Such models are integral to the development of context-aware surgical training systems and process monitoring platforms [11], [19] as well as for encoding adaptive robotic control policies in teleoperated environments [13], [20], [78]. However, their limited capacity to capture continuous biophysical dynamics can constrain their utility in applications where physiological fidelity is essential. Recognizing the limitations inherent in purely continuous or discrete approaches, hybrid modeling strategies have emerged as a state-of-the-art solution for surgical digital twins. These frameworks integrate continuous dynamic models with discrete state machines, enabling the simultaneous tracking of physiological changes and procedural events [8], [7], [19], [37]. For example, hybrid automata have been deployed to synchronize real-time updates of tissue deformation with the sequencing of surgical tool actions [7], [19]. This integration allows digital twins to provide context-sensitive support, adapting to abrupt workflow transitions and physiological perturbations alike--a critical requirement in both routine and emergent surgical scenarios [8], [11], [7]. B. Mutual Information and Information-Theoretic Approaches With the proliferation of multi-modal surgical data, information-theoretic concepts have become indispensable for quantifying uncertainty, relevance, and redundancy across heterogeneous information streams. Mutual information I(X; Y) has been adopted as a rigorous metric for selecting the most informative sensors, imaging modalities, or clinical parameters, thereby enhancing the efficiency and robustness of digital twin-enabled decision support [2], [3], [13], [34], [11], [51], [48], [26], [29]. This is formally captured as Eq.

The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long sequences. Addressing this, we introduce Subjective Depth Transformers (SDT) and Subjective Timescale Transformers (STT), two distinct architectures that leverage Bayesian surprise signals to dynamically route computation, learning where and when to compute within decoder-only TFs. SDT augments a decoder-only stack with alternating Decision and Dynamic layers: a Decision layer computes a full block 'posterior' and a lightweight 'prior,' while a Dynamic layer employs fixed-capacity Top-K routing based on Bayesian surprise (Expected and Unexpected Change), maintaining a static compute graph. STT extends this conditional computation to the temporal domain: a transition network predicts residual updates, forming a temporal 'change hypothesis' that informs a router to dynamically execute or bypass TF blocks for each token, managing KV-cache contributions. Both architectures exhibit the predicted shift from novelty to prediction driven gating over training, suggesting alignment with surprise based principles. While operating at reduced capacity, they offer preliminary insights into the compute-accuracy trade-offs of conditional computation. The proposed architectures establish a flexible framework for efficiency, reducing self-attention computation by 75% and KV-cache requirements by 50% within each compute skipping layer, setting a pathway for more efficient models.

We introduce a novel framework that integrates Semantic Digital Twins (SDTs) with Large Language Models (LLMs) to enable adaptive and goal-driven robotic task execution in dynamic environments. The system decomposes natural language instructions into structured action triplets, which are grounded in contextual environmental data provided by the SDT. This semantic grounding allows the robot to interpret object affordances and interaction rules, enabling action planning and real-time adaptability. In case of execution failures, the LLM utilizes error feedback and SDT insights to generate recovery strategies and iteratively revise the action plan. We evaluate our approach using tasks from the ALFRED benchmark, demonstrating robust performance across various household scenarios. The proposed framework effectively combines high-level reasoning with semantic environment understanding, achieving reliable task completion in the face of uncertainty and failure.

Computational models offer powerful tools for formalising psychological theories, making them both testable and applicable in digital contexts. However, they remain little used in the study of motivation within psychology. We focus on the "need for competence", postulated as a key basic human need within Self-Determination Theory (SDT) -- arguably the most influential psychological framework for studying intrinsic motivation (IM). The need for competence is treated as a single construct across SDT texts. Yet, recent research has identified multiple, ambiguously defined facets of competence in SDT. We propose that these inconsistencies may be alleviated by drawing on computational models from the field of artificial intelligence, specifically from the domain of reinforcement learning (RL). By aligning the aforementioned facets of competence -- effectance, skill use, task performance, and capacity growth -- with existing RL formalisms, we provide a foundation for advancing competence-related theory in SDT and motivational psychology more broadly. The formalisms reveal underlying preconditions that SDT fails to make explicit, demonstrating how computational models can improve our understanding of IM. Additionally, our work can support a cycle of theory development by inspiring new computational models formalising aspects of the theory, which can then be tested empirically to refine the theory. While our research lays a promising foundation, empirical studies of these models in both humans and machines are needed, inviting collaboration across disciplines.

Understanding the emotions in a dialogue usually requires external knowledge to accurately understand the contents. As the LLMs become more and more powerful, we do not want to settle on the limited ability of the pre-trained language model. However, the LLMs either can only process text modality or are too expensive to process the multimedia information. We aim to utilize both the power of LLMs and the supplementary features from the multimedia modalities. In this paper, we present a framework, Lantern, that can improve the performance of a certain vanilla model by prompting large language models with receptive-field-aware attention weighting. This framework trained a multi-task vanilla model to produce probabilities of emotion classes and dimension scores. These predictions are fed into the LLMs as references to adjust the predicted probabilities of each emotion class with its external knowledge and contextual understanding. We slice the dialogue into different receptive fields, and each sample is included in exactly t receptive fields. Finally, the predictions of LLMs are merged with a receptive-field-aware attention-driven weighting module. In the experiments, vanilla models CORECT and SDT are deployed in Lantern with GPT-4 or Llama-3.1-405B. The experiments in IEMOCAP with 4-way and 6-way settings demonstrated that the Lantern can significantly improve the performance of current vanilla models by up to 1.23% and 1.80%.

Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real-world applications that involve complex tasks with rich temporal and logical structures. In this paper, we propose temporal logic Specification-conditioned Decision Transformer (SDT), a novel framework that harnesses the expressive power of signal temporal logic (STL) to specify complex temporal rules that an agent should follow and the sequential modeling capability of Decision Transformer (DT). Empirical evaluations on the DSRL benchmarks demonstrate the better capacity of SDT in learning safe and high-reward policies compared with existing approaches. In addition, SDT shows good alignment with respect to different desired degrees of satisfaction of the STL specification that it is conditioned on.

A sequential detection and tracking (SDT) approach is proposed for detection and tracking of very low signal-to-noise (SNR) objects. The proposed approach is compared with two existing particle filter track-before-track (TBD) methods. It is shown that the former outperforms the latter. A conventional detection and tracking (CDT) approach, based on one-data-frame thresholding, is considered as a benchmark for comparison. Simulations demonstrate the performance.

Over the past decade, neural network (NN)-based controllers have demonstrated remarkable efficacy in a variety of decision-making tasks. However, their black-box nature and the risk of unexpected behaviors and surprising results pose a challenge to their deployment in real-world systems with strong guarantees of correctness and safety. We address these limitations by investigating the transformation of NN-based controllers into equivalent soft decision tree (SDT)-based controllers and its impact on verifiability. Differently from previous approaches, we focus on discrete-output NN controllers including rectified linear unit (ReLU) activation functions as well as argmax operations. We then devise an exact but cost-effective transformation algorithm, in that it can automatically prune redundant branches. We evaluate our approach using two benchmarks from the OpenAI Gym environment. Our results indicate that the SDT transformation can benefit formal verification, showing runtime improvements of up to 21x and 2x for MountainCar-v0 and CartPole-v0, respectively.

In recent years, ubiquitous semantic Metaverse has been studied to revolutionize immersive cyber-virtual experiences for augmented reality (AR) and virtual reality (VR) users, which leverages advanced semantic understanding and representation to enable seamless, context-aware interactions within mixed-reality environments. This survey focuses on the intelligence and spatio-temporal characteristics of four fundamental system components in ubiquitous semantic Metaverse, i.e., artificial intelligence (AI), spatio-temporal data representation (STDR), semantic Internet of Things (SIoT), and semantic-enhanced digital twin (SDT). We thoroughly survey the representative techniques of the four fundamental system components that enable intelligent, personalized, and context-aware interactions with typical use cases of the ubiquitous semantic Metaverse, such as remote education, work and collaboration, entertainment and socialization, healthcare, and e-commerce marketing. Furthermore, we outline the opportunities for constructing the future ubiquitous semantic Metaverse, including scalability and interoperability, privacy and security, performance measurement and standardization, as well as ethical considerations and responsible AI. Addressing those challenges is important for creating a robust, secure, and ethically sound system environment that offers engaging immersive experiences for the users and AR/VR applications.

Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, tree boosting methods...) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. In this paper, we propose deep boosting decision trees (DBDT), a novel approach for fraud detection based on gradient boosting and neural networks. In order to combine the advantages of both conventional methods and deep learning, we first construct soft decision tree (SDT), a decision tree structured model with neural networks as its nodes, and then ensemble SDTs using the idea of gradient boosting. In this way we embed neural networks into gradient boosting to improve its representation learning capability and meanwhile maintain the interpretability. Furthermore, aiming at the rarity of detected fraud cases, in the model training phase we propose a compositional AUC maximization approach to deal with data imbalances at algorithm level. Extensive experiments on several real-life fraud detection datasets show that DBDT can significantly improve the performance and meanwhile maintain good interpretability.