NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry

Neural network quantum state (NNQS) has emerged as a promising candidate for quantum many-body problems, but its practical applications are often hindered by the high cost of sampling and local energy calculation. We develop a high-performance NNQS method for \textit{ab initio} electronic structure calculations. The major innovations include: (1) A transformer based architecture as the quantum wave function ansatz; (2) A data-centric parallelization scheme for the variational Monte Carlo (VMC) algorithm which preserves data locality and well adapts for different computing architectures; (3) A parallel batch sampling strategy which reduces the sampling cost and achieves good load balance; (4) A parallel local energy evaluation scheme which is both memory and computationally efficient; (5) Study of real chemical systems demonstrates both the superior accuracy of our method compared to state-of-the-art and the strong and weak scalability for large molecular systems with up to $120$ spin orbitals.Comment: Accepted by SC'2

Collaborative filtering with diffusion-based similarity on tripartite graphs

Collaborative tags are playing more and more important role for the organization of information systems. In this paper, we study a personalized recommendation model making use of the ternary relations among users, objects and tags. We propose a measure of user similarity based on his preference and tagging information. Two kinds of similarities between users are calculated by using a diffusion-based process, which are then integrated for recommendation. We test the proposed method in a standard collaborative filtering framework with three metrics: ranking score, Recall and Precision, and demonstrate that it performs better than the commonly used cosine similarity.Comment: 8 pages, 4 figures, 1 tabl

Neuron-Specific HuR-Deficient Mice Spontaneously Develop Motor Neuron Disease

Human Ag R (HuR) is an RNA binding protein in the ELAVL protein family. To study the neuron-specific function of HuR, we generated inducible, neuron-specific HuR-deficient mice of both sexes. After tamoxifen-induced deletion of HuR, these mice developed a phenotype consisting of poor balance, decreased movement, and decreased strength. They performed significantly worse on the rotarod test compared with littermate control mice, indicating coordination deficiency. Using the grip-strength test, it was also determined that the forelimbs of neuron-specific HuR-deficient mice were much weaker than littermate control mice. Immunostaining of the brain and cervical spinal cord showed that HuR-deficient neurons had increased levels of cleaved caspase-3, a hallmark of cell apoptosis. Caspase-3 cleavage was especially strong in pyramidal neurons and α motor neurons of HuR-deficient mice. Genome-wide microarray and real-time PCR analysis further indicated that HuR deficiency in neurons resulted in altered expression of genes in the brain involved in cell growth, including trichoplein keratin filament-binding protein, Cdkn2c, G-protein signaling modulator 2, immediate early response 2, superoxide dismutase 1, and Bcl2. The additional enriched Gene Ontology terms in the brain tissues of neuron-specific HuR-deficient mice were largely related to inflammation, including IFN-induced genes and complement components. Importantly, some of these HuR-regulated genes were also significantly altered in the brain and spinal cord of patients with amyotrophic lateral sclerosis. Additionally, neuronal HuR deficiency resulted in the redistribution of TDP43 to cytosolic granules, which has been linked to motor neuron disease. Taken together, we propose that this neuron-specific HuR-deficient mouse strain can potentially be used as a motor neuron disease model

Stunting and soil-transmitted-helminth infections among school-age pupils in rural areas of southern China

<p>Abstract</p> <p>Background</p> <p>Stunting and soil-transmitted helminth (STH) infections including ascariasis, trichuriasis and hookworm remain major public health problems in school-age pupils in developing countries. The objectives of this study were to determine the prevalence of stunting for children and its association with three major soil-transmitted helminths (STH) in rural areas of southern China. The study also aims to determine risk factors for stunting and to provide guidance on the prevention and control of stunting and STH infections for future studies in this field.</p> <p>Results</p> <p>A cross-sectional survey was carried out in the poor rural areas in Guangxi Autonomous Regional and Hainan Province where STH prevalence was higher between September and November 2009. Pupils were from 15 primary schools. All the school-age pupils aged between 9 and 12 years old (mean age 11.2 ± 3.2 years), from grades three to six took part in this study. Study contents include questionnaire surveys, physical examination and laboratory methods (stool checking for eggs of three major STH infections and haemoglobin determination was performed for the anaemia test). Finally 1031 school-age pupils took part in survey. The results showed that the overall prevalence of stunting (HAZ < 2SD) was 25.6%, based on the WHO Child Growth Standards (2007). Risk factors for stunting based on logistic regression analyses were: (1) STH moderate-to-heavy intensity infections (OR = 1.93;95%CI:1.19,3.11); (2) anaemia (OR = 3.26;95%CI: 2.02,5.27); (3) education level of mother (OR = 2.13; 95%CI: 1.39,3.25). The overall prevalence of major STH infections was 36.7%, STH moderate-to-heavy intensity infections was 16.7%. The overall prevalence of ascariasis, trichuriasis, hookworm and co-infection were 18.5%, 11.2%, 14.7% and 9.1% respectively. The prevalence of anaemic children (HB < 12 g/dl) was 13.1%.</p> <p>Conclusion</p> <p>The present study showed that stunting was highly prevalent among the study population and STH infection is one of the important risk factors for stunting, with moderate-to-heavy intensity infections being the main predictor of stunting. Hence, additional interventions measures such as to promote de-worming treatment, to enhance health education and to improve hygiene and sanitation in order to reduce stunting in this population, are needed throughout the primary school age group.</p

A generalized model via random walks for information filtering

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation

Tag-Aware Recommender Systems: A State-of-the-art Survey

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.Comment: 19 pages, 3 figure

Detecting Depression Using Single-Channel EEG and Graph Methods

Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe