Exploring Communities in Large Profiled Graphs

Given a graph $G$ and a vertex $q\in G$, the community search (CS) problem aims to efficiently find a subgraph of $G$ whose vertices are closely related to $q$. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitate efficient and online solutions for PCS

Computer-Aided Value-Assessment Model: Review for Bilingual Teaching Courses Quantitative Analysis

AbstractIn order to review the effect on the bilingual teaching courses aided by computer, the comprehensive evaluation on the bilingual teaching course and research results of the bilingual teaching is required in the university. A full range system for bilingual teaching course performance assessment that is a novel quantized method is researched in this paper. Furthermore, the comprehensive evaluation processing and evaluation model have been accomplished. The application results for bilingual teaching course performance review assessment which is developed as a computer management system demonstrated its high operability and achieved accurate can be convenient for the same requirements

HIF-1α Contributes to Hypoxia-induced Invasion and Metastasis of Esophageal Carcinoma via Inhibiting E-cadherin and Promoting MMP-2 Expression

Hypoxia-inducible factor-1α (HIF-1α) has been found to enhance tumor invasion and metastasis, but no study has reported its action in esophageal carcinoma. The goal of this study was to explore the probable mechanism of HIF-1α in the invasion and metastasis of esophageal carcinoma Eca109 cells in vitro and in vivo. mRNA and protein expression of HIF-1α, E-cadherin and matrix metalloproteinase-2 (MMP-2) under hypoxia were detected by RT-PCR and Western blotting. The effects of silencing HIF-1α on E-cadherin, MMP-2 mRNA and protein expression under hypoxia or normoxia were detected by RT-PCR and Western blotting, respectively. The invasive ability of Eca109 cells was tested using a transwell chambers. We established an Eca109-implanted tumor model and observed tumor growth and lymph node metastasis. The expression of HIF-1α, E-cadherin and MMP-2 in xenograft tumors was detected by Western blotting. After exposure to hypoxia, HIF-1α protein was up-regulated, both mRNA and protein levels of E-cadherin were down-regulated and MMP-2 was up-regulated, while HIF-1α mRNA showed no significant change. SiRNA could block HIF-1α effectively, increase E-cadherin expression and inhibit MMP-2 expression. The number of invading cells decreased after HIF-1α was silenced. Meanwhile, the tumor volume was much smaller, and the metastatic rate of lymph nodes and the positive rate were lower in vivo. Our observations suggest that HIF-1α inhibition might be an effective strategy to weaken invasion and metastasis in the esophageal carcinoma Eca109 cell line

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed; if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT's prowess: it remarkably decreases Deit-S's FLOPs by 63\% and concurrently amplifies throughput twofold. Code of this project is at https://github.com/edgeai1/LF-ViT.git

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models

In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment of LLMs in real-world applications. To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation). To address these challenges, this work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation. Specially, we construct a new hallucination benchmark HaluEval 2.0, and designs a simple yet effective detection method for LLM hallucination. Furthermore, we zoom into the different training or utilization stages of LLMs and extensively analyze the potential factors that lead to the LLM hallucination. Finally, we implement and examine a series of widely used techniques to mitigate the hallucinations in LLMs. Our work has led to several important findings to understand the hallucination origin and mitigate the hallucinations in LLMs. Our code and data can be accessed at https://github.com/RUCAIBox/HaluEval-2.0.Comment: 24 pages, 8 figures, 13 table

Entanglement control in one-dimensional s=1/2s=1/2 random XY spin chain

The entanglement in one-dimensional random XY spin systems where the impurities of exchange couplings and the external magnetic fields are considered as random variables is investigated by solving the different spin-spin correlation functions and the average magnetization per spin. The entanglement dynamics near particular locations of the system is also studied when the exchange couplings (or the external magnetic fields) satisfy three different distributions(the Gaussian distribution, double-Gaussian distribution, and bimodal distribution). We find that the entanglement can be controlled by varying the strength of external magnetic field and the different distributions of impurities. Moreover, the entanglement of some nearest-neighboring qubits can be increased for certain parameter values of the three different distributions.Comment: 13 pages, 4 figure