Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphs of low average degree without independent transversals

An independent transversal of a graph G $G$ with a vertex partition P ${\mathscr{P}}$ is an independent set of G $G$ intersecting each block of P ${\mathscr{P}}$ in a single vertex. Wanless and Wood proved that if each block of P ${\mathscr{P}}$ has size at least t $t$ and the average degree of vertices in each block is at most t ∕ 4 $t\unicode{x02215}4$ , then an independent transversal of P ${\mathscr{P}}$ exists. We present a construction showing that this result is optimal: for any ε > 0 $\varepsilon \gt 0$ and sufficiently large t $t$ , there is a forest with a vertex partition into parts of size at least t $t$ such that the average degree of vertices in each block is at most (1 4 + ε ) t $(\frac{1}{4}+\varepsilon )t$ , and there is no independent transversal. This unexpectedly shows that methods related to entropy compression such as the Rosenfeld–Wanless–Wood scheme or the Local Cut Lemma are tight for this problem. Further constructions are given for variants of the problem, including the hypergraph version.
Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks such as semantic segmentation, requiring large datasets and substantial computational power. Continual learning for semantic segmentation (CSS) is an emerging trend that consists in updating an old model by sequentially adding new classes. However, continual learning methods are usually prone to catastrophic forgetting. This issue is further aggravated in CSS where, at each step, old classes from previous iterations are collapsed into the background. In this paper, we propose Local POD, a multi-scale pooling distillation scheme that preserves long- and short-range spatial relationships at feature level. Furthermore, we design an entropy-based pseudo-labelling of the background w.r. t. classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes. Finally, we introduce a novel rehearsal method that is particularly suited for segmentation. Our approach, called PLOP, significantly outperforms state-of-the-art methods in existing CSS scenarios, as well as in newly proposed challenging benchmarks.
The Decision-Conflict Logit

We study two random non-forced choice models that explain behavioural patterns suggesting that the no-choice outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable. The decision-conflict logit extends the logit model with an outside option by assigning a menu-dependent value to that option. This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as on how attractive options are added to it. The multicriteria logit is a special case of this model and introduces multiple utility functions that jointly predict behaviour in a multiplicative-logit way. Every multicriteria logit admits a tractable discrete-choice formulation.
Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions

To measure the difference between two probability distributions, referred to as the source and target, respectively, we exploit both the chain rule and Bayes' theorem to construct conditional transport (CT), which is constituted by both a forward component and a backward one. The forward CT is the expected cost of moving a source data point to a target one, with their joint distribution defined by the product of the source probability density function (PDF) and a source-dependent conditional distribution, which is related to the target PDF via Bayes' theorem. The backward CT is defined by reversing the direction. The CT cost can be approximated by replacing the source and target PDFs with their discrete empirical distributions supported on mini-batches, making it amenable to implicit distributions and stochastic gradient descent-based optimization. When applied to train a generative model, CT is shown to strike a good balance between mode-covering and mode-seeking behaviors and strongly resist mode collapse. On a wide variety of benchmark datasets for generative modeling, substituting the default statistical distance of an existing generative adversarial network with CT is shown to consistently improve the performance. PyTorch code is provided.
Universal Approximation Theorems for Differentiable Geometric Deep Learning

This paper addresses the growing need to process non-Euclidean data, by introducing a geometric deep learning (GDL) framework for building universal feedforward-type models compatible with differentiable manifold geometries. We show that our GDL models can approximate any continuous target function uniformly on compact sets of a controlled maximum diameter. We obtain curvature-dependent lower-bounds on this maximum diameter and upper-bounds on the depth of our approximating GDL models. Conversely, we find that there is always a continuous function between any two non-degenerate compact manifolds that any"locally-defined"GDL model cannot uniformly approximate. Our last main result identifies data-dependent conditions guaranteeing that the GDL model implementing our approximation breaks"the curse of dimensionality. "We find that any"real-world"(i.e. finite) dataset always satisfies our condition and, conversely, any dataset satisfies our requirement if the target function is smooth. As applications, we confirm the universal approximation capabilities of the following GDL models: Ganea et al. (2018)'s hyperbolic feedforward networks, the architecture implementing Krishnan et al. (2015)'s deep Kalman-Filter, and deep softmax classifiers. We build universal extensions/variants of: the SPD-matrix regressor of Meyer et al. (2011), and Fletcher (2003)'s Procrustean regressor. In the Euclidean setting, our results imply a quantitative version of Kidger and Lyons (2020)'s approximation theorem and a data-dependent version of Yarotsky and Zhevnerchuk (2019)'s uncursed approximation rates.
Chemical Studies on the Galeobdolon chinense

AIM To study the chemical constituents of Galeobdolon chinense . METHODS The chemical constituents of Galeobdolon chinense were isolated by using silica gel and Pre-HPLC chromatography. Their structures were identified by spectric evidences and chemical methods. RESULTS Four compounds were obtained and identified as succinic acid(Ⅰ), hyperoside(Ⅱ), rutin(Ⅲ) and amentoflavone(Ⅳ) respectively. CONCLUSION All compounds were isolated from this plant for the first time.
Size and Depth Separation in Approximating Natural Functions with Neural Networks

When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. However, not all functions are interesting from a practical viewpoint: functions of interest usually have a polynomially-bounded Lipschitz constant, and can be computed efficiently. We call functions that satisfy these conditions"benign", and explore the benefits of size and depth for approximation of benign functions with ReLU networks. As we show, this problem is more challenging than the corresponding problem for non-benign functions. We give barriers to showing depth-lower-bounds: Proving existence of a benign function that cannot be approximated by polynomial-size networks of depth $4$ would settle longstanding open problems in computational complexity. It implies that beyond depth $4$ there is a barrier to showing depth-separation for benign functions, even between networks of constant depth and networks of nonconstant depth. We also study size-separation, namely, whether there are benign functions that can be approximated with networks of size $O(s(d))$, but not with networks of size $O(s'(d))$. We show a complexity-theoretic barrier to proving such results beyond size $O(d\log^2(d))$, but also show an explicit benign function, that can be approximated with networks of size $O(d)$ and not with networks of size $o(d/\log d)$. For approximation in $L_\infty$ we achieve such separation already between size $O(d)$ and size $o(d)$. Moreover, we show superpolynomial size lower bounds and barriers to such lower bounds, depending on the assumptions on the function. Our size-separation results rely on an analysis of size lower bounds for Boolean functions, which is of independent interest: We show linear size lower bounds for computing explicit Boolean functions with neural networks and threshold circuits.
IRONY AND PARODY IN JACQUELINE WOODSON’S HARBOR ME: A POSTMODERNISM STUDY

This study concerns to discuss the Irony and Parody in Jacqueline Woodson’s novel entitled Harbor Me . This study is focused on postmodern theory, since the novel tells about social phenomena in daily life such as irony and parody. This study aims to disclose how irony and parody are reflected in Harbor Me novel by Jacqueline Woodson. This study was conducted by applying a qualitative type since the data are in the form of words. The source of data in this study is Harbor Me novel by Jacqueline Woodson. The data are the utterances of the characters and the author’s narration in Jacqueline Woodson’s Harbor Me . In analyzing the data, the writer used three concurrent processes. They are data reduction, data display, and conclusion drawing or verification. The result of this study found that irony and parody also as a theme in postmodernism literature. The irony and parody are used for illustrating the social condition in Harbor Me novel by Jacqueline Woodson. The characters experienced the irony as a real occurrence which is not accordance with their expectation. Meanwhile, they also experienced the parody for giving an insinuation by imitating others characters’ words and attitudes. In addition, the presentation of irony and parody demonstrate the influence of theme in postmodernism. It is because the characteristics of irony and parody as the parts of postmodernism which have been stated by Lyotard in Sarup (1993). Keywords : Postmodernism, Irony, Parody
Antanaitis_et_al_Diet in early Lithuanian prehistory and the new stable isotope evidence

This article reviews current scientific evidence of food resources exploited in the Lithuanian Stone and Bronze Ages and presents the new direct, biochemical stable isotope evidence. Stable carbon and nitrogen isotope analyses were performed on 75 Stone and Bronze Age animal bone samples and 23 human bone samples. We discuss how the obtained values relate to diet and other evidence of diet, compare the obtained values with regional stable isotope data, and consider sociocultural implications.
Obesity and severe coronavirus disease 2019: molecular mechanisms, paths forward, and therapeutic opportunities

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appears to have higher pathogenicity among patients with obesity. Obesity, termed as body mass index greater than 30 kg/m2, has now been demonstrated to be important comorbidity for disease severity during coronavirus disease 2019 (COVID-19) pandemic and associated with adverse events. Unraveling mechanisms behind this phenomenon can assist scientists, clinicians, and policymakers in responding appropriately to the COVID-19 pandemic. In this review, we systemically delineated the potential mechanistic links between obesity and worsening COVID-19 from altered physiology, underlying diseases, metabolism, immunity, cytokine storm, and thrombosis. Problematic ventilation caused by obesity and preexisting medical disorders exacerbate organ dysfunction for patients with obesity. Chronic metabolic disorders, including dyslipidemia, hyperglycemia, vitamin D deficiency, and polymorphisms of metabolism-related genes in obesity, probably aid SARS-CoV-2 intrusion and impair antiviral responses. Obesity-induced inadequate antiviral immunity (interferon, natural killer cells, invariant natural killer T cell, dendritic cell, T cells, B cell) at the early stage of SARS-CoV-2 infection leads to delayed viral elimination, increased viral load, and expedited viral mutation. Cytokine storm, with the defective antiviral immunity, probably contributes to tissue damage and pathological progression, resulting in severe symptoms and poor prognosis. The prothrombotic state, driven in large part by endothelial dysfunction, platelet hyperactivation, hypercoagulability, and impaired fibrinolysis in obesity, also increases the risk of severe COVID-19. These mechanisms in the susceptibility to severe condition also open the possibility for host-directed therapies in population with obesity. By bridging work done in these fields, researchers can gain a holistic view of the paths forward and therapeutic opportunities to break the vicious cycle of obesity and its devastating complications in the next emerging pandemic.
Occurrence and Ecological Risk Assessment of Diclofenac and Ibuprofen Residues in Wastewater from Three Wastewater Treatment Plants in South-Western Nigeria

Despite the frequent detection of non-steroidal anti-inflammatory drugs (NSAIDs) worldwide in the aquatic environment, there is currently a paucity of studies from Africa, including Nigeria, on the occurrence and potential ecological risks of two commonly used NSAIDs diclofenac and ibuprofen in wastewater and effluent receiving water. In this work, diclofenac and ibuprofen were determined in wastewater from three wastewater treatment plants (WWTPs) and effluent receiving water in South-Western Nigeria. Instrumental analysis was performed by high-performance liquid chromatography. Ecological risk was assessed for four trophic levels: fish, daphnia, algae and bacteria. The method quality parameters were satisfactory for the accurate determination of target NSAIDs in wastewater and effluent receiving water. Maximum concentrations of diclofenac and ibuprofen in wastewater were 166.1 μg L (UCH influent) and 62.0 μg L (Ijaiye effluent), respectively. Ibuprofen posed high risk to fish in all effluent and effluent receiving water samples. Bacteria were the most sensitive organisms to the presence of diclofenac while fish was the most sensitive to ibuprofen. To the best of our knowledge, this is the first report on the occurrence and ecological risk assessment of diclofenac and ibuprofen residues in wastewater from Nigerian municipal and hospital WWTPs. More efficient wastewater treatment processes and technologies are recommended for the investigated WWTPs to reduce the discharge of target NSAIDs and other pharmaceuticals into the Nigerian aquatic environment. DOI: https://dx.doi. org/10.4314/jasem.v25i3. 5 Copyright: Copyright © 2021 Ajibola et al. This is an open access article distributed under the Creative Commons Attribution License (CCL), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Dates: Received: 12 December 2020; Revised: 26 January 2021; Accepted: 12 February 2021
Effects of Feeding Strategies on Growth Performance and Economic Returns on the Production of Nile Tilapia (Oreochromis niloticus) in Fertilized Ponds

This study evaluated the effects of restrictive feeding strategies on the profitability of tilapia production in fertilized ponds. Sex-reversed tilapia fingerlings (~60g) were cultured for five months under three feeding strategies; alternate-day full ration (100%), 66.7% and 75% of full ration daily. Fish fed 75% of the full ration daily had the highest final weight (411.3±39.32 g), followed by fish fed 66.7% of the full ration daily (352.0±9.17 g) and the fish fed alternate-day full ration (227.8±22.17 g). Fish that received the alternate-day full ration had the lowest feed intake (223.8±29.77 g fish-1) and a better feed conversion ratio (1.30±0.12) compared to the fish fed 66.7% (1.51±0.12) and 75% (1.52±0.09) of full ration daily. Crude protein, dry matter, ash content and the hepatosomatic index of the fish were not affected by the feed restriction. The crude lipid, mesenteric fat and the viscerosomatic indices were significantly (P<0.05) lower in fish that received the alternate-day full ration. While the cost of production was lowest in the alternate-day ration, it had the lowest economic returns (US$ 82.95±8.60) compared to fish that received 66.7% and 75% of full ration daily (131.16±22.62 and 162.48±19.64 US$, respectively). These results indicated that tilapia can be cultured in fertilized ponds and fed with two-thirds of daily full ration given in intensive systems without any effect on economic returns.
Evaluation of Shear Strength for Steel Fiber Reinforced Concrete Beams

In this paper, shear strength of steel fiber reinforced concrete (SFRC) beams without stirrups is predicted using a rational and unified mechanical approach. Based on forces equilibrium conditions of the shear transfer mechanisms through the diagonal shear crack, the design equations have been developed. Relation for the shear resistance of steel fibers is proposed as a function of the post-cracking strength based on: 1) ultimate crack opening limit-based geometrical condition, 2) crack width-based constitutive tension softening law and 3) the 45-degree truss model. Based on a semi-empirical mixture rule, the post-cracking strength of SFRC is estimated which accounts for fiber content, fiber aspect ratio and the fiber efficiency factors of length, orientation and shape. Using an empirical equation, the concrete contribution to shear is obtained as a function of compressive strength. The shear strength predictions have been validated with 253 experimental results from 22 different investigations and showed a good agreement which strongly support the proposed analytical approach. Compared to the other models from the literature, the proposed method can be used in design practice for predicting the shear strength of SFRC beams without stirrups where most of the predictions agree conservatively with the experimental results. Accordingly, the proposed approach can be applied across a practical range of concrete strengths, shear span/depth ratios, fiber factors and types and steel ratios. Additionally, a parametric study is reported for the factors affecting the shear strength of SFRC beams. Keywords— Concrete beams; ultimate shear capacity; fiber volume; fiber aspect ratio; steel fibers. ——————————  ——————————
Surface melting over the Greenland ice sheet derived from enhanced resolution passive microwave brightness temperatures (1979–2019)

Abstract. Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.
Perceptions, Attitudes, and Practices towards Early Detection, Prevention and Survivorship of Cancer in Oman

oxidative stress within the arterial wall leading to the development of atherosclerosis and further amplified by obesity-induced cells. This further triggeran inflammatory cascade, and plaque progression. The above molecular and cellular events thus promote atheroma formation by incorporation ofdendritic cells and T cells into the lumen by adhesion molecules. Thus,in the arterial wall, the detrimental effect ofinflammation and oxidative stress synergize to accelerate the progression ofatheroma plaque ADITUM Journal Of Oncology and Cancer Screening
The genotype determination and molecular characterization of bovine leukemia virus in Turkey

Bovine leukemia virus (BLV) infection is widespread worldwide and causes significant economic losses in the livestock industry. In this study, to identify the genotypes and to investigate the molecular characterization of BLV strains, we analyzed 25 BLV strains from cattle housed in six herds located in several Turkish geographical regions. Phylogenetic analysis, based on a partial or full-length sequence of the env gp51 gene of BLVs, showed that all Turkish BLVs belonged to genotype 1. The alignment of the deduced amino acid sequences demonstrated fourteen amino acid substitutions in different regions of the env gp51. This study updates our knowledge of BLV in Turkey. Further studies are needed to understand the molecular epidemiology and implement a comprehensive BLV control program in Turkey.