ライフサイエンスコーパス: eigenvalue

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

2 skew-symmetric pairs with oppositely signed eigenvalues.

3 determine PCA modes (eigenvectors) and their eigenvalues.

4 pressions are obtained for the corresponding eigenvalues.

5 , and associated matrices have small leading eigenvalues.

6 determined by the corresponding transmission eigenvalues.

7 e dynamics associated with a pair of complex eigenvalues.

8 phase system, leading to modes with negative eigenvalues.

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

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

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

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

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

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

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

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

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

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

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

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

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

22 These eigenvalues and the functions describing transverse vari

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

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

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

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

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

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

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

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

31 Peculiarities of eigenvalue behaviour are considered for different permit

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

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

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

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

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

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

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

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

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

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

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

43 The eigenvalue distribution allows one to quantitatively ass

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

142 isplacement and principal component analysis eigenvalue spectrum analyses.

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

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

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

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

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

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

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

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

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

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

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