Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms

Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.) depending on the notion of context defined at training time. These properties manifest when querying the embedding space for the most similar vectors, and when used at the input layer of deep neural networks trained to solve downstream NLP problems. Meta-embeddings combine multiple sets of differently trained word embeddings, and have been shown to successfully improve intrinsic and extrinsic performance over equivalent models which use just one set of source embeddings. We introduce word prisms: a simple and efficient meta-embedding method that learns to combine source embeddings according to the task at hand. Word prisms learn orthogonal transformations to linearly combine the input source embeddings, which allows them to be very efficient at inference time. We evaluate word prisms in comparison to other meta-embedding methods on six extrinsic evaluations and observe that word prisms offer improvements in performance on all tasks.
Effects of microcystins toxins contaminated drinking water on hepatic problems in animals (cows and buffalos) and toxins removal chemical method

In present study, it is investigated the toxins in drinking water samples due to microbe’s activities, very harmful health effect on humans and animals and especially their liver functions can disturb badly. Liver and kidneys problems in cows and buffalos have a major economic impact on the beef and milk processing business for the reason that liver disease problem can shrunk body size and animal performance. For this purpose draw the blood samples of both cows and buffaloes for LFTs (liver function tests) and RFTs (renal function tests) medical laboratory test.This study had the documentary proof the bad health of large animals (cows and buffaloes) that was due to microcystins toxins effects, these findings in case of cows and buffaloes with hepatic problems, collected the 116 samples of buffaloes and 116 of cows, but 82.2% and 87.93% seen as positive in range for microcystins, respectively. In details, animal samples was 116 collected including 50 (43.1% of total sample) cows and 66 (56.8% of total sample) buffaloes but find 47 out of 50 (94%) of cows and 63 out of 66 (95.45%) buffaloes suffered from liver diseases were investigated. Liver swellings were confirmed by performing the LFTs biochemical tests profile of liver and disease sign and symptoms. Water sources treatment is only solution of these problems; in this case we use the coagulation process with ferric chloride solution of different doses. In this study, it has proved that microcystins are removed using the concentration of ferric chloride dose is 16 mg/l. Liver function tests were also played very important role to know the actual working positions of liver function, so values of Rfts indicated the abnormalities of liver in cows and buffaloes due to continues taking the microcystin toxins from water and food sources.
Local structure of sprinkled causal sets

We describe numerical and analytical investigations of causal sets sprinkled into spacetime manifolds. The first part of the paper is a numerical study of finite causal sets sprinkled into Alexandrov subsets of Minkowski spacetime of dimensions $1 + 1$, $1 + 2$ and $1 + 3$. In particular we consider the rank 2 past of sprinkled causet events, which is the set of events that are two links to the past. Assigning one of the rank 2 past events as `preferred past' for each event yields a `preferred past structure', which was recently proposed as the basis for a causal set d'Alembertian. We test six criteria for selecting rank 2 past subsets. One criterion performs particularly well at uniquely selecting -- with very high probability -- a preferred past satisfying desirable properties. The second part of the paper concerns (infinite) sprinkled causal sets for general spacetime manifolds. After reviewing the construction of the sprinkling process with the Poisson measure, we consider various specific applications. Among other things, we compute the probability of obtaining a sprinkled causal set of a given isomorphism class by combinatorial means, using a correspondence between causal sets in Alexandrov subsets of $1 + 1$ dimensional Minkowski spacetime and 2D-orders. These methods are also used to compute the expected size of the past infinity as a proportion of the total size of a sprinkled causal set.
Comparing Mortality Risk Predictive Ability of Different Scoring Systems in Cirrhotic Patients with Bacteremia

Patients with liver cirrhosis and bacteremia have substantially higher risk of mortality and morbidity. Our study aimed to investigate scoring systems that can predict the mortality risk in patients with cirrhosis and bacteremia. A single-center, retrospective cohort study was performed among adult patients who visited the emergency department from January 2015 to December 2018. All patients diagnosed with liver cirrhosis and bacteremia were enrolled and divided into survivor and nonsurvivor groups for comparison based on their 30-day in-hospital mortality event. The Pitt bacteremia score (PBS), model for end-stage liver disease (MELD) score, Child–Pugh score, and quick sequential Organ Failure Assessment (qSOFA) score were calculated and compared using the area under the receiver operating characteristic (AUROC) curves. A total of 127 patients (survivor: 86; nonsurvivor: 41) were eligible for this study. Compared with the nonsurvivor group, patients in the survivor group had significantly lower MELD score (22 ± 7 vs. 29 ± 5, p < 0.001), lower proportion of high qSOFA (score ≥ 2) (23.3% vs. 51.2%, p < 0.01), and high PBS (score ≥ 4) (7.0% vs. 34.1%, p < 0.001) category. There was also a significantly different distribution in Child–Pugh classification between the two groups (p < 0.01). The survivor group had significantly lower proportion of acute-on-chronic liver failure (27.9% vs. 68.3%, p < 0.001) and fewer number of organ failures (p < 0.001). In comparison of the discriminative ability in mortality risk prediction, PBS (AUROC = 0.83, 95% CI = 0.75–0.90, p < 0.001) and MELD scores (AUROC = 0.78, 95% CI = 0.70–0.86, p < 0.001) revealed a better predictive ability than Child–Pugh (AUROC = 0.69, 95% CI = 0.59–0.70, p < 0.01) and qSOFA scores (AUROC = 0.65, 95% CI = 0.54–0.75, p < 0.01). PBS and MELD scores both demonstrated a superior ability of predicting mortality risk in cirrhotic patients with bacteremia.
Stunting and Its Associated Factors among Early Adolescent School Girls of Gondar Town, Northwest Ethiopia: A School-Based Cross-Sectional Study

Introduction Stunting is a crucial indicator of long-term chronic undernutrition that reflects a failure to reach a linear growth. Adolescent girls are potentially at a higher risk of stunting as they are traditionally married at an early age in low-income countries. In Ethiopia, stunting has mostly been examined in early childhood, with limited information at the early adolescent age. Therefore, this study is aimed at determining the prevalence of stunting and its associated factors among early adolescent school girls age 10 to 14 in Gondar town. Methods We conducted a school-based cross-sectional study. A multistage sampling method was used to sample 662 adolescent girls in selected primary schools. A pretested, structured, and interviewer-administered questionnaire was used to collect the required data. Stata Version 14 and WHO Anthro-plus software were used to analyze the data. The bivariable and multivariable logistic regression model was fitted to identify factors associated with stunting. Adjusted Odds Ratio (AOR) with its 95% confidence interval (CI) was calculated, and a p value ≤ 0.05 was considered to declare statistically significant variables. Results The prevalence of stunting was 27.5% [95% CI: 25.5%-29.5%]. The odds of stunting were found to be higher among grade 5 students [AOR; 95% CI: 1.90; 1.13-3.20], those who reported a daily meal frequency of less than three [AOR; 95% CI: 2.37; 1.60-3.50], and those who were from food-insecure families [AOR; 95% CI: 2.52; 1.70-3.73]. Adolescent girls whose mothers were government employees [AOR; 95% CI: 0.48; 0.26–0.89] or merchants [AOR; 95% CI: 0.43; 0.28–0.67] were less likely to be stunted compared to those whose mothers were housewives. Conclusion Stunting among early adolescent girls is found to be a moderate public health problem. A school-based nutritional program might be helpful to reduce stunting in this group of adolescent girls.
Sampled Nonlocal Gradients for Stronger Adversarial Attacks

The vulnerability of deep neural networks to small and even imperceptible perturbations has become a central topic in deep learning research. The evaluation of new defense mechanisms for these so-called adversarial attacks has proven to be challenging. Although several sophisticated defense mechanisms were introduced, most of them were later shown to be ineffective. However, a reliable evaluation of model robustness is mandatory for deployment in safety-critical real-world scenarios. We propose a simple yet effective modification to the gradient calculation of state-of-the-art first-order adversarial attacks, which increases their success rate and thus leads to more accurate robustness estimates. Normally, the gradient update of an attack is directly calculated for the given data point. In general, this approach is sensitive to noise and small local optima of the loss function. Inspired by gradient sampling techniques from non-convex optimization, we propose to calculate the gradient direction of the adversarial attack as the weighted average over multiple points in the local vicinity. We empirically show that by incorporating this additional gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces. Additionally, we show that the proposed method achieves higher success rates than a variety of state-of-the-art attacks on the benchmark datasets MNIST, Fashion-MNIST, and CIFAR10.
Inflammatory bowel disease and pregnancy: fertility, complications and treatment

Inflammatory bowel disease (IBD) is commonly diagnosed and treated in the young population. Therefore, it is common that women anticipating or undergoing pregnancy will have to cope with the additional burden of their IBD. Pregnancy in an IBD patient also presents challenges for the practitioner, in that the usual diagnostic and therapeutic armamentarium of potential tests and therapies is disrupted. This review covers the implications of IBD for fertility, pregnancy and offspring, and discusses the management of IBD in pregnancy.
Development and validation of risk score for predicting spontaneous rupture of hepatocellular carcinoma

Purpose Spontaneous rupture is a potentially serious complication of liver cancer. A risk score was developed and validated for predicting spontaneous rupture based on a retrospective study. Methods Multiple logistic regression analysis was used to study the relationship between clinical variables and spontaneous rupture. The independent rupture predictors were converted into a score based on the odds ratio. Predicted attributes of the developed scores were then verified using a dataset in 2019. Results The incidence of spontaneous rupture was 5.5% from 2002 to 2019. A 10-point score (α-FP of ≥400 µg/L, 1; protrusion from liver surface, 2; ascites, 3; tumor size of >5 cm, 4) was derived for prediction of rupture and area under the receiver-operating characteristic curve was 0.9 (95% confidence interval, 0.87–0.92). When applying a cutoff value of 5 points or more, the specificity was 0.87 and the sensitivity was 0.84. A validation cohort consisting of 202 hepatocellular carcinoma patients reproduces the predictive, identification, and calibration characteristics. The observed rate of spontaneous rupture according to risk stratification of the score was 0.6% for those with a score of 0–4, 21.6% for a score of 5–7, and 36.4% for a score of 8–10 in the validation cohort. Conclusion Here, based on routine clinical data, we determine the factors that affect prognosis and propose an effective tool for predicting spontaneous rupture, which may be useful in guiding priority treatment of high-risk patients or clinical routine preventive treatment.
Starburst galaxies strike back: a multi-messenger analysis with Fermi-LAT and IceCube data

Starburst galaxies, which are known as ‘reservoirs’ of high-energy cosmic-rays, can represent an important high-energy neutrino ‘factory’ contributing to the diffuse neutrino flux observed by IceCube. In this paper, we revisit the constraints affecting the neutrino and gamma-ray hadronuclear emissions from this class of astrophysical objects. In particular, we go beyond the standard prototype-based approach leading to a simple power-law neutrino flux, and investigate a more realistic model based on a data-driven blending of spectral indexes, thereby capturing the observed changes in the properties of individual emitters. We then perform a multi-messenger analysis considering the extragalactic gamma-ray background (EGB) measured by Fermi-LAT and different IceCube data samples: the 7.5-yr high-energy starting events (HESE) and the 6-yr high-energy cascade data. Along with starburst galaxies, we take into account the contributions from blazars and radio galaxies as well as the secondary gamma-rays from electromagnetic cascades. Remarkably, we find that, differently from the highly-constrained prototype scenario, the spectral index blending allows starburst galaxies to account for up to $40{{\ \rm per\ cent}}$ of the HESE events at $95.4{{\ \rm per\ cent}}$ CL, while satisfying the limit on the non-blazar EGB component. Moreover, values of $\mathcal {O}(100\, \mathrm{PeV})$ for the maximal energy of accelerated cosmic-rays by supernovae remnants inside the starburst are disfavoured in our scenario. In broad terms, our analysis points out that a better modelling of astrophysical sources could alleviate the tension between neutrino and gamma-ray data interpretation.
Birkhoff’s Decomposition Revisited: Sparse Scheduling for High-Speed Circuit Switches

Data centers are increasingly using high-speed circuit switches to cope with the growing demand and reduce operational costs. One of the fundamental tasks of circuit switches is to compute a sparse collection of switching configurations to support a traffic demand matrix. Such a problem has been addressed in the literature with variations of the approach proposed by Birkhoff in 1946 to decompose a doubly stochastic matrix exactly. However, the existing methods are heuristic and do not have theoretical guarantees on how well a collection of switching configurations (i.e., permutations) can approximate a traffic matrix (i.e., a scaled doubly stochastic matrix). In this paper, we revisit Birkhoff’s approach and make three contributions. First, we establish the first theoretical bound on the sparsity of Birkhoff’s algorithm (i.e., the number of switching configurations necessary to approximate a traffic matrix). In particular, we show that by using a subset of the admissible permutation matrices, Birkhoff’s algorithm obtains an <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-approximate decomposition with at most <inline-formula> <tex-math notation="LaTeX">$O(\log (1 / \epsilon))$ </tex-math></inline-formula> permutations. Second, we propose a new algorithm, <monospace>Birkhoff+</monospace>, which combines the wealth of Frank-Wolfe with Birkhoff’s approach to obtain sparse decompositions in a fast manner. And third, we evaluate the performance of the proposed algorithm numerically and study how this affects the performance of a circuit switch. Our results show that <monospace>Birkhoff+</monospace> is superior to previous algorithms in terms of throughput, running time, and number of switching configurations.
Clostridium bolteae is elevated in neuromyelitis optica spectrum disorder in India and shares sequence similarity with AQP4

Objective To understand the role of gut microbiome in influencing the pathogenesis of neuromyelitis optica spectrum disorders (NMOSDs) among patients of south Indian origin. Methods In this case-control study, stool and blood samples were collected from 39 patients with NMOSD, including 17 with aquaporin 4 IgG antibodies (AQP4+) and 36 matched controls. 16S ribosomal RNA (rRNA) sequencing was used to investigate the gut microbiome. Peripheral CD4+ T cells were sorted in 12 healthy controls, and in 12 patients with AQP4+ NMOSD, RNA was extracted and immune gene expression was analyzed using the NanoString nCounter human immunology kit code set. Results Microbiota community structure (beta diversity) differed between patients with AQP4+ NMOSD and healthy controls (p < 0.001, pairwise PERMANOVA test). Linear discriminatory analysis effect size identified several members of the microbiota that were altered in patients with NMOSD, including an increase in Clostridium bolteae (effect size 4.23, p 0.00007). C bolteae was significantly more prevalent (p = 0.02) among patients with AQP4-IgG+ NMOSD (n = 8/17 subjects) compared with seronegative patients (n = 3/22) and was absent among healthy stool samples. C bolteae has a highly conserved glycerol uptake facilitator and related aquaporin protein (p59-71) that shares sequence homology with AQP4 peptide (p92-104), positioned within an immunodominant (AQP4 specific) T-cell epitope (p91-110). Presence of C bolteae correlated with expression of inflammatory genes associated with both innate and adaptive immunities and particularly involved in plasma cell differentiation, B cell chemotaxis, and Th17 activation. Conclusion Our study described elevated levels of C bolteae associated with AQP4+ NMOSD among Indian patients. It is possible that this organism may be causally related to the immunopathogenesis of this disease in susceptible individuals.
Intriguing Properties of Contrastive Losses

Contrastive loss and its variants have become very popular recently for learning visual representations without supervision. In this work, we first generalize the standard contrastive loss based on cross entropy to a broader family of losses that share an abstract form of $\mathcal{L}_{\text{alignment}} + \lambda \mathcal{L}_{\text{distribution}}$, where hidden representations are encouraged to (1) be aligned under some transformations/augmentations, and (2) match a prior distribution of high entropy. We show that various instantiations of the generalized loss perform similarly under the presence of a multi-layer non-linear projection head, and the temperature scaling ($\tau$) widely used in the standard contrastive loss is (within a range) inversely related to the weighting ($\lambda$) between the two loss terms. We then study an intriguing phenomenon of feature suppression among competing features shared acros augmented views, such as "color distribution" vs "object class". We construct datasets with explicit and controllable competing features, and show that, for contrastive learning, a few bits of easy-to-learn shared features could suppress, and even fully prevent, the learning of other sets of competing features. Interestingly, this characteristic is much less detrimental in autoencoders based on a reconstruction loss. Existing contrastive learning methods critically rely on data augmentation to favor certain sets of features than others, while one may wish that a network would learn all competing features as much as its capacity allows.
Learning and Evaluating Representations for Deep One-class Classification

We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are faithful to the target task. In particular, we present a novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations. Moreover, we argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as an average of normality scores from a surrogate classifier. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks. Finally, we present visual explanations, confirming that the decision-making process of our deep one-class classifier is intuitive to humans. The code is available at: this https URL.
The GALAH survey: tracing the Galactic disk with Open Clusters

Open clusters are unique tracers of the history of our own Galaxy's disk. According to our membership analysis based on \textit{Gaia} astrometry, out of the 226 potential clusters falling in the footprint of GALAH or APOGEE, we find that 205 have secure members that were observed by at least one of the survey. Furthermore, members of 134 clusters have high-quality spectroscopic data that we use to determine their chemical composition. We leverage this information to study the chemical distribution throughout the Galactic disk of 21 elements, from C to Eu, and including Fe. The radial metallicity gradient obtained from our analysis is $-$0.076$\pm$0.009 dex kpc$^{-1}$, which is in agreement with previous works based on smaller samples. Furthermore, the gradient in the [Fe/H] - guiding radius (r$_{\rm guid}$) plane is $-$0.073$\pm$0.008 dex kpc$^{-1}$. We also reveal trends in the [X/Fe] - r$_{\rm guid}$ - age space, which are important to understand production rates of different elements as a function of space and time. Finally, we observe subtle - but significant - differences on how field stars and open clusters trace the distribution of chemical elements throughout the Galactic disk. As it is discussed in the paper, these differences are probably due to the physical selection effect imposed by our Galaxy, which preserves the clusters living far from its gravitational potential perturbations (e.g., spirals, bars, molecular clouds), while it destroys all the others.
DTGAN: Dual Attention Generative Adversarial Networks for Text-to-Image Generation

Most existing text-to-image generation methods adopt a multi-stage modular architecture which has three significant problems: 1) Training multiple networks increases the run time and affects the convergence and stability of the generative model; 2) These approaches ignore the quality of early-stage generator images; 3) Many discriminators need to be trained. To this end, we propose the Dual Attention Generative Adversarial Network (DTGAN) which can synthesize high-quality and semantically consistent images only employing a single generator/discriminator pair. The proposed model introduces channel-aware and pixel-aware attention modules that can guide the generator to focus on text-relevant channels and pixels based on the global sentence vector and to fine-tune original feature maps using attention weights. Also, Conditional Adaptive Instance-Layer Normalization (CAdaILN) is presented to help our attention modules flexibly control the amount of change in shape and texture by the input natural-language description. Furthermore, a new type of visual loss is utilized to enhance the image resolution by ensuring vivid shape and perceptually uniform color distributions of generated images. Experimental results on benchmark datasets demonstrate the superiority of our proposed method compared to the state-of-the-art models with a multi-stage framework.