Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Five Health-Related Behaviors for Chronic Disease Prevention Among Sexual and Gender Minority Adults — 25 U.S. States and Guam, 2016

In recent decades, public health awareness of health disparities among lesbian, gay, bisexual, and transgender (LGBT) populations has increased (1). Healthy People 2020 included objectives to improve health of LGBT persons. † Five key health-related behaviors were found to be likely associated with reduced all-cause mortality: never smoking, performing regular physical activity, consuming no or moderate amounts of alcohol, having a normal body weight, and obtaining sufficient sleep daily (2). CDC estimated these five health-related behaviors among adults aged ≥21 years by sexual orientation and transgender status using data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) in 25 U.S. states and Guam. Patterns of these five health-related behaviors varied by sexual orientation among men and women, and among transgender adults. Lesbian and bisexual women were less likely to engage in all five health-related behaviors than were heterosexual women (5.4% and 6.9%, respectively, versus 10.6%). Compared with cisgender§ adults, male-to-female transgender adults were less likely to engage in any two of five health-related behaviors (12.3% versus 18.6%). Male-to-female transgender adults, however, were more likely to engage in any three of five health-related behaviors than were female-to-male transgender adults (47.2% versus 28.2%). The number of health-related behaviors did not differ between gay or bisexual men and heterosexual men. Continued efforts are needed to target LGBT populations for overall well-being, including strategies for health promotion and engagement in health-related behaviors.
Adaptive Control of Linear Systems with Time-Varying Parameters

A new method for the adaptive control of linear systems with time-varying parameters is proposed. The method does not require any restriction on the rates of parameter variations. For linear systems in parametric strict-feedback form a state feedback adaptive backstepping controller with nonlinear damping terms is proposed and stability properties are proved. For systems in observable canonical form an ISS Kreisselmeier filter and an adaptive observer backstepping controller with an additional linear damping term are proposed: these guarantee asymptotic output regulation and bounded states. Simulation results show that the proposed controllers have superior performance over the standard controllers in the presence of varying parameters.
The Influence of the Shape and Size of the Cell on Developing Military Passability Maps

The necessity to divide the analysed area into basic elements, regardless of the administrative division (cells or pixels, also called primary fields), and use them to prepare thematic maps emerged as early as by the end of the 19th century. The automation of map development processes brought a new approach to the function of cells, which made them a carrier that facilitates information processing, and presenting the results of analyses in the form of studies that very often function only in spatial information systems or on the Internet. Cells are currently used to conduct a series of advanced spatial analyses in practically all areas of application. The aim of the presented research was to analyse the influence of the shape and size of cells on the terrain classification results for the purposes of developing military passability maps. The research used the automatic terrain classification method, based on calculating the index of passability, calculated for cells of square, triangular, and hexagonal shapes and of different sizes, ranging from 100 m to 10,000 m. Indices of passability were determined basing on parameters derived from land cover elements that exist in the area of each of the adopted cells. Because of the fact that passability maps are mainly developed for military purposes, the study used a standardised vector spatial database—VMap Level 2. The studies have demonstrated that, if the surface areas of cells are identical, their shapes do not have a significant influence on the resulting passability map. The authors have also determined the sizes of cells that should be adopted for developing passability maps on various levels of accuracy, and, as a consequence, for being used on various levels of command of military troops.
Using Eye Tracking to Evaluate the Usability of Flow Maps

Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.
Combining Self-Training and Tri-Training for Course-Level Student Classification

Course-level student classification is one of the most popular tasks in the educational data mining area. It helps early identifying the students facing difficulties in a course by predicting their final study status after the end of the course. To perform this task more effectively, our work proposes a semi-supervised learning method, TriForest, which combines Self-Training and Tri-Training. TriForest has three base classifiers all of which are Random Forests in the learning process of the Self-Training framework. Meanwhile, TriForest explores the agreement mechanism of Tri-Training in selecting the most confidently predicted instances for training data set enhancement of each base classifier. In addition, our work also takes into account both study performance via grades and forum-related attributes in this classification task. The selected attributes are useful for the proposed method to differentiate successful students from students with failure in a course where the data sets of the past students can be used to build a classification model for the current students. Indeed, experimental results on real data sets have shown that with about 90% of Accuracy, our method can achieve better predictions than its base classifier and other semi-supervised learning methods. Such accurate student classifications help both students and lecturers forecast the students' study status and in-trouble students can be then supported appropriately for their ultimate success in the course.
Proof and Evaluation of Improved Slack Reclamation for Response Time Analysis of Real-Time Multiprocessor Systems

RTA (Response time analysis) is a popular technique to guarantee timing requirements for a real-time system, and therefore the RTA framework has been widely studied for popular scheduling algorithms such as EDF (Earliest Deadline First) and FP (Fixed Priority). While a number of extended techniques of RTA have been introduced, some of them cannot be used since they have not been proved and evaluated in terms of their correctness and empirical performance. In this letter, we address the state of the art technique of slack reclamation of the existing generic RTA framework for multiprocessors. We present its mathematical proof of correctness and empirical performance evaluation, which have not been revealed to this day. key words: response time analysis, real-time multiprocessor systems, slack reclamation
Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as " Weibo ") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.
Locally advanced skin cancers of the frail and elderly: consider adaptive split‐course radiotherapy

Symptomatic locally advanced skin cancers in frail elderly patients are difficult to treat. Definitive radiotherapy (RT) is an option. Attending for a fully fractionated course of RT can be problematic for patients but also for the families that need to provide transport and care for a traditionally out-patient service. This article is protected by copyright. All rights reserved.
SEREEGA: Simulating event-related EEG activity

Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which e ects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learn-ing algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of sim-ulated epochs of EEG data. It is modular and extensible, at initial release supporting ve different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general work ow of this toolbox, as well as a simulated data set demonstrating some of its functions. Highlights Simulated EEG data has a known ground truth, which can be used to validate methods. We present a general-purpose open-source toolbox to simulate EEG data. It provides a single framework to simulate many different types of EEG recordings. It is modular, extensible, and already includes a number of head models and signals. It supports noise, oscillations, event-related potentials, connectivity, and more.
A Multivariate Approach to Study Drivers of Land-Cover Changes through Remote Sensing in the Dry Chaco of Argentina

Land-cover changes are driven by different combinations of biophysical, economic, and cultural drivers that are acting at different scales. We aimed to (1) analyze trends in land use and land cover changes (conversion, abandonment, forest persistence) in the dry Chaco in central Argentina (1979 to 2010), and (2) examine how physical and socio-economic drivers have influenced those changes. Based on Landsat data, we obtained the proportion of 16 classes of land cover changes for 81 individual circular samples. We performed a Principal Component Analysis (PCA) to identify the main trends of change across the whole region. To explore the relationships between the changes in land cover and drivers, we developed a GIS comprising thematic maps representing the different drivers. The drivers were first correlated with the two first PCA axes, and in a second approximation were subjected to multiple regression analyses. We obtained in this way the best model to explain each PCA axis. The highest conversion, as indicated by PCA axis 1, was experienced by flat areas close to roads and with the highest annual rainfall. Besides agricultural expansion that was triggered by precipitation increase as a major driver of forest conversion, changes that were observed during the period 1979–2010, may have also been influenced by several other driving forces acting at different spatial scales and contexts.
ODAR: Aerial Manipulation Platform Enabling Omnidirectional Wrench Generation

We propose a novel aerial manipulation platform, an omnidirectional aerial robot, that is capable of omnidirectional wrench generation with opportunistically distributed/aligned Sectional rotors. To circumvent the tight thrust margin and weight budget of currently available rotor and battery technologies, we propose a novel design optimization framework, which maximizes the minimum-guaranteed control force/torque for any attitude while incorporating such important and useful aspects as interrotor aerointerference, anisotropic task requirement, gravity compensation, etc. We also provide a closed-form solution of infinity-norm optimal control allocation to avoid rotor saturation with the tight thrust margin. Further, we elaborate the notion of electronic speed controller induced singularity and devise a novel selective mapping algorithm to substantially subdue its destabilizing effect. Experiments are performed to validate the theory, which demonstrate such capabilities not possible with typical aerial manipulation systems, namely, separate translation and attitude control on SE(3), hybrid pose/wrench control with downward force of 60 N much larger than its own weight (2.6 kg), and peg-in-hole teleoperation with a radial tolerance of 0.5 mm.
miR-133: A Suppressor of Cardiac Remodeling?

Cardiac remodeling, which is characterized by mechanical and electrical remodeling, is a significant pathophysiological process involved in almost all forms of heart diseases. MicroRNAs (miRNAs) are a group of non-coding RNAs of 20–25 nucleotides in length that primarily regulate gene expression by promoting mRNA degradation or post-transcriptional repression in a sequence-specific manner. Three miR-133 genes have been identified in the human genome, miR-133a-1, miR-133a-2, and miR-133b, which are located on chromosomes 18, 20, and 6, respectively. These miRNAs are mainly expressed in muscle tissues and appear to repress the expression of non-muscle genes. Based on accumulating evidence, miR-133 participates in the proliferation, differentiation, survival, hypertrophic growth, and electrical conduction of cardiac cells, which are essential for cardiac fibrosis, cardiac hypertrophy, and arrhythmia. Nevertheless, the roles of miR-133 in cardiac remodeling are ambiguous, and the mechanisms are also sophisticated, involving many target genes and signaling pathways, such as RhoA, MAPK, TGFβ/Smad, and PI3K/Akt. Therefore, in this review, we summarize the critical roles of miR-133 and its potential mechanisms in cardiac remodeling.
LSTM-Based Goal Recognition in Latent Space

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.
On Performance of Time-Delay Linear Consensus Networks with Directed Interconnection Topologies

We study performance of noisy linear time-invariant (LTI) consensus networks in the presence of time-delay with directed graph topologies. The $\mathcal{H}_{2}$-norm squared of the network is adopted as the performance measure. One of our main results provides an explicit expression for the $\mathcal{H}_{2}$-norm of directed networks with normal Laplacian matrices. This expression is in terms of Laplacian eigenvalues and time-delay. Furthermore, we exploit topology of some well-known graphs and show how their $\mathcal{H}_{2}$-norm scale with network size and time delay. It is also proven that $\mathcal{H}_{2}$-norm of a directed network with normal Laplacian is an increasing function of time-delay.
Lactosylceramide synthases encoded by B4galt5 and 6 genes are pivotal for neuronal generation and myelin formation in mice

It is uncertain which β4-galactosyltransferase (β4GalT; gene name, B4galt), β4GalT-5 and/or β4GalT-6, is responsible for the production of lactosylceramide (LacCer) synthase, which functions in the initial step of ganglioside biosynthesis. Here, we generated conditional B4galt5 knockout (B4galt5 cKO) mice, using Nestin-Cre mice, and crossed these with B4galt6 KO mice to generate B4galt5 and 6 double KO (DKO) mice in the central nervous system (CNS). LacCer synthase activity and major brain gangliosides were completely absent in brain homogenates from the DKO mice, although LacCer synthase activity was about half its normal level in B4galt5 cKO mice and B4galt6 KO mice. The DKO mice were born normally but they showed growth retardation and motor deficits at 2 weeks and died by 4 weeks of age. Histological analyses showed that myelin-associated proteins were rarely found localized in axons in the cerebral cortex, and axonal and myelin formation were remarkably impaired in the spinal cords of the DKO mice. Neuronal cells, differentiated from neurospheres that were prepared from the DKO mice, showed impairments in neurite outgrowth and branch formation, which can be explained by the fact that neurospheres from DKO mice could weakly interact with laminin due to lack of gangliosides, such as GM1a. Furthermore, the neurons were immature and perineuronal nets (PNNs) were poorly formed in DKO cerebral cortices. Our results indicate that LacCer synthase is encoded by B4galt5 and 6 genes in the CNS, and that gangliosides are indispensable for neuronal maturation, PNN formation, and axonal and myelin formation.
Finite-Time Distributed Event-Triggered Consensus Control for General Linear Multi-Agent Systems

This paper considers the finite-time distributed event-triggered consensus with fully continuous communication free for multi-agent systems with general linear dynamics under a directed graph. By employing an external dynamic threshold, a novel distributed event-triggered consensus controller is proposed to achieve consensus in finite time. It should be emphasized that continuous communication is not required in both controller update and triggering condition detecting by this approach, which means fully continuous communication free. In this strategy, the triggering instants is reduced significantly and the high frequency triggering is restrained. Additionally, Zeno behavior is almost excluded in the framework of event-triggered finite-time consensus. Finally, numerical simulations are given to illustrate the theoretical results.
Characterization of three strains of Capnocytophaga canis isolated from patients with sepsis

Capnocytophaga canimorsus and Capnocytophaga cynodegmi, both commensal bacteria in the oral cavities of dogs and cats, are zoonotic pathogens. In particular, C. canimorsus causes sepsis and fatal septic shock. Recently, a novel Capnocytophaga species, C. canis, was isolated from the oral cavities of healthy dogs. It is reportedly oxidase‐negative and therefore considered avirulent in humans. In the present study, three strains of C. canis were isolated from Japanese patients with sepsis. All three strains, HP20001, HP33001 and HP40001, were oxidase‐positive. Nucleotide sequence identities of the 16S rRNA gene of the three strains to the C. canimorsus type strain ATCC35979, C. cynodegmi type strain ATCC49044 and C. canis type strain LMG29146 were 96.9–97.0%, 96.9–97.0% and 99.7–99.8%, respectively. Multi‐locus sequence analysis based on seven house‐keeping genes, dnaJ, fumC, glyA, gyrB, murG, trpB and tuf, revealed that the oxidase‐positive C. canis strains isolated in Japan and oxidase‐negative strains of C. canis from canine oral cavities in Switzerland were clustered in different genetic subgroups. These results indicate that the virulence of C. canis strains in humans is associated with oxidase activity.
Counterexamples to Parametric Convergence in Recursive Networked Identification

This paper studies a condition for global parametric convergence of a recursive identification algorithm with an output quantizer. The problem is addressed by an analytic calculation of the associated ODE for a low order test model. The ODE is then analysed numerically to investigate the possibilities for proving parametric convergence. The main contribution of the paper shows that a monotone quantizer is not a necessary condition for global parametric convergence to the true parameter vector w.p.1.
Solving Multi-Document Summarization as an Orienteering Problem

With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.