On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation

Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.Comment: SDM 2018 (Full version including all proofs

A geometric model for the module category of a skew-gentle algebra

In this article, we realize skew-gentle algebras as skew-tiling algebras associated to admissible partial triangulations of punctured marked surfaces. Based on this, we establish a bijection between tagged permissible curves and certain indecomposable modules, interpret the Auslander-Reiten translation via the tagged rotation, and show intersection-dimension formulas. As an application, we classify support $\tau$-tilting modules for skew-gentle algebras.Comment: Improved the introductio

Adaptive Data Augmentation for Contrastive Learning

In computer vision, contrastive learning is the most advanced unsupervised learning framework. Yet most previous methods simply apply fixed composition of data augmentations to improve data efficiency, which ignores the changes in their optimal settings over training. Thus, the pre-determined parameters of augmentation operations cannot always fit well with an evolving network during the whole training period, which degrades the quality of the learned representations. In this work, we propose AdDA, which implements a closed-loop feedback structure to a generic contrastive learning network. AdDA works by allowing the network to adaptively adjust the augmentation compositions according to the real-time feedback. This online adjustment helps maintain the dynamic optimal composition and enables the network to acquire more generalizable representations with minimal computational overhead. AdDA achieves competitive results under the common linear protocol on ImageNet-100 classification (+1.11% on MoCo v2).Comment: Accepted by ICASSP 202

Iron-boron pair dissociation in silicon under strong illumination

The dissociation of iron-boron pairs (FeB) in Czochralski silicon under strong illumination was investigated. It is found that the dissociation process shows a double exponential dependence on time. The first fast process is suggested to be caused by a positive Fe in FeB capturing two electrons and diffusion triggered by the electron-phonon interactions, while the second slow one would involve the capturing of one electron followed by temperature dependent dissociation with an activation energy of (0.21 +/- 0.03) eV. The results are important for understanding and controlling the behavior of FeB in concentrator solar cells

Effects of intratracheal administration of nuclear factor-kappaB decoy oligodeoxynucleotides on long-term cigarette smoke-induced lung inflammation and pathology in mice

To determine if nuclear factor-κB (NF-κB) activation may be a key factor in lung inflammation and respiratory dysfunction, we investigated whether NF-κB can be blocked by intratracheal administration of NF-κB decoy oligodeoxynucleotides (ODNs), and whether decoy ODN-mediated NF-κB inhibition can prevent smoke-induced lung inflammation, respiratory dysfunction, and improve pathological alteration in the small airways and lung parenchyma in the long-term smoke-induced mouse model system. We also detected changes in transcriptional factors. In vivo, the transfection efficiency of NF-κB decoy ODNs to alveolar macrophages in BALF was measured by fluorescein isothiocyanate (FITC)-labeled NF-κB decoy ODNs and flow cytometry post intratracheal ODN administration. Pulmonary function was measured by pressure sensors, and pathological changes were assessed using histology and the pathological Mias software. NF-κB and activator protein 1(AP-1) activity was detected by the electrophoretic motility shift assay (EMSA). Mouse cytokine and chemokine pulmonary expression profiles were investigated by enzyme-linked immunosorbent assay (ELISA) in bronchoalveolar lavage fluid (BALF) and lung tissue homogenates, respectively, after repeated exposure to cigarette smoke. After 24 h, the percentage of transfected alveolar macrophages was 30.00 ± 3.30%. Analysis of respiratory function indicated that transfection of NF-κB decoy ODNs significantly impacted peak expiratory flow (PEF), and bronchoalveolar lavage cytology displayed evidence of decreased macrophage infiltration in airways compared to normal saline-treated or scramble NF-κB decoy ODNs smoke exposed mice. NF-κB decoy ODNs inhibited significantly level of macrophage inflammatory protein (MIP) 1α and monocyte chemoattractant protein 1(MCP-1) in lung homogenates compared to normal saline-treated smoke exposed mice. In contrast, these NF-κB decoy ODNs-treated mice showed significant increase in the level of tumor necrosis factor-α(TNF-α) and pro-MMP-9(pro-matrix metalloproteinase-9) in mice BALF. Further measurement revealed administration of NF-κB decoy ODNs did not prevent pathological changes. These findings indicate that NF-κB activation play an important role on the recruitment of macrophages and pulmonary dysfunction in smoke-induced chronic lung inflammation, and with the exception of NF-κB pathway, there might be complex mechanism governing molecular dynamics of pro-inflammatory cytokines expression and structural changes in small airways and pulmonary parenchyma in vivo

The Effect of Product Recommendations on Online Investor Behaviors

Despite the popularity of product recommendations on online investment platforms, few studies have explored their impact on investor behaviors. Using data from a global e-commerce platform, we apply regression discontinuity design to causally examine the effects of product recommendations on online investors' mutual fund investments. Our findings indicate that recommended funds experience a significant rise in purchases, especially among low socioeconomic status investors who are most influenced by these recommendations. However, investors tend to suffer significantly worse investment returns after purchasing recommended funds, and this negative impact is also most significant for investors with low socioeconomic status. To explain this disparity, we find investors tend to gather less information and expend reduced effort in fund research when buying recommended funds. Furthermore, investors' redemption timing of recommended funds is less optimal than non-recommended funds. We also find that recommended funds experience a larger return reversal than non-recommended funds. In conclusion, product recommendations make investors behave more irrationally and these negative consequences are most significant for investors with low socioeconomic status, which can amplify wealth inequality among investors in financial markets