Edges of the Barvinok-Novik orbitope

Here we study the k^th symmetric trigonometric moment curve and its convex hull, the Barvinok-Novik orbitope. In 2008, Barvinok and Novik introduce these objects and show that there is some threshold so that for two points on S^1 with arclength below this threshold, the line segment between their lifts on the curve form an edge on the Barvinok-Novik orbitope and for points with arclenth above this threshold, their lifts do not form an edge. They also give a lower bound for this threshold and conjecture that this bound is tight. Results of Smilansky prove tightness for k=2. Here we prove this conjecture for all k.Comment: 10 pages, 3 figures, corrected Lemma 4 and other minor revision

Quantum Searching via Entanglement and Partial Diffusion

In this paper, we will define a quantum operator that performs the inversion about the mean only on a subspace of the system (Partial Diffusion Operator). This operator is used in a quantum search algorithm that runs in O(sqrt{N/M}) for searching an unstructured list of size N with M matches such that 1<= M<=N. We will show that the performance of the algorithm is more reliable than known {fixed operators quantum search algorithms} especially for multiple matches where we can get a solution after a single iteration with probability over 90% if the number of matches is approximately more than one-third of the search space. We will show that the algorithm will be able to handle the case where the number of matches M is unknown in advance such that 1<=M<=N in O(sqrt{N/M}). A performance comparison with Grover's algorithm will be provided.Comment: 19 pages. Submitted to IJQI. Please forward comments/enquires for the first author to [email protected]

Psychosocial characteristics and social networks of suicidal prisoners: towards a model of suicidal behaviour in detention

Prisoners are at increased risk of suicide. Investigation of both individual and environmental risk factors may assist in developing suicide prevention policies for prisoners and other high-risk populations. We conducted a matched case-control interview study with 60 male prisoners who had made near-lethal suicide attempts in prison (cases) and 60 male prisoners who had not (controls). We compared levels of depression, hopelessness, self-esteem, impulsivity, aggression, hostility, childhood abuse, life events (including events occurring in prison), social support, and social networks in univariate and multivariate models. A range of psychosocial factors was associated with near-lethal self-harm in prisoners. Compared with controls, cases reported higher levels of depression, hopelessness, impulsivity, and aggression, and lower levels of self-esteem and social support (all p values <0.001). Adverse life events and criminal history factors were also associated with near-lethal self-harm, especially having a prior prison spell and having been bullied in prison, both of which remained significant in multivariate analyses. The findings support a model of suicidal behaviour in prisoners that incorporates imported vulnerability factors, clinical factors, and prison experiences, and underscores their interaction. Strategies to reduce self-harm and suicide in prisoners should include attention to such factors

Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Prevention of suicidal behaviour in prisons: an overview of initiatives based on a systematic review of research on near-lethal suicide attempts

Background: Worldwide, prisoners are at high risk of suicide. Research on near-lethal suicide attempts can provide important insights into risk and protective factors, and inform suicide prevention initiatives in prison. Aims: To synthesize findings of research on near-lethal attempts in prisons, and consider their implications for suicide prevention policies and practice, in the context of other research in custody and other settings. Method: We searched two bibliographic indexes for studies in any language on near-lethal and severe self-harm in prisoners, supplemented by targeted searches over the period 2000–2014. We extracted information on risk factors descriptively. Data were not meta-analyzed owing to heterogeneity of samples and methods. Results: We identified eight studies reporting associations between prisoner near-lethal attempts and specific factors. The latter included historical, prison-related, and clinical factors, including psychiatric morbidity and comorbidity, trauma, social isolation, and bullying. These factors were also identified as important in prisoners' own accounts of what may have contributed to their attempts (presented in four studies). Conclusion: Factors associated with prisoners' severe suicide attempts include a range of potentially modifiable clinical, psychosocial, and environmental factors. We make recommendations to address these factors in order to improve detection, management, and prevention of suicide risk in prisoners

Five-State Study of ACA Marketplace Competition

The health insurance marketplaces created by the Affordable Care Act (ACA) were intended to broaden health insurance coverage by making it relatively easy for the uninsured, armed with income-related federal subsidies, to choose health plans that met their needs from an array of competing options. The further hope was that competition among health plans on the exchanges would lead to lower costs and higher value for consumers, because inefficient, low-value plans would lose out in the competitive market place. This study sought to understand the diverse experience in five states under the ACA in order to gain insights for improving competition in the private health insurance industry and the implementation of the ACA.In spring 2016, the insurance marketplaces had been operating for nearly three full years. There were numerous press stories of plans' decisions to enter or leave selected states or market areas within states and to narrow provider networks by including fewer choices among hospitals, medical specialists, and other providers. There were also beginning to be stories of insurer requests for significant premium increases. However, there was no clear understanding of how common these practices were, nor how and why practices differed across carriers, markets, and state regulatory settings.This project used the ACA Implementation Research Network to conduct field research in California, Michigan, Florida, North Carolina, and Texas. In each state, expert field researchers engaged directly with marketplace stakeholders, including insurance carriers, provider groups, state regulators, and consumer engagement organizations, to identify and understand their various decisions. This focus included an effort to understand why carriers choose to enter or exit markets and the barriers they faced, how provider networks were built, and how state regulatory decisions affected decision-making. Ultimately, it sought to find where and why certain markets are successful and competitive and how less competitive markets might be improved.The study of five states was not intended to provide statistically meaningful generalizations about the functioning of the marketplace exchanges. Rather, it was intended to accomplish two other objectives. First, the study was designed to generate hypotheses about the development and evolution of the exchanges that might be tested with "harder" data from all the exchanges. Second, it sought to describe the potentially idiosyncratic nature of the marketplaces in each of the five states. Political and economic circumstances may differ substantially across markets. Policymakers and market participants need to appreciate the nuances of different local settings if programs are to be successful. What works in Michigan may not work in Texas and vice versa. Field research of this sort can give researchers and policymakers insight into how idiosyncratic local factors matter in practice.In brief, our five states had four years of experience in the open enrollment periods from 2014 through 2017. The states array themselves in a continuum of apparent success in enhancing and maintaining competition among insurers. California and Michigan appear to have had success in nurturing insurer competition, in at least the urban areas of their states. Florida, North Carolina, and Texas were less successful. This divergence is recent, however. As recently as the 2015 and 2016 open enrollment periods, all of the states had what appeared to be promising, if not always robust, insurance competition. Large changes occurred in the run-up to the 2017 open enrollment period

Learning Arbitrary Statistical Mixtures of Discrete Distributions

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$ directly, but only indirectly and in very noisy fashion, by sampling from $[n]$ repeatedly, independently $K$ times from the distribution $p$. The problem is to infer $\vartheta$ to high accuracy in transportation (earthmover) distance. We give the first efficient algorithms for learning this mixture model without making any restricting assumptions on the structure of the distribution $\vartheta$. We bound the quality of the solution as a function of the size of the samples $K$ and the number of samples used. Our model and results have applications to a variety of unsupervised learning scenarios, including learning topic models and collaborative filtering.Comment: 23 pages. Preliminary version in the Proceeding of the 47th ACM Symposium on the Theory of Computing (STOC15