Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact

Throughout history, a relatively small number of individuals have made a profound and lasting impact on science and society. Despite long-standing, multi-disciplinary interests in understanding careers of elite scientists, there have been limited attempts for a quantitative, career-level analysis. Here, we leverage a comprehensive dataset we assembled, allowing us to trace the entire career histories of nearly all Nobel laureates in physics, chemistry, and physiology or medicine over the past century. We find that, although Nobel laureates were energetic producers from the outset, producing works that garner unusually high impact, their careers before winning the prize follow relatively similar patterns as ordinary scientists, being characterized by hot streaks and increasing reliance on collaborations. We also uncovered notable variations along their careers, often associated with the Nobel prize, including shifting coauthorship structure in the prize-winning work, and a significant but temporary dip in the impact of work they produce after winning the Nobel. Together, these results document quantitative patterns governing the careers of scientific elites, offering an empirical basis for a deeper understanding of the hallmarks of exceptional careers in science

Lyapunov Functions in Piecewise Linear Systems: From Fixed Point to Limit Cycle

This paper provides a first example of constructing Lyapunov functions in a class of piecewise linear systems with limit cycles. The method of construction helps analyze and control complex oscillating systems through novel geometric means. Special attention is stressed upon a problem not formerly solved: to impose consistent boundary conditions on the Lyapunov function in each linear region. By successfully solving the problem, the authors construct continuous Lyapunov functions in the whole state space. It is further demonstrated that the Lyapunov functions constructed explain for the different bifurcations leading to the emergence of limit cycle oscillation

A Data Preprocessing Algorithm for Classification Model Based On Rough Sets

AbstractAimed to solve the limitation of abundant data to constructing classification modeling in data mining, the paper proposed a novel effective preprocessing algorithm based on rough sets. Firstly, we construct the relation Information System using original data sets. Secondly, make use of attribute reduction theory of Rough sets to produce the Core of Information System. Core is the most important and necessary information which cannot reduce in original Information System. So it can get a same effect as original data sets to data analysis, and can construct classification modeling using it. Thirdly, construct indiscernibility matrix using reduced Information System, and finally, get the classification of original data sets. Compared to existing techniques, the developed algorithm enjoy following advantages: (1) avoiding the abundant data in follow-up data processing, and (2) avoiding large amount of computation in whole data mining process. (3) The results become more effective because of introducing the attributes reducing theory of Rough Sets

Is pension insurance a barrier to entrepreneurship? New evidence from China

This article provides evidence of the impact of pension insurance on entrepreneurship. It uses recent, nationally representative sample data from the Chinese General Social Survey (2013). We use a probit regression model to investigate whether the pension insurance converge rate affects the probability of a person becoming an entrepreneur. We find that the presence of both basic pension and business pension insurance reduce individual entrepreneurial probability. We also find that the two types of pension insurance do not appear to increase entrepreneurship among any particular subgroup, based on geo graphical regions, gender, education, social connection or marital status. Moreover, we argue that the basic pension and business pension insurance actually have a negative effect on the probability of small business entrepreneurship. Even, we have found there seems to be one important exception to this general pattern. For, most importantly, basic pension and business pension insurance have a positive effect on the probability of one particular kind of entrepreneurship: Innovation-driven entrepreneurship. Exploring possible mechanisms, we find that the important transmission channels through which pension insurance affects business creation is the lack of security and total family income

Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202

L2L_2BN: Enhancing Batch Normalization by Equalizing the L2L_2 Norms of Features

In this paper, we show that the difference in $l_2$ norms of sample features can hinder batch normalization from obtaining more distinguished inter-class features and more compact intra-class features. To address this issue, we propose an intuitive but effective method to equalize the $l_2$ norms of sample features. Concretely, we $l_2$-normalize each sample feature before feeding them into batch normalization, and therefore the features are of the same magnitude. Since the proposed method combines the $l_2$ normalization and batch normalization, we name our method $L_2$BN. The $L_2$BN can strengthen the compactness of intra-class features and enlarge the discrepancy of inter-class features. The $L_2$BN is easy to implement and can exert its effect without any additional parameters or hyper-parameters. Therefore, it can be used as a basic normalization method for neural networks. We evaluate the effectiveness of $L_2$BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The results demonstrate that the $L_2$BN can boost the generalization ability of various neural network models and achieve considerable performance improvements