Research and development in Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft Systems (UAS) has witnessed unprecedented scientific and commercial interest and growth, particularly during the last two decades. Although military applications dominated the global market for years, interest in using UAVs in civil and public domains increases exponentially, worldwide, albeit challenges related to integrating unmanned aviation into the national airspace. Sample applications include, but are not limited to, surveillance [1], search and rescue [2], aerial photography [3], fire monitoring [4], agriculture [5], and aerial delivery [6]. The listed applications refer to solely passive tasks, that is, tasks in which no UAV interaction with the environment is needed. However, contact with the environment is required in industrial and maintenance applications like bridge inspection, water damn inspection, high-voltage transmission line inspection [7], assembly tasks [8] or construction [9].

In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes. To broaden the applicability of image retrieval methods for diverse purposes and uncover more general patterns, we innovatively introduce a crucial factor from computational aesthetics, namely image composition, into this topic. By explicitly integrating composition-related information extracted by CNN into the designed retrieval model, our method considers both the image's composition rules and semantic information. Qualitative and quantitative experiments demonstrate that the image retrieval network guided by composition information outperforms those relying solely on content information, facilitating the identification of images in databases closer to the target image in human perception.

Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-item interaction matrices has limitations, especially in scenarios where there is a lack of interaction data for new items. Knowledge graph (KG)-based recommendation systems have emerged as a promising solution. However, most KG-based methods adopt node embeddings, which do not provide personalized recommendations for different users and cannot generalize well to the new items. To address these limitations, we propose Knowledge-enhanced User-Centric subgraph Network (KUCNet), a subgraph learning approach with graph neural network (GNN) for effective recommendation. KUCNet constructs a U-I subgraph for each user-item pair that captures both the historical information of user-item interactions and the side information provided in KG. An attention-based GNN is designed to encode the U-I subgraphs for recommendation. Considering efficiency, the pruned user-centric computation graph is further introduced such that multiple U-I subgraphs can be simultaneously computed and that the size can be pruned by Personalized PageRank. Our proposed method achieves accurate, efficient, and interpretable recommendations especially for new items. Experimental results demonstrate the superiority of KUCNet over state-of-the-art KG-based and collaborative filtering (CF)-based methods.

Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-generated contents, and biases within the context of social media analysis (analyzing works that use the tools offered by complex network analysis and natural language processing) through the lens of three key points of view: online debates, online support, and human-AI interactions. On the one hand, we delineate the phenomenon of online debates, where polarization, misinformation, and echo chamber formation often proliferate, driven by algorithmic biases and extreme mechanisms of homophily. On the other hand, we explore the emergence of online support groups through users' self-disclosure and social support mechanisms. Online debates and support mechanisms present a duality of both perils and possibilities within social media; perils of segregated communities and polarized debates, and possibilities of empathy narratives and self-help groups. This dichotomy also extends to a third perspective: users' reliance on AI-generated content, such as the ones produced by Large Language Models, which can manifest both human biases hidden in training sets and non-human biases that emerge from their artificial neural architectures. Analyzing interdisciplinary approaches, we aim to deepen the understanding of the complex interplay between social interactions, user-generated content, and biases within the realm of social media ecosystems.

This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unobserved latent factors; the other part models the effects of the observed covariates through a coefficient matrix which is composed of high-dimensional column vectors. We model the observational pattern of the responses through a logistic regression of the covariates, and allow its probability to go to zero as the sample size increases. We apply an iterative least squares (LS) estimation approach in our considered context. The iterative LS methods in general enjoy a low computational cost, but deriving the statistical properties of the resulting estimators is a challenging task. We show that our method only needs a few iterations, and the resulting entry-wise estimators of the low-rank matrix and the coefficient matrix are guaranteed to have asymptotic normal distributions. As a result, individual inference can be conducted for each entry of the unknown matrices. We also propose a simultaneous testing procedure with multiplier bootstrap for the high-dimensional coefficient matrix. This simultaneous inferential tool can help us further investigate the effects of covariates for the prediction of missing entries.

Reddit will enter a new era as a publicly traded company with a market value of 6.4bn after the social media platform's initial public offering was priced at 34 per share. The price, announced late on Wednesday, came in at the top of the target range set by Reddit's investment bankers as they spent the past few weeks gauging investor demand for the stock. It sets the stage for Reddit's shares to begin trading Thursday on the New York stock exchange under the ticker symbol RDDT in the largest initial public offering by a social media company in years. The platform, which is hoping to raise 748m, is set to sell 22m shares. The company's latest 6.4bn valuation is a drop from 2021, when it was valued at 10bn during a private funding round.

Willonius Hatcher was looking for a way in. He'd tried just about everything to break into Hollywood, and because there no longer exists a traditional entry point into its hallowed pantheon of performers--we can thank the internet for doing away with all notions of conventional success--the pursuit of it sometimes felt like a mirage. He could see it, and he knew he could get there because he believed in his talent, only the closer he got the farther the door seemed. He'd done the stand-up circuit, short film work, sketches, even video editing. None of them got him fully in the door.

'Seen and Unseen': Fox News' Raymond Arroyo has the latest on President Biden's dog Commander and the controversy surrounding Google's A.I.-generated historical images on'The Ingraham Angle.' BEHIND THE CURTAIN: Google abandoned "fairness" and took major "shortcuts" to launch the Gemini artificial intelligence chatbot despite internal concerns, according to a former high-level employee. IN THE WORKS: Apple is in talks with Google to use its new Gemini artificial intelligence models to power the AI features for iPhones after previously discussing the prospect with ChatGPT maker OpenAI, according to a new report. 'CRAZY AND WEIRD': LSU women's basketball star Angel Reese took to social media Monday to call out those allegedly creating AI-generated photos of the college basketball player. Angel Reese #10 of the LSU Lady Tigers looks on against the Tennessee Lady Vols in the first quarter at Thompson-Boling Arena on February 25, 2024 in Knoxville, Tennessee. LAWN BEAST: Imagine a future where the hum of lawn mowers and the rustle of leaves being raked are sounds of the past, replaced by quiet and efficient robots.

From Kirsten Dunst and Jesse Plemons, to Maggie Gyllenhaal and Peter Sarsgaard, celebrity couples often look weirdly alike. In fact, the phenomenon has coined the term'doppelbanger' – two people with similar facial features who are intimately involved. This has led to speculation that looking similar is somehow conducive to a better or more long-lasting relationship. But researchers in Germany, who used artificial intelligence (AI) to analyse celebrity photos, have found there is no evidence to support this. Celebrity couples who look alike are no more likely to stay together than pairs who have different faces, such as David and Victoria Beckham, and Emily Blunt and John Krasinski, they say.

France's competition watchdog has fined Google 250 million euros ( 272m) for breaching commitments to media companies on content licensing. The French Competition Authority said on Wednesday that it was imposing the fine as part of additional measures over a 2019 case that organisations representing French magazines and newspapers had lodged against the United States tech giant and other online platforms. The media outlets accused the tech companies of making billions from their content without sharing the revenue with those who gathered it. In 2021, the watchdog fined Google 500 million euros ( 592m) for failing to negotiate in good faith. The dispute appeared to be resolved in 2022 when the company dropped its appeal against the fine.