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The whole idea of my work came from me questioning about the relationship between industrialization and human beings. Our generation look and think conforming to a public standard. When being asked question our answer is not based on our preferences, but whether it matches what we’ve been taught

Research on the Similarities between the Plot of Ji Chun Tai and Content of Sichuan Opera

Ji Chun Tai is the masterpiece of Sichuan dialect on late Qing Dynasty, composed of 40 vernacular short stories. It is divided into four parts, namely, Yuan Ji, Heng Ji, Li Ji, and Zhen Ji. Each part contains ten short stories. The author of Ji Chun Tai is a literator from Zhong Jiang who failed in imperial examination System in late Qing Dynasty. There are a large number of Sichuan Opera elements in those forty vernacular short stories. Generally speaking, the plot of Ji Chun Tai is full of ups and downs, together with relatively concentrated conflicts, which reflects the characteristics of Sichuan opera. Besides, the thought of persuasion and punishment, strong superstitious color, and detective story in Ji Chui Tai are combined together to reflect the characteristics of Sichuan Opera

Kinetin induces microtubular breakdown, cell cycle arrest and programmed cell death in tobacco BY-2 cells

Plant cells can undergo regulated cell death in response to exogenous factors (often in a stress context), but also as regular element of development (often regulated by phytohormones). The cellular aspects of these death responses differ, which implies that the early signalling must be different. We use cytokinin-induced programmed cell death as paradigm to get insight into the role of the cytoskeleton for the regulation of developmentally induced cell death, using tobacco BY-2 cells as experimental model. We show that this PCD in response to kinetin correlates with an arrest of the cell cycle, a deregulation of DNA replication, a loss of plasma membrane integrity, a subsequent permeabilisation of the nuclear envelope, an increase of cytosolic calcium correlated with calcium depletion in the culture medium, an increase of callose deposition and the loss of microtubule and actin integrity. We discuss these findings in the context of a working model, where kinetin, mediated by calcium, causes the breakdown of the cytoskeleton, which, either by release of executing proteins or by mitotic catastrophe, will result in PCD

Selective Differential Privacy for Language Modeling

With the increasing applications of language models, it has become crucial to protect these models from leaking private information. Previous work has attempted to tackle this challenge by training RNN-based language models with differential privacy guarantees. However, applying classical differential privacy to language models leads to poor model performance as the underlying privacy notion is over-pessimistic and provides undifferentiated protection for all tokens in the data. Given that the private information in natural language is sparse (for example, the bulk of an email might not carry personally identifiable information), we propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility. To realize such a new notion, we develop a corresponding privacy mechanism, Selective-DPSGD, for RNN-based language models. Besides language modeling, we also apply the method to a more concrete application--dialog systems. Experiments on both language modeling and dialog system building show that the proposed privacy-preserving mechanism achieves better utilities while remaining safe under various privacy attacks compared to the baselines. The data and code are released at https://github.com/wyshi/lm_privacy to facilitate future research .Comment: NAACL 202

Towards Efficient Data Valuation Based on the Shapley Value

"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets