Foreword

In this paper, we show that the consistency of closed-loop subspace identification methods (SIMs) can be achieved through innovation estimation. Based on this analysis, a sufficient condition for the consistency of a new proposed closed-loop SIM is given, A consistent estimate of the Kalman gain under closed-loop conditions is also provided based on the algorithm. A multi-input-multi-output simulation shows that itis consistent under closed-loop conditions, when traditional SIMs fail to provide consistent estimates

The Forgotten History: Textbook Controversy and Sino-Japanese Relations

History plays an important role in shaping the relations between Japan and China. Because Japan�s military expansionism during 1931-1945 has left a deep scar in the memories of the Chinese population, the issue of history remains at the core of Sino-Japanese diplomacy. Since the 1980s, the Chinese government has consistently accused the Japanese government of revising and obscuring Japan�s wartime history, notably that of the Japanese military aggression in China during 1931-1945. China�s reaction against the Japanese government�s whitewashing of history demonstrates the fear that, by rendering Japanese youths oblivious of their nation�s militarist past, Japan may repeat its past. While diplomatic negotiations to improve Sino-Japanese relations have taken place, disagreement over historical interpretation continues to fuel the discontent between the two countries. To better understand the dynamics of the Sino-Japanese relations, the research investigates the origins and nature of the textbook controversy by discussing how the controversy came about and how each government responded to the issue. In addition, the analysis of ultranationalist movement in Japan allows us to understand the public reaction to the controversy as well as its political repercussions. I also explore the Franco-German case of postwar reconciliation and development of preventive institutions. By comparing the postwar experience of China and Japan to that of Europe, we can gain an insight about the creative ways of constructing a common history between historically hostile nations. Finally, the assessment of Japanese leadership since 2000 enables us to evaluate the future development surrounding the problem of history and its impact on bilateral relations

Human vs. machine as message source in advertising: examining the persuasiveness of brand influencer type and the mediating role of source credibility for advertising effectiveness in social media advertising

[EMBARGOED UNTIL 5/31/2023] Message source effects on persuasion of target audiences have been examined for decades by scholars in advertising, consumer behavior, communication, and psychology, among others. Myriads of studies are available on the subject, but in nearly every instance "source" is defined as a human and rarely is source defined as non-human, or machine. The rise of artificial intelligence (AI) as a message source urges scientific inquiry of the validity of those established theories in a new technology context. The focus of this dissertation is on that of the machine as source. By "machine" this study refers broadly to AI agents, defined to mean digitally created artificial beings that can think and perform tasks like a human. The specific AI agent examined here is that of SMIs, defined as AI agents who are associated with fame and perform human tasks using software and algorithms. The context of the study is social media, defined as "digital networked tools or technologies that enable communication, collaboration, and creative expression across social networks" (McMillan and Childers, 2017, p. 52). Influencer marketing is a crucial component of social media marketing, which is projected to become a $10 billion market by 2023 (Tan, 2019). The primary contribution of this study is, therefore, to understand SMIs' effectiveness in social media advertising. Considering that 95 percent of consumer interaction is projected to be powered by AI by the year of 2025 (Finance Digest, 2020), research to understand the impact of this transformation of message source (from human to machine) is urgently needed but rarely conducted to date. The most apparent machine sources, SMIs, are already being put to use in practice without fully understanding their effectiveness and risk, to replace human influencers. Human influencers, here, specifically refer to social media influencers (SMIs) "who have built a sizable social network of people following them and are seen as self-made microcelebrities" (Shan et al., 2020, p. 2). Indeed, this very notion is reflected in the warnings of the infamous physicist Stephen Hawking who predicted that, someday, machines may even replace humans. As much as this may sound like a futuristic movie, machines are beginning to replace humans in fields as far and wide as medicine, engineering, and transportation. For example, AI is cleaning floors at airports, taking people's temperatures, and even making salad in hospital dining-halls in response to the coronavirus pandemic (Semuels, 2020). In advertising, AI is taking over the work humans have traditionally done, from content matching to advertising creation (Rodgers, 2021). An SMI, Lil Miquela, even takes the spot that is usually reserved for a human and is named as one of Time's 25 most influential people on the internet in 2018 (Time, 2018). The proposed research is supported by survey results from consumers worldwide that paint a mixed picture --some consumers embrace AI for its potential benefits, whereas others fear that AI will hurt their privacy and ability to control their jobs, lives, and futures (Zhang and Dafoe, 2019), suggesting potential drawbacks of AI technology. This suggests that SMIs could trigger various perceptions among consumers that may lead to different outcomes. The challenge is to know the underlying psychological mechanisms to explain potential positive/negative outcomes, yet studies on the subject are rare but urgently needed. This dissertation investigates this phenomenon, specifically, potential benefits and drawbacks of using SMIs compared to (human) SMIs in social media advertising. Based on established theories of persuasion on advertising and brand endorsers, this dissertation identifies a crucial processing mechanism - source credibility - that is used to explain instances under which influencer type (i.e., AI vs. human) differentially influences advertising outcomes (i.e., attitude toward the advertisement, attitude toward brand, and purchase intentions). Source credibility is the source's truthfulness and believability perceived by the consumers (Roy et al., 2017). The treatment of source credibility as a mediating factor is a unique aspect of the research and that diverges from prior approaches that treat it as a predictor of persuasion. Rather, this research conceptualizes source credibility as dynamic, constantly changing, and not related in a simple way to the persuasiveness of an influencer type. Three dimensions of source credibility - expertise, trustworthiness, and physical attractiveness - are proposed to explain how an AI influencer may perform better/worse than a (human) SMI on advertising outcomes. A review of five decades' of source credibility studies noted several discrepancies regarding three dimensions of source credibility, suggesting gaps or unresolved issues in source credibility that deserve attention (see Pornpitakpan, 2004). Additionally, initial negative/positive dispositions toward the brand influencers, which has hardly been examined (see Pornpitakpan, 2004), will be measured in an effort to assess how much, if any, of a shift is detected in source credibility perceptions. To summarize, this research examines effectiveness of influencer type (artificial intelligence influencer vs. SMI) on persuasion of social media advertising (attitude toward the ad, attitude toward the brand, and purchase intentions). One mediator, source credibility, is proposed to explain the results. This is accomplished with a pilot study and an experimental study conducted in an online setting.Includes bibliographical references

Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture

Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed. With the considerable benefits in computation speed and energy efficiency, there are significant interests in leveraging ONNs into medical sensing, security screening, drug detection, and autonomous driving. However, due to the challenge of implementing reconfigurability, deploying multi-task learning (MTL) algorithms on ONNs requires re-building and duplicating the physical diffractive systems, which significantly degrades the energy and cost efficiency in practical application scenarios. This work presents a novel ONNs architecture, namely, \textit{RubikONNs}, which utilizes the physical properties of optical systems to encode multiple feed-forward functions by physically rotating the hardware similarly to rotating a \textit{Rubik's Cube}. To optimize MTL performance on RubikONNs, two domain-specific physics-aware training algorithms \textit{RotAgg} and \textit{RotSeq} are proposed. Our experimental results demonstrate more than 4$\times$ improvements in energy and cost efficiency with marginal accuracy degradation compared to the state-of-the-art approaches.Comment: To appear at 32nd International Joint Conference on Artificial Intelligence (IJCAI'23