Quasi-Variational Inequalities without Concavity Assumptions

This paper generalizes a foundational quasi-variationalinequality by relaxing the (0-diagonal) concavity condition. The approach adopted in this paper is based on continuous selection-type arguments and hence it is quite different from the approach used in the literature. Thus it enables us to prose the existence of equilibrium of the constrained noncooperative games without assuming the (quasi) convexity of loss functions

Quasi-Variational Inequalities without Concavity Assumptions

This paper generalizes a foundational quasi-variationalinequality by relaxing the (0-diagonal) concavity condition. The approach adopted in this paper is based on continuous selection-type arguments and hence it is quite different from the approach used in the literature. Thus it enables us to prose the existence of equilibrium of the constrained noncooperative games without assuming the (quasi) convexity of loss functions

Monitoring Breast Tumor Lung Metastasis by U-SPECT-II/CT with an Integrin αvβ3-Targeted Radiotracer 99mTc-3P-RGD2

Purpose: The purpose of this study was to evaluate the capability of u-SPECT-II/CT to monitor the progression of breast cancer lung metastasis using 99mTc-3P-RGD2 as a radiotracer

A Method for forcasting Salinity Process in Seawater-Intruded Rivers

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Phase Changes in the Evolution of the IPv4 and IPv6 AS-Level Internet Topologies

In this paper we investigate the evolution of the IPv4 and IPv6 Internet topologies at the autonomous system (AS) level over a long period of time.We provide abundant empirical evidence that there is a phase transition in the growth trend of the two networks. For the IPv4 network, the phase change occurred in 2001. Before then the network's size grew exponentially, and thereafter it followed a linear growth. Changes are also observed around the same time for the maximum node degree, the average node degree and the average shortest path length. For the IPv6 network, the phase change occurred in late 2006. It is notable that the observed phase transitions in the two networks are different, for example the size of IPv6 network initially grew linearly and then shifted to an exponential growth. Our results show that following decades of rapid expansion up to the beginning of this century, the IPv4 network has now evolved into a mature, steady stage characterised by a relatively slow growth with a stable network structure; whereas the IPv6 network, after a slow startup process, has just taken off to a full speed growth. We also provide insight into the possible impact of IPv6-over-IPv4 tunneling deployment scheme on the evolution of the IPv6 network. The Internet topology generators so far are based on an inexplicit assumption that the evolution of Internet follows non-changing dynamic mechanisms. This assumption, however, is invalidated by our results.Our work reveals insights into the Internet evolution and provides inputs to future AS-Level Internet models.Comment: 12 pages, 21 figures; G. Zhang et al.,Phase changes in the evolution of the IPv4 and IPv6 AS-Level Internet topologies, Comput. Commun. (2010

Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity

Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels. Recently, the emergence of LLMs has introduced new solutions to such problems by leveraging graph structures to generate supplementary user profiles. However, previous approaches have not fully utilized the graph understanding capabilities of LLMs and have struggled to adapt to complex streaming data environments. In this work, we propose a fine-grained streaming data synthesis framework that categorizes sparse users into three categories: Mid-tail, Long-tail, and Extreme. Specifically, we design LLMs to comprehensively understand three key graph elements in streaming data, including Local-global Graph Understanding, Second-Order Relationship Extraction, and Product Attribute Understanding, which enables the generation of high-quality synthetic data to effectively address sparsity across different categories. Experimental results on three real datasets demonstrate significant performance improvements, with synthesized data contributing to MSE reductions of 45.85%, 3.16%, and 62.21%, respectively