphical models are popular for inferring biol

137 Gaussian graphical models are used to construct associat

138 tional inference approach to the learning of Gaussian graphical models of gene regulatory networks, t

139 erved and varying network structure, and use Gaussian graphical models to represent the network struc

140 demonstrated rigorously for the two-pattern Gaussian Hopfield model.

141 es bleaching with a circular laser beam of a Gaussian intensity profile.

142 and dark-field are retrieved through a multi-Gaussian interpolation of the beam.

143 Each of these Gaussians is centered about the favored temperature of t

144 rametric method is combined with a judicious Gaussian kernel density estimation in the remaining low-

145 rage, numerically simulated low pass RC, and Gaussian kernels, is compared.

146 oom temperature and emits spatially coherent Gaussian laser beams.

147 Twisted Laguerre-Gaussian lasers, with orbital angular momentum and chara

148 Further, we implement a dithered Gaussian lattice to minimize sample-induced illumination

149 variations from one protein imposed by a non-Gaussian Levy noise, which is able to describe even larg

150 results also imply that the presence of non-Gaussian Levy noises has fundamentally changed the escap

151 On-axis Laguerre-Gaussian (LG) beams are expanded into off-axis OAM spect

152 thways generate short conversion tracts with Gaussian-like distributions.

153 ODNs generated short conversion tracts with Gaussian-like distributions.

154 orientation, and the local flow around it is Gaussian-like for immotile bacteria, wild-type cells exh

155 hase EPR spectra of the radical cations have Gaussian lineshapes with linewidths proportional to N(-0

156 on similar devices, deviations from the main Gaussian lobe up to 25 microns from the focus and down t

157 tur distribution of the singular values of a Gaussian matrix, in agreement with previous work on high

158 but rather its ability to generate negative Gaussian membrane curvature.

159 l operation were quantified using an inverse Gaussian method (EPA's OTM 33a) in four major U.S. basin

160 profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling.

161 les [Formula: see text], where a conditional Gaussian mixture in a high-dimensional subspace via an e

162 ew outlier detection method and an aggregate Gaussian mixture model (AGMM).

163 issen, utilizes a modified Dirichlet process Gaussian mixture model (DPGMM) to fit the number of mixt

164 AA algorithm using an MR imaging-constrained gaussian mixture model (GMM).

165 In this calling procedure, a Gaussian Mixture Model and a Dirichlet Process Gaussian

166 We introduce the Gaussian Mixture model And Proportion test (GMAP) algori

167 Here the authors introduce GMAP, the Gaussian Mixture model And Proportion test, to identify

168 ussian Mixture Model and a Dirichlet Process Gaussian Mixture Model are integrated to infer genotypes

169 Specifically, a Gaussian mixture model is used to capture both binding a

170 l group aged between 20 and 30 years; (ii) a Gaussian mixture model that assigned each subject a prob

171 It combines the constrained Gaussian mixture model that incorporates the biological

172 We then built a Gaussian mixture model to represent the mixed population

173 When we applied a Gaussian mixture model to the AFM data, we observed a di

174 We propose the Gaussian mixture model with partitioning approach for cl

175 thway were used to cluster samples using the Gaussian mixture model.

176 The aim of our study was to create a novel Gaussian mixture modeling (GMM) pipeline to model the co

177 We demonstrate, using Gaussian mixture modeling, that the sample of 730 studie

178 Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrin

179 We apply large-scale Gaussian mixture models to massive datasets using Hadoop

180 ethod based on expectation maximization of a Gaussian mixture that accounts for localization uncertai

181 he structures within FAs, characterized as a Gaussian mixture, typically have areas between 0.01 and

182 A bottom-up graphic Gaussian model (GGM) algorithm was developed for constru

183 IM distributions are well characterized by a Gaussian model and separation efficiency can be predicte

184 Our model is a linear-Gaussian model and uses two types of hidden variables.

185 We employed a difference-of-Gaussian model to capture the center-surround configurat

186 ode is achieved by superposition of Laguerre-Gaussian modes and manifests exotic flower-like localiza

187 delay associated with the space of Laguerre-Gaussian modes, and an interferometer incorporating such

188 lf-reconstruct earlier upon propagation than Gaussian modes.

189 d using Hermite-Gaussian and radial Laguerre-Gaussian modes.

190 ating that colloidal membranes have positive Gaussian modulus.

191 screening (VS) algorithm, LIGSIFT, that uses Gaussian molecular shape overlay for fast small molecule

192 sion, support vector machine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separ

193 idely used elastic network models (ENMs)-the Gaussian Network Model (GNM) and the Anisotropic Network

194 Gaussian network model (GNM) is a simple yet powerful mo

195 Gaussian network model (GNM), regarded as the simplest a

196 -established protein-modeling framework, the Gaussian Network Model (GNM), to model chromatin dynamic

197 ctors and with fluctuations predicted by the Gaussian network model.

198 noise has been intensively investigated, but Gaussian noise cannot incorporate large bursts typically

199 The dynamics of the system induced by Gaussian noise has been intensively investigated, but Ga

200 d various types of fluctuating speech-shaped Gaussian noise including those with both regularly and i

201 ecoherence will remain unavoidable as is the Gaussian noise of classic circuits imposed by the Browni

202 chinchilla auditory nerve fiber responses to Gaussian noise to reveal pronounced distortions in tonot

203 nalyses of auditory nerve fiber responses to Gaussian noise to uncover pronounced distortions in codi

204 inant frequency of TFS coding in response to Gaussian noise was 2.4 kHz in noise-overexposed fibers c

205 ys suffer from various degenerations such as Gaussian noise, Poisson noise, speckle noise and pupil l

206 the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known K

207 ape mechanism in such a system compared with Gaussian noise.

208 are optimal proportions of Gaussian and non-Gaussian noises, which maximise the quantities MFET and

209 Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and a

210 d relative cerebral blood volume (nrCBV) and Gaussian-normalized relative cerebral blood flow (nrCBF)

211 ormalized relative cerebral blood volume and Gaussian-normalized relative cerebral blood flow values

212 nt images and voxel-wise subtraction between Gaussian-normalized relative cerebral blood volume (nrCB

213 KN2A loss, with both demonstrating increased Gaussian-normalized relative cerebral blood volume and G

214 -classical one and a Gaussian state to a non-Gaussian one by applying a sequence of operations determ

215 Moreover, we show that Bessel-Gaussian orbital-angular-momentum beams are more toleran

216 y Equivalence Search (IMaGES) and Linear non-Gaussian Orientation, Fixed Structure (LOFS) algorithms.

217 hot rubidium vapor is shown to result in non-Gaussian output mode structures that may be controlled b

218 iterature on the propagation of superluminal Gaussian packets, strongly distorted sinusoidal packets

219 e classes of attenuation maps using unimodal gaussians parameterized over a patient population.

220 ght-atom interactions to produce tunably non-Gaussian, partially self-healing optical modes.

221 the Fokker-Planck equation with strongly non-Gaussian PDFs in much higher dimensions even with orders

222 ry of both the transient and equilibrium non-Gaussian PDFs requires only [Formula: see text] samples!

223 bound ion is expelled first, giving distinct Gaussian peak shaped ion transfer voltammetric waves tha

224 A poorly packed column can produce non-Gaussian peak shapes and lower detection sensitivities.

225 fluorophore spectra comprise a well-defined Gaussian peak with a full width at half-maximum ranging

226 The framework relies on inverse-Gaussian point-processes with Laguerre expansion of the

227 a relatively simple data distribution (e.g., Gaussian, Poisson, negative binomial, etc.), which may n

228 Rg, of the protein due to the application of Gaussian polymer chain end-to-end distribution to extrac

229 We trained Gaussian process (GP) classification and regression mode

230 of microbial population growth curves using Gaussian process (GP) regression.

231 surrogate model is constructed by teaching a Gaussian process adsorption energies based on group addi

232 Regional and whole-brain gaussian process classifiers using a nested leave-one-ou

233 Gaussian process classifiers were employed to evaluate p

234 Gaussian process emulation techniques have been used wit

235 ata assimilation output; and (iii) dynamical Gaussian process model regression.

236 p a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and iden

237 Our results indicate that Gaussian process models have the potential to improve th

238 ombines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditio

239 Linear and Gaussian process regression models further validate that

240 We used Gaussian process regression to forecast future coverage

241 imation model, spatiotemporal smoothing, and Gaussian process regression to synthesise data and gener

242 Gaussian process regression was used to generate ARIs fo

243 vity map at 30 arc-seconds( 1 km) based on a Gaussian Process Regression(GPR).

244 Our framework is based on Gaussian Process regression, a Bayesian learning techniq

245 of machine learning base-learners including Gaussian process regression, Lasso, and random forest on

246 We use a non-parametric approach based on Gaussian Process regression.

247 ssion method (DisMod-MR) or spatial-temporal Gaussian process regression.

248 Our approach is based on Gaussian processes and applies to a wide range of data.

249 Multi-response Gaussian processes are used for the supervised learning

250 modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spi

251 regression model between PM2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting.

252 Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high f

253 Gaussian processes model the stochastic nature of the sp

254 gene expression data, when available, using Gaussian processes to model the dynamics of gene express

255 IBD statistics as a collection of stationary Gaussian processes.

256 ul methods of non-parametric regression with Gaussian processes.

257 ad analysis measures the beam diameter for a Gaussian profile and line scans measure an "effective" s

258 ange pixels, RGB pixel designs, and in-plane Gaussian profile pixels that have the potential to enabl

259 on (PSF) of an XYZ image stack into a narrow Gaussian profile.

260 any signals exchanged, we prove that two-way Gaussian protocols are immune to coherent attacks.

261 ero transmission (near-critical coupling) of Gaussian pulses propagating through a nano-fibre with a

262 Numerical simulations show that initial Gaussian pulses will evolve into the parabolic pulses in

263 e constructed a covariance matrix based on a Gaussian radial basis function.

264 We then developed a Bayesian Gaussian Regression model to measure the relationship am

265 VLT-R-DR) after four ECT treatments, using a Gaussian repeated measures model in all patients receivi

266 Using the Gaussian-Schell model, the properties of the harmonic be

267 ion functions that do not follow the assumed Gaussian shape.

268 stribution of filament curvatures with a non-Gaussian shape.

269 ; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific

270 Gaussian spatial-mode light tuned near to the atomic res

271 imentally realized, but these states display Gaussian spin distribution functions with a non-negative

272 al state to a highly non-classical one and a Gaussian state to a non-Gaussian one by applying a seque

273 Importantly, both the variance and the non-Gaussian statistical features in different Nino regions

274 e experiment indicates that the observed non-Gaussian statistics and dynamic heterogeneity are inhere

275 ic enhancement, and observed states with non-Gaussian statistics consistent with oversqueezed states.

276 ve muscle cell membranes does not follow the Gaussian statistics for normal Brownian diffusion.

277 ng accurate filter results and capturing non-Gaussian statistics of the true signal.

278 serving low-density fluctuations relative to Gaussian statistics--facilitates this nonclassical behav

279 time, so that their displacement follows the Gaussian statistics.

280 ral dynamical regimes including strongly non-Gaussian statistics.

281 We also developed a method to generate Gaussian stimuli that evoke spike trains with prescribed

282 rosophila ORNs in vivo with naturalistic and Gaussian stimuli, we show that ORNs adapt to stimulus me

283 turbulent dynamical systems with conditional Gaussian structures, which contain many strong non-Gauss

284 r diamagnetism by far exceeding the standard Gaussian superconducting fluctuations is observed below

285 lar, the addition of NEM-HMM increased a non-Gaussian tail in the path curvature distribution as well

286 (RT) level and with excessively long RTs (ex-Gaussian tau) related to cognitive failures.

287 m the median behavior are multiplicative and Gaussian-that is, they are proportionally larger for lar

288 of freedom coupled via a smooth homogeneous Gaussian vector field with both gradient and divergence-

289 A two-dimensional Gaussian was used to model the pRF in each voxel, and we

290 We consider inelastic scattering of Gaussian wave packets with parameters close to a zero of

291 ~150 THz) THz to infrared (IR) source with a Gaussian wavefront, emitted from nano-pore-structured me

292 zation in 3D, analytical functions such as a Gaussian, which are computationally inexpensive, may not

293 However, the more generally used Gaussian white noise stimuli were not effective since th

294 oise stimulation (tRNS; 100-640 Hz zero-mean Gaussian white noise) to the occipital region of human p

295 at, even in the absence of correlations, for Gaussian white noise, the conventional analysis leads to

296 rom naturalistic light contrast changes than Gaussian white-noise stimuli, and we explicate why this

297 meters, namely, transmitted power and scaled Gaussian width.

298 s of processes whose noise can be modeled by Gaussian, Wiener, or Ornstein-Uhlenbeck processes.

299 see text], the velocity distribution is non-Gaussian with a tail extending to significant negative v

300 In each case, forcing statistics are non-Gaussian, with long tails corresponding to rare intermit