Chinese Narratives of "National Humiliation" and Japan's Role in the Construction of China's National Identity, 1915 to the early 2000s

The sense of national humiliation in China derives from a huge psychological gap between a glorious Empire and a peripheral nation-state which invaded by foreign imperialists in the 19th century and the early 20th century. This gap let Chinese people tend to define the period from 1840s to 1940s as the “Century of National Humiliation”. Although, Chinese people suffer a lot during this “Century”, direct experience and the meaning attached it are not the same. Apart from history itself, this thesis argues that narratives of national humiliation are significant in constructing China’s national identity. In this sense, it will focus on China’s humiliation narratives in different periods, and try to find out what kind of role Japan plays in the construction of China’s national identity. In the first place, this thesis will focus on the narratives of humiliation/victim in different periods of China since its popularization in 1915, and try to give a comprehensive picture of the origin and evolution of this narrative. More specifically, it will examine Chinese humiliation narratives in the following three main periods chronologically: the origins and evolution of “national humiliation” in the pre-1949 era, the absence of “national humiliation” from the 1950s to the 1980s, and the reinvention of “national humiliation” in the post-1989 era. It argues that the narratives of the national past help construct China’s identities in different periods with different meanings. In the second place, this thesis examines not only the discourse of humiliation per se, but also the role that Japan assumes in both victim narratives and the none-victim narratives, and will utilize a social “self/other” approach to analyze Japan’s role in the construction of Chinese national identity. Overall, looking back on Chinese humiliation narratives in three main periods, this thesis concludes that China's national humiliation discourse is an integral part of the shaping of national identity and Japan plays an important role in this process. It also finds out that there is no certain consistency in the interpretations of the national humiliation throughout the last 100 years in China. The national humiliation discourse had once disappeared in China during Mao’s era from 1950s to 1980s. However, whether humiliation discourse dominants Chinese civil society or not, the ruling governments always play an essential role in shaping the nation’s identity. Besides, Japan has been an indispensable “other” in China’s construction of national identity. The popularization and intensification of humiliation discourse in China have always associated with anti-Japanese sentiments. Therefore, in Chinese context, Japan always assumes the role as an “enemy” when the humiliation/victim narrative dominates the civil society

Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc

LED receiver impedance and its effects on LED-LED visible light communications

This paper experimentally demonstrates that the AC impedance spectrum of the LED as a photodetector heavily depends on the received optical power, which may cause the impedance mismatch between the LED and the post trans-impedance amplifier. The optical power dependent impedance of the LED is well fitted by a modified dispersive carrier transport model for inorganic semiconductors. The bandwidth of the LED-LED visible light communication link is further shown to decrease with the optical power received by the LED. This leads to a trade-off between link bandwidth and SNR, and consequently affects the choice of the proper dada modulation scheme.Comment: 9 pages, 9 figures, submitted to Optics Expres

Energy-recycling Blockchain with Proof-of-Deep-Learning

An enormous amount of energy is wasted in Proofof-Work (PoW) mechanisms adopted by popular blockchain applications (e.g., PoW-based cryptocurrencies), because miners must conduct a large amount of computation. Owing to this, one serious rising concern is that the energy waste not only dilutes the value of the blockchain but also hinders its further application. In this paper, we propose a novel blockchain design that fully recycles the energy required for facilitating and maintaining it, which is re-invested to the computation of deep learning. We realize this by proposing Proof-of-Deep-Learning (PoDL) such that a valid proof for a new block can be generated if and only if a proper deep learning model is produced. We present a proof-of-concept design of PoDL that is compatible with the majority of the cryptocurrencies that are based on hash-based PoW mechanisms. Our benchmark and simulation results show that the proposed design is feasible for various popular cryptocurrencies such as Bitcoin, Bitcoin Cash, and Litecoin.Comment: 5 page

A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)

The alignment of heterogeneous sequential data (video to text) is an important and challenging problem. Standard techniques for this task, including Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from inherent drawbacks. Mainly, the Markov assumption implies that, given the immediate past, future alignment decisions are independent of further history. The separation between similarity computation and alignment decision also prevents end-to-end training. In this paper, we propose an end-to-end neural architecture where alignment actions are implemented as moving data between stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture supports a large variety of alignment tasks, including one-to-one, one-to-many, skipping unmatched elements, and (with extensions) non-monotonic alignment. Extensive experiments on semi-synthetic and real datasets show that our algorithm outperforms state-of-the-art baselines.Comment: Accepted at CVPR 2018 (Spotlight). arXiv file includes the paper and the supplemental materia

Understanding Actors and Evaluating Personae with Gaussian Embeddings

Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for embedding actors, movies, character roles, genres, and descriptive keywords as Gaussian distributions and translation vectors, where the Gaussian variance corresponds to actors' versatility. Empirical results indicate that (1) the technique considerably outperforms TransE (Bordes et al. 2013) and ablation baselines and (2) automatically identified persona topics (Bamman, O'Connor, and Smith 2013) yield statistically significant improvements in both tasks, whereas simplistic persona descriptors including age and gender perform inconsistently, validating prior research.Comment: Accepted at AAAI 201