Infra-Red Fixed Points in an Asymptotically Non-Free Theory

We investigate the infrared fixed point structure in asymptotically free and asymptotically non-free theory. We find that the ratios of couplings converge strongly to their infrared fixed points in the asymptotically non-free theory.Comment: 12 pages + 8 eps figures, LaTeX, typos corrected, revised version to be publishe

Reappraisal of Braverman thesis for its evolutionary succession

鈴木良始教授古稀祝賀記念号(Honorable issue in commemoration of Prof. Yoshiji Suzuki's 70 years of age)application/pdfdepartmental bulletin pape

Observation of B+→ψ(3770)K+

journal articl

Measurement of branching fraction and time-dependent CP asymmetry parameters in B^0 → D^*+D^*-K^{0}_{S}decays

We present a measurement of the branching fraction and time-dependent CP violation parameters for B^0 → D^*+D^*-K^{0}_{S} decays. These results are obtained from a 414 fb^{-1} data sample that contains 449×10^6 B\bar{B} pairs collected at the Υ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e^+e^- collider. We obtain the branching fraction, B(B^0→D^*+D*^-K^{O}_{S})= [3.4±0.4(stat)±0.7(syst)]×10^-3, which is in agreement with the current world average. We also obtain an upper limit on the product branching fraction for a possible two-body decay, B(B^0 → D_s1^+(2536)D^*-)B(D_s1^+(2536)→ D^*+K^{O}_{S})s^+, where s^±[equivalent]m2(D^*±K^{0}_{S}), we extract the CP violation parameters, J_c/J_0=0.60^{+0.25}_{-0.28}(stat)±0.08(syst), 2J_s1/J_0sin2φ_1=-0.17^{+0.42}_{-0.42}(stat)±0.09(syst), 2J_s2/J_0cos2φ_1=-0.23^{+0.43}_{-0.41}(stat)±0.13(syst). A large value of J_c/J_0 would indicate a significant resonant contribution from a broad unknown D_s^**+ state. Although the sign of the factor, 2J_s2/J_0, can be deduced from theory, no conclusion can be drawn regarding the sign of cos2φ_1 given the errors.journal articl

Fig S2: MCMC diagnostics for two representative subjects. from Temporal exponential random graph models of longitudinal brain networks after stroke

Each panel corresponds to a model parameter as described in the main text: A) edges, B) temporal edges, C) temporal triangles, D) stability. MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. In each panel the left side plot shows the MCMC sample statistics while the right plot shows the distribution of the difference between the observed and simulated values. MCMC sample statistics are varying randomly around the observed values at each step (so the chain is “mixing” well) and the difference between the observed and simulated values of the sample statistics have a roughly bell-shaped distribution, centered at 0. The sawtooth pattern visible on the degree term deviation plot is due to the combination of discrete values and small range in the statistics, so this is an inherent property of the statistic, not a problem with the fit (Hunter et al., 2008). As a side note, the same values were obtained for the model parameters when using MPLE instead of MCMCLE

Tab S1: tERGM performance for different observation periods of temporal brain networks from Temporal exponential random graph models of longitudinal brain networks after stroke

Two observation windows were considered covering acute and chronic phases, i.e, from 2 weeks to 3 months and from 2 weeks to 1 year. Link prediction performance is assessed by generating new network samples from the fitted tERGMs for each subject and time step. The performance is measured by the area under the precision-recall and receiver operating characteristic curves, i.e., AUP and AUR. The grand-average of the mean values across time are reported for each group -i.e., sucbortical/cortical/healthy- along with their standard deviations

Image_1_The Impact of the Geometric Correction Scheme on MEG Functional Topology at Rest.TIF

Spontaneous activity is correlated across brain regions in large scale networks (RSN) closely resembling those recruited during several behavioral tasks and characterized by functional specialization and dynamic integration. Specifically, MEG studies revealed a set of central regions (dynamic core) possibly facilitating communication among differently specialized brain systems. However, source projected MEG signals, due to the fundamentally ill-posed inverse problem, are affected by spatial leakage, leading to the estimation of spurious, blurred connections that may affect the topological properties of brain networks and their integration. To reduce leakage effects, several correction schemes have been proposed including the Geometric Correction Scheme (GCS) whose theory, simulations and empirical results on topography of a few RSNs were already presented. However, its impact on the estimation of fundamental graph measures used to describe the architecture of interactions among brain regions has not been investigated yet. Here, we estimated dense, MEG band-limited power connectomes in theta, alpha, beta, and gamma bands from 13 healthy subjects (all young adults). We compared the connectivity and topology of MEG uncorrected and GCS-corrected connectomes. The use of GCS considerably reorganized the topology of connectivity, reducing the local, within-hemisphere interactions mainly in the beta and gamma bands and increasing across-hemisphere interactions mainly in the alpha and beta bands. Moreover, the number of hubs decreased in the alpha and beta bands, but the centrality of some fundamental regions such as the Posterior Cingulate Cortex (PCC), Supplementary Motor Area (SMA) and Middle Prefrontal Cortex (MPFC) remained strong in all bands, associated to an increase of the Global Efficiency and a decrease of Modularity. As a comparison, we applied orthogonalization on connectomes and ran the same topological analyses. The correlation values were considerably reduced, and orthogonalization mainly decreased local within-hemisphere interactions in all bands, similarly to GCS. Notably, the centrality of the PCC, SMA and MPFC was preserved in all bands, as for GCS, together with other hubs in the posterior parietal regions. Overall, leakage correction removes spurious local connections, but confirms the role of dynamic hub regions, specifically the anterior and posterior cingulate, in integrating information in the brain at rest.</p

File S2: from Temporal exponential random graph models of longitudinal brain networks after stroke

Multidomain behavioral recovery of stroke patients and helathy control

File_S1 from Temporal exponential random graph models of longitudinal brain networks after stroke

Clinical and demographic information of stroke patients and healthy control

Text S1: Equations for static graph metrics used in the tERGM from Temporal exponential random graph models of longitudinal brain networks after stroke

Static graph metrics are defined by removing any reference to the past networks. This only affects the definition of edges between components (E) and triangles within components (T), according to the following equation