Distributed and federated learning are important tools for high-dimensional classification of large datasets. To reduce computational costs and overcome the curse of dimensionality, feature screening plays a pivotal role in eliminating irrelevant features during data preprocessing. However, data heterogeneity, particularly label shifting across different clients, presents significant challenges for feature screening. This paper introduces a general framework that unifies existing screening methods and proposes a novel utility, label-shift robust federated feature screening (LR-FFS), along with its federated estimation procedure. The framework facilitates a uniform analysis of methods and systematically characterizes their behaviors under label shift conditions. Building upon this framework, LR-FFS leverages conditional distribution functions and expectations to address label shift without adding computational burdens and remains robust against model misspecification and outliers. Additionally, the federated procedure ensures computational efficiency and privacy protection while maintaining screening effectiveness comparable to centralized processing. We also provide a false discovery rate (FDR) control method for federated feature screening. Experimental results and theoretical analyses demonstrate LR-FFS's superior performance across diverse client environments, including those with varying class distributions, sample sizes, and missing categorical data.

The rapid development of online recruitment platforms has created unprecedented opportunities for job seekers while concurrently posing the significant challenge of quickly and accurately pinpointing positions that align with their skills and preferences. Job recommendation systems have significantly alleviated the extensive search burden for job seekers by optimizing user engagement metrics, such as clicks and applications, thus achieving notable success. In recent years, a substantial amount of research has been devoted to developing effective job recommendation models, primarily focusing on text-matching based and behavior modeling based methods. While these approaches have realized impressive outcomes, it is imperative to note that research on the explainability of recruitment recommendations remains profoundly unexplored. To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations. Specifically, we first design a hierarchical representation disentangling module to explicitly mine the hierarchical skill-related factors implied in hidden representations of job seekers and jobs. Subsequently, we propose level-aware association modeling to enhance information communication and robust representation learning both inter- and intra-level, which consists of the interlevel knowledge influence module and the level-wise contrastive learning. Finally, we devise an interaction diagnosis module incorporating a neural diagnosis function for effectively modeling the multi-level recruitment interaction process between job seekers and jobs, which introduces the cognitive measurement theory.

Communication and tech companies are already planning for 6G wireless networks, even though 5G has yet to be fully rolled out globally. With improved data latency, security, reliability, and the ability to process massive volumes of global data in real time, experts like Qin believe 6G is set to transform our leisure and work. Among the new use cases for 6G networks envisioned by Vivo are mixed reality, holographic and multi-sensory communication, interactive 3D virtual digital humans, collaborative robots, and automated driving. There are expectations for 6G to be deployed by 2030. The UN's telecoms agency, International Telecommunication Union (ITU), has stated it plans to finish the initial 6G standardization process no later than the year 2030.

Hong Qin, a computer science professor at the University of Tennessee at Chattanooga, was born in a town on the eastern coast of China not far from the birthplace of Confucius. The great Chinese philosopher once said, "Real knowledge is to know the extent of one's ignorance." Confucius was probably onto something when he said real knowledge is knowing your limits. Qin (pronounced "chin") works in a field, computational biology, that's so intricate that it helps to have an appreciation for the limits of the human brain. More and more, human researchers such as Qin are humbling themselves and allowing artificial intelligence models and supercomputers do the heavy lifting of scientific discovery.

Differentiation is an important inference method in Bayesian networks and intervention is a basic notion in causal Bayesian networks. In this paper, we reveal the connection between differentiation and intervention in Bayesian networks.

Avatarify, an AI face animator app that has gone viral in TikTok videos, has been removed from Apple's China app store, and analysts said the move was mainly due to concerns about invasion of privacy. "The potential risk of invasion of privacy is the major reason behind the removal of AI apps like Avatarify," said Qin An, head of the Beijing-based Institute of China Cyberspace Strategy, on Monday. Unscrupulous individuals or groups might make money from these apps by using them to attract the public's attention first and then violating personal privacy, according to Qin. Qin's comments came after Avatarify - a face-changing app adopting artificial intelligence (AI) to allow users to replace their own faces with other people's faces for photography and videos - was removed from Apple's China app store. The app started trending on February 17 and ranked first on February 28 with more than 1.5 million downloads, according to a report by thepaper.cn. Chinese netizens have used the app to make funny videos, which went viral and started trending on Douyin, the Chinese version of TikTok.

A novel computer algorithm, or set of rules, that accurately predicts the orbits of planets in the solar system could be adapted to better predict and control the behavior of the plasma that fuels fusion facilities designed to harvest on Earth the fusion energy that powers the sun and stars. The algorithm, devised by a scientist at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL), applies machine learning, the form of artificial intelligence (AI) that learns from experience, to develop the predictions. "Usually in physics, you make observations, create a theory based on those observations, and then use that theory to predict new observations," said PPPL physicist Hong Qin, author of a paper detailing the concept in Scientific Reports. "What I'm doing is replacing this process with a type of black box that can produce accurate predictions without using a traditional theory or law." Qin (pronounced Chin) created a computer program into which he fed data from past observations of the orbits of Mercury, Venus, Earth, Mars, Jupiter, and the dwarf planet Ceres.

The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each round under given constraints so as to maximize the sum of rewards of arms. Several existing algorithms have regret bounds that are optimal with respect to the number of rounds $T$. However, there is a gap of $\tilde{O}(\max(\sqrt{d}, \sqrt{k}))$ between the current best upper and lower bounds, where $d$ is the dimension of the feature vectors, $k$ is the number of the chosen arms in a round, and $\tilde{O}(\cdot)$ ignores the logarithmic factors. The dependence of $k$ and $d$ is of practical importance because $k$ may be larger than $T$ in real-world applications such as recommender systems. In this paper, we fill the gap by improving the upper and lower bounds. More precisely, we show that the C${}^2$UCB algorithm proposed by Qin, Chen, and Zhu (2014) has the optimal regret bound $\tilde{O}(d\sqrt{kT} + dk)$ for the partition matroid constraints. For general constraints, we propose an algorithm that modifies the reward estimates of arms in the C${}^2$UCB algorithm and demonstrate that it enjoys the optimal regret bound for a more general problem that can take into account other objectives simultaneously. We also show that our technique would be applicable to related problems. Numerical experiments support our theoretical results and considerations.

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach to learn discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are also applicable when effects of special relativity and general relativity are important. The illustrated advantages of discrete field theories relative to continuous theories in terms of machine learning compatibility are consistent with Bostrom's simulation hypothesis.

Unlike the previous three industrial revolutions (18th, 19th, and 20th Centuries), the 4th will be more decentralized, automated, and controlled interdependently (Qin, Liu, & Grosvenor, 2016). In the first industrial revolution, the factory achieved production primarily through machines powered by water and steam and heavy manpower. In the second, operations became slightly more complexed through machines powered by electricity supported by mass production and division of labour. The third industrial revolution ushered in the use of electronics and information technology, adding more complexity to the production process in making it more automated (Brettel, Friederichsen, Keller, & Rosenberg, 2014; Wolfgang, 2016). Undoubtedly, these three industrial revolutions would have impacted their countries' economies.