Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

This paper proposes a new neural architecture for collaborative ranking with implicit feedback. Our model, LRML (Latent Relational Metric Learning) is a novel metric learning approach for recommendation. More specifically, instead of simple push-pull mechanisms between user and item pairs, we propose to learn latent relations that describe each user item interaction. This helps to alleviate the potential geometric inflexibility of existing metric learning approaches. This enables not only better performance but also a greater extent of modeling capability, allowing our model to scale to a larger number of interactions. In order to do so, we employ a augmented memory module and learn to attend over these memory blocks to construct latent relations. The memory-based attention module is controlled by the user-item interaction, making the learned relation vector specific to each user-item pair. Hence, this can be interpreted as learning an exclusive and optimal relational translation for each user-item interaction. The proposed architecture demonstrates the state-of-the-art performance across multiple recommendation benchmarks. LRML outperforms other metric learning models by 6%-7.5% in terms of Hits@10 and nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover, qualitative studies also demonstrate evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of LRML to uncover hidden relational structure within implicit datasets.
Cannabidiol protects livers against nonalcoholic steatohepatitis induced by high‐fat high cholesterol diet via regulating NF‐κB and NLRP3 inflammasome pathway

Cannabidiol (CBD), an abundant nonpsychoactive constituent of marijuana, has been reported previously to protect against hepatic steatosis. In this study, we studied further the functions and mechanisms of CBD on liver inflammation induced by HFC diet. Mice feeding an HFC diet for 8 weeks were applied to test the protective effect of CBD on livers. RAW264.7 cells were incubated with LPS + ATP ± CBD to study the mechanisms of the effect of CBD against inflammasome activation. We found that CBD alleviated liver inflammation induced by HFC diet. CBD significantly inhibited the nuclear factor‐κappa B (NF‐κB) p65 nuclear translocation and the activation of nucleotide‐binding domain like receptor protein 3 (NLRP3) inflammasome both in vivo and in vitro studies, which lead to the reduction of the expression of inflammation‐related factors in our studies. In addition, Inhibitor of activation of NF‐κB partly suppressed the NLRP3 inflammasome activation, while adding CBD further inhibited NF‐κB activation and correspondingly suppressed the NLRP3 inflammasome activation in macrophages. In conclusion, the suppression of the activation of NLRP3 inflammasome through deactivation of NF‐κB in macrophages by CBD might be one mechanism of its anti‐inflammatory function in the liver.
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network

Recommender systems are becoming more and more important in our daily lives. However, traditional recommendation methods are challenged by data sparsity and efficiency, as the numbers of users, items, and interactions between the two in many real-world applications increase fast. In this paper, we propose a novel clustering recommender system based on node2vec technology and rich information network, namely, N2VSCDNNR, to solve these challenges. In particular, we use a bipartite network to construct the user–item network and represent the interactions among users (or items) by the corresponding one-mode projection network. In order to alleviate the data sparsity problem, we enrich the network structure according to user and item categories, and construct the one-mode projection category network. Then, considering the data sparsity problem in the network, we employ node2vec to capture the complex latent relationships among users (or items) from the corresponding one-mode projection category network. Moreover, considering the dependence on parameter settings and information loss problem in clustering methods, we use a novel spectral clustering method, which is based on dynamic nearest-neighbors (DNNs) and a novel automatically determining cluster number (ADCN) method that determines the cluster centers based on the normal distribution method, to cluster the users and items separately. After clustering, we propose the two-phase personalized recommendation to realize the personalized recommendation of items for each user. A series of experiments validate the outstanding performance of our N2VSCDNNR over several advanced embedding and side information based recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time complexity than the baseline methods in online recommendations, indicating its potential to be widely applied in large-scale systems.
Vanishing conditions on Weyl tensor for Einstein-type manifolds

In this paper we consider an Einstein-type equation which generalizes important geometric equations, like static and critical point equations. We prove that a complete Einstein-type manifold with fourth-order divergence-free Weyl tensor and zero radial Weyl curvature is locally a warped product with $(n-1)$-dimensional Einstein fibers, provided that the potential function is proper. As a consequence, we prove a result about the nonexistence of multiple black holes in static spacetimes. Lastly, we prove an estimate for the volume growth of geodesic balls and a weak version of the Omori-Yau maximum principle.
Chinese American Women's Experiences with Postpartum Depressive Symptoms and Mental Health Help-Seeking Behaviors

Purpose: The objective of this study was to explore the perspectives of postpartum depression (PPD) and mental health help-seeking behaviors among Chinese American women. Study Design and Methods: Using a qualitative design, Chinese American women, who had given birth in the past year, participated in a semistructured interview (English or Mandarin). Depressive symptoms and mental health services questionnaires were also conducted. Results: All 15 participants were married and between 29 and 39 years of age. Content analysis revealed two main themes including culture-specific postpartum traditions and mental health help-seeking. Nine reported sadness or PPD symptoms, including three who scored above the cutoff of the Edinburgh Postnatal Depression Scale (EPDS score ≥9) for risk of PPD and others who disclosed such information during the interview. Many women shared that they experienced postpartum depressive symptoms, but some did not believe depression was applicable to Chinese women. Clinical Implications: Healthcare professionals working with Chinese American women must be aware of culture-specific childbearing traditions to promote maternal–infant well-being outcomes.
Minimizing Polarization and Disagreement in Social Networks

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: \beginquotation \noindent Given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes \em disagreementand \em controversy simultaneously? \endquotation \noindent This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the others viewpoint of the world, and decrease overall levels of polarization and controversy? Such decisions have an important global effect on society \citewilliams2007social. Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a graph with $O(n/ε^2)$ edges that is a $(1+ε)$ approximation to the optimum. Our formulation is an instance of optimization over \em graph topologies, see also \citeboyd2004fastest,daitch2009fitting,sun2006fastest. Furthermore, for a given graph, we show how to optimize the same objective over the agents» innate opinions in polynomial time. Finally, we perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both controversy and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a reduction in controversy and disagreement of the order $6.2 \times 10^4$.
Modeling of multimodal transportation systems of large networks. (Modélisation des systèmes de transport multimodaux de grands réseaux)

The objective of this work consists on the modeling of traffic flow of a large multimodaltransportation network. The work is organized in two parts: a theoretical study part anda development part. The theoretical study emphasizes on how a multimodal network canbe model and how its performance in terms of supply can be optimized. To do so, threemain studies are discussed: the traffic flow prediction and regulation on large surface net-works, the vehicular multimodality in big surface networks taking into account new forms ofmobility, and finally the impact of the information on the cost of the itineraries. The devel-opment part consists on the conception of a traffic flow simulator to regulate the vehicularmultimodal traffic. The developed simulator should assist transport operators and territorialcommunities in their traffic flow management strategies
The Contribution of Changes in Adenylyl Cyclase Signaling System of the Brain and Myocardium to Etiology and Pathogenesis of Diabetes Mellitus

The functional changes in hormone-sensitive adenylyl cyclase (AC) signaling system of the central nervous system (CNS) and periphery play a crucial role in etiology and pathogenesis of diabetes mellitus (DM). The identification of these changes in AC signaling system and the abnormalities in AC signaling network are necessary for creation of the new strategies to treat and prevent diabetic pathology. In this chapter, our data and the results of other authors on the changes in hormone-sensitive adenylyl cyclase signaling system (ACSS) in the diabetic brain and heart and on their contribution to etiology and pathogenesis of DM and its complications, diabetic cardiomyopathy in particular, are presented and analyzed, and the promising approaches to treat DM and its complica- tions, which are based on the restoration of AC signaling cascades and their functional interaction, are discussed.
Making the Last Iterate of SGD Information Theoretically Optimal

Stochastic gradient descent (SGD) is one of the most widely used algorithms for large scale optimization problems. While classical theoretical analysis of SGD for convex problems studies (suffix) \emph{averages} of iterates and obtains information theoretically optimal bounds on suboptimality, the \emph{last point} of SGD is, by far, the most preferred choice in practice. The best known results for last point of SGD \cite{shamir2013stochastic} however, are suboptimal compared to information theoretic lower bounds by a $\log T$ factor, where $T$ is the number of iterations. \cite{harvey2018tight} shows that in fact, this additional $\log T$ factor is tight for standard step size sequences of $\OTheta{\frac{1}{\sqrt{t}}}$ and $\OTheta{\frac{1}{t}}$ for non-strongly convex and strongly convex settings, respectively. Similarly, even for subgradient descent (GD) when applied to non-smooth, convex functions, the best known step-size sequences still lead to $O(\log T)$-suboptimal convergence rates (on the final iterate). The main contribution of this work is to design new step size sequences that enjoy information theoretically optimal bounds on the suboptimality of \emph{last point} of SGD as well as GD. We achieve this by designing a modification scheme, that converts one sequence of step sizes to another so that the last point of SGD/GD with modified sequence has the same suboptimality guarantees as the average of SGD/GD with original sequence. We also show that our result holds with high-probability. We validate our results through simulations which demonstrate that the new step size sequence indeed improves the final iterate significantly compared to the standard step size sequences.
Race-Ethnicity, Poverty, Urban Stressors, and Telomere Length in a Detroit Community-based Sample

Residents of distressed urban areas suffer early aging-related disease and excess mortality. Using a community-based participatory research approach in a collaboration between social researchers and cellular biologists, we collected a unique data set of 239 black, white, or Mexican adults from a stratified, multistage probability sample of three Detroit neighborhoods. We drew venous blood and measured telomere length (TL), an indicator of stress-mediated biological aging, linking respondents’ TL to their community survey responses. We regressed TL on socioeconomic, psychosocial, neighborhood, and behavioral stressors, hypothesizing and finding an interaction between poverty and racial-ethnic group. Poor whites had shorter TL than nonpoor whites; poor and nonpoor blacks had equivalent TL; and poor Mexicans had longer TL than nonpoor Mexicans. Findings suggest unobserved heterogeneity bias is an important threat to the validity of estimates of TL differences by race-ethnicity. They point to health impacts of social identity as contingent, the products of structurally rooted biopsychosocial processes.
The K2-HERMES Survey: age and metallicity of the thick disc

Asteroseismology is a promising tool to study Galactic structure and evolution because it can probe the ages of stars. Earlier attempts comparing seismic data from the Kepler satellite with predictions from Galaxy models found that the models predicted more low-mass stars compared to the observed distribution of masses. It was unclear if the mismatch was due to inaccuracies in the Galactic models, or the unknown aspects of the selection function of the stars. Using new data from the K2 mission, which has a well-defined selection function, we find that an old metal-poor thick disc, as used in previous Galactic models, is incompatible with the asteroseismic information. We use an importance-sampling framework, which takes the selection function into account, to fit for the metallicities of a population synthesis model using spectroscopic data. We show that spectroscopic measurements of [Fe/H] and [α/Fe] elemental abundances from the GALAH survey indicate a mean metallicity of log (Z/Z⊙) = −0.16 for the thick disc. Here Z is the effective solar-scaled metallicity, which is a function of [Fe/H] and [α/Fe]. With the revised disc metallicities, for the first time, the theoretically predicted distribution of seismic masses show excellent agreement with the observed distribution of masses. This indirectly verifies that the asteroseismic mass scaling relation is good to within five per cent. Assuming the asteroseismic scaling relations are correct, we estimate the mean age of the thick disc to be about 10 Gyr, in agreement with the traditional idea of an old α-enhanced thick disc.
The Discrepancy of Neurological Diseases between China and Western Countries in Recent Two Decades

IntroductIon Neurological diseases are characterized by high frequency of disability and mortality with pathogenesis largely unknown. Genetic, refractory, and rare diseases account for a large proportion of neurological diseases; sympathetically, limited curable treatments are available. Hundreds of millions of people worldwide are affected by neurological disorders. The diagnosis and treatment of neurological disorders have made significant progress in recent years, especially in the past two decades with the rapid development of technology. For example, regarding acute ischemic stroke, the disability, mortality, and recurrence rate were notably decreasing with the improvement of intravenous and intra‐artery treatment and with the emphasis on secondary prevention in recent years worldwide. More surprisingly, gene therapy is emerging as a powerful approach to help patients suffering from gene defects with the potential to treat and even cure the genetic disorders. Almost at the same time, remarkable changes have been made in terms of diagnosis, treatment, and prevention of neurological diseases in China. Even more importantly, China is a country having very large geographical areas with disequilibrium of economic development and having enormous population covering diversified cultures and customs. Therefore, risk factors and spectrum for some of the neurological disease are different from Western countries. Hereby in this paragraph, we reviewed the progression of neurological diseases in the recent two decades and further compared the differences between China and Western countries. The comparison will help medical practitioners and researchers to better understand the gap, to make better medical decisions for patients, and to encourage progress in research field. stroke and cerebrovascular dIsease Stroke is the second most common cause of death worldwide, and a major global cause of disability. [1] There are significant geographical differences in stroke burden by regions and country income levels. In the past two decades, within Western countries, there has been a great reduction in stroke incidence, mortality, mortality‐to‐incidence ratios, and disability‐adjusted life years (DALYs) lost, which could be explained by the good health services and strategies for stroke prevention and medical care in these countries. Conversely, in China, stroke incidence has increased, and mortality‐to‐incidence ratios and DALYs lost are much higher than those in high‐income countries. Although reductions in stroke mortality have taken place globally, compared to the high‐income countries, a two‐fold higher stroke mortality rate was observed in the low‐income and middle‐income countries.
[Body composition, biochemical and clinical changes of adolescents with excessive adiposity].

BACKGROUND adolescents with excess body fat and eutrophic had the same metabolic changes expected in obese individuals. OBJECTIVE to evaluate body composition, anthropometric changes, biochemical and clinical characteristics of female adolescents. METHODS a total of 113 adolescents from public schools in Viçosa, MG, divided into three groups: group 1 - consisting of eutrophic adolescents with excess body fat; group 2 - eutrophic with body fat within normal limits; and group 3 - with excess weight and body fat. Weight, height, waist and hip circumference, blood pressure were measured. The body mass index (BMI) and waist-hip ratio were calculated. The percentage of body fat was obtained by bioelectrical impedance horizontally, following its own protocol for this evaluation. The assessment of the percentage of body fat and biochemistry was performed after 12 hours of fasting, and analyzed the lipid profile, blood glucose and insulin, homocysteine, leptin and C-reactive protein. Insulin resistance was calculated by HOMA index. RESULTS the group of eutrophic adolescents, with higher adiposity, behaved in relation to blood pressure, HDL and glucose levels, similarly to adolescents who are overweight. It can be seen that the HOMA index, insulin and leptin increased with increasing body fat. More than half of adolescents had total cholesterol and CRP levels above recommended levels. The most obvious metabolic disorder related to the lipid profile for both groups studied. CONCLUSION excess adiposity in normal weight adolescents may be related to clinical and biochemical changes similar to those found in adolescents who are overweight.
Sex‐ and Gender‐Stratified Risks of Psychological Factors for Incident Ischemic Heart Disease: Systematic Review and Meta‐Analysis

Background Psychological factors are associated with an increased risk of developing ischemic heart disease (IHD). Women more often report psychological factors, and sex and gender differences are present in IHD. In this meta‐analysis we examine the risks of psychological factors for IHD incidence in women and men. We hypothesize that a broad range of psychological factors are related to a higher risk for incident IHD, with a higher risk for women. Methods and Results PubMed, EMBASE, and PsycINFO were searched for studies assessing the risk between psychological factors and incident IHD. Psychological factors included depression, anxiety or panic disorder, social support, hostility, anger, personality (type D), type A behavior pattern, posttraumatic stress disorder, and psychological distress. In the primary analyses, 62 studies (77 separate reports) that included 2 145 679 women and 3 119 879 men and reported confounder‐adjusted hazard ratios or relative risks were included. Pooled effect confounder‐adjusted estimates from random‐effects models showed that psychological factors (all combined) were associated with incident IHD in women (hazard ratio: 1.22; 95% CI, 1.14–1.30) and men (hazard ratio: 1.25; 95% CI, 1.19–1.31). No sex and gender differences were found for these pooled effect estimates (P=0.547). Conclusions Psychological factors are associated with incident IHD in both women and men, but no significant differences were observed between women and men. IHD is predominantly being studied as obstructive coronary artery disease, which is more prevalent in men. Data are needed on psychological predictors and other manifestations of IHD such as coronary microvascular disease, which is more common in women.
TextCohesion: Detecting Text for Arbitrary Shapes

In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions. These components are easier to handle than the entire text instance. A confidence scoring mechanism is designed to filter characters that are similar to text. Our method can integrate text contexts intensively when backgrounds are complex. Experiments on two curved challenging benchmarks demonstrate that TextCohesion outperforms state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and bfseries86.3% on SCUT-CTW1500.