Miracle-dz/UniFAR-FTUs huggingface dataset:
Ratio of Vestibular Endolymph in Patients with Isolated Lateral Semicircular Canal Dysplasia.

PURPOSE Isolated vestibular-lateral semicircular canal dysplasia (LSCCD) is one of the most common anomalies of the inner ear. However, endolymphatic size in LSCCD is unknown. We measured the size of the endolymph in the vestibule of patients with LSCCD and compared it with that measured in patients without LSCCD. METHODS We extracted 1102 magnetic resonance (MR) studies for the evaluation of endolymphatic hydrops (EH) from our database of radiology reports. Among these, we found 15 ears from 11 patients with LSCCD; 4 patients had bilateral abnormalities. Seven of the 15 ears demonstrated aplasia and 8 ears, hypoplasia of the lateral semicircular canal (LSCC). The control group consisted of 26 ears from 13 randomly selected patients without LSCCD. We measured the area of endolymph in the vestibule (ELA), total area of vestibular lymph fluid (TLA), and area of the central bony island (CBI) of the LSCC from axial MR images obtained after intratympanic or intravenous administration of gadolinium-based contrast material. The ratio of endolymphatic area to total lymphatic area (%EL) was defined as %EL = ELA/TLA × 100.We evaluated the correlation between %EL and the area of the CBI and compared age, %EL, degree of cochlear EH, hearing level, and presence of rotating vertigo among the 3 groups (aplasia, hypoplasia, control). RESULTS The mean %EL was 76.7% in the aplasia group, 50.0% in the hypoplasia group, and 27.8% in the control group (P < 0.001). There was a relatively strong linear correlation between the area of the CBI and %EL (r = -0.767). Patient age, mean hearing level, degree of cochlear EH, or presence of vertigo attacks did not differ significantly among the groups (P > 0.05). CONCLUSION The size of vestibular endolymph was larger in the groups with aplasia or hypoplasia than the control group. Thus, the current diagnostic cut-off value for significant vestibular EH (>50%) might not be appropriate for ears with LSCCD.
BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset

Named-entity recognition (NER) can still be regarded as work in progress for a number of Asian languages due to the scarcity of annotated corpora. For this reason, with this paper we publicly release an entity-annotated Persian dataset and we present a performing approach for Persian NER based on a deep learning architecture. In addition to the entity-annotated dataset, we release a number of word embeddings (including GloVe, skip-gram, CBOW and Hellinger PCA) trained on a sizable collation of Persian text. The combination of the deep learning architecture (a BiLSTM-CRF) and the pre-trained word embeddings has allowed us to achieve a 77 . 45% CoNLL F 1 score, a result that is more than 12 percentage points higher than the best previous result and interesting in absolute terms.
Communication and collaboration among return-to-work stakeholders

Abstract Purpose: Workers who are injured or become ill on the job are best able to return-to-work when stakeholders involved in their case collaborate and communicate. This study examined health care providers’ and case managers’ engagement in rehabilitation and return-to-work following workplace injury or illness. Method: In-depth interviews were conducted with 97 health care providers and 34 case managers in four Canadian provinces about their experiences facilitating rehabilitation and return-to-work, and interacting with system stakeholders. Results: A qualitative thematic content analysis demonstrated two key findings. Firstly, stakeholders were challenged to collaborate as a result of: barriers to interdisciplinary and cross-professional communication; philosophical differences about the timing and appropriateness of return-to-work; and confusion among health care providers about the workers' compensation system. Secondly, these challenges adversely affected the co-ordination of patient care, and consequentially, injured workers often became information conduits, and effective and timely treatment and return-to-work was sometimes negatively impacted. Conclusions: Communication challenges between health care providers and case managers may negatively impact patient care and alienate treating health care providers. Discussion about role clarification, the appropriateness of early return-to-work, how paperwork shapes health care providers’ role expectations, and strengthened inter-professional communication are considered. Implications for Rehabilitation Administrative and conceptual barriers in workers’ compensation systems challenge collaboration and communication between health care providers and case managers. Injured workers may become conduits of incorrect information, resulting in adversarial relationships, overturned health care providers’ recommendations, and their disengagement from rehabilitation and return-to-work. Stakeholders should clarify the role of health care providers during rehabilitation and return-to-work and the appropriateness of early return-to-work to mitigate recurring challenges. Communication procedures between health care specialists may disrupt these challenges, increasing the likelihood of timely and effective rehabilitation and return-to-work.
Worldwide emergence of resistance to antifungal drugs challenges human health and food security

The recent rate of emergence of pathogenic fungi that are resistant to the limited number of commonly used antifungal agents is unprecedented. The azoles, for example, are used not only for human and animal health care and crop protection but also in antifouling coatings and timber preservation. The ubiquity and multiple uses of azoles have hastened the independent evolution of resistance in many environments. One consequence is an increasing risk in human health care from naturally occurring opportunistic fungal pathogens that have acquired resistance to this broad class of chemicals. To avoid a global collapse in our ability to control fungal infections and to avoid critical failures in medicine and food security, we must improve our stewardship of extant chemicals, promote new antifungal discovery, and leverage emerging technologies for alternative solutions.
Diacritics Restoration Using Neural Networks

In this paper, we describe a novel combination of a character-level recurrent neural-network based model and a language model applied to diacritics restoration. In many cases in the past and still at present, people often replace characters with diacritics with their ASCII counterparts. Despite the fact that the resulting text is usually easy to understand for humans, it is much harder for further computational processing. This paper opens with a discussion of applicability of restoration of diacritics in selected languages. Next, we present a neural network-based approach to diacritics generation. The core component of our model is a bidirectional recurrent neural network operating at a character level. We evaluate the model on two existing datasets consisting of four European languages. When combined with a language model, our model reduces the error of current best systems by 20% to 64%. Finally, we propose a pipeline for obtaining consistent diacritics restoration datasets for twelve languages and evaluate our model on it. All the code is available under open source license on https://github.com/arahusky/diacritics_restoration .
SumeCzech: Large Czech News-Based Summarization Dataset

Document summarization is a well-studied NLP task. With the emergence of artiﬁcial neural network models, the summarization performance is increasing, as are the requirements on training data. However, only a few datasets are available for Czech, none of them particularly large. Additionally, summarization has been evaluated predominantly on English, with the commonly used ROUGE metric being English-speciﬁc. In this paper, we try to address both issues. We present SumeCzech, a Czech news-based summarization dataset. It contains more than a million documents, each consisting of a headline, a several sentences long abstract and a full text. The dataset can be downloaded using the provided scripts available at http://hdl.handle. net/11234/1-2615 . We evaluate several summarization baselines on the dataset, including a strong abstractive approach based on Transformer neural network architecture. The evaluation is performed using a language-agnostic variant of ROUGE.
Deriving metallicities from calcium triplet spectroscopy in combination with near-infrared photometry

Context. When established with sufficient precision the ages, metallicities and kinematics of the Galactic globular clusters (GGCs) can shed much light on the dynamical and chemical evolution of the Galactic halo and bulge. While the most fundamental way to determine GC abundances is via high resolution spectroscopy, in practice this method is limited to only the brighter stars in the nearest and less reddened objects. This restriction has, over the years, led to the development of a large number of techniques that measure the overall abundance indirectly, from parameters that correlate with overall metallicity. One of the most efficient methods is the measurement of the equivalent width (EW) of the Calcium II Triplet (CaT) at λ ≈ 8500A in red giants, corrected for the luminosity and temperature effects via V magnitude differences from the horizontal branch (HB). Aims. We establish a similar method in the NIR, combining the power of the differential magnitudes technique with the advantages of NIR photometry in minimizing differential reddening effects Methods. We use the Ks magnitude difference between the star and the reddest part of the HB (RHB) or of the Red Clump (RC) to generate reduced equivalent widths (rEW) from the datasets presented in Saviane et al. (2012) and Rutledge et al. (1997) Subsequently we calibrated these rEW against three different metallicity scales: the one presented in Carretta et al. (2009), the metallicity values given in Harris (2010) and a version of the former corrected via high-resolution spectroscopic metallicities. Results. We calculated the calibration relations for the two datasets and the three metallicity scales and found that they are approximately equivalent, with differences almost negligible. We compared our nIR calibrations with the corresponding optical ones, and found them to be equivalent, establishing that the luminosity-corrected rEW using the Ks magnitude is compatible with the one obtained from the V magnitude. We then used the metallicities obtained from the calibration to investigate the internal metallicity distributions of the GCs. Conclusions. We have established that the ([Fe/H]:rEW) relation is independent from the magnitude used for the luminosity correction and find that the calibration relations only change slightly for different metallicity scales. The CaT technique using NIR photometry is thus a powerful tool to derive metallicities. In particular, it can be used to study the internal metallicity spread of a GC. We confirm the presence of at least two metallicity populations in NGC 6656 and find that several other GCs present peculiar metallicity distributions.
Use of Mother Tongue in EFL Classes of Secondary Schools In Jabodebek: Students’ and Teachers’ Perception

This study aims to investigate senior high school students and teachers’ perception of the use of Indonesian in their English classes. To achieve the objective, two sets of questionnaires were administered to gauge the perceptions of 556 students and 15 teachers of 10 senior high schools in Jabodebek (Jakarta, Bogor, Depok, and Bekasi). The findings revealed: (1) the majority of both students and teachers preferred to use Indonesian in their English classes; (2) they believed Indonesian is helpful in language skills development, language components learning, learning materials understanding and classroom interactions; (3) the students preferred the predominantly use of Indonesian, while the teachers preferred the predominantly use of English during the class hour; (4) the higher their grade, the higher amount of English the students would like to have in their English classrooms; and (5) teachers with longer teaching experience tended to expect the use of bigger proportion of Indonesian in English classrooms.
False positive reduction for lung nodule CAD

Computer-aided detection (CAD) algorithms 'automatically' identify lung nodules on thoracic multi-slice CT scans (MSCT) thereby providing physicians with a computer-generated 'second opinion'. While CAD systems can achieve high sensitivity, their limited specificity has hindered clinical acceptance. To overcome this problem, we propose a false positive reduction (FPR) system based on image processing and machine learning to reduce the number of false positive lung nodules identified by CAD algorithms and thereby improve system specificity. To discriminate between true and false nodules, twenty-three 3D features were calculated from each candidate nodule's volume of interest (VOI). A genetic algorithm (GA) and support vector machine (SVM) were then used to select an optimal subset of features from this pool of candidate features. Using this feature subset, we trained an SVM classifier to eliminate as many false positives as possible while retaining all the true nodules. To overcome the imbalanced nature of typical datasets (significantly more false positives than true positives), an intelligent data selection algorithm was designed and integrated into the machine learning framework, thus further improving the FPR rate. Three independent datasets were used to train and validate the system. Using two datasets for training and the third for validation, we achieved a 59.4% FPR rate while removing one true nodule on the validation datasets. In a second experiment, 75% of the cases were randomly selected from each of the three datasets and the remaining cases were used for validation. A similar FPR rate and true positive retention rate was achieved. Additional experiments showed that the GA feature selection process integrated with the proposed data selection algorithm outperforms the one without it by 5%-10% FPR rate. The methods proposed can be also applied to other application areas, such as computer-aided diagnosis of lung nodules.
Nipple Reduction Using the Modified Top Hat Flap

Background: Large nipples, disproportionate to the small areola and breast size, are an ethnic characteristic frequently encountered among Asian female patients. Patients seek correction to improve cosmesis and alleviate psychological and physical discomfort. The authors present a new technique of nipple reduction and describe its potential advantages over other techniques. Methods: Between March of 2003 and April of 2005, 34 nipple reductions were performed in 19 female patients (mean age, 40.5 ± 5.6 years) using the modified top hat flap. The neonipple is designed to reduce the nipple diameter at the superior pole of the nipple while preserving the subdermal plexus. A crescent-shaped section of nipple skin below the proposed neonipple is excised, maintaining the integrity of the neonipple and the central nipple core. Two lateral wing flaps are elevated and trimmed to reduce both nipple height and diameter at the lateral walls of the nipple. The flaps of the neonipple are then sutured to the areola. Results: Postoperative recovery was rapid and uneventful and no complications were encountered. The mean diameter of the hypertrophic nipple was 16.3 ± 2.6 mm (range, 16 to 30 mm). The mean diameter of the neonipple was 7.9 ± 1.7 mm (range, 5 to 11 mm), with an average reduction of 8.4 ± 1.6 mm (range, 5 to 20 mm). At 17.2 ± 2.9 months of follow-up, the neonipple had a natural appearance, with less projection and an inconspicuous scar. There was no statistically significant difference on monofilament sensation testing (p = 0.5829) between reduction nipple and areola in 11 nipples of seven patients. Conclusions: The modified top hat flap requires minimal preoperative planning, is easy to perform, and yields reproducible results. This technique decreases both the diameter and height of any size nipple and can be modified to meet patient preferences. Because the continuity of the neonipple with the subdermal arterial plexus is maintained and the majority of the parenchymal elements are preserved, nipple sensation and circulation remain largely unaffected.
Sarcasm Target Identification: Dataset and An Introductory Approach

Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identiﬁcation ( i.e. , extracting the target of ridicule in a sarcastic sentence). As a benchmark, we introduce a new dataset for the task. This dataset is manually annotated for the sarcasm target in book snippets and tweets based on our formulation of the task. We then introduce an automatic approach for sarcasm target identiﬁcation. It is based on a combination of two types of extractors: one based on rules, and another consisting of a statistical classiﬁer. Our introductory approach establishes the viability of sarcasm target identiﬁcation, and will serve as a baseline for future work.
Frequency Analysis of Honey Bee Buzz for Automatic Recognition of Health Status: A Preliminary Study

The study of honey bee health has received special attention in the last years. Researchers has been monitoring physical variables to determinate the status of the colony. This is a ﬁrst approach in the development of a real time monitoring system to provide useful information to beekeepers that will help them to prevent colony losses. This study presents an analysis of the sound from two colonies of bees in the Mel frequency domain. The ﬁrst is a healthy colony with queen and the second one is a hive with no queen and with a reduced population. Sound samples were acquired for each colony and characterized using Mel Frequency Cepstral Coeﬃcients (MFCC). To summarizes the information, statistical descriptors was obtained for each Mel coeﬃcient. An exploratory analysis of samples revealed two diﬀerent hive characteristics; the presence and lack of a queen bee. For honey bee buzz recognition, a Logistic Regression Model was used. The preliminary results show that it is possible to classify both characteristics obtaining high classiﬁcation rates using a reduced set of features.
The plethysmographic variability index does not predict fluid responsiveness estimated by esophageal Doppler during kidney transplantation

Abstract Research is ongoing to find a noninvasive method of monitoring, which can predict fluid responsiveness in patients undergoing kidney transplantation. To compare the responses to fluid challenges with the Pleth Variability Index, a noninvasive dynamic index derived from plethysmographic variability (Radical 7 pulse oximeter; Masimo Corporation, Irvine, CA), and the esophageal Doppler, the criterion standard. Observational study. University hospital; study from May 2011 and May 2012. Forty-eight patients with end-renal function were included and 44 analyzed. Patients with cardiac failure were not eligible. Fluid challenges were administered during maintenance of general anesthesia but before skin incision and repeated if the patient was deemed to be a “responder” (increase in stroke volume ≥10%). The primary endpoint was to assess if the Pleth Variability Index is an accurate predictor of fluid responsiveness. Among 76 fluid challenges, 38 were considered as positive (increase in stroke volume measured by Doppler ≥10%). Pleth Variability Index was similar at baseline between responders and nonresponder patients. Fluid challenges were associated with a significant decrease in Pleth Variability Index in overall cases (12 [8–14] vs 10 [6–17], P = .050), but it was not able to discriminate between responders (12 [8–15] vs 10 [5–15], P = .650) and nonresponders (11 [6–16] vs 8 [5–14], P = .047). The area under the Receiver Operating Characteristic curve for Pleth Variability Index was 0.49 (0.36–0.62). Pleth Variability Index is not an accurate predictor of fluid responsiveness during kidney transplantation.
Integrating Implicit Leadership Theories, Leader–Member Exchange, Self-Efficacy, and Attachment Theory to Predict Job Performance

The impact of implicit leadership theories on performance and the mechanism linking them have received insufficient theoretical and research attention. Drawing on Bandura's social cognitive theory, the present study contributes theory through examining the assertion that higher congruence between followers' implicit leadership theory and the characteristics of supervisors enhance job performance through higher quality of leader–member exchange and self-efficacy. Moreover, in the proposed model, attachment insecurity was considered as the antecedent of the congruence and leader–member exchange in addition to the moderator of the relationship between them. Capitalizing upon Structural Equation Modeling (SEM), this study tested the model in a field study using a sample of employees in knowledge-oriented firms in Iran. The results suggest that the congruence between followers' implicit leadership theory and the characteristics of supervisors does not directly impact performance and leader–member exchange and self-efficacy are the full mediators. The results also showed that attachment insecurity is the predictor of neither the congruence nor the leader–member exchange. Additionally, attachment insecurity moderates the relationship between these two variables in a way that when attachment insecurity is high, the congruence has more positive impact on leader–member exchange.
Constructing Narrative Event Evolutionary Graph for Script Event Prediction

Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.