Miracle-dz/UniFAR-FTUs huggingface dataset:
Stylometry in a Bilingual Setup

The method of stylometry by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. For instance, in a bilingual Czech-German text collection containing parallel texts (originals and translations in both directions, along with Czech and German translations from other languages), authors would not cluster across languages, since frequency word lists for any Czech texts are obviously going to be more similar to each other than to a German text, and the other way round. We have tried to come up with an interlingua that would remove the language-specific features and possibly keep the linguistically independent features of individual author signal, if they exist. We have tagged, lemmatized, and parsed each language counterpart with the corresponding language model in UDPipe, which provides a linguistic markup that is cross-lingual to a significant extent. We stripped the output of language-dependent items, but that alone did not help much. As a next step, we transformed the lemmas of both language counterparts into shared pseudolemmas based on a very crude Czech-German glossary, with a 95.6% success. We show that, for stylometric methods based on the most frequent words, we can do without translations.
Prevalence and clinical profiles of 'autoantibody-negative' systemic sclerosis subjects.

OBJECTIVES To determine the prevalence of autoantibody negative systemic sclerosis (SSc) and to identify the clinical correlates thereof. METHODS Clinical data and sera from 874 SSc subjects were collected and autoantibodies were tested in a central laboratory using 1) indirect immunofluorescence (IIF), 2) commercially available ELISA, addressable laser bead immunoassay (ALBIA), and line immunoassay (LIA), and 3) a sensitive immunoprecipitation (IP) assay. RESULTS Fifteen (15; 1.7%) subjects were autoantibody negative by IIF, ELISA, ALBIA, LIA and IP, and 16 (1.8%) were antinuclear antibody (ANA) positive by IIF but otherwise negative by ELISA, ALBIA, LIA and IP. Thirty-seven (37; 4.2%) were ANA positive by IIF, autoantibody negative by commercially available immunoassays, but had autoantibodies identified by IP (including Th/To in 20). Autoantibody-negative subjects had generally less severe disease than positive subjects. CONCLUSIONS Autoantibody-negative SSc is rare (<2%) and appears to be associated with a favourable prognosis.
Burmese Speech Corpus, Finite-State Text Normalization and Pronunciation Grammars with an Application to Text-to-Speech

This paper introduces an open-source crowd-sourced multi-speaker speech corpus along with the comprehensive set of finite-state transducer (FST) grammars for performing text normalization for the Burmese (Myanmar) language. We also introduce the open-source finite-state grammars for performing grapheme-to-phoneme (G2P) conversion for Burmese. These three components are necessary (but not sufficient) for building a high-quality text-to-speech (TTS) system for Burmese, a tonal Southeast Asian language from the Sino-Tibetan family which presents several linguistic challenges. We describe the corpus acquisition process and provide the details of our finite state-based approach to Burmese text normalization and G2P. Our experiments involve building a multi-speaker TTS system based on long short term memory (LSTM) recurrent neural network (RNN) models, which were previously shown to perform well for other languages in a low-resource setting. Our results indicate that the data and grammars that we are announcing are sufficient to build reasonably high-quality models comparable to other systems. We hope these resources will facilitate speech and language research on the Burmese language, which is considered by many to be low-resource due to the limited availability of free linguistic data.
Class-based LSTM Russian Language Model with Linguistic Information

In the paper, we present class-based LSTM Russian language models (LMs) with classes generated with the use of both word frequency and linguistic information data, obtained with the help of the “VisualSynan” software from the AOT project. We have created LSTM LMs with various numbers of classes and compared them with word-based LM and class-based LM with word2vec class generation in terms of perplexity, training time, and WER. In addition, we performed a linear interpolation of LSTM language models with the baseline 3-gram language model. The LSTM language models were used for very large vocabulary continuous Russian speech recognition at an N-best list rescoring stage. We achieved significant progress in training time reduction with only slight degradation in recognition accuracy comparing to the word-based LM. In addition, our LM with classes generated using linguistic information outperformed LM with classes generated using word2vec. We achieved WER of 14.94 % at our own speech corpus of continuous Russian speech that is 15 % relative reduction with respect to the baseline 3-gram model.
Explorer Learning to Generate Product Reviews from Attributes

Automatically generating product reviews is a meaningful, yet not well-studied task in sentiment analysis. Traditional natural language generation methods rely extensively on hand-crafted rules and predefined templates. This paper presents an attention-enhanced attribute-to-sequence model to generate product reviews for given attribute information, such as user, product, and rating. The attribute encoder learns to represent input attributes as vectors. Then, the sequence decoder generates reviews by conditioning its output on these vectors. We also introduce an attention mechanism to jointly generate reviews and align words with input attributes. The proposed model is trained end-to-end to maximize the likelihood of target product reviews given the attributes. We build a publicly available dataset for the review generation task by leveraging the Amazon book reviews and their metadata. Experiments on the dataset show that our approach outperforms baseline methods and the attention mechanism significantly improves the performance of our model.
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings

We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.
Inferences for Lexical Semantic Resource Building with Less Supervision

Lexical semantic resources may be built using various approaches such as extraction from corpora, integration of the relevant pieces of knowledge from the pre-existing knowledge resources, and endogenous inference. Each of these techniques needs human supervision in order to deal with the potential errors, mapping difficulties or inferred candidate validation. We detail how various inference processes can be employed for the less supervised lexical semantic resource building. Our experience is based on the combination of different inference techniques for multilingual resource building and evaluation.
Multiword Expression aware Neural Machine Translation

Multiword Expressions (MWEs) are a frequently occurring phenomenon found in all natural languages that is of great importance to linguistic theory, natural language processing applications, and machine translation systems. Neural Machine Translation (NMT) architectures do not handle these expressions well and previous studies have rarely addressed MWEs in this framework. In this work, we show that annotation and data augmentation, using external linguistic resources, can improve both translation of MWEs that occur in the source, and the generation of MWEs on the target, and increase performance by up to 5.09 BLEU points on MWE test sets. We also devise a MWE score to specifically assess the quality of MWE translation which agrees with human evaluation. We make available the MWE score implementation – along with MWE-annotated training sets and corpus-based lists of MWEs – for reproduction and extension.
Recovery from ischemic acute kidney injury by spironolactone administration.

BACKGROUND Prophylactic mineralocorticoid receptor (MR) antagonism with spironolactone (Sp) in rats completely prevents renal damage induced by ischemia. Because acute renal ischemia cannot typically be predicted, this study was designed to investigate whether Sp could prevent renal injury after an ischemic/reperfusion insult. METHODS Six groups of male Wistar rats were studied: rats that received a sham abdominal operation (S); rats that underwent 20 min of ischemia and reperfusion for 24 h (I/R) and four groups of rats treated with Sp (20 mg/kg) 0, 3, 6 or 9 h after ischemia. RESULTS As expected, I/R resulted in renal dysfunction characterized by a fall in renal blood flow and glomerular filtration rate and severe tubular injury which was confirmed by a significant increase in tubular damage biomarkers including kidney injury molecule-1, heat shock protein 72 and urinary protein excretion. The renal injury induced by I/R was in part due to Rho-kinase, endothelin and angiotensin II type 1 receptor upregulation. Interestingly, Sp administration at 0 and 3 h after ischemia completely reversed and prevented the damage induced by I/R. The protection induced by Sp given 6 h after ischemia was partial, but no protection was observed by administering Sp 9 h after ischemia. CONCLUSION Our results show that MR antagonism administered, either immediately or 3 h after I/R, effectively prevented ischemic acute renal injury, indicating that spironolactone is a promising agent for preventing acute kidney injury once an ischemic insult has occurred.
Effects of the rate of computer-mediated speech on emotion-related subjective and physiological responses

In this paper, we describe the results of a psychophysiological study on user's judgements on, and physiological responses to, audio news, which were presented on computer with a fast and slow reading rate. Several effects of condition, age, gender, level of education and level of computer-user experience on the evaluation of the understandability, valence, arousal and interest of the news, as well as on electrodermal and facial muscle activity were found. For example, the fast news was judged as more arousing, but the slow news as more understandable. The younger subjects judged the fast news as more positive and interesting, whereas the opposite was true for older subjects. The results have several methodological and practical implications on presenting and studying speech information in computer systems, such as speech interfaces and news services, given the possibilities afforded by modern technology to present multimodal information effectively and adapt the information and/or interfaces to fit the characteristics of the user (e.g. age).
Knowledge management in pursuit of learning: the Learning with Knowledge Cycle

This paper suggests that knowledge and learning are closely intertwined. An overview of some of the key differences between the respective concepts of the learning organisation and knowledge management forms a basis for exploring the link between these two concepts. Both concepts are under evolution. A model that contributes to the formation of an ontology for analytic discussion, and the implementation of knowledge management, the Learning with Knowledge Cycle, is proposed. The stages in this Cycle are: knowledge creation and construction, knowledge articulation, knowledge repository updating, knowledge access, knowledge use and knowledge revision. The Cycle represents the learning process within organisations and is applicable to both tacit and explicit knowledge. All stages in the Cycle contribute to effective organisational learning. Examples of the knowledge-based activities that contribute to each stage in the Cycle are given. Knowledge management is concerned with the facilitation of the activities that support each of the stages in the Cycle.
Insertion element IS986 from Mycobacterium tuberculosis: a useful tool for diagnosis and epidemiology of tuberculosis

IS986 of Mycobacterium tuberculosis belongs to the IS3-like family of insertion sequences, and it has previously been shown to be present in multiple copies in the chromosome of M. tuberculosis. In this study we investigated the value of a IS986-based DNA probe in the diagnosis and epidemiology of tuberculosis. IS986 was found only in species belonging to the M. tuberculosis complex. Independent isolates of M. tuberculosis complex strains showed a very high degree of polymorphism of restriction fragments which contained IS986 DNA. In contrast, Mycobacterium bovis BCG vaccine strains as well as clinical isolates of M. bovis BCG contained one copy of IS986, which was present at the same location in the chromosome. Different M. tuberculosis isolates from a recent M. tuberculosis outbreak showed an identical banding pattern. We concluded that IS986 is an extremely suitable tool for the diagnosis and epidemiology of tuberculosis.
Exo‐glucosidase activity and substrate specificity of the beta‐glycosidase isolated from the extreme thermophile Sulfolobus solfataricus

The enzyme with beta‐galactosidase activity from Sulfolobus solfataricus strain MT‐4, like other enzymes of this type isolated from thermophilic sources, has broad specificity for beta‐D‐gluco‐, fuco‐ and galacto‐sides. The beta‐galactosidase activity was purified by a new procedure that improved yields (44%) and final specific activity (182 units mg‐1 at 75 degrees C using chromogenic beta‐D‐galactoside as substrate). The enzyme hydrolysed a large number of beta‐linked glycoside dimers and oligomers; chromogenic beta‐glucosides and beta‐fucosides are the preferred substrates, and kinetic analysis indicated that they bind to a common catalytic site. The order of catalytic efficiency was beta 1–3 > beta 1–4 > beta 1–6 and cellotetraose > cellotriose > cellobiose for glucose dimers and oliogomers respectively. The cleavage occurred at the non‐reducing end of the oligosaccharide, and the enzyme showed noticeable specificity also for the aglycone part of substrates. From these results the enzyme from S. solfataricus strain MT‐4 is defined as a true glycosyl hydrolase with remarkable exo‐glucosidase activity and it is designated S beta‐gly.
Cross-lingual Structure Transfer for Zero-resource Event Extraction

Most of the current cross-lingual transfer learning methods for Information Extraction (IE) have been only applied to name tagging. To tackle more complex tasks such as event extraction we need to transfer graph structures (event trigger linked to multiple arguments with various roles) across languages. We develop a novel share-and-transfer framework to reach this goal with three steps: (1) Convert each sentence in any language to language-universal graph structures; in this paper we explore two approaches based on universal dependency parses and complete graphs, respectively. (2) Represent each node in the graph structure with a cross-lingual word embedding so that all sentences in multiple languages can be represented with one shared semantic space. (3) Using this common semantic space, train event extractors from English training data and apply them to languages that do not have any event annotations. Experimental results on three languages (Spanish, Russian and Ukrainian) without any annotations show this framework achieves comparable performance to a state-of-the-art supervised model trained from more than 1,500 manually annotated event mentions.
TOWARD UNDERSTANDING THE B[e] PHENOMENON. V. NATURE AND SPECTRAL VARIATIONS OF THE MWC 728 BINARY SYSTEM

We report the results of a long-term spectroscopic monitoring of the FS CMa type object MWC 728. We found that it is a binary system with a B5 ve ( T eff = 14,000 ± 1000 K) primary and a G8 iii type ( T eff ∼ 5000 K) secondary. Absorption line positions of the secondary vary, with a semi-amplitude of ∼20 km s−1 and a period of 27.5 days. The system’s mass function is 2.3 × 10−2 M ⊙ , and its orbital plane is ∼13°–15° tilted from the plane of the sky. The primary’s v sin i ∼ 110 km s−1, combined with this tilt, implies that it rotates at a nearly breakup velocity. We detected strong variations of the Balmer and He i emission-line profiles on timescales from days to years. This points to a variable stellar wind of the primary in addition to the presence of a circum-primary gaseous disk. The strength of the absorption-line spectrum, along with the optical and near infrared (IR) continuum, suggest that the primary contributes ∼60% of the V-band flux, the disk contributes ∼30%, and the secondary contributes ∼10%. The system parameters, along with the interstellar extinction, suggest a distance of ∼1 kpc, that the secondary does not fill its Roche lobe, and that the companions’ mass ratio is q ∼ 0.5. Overall, the observed spectral variability and the presence of a strong IR-excess are in agreement with a model of a close binary system that has undergone a non-conservative mass-transfer.
Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks

Pre-trained word embeddings are widely used in various fields. However, the coverage of pre-trained word embeddings only includes words that appeared in corpora where pre-trained embeddings are learned. It means that the words which do not appear in training corpus are ignored in tasks, and it could lead to the limited performance of neural models. In this paper, we propose a simple yet effective method to represent out-of-vocabulary (OOV) words. Unlike prior works that solely utilize subword information or knowledge, our method makes use of both information to represent OOV words. To this end, we propose two stages of representation learning. In the first stage, we learn subword embeddings from the pre-trained word embeddings by using an additive composition function of subwords. In the second stage, we map the learned subwords into semantic networks (e.g., WordNet). We then re-train the subword embeddings by using lexical entries on semantic lexicons that could include newly observed subwords. This two-stage learning makes the coverage of words broaden to a great extent. The experimental results clearly show that our method provides consistent performance improvements over strong baselines that use subwords or lexical resources separately.
Role of spermatogonia in the repair of the seminiferous epithelium following x-irradiation of the rat testis.

In the normal adult rat testis, type A0 spermatogonia do not appear to participate to a significant extent in the production of spermatocytes, while type A1 spermatogonia periodically initiate a series of divisions resulting in the production of spermatocytes and new type A1 spermatogonia. The behavior of type A0 and A1 spermatogonia was investigated following administration of a single dose of x-rays (300 r) to the testis. Using whole mounts of seminiferous tubules, the type A0 and A1 cells were counted at various intervals after irradiation. At 8 and 13 days after irradiation, type A1 spermatogonia reached lowest values, i.e., 6% and 3% of non-irradiated control, while type A0 reached the lowest value, i.e., 62% of control at eight days. Thereafter the numbers of type A0 and A1 progressively increased to return to normal at 39 days. It was thus concluded that the type A0 were comparatively more resistant to x-irradiation than type A1 spermatogonia. To verify if the surviving type A0 proliferated in the irradiated testis, animals were injected with 3H-thymidine three hours before they were sacrificed at various times after x-irradiation. In irradiated testes the labeling indices of the surviving type A (A0, A1–A4) were the same as in the non-irradiated testes except in stages V-VI of the cycle of the seminiferous epithelium. While in the controls only 2% of type A cells were labeled at these two stages of the cycle, after irradiation the labeling index of type A reached a maximum of 31% at 13 days to return to control values by 39 days. Since at 13 days after irradiation type A0 spermatogonia were the predominant component of the spermatogonial population, it was concluded that these cells must have incorporated 3H-thymidine and thereby contributed to the reconstruction of the spermatogonial population partially destroyed by irradiation.
Imbalance optimization in scientific workflows

Scientific workflows are a means of defining and orchestrating large, complex, multi-stage computations that perform data analysis and/or simulation. Task clustering is a runtime optimization technique that merges multiple short workflow tasks into a single job such that the job execution overhead is reduced and the overall runtime performance of the workflow is significantly improved. However, current task clustering strategies fail to consider the imbalance problem of both task runtime and task dependency. In our work, we first investigate the different causes of runtime imbalance and dependency imbalance. We then introduce a series of metrics based on our prior work to measure the severity of runtime and dependency imbalance respectively. Finally, we study a wide range of real scientific workflows to generalize the relationship between these metrics and balancing methods.
Nonuniformly-wound helical antennas

We investigate nonuniformly-wound helical antennas without ground plane (antenna counterbalance is a wire pigtail), with aim to maximize RHCP gain. By optimizing radii and pitches of these antennas, we obtain antennas with the gain that is very close to the gain of the optimal uniformly-wound helical antennas with infinite ground plane, while the obtained nonuniform antennas are smaller and handier.
Mediator framework for inserting xDRs into Hadoop

During the last decades, we assisted to what is called “information explosion”. With the advent of the new technologies and new contexts, the volume, velocity and variety of data has increased exponentially, becoming what is known today as big data. Among them, we emphasize telecommunications operators, which gather, using network monitoring equipment, millions of network event records, the Call Detail Records (CDRs) and the Event Detail Records (EDRs), commonly known as xDRs. These records are stored and later processed to compute network performance and quality of service metrics. With the ever increasing number of collected xDRs, its generated volume needing to be stored has increased exponentially, making the current solutions based on relational databases not suited anymore. To tackle this problem, the relational data store can be replaced by Hadoop File System (HDFS). However, HDFS is simply a distributed file system, this way not supporting any aspect of the relational paradigm. To overcome this difficulty, this paper presents a framework that enables the current systems inserting data into relational databases, to keep doing it transparently when migrating to Hadoop. As proof of concept, the developed platform was integrated with the Altaia - a performance and QoS management of telecommunications networks and services.