Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Placement of Detectors to Minimize Casualties on a Manmade Attack

This study proposes a mathematical model to optimally locate a set of detectors in such a way that the expected number of casualties in a given threat area can be minimized. Detectors may not be perfectly reliable, which is often a function of how long an attacker would stay within the detectors effective detection radius. To accurately detect any threat event and to avoid any false alarm, we assume that a set of backup/secondary detectors are available to support the primary detectors. The problem is formulated as a nonlinear binary integer programming model and then solved as a linearized branch-and-bound algorithm. A number of sensitivity analyses are performed to illustrate the robustness of the model and to draw key managerial insights. Experimental results reveal that a two-layer detection will significantly minimize the expected number of casualties in a threat area over a one-layer detection problem.
COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms

It is crucial for policymakers to understand the community prevalence of COVID-19 so combative resources can be effectively allocated and prioritized during the COVID-19 pandemic. Traditionally, community prevalence has been assessed through diagnostic and antibody testing data. However, despite the increasing availability of COVID-19 testing, the required level has not been met in parts of the globe, introducing a need for an alternative method for communities to determine disease prevalence. This is further complicated by the observation that COVID-19 prevalence and spread vary across different spatial, temporal, and demographic verticals. In this study, we study trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as a complement to COVID-19 testing reports. This allows us to assess community disease prevalence, even in areas with low COVID-19 testing ability. Using individually reported symptom data from various populations, our method predicts the likely percentage of the population that tested positive for COVID-19. We achieved a mean absolute error (MAE) of 1.14 and mean relative error (MRE) of 60.40% with 95% confidence interval as [60.12, 60.67]. This implies that our model predicts +/- 1140 cases than the original in a population of 1 million. In addition, we forecast the location-wise percentage of the population testing positive for the next 30 days using self-reported symptoms data from previous days. The MAE for this method is as low as 0.15 (MRE of 11.28% with 95% confidence interval [10.9, 11.6]) for New York. We present an analysis of these results, exposing various clinical attributes of interest across different demographics. Lastly, we qualitatively analyze how various policy enactments (testing, curfew) affect the prevalence of COVID-19 in a community.
On maximum-likelihood estimation in the all-or-nothing regime

We study the problem of estimating a rank-1additive deformation of a Gaussian tensor according to the maximum-likelihood estimator (MLE). The analysis is carried out in the sparse setting, where the underlying signal has a support that scales sublinearly with the total number of dimensions. We show that for Bernoulli distributed signals, the MLE undergoes an all-or-nothing (AoN) phase transition, already established for the minimum mean-square-error estimator (MMSE) in the same problem. The result follows from two main technical points: (i) the connection established between the MLE and the MMSE, using the first and second-moment methods in the constrained signal space, (ii) a recovery regime for the MMSE stricter than the simple error vanishing characterization given in the standard AoN, that is here proved as a general result. A full version of this paper is accessible at: https://arxiv.org/pdf/2101.09994. pdf
Non-Equilibrium Entropy and Irreversibility in Generalized Stochastic Loewner Evolution from an Information-Theoretic Perspective

In this study, we theoretically investigated a generalized stochastic Loewner evolution (SLE) driven by reversible Langevin dynamics in the context of non-equilibrium statistical mechanics. Using the ability of Loewner evolution, which enables encoding of non-equilibrium systems into equilibrium systems, we formulated the encoding mechanism of the SLE by Gibbs entropy-based information-theoretic approaches to discuss its advantages as a means to better describe non-equilibrium systems. After deriving entropy production and flux for the 2D trajectories of the generalized SLE curves, we reformulated the system’s entropic properties in terms of the Kullback–Leibler (KL) divergence. We demonstrate that this operation leads to alternative expressions of the Jarzynski equality and the second law of thermodynamics, which are consistent with the previously suggested theory of information thermodynamics. The irreversibility of the 2D trajectories is similarly discussed by decomposing the entropy into additive and non-additive parts. We numerically verified the non-equilibrium property of our model by simulating the long-time behavior of the entropic measure suggested by our formulation, referred to as the relative Loewner entropy.
Black hole fueling in galaxy mergers: A high-resolution analysis

Using parsec scale resolution hydrodynamical adaptive mesh refinement simulations we have studied the mass transport process throughout a galactic merger. The aim of such study is to connect both the peaks of mass accretion rate on to the BHs and star formation bursts with both gravitational and hydrodynamic torques acting on the galactic gaseous component. Our merger initial conditions were chosen to mimic a realistic system. The simulations include gas cooling, star formation, supernovae feedback, and AGN feedback. Gravitational and hydrodynamic torques near pericenter passes trigger gas funneling to the nuclei which is associated with bursts of star formation and black hole growth. Such episodes are intimately related with both kinds of torques acting on the galactic gas. Pericenters trigger both star formation and mass accretion rates of ∼ few (1–10) M⊙ yr−1. Such episodes last ∼ (50–75) Myrs. Close passes also can produce black hole accretion that approaches and reaches the Eddington rate, lasting ∼ few Myrs. Our simulation shows that both gravitational and hydrodynamic torques are enhanced at pericenter passes with gravitational torques tending to have higher values than the hydrodynamic torques throughout the merger. We also find that in the closest encounters, hydrodynamic and gravitational torques can be comparable in their effect on the gas, the two helping in the redistribution of both angular momentum and mass in the galactic disc. Such phenomena allow inward mass transport on to the BH influence radius, fueling the compact object and lighting up the galactic nuclei.
Efficient Evader Detection in Mobile Sensor Networks

Suppose one wants to monitor a domain with sensors each sensing small ball-shaped regions, but the domain is hazardous enough that one cannot control the placement of the sensors. A prohibitively large number of randomly placed sensors would be required to obtain static coverage. Instead, one can use fewer sensors by providing only mobile coverage, a generalization of the static setup wherein every possible intruder is detected by the moving sensors in a bounded amount of time. Here, we use topology in order to implement algorithms certifying mobile coverage that use only local data to solve the global problem. Our algorithms do not require knowledge of the sensors' locations. We experimentally study the statistics of mobile coverage in two dynamical scenarios. We allow the sensors to move independently (billiard dynamics and Brownian motion), or to locally coordinate their dynamics (collective animal motion models). Our detailed simulations show, for example, that collective motion enhances performance: The expected time until the mobile sensor network achieves mobile coverage is lower for the D'Orsogna collective motion model than for the billiard motion model. Further, we show that even when the probability of static coverage is low, all possible evaders can nevertheless be detected relatively quickly by mobile sensors.
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation

Semantic segmentation using convolutional neural networks (CNN) is a crucial component in image analysis. Training a CNN to perform semantic segmentation requires a large amount of labeled data, where the production of such labeled data is both costly and labor intensive. Semi-supervised learning algorithms address this issue by utilizing unlabeled data and so reduce the amount of labeled data needed for training. In particular, data augmentation techniques such as CutMix and ClassMix generate additional training data from existing labeled data. In this paper we propose a new approach for data augmentation, termed ComplexMix, which incorporates aspects of CutMix and ClassMix with improved performance. The proposed approach has the ability to control the complexity of the augmented data while attempting to be semantically-correct and address the tradeoff between complexity and correctness. The proposed ComplexMix approach is evaluated on a standard dataset for semantic segmentation and compared to other state-of-the-art techniques. Experimental results show that our method yields improvement over state-of-the-art methods on standard datasets for semantic image segmentation.
Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data

Automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI) is gaining importance in many medical diagnostic applications. This report presents two improvements for brain neoplasm detection in MRI data: an advanced preprocessing technique is proposed to improve the area of interest in MRI data and a hybrid technique using Convolutional Neural Network (CNN) for feature extraction followed by Support Vector Machine (SVM) for classification. The learning algorithm for SVM is modified with the addition of cost function to minimize false positive prediction addressing the errors in MRI data diagnosis. The proposed approach can effectively detect the presence of neoplasm and also predict whether it is cancerous (malignant) or non-cancerous (benign). To check the effectiveness of the proposed preprocessing technique, it is inspected visually and evaluated using training performance metrics. A comparison study between the proposed classification technique and the existing techniques was performed. The result showed that the proposed approach outperformed in terms of accuracy and can handle errors in classification better than the existing approaches.
Implication of the Temperature-Dependent Charge Barrier Height of Amorphous Germanium Contact Detector in Searching for Rare Event Physics

The exploration of germanium (Ge) detectors with amorphous Ge (a-Ge) contacts has drawn attention to the searches for rare-event physics such as dark matter and neutrinoless double-beta decay. The charge barrier height (CBH) of the a-Ge contacts deposited on the detector surface is crucial to suppress the leakage current of the detector in order to achieve la ow-energy detection threshold and high-energy resolution. The temperature-dependent CBH of a-Ge contacts for three Ge detectors is analyzed to study the bulk leakage current (BLC) characteristics. The detectors were fabricated at the University of South Dakota using homegrown crystals. The CBH is determined from the BLC when the detectors are operated in the reverse bias mode with a guard-ring structure, which separates the BLC from the surface leakage current (SLC). The results show that CBH is temperature dependent. The direct relation of the CBH variation to temperature is related to the barrier inhomogeneities created on the interface of a-Ge and crystalline Ge. The inhomogeneities that occur at the interface were analyzed using the Gaussian distribution model for three detectors. The CBH of a-Ge contact is projected to zero temperature. The implication of the CBH at zero temperature is discussed for Ge detectors with a-Ge contacts in searching for rare-event physics.
Population and Inequality Dynamics in Simple Economies

While the use of spatial agent-based and individual-based models has flourished across many scientific disciplines, the complexities these models generate are often difficult to manage and quantify. This research reduces population-driven, spatial modeling of individuals to the simplest configurations and parameters: an equal resource opportunity landscape with equally capable individuals; and asks the question, "Will valid complex population and inequality dynamics emerge from this simple economic model? " Two foraging economies are modeled: subsistence and surplus. The resulting, emergent population dynamics are characterized by their sensitivities to agent and landscape parameters. The various steady and oscillating regimes of single-species population dynamics are generated by appropriate selection of model growth parameters. These emergent dynamics are shown to be consistent with the equation-based, continuum modeling of single-species populations in biology and ecology. The intrinsic growth rates, carry capacities, and delay parameters of these models are implied for these simple economies. Aggregate measures of individual distributions are used to understand the sensitivities to model parameters. New local measures are defined to describe complex behaviors driven by spatial effects, especially extinctions. This simple economic model is shown to generate significantly complex population and inequality dynamics. Model parameters generating the intrinsic growth rate have strong effects on these dynamics, including large variations in inequality. Significant inequality effects are shown to be caused by birth costs above and beyond their contribution to the intrinsic growth rate. The highest levels of inequality are found during the initial non-equilibrium period and are driven by factors different than those driving steady state inequality.
Numerical issues in maximum likelihood parameter estimation for Gaussian process regression

This article focuses on numerical issues in maximum likelihood parameter estimation for Gaussian process regression (GPR). This article investigates the origin of the numerical issues and provides simple but effective improvement strategies. This work targets a basic problem but a host of studies, particularly in the literature of Bayesian optimization, rely on off-the-shelf GPR implementations. For the conclusions of these studies to be reliable and reproducible, robust GPR implementations are critical.
Hierarchical Domain Invariant Variational Auto-Encoding with weak domain supervision

We address the task of domain generalization, where the goal is to train a predictive model based on a number of domains such that it is able to generalize to a new, previously unseen domain. We choose a generative approach within the framework of variational autoencoders and propose a weakly supervised algorithm that is able to account for incomplete and hierarchical domain information. We show that our method is able to learn representations that disentangle domain-specific information from class-label specific information even in complex settings where an unobserved substructure is present in domains. Our interpretable method outperforms previously proposed generative algorithms for domain generalization and achieves competitive performance compared to state-of-the-art approaches, which are based on complex image-processing steps, on the standard domain generalization benchmark dataset PACS.
A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts

Objective: This work aimed to demonstrate the effectiveness of a hybrid approach based on Sentence BERT model and retrofitting algorithm to compute relatedness between any two biomedical concepts. Materials and Methods: We generated concept vectors by encoding concept preferred terms using ELMo, BERT, and Sentence BERT models. We used BioELMo and Clinical ELMo. We used Ontology Knowledge Free (OKF) models like PubMedBERT, BioBERT, BioClinicalBERT, and Ontology Knowledge Injected (OKI) models like SapBERT, CoderBERT, KbBERT, and UmlsBERT. We trained all the BERT models using Siamese network on SNLI and STSb datasets to allow the models to learn more semantic information at the phrase or sentence level so that they can represent multi-word concepts better. Finally, to inject ontology relationship knowledge into concept vectors, we used retrofitting algorithm and concepts from various UMLS relationships. We evaluated our hybrid approach on four publicly available datasets which also includes the recently released EHR-RelB dataset. EHR-RelB is the largest publicly available relatedness dataset in which 89% of terms are multi-word which makes it more challenging. Results: Sentence BERT models mostly outperformed corresponding BERT models. The concept vectors generated using the Sentence BERT model based on SapBERT and retrofitted using UMLS-related concepts achieved the best results on all four datasets. Conclusions: Sentence BERT models are more effective compared to BERT models in computing relatedness scores in most of the cases. Injecting ontology knowledge into concept vectors further enhances their quality and contributes to better relatedness scores.
Deep Learning Generalization and the Convex Hull of Training Sets

We study the generalization of deep learning models in relation to the convex hull of their training sets. A trained image classifier basically partitions its domain via decision boundaries and assigns a class to each of those partitions. The location of decision boundaries inside the convex hull of training set can be investigated in relation to the training samples. However, our analysis shows that in standard image classification datasets, all testing images are considerably outside that convex hull, in the pixel space, in the wavelet space, and in the internal representations learned by deep networks. Therefore, the performance of a trained model partially depends on how its decision boundaries are extended outside the convex hull of its training data. From this perspective which is not studied before, over-parameterization of deep learning models may be considered a necessity for shaping the extension of decision boundaries. At the same time, over-parameterization should be accompanied by a specific training regime, in order to yield a model that not only fits the training set, but also its decision boundaries extend desirably outside the convex hull. To illustrate this, we investigate the decision boundaries of a neural network, with various degrees of parameters, inside and outside the convex hull of its training set. Moreover, we use a polynomial decision boundary to study the necessity of over-parameterization and the influence of training regime in shaping its extensions outside the convex hull of training set.
Computational Workflows for Designing Input Devices

Input devices, such as buttons and sliders, are the foundation of any interface. The typical user-centered design workflow requires the developers and users to go through many iterations of design, implementation, and analysis. The procedure is inefficient, and human decisions highly bias the results. While computational methods are used to assist various design tasks, there has not been any holistic approach to automate the design of input components. My thesis proposed a series of Computational Input Design workflows: I envision a sample-efficient multi-objective optimization algorithm that cleverly selects design instances, which are instantly deployed on physical simulators. A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators. The new workflows derive Pareto-optimal designs with high efficiency and automation. I demonstrate designing a push-button via the proposed methods. The resulting designs outperform the known baselines. The Computational Input Design process can be generalized to other devices, such as joystick, touchscreen, mouse, controller, etc.
Generating Black-Box Adversarial Examples in Sparse Domain

Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many applications, recent investigations show that such networks are highly vulnerable to adversarial attacks. The black-box adversarial attack is one type of attack that the attacker does not have any knowledge about the model or the training dataset, but it has some input data set and their labels. In this paper, we propose a novel approach to generate a black-box attack in sparse domain whereas the most important information of an image can be observed. Our investigation shows that large sparse (LaS) components play a critical role in the performance of image classifiers. Under this presumption, to generate adversarial example, we transfer an image into a sparse domain and put a threshold to choose only <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components. In contrast to the very recent works that randomly perturb <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> low frequency (LoF) components, we perturb <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components either randomly (query-based) or in the direction of the most correlated sparse signal from a different class. We show that LaS components contain some middle or higher frequency components information which leads fooling image classifiers with a fewer number of queries. We demonstrate the effectiveness of this approach by fooling six state-of-the-art image classifiers, the TensorFlow Lite (TFLite) model of Google Cloud Vision platform, and YOLOv5 model as an object detection algorithm. Mean squared error (MSE) and peak signal to noise ratio (PSNR) are used as quality metrics. We also present a theoretical proof to connect these metrics to the level of perturbation in the sparse domain.
MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for FMCW Automotive Radar

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform (see Fig. 1).