UMBC Mathematics and Statistics Department
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Flipping the switch on the hub cell: Islet desynchronization through cell silencing
(PLOS, 20210408)Pancreatic β cells, responsible for secreting insulin into the bloodstream and maintaining glucose homeostasis, are organized in the islets of Langerhans as clusters of electrically coupled cells. Gap junctions, connecting ... 
On the negative dependence inequalities and maximal score in roundrobin tournament
We extend Huber's (1963) inequality for the joint distribution function of negative dependent scores in the roundrobin tournament. As a byproduct, this extension implies convergence in probability of the maximal score in ... 
Fully Distributed Optimization based CAV Platooning Control under Linear Vehicle Dynamics
This paper develops distributed optimization based, platoon centered CAV car following schemes, motivated by the recent interest in CAV platooning technologies. Various distributed optimization or control schemes have been ... 
Weak Solutions for a Poroelastic Plate System
(20210316)We consider a recent plate model obtained as a scaled limit of the three dimensional Biot system of poroelasticity. The result is a "2.5" dimensional linear system that couples traditional EulerBernoulli plate dynamics ... 
TeamBased Online Multidisciplinary Education on Big Data + HighPerformance Computing + Atmospheric Sciences
(UMBC HPCF, 2020)Given the context of many institutions moving to online instruction due to the COVID19 pandemic in 2020, we share our experiences of an online teambased multidisciplinary education program on big data + high performance ... 
State and parameter estimation from exact partial state observation in stochastic reaction networks
(20201209)We consider chemical reaction networks modeled by a discrete state and continuous in time Markov process for the vector copy number of the species and provide a novel particle filter method for state and parameter estimation ... 
ModelingToolkit: A Composable Graph Transformation System For EquationBased Modeling
Getting good performance out of numerical equation solvers requires that the user has provided stable and efficient functions representing their model. However, users should not be trusted to write good code. In this ... 
Nested Group Testing Procedures for Screening
This article reviews a class of adaptive group testing procedures that operate under a probabilistic model assumption as follows. Consider a set of N items, where item i has the probability p (pi in the generalized group ... 
A generalized model of flocking with steering
(20210207)We introduce and analyze a model for the dynamics of flocking and steering of a finite number of agents. In this model, each agent's acceleration consists of flocking and steering components. The flocking component is a ... 
A High‐Dimensional Classification Rule Using Sample Covariance Matrix Equipped With Adjusted Estimated Eigenvalues
(Wiley Online Library, 20210203)High‐dimensional classification have had challenges mainly due to the singularity issue of the sample covariance matrix. In this work, we propose a different approach to get a more reliable sample covariance matrix by ... 
On Surrogate Learning for Linear Stability Assessment of NavierStokes Equations with Stochastic Viscosity
(20210228)We study linear stability of solutions to the Navier\textendash Stokes equations with stochastic viscosity. Specifically, we assume that the viscosity is given in the form of a~stochastic expansion. Stability analysis ... 
Application of adaptive ANOVA and reduced basis methods to the stochastic StokesBrinkman problem
(20210213)The StokesBrinkman equations model fluid flow in highly heterogeneous porous media. In this paper, we consider the numerical solution of the StokesBrinkman equations with stochastic permeabilities, where the permeabilities ... 
The microglia response to electrical overstimulation of the retina imaged under a transparent stimulus electrode
(IOP, 20210108)Objective: We investigated using the morphological response of retinal microglia as indicators of tissue damage from electrical overstimulation by imaging them through an optically transparent stimulus electrode. Approach: ... 
Adaptively Solving the LocalMinimum Problem for Deep Neural Networks
This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the ... 
Stochastic Approximation Algorithm for Estimating Mixing Distribution for Dependent Observations
Estimating the mixing density of a mixture distribution remains an interesting problem in statistics literature. Using a stochastic approximation method, Newton and Zhang (1999) introduced a fast recursive algorithm for ... 
Incorporating age and delay into models for biophysical systems
In many biological systems, chemical reactions or changes in a physical state are assumed to occur instantaneously. For describing the dynamics of those systems, Markov models that require exponentially distributed interevent ... 
Longtime dynamics of a hingedfree plate driven by a nonconservative force
(20200706)A partially hinged, partially free rectangular plate is considered, with the aim to address the possible unstable end behaviors of a suspension bridge subject to wind. This leads to a nonlinear plate evolution equation ... 
The Simultaneous Assessment of Normality and Homoscedasticity in OneWay Random Effects Models
(Society of Statistics, Computer and Applications, 20200628)The article investigates the simultaneous assessment of normality and homoscedasticity in a oneway random effects model. Test procedures are developed assuming a smooth alternative to the normal distribution, specified ... 
Individual and communitylevel risk for COVID19 mortality in the United States
(Nature, 20201211)Reducing COVID19 burden for populations will require equitable and effective riskbased allocations of scarce preventive resources, including vaccinations. To aid in this effort, we developed a general population risk ... 
LowOrder Model of Biological Neural Networks
(20201212)A biologically plausible loworder model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning ...