Miracle-dz/UniFAR-FTUs huggingface dataset:
IrisNet: Deep Learning for Automatic and Real-time Tongue Contour Tracking in Ultrasound Video Data using Peripheral Vision

The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automatic and accurate tracking of tongue contours in real-time ultrasound data became practical only in recent years. Recent studies have shown that the performance of deep learning techniques is significant in the tracking of ultrasound tongue contours in real-time applications such as pronunciation training using multimodal ultrasound-enhanced approaches. Due to the high correlation between ultrasound tongue datasets, it is feasible to have a general model that accomplishes automatic tongue tracking for almost all datasets. In this paper, we proposed a deep learning model comprises of a convolutional module mimicking the peripheral vision ability of the human eye to handle real-time, accurate, and fully automatic tongue contour tracking tasks, applicable for almost all primary ultrasound tongue datasets. Qualitative and quantitative assessment of IrisNet on different ultrasound tongue datasets and PASCAL VOC2012 revealed its outstanding generalization achievement in compare with similar techniques.
Fatal Streptococcus iniae Infection in a Juvenile Free-Ranging Short-Beaked Common Dolphin (Delphinus delphis)

Simple Summary Streptococcus iniae (S. iniae) is a significant aquatic bacterial pathogen that has caused devastating losses to wild and cultured fish populations worldwide and is an important zoonotic pathogen in humans. Previously reported in captive dolphins only, this case report describes S. iniae associated infection and mortality in a free-ranging short-beaked common dolphin (Delphinus delphis). Unreported previously in wild marine mammals, its detection highlights a need for further investigation into the epidemiology of S. iniae to better understand the risks for wild marine mammal populations and potential transmission cycles between marine mammals, humans and wild and intensive fish populations. Abstract Streptococcus iniae (S. iniae) is a significant aquatic pathogen of farmed fish species, important zoonotic pathogen, and reported cause of disease in captive Amazon River dolphins (Inia geoffrensis) and a bottlenose dolphin (Tursiops truncatus). Here we report S. iniae as the cause of subcutaneous abscesses, sepsis and mortality in a juvenile free-ranging short-beaked common dolphin (Delphinus delphis) found deceased on a metropolitan Australian beach. Body surfaces were covered by multifocal, depressed, deep, irregular cutaneous ulcerations, which microscopically were characterised by ruptured subcutaneous abscesses with intralesional cocci. Routine microbiological investigations revealed a heavy growth of beta-haemolytic Streptococcus sp. identified as Streptococcus iniae in skin lesions as well as from heart blood, the latter supportive of sepsis. Tissues were negative for cetacean morbillivirus and no other disease processes were identified. S. iniae has not been reported in free-ranging marine mammals, nor in Australian delphinids, previously. More notably a pathogen of captive animals, this case report identifies S. iniae as a pathogen of wild dolphins also. In addition to expanding the host reservoir of a significant zoonotic pathogen, determining the source of infection as well as possible consequences for other marine mammals and wild and intensive fish stocks warrants further investigations.
Expression of heat shock protein (HSP) genes and antioxidant enzyme genes in hybrid rice II YOU 838 during heat stress

II YOU 838 (Oryza sativa subsp. indica), crossed by the maternal II-32A and paternal Fu Hui 838, was one of the most widely cultivated hybrid rice in China. Fu Hui 838, which has resistance to high temperature, was generated by mutation technology in 1990. Previous field-testing showed that II YOU 838 had tolerance to high temperature stress and this was confirmed in the present study. The mechanism of heat tolerance of II YOU 838 is not understood. The present study reports gene expression of a representative sample of heat-responsive proteins in II YOU 838 flag leaves subjected to heat stress during flowering. Differential expression of the heat shock protein 70 (HSP70), heat shock protein 90 (HSP90), small heat shock protein (smHSP), superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) were studied under heat stress and optimum temperatures in flag leaves of II YOU 838. All six genes studied were responsive to high temperatures. Quantitative real-time PCR showed increased expression of the heat shock protein genes and antioxidant enzyme genes in ﬂag leaves under heat stress. With increasing number of days gene expression decreased under high temperature. Peak expression of SOD, POD, hsp70 and hsp90 was on Day 2 under 39 ℃. On Day 3, the expression of CAT under 39 ℃ was the highest. The expression of smhsp was highest on Day 3 under 27 ℃, followed by that on Day 2 under 27 ℃. The maximum expression values were observed on Day 2 or Day 3 after beginning of heat stress. This suggests that hsp90, hsp70, SOD and POD are principally involved in early responses to heat in rice ﬂag leaves, and that smhsp may play a role in the recovery mechanism in rice after heat stress. This may provide insights into the mechanism of heat-tolerance in rice
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

Abstract. Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.
Morse theory with homotopy coherent diagrams.

We study Morse theory on noncompact manifolds equipped with exhaustions by compact pieces, defining the Morse homology of a pair which consists of the manifold and related geometric/homotopy data. We construct a collection of Morse data parametrized by cubes of arbitrary dimensions. From this collection, we obtain a family of linear maps subject to some coherency conditions, which can be packaged into a homotopy coherent diagram. We introduce a chain complex which is a colimit for the diagram and show that it computes the Morse homology.
Turystyka pielgrzymkowa zrównoważona – Karta sanktuarium dostępnego (Ksd)

Poznanie społecznych i przestrzennych uwarunkowań zrównoważonego rozwoju turystyki kulturowo – religijnej wydaje się problemem ważnym, ponieważ zainteresowanie tą formą w Polsce nie słabnie. Wśród nich jest wiele osób z niepełnosprawnością, starszych, rodzin z małymi dziećmi. Problematyka dostępności sanktuariów dla niepełnosprawnych nie była dotąd materiałem szerszych i pogłębionych studiów naukowych w Polsce, co zachęca do podjęcia rozważań nad tą problematyką. Szczegółowa analiza problemu możliwości znoszenia barier dostępności sanktuariów wydawała się autorom szczególnie istotna w kontekście spodziewanego ruchu pielgrzymkowego w Polsce w roku 2016 z okazji Światowych Dni Młodzieży. Jednym z założonych i osiągniętych efektów aplikacyjnych dorobku badań jest opracowanie ‘Karty Sanktuarium Dostępnego’(KSD), pomocnego przy ocenie dostępności miejsc kultu religijnego dla osób z różnego rodzaju niepełnosprawnością. W przedstawionym tekście zaprezentowano dorobek badań ds. -144 oraz ds. -245 AWF Warszawa (granty Ministerstwa Nauki i Szkolnictwa Wyższego na lata 2014 – 2018).
Effectiveness of self-supervised pre-training for speech recognition

We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on testclean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data.
Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona

Understanding and monitoring the complex and dynamic processes of the Sun is important for a number of human activities on Earth and in space. For this reason, NASA's Solar Dynamics Observatory (SDO) has been continuously monitoring the multi-layered Sun's atmosphere in high-resolution since its launch in 2010, generating terabytes of observational data every day. The synergy between machine learning and this enormous amount of data has the potential, still largely unexploited, to advance our understanding of the Sun and extend the capabilities of heliophysics missions. In the present work, we show that deep learning applied to SDO data can be successfully used to create a high-fidelity virtual telescope that generates synthetic observations of the solar corona by image translation. Towards this end we developed a deep neural network, structured as an encoder-decoder with skip connections (U-Net), that reconstructs the Sun's image of one instrument channel given temporally aligned images in three other channels. The approach we present has the potential to reduce the telemetry needs of SDO, enhance the capabilities of missions that have less observing channels, and transform the concept development of future missions.
Exercise and the Eye: A Call for Prospective, Outcomes-Based Research Collaborations between Exercise Physiologists and Ophthalmologists

Kleinman. Int J Sports Exerc Med 2018, 4:094 Citation: Kleinman DM (2018) Exercise and the Eye: A Call for Prospective, Outcomes-Based Research Collaborations between Exercise Physiologists and Ophthalmologists. Int J Sports Exerc Med 4:094. doi.org/10.23937/2469-5718/1510094 Accepted: July 03, 2018; Published: July 05, 2018 Copyright: © 2018 Kleinman DM. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Exercise and the Eye: A Call for Prospective, Outcomes-Based Research Collaborations between Exercise Physiologists and Ophthalmologists
Acamprosate may be safer than baclofen for the treatment of alcohol use disorder in patients with cirrhosis: a first description of use in real-world clinical practice

Objectives Patients with alcohol use disorder (AUD) and liver cirrhosis benefit from stopping alcohol intake. Baclofen has been trialled for AUD in cirrhosis and appears to be effective. However, in patients without cirrhosis acamprosate is safer and more efficacious. Acamprosate is rarely used in cirrhosis due to safety concerns: the only published report was for 24 h in a controlled setting. Our centre uses both medications off-label in cirrhotic patients. We performed an audit to pragmatically compare the safety of acamprosate to baclofen in these patients. Methods The electronic records of patients prescribed acamprosate or baclofen between 01/04/17 and 31/03/20 were retrospectively reviewed. Adverse events and abstinence at last follow-up were compared by Student’s t-test, Mann–Whitney U or chi-square test. Confounding variables were evaluated by logistic regression. Results In total 48 cirrhotic patients taking acamprosate (median 84 days, range 2–524); 44 baclofen (247 days, 8–910) met inclusion criteria. At baseline, 41% had Childs-Pugh B or C cirrhosis. More patients taking baclofen had an unplanned hospital admission or attendance (23 vs 13; P = 0.013) and the mean number per patient was higher (1.6 vs 0.6; P = 0.032). Sub-group analysis revealed increased admissions in actively drinking patients prescribed baclofen to achieve abstinence (mean 2.4 vs 0.6; P = 0.020); acamprosate use was associated with a reduced chance of admission or attendance (OR, 0.284; 0.095–0.854; P = 0.025) independent of treatment length. No difference in efficacy was observed. Conclusions In patients with cirrhosis, acamprosate was associated with fewer unplanned admissions than baclofen, hence may be safer despite historical concerns.
Multi-day Residential EV Charging Strategy Using Reinforcement Learning

The widespread adoption of electric vehicles (EV) is expected to introduce significant demand on power systems. Uncontrolled charging which coincides with peak household electricity consumption is an issue that will have to be addressed. A charging strategy that spans across a week is proposed to maximise utilisation of an EV's battery capacity. This allows for charging to be delayed to a more suitable time such as the weekend where self-consumption of energy generated from residential rooftop solar is a possibility. A dynamic reward function which considers the range of an EV in relation to the energy requirements of the next trip is proposed. Agents are rewarded for charging at optimal times through a model-free reinforcement learning approach. An optimal charging policy is obtained via Q-learning which demonstrates the viability of a multi-day charging strategy.
Distributed Event-Triggered Consensus Control of Multi-Agent Systems under Denial-of-Service Jamming Attacks

This work focuses on the consensus problem of multi-agent systems (MASs) under event-triggered control (ETC) subject to denial-of-service (DoS) jamming attacks. To reduce the cost of communication networks, a novel event-triggering mechanism (ETM) is applied to the sleeping interval to determine whether the sampled signal should be transmitted or not. Unlike periodic DoS attacks model, the DoS attacks occurrence are irregular, where attack attributes such as attack frequency and attack duration are taken into account. Moreover, compared with the fixed topological graph, the communication topologies may change due to DoS jamming attacks in this work. In view of this, based on the piecewise Lyapunov functional, sufficient conditions are derived to guarantee that consensus problem of the MASs can be solved. Finally, the effectiveness and correctness of the theoretical results are verified by a numerical example.
ECONOMIC PLURALISM AND THE PROCESS OF ENTREPRENEURIAL PLURALISM IN COMMUNITARIAN TOURISM ACTIVITIES DEVELOPED BY THE BELA VISTA DO JARAQUI COMMUNITY - MANAUS - AM

This study seeks to contribute to discussions related to other economic and entrepreneurial realities existing in Brazil, excluded because they do not fit the globalizing reality, but which contribute, in a way, to the improvement of life in lower social classes, adding in sociocultural aspects, feeding both collective labor forces and the formal economy of many capitalist companies, in which tourism is also inserted. That said, the aim of this article proposes to verify the entrepreneurial pluralism based on the economic pluralism of Zaoual (2006), identifying different entrepreneurial forms related to communitarian tourism developed in the Bela Vista do Jaraqui community, in Manaus-Amazonas. From the perspective of the dialectic of qualitative approach, we conducted field and participant research with the community with the intention of interviewing and observing the entrepreneurial practices related to communitarian tourism. The results point to the presence of entrepreneurial forms, which appear as: Bezamat's turner entrepreneurship (2017), Zaoual's situated entrepreneurship (2006), and Mandrysz's social entrepreneurship (2020). In addition, the studies found some weaknesses in relation to community and institutional arrangements.
Issues associated with the use of National Health Insurance contributions in determining public policy program beneficiaries

ABSTRACT Scholars, practitioners, and policymakers agree that the eligibility criteria used for determining welfare benefit recipients must be efficient, equitable, and possess few measurement errors. This study analyzes Korea’s system of using contributions to the National Health Insurance as an eligibility criterion for determining welfare benefit recipients and evaluates whether the system has these aforementioned characteristics, using the case of the COVID-19 stimulus payment distributed in the city of Jeonju. The analysis shows that while the system is favorable from an efficiency perspective, it is less desirable in terms of the other two characteristics. Based on the findings, this study proposes using tax return and employment insurance data, as such databases can help solve the equity and measurement error issues associated with the use of the current system.
Challenges in Treating Charcot-Marie-Tooth Disease and Related Neuropathies: Current Management and Future Perspectives

There is still no effective drug treatment available for Charcot-Marie-Tooth neuropathies (CMT). Current management relies on rehabilitation therapy, surgery for skeletal deformities, and symptomatic treatment of pain; fatigue and cramps are frequent complaints that are difficult to treat. The challenge is to find disease-modifying therapies. Several approaches, including gene silencing, to counteract the PMP22 gene overexpression in the most frequent CMT1A type are under investigation. PXT3003 is the compound in the most advanced phase for CMT1A, as a second-phase III trial is ongoing. Gene therapy to substitute defective genes or insert novel ones and compounds acting on pathways important for different CMT types are being developed and tested in animal models. Modulation of the Neuregulin pathway determining myelin thickness is promising for both hypo-demyelinating and hypermyelinating neuropathies; intervention on Unfolded Protein Response seems effective for rescuing misfolded myelin proteins such as P0 in CMT1B. HDAC6 inhibitors improved axonal transport and ameliorated phenotypes in different CMT models. Other potential therapeutic strategies include targeting macrophages, lipid metabolism, and Nav1.8 sodium channel in demyelinating CMT and the P2X7 receptor, which regulates calcium influx into Schwann cells, in CMT1A. Further approaches are aimed at correcting metabolic abnormalities, including the accumulation of sorbitol caused by biallelic mutations in the sorbitol dehydrogenase (SORD) gene and of neurotoxic glycosphingolipids in HSN1.
Obtaining Porous Zinc Oxide Ceramics Using Replica Technique: Application in Photocatalysis

26, 2021 This research investigated the production of porous zinc oxide (ZnO) ceramics obtained by the replica method for water depollution by photocatalytic processes. Five photo-decolorization cycles were performed to analyze the reuse potential of the ceramics. Statistical analyses using R programming were conducted to investigate possible significant differences between them. The ceramics porosities were between 46.74 and 62.50% (depending on the composition). The most successful results were achieved in prepared ceramics from slurries containing 65% ZnO and 1% carboxymethylcellulose, in which the dye decolorization results reached 90.5% after 5 cycles, indicating a high reuse potential of these ceramics. Multivariate analysis proved the negative effect of increasing the heat treatment temperature on the evaluated properties. The best processing conditions to obtain porous ZnO ceramics by the replica technique were established through statistical tools, with very satisfactory results in the photo-decolorization potential of the
Space–Time Trade-off of Precursory Seismicity in New Zealand and California Revealed by a Medium-Term Earthquake Forecasting Model

The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) medium-term earthquake forecasting model is based on the precursory scale increase (Ψ) phenomenon and associated scaling relations, in which the precursor magnitude MP is predictive of the mainshock magnitude Mm, precursor time TP and precursory area AP. In early studies of Ψ, a relatively low correlation between TP and AP suggested the possibility of a trade-off between time and area as a second-order effect. Here, we investigate the trade-off by means of the EEPAS model. Existing versions of EEPAS in New Zealand and California forecast target earthquakes of magnitudes M > 4.95 from input catalogues with M > 2.95. We systematically vary one parameter each from the EEPAS distributions for time and location, thereby varying the temporal and spatial scales of these distributions by two orders of magnitude. As one of these parameters is varied, the other is refitted to a 20-year period of each catalogue. The resulting curves of the temporal scaling factor against the spatial scaling factor are consistent with an even trade-off between time and area, given the limited temporal and spatial extent of the input catalogue. Hybrid models are formed by mixing several EEPAS models, with parameter sets chosen from points on the trade-off line. These are tested against the original fitted EEPAS models on a subsequent period of the New Zealand catalogue. The resulting information gains suggest that the space–time trade-off can be exploited to improve forecasting.
Quantifying Stridor Associated with Parkinsonism and Deep Brain Stimulation—A Case Report

We present a patient with a diagnosis of Parkinson’s disease (PD) with an atypical presentation of stridor responsive to medication. The patient was treated with subthalamic nucleus (STN) deep brain stimulation (DBS) for medically refractory motor symptoms. The stridor deteriorated following DBS, and novel parameter adjustments were used to improve it. Laryngoscopy was performed before and after adjustment but not in each test condition for reasons of patient comfort. A laryngograph was instead used to provide an objective measure of vocal fold closure and stridor during adjustment along with a perceptual speech rating—the DAB scale.