ライフサイエンスコーパス: eigenvalue

1 llent agreement with the relativistic energy eigenvalue.

2 e dynamics associated with a pair of complex eigenvalues.

3 phase system, leading to modes with negative eigenvalues.

4 of the OAM and SAM operators with fractional eigenvalues.

5 epidemiology, and the calculation of matrix eigenvalues.

6 ation of the variances associated with these eigenvalues.

7 MIs), which enforce a regional allocation of eigenvalues.

8 ctors of the density matrix with the largest eigenvalues.

9 igenchannel are due to correlation among the eigenvalues.

10 ion tensor possibly featuring three distinct eigenvalues.

11 , and associated matrices have small leading eigenvalues.

12 ue exists as well as a finite number of zero eigenvalues.

13 skew-symmetric pairs with oppositely signed eigenvalues.

14 determine PCA modes (eigenvectors) and their eigenvalues.

15 pressions are obtained for the corresponding eigenvalues.

16 determined by the corresponding transmission eigenvalues.

17 Using factor analysis (eigenvalue = 1.73) to compare characteristics identified

18 to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that d

19 The first PC (eigenvalue 4.2) showed one major pattern of clinical fea

20 ssociated with 0.27 SD lower darkgrey module eigenvalues (95% confidence interval [CI]; 0.12, 0.42; P

21 g, developed as a fast numerical solution to eigenvalue and linear least-squares problems of the form

22 ions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for effici

23 computation of lower bounds into a staple of eigenvalue and spectral problems in physics and chemistr

24 We propose eigenvalue and stable state separation sensitivity analy

25 data was improved significantly by adjusting eigenvalues and correlation times of the dominant modes.

26 e known, then they can be used to obtain the eigenvalues and eigenfunctions for the same potential, o

27 pecifically, our method explicitly finds the eigenvalues and eigenfunctions of the diffusion generato

28 rms of a set of equilibrium averages and the eigenvalues and eigenfunctions of the diffusion operator

29 (EPs) are degeneracies at which two or more eigenvalues and eigenstates of a physical system coalesc

30 s around exceptional points (EPs), where the eigenvalues and eigenstates of the system coalesce.

31 ian systems and represent the coalescence of eigenvalues and eigenstates.

32 ional points in parameter space whereby some eigenvalues and eigenvectors coalesce simultaneously.

33 tems is exceptional points (EPs), where both eigenvalues and eigenvectors coalesce.

34 the spectra of non-Hermitian operators where eigenvalues and eigenvectors coalesce.

35 s of science and engineering require finding eigenvalues and eigenvectors of large matrices.

36 y, known as exceptional point (EP), at which eigenvalues and eigenvectors of the corresponding non-He

37 ation can be predicted with the only help of eigenvalues and eigenvectors of the graph Laplacian matr

38 The finite-basis eigenvalues and eigenvectors of the Hamiltonian were obt

39 he evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housin

40 umber of infected individuals and the set of eigenvalues and eigenvectors.

41 l incorporating principal component analysis eigenvalues and re-parameterization (Pfa), and (iii) a m

42 accination rates, vaccination effectiveness, eigenvalues and reproduction numbers ([Formula: see text

43 Key indices such as eigenvalues and the basic reproduction number were utili

44 urring in all open physical systems when two eigenvalues and the corresponding eigenstates coalesce.

45 xceptional point (EP), which occurs when the eigenvalues and the corresponding eigenstates of a syste

46 point, featuring the coalescence of both the eigenvalues and the eigenmodes of the systems.

47 taining a strong separation between the bulk eigenvalues and the eigenvalues relevant to community st

48 which is solved analytically to produce the eigenvalues and the eigenvectors that specify the NMR pa

49 These eigenvalues and the functions describing transverse vari

50 the markers, the higher the variance of the eigenvalues and the lower the number of independent test

51 ts (EP) are non-Hermitian degeneracies where eigenvalues and their corresponding eigenvectors coalesc

52 transmission pull down all the transmission eigenvalues and thereby produce dips in the transmittanc

53 instein and Temple lower bounds based on the eigenvalues and variances converge very slowly and their

54 h the band crossings are implied by symmetry eigenvalues), and topological insulators.

55 From the reorientational eigenmodes, their eigenvalues, and correlation times, NMR relaxation data

56 ntify protein mobility, PCA modes with their eigenvalues, and displacement vector (DV) projections on

57 that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto th

58 At an EP, two or more eigenvalues, and the corresponding eigenstates, coalesce

59 meter space of a system at which two or more eigenvalues, and their corresponding eigenvectors, coale

60 on coefficient, fractional anistropy, tensor eigenvalues, and tract length were measured.

61 Simple analytical approximations to the eigenvalues are also provided for the limiting cases of

62 operty of bipartite networks: large dominant eigenvalues are associated with highly nested configurat

63 terms of Heun confluent functions, while the eigenvalues are determined via the solutions of a simple

64 By treating the three eigenvalues as a vector, called Hessian vector, which is

65 of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading.

66 Peculiarities of eigenvalue behaviour are considered for different permit

67 ronic circuits that benefit from the drastic eigenvalues bifurcation near a divergent exceptional poi

68 that M is characterized by positive dominant eigenvalues; by contrast, rapidly changing environments

69 bbit ventricular myocytes, we show that this eigenvalue can be estimated in practice by pacing these

70 We conjecture that the three eigenvalues contain important information of the Hessian

71 Dominant eigenvalues decay initially as a power law, unveiling a

72 We describe the use of the matrix eigenvalue decomposition (EVD) and pseudoinverse project

73 are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image

74 ssifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the pro

75 We developed a method, AlloHubMat, that uses eigenvalue decomposition of mutual information derived f

76 a particular gene, termed Meff, by using the eigenvalue decomposition of the genotype correlation mat

77 od, which can be formulated as a generalized eigenvalue decomposition problem, offers a model-free es

78 The third eigenvalue decreased in ischemic gray (P = .001) and whi

79 The first and second eigenvalues decreased in both ischemic gray and ischemic

80 that corresponds to the smallest or largest eigenvalues, depending on the setting.

81 persistence through a threshold value of the eigenvalue determined by ecological features of the foca

82 een eigenchannel velocities and transmission eigenvalues determines the energy density within the med

83 f T and the effective number of transmission eigenvalues determines the probability distributions of

84 y richer economic information in the largest eigenvalues deviating from RMT predictions for the housi

85 The eigenvalue distribution allows one to quantitatively ass

86 act analytical expression for the covariance eigenvalue distribution in the large-network limit can b

87 In addition, the eigenvalue distribution is found to follow a finite-widt

88 eate a power-law signature in the covariance eigenvalue distribution.

89 e exponent beta, where phase transitions for eigenvalue distributions occur.

90 lations to around 1% on average and the core eigenvalue down to under 100 pcm.

91 S also relies on intrinsic dynamic noise and eigenvalue dropout to find ground states more efficientl

92 of such cutoffs (high multiplicity of second eigenvalues due to symmetry) is explored.

93 ered collective modes and positive shifts in eigenvalues due to the constraining effect of bt10 bindi

94 Principal component analysis, based on an eigenvalue-eigenvector analysis of the scaled sensitivit

95 o solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum

96 Here, we present a way to solve the extremal eigenvalue/eigenvector problem, turning it into a nonlin

97 he only space group where symmetry indicated eigenvalues enumerate all possible invariants due to abs

98 The other is that one of transformation eigenvalues equal to 1, i.e., lambda2 = 1, indicating a

99 and to an arguably simpler Perron-Frobenius eigenvalue equation of the type that occurs in the study

100 btained by solving the resulting generalized eigenvalue equation, and the flux autocorrelation functi

101 The resulting TDA eigenvalue equations are solved on a D-Wave quantum anne

102 lower bound for its initial input, but Ritz eigenvalue estimates can also be used.

103 of an optimal scaling parameter for accurate eigenvalue estimation.

104 des and find the spectra of all transmission eigenvalues, even at dips in the lowest transmission eig

105 eatures including a spectral gap in which no eigenvalue exists as well as a finite number of zero eig

106 cle law, whereas the other predicts that the eigenvalues follow a power-law distribution.

107 temperatures T We find that the spectrum of eigenvalues [Formula: see text] has a sharp maximum near

108 by analysing the statistical distribution of eigenvalues from noisy image patch matrices and leveragi

109 n to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challen

110 All factors having an Eigenvalue greater than 1 were considered.

111 Factor analysis yielded a single eigenvalue >1 (3.712), whereas confirmatory factor analy

112 Based on the eigenvalue >1, a total of eight PCs were formed contribu

113 erformed to identify unidimensional domains (eigenvalue >1.0) and Rasch analyses (differential item f

114 Moreover, PCA (49.63% with Eigenvalues > 1) and CA (clusters 1 and 2) methods confi

115 s p < .05, rotated factor loading > 0.5, and Eigenvalues > or = 1.

116 EFA identified five components (eigenvalues >/= 1) explaining 35% of the overall varianc

117 actor analysis demonstrated two factors with eigenvalues >2 that explained 52.2% of the variance, mai

118 or analysis identified a 5-factor structure (eigenvalues >or=1).

119 ic and [Formula: see text]-broken phases for eigenvalues have extensively been studied in the last de

120 of practical importance, efforts to correct eigenvalues have little value in comparison to the JSE c

121 , no distinct time separation exists for the eigenvalues, hence multiple (slow) eigenmodes contribute

122 he execution time for the calculations of i) eigenvalues, ii) Cholesky decomposition, iii) Sylvester'

123 d in the spectral vicinity of exact embedded eigenvalues in spite of deep surface modulation and vert

124 sults indicate that the multiplicity of zero eigenvalues in the eigenvalue spectrum can serve as a va

125 ied via Tokunaga c-value, the number of zero eigenvalues increases indicating that basins in humid cl

126 We show that multiplicity of zero eigenvalues is sufficient to determine the minimum set o

127 ic and [Formula: see text]-broken phases for eigenvalues is theoretically demonstrated in heterostruc

128 r with the least negative (but nonvanishing) eigenvalue lambda(1) = mu(1) + iomega(1).

129 The eigenvalues lambda(k)=1, therefore, define the "common H

130 meter E i = 13Deltalambdai, derived from the eigenvalues (lambda(i)) corresponding to the MESP minima

131 iffusivity [MD], fractional anisotropy [FA], eigenvalues [lambda(i)]) imaging parameters and urine ou

132 Mean diffusivity and eigenvalues lambda1 and lambda2 were significantly (P <

133 " This probability is related to the maximum eigenvalue (lambda1) of the adjacency matrix of the RyR2

134 The tensor eigenvalues (lambda1, lambda2, and lambda3), the mean di

135 In past work, by an eigenvalue mathematical analysis, we made an initial pre

136 on in the HL regime, while bias in estimated eigenvalues may have little effect.

137 The three eigenvalues, mean diffusivity, and FA were significantly

138 ture of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selecte

139 also exist in the extensively-studied large-eigenvalue modes.

140 ally, this is an observation of an 'embedded eigenvalue'--namely, a bound state in a continuum of rad

141 ces to the case of many parameters, ramified eigenvalues, not necessarily hermitian matrices, etc.

142 of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful i

143 ify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems.

144 ee independent methods: analysis of symmetry eigenvalues, numerical calculations of the nested Wannie

145 ining intrinsic kinetic isotope effects from eigenvalues obtained in transient kinetic experiments.

146 Additionally, the first contrast eigenvalue of 2.34 and a raw variance of less than 50% i

147 f the variance, resulting in a food security eigenvalue of 3.603 with a 0.901 proportion.

148 rne provide an approximation for the leading eigenvalue of a food web community matrix involving coef

149 e estimation algorithm efficiently finds the eigenvalue of a given eigenvector but requires fully coh

150 nd that stability is governed by the maximum eigenvalue of a modified adjacency matrix, and we test t

151 the eigenvector associated with the largest eigenvalue of a quadratic form computed through suitable

152 enological model and is based on the leading eigenvalue of a suitable landscape matrix.

153 ed reproduction number Lambda0 (the dominant eigenvalue of G0) must be larger than unity.

154 information of the cluster using the maximum eigenvalue of its adjacency matrix.

155 nd extracts eigenvectors associated with the eigenvalue of minimal magnitude ("shapelet laws") that c

156 in the spatial system based on the principal eigenvalue of our linear problem, lambda1.

157 the first principal component (PC1) and the eigenvalue of PC1.

158 previous theoretical studies showed that the eigenvalue of the alternating eigenmode represents an id

159 The eigenvector associated with the largest eigenvalue of the covariance matrix is helpful for ident

160 is found that the vibration frequency is an eigenvalue of the delaminated lamina determined only by

161 he eigenvector corresponding to the smallest eigenvalue of the diffusion tensor obtained from diffusi

162 old is inversely proportional to the maximum eigenvalue of the network.

163 onship between the hysteresis and the middle eigenvalue of the transformation stretch tensor as predi

164 show that for L approximately 4xi, a single eigenvalue of the transmission matrix (TM) dominates tra

165 fficient of variation (C.O.V.) of first mode eigenvalue of TSDT, FSDT and CBT are approximately ident

166 variance explained by measures of 55.3% and eigenvalues of 1.9, 1.4, 1.2, 1.0 and 0.9.

167 he existence of exceptional points where the eigenvalues of a non-Hermitian Hamiltonian or a Liouvill

168 ind that, for sufficiently large delays, the eigenvalues of a randomly coupled system are complex eve

169 We construct an Euler product from the Hecke eigenvalues of an automorphic form on a classical group

170 ates probed in these experiments have energy eigenvalues of approximately 3,330 cm(-1) and lie above

171 feedback loops in a network, as well as the eigenvalues of associated matrices, is determined by a s

172 l dimension grows exponentially, finding the eigenvalues of certain operators is one such intractable

173 hical index with defining structure built on eigenvalues of chemical matrices relying on distances in

174 The Ritz upper bound to eigenvalues of Hermitian operators is essential for many

175 limited perturbations, as determined by the eigenvalues of Jacobian matrices.

176 level spacings would be the same as for the eigenvalues of large random matrices.

177 ed spectral topological indices derived from eigenvalues of temperature-dependent chemical matrices.

178 ove that (under certain mild conditions) the eigenvalues of the (normalized) Laplacian of a random po

179 stem about the steady state, and determining eigenvalues of the associated coefficient matrix.

180 t homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian.

181 " component by testing the statistics of the eigenvalues of the correlation matrix against a "null hy

182 However, the energy eigenvalues of the coshine Yukawa potential model, are m

183 In this study, we propose the use of eigenvalues of the covariance matrix of multiple time se

184 relation as reflected in the distribution of eigenvalues of the covariance matrix of the dynamic fluc

185 of the largest eigenvalue to the sum of the eigenvalues of the cross-spectral matrix at a given freq

186 s disease by analysing each of the component eigenvalues of the diffusion tensor in isolation to test

187 ce apparent diffusion coefficient (ADC), and eigenvalues of the diffusion tensor in lesions and contr

188 rearrangements and the largest and smallest eigenvalues of the dynamical matrix.

189 ion of the parameter space quantified by the eigenvalues of the Fisher Information Matrix.

190 ve is determined by "spectral" data, namely, eigenvalues of the Frobenius operator of k acting on the

191 n abundance fit a geometric series as do the eigenvalues of the integral transform which kernel is a

192 ematical connec- tion between the spectra of eigenvalues of the Laplacian matrix and the behaviour of

193 ut not trivial consequence of the spectra of eigenvalues of the Laplacian matrix, where behaviour may

194 n terms of nonlinear dynamics, the Laplacian eigenvalues of the nominal interconnections, and the var

195 ed in a genome scan from the variance of the eigenvalues of the observed marker correlation matrix.

196 SignificanceWe show that the eigenvalues of the self-adjoint extension of the prolate

197 sequence works to lower the maximal absolute eigenvalues of the stochastic model, thereby contributin

198 m accidental degeneracy, whereas the complex eigenvalues of the system are deformed into a two-dimens

199 is (PCA) of the ratio of the second to first eigenvalues of the T-wave vector (PCA ratio) (>32.0% in

200 antified by the ratio of the second to first eigenvalues of the T-wave vector by PCA (PCA ratio); QTd

201 explained in terms of a charge model for the eigenvalues of the TM tau in which the Coulomb interacti

202 is to relate them to the eigenfunctions and eigenvalues of the transfer operator.

203 n size, because such changes affect only the eigenvalues of the transition matrix, not the eigenvecto

204 e transmittance, are given by the sum of the eigenvalues of the transmission matrix.

205 nd to points in parameter space at which the eigenvalues of the underlying system and the correspondi

206 t of variation allowed is constrained by the eigenvalues of this principal component analysis.

207 We show that the eigenvalues of this random walk can be naturally indexed

208 e and readout errors, and measure the energy eigenvalues of this wire with an error of approximately

209 een charges mimics the repulsion between the eigenvalues of TM.

210 y, respectively, of semisimple matrices, the eigenvalues of which are ramified on D as functions of x

211 physical meaning of the multiplicity of zero eigenvalues on the dynamics of the river network.

212 First, we explain the observed range of zero eigenvalues on the spectra using the notion of multiplic

213 Ritz eigenvalues only provide upper bounds for the energy lev

214 er the bulk gap as a single sheet of complex eigenvalues or with a single exceptional point.

215 tructed by binding triples identified by the eigenvalue pattern of the dependence model, and are furt

216 We further investigate the eigenvalue pattern of the proposed method, and we discov

217 cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have poten

218 of lasing regimes, we present the concept of eigenvalue probability distributions.

219 We solve the fluid-solid interaction eigenvalue problem for the axial wavenumber, fluid press

220 odynamic model takes the form of a nonlinear eigenvalue problem for the swimming speed and locomotion

221 ly much more desirable than solving the full eigenvalue problem for the whole assembled structure.

222 We encode this intuition as an eigenvalue problem in a manner analogous to Google's Pag

223 ifically, we start by presenting the Floquet eigenvalue problem in driven two-dimensional PhCs.

224 design sensitivity for the mixed variational eigenvalue problem is derived using the adjoint method a

225 state dynamics can be determined solving the eigenvalue problem of a matrix representing the regulato

226 structures can be understood by solving the eigenvalue problem of Maxwell's equations for static lin

227 Finally, the vibration and eigenvalue problem of the actuated nano-manipulator subj

228 static stability of the poles by solving the eigenvalue problem that links the 2D model to a 3D frame

229 is computed as the solution to a generalized eigenvalue problem, and its performance for fold classif

230 this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecule

231 ite down quantum evolution as a ground-state eigenvalue problem.

232 ize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and

233 ion matrix, with the corresponding series of eigenvalues proportional to the series of the "fractions

234 e led to a formal hypothesis test of the top eigenvalue, providing another way to achieve dimension r

235 In this study, we propose LD eigenvalue regression (LDER), an extension of LDSC, by m

236 el statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions

237 e introduce BiVariate Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions

238 aration between the bulk eigenvalues and the eigenvalues relevant to community structure even in the

239 wed that the ten components with the highest Eigenvalues represented 41% of the variability of the sa

240 The resulting expressions contain eigenvalues representing the dispersion and skewness of

241 etween the conductivity and diffusion tensor eigenvalues (respectively, final sigma and d) in agreeme

242 Its eigenvalues satisfy lambda(k)>/=1.

243 Eigenvalue sensitivity analysis and separation sensitivi

244 We use eigenvalue sensitivity analysis and stable state separat

245 While eigenvalue sensitivity analysis is an established techni

246 a "glass transition" temperature, T(g), the eigenvalues show a distinct time separation, and the rat

247 The number of negative eigenvalues shows no transition with temperature.

248 LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfull

249 , spherical anisotropy coefficient (CS), and eigenvalue skewness (SK), as well as normalized signal i

250 atistical mechanical model that combines the eigenvalue solutions of the rate matrix and the free-ene

251 on for effective size is obtained by finding eigenvalue solutions to the recurrence equations for inb

252 e this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepare

253 by a combination of a fast mixed variational eigenvalue solver and distributed Graphic Processing Uni

254 f river network topology, we investigate the eigenvalue spectra of its connectivity matrix (i.e., adj

255 Next, we show that the eigenvalue spectra of such complex networks follow disti

256 drainage river networks, we investigate the eigenvalue spectra of their adjacency matrix.

257 isplacement and principal component analysis eigenvalue spectrum analyses.

258 combinations of these modes approximate the eigenvalue spectrum and eigenfunctions in a systematical

259 the multiplicity of zero eigenvalues in the eigenvalue spectrum can serve as a valuable tool for und

260 An analytical link between the eigenvalue spectrum of the dynamics, the heterogeneity o

261 ociated with abrupt phase transitions in the eigenvalue spectrum.

262 d Wannier-Stark states and their equidistant eigenvalue spectrum.

263 containing examples for which the low-energy eigenvalue splitting vanishes, and hence quantum tunneli

264 ressed, and we provide a lower bound for the eigenvalue splitting.

265 fficient for determining differences between eigenvalues such as tunneling splittings and spectral fe

266 changing environments favor Ms with dominant eigenvalues that are negative, as offspring favor a phen

267 ariance matrix has one (or more) outstanding eigenvalues that cannot be easily equalized because of s

268 In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap

269 calculate analytically the distributions of eigenvalues that generalize Wigner's as well as Girko's

270 ticular, the statistical fluctuations of the eigenvalues ("the energy levels") follow certain univers

271 alue of the observable, rather than a random eigenvalue thereof.

272 Global coherence, the ratio of the largest eigenvalue to the sum of the eigenvalues of the cross-sp

273 sible to annihilate), and measure the mirror eigenvalues to elucidate the braiding consequence.

274 Controlling (splitting or shifting) these eigenvalues to fully tune the frequency response, howeve

275 o utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely t

276 t encircle this structure cause the system's eigenvalues to trace out non-commutative braids.

277 ses the time-symmetric formulation to assign eigenvalues to unmeasured observables of a system, which

278 nces the frequency selectivity by moving the eigenvalues toward the imaginary axis; spontaneously osc

279 iduals: first residual contrast values <2.00 eigenvalue units) and item fit (outfit mean square value

280 The mode eigenvalues, which measure flexibility, follow simple sc

281 Correlation of unrestricted natural orbital eigenvalues with previous experimental models suggested