Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating patterns in retention, continuation, gaps, and re-engagement in HIV care in a Medicaid-insured population, 2006–2012, United States

ABSTRACT We used the US-based MarketScan® Medicaid Multi-state Databases to determine the un-weighted proportion of publically insured persons with HIV that were retained, continued, and re-engaged in care. Persons were followed for up to 84 months. Cox proportional hazards models were conducted to determine factors associated with gaps in care. Of the 6463 HIV cases identified in 2006, 61% were retained during the first 24 months, and 53% continued in care through 78 months. Between 8% and 30% experienced a gap in care, and 59% of persons who experienced a gap in care later re-engaged in care. Persons with one or more Charlson co-morbidities (HR 0.72, 95% CI 0.64–0.81), ages 40–59 (0.79, 0.71–0.88), mental illness diagnosis (0.79, 0.72–0.87), hepatitis C co-infection (0.83, 0.75–0.93), and female sex (0.86, 0.78–0.94) were less likely to experience a gap in care. Between 27% and 38% of those not retained in care continued to receive HIV-related laboratory services. This Medicaid claims database combines features of both clinic visits-based and surveillance lab-based surrogate measures to give a more complete picture of engagement in care than single-facility-based studies.
Ultrastructural evaluation of calcitonin gene‐related peptide immunoreactivity in the human cochlea and vestibular endorgans

Calcitonin gene‐related peptide (CGRP) is a neuropeptide widely distributed in the peripheral and central nervous system. Demonstrated in the efferent systems of the mammalian cochlea and vestibule, immunoreactive patterns of CGRP may vary by species. There is, however, no information in the literature investigating CGRP localization in the human cochlea. In the present study, the ultrastructural localization of CGRP immunoreactivity was evaluated in the human inner ear with immunoelectron microscopy. It was found that, in human cochlea, CGRP immunoreactivity was located in unmyelinated nerve fibres of the spiral lamina, inner spiral fibres beneath inner hair cells, tunnel spiral fibres, tunnel crossing fibres and outer radial fibres. In endorgans of human vestibule, CGRP immunoreactivity was located in vesiculated nerve fibres and bouton‐type nerve terminals which were seen to contact afferent nerve chalices surrounding type I sensory cells and afferent nerve fibres, or to form an en passant contact with afferent dendrites. CGRP immunoreactivity appeared to be confined to efferent systems in all cases. This study presents evidence that CGRP could serve a role in neurotransmission or neuroregulation in both cochlear and vestibular efferent systems of human.
A Fast Solver for HSS Representations via Sparse Matrices

In this paper we present a fast direct solver for certain classes of dense structured linear systems that works by first converting the given dense system to a larger system of block sparse equations and then uses standard sparse direct solvers. The kind of matrix structures that we consider are induced by numerical low rank in the off-diagonal blocks of the matrix and are related to the structures exploited by the fast multipole method (FMM) of Greengard and Rokhlin. The special structure that we exploit in this paper is captured by what we term the hierarchically semiseparable (HSS) representation of a matrix. Numerical experiments indicate that the method is probably backward stable.
Blindsight: eyes-free access to mobile phones

Many mobile phones integrate services such as personal calendars. Given the social nature of the stored data, however, users often need to access such information as part of a phone conversation. In typical non-headset use, this re-quires users to interrupt their conversations to look at the screen. We investigate a counter-intuitive solution: to avoid the need for interruption we replace the visual interface with one based on auditory feedback. Surprisingly, this can be done without interfering with the phone conversation. We present blindSight, a prototype application that replaces the traditionally visual in-call menu of a mobile phone. Users interact using the phone keypad, without looking at the screen. BlindSight responds with auditory feedback. This feedback is heard only by the user, not by the person on the other end of the line. We present the results of two user studies of our prototype. The first study verifies that useful keypress accuracy can be obtained for the phone-at-ear position. The second study compares the blindSight system against a visual baseline condition and finds a preference for blindSight.
Facial biometrics using Akinlolu-Raji image-processing algorithm and anthropological facts which prove that Kebbi and Zamfara Hausas are Hausa Bakwai

Background: Anthropology studies human biology and culture. This study aims to develop a novel image-processing algorithm which can be used to compute cephalometric measurements for face recognition of individuals of any ethnic group or tribe and to produce forensic facial database of Hausas. Materials and Methods: Three hundred Hausas of Kebbi State (150 males and 150 females, aged 18-36 years) were selected as subjects for the study with informed consents and when established as Hausas by parents and grandparents. Height, body weight, and cephalometric parameters (evaluated on three-dimensional facial photographs) were measured on subjects. The novel Akinlolu-Raji image-processing algorithm was developed using modified row method of computer programming. Facial width, total face height, short forehead height, long forehead height, upper face height, nasal bridge length, nose height, morphological face height, and lower face height computed from readings of Akinlolu-Raji image-processing algorithm were analyzed using z-test (P ? 0.05) of 2010 Microsoft Excel statistical software. Results: Biological examination of the anthropological history of Hausas proved that Hausas of Kebbi and Zamfara belong to the Hausa Bakwai States. Comparative statistical analyzes of facial measurements showed sexual dimorphism (P > 0.05) with nonsignificant higher averages in Hausa males compared to females. Hausa males have the leptoprosopic face type while Hausa females have the hyperleptoprosopic face type based on classifications of face types from facial indices. Conclusions: Hausas of Kebbi and Zamfara belong to the Hausa Bakwai States. The novel Akinlolu-Raji image-processing algorithm can be used to compute biometrics for face recognition. Furthermore, sexual dimorphism exists between Hausa males and females.
Inter-organizational Knowledge Transfer in the Buyer-Supplier Relationship: A Buyer's Perspective

We delineate an integrative view of inter-organizational knowledge transfer (IOKT) in the upstream supply chain relationship from a buyer's perspective. For this purpose, we suggest that the concept of IOKT can be classified into the source and the recipient type of knowledge transfer according to the direction of knowledge flow. A three-stage model (motivation-behaviors-performance) is adopted to examine the relationships between IOKT and its antecedent factors, and, in turn, between IOKT and organizational performance. By analyzing 140 buyer-supplier relationships using the partial least squares (PLS) method, we verify our research hypotheses. The results show that each of these two sub-concepts of knowledge transfer has different motivational and resultant mechanism. In addition, we also consider information sharing as another key construct of IOKT-related behavior, examining its relationship with motivational and resultant factors. Contributions of this research and future research opportunities are also discussed
Consensus for formation control of nonholonomic mobile robots

In this article we present novel formation control laws based on artificial potential fields and consensus algorithms for a group of unicycles enabling arbitrary formation patterns for these nonholonomic vehicles. Given connected and balanced graphs we are able to prove stability of the rendezvous controller by applying the LaSalle-Krasovskii invariance principle. Further, we introduce obstacle avoidance, enabling a reactive behavior of the robotic group in unknown environments. The effectiveness of the proposed controllers is shown using computer simulations and finally, a classification w.r. t. existing solutions is done.
Direct Modeling of Complex Invariances for Visual Object Features

View-invariant object representations created from feature pooling networks have been widely adopted in state-of-the-art visual recognition systems. Recently, the research community seeks to improve these view-invariant representations further by additional invariance and receptive field learning, or by taking on the challenge of processing massive amounts of learning data. In this paper we consider an alternate strategy of directly modeling complex invariances of object features. While this may sound like a naive and inferior approach, our experiments show that this approach can achieve competitive and state-of-the-art accuracy on visual recognition data sets such as CIFAR-10 and STL-10. We present an highly applicable dictionary learning algorithm on complex invariances that can be used in most feature pooling network settings. It also has the merits of simplicity and requires no additional tuning. We also discuss the implication of our experiment results concerning recent observations on the usefulness of pre-trained features, and the role of direct invariance modeling in invariance learning.
MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM

Simultaneous Localisation and Mapping (SLAM) systems that recover the trajectory of a robot or mobile device are characterised by a front-end and back-end. The front-end uses sensor observations to identify loop closures; the back-end optimises the estimated trajectory to be consistent with these closures. The GraphSLAM framework formulates the back-end problem as a graph-based optimisation on a pose graph. This paper describes a back-end system optimised for very dense sequence-based loop closures. This arises when the frontend generates magnetic loop closures, among other things. Magnetic measurements are fast varying, which is good for localisation, but the requirement for high sampling rates (50 Hz+) produces many more loop closures than conventional systems. To date, however, there is no study optimising GraphSLAM back-end for sequence-based magnetic loop closures. Hence we introduce a novel variant of the Stochastic Gradient Descent-based SLAM algorithm called MSGD (Magnetic-SGD). We use high-accuracy groundtruth system and extensive real datasets to evaluate MSGD against state-of-the-art back-end algorithms. We demonstrate MSGD is at least as good as the best competitor algorithm in terms of quality, while being faster and more scalable.
SVitchboard 1: small vocabulary tasks from Switchboard

We present a conversational telephone speech data set designed to support research on novel acoustic models. Small vocabulary tasks from 10 words up to 500 words are deﬁned using subsets of the Switchboard-1 corpus; each task has a completely closed vocabulary (an OOV rate of 0%). We justify the need for these tasks, describe the algorithm for selecting them from a large corpus, give a statistical analysis of the data and present baseline whole-word hidden Markov model recognition results. The goal of the paper is to deﬁne a common data set and to encourage other researchers to use it.
Factors Influencing Sexual Satisfaction in Patients with Breast Cancer Participating in a Support Group and Non Support Group

Purpose: This study was to identify the influence of sexual behavior, body image, social support, and other characteristics on sexual satisfaction in patients with breast cancer according to their participation in a support group. Method: Data was collected by self-report questionnaires. Participants included 63 patients attending a support group and 76 patients who did not participate in the support group. The questionnaire sections consisted of sexual satisfaction, sexual behavior, body image, social support and information on general characteristics, disease-related characteristics, and sexual life-related characteristics. Result: There was no statistically significant difference in sexual behavior, body image and sexual satisfaction between the two groups. Social support scores were significantly higher in the support group. Sexual satisfaction was positively related with sexual behavior, post-op change of sexual intercourse frequency, body image, and patient's education level, and negatively related to age in the support group. Sexual satisfaction was positively related with sexual behavior, social support and body image in the non support group. Sexual behavior is predictable 37.0% of sexual satisfaction in the support group. Sexual behavior, body image, and social support is predictable for 38.0% of the sexual satisfaction in non support group participants. Conclusion: Implications point to the need for the development and implementation of programs that focus specifically on sexual life issues for breast cancer patients, as well as further research measuring the effects of such intervention programs. Continuous education and counseling through participation in support groups can contribute to promote and affirm a healthy sexual life for patients with breast cancer.
Boundary elements method computations of 3D transient scattering from lossy dielectric objects

A method for computing the transient scattering from arbitrarily shaped lossy dielectric objects is presented. Starting from Green's function for lossy media and the surface integral equation formulation of electromagnetic scattering, a combination of integral equations which is capable of dealing with the scattering process in the time domain without instability is given. The discretization of the scatterers using eight-noded boundary elements is discussed. Semidiscontinuous boundary elements are used for objects with edges and vertices. Some examples show the applicability of the method. The results are compared with the analytical solution obtained via the Mie series. >
Evaluating Active Learning Strategies for Image Annotation of Intestinal Parasites

Manually annotating large datasets is unfeasible and, to do it automatically with a pattern classifier, it depends on the quality of a much smaller training set. Active learning techniques have been proposed to select those relevant samples from large datasets by prompting an user with label suggestions to be confirmed or corrected. In this work we explore variations of an active learning methodology that, given an organization of the data computed beforehand and only once, allows interactive response times during the active learning process involving an expert. We use the optimum-path forest (OPF) data clustering algorithm for the a priori organization and some combinations of active learning algorithms and classifiers to test the methodology. The active learning algorithms considered are the root distance-based sampling (RDS), a new variation of it that we call root path-weight-based sampling (RWS) and two additional random selection baselines. The included classifiers are the OPF-based supervised and semi-supervised learning methods, and an ensemble of logistic regression classifiers. We tested each combination of active learning and classification algorithm against a dataset extracted from images of intestinal parasites, in which the presence of a large diverse class, namely impurities, mixed with the actual parasites poses a challenge for learning methods.
Malnutrition, Infection and Arteriovenous Fistula Failure: Is There a Link?

Introduction The histology of neointimal hyperplasia, the primary cause of arteriovenous fistula (AVF) stenosis, resembles the histology of atherosclerosis. We evaluated classic atherogenic risk factors such as hypertension, smoking, diabetes, cholesterol, and evaluated the role of expanded risk factors such as: cytomegalovirus (CMV), Helicobacter pylori (H. pylori), Chlamydia pneumoniae (C. pneumoniae), infection, and malnutrition, as possible causes of AVF failure in hemodialysis (HD) patients. Methods AVF of 91 HD patients were monitored by on-line blood flow measurement (Qac); levels of albumin, fibrinogen, C-reactive protein and plasma cholesterol were recorded. Nutrition was evaluated via the Malnutrition Inflammation Score and the normalized protein intake (nPCR). Seropositivity to CMV, C. pneumoniae and H. Pylori were assessed. Results Twenty-one patients had at least one episode of vascular access thrombosis; 17 patients had stenotic lesions. Analysis of survival tables revealed that patients who had high IgG CMV antibody levels had a higher probability of AVF failure than patients with lower CMV antibody levels. The difference in the empirical survival functions was statistically significant when we stratified by CMV antibody levels, unlike H. pylori or C. pneumoniae. In a logistic regression model, CMV, increased cholesterol, and decreases in nPCR and Qac significantly increased the risk of AVF failure. Conclusion Our study suggests that CMV infection, total plasma cholesterol, decreased Qac, and nPCR are important risk factors of AVF failure in HD patients.
Solving the maximum clique problem using PUBB

Given an (undirected) graph G=(V, E), a clique of G is a subset of vertices in which every pair is connected by an edge. The problem of finding a clique of maximum size is a classical NP-hard problem, and many algorithms, both heuristic and exact, have been proposed. While the philosophy behind the heuristic algorithms varies greatly, almost all of the exact algorithms are designed in the branch-and-bound framework. As is well known, branch-and-bound is well suited to parallelization, and PUBB is a software utility which implements a generic version of it. The authors show effectiveness of parallelization of branch-and-bound for the maximum clique problem. Especially, by using PUBB with good heuristics and branching techniques, they were able to solve five previously unsolved DIMACS benchmark problems to optimality.
Dual Hybrid Boundary Node Method for Transient Eddy Current Problem

Dual hybrid boundary node method (DHBNM) is presented for solving transient eddy current problems in the paper. With difference to the traditional boundary element method (BEM), the DHBNM combines dual reciprocity method (DRM) and hybrid boundary node method (HBNM), which does not require the ‘boundary element mesh’, either for the purpose of interpolation of the solution variables, or for the integration of the ‘energy’. In this method, the solution composes into two parts, i. e. , the complementary solution and the particular solution. The complementary solution is solved by HBNM, and the particular one is obtained by DRM. Theoretical analysis in details is given and a transient eddy current example is also presented to prove the proposed theory.
Combining Word Sense and Usage for Modeling Frame Semantics

Models of lexical semantics are core paradigms in most NLP applications, such as dialogue, information extraction and document understanding. Unfortunately, the coverage of currently available resources (e.g. FrameNet) is still unsatisfactory. This paper presents a largely applicable approach for extending frame semantic resources, combining word sense information derived from WordNet and corpus-based distributional information. We report a large scale evaluation over the English FrameNet, and results on extending FrameNet to the Italian language, as the basis of the development of a full FrameNet for Italian.
EvoNF: a framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm will converge and the tuning of fuzzy inference system will be successful. Success of evolutionary search procedures for the optimization of fuzzy inference system is well proven and established in many application areas. In this paper, we explore how the optimization of fuzzy inference systems can be further improved using a meta-heuristic approach combining neural network learning and evolutionary computation. The proposed technique can be considered as a methodology to integrate neural networks, fuzzy inference systems and evolutionary search procedures. We present the theoretical frameworks and some experimental results to demonstrate the efficiency of the proposed technique.
Object Detection and Tracking of Pedestrian for ADAS

Pedestrian protection system in advanced driver assistance system is become active research area in many developed and also developing countries for improving traffic safety. The main challenge of pedestrian detection is development of reliable and affordable on board pedestrian detection system. Due to non-rigid appearance of pedestrians like different clothes, aspect ratio, changing size and dynamic shape etc. and varying environmental conditions, it is very hard to cope with the high robustness of this system. In this survey we explicitly addressed the detail overview of current advances in the field. Image segmentation, feature extraction and subsequent classification process are discussed separately to focus on novelty of recent research. Moreover, we discussed limitation of state of art and promising direction for further work.
Cognitive Effects of ThinkRx Cognitive Rehabilitation Training for Eleven Soldiers with Brain Injury: A Retrospective Chart Review

Cognitive rehabilitation training is a promising technique for remediating the cognitive deficits associated with brain injury. Extant research is dominated by computer-based interventions with varied results. Results from clinician-delivered cognitive rehabilitation are notably lacking in the literature. The current study examined the cognitive outcomes following ThinkRx, a clinician-delivered cognitive rehabilitation training program for soldiers recovering from traumatic brain injury and acquired brain injury. In a retrospective chart review, we examined cognitive outcomes of 11 cases who had completed an average of 80 h of ThinkRx cognitive rehabilitation training delivered by clinicians and supplemented with digital training exercises. Outcome measures included scores from six cognitive skill batteries on the Woodcock Johnson – III Tests of Cognitive Abilities. Participants achieved gains in all cognitive skills tested and achieved statistically significant changes in long-term memory, processing speed, auditory processing, and fluid reasoning with very large effect sizes. Clinically significant changes in multiple cognitive skills were also noted across cases. Results of the study suggest that ThinkRx clinician-delivered cognitive training supplemented with digital exercises may be a viable method for targeting the cognitive deficits associated with brain injury.
The smoke detection for early fire-alarming system base on video processing

The paper presents an smoke-detection method for early fire-alarming system based on video processing. The basic strategy of smoke-pixel judgment is composed of two decision rules: a chromaticity-based static decision rule and a diffusion-based dynamic characteristic decision rule. The chromatic decision rule is deduced by grayish color of smoke and dynamic decision rule is dependent on the spreading attributes of smoke. Experimental results show that the proposed method can provide an early alarm at a lower false alarm rate before the fire burns up, and hence is very attractive for the important military, social security, commercial applications, and so on.