Deformed Statistics Kullback-Leibler Divergence Minimization within a Scaled Bregman Framework

The generalized Kullback-Leibler divergence (K-Ld) in Tsallis statistics [constrained by the additive duality of generalized statistics (dual generalized K-Ld)] is here reconciled with the theory of Bregman divergences for expectations defined by normal averages, within a measure-theoretic framework. Specifically, it is demonstrated that the dual generalized K-Ld is a scaled Bregman divergence. The Pythagorean theorem is derived from the minimum discrimination information-principle using the dual generalized K-Ld as the measure of uncertainty, with constraints defined by normal averages. The minimization of the dual generalized K-Ld, with normal averages constraints, is shown to exhibit distinctly unique features.Comment: 16 pages. Iterative corrections and expansion

Leadless Cardiac Pacemakers Back to the Future

AbstractDespite significant advances in battery longevity, lead performance, and programming features since the first implanted permanent pacemaker was developed, the basic design of cardiac pacemakers has remained relatively unchanged over the past 50 years. Because of inherent limitations in their design, conventional (transvenous) pacemakers are prone to multiple potential short- and long-term complications. Accordingly, there has been intense interest in a system able to provide the symptomatic and potentially lifesaving therapies of cardiac pacemakers while mitigating many of the risks associated with their weakest link—the transvenous lead. Leadless cardiac pacing represents the future of cardiac pacing systems, similar to the transition that occurred from the use of epicardial pacing systems to the familiar transvenous systems of today. This review summarizes the current evidence and potential benefits of leadless pacing systems, which are either commercially available (in Europe) or under clinical investigation

Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, specially when the objective is to improve the performance of a stochastic system. However, the performance of these methods depends on several parameters, such as the choice of a suitable smoothing kernel. Different kernels have been studied in literature, which include Gaussian, Cauchy and uniform distributions among others. This paper studies a new class of kernels based on the q-Gaussian distribution, that has gained popularity in statistical physics over the last decade. Though the importance of this family of distributions is attributed to its ability to generalize the Gaussian distribution, we observe that this class encompasses almost all existing smoothing kernels. This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution. Using the derived gradient estimates, we propose two-timescale algorithms for optimization of a stochastic objective function in a constrained setting with projected gradient search approach. We prove the convergence of our algorithms to the set of stationary points of an associated ODE. We also demonstrate their performance numerically through simulations on a queuing model

Continuity and Stability of Partial Entropic Sums

Extensions of Fannes' inequality with partial sums of the Tsallis entropy are obtained for both the classical and quantum cases. The definition of kth partial sum under the prescribed order of terms is given. Basic properties of introduced entropic measures and some applications are discussed. The derived estimates provide a complete characterization of the continuity and stability properties in the refined scale. The results are also reformulated in terms of Uhlmann's partial fidelities.Comment: 9 pages, no figures. Some explanatory and technical improvements are made. The bibliography is extended. Detected errors and typos are correcte

Structural basis for chemokine recognition and activation of a viral G protein-coupled receptor

Chemokines are small proteins that function as immune modulators through activation of chemokine G protein–coupled receptors (GPCRs). Several viruses also encode chemokines and chemokine receptors to subvert the host immune response. How protein ligands activate GPCRs remains unknown. We report the crystal structure at 2.9 angstrom resolution of the human cytomegalovirus GPCR US28 in complex with the chemokine domain of human CX3CL1 (fractalkine). The globular body of CX3CL1 is perched on top of the US28 extracellular vestibule, whereas its amino terminus projects into the central core of US28. The transmembrane helices of US28 adopt an active-state–like conformation. Atomic-level simulations suggest that the agonist-independent activity of US28 may be due to an amino acid network evolved in the viral GPCR to destabilize the receptor’s inactive state.Swiss National Science FoundationNational Institutes of Health (U.S.) (Pioneer Award)Virginia and D.K. Ludwig Fund for Cancer Researc