With the widespread adoption of Extended Reality (XR) headsets, spatial computing technologies are gaining increasing attention. Spatial computing enables interaction with virtual elements through natural input methods such as eye tracking, hand gestures, and voice commands, thus placing natural human-computer interaction at its core. While previous surveys have reviewed conventional XR interaction techniques, recent advancements in natural interaction, particularly driven by artificial intelligence (AI) and large language models (LLMs), have introduced new paradigms and technologies. In this paper, we review research on multimodal natural interaction for wearable XR, focusing on papers published since 2022 in six top venues: ACM CHI, UIST, IMWUT (Ubicomp), IEEE VR, ISMAR, and TVCG. We classify and analyze these studies based on application scenarios, operation types, and interaction modalities. This analysis provides a structured framework for understanding how researchers are designing advanced natural interaction techniques in XR. Based on these findings, we discuss the challenges in natural interaction techniques and suggest potential directions for future research. This review provides valuable insights for researchers aiming to design natural and efficient interaction systems for XR, ultimately contributing to the advancement of spatial computing.
Text classification is a pivotal task in natural language understanding, and its performance has seen remarkable advancements with the rise of Pre-trained Language Models (PLMs). Recently, the proliferation of PLMs has made it increasingly challenging to choose the most suitable model for a given dataset. Since fine-tuning the sheer number of models is impractical, Transferability Estimation (TE) has become a promising solution to efficient model selection. Unlike current TE methods that focus solely on fixed and hard class assignments to evaluate the quality of model-encoded features, our approach further takes into account the inter-sample and inter-model variations represented by soft class assignments. We achieve this by utilizing class embeddings to predict posterior class assignments, with the logarithm of the maximum posterior evidence serving as the transferability score. Moreover, we found that the informative sub-space of the dataset can lead to more accurate calculation of soft class assignments, where we achieve efficient annotation of informative samples by eliciting the powerful judging ability of large language model. The resulting posterior evidence over the informative sub-space, LogIPE, enables us to capture subtle differences between models, enhancing the accuracy of model selection and validated by extensive experiments conducted on a wide range of text classification datasets as well as candidate PLMs.
In this paper, we identify the distinction between non-brute-force computation and brute-force computation as the most fundamental problem in computer science. Subsequently, we prove, by the diagonalization method, that constructed self-referential CSPs cannot be solved by non-brute-force computation, which is stronger than P
Reasoning has long been regarded as a distinctive hallmark of human cognition, and recent advances in the artificial intelligence community have increasingly focused on the reasoning large language models (
In this paper, we conduct a comprehensive survey on federated
Artificial intelligence-enabled database technology, known as AI4DB (Artificial Intelligence for Databases), is an active research area attracting significant attention and innovation. This survey first introduces the background of learning-based database techniques. It then reviews advanced query optimization methods for learning databases, focusing on four popular directions: cardinality/cost estimation, learning-based join order selection, learning-based end-to-end optimizers, and text-to-SQL models. Cardinality/cost estimation is classified into supervised and unsupervised methods based on learning models, with illustrative examples provided to explain the working mechanisms. Detailed descriptions of various query optimizers are also given to elucidate the working mechanisms of each component in learning query optimizers. Additionally, we discuss the challenges and development opportunities of learning query optimizers. The survey further explores text-to-SQL models, a new research area within AI4DB. Finally, we consider the future development prospects of learning databases.
