Wang Xitian and the Chinese Experience in Imperial Tokyo, 1899-1923: Class, Violence, and the Formation of a New National Consciousness

A 2021-2022 Williams Prize for best essay in East Asian Studies was awarded to Isabella Yang (Saybrook ‘22) for her essay submitted to the Department of History, Wang Xitian and the Chinese Experience in Imperial Tokyo, 1899-1923: Class, Violence, and the Formation of a New National Consciousness” (Daniel Botsman, Professor of History, advisor). Drawing upon a remarkable array of sources in Japanese, Chinese and English, Isabella Yang, in her thesis “Wang Xitian and the Chinese Experience in Imperial Tokyo, 1899-1923: Class, Violence, and the Formation of a New National Consciousness,” has crafted a genuinely path-breaking account of an aspect of Tokyo\u27s pre-war history that has been almost entirely neglected in English: the experience of Chinese students and workers in the city in the early decades of the 20th century. Although the essay begins and finishes with a focus on one extraordinary individual, a Chinese student and activist, named Wang Xitian, who was brutally murdered by Japanese soldiers in the aftermath of the Great Kantō Earthquake of 1923, it offers far more than a simple biography. It begins by tracing the development of the now almost entirely forgotten Kanda Chinatown that formed in the center of Tokyo in the first years of the 20th century, as elite Chinese students began to flock to Japan to take advantage of opportunities to pursue higher education. That Chinese students, especially famous writers such as Lu Xun, came to Japan in this period is, of course, well known, but Yang’s contribution here is to ground the experience of those students in the history of Tokyo as a city, paying close attention to the specific neighborhoods where they studied and lived. The second part of the essay, which is even more impressive in its research, explores the development of a very different kind of Chinese community, one formed by poor laborers from Zhejiang in the Oshima-machi neighborhood of Eastern Tokyo in the years after World War I. Needless to say, this group is much less well documented than the elite students, but Yang was able to locate published collections of primary documents in both Japanese and Chinese to explore the history of this community, and she also scoured collections of pre-war Japanese newspapers to trace a series of police crack downs that targeted working-class Chinese migrants in the city in the years leading up to the 1923 earthquake. She then discusses how, in the aftermath of the earthquake, the Chinese residents of Oshima-machi became targets of a little-known massacre. Telling the story of this reprehensible moment in modem Japanese history is an important contribution in itself. But to her credit, Yang is not only interested in exposing the facts of the Oshima-machi massacre and Wang\u27s murder. She also builds a compelling argument about how Wang’s life, and activities as a social worker, show how the growing nationalist consciousness that took root among Chinese students in Tokyo, in some cases also created links across the class divide that, at an earlier point, would have separated the elite students of Kanda from the poorer migrant workers in Oshima-machi. Yang’s work is not only path-breaking, but it is also compellingly written and organized, with helpful maps to assist the reader navigate the relevant geographies of both Tokyo and Zhejiang. In short, it is a truly remarkable thesis in all respects

Tuning for robust and optimal dynamic positioning control in BlueROV2

A tuning approach for the robust and optimal dynamic positioning control of BlueROV2 subjected to currents with varying speeds and headings is presented. A 2D planar dynamic model of BlueROV2 is developed in Matlab/Simulink and used for the study. The surge, sway and yaw motions are controlled by individual PID controllers. An extensive sensitivity study is carried out on a total of nine cases with different current speeds, current headings, and measurement noise levels. The results show that tuning a model solely using step responses from a linearized model might not produce optimal results. Further it is important to verify the system responses in time domain after tuning. Finally, it is observed that re-tuning the controllers for each simulation case may lead to better performance. However, it is also shown that the base case controller gains are sufficiently robust and lead to good performances for the other simulation cases.publishedVersio

Reasons for Teenagers’ Habitual Use of Social Media: A Case Study of TikTok

With the development of Internet and 5G, new social media has been constantly developing and updating. People are getting more and more used to get information from social media. People’s lives have been filled with applications, such as TikTok and Instagram. These applications not only bring much fun and convenience to people, but also make it possible for people’s fragmented time to be used wisely. However, at the same time, many people, especially teenagers with poor self-control, would easily be overdependent on the social media. As one of the most famous social media at present, with the help of big data, TikTok has successfully made some teenagers seriously depend on its platform by inferring the users’ minds and accurately showing them the content they demand. This paper takes TikTok as a case study and teenagers as the research object to analysis the reasons why teenagers use social media habitually, and provide some reasonable solutions to reduce teenagers’ media dependency. In short, teenagers get addicted to TikTok primarily because of their self-control is not strong enough so that they fell into the trap of TikTok. TikTok and other social media use big data to predict users’ preference, and take advantage of users’ psychology to make teenagers get addicted to social media without realizing. In order to get rid of the traps, network supervision departments should strengthen the management, TikTok and other social media should recommend more useful short videos to teenagers, and teenagers themselves can also take advantage of big data

A bibliometric analysis on the spillover effect of green consumption behavior

The research employs a bibliometric analysis approach to assess the existing landscape of the spillover effects literature associated with green consumption behavior, utilizing a sample of 176 documents sourced from the Web of Science database. Through document co-citation analysis, the knowledge structure is systematically organized and clarified, leading to the development of a framework based on an antecedent-behavior-consequence logic. This framework highlights the dynamic nature of green consumption behavior, ultimately aiming to provide theoretical support and practical guidance for researchers and practitioners

An Efficient Inference Framework for Early-exit Large Language Models

Building efficient inference framework has gained increasing interests for research community. Early-exit models, a variant of LLMs, improves the inference efficiency of LLMs by skipping rest layers and directly generate output tokens when they are confident enough. However, there is no work of LLM inference framework that takes early-exit models into consideration. This is non-trivial as prior art on LLM inference cannot be directly applied to early-exit models. In this work, we solves two key challenges in building efficient inference framework for early-exit models: (1) batch inference at iteration-level granularity; and (2) KV cache management. For the former, we propose to process the batch until all sequences surpass the early-exit confidence threshold. For the latter, we propose to fill the KV cache of rest layers before the iteration terminates. Our evaluation shows that, compared with the original vLLM operating at full layers, our solution achieves up to 1.25x speed up

Spatial enhancement due to statistical learning tracks the estimated spatial probability

It is well known that attentional selection is sensitive to the regularities presented in the display. In the current study we employed the additional singleton paradigm and systematically manipulated the probability that the target would be presented in one particular location within the display (probabilities of 30%, 40%, 50%, 60%, 70%, 80%, and 90%). The results showed the higher the target probability, the larger the performance benefit for high- relative to low-probability locations both when a distractor was present and when it was absent. We also showed that when the difference between high- and low-probability conditions was relatively small (30%) participants were not able to learn the contingencies. The distractor presented at a highprobability target location caused more interference than when presented at a low-probability target location. Overall, the results suggest that attentional biases are optimized to the regularities presented in the display tracking the experienced probabilities of the locations that were most likely to contain a target. We argue that this effect is not strategic in nature nor the result of repetition priming. Instead, we assume that through statistical learning the weights within the spatial priority map are adjusted optimally, generating the efficient selection priorities.info:eu-repo/semantics/publishedVersio