Multimodal Emotion Recognition Model using Physiological Signals

As an important field of research in Human-Machine Interactions, emotion recognition based on physiological signals has become research hotspots. Motivated by the outstanding performance of deep learning approaches in recognition tasks, we proposed a Multimodal Emotion Recognition Model that consists of a 3D convolutional neural network model, a 1D convolutional neural network model and a biologically inspired multimodal fusion model which integrates multimodal information on the decision level for emotion recognition. We use this model to classify four emotional regions from the arousal valence plane, i.e., low arousal and low valence (LALV), high arousal and low valence (HALV), low arousal and high valence (LAHV) and high arousal and high valence (HAHV) in the DEAP and AMIGOS dataset. The 3D CNN model and 1D CNN model are used for emotion recognition based on electroencephalogram (EEG) signals and peripheral physiological signals respectively, and get the accuracy of 93.53% and 95.86% with the original EEG signals in these two datasets. Compared with the single-modal recognition, the multimodal fusion model improves the accuracy of emotion recognition by 5% ~ 25%, and the fusion result of EEG signals (decomposed into four frequency bands) and peripheral physiological signals get the accuracy of 95.77%, 97.27% and 91.07%, 99.74% in these two datasets respectively. Integrated EEG signals and peripheral physiological signals, this model could reach the highest accuracy about 99% in both datasets which shows that our proposed method demonstrates certain advantages in solving the emotion recognition tasks.Comment: 10 pages, 10 figures, 6 table

Safety and efficacy of a novel double-lumen tracheal tube in neonates with RDS: A prospective cohort study

BackgroundThe purpose of this study was to assess the safety and efficacy of a new double-lumen tracheal tube for neonates, with a conventional tracheal tube as a control.MethodNewborns with respiratory distress syndrome (RDS) requiring endotracheal intubation admitted to the tertiary neonatal intensive care unit (NICU) of Qujing Maternal and Child Healthcare Hospital in Yunnan Province between March 2021 and May 2022 were enrolled in this prospective cohort study. Outcome indicators related to effectiveness included mainly the number of intubations, duration of ventilation, duration of oxygenation, and length of stay; safety indicators included any clinical adverse effects during and after intubation. Appropriate stratified and subgroup analyses were performed according to the purpose of intubation, gestational age, and whether the drug was administered via endotracheal tube.ResultA total of 101 neonates were included and divided into two groups based on the choice of tracheal tube: the conventional (n = 50) and new (n = 51) tracheal tube groups. There was no statistical difference between the two groups in terms of adverse effects during and after intubation (p > 0.05). In neonates who were mechanically ventilated without endotracheal surfactant therapy or newborns receiving InSurE technique followed by non-invasive ventilation, no significant differences were found between the two groups regarding any of the efficacy indicators (p > 0.05). However, for neonates on invasive mechanical ventilation, the new tracheal tube allowed for a significant reduction in the duration of mechanical ventilation (96.50[74.00, 144.00] vs. 121.00[96.00, 196.50] hours, p = 0.037) and total ventilation (205.71 ± 80.24 vs. 277.56 ± 117.84 h, p = 0.027), when used as a route for endotracheal drug delivery. Further analysis was performed according to gestational age for newborns requiring intratracheal surfactant administration during mechanical ventilation, and the data showed that for preterm infants, the new tracheal tube not only shortened the duration of mechanical ventilation (101.75 ± 39.72 vs. 155.50 ± 51.49 h, p = 0.026) and total ventilation (216.00 ± 81.60 vs. 351.50 ± 113.79 h, p = 0.010), but also demonstrated significant advantages in reducing the duration of oxygen therapy (9.75 ± 6.02 vs. 17.33 ± 8.43 days, p = 0.042); however, there was no statistical difference in efficacy outcomes between the two groups in full-term infants (p > 0.05).ConclusionThe efficacy and safety of this new tracheal tube are promising in neonates with RDS, especially those requiring surfactant administration via a tracheal tube during mechanical ventilation. Given the limitations of this study, however, the clinical feasibility of this catheter needs to be further confirmed in prospective randomized trials with larger sample sizes.Clinical Trial Registrationhttp://www.chictr.org.cn/showproj.aspx?proj=12207

Cross-Lingual Knowledge Editing in Large Language Models

Knowledge editing aims to change language models' performance on several special cases (i.e., editing scope) by infusing the corresponding expected knowledge into them. With the recent advancements in large language models (LLMs), knowledge editing has been shown as a promising technique to adapt LLMs to new knowledge without retraining from scratch. However, most of the previous studies neglect the multi-lingual nature of some main-stream LLMs (e.g., LLaMA, ChatGPT and GPT-4), and typically focus on monolingual scenarios, where LLMs are edited and evaluated in the same language. As a result, it is still unknown the effect of source language editing on a different target language. In this paper, we aim to figure out this cross-lingual effect in knowledge editing. Specifically, we first collect a large-scale cross-lingual synthetic dataset by translating ZsRE from English to Chinese. Then, we conduct English editing on various knowledge editing methods covering different paradigms, and evaluate their performance in Chinese, and vice versa. To give deeper analyses of the cross-lingual effect, the evaluation includes four aspects, i.e., reliability, generality, locality and portability. Furthermore, we analyze the inconsistent behaviors of the edited models and discuss their specific challenges

Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network

IntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.Results and discussionNumerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment

When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective!

Recently, graph pre-training has attracted wide research attention, which aims to learn transferable knowledge from unlabeled graph data so as to improve downstream performance. Despite these recent attempts, the negative transfer is a major issue when applying graph pre-trained models to downstream tasks. Existing works made great efforts on the issue of what to pre-train and how to pre-train by designing a number of graph pre-training and fine-tuning strategies. However, there are indeed cases where no matter how advanced the strategy is, the "pre-train and fine-tune" paradigm still cannot achieve clear benefits. This paper introduces a generic framework W2PGNN to answer the crucial question of when to pre-train (i.e., in what situations could we take advantage of graph pre-training) before performing effortful pre-training or fine-tuning. We start from a new perspective to explore the complex generative mechanisms from the pre-training data to downstream data. In particular, W2PGNN first fits the pre-training data into graphon bases, each element of graphon basis (i.e., a graphon) identifies a fundamental transferable pattern shared by a collection of pre-training graphs. All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training. In this manner, the feasibility of pre-training can be quantified as the generation probability of the downstream data from any generator in the generator space. W2PGNN provides three broad applications, including providing the application scope of graph pre-trained models, quantifying the feasibility of performing pre-training, and helping select pre-training data to enhance downstream performance. We give a theoretically sound solution for the first application and extensive empirical justifications for the latter two applications