Miracle-dz/UniFAR-FTUs huggingface dataset:
Atrial Fibrillation Genetic Risk and Ischemic Stroke Mechanisms

Background and Purpose— Atrial fibrillation (AF) is a leading cause of cardioembolic stroke, but the relationship between AF and noncardioembolic stroke subtypes are unclear. Because AF may be unrecognized, and because AF has a substantial genetic basis, we assessed for predisposition to AF across ischemic stroke subtypes. Methods— We examined associations between AF genetic risk and Trial of Org 10172 in Acute Stroke Treatment stroke subtypes in 2374 ambulatory individuals with ischemic stroke and 5175 without from the Wellcome Trust Case-Control Consortium 2 using logistic regression. We calculated AF genetic risk scores using single-nucleotide polymorphisms associated with AF in a previous independent analysis across a range of preselected significance thresholds. Results— There were 460 (19.4%) individuals with cardioembolic stroke, 498 (21.0%) with large vessel, 474 (20.0%) with small vessel, and 814 (32.3%) individuals with strokes of undetermined cause. Most AF genetic risk scores were associated with stroke, with the strongest association (P=6×10−4) attributed to scores of 944 single-nucleotide polymorphisms (each associated with AF at P<1×10−3 in a previous analysis). Associations between AF genetic risk and stroke were enriched in the cardioembolic stroke subset (strongest P=1.2×10−9, 944 single-nucleotide polymorphism score). In contrast, AF genetic risk was not significantly associated with noncardioembolic stroke subtypes. Conclusions— Comprehensive AF genetic risk scores were specific for cardioembolic stroke. Incomplete workups and subtype misclassification may have limited the power to detect associations with strokes of undetermined pathogenesis. Future studies are warranted to determine whether AF genetic risk is a useful biomarker to enhance clinical discrimination of stroke pathogeneses.
Pattern-Reconfigurable Antenna With Five Switchable Beams in Elevation Plane

Pattern-reconfigurable antennas with multiple switchable beams, especially with both boresight and endfire directions, are highly desired for wireless communications. In this letter, a novel pattern-reconfigurable antenna is proposed that provides an efficient solution. By reconfiguring parasitic striplines placed around a radiating dipole and reflecting metal pieces under the dipole using p-i-n diodes, the antenna main beam can be switched to five directions in the elevation plane, approximately from <inline-formula><tex-math notation="LaTeX">$-90^{\circ }$ </tex-math></inline-formula> (left endfire), <inline-formula><tex-math notation="LaTeX">$-45^{\circ }$</tex-math> </inline-formula>, 0<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> (boresight), <inline-formula><tex-math notation="LaTeX">$+45^{\circ }$</tex-math></inline-formula>, to <inline-formula> <tex-math notation="LaTeX">$+90^{\circ }$</tex-math></inline-formula> (right endfire). The proposed antenna operates at 2.45 GHz with dimensions of about 0.57<inline-formula><tex-math notation="LaTeX">$\lambda$</tex-math> </inline-formula><inline-formula><tex-math notation="LaTeX">$\times\, 0.45\lambda$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$\times\, 0.28\lambda$</tex-math></inline-formula>. An antenna prototype is fabricated and measured. For all five directional beams, the measured <inline-formula><tex-math notation="LaTeX"> $|S_{11}|$</tex-math></inline-formula> values are below <inline-formula><tex-math notation="LaTeX">$-$</tex-math> </inline-formula>13 dB, and the measured realized gains range from 5.2 to 6.5 dBi. They agree reasonably well with the simulated ones.
Mourning the support of women postpartum: The experiences of migrants in Cape Town, South Africa

ABSTRACT The maintenance of social networks amongst migrant diasporas has been previously emphasized. When caring for a new baby in particular, however, hands-on social supports are needed. These social supports are poorly understood for migrants. This qualitative study of maternal postpartum support included 23 in-depth interviews with postpartum migrant women and nine focus groups with adult men and women (N = 48) in Cape Town. The absence of nonworking women specifically, and social support generally, was central to migrants' descriptions of stress and infant feeding. The absence of elder and other nonworking women in migrant contexts may add vulnerability to already marginal communities.
An Integrated Model of Phenotypic Trait Changes and Site‐Specific Sequence Evolution

Abstract. — Recent years have seen a constant rise in the availability of trait data, including morphological features, ecological preferences, and life history characteristics. These phenotypic data provide means to associate genomic regions with phenotypic attributes, thus allowing the identification of phenotypic traits associated with the rate of genome and sequence evolution. However, inference methodologies that analyze sequence and phenotypic data in a unified statistical framework are still scarce. Here, we present TraitRateProp, a probabilistic method that allows testing whether the rate of sequence evolution is associated with a binary phenotypic character trait. The method further allows the detection of specific sequence sites whose evolutionary rate is most noticeably affected following the character transition, suggesting a shift in functional/structural constraints. TraitRateProp is first evaluated in simulations and then applied to study the evolutionary process of plastid plant genomes upon a transition to a heterotrophic lifestyle. To this end, we analyze 20 plastid genes across 85 orchid species, spanning different lifestyles and representing different genera in this large family of flowering plants. Our results indicate higher evolutionary rates following repeated transitions to a heterotrophic lifestyle in all but four of the loci analyzed. [Evolutionary models; evolutionary rate; genotype‐phenotype; orchids; plastome; rate shift. ]
Profiling students from their questions in a blended learning environment

Automatic analysis of learners' questions can be used to improve their level and help teachers in addressing them. We investigated questions (N=6457) asked before the class by 1st year medicine/pharmacy students on an online platform, used by professors to prepare their on-site Q&A session. Our long-term objectives are to help professors in categorizing those questions, and to provide students with feedback on the quality of their questions. To do so, first we manually categorized students' questions, which led to a taxonomy then used for an automatic annotation of the whole corpus. We identified students' characteristics from the typology of questions they asked using K-Means algorithm over four courses. The students were clustered by the proportion of each question asked in each dimension of the taxonomy. Then, we characterized the clusters by attributes not used for clustering such as the students' grade, the attendance, the number and popularity of questions asked. Two similar clusters always appeared: a cluster (A), made of students with grades lower than average, attending less to classes, asking a low number of questions but which are popular; and a cluster (D), made of students with higher grades, high attendance, asking more questions which are less popular. This work demonstrates the validity and the usefulness of our taxonomy, and shows the relevance of this classification to identify different students' profiles.
Minimum spanning tree based stereo matching using image edge and brightness information

In this paper, we propose a MST-based stereo matching method using image edge and brightness information due to the classical MST based methods were used to produce the inaccurate matching weight in the areas of image boundaries and similar color background. Firstly, we applied the bilateral filtering to the original images to restrain the image noise and preserve the image edges. Secondly, we planned the self-adaptive weight function by combining the image edges and brightness information to improve the accuracy of the matching cost computation. Finally, we compute the disparity and refine the result according to the cost aggregation. The standard image sets of Middlebury database are employed to indicate the performances of the proposed and the NLCA method. The experimental results show the percentage of bad matching pixels of the proposed method were reduced 5.93% & 5.32% compared to those of the NLCA method with the Middlebury data. The compared results demonstrate that the proposed method has the higher matching accuracy and better robustness.
A Comparison of En Face Optical Coherence Tomography and Fundus Autofluorescence in Stargardt Disease

Purpose To compare morphologic changes on en face images derived from wide-field swept-source optical coherence tomography (ssOCT) to hypo- and hyperautofluorescent (hypoAF, hyperAF) areas on short-wavelength autofluorescence (SW-AF), and near-infrared (NIR)-AF in recessive Stargardt disease (STGD1). Methods Wide-field ssOCT cube scans were obtained from 16 patients (16 eyes). Averaged B-scans and SW-AF images were obtained using Spectralis HRA+OCT. NIR-AF images were obtained from 6 eyes. The inner/outer segment (IS/OS), OS/RPE, and RPE/Bruch's membrane boundaries were segmented, and en face slab images generated. A subRPE slab image was used to measure the abnormal RPE area, and an IS/OS slab image, the IS/OS junction loss area. These were compared to hypo- and abnormal SW-AF areas, and hypoNIR-AF areas. A preRPE(OS) slab image was used to evaluate the spatial and intraretinal locations of flecks. Results For all eyes, RPE atrophy was visualized as a central hyperreflective area on the subRPE slab, and IS/OS junction loss as an abnormal reflective area on the IS/OS slab; the latter was significantly larger (P = 0.04). There was good agreement between the hyperreflective area on the subRPE slab image and hypoSW-AF area, and between the abnormal reflective area on the IS/OS slab and hypo-hyperSW-AF area; the hypoNIR-AF area indicated that the hyperreflective area on the subRPE slab underestimated RPE atrophy. The spatial locations of hyperreflective flecks on the en face preRPE(OS) slab image corresponded to those on the SW-AF images. Conclusions Wide-field en face OCT imaging has the potential to be a clinically useful tool for the management of STGD1.
onflicts of interest distort public evaluations : Evidence from CAA football coaches

This paper investigates how private incentives distort public evaluations. Exploiting a unique empirical setting, we study the influence of conflicts of interest among NCAA football coaches participating in the USA Today Coaches Poll of the top 25 teams from 2005 to 2010. The research design takes advantage of a situation where many agents are evaluating the same thing, private incentives are clearly defined and measurable, and there exists an alternative source of computer rankings that is bias free. We find evidence that coaches distort their rankings to reflect their own financial and reputational interests. Most importantly, we find that coaches show more favoritism toward their own team when they stand to gain more financially and toward other teams when it generates higher direct financial payoffs for their own university. Additionally, coaches boost the ranking of their own team and teams from their same athletic conference, even after accounting for direct financial incentives. Coaches also rank teams they defeated more favorably, thereby making their own team look better. © 2014 Elsevier B.V. All rights reserved.
Forecast customer flow using long short-term memory networks

Customer flow forecast is of practical importance in business intelligence domain. This paper particularly investigates an interesting issue, i.e., how to forecast off-line customer flow for over two thousand shops by considering both online customer behaviors and off-line periodic customer behaviors. Apparently, it is difficult to directly model these underlying dependent variables via traditional regression models. To this end, the proposed approach first introduces various extra information to incorporate more underlying factors. Then, the hierarchical linear model is performed to screen out insignificant factors. On the basis of this reduced feature space, the second-order flow factor is incorporated to model the variance term. The combined new feature set is then used for the learning of a number of Long Short Term Memory (LSTM) models. The rigorous experiments have been performed and the promising results demonstrate the superiority of the proposed approach which indicates the wide applicability of the proposed forecast model.
Depth super-resolution via fully edge-augmented guidance

Recently, convolutional neural networks (CNNs) have been widely used for image processing problems. In this work, we present an end-to-end depth map super-resolution method based on CNN. Standing on a residual learning architecture, the proposed network learns joint features to get a high-resolution (HR) depth map from a low-resolution (LR) one with the multi-layers guidance of a HR color image. Furthermore, in order to focus on the boundaries of depth map, we generate an edge-attention map from the associated HR color images as a guidance. Experimental results show that the proposed network outperforms the state-of-the-art depth map super-resolution methods.
Diversified shared latent structure based localization for blind persons

Indoor localization systems for blind person aims to help visually impaired people localize themselves in indoor environments. Most approaches employ the RGBD camera and LIDAR for accurate localization, yet these devices are not cheap and portable for blind persons. Instead, WiFi signals are quite ubiquitous in most indoor areas, like shopping mall, hospital etc. Therefore, we propose a diversified shared latent variable model that exploits the availability of WiFi for localization. More specifically, the observation spaces in our model, WiFi strength measurements and their corresponding locations, share a single and reduced dimensionality latent space. By building and incorporating a kernel based diversity prior, the learned latent variables are inclined to extract more features of the WiFi signals, such as the coverage area, and thus further enhance the accuracy of localization. The experimental results illustrate our proposed model is accurate and efficient for indoor localization issue.
Accelerating Natural Gradient with Higher-Order Invariance

An appealing property of the natural gradient is that it is invariant to arbitrary differentiable reparameterizations of the model. However, this invariance property requires infinitesimal steps and is lost in practical implementations with small but finite step sizes. In this paper, we study invariance properties from a combined perspective of Riemannian geometry and numerical differential equation solving. We define the order of invariance of a numerical method to be its convergence order to an invariant solution. We propose to use higher-order integrators and geodesic corrections to obtain more invariant optimization trajectories. We prove the numerical convergence properties of geodesic corrected updates and show that they can be as computationally efficient as plain natural gradient. Experimentally, we demonstrate that invariance leads to faster optimization and our techniques improve on traditional natural gradient in deep neural network training and natural policy gradient for reinforcement learning.
Leveraging the Exact Likelihood of Deep Latent Variable Models

Deep latent variable models (DLVMs) combine the approximation abilities of deep neural networks and the statistical foundations of generative models. Variational methods are commonly used for inference; however, the exact likelihood of these models has been largely overlooked. The purpose of this work is to study the general properties of this quantity and to show how they can be leveraged in practice. We focus on important inferential problems that rely on the likelihood: estimation and missing data imputation. First, we investigate maximum likelihood estimation for DLVMs: in particular, we show that most unconstrained models used for continuous data have an unbounded likelihood function. This problematic behaviour is demonstrated to be a source of mode collapse. We also show how to ensure the existence of maximum likelihood estimates, and draw useful connections with nonparametric mixture models. Finally, we describe an algorithm for missing data imputation using the exact conditional likelihood of a deep latent variable model. On several data sets, our algorithm consistently and significantly outperforms the usual imputation scheme used for DLVMs.
Diabetes self-management among low-income spanish-speaking patients: A pilot study

Background: The prevalence of type 2 diabetes and diabetesrelated morbidity and mortality is higher among low-income Hispanics when compared to that of Whites. However, little is known about how to effectively promote self-management in this population.Purpose: The objectives were first to determine the feasibility of conducting a randomized clinical trial of an innovative self-management intervention to improve metabolic control in low-income Spanish-speaking individuals with type 2 diabetes and second to obtain preliminary data of possible intervention effects.Methods: Participants for this pilot study were recruited from a community health center, an elder program, and a community-wide database developed by the community health center, in collaboration with other agencies serving the community, by surveying households in the entire community. Participants were randomly assigned to an intervention (n = 15) or a control (n = 10) condition. Assessments were conducted at baseline and at 3 months and 6 months postrandomization. The intervention consisted of 10 group sessions that targeted diabetes knowledge, attitudes, and self-management skills through culturally specific and literacysensitive strategies. The intervention used a cognitivebehavioral theoretical framework.Results: Recruitment rates at the community health center, elder program, and community registry were 48%, 69%, and 8%, respectively. Completion rates for baseline, 3-month, and 6-month assessments were 100%, 92%, and 92%, respectively. Each intervention participant attended an average of 7.8 out of 10 sessions, and as a group the participants showed high adherence to intervention activities (93% turned in daily logs, and 80% self-monitored glucose levels at least daily). There was an overall Group × Time interaction (p = .02) indicating group differences in glycosylated hemoglobin over time. The estimated glycosylated hemoglobin decrease at 3 months for the intervention group was −0.8% (95% confidence intervals = −1.1%, −0.5%) compared with the change in the control group (p = .02). At 6 months, the decrease in the intervention group remained significant, −0.85% (95% confidence intervals = −1.2, −0.5), and the decrease was still significantly different from that of the controls (p = .005). There was a trend toward increased physical activity in the intervention group as compared to that of the control group (p = .11) and some evidence (nonsignificant) of an increase in blood glucose self-monitoring in the intervention participants but not the control participants. Adjusting for baseline depressive scores, we oberved a significant difference in depressive symptoms between intervention participants and control participants at the 3-month assessment (p = .02). Conclusions: Low-income Spanish-speaking Hispanics are receptive to participate in diabetes-related research. This study shows that the pilot-tested diabetes self-management program is promising and warrants the conduct of a randomized clinical trial.
Pattern Division for Massive MIMO Networks With Two-Stage Precoding

In massive multiple-input multiple-output networks with two-stage precoding, the user clusters with serious angle-spreading-range (ASR) overlapping should be divided into different patterns and scheduled in orthogonal sub-channels to achieve optimal performance. In this letter, we propose one graph theory-based pattern division (GT-PD) scheme to deal with the ASR overlapping with a limited number of sub-channels. First, we depict the ASR overlapping as an undirected weighted graph, where the weight of each edge indicates the strength of the ASR overlapping between two connected clusters. Then, we separately denote each user cluster and pattern as a vertex and a color, and transform the pattern division into a graph coloring problem with limited colors. In addition, the GT-PD scheme is developed with the help of the Dsatur algorithm. Finally, numerical results are provided to corroborate the efficiency of the proposed scheme.
Distributed leadership in health care teams: Constellation role distribution and leadership practices.

BACKGROUND AND PURPOSE Recent literature has been critical of research that adopts a narrow focus on single leaders and on leadership attributes and has called for attention to leadership that is distributed among individuals and to practices in which leaders engage. We conducted a study of health care teams where we attended to role distribution among leadership constellation members and to loose or tight coupling practices between leaders and the remainder of the team. This focus provides insights into how leadership can be practiced and structured to enhance team functioning. METHODOLOGY A qualitative, multicase study of four teams was conducted. Data collection involved 44 interviews with almost all the members of the teams and 18 team meeting observations. Thematic analysis was conducted by the two authors. FINDINGS Leadership constellations can give rise to leadership role overlaps and gaps that may create ambiguity within teams, ambiguity is diminished if the leaders can agree on which leader assumes ultimate authority in an area, the presence of more leaders does not necessarily entail more comprehensive fulfillment of team needs, and teams' needs for tight or loose leadership practices are influenced by contextual factors that we elaborate. PRACTICE IMPLICATIONS (a) It is important to recognize areas of overlap and gaps in leadership roles and to provide clarity about role boundaries to avoid ambiguity. Role mapping exercises and open discussions should be considered. (b) Attempting to spread formal leadership responsibilities informally among individuals is not always a workable strategy for addressing team needs.
Low salt and low calorie diet does not reduce more body fat than same calorie diet: a randomized controlled study

Background Recent several observational studies have reported that high salt intake is associated with obesity. But it is unclear whether salt intake itself induce obesity or low salt diet can reduce body fat mass. We investigated whether a low salt diet can reduce body weight and fat amount. Matrials and Methods The randomized, open-label pilot trial was conducted at a single institution. A total of 85 obese people were enrolled. All participants were served meals three times a day, and provided either a low salt diet or control diet with same calorie. Visceral fat was measured with abdominal computer tomography, while body fat mass and total body water was measured with bio-impedance. Results Reductions in body weight (–6.3% vs. –5.0%, p = 0.05) and BMI (–6.6% vs. –5.1%, p = 0.03) were greater in the low salt group than in the control group. Extracellular water and total body water were significantly reduced in the low salt group compared to the control group. However, changes in body fat mass, visceral fat area, and skeletal muscle mass did not differ between the two groups. Changes in lipid profile, fasting glucose, and HOMA-IR did not differ between the two groups. Conclusions A two-month low salt diet was accompanied by reduction of body mass index. However, the observed decrease of body weight was caused by reduction of total body water, not by reduction of body fat mass or visceral fat mass.