Miracle-dz/UniFAR-FTUs huggingface dataset:
S EMANTIC H ASHING WITH L OCALITY S ENSITIVE E M - BEDDINGS

Semantic hashing methods have been explored for learning transformations into binary vector spaces. These learned binary representations may then be used in hashing based retrieval methods, typically by retrieving all neighboring elements in the Hamming ball with radius 1 or 2. Prior studies focus on tasks with a few dozen to a few hundred semantic categories at most, and it is not currently well known how these methods scale to domains with richer semantic structure. In this study, we focus on learning embeddings for the use in exact hashing retrieval, where Approximate Nearest Neighbor search comprises of a simple table lookup. We propose similarity learning methods in which the optimized base similarity is the angular similarity (the probability of collision under SimHash.) We demonstrate the benefits of these embeddings on a variety of domains, including a coocurrence modelling task on a large scale text corpus; the rich structure of which cannot be handled by a few hundred semantic groups.
Organic fertilization as an alternative to the chemical in cherry tomato growing under irrigation depths

Two experiments were carried out to evaluate the effects of organic fertilization as an alternative to the chemical on the yield and quality of cherry tomato grown under different irrigation depths in protected environment, in 2013 and 2014. The experimental design was in randomized blocks (5 x 3 factor), with five irrigation depths as the factors (70%, 85%, 100%, 115% and 130% of ETc) and three types of fertilization: no fertilized soil (control), NPK fertilized soil (Chemical) and earthworm humus fertilized soil (organic). The following were evaluated in this study: water use efficiency, number of aborted flowers, total soluble solid content, transverse and longitudinal diameter of the fruits, number of fruits and fruit yield per plant. Organic fertilization is an alternative to the chemical with no losses in yield and fruit quality. The increase in water depth enhanced yield and fruit diameters and reduced water use efficiency. Water use efficiency was more sensitive to the increments in water depth than to different types of fertilization. The values of °Brix obtained in the experiment were classified as acceptable for the domestic and international market when submitted to organic fertilization.
Changes in left ventricular and atrial mechanics and function after dialysis in patients with end-stage renal disease.

Background Hemodialysis (HD) can influence end-stage renal disease (ESRD) patients' circulatory system. The present study aimed to evaluate the effect of volume depletion on left ventricular (LV) and left atrial (LA) function and determine the volume-independent parameters before and after HD in patients with ESRD. Methods Between January 2018 and January 2019, we recruited long-term HD patients (n=40, 51.0±16.4 years), excluding those with structural cardiac disease. Echocardiographic parameters, including LV and LA volumes, flow Doppler, pulsed tissue Doppler, and speckle tracking echocardiography (STE) before and after HD (within 24 h), were examined, and the values were compared. Results Following HD, alteration in LV end-systolic volume was not detected, whereas LV end-diastolic volume (90.18±23.91 vs. 84.21±23.54 mL, P=0.036) and LV ejection fraction (LVEF; 64.63%±6.56% vs. 62.84%±6.56%, P=0.049) decreased. Peak early diastolic trans-mitral flow velocity (E-wave; 82.22±20.13 vs. 72.43±18.32 cm/s, P<0.001), peak early diastolic tissue Doppler velocity (e'; 6.45±1.88 vs. 5.77±1.63 cm/s, P<0.001) at the septal side of the mitral annulus, the ratio of early to late Doppler velocities of diastolic mitral inflow (0.90±0.27 vs. 0.79±0.23, P<0.001), and the average E/e' ratio (12.54±4.08 vs. 11.28±4.52, P=0.049) decreased significantly. No significant difference was found in peak blood flow velocity at the mitral valve during late diastole and e' at the lateral side of the mitral annulus after HD. LA volume index (35.55±12.61 vs. 30.22±9.80 mL/m2, P<0.001), tricuspid regurgitation velocity (260.11±36.54 vs. 242.37±32.22 cm/s, P=0.002), and pulmonary artery systolic pressure (33.63±11.29 vs. 29.94±7.80 mmHg, P=0.006) significantly decreased. LV global longitudinal systolic strain (GLS) of 4-chamber view (-24.37%±3.02% vs. -23.38%±3.33%, P=0.019), rather than global circumferential systolic strain, exhibited significant change after HD. Significant changes were also found in LV longitudinal early diastolic strain rate (LSRe; 1.17±0.25 vs. 1.05±0.24 s-1, P<0.001) and early diastolic global radial velocity (Ve; 2.62±0.59 vs. 2.25±0.67 cm/s, P=0.011) after HD, but not in other strain rates and global radial velocity measurements. LA maximal volume (35.55±12.61 vs. 30.22±9.80 mL/m2, P<0.001), LA total emptying fraction (54.19%±10.39% vs. 49.63%±11.05%, P=0.009), and LA passive emptying fraction (32.23%±12.86% vs. 26.81%±9.28%, P=0.004) decreased significantly after HD, while LA minimal volume, the volume at the onset of atrial systole, and LA active emptying fraction after HD were not significantly different. Conclusions Most indices of systolic (LVEF and GLS of 4-chamber view) and early diastolic function (E-wave, e', LSRe, global radial Ve, and LA passive emptying fraction) were preload dependent. Late diastolic indices, including LV late diastolic global longitudinal strain rate, late diastolic global radial velocity, and LA active emptying fraction, did not change with volume depletion.
Prevalence and Factors Associated with Opisthorchis Viverrini Infection in Lao PDR

In many countries of South-East Asia infection with the liver fluke Opisthorchis viverrini (OV) is of considerable importance as a risk factor for cholangiocellular carcinoma (CC) development (Haswell-Elkins et al., 1994; Sriamporn et al., 2004; Sripa et al., 2011). In Viet Nam, Clonorchis sinensis may also be found (Doanh and Nawa, 2016). Infection with liver flukes (Opisthorchis viverrini, Opisthorchis felineus and Clonorchis sinensis) has been determined to be a class I carcinogen by the International Agency for Cancer Research evaluation committee (IARC, 1994). It has proved very difficult to change the dietary lifestyle habit of eating raw fish containing liver fluke eggs (Jongsuksuntigul and Imsomboon, 2003; Sripa et al., 2011) and a complicating factor is the presence of reservoir canine and feline hosts (Enes et al., 2010; Aunpromma et al., 2012). Furthermore, while the antihelminthic drug praziquantel may be used to control OV infection it maygive rise to an unwise dependency (Saengsawang et al., 2013) and repeated cycles of re-infection and treatment with praziquantel may increase the risk of CC (Kamsa-ard et al., 2015). As in neighbouring Thailand and Cambodia (Sithithaworn et al., 2012), OV has long been known in Laos (Bedier and Chesnau, 1929) and more recent reports have confirmed its presence as a common parasite in central and southern parts of the country ( Kobayashi et
The Consumption of Food at Video Game Events

This paper develops the discussion from a completed ethnographic PhD thesis (Law 2016); it draws on the data gathered from interviews, focus groups and participant observations to consider the consumption of food at video game events. To date, there is still little empirical research conducted on video gamers that attend various video game events; in particular, the relationship between the consumption of food at video game events. Though it was not the intention of the research to explore the consumption of food amongst video gamers, the subsequent analysis of this data highlighted patterns in relation to the consumption of food amongst male and female gamers at video game events. This paper argues that the consumption of food provided a social significance to connect gamers together through ritual practices, such as eating and drinking together – which little has been explored within games studies.
Post-depositional loss of nitrate and chloride in Antarctic snow by photolysis and sublimation: a field investigation

Nitrate in snow is subject to post-depositional processing, which leads to net loss and redistribution within the snowpack (Röthlisberger et al. 2000; Jacobi & Hilker 2007; Frey et al. 2009). The relative importance of the volatilization of nitric acid (HNO 3 ) and the photolysis of nitrate in the post-depositional loss process have long been debated (Röthlisberger et al. 2000; Blunier et al. 2005). Nitrate photolysis in/on the snowpack is a source of reactive nitrogen oxides (NO y ) in the atmosphere (Honrath et al. 1999; Jones et al. 2001; Shi et al. 2018). The photochemistry of nitrate in the snowpack has significant implications because its photoproducts—NO, NO 2 and radicals—are intimately linked to reactions involving ozone, hydrocarbons and halogens. Nitrate undergoes photolysis in the quasi-liquid layer of ice and either NO 2 (Eqn. 1) or NO 2 − (Eqn. 2) is produced (Mack & Bolton 1999; Grannas et al. 2007; Meusinger et al. 2014).
Attitude Towards Shopping Mall Purchasing Behavior Management Dynamics

Raju Bhai Manandhar* Abstract Consume attitude is multidimensional and it has been one of the main concepts used to explain individual differences. This study aims to examine the relationship between overall attitude and shopping mall purchasing behavior and impact of advertising and purpose to visit on shopping mall purchasing behavior in Nepalese consumers. The present study is descriptive and analytical in nature. Structured questionnaire technique under survey approach was applied for collecting primary data with five point Likert scale questionnaire. The population of this study targeted the consumers of shopping malls in Kathmandu valley. The sample size has been derived for unknown population that is 384. The judgmental sampling technique was used in this research to make this study more inclusive and representatives. Descriptive statistics and inferential statistics (correlation analysis and regression analysis) have been used to analyze the data. It is found that there is strong association between shopping mall purchase behavior and overall consumer attitude. The study found that purpose to visit has impact on shopping mall purchase behavior. The study also found that advertisement has no significant impact on shopping mall purchase behavior.
Profitability and Resource Use Efficiency of Mustard Cultivation

Climatic condition is conducive to cultivate mustard in Bangladesh and there are ample opportunity to improve the present scenario of production, since the demand of oilseeds is high. But economic analysis of production of this crop remains unnoticed most of the time. For assessment of the profitability and resource use efficiency, a field level study was conducted with 100 mustard growers, who were selected purposively and interviewed with pre tested questionnaire from Manikganj district during the period May to August 2019. Applying the Cobb-Douglas production model the results suggested that farmers earned 18577.91 Tk ha-1 by producing 1683.75 kg ha-1 Mustard with the Benefit Cost Ratio (BCR) 1.28. Land preparation cost, seed, human labour, irrigation and fertilizer had a significant positive effect on the yield of Mustard while insecticides had negative insignificant effect. Farmers were inefficient in case of resource use. Mustard cultivation is profitable in Bangladesh and has the potentiality to minimize import cost of oilseeds. Mustard production can be increased further by ensuring adequate supply of labor at peak period with reasonable wage rate, incentive price of produce for farmers, sufficient drainage system after flood, collateral free and easy access to credit, crop insurance to mustard growers. SAARC J. Agri. , 18(2): 195-206 (2020)
US disparities in affluence by household structure, 1959 to 2017

Background: This study examines trends in affluence ‒ as indicated by high household income ‒ by household structure over the 1959 to 2017 period. I contrast the experiences of married-couple households, whose share of all households declined substantially over time, with those of single-parent households, cohabiting couples, individuals living alone, and people living with nonrelatives. Methods: I use data from multiple censuses and the American Community Survey and logistic regression. Results: Levels of absolute affluence rose substantially for all household types, reflecting rising living standards. Married-couple households were the most likely to be affluent and single-parent households were the least. Moreover, the affluence gap between married-couple households and all others widened. Married couples fared better because they experienced larger increases in wages and other important sources of income, such as from investments and retirement. Conclusions: The findings suggest that married-couple households benefit from a collective work strategy and economies of scale that increase their likelihood of affluence. Positive selectivity into marriage may also have increased over time. Contribution: This study provides timely new information on changing gaps in affluence by household structure during a period of substantial change in household living arrangements and economic well-being.
Comparison of preprints and final journal publications from COVID-19 Studies: Discrepancies in results reporting and spin in interpretation

Objective: To compare results reporting and the presence of spin in COVID-19 study preprints with their finalized journal publications Design: Cross-sectional Setting: International medical literature Participants: Preprints and final journal publications of 67 interventional and observational studies of COVID-19 treatment or prevention from the Cochrane COVID-19 Study Register published between March 1, 2020 and October 30, 2020 Main outcome measures: Study characteristics and discrepancies in 1) Results reporting (number of outcomes, outcome descriptor, measure (e.g., PCR test), metric (e.g., mean change from baseline), assessment time point (e.g., 1 week post treatment), data reported (e.g., effect estimate and measures of precision), reported statistical significance of result, type of statistical analysis (e.g., chi-squared test), subgroup analyses (if any), whether outcome was identified as primary or secondary and 2) Spin (reporting practices that distort the interpretation of results so that results are viewed more favorably). Results: Of 67 included studies, 23 (34%) had no discrepancies in results reporting between preprints and journal publications. Fifteen (22%) studies had at least one outcome that was included in the journal publication, but not the preprint; 8 (12%) had at least one outcome that was reported in the preprint only. For outcomes that were reported in both preprints and journals, common discrepancies were differences in numerical values and statistical significance, additional statistical tests and subgroup analyses conducted in journal publications, and longer follow-up times for outcome assessment in journal publications. At least one instance of spin occurred in both preprints and journals in 23 / 67 (34%) studies, the preprint only in 5 (7%) studies, and the journal publications only in 2 (3%) of studies. Spin was removed between the preprint and journal publication in 5/67 (7%) studies; but added in 1/67 (1%) study. Conclusions: The COVID-19 preprints and their subsequent journal publications were largely similar in reporting of study characteristics, outcomes and spin. All COVID-19 studies published as preprints and journal publications should be critically evaluated for discrepancies and spin.
Automated Mathematical Equation Structure Discovery for Visual Analysis

Finding the best mathematical equation to deal with the different challenges found in complex scenarios requires a thorough understanding of the scenario and a trial and error process carried out by experts. In recent years, most state-of-the-art equation discovery methods have been widely applied in modeling and identification systems. However, equation discovery approaches can be very useful in computer vision, particularly in the field of feature extraction. In this paper, we focus on recent AI advances to present a novel framework for automatically discovering equations from scratch with little human intervention to deal with the different challenges encountered in real-world scenarios. In addition, our proposal can reduce human bias by proposing a search space design through generative network instead of hand-designed. As a proof of concept, the equations discovered by our framework are used to distinguish moving objects from the background in video sequences. Experimental results show the potential of the proposed approach and its effectiveness in discovering the best equation in video sequences. The code and data are available at: https://github.com/carolinepacheco/equation-discovery-scene-analysis
Zero-Shot Cross-Lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders

Previous work mainly focuses on improving cross-lingual transfer for NLU tasks with a multilingual pretrained encoder (MPE), or improving the performance on supervised machine translation with BERT. However, it is under-explored that whether the MPE can help to facilitate the cross-lingual transferability of NMT model. In this paper, we focus on a zero-shot cross-lingual transfer task in NMT. In this task, the NMT model is trained with parallel dataset of only one language pair and an off-the-shelf MPE, then it is directly tested on zero-shot language pairs. We propose SixT, a simple yet effective model for this task. SixT leverages the MPE with a two-stage training schedule and gets further improvement with a position disentangled encoder and a capacity-enhanced decoder. Using this method, SixT significantly outperforms mBART, a pretrained multilingual encoder-decoder model explicitly designed for NMT, with an average improvement of 7.1 BLEU on zero-shot any-to-English test sets across 14 source languages. Furthermore, with much less training computation cost and training data, our model achieves better performance on 15 any-to-English test sets than CRISS and m2m-100, two strong multilingual NMT baselines.
Paris Agreement requires substantial, broad, and sustained engagements beyond COVID-19 recovery packages

It has been claimed that COVID-19 public stimulus packages could be sufficient to meet the short-term energy investment needs to leverage a shift toward a pathway consistent with the 1.5 °C target of the Paris Agreement. Here we provide complementary perspectives to reiterate that substantial, broad, and sustained engagements beyond recovery packages will be needed for achieving the Paris Agreement long-term targets. Low-carbon investments will need to scale up and persist over the next several decades following short-term stimulus packages. The required total energy investments in the real world can be larger than the currently available estimates from Integrated Assessment Models (IAMs). Existing databases from IAMs are not sufficient for analyzing the effect of public spending on emission reduction. To inform what role COVID-19 stimulus packages and public investments may play for reaching the Paris Agreement targets, explicit modelling of such policies is required.
Neutrino-Nucleon Cross-Section Model Tuning in GENIE v3

We summarise the results of a study performed within the GENIE global analysis framework, revisiting the GENIE bare-nucleon cross-section tuning and, in particular, the tuning of a) the inclusive cross-section, b) the cross-section of low-multiplicity inelastic channels (single-pion and double-pion production), and c) the relative contributions of resonance and non-resonance processes to these final states. The same analysis was performed with several different comprehensive cross-section model sets available in GENIE Generator v3. In this work we performed a careful investigation of the observed tensions between exclusive and inclusive data, and installed analysis improvements to handle systematics in historic data. All tuned model configurations discussed in this paper are available through public releases of the GENIE Generator. With this paper we aim to support the consumers of these physics tunes by providing comprehensive summaries of our alternate model constructions, of the relevant datasets and their systematics, and of our tuning procedure and results.
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples

Capturing word meaning in context and distinguishing between correspondences and variations across languages is key to building successful multilingual and cross-lingual text representation models. However, existing multilingual evaluation datasets that evaluate lexical semantics “in-context” have various limitations. In particular, 1) their language coverage is restricted to high-resource languages and skewed in favor of only a few language families and areas, 2) a design that makes the task solvable via superficial cues, which results in artificially inflated (and sometimes super-human) performances of pretrained encoders, and 3) no support for cross-lingual evaluation. In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs. We conduct a series of experiments in a wide range of setups and demonstrate the challenging nature of AM2iCo. The results reveal that current SotA pretrained encoders substantially lag behind human performance, and the largest gaps are observed for low-resource languages and languages dissimilar to English.
GooAQ: Open Question Answering with Diverse Answer Types

While day-to-day questions come with a variety of answer types, the current question-answering (QA) literature has failed to adequately address the answer diversity of questions. To this end, we present GooAQ, a large-scale dataset with a variety of answer types. This dataset contains over 5 million questions and 3 million answers collected from Google. GooAQ questions are collected semi-automatically from the Google search engine using its autocomplete feature. This results in naturalistic questions of practical interest that are nonetheless short and expressed using simple language. GooAQ answers are mined from Google's responses to our collected questions, specifically from the answer boxes in the search results. This yields a rich space of answer types, containing both textual answers (short and long) as well as more structured ones such as collections. We benchmarkT5 models on GooAQ and observe that: (a) in line with recent work, LM's strong performance on GooAQ's short-answer questions heavily benefit from annotated data; however, (b) their quality in generating coherent and accurate responses for questions requiring long responses (such as 'how' and 'why' questions) is less reliant on observing annotated data and mainly supported by their pre-training. We release GooAQ to facilitate further research on improving QA with diverse response types.