Miracle-dz/UniFAR-FTUs huggingface dataset:
TTS applied to the generation of datasets for automatic speech recognition

Abstract Despite automatic speech recognition (ASR) systems evolution with deep learning methods, for languages with a shortage of open/public resources, the resulting systems still present low-quality performance. On the other hand, Text-to-Speech (TTS) has also evolved in the last decade, allowing for zero-shot multi-speaker TTS (ZS-TTS) models to generate speech of a target speaker using only a few seconds of its speech. These advances motivated the use of ZS-TTS in the training of ASR systems to improve the performance of the models. However, ZS-TTS models still require a large number of diverse speakers and hours of speech during training, thus hindering their practical use in languages with less accessible data. In this work, we explored ZS-TTS in scenarios with few available speakers. We proposed the use of flow-based models due to its state-of-the-art (SOTA) results and explored the use of multilingual models, seeking to leverage available data from languages with many available speakers. The results achieved by this work made possible the development of ZS-TTS and zero-shot voice conversion (VC) systems in languages with few available speakers. The approach proposed in this work was applied to improve ASR systems in two languages, simulating a scenario with only one speaker available for the training of the ZS-TTS model. Despite using only one speaker in the target languages, our data augmentation approach achieved results comparable to the SOTA in the English language.
Text Summarization and Temporal Learning Models Applied to Portuguese Fake News Detection in a Novel Brazilian Corpus Dataset

Streaming content advances and the appearance of online media raised the ability for massive content sharing that reaches thousands of people worldwide in a real-time fashion. Fake news spreading is nowadays the main concern of several authorities worldwide due to the negative impact and potential to induce social and political instability in our society. Therefore, fake news detection and suppression gained increased attention as an important topic in nat-ural language processing and machine learning academic research. Regardless of the state-of-the-art methods available for fake news detection, a good corpus revealing novel language-specific counterfeit aspects is also important to exploit and distinguish between real and fake news in the context of social and political impacts for specific regions. This paper extends a previous Brazilian Portuguese corpora dataset and proposes using and comparing several deep learning and classical machine learning models to detect counterfeit content in the Portuguese language. Moreover, we propose using text summarization to achieve concise news summaries and prevent losing relevant information. This work presents an updated and balanced version of the FakeRecogna dataset for detecting fake news articles using a temporal learning approach based on efficient and well-known deep learning models.
A Corpus of Stock Market Tweets Annotated with Named Entities

In this work, we present a corpus of stock market tweets written in Brazilian Portuguese and annotated with named entities according to HAREM’s taxonomy. The corpus consists of 4,048 tweets and was originally built for research on emotion classification, being already annotated with it. By identifying the named entities present in the corpus, we intend it to enable new studies regarding possible correlations between named entities and emotions, along with other research on how such entities are used in this domain and linguistic genre. The annotation was manually carried out by one of the researchers and, out of the 84.397 tokens present in the corpus, 23.453 were annotated with named entities.
Sequence-to-sequence and transformer approaches to Portuguese text style transfer

In Natural Language Generation, text style transfer is the task of rewriting a given source text according to a target style of interest while preserving as much as possible of its meaning. As a means to foster research in this field, this paper presents a range of style transfer models using sequence-to-sequence and transformer architectures alike. In doing so, we would like to compare alternative approaches for the task, and identify opportunities to move towards more robust style transfer in Portuguese.
Investigating the Generalizability of Portuguese Readability Assessment Models Trained Using Linguistic Complexity Features

This paper discusses our effort to build an automatic Portuguese readability assessment sys-tem aimed at Portuguese language learners. We demonstrate that using linguistic complexity features combined with traditional machine learning techniques allows for more control over selecting features that are more informative, resulting in models that generalize better to texts in authentic language learning environments. Using 489 linguistic complexity measures automatically extracted from a corpus of 500 texts annotated according to their level, we train random forest and LogitBoost classifiers to predict the CEFR level of a given text. Sub-sequently, we investigate the models’ generalizability by testing them on an independently collected and annotated corpus. We conclude that through using more informative features, the models’ capacity to generalize to novel data can increase even if performance on the test set extracted from the training data decreases.
Dynamics of morphogen source formation in a growing tissue

A tight regulation of morphogen production is key for morphogen gradient formation and thereby for reproducible and organised organ development. Although many genetic interactions involved in the establishment of morphogen production domains are known, the biophysical mechanisms of morphogen source formation are poorly understood. Here we addressed this by focusing on the morphogen Shh in the vertebrate neural tube. Shh is produced by the adjacently located notochord and by the floor plate of the neural tube. Using a data-constrained computational screen, we identified different possible mechanisms by which floor plate formation can occur, only one of which is consistent with experimental data. In this mechanism, the floor plate is established rapidly in response to Shh from the notochord and the dynamics of regulatory interactions within the neural tube. In this process, uniform activators and Shh-dependent repressors are key for establishing the floor plate size. Subsequently, the floor plate becomes insensitive to Shh and increases in size due to tissue growth, leading to scaling of the floor plate with neural tube size. In turn, this results in scaling of the Shh amplitude with tissue growth. Thus, this mechanism ensures a separation of time scales in floor plate formation, so that the floor plate domain becomes growth dependent after an initial rapid establishment phase. Our study raises the possibility that the time scale separation between specification and growth might be common strategy for scaling the morphogen gradient amplitude in growing organs. The model that we developed provides a new opportunity for quantitative studies of morphogen source formation in growing tissues.
Creation and manipulation of bipartite expression transgenes in C. elegans using phiC31 recombinase

Bipartite expression systems are widely used in model organisms to express specific gene products in a cell-specific context. They are typically encoded as two independent, unlinked transgenic insertions: a driver and a reporter. Herein, I outline an efficient method named recombination-mediated homolog exchange (RMHE) that uses genetically encoded phiC31 recombinase to convert driver and reporter transgenes integrated at the same genetic position from a trans configuration where one component is on each chromosome, to a cis configuration where the driver and reporter are on the same chromosome. This approach is based upon the development of a set of 3’ attB tagged driver lines and 5’ attP tagged reporter lines. This genetic based approach leverages both the power of combinatorial re-assortment of drivers and reporters and the simplicity of single locus genetics. I also describe a novel microinjection-based approach named recombination-mediated integration (RMI) that utilizes the individual attB driver and attP reporter lines as landing site for the phiC31 recombinase mediated integration of whole driver and reporter plasmids into the existing reporter and driver lines, respectively. Thus, this work outlines both a novel genetic based and a novel microinjection-based method to create cis-linked driver/reporter pairs. These new tools increase the utility of bipartite systems for C. elegans genetics by reducing the complexity of reporter system segregation in crosses and thus can greatly simplify the use of bipartite reporter systems during genetic analysis.
Sexual dimorphism in the distribution and density of regulatory interneurons in the striatum

Dysfunction of the cortico-basal circuitry – including its primary input nucleus, the striatum – contributes to neuropsychiatric disorders, including autism and Tourette Syndrome (TS). These conditions show marked sexual dimorphism, occurring more often in males than in females. Regulatory interneurons, including cholinergic interneurons (CINs) and parvalbumin-expressing GABAergic fast spiking interneurons (FSIs), are implicated in human neuropsychiatric disorders such as TS, and ablation of these interneurons produces relevant behavioral pathology in male mice, but not in females. Here we investigate sexual dimorphism in the density and distribution of striatal interneurons, using stereological quantification of CINs, FSIs, and somatostatin-expressing (SOM) GABAergic interneurons in the dorsal and ventral striatum in male and female mice. Males have a higher density of CINs than females, specifically in the dorsal striatum; females have equal distribution between dorsal and ventral striatum. FSIs showed similar effects, with a greater dorsal-ventral density gradient in males than in females. SOM interneurons were denser in the ventral than in the dorsal striatum, with no sexual dimorphism. These sex differences in the density and distribution of FSIs and CINs suggest a potential source of the sexual dimorphism seen in TS and autism spectrum disorder.
Influence of environmental variables, small-scale fisheries and vessel traffic on the distribution and behavior of bottlenose dolphins in a tropical lagoon

The distribution of marine predators is influenced by a variety of natural and, in some cases, anthropogenic environmental factors. In particular, the optimal foraging theory predicts that micro-habitat selection should be the result of a trade-off between prey availability, energy expenditure, and predation risk. In addition, the risk-disturbance hypothesis suggests that anthropogenic disturbance may be perceived by animals in the same way as predation risk. Habitat selection may also be locally influenced by individual behavior and physiological state (defining functional habitats): variation in their needs should affect their optimal trade-off. We tested these hypotheses in a population of bottlenose dolphins living in a tropical lagoon using a habitat modeling approach. Bottlenose dolphins were predominantly distributed within the lagoon, with a preference for the vicinity of fringing and inner reefs (with lower predation risk than the outer reef), and were located in areas of high fish productivity, consistent with optimal habitat selection. We also observed an interaction between habitat and dolphin behavior, suggesting the existence of functional habitats: foraging was more common in nearshore habitats with probable higher prey density while resting and socializing were more common further from shore. Similarly, females with calves were preferentially found in shallower waters compared to other social groups. We did not observe any effects of anthro-pogenic disturbance variables and therefore cannot conclude on the risk-disturbance hypothesis for this population.
Evolution Under Thermal Stress Affects Escherichia coli’s Resistance to Antibiotics

Exposure to both antibiotics and temperature changes can induce similar physiological responses in bacteria. Thus, changes in growth temperature may affect antibiotic resistance. Previous studies have found that evolution under antibiotic stress causes shifts in the optimal growth temperature of bacteria. However, little is known about how evolution under thermal stress affects antibiotic resistance. We examined 100+ heat-evolved strains of Escherichia coli that evolved under thermal stress. We asked whether evolution under thermal stress affects optimal growth temperature, if there are any correlations between evolving in high temperatures and antibiotic resistance, and if these strains’ antibiotic efficacy changes depending on the local environment’s temperature. We found that: (1) surprisingly, most of the heat-evolved strains displayed a decrease in optimal growth temperature and overall growth relative to the ancestor strain, (2) there were complex patterns of changes in antibiotic resistance when comparing the heat-evolved strains to the ancestor strain, and (3) there were few significant correlations among changes in antibiotic resistance, optimal growth temperature, and overall growth. Importance Escherichia coli, a bacteria species often found within the intestinal tract of warm-blooded organisms, can be harmful to humans. Like all species of bacteria, E. coli can evolve, particularly in the presence of stressful conditions such as extreme temperatures or antibiotic treatments. Recent evidence suggests that when encountering one source of stress, an organism’s ability to deal with a different source of stress is also affected. With global climate change and the continued evolution of antibiotic-resistant bacteria, the need to further investigate how temperature and antibiotics interact is clear. The significance of our research is in identifying possible correlations between temperature and antibiotic stress, broadening our understanding of how stressors affect organisms, and allowing for insights into possible future evolutionary pathways.
Auditory Feature-based Perceptual Distance

Studies comparing acoustic signals often rely on pixel-wise differences between spectrograms, as in for example mean squared error (MSE). Pixel-wise errors are not representative of perceptual sensitivity, however, and such measures can be highly sensitive to small local signal changes that may be imperceptible. In computer vision, high-level visual features extracted with convolutional neural networks (CNN) can be used to calculate the fidelity of computer-generated images. Here, we propose the auditory perceptual distance (APD) metric based on acoustic features extracted with an unsupervised CNN and validated by perceptual behavior. Using complex vocal signals from songbirds, we trained a Siamese CNN on a self-supervised task using spectrograms rescaled to match the auditory frequency sensitivity of European starlings, Sturnus vulgaris. We define APD for any pair of sounds as the cosine distance between corresponding feature vectors extracted by the trained CNN. We show that APD is more robust to temporal and spectral translation than MSE, and captures the sigmoidal shape of typical behavioral psychometric functions over complex acoustic spaces. When fine-tuned using starlings’ behavioral judgments of naturalistic song syllables, the APD model yields even more accurate predictions of perceptual sensitivity, discrimination, and categorization on novel complex (high-dimensional) acoustic dimensions, including diverging decisions for identical stimuli following different training conditions. Thus, the APD model outperforms MSE in robustness and perceptual accuracy, and offers tunability to match experience-dependent perceptual biases.
Holocene evolution of a barrier island system, Ria Formosa, South Portugal

Holocene evolution of the Ria Formosa barrier island system was studied through the examination of a large subsurface dataset acquired from 191 boreholes and five seismic refraction profiles. Two boreholes with total depths of 26 and 16.5 m were selected for a multi-proxy detailed laboratory analysis, including mean grain size distribution, organic matter (OM) content, color variation, shell identification, and benthic foraminifera assemblages. Selected cores are thought to be representative of the identified depositional sub-basins. Subsurface age data from 16 AMS 14C dated samples were plotted against depth and resulted in a coherent age model of sedimentary infill. The system evolution was largely controlled by sediment availability, accommodation space, and Holocene sea level rise, first at a rapid rate of 7 mm/yr from 10 kcal yr BP to 7.25 kcal yr BP, followed by a slowdown to 1.1 mm/yr until present. A conceptual model for the origin and Holocene evolution of the Ria Formosa barrier island system implies three main steps, leading to the present system geomorphology: (1) marine flooding of incised palaeovalleys by the rapid transgression of palaeovalleys in the early Holocene; (2) development of a proto-barrier island chain perched on Pleistocene detritic headlands and steeper interfluve areas during the early to middle Holocene; and (3) full development of the barrier islands chain and enclosing of the coastal lagoon, followed by the maturation of the system with subsequent siltation and salt marsh expansion from the middle Holocene until present. The onset of barrier system formation dates back to ca. 8 kcal yr BP, predating previously proposed age.
J an 2 01 7 MCMC sampling colourings and independent sets of G ( n , d / n ) near uniqueness threshold

Sampling fromGibbs distributionis a central problem in computer science. In this work we focu s on thek-colouring modeland thehard-core model with fugacityλ when the underlying graph is an instance of Erdős-Rényi random graph G(n, d/n), whered is fixed. We use theMarkov Chain Monte Carlomethod for sampling from the aforementioned distributions. In particular, we consider Glauber (block) dynamics . We provide significantly improved bounds forrapid mixing in terms of the number of colours and the fugacity for the corr esponding models. For both models the bounds we get are only within smal l constant factors from the optimal ones, conjectured by the statistical physicists. We usePath Couplingto show rapid mixing. For k andλ in the range of our interest the technical challenge is to cope with the high degree vertices, i.e. vert ices of degree much larger than the expected degree d. The natural approach to this problem is to consider block up dates rather than single vertex updates for the Markov chain. Using appropria tely defined blocks the effect of high degree vertices somehow diminishes. However, devising suc h a onstruction of blocks is a non trivial task. We develop for a first time a weighting schema for the paths of t he underlying graph. Vertices which belong to “light” paths, only, can be placed at the boun daries of the blocks. This gives rise to simple structured blocks, i.e. trees with at most one extra e dge.
Documentation of Some Identified Functions and Contexts of Traditional Pottery in Northern Ghana

Aims: The intent of this research was to observe and document some identified traditional pottery functions and contexts in some parts of northern Ghana. Study Design: Due to the peculiar nature of the research, the qualitative research methods were adopted for the study with the aiming of assembly necessary details requisite for a good description of the functions and contexts of the pottery. Purpose of Study: The purpose of the study was to make a field trip to seven selected traditional potting centre in northern Ghana, to collect data in relation to context and functions of the potteries. These centres include Zubulga, Palnaba, Tuggo, Sirigu, Naakpili, Lugshigu and Kukuo. Methodology: Out of a total of 41 districts (that is, 20 in the northern region, 11 in the upper west region and 10 in the upper east region), seven were selected as the representative population. The criteria for inclusion were based on: the geographical location and how it affects the decorative Original Research Article Yussif et al.; ARJASS, 6(3): 1-9, 2018; Article no.ARJASS. 41150 2 techniques of the traditional pottery; the variety in the use of tools, materials and methods and availability of clay as the main material in the area. This research was designed to identify and document the traditional pottery functions and contexts of the pots. The objectives therefore were: to observe, comparatively analyze and record some purposively selected traditional pottery functions and contexts in some parts of northern Ghana. The descriptive approach of the qualitative research methodology was employed. Interview and observational were used as the data collection methods. They were used to ascertain data that were analyzed to conclude on the traditional pottery functions and contexts by the potters in the selected areas of northern Ghana. Results: The data analyzes indicated that the potters make interesting patterns of decorative techniques: Specifically, nine functions and contexts were identified and documented by the researchers at the time of their studies. Studies made by the researchers clearly shows that traditional pottery found in northern Ghana are generally produced based on domestic functions, religious functions, and functions related to rites of passage. On the flip side of the coin, contexts of traditional pottery found at potting centres in northern Ghana included the courtyards, bedrooms, bathrooms, graveyards and the kitchens of their owners. Conclusion: Evidently, it can be concluded that, since not all forty-one districts pottery centres were covered, some of the traditional pottery functions and contexts in northern Ghana has been left undocumented. It is therefore recommended that the study should continue in the rest of the areas.
Multi-Task Classifier Sharing for Cross-Dataset Time-Series Classification

Multi-task learning has shown its advantages in NLP and CV domains but has not been widely applied for time-series classification. In this paper, we propose a novel multi-task learning approach for time-series classification based on intelligent classifier sharing. Based on the proposed method, we demonstrate that a time-series classification task can benefit from semantically relevant ones automatically without human intervene. Experiments are carried out on the 15 largest datasets in UCR repository. Overall, the average accuracy of 15 datasets has been enhanced from 86.8% to 88.2% compared to the best state-of-the-art model in the literature. Analyses also verify that the task relations captured by our Multi-Task Classifier Sharing (MTCS) method present good interpretability.