Edges and gradients in lightness illusions: Role of optical veiling glare.

Lightness Illusions (Contrast, Assimilation, and Natural Scenes with Edges and Gradients) show that appearances do not correlate with the light sent from the scene to the eye. Lightness Illusions begin with a control experiment that includes two identical Gray Regions-Of-Interest(GrayROI) that have equal appearances in uniform surrounds. The Illusion experiment modifies "the-rest-of-the-scene" to make these GrayROIs appear different from each other.

Preliminary evaluation of a self-guided fall risk assessment tool for older adults.

Falls are a major health problem for older adults with significant physical and psychological consequences. The first step of successful fall prevention is to identify those at risk of falling. Recent technology advancement offers the possibility of objective, lowcost and self-guided fall risk assessment.

Authenticity and credibility aware detection of adverse drug events from social media.

Objectives: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

Authenticity and credibility aware detection of adverse drug events from social media.

Objectives: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

Validating Virtual Time to Contact With Home Based Technology in Young and Older Adults.

Virtual time to contact (VTC) is a measure of postural stability that estimates the virtual time it would take to reach an individual's stability boundary. This study aimed to validate VTC as measured by a depth sensor, and to determine if VTC from the depth sensor distinguishes between older adult fallers and non-fallers compared to a force platform. VTC was assessed in 10 young and 20 older adults by having participants lean in a circular direction followed by five balance tests: eyes open, dual task, eyes open foam, eyes closed, and eyes closed foam.

Wideband compressive beamforming tomography for drive-by large-scale acoustic source mapping.

Noise-mapping is an effective sound visualization tool for the identification of urban noise hotspots, which is crucial to taking targeted measures to tackle environmental noise pollution. This paper develops a high-resolution wideband acoustic source mapping methodology using a portable microphone array, where the joint localization and power spectrum estimation of individual sources sparsely distributed over a large region are achieved by tomographic imaging with the multi-frequency delay-and-sum beamforming power outputs from multiple array positions. Exploiting the fact that a wideband source has a common spatial signal-support across the frequency spectrum, two-dimensional tomographic maps are produced by applying compressive sensing techniques including group least absolute shrinkage selection operator formulation and sparse Bayesian learning to promote group sparsity over multiple frequency bands.

Calculating Retinal Contrast from Scene Content: A Program.

This paper describes a computer program for calculating the contrast image on the human retina from an array of scene luminances. We used achromatic transparency targets and measured test target's luminances with meters. We used the CIE standard Glare Spread Function (GSF) to calculate the array of retinal contrast.

Large-region acoustic source mapping using a movable array and sparse covariance fitting.

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size.

Drive-by large-region acoustic noise-source mapping via sparse beamforming tomography.

Environmental noise is a risk factor for human physical and mental health, demanding an efficient large-scale noise-monitoring scheme. The current technology, however, involves extensive sound pressure level (SPL) measurements at a dense grid of locations, making it impractical on a city-wide scale. This paper presents an alternative approach using a microphone array mounted on a moving vehicle to generate two-dimensional acoustic tomographic maps that yield the locations and SPLs of the noise-sources sparsely distributed in the neighborhood traveled by the vehicle.

Detecting signals of detrimental prescribing cascades from social media.

Motivation: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths.

Representation of Stimulus Speed and Direction in Vibrissal-Sensitive Regions of the Trigeminal Nuclei: A Comparison of Single Unit and Population Responses.

The rat vibrissal (whisker) system is one of the oldest and most important models for the study of active tactile sensing and sensorimotor integration. It is well established that primary sensory neurons in the trigeminal ganglion respond to deflections of one and only one whisker, and that these neurons are strongly tuned for both the speed and direction of individual whisker deflections. During active whisking behavior, however, multiple whiskers will be deflected simultaneously.

A minimum-error, energy-constrained neural code is an instantaneous-rate code.

Sensory neurons code information about stimuli in their sequence of action potentials (spikes). Intuitively, the spikes should represent stimuli with high fidelity. However, generating and propagating spikes is a metabolically expensive process.

A Phase-Locked Loop Epilepsy Network Emulator.

Most seizure forecasting employs statistical learning techniques that lack a representation of the network interactions that give rise to seizures. We present an epilepsy network emulator (ENE) that uses a network of interconnected phase-locked loops (PLLs) to model synchronous, circuit-level oscillations between electrocorticography (ECoG) electrodes. Using ECoG data from a canine-epilepsy model (Davis et al.

Tactile soft-sparse mean fluid-flow imaging with a robotic whisker array.

An array of whiskers is critical to many mammals to survive in their environment. However, current engineered systems generally employ vision, radar or sonar to explore the surroundings, not having sufficiently benefited from tactile perception. Inspired by the whisking animals, we present here a novel tomography-based tactile fluid-flow imaging technique for the reconstruction of surroundings with an artificial whisker array.

A stimulus-dependent spike threshold is an optimal neural coder.

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems.

Defense of Cyber Infrastructures Against Cyber-Physical Attacks Using Game-Theoretic Models.

The operation of cyber infrastructures relies on both cyber and physical components, which are subject to incidental and intentional degradations of different kinds. Within the context of network and computing infrastructures, we study the strategic interactions between an attacker and a defender using game-theoretic models that take into account both cyber and physical components. The attacker and defender optimize their individual utilities, expressed as sums of cost and system terms.

Deterministic identification of specific individuals from GWAS results.

Motivation: Genome-wide association studies (GWASs) are commonly applied on human genomic data to understand the causal gene combinations statistically connected to certain diseases. Patients involved in these GWASs could be re-identified when the studies release statistical information on a large number of single-nucleotide polymorphisms. Subsequent work, however, found that such privacy attacks are theoretically possible but unsuccessful and unconvincing in real settings.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement.

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion.

Emotion modulates eye movement patterns and subsequent memory for the gist and details of movie scenes.

A basic question in vision research regards where people look in complex scenes and how this influences their performance in various tasks. Previous studies with static images have demonstrated a close link between where people look and what they remember. Here, we examined the pattern of eye movements when participants watched neutral and emotional clips from Hollywood-style movies.

Neighborhood repulsed metric learning for kinship verification.

Kinship verification from facial images is an interesting and challenging problem in computer vision, and there are very limited attempts on tackle this problem in the literature. In this paper, we propose a new neighborhood repulsed metric learning (NRML) method for kinship verification. Motivated by the fact that interclass samples (without a kinship relation) with higher similarity usually lie in a neighborhood and are more easily misclassified than those with lower similarity, we aim to learn a distance metric under which the intraclass samples (with a kinship relation) are pulled as close as possible and interclass samples lying in a neighborhood are repulsed and pushed away as far as possible, simultaneously, such that more discriminative information can be exploited for verification.

3D object retrieval using salient views.

This paper presents a method for selecting salient 2D views to describe 3D objects for the purpose of retrieval. The views are obtained by first identifying salient points via a learning approach that uses shape characteristics of the 3D points (Atmosukarto and Shapiro in International workshop on structural, syntactic, and statistical pattern recognition, 2008; Atmosukarto and Shapiro in ACM multimedia information retrieval, 2008). The salient views are selected by choosing views with multiple salient points on the silhouette of the object.

Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.

Craniosynostosis is the premature fusion of the bones of the calvaria resulting in abnormal skull shapes that can be associated with increased intracranial pressure. While craniosynostoses of multiple different types can be easily diagnosed, quantifying the severity of the abnormality is much more subjective and not a standard part of clinical practice. For this purpose we have developed a severity-based retrieval system that uses a logistic regression approach to quantify the severity of the abnormality of each of three types of craniosynostoses.

High-order local spatial context modeling by spatialized random forest.

In this paper, we propose a novel method for spatial context modeling toward boosting visual discriminating power. We are particularly interested in how to model high-order local spatial contexts instead of the intensively studied second-order spatial contexts, i.e.

Image transformation based on learning dictionaries across image spaces.

In this paper, we propose a framework of transforming images from a source image space to a target image space, based on learning coupled dictionaries from a training set of paired images. The framework can be used for applications such as image super-resolution and estimation of image intrinsic components (shading and albedo). It is based on a local parametric regression approach, using sparse feature representations over learned coupled dictionaries across the source and target image spaces.

Discriminative multimanifold analysis for face recognition from a single training sample per person.

Conventional appearance-based face recognition methods usually assume that there are multiple samples per person (MSPP) available for discriminative feature extraction during the training phase. In many practical face recognition applications such as law enhancement, e-passport, and ID card identification, this assumption, however, may not hold as there is only a single sample per person (SSPP) enrolled or recorded in these systems. Many popular face recognition methods fail to work well in this scenario because there are not enough samples for discriminant learning.