Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.

Question Under Discussion (QUD), which is originally a linguistic analytic framework, gains increasing attention in the community of natural language processing over the years. Various models have been proposed for implementing QUD for discourse processing. This survey summarizes these models, with a focus on application to written texts, and examines studies that explore the relationship between QUD and mainstream discourse frameworks, including RST, PDTB and SDRT. Some questions that may require further study are suggested.

Environmental crime currently represents the third largest criminal activity worldwide while threatening ecosystems as well as human health. Among the crimes related to this activity, improper waste management can nowadays be countered more easily thanks to the increasing availability and decreasing cost of Very-High-Resolution Remote Sensing images, which enable semi-automatic territory scanning in search of illegal landfills. This paper proposes a pipeline, developed in collaboration with professionals from a local environmental agency, for detecting candidate illegal dumping sites leveraging a classifier of Remote Sensing images. To identify the best configuration for such classifier, an extensive set of experiments was conducted and the impact of diverse image characteristics and training settings was thoroughly analyzed. The local environmental agency was then involved in an experimental exercise where outputs from the developed classifier were integrated in the experts' everyday work, resulting in time savings with respect to manual photo-interpretation. The classifier was eventually run with valuable results on a location outside of the training area, highlighting potential for cross-border applicability of the proposed pipeline.

Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery. In recent years, with the rapid development of deep learning (DL) techniques, DL-based small molecule drug discovery methods have achieved excellent performance in prediction accuracy, speed, and complex molecular relationship modeling compared to traditional machine learning approaches. These advancements enhance drug screening efficiency and optimization, and they provide more precise and effective solutions for various drug discovery tasks. Contributing to this field's development, this paper aims to systematically summarize and generalize the recent key tasks and representative techniques in DL-based small molecule drug discovery in recent years. Specifically, we provide an overview of the major tasks in small molecule drug discovery and their interrelationships. Next, we analyze the six core tasks, summarizing the related methods, commonly used datasets, and technological development trends. Finally, we discuss key challenges, such as interpretability and out-of-distribution generalization, and offer our insights into future research directions for DL-assisted small molecule drug discovery.

Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature.

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, we highlight future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis.

In this survey, we present community appeals for a unified framework [Ci et al., 2023; a comprehensive overview of HcFMs by proposing Wang et al., 2023; Chen et al., 2024; Huang et al., 2024a] to a taxonomy that categorizes current approaches unlock systematic understanding and a wide range of humancentric into four groups: (1) Human-centric Perception applications for everybody. Foundation Models that capture fine-grained features Inspired by rapid advancements of general foundation models, for multi-modal 2D and 3D understanding; (2) e.g., large language models (LLMs), large vision models Human-centric AIGC Foundation Models that generate (LVMs) and text-to-image generative models, and their high-fidelity, diverse human-related content; presents of a paradigm shift from end-to-end learning of (3) Unified Perception and Generation Models that task-specific models to generalist models, a recent trend is integrate these capabilities to enhance both human to develop Human-centric Foundation Models (HcFM) that understanding and synthesis; and (4) Human-centric satisfy three criteria, namely generalization, broad applicability, Agentic Foundation Models that extend beyond perception and high fidelity. Generalization ensures robustness and generation to learn human-like intelligence to unseen conditions, enabling the model to perform consistently and interactive behaviors for humanoid embodied across varied environments.

Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs). Efforts in leveraging vision models for time series analysis have also been made along the way but are less visible to the community due to the predominant research on sequence modeling in this domain. However, the discrepancy between continuous time series and the discrete token space of LLMs, and the challenges in explicitly modeling the correlations of variates in multivariate time series have shifted some research attentions to the equally successful Large Vision Models (LVMs) and Vision Language Models (VLMs). To fill the blank in the existing literature, this survey discusses the advantages of vision models over LLMs in time series analysis. It provides a comprehensive and in-depth overview of the existing methods, with dual views of detailed taxonomy that answer the key research questions including how to encode time series as images and how to model the imaged time series for various tasks. Additionally, we address the challenges in the pre- and post-processing steps involved in this framework and outline future directions to further advance time series analysis with vision models.

While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information commonly encountered in real-world scenarios, remains in its infancy. Consequently, effectively integrating the text modality remains challenging. In this work, we highlight an intuitive yet significant observation that has been overlooked by existing works: time-series-paired texts exhibit periodic properties that closely mirror those of the original time series. Building on this insight, we propose a novel framework, Texts as Time Series (TaTS), which considers the time-series-paired texts to be auxiliary variables of the time series. TaTS can be plugged into any existing numerical-only time series models and enable them to handle time series data with paired texts effectively. Through extensive experiments on both multimodal time series forecasting and imputation tasks across benchmark datasets with various existing time series models, we demonstrate that TaTS can enhance predictive performance and achieve outperformance without modifying model architectures.

With the rapid development of artificial intelligence and computer vision technologies, emotion recognition has become an important research direction in various fields such as human-computer interaction (HCI), intelligent customer service, and mental health monitoring [Poria et al., 2017a]. The goal of emotion recognition is to analyze an individual's emotional state through multimodal information, such as speech, text, and visual data, to achieve emotional understanding in intelligent systems. However, traditional emotion recognition methods mainly focus on feature extraction and emotion classification from a single modality, which limits their effectiveness in complex real-world applications. In recent years, with the continuous advancement of multimodal learning and deep learning technologies, multimodal emotion recognition (MER) has gradually become a research hotspot. MER improves the accuracy and robustness of emotion classification by integrating multiple data sources.