Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data

Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar effects of observed characteristics, but the grouping is typically unknown in advance. We first conduct a local analysis which reveals that the variances of the individual coefficient estimates contain useful information for the estimation of group structure. We then propose a method to estimate unobserved groupings for general panel data models that explicitly account for the variance information. Our proposed method remains computationally feasible with a large number of individuals and/or repeated measurements on each individual. The developed ideas can also be applied even when individual-level data are not available and only parameter estimates together with some quantification of estimation uncertainty are given to the researcher. A thorough simulation study demonstrates superior performance of our method than existing methods and we apply the method to two empirical applications

An analysis of the extent to which employees of post-80s and 90s in China can be motivated by financial and non-financial rewards.

As Chinese post-80s and 90s employees whose values of job motivation, rewards and satisfaction have huge difference with workforce of other generation due to special social context they have experienced, it is significant to examine factors that motivate them effectively. There are three main research focus, the first focus is to find out factors that demotivate and dissatisfy them; the second focus is to identify their preference of rewards within financial and non-financial rewards; the last focus is to explore factors that influence the retention of this special cohort. With the use of in-depth interview as qualitative research approach, 15 Chinese post-80s and 90s participants are invited to have a better understanding of their opinions. The research found that financial rewards is not the only factors that have important effect on motivating and satisfying Chinese post-80s and 90s employees, some non-financial rewards like recognition, harmonious superior-subordinate relationship, and management style play an increasingly essential role. Therefore, organizations should improve their motivation strategy and rewards management to cater for Chinese post-80s and 90s employees' higher level of needs like work-life balance, self-improvement and self-actualization. Key words: Chinese post-80s and 90s, motivation, financial and non-financial rewards

Sparse Fr\'echet Sufficient Dimension Reduction with Graphical Structure Among Predictors

Fr\'echet regression has received considerable attention to model metric-space valued responses that are complex and non-Euclidean data, such as probability distributions and vectors on the unit sphere. However, existing Fr\'echet regression literature focuses on the classical setting where the predictor dimension is fixed, and the sample size goes to infinity. This paper proposes sparse Fr\'echet sufficient dimension reduction with graphical structure among high-dimensional Euclidean predictors. In particular, we propose a convex optimization problem that leverages the graphical information among predictors and avoids inverting the high-dimensional covariance matrix. We also provide the Alternating Direction Method of Multipliers (ADMM) algorithm to solve the optimization problem. Theoretically, the proposed method achieves subspace estimation and variable selection consistency under suitable conditions. Extensive simulations and a real data analysis are carried out to illustrate the finite-sample performance of the proposed method

Graph Force Learning

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning

CURRENT SITUATION INVESTIGATION AND COUNTERMEASURE RESEARCH OF STUDENTS’ ONLINE LEARNING DURING THE EPIDEMIC PERIOD: A CASE STUDY OF ZHEJIANG PROVINCE, CHINA

A survey of 538 students in 6 primary and secondary schools and colleges in Hangzhou, Ningbo and Jiaxing, Zhejiang Province, China has found: (1) Chinese schools suspended offline teaching in February-May, 2020 due to the novel coronavirus outbreak. All students studied online at home and 93% of them studied 2-7 hours a day online on average. Among all of them, students in primary schools spent least time online and college students spent most time. The science courses in middle school accounted for 46% of total studied courses, English accounted for 17%, and university major courses accounted for 21%. Furthermore, students spent 1-7 hours per day on watching TV and playing video games, and 1-4 hours on homework to review lessons. (2) After the end of the epidemic in China, more than 51% of students are still studying online for 1-4 hours a day, the epidemic situation has made online teaching in China popularized 10-20 years in advance, and students' online learning has become normal. (3) 32% of students like to study online, and they think that online class has the following advantages: numerous high-quality courseware that can be learned at any time anywhere, easy to communicate, save the time to go and from school, high learning efficiency, and online tutoring class charges are cheaper than offline ones. (4) The proportion of students who feel neutral and dislike the online study account for 56% and 9% respectively; they think online learning has the following problems: the online courses provided by schools are boring but they were forced to learn, and also have to clock in, which cannot bring the advantages of online education; the price of online tutoring course is very high; communication is not as easy as offline; the submission and correction of homework is more complicated than offline, and the learning effect is not good; students’ eyesight is decreased rapidly; online examination is not allowed. (5) 21% of parents are very supportive of online teaching, 62% of parents think it is acceptable, 17% of parents do not support or oppose, the reason for opposition is that their children do not have enough self-control, online learning effect is more difficult to ensure, eyesight loss is faster and so on. Therefore, the following countermeasures are put forward: (1) students are ought to be guided to pay attention to online learning; (2) to strengthen the reform of teaching methods, improve courseware quality, control teaching time, and leave students time for notes to ensure recess; (3) reduce video and broadcast courses, advocate live courses, strengthen the communication and interaction between teachers and students; (4) reform to simplify the online homework submission method, explore a reasonable online examination model; (5) strengthens the home-school cooperation, encourages the supervision function of parents, and strengthens the online teaching results. Article visualizations

Graph Force Learning

Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning. © 2020 IEEE

An Emerging Coding Paradigm VCM: A Scalable Coding Approach Beyond Feature and Signal

In this paper, we study a new problem arising from the emerging MPEG standardization effort Video Coding for Machine (VCM), which aims to bridge the gap between visual feature compression and classical video coding. VCM is committed to address the requirement of compact signal representation for both machine and human vision in a more or less scalable way. To this end, we make endeavors in leveraging the strength of predictive and generative models to support advanced compression techniques for both machine and human vision tasks simultaneously, in which visual features serve as a bridge to connect signal-level and task-level compact representations in a scalable manner. Specifically, we employ a conditional deep generation network to reconstruct video frames with the guidance of learned motion pattern. By learning to extract sparse motion pattern via a predictive model, the network elegantly leverages the feature representation to generate the appearance of to-be-coded frames via a generative model, relying on the appearance of the coded key frames. Meanwhile, the sparse motion pattern is compact and highly effective for high-level vision tasks, e.g. action recognition. Experimental results demonstrate that our method yields much better reconstruction quality compared with the traditional video codecs (0.0063 gain in SSIM), as well as state-of-the-art action recognition performance over highly compressed videos (9.4% gain in recognition accuracy), which showcases a promising paradigm of coding signal for both human and machine vision