Optimality of Graphlet Screening in High Dimensional Variable Selection

Consider a linear regression model where the design matrix X has n rows and p columns. We assume (a) p is much large than n, (b) the coefficient vector beta is sparse in the sense that only a small fraction of its coordinates is nonzero, and (c) the Gram matrix G = X'X is sparse in the sense that each row has relatively few large coordinates (diagonals of G are normalized to 1). The sparsity in G naturally induces the sparsity of the so-called graph of strong dependence (GOSD). We find an interesting interplay between the signal sparsity and the graph sparsity, which ensures that in a broad context, the set of true signals decompose into many different small-size components of GOSD, where different components are disconnected. We propose Graphlet Screening (GS) as a new approach to variable selection, which is a two-stage Screen and Clean method. The key methodological innovation of GS is to use GOSD to guide both the screening and cleaning. Compared to m-variate brute-forth screening that has a computational cost of p^m, the GS only has a computational cost of p (up to some multi-log(p) factors) in screening. We measure the performance of any variable selection procedure by the minimax Hamming distance. We show that in a very broad class of situations, GS achieves the optimal rate of convergence in terms of the Hamming distance. Somewhat surprisingly, the well-known procedures subset selection and the lasso are rate non-optimal, even in very simple settings and even when their tuning parameters are ideally set

Number-resolved master equation approach to quantum transport under the self-consistent Born approximation

We construct a particle-number(n)-resolved master equation (ME) approach under the self-consistent Born approximation (SCBA) for quantum transport through mesoscopic systems. The formulation is essentially non-Markovian and incorporates the interlay of the multi-tunneling processes and many-body correlations. The proposed n-SCBA-ME goes completely beyond the scope of the Born-Markov master equation, being applicable to transport under small bias voltage, in non-Markovian regime and with strong Coulomb correlations. For steady state, it can recover not only the exact result of noninteracting transport under arbitrary voltages, but also the challenging nonequilibrium Kondo effect. Moreover, the n-SCBA-ME approach is efficient for the study of shot noise.We demonstrate the application by a couple of representative examples, including particularly the nonequilibrium Kondo system.Comment: arXiv admin note: substantial text overlap with arXiv:1302.638

The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study

Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature. Scientific literature tagging is beyond a pure multi-label text classification task because papers on the Web are prevalently accompanied by metadata information such as venues, authors, and references, which may serve as additional signals to infer relevant tags. Although there have been studies making use of metadata in academic paper classification, their focus is often restricted to one or two scientific fields (e.g., computer science and biomedicine) and to one specific model. In this work, we systematically study the effect of metadata on scientific literature tagging across 19 fields. We select three representative multi-label classifiers (i.e., a bag-of-words model, a sequence-based model, and a pre-trained language model) and explore their performance change in scientific literature tagging when metadata are fed to the classifiers as additional features. We observe some ubiquitous patterns of metadata's effects across all fields (e.g., venues are consistently beneficial to paper tagging in almost all cases), as well as some unique patterns in fields other than computer science and biomedicine, which are not explored in previous studies.Comment: 11 pages; Accepted to WWW 202

Up-regulation of microRNA-183 reduces FOXO1 expression in gastric cancer patients with Helicobacter pylori infection

The aim of the study is to detect the expression of FOXO1 mRNA and protein in samples from gastric cancer patients with Helicobacter pylori (H. pylori) infection, and to investigate the relationship between FOXO1 expression and miR-183 expression. Twenty-six gastric cancer patients with H. pylori infection and 26 gastric cancer patients without H. pylori infection were included into experimental group and control group, respectively. Tumor tissues and peripheral blood were collected from all subjects. QRT-PCR was used to determine the expression of miRNA and mRNA. Western blotting was carried out to measure protein expression. Dual luciferase reporter assay was used to identify direct interaction between miRNA and 3’-UTR of mRNA. Cell proliferation was examined by CCK-8 assay. FOXO1 mRNA and protein expression was downregulated in gastric cancer patients, being possibly related to H. pylori infection. The expression of miR-183 in tumor tissues and serum from gastric cancer patients with H. pylori infection was elevated, and probably regulated the expression of FOXO1 by direct targeting. Stimulation by H. pylori up-regulated the expression of miR-183 in gastric cancer AGS cells, and reduced the levels of FOXO1 mRNA and protein. Inhibition of miR183 elevated the expression of FOXO1 and suppressed the proliferation of AGS cells. The present study demonstrates that the expression of FOXO1 in tumor tissues and blood from gastric cancer patients with H. pylori infection is significantly down-regulated, and may be related to the up-regulation of miR-183. H. pylori may regulate FOXO1 expression through miR-183 to affect the pathological process of gastric cancer

Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure. As pretrained language models (PLMs) have demonstrated their effectiveness in obtaining widely generalizable text representations, a substantial amount of effort has been made to incorporate PLMs into representation learning on text-rich networks. However, few of them can jointly consider heterogeneous structure (network) information as well as rich textual semantic information of each node effectively. In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. Specifically, we inject heterogeneous structure information into each Transformer layer when encoding node texts. Meanwhile, Heterformer is capable of characterizing node/edge type heterogeneity and encoding nodes with or without texts. We conduct comprehensive experiments on three tasks (i.e., link prediction, node classification, and node clustering) on three large-scale datasets from different domains, where Heterformer outperforms competitive baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer

Research on Model of Harmony Sustainable Development between ECS and SRS： from Perspective of Resource Entropy

Combining the theory of dissipative structure systems, resource entropy and stakeholder, the article gave the definition of Stakeholder Resource System (SRS), analyzed sustainable development model of Enterprise Complex System (ESC) from the perspective of resource entropy, as well as sustainable development model of stakeholder resource system. Finally, based on the above analysis, the article studied Harmony Sustainable Development model of ECS and SRS. Key words: Stakeholder Resource System; Enterprise Complex System; Resource Entropy; Harmony Sustainable Development Résumé: En combinant la théorie du système de structure dissipative, l'entropie des ressources et les intervenants, l'article a donné une définition du système des ressources des intervenants(SRS), analysé à la fois des modèles de développement durable du système complexe de l'entreprise(CES) du point de vue de l'entropie des ressources et des modèles de développement durable du système des ressources des parties prenantes. Enfin, à part l'analyse ci-dessus, l'article a étudié également le modèle du développement durable harmonieux d'ECS et de SRS. Mots-Clés: système des ressources des intervenants, système complexe de l’entreprise, entropie des ressources, développement durable harmonieu

Estradiol regulates miR-135b and mismatch repair gene expressions via estrogen receptor-β in colorectal cells.

Estrogen has anti-colorectal cancer effects which are thought to be mediated by mismatch repair gene (MMR) activity. Estrogen receptor (ER) expression is associated with microRNA (miRNA) expression in ER-positive tumors. However, studies of direct link between estrogen (especially estradiol E2), miRNA expression, and MMR in colorectal cancer (CRC) have not been done. In this study, we first evaluated the effects of estradiol (E2) and its antagonist ICI182,780 on the expression of miRNAs (miR-31, miR-155 and miR-135b) using COLO205, SW480 and MCF-7 cell lines, followed by examining the association of tissue miRNA expression and serum E2 levels using samples collected from 18 colorectal cancer patients. E2 inhibited the expressions of miRNAs in COLO205 cells, which could be reversed by E2 antagonist ICI 182.780. The expression of miR-135b was inversely correlated with serum E2 level and ER-β mRNA expression in CRC patients' cancer tissues. There were significant correlations between serum E2 level and expression of ER-β, miR-135b, and MMR in colon cancer tissue. This study suggests that the effects of estrogen on MMR function may be related to regulating miRNA expression via ER-β, which may be the basis for the anti-cancer effect in colorectal cells