Hamiltonian Variational Auto-Encoder

Variational Auto-Encoders (VAEs) have become very popular techniques to perform inference and learning in latent variable models as they allow us to leverage the rich representational power of neural networks to obtain flexible approximations of the posterior of latent variables as well as tight evidence lower bounds (ELBOs). Combined with stochastic variational inference, this provides a methodology scaling to large datasets. However, for this methodology to be practically efficient, it is necessary to obtain low-variance unbiased estimators of the ELBO and its gradients with respect to the parameters of interest. While the use of Markov chain Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo (HMC) has been previously suggested to achieve this [23, 26], the proposed methods require specifying reverse kernels which have a large impact on performance. Additionally, the resulting unbiased estimator of the ELBO for most MCMC kernels is typically not amenable to the reparameterization trick. We show here how to optimally select reverse kernels in this setting and, by building upon Hamiltonian Importance Sampling (HIS) [17], we obtain a scheme that provides low-variance unbiased estimators of the ELBO and its gradients using the reparameterization trick. This allows us to develop a Hamiltonian Variational Auto-Encoder (HVAE). This method can be reinterpreted as a target-informed normalizing flow [20] which, within our context, only requires a few evaluations of the gradient of the sampled likelihood and trivial Jacobian calculations at each iteration.Comment: Accepted as a poster in the proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS

Provoking Punches: Factors Influencing Perceived Violent Reactions to Negative Situations

Purpose: Violence among college students is an important area of research as this group is at an increased risk of both engaging in and being a victim of violence. As such, the current research aimed to examine factors that may influence violent tendencies among a sample of college students. Method: Data from 101 completed surveys were analyzed. Principal components factor analysis and Cronbach’s alpha resulted in the creation of six independent variables (gun experience, weapons support, anger contagion, witness violence, violent community, and aggressive beliefs) and four dependent variables (competition for resources, social attacks, physical attacks, and unfair situations). OLS regression was used to estimate the impact of each variable on perceptions of reacting with violence to four negative situations. Results: Gun experience and violent community significantly predicted responding violently to both social and physical attacks, while gun support was only predictive of violence in competition for resources. Additionally, aggressive beliefs predicted perceptions of violent responses to physical attacks and in unfair situations. Finally, anger contagion was associated with students reporting an increased likelihood of responding violently to social attacks. Conclusions: While research shows the importance of understanding violence exposure and aggressive norms in creating and improving violence prevention programs and anti-violence strategies, the role that perceptions play is largely absent. Furthermore, this research supports the importance of implementing these programs and strategies among college students/young adults to potentially reduce violence and aggression within this age group

À la verticale du bâti : étude du potentiel d'adaptation des constructions en hauteur sur le parcellaire originel du quartier Bui Thi Xuan à Hanoi, Vietnam

Cette étude concerne l’architecture récente du quartier Bui Thi Xuan à Hanoi, au Vietnam. Depuis l’instauration de la politique de libéralisation économique du doi moi en 1986, le secteur privé vietnamien participe activement aux domaines de la construction et de la rénovation. La recherche examine particulièrement les bâtiments en béton qui sont érigés en hauteur sur des parcelles étroites et profondes, et souvent rénovés tôt après leur construction. La rapidité et la fréquence des interventions font douter de la qualité des édifices à long terme. Le travail vise à évaluer comment ces bâtiments peuvent être adaptés pour répondre aux contraintes de densification urbaine actuelles et projetées. Par une analyse systémique, l’étude discutera de la viabilité des interventions en fonction des critères d’adaptabilité et de qualité environnementale. La présentation des rénovations récentes permettra ensuite de déduire le processus évolutif réel des édifices. Pour conclure, le travail démontrera que ce type architectural, dans son état actuel, détient un potentiel d’adaptation qui est toutefois sous-exploité.This study examines the recent architecture of the Bui Thi Xuan neigbourhood in Hanoi, Vietnam. Since 1986, when the doi moi policy came into effect, the country has been experiencing a strong economic boom. As a result, the private sector plays an active part in the construction and renovation industries. The research focuses on a particular type of urban infill, where tall concrete structures are built within narrow and deep lots, and are often quickly renovated thereafter. The purpose of this study is to determine how these buildings can be physically and spatially modified in response to an already occurring urban densification. A systemic analysis of the building stock will reveal the viability of such options in terms of adaptability and environmental quality criteria. Recent renovations will then show how these structures are actually evolving within their environment. The research concludes with a demonstration of how these buildings, as they stand, have an under-exploited capacity to provide a healthy and adaptable environment

Recent developments of MCViNE and its applications at SNS

MCViNE is an open source, object-oriented Monte Carlo neutron ray-tracing simulation software package. Its design allows for flexible, hierarchical representations of sophisticated instrument components such as detector systems, and samples with a variety of shapes and scattering kernels. Recently this flexible design has enabled several applications of MCViNE simulations at the Spallation Neutron Source (SNS) at Oak Ridge National Lab, including assisting design of neutron instruments at the second target station and design of novel sample environments, as well as studying effects of instrument resolution and multiple scattering. Here we provide an overview of the recent developments and new features of MCViNE since its initial introduction (Jiao et al 2016 Nucl. Instrum. Methods Phys. Res., Sect. A 810, 86–99), and some example applications

Endothelial dysfunction in children with steroid-resistant nephrotic syndrome

Background: Steroid-resistant nephrotic syndrome (SRNS) is associated with early atherosclerosis because of comorbidities including persistent hyperlipidemia and hypertension. The aim of this study was to determine the incidence of abnormal carotid intima-media thickening (cIMT) as an early sign of atherosclerosis in a small group of children with SRNS. Methods: A total of 8 children with SRNS (mean age, 10.8±4.2 years at recruitment andmeandisease duration, 40.9±20.7 months) were studied; all children were normotensive. B-mode ultrasound was used to measure cIMT, and the results were compared with healthy controls. Results: Children with SRNS had significantly thicker CIMT (0.44±0.04 mm), compared to the controls (0.37±0.59mm)(P < 0.01). A high level of total cholesterol (5.4±2.0 mmol/L; normal < 5.2 mmol/L) was reported in these children, while normal levels of lowdensity lipoprotein, very-low-density lipoprotein, triglyceride, and high-density lipoprotein were found. Also, the mean creatinine level was 45.1±15.0 µmol/L, and the mean urea level was 4.2±1.8 mmol/L. Conclusions: Children with SRNS had an abnormal vascular phenotype with a thicker CIMT, compared to the controls and showed evidence of hypercholesterolemia

Intelligent chilled mirror humidity sensor

A new, intelligent, chilled mirror humidity instrument has been designed for use on buoys and ships. The design goal is to make high quality dew point temperature measurements for a period of up to one year from an unattended platform, while consuming as little power as possible. Nominal system accuracy is 0.3°C, and a measure of data quality is provided to indicate possible drift in calibration. Energy consumption is typically 800 Joules per measurement; standby power consumption is 0.05 watts. Control of the instrument is managed by an onboard central processing unit which is programmable in BASIC, and communication to an external data logger is provided through an RS232 compatible interface. This report describes the preliminary sensor tests that led to this new design and provides the complete technical description required for fabrication.Funding was provided by the Office of Naval Research under contract Number N00014-84-C-0134, and the National Science Foundation through grant Number OCE87- 09614

Learning Deep Features in Instrumental Variable Regression

Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by using an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner. DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task