Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mathematical Theory of Attention

Attention is a powerful component of modern neural networks across a wide variety of domains. However, despite its ubiquity in machine learning, there is a gap in our understanding of attention from a theoretical point of view. We propose a framework to fill this gap by building a mathematically equivalent model of attention using measure theory. With this model, we are able to interpret self-attention as a system of self-interacting particles, we shed light on self-attention from a maximum entropy perspective, and we show that attention is actually Lipschitz-continuous (with an appropriate metric) under suitable assumptions. We then apply these insights to the problem of mis-specified input data; infinitely-deep, weight-sharing self-attention networks; and more general Lipschitz estimates for a specific type of attention studied in concurrent work.
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods

This paper provides a method to classify sentiment with robust model based ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To reduce domain bias, we first train tweet dataset for pre-trained language model. Besides, each classifier has its strengths and weakness, we leverage different types of models with ensemble methods: average and power weighted sum. From the experiments, we show that our approach has achieved positive effect for sentiment classification. Our system reached third place among 26 teams from the evaluation in SocialNLP 2020 EmotionGIF competition.
Broadleaf Herbicide Effects on Clethodim and Quizalofop-P Efficacy on Volunteer Wheat (Triticum aestivum)1

Field experiments were conducted at three locations in 2003 and 2004 to examine clethodim and quizalofop-P efficacy on spring wheat seedlings when applied alone or in tank mixtures with herbicides used to control broadleaf weeds. Clethodim at the recommended rate of 30 g/ha reduced spring wheat biomass by 63 to 98% and was only >90% in three of six site years. In contrast, quizalofop-P at the recommended rate of 36 g/ha reduced wheat biomass >90% in all cases. Clethodim or quizalofop-P could be tank mixed with 2,4-D ester, bromoxynil, or bromoxynil plus MCPA ester with little risk of reduced efficacy on wheat. However, 2,4-D amine was highly antagonistic to both herbicides. The commercial mixture of thifensulfuron plus tribenuron reduced clethodim, but not quizalofop-P, efficacy on wheat. Herbicide options exist for simultaneous control of volunteer glyphosate-resistant canola and glyphosate-resistant wheat if the latter technology were to be commercialized in the future. Nomenclature: Bromoxynil; clethodim; 2,4-D; MCPA; quizalofop-P; thifensulfuron; tribenuron; canola, Brassica napus L. ; wheat, Triticum aestivum L. Additional index words: Antagonism, glyphosate-resistant crops, herbicide interaction, herbicide mixture, volunteer management. Abbreviations: ACCase, acetyl-CoA carboxylase (EC 6.4.1.2).
Are Labels Necessary for Classifier Accuracy Evaluation?

To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composed of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many real-world scenarios involve unlabeled test data, rendering common model evaluation methods infeasible. We investigate this important and under-explored problem, Automatic model Evaluation (AutoEval). Specifically, given a labeled training set and a model, we aim to estimate the model accuracy on unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets generated from the original training set via various image transformations such as rotation, background substitution, foreground scaling, etc. As the classification accuracy of the model on each sample (dataset) is known from the original dataset labels, our task can be solved via regression. Using the feature statistics to represent the distribution of a sample dataset, we can train regression techniques (e.g., a regression neural network) to predict model performance. Using synthetic meta-dataset and real-world datasets in training and testing, respectively, we report reasonable and promising estimates of the model accuracy. We also provide insights into the application scope, limitation, and future directions of AutoEval.
Stokes flow due to point torques and sources in a spherical geometry

Singularity representations of the Stokes flow due to point torques and sources in a spherical geometry are derived. Both rigid spheres with no slip and spherical bubbles are considered. Surprisingly, for an axisymmetric torque with no slip the solution consists of a single point image torque, similar to point charges in electrostatics.
Oxidative stability in grinds from omega-3 enhanced pork

Juárez, M. , Dugan, M. E. R. , Larsen, I. L. , Thacker, R. , Rolland, D. C. and Aalhus, J. L. 2011. Oxidative stability in grinds from omega-3 enhanced pork. Can. J. Anim. Sci. 91: 623-634. In order to investigate the effect of enhancing n-3 fatty acid levels in carcasses on the retail and sensory characteristics of pork grinds, as well as the ability of increasing dietary levels of vitamin E to stabilize the increase in PUFA levels from flaxseed supplementation, 81 pigs were used in a 3×3 factorial experiment. Dietary treatments included three lengths of time (0, 3 and 6 wk) of flaxseed supplementation and three levels of vitamin E (40, 200 and 400 mg kg feed-1). α-Tocopherol tissue levels increased with dietary vitamin E supplementation (P<0.001), and fat softness (P=0.018) and total PUFA and n-3 fatty acids (P<0.001) increased with flaxseed supplementation. Most other animal performance, carcass and lean pork quality traits were not affected (P>0.05) by dietary treatments. In ground pork, the increase in TBARS after 6 d in retail was lower (P<0.001) with increasing dietary vitamin E levels. However, vitamin E had no effect (P>0.05) on the higher (P<0.001) TBARS levels observed in grinds from flaxseed fed pigs. The dietary addition of flaxseed negatively modified (P<0.05) all the sensory attributes of both cooked and reheated products. Increasing the fat content of the grinds up to 30% seemed to counteract (P>0.05) the negative effects of feeding flaxseed for 3 wk. Feeding vitamin E was not able to prevent negative effects (P<0.05) observed during retail display in grinds with either increasing fat content or higher n-3 fatty acids from dietary flaxseed. However, increasing dietary vitamin E only showed a protective effect (P<0.05) on retail appearance after long periods of ageing.
Giberela do trigo: aspectos epidemiológicos e modelos de previsão

A giberela ou fusariose da espiga e, atualmente, uma das mais importantes doencas da cultura do trigo (Triticum aestivum). Epidemias severas tem sido observadas nos ultimos anos em diversas regioes do mundo, promovendo perdas na produtividade e na qualidade dos graos. No Brasil, a giberela alcancou o status de principal doenca nas regioes triticolas, principalmente no Sul do Brasil. A sua natureza esporadica esta associada a dependencia climatica, sendo as mais severas epidemias observadas em anos com maior frequencia de chuvas, durante a fase de florescimento e enchimento de graos. Poucas sao as cultivares com resistencia genetica aceitavel e essas normalmente falham em anos muito favoraveis a epidemias. Medidas de controle com fungicidas tem sido preconizadas, entretanto, a dificuldade reside no momento e na tecnologia de aplicacao, para se obter resultados satisfatorios. O melhor entendimento dos fatores que influenciam a dinâmica da doenca pode auxiliar no manejo da cultura visando a minimizar os riscos de epidemias. Aspectos como aerobiologia e tipo de inoculo, distribuicao espacial e gradiente espacial da doenca podem contribuir no conhecimento da associacao e a importância de fontes de inoculo local ou distante ao campo. Recentemente, uma grande atencao tem sido dada ao desenvolvimento de modelos de previsao ou de risco de giberela. Tais modelos podem auxiliar na tomada de decisao no acionamento de medidas de controle bem como alertar niveis epidemicos da doenca em uma determinada regiao, ou mesmo quanto a alta contaminacao do trigo com micotoxinas de Fusarium spp.
Bayesian hierarchical spatial regression models for spatial data in the presence of missing covariates with applications

In many applications, survey data are collected from different survey centers in different regions. It happens that in some circumstances, response variables are completely observed while the covariates have missing values. In this article, we propose a joint spatial regression model for the response variable and missing covariates via a sequence of one‐dimensional conditional spatial regression models. We further construct a joint spatial model for missing covariate data mechanisms. The properties of the proposed models are examined and a Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. In addition, the Bayesian model comparison criteria, the modified deviance information criterion and the modified logarithm of the pseudo‐marginal likelihood, are developed to assess the fit of spatial regression models for spatial data. Extensive simulation studies are carried out to examine empirical performance of the proposed methods. We further apply the proposed methodology to analyze a real dataset from a Chinese Health and Nutrition Survey conducted in 2011.
The giant erinaceid insectivore, Deinogalerix Freudenthal, from the upper Miocene of Gargano, Italy

A detailed description of Deinogalerix is provided. In addition to the type species, four new species are distinguished: D. freudenthali, D. minor, D. brevirostris, and D. intermedius. There were two lineages, which differed in size. Deinogalerix was not directly derived from any of the Galericinae known from Europe but was probably an immigrant from Asia. It is interpreted as a predator which captured prey by a snapping action of the jaws.
Origin of enhanced chemical precompression in cerium hydride CeH$_{9}$

The rare-earth metal hydrides with clathrate structures have been highly attractive because of their promising high-$T_{\rm c}$ superconductivity at high pressure. Recently, cerium hydride CeH$_9$ composed of Ce-encapsulated clathrate H cages was synthesized at much lower pressures of 80$-$100 GPa, compared to other experimentally synthesized rare-earth hydrides such as LaH$_{10}$ and YH$_6$. Based on density-functional theory calculations, we find that the Ce 5$p$ semicore and 4$f$/5$d$ valence states strongly hybridize with the H 1$s$ state, while a transfer of electrons occurs from Ce to H atoms. Further, we reveal that the delocalized nature of Ce 4$f$ electrons plays an important role in the chemical precompression of clathrate H cages. Our findings not only suggest that the bonding nature between the Ce atoms and H cages is characterized as a mixture of ionic and covalent, but also have important implications for understanding the origin of enhanced chemical precompression that results in the lower pressures required for the synthesis of CeH$_9$.
On the sedimentation of a droplet in Stokes flow

This paper is dedicated to the analysis of the transport-Stokes equation which describes sedimentation of inertialess suspensions in a viscous flow at mesoscopic scaling. First we present a global existence and uniqueness result for $L^1\cap L^\infty$ initial densities with finite first moment. Secondly, we consider the case where the initial data is the characteristic function of an axisymmetric bounded domain and investigate the regularity of its surface. Using spherical parametrisation, a hyperbolic equation for the evolution of the radius of the droplet is derived and we present a local existence and uniqueness result. Finally, we investigate the case where the initial shape of the droplet is spherical and show that the solution corresponds to the Hadamard and Rybczynski result. We present numerical simulations in the spherical case.
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths

Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error. However, compressive networks are desired in many real world applications and direct training of small networks may be trapped in local optima. In this paper, instead of pruning or distilling over-parameterized models to compressive ones, we propose a new approach based on differential inclusions of inverse scale spaces. Specifically, it generates a family of models from simple to complex ones that couples a pair of parameters to simultaneously train over-parameterized deep models and structural sparsity on weights of fully connected and convolutional layers. Such a differential inclusion scheme has a simple discretization, proposed as Deep structurally splitting Linearized Bregman Iteration (DessiLBI), whose global convergence analysis in deep learning is established that from any initializations, algorithmic iterations converge to a critical point of empirical risks. Experimental evidence shows that DessiLBI achieve comparable and even better performance than the competitive optimizers in exploring the structural sparsity of several widely used backbones on the benchmark datasets. Remarkably, with early stopping, DessiLBI unveils "winning tickets" in early epochs: the effective sparse structure with comparable test accuracy to fully trained over-parameterized models.
The Berkani's property and a note on some recent results

In this paper, we continue the study of Berkani's property introduced in [Berkani M, Kachad M. New Browder and Weyl type theorems. Bull Korean Math Soc. 2015;52:439–452. ], in connection with other Weyl-type theorems. Moreover, we give counterexamples to show that some recent results related to this property, which are announced and proved by P. Aiena and M. Kachad in [Aiena P, Kachad M. Property and perturbations. Ann Funct Anal. 2020;11:29–46. ] are false. Furthermore, we specify the mistakes committed in each of them and we give the correct versions. We also give a global note on the paper [Jayanthi N, Vasanthakumar P. A new Browder type property. Inter J Math Anal. 2020;14:1–11].
<inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq1-3049692.gif"/></alternatives></inline-formula>: Hybrid Associations Models for Sequential Recommendation

Sequential recommendation aims to identify and recommend the next few items for a user that the user is most likely to purchase/review, given the user's purchase/rating trajectories. It becomes an effective tool to help users select favorite items from a variety of options. In this manuscript, we developed hybrid associations models (<inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}\limits$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq2-3049692.gif"/></alternatives></inline-formula>) to generate sequential recommendations using three factors: 1) users’ long-term preferences, 2) sequential, high-order and low-order association patterns in the users’ most recent purchases/ratings, and 3) synergies among those items. <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}\limits$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq3-3049692.gif"/></alternatives></inline-formula> uses simplistic pooling to represent a set of items in the associations, and element-wise product to represent item synergies of arbitrary orders. We compared <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}\limits$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq4-3049692.gif"/></alternatives></inline-formula> models with the most recent, state-of-the-art methods on six public benchmark datasets in three different experimental settings. Our experimental results demonstrate that <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}\limits$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq5-3049692.gif"/></alternatives></inline-formula> models significantly outperform the state of the art in all the experimental settings. with an improvement as much as 46.6 percent. In addition, our run-time performance comparison in testing demonstrates that <inline-formula><tex-math notation="LaTeX">$\mathop {\mathtt {HAM}}\limits$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">HAM</mml:mi></mml:math><inline-graphic xlink:href="ning-ieq6-3049692.gif"/></alternatives></inline-formula> models are much more efficient than the state-of-the-art methods. and are able to achieve significant speedup as much as 139.7 folds.
Predicting potential drug targets and repurposable drugs for COVID-19 via a deep generative model for graphs

Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options
Cardiovascular, endothelial function, and immune markers in response to treatment with a polysaccharide in HIV+ adults in a randomized, double-blind placebo-controlled trial

Background and Aim: Given the ongoing problems of hypertension and endothelial dysfunction in the HIV population, the primary objective of the study was to assess the cardiovascular, endothelial function, and immune markers in response to rice bran arabinoxylan compound (RBAC) treatment in a sample of HIV+ adults on antiretroviral therapy (ART). Study Design: A randomized, double-blind placebo-controlled trial of 6 months was used to execute the study. Materials and Methods: Forty-seven subjects were enrolled and randomly assigned to one of two study conditions (n=22 RBAC and n=25 placebo) for 6 months with assessments at baseline and 3 and 6 months. A multivariate repeated measures analysis of variance model was used to assess the differences between RBAC and placebo groups in cardiovascular (systolic blood pressure), endothelial function (skin blood flow in response to nitric oxide), and immune (CD4+ cell count) markers from baseline to 6 months. Results: The effect of treatment (RBAC versus placebo) was significant (Wilks’ λ=0.92, F[3, 102]=3.07, P=0.03). The effect of time was significant (Wilks’ λ=0.10, F[2, 103]=474.6, P<0.001). The overall interaction between treatment and time was significant (Wilks’ λ=0.92, F[2, 103]=4.58, P=0.01). Time contrasts showed that a difference in the overall dependent variable did not occur from baseline to 3 months (F[1, 104]=2.7, P=0.10), marginally occurred from baseline to 6 months (F[1, 104]=3.2, P=0.08), and was significant from 3 to 6 months (F[1, 104]=6.43, P=0.01). Conclusions: The overall significant interaction suggests varying responses in the dependent variables between RBAC and placebo over time, which is being driven by systolic blood pressure, as it decreased in the RBAC group, but increased in the placebo group. In addition, CD4+ manifested a non-significant increase from baseline to 3 months then decreased from 3 to 6 months in the RBAC group, whereas it decreased at 3 months followed by a slight increase at 6 months in the placebo group. Skin blood flow in response to nitric oxide improved non-significantly overall in both groups, but worsened from 3 to 6 months in the placebo group. Thus, RBAC treatment may contribute to modest short-term improvements in systolic blood pressure, endothelial function, and CD4+ cell count, which could help improve the overall health profile of HIV+ adults. Relevance for Patients: Persons with HIV on ART suffer disproportionately from hypertension and endothelial dysfunction compared to the non-infected population, and conventional medical therapy does not alleviate these issues. RBAC is a safe, low-risk alternative that may help to improve the overall quality of life of these patients through modest improvements in these biomarkers plus CD4+ cell count.
RFLP of analyses of an intergenic spacer region of chloroplast DNA in some wild wheat species

Several studies are being made to get high productive wheats throughout the world because they provide the most of human energy and protein needs. In this study, 11 wheat species of Triticum and Aegilops were investigated. One of the intergenic regions of cpDNA was studied. This region was amplified with PCR and digested with 6 restriction endonucleases (Hpa II, Alu I, Hinc II, Ava III, Nde I and Hae III). According to results, amplified products can be divided into the three groups which are Group 1 (~2000 bp), Group 2 (~1000 bp) and Group 3 (~700 bp). Group 1 contains Ae. neglecta Req. ex Bertol. (syn.:Ae. triaristata), Ae. triuncialis, Ae. mutica Boiss. and T. monococcum L. var. monococcum. Group 2 contains T. aestivum L. , Ae. biuncialis Vis. , T. turgidum L. (Syn.: T. turgidum var. turgidum) and T. carthlicum Nevski. Group 3 contains Ae. tauschii Cosson. subsp. tauschii and Ae. cylindrica Host. Data obtained from the restriction enzyme digestions were evaluated by using numerical taxonomy system and a phenogram was constructed using SAHN clustering unweighted pair group method using arithmetic averages.