Miracle-dz/UniFAR-FTUs huggingface dataset:
Actively Estimating Crowd Annotation Consensus

The rapid growth of storage capacity and processing power has caused machine learning applications to increasingly rely on using immense amounts of labeled data. It has become more important than ever to have fast and inexpensive ways to annotate vast amounts of data. With the emergence of crowdsourcing services, the research direction has gravitated toward putting the wisdom of crowds to better use. Unfortunately, spammers and inattentive annotators pose a threat to the quality and trustworthiness of the consensus. Thus, high quality consensus estimation from crowd annotated data requires a meticulous choice of the candidate annotator and the sample in need of a new annotation. Due to time and budget limitations, it is of utmost importance that this choice is carried out while the annotation collection is in progress. We call this process active crowd-labeling. To this end, we propose an active crowd-labeling approach for actively estimating consensus from continuous-valued crowd annotations. Our method is based on annotator models with unknown parameters, and Bayesian inference is employed to reach a consensus in the form of ordinal, binary, or continuous values. We introduce ranking functions for choosing the candidate annotator and sample pair for requesting an annotation. In addition, we propose a penalizing method for preventing annotator domination, investigate the explore-exploit trade-off for incorporating new annotators into the system, and study the effects of inducing a stopping criterion based on consensus quality. We also introduce the crowd-labeled Head Pose Annotations datasets. Experimental results on the benchmark datasets used in the literature and the Head Pose Annotations datasets suggest that our method provides high-quality consensus by using as few as one fifth of the annotations (~80% cost reduction), thereby providing a budget and time-sensitive solution to the crowd-labeling problem.
Epidemic Spreading Model to Characterize Misfolded Proteins Propagation in Aging and Associated Neurodegenerative Disorders

Misfolded proteins (MP) are a key component in aging and associated neurodegenerative disorders. For example, misfolded Amyloid-ß (Aß) and tau proteins are two neuropathogenic hallmarks of Alzheimer's disease. Mechanisms underlying intra-brain MP propagation/deposition remain essentially uncharacterized. Here, is introduced an epidemic spreading model (ESM) for MP dynamics that considers propagation-like interactions between MP agents and the brain's clearance response across the structural connectome. The ESM reproduces advanced Aß deposition patterns in the human brain (explaining 46∼56% of the variance in regional Aß loads, in 733 subjects from the ADNI database). Furthermore, this model strongly supports a) the leading role of Aß clearance deficiency and early Aß onset age during Alzheimer's disease progression, b) that effective anatomical distance from Aß outbreak region explains regional Aß arrival time and Aß deposition likelihood, c) the multi-factorial impact of APOE e4 genotype, gender and educational level on lifetime intra-brain Aß propagation, and d) the modulatory impact of Aß propagation history on tau proteins concentrations, supporting the hypothesis of an interrelated pathway between Aß pathophysiology and tauopathy. To our knowledge, the ESM is the first computational model highlighting the direct link between structural brain networks, production/clearance of pathogenic proteins and associated intercellular transfer mechanisms, individual genetic/demographic properties and clinical states in health and disease. In sum, the proposed ESM constitutes a promising framework to clarify intra-brain region to region transference mechanisms associated with aging and neurodegenerative disorders.
Genome Sequences of Lactococcus lactis MG1363 (Revised) and NZ9000 and Comparative Physiological Studies

ABSTRACT Lactococcus lactis NZ9000 and its parent MG1363 are the most commonly used lactic acid bacteria for expression and physiological studies. We noted unexpected but significant differences in the growth behaviors of both strains. We sequenced the entire genomes of the original NZ9000 and MG1363 strains using an ultradeep sequencing strategy. The analysis of the L. lactis NZ9000 genome yielded 79 differences, mostly point mutations, with the annotated genome sequence of L. lactis MG1363. Resequencing of the MG1363 strain revealed that 73 out of the 79 differences were due to errors in the published sequence. Comparative transcriptomic studies revealed several differences in the regulation of genes involved in sugar fermentation, which can be explained by two specific mutations in a region of the ptcC promoter with a key role in the regulation of cellobiose and glucose uptake.
The association between sensation seeking and e-cigarette use in Texas young adults: A cross-sectional study

ABSTRACT Objective: To examine the associations between sensation seeking and ever and current e-cigarette use in Texas young adults (18–29 years old). Current cigarette use was examined as a potential effect modifier of the associations. Participants: Participants included college students enrolled in four-year and two-year colleges in four metropolitan areas in Texas (n = 5,418) who completed the survey between November 2014 and February 2015. Methods: This cross-sectional study utilized mixed effects logistic regression to determine the associations between mean sensation seeking scores and ever and current e-cigarette use after controlling for covariates. Results: After controlling for covariates, significant associations between sensation seeking and both ever and current e-cigarette use were observed; however, these associations were significant for noncurrent smokers only (adjusted odds rations [AOR] = 1.55, 95% confidence interval (CI) = 1.39, 1.73; AOR = 1.82, 95% CI = 1.54, 2.15, respectively). Conclusions: Sensation seeking is an important factor in identifying college students who may be at increased risk for e-cigarette use behaviors.
Sub-acute nickel exposure impairs behavior, alters neuronal microarchitecture, and induces oxidative stress in rats’ brain

Abstract Nickel (Ni) is a heavy metal with wide industrial uses. Environmental and occupational exposures to Ni are potential risk factors for neurological symptoms in humans. The present study investigated the behavior and histomorphological alterations in brain of rats sub-acutely exposed to nickel chloride (NiCl2) and the possible involvement of oxidative stress. Rats were administered with 5, 10 or 20 mg/kg NiCl2 via intraperitoneal injections for 21 days. Neurobehavioral assessment was performed using the Y-maze and open field test (OFT). Histomorphological analyses of brain tissues, as well as biochemical determination of oxidative stress levels were performed. Results showed that Ni treatments significantly reduced body weight and food intake. Cognitive and motor behaviors on the Y-maze and OFT, respectively, were compromised following Ni treatments. Administration of Ni affected neuronal morphology in the brain and significantly reduced percentage of intact neurons in both hippocampus and striatum. Additionally, markers of oxidative stress levels and nitric oxide (NO) levels were significantly altered following Ni treatments. These data suggest that compromised behavior and brain histomorphology following Ni exposures is associated with increase in oxidative stress.
Torque Teno Virus 10 Isolated by Genome Amplification Techniques from a Patient with Concomitant Chronic Lymphocytic Leukemia and Polycythemia Vera

An infectious etiology has been proposed for many human cancers, but rarely have specific agents been identified. One difficulty has been the need to propagate cancer cells in vitro to produce the infectious agent in detectable quantity. We hypothesized that genome amplification from small numbers of cells could be adapted to circumvent this difficulty. A patient with concomitant chronic lymphocytic leukemia (CLL) and polycythemia vera (PV) requiring therapeutic phlebotomy donated a large amount of phlebotomized blood to test this possibility. Using genome amplification methods, we identified a new isolate (BIS8-17) of torque teno virus (TTV) 10. The presence of blood isolate sequence 8–17 (BIS8-17) in the original plasma was confirmed by polymerase chain reaction (PCR), validating the approach, since TTV is a known plasma virus. Subsequent PCR testing of plasmas from additional patients showed that BIS8-17 had a similar incidence (~20%) in CLL (n = 48) or PV (n = 10) compared with healthy controls (n = 52). CLL cells do not harbor BIS8-17; PCR did not detect it in CLL peripheral blood genomic deoxyribonucleic acid (DNA) (n = 20). CLL patient clinical outcome or prognostic markers (immunoglobulin heavy chain variable region [IGHV] mutation, CD38 or zeta-chain associated protein kinase 70kDa [ZAP-70]) did not correlate with BIS8-17 infection. Although not causative to our knowledge, this is the first reported isolation and detection of TTV in either CLL or PV. TTV could serve as a covirus with another infectious agent or TTV variant with rearranged genetic components that contribute to disease pathogenesis. These results prove that this method identifies infectious agents and provides an experimental methodology to test correlation with disease.
Autoresonant (Nonstationary) Excitation of the Diocotron Mode in Non-neutral Plasmas

We report on the autoresonant (nonlinear phase locking) manipulation of the diocotron mode in a non-neutral plasma. Autoresonance is a very general phenomena in driven nonlinear oscillator and wave systems, and allows us to control the amplitude of a nonlinear wave without the use of feedback. These are the first controlled laboratory studies of autoresonance in a collective plasma system.
Recognition and Vulnerability Analysis of Key Nodes in Power Grid Based on Complex Network Centrality

The analysis of blackouts, which can inevitably lead to catastrophic damage to power grids, helps to explore the nature of complex power grids but becomes difficult using conventional methods. This brief studies the vulnerability analysis and recognition of key nodes in power grids from a complex network perspective. Based on the ac power flow model and the network topology weighted with admittance, the cascading failure model is established first. The node electrical centrality is further pointed out, using complex network centrality theory, to identify the key nodes in power grids. To effectively analyze the behavior and verify the correctness of node electrical centrality, the net-ability and vulnerability index are introduced to describe the transfer ability and performance under normal operation and assess the vulnerability of the power system under cascading failures, respectively. Simulation results of IEEE 30-bus and IEEE 57-bus test cases show that the key nodes can be effectively identified with high electrical centrality, the resultant cascading failures that eventually lead to a severe decrease in net-ability, verifying the correctness and effectiveness of the analysis.
Cracking Down MapReduce Failure Amplification through Analytics Logging and Migration

MapReduce is popular for big data analytics because it offers easy-to-use map and reduce user interfaces while hiding the complexity of system scalability and fault resiliency issues. While a large body of literature has focused on improving the performance and scalability of MapReduce, the issue of fault resiliency has thus far received little attention. In this paper, we take on an effort to investigate the fault resiliency of MapReduce using YARN (the next-generation Hadoop) as a case study. We reveal that the failures of a MapTask, a ReduceTask or a compute node can cause distinctly different impact to MapReduce programs. Particularly, YARN MapReduce is not able to gracefully handle failures that involve ReduceTasks, causing prolonged task execution, delayed job completion, and, more severely, failure amplifications due to the cascading effects to other tasks. These problems together cause the performance collapse of MapReduce jobs. In this paper, we introduce a new fault-tolerant framework that can crack down failure amplification and gracefully handle failure scenarios. It is designed with two key fault handling techniques: analytics logging and speculative fast migration. Analytics logging is a light-weight mechanism that logs the key progress information of MapReduce tasks, speculative fast migration handles node failures by proactively re-executing MapTasks, migrating ReduceTasks, and collective merging with a pipeline of shuffle/merge and reduce stages. Our performance evaluation demonstrates that these techniques can eliminate failure amplification and deliver fast job execution compared to the existing task re-execution mechanism in MapReduce.
Style and Identity Recognition

Knowledge of the author’s identity and style can by used in the fight against forged and and anonymous documents and illegal actions in the Internet. Nowadays, there are many systems dedicated to solving stylometric tasks, but they are predominantly designed only for a specific task; they are used exclusively by their owners; or they do not natively support any Slavic languages. Therefore, we present new open-source modular system Style & Identity Recognition (SIR). The system is designed to support any stylometric tasks with minimal efforts (or event by default) by combining dynamic stylometry features selection and prediction driven by input data labels. The system is free for non-commercial applications and easy to use, therefore it can be helpful for people dealing with threatening e-mails or sms, children forum protection against pedophiles and other tasks. Being customizable and freely accessible, it can be also used as a baseline for other systems solving stylometry tasks. System combines machine learning techniques and nature language processing tools. It is written in Python and it is dependent on other open-source Python libraries.
Improving efficiency using the Rao–Blackwell theorem in corrected and conditional score estimation methods for joint models

Longitudinal covariates in survival models are generally analyzed using random effects models. By framing the estimation of these survival models as a functional measurement error problem, semiparametric approaches such as the conditional score or the corrected score can be applied to find consistent estimators for survival model parameters without distributional assumptions on the random effects. However, in order to satisfy the standard assumptions of a survival model, the semiparametric methods in the literature only use covariate data before each event time. This suggests that these methods may make inefficient use of the longitudinal data. We propose an extension of these approaches that follows a generalization of Rao–Blackwell theorem. A Monte Carlo error augmentation procedure is developed to utilize the entirety of longitudinal information available. The efficiency improvement of the proposed semiparametric approach is confirmed theoretically and demonstrated in a simulation study. A real data set is analyzed as an illustration of a practical application.
Considering transmission period via network for tele-haptics

In haptic communication via the network, transmission period is not able to be designed uniquely since it restricted by the line bandwidth, transmission protocol, hardware constraints and so on. To address this problem, downsampling is often employed. Additionally, to enhance the performance of downsampled system, multirate control is proposed. However, the quantitative analysis on downsampling and multirate control are not treated. Therefore, this paper treats downsampling and multirate control system, and performs quantitative analysis by using hybrid parameter. In order to reveal the most effective combination of holder and single or multirate, holders (ZOH, DFOH and PFOH) are considered explicitly, and it is formulated to Laplace domain. The effectiveness of each combination is verified through experiments.
Natural History of Conservatively Managed Ureteral Stones: Analysis of 6600 Patients.

INTRODUCTION Ureteral colic has a lifetime prevalence of 10%-15% and is one of the most common emergency urologic presentations. Current European Association of Urology recommends conservative management for "small" (<6 mm) ureteral stones if active removal is not indicated. It is important to understand the natural history of ureteral stone disease to help counsel patients with regard to their likelihood of stone passage and anticipated time frame with which they could be safely observed. We aimed to conduct a systematic review to better establish the natural history of stone expulsion. METHODOLOGY Literature search was performed using Cochrane and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Outcome measures were patient and stone demographics, expulsion rates, expulsion times, and side effect of the medication. A cumulative analysis, with subgroup analysis, was performed on stone location and size. The results were depicted as percentages and an intention-to-treat basis was used. RESULTS The literature search identified 70 studies and a total of 6642 patients, with a median age of 46 and range of 18-74 years. Overall, 64% of patients successfully passed their stones spontaneously. About 49% of upper ureteral stones, 58% of midureteral stones, and 68% of distal ureteral stones passed spontaneously. Almost 75% of stones <5 mm and 62% of stones ≥5 mm passed spontaneously. The average time to stone expulsion was about 17 days (range 6-29 days). Nearly 5% of participants required rehospitalization due to a deterioration of their condition and only about 1% of patients experienced side effects from analgesia provided. CONCLUSION We believe this current review is the largest study for the evaluation of natural history of ureteral stones. The evidence suggests that ureteral stones will pass without intervention in 64% of patients, however, this varies from nearly 50%-75% depending on the size and location, in the span of 1-4 weeks.
Pathophysiological and behavioral deficits in developing mice following rotational acceleration-deceleration traumatic brain injury

ABSTRACT Abusive head trauma (AHT) is the leading cause of death from trauma in infants and young children. An AHT animal model was developed on 12-day-old mice subjected to 90° head extension-flexion sagittal shaking repeated 30, 60, 80 and 100 times. The mortality and time until return of consciousness were dependent on the number of repeats and severity of the injury. Following 60 episodes of repeated head shakings, the pups demonstrated apnea and/or bradycardia immediately after injury. Acute oxygen desaturation was observed by pulse oximetry during respiratory and cardiac suppression. The cerebral blood perfusion was assessed by laser speckle contrast analysis (LASCA) using a PeriCam PSI system. There was a severe reduction in cerebral blood perfusion immediately after the trauma that did not significantly improve within 24 h. The injured mice began to experience reversible sensorimotor function at 9 days postinjury (dpi), which had completely recovered at 28 dpi. However, cognitive deficits and anxiety-like behavior remained. Subdural/subarachnoid hemorrhage, damage to the brain-blood barrier and parenchymal edema were found in all pups subjected to 60 insults. Proinflammatory response and reactive gliosis were upregulated at 3 dpi. Degenerated neurons were found in the cerebral cortex and olfactory tubercles at 30 dpi. This mouse model of repetitive brain injury by rotational head acceleration-deceleration partially mimics the major pathophysiological and behavioral events that occur in children with AHT. The resultant hypoxia/ischemia suggests a potential mechanism underlying the secondary rotational acceleration-deceleration-induced brain injury in developing mice. Summary: A neonatal mouse model that mimics abused head trauma in children offers a novel and reliable tool for finding mechanisms underlying the trauma and assessing treatments.
In-vivo performance of impedance spectroscopy, laser fluorescence, and bitewing radiographs for occlusal caries detection.

OBJECTIVE To evaluate the clinical performance of impedance spectroscopy, laser fluorescence, and bitewing radiographs in detecting occlusal caries and compare them with visual scores. METHOD AND MATERIALS In 62 adults, one occlusal surface per person was selected and independently examined by two examiners using the visual ICDAS scoring system, CarieScan PRO (ACIS), DIAGNOdent pen (LF-pen), and bitewing radiographs. The procedures were repeated within 1 to 4 weeks. The diagnostic performance was expressed as sensitivity, specificity, predictive values, accuracy, and diagnostic odds ratio (DOR) using ICDAS as gold standard. Intra- and inter-examiner reproducibility was assessed with intra-class correlation coefficients (ICC) for numerical results and kappa values for categorical data. Five patients dropped out due to no-shows or restorative care. RESULTS In total, 54% of the teeth had early or no lesions (ICDAS 0 to 2) while 46% exhibited moderate to extensive lesions (ICDAS 3 to 5). The Spearman correlation coefficients were 0.65, 0.60, and 0.71 for ICDAS vs ACIS, LF-pen, and bitewing radiographs, respectively. ACIS demonstrated high specificity and positive predictive values but low sensitivity, whereas LF-pen had moderate sensitivity and high specificity. Accuracy and DOR was highest for bitewing radiographs. The ICC values ranged between 0.65 and 0.88 for ACIS and 0.89 and 0.94 for LF-pen. The weighted kappa values were 0.81 to 0.91 for ICDAS and 0.90 to 0.92 for bitewing radiographs. CONCLUSION All three methods were useful for detecting occlusal caries but bitewing radiography exhibited the best performance when compared with visual scoring. ACIS displayed the highest specificity and positive predictive value but the sensitivity was low and the clinical handling was less convenient. Further clinical studies are needed to evaluate the long-term effects of early caries detection on dental health.
A novel submodule capacitor voltage balancing scheme for hybrid cascaded multilevel converter by injection of zero sequence current

Multilevel converters with fault tolerant capability are getting increased importance in the HVDC systems. This paper describes the working principle and submodule capacitor voltage balancing issues of the Hybrid Cascaded Multilevel converter (HCMC). The converter involves lesser number of sub-modules and provides excellent DC side fault tolerant capability in an HVDC system. The paper analyzes the submodule capacitor voltage balancing issues associated with this converter. A novel submodule capacitor voltage balancing scheme for the HCMC by the injection of zero sequence current is proposed in this paper. The efficacy of the proposed scheme is verified with the help of simulation study done in PSCAD/EMTDC.