Data-Driven Inference, Reconstruction, and Observational Completeness of Quantum Devices

The range of a quantum measurement is the set of output probability distributions that can be produced by varying the input state. We introduce data-driven inference as a protocol that, given a set of experimental data as a collection of output distributions, infers the quantum measurement which is, i) consistent with the data, in the sense that its range contains all the distributions observed, and, ii) maximally noncommittal, in the sense that its range is of minimum volume in the space of output distributions. We show that data-driven inference is able to return a measurement up to symmetries of the state space (as it is solely based on observed distributions) and that such limit accuracy is achieved for any data set if and only if the inference adopts a (hyper)-spherical state space (for example, the classical or the quantum bit). When using data-driven inference as a protocol to reconstruct an unknown quantum measurement, we show that a crucial property to consider is that of observational completeness, which is defined, in analogy to the property of informational completeness in quantum tomography, as the property of any set of states that, when fed into any given measurement, produces a set of output distributions allowing for the correct reconstruction of the measurement via data-driven inference. We show that observational completeness is strictly stronger than informational completeness, in the sense that not all informationally complete sets are also observationally complete. Moreover, we show that for systems with a (hyper)-spherical state space, the only observationally complete simplex is the regular one, namely, the symmetric informationally complete set.Comment: 15 pages, 12 figures, minor update

Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision suppor

Experimental Assessment of ‘subgrid’ scale Probability Density Function Models for Large Eddy Simulation

Filtered density functions (FDFs) of mixture fraction are quantified by analyzing experimental data obtained from two-dimensional planar laser-induced fluorescence scalar measurements in the isothermal swirling flow of a combustor operating at a Reynolds number of 28,662 for three different swirl numbers (0.3, 0.58 and 1.07). Two-dimensional filtering using a box filter was performed on the measured scalar to obtain the filtered variables used for presumed FDF for Large Eddy Simulations (LES). A dependant variable from the measured scalar, which was a pre-computed temperature, was integrated over the experimentally obtained FDF as well as over the presumed beta or top-hat FDFs and a relative error in temperature prediction was calculated. The experimentally measured FDFs depended on swirl numbers and axial and radial positions in the flow. The FDFs were unimodal in the regions of low variance and bimodal in the regions of high variance. The influence of the filter spatial dimension on the measured FDF was evaluated and consequences for subgrid modeling for LES discussed

Comment on `Pressure of Hot QCD at large N_f'

It is argued why quasiparticle models can be useful to describe the thermodynamics of hot QCD excluding, however, the case of a large number of flavors, for which exact results have been calculated by Moore.Comment: 5 pages, 2 figures (version accepted for publication

Comparing EEG patterns of actual and imaginary wrist movements - a machine learning approach

Our goal is to develop an algorithm for feature extraction and classification to be used in building brain-computer interfaces. In this paper, we present preliminary results for classifying EEG data of imaginary wrist movements. We have developed an algorithm based on the spatio-temporal features of the recorded EEG signals. We discuss the differences between the feature vectors selected for both actual and imaginary wrist movements and compare classification results

IMPACT OF RISK AVERSE BEHAVIOR ON FERTILIZER DEMAND FOR TAME FORAGES

Risk and Uncertainty,

Investigating the appropriateness and relevance of mobile web accessibility guidelines

The Web Accessibility Initiative (WAI) of the World Wide Web Consortium (W3C) develop and maintain guidelines for making the web more accessible to people with disabilities. WCAG 2.0 and the MWBP 1.0 are internationally regarded as the industry standard guidelines for web accessibility. Mobile testing sessions conducted by AbilityNet document issues raised by users in a report format, relating issues to guidelines wherever possible. This paper presents the results of a preliminary investigation that examines how effectively and easily these issues can be related by experts to the guidelines provided by WCAG 2.0 and MWBP 1.0. Copyright 2014 ACM

Individual differences in level of wisdom are associated with brain activation during a moral decision-making task.

IntroductionWisdom is reportedly associated with better health and quality of life. However, our knowledge of the neurobiology of wisdom is still in the early stages of development. We aimed to improve our understanding by correlating a psychometric measure of the trait with patterns of brain activation produced by a cognitive task theorized to be relevant to wisdom: moral decision-making. In particular, we aimed to determine whether individual differences in wisdom interact with moral task complexity in relation to brain activation.MethodsParticipants were 39 community-dwelling men and women aged 27-76 years, who completed moral and nonmoral decision-making tasks while undergoing functional magnetic resonance imaging. Brain activation in select regions of interest was correlated with participants' scores on the San Diego Wisdom Scale (SD-WISE).ResultsIndividual differences in wisdom were found to interact with brain response to moral versus nonmoral and moral personal versus impersonal dilemmas, particularly in regions in or near the default mode network. Persons with higher scores on the SD-WISE had less contrast between moral and nonmoral dilemmas and greater contrast between moral-personal and moral-impersonal dilemmas than individuals with lower SD-WISE scores.ConclusionsResults confirmed our hypothesis that individual differences in level of wisdom would interact with moral condition in relation to brain activation, and may underscore the relevance of considering one's own and others' actions and experiences in the context of wise thinking. Future studies are needed to replicate these findings and to examine specific neurocircuits

results from the World Mental Health Survey Initiative

Purpose: Understanding the effects of war on mental disorders is important for developing effective post-conflict recovery policies and programs. The current study uses cross-sectional, retrospectively reported data collected as part of the World Mental Health (WMH) Survey Initiative to examine the associations of being a civilian in a war zone/region of terror in World War II with a range of DSM-IV mental disorders. Methods: Adults (n = 3370) who lived in countries directly involved in World War II in Europe and Japan were administered structured diagnostic interviews of lifetime DSM-IV mental disorders. The associations of war-related traumas with subsequent disorder onset-persistence were assessed with discrete-time survival analysis (lifetime prevalence) and conditional logistic regression (12-month prevalence). Results: Respondents who were civilians in a war zone/region of terror had higher lifetime risks than other respondents of major depressive disorder (MDD; OR 1.5, 95% CI 1.1, 1.9) and anxiety disorder (OR 1.5, 95% CI 1.1, 2.0). The association of war exposure with MDD was strongest in the early years after the war, whereas the association with anxiety disorders increased over time. Among lifetime cases, war exposure was associated with lower past year risk of anxiety disorders (OR 0.4, 95% CI 0.2, 0.7). Conclusions: Exposure to war in World War II was associated with higher lifetime risk of some mental disorders. Whether comparable patterns will be found among civilians living through more recent wars remains to be seen, but should be recognized as a possibility by those projecting future needs for treatment of mental disorders.publishersversionpublishe