Symmetron Cosmology

The symmetron is a scalar field associated with the dark sector whose coupling to matter depends on the ambient matter density. The symmetron is decoupled and screened in regions of high density, thereby satisfying local constraints from tests of gravity, but couples with gravitational strength in regions of low density, such as the cosmos. In this paper we derive the cosmological expansion history in the presence of a symmetron field, tracking the evolution through the inflationary, radiation- and matter-dominated epochs, using a combination of analytical approximations and numerical integration. For a broad range of initial conditions at the onset of inflation, the scalar field reaches its symmetry-breaking vacuum by the present epoch, as assumed in the local analysis of spherically-symmetric solutions and tests of gravity. For the simplest form of the potential, the energy scale is too small for the symmetron to act as dark energy, hence we must add a cosmological constant to drive late-time cosmic acceleration. We briefly discuss a class of generalized, non-renormalizable potentials that can have a greater impact on the late-time cosmology, though cosmic acceleration requires a delicate tuning of parameters in this case.Comment: 42 page

Biologically Inspired Feedback Design for Drosophila Flight

We use a biologically motivated model of the Drosophila's flight mechanics and sensor processing to design a feedback control scheme to regulate forward flight. The model used for insect flight is the grand unified fly (GUF) [3] simulation consisting of rigid body kinematics, aerodynamic forces and moments, sensory systems, and a 3D environment model. We seek to design a control algorithm that will convert the sensory signals into proper wing beat commands to regulate forward flight. Modulating the wing beat frequency and mean stroke angle produces changes in the flight envelope. The sensory signals consist of estimates of rotational velocity from the haltere organs and translational velocity estimates from visual elementary motion detectors (EMD's) and matched retinal velocity filters. The controller is designed based on a longitudinal model of the flight dynamics. Feedforward commands are generated based on a desired forward velocity. The dynamics are linearized around this operating point and a feedback controller designed to correct deviations from the operating point. The control algorithm is implemented in the GUF simulator and achieves the desired tracking of the forward reference velocities and exhibits biologically realistic responses

Visual identification of individual Holstein-Friesian cattle via deep metric learning

Holstein-Friesian cattle exhibit individually-characteristic black and white coat patterns visually akin to those arising from Turing's reaction-diffusion systems. This work takes advantage of these natural markings in order to automate visual detection and biometric identification of individual Holstein-Friesians via convolutional neural networks and deep metric learning techniques. Existing approaches rely on markings, tags or wearables with a variety of maintenance requirements, whereas we present a totally hands-off method for the automated detection, localisation, and identification of individual animals from overhead imaging in an open herd setting, i.e. where new additions to the herd are identified without re-training. We propose the use of SoftMax-based reciprocal triplet loss to address the identification problem and evaluate the techniques in detail against fixed herd paradigms. We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified - achieving 98.2% accuracy when trained on just half of the population. This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary research such as behavioural analysis, disease outbreak tracing, and more. Key parts of the source code, network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and Electronics in Agriculture; Source code and network weights available at https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1

Is contact nucleation caused by pressure perturbation?

The reason why ice nucleation is more efficient by contact nucleation than by immersion nucleation has been elusive for over half a century. Six proposed mechanisms are summarized in this study. Among them, the pressure perturbation hypothesis, which arose from recent experiments, can qualitatively explain nearly all existing results relevant to contact nucleation. To explore the plausibility of this hypothesis in a more quantitative fashion and to guide future investigations, this study assessed the magnitude of pressure perturbation needed to cause contact nucleation and the associated spatial scales. The pressure perturbations needed were estimated using measured contact nucleation efficiencies for illite and kaolinite, obtained from previous experiments, and immersion freezing temperatures, obtained from well-established parameterizations. Pressure perturbations were obtained by assuming a constant pressure perturbation or a Gaussian distribution of the pressure perturbation. The magnitudes of the pressure perturbations needed were found to be physically reasonable, being achievable through possible mechanisms, including bubble formation and breakup, Laplace pressure arising from the distorted contact line, and shear. The pressure perturbation hypothesis provides a physically based and experimentally constrainable foundation for parameterizing contact nucleation that may be useful in future cloud-resolving models

Structure formation constraints on the Jordan-Brans-Dicke theory

We use cosmic microwave background data from WMAP, ACBAR, VSA and CBI, and galaxy power spectrum data from 2dF, to constrain flat cosmologies based on the Jordan-Brans-Dicke theory, using a Markov Chain Monte Carlo approach. Using a parametrization based on \xi=1/4\omega, and performing an exploration in the range \ln\xi \in [-9,3], we obtain a 95% marginalized probability bound of \ln\xi < -6.2, corresponding to a 95% marginalized probability lower bound on the Brans-Dicke parameter \omega>120.Comment: 6 pages, 5 figures, RevTeX. Updated to match version accepted by PRD. Significant updates. Headline constraint tightened to omega > 120 (95% conf) by improved statistical analysi

Chimpanzee Rights: The Philosophers' Brief

In December 2013, the Nonhuman Rights Project (NhRP) filed a petition for a common law writ of habeas corpus in the New York State Supreme Court on behalf of Tommy, a chimpanzee living alone in a cage in a shed in rural New York (Barlow, 2017). Under animal welfare laws, Tommy’s owners, the Laverys, were doing nothing illegal by keeping him in those conditions. Nonetheless, the NhRP argued that given the cognitive, social, and emotional capacities of chimpanzees, Tommy’s confinement constituted a profound wrong that demanded remedy by the courts. Soon thereafter, the NhRP filed habeas corpus petitions on behalf of Kiko, another chimpanzee housed alone in Niagara Falls, and Hercules and Leo, two chimpanzees held in research facilities at Stony Brook University. Thus began the legal struggle to move these chimpanzees from captivity to a sanctuary, an effort that has led the NhRP to argue in multiple courts before multiple judges. The central point of contention has been whether Tommy, Kiko, Hercules, and Leo have legal rights. To date, no judge has been willing to issue a writ of habeas corpus on their behalf. Such a ruling would mean that these chimpanzees have rights that confinement might violate. Instead, the judges have argued that chimpanzees cannot be bearers of legal rights because they are not, and cannot be persons. In this book we argue that chimpanzees are persons because they are autonomous

The impact of adolescent exposure to medical marijuana laws on high school completion, college enrollment and college degree completion

Background: There is concern that medical marijuana laws (MMLs) could negatively affect adolescents. To better understand these policies, we assess how adolescent exposure to MMLs is related to educational attainment. Methods: Data from the 2000 Census and 2001-2014 American Community Surveys were restricted to individuals who were of high school age (14-18) between 1990 and 2012 (n = 5,483,715). MML exposure was coded as: (i) a dichotomous any MML indicator, and (ii) number of years of high school age exposure. We used logistic regression to model whether MMLs affected: (a) completing high school by age 19; (b) beginning college, irrespective of completion; and (c) obtaining any degree after beginning college. A similar dataset based on the Youth Risk Behavior Survey (YRBS) was also constructed for confirmatory analyses assessing marijuana use. Results: MMLs were associated with a 0.40 percentage point increase in the probability of not earning a high school diploma or GED after completing the 12th grade (from 3.99% to 4.39%). High school MML exposure was also associated with a 1.84 and 0.85 percentage point increase in the probability of college non-enrollment and degree non-completion, respectively (from 31.12% to 32.96% and 45.30% to 46.15%, respectively). Years of MML exposure exhibited a consistent dose response relationship for all outcomes. MMLs were also associated with 0.85 percentage point increase in daily marijuana use among 12th graders (up from 1.26%). Conclusions: Medical marijuana law exposure between age 14 to 18 likely has a delayed effect on use and education that persists over time. (C) 2016 Elsevier Ireland Ltd. All rights reserved

The Mass Distribution of Stellar-Mass Black Holes

We perform a Bayesian analysis of the mass distribution of stellar-mass black holes using the observed masses of 15 low-mass X-ray binary systems undergoing Roche lobe overflow and five high-mass, wind-fed X-ray binary systems. Using Markov Chain Monte Carlo calculations, we model the mass distribution both parametrically---as a power law, exponential, gaussian, combination of two gaussians, or log-normal distribution---and non-parametrically---as histograms with varying numbers of bins. We provide confidence bounds on the shape of the mass distribution in the context of each model and compare the models with each other by calculating their relative Bayesian evidence as supported by the measurements, taking into account the number of degrees of freedom of each model. The mass distribution of the low-mass systems is best fit by a power-law, while the distribution of the combined sample is best fit by the exponential model. We examine the existence of a "gap" between the most massive neutron stars and the least massive black holes by considering the value, M_1%, of the 1% quantile from each black hole mass distribution as the lower bound of black hole masses. The best model (the power law) fitted to the low-mass systems has a distribution of lower-bounds with M_1% > 4.3 Msun with 90% confidence, while the best model (the exponential) fitted to all 20 systems has M_1% > 4.5 Msun with 90% confidence. We conclude that our sample of black hole masses provides strong evidence of a gap between the maximum neutron star mass and the lower bound on black hole masses. Our results on the low-mass sample are in qualitative agreement with those of Ozel, et al (2010).Comment: 56 pages, 22 figures, 9 tables, as accepted by Ap

Functional mammalian spliceosomal complex E contains SMN complex proteins in addition to U1 and U2 snRNPs

Copyright @ 2011 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.Spliceosomes remove introns from primary gene transcripts. They assemble de novo on each intron through a series of steps that involve the incorporation of five snRNP particles and multiple non-snRNP proteins. In mammals, all the intermediate complexes have been characterized on one transcript (MINX), with the exception of the very first, complex E. We have purified this complex by two independent procedures using antibodies to either U1-A or PRPF40A proteins, which are known to associate at an early stage of assembly. We demonstrate that the purified complexes are functional in splicing using commitment assays. These complexes contain components expected to be in the E complex and a number of previously unrecognized factors, including survival of motor neurons (SMN) and proteins of the SMN-associated complex. Depletion of the SMN complex proteins from nuclear extracts inhibits formation of the E complex and causes non-productive complexes to accumulate. This suggests that the SMN complex stabilizes the association of U1 and U2 snRNPs with pre-mRNA. In addition, the antibody to PRPF40A precipitated U2 snRNPs from nuclear extracts, indicating that PRPF40A associates with U2 snRNPs