Concepts of quantum non-Markovianity: a hierarchy

Markovian approximation is a widely-employed idea in descriptions of the dynamics of open quantum systems (OQSs). Although it is usually claimed to be a concept inspired by classical Markovianity, the term quantum Markovianity is used inconsistently and often unrigorously in the literature. In this report we compare the descriptions of classical stochastic processes and quantum stochastic processes (as arising in OQSs), and show that there are inherent differences that lead to the non-trivial problem of characterizing quantum non-Markovianity. Rather than proposing a single definition of quantum Markovianity, we study a host of Markov-related concepts in the quantum regime. Some of these concepts have long been used in quantum theory, such as quantum white noise, factorization approximation, divisibility, Lindblad master equation, etc.. Others are first proposed in this report, including those we call past-future independence, no (quantum) information backflow, and composability. All of these concepts are defined under a unified framework, which allows us to rigorously build hierarchy relations among them. With various examples, we argue that the current most often used definitions of quantum Markovianity in the literature do not fully capture the memoryless property of OQSs. In fact, quantum non-Markovianity is highly context-dependent. The results in this report, summarized as a hierarchy figure, bring clarity to the nature of quantum non-Markovianity.Comment: Clarifications and references added; discussion of the related classical hierarchy significantly improved. To appear in Physics Report

A nested mixture model for protein identification using mass spectrometry

Mass spectrometry provides a high-throughput way to identify proteins in biological samples. In a typical experiment, proteins in a sample are first broken into their constituent peptides. The resulting mixture of peptides is then subjected to mass spectrometry, which generates thousands of spectra, each characteristic of its generating peptide. Here we consider the problem of inferring, from these spectra, which proteins and peptides are present in the sample. We develop a statistical approach to the problem, based on a nested mixture model. In contrast to commonly used two-stage approaches, this model provides a one-stage solution that simultaneously identifies which proteins are present, and which peptides are correctly identified. In this way our model incorporates the evidence feedback between proteins and their constituent peptides. Using simulated data and a yeast data set, we compare and contrast our method with existing widely used approaches (PeptideProphet/ProteinProphet) and with a recently published new approach, HSM. For peptide identification, our single-stage approach yields consistently more accurate results. For protein identification the methods have similar accuracy in most settings, although we exhibit some scenarios in which the existing methods perform poorly.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS316 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both \emph{in vivo} and \emph{in vitro}. In this paper we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.Comment: Non-final version submitted to Chao

A Behavioural Finance Explanation of a Gearing-ß Inverse Association Referencing Weill’s Liquidity Result (in English)

The authors investigated Arnold’s conjecture that Leverage (Financial Gearing) and Operating Gearing should be positively related to the equity ß of the Sharpe/Lintner CAPM. They find for a sample of the S&P 500 firms that have been on that index continuously for more than 15 years, that ß is negatively associated with Leverage and Operating Gearing. Using Weill’s results for transitional economies, the authors suggest that liquidity may provide an explanation for this anomalous ß-Gearing inversion. The implications are: that (1) one should revaluate the positive associations posited for Financial and Operating gearing with ß and (2) consider the possibility of managing liquidity as a way to affect ß.financial gearing; leverage; liquidity; beta

Isotropic and Anisotropic Lattice Spacing Corrections for I=2 pi-pi Scattering from Effective Field Theory

The calculation of the finite lattice spacing corrections for I=2 pi-pi scattering is carried out for isotropic and anisotropic Wilson lattice actions. Pion masses and decay constants are also determined in this context. These results correct the phase shift calculated from the lattice, which is connected to the scattering length and effective range in this low energy scattering process. When in terms of the lattice-physical parameters for either Wilson action, these lattice spacing effects first appear at the next-to-leading order counter-terms.Comment: 15 pages, additional discussion of pion decay constant, version published in PR

Disruption of nNOS-NOS1AP protein-protein interactions suppresses neuropathic pain in mice

Elevated N-methyl-D-aspartate receptor (NMDAR) activity is linked to central sensitization and chronic pain. However, NMDAR antagonists display limited therapeutic potential because of their adverse side effects. Novel approaches targeting the NR2B-PSD95-nNOS complex to disrupt signaling pathways downstream of NMDARs show efficacy in preclinical pain models. Here, we evaluated the involvement of interactions between neuronal nitric oxide synthase (nNOS) and the nitric oxide synthase 1 adaptor protein (NOS1AP) in pronociceptive signaling and neuropathic pain. TAT-GESV, a peptide inhibitor of the nNOS-NOS1AP complex, disrupted the in vitro binding between nNOS and its downstream protein partner NOS1AP but not its upstream protein partner postsynaptic density 95 kDa (PSD95). Putative inactive peptides (TAT-cp4GESV and TAT-GESVΔ1) failed to do so. Only the active peptide protected primary cortical neurons from glutamate/glycine-induced excitotoxicity. TAT-GESV, administered intrathecally (i.t.), suppressed mechanical and cold allodynia induced by either the chemotherapeutic agent paclitaxel or a traumatic nerve injury induced by partial sciatic nerve ligation. TAT-GESV also blocked the paclitaxel-induced phosphorylation at Ser15 of p53, a substrate of p38 MAPK. Finally, TAT-GESV (i.t.) did not induce NMDAR-mediated motor ataxia in the rotarod test and did not alter basal nociceptive thresholds in the radiant heat tail-flick test. These observations support the hypothesis that antiallodynic efficacy of an nNOS-NOS1AP disruptor may result, at least in part, from blockade of p38 MAPK-mediated downstream effects. Our studies demonstrate, for the first time, that disrupting nNOS-NOS1AP protein-protein interactions attenuates mechanistically distinct forms of neuropathic pain without unwanted motor ataxic effects of NMDAR antagonists