Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug-resistant tuberculosis

Purpose of review This review discusses the recent evidence on epidemiology, diagnosis, and treatment of drug-resistant and multidrug-resistant (MDR) tuberculosis (TB), an area where solutions for better diagnosis and treatment continually develop. Recent findings The prevalence of drug resistance has been constantly rising during the recent years. It has peaked in eastern European countries such as Belarus, where a record of 35.5% MDR-TB amongst new cases have been reported from Minsk. New diagnostic tools are becoming available. Xpert MTB/RIF is by far the most promising of these new techniques. Clinical management of drug-resistant TB is still cumbersome. However, after over 40 years of neglect, new drugs are becoming readily available: delamanid, bedaquiline, and PA-824 combined into innovative regimens raise hopes for substantially higher success rates. Summary The innovative diagnostic tools recently validated are changing the traditional paradigms of TB diagnosis, for too long based on sputum smear, culture, and drug susceptibility testing. New anti-TB compounds, which can be combined with several ‘old’ drugs with new indications, are gradually modifying the chances of cure for MDR-TB cases. Although initial evidence appears promising, the market use of new drugs must be accompanied by a serious public health approach aimed at preventing the development of further drug resistance.
Recurrent neural network-based fault detector for aileron failures of aircraft

This paper empirically investigate the design of a fault detection mechanism based on Long Short Term Memory (LSTM) neural network. Given an equation based model that approximate the behavior of aircraft ailerons, the fault detector aims at predicting the state of aircraft: the normal state for which no failure are observed, or four different failure states, e.g. a delay changes. This is achieved by collecting a limited amount of command and responses data by varying the parameters of the aileron model, such that a LSTM network is used to predict the state of the aircraft of sequence of the pair commands/responses. In this empirical study we empirically demonstrated LSTM networks can be a promising approach for fault detection, and achieve reasonable performances despite a limited amount of data, in particular avoiding overfitting of the model.
Konkanverter - A Finite State Transducer based Statistical Machine Transliteration Engine for Konkani Language

We have developed a finite state transducer based transliteration engine called Konkanverter that performs statistical machine transliteration between three different scripts used to write the Konkani language. The statistical machine transliteration system consists of cascading finite state transducers combining both rule-based and statistical approaches. Based on the limited availability of parallel corpora, this cascading approach is found to perform significantly better than a pure rule-based approach or pure statistical approach.
Database-Inspired Search

"W3QL: A Query Language for the WWW", published in 1995, presented a language with several distinctive features. Employing existing indexes as access paths, it allowed the selection of documents using conditions on semi-structured documents and maintaining dynamic views of navigational queries. W3QL was capable of automatically filling out forms and navigating through them. Finally, in the SQL tradition, it was a declarative query language, that could be the subject of optimization.Ten years later, we examine some current trends in the domain of search, namely the emergence of system-level search services and of the semantic web. In this context, we explore whether W3QL's ideas are still relevant to help improve information search and retrieval. We identify two main environments for searching, the enterprise and the web at large. Both environments could benefit from database-inspired integration language, and an execution system that implements it.
sonLP: Social network link prediction by principal component regression

Social networks are driven by social interaction and therefore dynamic. When modeled as a graph, nodes and links are continually added and deleted, and there is considerable interest in social network analysis on predicting link formation. Current work has not adequately addressed three issues: (1) Most link predictors start with using features from the link topology as input. How do features in other dimensions of the social network data affect link formation? (2) The dynamic nature of social networks implies the features driving link formation are constantly changing. How can a predictor automatically select the features that are important for link formation? (3) Node pairs that are not linked can outnumber links by orders of magnitude, but previous work do not address this imbalance. How can we design a predictor that is robust with respect to link imbalance? This paper presents sonLP, a social network link predictor. It uses principal component analysis to identify features that are important to link prediction, its tradeoff between true and false positives is near optimal for a wide range of link imbalance, and it has optimal time complexity. Experiments with coauthorship prediction in the ACM researcher community also show the importance of using features outside the links' dimension.
Development of Phasor Estimation Algorithm for P-Class PMU Suitable in Protection Applications

This paper proposes a phasor estimation algorithm for P-class phasor measurement unit suitable in protection applications using Hilbert transform and convolution of a signal. As the protective relay requires extracted fundamental component of the phasor for its operation, the author’s introduced an algorithm to estimate a robust phasor corresponding to the fundamental component which is close to the actual signal in ${L_{2}}$ -norm. Though IEEE C37.118. 1a-2014 standard does not specify the accuracy requirements of phasor under transient condition, the performance of phasor estimator is tested under different dynamic conditions as per IEEE C37.118. 1a-2014 standard. The effectiveness of proposed algorithm has also been verified on modified two area power system during fault along with the data generated by the experimental setup in laboratory. The results revealed that the proposed algorithm estimates the phasor accurately irrespective of distortion present in the sinusoidal signals. Furthermore, the proposed estimator inherently filters harmonics, immune to decaying dc components, detects sharp changes in a signal during faults, and effectively works under complex modulated conditions. The above scenario appears frequently in a power system with distributed energy sources. The simplicity, robustness, and generality of the proposed algorithm suits for wide area measurement systems to measure the voltage and current phasors during disturbance in the smart power system networks.
Optical MEMS in Bonded Silicon on Insulator

Bonded silicon on insulator (BSOI) material provides an excellent solution for optical MEMS devices, which typically require optical surfaces with high flatness and low roughness to be combined with high quality mechanical parts and low power, high force microactuators. This paper will review a number of different approaches to optical MEMS, including surface tension self-assembly for 3D components such as fixed mirrors and lenses, torsion mirror scanners and active corner cube reflectors, and deep reactive ion etching for variable attenuators, blazed diffraction gratings and tuneable external cavity lasers. Current performance and limitations of tuneable components will be given. Methods of mounting additional macroscopic components such as filters and optical fibres will also be described, together with latching actuators for linear, rotary and
Association between depressive symptoms and morningness-eveningness, sleep duration and rotating shift work in Japanese nurses

ABSTRACT Higher depressive symptoms have been reported in rotating shift workers compared with day workers. Depressive symptoms in adults who do not engage in night work have also been shown to be associated with chronotype and sleep duration. This study examines associations between depressive symptoms, morningness-eveningness (i.e. the degree to which people prefer to be active in the morning or the evening), sleep duration and rotating shift work. Japanese nurses (1252 day workers and 1780 rotating shift workers, aged 20–59) were studied using a self-administered questionnaire. The questionnaire covered depressive symptoms, morningness-eveningness, sleep habits and demographic characteristics of the participants. The Center for Epidemiologic Studies Depression Scale (CES-D) was used to determine the levels of depressive symptoms. A Japanese version of the Morningness-Eveningness Questionnaire (MEQ) was used to measure morningness-eveningness. The CES-D score of shift workers was significantly (p < 0.05) higher than that of day workers. The MEQ score was significantly (p < 0.05) lower (i.e. greater eveningness) in shift workers than in day workers. Sleep duration on the day shift was significantly (p < 0.05) shorter in shift workers than in day workers. Simple linear regression revealed that the MEQ score, sleep duration on the day shift and current work shift (i.e. rotating shift work) were significantly (p < 0.05) associated with the CES-D score. Multivariate linear regression indicated that greater eveningness and shorter sleep duration were independently associated with higher CES-D scores, while rotating shift work was not. These associations between the MEQ score, the sleep duration and the CES-D score were also confirmed in both day workers and shift workers when the groups were analyzed separately. These results suggest that greater eveningness and shorter sleep duration on the day shift were independently associated with higher levels of depressive symptoms, which may explain associations between rotating shift workers and depressive symptoms. These findings have important implications for the development of novel strategies for preventing poor mental health in day workers and rotating shift workers.
Processing negative emotions through social communication: Multimodal database construction and analysis

Emotion-rich data is pre-requisite in the efforts of transferring emotional aspects of human communication into Human-Computer Interaction (HCI). An important facet of human social-affective interaction is its ability to facilitate social sharing of emotion, a fundamental part of the emotional processes. When conducted properly, such an interaction can give a positive effect to emotion-related problems. However, there is still a lack of resources that are: 1) explicitly designed for studying the emotional problems commonly encountered in everyday life, and 2) involving a professional as an expert in the conversation. In this paper, we present recordings of dyadic social-affective interactions between a professional counselor as an expert and 30 participants, summing up to 23 hours and 41 minutes of material. In each interaction, a negative emotion inducer is shown to the dyad, and the goal of the expert is to aid emotion processing and elicit a positive emotional change through the interaction. Specifically, we aim to observe how an external party can guide and facilitate emotion processing, especially after a negative emotional response in a commonly encountered social situation. The construction, development, and analysis of the database is detailed in this paper.
First-Principles Study of Metal -Ceramic Interfaces

Despite their use in a variety of applications, the fundamental properties of metal–ceramic interfaces are still poorly understood. Historically, this has been due to experimental complications associated with the study of a buried interface, and to theoretical difficulties caused by complex interfacial bonding interactions. However, the advent of first-principles techniques based on Density Functional Theory (DFT) has led to new opportunities for highly-accurate computer simulations of the atomic and electronic structure of interfaces. In this study we present a series of DFT calculations on a broad class of Al/ceramic interface systems, with the goal of explaining and predicting the nature of metal–ceramic adhesion. Since the strength of interfacial bonding, as quantified by the ideal work of adhesion Ï , plays a crucial role in determining the mechanical properties of an interface, we have systematically calculated Ï for 20 different interface geometries including oxide («–Al 2 O 3), carbide (WC, VC), and nitride (VN) ceramic compounds with the goal of identifying trends. In order to identify the optimal atomic structures we consider the effects of several stacking sequences, interface terminations (both polar and non-polar), and allow for full atomic relaxations. For each system we use a variety of methods to carefully analyze the interfacial electronic structure in order to determine the nature of the metal–ceramic bonding. Although we find significant differences in both the interfacial bonding and the inherent electronic structures of the bulk ceramics, our calculations reveal a trend in Ï with respect to the summed surface energies of the interfaced materials (Al · ceramic). Specifically, we find that surfaces having larger 's adhere at interfaces more strongly than those with small 's. We argue that this behavior is consistent with the intuitive notion of the surface energy being a measure of surface reactivity, and hence it could be used as a guide in the design of interfacial properties. iii To my parents. iv Acknowledgments My greatest debt is to my advisor, Jim Adams, whose guidance, patience, imagination, and insight have been a positive influence on both my professional and personal development. served as my industrial advisor, and is responsible (or to blame) for introducing me to the practical aspects of ab initio simulation techniques. His enthusiasm, determination, energy, and friendship are greatly appreciated.haripande. (Additional thanks to Richard Martin for serving as my de facto UIUC advisor during Jim's absence.) I have grown immensely from many interactions with other group …
Agree to disagree: Improving disagreement detection with dual GRUs

This paper presents models for detecting agreement/disagreement in online discussions. In this work we show that by using a Siamese inspired architecture to encode the discussions, we no longer need to rely on hand-crafted features to exploit the meta thread structure. We evaluate our model on existing online discussion corpora ABCD, IAC and AWTP. Experimental results on ABCD dataset show that by fusing lexical and word embedding features, our model achieves the state of the art performance of 0.804 average F1 score. We also show that the model trained on ABCD dataset performs competitively on relatively smaller annotated datasets (IAC and AWTP).
Multiresolution wavenet PID control for global regulation of robots

A novel global PID control scheme for nonlinear MIMO systems is proposed and synthesized for a robot manipulator. Identification process is used for online tuning of the discrete linear PID feedback gains. Inverse dynamics identification is based on radial basis neural network with daughter RASP1 wavelets activation functions in cascaded with an infinite impulse response (IIR) filter in the output to prune irrelevant signals and nodes. The closed-loop system guarantees global regulation for a class of nonlinear dynamical systems, convenient for instance in plants whose dynamics are rather uncertain or unknown, such as in commercial robots. Real-time experimental study is carried out on a three degrees of freedom robotic haptic interface, the PHANToM Premium 1.0A. Results highlight the performance in global regulation with smooth control effort, without using the mathematical model of the robot, nor cumbersome tuning procedures typical of PID for robots.
Myocardial microvascular function during acute coronary artery stenosis: effect of hypertension and hypercholesterolaemia.

AIMS Coronary collateral arteries (CCA) reduce cardiovascular events. We tested the hypothesis that new microvessels that proliferate in early atherosclerosis may be associated with myocardial protection during acute subtotal coronary artery obstruction (CAO). METHODS AND RESULTS Acute left anterior descending CAO was induced by a balloon catheter in pigs after 12 weeks of high-cholesterol (HC) diet, renovascular hypertension (HTN), or normal control. Cardiac structure, myocardial perfusion, and functional response to iv adenosine and CAO were studied in vivo using electron beam computed tomography (CT). The intra-myocardial microvessels were subsequently evaluated ex vivo using micro-CT, and myocardial expression of growth factors using immunoblotting. Basal myocardial perfusion and microvascular permeability were similar among the groups, whereas their responses to adenosine were attenuated in HC and HTN. A significant decline in myocardial perfusion in normal pigs during acute CAO was attenuated in HC and abolished in HTN. CAO also elicited an increase in normal anterior wall microvascular permeability (+202 +/- 59%, P < 0.05), which was attenuated in HC and HTN (+55 +/- 9 and +31 +/- 8%, respectively, P < 0.05 vs. normal). Microvascular (<200 microm) spatial density was significantly elevated in HC and HTN, accompanied by increased myocardial growth factor expression. CONCLUSION This study demonstrates that early exposure to the cardiovascular risk factors HC and HTN protects the heart from decreases in myocardial perfusion during acute subtotal CAO. This protective effect is associated with and potentially mediated by pre-emptive development of intra-myocardial microvessels that might serve as recruitable CCA.
Discrete-time observer-based gain-scheduled output feedback controller design with simultaneous optimization of scaling matrices

It is well known that Gain-Scheduled (GS) controllers are very effective for controlling plant systems whose characteristics vary with measurable parameters. Thus, there have been many reports on GS output-feedback controller design problem; however, in controller design for practical systems, multiple uncertainty blocks are often introduced to represent several design requirements. Then, the design problem of GS output feedback controllers and scaling matrices for the multiple uncertainty blocks become a non-convex problem. Furthermore, there are very few reports on the design method for observer-based GS output feedback controllers which have simpler structure than full-order H∞-type GS output feedback controllers. On this issue, we propose a design method for observer-based GS output feedback controllers with the simultaneous optimization of the scaling matrices in terms of dilated Linear Matrix Inequality (LMI). A numerical example demonstrates the effectiveness of the simultaneous optimization of GS controller gains and scaling matrices.
A Predictive Load Balancing Service for Cloud-Replicated Databases

Cloud computing emerges as an alternative to promote quality of service for data-driven applications. Database Management Systems must be available to support the deployment of cloud applications resorting to databases. Many solutions use database replication as a strategy to increase availability and decentralize the workload of database transactions between replicas. By the distribution of database transactions between replicas, load balancing techniques improve the computational resources utilization. However, several solutions use the current state of the database service in making decisions for the distribution of transactions. This paper proposes a predictive load balancing service for replicated cloud databases.
Proteomic analysis of Singapore grouper iridovirus envelope proteins and characterization of a novel envelope protein VP088

Singapore grouper iridovirus (SGIV) is an enveloped virus causing heavy economic losses to marine fish culture. The envelope fractions of SGIV were separated from the purified virions by Triton X‐100 treatment, and subjected to 1‐DE‐MALDI‐TOF/TOF‐MS/MS and LC‐MALDI‐TOF/TOF‐MS/MS analysis. A total of 19 virus‐encoded envelope proteins were identified in this study and 73.7% (13/17) of them were predicted to be membrane proteins. Three viral envelope proteins were uniquely identified by 1‐DE‐MALDI, whereas another ten proteins were identified only by LC‐MALDI, with six proteins identified by both workflows. VP088 was chosen as a representative of proteomic identification and characterized further. VP088 was predicted to be a viral transmembrane envelope protein which contains two RGD (Arg–Gly–Asp) motifs, three transmembrane domains, and five N‐glycosylation sites. VP088 gene transcript was first detected at 12 h p.i. and reached the peak at 48 h p.i. Combined with the drug inhibition assay, VP088 gene was identified as a late (L) gene. Recombinant VP088 (rVP088) was expressed in Escherichia coli, and the specific antiserum against rVP088 was raised. VP088 was proved to be a viral envelope protein by Western blot and immunoelectron microscopy (IEM). Furthermore, rVP088 can bind to a 94 kDa host cell membrane protein, suggesting that VP088 might function as an attaching protein. Neutralization assay also suggested that VP088 is involved in SGIV infection. This study will lead to a better understanding of molecular mechanisms of the iridoviral pathogenesis and virus–host interactions.
Multimodal monitoring of Parkinson's and Alzheimer's patients using the ICT4LIFE platform

The analysis of multimodal data collected by innovative imaging sensors, Internet of Things (IoT) devices and user interactions, can provide smart and automatic distant monitoring of patients and reveal valuable insights for early detection and/or prevention of events related to their health situation. In this paper, we present a platform called ICT4LIFE which starting from low-level data capturing and performing multimodal fusion to extract relevant features, can perform high-level reasoning to provide relevant data on monitoring and evolution of the patient, and trigger proper actions for improving the quality of life of the patient.
Executive Dysfunctions Predict Self-Restricted Driving Habits in Elderly People with or without Alzheimer’s Dementia

Abstract Introduction The purpose of this study was to elucidate the impact of specific cognitive functions on self-restricted driving habits in healthy elderly drivers and patients suffering from mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Method Our study population included 35 cognitively healthy controls, 10 MCI patients, and 16 patients with AD. All participants completed a neuropsychological examination and a self-reported questionnaire assessing driving habits and patterns. Results In challenging driving conditions, patients with MCI or AD showed significantly more driving self-restriction than healthy subjects (effect size d=1.06, p=0.007). Ordinal regression analysis across the entire group revealed that deficits in executive functions and reaction had a higher impact on driving restriction (p=0.002) than deficits in memory functions (p=0.570). Additionally, our data showed that 40% of patients with mild to moderate AD still drive in challenging conditions. Discussion Our results illustrate that elderly individuals use self-imposed driving restrictions as compensatory strategies. These restrictions increase with cognitive decline mainly in the field of executive functions, but they do not change once patients convert from MCI to AD.