Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Better Universal Representations from Pre-trained Contextualized Language Models

Pre-trained contextualized language models such as BERT have shown great effectiveness in a wide range of downstream natural language processing (NLP) tasks. However, the effective representations offered by the models target at each token inside a sequence rather than each sequence and the fine-tuning step involves the input of both sequences at one time, leading to unsatisfying representation of each individual sequence. Besides, as sentence-level representations taken as the full training context in these models, there comes inferior performance on lower-level linguistic units (phrases and words). In this work, we present a novel framework on BERT that is capable of generating universal, fixed-size representations for input sequences of any lengths, i.e., words, phrases, and sentences, using a large scale of natural language inference and paraphrase data with multiple training objectives. Our proposed framework adopts the Siamese network, learning sentence-level representations from natural language inference dataset and phrase and word-level representations from paraphrasing dataset, respectively. We evaluate our model across different granularity of text similarity tasks, including STS tasks, SemEval2013 Task 5(a) and some commonly used word similarity tasks, where our model substantially outperforms other representation models on sentence-level datasets and achieves significant improvements in word-level and phrase-level representation.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest. In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce. Meta-learning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples. In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances. Meta-learning approaches have so far been typically tested in an N-way, K-shot classification setting where each task has N classes with K examples per class. Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes. We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.
Leptonic unitarity triangles

We present a comprehensive analysis of leptonic unitarity triangles, using both current neutrino oscillation data and projections of next-generation oscillation measurements. Future experiments, sensitive to the degree of CP violation in the lepton sector, will enable the construction of precise triangles. We show how unitarity violation could manifest in the triangles and discuss how they serve as unitarity tests. We also propose the use of Jarlskog factors as a complementary means of probing unitarity. This analysis highlights the importance of testing the unitarity of the leptonic mixing matrix, an understanding of which is crucial for deciphering the nature of the neutrino sector.
Effect of a molluscicide and an insecticide on the establishment of direct-drilled ryegrass, tall fescue and phalaris.

The effect of a molluscicide or an insecticide on the establishment, by direct dril l ing of ‘Grasslands Roa’ tall fescue (Festuca arundinacea Schreb.) ‘Ellett’ perennial ryegrass (Lolium perenne L), and ‘Grasslands Maru’ phalaris (Phalaris aquatica L.) was measured in spring and autumn establishment over 2 years. In autumn after a wet summer the application of a molluscicide significantly increased seedling numbers and establishment yield for ryegrass and tall fescue. A molluscicide applied in autumn after a dry summer or in spring had n o e f f e c t . Application of insecticide significantly improved the establishment of ryegrass and tall fescue in only one of the two springs and had no effect in autumn. Phalaris was the least responsive of the pasture species to either an insecticide or molluscicide. These differences observed at establishment resulting from the applications of a pesticide were not apparent in an assessment made 1 year later. For tall fescue the results recorded on seedling numbers and yield of sown species at establishment and 1 year would suggest that for this species establishment by direct drilling is not recommended.
The Dynamics and Geometry of Semi-Hyperbolic Rational Semigroups

We deal with various classes of finitely generated semi-hyperbolic rational semigroups $G$ acting on the Riemann sphere. Our primary tool is the associated skew product map $\tilde f$. For every real number $t\ge 0$ we define the topological pressure $P(t)$ ascribed to the (possibly) unbounded potential $-t\log|\tilde f'|$. We show that for some non-degenerate open (in $[0,+\infty)$) interval containing $0$ and the Hausdorff dimension of the Julia set $J(G)$, the function $t\mapsto P(t)$ is real-analytic. We further show that for all $t$ in such an interval there exist a unique $t$-conformal measure $m_t$ and a unique Borel probability $\tilde f$-invariant measure $\mu_t$ absolutely continuous with respect to $m_t$. We then show that $\mu_t$ and $m_t$ are equivalent measures and that the dynamical system $(\tilde f,\mu_t)$ is metrically exact (thus ergodic), and that for Holder continuous observables $(\tilde f,\mu_t)$ satisfies the CLT, LIL, and the Exponential Decay of Correlations. We prove a Variational Principle for the potentials $-t\log|\tilde f'|$, and we characterize the measures $\mu_t$ as their unique equilibrium states. Concerning geometry, we first introduce the Nice Open Set Condition, and we prove that each *semi-hyperbolic rational semigroup satisfying this condition is of finite type. We then introduce the class of non-exceptional semigroups and perform a full multifractal analysis of the equilibrium states $\mu_t$. In particular, we show that the corresponding multifractal spectrum is non-trivial for every non-exceptional totally non-recurrent rational semigroup satisfying the Nice Open Set Condition. Finally, we settle a long standing problem in the theory of rational semigroups by proving that for our class of semigroups the Hausdorff dimension of each fiber Julia set is strictly smaller than the Hausdorff dimension of the global Julia set of the semigroup.
A Taxonomy of the Genus Corallina (Corallinaceae, Rhodophyta) in Korea

Three species of the genus Corallina (Corallinaceae, Rhodophyta), Corallina officinalis Linnaeus, C. pilulifera Postels et Ruprecht, and C. confusa Yendo, were investigated taxonomically from the coasts of Korea during June 1992 and April 1993 in order to clarify and re-evaluate the characteristics of the species. Habit, vegetative morphology, reproductive structures, and phonology were discussed on the point of taxonomic criteria. The initial separation of these three species was based on the number of tiers of medullary cells per intergeniculum. They were distinctive respectively in the prostrate lower branches, shape of intergenicula, and terminal conceptacle with antennae, as well as intergenicular length, number of tiers per intergeniculum, and number and area of epithallial cell concavity per unit area.
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework

As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a main challenge limiting the broad use of distributed signal processing techniques is the issue of privacy in handling sensitive data. To address this privacy issue, we propose a novel yet general subspace perturbation method for privacy-preserving distributed optimization, which allows each node to obtain the desired solution while protecting its private data. In particular, we show that the dual variable introduced in each distributed optimizer will not converge in a certain subspace determined by the graph topology. Additionally, the optimization variable is ensured to converge to the desired solution, because it is orthogonal to this non-convergent subspace. We therefore propose to insert noise in the non-convergent subspace through the dual variable such that the private data are protected, and the accuracy of the desired solution is completely unaffected. Moreover, the proposed method is shown to be secure under two widely-used adversary models: passive and eavesdropping. Furthermore, we consider several distributed optimizers such as ADMM and PDMM to demonstrate the general applicability of the proposed method. Finally, we test the performance through a set of applications. Numerical tests indicate that the proposed method is superior to existing methods in terms of several parameters like estimated accuracy, privacy level, communication cost and convergence rate.
The use of pseudo-multivariate standard error to improve the sampling design of coral monitoring programs

The characteristics of coral reef sampling and monitoring are highly variable, with numbers of units and sampling effort varying from one study to another. Numerous works have been carried out to determine an appropriate effect size through statistical power; however, these were always from a univariate perspective. In this work, we used the pseudo multivariate dissimilarity-based standard error (MultSE) approach to assess the precision of sampling scleractinian coral assemblages in reefs of Venezuela between 2017 and 2018 when using different combinations of number of transects, quadrats and points. For this, the MultSE of 36 sites previously sampled was estimated, using four 30m-transects with 15 photo-quadrats each and 25 random points per quadrat. We obtained that the MultSE was highly variable between sites and is not correlated with the univariate standard error nor with the richness of species. Then, a subset of sites was re-annotated using 100 uniformly distributed points, which allowed the simulation of different numbers of transects per site, quadrats per transect and points per quadrat using resampling techniques. The magnitude of the MultSE stabilized by adding more transects, however, adding more quadrats or points does not improve the estimate. For this case study, the error was reduced by half when using 10 transects, 10 quadrats per transect and 25 points per quadrat. We recommend the use of MultSE in reef monitoring programs, in particular when conducting pilot surveys to optimize the estimation of the community structure.
Silvopastoral systems: technical, environmental and socio-economic challenges

In this article agroforestry is discussed with examples from coconut and oil palm plantations with animal interactions. Reference is also given to regeneration of forests with interaction with complementary crops and crop-animal-soil interactions in general. Feed evaluation systems more adapted to consumption of tree forages must be developed to support silvopastoral systems.
Relationship between Wing-Form Response to Nymphal Density and Black Colouration of Adult Body in the Brown Planthopper, Nilaparvata lugens (Homoptera:Delphacidae)

During successive selection for adults with a special wing form from a population oz the brown planthopper, IViloparvata l gens, a negative corrclation was found between the propertion of brachypters and the grade of black pigmentation in thc adults: population including more blackish individuals tendedi to exhibit a lower ratio ofbrachypters. The selection for blackish macropters finally generated a population producing predominantly rnacropters, whilc the selection for yellowish brown brachypters produced a population exhibiting totally brachypters, over broad ranges of nymphal densities in both sexes, Two populations from Malaysia and the Philippines were quite similar to this brachypter producing population in their wing-form responses to density. All of 14 populatiens cellected in Saga and Nagasaki Prefectures during 1986 and 1987 were highly macropterous and their body colours were
The aetiology of amoebic gill disease (AGD) and aspects of the host immune response to infection

Amoebic gill disease (AGD) is an ectoparasitic condition of some marine fish. Atlantic salmon are highly susceptible to AGD and the cost of mitigation is a significant financial burden for the aquaculture industry, particularly in Tasmania, Australia. Despite a considerable research effort over the past 20 years, two fundamental questions remain unanswered: 1. Which species of amoeba causes AGD? Two species of amoebae, Neoparamoeba pemaquidensis and Neoparamoeba branchiphila have been isolated from the gills of AGD-affected fish. Based on morphology alone, either species may be associated with AGD yet all attempts to experimentally induce AGD with cultured strains of either species have been unsuccessful. 2. Why are Atlantic salmon highly susceptible to AGD and is this susceptibility linked to aberrant innate and adaptive immune responses? In this thesis, the aetiology of AGD was resolved by identifying and characterising a new species of amoeba, Neoparamoeba perurans and demonstrating that this species is the only known aetiological agent of AGD globally. In so doing, the phylogeny of Neoparamoeba was resolved and the strict co-evolution of Neoparamoeba species with their endosymbiont, Perkinsela amoebae-like organism was confirmed as a defining characteristic amongst all members of the Neoparamoeba. Following this, the mechanisms that underpin the susceptibility of Atlantic salmon to AGD were assessed. Global gene expression profiling the gills of AGD-affected fish revealed that transcripts associated with the immune response were almost universally down-regulated in AGD-lesions specifically. In AGD-affected tissue, significant, coordinated down-regulation of the major histocompatibility complex class I (MHC I), and possibly the MHC II pathway-related genes occurred during the later stages of infection and appeared to be mediated by down-regulation of interferon-regulatory factor (IRF)-1, independent of interferon-a, interferon-y (IFN- a) and IRF-2 expression. Stimulating AGD-lesions ex vivo with recombinant IFN-y failed to restore the expression of IRF-1 and the MHC I receptor molecule, thus confirming earlier observations that the MHC I antigen presentation pathway appears to be modulated independently of IFN-y in AGD lesions. Within the AGD lesion microenvironment, suppression of the MHC I and possibly the MHC II pathways may inhibit the development of acquired immunity and could explain the unusually high susceptibility of Atlantic salmon to AGD. Whilst the data are preliminary, the immunologically unresponsiveness of the AGD lesion microenvironment is possibly linked with a disruption in the NF-kB signalling pathway which may permit N. perurans to evade the host immune response. Finally it is proposed that an understanding of the mechanisms of localised immunosuppression will be particularly important for the development of new treatments for AGD since systemic immunostimulation may be ineffective without simultaneous disruption of the immune privilege-like microenvironment within AGD lesions.
Clinical Characteristics and Short-Term Outcomes of Severe Patients With COVID-19 in Wuhan, China

Background: A novel pneumonia (COVID-19) spread rapidly throughout worldwide, in December, 2019. Most of the deaths have occurred in severe and critical cases, but information on prognostic risk factors for severely ill patients is incomplete. Further research is urgently needed to guide clinicians, and we therefore prospectively evaluate the clinical outcomes of 114 severely ill patients with COVID-19 for short-term at the Union Hospital in Wuhan, China. Methods: In this single-centered, prospective, and observational study, we enrolled 114 severely ill patients with confirmed COVID-19 from Jan 23, 2020, to February 22, 2020. Epidemiological, demographic, laboratory, treatment, and outcome data were recorded, and the risk factors for poor outcome were analyzed. Results: Among the 114 enrolled patients with a mean age of 63.96 ± 13.41 years, 94 (82.5%) patients were classified as a good outcome group. Common clinical manifestations included fever, cough, and fatigue. Compared with the good outcome group, 20 (17.5%) patients in the poor outcome group more frequently exhibited lymphopenia, and lower levels of albumin, partial arterial oxygen pressure, higher levels of lactate dehydrogenase, creatine kinase, hypersensitive troponin I, C-reactive protein, ferritin, blood urea nitrogen, and D-dimer, as well as markedly higher levels of IL-6 and IL-10. Absolute numbers of T lymphocytes, CD8 + T cells, decreased in almost all the patients and were markedly lower in the poor outcome group than the good outcome group. We also found that traditional Chinese medicine can significantly improve the patient's condition, which is conducive to the transformation from a severe to mild condition. In addition, univariate and multivariate Cox analyses of potential factors for poor outcome patients indicated that cytokine storms and uncontrolled inflammation responses as well as liver, kidney, and cardiac dysfunction are related to the development of a poor outcome. Conclusion: In summary, we reported this single-centered, prospective, and observational study for short-term outcome in severe patients with COVID-19. We found that cytokine storms and uncontrolled inflammation responses as well as liver, kidney, and cardiac dysfunction may play important roles in the final outcome of severely ill patients with COVID-19. Our study will allow clinicians to benefit and rapidly estimate the likelihood of a short-term poor outcome for severely ill patients.
Genetic structure of SARS-CoV-2 in Western Germany reflects clonal superspreading and multiple independent introduction events

We whole-genome sequenced 55 SARS-CoV-2 isolates from Western Germany and investigated the genetic structure of SARS-CoV-2 outbreaks in the Heinsberg district and Dusseldorf. While the genetic structure of the Heinsberg outbreak indicates a clonal origin, reflective of superspreading dynamics during the carnival season, distinct viral strains are circulating in Dusseldorf, reflecting the city's international links. Limited detection of Heinsberg strains in the Dusseldorf area despite geographical proximity may reflect efficient containment and contact tracing efforts.
On the reliability of model-based predictions in the context of the current COVID epidemic event: impact of outbreak peak phase and data paucity

The pandemic spread of the COVID-19 virus has, as of 20th of April 2020, reached most countries of the world. In an effort to design informed public health policies, many modelling studies have been performed to predict crucial outcomes of interest, including ICU solicitation, cumulated death counts, etc. The corresponding data analyses however, mostly rely on restricted (openly available) data sources, which typically include daily death rates and confirmed COVID cases time series. In addition, many of these predictions are derived before the peak of the outbreak has been observed yet (as is still currently the case for many countries). In this work, we show that peak phase and data paucity have a substantial impact on the reliability of model predictions. Although we focus on a recent model of the COVID pandemics, our conclusions most likely apply to most existing models, which are variants of the so-called 'Susceptible-Infected-Removed' or SIR framework. Our results highlight the need for performing systematic reliability evaluations for all models that currently inform public health policies. They also motivate a plea for gathering and opening richer and more reliable data time series (e.g., ICU occupancy, negative test rates, social distancing commitment reports, etc).
DNA Barcoding of Nematodes Using the MinION

Many nematode species are parasitic and threaten the health of plants and animals, including humans, on a global scale. Advances in DNA sequencing techniques have allowed for the rapid and accurate identification of many organisms including nematodes. However, the steps taken from sample collection in the field to molecular analysis and identification can take many days and depend on access to both immovable equipment and a specialized laboratory. Here, we present a protocol to genetically identify nematodes using 18S SSU rRNA sequencing using the MinION, a portable third generation sequencer, and proof that it is possible to perform all the molecular preparations on a fully portable molecular biology lab – the Bentolab. We show that both parasitic and free-living nematode species (Anisakis simplex, Panagrellus redivivus, Turbatrix aceti, and Caenorhabditis elegans) can be identified with a 96–100% accuracy compared to Sanger sequencing, requiring only 10–15 min of sequencing. This protocol is an essential first step toward genetically identifying nematodes in the field from complex natural environments (such as feces, soil, or marine sediments). This increased accessibility could in turn improve global information of nematode presence and distribution, aiding near-real-time global biomonitoring.
Multi-Platform Detection of Small Ruminant Lentivirus Antibodies and Provirus as Biomarkers of Production Losses

Small ruminant lentiviruses (SRLVs) are endemic in most areas of Europe, causing a chronic infection and a multisystemic disease affecting the udder, carpal joints, lungs, and central nervous system. Due to the lack of treatments and protective vaccination strategies, infection control is focused on the identification of infected animals through serological or molecular techniques. However, antigenic and genetic heterogeneity of SRLVs represent a clear drawback for diagnosis. Infected animals may present lower animal production parameters such as birth weight or milk production and quality, depending on productive systems considered and, likely, to the diagnostic method applied. In this study, four sheep flocks dedicated to dairy or meat production were evaluated using three different ELISA and two PCR strategies to classify animal population according to SRLV infection status. Productive parameters were recorded along one whole lactation or reproductive period and compared between positive and negative animals. SRLV was present in 19% of the total population, being unequally distributed in the different flocks. Less than half of the infected animals were detected by a single diagnostic method, highlighting the importance of combining different diagnostic techniques. Statistical analysis employing animal classification using all the diagnostic methods associated lambing size, lamb weight at birth, and daily weight gain with SRLV infection status in meat flocks. Milk production, somatic cell count, fat, and protein content in the milk were associated with SRLV infection in dairy flocks, to a greater extent in the flock showing higher seroprevalence. A multi-platform SRLV diagnostic strategy was useful for ensuring correct animal classification, thus validating downstream studies investigating production traits.