This study presents a practically deployable anti-spoofing system for fingerprint biometrics based on electric finger channel response (FCR) to detect falsification attempts using artificial fake fingerprints. Meanwhile, electric signals passed through the human body are particularly vulnerable to distortion from electromagnetic interference (EMI) induced into the body channel by the body antenna effects. The proposed EMI-robust fake fingerprint detection (FFD) employed a neural fully-connected filter trained on the proposed detection features extracted from the amplitude variation and time dispersion characteristics of FCR. A valid FCR dataset for feature analysis was acquired using custom-developed devices in a designated experimental setup involving 20 subjects. Following compatibility verification of the FCR-based FFD with a capacitive-sensing fingerprint scanner in the implemented prototype, the performance evaluation under onsite EMI conditions—primarily modeled as wide-band pulsed-radiated emission and narrow-band sinusoidal wave interferences—showed that the proposed FFD achieved false rejection and acceptance rates of 0.9$\%$and 0.5$\%$, respectively.

This study presents a highly reliable approach to prevent fingerprint spoofing attacks based on electric body pulse responses (BPRs) in personal Internet of Things (IoT) gadgets. Real fingerprint pulse response (RFPR) and fake fingerprint pulse response (FFPR) data were collected from ten subjects for four weeks. The FFPR was obtained by wearing a fake fingerprint made of artificial substances, such as conductive silicone, over the finger. We analyzed different patterns of FFPR compared to RFPR using an electric circuit model of the proposed fingerprint anti-spoofing system based on BPRs. Simple features comprising ten, five, or three datapoints were selected by the minimum redundancy maximum relevance (MRMR) algorithm and led to reduction in processing complexity. We also validated its robustness to sampling offset errors caused by practical sampling operations in devices based on the evaluation of classification accuracy using machine learning algorithms, such as${k}$-nearest neighbor (KNN) and support vector machine (SVM). Finally, the effectiveness of the selected feature was evaluated using unsupervised anomaly detection algorithms, such as principal component analysis (PCA), one-class SVM (OC-SVM), and variational autoencoder (VAE), in a practical scenario with sampling offset errors in the training and test data. The VAE outperformed PCA and OC-SVM by achieving a detection accuracy of 99.76% using raw data under 100 datapoints and 97.60% with reduced features having only five datapoints, regardless of sampling offset errors. Therefore, the proposed anomaly detection system based on EPRs can provide promising fingerprint spoof detection in IoT devices with limited computing resources.

MOTIVATION: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. RESULTS: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. AVAILABILITY AND IMPLEMENTATION: The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. RESULTS: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge. RESULTS: The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer's disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful results. AVAILABILITY: Software is freely available on request.

Alzheimer’s disease (AD) is the most common form of de-mentia that causes progressive impairment of memory and other cognitive functions. Multivariate regression models have been studied in AD for revealing relationships between neuroimaging measures and cognitive scores to understand how structural changes in brain can influence cognitive sta-tus. Existing regression methods, however, do not explic-itly model dependence relation among multiple scores de-rived from a single cognitive test. It has been found that such dependence can deteriorate the performance of these methods. To overcome this limitation, we propose an effi-cient sparse Bayesian multi-task learning algorithm, which adaptively learns and exploits the dependence to achieve improved prediction performance. The proposed algorithm is applied to a real world neuroimaging study in AD to pre-dict cognitive performance using MRI scans. The effective-ness of the proposed algorithm is demonstrated by its supe-rior prediction performance over multiple state-of-the-art competing methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are con-sistent with prior knowledge. 1.

Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Regression analysis has been studied to relate neuroimaging measures to cognitive status. However, whether these measures have further predictive power to infer a trajectory of cognitive performance over time is still an under-explored but important topic in AD research. We propose a novel high-order multi-task learning model to address this issue. The proposed model explores the temporal correlations existing in data features and regression tasks by the structured sparsity-inducing norms. In addition, the sparsity of the model enables the selection of a small number of MRI measures while maintaining high prediction accuracy. The empirical studies, using the baseline MRI and serial cognitive data of the ADNI cohort, have yielded promising results.

MOTIVATION: Recent advances in high-throughput genotyping and brain imaging techniques enable new approaches to study the influence of genetic variation on brain structures and functions. Traditional association studies typically employ independent and pairwise univariate analysis, which treats single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) as isolated units and ignores important underlying interacting relationships between the units. New methods are proposed here to overcome this limitation. RESULTS: Taking into account the interlinked structure within and between SNPs and imaging QTs, we propose a novel Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) method to identify quantitative trait loci for multiple disease-relevant QTs and apply it to a study in mild cognitive impairment and Alzheimer's disease. Built upon regression analysis, our model uses a new form of regularization, group ℓ(2,1)-norm (G(2,1)-norm), to incorporate the biological group structures among SNPs induced from their genetic arrangement. The new G(2,1)-norm considers the regression coefficients of all the SNPs in each group with respect to all the QTs together and enforces sparsity at the group level. In addition, an ℓ(2,1)-norm regularization is utilized to couple feature selection across multiple tasks to make use of the shared underlying mechanism among different brain regions. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected SNP predictors relevant to the imaging QTs. AVAILABILITY: Software is publicly available at: http://ranger.uta.edu/%7eheng/imaging-genetics/.

MOTIVATION: Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic. RESULTS: Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel 'task-correlated longitudinal sparse regression' model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ(2,1)-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs. AVAILABILITY: Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/ CONTACT: [email protected] or [email protected].

We present a dynamic controller to physically simulate under-actuated three-dimensional full-body biped locomotion. Our data-driven controller takes motion capture reference data to reproduce realistic human locomotion through realtime physically based simulation. The key idea is modulating the reference trajectory continuously and seamlessly such that even a simple dynamic tracking controller can follow the reference trajectory while maintaining its balance. In our framework, biped control can be facilitated by a large array of existing data-driven animation techniques because our controller can take a stream of reference data generated on-the-fly at runtime. We demonstrate the effectiveness of our approach through examples that allow bipeds to turn, spin, and walk while steering its direction interactively.

Recent advances in brain imaging and high throughput genotyping techniques enable new approaches to study the influence of genetic variation on brain structure and function. However, major computational challenges are bottlenecks for comprehensive joint analysis of these high-dimensional image and genomic data. We report our initial progress in developing an imaging genomic browsing system for integrated exploration of neuroimaging and genomic data. We describe a method for synthesizing a set of realistic neuroimaging and genomic data, where the relationships between imaging phenotypes and genotypes are known. This data set is used to demonstrate the functionality of our system, which is designed for effectively exploring the neuroanatomical distribution of statistical results that measure the associations between brain imaging phenotypes and genotypes on a genome-wide scale. The proposed system has substantial potential for enabling discovery of important imaging genomic associations through visual evaluation and can be extended towards several directions.

A scheme for enhancing the TCP performance when a handoff occurs within a heterogeneous mobile network is proposed. Currently, the research on the interworking between 3G cellular networks and a WLAN is actively being conducted. To integrate these two technologies, there are many issues such as the network architecture, mobility management, and security which should he solved. Implementing a seamless handoff between WLAN and 3G networks is an essential issue. In this paper, we present some issues to develop a network that supports interworking between WLANs and 3G cellular networks, especially focusing on the TCP challenges. We examine a TCP performance during an intersystem handoff between a WLAN and a 3G cellular network and discuss some problems that the current TCP experiences during intersystem handoff and propose some schemes to improve TCP performance.

Current trends show that 3G cellular network and WLAN will coexist and work together to support more users with higher data rate services over a wider area. In this hybrid mobile network, the WLAN provides high bandwidth data service over a small area while the 3G cellular network provides a higher mobility with lower bandwidth data service. To provide seamless data service to mobile users, vertical handoff between WLAN and 3G cellular network has to be implemented. We present the challenges in the vertical handoff. We propose a TCP scheme for a seamless vertical handoff, and present a performance evaluation. In the proposed scheme, the TCP sender and receiver use the Handoff (HO) option field in TCP header to recognize an impending handoff and a completing handoff. After a vertical handoff, the sender tries to readjust its data rate, since the new network has drastically different characteristics in contrast with horizontal handoff where keeping the same data rate improves performance. The proposed scheme can be implemented incrementally. It is not necessary to change all of the TCP senders for compatibility since it uses an optional field in TCP header.