Vlasov Description Of Dense Quark Matter

We discuss properties of quark matter at finite baryon densities and zero temperature in a Vlasov approach. We use a screened interquark Richardson's potential consistent with the indications of Lattice QCD calculations. We analyze the choices of the quark masses and the parameters entering the potential which reproduce the binding energy (B.E.) of infinite nuclear matter. There is a transition from nuclear to quark matter at densities 5 times above normal nuclear matter density. The transition could be revealed from the determination of the position of the shifted meson masses in dense baryonic matter. A scaling form of the meson masses in dense matter is given.Comment: 15 pages 4 figure

Pairing Correlations in the Two-Dimensional Hubbard Model

We present the results of a quantum Monte Carlo study of the extended $s$ and the $d_{x^2-y^2}$ pairing correlation functions for the two-dimensional Hubbard model, computed with the constrained-path method. For small lattice sizes and weak interactions, we find that the $d_{x^2-y^2}$ pairing correlations are stronger than the extended $s$ pairing correlations and are positive when the pair separation exceeds several lattice constants. As the system size or the interaction strength increases, the magnitude of the long-range part of both correlation functions vanishes.Comment: 4 pages, RevTex, 4 figures included; submitted to Phys. Rev. Let

A Constrained Path Quantum Monte Carlo Method for Fermion Ground States

We propose a new quantum Monte Carlo algorithm to compute fermion ground-state properties. The ground state is projected from an initial wavefunction by a branching random walk in an over-complete basis space of Slater determinants. By constraining the determinants according to a trial wavefunction $|\Psi_T \rangle$, we remove the exponential decay of signal-to-noise ratio characteristic of the sign problem. The method is variational and is exact if $|\Psi_T\rangle$ is exact. We report results on the two-dimensional Hubbard model up to size $16\times 16$, for various electron fillings and interaction strengths.Comment: uuencoded compressed postscript file. 5 pages with 1 figure. accepted by PRL

Peroxisome proliferator-activated receptor (PPAR)alpha expression in T cells mediates gender differences in development of T cell-mediated autoimmunity.

Peroxisome proliferator-activated receptor (PPAR)alpha is a nuclear receptor that mediates gender differences in lipid metabolism. PPARalpha also functions to control inflammatory responses by repressing the activity of nuclear factor kappaB (NF-kappaB) and c-jun in immune cells. Because PPARalpha is situated at the crossroads of gender and immune regulation, we hypothesized that this gene may mediate sex differences in the development of T cell-mediated autoimmune disease. We show that PPARalpha is more abundant in male as compared with female CD4(+) cells and that its expression is sensitive to androgen levels. Genetic ablation of this gene selectively removed the brake on NF-kappaB and c-jun activity in male T lymphocytes, resulting in higher production of interferon gamma and tumor necrosis factor (but not interleukin 17), and lower production of T helper (Th)2 cytokines. Upon induction of experimental autoimmune encephalomyelitis, male but not female PPARalpha(-/-) mice developed more severe clinical signs that were restricted to the acute phase of disease. These results suggest that males are less prone to develop Th1-mediated autoimmunity because they have higher T cell expression of PPARalpha

Numerical Study of a Two-Dimensional Quantum Antiferromagnet with Random Ferromagnetic Bonds

A Monte Carlo method for finite-temperature studies of the two-dimensional quantum Heisenberg antiferromagnet with random ferromagnetic bonds is presented. The scheme is based on an approximation which allows for an analytic summation over the realizations of the randomness, thereby significantly alleviating the ``sign problem'' for this frustrated spin system. The approximation is shown to be very accurate for ferromagnetic bond concentrations of up to ten percent. The effects of a low concentration of ferromagnetic bonds on the antiferromagnetism are discussed.Comment: 11 pages + 5 postscript figures (included), Revtex 3.0, UCSBTH-94-2

Truncated-Determinant Diagrammatic Monte Carlo for Fermions with Contact Interaction

For some models of interacting fermions the known solution to the notorious sign-problem in Monte Carlo (MC) simulations is to work with macroscopic fermionic determinants; the price, however, is a macroscopic scaling of the numerical effort spent on elementary local updates. We find that the {\it ratio} of two macroscopic determinants can be found with any desired accuracy by considering truncated (local in space and time) matices. In this respect, MC for interacting fermionic systems becomes similar to that for the sign-problem-free bosonic systems with system-size independent update cost. We demonstrate the utility of the truncated-determinant method by simulating the attractive Hubbard model within the MC scheme based on partially summed Feynman diagrams. We conjecture that similar approach may be useful in other implementations of the sign-free determinant schemes.Comment: results of the actual Hubbard model simulations are adde

Graphene Transport at High Carrier Densities using a Polymer Electrolyte Gate

We report the study of graphene devices in Hall-bar geometry, gated with a polymer electrolyte. High densities of 6 $\times 10^{13}/cm^{2}$ are consistently reached, significantly higher than with conventional back-gating. The mobility follows an inverse dependence on density, which can be correlated to a dominant scattering from weak scatterers. Furthermore, our measurements show a Bloch-Gr\"uneisen regime until 100 K (at 6.2 $\times10^{13}/cm^{2}$), consistent with an increase of the density. Ubiquitous in our experiments is a small upturn in resistivity around 3 $\times10^{13}/cm^{2}$, whose origin is discussed. We identify two potential causes for the upturn: the renormalization of Fermi velocity and an electrochemically-enhanced scattering rate.Comment: 13 pages, 4 figures, Published Versio

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees

Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimate the expected cumulative reward following a state-action pair. The Q function neural network contains a lot of implicit knowledge about the RL problems, but often remains unexamined and uninterpreted. To our knowledge, this work develops the first mimic learning framework for Q functions in DRL. We introduce Linear Model U-trees (LMUTs) to approximate neural network predictions. An LMUT is learned using a novel on-line algorithm that is well-suited for an active play setting, where the mimic learner observes an ongoing interaction between the neural net and the environment. Empirical evaluation shows that an LMUT mimics a Q function substantially better than five baseline methods. The transparent tree structure of an LMUT facilitates understanding the network's learned knowledge by analyzing feature influence, extracting rules, and highlighting the super-pixels in image inputs.Comment: This paper is accepted by ECML-PKDD 201

Estimating the reproducibility & transparency of smoking cessation behaviour change interventions

Introduction: Activities promoting research reproducibility and transparency are crucial for generating trustworthy evidence. Evaluation of smoking interventions is one area where vested interests may motivate reduced reproducibility and transparency. / Aims: Assess markers of transparency and reproducibility in smoking behaviour change intervention evaluation reports. Methods: One hundred evaluation reports of smoking behaviour change intervention randomised controlled trials published in 2018-2019 were identified. Reproducibility markers of pre-registration, protocol sharing, data-, materials- and analysis script-sharing, replication of a previous study and open access publication were coded in identified reports. Transparency markers of funding and conflict of interest declarations were also coded. Coding was performed by two researchers, with inter-rater reliability calculated using Krippendorff’s alpha. / Results: Seventy-one percent of reports were open access and 73% pre-registered. However, only 13% provided accessible materials, 7% accessible data and 1% accessible analysis scripts. No reports were replication studies. Ninety-four percent of reports provided a funding source statement and eighty-eight percent of reports provided a conflict of interest statement. / Conclusions: Open data, materials, analysis and replications are rare in smoking behaviour change interventions, whereas funding source and conflict of interest declarations are common. Future smoking research should be more reproducible to enable knowledge accumulation