Miracle-dz/UniFAR-FTUs huggingface dataset:
LPSM: Fitting shape model by linear programming

We propose a shape model fitting algorithm that uses linear programming optimization. Most shape model fitting approaches (such as ASM, AAM) are based on gradient-descent-like local search optimization and usually suffer from local minima. In contrast, linear programming (LP) techniques achieve globally optimal solution for linear problems. In [1], a linear programming scheme based on successive convexification was proposed for matching static object shape in images among cluttered background and achieved very good performance. In this paper, we rigorously derive the linear formulation of the shape model fitting problem in the LP scheme and propose an LP shape model fitting algorithm (LPSM). In the experiments, we compared the performance of our LPSM with the LP graph matching algorithm(LPGM), ASM, and a CONDENSATION based ASM algorithm on a test set of PUT database. The experiments show that LPSM can achieve higher shape fitting accuracy. We also evaluated its performance on the fitting of some real world face images collected from internet. The results show that LPSM can handle various appearance outliers and can avoid local minima problem very well, as the fitting is carried out by LP optimization with l1 norm robust cost function.
Accurate Dynamic Modeling of Helical Ionic Polymer-Metal Composite Actuator Based on Intrinsic Equations

This paper presents dynamic modeling of an innovative contractible ionic polymer-metal composites (IPMC) actuator with a helical configuration. The helical shaped IPMC actuator is fabricated through the thermal treatment of an IPMC strip, which is helically coiled on a glass rod. This type of a soft actuator can be used to realize not only bending motion but also torsional and longitudinal motion. For the first time, an explicit analytical expression is developed for the computation of mode shapes and dynamic responses of a helical IPMC actuator based on the intrinsic equations of the naturally curved and twisted beam. The numerical transfer-matrix method is used to solve the systems of 12 linear ordinary differential equations with boundary conditions. In particular, the effect of the structural parameters on the performance of the helical IPMC actuator is evaluated, using experimental results and an analytical model. The validation of the proposed model is achieved through comparison with computational results using a commercial finite-element (FE) program as well as experimental results. The present experimental and theoretical results show that diameter, among the structural parameters, plays an important role in the actuation performance of a helical IPMC actuator. The proposed modeling is general and can also be used in solving other cylindrical or noncylindrical helical IPMC actuators with different cross-sectional shapes as well as various end conditions.
The Genetic Contribution of West-African Ancestry to Protection against Central Obesity in African-American Men but Not Women: Results from the ARIC and MESA Studies

Over 80% of African-American (AA) women are overweight or obese. A large racial disparity between AA and European-Americans (EA) in obesity rates exists among women, but curiously not among men. Although socio-economic and/or cultural factors may partly account for this race-by-sex interaction, the potential involvement of genetic factors has not yet been investigated. Among 2814 self-identified AA in the Atherosclerosis Risk in Communities study, we estimated each individual's degree of West-African genetic ancestry using 3437 ancestry informative markers. We then tested whether sex modifies the association between West-African genetic ancestry and body mass index (BMI), waist-circumference (WC), and waist-to-hip ratio (WHR), adjusting for income and education levels, and examined associations of ancestry with the phenotypes separately in males and females. We replicated our findings in the Multi-Ethnic Study of Atherosclerosis (n = 1611 AA). In both studies, we find that West-African ancestry is negatively associated with obesity, especially central obesity, among AA men, but not among AA women (pinteraction = 4.14 × 10−5 in pooled analysis of WHR). In conclusion, our results suggest that the combination of male gender and West-African genetic ancestry is associated with protection against central adiposity, and suggest that the large racial disparity that exists among women, but not men, may be at least partly attributed to genetic factors.
Learning Hypotheses from Triadic Labeled Data

We propose extensions of the classical JSM-method and the Naive Bayesian classifier for the case of triadic relational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also evaluated the time performance of the proposed methods.
Comparative Analysis of Textural Features Derived from GLCM for Ultrasound Liver Image Classification

— Comparative analysis of nine textural feature measures derived from gray-level co-occurrence matrix obtained from the region(s) of interest (ROI) among the normal and abnormal anatomical structures that appear in the patient’s ultrasound liver images is presented in this paper. S election of the most robust discriminating features for classification experiment is performed through analysis of each feature classes’ separability power. The results analysis shows that cluster prominence, cluster shade, maximum probability, and entropy have high classes’ separability power and were selected for the classification of liver ultrasound images into normal liver (NL), primary liver cell carcinoma (PLCC) and hepatocellular carcinoma (HCC) at 0.4, 0.4, 0.2 and 0.6 sensitivity respectively.
Estimation method of initial labels for propagation-based saliency detection

We propose an estimation method of initial labels based on scale-invariant feature transform (SIFT), high dimensional color transform (HDCT), and machine learning for propagation-based saliency detection. The label propagation strategy is efficient for saliency detection, but its accuracy depends on the distribution of initial labels. In this paper, the proposed method respectively estimates initial labels of fore/background based on the machine learning with HDCT and the density of SIFT feature points. Consequently, initial labels are certainly distributed all over the region and inaccurate saliencies are attenuated in resultant saliency maps. Through simulations, we show that the proposed method outperforms the state-of-the-art methods objectively and perceptually.
Representative Critical Reliability Paths for low-cost and accurate on-chip aging evaluation

Aging of transistors degrades circuit performance and can potentially lead to functional failure in the field. This has become a major reliability concern especially when technology further scales to 45 nm and below. It is thus necessary to design on-chip structures that can provide accurate aging evaluation with no performance penalty. In this paper, we propose a novel methodology to accurately evaluate aging in the field. Representative Critical Reliability Paths (RCRP-s) are synthesized as a stand-alone circuit to represent the aging of critical reliability paths, which are defined as paths that can potentially become critical at some point in time due to aging. By monitoring the RCRPs, aging of the critical reliability paths can be efficiently and accurately evaluated with no impact on the normal operation of the chip. The aging evaluation results can then be exploited to guide on-chip performance calibration to ensure lifetime reliability. Simulation results demonstrate the efficiency of the proposed structure.
Computational aspects of distributed optimization in model predictive control

This paper presents a systematic computational study on the performance of distributed optimization in model predictive control (MPC). We consider networks of dynamically coupled systems, which are subject to input and state constraints. The resulting MPC problem is structured according to the system's dynamics, which makes the problem suitable for distributed optimization. The influence of fundamental aspects of distributed dynamic systems on the performance of two particular distributed optimization methods is systematically analyzed. The methods considered are dual decomposition based on fast gradient updates (DDFG) and the alternating direction method of multipliers (ADMM), while the aspects analyzed are coupling strength, stability, initial state, coupling topology and network size. The methods are found to be sensitive to coupling strength and stability, but relatively insensitive to initial state and topology. Moreover, they scale well with the number of subsystems in the network.
Crazy Mad Nutters: The Language of Mental Health

Many people with mental illnesses face challenges posed by stigma perpetuated by fear and misconception in society at large. This societal stigma against mental health conditions is present in everyday language. In this study we take a set of 14 words with the potential to stigmatize mental health and sample Twitter as an approximation of contemporary discourse. Annotation reveals that these words are used with different senses, from expressive to stigmatizing to clinical.We use these wordsense annotations to extract a set of mental health–aware Twitter users, and compare their language use to that of an ageand gendermatched comparison set of users, discovering a difference in frequency of stigmatizing senses as well as a change in the target of pejorative senses. Such analysis may provide a first step towards a tool with the potential to help everyday people to increase awareness of their own stigmatizing language, and to measure the effectiveness of anti-stigma campaigns to change our discourse.
Distance‐weighted discrimination of face images for gender classification

We illustrate the advantages of distance‐weighted discrimination for classification and feature extraction in a high‐dimension low sample size (HDLSS) situation. The HDLSS context is a gender classification problem of face images in which the dimension of the data is several orders of magnitude larger than the sample size. We compare distance‐weighted discrimination with Fisher's linear discriminant, support vector machines and principal component analysis by exploring their classification interpretation through insightful visuanimations and by examining the classifiers' discriminant errors. This analysis enables us to make new contributions to the understanding of the drivers of human discrimination between men and women. Copyright © 2017 John Wiley & Sons, Ltd.
Slice embedding solutions for distributed service architectures

Network virtualization provides a novel approach to running multiple concurrent virtual networks over a common physical network infrastructure. From a research perspective, this enables the networking community to concurrently experiment with new Internet architectures and protocols. From a market perspective, on the other hand, this paradigm is appealing as it enables infrastructure service providers to experiment with new business models that range from leasing virtual slices of their infrastructure to hosting multiple concurrent network services. In this article, we present the slice embedding problem and recent developments in the area. A slice is a set of virtual instances spanning a set of physical resources. The embedding problem consists of three main tasks: (1) resource discovery, which involves monitoring the state of the physical resources, (2) virtual network mapping, which involves matching users' requests with the available resources, and (3) allocation, which involves assigning the resources that match the users' requests. We also outline how these three tasks are tightly coupled, and how there exists a wide spectrum of solutions that either solve a particular task or jointly solve multiple tasks along with the interactions among them. To dissect the space of solutions, we introduce three main classification criteria, namely: (1) the type of constraints imposed by the user, (2) the type of dynamics considered in the embedding process, and (3) the allocation strategy adopted. Finally, we conclude with a few interesting research directions.
Characterizing Guidance in Visual Analytics

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.
Healthy Option Preferences of Rural Restaurant Customers

In preparation for an intervention study in three rural Iowa restaurants, 250 customers were surveyed regarding their interest in dietary change, perceptions of the restaurant, and interest in healthy options. Customers were ages 18 to 88, with a mean age of 52, and 53% were women. Most agreed that the restaurant offers healthy meals. Options customers stated they were most likely to order if available included meat that is baked or broiled, whole-wheat bread, fresh fruit or steamed vegetables, and regular salad dressing on the side. They were least interested in low-fat sour cream, low-fat salad dressing, low-fat milk, low-calorie dessert, and holding high-fat ingredients. Women were more likely to indicate interest in healthy options than were men. Interest in several options was also positively associated with age. Increasing the healthy options in restaurants may be especially effective in changing the dietary intake of women and older adults.
Calculation of the Kullback-Leibler distance between point process models

We have developed a method for quantifying neural response changes in terms of the Kullback-Leibler distance between the intensity functions for each stimulus condition. We use empirical histogram estimates to characterize the intensity function of the neural response. A critical factor in determining the histogram estimates is selection of bin-width. We analytically derive the Kullback-Leibler distance between two Poisson processes and two dead time modified Poisson processes in terms of the bin-width selected. Our results show that, for constant intensity processes having the same number of expected counts, the distance between the dead time modified processes is larger than between the Poisson processes.
Mechanical and dynamical stability of TiAsTe compound from ab initio calculations

The first-principles calculations are employed to provide a fundamental understanding of the structural features and relative thermodynamical, mechanical and phonon stability of TiAsTe compound. The calculated lattice parameters are in good agreement with available experimental results. We have computed elastic constants, its derived moduli and ratios that characterize mechanical properties for the first time. The calculated elastic constants indicate that these materials are mechanically stable at ambient condition. The minimum thermal conductivities of TiAsTe are calculated using both Clarke’s model and Cahill’s model. Furthermore, the elastic anisotropy has been visualized in detail by plotting the directional dependence of compressibility, Young’s modulus and shear modulus. Our results suggest strong elastic anisotropy for this compound. Additionally, the phonon spectra and phonon density of states are also obtained and discussed. The full phonon dispersion calculations confirm the dynamic stability of TiAsTe.
The biochemical and neurologic basis for the treatment of male erectile dysfunction.

Male erectile dysfunction (MED) is defined as the “inability to achieve or maintain an (penile) erection adequate for sexual satisfaction” ([National Institutes of Health Consensus Statement, 1993][1]). Erectile dysfunction occurs in varying degrees in an estimated 20 to 30 million U.S. men and
A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

This study presents a progressive FastICA peel-off (PFP) framework for high-density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a peel-off strategy, i.e., removal of the estimated motor unit action potential trains from the previous step, is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. A constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and SNRs (20, 10, and 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous muscle contraction at different intensities. On average 14.1 ±5.0 motor units were identified from each trial of experimental surface EMG signals.
Relationship learning as a dimension of relationship quality : tentative evidence from transnational buyer-supplier relationships

While relationship learning has been addressed in marketing theory, it has not yet been explored as a possible dimension of the second-order construct of relationship quality (RQ). This construct has so far been mostly conceptualized to consist of trust and commitment, sometimes also satisfaction; however, some see the latter more as a consequence of the former two. Additionally, while RQ and its multidimensionality have been researched in the marketing literature, this area has remained virtually unexplored in the supply and operations management literature. The purpose of this paper is to analyze the multidimensional nature of the second-order construct of RQ in a particular setting of transnational corporation (TNC) buyer-supplier relationships. Our paper aims to determine if relationship learning can be considered an important dimension of RQ, alongside trust and commitment. In our study of relationship learning, we follow Jean, Sinkovics and Kim (2010) and Jean and Sinkovics (2010), who have focused on the governance aspect of relationship learning in managing supply performance outcomes. We employ an indirect testing approach by testing RQ as a second-order reflective construct comprised of trust, commitment and relationship within a simple variance-based Structural Equation Modeling (SEM). Our results confirm that relationship learning is an important dimension of the second-order construct of RQ. This was done on a tentative sample of 11 TNC purchasing managers and 55 evaluated suppliers, albeit with some research limitations which we acknowledge. Our research calls for additional cross-validation of our tentative results.
Clinical proteomics in obstetrics and neonatology

Clinical proteomics has been applied to the identification of biomarkers of obstetric and neonatal disease. We will discuss a number of encouraging studies that have led to potentially valid biomarkers in the context of Down's syndrome, preterm birth, amniotic infections, preeclampsia, intrauterine growth restriction and obstructive uropathies. Obtaining noninvasive biomarkers (e.g., from the maternal circulation, urine or cervicovaginal fluid) may be more feasible for obstetric diseases than for diseases of the fetus, for which invasive methods are required (e.g., amniotic fluid, fetal urine). However, studies providing validated proteomics-identified biomarkers are limited. Efforts should be made to save well-characterized samples of these invasive body fluids so that many valid biomarkers of pregnancy-related diseases will be identified in the coming years using proteomics based analysis upon adoption of ‘clinical proteomics guidelines’.
Comparison of Wall Pressures Measured in the Model Silo with DEM Simulation

Wall stresses are very important parameter in processes run in silos and tanks. DEM method appeared to be a very useful numerical tool in simulation of granular materials [1]. The paper presents the comparison of measured wall stresses exerted by 20,400 pea grains with DEM simulation in the model of silo by the DEMMAT code [2]. Two sensors of 55 mm in diameter were placed on the lateral wall and wall stresses were registered.
Analysis on Fatigue Damage of Metal Rubber Vibration Isolator

The fatigue damage test of metal rubber vibration isolator is performed, the result of which shows that fatigue damage performance of metal rubber is different from conventional solid materials. The damage of metal rubber which is accumulated with its inner partial broken and wore wires, results in deterioration of its macro-mechanical characteristic, macro-cracking and final fatigue failure, so it does not rupture as sudden failure of solid material. It is difficult to describe damage evolution of metal rubber in the microscopic perspective, and from a macro point of view, it is feasible method to describe degree of fatigue damage which bases on the changes of mechanical properties. In order to reflect the incremental cumulative damage degree of metal rubber, the stiffness damage and damping damage are used to accurately describe the fatigue damage process of metal robber, and the comprehensive damage factor is proposed to be the fatigue failure criterion. Damage factor can predict fatigue life of metal rubber specimen accurately, so it has reference significance to design of metal rubber
A Generic Open World Named Entity Disambiguation Approach for Tweets

Social media is a rich source of information. To make use of this information, it is sometimes required to extract and disambiguate named entities. In this paper we focus on named entity disambiguation (NED) in twitter messages. NED in tweets is challenging in two ways. First, the limited length of Tweet makes it hard to have enough context while many disambiguation techniques depend on it. The second is that many named entities in tweets do not exist in a knowledge base (KB). In this paper we share ideas from information retrieval (IR) and NED to propose solutions for both challenges. For the first problem we make use of the gregarious nature of tweets to get enough context needed for disambiguation. For the second problem we look for an alternative home page if there is no Wikipedia page represents the entity. Given a mention, we obtain a list of Wikipedia candidates from YAGO KB in addition to top ranked pages from Google search engine. We use Support Vector Machine (SVM) to rank the candidate pages to find the best representative entities. Experiments conducted on two data sets show better disambiguation results compared with the baselines and a competitor.