Approximation Algorithm for Unrooted Prize-Collecting Forest with Multiple Components and Its Application on Prize-Collecting Sweep Coverage

In this paper, we introduce a polynomial-time 2-approximation algorithm for the Unrooted Prize-Collecting Forest with $K$ Components (URPCF$_K$) problem. URPCF$_K$ aims to find a forest with exactly $K$ connected components while minimizing both the forest's weight and the penalties incurred by unspanned vertices. Unlike the rooted version RPCF$_K$, where a 2-approximation algorithm exists, solving the unrooted version by guessing roots leads to exponential time complexity for non-constant $K$. To address this challenge, we propose a rootless growing and rootless pruning algorithm. We also apply this algorithm to improve the approximation ratio for the Prize-Collecting Min-Sensor Sweep Cover problem (PCMinSSC) from 8 to 5. Keywords: approximation algorithm, prize-collecting Steiner forest, sweep cover

Electrocardiogram Recognization Based on Variational AutoEncoder

Subtle distortions on electrocardiogram (ECG) can help doctors to diagnose some serious larvaceous heart sickness on their patients. However, it is difficult to find them manually because of disturbing factors such as baseline wander and high-frequency noise. In this chapter, we propose a method based on variational autoencoder to distinguish these distortions automatically and efficiently. We test our method on three ECG datasets from Physionet by adding some tiny artificial distortions. Comparing with other approaches adopting autoencoders [e.g., contractive autoencoder, denoising autoencoder (DAE)], the results of our experiment show that our method improves the performance of publically available on ECG analysis on the distortions

CULTURAL ADAPTATION OF CHINESE STUDENTS AS THE NEED OF THEIR EDUCATION ABROAD PROCESS

The article is devoted to a sharp problem of educational practice: ways of cultural adaptation of foreign (in particular, Chinese) students in new host society as a condition of their successful education abroad. Chinese students now form the largest abroad students` community in main countries of the world. As the Chinese way of living and the education traditions differ a lot from the countries` they usually would like to continue their education at, cultural adaptation is seen in the article as the need for a comfortable education process both for the students came and the University host them. The Main methods of Chinese students` cultural adaptation process are under consideration of the research, paying attention to the period (stage) a student finds himself / herself at that also can be seen as the aim of the research. As to the methods, the ones traditionally used in social studies and general research work were used: ethnographic descriptions and census data, scientific method to collect empirical evidence, method of analysis, etc. As the result was presented a set of methods that can be used by a host University on condition of the Chinese students` cooperation to level down the cultural shock period for the student and to speed up his/her entering the host culture society.

DEPHN: Different Expression Parallel Heterogeneous Network using virtual gradient optimization for Multi-task Learning

Recommendation system algorithm based on multi-task learning (MTL) is the major method for Internet operators to understand users and predict their behaviors in the multi-behavior scenario of platform. Task correlation is an important consideration of MTL goals, traditional models use shared-bottom models and gating experts to realize shared representation learning and information differentiation. However, The relationship between real-world tasks is often more complex than existing methods do not handle properly sharing information. In this paper, we propose an Different Expression Parallel Heterogeneous Network (DEPHN) to model multiple tasks simultaneously. DEPHN constructs the experts at the bottom of the model by using different feature interaction methods to improve the generalization ability of the shared information flow. In view of the model's differentiating ability for different task information flows, DEPHN uses feature explicit mapping and virtual gradient coefficient for expert gating during the training process, and adaptively adjusts the learning intensity of the gated unit by considering the difference of gating values and task correlation. Extensive experiments on artificial and real-world datasets demonstrate that our proposed method can capture task correlation in complex situations and achieve better performance than baseline models\footnote{Accepted in IJCNN2023}

Improved Approximation Algorithm for Minimum-Weight (1,m)(1,m)--Connected Dominating Set

The classical minimum connected dominating set (MinCDS) problem aims to find a minimum-size subset of connected nodes in a network such that every other node has at least one neighbor in the subset. This problem is drawing considerable attention in the field of wireless sensor networks because connected dominating sets can serve as virtual backbones of such networks. Considering fault-tolerance, researchers developed the minimum $k$-connected $m$-fold CDS (Min$(k,m)$CDS) problem. Many studies have been conducted on MinCDSs, especially those in unit disk graphs. However, for the minimum-weight CDS (MinWCDS) problem in general graphs, algorithms with guaranteed approximation ratios are rare. Guha and Khuller designed a $(1.35+\varepsilon)\ln n$-approximation algorithm for MinWCDS, where $n$ is the number of nodes. In this paper, we improved the approximation ratio to $2H(\delta_{\max}+m-1)$ for MinW$(1,m)$CDS, where $\delta_{\max}$ is the maximum degree of the graph