Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft segmentation-guided bipartite graph image stylization

In this paper, we propose a photo stylistic brush, an automatic robust style transfer approach based on soft segmentation-guided bipartite graph. A two-step bipartite graph algorithm with different granularity levels is employed to aggregate pixels into superpixel and find their correspondences. In the first step, with the extracted hierarchical features, a bipartite graph is constructed to describe the content similarity for pixel partition to produce superpixels. In the second step, superpixels in the input/reference image are rematched to form a new soft segmentation-guided bipartite graph, and superpixel-level correspondences are generated by a bipartite matching. Finally, the refined correspondence guides our approach to perform the transfer in a decorrelated color space. Extensive experimental results demonstrate the effectiveness and robustness of the proposed method for transferring various styles of exemplar images, even for some challenging cases, such as night images.
Effect of a single dose of esmolol on the bispectral index to endotracheal intubation during desflurane anesthesia

Background In this prospective, randomized, double-blind, placebo-controlled trial, we investigated the effect of a single dose of esmolol on the bispectral index (BIS) to endotracheal intubation during desflurane anesthesia. Methods After induction of anesthesia, 60 patients were mask-ventilated with desflurane (end-tidal 1 minimum alveolar concentration) for 5 min and then received either normal saline, esmolol 0.5 or 1 mg/kg, 1 min prior to intubation (control, esmolol-0.5 and esmolol-1 groups, n = 20/group). BIS, mean arterial pressure, and heart rate were measured prior to anesthesia induction and esmolol administration, immediately preceding intubation (time point 0), and every minute for 5 min after intubation (time point 1 to 5). At time point 0, 1 and 5, 5 ml of arterial blood was taken to measure plasma concentrations of norepinephrine and epinephrine. Results BIS increased significantly at 1 min after intubation when compared with pre-intubation values in all groups. Both mean arterial pressure and heart rate increased significantly 1 min after intubation when compared with preintubation values for all groups. Plasma epinephrine concentrations did not increase significantly after tracheal intubation in any of the groups. Norepinephrine increased at 1 min after intubation when compared with the preintubation values in the esmolol groups (P < 0.05). Conclusions A single bolus of esmolol was unable to blunt the increase in BIS to endotracheal intubation during desflurane anesthesia.
Label propagation based saliency detection via graph design

Saliency detection has been widely used as the pre-processing of the computer-vision tasks. Existing propagation based saliency detection methods simply select a k-regular graph for saliency propagation, which usually leads to the mistaken highlighting of the long-range smooth background regions. In this paper, we design a novel graph for label propagation based saliency detection by considering the local consistency and the global symmetry of the image scene and updating the graph model based on smoothness assumption and cluster assumption. Then, we label the reliable seeds and propagate the saliency value through the designed graph. On two widely used large open benchmark data sets, the proposed method significantly outperforms thirteen state-of-the-arts under either quantitative or qualitative evaluation.
ACL Repair Might Induce Further Abnormality of Gamma Loop in the Intact Side of the Quadriceps Femoris

The purpose of this study was to investigate the effect of surgery on the gamma-loop in the quadriceps of patients with ACL injuries. We compared the response to vibration stimulation in subjects with ACL repair, subjects with ACL rupture, and normal subjects, by measuring the maximal strength and integrated electromyography (I-EMG) of the quadriceps. Pre-vibration data were obtained from each subject by measuring the MVC of the knee extension and the I-EMG from the vastus medialis, vastus lateralis, and rectus femoris. Vibration stimulation was applied to the infrapatellar tendon, followed immediately by repeating maximal strength and I-EMG recording. The results of this study indicated that alpha motor neuron activity of the intact side of the vastus lateralis in response to prolonged vibration stimulation was altered by surgery, but no effect was detected in the injured side. The results could suggest that abnormality of the gamma-loop existed even in the quadriceps of patients with ACL rupture since the vibration stimulus failed to elicit changes in ACL-rupture group as compared with those of normal subjects. In comparison, abnormality of the gamma-loop in the intact side of the QF was probably induced by the rupture, and further abnormality of gamma-loop was induced by surgery.
Effect of wavelet and hybrid classification on action recognition

Any action dataset may contain similar classes such as running, walking and jogging. Therefore, equivalent probabilities may be provided for different classes upon action classification. In this case, the classifier cannot indubitably assign a class to a given sample. To address this problem, we propose a new hybrid classifier to automatically compress the features and classify them using SVM with polynomial or sigmoid kernels. Furthermore, we hypothesize that motion saliency detection can strength the power of motion feature extraction in the bag of visual words framework (BoVW). To this end, we evaluate the effect of 3D-discrete wavelet transform (3D-DWT), as the preprocessing step, on motion feature extraction. The experimental results show that the proposed framework achieves promising results on KTH, Weizmann, and URADL datasets, and outperforms recent state-of-the-art approaches.
Brief Report: HIV Prevalence and the Prevalence of Unsuppressed HIV in New York City, 2010–2014

Background: The widespread use of antiretroviral treatment made HIV prevalence no longer a good measure of population-level transmission risk. The objective of this analysis was to use the prevalence of unsuppressed HIV to describe population-level HIV transmission risk. Methods: Using New York City (NYC) HIV surveillance data, we reported HIV prevalence and the prevalence of unsuppressed HIV, defined as the number of persons living with HIV with an unsuppressed viral load divided by population size. Results: The estimated number of persons living with HIV in NYC increased from 79,100 [95% confidence interval (CI): 78,200 to 80,000] in 2010 to 81,700 (95% CI: 80,500 to 82,900) in 2014. HIV prevalence (≥18 years old) remained unchanged at 1.22% (95% CI: 1.21% to 1.24%) in 2010 and 1.22% (95% CI: 1.20% to 1.24%) in 2014. The prevalence of unsuppressed HIV (≥18 years old) steadily decreased from 0.49% (95% CI: 0.48% to 0.51%) in 2010 to 0.34% (95% CI: 0.32% to 0.36%) in 2014. Men had both higher HIV prevalence (1.86% vs. 0.65% in 2014) and higher prevalence of unsuppressed HIV (0.51% vs. 0.18% in 2014) than women. In 2014, the black–white ratio of prevalence of unsuppressed HIV was 5.8 among men and 26.3 among women, and the Hispanic–white ratio was 2.7 among men and 10.0 among women. Conclusions: The prevalence of unsuppressed HIV has been steadily decreasing in NYC. As antiretroviral treatment continues to expand, programs should consider using the prevalence of unsuppressed HIV to measure population-level transmission risk.
EMORY-BASED P ARAMETER A DAPTATION

Deep neural networks have excelled on a wide range of problems, from vision to language and game playing. Neural networks very gradually incorporate information into weights as they process data, requiring very low learning rates. If the training distribution shifts, the network is slow to adapt, and when it does adapt, it typically performs badly on the training distribution before the shift. Our method, Memory-based Parameter Adaptation, stores examples in memory and then uses a context-based lookup to directly modify the weights of a neural network. Much higher learning rates can be used for this local adaptation, reneging the need for many iterations over similar data before good predictions can be made. As our method is memory-based, it alleviates several shortcomings of neural networks, such as catastrophic forgetting, fast, stable acquisition of new knowledge, learning with an imbalanced class labels, and fast learning during evaluation. We demonstrate this on a range of supervised tasks: large-scale image classification and language modelling.
Learnable contextual regularization for semantic segmentation of indoor scene images

Semantic segmentation of indoor scene images has a wide range of applications. However, due to a large number of classes and uneven distribution in indoor scenes, mislabels are often made when facing small objects or boundary regions. Technically, contextual information may benefit for segmentation results, but has not yet been exploited sufficiently. In this paper, we propose a learnable contextual regularization model for enhancing the semantic segmentation results of color indoor scene images. This regularization model is combined with a deep convolutional segmentation network without significantly increasing the number of additional parameters. Our model, derived from the inherent contextual regularization on the indoor scene objects, benefits much from the learnable constraint layers bridging the lower layers and the higher layers in the deep convolutional network. The constraint layers are further integrated with a weighted L1-norm based contextual regularization between the neighboring pixels of RGB values to improve the segmentation results. Experimental results on NYUDv2 indoor scene dataset demonstrate the effectiveness and efficiency of the proposed method.
Stroke Recovery and Prevention Barriers Among Young African-American Men: Potential Avenues to Reduce Health Disparities

Abstract Background: African Americans (AAs) who experience a first time stroke are younger and have double the stroke rate and more poststroke complications than other Americans. Objective: To assess perceived poststroke care barriers among younger AA men and their care partners (CPs) in order to inform the development of acceptable and effective improvements in poststroke care for this high-risk group. Methods: Ten community-dwelling AA stroke survivors and 7 of their CPs participated in focus groups and advisory board meetings. Survivors had stroke or transient ischemic attack within 1 year and a Barthel Index score >60. In focus groups, using a semi-structured interview guide, survivors and CPs identified self-perceived barriers and facilitators to poststroke care. Thematic analysis of session transcripts and the constant comparative method were used to generate themes. Results: Survivor age ranged from 34 to 64 years. Mean Barthel score was 95.5. CPs, all AA women, ranged in age from 49 to 61 years. Five CPs were wives, 1 was a fiancée, and 1 was a niece. Participants cited multiple personal, social, and societal stroke recovery challenges. Although hypertension and smoking risks were acknowledged, stress, depression, posttraumatic stress disorder, anger/frustration, personal identity change, and difficulty communicating unique needs as AA men were more frequently noted. Facilitators included family support, stress reduction, and dietary changes. Conclusions: Younger AA men and their CPs perceive multiple poststroke care barriers. Biological risk reduction education may not capture all salient aspects of health management for AA stroke survivors. Leveraging family and community strengths, addressing psychological health, and directly engaging patients with health care teams may improve care management.
A 65-nm ReRAM-Enabled Nonvolatile Processor With Time-Space Domain Adaption and Self-Write-Termination Achieving > 4× Faster Clock Frequency and > 6× Higher Restore Speed

With an ever-increasing demand for energy efficiency, processors with instant-on and zero leakage features are highly appreciated in energy harvesting as well as “normally off” applications. Recently, zero-standby power and fast switching nonvolatile processors (NVPs) have been proposed based on emerging nonvolatile memories (NVMs), such as ferroelectric RAM or spin-transfer-torque magnetic RAM. However, previous NVPs store all data to NVM upon every power interruption, resulting in high-energy consumption and degraded NVM endurance. This paper presents a 65-nm fully CMOS-logic-compatible ReRAM-based NVP supporting time-space domain adaption. It incorporates adaptive nonvolatile controller, nonvolatile flip-flops, and nonvolatile static random access memory (nvSRAM) with self-write termination. Data redundancy in both time and space domain is fully exploited to reduce store/restore time/energy and boost clock frequency. The NVP operates at >100 MHz and achieves 20 ns/0.45 nJ restore time/energy, realizing >6 $\times $ and >6000 $\times $ higher speed and energy efficiency of restore and >4 $\times $ faster operating frequency compared with that of state of the art.
Design and performance analysis of solar cell antenna

This paper describes about the solarcell antenna which is an integrated device used for trans-receiving the RF signal and generates DC voltage simultaneously. Solarcell Antenna consists of Microstrip patch antenna and Solarcells, during integration various parameters of antenna such as substrate (dielectric materials used), shape of the patch, dimensions of feed are to be obtained for desired frequency range. Different shapes of patches, dielectric materials are taken into account and compared with the results in which Microstrip Patch model suits the best for 2.4 GHz frequency. We have integrated Solarcells and Microstrip patch antenna using CST Microwave studio software so that it can act as stand-alone system in remote places for various applications such as disaster alert system, moving vehicles, remote sensing, and meteorological surveillance.
Taxonomy of Tricholoma in northern Europe based on ITS sequence data and morphological characters

Based on molecular and morphological data we investigated the taxonomy and phylogeny of the ectomycorrhizal genus Tricholoma in northern Europe. Our phylogenetic tree confirmed the presence of at least 72 well circumscribed species within the region. Of these, three species, viz. T. boreosulphurescens, T. bryogenum and T. ilkkae are described as new to science, based on morphological, distributional, ecological and molecular data. Several other terminal branches represent putative cryptic taxa nested within classical species or species groups. Molecular type studies and/or designation of sequenced neotypes are needed in these groups, before the taxonomy can be settled. In general our phylogenetic analysis supported previous suprageneric classification systems, but with some substantial changes. Most notably, T. virgatum and allies were found to belong to sect. Tricholoma rather than sect. Atrosquamosa, while T. focale was found to be clearly nested in sect. Genuina rather than in sect. Caligata. In total, ten sections are accepted, with five species remaining unassigned. The combination of morphological and molecular data showed pileus colour, pileipellis structure, presence of clamp connections and spore size to be rather conservative characters within accepted sections, while the presence of a distinct ring, and especially host selection were highly variable within these.
A Stiffness Degradation Model for Graphite/Epoxy Laminates

Stiffness reduction in composite laminates is an important measure of fa tigue damage. The determination of fatigue damage and the prediction of fatigue life can be made through the development of a stiffness degradation model. This paper proposes a stiffness degradation model that can be used to predict the statistical distribution of the residual stiffness of composite laminates subjected to fatigue cycling. Based on the pro posed model, two analytical methods are presented, which are capable of predicting the stiffness degradation of a particular composite specimen under cyclic loading. One method is based on the linear regression analysis and the other on the Bayesian approach. Experi ments have been performed on graphite/epoxy [90, +45, —45,0], laminates to generate sta tistically significant data for evaluating the proposed analytical models and for verifying the predicted results. It is shown that theoretical predictions for the stiffness degradation of an individual specimen and for the statistical distribution of the entire population corre late reasonably well with experimental results.
Global mapping of transcription factor motifs in human aging

Biological aging is a complex process dependent on the interplay of cell autonomous and tissue contextual changes which occur in response to cumulative molecular stress and manifest through adaptive transcriptional reprogramming. Here we describe a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissue sites collected at different biological ages and from 7 different in-vitro aging models. In-vitro aging platforms included replicative senescence and an energy restriction model in quiescence (ERiQ), in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC. TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the ERiQ profile was shared with the majority of in-vivo aged tissues. Fitting motifs in a minimalistic protein-protein network allowed to probe for connectivity to distinct stress sensors. The DNA damage sensors ATM and ATR linked to the subnetwork associated with senescence. By contrast, the energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that metabolic dysfunction may be linked to transcriptional patterns characteristic of many aged tissues and distinct from cumulative DNA damage associated with senescence.
Non-stationary source separation and system identification for MIMO-AR mixtures using HM-GMM

A technique for non-stationary source separation is proposed for multiple-input multiple-output auto-regressive (MIMO-AR) mixtures. Hidden Markov Gaussian mixture model (HM-GMM) is employed in this paper to represent source model. Expectation-maximum algorithm is used to achieve MIMO-AR model identification, and then blind source separation (BSS) is achieved by matrix joint diagonalization. Simulation results show its effectiveness, not only for system identification, but also for BSS.
Clostridioides difficile Biology: Sporulation, Germination, and Corresponding Therapies for C. difficile Infection

Clostridioides difficile is a Gram-positive, spore-forming, toxin-producing anaerobe, and an important nosocomial pathogen. Due to the strictly anaerobic nature of the vegetative form, spores are the main morphotype of infection and transmission of the disease. Spore formation and their subsequent germination play critical roles in C. difficile infection (CDI) progress. Under suitable conditions, C. difficile spores will germinate and outgrow to produce the pathogenic vegetative form. During CDI, C. difficile produces toxins (TcdA and TcdB) that are required to initiate the disease. Meanwhile, it also produces spores that are responsible for the persistence and recurrence of C. difficile in patients. Recent studies have shed light on the regulatory mechanisms of C. difficile sporulation and germination. This review is to summarize recent advances on the regulation of sporulation/germination in C. difficile and the corresponding therapeutic strategies that are aimed at these important processes.
Belongingness and Burdensomeness in Adolescents: Slovene Translation and Validation of the Interpersonal Needs Questionnaire

The Interpersonal Needs Questionnaire (INQ) for assessing thwarted belongingness (TB) and perceived burdensomeness (PB) has not been validated with community adolescents. We translated and administered the INQ to 307 Slovenian adolescents twice over 2-3 months and found that the 15-item version (INQ-15) did not fit without modification. TB and PB scales correlated with concurrent and later suicide ideation and lifetime suicide attempt history. The latent PB factor was associated with concurrent and later ideation controlling for TB, age, gender, depressive symptoms, binge drinking, and peer victimization. Suicide ideation and binge drinking were independently related to attempt history. Assessing and intervening on PB and binge drinking may be promising approaches to suicide prevention with community adolescents.
The bi-objective workflow satisfiability problem and workflow resiliency

A computerized workflow management system may enforce a security policy, specified in terms of authorized actions and constraints, thereby restricting which users can perform particular steps in a workflow. The existence of a security policy may mean it is impossible to find a valid plan (an assignment of steps to authorized users such that all constraints are satisfied). Work in the literature focuses on the workflow satisfiability problem, a \emph{decision} problem that outputs a valid plan if the instance is satisfiable (and a negative result otherwise). In this paper, we introduce the \textsc{Bi-Objective Workflow Satisfiability Problem} (\BOWSP), which enables us to solve \emph{optimization} problems related to workflows and security policies. In particular, we are able to compute a "least bad" plan when some components of the security policy may be violated. In general, \BOWSP is intractable from both the classical and parameterized complexity point of view. We prove there exists an fixed-parameter tractable (FPT) algorithm to compute a Pareto front for \BOWSP if we restrict our attention to user-independent constraints. We also present a second algorithm to compute a Pareto front which uses mixed integer programming (MIP). We compare the performance of both our algorithms on synthetic instances, and show that the FPT algorithm outperforms the MIP-based one by several orders of magnitude on most of the instances. Finally, we study the important question of workflow resiliency and prove new results establishing that known decision problems are fixed-parameter tractable when restricted to user-independent constraints. We then propose a new way of modeling the availability of users and demonstrate that many questions related to resiliency in the context of this new model may be reduced to instances of \BOWSP.
Glass Ceilings or Glass Doors? Wage Disparity Within and Between Firms

We investigate whether immigrant and minority workers’ poor access to high-wage jobs—that is, glass ceilings—is attributable to poor access to jobs in high-wage firms, a phenomenon we call glass doors. Our analysis uses linked employer–employee data to measure mean and quantile-wage differentials of immigrants and ethnic minorities, both within and across firms. We find that glass ceilings exist for some immigrant groups, and that they are driven in large measure by glass doors. For some immigrant groups, the sorting of these workers across firms accounts for as much as half of the economy-wide wage disparity they face. This article has the supplement materials online.