Miracle-dz/UniFAR-FTUs huggingface dataset:
Word sense disambiguation features for taxonomy extraction

Many NLP tasks, such as fact extraction, coreference reso- lution and alike, rely on existing lexical taxonomies or ontologies. One of possible ways to create a lexical taxonomy is to extract taxonomic re- lations from monolingual dictionary or encyclopedia: a semi-formalized resource designed to contain many such relations. Word-sense disam- biguation (WSD) is a mandatory tool in such approaches. Quality of extracted taxonomy greatly depends on WSD results. Most WSD approaches can be formulated as machine learning task. For this sake feature representation ranges from collocation vectors as in Lesk algorithm or neural network features in word2vec to highly specialized vector sense representation models such as AdaGram. In this paper we apply several WSD algorithms to dictionary dentions. Our main focus is on inuence of dierent approaches to extract WSD features from dictionary denitions on WSD performance.
Crosstalk between mitogen-activated protein kinase inhibitors and transforming growth factor-β signaling results in variable activation of human dermal fibroblasts

Fibroblast activation is a key step in the establishment of skin fibrosis induced by acute injury, and it is characterized by the differentiation of plastic resident tissue fibroblasts into contractile, extracellular matrix-secreting myofibroblasts. As fibroblast activation must be regulated in vivo, fibroblasts receive signals from the surrounding environment that initiate their fibrotic program. Thus, the present study investigated the effects of mitogen-activated protein kinase (MAPK) signaling pathways on fibroblast activation. It was demonstrated in primary human dermal fibroblasts that small molecule-mediated inhibition of extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) potentiated fibroblast activation, and that small molecule-mediated inhibition of p38 antagonized fibroblast activation. ERK and JNK inhibition cooperatively enhanced fibroblast activation mediated by treatment with exogenous transforming growth factor (TGF)-β1, and p38 inhibition antagonized ERK inhibitor-mediated or JNK inhibitor-mediated fibroblast activation. Transcript analysis demonstrated that ERK and JNK inhibitor-mediated fibroblast activation was accompanied by distinct changes in the expression of TGF-β-associated ligands and receptors, and that p38 inhibitor-mediated antagonism of fibroblast activation was accompanied by a distinct expression paradigm of TGF-β-associated genes, including upregulation of beta-glycan. ERK inhibitor-mediated and JNK inhibitor-mediated fibroblast activation was partially antagonized by small molecule-mediated inhibition of TGF-β receptor (R)1, indicating that these mechanisms of fibroblast activation are partially dependent on TGF-β/TGF-βR signaling. These data collectively demonstrate and provide partial explanations of the varied effects and pathway dependencies of MAPK inhibitor-mediated effects on fibroblast activation.
Challenges in administrative data linkage for research

Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the ‘black box’ of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i) the data linkage environment and privacy preservation; (ii) the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods) and (iii) linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.
Robust Audio Adversarial Example for a Physical Attack

We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not able to perform such a physical attack because of reverberation and noise from playback environments. In contrast, our method obtains robust adversarial examples by simulating transformations caused by playback or recording in the physical world and incorporating the transformations into the generation process. Evaluation and a listening experiment demonstrated that our adversarial examples are able to attack without being noticed by humans. This result suggests that audio adversarial examples generated by the proposed method may become a real threat.
A Detailed Full-Cell Model of a 2018 Commercial PERC Solar Cell in Quokka3

An unprecedented detailed model of a full-size passivated emitter and rear cell (PERC) solar cell design, as manufactured at a current Trina Solar production-line during ramp-up, is presented. Combining a newly proposed multidomain approach with the multiscale skin-concept of Quokka3, the 15.6 cm × 15.6 cm three-dimensional cell geometry including the details of the emitter skins can thoroughly be solved within a single simulation. The multidomain approach uses an inner and two edge domains as irreducible symmetry elements, each containing the unequal front and rear pitch, the dashed rear contacts, as well as part of the busbars and consequently the full finger resistance. The full-cell current density is then determined by simple area-averaging, opposed to the more complicated common approach of coupling it with a distributed network model. The multiscale skin approach enables to model all emitter parts of the PERC cell in detail (accounting for dopant profiles, front surface recombination, Fermi–Dirac statistics, etc.), whereas the other skin regions can still be described by their lumped properties, i.e., Rsheet and J0/Seff. A complete set of carefully established electrical and optical input parameters as well as a detailed loss breakdown is presented, providing fellow researchers with a point of reference for modeling a state-of-the-art PERC solar cell in 2018.
Novel Tretinoin 0.05% Lotion for the Once-Daily Treatment of Moderate-to-Severe Acne Vulgaris: Assessment of Efficacy and Safety in Patients Aged 9 Years and Older.

BACKGROUND Topical tretinoin has been extensively studied in clinical trials, and its essential role in the treatment of acne vulgaris (acne) established through evidence-based guidelines. OBJECTIVE To evaluate efficacy, safety, and tolerability of a novel tretinoin 0.05% lotion in moderate-to-severe acne in patients aged 9 years and older. METHODS A total of 1640 patients, 9-58 years of age were randomized to receive tretinoin 0.05% lotion or vehicle in two double-blind, placebo-controlled 12-week, 2-arm, parallel group studies evaluating safety and efficacy (inflammatory and noninflammatory lesion counts and acne severity using Evaluator Global Severity Scores [EGSS]). In addition, patients completed a patient satisfaction survey (PSS), Acne-specific quality of life (QoL) questionnaire and assessed their facial skin for shininess/oiliness improvement. The data from these two independent studies were pooled and analyzed. RESULTS Tretinoin 0.05% lotion demonstrated statistically significant superiority to vehicle in reducing inflammatory and noninflammatory lesion counts (both P less than .001) at week 12 and improving acne severity (P less than .001). At week 12, mean percent change in inflammatory and noninflammatory lesions were 52% and 46%, respectively. Treatment success (a 2-grade improvement in EGSS and 'clear' or 'almost clear' was reported in 18% of patients. Tretinoin 0.05% lotion also showed significantly greater benefits relative to vehicle control in terms of patient satisfaction (P less than .001) and acne-specific QoL domains. Tretinoin 0.05% lotion was very well tolerated with no substantive differences in cutaneous tolerability among treatment groups. No patients discontinued treatment because of adverse events. LIMITATIONS Data from controlled studies may differ from clinical practice. CONCLUSIONS Tretinoin 0.05% lotion provides statistically significant greater efficacy than vehicle with a highly favorable safety and tolerability profile in moderate-to-severe acne patients. J Drugs Dermatol. 2018;17(10):1084-1091.
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on properties of their inputs (e.g. the i.i.d. assumption), tend to fail silently. This paper explores the problem of building ML systems that fail loudly, investigating methods for detecting dataset shift, identifying exemplars that most typify the shift, and quantifying shift malignancy. We focus on several datasets and various perturbations to both covariates and label distributions with varying magnitudes and fractions of data affected. Interestingly, we show that across the dataset shifts that we explore, a two-sample-testing-based approach, using pre-trained classifiers for dimensionality reduction, performs best. Moreover, we demonstrate that domain-discriminating approaches tend to be helpful for characterizing shifts qualitatively and determining if they are harmful.
Abnormal-Node Detection Based on Spatio-Temporal and Multivariate-Attribute Correlation in Wireless Sensor Networks

In wireless sensor networks (WSNs), data can be subject to malicious attacks and failures, leading to unreliability. This vulnerability poses a challenge to environmental monitoring applications by creating false alarms. To guarantee a trustworthy system, we therefore need to detect abnormal nodes. In this paper, we propose a new framework for detecting abnormal nodes in clustered heterogeneous WSNs. It makes use of observed spatiotemporal (ST) and multivariate-attribute (MVA) sensor correlations, while considering the background knowledge of the monitored environment. Based on the ST correlations, the collected data is analyzed by computing the crosscorrelation between sensor streams. A new method is proposed for evaluating the intensity of the correlation between two sensor streams. The crosscorrelation value obtained is compared against two thresholds, the lag threshold and the correlation threshold. Based on available background knowledge and the observed MVA correlations, a number of rules are presented to detect abnormal nodes while identifying real events. Our experiments on real-world sensor data demonstrate that our approach captures the correlation and discovers abnormal nodes efficiently.
A 3D Convolutional Neural Network Framework for Polyp Candidates Detection on the Limited Dataset of CT Colonography

Proper training of convolutional neural networks (CNNs) requires annotated training datasets oflarge size, which are not currently available in CT colonography (CTC). In this paper, we propose a well-designed framework to address the challenging problem of data shortage in the training of 3D CNN for the detection of polyp candidates, which is the first and crucial part of the computer-aided diagnosis (CAD) of CTC. Our scheme relies on the following two aspects to reduce overfitting: 1) mass data augmentation, and 2) a flat 3D residual fully convolutional network (FCN). In the first aspect, we utilize extensive rotation, translation, and scaling with continuous value to provide numerous data samples. In the second aspect, we adapt the well-known V-Net to a flat residual FCN to resolve the problem of detection other than segmentation. Our proposed framework does not rely on accurate colon segmentation nor any electrical cleansing of tagged fluid, and experimental results show that it can still achieve high sensitivity with much fewer false positives. Code has been made available at: http://github.com/chenyzstju/ctc_screening_cnn.
Sensitivity of Cross-Trained Deep CNNs for Retinal Vessel Extraction

Automatic segmentation of vascular network is a critical step in quantitatively characterizing vessel remodeling in retinal images and other tissues. We proposed a deep learning architecture consists of 14 layers to extract blood vessels in fundoscopy images for the popular standard datasets DRIVE and STARE. Experimental results show that our CNN characterized by superior identifying for the foreground vessel regions. It produces results with sensitivity higher by 10% than other methods when trained by the same data set and more than 1% with cross training (trained on DRIVE, tested with STARE and vice versa). Further, our results have better accuracy $> 0 . 95$% compared to state of the art algorithms.
Learning syntactic tagging of Macedonian language

This paper presents the creation of machine learning based systems for Part-of-speech tagging of Macedonian language. Four well-known PoS tagger systems implemented for English and Slavic languages: TnT, cyclic dependency network, guided learning framework for bidirectional sequence classification, and dynamic features induction were trained. Orwell’s novel “1984” was manually tagged from the authors and it was used split into training and test set. After the training of the models, a comparison between the models was made. At the end, a POS tagger with an accuracy that reaches 97.5% was achieved, making it very appropriate for the future grammatical tagging of the National corpus of Macedonian language, which is currently in its initial stage. The Part-of-speech tagger that was create is published online and free to use.
InK: Reactive Kernel for Tiny Batteryless Sensors

Tiny energy harvesting battery-free devices promise maintenance free operation for decades, providing swarm scale intelligence in applications from healthcare to building monitoring. These devices operate intermittently because of unpredictable, dynamic energy harvesting environments, failing when energy is scarce. Despite this dynamic operation, current programming models are static; they ignore the event-driven and time-sensitive nature of sensing applications, focusing only on preserving forward progress while maintaining performance. This paper proposes InK; the first reactive kernel that provides a novel way to program these tiny energy harvesting devices that focuses on their main application of event-driven sensing. InK brings an event-driven paradigm shift for batteryless applications, introducing building blocks and abstractions that enable reacting to changes in available energy and variations in sensing data, alongside task scheduling, while maintaining a consistent memory and sense of time. We implemented several event-driven applications for InK, conducted a user study, and benchmarked InK against the state-of-the-art; InK provides up to 14 times more responsiveness and was easier to use. We show that InK enables never before seen batteryless applications, and facilitates more sophisticated batteryless programs.
Correlation Analyses Between Personality Traits and Personal Behaviors Under Specific Emotion States Using Physiological Data from Wearable Devices

In addition to the computational ability, human-like characteristics such as behavior, emotion, and personality can also be augmented to many personalized computers, applications robots and other systems. In recent years, there are many studies about human characteristics by using rich personal data collected from information systems and ubiquitous devices such as wearables. Besides separated studies on each aspect of human behavior, emotion and personality by using the personal data, it is also necessary to further study various relationships among these human characteristics. Therefore, this research is to examine how personality traits are associated with personal behaviors under specific emotional states based on physiological data collected from three wearable devices, Emotive Insight, Spire Stone and Huawei Fit Watch. Experimental data was gathered from 50 participants subjected to; a Big Five Inventory (BFI) questionnaire to get their personality traits, presenting before a crowd and/or watching a movie where physiological data measured by wearables. Attributes of personal behavior, e.g. blink, wink, surprise, furrow, smile, and clench, are analyzed correlatively with the participants' personality traits under respective emotion states of excitement, relaxation, stress, engagement, interest, and focus. Finally, we identify significant attribute correlations and find that correlations between the personality traits and the personal behaviors are greatly depended on the emotional states.
Identification of Potential Gene Interactions in Heart Failure Caused by Idiopathic Dilated Cardiomyopathy

Background Many heart failure (HF) cases are caused by idiopathic dilated cardiomyopathy (iDCM). This study explored the mechanisms of the development and progression of HF caused by iDCM. Material/Methods The gene expression profiles of 102 samples were downloaded from the GEO database (GSE5406). Differentially expressed genes (DEGs) were identified through GO analysis and a KEGG pathway analysis, respectively. A protein–protein interaction (PPI) network was constructed and analyzed to screen potential regulatory proteins. In addition, MCODE and a cytoHubba plugin were used to identify the module and hub genes of DEGs. Finally, transcription factors (TFs) were predicted using PASTAA. We did not perform whole-exome sequencing (WES) for detecting mitochondrial DNA (mtDNA). Results A total of 197 DEGs were screened, and 3 modules, and 4 upregulated and 11 downregulated hub genes were screened. The GO analysis focused on the terms and 12 KEGG pathways were enriched. The FOS, TIMP1, and SERPINE1 hub genes, as well as some key TFs, demonstrated important roles in the progression of HF caused by iDCM. CEBPD, CEBOB, CDC37L1, and SRGN may be new targets for HF in iDCM patients. Conclusions The identified DEGs and their enriched pathways provide references for exploring the mechanisms of the development and progression of HF patients with iDCM. Moreover, modules, hub genes, and TFs may be useful in the treatment and diagnosis of HF patients with iDCM. However, mtDNA was not investigated.
Applications of Metaheuristics in Reservoir Computing Techniques: A Review

Reservoir computing approaches have been around for almost two decades. They were developed to solve the difficult gradient-descent training of recurrent neural networks. However, in reservoir computing, the choice of parameters and architecture often leads to an optimization challenge. Most early applications have used trial and error with expert knowledge to select the right value for parameter(s)/architecture. This approach is usually cumbersome and difficult considering the large search space. Metaheuristics have been known to perform well in solving such kinds of problems. This review discusses areas where metaheuristics are used in the echo state network—a pioneer in the reservoir computing field. In addition, trends and research gaps are also discussed.
Predictors of cardiovascular outcome and rehospitalization in elderly patients with heart failure

Heart failure (HF) is a major public health problem and represents the only cardiac disease continuing to increase in prevalence, in particular among elderly patients. The frequent rehospitalizations have a negative impact on quality of life of patients with HF, constituting a substantial cost for patients and the health system. The aim of this review was to look into biochemical, echocardiographic and socioeconomical parameters as predictors of clinical outcomes and rehospitalizations.
Scheduling in Concentrating Solar Power Plants Based on Model Predictive Control and Binary-Regularization Methods

This paper proposes to use model predictive control with binary-regularization $(\mathbf{MPC}^{BR})$ to manage the generation problem in concentrating solar power (CSP) plants with thermal energy storage (TES). The main advantage of the proposed method is to allow rescheduling the generation at regular periods taking advantage of the most recent energy prices and weather forecast, and of the current plant's state. A second benefit of the $\mathbf{MPC} ^{BR}$ formulation is the inclusion of a turbine protection method based on a binary-regularization term that penalizes for changes of the turbine output and binary constraints that limit the number of daily turbine startups. Binary variables are used to avoid penalization of the turbine start-up and shutdown. These extensions of the classical MPC approach aim to preserve the turbine lifetime. An interesting question is if this protection mechanism affect the economic results of the CSP plant. An included economic study shows that the proposed scheduling method provides a good trade-off between the economic profits obtained from energy sales and the protection of the generation block. The study is based on a realistic simulation of a 50 MW parabolic trough collector-based CSP with TES under the assumption of participation in the Spanish day-ahead energy market scenario.
Investigation of Enhanced Tacotron Text-to-speech Synthesis Systems with Self-attention for Pitch Accent Language

End-to-end speech synthesis is a promising approach that directly converts raw text to speech. Although it was shown that Tacotron2 outperforms classical pipeline systems with regards to naturalness in English, its applicability to other languages is still unknown. Japanese could be one of the most difficult languages for which to achieve end-to-end speech synthesis, largely due to its character diversity and pitch accents. Therefore, state-of-the-art systems are still based on a traditional pipeline framework that requires a separate text analyzer and duration model. Towards end-to-end Japanese speech synthesis, we extend Tacotron to systems with self-attention to capture long-term dependencies related to pitch accents and compare their audio quality with classical pipeline systems under various conditions to show their pros and cons. In a large-scale listening test, we investigated the impacts of the presence of accentual-type labels, the use of force or predicted alignments, and acoustic features used as local condition parameters of the Wavenet vocoder. Our results reveal that although the proposed systems still do not match the quality of a top-line pipeline system for Japanese, we show important stepping stones towards end-to-end Japanese speech synthesis.