A sharp bound for the resurgence of sums of ideals

We prove a sharp upper bound for the resurgence of sums of ideals involving disjoint sets of variables, strengthening work of Bisui--H\`a--Jayanthan--Thomas. Complete solutions are delivered for two conjectures proposed by these authors. For given real numbers $a$ and $b$, we consider the set Res$(a,b)$ of possible values of the resurgence of $I+J$ where $I$ and $J$ are ideals in disjoint sets of variables having resurgence $a$ and $b$, respectively. Some questions and partial results about Res$(a,b)$ are discussed.Comment: 14 pages, 01 figur

HYDROLOGICAL SURVEY OF THE HUONG RIVER IN 2005

Joint Research on Environmental Science and Technology for the Eart

DETERMINATION OF ARSENIC (ILL AND V) BY ANODIC STRIPPING VOLTAMMETRY ON GOLD FILM ELECTRODE

Joint Research on Environmental Science and Technology for the Eart

ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese

Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several significant drawbacks, including imbalanced data and noisy data on social media platforms. Imbalanced and noisy are two essential issues that need to be addressed in Vietnamese social media texts. Graph Convolutional Networks can address the problems of imbalanced and noisy data in text classification on social media by taking advantage of the graph structure of the data. This study presents a novel approach based on contextualized language model (PhoBERT) and graph-based method (Graph Convolutional Networks). In particular, the proposed approach, ViCGCN, jointly trained the power of Contextualized embeddings with the ability of Graph Convolutional Networks, GCN, to capture more syntactic and semantic dependencies to address those drawbacks. Extensive experiments on various Vietnamese benchmark datasets were conducted to verify our approach. The observation shows that applying GCN to BERTology models as the final layer significantly improves performance. Moreover, the experiments demonstrate that ViCGCN outperforms 13 powerful baseline models, including BERTology models, fusion BERTology and GCN models, other baselines, and SOTA on three benchmark social media datasets. Our proposed ViCGCN approach demonstrates a significant improvement of up to 6.21%, 4.61%, and 2.63% over the best Contextualized Language Models, including multilingual and monolingual, on three benchmark datasets, UIT-VSMEC, UIT-ViCTSD, and UIT-VSFC, respectively. Additionally, our integrated model ViCGCN achieves the best performance compared to other BERTology integrated with GCN models

Coping with noise in joint remote preparation of a general two-qubit state by using nonmaximally entangled quantum channel

Noise is unavoidable in practice and its presence makes quantum protocols imperfect. In this paper we consider a way to cope with typical types of noise in joint remote preparation of an arbitrary 2-qubit state. The idea is to use nonmaximally (in stead of maximally) entangled states as the initial quantum channel. Because noise changes the initial quantum channel we can beforehand tailor it to be nonmaximally entangled by introducing free parameters which, depending on given types of noise, can be controlled so that due to the affect of noise the initial quantum channel becomes closest to the maximally entangled one, thus optimizing the performance of the joint remote state preparation protocol. The dependence of the optimal averaged fidelities on the strength of various types of noise is represented by phase diagrams that clearly separate the quantum domain from the classical one