The sole aim of this book is to give a self-contained introduction to concepts and mathematical tools in Bayesian matrix decomposition in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning Bayesian matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of variational inference for conducting the optimization. We refer the reader to literature in the field of Bayesian analysis for a more detailed introduction to the related fields. This book is primarily a summary of purpose, significance of important Bayesian matrix decomposition methods, e.g., real-valued decomposition, nonnegative matrix factorization, Bayesian interpolative decomposition, and the origin and complexity of the methods which shed light on their applications. The mathematical prerequisite is a first course in statistics and linear algebra. Other than this modest background, the development is self-contained, with rigorous proof provided throughout.

Policy optimization methods are popular reinforcement learning algorithms in practice. Recent works have built theoretical foundation for them by proving $\sqrt{T}$ regret bounds even when the losses are adversarial. Such bounds are tight in the worst case but often overly pessimistic. In this work, we show that in tabular Markov decision processes (MDPs), by properly designing the regularizer, the exploration bonus and the learning rates, one can achieve a more favorable polylog$(T)$ regret when the losses are stochastic, without sacrificing the worst-case guarantee in the adversarial regime. To our knowledge, this is also the first time a gap-dependent polylog$(T)$ regret bound is shown for policy optimization. Specifically, we achieve this by leveraging a Tsallis entropy or a Shannon entropy regularizer in the policy update. Then we show that under known transitions, we can further obtain a first-order regret bound in the adversarial regime by leveraging the log-barrier regularizer.

How do you incentivize self-interested agents to $\textit{explore}$ when they prefer to $\textit{exploit}$? We consider complex exploration problems, where each agent faces the same (but unknown) MDP. In contrast with traditional formulations of reinforcement learning, agents control the choice of policies, whereas an algorithm can only issue recommendations. However, the algorithm controls the flow of information, and can incentivize the agents to explore via information asymmetry. We design an algorithm which explores all reachable states in the MDP. We achieve provable guarantees similar to those for incentivizing exploration in static, stateless exploration problems studied previously. To the best of our knowledge, this is the first work to consider mechanism design in a stateful, reinforcement learning setting.

The use of AI/ML capabilities for improving HCI/UX work and delivering better UX in solutions is becoming a trend (Abbas et al., 2022; Wu et al., 2019; Nikiforova et al., 2021) and creates many new opportunities for HCI/UX professionals (Holmquist, 2017; Yang et al., 2020). Some even speculate "AI/ML is the new UX" (Yang et al., 2018). Researchers proposed that AI can perform as an assistant, collaborator, researcher, or facilitator (Bertão & Joo, 2021; Main & Grierson, 2020). AI technology will change the role of designers in the design process and generate an opportunity for creative collaboration between AI and designers (McCormack et al., 2020). Also, companies are moving fast to adopt AI for improving customer experience (CX).

Narrative is a ubiquitous component of human communication. Understanding its structure plays a critical role in a wide variety of applications, ranging from simple comparative analyses to enhanced narrative retrieval, comprehension, or reasoning capabilities. Prior research in narratology has highlighted the importance of studying the links between cognitive and linguistic aspects of narratives for effective comprehension. This interdependence is related to the textual semantics and mental language in narratives, referring to characters' motivations, feelings or emotions, and beliefs. However, this interdependence is hardly explored for modeling narratives. In this work, we propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state along with linguistic information at the syntactic and semantic levels. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. To this end, we implement a computational model that leverages the protagonist's mental state information obtained from a pre-trained model trained on social commonsense knowledge and integrates their representations with contextual semantic embed-dings using a multi-feature fusion approach. Evaluating against prior zero-shot and supervised baselines, we find that our model is able to achieve significant improvements in the task of identifying climax and resolution.

Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy.

If you're dating and haven't found any success on the endless dating apps in the App Store, you may want to consider joining the group of singles using Instagram as a replacement. DMing (direct messaging) on the social platform--with over 1 billion monthly users--isn't new, but young people are now using it, and Instagram's newer app features, to find better dating success than from traditional dating apps. These Instagram users are not alone– successful celebrity couples like Joe Jonas and Sophie Turner, and Simone Biles and Jonathan Owens, haven't shied away from the fact they've connected via the platform's DMs. Even Meghan Markle wouldn't have become royalty had her friend not posted an Instagram photo of Markle that caught the attention of her now-husband, Prince Harry. Shooting your shot in the DMs can seem daunting, so here are some tips for flirting and dating via Instagram, according to the Gen Z users finding success with it.

William Shatner, 'Star Trek' alum and author of'Boldly Go,' spoke to Fox News Digital about his decadeslong friendship with Leonard Nimoy, as well as his iconic on-screen kiss with Nichelle Nichols. "Star Trek" fans can bask in nostalgia, as the cast of the iconic science fiction series has reunited. After more than two decades, "Star Trek: Nemesis" actors, including Gates McFadden, LeVar Burton, Jonathan Frakes and Patrick Stewart, revealed the decision to reprise their famous roles and what it was like working together on the spacecraft again on "Star Trek: Picard." Stewart, who's known for his role as Jean Luc Picard in the "Star Trek" franchise, gave fans a preview of what they can expect in the current series. "Star Trek: Nemesis" actors, including, from left, Jonathan Frakes, Patrick Stewart, Gates McFadden, LeVar Burton and Michael Dorn, reprise their famous roles on "Star Trek: Picard."

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' The Bing artificially intelligent chatbot can do a lot – including insult its users. In a Wednesday blog post, Microsoft said that the search engine tool was responding to certain inquiries with a "style we didn't intend." Following testing in 169 countries, over the first seven days, the tech giant said that while feedback on answers generated by the new Bing has been mostly positive, there were also noted challenges with answers that need timely data. Microsoft noted that Bing can be repetitive or "be prompted/provoked to give responses that are not necessarily helpful or in line with our designed tone."

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' Billionaire Elon Musk took another swing at artificial intelligence service ChatGPT and the mainstream media on Thursday with a viral meme that accumulated over 254,000 likes on Twitter. Musk has emerged as a major critic of ChatGPT amid accusations that the artificial intelligence (AI) bot engages in liberal bias. The Tesla CEO and owner of Twitter shared a meme with the caption, "ChatGPT to the mainstream media." "Look at me," the meme read.