Miracle-dz/UniFAR-FTUs huggingface dataset:
Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English

The vast majority of language pairs in the world are low-resource because they have little, if any, parallel data available. Unfortunately, machine translation (MT) systems do not currently work well in this setting. Besides the technical challenges of learning with limited supervision, there is also another challenge: it is very difficult to evaluate methods trained on low resource language pairs because there are very few freely and publicly available benchmarks. In this work, we take sentences from Wikipedia pages and introduce new evaluation datasets in two very low resource language pairs, Nepali-English and Sinhala-English. These are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.
Connecting the Unconnected: Toward Frugal 5G Network Architecture and Standardization

This article adopts a holistic approach to address the problem of poor broadband connectivity in rural areas by suggesting a novel wireless network architecture called "Frugal 5G Network. " To arrive at the Frugal 5G Network architecture, we take into consideration rural connectivity needs and the characteristics specific to rural areas. As part of the proposed Frugal 5G Network, we define a heterogeneous access network wherein macrocells provide carpet coverage, while wireless local area networks (WLANs) provide additional capacity to serve village clusters. WLAN is backhauled via a wireless network also called the wireless middle mile network. We define software defined networking and network function virtualization based architecture to make the network flexible and scalable. The concepts of fog computing have also been employed in the network architecture to bring intelligence to the edge (i.e., to the access network). The proposed network architecture is developed independent of 3GPP 5G standards and can serve as a potential solution toward IEEE P2061, a standardization project that aims to design an architecture to facilitate rural broadband communication.
Dietary Omega-3 Polyunsaturated Fatty Acid Deprivation Does Not Alter Seizure Thresholds but May Prevent the Anti-seizure Effects of Injected Docosahexaenoic Acid in Rats

Background: Brain concentrations of omega-3 docosahexaenoic acid (DHA, 22:6n-3) have been reported to positively correlate with seizure thresholds in rodent seizure models. It is not known whether brain DHA depletion, achieved by chronic dietary omega-3 polyunsaturated fatty acid (PUFA) deficiency, lowers seizure thresholds in rats. Objective: The present study tested the hypothesis that lowering brain DHA concentration with chronic dietary n-3 PUFA deprivation in rats will reduce seizure thresholds, and that compared to injected oleic acid (OA), injected DHA will raise seizure thresholds in rats maintained on n-3 PUFA adequate and deficient diets. Methods: Rats (60 days old) were surgically implanted with electrodes in the amygdala, and subsequently randomized to the AIN-93G diet containing adequate levels of n-3 PUFA derived from soybean oil or an n-3 PUFA-deficient diet derived from coconut and safflower oil. The rats were maintained on the diets for 37 weeks. Afterdischarge seizure thresholds (ADTs) were measured every 4–6 weeks by electrically stimulating the amygdala. Between weeks 35 and 37, ADTs were assessed within 1 h of subcutaneous OA or DHA injection (600 mg/kg). Seizure thresholds were also measured in a parallel group of non-implanted rats subjected to the maximal pentylenetetrazol (PTZ, 110 mg/kg) seizure test. PUFA composition was measured in the pyriform-amygdala complex of another group of non-implanted rats sacrificed at 16 and 32 weeks. Results: Dietary n-3 PUFA deprivation did not significantly alter amygdaloid seizure thresholds or latency to PTZ-induced seizures. Acute injection of OA did not alter amygdaloid ADTs of rats on the n-3 PUFA adequate or deficient diets, whereas acute injection of DHA significantly increased amygdaloid ADTs in rats on the n-3 PUFA adequate control diet as compared to rats on the n-3 PUFA deficient diet (P < 0.05). Pyriform-amygdala DHA percent composition did not significantly differ between the groups, while n-6 docosapentaenoic acid, a marker of n-3 PUFA deficiency, was significantly increased by 2.9-fold at 32 weeks. Conclusion: Chronic dietary n-3 PUFA deficiency does not alter seizure thresholds in rats, but may prevent the anti-seizure effects of DHA.
Chlamydia pecorum Associated With an Outbreak of Infectious Keratoconjunctivitis in Semi-domesticated Reindeer in Sweden

Infectious keratoconjunctivitis (IKC), the most common ocular disease in ruminants worldwide, has affected semi-domesticated Eurasian reindeer (Rangifer tarandus tarandus) for over 100 years, both as individual cases and in outbreaks affecting tens to hundreds of animals. Recurrent IKC outbreaks have been affecting a semi-domesticated reindeer herd in Östra Kikkejaure (Norrbotten county, Sweden) from 2014. The latest episode of these recurrent outbreaks, in winter 2016/2017, was investigated in this study. Clinical findings were in line with previous reports of IKC in semi-domesticated reindeer and the clinical signs displayed by the affected animals (n = 30) included increased lacrimation, follicular conjunctivitis, purulent secretions around the affected eyes and corneal edema. Laboratory analyses of the samples revealed the presence of Chlamydiaceae in most samples obtained from the clinically affected animals (98.3%, n = 60), but also a high seroprevalence of cervid herpesvirus 2 (CvHV2) antibodies (56.6%, n = 53). Moraxella bovoculi was isolated from nine IKC-affected animals during the outbreak (45.0%, n = 20). All affected animals were treated with long-acting antibiotics and recovered from the disease, testing negative for the presence of Chlamydiaceae DNA by PCR 16 days and 3 months after the initial treatment. For the first time, Chlamydia pecorum was identified in semi-domesticated reindeer, and the involvement of Chlamydiaceae in a clinical outbreak of IKC is reported. The CvHV2 seroprevalence (56.6%) and the data obtained from a previous outbreak in 2014 also suggest the involvement of the reindeer alphaherpesvirus in the recurrent outbreaks.
DNA Uptake by Type IV Filaments

Bacterial uptake of DNA through type IV filaments is an essential component of natural competence in numerous gram-positive and gram-negative species. Recent advances in the field have broadened our understanding of the structures used to take up extracellular DNA. Here, we review seminal experiments in the literature describing DNA binding by type IV pili, competence pili and the flp pili of Micrococcus luteus; collectively referred to here as type IV filaments. We compare the current state of the field on mechanisms of DNA uptake for these three appendage systems and describe the current mechanistic understanding of both DNA-binding and DNA-uptake by these versatile molecular machines.
Advances in the Study of Chronic Active Epstein-Barr Virus Infection: Clinical Features Under the 2016 WHO Classification and Mechanisms of Development

Chronic active Epstein-Barr virus infection (CAEBV) is one of the Epstein-Barr virus (EBV)-positive T- or NK-lymphoproliferative diseases. It is considered rare and geographically limited to Japan and East Asia. However, CAEBV is drawing international attention, and the number of case reported worldwide is increasing, after its classification in the EBV-positive T- or NK-cell neoplasms, in the 2016 WHO classification. In this article, I review current advances in the study of CAEBV under the new definition and show future directions. In CAEBV, EBV-infected T or NK cells clonally proliferate and infiltrate multiple organs, leading to their failure. These characteristics define CAEBV as a lymphoid neoplasm. However, the main symptom of CAEBV is inflammation. Recently, the mechanisms underlying the development of CAEBV have gradually become clearer. EBV infection of T or NK cells can occur during the acute phase of primary infection with a high EBV load in the peripheral blood. In addition, it was reported that cytotoxic T cells decreased in numbers or showed dysfunction in CAEBV. These findings suggest that undetermined immunosuppressive disorders may underlie persistent infection of T or NK cells. Furthermore, EBV itself contributes to the survival of host cells. In vitro EBV infection of T cells induced intercellular survival-promoting pathways. Constitutive activation of NF-kB and STAT3 was observed in EBV-positive T or NK cells in CAEBV, promoting not only cell survival but also CAEBV development. During the disease course, CAEBV can lead to two lethal conditions: hemophagocytic lymphohistiocytosis and chemotherapy-resistant lymphoma. It is necessary to start treatment before these conditions develop. At present, the only effective treatment strategy for eradicating EBV-infected T or NK cells is allogeneic stem cell transplantation (allo-HSCT). However, patients with an active disease, in which the condition is accompanied by fever, liver dysfunction, progressive skin lesions, vasculitis, or uveitis, had worse outcomes after allo-HSCT, than patients with an inactive disease had. Unfortunately, current chemotherapies are insufficient to improve the activity of CAEBV. Based on the molecular mechanisms for the development of the disease, the NF-kB, or JAK/STAT mediating pathways are attractive candidate targets for new treatments.
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning

We consider the dynamics of a linear stochastic approximation algorithm driven by Markovian noise, and derive finite-time bounds on the moments of the error, i.e., deviation of the output of the algorithm from the equilibrium point of an associated ordinary differential equation (ODE). We obtain finite-time bounds on the mean-square error in the case of constant step-size algorithms by considering the drift of an appropriately chosen Lyapunov function. The Lyapunov function can be interpreted either in terms of Stein's method to obtain bounds on steady-state performance or in terms of Lyapunov stability theory for linear ODEs. We also provide a comprehensive treatment of the moments of the square of the 2-norm of the approximation error. Our analysis yields the following results: (i) for a given step-size, we show that the lower-order moments can be made small as a function of the step-size and can be upper-bounded by the moments of a Gaussian random variable; (ii) we show that the higher-order moments beyond a threshold may be infinite in steady-state; and (iii) we characterize the number of samples needed for the finite-time bounds to be of the same order as the steady-state bounds. As a by-product of our analysis, we also solve the open problem of obtaining finite-time bounds for the performance of temporal difference learning algorithms with linear function approximation and a constant step-size, without requiring a projection step or an i.i. d. noise assumption.
Direct Stimulation of Human Hippocampus During Verbal Associative Encoding Enhances Subsequent Memory Recollection

Previous studies have reported conflicting results regarding the effect of direct electrical stimulation of the human hippocampus on memory performance. A major function of the hippocampus is to form associations between individual elements of experience. However, the effect of direct hippocampal stimulation on associative memory remains largely inconclusive, with most evidence coming from studies employing non-invasive stimulation. Here, we therefore tested the hypothesis that direct electrical stimulation of the hippocampus specifically enhances hippocampal-dependent associative memory. To test this hypothesis, we recruited surgical patients with implanted subdural electrodes to perform a word pair memory task during which the hippocampus was stimulated. Our results indicate that stimulation of the hippocampus during encoding helped to build strong associative memories and enhanced recollection in subsequent trials. Moreover, stimulation significantly increased theta power in the lateral middle temporal cortex during successful memory encoding. Overall, our findings indicate that hippocampal stimulation positively impacts performance during a word pair memory task, suggesting that successful memory encoding involves the temporal cortex, which may act together with the hippocampus.
Predicting Directions of Changes in Genotype Proportions Between Norovirus Seasons in Japan

The norovirus forecasting system (NOROCAST) has been developed for predicting directions of changes in genotype proportions between human norovirus (HuNoV) seasons in Japan through modeling herd immunity to structural protein 1 (VP1). Here 404 nearly complete genomic sequences of HuNoV were analyzed to examine whether the performance of NOROCAST could be improved by modeling herd immunity to VP2 and non-structural proteins (NS) in addition to VP1. It was found that the applicability of NOROCAST may be extended by compensating for unavailable sequence data and observed genotype proportions of 0 in each season. Incorporation of herd immunity to VP2 and NS did not appear to improve the performance of NOROCAST, suggesting that VP1 may be a suitable target of vaccines.
Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype per se for non-chemically induced injuries revealed the robust performance of the DNN models across these additional external testing datasets. Thus, the DNN models learned features specific to the injury phenotype contained in the gene expression data.
Google Classroom as a Tool of Support of Blended Learning for Geography Students

The article reveals the experience of organizing blended learning for geography students using Google Classroom, and discloses its potential uses in the study of geography. For the last three years, the authors have tested such in-class and distance courses as “Cartography and Basics of Topography”, “Population Geography”, “Information Systems and Technologies in Tourism Industry”, “Regional Economic and Social World Geography (Europe and the CIS)”, “Regional Economic and Social World Geography (Africa, Latin America, Asia, Anglo-America, Australia and Oceania)”, “Socio-Economic Cartography”. The advantages of using the specified interactive tool during the study of geographical disciplines are highlighted out in the article. As it has been established, the organization of the learning process using Google Classroom ensures the unity of in-class and out-of-class learning; it is designed to realize effective interaction of the subjects learning in real time; to monitor the quality of training and control the students’ learning achievements in class as well as out of it, etc. The article outlines the disadvantages that should be taken into account when organizing blended learning using Google Classroom, including the occasional predominance of students’ external motivation in education and their low level of readiness for work in the classroom; insufficient level of material and technical support in some classrooms; need for out-of-class pedagogical support; lack of guidance on the content aspect of Google Classroom pages, etc. Through the test series conducted during 2016-2017, an increase in the number of geography students with a sufficient level of academic achievements and a decrease of those with a low level of it was revealed.
Angiotensin II-Regulated Autophagy Is Required for Vascular Smooth Muscle Cell Hypertrophy

Hypertension is a disease associated to increased plasma levels of angiotensin II (Ang II). Ang II can regulate proliferation, migration, ROS production and hypertrophy of vascular smooth muscle cells (VSMCs). However, the mechanisms by which Ang II can affect VSMCs remain to be fully elucidated. In this context, autophagy, a process involved in self-digestion of proteins and organelles, has been described to regulate vascular remodeling. Therefore, we sought to investigate if Ang II regulates VSMC hypertrophy through an autophagy-dependent mechanism. To test this, we stimulated A7r5 cell line and primary rat aortic smooth muscle cells with Ang II 100 nM and measured autophagic markers at 24 h by Western blot. Autophagosomes were quantified by visualizing fluorescently labeled LC3 using confocal microscopy. The results showed that treatment with Ang II increases Beclin-1, Vps34, Atg-12–Atg5, Atg4 and Atg7 protein levels, Beclin-1 phosphorylation, as well as the number of autophagic vesicles, suggesting that this peptide induces autophagy by activating phagophore initiation and elongation. These findings were confirmed by the assessment of autophagic flux by co-administering Ang II together with chloroquine (30 μM). Pharmacological antagonism of the angiotensin type 1 receptor (AT1R) with losartan and RhoA/Rho Kinase inhibition prevented Ang II-induced autophagy. Moreover, Ang II-induced A7r5 hypertrophy, evaluated by α-SMA expression and cell size, was prevented upon autophagy inhibition. Taking together, our results suggest that the induction of autophagy by an AT1R/RhoA/Rho Kinase-dependent mechanism contributes to Ang II-induced hypertrophy in VSMC.
Distributed optimization for a class of high‐order nonlinear multiagent systems with unknown dynamics

In this paper, we study a distributed optimization problem for a class of high‐order multiagent systems with unknown dynamics. In comparison with existing results for integrators or linear agents, we need to overcome the difficulties brought by the unknown nonlinearities and the optimization requirement. For this purpose, we employ an embedded control‐based design and first convert this problem into an output stabilization problem. Then, two kinds of adaptive controllers are given for these agents to drive their outputs to the global optimal solution under some mild conditions. Finally, we show that the estimated parameter vector converges to the true parameter vector under some well‐known persistence of excitation condition. The efficacy of these algorithms was verified by a simulation example.
End-to-End Single Image Fog Removal Using Enhanced Cycle Consistent Adversarial Networks

Single image defogging is a classical and challenging problem in computer vision. Existing methods towards this problem mainly include handcrafted priors based methods that rely on the use of the atmospheric degradation model and learning-based approaches that require paired fog-fogfree training example images. In practice, however, prior-based methods are prone to failure due to their own limitations and paired training data are extremely difficult to acquire. Moreover, there are few studies on the unpaired trainable defogging network in this field. Thus, inspired by the principle of CycleGAN network, we have developed an end-to-end learning system that uses unpaired fog and fogfree training images, adversarial discriminators and cycle consistency losses to automatically construct a fog removal system. Similar to CycleGAN, our system has two transformation paths; one maps fog images to a fogfree image domain and the other maps fogfree images to a fog image domain. Instead of one stage mapping, our system uses a two stage mapping strategy in each transformation path to enhance the effectiveness of fog removal. Furthermore, we make explicit use of prior knowledge in the networks by embedding the atmospheric degradation principle and a sky prior for mapping fogfree images to the fog images domain. In addition, we also contribute the first real world nature fog-fogfree image dataset for defogging research. Our multiple real fog images dataset (MRFID) contains images of 200 natural outdoor scenes. For each scene, there is one clear image and corresponding four foggy images of different fog densities manually selected from a sequence of images taken by a fixed camera over the course of one year. Qualitative and quantitative comparison against several state-of-the-art methods on both synthetic and real world images demonstrate that our approach is effective and performs favorably for recovering a clear image from a foggy image.
F-BLEAU: Fast Black-Box Leakage Estimation

We consider the problem of measuring how much a system reveals about its secret inputs. We work in the black-box setting: we assume no prior knowledge of the system's internals, and we run the system for choices of secrets and measure its leakage from the respective outputs. Our goal is to estimate the Bayes risk, from which one can derive some of the most popular leakage measures (e.g., min-entropy leakage). The state-of-the-art method for estimating these leakage measures is the frequentist paradigm, which approximates the system's internals by looking at the frequencies of its inputs and outputs. Unfortunately, this does not scale for systems with large output spaces, where it would require too many input-output examples. Consequently, it also cannot be applied to systems with continuous outputs (e.g., time side channels, network traffic). In this paper, we exploit an analogy between Machine Learning (ML) and black-box leakage estimation to show that the Bayes risk of a system can be estimated by using a class of ML methods: the universally consistent learning rules; these rules can exploit patterns in the input-output examples to improve the estimates' convergence, while retaining formal optimality guarantees. We focus on a set of them, the nearest neighbor rules; we show that they significantly reduce the number of black-box queries required for a precise estimation whenever nearby outputs tend to be produced by the same secret; furthermore, some of them can tackle systems with continuous outputs. We illustrate the applicability of these techniques on both synthetic and real-world data, and we compare them with the state-of-the-art tool, leakiEst, which is based on the frequentist approach.