Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of weather and policy intervention on COVID-19 infection in Ghana

Even though laboratory and epidemiological studies have demonstrated the effects of ambient temperature on the transmission and survival of coronaviruses, not much has been done on the effects of weather on the spread of COVID-19. This study investigates the effects of temperature, humidity, precipitation, wind speed and the specific government policy intervention of partial lockdown on the new cases of COVID-19 infection in Ghana. Daily data on confirmed cases of COVID-19 from March 13, 2020 to April 21, 2020 were obtained from the official website of Our World in Data (OWID) dedicated to COVID-19 while satellite climate data for the same period was obtained from the official website of NASA's Prediction of Worldwide Energy Resources (POWER) project. Considering the nature of the data and the objectives of the study, a time series generalized linear model which allows for regressing on past observations of the response variable and covariates was used for model fitting. The results indicate significant effects of maximum temperature, relative humidity and precipitation in predicting new cases of the disease. Also, results of the intervention analysis indicate that the null hypothesis of no significant effect of the specific policy intervention of partial lockdown should be rejected (p-value=0.0164) at a 5\% level of significance. These findings provide useful insights for policymakers and the public.
Allergen-specific immunoglobulin, histamine and T-cell responses induced by soybean glycinin and β-conglycinin in BALB/c mice of oral sensitisation

Glycinin and β-conglycinin are major soybean allergens involved in food hypersensitivity. However, the mechanism of immune responses induced by glycinin and β-conglycinin has not been fully understood. Balb/c mice were oral sensitised with different doses (0.1, 1.0 and 10 mg/day) of soybean glycinin and β-conglycinin for five weeks. Allergen-specific immunoglobulin (Ig), serum histamine and T-cell responses were tested to assess the allergenic activity of glycinin and β-conglycinin. Mice sensitised with 0.1 or 1.0 mg/day allergens induced high levels of specific IgE, IgG1 and serum histamine compared with mice treated with saline. Furthermore, specific T-cell proliferation and significant up-regulation of interleukin (IL)-4, IL-5 and interferon (IFN)-γ were observed in splenocytes from mice gavaged with 0.1 or 1.0 mg/day soybean proteins. Low doses of glycinin or β-conglycinin can induce allergic reactions in BALB/c mice, which might be associated with increased IgE and cytokine production.
CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation

Emotion-controllable response generation is an attractive and valuable task that aims to make open-domain conversations more empathetic and engaging. Existing methods mainly enhance the emotion expression by adding regularization terms to standard cross-entropy loss and thus influence the training process. However, due to the lack of further consideration of content consistency, the common problem of response generation tasks, safe response, is intensified. Besides, query emotions that can help model the relationship between query and response are simply ignored in previous models, which would further hurt the coherence. To alleviate these problems, we propose a novel framework named Curriculum Dual Learning (CDL) which extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively. CDL utilizes two rewards focusing on emotion and content to improve the duality. Additionally, it applies curriculum learning to gradually generate high-quality responses based on the difficulties of expressing various emotions. Experimental results show that CDL significantly outperforms the baselines in terms of coherence, diversity, and relation to emotion factors.
Automatic Catalog of RRLyrae from ~ 14 million VVV Light Curves: How far can we go with traditional machine-learning?

Context. The creation of a 3D map of the bulge using RR Lyrae (RRL) is one of the main goals of the VISTA Variables in the Via Lactea Survey (VVV) and VVV(X) surveys. The overwhelming number of sources undergoing analysis undoubtedly requires the use of automatic procedures. In this context, previous studies have introduced the use of machine learning (ML) methods for the task of variable star classification. Aims. Our goal is to develop and test an entirely automatic ML-based procedure for the identification of RRLs in the VVV Survey. This automatic procedure is meant to be used to generate reliable catalogs integrated over several tiles in the survey. Methods. Following the reconstruction of light curves, we extracted a set of period- and intensity-based features, which were already defined in previous works. Also, for the first time, we put a new subset of useful color features to use. We discuss in considerable detail all the appropriate steps needed to define our fully automatic pipeline, namely: the selection of quality measurements; sampling procedures; classifier setup, and model selection. Results. As a result, we were able to construct an ensemble classifier with an average recall of 0.48 and average precision of 0.86 over 15 tiles. We also made all our processed datasets available and we published a catalog of candidate RRLs. Conclusions. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, our results indicate that color is an informative feature type of the RRL objective class that should always be considered in automatic classification methods via ML. We also argue that recall and precision in both tables and curves are high-quality metrics with regard to this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates, it is important to use the original distribution more abundantly than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step and that most errors in the identification of RRLs are related to low-quality observations of some sources or to the increased difficulty in resolving the RRL-C type given the data.
Splenic Lymphoma with Villous Lymphocytes

Splenic lymphoma with villous lymphocytes (SLVL) is a recently identified B cell lymphoproliferative disorder characterised by splenomegaly and the presence in the peripheral blood of lymphocytes with ‘villous’ projections. These villous lymphocytes are slightly larger than those found in CLL, have a round nucleus, a visible nucleolus in 50% of cases and have a variable amount of basophilic cytoplasm and characteristically, an irregular cell surface with thin, short membrane villi that are unevenly distributed and most often seen at the poles of the cell. Small numbers of plasmacytoid cells are present in most cases. There may be a variable degree of anaemia or thrombocytopenia. Immunologically the cells display a B cell phenotype with surface immunoglobulin of moderate to strong intensity, CD19, CD20, CD22, class II MHC antigens and FMC-7 expression, but they are often negative with CD5 and CD23, commonly positive in CLL, and they also are negative with CD25, HC2 and B-ly-7, typical markers in hairy cell.
Knowledge Base Inference for Regular Expression Queries

Two common types of tasks on Knowledge Bases have been studied -- single link prediction (Knowledge Base Completion) and path query answering. However, our analysis of user queries on a real-world knowledge base reveals that a significant fraction of queries specify paths using regular expressions(regex). Such regex queries cannot be handled by any of the existing link prediction or path query answering models. In response, we present Regex Query Answering, the novel task of answering regex queries on incomplete KBs. We contribute two datasets for the task, including one where test queries are harvested from actual user querylogs. We train baseline neural models for our new task and propose novel ways to handle disjunction and Kleene plus regex operators.
An Evaluation of Visualization Methods for Population Statistics Based on Choropleth Maps

We evaluate several augmentations to the choropleth map to convey additional information, including glyphs, 3D, cartograms, juxtaposed maps, and shading methods. While choropleth maps are a common method used to represent societal data, with multivariate data they can impede as much as improve understanding. In particular large, low population density regions often dominate the map and can mislead the viewer as to the message conveyed. Our results highlight the potential of 3D choropleth maps as well as the low accuracy of choropleth map tasks with multivariate data. We also introduce and evaluate popcharts, four techniques designed to show the density of population at a very fine scale on top of choropleth maps. All the data, results, and scripts are available from this https URL.
Generating Persona-Consistent Dialogue Responses Using Deep Reinforcement Learning

Recent transformer-based open-domain dialogue agents are trained by reference responses in a fully supervised scenario. Such agents often display inconsistent personalities as training data potentially contain contradictory responses to identical input utterances and no persona-relevant criteria are used in their training losses. We propose a novel approach to train transformer-based dialogue agents using actor-critic reinforcement learning. We define a new reward function to assess generated responses in terms of persona consistency, topic consistency, and fluency. Our reference-agnostic reward relies only on a dialogue history and a persona defined by a list of facts. Automatic and human evaluations on the PERSONACHAT dataset show that our proposed approach increases the rate of persona-consistent responses compared with its peers that are trained in a fully supervised scenario using reference responses.
Near infrared reflectance spectroscopy prediction and heritability of neutral detergent-soluble fiber in alfalfa

Increasing neutral detergent-soluble fiber (NDSF) concentration has been considered a strategy for breeding alfalfa (Medicago sativa L.) with improved quality. Pectin is the main component of NDSF and is highly digestible, but the assay for NDSF is time-consuming, The objectives were (i) to determine the potential use of near infrared reflectance spectroscopy (NIRS) to predict NDSF concentration in alfalfa and (ii) to estimate NDSF heritability and gain from selection. Herbage was sampled five times across 2 yr from two populations of half-sib (HS) progeny tested at Ithaca, NY. Calibration equations were developed by multiple linear regression of laboratory values of NDSF on NIRS spectra. Samples from one harvest were assayed twice to compare extraction methods. Means of NDSF from 90- and 80%-ethanol extractions were 213.1 ± 7.21 and 179.5 ± 5.89 g kg - respectively. Assay standard deviations were low for both ethanol concentrations. Because a better calibration equation was obtained with the 80%-ethanol assay, all remaining samples were assayed by that technique. Standard errors of calibration were small, ranging from 4.71 to 7.54 g kg - 1 . Coefficients of multiple determination (R 2 ) ranged from 0.72 to 0.97. Most heritability estimates for NDSF were significantly greater than zero, ranging from -0.03 ± 0.20 to 0.56 ± 0.17 and averaging 0.41. Expected gain from selection of parents based on HS progeny ranged from 4.6 to 19.0 g kg -1 and averaged 10.3 g kg -1 . Selecting parents with higher NDSF concentrations predicted by NIRS evaluations of HS progenies may improve forage quality of alfalfa.
Detecting ultra-high-energy cosmic ray anisotropies through harmonic cross-correlations

We propose an observable for ultra-high-energy cosmic ray (UHECR) physics: the harmonic-space cross-correlation power spectrum between the arrival directions of UHECRs and the large-scale cosmic structure mapped by galaxies. This cross-correlation has not yet been considered in the literature, and it permits a direct theoretical modelling of the main astrophysical components. We describe the expected form of the cross-correlation and show how, if the distribution of UHECR sources traces the large-scale cosmic structure, it could be easier to detect with current data than the UHECR auto-correlation. Moreover, the cross-correlation is more sensitive to UHECR anisotropies on smaller angular scales, it is more robust to systematic uncertainties, and it could be used to determine the redshift distribution of UHECR sources, making it a valuable tool for determining their origins and properties.
Consistent modeling of velocity statistics and redshift-space distortions in one-loop perturbation theory

The peculiar velocities of biased tracers of the cosmic density field contain important information about the growth of large scale structure and generate anisotropy in the observed clustering of galaxies. Using N-body data, we show that velocity expansions for halo redshift-space power spectra are converged at the percent-level at perturbative scales for most line-of-sight angles μ when the first three pairwise velocity moments are included, and that the third moment is well-approximated by a counterterm-like contribution. We compute these pairwise-velocity statistics in Fourier space using both Eulerian and Lagrangian one-loop perturbation theory using a cubic bias scheme and a complete set of counterterms and stochastic contributions. We compare the models and show that our models fit both real-space velocity statistics and redshift-space power spectra for both halos and a mock sample of galaxies at sub-percent level on perturbative scales using consistent sets of parameters, making them appealing choices for the upcoming era of spectroscopic, peculiar-velocity and kSZ surveys.
Cross-Linguistic Syntactic Evaluation of Word Prediction Models

A range of studies have concluded that neural word prediction models can distinguish grammatical from ungrammatical sentences with high accuracy. However, these studies are based primarily on monolingual evidence from English. To investigate how these models’ ability to learn syntax varies by language, we introduce CLAMS (Cross-Linguistic Assessment of Models on Syntax), a syntactic evaluation suite for monolingual and multilingual models. CLAMS includes subject-verb agreement challenge sets for English, French, German, Hebrew and Russian, generated from grammars we develop. We use CLAMS to evaluate LSTM language models as well as monolingual and multilingual BERT. Across languages, monolingual LSTMs achieved high accuracy on dependencies without attractors, and generally poor accuracy on agreement across object relative clauses. On other constructions, agreement accuracy was generally higher in languages with richer morphology. Multilingual models generally underperformed monolingual models. Multilingual BERT showed high syntactic accuracy on English, but noticeable deficiencies in other languages.
The Parsec-scale Jet of the Neutrino-emitting Blazar TXS 0506+056

Recently the IceCube Collaboration detected very high energy (VHE) neutrinos and associated them with the blazar TXS 0506+056, raising a possible association of VHE neutrinos with this and other individual blazars. Very long baseline interferometry (VLBI) is so far the only technique enabling the imaging of the innermost jet at milliarcsec resolution (parsec scale), where the high-energy emission possibly originates from. Here, we report on the radio properties of the parsec-scale jet in TXS 0506+056 derived from the analysis of multiepoch, multifrequency archive VLBI data. The half opening angle of the jet beam is about 3. °8, and the jet inclination angle is about 20°. The overall jet structure shows a helical trajectory with a precessing period of 5–6 yr, likely originating from instabilities operating at parsec scales. The calculated beaming parameters (Doppler boosting factor, bulk Lorentz factor) suggest a moderately relativistic jet. The parsec-scale magnetic field strength is estimated in the contexts of core shift and variability, and is in general agreement in the range of 0.2–0.7 G. And it is found to decrease from a relatively larger value during the quiescent period before the ongoing flare. This suggests a conversion of magnetic field energy density to particle energy density that helps accelerate injected particles at the jet base and results in variable shocked emission. The neutrino event could be associated with the onset of energetic particle injection into the jet. This scenario then supports the lepto-hadronic origin of the VHE neutrinos and γ-ray emission owing to a cospatial origin.
Effects of Residual Hatchery‐Reared Steelhead on Growth of Wild Rainbow Trout and Spring Chinook Salmon

Abstract We investigated the effects of nonmigrant (residual) juvenile hatchery steelhead (anadromous rainbow trout Oncorhynchus mykiss) on growth of wild rainbow trout and juvenile spring Chinook salmon O. tshawytscha to examine how increased densities of residual hatchery steelhead might affect the growth of preexisting wild rainbow trout and Chinook salmon. We used screened enclosures in a natural stream to examine food utilization and physiological stress, factors that might affect fish growth. The presence of residual hatchery steelhead led to reduced growth of wild rainbow trout (1993: P = 0.019; 1994: P = 0.020) but not of spring Chinook salmon (P = 0.360). Enclosures did not reduce the total number of food items available but did influence the species composition of aquatic and terrestrial invertebrates. The food habits of paired and unpaired fish differed; however, the power of those tests was low. Cortisol level, a measure of physiological stress, did not differ between paired and unpaired fish .
Biomedical Entity Representations with Synonym Marginalization

Biomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their surface forms, normalization of biomedical entities is very challenging. In this paper, we focus on learning representations of biomedical entities solely based on the synonyms of entities. To learn from the incomplete synonyms, we use a model-based candidate selection and maximize the marginal likelihood of the synonyms present in top candidates. Our model-based candidates are iteratively updated to contain more difficult negative samples as our model evolves. In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates. On four biomedical entity normalization datasets having three different entity types (disease, chemical, adverse reaction), our model BioSyn consistently outperforms previous state-of-the-art models almost reaching the upper bound on each dataset.
Effects of sample size on fish parasite prevalence, mean abundance and mean intensity estimates

Summary This study considers the effects of sample size on estimates of three parasitological indices (prevalence, mean abundance and mean intensity) in four different host–parasite systems, each showing a different pattern of infection. Monte Carlo simulation procedures were used in order to obtain an estimation of the parasitological indices, as well as their variance and bias, based on samples of different size. Although results showed that mean values of all indices were similar irrespective of sample size, estimates of prevalence were not significantly affected by sample size whereas mean abundance and mean intensity were affected in at least one sample. Underestimation of values was more perceptible in small (<40) sample sizes. Distribution of the estimated values revealed a different arrangement according to the host–parasite system and to the parasitological parameter. Monte Carlo simulation procedures are, therefore, suggested to be included in studies concerning estimation of parasitological parameters.
Star Formation and Morphological Properties of Galaxies in the Pan-STARRS 3π Survey. I. A Machine-learning Approach to Galaxy and Supernova Classification

We present a classification of galaxies in the Pan-STARRS1 (PS1) 3π survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features (colors and moments) from the PS1 data release 2. The labels for the morphological classification are taken from Huertas-Company et al., while labels for the star formation fraction (SFF) are from the Blanton et al. catalog. We find that colors provide more predictive accuracy than photometric moments. We morphologically classify galaxies as either early- or late-type, and our RF model achieves a 78% classification accuracy. Our second model classifies galaxies as having either a low-to-moderate or high SFF. This model achieves an 89% classification accuracy. We apply both RF classifiers to the entire PS1 3π dataset, which allows us to assign two scores to each PS1 source: PHSFF, which quantifies the probability of having a high SFF; and Pspiral, which quantifies the probability of having a late-type morphology. Finally, as a proof of concept, we apply our classification framework to supernova (SN) host galaxies from the Zwicky Transient Factory and the Lick Observatory Supernova Search samples. We show that by selecting PHSFF or Pspiral, it is possible to significantly enhance or suppress the fraction of core-collapse SNe (or thermonuclear SNe) in the sample with respect to random guessing. This result demonstrates how contextual information can aid transient classifications at the time of first detection. In the current era of spectroscopically starved time-domain astronomy, prompt automated classification is paramount. Our table is available at 10.5281/zenodo.3990545.
Diverse Visuo-Lingustic Question Answering (DVLQA) Challenge

Existing question answering datasets mostly contain homogeneous contexts, based on either textual or visual information alone. On the other hand, digitalization has evolved the nature of reading which often includes integrating information across multiple heterogeneous sources. To bridge the gap between two, we compile a Diverse Visuo-Lingustic Question Answering (DVLQA) challenge corpus, where the task is to derive joint inference about the given image-text modality in a question answering setting. Each dataset item consists of an image and a reading passage, where questions are designed to combine both visual and textual information, i.e. ignoring either of them would make the question unanswerable. We first explore the combination of best existing deep learning architectures for visual question answering and machine comprehension to solve DVLQA subsets and show that they are unable to reason well on the joint task. We then develop a modular method which demonstrates slightly better baseline performance and offers more transparency for interpretation of intermediate outputs. However, this is still far behind the human performance, therefore we believe DVLQA will be a challenging benchmark for question answering involving reasoning over visuo-linguistic context. The dataset, code and public leaderboard will be made available at this https URL.