Output Feedback Fractional-Order Nonsingular Terminal Sliding Mode Control of Underwater Remotely Operated Vehicles

For the 4-DOF (degrees of freedom) trajectory tracking control problem of underwater remotely operated vehicles (ROVs) in the presence of model uncertainties and external disturbances, a novel output feedback fractional-order nonsingular terminal sliding mode control (FO-NTSMC) technique is introduced in light of the equivalent output injection sliding mode observer (SMO) and TSMC principle and fractional calculus technology. The equivalent output injection SMO is applied to reconstruct the full states in finite time. Meanwhile, the FO-NTSMC algorithm, based on a new proposed fractional-order switching manifold, is designed to stabilize the tracking error to equilibrium points in finite time. The corresponding stability analysis of the closed-loop system is presented using the fractional-order version of the Lyapunov stability theory. Comparative numerical simulation results are presented and analyzed to demonstrate the effectiveness of the proposed method. Finally, it is noteworthy that the proposed output feedback FO-NTSMC technique can be used to control a broad range of nonlinear second-order dynamical systems in finite time

Immobilized biocatalytic process to prepare enantiopure pregabalin intermediate using engineered hydantoinase

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Towards the multimodal unit of meaning: a multimodal corpus-based approach

While there has been a wealth of research that uses textually rendered spoken corpora (i.e. written transcripts of spoken language) and corpus methods to investigate utterance meaning in various contexts, multimodal corpus-based research beyond the text is still rare. Monomodal corpus-based research often limits our description and understanding of the meaning of words and phrases, mainly due to the fact that meaning is constructed by multiple modes (e.g. speech, gesture, prosody, etc.). Hence, focusing on speech and gesture, the thesis explores multimodal corpus-based approaches for investigating multimodal units of meaning, using recurrent phrases, e.g. “(do) you know/see what I mean”, and gesture as two different, yet complementary points of entry. The primary goal is to identify the patterned uses of gesture and speech that can assist in the description of multimodal units of meaning. The Nottingham Multimodal Corpus (250,000 running words) is used as the data base for the research. The main original contributions of the thesis include a new coding scheme for segmenting gestures, two multimodal profiles for a target recurrent speech and gesture pattern, and a new framework for classifying and describing the role of gestures in discourse. Moreover, the thesis makes important implications for our understanding of the temporal, cognitive and functional relationship between speech and gesture; it also discusses potential applications, particularly in English language teaching and Human-Computer Interaction. These findings are of value to the methodological and theoretical development of multimodal corpus-based research on units of meaning

Recovery type a posteriori error estimation of an adaptive finite element method for Cahn--Hilliard equation

In this paper, we derive a novel recovery type a posteriori error estimation of the Crank-Nicolson finite element method for the Cahn--Hilliard equation. To achieve this, we employ both the elliptic reconstruction technique and a time reconstruction technique based on three time-level approximations, resulting in an optimal a posteriori error estimator. We propose a time-space adaptive algorithm that utilizes the derived a posteriori error estimator as error indicators. Numerical experiments are presented to validate the theoretical findings, including comparing with an adaptive finite element method based on a residual type a posteriori error estimator.Comment: 36 pages, 7 figure

Towards a corpus-based description of speech-gesture units of meaning: The case of the circular gesture

The theories and methods in corpus linguistics (CL) have had an impact on numerous areas in applied linguistics. However, the interface between CL and multimodal speech-gesture studies remains underexplored. One fundamental question is whether it is possible, and even appropriate, to apply the theories and paradigms established based on textual data to multimodal data. To explore this, we examine how CL can assist investigating lexico-grammatical patterns of speech co-occurring with a recurrent gesture (i.e. the circular gesture). Sinclair’s (1996) unit of meaning model is used to describe the co-gestural speech patterns. The study draws on a subset of the Nottingham Multimodal Corpus, in which 570 instances of circular gestures and their co-occurring speech are identified and analysed. We argue that Sinclair’s unit of meaning model can be extended to include speech-gesture patterns, and that those descriptions enable a more nuanced understanding of meaning in context

Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

Replay-based methods in class-incremental learning (CIL) have attained remarkable success, as replaying the exemplars of old classes can significantly mitigate catastrophic forgetting. Despite their effectiveness, the inherent memory restrictions of CIL result in saving a limited number of exemplars with poor diversity, leading to data imbalance and overfitting issues. In this paper, we introduce a novel exemplar super-compression and regeneration method, ESCORT, which substantially increases the quantity and enhances the diversity of exemplars. Rather than storing past images, we compress images into visual and textual prompts, e.g., edge maps and class tags, and save the prompts instead, reducing the memory usage of each exemplar to 1/24 of the original size. In subsequent learning phases, diverse high-resolution exemplars are generated from the prompts by a pre-trained diffusion model, e.g., ControlNet. To minimize the domain gap between generated exemplars and real images, we propose partial compression and diffusion-based data augmentation, allowing us to utilize an off-the-shelf diffusion model without fine-tuning it on the target dataset. Therefore, the same diffusion model can be downloaded whenever it is needed, incurring no memory consumption. Comprehensive experiments demonstrate that our method significantly improves model performance across multiple CIL benchmarks, e.g., 5.0 percentage points higher than the previous state-of-the-art on 10-phase Caltech-256 dataset.Comment: Code: https://github.com/KerryDRX/ESCOR

Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control

Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient's surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning anesthesia strategies on real clinical datasets, is proposed. Conservative Q-Learning was first introduced to alleviate the problem of Q function overestimation in an offline context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient's vital signs. In addition, the confidence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.Comment: 11 pages, 7 figure

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory.Comment: MICCAI-202