655 research outputs found
The Rise of Technocratic Leadership in the 1990s in the People’s Republic of China
The transformation of China’s political elite provides important insights into the nation’s political metamorphosis and the changes in cadre selection criteria. The current literature explains the composition of Chinese political elites by referencing cross-sectional biographic data and describing how the revolutionary veterans stepped down and were replaced by the technocrats who emerged in the 1980s and 1990s. However, explanations for the rise of the technocrats have largely been limited to socioeconomic factors. By analyzing the longitudinal data of Chinese provincial leaders during the period of 1990–2013, this article shows the rise of technocrats in Chinese politics in the 1990s but also provides an explanation for it from the perspectives of individuals’ career paths and the contemporaneous political and policy landscapes. These explanations were drawn from analyses of the expansion of higher education and faculty restructuring in the 1950s, graduate job assignments, the recruitment and promotion of young and middle-aged cadres, and the cadre policy known as the Four Modernizations of the early 1980s. This article presents the interactions among individuals’ career opportunities, group composition characteristics, and socioeconomic and macropolitical dynamics. It also reveals how the Chinese Communist Party legitimizes its ruling power and maintains state capacity and political order through elite recruitment
Towards Optimal Discrete Online Hashing with Balanced Similarity
When facing large-scale image datasets, online hashing serves as a promising
solution for online retrieval and prediction tasks. It encodes the online
streaming data into compact binary codes, and simultaneously updates the hash
functions to renew codes of the existing dataset. To this end, the existing
methods update hash functions solely based on the new data batch, without
investigating the correlation between such new data and the existing dataset.
In addition, existing works update the hash functions using a relaxation
process in its corresponding approximated continuous space. And it remains as
an open problem to directly apply discrete optimizations in online hashing. In
this paper, we propose a novel supervised online hashing method, termed
Balanced Similarity for Online Discrete Hashing (BSODH), to solve the above
problems in a unified framework. BSODH employs a well-designed hashing
algorithm to preserve the similarity between the streaming data and the
existing dataset via an asymmetric graph regularization. We further identify
the "data-imbalance" problem brought by the constructed asymmetric graph, which
restricts the application of discrete optimization in our problem. Therefore, a
novel balanced similarity is further proposed, which uses two equilibrium
factors to balance the similar and dissimilar weights and eventually enables
the usage of discrete optimizations. Extensive experiments conducted on three
widely-used benchmarks demonstrate the advantages of the proposed method over
the state-of-the-art methods.Comment: 8 pages, 11 figures, conferenc
The Proximal Operator of the Piece-wise Exponential Function and Its Application in Compressed Sensing
This paper characterizes the proximal operator of the piece-wise exponential
function with a given shape parameter ,
which is a popular nonconvex surrogate of -norm in support vector
machines, zero-one programming problems, and compressed sensing, etc. Although
Malek-Mohammadi et al. [IEEE Transactions on Signal Processing,
64(21):5657--5671, 2016] once worked on this problem, the expressions they
derived were regrettably inaccurate. In a sense, it was lacking a case. Using
the Lambert W function and an extensive study of the piece-wise exponential
function, we have rectified the formulation of the proximal operator of the
piece-wise exponential function in light of their work. We have also undertaken
a thorough analysis of this operator. Finally, as an application in compressed
sensing, an iterative shrinkage and thresholding algorithm (ISTA) for the
piece-wise exponential function regularization problem is developed and fully
investigated. A comparative study of ISTA with nine popular non-convex
penalties in compressed sensing demonstrates the advantage of the piece-wise
exponential penalty
On Choosing Initial Values of Iteratively Reweighted Algorithms for the Piece-wise Exponential Penalty
Computing the proximal operator of the sparsity-promoting piece-wise
exponential (PiE) penalty with a given shape parameter
, which is treated as a popular nonconvex surrogate of -norm,
is fundamental in feature selection via support vector machines, image
reconstruction, zero-one programming problems, compressed sensing, etc. Due to
the nonconvexity of PiE, for a long time, its proximal operator is frequently
evaluated via an iteratively reweighted algorithm, which substitutes
PiE with its first-order approximation, however, the obtained solutions only
are the critical point. Based on the exact characterization of the proximal
operator of PiE, we explore how the iteratively reweighted solution
deviates from the true proximal operator in certain regions, which can be
explicitly identified in terms of , the initial value and the
regularization parameter in the definition of the proximal operator. Moreover,
the initial value can be adaptively and simply chosen to ensure that the
iteratively reweighted solution belongs to the proximal operator of
PiE
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