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Telepsychiatry:The Evaluation and Treatment of Seniors in Rural Retirement Communities

Journal of Geriatric Medicine 2019年第2期1卷 23-31页

作者： Susanne Inez Steinberg Robert Gallop Medical Director&Psychiatrist Penn Med Princeton House Behavioral Health MoorestownNJ 08057United States Applied Statistics Program Department of MathematicsWest Chester UniversityWest ChesterPA 19383United States

Few studies examine the benefits from geriatric telepsychiatry in rural retirement ***:*** demonstrate that using telepsychiatry(a)standard approaches to psychiatric evaluation would yield diagnoses in Diagnostic and Statistical Manual of Mental Disorders(DSM-V);(b)psychotherapies and pharmacotherapy could be effectively *** examine the relationships among cognition,mood,agitation and functions at baseline and the response to treatment over ***:Prospective longitudinal ***:Geriatric Depression scale(GDS),Mini Mental State Exam(MMSE),Barthel Index(BI),Pittsburgh Agitation Scale(PAS).Setting:Video ***:Psychotherapy,***:428 Seniors over 55,met criteria for at least one DSM-V ***:Treatments were administered for a full range of psychiatric diagnoses and age-related medical *** most frequently prescribed pharmacological agents were:antidepressants(78%)antipsychotics(64%),memory enhancers(38%).Participants(66%)received psychotherapy:individual(31%),couple(7%),family(13%).Variation in the MMSE scores were observed:55%remained stable,11%declined,18%*** Scores improved from baseline to 26 weeks(p=0.02,d=0.99:95%CI 0.39-1.56).PAS scores declined from baseline to 52 weeks(McNemar’s S=11.27,p=0.0008,d=1.17:95%CI 0.63-1.68).Function(BI)at week 26 was not statistically significantly different from baseline(t(26)=1.66,p=0.11,d=0.65:95%CI-0.16-1.42).Participants maintained independence(64.5%)at 52 weeks(McNemar’s S=6.23,p=0.013,d=0.79:95%CI 0.19-1.36)Conclusion:This study demonstrates the feasibility and benefit of providing a full complement of services via telepsychiatry to seniors and provides a rationale for more comprehensive reimbursement plans.

关键词： Psychotherapy Psychopharmacology Telepsychiatry Seniors Retirement communities Nursing Homes

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Adversarially robust clustering with optimality guarantees

arXiv

引用

arXiv 2023年

作者： Jana, Soham Yang, Kun Kulkarni, Sanjeev The Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN United States The Data Science Institute Center For Research Computing Southern Methodist University DallasTX United States The Department of Operations Research and Financial Engineering Princeton University PrincetonNJ United States

We consider the problem of clustering data points coming from sub-Gaussian mixtures. Existing methods that provably achieve the optimal mislabeling error, such as the Lloyd algorithm, are usually vulnerable to outliers. In contrast, clustering methods seemingly robust to adversarial perturbations are not known to satisfy the optimal statistical guarantees. We propose a simple robust algorithm based on the coordinatewise median that obtains the optimal mislabeling rate even when we allow adversarial outliers to be present. Our algorithm achieves the optimal error rate in constant iterations when a weak initialization condition is satisfied. In the absence of outliers, in fixed dimensions, our theoretical guarantees are similar to that of the Lloyd algorithm. Extensive experiments on various simulated and public datasets are conducted to support the theoretical guarantees of our *** Codes 62G30, 62G35, 62H30 Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Neural canonical transformation with symplectic flows

arXiv

引用

arXiv 2019年

作者： Li, Shuo-Hui Dong, Chen-Xiao Zhang, Linfeng Wang, Lei Institute of Physics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing100190 China Songshan Lake Materials Laboratory Dongguan Guangdong523808 China

Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. We construct flexible and powerful canonical transformations as generative models using symplectic neural networks. The model transforms physical variables towards a latent representation with an independent harmonic oscillator Hamiltonian. Correspondingly, the phase space density of the physical system flows towards a factorized Gaussian distribution in the latent space. Since the canonical transformation preserves the Hamiltonian evolution, the model captures nonlinear collective modes in the learned latent representation. We present an efficient implementation of symplectic neural coordinate transformations and two ways to train the model. The variational free energy calculation is based on the analytical form of physical Hamiltonian. While the phase space density estimation only requires samples in the coordinate space for separable Hamiltonians. We demonstrate appealing features of neural canonical transformation using toy problems including two-dimensional ring potential and harmonic chain. Finally, we apply the approach to real-world problems such as identifying slow collective modes in alanine dipeptide and conceptual compression of the MNIST dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Hamiltonians

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Modeling binary time series using gaussian processes with application to predicting sleep states

arXiv

引用

arXiv 2017年

作者： Gao, Xu Shahbaba, Babak Ombao, Hernando Department of Statistics University of California IrvineCA United States Department of Cognitive Sciences University of California IrvineCA United States Program on Applied Mathematics and Computational Science King Abdullah University of Science and Technology

Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates on the binary-valued response. The Gaussian process captures the residual variations in the binary response that are not explained by covariates and past realizations. We develop a frequentist modeling framework that provides efficient inference and more accurate predictions. Results demonstrate the advantages of improved prediction rates over existing approaches such as logistic regression, generalized additive mixed model, models for ordinal data, gradient boosting, decision tree and random forest. Using our proposed model, we show that previous sleep state and heart rates are significant predictors for future sleep states. Simulation studies also show that our proposed method is promising and robust. To handle computational complexity, we utilize Laplace approximation, golden section search and successive parabolic interpolation. With this paper, we also submit an R-package (HIBITS) that implements the proposed procedure. Copyright © 2017, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

引用

Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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The Disordered Heterogeneous Universe: Galaxy Distribution and Clustering Across Length Scales

arXiv

引用

arXiv 2022年

作者： Philcox, Oliver H.E. Torquato, Salvatore Department of Astrophysical Sciences Princeton University PrincetonNJ08540 United States School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Center for Theoretical Physics Department of Physics Columbia University New YorkNY10027 United States Simons Foundation New YorkNY10010 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States

The studies of disordered heterogeneous media and galaxy cosmology share a common goal: analyzing the disordered distribution of particles and/or building blocks at ‘microscales’ to predict physical properties of the medium at ‘macroscales’, whether it be a liquid, colloidal suspension, composite material, galaxy cluster, or entire Universe. The theory of disordered heterogeneous media provides an array of theoretical and computational techniques to characterize a wide class of complex material microstructures. In this work, we apply them to describe the disordered distributions of galaxies obtained from recent suites of dark matter simulations. We focus on the determination of lower-order correlation functions, ‘void’ and ‘particle’ nearest-neighbor functions, certain cluster statistics, pair-connectedness functions, percolation properties, and a scalar order metric to quantify the degree of order. Compared to analogous homogeneous Poisson and typical disordered systems, the cosmological simulations exhibit enhanced large-scale clustering and longer tails in the void and particle nearest-neighbor functions, due to the presence of quasi-long-range correlations imprinted by early Universe physics, with a minimum particle separation far below the mean nearest-neighbor distance. On large scales, the system appears ‘hyperuniform’, as a result of primordial density fluctuations, whilst on the smallest scales, the system becomes almost ‘antihyperuniform’, as evidenced by its number variance. Additionally, via a finite scaling analysis, we compute the percolation threshold of the galaxy catalogs, finding this to be significantly lower than for Poisson realizations (at reduced density ηc = 0.25 in our fiducial analysis compared to ηc = 0.34), with strong dependence on the mean density;this is consistent with the observation that the galaxy distribution contains voids of up to 50% larger radius. However, the two sets of simulations appear to share the same fractal dimension

关键词： Galaxies

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Hard convex lens-shaped particles: Characterization of dense disordered packings

引用

Physical Review E 2019年第6期100卷 062902-062902页

作者： Giorgio Cinacchi Salvatore Torquato Departamento de Física Teórica de la Materia Condensada Instituto de Física de la Materia Condensada (IFIMAC) Instituto de Ciencias de Materiales “Nicolás Cabrera” Universidad Autónoma de Madrid Ciudad Universitaria de Cantoblanco E-28049 Madrid Spain Department of Chemistry and Department of Physics Institute for the Science and Technology of Materials Program for Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Among the family of hard convex lens-shaped particles (lenses), the one with aspect ratio equal to 2/3 is “optimal” in the sense that the maximally random jammed (MRJ) packings of such lenses achieve the highest packing fraction ϕMRJ≃0.73 [G. Cinacchi and S. Torquato, Soft Matter 14, 8205 (2018)]. This value is only a few percent lower than ϕDKP=0.76210⋯, the packing fraction of the corresponding densest-known crystalline (degenerate) packings [G. Cinacchi and S. Torquato, J. Chem. Phys. 143, 224506 (2015)]. By exploiting the appreciably reduced propensity that a system of such optimal lenses has to positionally and orientationally order, disordered packings of them are progressively generated by a Monte Carlo method–based procedure from the dilute equilibrium isotropic fluid phase to the dense nonequilibrium MRJ state. This allows us to closely monitor how the (micro)structure of these packings changes in the process of formation of the MRJ state. The gradual changes undergone by the many structural descriptors calculated here can coherently and consistently be traced back to the gradual increase in contacts between the hard particles until the isostatic mean value of ten contact neighbors per lens is reached at the effectively hyperuniform MRJ state. Compared to the MRJ state of hard spheres, the MRJ state of such optimal lenses is denser (less porous), more disordered, and rattler-free. This set of characteristics makes them good glass formers. It is possible that this conclusion may also hold for other hard convex uniaxial particles with a correspondingly similar aspect ratio, be they oblate or prolate, and that, by using suitable biaxial variants of them, that set of characteristics might further improve.

关键词： Granular packing Jamming Colloids Disordered systems Granular materials Lyotropic liquid crystals Monte Carlo methods

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Relation between the Kolmogorov and Batchelor constants

引用

The Physics of Fluids 1987年第1期30卷 3-3页

作者： Victor Yakhot Steven A. Orszag Applied and Computational Mathematics Princeton University Princeton New Jersey 08544

It is shown that the renormalization group theory of turbulence leads to the relation Ba=CKPt between the turbulent Prandtl number Pt, the Kolmogorov constant CK, and the Batchelor constant Ba.

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Unsupervised particle sorting for high-resolution single-particle cryo-EM

arXiv

引用

arXiv 2019年

作者： Zhou, Ye Moscovich, Amit Bendory, Tamir Bartesaghi, Alberto Department of Computer Science Duke University Durham United States Program in Applied and Computational Mathematics Princeton University United States School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Biochemistry Duke University School of Medicine Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle *** Codes 62P10 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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mechanoChemML: A software library for machine learning in computational materials physics

arXiv

引用

arXiv 2021年

作者： Zhang, X. Teichert, G.H. Wang, Z. Duschenes, M. Srivastava, Siddhartha Livingston, E. Holber, J. Shojaei, M. Faghih Sundararajan, A. Garikipati, K. Department of Mechanical Engineering University of Michigan United States Applied Physics Program University of Michigan United States Department of Mathematics University of Michigan United States Michigan Institute for Computational Discovery & Engineering University of Michigan United States

We present mechanoChemML, a machine learning software library for computational materials physics. mechanoChemML is designed to function as an interface between platforms that are widely used for machine learning on one hand, and others for solution of partial differential equations-based models of physics. Of special interest here, and the focus of mechanoChemML, are applications to computational materials physics. These typically feature the coupled solution of material transport, reaction, phase transformation, mechanics, heat transport and electrochemistry. Central to the organization of mechanoChemML are machine learning workflows that arise in the context of data-driven computational materials physics. The mechanoChemML code structure is described, the machine learning workflows are laid out and their application to the solution of several problems in materials physics is outlined. © 2021, CC BY-NC-SA.

关键词： Partial differential equations

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