‘I’m ugly, but gentle’: performing ‘little character’ in post-Mao Chinese comedies

Stars are often associated with glamour and beauty, but in this paper I would like to question how the concept of “chou” (literally meaning ugliness) is embraced in contemporary Chinese cinema. The popularity of chouxing (ugly star) in the Chinese cinema since the late 1980s has challenged the star system in Chinese film industry during the previous decades when a male actor’s handsome appearance was regarded as an important criterion for him being cast as a leading man. Directing the public attention to a male star’s physical appearance by stressing the attributive adjective chou, this newly-coined word raises a question: how the cinematic emphasis on a male star’s physical appearance engages with the social construction of a star’s screen charisma under the transnational context? To answer the question, this article takes Ge You (b.1957) as a case study and explores the star’s impersonation of xiao renwu (little character) in Chinese comedies. I argue that the Chinese cinema’s emphasis of a chouxing’s physical appearance is a visual manifest of the character’s imperfectness and ordinariness. Nonetheless, despite the fact that the cinematic emphasis of the star’s unattractive appearance often signifies a little character’s unprivileged social status, it neither marginalises nor makes the character a social outsider. Instead, the imperfectness and ordinariness has endowed the little character with the power as an insider of the Chinese society

Fine-grained Image Classification by Exploring Bipartite-Graph Labels

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrates BGL advances previous works in fine-grained object recognition. An online demo is available at http://www.f-zhou.com/fg_demo/

Large deviations for two scale chemical kinetic processes

We formulate the large deviations for a class of two scale chemical kinetic processes motivated from biological applications. The result is successfully applied to treat a genetic switching model with positive feedbacks. The corresponding Hamiltonian is convex with respect to the momentum variable as a by-product of the large deviation theory. This property ensures its superiority in the rare event simulations compared with the result obtained by formal WKB asymptotics. The result is of general interest to understand the large deviations for multiscale problems

Two-scale large deviations for chemical reaction kinetics through second quantization path integral

Motivated by the study of rare events for a typical genetic switching model in systems biology, in this paper we aim to establish the general two-scale large deviations for chemical reaction systems. We build a formal approach to explicitly obtain the large deviation rate functionals for the considered two-scale processes based upon the second-quantization path integral technique. We get three important types of large deviation results when the underlying two times scales are in three different regimes. This is realized by singular perturbation analysis to the rate functionals obtained by path integral. We find that the three regimes possess the same deterministic mean-field limit but completely different chemical Langevin approximations. The obtained results are natural extensions of the classical large volume limit for chemical reactions. We also discuss its implication on the single-molecule Michaelis-Menten kinetics. Our framework and results can be applied to understand general multi-scale systems including diffusion processes

Entanglement renormalization and integral geometry

We revisit the applications of integral geometry in AdS$_3$ and argue that the metric of the kinematic space can be realized as the entanglement contour, which is defined as the additive entanglement density. From the renormalization of the entanglement contour, we can holographically understand the operations of disentangler and isometry in multi-scale entanglement renormalization ansatz. Furthermore, a renormalization group equation of the long-distance entanglement contour is then derived. We then generalize this integral geometric construction to higher dimensions and in particular demonstrate how it works in bulk space of homogeneity and isotropy.Comment: 40 pages, 7 figures. v2: discussions on the general measure added, typos fixed; v3: sections reorganized, various points clarified, to appear in JHE