Nonverbal communication plays a crucial role in both human-human and human-robot interactions (HRIs), where facial expressions convey emotions, intentions and trust. Enabling humanoid robots to generate human-like facial reactions in response to human speech and facial behaviours remains significant challenges. In this work, we leverage human-human interaction (HHI) datasets to train a humanoid robot, allowing it to learn and imitate facial reactions to both speech and facial expression inputs. Specifically, we extend a sequence-to-sequence (Seq2Seq)-based framework that enables robots to simulate human-like virtual facial expressions that are appropriate for responding to the perceived human user behaviours. Then, we propose a deep neural network-based motor mapping model to translate these expressions into physical robot movements. Experiments demonstrate that our facial reaction–motor mapping framework successfully enables robotic self-reactions to various human behaviours, where our model can best predict 50 frames (two seconds) of facial reactions in response to the input user behaviour of the same duration, aligning with human cognitive and neuromuscular processes. Our code is provided at https://github.com/mrsgzg/Robot_Face_Reaction.

In this paper, the design of a power data center based on dempster-shafer and wireless sensor network sharing is proposed to solve the problems of poor intelligent scheduling ability and long transmission delay in traditional power information management systems. By constructing a wireless sensor information collection model of power big data under the smart power management microservice architecture, combined with the wireless sensor network sharing and scheduling scheme, an intelligent deployment routing algorithm for network nodes is established. The power data center prediction and feature extraction under the wireless sensor network sharing and fusion mechanism are realized using Dempster-Shafer evidence theory system, and an intelligent power management microservice platform based on the design of power data center is constructed. The test results show that the corresponding value of this method is generally higher than that of the reference in the comparison of the balanced dispatching output. The transmission delay of this method is reduced by 43.20% and 52.00%, respectively, compared with the average transmission delay of references (Wankai et al. in Power Syst Technol 46:4473–4481, 2022) and (Changyue et al. in IEEE Trans Power Electron 33:10157–10168, 2018). These results show that the method in this study has a significant effect on reducing the transmission delay in the design of a power data center. This method can improve the fusion level and intelligent scheduling ability of power data, improve data clustering and stability of power data transmission, and improve the sharing level of power data. It is proven that the power data center and information management system under the intelligent power management micro-service framework constructed by this method have improved the power service level. The analysis shows that this method has a small delay in power data output and a high stability in information collection and transmission.

Artificial intelligence (AI) has made remarkable progress across various domains, with large language models like ChatGPT gaining substantial attention for their human-like text-generation capabilities. Despite these achievements, improving spatial reasoning remains a significant challenge for these models. Benchmarks like StepGame evaluate AI spatial reasoning, where ChatGPT has shown unsatisfactory performance. However, the presence of template errors in the benchmark has an impact on the evaluation results. Thus there is potential for ChatGPT to perform better if these template errors are addressed, leading to more accurate assessments of its spatial reasoning capabilities. In this study, we refine the StepGame benchmark, providing a more accurate dataset for model evaluation. We analyze GPT’s spatial reasoning performance on the rectified benchmark, identifying proficiency in mapping natural language text to spatial relations but limitations in multi-hop reasoning. We provide a flawless solution to the benchmark by combining template-to-relation mapping with logic-based reasoning. This combination demonstrates proficiency in performing qualitative reasoning on StepGame without encountering any errors. We then address the limitations of GPT models in spatial reasoning. To improve spatial reasoning, we deploy Chain-of-Thought and Tree-of-thoughts prompting strategies, offering insights into GPT’s cognitive process. Our investigation not only sheds light on model deficiencies but also proposes enhancements, contributing to the advancement of AI with more robust spatial reasoning capabilities.

In this work, the problem of bootstrapping knowledge in language and vision for autonomous robots is addressed through novel techniques in grammar induction and word grounding to the perceptual world. In particular, we demonstrate a system, called OLAV, which is able, for the first time, to (1) learn to form discrete concepts from sensory data; (2) ground language (n-grams) to these concepts; (3) induce a grammar for the language being used to describe the perceptual world; and moreover to do all this incrementally, without storing all previous data. The learning is achieved in a loosely-supervised manner from raw linguistic and visual data. Moreover, the learnt model is transparent, rather than a black-box model and is thus open to human inspection. The visual data is collected using three different robotic platforms deployed in real-world and simulated environments and equipped with different sensing modalities, while the linguistic data is collected using online crowdsourcing tools and volunteers. The analysis performed on these robots demonstrates the effectiveness of the framework in learning visual concepts, language groundings and grammatical structure in these three online settings.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment. RESULTS: In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development. CONCLUSIONS: The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

Diabetic retinopathy severity assessment is an important domain in which deep learning has benefited medical imaging analysis. In this regard, CNNs which perform well in ImageNet are incapable of extracting subtle lesion features from high-resolution retinal fundus images. So novel convolutional networks with higher input size were developed. But no prior work give deep investigation on the impact of image resolution in the context of DR severity assessment. In this paper, we first explore how the performance of diabetic retinopathy severity assessment task would change if higher-resolution input images were used. Next, we adopt the stepwise strategy of training convolutional networks with high input scales to avoid overfitting. Finally, rigorous analyses on the impact of image resolution are given, showing that as model expands with higher input image resolutions, the performance grows logarithmically while both time and space complexity increase exponentially. Our model obtains new state-of-the-art kappa score in the task of diabetic retinopathy severity assessment task on EyePACS dataset with convolutional networks whose input size is 896 × 896, and great progress in classification of mild diabetic retinopathy. There is great potential for generalizing this solution to other medical image analysis problems.