Charged Higgs bosons from the 3-3-1 models and the R(D(∗))\mathcal{R}(D^{(*)}) anomalies

Several anomalies in the semileptonic B-meson decays such as $\mathcal{R}(D^{(*)})$ have been reported by $BABAR$, Belle, and LHCb collaborations recently. In this paper, we investigate the contributions of the charged Higgs bosons from the 3-3-1 models to the $\mathcal{R}(D^{(*)})$ anomalies. We find that, in a wide range of parameter space, the 3-3-1 models might give reasonable explanations to the $\mathcal{R}(D^{(*)})$ anomalies and other analogous anomalies of the B meson's semileptonic decays.Comment: Accpeted by Physical Review

Environments for sonic ecologies

This paper outlines a current lack of consideration for the environmental context of Evolutionary Algorithms used for the generation of music. We attempt to readdress this balance by outlining the benefits of developing strong coupling strategies between agent and en- vironment. It goes on to discuss the relationship between artistic process and the viewer and suggests a placement of the viewer and agent in a shared environmental context to facilitate understanding of the artistic process and a feeling of participation in the work. The paper then goes on to outline the installation ‘Excuse Me and how it attempts to achieve a level of Sonic Ecology through the use of a shared environmental context

Sleep disorders and suicide attempts following discharge from residential treatment

IntroductionSuicide is a significant public health concern and its prevention remains a top clinical priority of the Veterans Health Administration. Periods of transition in care (e.g., moving from inpatient to outpatient care) represent a period of increased risk. Sleep disorders are prevalent amongst Veterans and are modifiable risk factor for suicide. The present study examined the relationship of sleep disorders to time to suicide attempt amongst Veterans known to have attempted suicide in the 180 days following discharge from a Mental Health Residential Rehabilitation Treatment Program.MethodThe present sample was comprised of all Veterans enrolled in services with the Veterans Health Administration known to have attempted suicide following discharge from a Mental Health Residential Rehabilitation Treatment Program during Fiscal Years 13 and 14 (N = 1,489). To create this sample, electronic medical record data were extracted from two VHA data sources: the Corporate Data Warehouse and the Suicide Prevention Application Network.ResultsCox regression models revealed that Veterans with a sleep disturbance (N = 1,211) had a shorter time to suicide attempt than those without a sleep disturbance [Hazard Ratio (HR) = 1.16, CI (1.02–1.32)]. A subsequent Cox regression model including age, insomnia, nightmare disorder, and alcohol dependence revealed that sleep-related breathing disorders [HR = 1.19, CI (1.01–1.38)], alcohol dependence [HR = 1.16, CI (1.02–1.33)], and age group were associated with increased risk.ConclusionFindings indicate that sleep disturbance, primarily driven by sleep-related breathing disorders, was associated with time to suicide attempt in this sample of high-risk Veterans known to have attempted suicide in the 180 days following their discharge from a Mental Health Residential Rehabilitation Treatment Program. These findings reveal an opportunity to reduce risk through the screening and treatment of sleep disorders in high-risk populations

Real-Time Decision Fusion for Multimodal Neural Prosthetic Devices

The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device., i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions.Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future

Age is in the Eye of the Beholder: Examining the Cues Employed to Construct the Illusion of Youth in Teen Pornography

Past research has identified a subgenre of mainstream pornography that attempts to create the illusion for consumers that sex is occurring between an adult and a minor (i.e., a child or young adolescent under the age of 18). This illusion is established through various textual, verbal, visual, and behavioural cues. Although the construction of adult–minor relationships in pornography has received some scholarly attention, there has been no attempt to investigate this phenomenon within pornographic videos available via the Internet. The current study addressed this omission by analyzing for content 150 of the most popular “teen” pornography videos available on three pornography websites. We coded for textual, visual, verbal, and behavioural content that connoted sexual activity between an adult and a minor. Results indicated that a small number of videos (28, 18.7 % of the sample) contained a disproportionate percentage of cues (54.2 %), with the remaining videos containing little or no youth sexualized content. We conclude that only a subsample of videos clearly attempted to portray adult–minor relationships. The prevalence of various cues within the sample was quantified and discussed, as were limitations associated with this study and directions for future research

Generalized Phase-Space Techniques to Explore Quantum Phase Transitions in Critical Quantum Spin Systems

We apply the generalized Wigner function formalism to detect and characterize a range of quantum phase transitions in several cyclic, finite-length, spin-$\frac{1}{2}$ one-dimensional spin-chain models, viz., the Ising and anisotropic $XY$ models in a transverse field, and the $XXZ$ anisotropic Heisenberg model. We make use of the finite system size to provide an exhaustive exploration of each system's single-site, bipartite and multi-partite correlation functions. In turn, we are able to demonstrate the utility of phase-space techniques in witnessing and characterizing first-, second- and infinite-order quantum phase transitions, while also enabling an in-depth analysis of the correlations present within critical systems. We also highlight the method's ability to capture other features of spin systems such as ground-state factorization and critical system scaling. Finally, we demonstrate the generalized Wigner function's utility for state verification by determining the state of each system and their constituent sub-systems at points of interest across the quantum phase transitions, enabling interesting features of critical systems to be intuitively analyzed.Comment: 20 pages, 8 figure

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed

Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update

CYP2D6 and CYP2C19 polymorphisms affect the exposure, efficacy and safety of tricyclic antidepressants (TCAs), with some drugs being affected by CYP2D6 only (e.g., nortriptyline and desipramine) and others by both polymorphic enzymes (e.g., amitriptyline, clomipramine, doxepin, imipramine, and trimipramine). Evidence is presented for CYP2D6 and CYP2C19 genotype-directed dosing of TCAs. This document is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Tricyclic Antidepressants

Why Treat Insomnia?

“Why treat insomnia?” This question grows out of the perspective that insomnia is a symptom that should only receive targeted treatment when temporary relief is needed or until more comprehensive gains may be achieved with therapy for the parent or precipitating medical or psychiatric disorders. This perspective, however, is untenable given recent data regarding the prevalence, course, consequences, and costs of insomnia. Further, the emerging data that the treatment of insomnia may promote better medical and mental health (alone or in combination with other therapies) strongly suggests that the question is no longer “why treat insomnia,” but rather “when isn’t insomnia treatment indicated?” This perspective was recently catalyzed with the American College of Physicians’ recommendation that chronic insomnia should be treated and that the first line treatment should be cognitive-behavioral therapy for insomnia (CBT-I)