Existence results for mean field equations

Let $\Omega$ be an annulus. We prove that the mean field equation -\Delta\psi=\frac{e\sp{-\beta\psi}}{\int\sb{\Omega}e\sp{-\beta\psi}} admits a solution with zero boundary for $\beta\in (-16\pi,-8\pi)$. This is a supercritical case for the Moser-Trudinger inequality.Comment: Filling a gap in the argument and adding 2 referrence

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

The η(2225)\eta(2225) observed by the BES Collaboration

In the framework of the $^3P_0$ meson decay model, the strong decays of the $3 ^1S_0$ and $4 ^1S_0$ $s\bar{s}$ states are investigated. It is found that in the presence of the initial state mass being 2.24 GeV, the total widths of the $3 ^1S_0$ and $4 ^1S_0$ $s\bar{s}$ states are about 438 MeV and 125 MeV, respectively. Also, when the initial state mass varies from 2220 to 2400 MeV, the total width of the $4 ^1S_0$ $s\bar{s}$ state varies from about 100 to 132 MeV, while the total width of the $3 ^1S_0$ $s\bar{s}$ state varies from about 400 to 594 MeV. A comparison of the predicted widths and the experimental result of $(0.19\pm 0.03^{+0.04}_{-0.06})$ GeV, the width of the $\eta(2225)$ with a mass of $(2.24^{+0.03+0.03}_{-0.02-0.02})$ GeV recently observed by the BES Collaboration in the radiative decay $J/\psi\to\gamma\phi\phi\to\gamma K^+K^-K^0_SK^0_L$, suggests that it would be very difficult to identify the $\eta(2225)$ as the $3 ^1S_0$ $s\bar{s}$ state, and the $\eta(2225)$ seams a good candidate for the $4 ^1S_0$ $s\bar{s}$ state.Comment: 14 pages, 3 figures, typos corrected, Accepted by Physical Review

Three realizations of quantum affine algebra Uq(A2(2))U_q(A_2^{(2)})

In this article we establish explicit isomorphisms between three realizations of quantum twisted affine algebra $U_q(A_2^{(2)})$: the Drinfeld ("current") realization, the Chevalley realization and the so-called $RLL$ realization, investigated by Faddeev, Reshetikhin and Takhtajan.Comment: 15 page

Charge and spin Hall effect in graphene with magnetic impurities

We point out the existence of finite charge and spin Hall conductivities of graphene in the presence of a spin orbit interaction (SOI) and localized magnetic impurities. The SOI in graphene results in different transverse forces on the two spin channels yielding the spin Hall current. The magnetic scatterers act as spin-dependent barriers, and in combination with the SOI effect lead to a charge imbalance at the boundaries. As indicated here, the charge and spin Hall effects should be observable in graphene by changing the chemical potential close to the gap.Comment: 7 page

On the \phi(1020)f_0(980) S-wave scattering and the Y(2175) resonance

We have studied the \phi(1020)f_0(980) S-wave scattering at energies around threshold employing chiral Lagrangians coupled to vector mesons through minimal coupling. The interaction kernel is obtained by considering the f_0(980) as a K\bar{K} bound state. The Y(2175) resonance is generated in this approach by the self-interactions between the \phi(1020) and the f_0(980) resonances. We are able to describe the e^+e^-\to \phi(1020)f_0(980) recent scattering data to test experimentally our scattering amplitudes, concluding that the Y(2175) resonance has a large \phi(1020)f_0(980) meson-meson component.Comment: 20 pages, 8 figure

A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions