Best Practices in Mental Health at Corrections Facilities

Police, court personnel, and correctional staff interact with, stabilize, and treat more persons with mental illness than any other system in America—making criminal justice agencies the largest mental health provider in the United States. Yet a wide gap exists between the training of corrections staff and the enormous responsibility they have for day-to-day management of mental health issues. To narrow this gap in jail and prison settings, the best practices include training programs, screening procedures, communication between staff, and good documentation. Quality mental health services help maintain security by reducing inmate and staff stress levels and helping to facilitate offender participation in rehabilitative programming. They increase the likelihood of successful reintegration of mentally ill offenders into the community by promoting adequate community based mental health care follow-up, thereby contributing to reduced recidivism. By following these best practices, correctional organizations can also reduce the likelihood of expensive civil litigation or other legal actions that can result from inadequate correctional mental health services

The Price of Information in Combinatorial Optimization

Consider a network design application where we wish to lay down a minimum-cost spanning tree in a given graph; however, we only have stochastic information about the edge costs. To learn the precise cost of any edge, we have to conduct a study that incurs a price. Our goal is to find a spanning tree while minimizing the disutility, which is the sum of the tree cost and the total price that we spend on the studies. In a different application, each edge gives a stochastic reward value. Our goal is to find a spanning tree while maximizing the utility, which is the tree reward minus the prices that we pay. Situations such as the above two often arise in practice where we wish to find a good solution to an optimization problem, but we start with only some partial knowledge about the parameters of the problem. The missing information can be found only after paying a probing price, which we call the price of information. What strategy should we adopt to optimize our expected utility/disutility? A classical example of the above setting is Weitzman's "Pandora's box" problem where we are given probability distributions on values of $n$ independent random variables. The goal is to choose a single variable with a large value, but we can find the actual outcomes only after paying a price. Our work is a generalization of this model to other combinatorial optimization problems such as matching, set cover, facility location, and prize-collecting Steiner tree. We give a technique that reduces such problems to their non-price counterparts, and use it to design exact/approximation algorithms to optimize our utility/disutility. Our techniques extend to situations where there are additional constraints on what parameters can be probed or when we can simultaneously probe a subset of the parameters.Comment: SODA 201

Representation learning of drug and disease terms for drug repositioning

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.Comment: Accepted to appear in 3rd IEEE International Conference on Cybernetics (Spl Session: Deep Learning for Prediction and Estimation

Geodesic completeness and the lack of strong singularities in effective loop quantum Kantowski-Sachs spacetime

Resolution of singularities in the Kantowski-Sachs model due to non-perturbative quantum gravity effects is investigated. Using the effective spacetime description for the improved dynamics version of loop quantum Kantowski-Sachs spacetimes, we show that even though expansion and shear scalars are universally bounded, there can exist events where curvature invariants can diverge. However, such events can occur only for very exotic equations of state when pressure or derivatives of energy density with respect to triads become infinite at a finite energy density. In all other cases curvature invariants are proved to remain finite for any evolution in finite proper time. We find the novel result that all strong singularities are resolved for arbitrary matter. Weak singularities pertaining to above potential curvature divergence events can exist. The effective spacetime is found to be geodesically complete for particle and null geodesics in finite time evolution. Our results add to a growing evidence for generic resolution of strong singularities using effective dynamics in loop quantum cosmology by generalizing earlier results on isotropic and Bianchi-I spacetimes.Comment: Revised version. Discussion in the proof on absence of strong singularities expanded. References added. To appear in CQ

Application of Generalized Partial Volume Estimation for Mutual Information based Registration of High Resolution SAR and Optical Imagery

Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance has been thoroughly explored for registration of high resolution SAR (TerraSAR-X) and optical (IKONOS-2) satellite images. Our experiments highlight possibility of an inconsistent MI behavior with different joint histogram bin size which gets reduced with an increase in order of B-spline kernel employed in GPVE. In general, bin size reduction and/or increasing B-spline order have a smoothing affect on MI surfaces and even the lowest order B-spline with a suitable histogram bin size can achieve same pixel level accuracy as achieved by the higher order kernels more consistently

Generic absence of strong singularities and geodesic completeness in modified loop quantum cosmologies

Different regularizations of the Hamiltonian constraint in loop quantum cosmology yield modified loop quantum cosmologies, namely mLQC-I and mLQC-II, which lead to qualitatively different Planck scale physics. We perform a comprehensive analysis of resolution of various singularities in these modified loop cosmologies using effective spacetime description and compare with earlier results in standard loop quantum cosmology. We show that the volume remains non-zero and finite in finite time evolution for all considered loop cosmological models. Interestingly, even though expansion scalar and energy density are bounded due to quantum geometry, curvature invariants can still potentially diverge due to pressure singularities at a finite volume. These divergences are shown to be harmless since geodesic evolution does not break down and no strong singularities are present in the effective spacetimes of loop cosmologies. Using a phenomenological matter model, various types of exotic strong and weak singularities, including big rip, sudden, big freeze and type-IV singularities, are studied. We show that as in standard loop quantum cosmology, big rip and big freeze singularities are resolved in mLQC-I and mLQC-II, but quantum geometric effects do not resolve sudden and type-IV singularities.Comment: Minor revision to match published version in CQ

Generic absence of strong singularities in loop quantum Bianchi-IX spacetimes

We study the generic resolution of strong singularities in loop quantized effective Bianchi-IX spacetime in two different quantizations - the connection operator based `A' quantization and the extrinsic curvature based `K' quantization. We show that in the effective spacetime description with arbitrary matter content, it is necessary to include inverse triad corrections to resolve all the strong singularities in the `A' quantization. Whereas in the `K' quantization these results can be obtained without including inverse triad corrections. Under these conditions, the energy density, expansion and shear scalars for both of the quantization prescriptions are bounded. Notably, both the quantizations can result in potentially curvature divergent events if matter content allows divergences in the partial derivatives of the energy density with respect to the triad variables at a finite energy density. Such events are found to be weak curvature singularities beyond which geodesics can be extended in the effective spacetime. Our results show that all potential strong curvature singularities of the classical theory are forbidden in Bianchi-IX spacetime in loop quantum cosmology and geodesic evolution never breaks down for such events.Comment: 23 page