ライフサイエンスコーパス: Gaussian

1 luctuations at the promoter are markedly non-Gaussian.

2 ils (higher settling rates) than the inverse Gaussian.

3 due-position protein stability effects to be Gaussian.

4 (BAK1) conformational ensemble, we performed Gaussian accelerated molecular dynamics simulations on e

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

6 Here, solution NMR experiments and a novel Gaussian-accelerated molecular dynamics (GaMD) simulatio

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

8 rial stress control, demonstrated using both Gaussian and Bessel writing beams.

9 nheritance patterns of F(2) progeny were non-Gaussian and deviated from Mendelian expectations.

10 found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvat

11 The CNNs were compared with gaussian and median filters.

12 Spectrum can automatically find K for both Gaussian and non-Gaussian structures.

13 makes no model assumptions and measures non-Gaussian and nonlinear relationships.

14 ian distribution, which includes the inverse Gaussian and other diffusion and nondiffusion models; th

15 histograms of smFRET values to a sum of two Gaussians and the autocorrelations to an exponential and

16 nance energy transfer values to a sum of two Gaussians and the autocorrelations to an exponential and

17 ails (lower settling rates) than the inverse Gaussian; and the gamma and exponential probability dist

18 In particular, the Gaussian approximation breaks down whenever a qubit is s

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

20 loped semiclassical approaches, based on the Gaussian approximation, which retain phase and width inf

21 ution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, rese

22 to both scrutinize the applicability of the Gaussian assumption and capture distinctive non-Gaussian

23 used but neural activity does not follow its Gaussian assumption.

24 eater computational performance than WEGA, a Gaussian-based shape similarity method.

25 uantum electron dynamics with an emphasis on Gaussian basis set methods.

26 nal reaction time was calculated using an ex-Gaussian Bayesian model.

27 case when the system is much larger than the Gaussian beam and can be considered to be infinite.

28 he optical system has been studied using the Gaussian beam approximation to design the incident beam

29 r by more than one order of magnitude than a Gaussian beam illumination and matched exactly those of

30 A Gaussian beam self-traps when localized isomerization-in

31 -coupled surface plasmon resonance system by Gaussian beam shaping and multivariate data analysis.

32 ser beam is well approximated by an infinite Gaussian beam with a waist that is small compared to the

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

34 he coherent combination of multiple tailored Gaussian beams emitted from a multicore fibre (MCF) ampl

35 ations of second-harmonic vortex and Hermite-Gaussian beams in the recently-developed three-dimension

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

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

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

39 hat upstream ESCRT-induced alteration of the Gaussian bending rigidity and their crowding in concert

40 mnet to address the above issues for linear (Gaussian), binomial (logistic), and multinomial GLMs.

41 ete protein unfolding, from native dimers to Gaussian chains, or a partial unfolding with oligomeriza

42 Here, we consider Gaussian channels that model energy loss and thermal noi

43 with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of seq

44 ab, a new method for simulating multivariate Gaussian clusters.

45 sian spots each containing a single Laguerre-Gaussian component, using just a spatial light modulator

46 erize players' goals as a dynamic mixture of Gaussian components.

47 We develop a log-Gaussian Cox process model to analyse the opportunistic

48 e no generalizable method for changing their Gaussian curvature has been devised.

49 fier recognizing sequences inducing negative Gaussian curvature in target membranes.

50 embrane neck, where the steep decline in the Gaussian curvature likely triggers ESCRT-III/VPS4 assemb

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

52 eriments, including those examining negative Gaussian curvature, can arise from translocation dynamic

53 e revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most ab

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

55 mpatibility constraints that prohibit finite Gaussian curvature.

56 bactofilin CcmA is most abundant at positive Gaussian curvature.

57 ethod, voltage-tunable negative and positive Gaussian curvatures shapes are produced.

58 of considerably higher positive and negative Gaussian curvatures than those present in straight- or c

59 ssels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosi

60 romote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this pattern

61 s lose helical shape and associated positive Gaussian curvatures.

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

63 ding higher-order spectrum of engineered non-Gaussian dephasing noise using a superconducting qubit a

64 encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from

65 Circuits based on these dual-gated Gaussian devices enable a variety of biological spiking

66 cytoarchitectural signature inferred by non-Gaussian diffusion barriers inside the cortical plate du

67 ss the entire cortex was delineated with non-Gaussian diffusion kurtosis imaging as well as conventio

68 ties of semiconducting polymers based on the Gaussian disorder model (GDM) for site energies while em

69 n), using a mobile laboratory and an inverse Gaussian dispersion method (OTM 33A).

70 al atmospheric stability classes; a modified Gaussian dispersion methodology using empirically measur

71 wavelets) and GMM fitting parameters (e.g., Gaussian distances).

72 f gene expression violates the assumption of Gaussian distributed errors in linear regression for eQT

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

74 dified Gaussian (EMG) fitting model for near-Gaussian distributed subpeaks, polynomial fitting for hi

75 fective half-lives across the population are gaussian-distributed (i.e., follow a normal distribution

76 orov-Smirnov-distance between a hypothetical Gaussian distribution and the observed distribution of t

77 ults was controlled and validated by a mixed Gaussian distribution estimation method.

78 ffects is known to be well approximated by a Gaussian distribution from previous empirical fits.

79 ved distribution appears inconsistent with a Gaussian distribution of binding energies.

80 t was performed depending on Gaussian or non-Gaussian distribution of data.

81 We show that a Gaussian distribution of heteropolymer segments, coupled

82 cement of a 150-nm-diameter particle and non-Gaussian distribution of increments.

83 neities are responsible for the generic, non-Gaussian distribution of increments.

84 rn, and a model that implements a 0-inflated Gaussian distribution of mean group abundance for each t

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

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

87 h characterized by an interlobe distance and Gaussian distribution width (disorder).

88 at the underlying startle response has a non-Gaussian distribution, and that the traditional PPI metr

89 a broader model set: the generalized inverse Gaussian distribution, which includes the inverse Gaussi

90 model can effectively learn low-dimensional Gaussian distributions from the original high-dimensiona

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

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

93 The Difference of Gaussian (DoG) detector has been used to overcome these

94 vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networ

95 ibutions (DPDs) ranging from the case of the Gaussian DPD to the case of the uniform with finite supp

96 work, we investigate a parametric family of Gaussian DPPs with a clearly interpretable effect of par

97 We propose that the observed non-Gaussian dynamics indicate a hopping diffusion mechanism

98 I data based on the multi-graph unsupervised Gaussian embedding method (MG2G).

99 obtained by using an exponentially modified Gaussian (EMG) fitting model for near-Gaussian distribut

100 Generalized estimating equations with Gaussian family, identity link, and an exchangeable work

101 Strikingly, the non-Gaussian feature is independent of the cytoskeleton cond

102 fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machi

103 and postreconstruction smoothing with a 5-mm gaussian filter.

104 he ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of cou

105 tases were found between standard, CNN-, and gaussian-filtered bone scans.

106 g the clinical protocol with additional 2-mm gaussian filtering (hereafter referred to as "clinical+G

107 per-resolution reconstruction, combined with Gaussian filtering and application of the Richardson-Luc

108 CT images after applying three Laplacian-of-Gaussian filters known as spatial scaling factors (SSFs)

109 We began by applying a Gaussian finite mixture model to 998 sampled illuminatio

110 increase in computation time as compared to Gaussian fitting.

111 s chosen by traditional analyses that assume Gaussian fluctuations or use the central limit theorem.

112 ngle-cell experiments with more complex, non-Gaussian fluctuations.

113 nipulate photons to create excitation beams (Gaussian, focused and collimated) for lab-on-chip applic

114 es around a central axis, and (2) a Laguerre-Gaussian ([Formula: see text]) beam with a helical phase

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

116 of angular velocity (provided by LC4) and a Gaussian function of angular size (provided by LPLC2) re

117 We designed a loss function and a 2 D Gaussian function specifically for the characteristics o

118 on is found to be the time derivative of the Gaussian function.

119 he asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional s

120 Using a regularized Gaussian Graphical Model, we construct a transcriptional

121 Gaussian graphical models are used to construct associat

122 he following features: (i) they are based on Gaussian graphical models which can capture the changes

123 oped for testing the difference in different Gaussian graphical models.

124 Data were analysed using Gaussian graphical models: a form of network analysis wh

125 cedented electrostatic control of dual-gated Gaussian heterojunction transistors for simplified spiki

126 commonly employed, noise is generically non-Gaussian in nature.

127 The statistical matching of the known nearly Gaussian incoming Gibbs state at the ADC completely dete

128 t new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-dr

129 similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity inf

130 ofile kernel similarity (LNCGS), the disease Gaussian interaction profile kernel similarity (DISGS),

131 cRNA function similarity (LNCFS), the lncRNA Gaussian interaction profile kernel similarity (LNCGS),

132 SAFD fits a three-dimensional Gaussian into the profile data of a feature, without dat

133 of the density of data points on MDiMs using Gaussian kernels followed by a curve fitting with an ada

134 ushing but also pulling forces from a single Gaussian laser beam.

135 ea estimation hierarchical model with latent Gaussian layers to account for space and time correlatio

136 istance in different forms, including: (1) a Gaussian-like beam dot that revolves around a central ax

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

138 ed a generalized additive model (GAM) with a Gaussian link to examine the city-specific short-term as

139 We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of

140 nstant birth-death model, combined with both Gaussian Markov random field (GMRF) and horseshoe Markov

141 ampling e.g. standard Hadamard protocols and Gaussian matrix methods, this approach results in a sign

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

143 ADC in the lower distribution using a double Gaussian mixed model.

144 tive binomial mixed models and zero-inflated Gaussian mixed models.

145 ive binomial mixed models, and zero-inflated Gaussian mixed models.

146 By means of a Gaussian mixed-effects model implemented in the new R/Bi

147 This article contributes spatial-Dirichlet Gaussian mixture model (DGMM), an algorithm and a workfl

148 spectrum of hearing loss profiles, we used a Gaussian Mixture Model (GMM) to segment audiograms witho

149 atients with breast cancer, we developed the Gaussian mixture model (GMM)-based classifier.

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

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

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

153 ach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segment

154 eep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that acc

155 approached via K-means (or, more generally, Gaussian mixture model) clustering composed with either

156 We propose a Gaussian mixture model-based multiplet identification me

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

158 lassification (optimal cut point, 1.56 SUVR) gaussian mixture modeling (optimal cut point, 1.55 SUVR)

159 ng the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into ful

160 beta-positive/Abeta-negative classification, gaussian mixture modeling, and comparison with cerebrosp

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

162 Using Gaussian mixture modelling we identify that Victoria Cro

163 stering algorithms like K-Means and standard Gaussian mixture models (GMM) fail to account for the st

164 n this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps.

165 Gaussian mixture models were used for the magnitude and

166 ally developed for this application, include Gaussian mixture models, Euler characteristic curves and

167 ent state-of-the-art filtration methods like Gaussian Mixture Models, Random Forests and CNNs designe

168 tionally efficient comparison metric between Gaussian mixture models.

169 ferring the parameters of a general class of Gaussian mixture process noise models from noisy and lim

170 Although Gaussian mixture process noise models have been consider

171 vides a framework for efficient inference of Gaussian mixture process noise models, with application

172 her representation of the process noise as a Gaussian mixture significantly improves state estimation

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

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

175 osed into a superposition of the fundamental Gaussian mode and high-order modes of a few-mode fiber.

176 Therefore, we chose an inverse Gaussian model as our principal probability model to cha

177 walk with drift diffusion yields an inverse Gaussian model as the interpulse interval distribution.

178 ed by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodali

179 eries was accurately described by an inverse Gaussian model measured by Kolmogorov-Smirnov measures.

180 We found that the use of a Gaussian model to initialize the MLE suppresses the adve

181 e demonstrate the proposed framework for the Gaussian model with arbitrary covariance structures.

182 An analysis that uses a two-dimensional Gaussian model, provides evidence for six families of pa

183 To analyze deviations from the inverse Gaussian model, we considered a broader model set: the g

184 idea of jointly inferring different types of Gaussian models associated with different parts of the t

185 lf-reconstruct earlier upon propagation than Gaussian modes.

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

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

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

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

190 specifically at the time of presentation of Gaussian noise (but not 8 kHz tone) between conditioning

191 ence of a finite and optimum amount of white Gaussian noise at a frugal energy expenditure of few ten

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

193 an signatures, a tool for characterizing non-Gaussian noise is essential.

194 This first experimental demonstration of non-Gaussian noise spectroscopy represents a major step towa

195 FrA pyramidal neurons was more pronounced to Gaussian noise than to pure frequency tones, and that th

196 hing during the measurements), 3) stochastic Gaussian noise, and 4) uncertainty in the exact time poi

197 ized relative cerebral blood volume (nrCBV), Gaussian-normalized relative blood flow (nrCBF), and tum

198 recovery (FLAIR) signal abnormality volume, Gaussian-normalized relative cerebral blood volume (nrCB

199 ment uses a simple analytical model based on Gaussian optics, numerical propagation calculations, and

200 uskal-Wallis test was performed depending on Gaussian or non-Gaussian distribution of data.

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

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

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

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

205 , and (iv) the intensity ratio of two fitted Gaussian peaks.

206 Mixed Gaussian phylogenetic models (MGPMs) incorporate the ide

207 Gaussian phylogenetic models like Brownian motion and Or

208 read function, small voxel sizes, and narrow gaussian postfiltering helped minimize feature variation

209 Gaussian probability density function analysis indicates

210 Quantitative analysis of REES data using Gaussian probability distribution function clearly indic

211 tablish that a physiologically based inverse Gaussian probability model provides a parsimonious and a

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

213 lgorithms including neural networks (NN) and Gaussian process (GP), we observe that NN provides excel

214 introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dyna

215 asures of model uncertainty achieved through Gaussian Process based Bayesian models.

216 Using Gaussian process classification, we create a classifier

217 sequential Monte Carlo ABC or (ii) ABC with Gaussian process emulation.

218 ingle cell is modeled as a noisy draw from a Gaussian process in high dimensions from low-dimensional

219 cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching a

220 The Gaussian Process Latent Variable Model (GPLVM) is a popu

221 This model is called the Gaussian process latent variable model (GPLVM).

222 hitherto underused in battery diagnosis-with Gaussian process machine learning.

223 The same Gaussian Process model best captured human search decisi

224 Our Gaussian process model takes the entire spectrum as inpu

225 We used a Bayesian multinomial logit Gaussian process model to produce estimates of public pe

226 scharge date were compared by a Hierarchical Gaussian Process model.

227 Gaussian process models highlighted calpain activity as

228 We used Gaussian process models to distinguish the temporal sequ

229 With Gaussian process models trained on a limited experimenta

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

231 Gaussian process regression (GPR) techniques have emerge

232 ncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (O

233 We present LonGP, an additive Gaussian process regression model that is specifically d

234 al species co-cultured with MSH1, we built a Gaussian process regression model to predict the Gompert

235 zed observational surveys and spatiotemporal Gaussian process regression modeling in the context of t

236 We used spatiotemporal Gaussian process regression to estimate a complete time

237 Spatiotemporal Gaussian process regression was used to ensure estimates

238 tion and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function

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

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

241 st, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correla

242 Together with experimental fitness data and Gaussian process regression, the latent space representa

243 predictive uncertainties recovered from the Gaussian Process to improve the transparency and trustwo

244 netics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning

245 Previous work has used Gaussian processes (GPs) in order to achieve this, but t

246 and data inversion permit us to identify non-Gaussian processes and, regardless of Gaussianity, to re

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

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

249 Gaussian processes model the stochastic nature of the sp

250 we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a

251 ic Block Models for community formation with Gaussian processes to model changes in the community str

252 ng a reinforcement learning approach, we use Gaussian Processes to model the policy and value functio

253 Bernoulli, support vector, random forest and Gaussian processes) analyses and to develop and evaluate

254 re, we model the experimental variance using Gaussian Processes, and subsequently, leverage uncertain

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

256 he proposed calibration approach is based on Gaussian radial basis function support vector classifier

257 lop full-brain parametric maps, implementing Gaussian random field theory to estimate inter-voxel dep

258 An integrate-and-fire process modeled as a Gaussian random walk with drift diffusion yields an inve

259 e training data are only drawn from the near-Gaussian regime of the tKdV model solutions without the

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

261 ition to neuromorphic computing, the tunable Gaussian response has significant implications for a ran

262 ) for twisted space-frequency and space-time Gaussian Schell-model (GSM) beams.

263 h-year cohort rises and falls according to a Gaussian-shaped curve.

264 shaped focal spot and a concentric beam with Gaussian-shaped focal spot can be generated at the same

265 hod enables state estimation on multivariate Gaussian signals.

266 ssian assumption and capture distinctive non-Gaussian signatures, a tool for characterizing non-Gauss

267 monstrate selective upconversion of Laguerre-Gaussian spatial modes mixed with turbulent noise.

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

269 ng a beam into a Cartesian grid of identical Gaussian spots each containing a single Laguerre-Gaussia

270 iciently computes density maps by fast multi-Gaussian spreading of atomic densities onto a three-dime

271 recursive Bayesian estimation algorithm for Gaussian states phase estimation has been proposed.

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

273 State-space models with Gaussian statistics are widely used for estimation of su

274 While the assumption that noise obeys Gaussian statistics is commonly employed, noise is gener

275 enhanced diffusion coefficient(3-10) and non-Gaussian statistics of the tracer displacements(6,9,10).

276 Gaussian statistics, however, fail to capture several ke

277 share the same dynamic heterogeneity and non-Gaussian statistics.

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

279 tomatically find K for both Gaussian and non-Gaussian structures.

280 esented as a sum of signals from anisotropic Gaussian sub-domains to the extent that this approximati

281 Development of a Gaussian support vector machine classifier based on HS-2

282 sults for classification of brainwaves using Gaussian synapse based probabilistic neural networks.

283 mplitude, mean and standard deviation of the Gaussian synapse via threshold engineering in dual gated

284 In this article we, therefore, introduce Gaussian synapses based on heterostructures of atomicall

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

286 that is well approximated by a multivariate Gaussian, thus facilitating downstream analysis.

287 ow water reveal a remarkable transition from Gaussian to anomalous behavior as surface waves cross an

288 f a heterogeneous cytoplasm as cause for non-Gaussian transport.

289 ic analysis of four possible origins for non-Gaussian transport: 1) sample-based variability, 2) rare

290 f the Riemann zeta function, this proves the Gaussian unitary ensemble random matrix model prediction

291 The laser beam profile was determined to be Gaussian using a knife-edge technique.

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

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

294 ation over an input signal ("evidence") plus Gaussian white noise.

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

296 in a small, approximately circular area with Gaussian width sigma = 0.06 mum.

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

298 tes, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy.

299 onse of each fMRI voxel was characterized as Gaussian, with independent center frequency and bandwidt

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