Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study on Conductive Knitted Fabric Electrodes for Long-Term Electrocardiography Monitoring: Silver-Plated and PEDOT:PSS Coated Fabrics

Long-term monitoring of the electrical activity of the heart helps to detect the presence of potential dysfunctions, enabling the diagnosis of a wide range of cardiac pathologies. However, standard electrodes used for electrocardiogram (ECG) acquisition are not fully integrated into garments, and generally need to be used with a gel to improve contact resistance. This article is focused on the development of washable screen-printed cotton, with and without Lycra, textile electrodes providing a medical quality ECG signal to be used for long-term electrocardiography measurements. Several samples with different Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) concentrations were investigated. Silver-plated knitted fabric electrodes were also used for comparison, within the same process of ECG signal recording. The acquisition of ECG signals carried out by a portable medical device and a low-coast Arduino-based device on one female subject in a sitting position. Three textile electrodes were placed on the right and left forearms and a ground electrode was placed on the right ankle of a healthy female subject. Plastic clamps were applied to maintain electrodes on the skin. The results obtained with PEDOT:PSS used for electrodes fabrication have been presented, considering the optimal concentration required for medical ECG quality and capacity to sustain up to 50 washing cycles. All the ECG signals acquired and recorded, using PEDOT:PSS and silver-plated electrodes, have been reviewed by a cardiologist in order to validate their quality required for accurate diagnosis.
There and back again: Outlier detection between statistical reasoning and data mining algorithms

Outlier detection has been a topic in statistics for centuries. Over mainly the last two decades, there has been also an increasing interest in the database and data mining community to develop scalable methods for outlier detection. Initially based on statistical reasoning, however, these methods soon lost the direct probabilistic interpretability of the derived outlier scores. Here, we detail from a joint point of view of data mining and statistics the roots and the path of development of statistical outlier detection and of database‐related data mining methods for outlier detection. We discuss their inherent meaning, review approaches to again find a statistically meaningful interpretation of outlier scores, and sketch related current research topics.
Algorithmic mechanism design (extended abstract)

We consider algorithmic problems in a distributed setting where the participants cannot be assumed to follow the algorithm but rather their own self-interest. As such participants, termed agents, are capable of manipulating the algorithm, the algorithm designer should ensure in advance that the agents' interests are best served by behaving correctly. Following notions from the field of mechanism design, we suggest a framework for studying such algorithms. Our main technical contribution concerns the study of a representative task scheduling problem for which the standard mechanism design tools do not suffice. Journal of Economic Literature Classification Numbers: C60, C72, D61, D70, D80.
Does race predict the development of metastases in men who receive androgen‐deprivation therapy for a biochemical recurrence after radical prostatectomy?

In this study among men who underwent radical prostatectomy (RP), African American men (AAM) were 28% more likely to develop recurrent disease compared with Caucasian men (CM). However, among those who had nonmetastatic, castration‐resistant prostate cancer (CRPC), race did not predict metastases or overall survival. Whether race predicts metastases among men who receive androgen‐deprivation therapy (ADT) after a biochemical recurrence (BCR) (ie, before CRPC but after BCR) is untested.
Ion Channels in Pulmonary Hypertension: A Therapeutic Interest?

Pulmonary arterial hypertension (PAH) is a multifactorial and severe disease without curative therapies. PAH pathobiology involves altered pulmonary arterial tone, endothelial dysfunction, distal pulmonary vessel remodeling, and inflammation, which could all depend on ion channel activities (K+, Ca2+, Na+ and Cl−). This review focuses on ion channels in the pulmonary vasculature and discusses their pathophysiological contribution to PAH as well as their therapeutic potential in PAH.
Trajectory-based social circle inference

Learning explicit and implicit patterns in human trajectories plays an important role in many Location-Based Social Networks (LBSNs) applications, such as trajectory classification (e.g., walking, driving, etc.), trajectory-user linking, friend recommendation, etc. A particular problem that has attracted much attention recently - and is the focus of our work - is the Trajectory-based Social Circle Inference (TSCI), aiming at inferring user social circles (mainly social friendship) based on motion trajectories and without any explicit social networked information. Existing approaches addressing TSCI lack satisfactory results due to the challenges related to data sparsity, accessibility and model efficiency. Motivated by the recent success of machine learning in trajectory mining, in this paper we formulate TSCI as a novel multi-label classification problem and develop a Recurrent Neural Network (RNN)-based framework called DeepTSCI to use human mobility patterns for inferring corresponding social circles. We propose three methods to learn the latent representations of trajectories, based on: (1) bidirectional Long Short-Term Memory (LSTM); (2) Autoencoder; and (3) Variational autoencoder. Experiments conducted on real-world datasets demonstrate that our proposed methods perform well and achieve significant improvement in terms of macro-R, macro-F1 and accuracy when compared to baselines.
Machine-assisted map editing

Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.
Cutting resilient networks - complete binary trees

In our previous work, we introduced the random $k$-cut number for rooted graphs. In this paper, we show that the distribution of the $k$-cut number in complete binary trees of size n, after rescaling, is asymptotically a periodic function of $\lg n − \lg \lg n$. Thus there are different limit distributions for different subsequences, where these limits are similar to weakly $1$-stable distributions. This generalizes the result for the case $k = 1$, i.e., the traditional cutting model, by Janson (2004).
Hybrid TSR–PSR Alternate Energy Harvesting Relay Network over Rician Fading Channels: Outage Probability and SER Analysis

In this research, we investigate a hybrid time-switching relay (TSR)–power-splitting relay (PSR) alternate energy harvesting (EH) relaying network over the Rician fading channels. For this purpose, the amplify-and-forward (AF) mode is considered for the alternative hybrid time TSR–PSR. The system model consists of one source, one destination and two alternative relays for signal transmission from the source to the destination. In the first step, the exact and asymptotic expressions of the outage probability and the symbol errors ratio (SER) are derived. Then, the influence of all system parameters on the system performance is investigated, and the Monte Carlo simulation verifies all results. Finally, the system performances of TSR–PSR, TSR, and PSR cases are compared in connection with all system parameters.
Subjective Satiety Following Meals Incorporating Rice, Pasta and Potato

The satiating capacity of carbohydrate staples eaten alone is dependent upon the energy density of the food but relative satiety when starchy staples are incorporated into mixed meals is uncertain. Our aim was to assess the satiating effects of three carbohydrate staples; jasmine rice, penne pasta, and Agria potato, each consumed within a standard mixed meal. Cooked portions of each staple containing 45 g carbohydrate were combined with 200 g of meat sauce and 200 g of mixed vegetables in three mixed meals. The quantities of staple providing 45 g carbohydrate were: Rice, 142 g; pasta, 138 g and potato 337 g. Participants (n = 14) consumed each of the mixed meals in random order on separate days. Satiety was assessed with using visual analogue scales at baseline and for 3 h post meal. In an area-under-the-curve comparison, participants felt less hungry (mean (SD)) following potato 263 (230) than following rice 374 (237) or pasta 444 (254) mm∙min, and felt fuller, more satisfied, and wanted to eat less following the potato compared with the rice and pasta meals (p for all <0.01). The superior satiating effect of potato compared with rice and pasta in a mixed meal was consistent with its lower energy density.
Evolution at two time frames: Polymorphisms from an ancient singular divergence event fuel contemporary parallel evolution

When species occur in repeated ecologically distinct habitats across their range, adaptation may proceed surprisingly fast and result in parallel evolution. There is increasing evidence that such cases of rapid parallel evolution are fueled by standing genetic variation, but the origin of this genetic variation remains poorly understood. In Pogonus chalceus beetles, short- and long-winged ecotypes have diverged in response to contrasting hydrological regimes and can be repeatedly found along the Atlantic European coast. By analyzing genomic variation across the beetles’ distribution, we reveal that genomically widespread short-wing selected alleles evolved during a singular divergence event, estimated at ~0.19 Mya. The ancient and differentially selected alleles are currently polymorphic in all populations across the range, allowing for the fast evolution of one ecotype from a small number of random individuals, as low as 5 to 15, of the populations of the other ecotype. Our results suggest that cases of fast parallel ecological divergence might be the result of evolution at two different time frames: divergence in the past, followed by repeated selection on the divergently evolved alleles after admixture. We suggest that this mechanism may be common and potentially further driven by periods of geographic isolation imposed by large-scale environmental changes such as glacial cycles.
Automatic intersection extraction and building arrangement with StarCraft II maps

In StarCraft, buildings arrangement near the intersections is one of most the critical strategic decisions in the early stage. The high time complexity of the buildings arrangement makes it difficult for AI bot to make the real-time decision. This paper presents an approach to analyze the intersection in StarCraft II maps. We propose a radarlike algorithm to automatically detect the intersection and use a designed heuristic search algorithm to arrange the building for building the wall. Our method can obtain the optimal solution while meeting the real-time requirement.
A Small Regulatory RNA Controls Cell Wall Biosynthesis and Antibiotic Resistance

The alphaproteobacterium Agrobacterium tumefaciens is able to infect various eudicots causing crown gall tumor formation. Based on its unique ability of interkingdom gene transfer, Agrobacterium serves as a crucial biotechnological tool for genetic manipulation of plant cells. The presence of hundreds of putative sRNAs in this organism suggests a considerable impact of riboregulation on A. tumefaciens physiology. Here, we characterized the biological function of the sRNA PmaR that controls various processes crucial for growth, motility, and virulence. Among the genes directly targeted by PmaR is ampC coding for a beta-lactamase that confers ampicillin resistance, suggesting that the sRNA is crucial for fitness in the competitive microbial composition of the rhizosphere. ABSTRACT Small regulatory RNAs play an important role in the adaptation to changing conditions. Here, we describe a differentially expressed small regulatory RNA (sRNA) that affects various cellular processes in the plant pathogen Agrobacterium tumefaciens. Using a combination of bioinformatic predictions and comparative proteomics, we identified nine targets, most of which are positively regulated by the sRNA. According to these targets, we named the sRNA PmaR for peptidoglycan biosynthesis, motility, and ampicillin resistance regulator. Agrobacterium spp. are long known to be naturally resistant to high ampicillin concentrations, and we can now explain this phenotype by the positive PmaR-mediated regulation of the beta-lactamase gene ampC. Structure probing revealed a spoon-like structure of the sRNA, with a single-stranded loop that is engaged in target interaction in vivo and in vitro. Several riboregulators have been implicated in antibiotic resistance mechanisms, such as uptake and efflux transporters, but PmaR represents the first example of an sRNA that directly controls the expression of an antibiotic resistance gene. IMPORTANCE The alphaproteobacterium Agrobacterium tumefaciens is able to infect various eudicots causing crown gall tumor formation. Based on its unique ability of interkingdom gene transfer, Agrobacterium serves as a crucial biotechnological tool for genetic manipulation of plant cells. The presence of hundreds of putative sRNAs in this organism suggests a considerable impact of riboregulation on A. tumefaciens physiology. Here, we characterized the biological function of the sRNA PmaR that controls various processes crucial for growth, motility, and virulence. Among the genes directly targeted by PmaR is ampC coding for a beta-lactamase that confers ampicillin resistance, suggesting that the sRNA is crucial for fitness in the competitive microbial composition of the rhizosphere.
Nutritional strategies to alleviate heat stress effects through feed restrictions and feed additives (vitamins and minerals) in broilers under summer conditions

This experiment was aimed to determine the best strategies for alleviating the detrimental effects of heat stress by feed restriction (FR) in hot times and dietary supplementation with Vit. +Min. (vitamins and minerals) by their impacts on growth parameters, mortality rate, serum biochemistry indices and carcass traits of broiler under summer conditions in the open-sided house. A total of 420 broilers of Ross 308 (unsexed 7-day-old) were equally divided into 7 treatments (T). Throughout the experiment, chicks in T1 (control) fed ad libitum on basal diets, FR was applied in T2, T3 and T4 as followed 2, 3, 4 hours (h) without feeding respectively. Supplemental vitamin C (Vit.C) + vitamin E (Vit.E) + chromium (Cr) + zinc (Zn) as followed 100+100+0.5+50 (T5), 150+150+1.0+75 (T6), 200+200+1.5+100 (T7) mg/kg diet respectively. It was found that FR and feed additives improved body weight, weight gain, and significantly decreased feed conversion, mortality rate, and abdominal fat of stressed broiler. Applying FR (3, 4 h) and supplementation with Vit. +Min. (T6, T7) significantly increased total serum protein and statically decreased total serum cholesterol. Overall results conducted that FR for 3 h (T3) and dietary supplementation (T7) have the most significant results which indicate the vital role of FR and supplementation with Vit. +Min. in stressed broilers.
Automatic Classification of Online Doctor Reviews: Evaluation of Text Classifier Algorithms

Background An increasing number of doctor reviews are being generated by patients on the internet. These reviews address a diverse set of topics (features), including wait time, office staff, doctor’s skills, and bedside manners. Most previous work on automatic analysis of Web-based customer reviews assumes that (1) product features are described unambiguously by a small number of keywords, for example, battery for phones and (2) the opinion for each feature has a positive or negative sentiment. However, in the domain of doctor reviews, this setting is too restrictive: a feature such as visit duration for doctor reviews may be expressed in many ways and does not necessarily have a positive or negative sentiment. Objective This study aimed to adapt existing and propose novel text classification methods on the domain of doctor reviews. These methods are evaluated on their accuracy to classify a diverse set of doctor review features. Methods We first manually examined a large number of reviews to extract a set of features that are frequently mentioned in the reviews. Then we proposed a new algorithm that goes beyond bag-of-words or deep learning classification techniques by leveraging natural language processing (NLP) tools. Specifically, our algorithm automatically extracts dependency tree patterns and uses them to classify review sentences. Results We evaluated several state-of-the-art text classification algorithms as well as our dependency tree–based classifier algorithm on a real-world doctor review dataset. We showed that methods using deep learning or NLP techniques tend to outperform traditional bag-of-words methods. In our experiments, the 2 best methods used NLP techniques; on average, our proposed classifier performed 2.19% better than an existing NLP-based method, but many of its predictions of specific opinions were incorrect. Conclusions We conclude that it is feasible to classify doctor reviews. Automatically classifying these reviews would allow patients to easily search for doctors based on their personal preference criteria.
Theft prediction with individual risk factor of visitors

Location-Based Social Networks (LBSN) provides unprecedented opportunities to tackle various social problems. In this study, we identify a number of crime-prediction-specific dynamic features which, for the first time, explore crime risk factors implicitly associated with the visitors. The reliable correlations between the proposed dynamic features and crime event occurrences have been observed. The evaluations on large real world data sets verify that the crime prediction performance can be notably improved with the inclusion of proposed crime-prediction-specific dynamic features.
Evaluation of the National Healthcare Safety Network Hemovigilance Module for transfusion‐related adverse reactions in the United States

The National Healthcare Safety Network (NHSN) Hemovigilance Module (HM) collects data on the frequency, severity, and imputability of transfusion‐associated adverse events. These events contribute to significant morbidity and mortality among transfusion patients. We report results from the first systematic assessment of eight attributes of the HM.
Dependency Grammar Induction with a Neural Variational Transition-based Parser

Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-ofthe-art results on this task but they require O(n3) run time. Transition-based models enable faster inference with O(n) time complexity, but their performance still lags behind. In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with O(n) time complexity. We train the parser with an integration of variational inference, posterior regularization and variance reduction techniques. The resulting framework outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to graph-based models, both on the English Penn Treebank and on the Universal Dependency Treebank. In an empirical comparison, we show that our approach substantially increases parsing speed over graphbased models.
Detecting latest local events from geotagged tweet streams

Geotagged tweet streams contain invaluable information about the real-world local events like sports games, protests and traffic accidents. Timely detecting and extracting such events has various applications but yet unsolved challenges. In this paper, we present DeLLe, a methodology for automatically Detecting Latest Local Events from geotagged tweet streams. DeLLe first finds unusual locations which have aggregated unexpected number of tweets, and then ranks the unusual locations to select the top ones that are likely to be local event candidates. We evaluate DeLLe on the city of Seattle, WA as well as a larger city of New York. The results show that the proposed method generally outperforms competitive baseline approaches.
Results of a Randomized Trial of Web-Based Retail Onsite Responsible Beverage Service Training: WayToServe.

OBJECTIVE Research in Responsible Beverage Service (RBS) training indicates that such training can prevent over-service of alcohol and reduce drunk driving. However, reviews of the RBS literature suggest that the quality of training methods and the stability of implementation are important factors in determining RBS training effectiveness. Most RBS classes have been taught live, where the quality of instruction varies across instructors and classes, and the stability of the implementation of an RBS curriculum is variable. Web-based RBS training may carry important advantages by stabilizing instruction and implementation factors. Randomized trial results of a web-based onsite RBS training program (WayToServe® [WTS]) are reported here. It was hypothesized that servers trained by WTS would refuse alcohol service at significantly higher rates compared with Usual and Customary (UC) live training. METHOD On-site alcohol-serving establishments in New Mexico communities were randomized to receive WTS training (n = 154) or the Usual and Customary live RBS training (n = 155). Premises were assessed at baseline, immediate post-training, 6-months post-training, and 1-year post-training intervals. Pseudo-intoxicated patron protocols were used to assess premise alcohol service during the early to mid-evening hours of 6:30 P.M. to 8:30 P.M. , with the percentage of alcohol service refusals to apparently intoxicated pseudo-patrons as the primary outcome variable. RESULTS Results indicate significantly higher refusal rates for WTS than for UC premises at the immediate (WTS = 68% vs. UC = 49%) and the 1-year post-training assessment points (WTS = 68% vs. UC = 58%) but not at the 6-month post-training assessment (WTS = 69% vs. UC = 64%). Differences in refusal rates based on pseudo-patron age were observed where younger pseudo-patrons were consistently refused more often than older pseudo-patrons. CONCLUSIONS Effective RBS training can be delivered online, making it a potentially cost-effective way of reaching large alcohol server populations.