Evolving stochastic learning algorithm based on Tsallis entropic index

In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks. The new algorithm combines deterministic and stochastic search steps by employing a different adaptive stepsize for each network weight, and applies a form of noise that is characterized by the nonextensive entropic index q, regulated by a weight decay term. The behavior of the learning algorithm can be made more stochastic or deterministic depending on the trade off between the temperature T and the q values. This is achieved by introducing a formula that defines a time-dependent relationship between these two important learning parameters. Our experimental study verifies that there are indeed improvements in the convergence speed of this new evolving stochastic learning algorithm, which makes learning faster than using the original Hybrid Learning Scheme (HLS). In addition, experiments are conducted to explore the influence of the entropic index q and temperature T on the convergence speed and stability of the proposed method

Improved sign-based learning algorithm derived by the composite nonlinear Jacobi process

In this paper a globally convergent first-order training algorithm is proposed that uses sign-based information of the batch error measure in the framework of the nonlinear Jacobi process. This approach allows us to equip the recently proposed Jacobi–Rprop method with the global convergence property, i.e. convergence to a local minimizer from any initial starting point. We also propose a strategy that ensures the search direction of the globally convergent Jacobi–Rprop is a descent one. The behaviour of the algorithm is empirically investigated in eight benchmark problems. Simulation results verify that there are indeed improvements on the convergence success of the algorithm

Return to Sport after Surgical Treatment of Lisfranc Injuries in Athletes: A Retrospective Case Series

Introduction A Lisfranc injury can be a devastating injury in athletes,and if inadequately treated, may lead to chronic pain and lossof function. The purpose of this study was to determine the rate andtime until return to sport after surgical fixation for a ligamentous Lisfrancinjury. We hypothesized that open reduction and screw fixationof a ligamentous Lisfranc injury can be a successful treatment in theathletic population and allow patients to return to sport at close totheir preinjury level of play. Methods All patients who were analyzed underwent repair of aligamentous Lisfranc injury with open reduction and screw fixationby a single surgeon, were between 18 - 40 years old at time of theirfinal follow up, and were identified as being an athlete (either recreationalor competitive). Eligible patients were given a questionnairethat included if they were able to return to sport, time until return tosport, subjective percentage of pre-injury level of play, current pain(0 - 10), and complications. Results Eleven patients were identified as athletes. Ten (91%) wereavailable for follow-up with a mean of 36.5 months (range, 14 - 60).The average age was 25.4 years (range, 15 - 37) at time of surgery.Eighty percent (8/10) were able to return to sport. The average timeuntil return to sport was 29.4 weeks (range, 22 - 52) with an averagesubjective value of their pre-injury level of play of 87% (range, 70 -100%). However, 67% (6/9) of the athletes had occasional pain withsport with an average pain level of 2.1 (range, 0 - 5). Two patientshad complications, a superficial infection and a deep vein thrombosis. Conclusion Most athletes were able to return to sport after undergoingopen reduction and internal fixation of a ligamentous Lisfrancinjury by less than 30 weeks post-surgery with a subjective value of87% of their previous function. However, the majority of the patientsalso experienced some residual pain with their respective sport.These findings suggested that athletes with a ligamentous Lisfrancinjury can have reliably good outcomes with operative repair

Leptogenesis through direct inflaton decay to light particles

We present a scenario of nonthermal leptogenesis following supersymmetric hybrid inflation, in the case where inflaton decay to both heavy right handed neutrino and SU(2)_L triplet superfields is kinematically disallowed. Lepton asymmetry is generated through the decay of the inflaton into light particles by the interference of one-loop diagrams with right handed neutrino and SU(2)_L triplet exchange respectively. We require superpotential couplings explicitly violating a U(1) R-symmetry and R-parity. However, the broken R-parity need not have currently observable low-energy signatures. Also, the lightest sparticle can be stable. Some R-parity violating slepton decays may, though, be detectable in the future colliders. We take into account the constraints from neutrino masses and mixing and the preservation of the primordial lepton asymmetry.Comment: 11 pages including 3 figures, uses Revtex, minor corrections, references adde

Pulsar kicks from a dark-matter sterile neutrino

We show that a sterile neutrino with mass in the 1-20 keV range and a small mixing with the electron neutrino can simultaneously explain the origin of the pulsar motions and the dark matter in the universe. An asymmetric neutrino emission from a hot nascent neutron star can be the explanation of the observed pulsar velocities. In addition to the pulsar kick mechanism based on resonant neutrino transitions, we point out a new possibility: an asymmetric off-resonant emission of sterile neutrinos. The two cases correspond to different values of the masses and mixing angles. In both cases we identify the ranges of parameters consistent with the pulsar kick, as well as cosmological constraints.Comment: 5 pages, 2 figures; final version; discussion and references adde

Electronic excitations and the tunneling spectra of metallic nanograins

Tunneling-induced electronic excitations in a metallic nanograin are classified in terms of {\em generations}: subspaces of excitations containing a specific number of electron-hole pairs. This yields a hierarchy of populated excited states of the nanograin that strongly depends on (a) the available electronic energy levels; and (b) the ratio between the electronic relaxation rate within the nano-grain and the bottleneck rate for tunneling transitions. To study the response of the electronic energy level structure of the nanograin to the excitations, and its signature in the tunneling spectrum, we propose a microscopic mean-field theory. Two main features emerge when considering an Al nanograin coated with Al oxide: (i) The electronic energy response fluctuates strongly in the presence of disorder, from level to level and excitation to excitation. Such fluctuations produce a dramatic sample dependence of the tunneling spectra. On the other hand, for excitations that are energetically accessible at low applied bias voltages, the magnitude of the response, reflected in the renormalization of the single-electron energy levels, is smaller than the average spacing between energy levels. (ii) If the tunneling and electronic relaxation time scales are such as to admit a significant non-equilibrium population of the excited nanoparticle states, it should be possible to realize much higher spectral densities of resonances than have been observed to date in such devices. These resonances arise from tunneling into ground-state and excited electronic energy levels, as well as from charge fluctuations present during tunneling.Comment: Submitted to the Physical Review

How Smart was T. Rex? Testing Claims of Exceptional Cognition in Dinosaurs and the Application of Neuron Count Estimates in Paleontological Research

Recent years have seen increasing scientific interest in whether neuron counts can act as correlates of diverse biological phenomena. Lately, Herculano-Houzel (2023) argued that fossil endocasts and comparative neurological data from extant sauropsids allow to reconstruct telencephalic neuron counts in Mesozoic dinosaurs and pterosaurs, which might act as proxies for behaviors and life history traits in these animals. According to this analysis, large theropods such as Tyrannosaurus rex were long-lived, exceptionally intelligent animals equipped with “macaque- or baboon-like cognition” whereas sauropods as well as most ornithischian dinosaurs would have displayed significantly smaller brains and an ectothermic physiology. Besides challenging established views on Mesozoic dinosaur biology, these claims raise questions on whether neuron count estimates could benefit research on fossil animals in general. Here, we address these findings by revisiting Herculano-Houzel’s (2023) work, identifying several crucial shortcomings regarding analysis and interpretation. We present revised estimates of encephalization and telencephalic neuron counts in dinosaurs, which we derive from phylogenetically informed modeling and an amended dataset of endocranial measurements. For large-bodied theropods in particular, we recover significantly lower neuron counts than previously proposed. Furthermore, we review the suitability of neurological variables such as neuron numbers and relative brain size to predict cognitive complexity, metabolic rate and life history traits in dinosaurs, coming to the conclusion that they are flawed proxies of these biological phenomena. Instead of relying on such neurological estimates when reconstructing Mesozoic dinosaur biology, we argue that integrative studies are needed to approach this complex subject

Caenorhabditis elegans orthologs of human genes differentially expressed with age are enriched for determinants of longevity

We report a systematic RNAi longevity screen of 82 Caenorhabditis elegans genes selected based on orthology to human genes differentially expressed with age. We find substantial enrichment in genes for which knockdown increased lifespan. This enrichment is markedly higher than published genomewide longevity screens in C. elegans and similar to screens that preselected candidates based on longevity-correlated metrics (e.g., stress resistance). Of the 50 genes that affected lifespan, 46 were previously unreported. The five genes with the greatest impact on lifespan (>20% extension) encode the enzyme kynureninase (kynu-1), a neuronal leucine-rich repeat protein (iglr-1), a tetraspanin (tsp-3), a regulator of calcineurin (rcan-1), and a voltage-gated calcium channel subunit (unc-36). Knockdown of each gene extended healthspan without impairing reproduction. kynu-1(RNAi) alone delayed pathology in C. elegans models of Alzheimer's disease and Huntington's disease. Each gene displayed a distinct pattern of interaction with known aging pathways. In the context of published work, kynu-1, tsp-3, and rcan-1 are of particular interest for immediate follow-up. kynu-1 is an understudied member of the kynurenine metabolic pathway with a mechanistically distinct impact on lifespan. Our data suggest that tsp-3 is a novel modulator of hypoxic signaling and rcan-1 is a context-specific calcineurin regulator. Our results validate C. elegans as a comparative tool for prioritizing human candidate aging genes, confirm age-associated gene expression data as valuable source of novel longevity determinants, and prioritize select genes for mechanistic follow-up

Persistence of a Continuous Stochastic Process with Discrete-Time Sampling: Non-Markov Processes

We consider the problem of `discrete-time persistence', which deals with the zero-crossings of a continuous stochastic process, X(T), measured at discrete times, T = n(\Delta T). For a Gaussian Stationary Process the persistence (no crossing) probability decays as exp(-\theta_D T) = [\rho(a)]^n for large n, where a = \exp[-(\Delta T)/2], and the discrete persistence exponent, \theta_D, is given by \theta_D = \ln(\rho)/2\ln(a). Using the `Independent Interval Approximation', we show how \theta_D varies with (\Delta T) for small (\Delta T) and conclude that experimental measurements of persistence for smooth processes, such as diffusion, are less sensitive to the effects of discrete sampling than measurements of a randomly accelerated particle or random walker. We extend the matrix method developed by us previously [Phys. Rev. E 64, 015151(R) (2001)] to determine \rho(a) for a two-dimensional random walk and the one-dimensional random acceleration problem. We also consider `alternating persistence', which corresponds to a < 0, and calculate \rho(a) for this case.Comment: 14 pages plus 8 figure

Ideal Spin Filters: Theoretical Study of Electron Transmission Through Ordered and Disordered Interfaces Between Ferromagnetic Metals and Semiconductors

It is predicted that certain atomically ordered interfaces between some ferromagnetic metals (F) and semiconductors (S) should act as ideal spin filters that transmit electrons only from the majority spin bands or only from the minority spin bands of the F to the S at the Fermi energy, even for F with both majority and minority bands at the Fermi level. Criteria for determining which combinations of F, S and interface should be ideal spin filters are formulated. The criteria depend only on the bulk band structures of the S and F and on the translational symmetries of the S, F and interface. Several examples of systems that meet these criteria to a high degree of precision are identified. Disordered interfaces between F and S are also studied and it is found that intermixing between the S and F can result in interfaces with spin anti-filtering properties, the transmitted electrons being much less spin polarized than those in the ferromagnetic metal at the Fermi energy. A patent application based on this work has been commenced by Simon Fraser University.Comment: RevTeX, 12 pages, 5 figure