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

Dynamic design and analysis of subsea CO2 discharging flowline for cargo submarines used for CCS in low-carbon and renewable energy value chains

Developing offshore low carbon and renewable energy value chains to realize a net-zero energy future requires combining offshore renewable energy and carbon capture storage (CCS) solutions. The subsea shuttle tanker (SST) was presented in recently published works to accelerate the adoption of offshore CCS systems. The SST is a novel underwater vessel designed to transport CO2 autonomously from offshore facilities to subsea wells for direct injection at marginal fields using a flowline connected. The SST will be subjected to stochastic currents and experience dynamic responses during this offloading process. The offloading flowline must be designed to handle this dynamic response. As such, this paper establishes the baseline design for this flowline. The cross-section and global configuration designs drive the flowline design. For the cross-section design, the pressure containment, collapse and local buckling criteria defined in DNV-OS-F101 are applied to validate the required structural capacity at specified water depths. For the configuration design, the principle factors concerning the water depth, internal flow rate, and current speed are investigated to further validate the stress capacity according to the allowed von Mises stress level for a more robust baseline design. Finally, the flowline connecting and disassembly methodology is proposed, and the critical factor of well-coordinated speed between flowline and SST is investigated to avoid overbending during the lifting and lowering phases.publishedVersio

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

Optimal Resource Allocation for Multi-UAV Assisted Visible Light Communication

In this paper, the optimization of deploying unmanned aerial vehicles (UAVs) over a reconfigurable intelligent surfaces (RISs)-assisted visible light communication (VLC) system is studied. In the considered model, UAVs are required to simultaneously provide wireless services as well as illumination for ground users. To meet the traffic and illumination demands of the ground users while minimizing the energy consumption of the UAVs, one must optimize UAV deployment, phase shift of RISs, user association and RIS association. This problem is formulated as an optimization problem whose goal is to minimize the transmit power of UAVs via adjusting UAV deployment, phase shift of RISs, user association and RIS association. To solve this problem, the original optimization problem is divided into four subproblems and an alternating algorithm is proposed. Specifically, phases alignment method and semidefinite program (SDP) algorithm are proposed to optimize the phase shift of RISs. Then, the UAV deployment optimization is solved by the successive convex approximation (SCA) algorithm. Since the problems of user association and RIS association are integer programming, the fraction relaxation method is adopted before using dual method to find the optimal solution. For simplicity, a greedy algorithm is proposed as an alternative to optimize RIS association. The proposed two schemes demonstrate the superior performance of 34:85% and 32:11% energy consumption reduction over the case without RIS, respectively, through extensive numerical study

Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac

Design and Control of the "TransBoat": A Transformable Unmanned Surface Vehicle for Overwater Construction

This paper presents the TransBoat, a novel omnidirectional unmanned surface vehicle (USV) with a magnetbased docking system for overwater construction with wave disturbances. This is the first such USV that can build overwater structures by transporting modules. The TransBoat incorporates two features designed to reject wave disturbances. First, the TransBoat's expandable body structure can actively transform from a mono-hull into a multi-hull for stabilization in turbulent environments by extending its four outrigger hulls. Second, a real-time nonlinear model predictive control (NMPC) scheme is proposed for all shapes of the TransBoat to enhance its maneuverability and resist disturbance to its movement, based on a nonlinear dynamic model. An experimental approach is proposed to identify the parameters of the dynamic model, and a subsequent trajectory tracking test validates the dynamics, NMPC controller and system mobility. Further, docking experiments identify improved performance in the expanded form of the TransBoat compared with the contracted form, including an increased success rate (of ~ 10%) and reduced docking time (of ~ 40 s on average). Finally, a bridge construction test verifies our system design and the NMPC control method