Car-following Model of Connected Cruise Control Vehicles to Mitigate Traffic Oscillations

With the aim of mitigating traffic oscillations, this paper extends a car-following model for Connected Cruise Control (CCC) systems by considering electronic throttle angles of multiple cars ahead. The linear stability condition of the proposed model is derived and numerical simulations are performed. It has been found that the proposed model is prominently better than the previous model, i.e. full velocity difference model, from the perspective of mitigating traffic oscillations. Additionally, the proposed model can also reduce fuel consumption, emissions, i.e. CO, HC and NOX, safety risk, and improve driving comfort at the same time. Simulation results suggest that the CCC car-following control design should consider the effect of multiple electronic throttle angles from the preceding cars.</p

Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to maintain and take the own views of the system into account, which is a crucial process to achieve the empathy called self-other awareness. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses

An Efficient End-to-End Transformer with Progressive Tri-modal Attention for Multi-modal Emotion Recognition

Recent works on multi-modal emotion recognition move towards end-to-end models, which can extract the task-specific features supervised by the target task compared with the two-phase pipeline. However, previous methods only model the feature interactions between the textual and either acoustic and visual modalities, ignoring capturing the feature interactions between the acoustic and visual modalities. In this paper, we propose the multi-modal end-to-end transformer (ME2ET), which can effectively model the tri-modal features interaction among the textual, acoustic, and visual modalities at the low-level and high-level. At the low-level, we propose the progressive tri-modal attention, which can model the tri-modal feature interactions by adopting a two-pass strategy and can further leverage such interactions to significantly reduce the computation and memory complexity through reducing the input token length. At the high-level, we introduce the tri-modal feature fusion layer to explicitly aggregate the semantic representations of three modalities. The experimental results on the CMU-MOSEI and IEMOCAP datasets show that ME2ET achieves the state-of-the-art performance. The further in-depth analysis demonstrates the effectiveness, efficiency, and interpretability of the proposed progressive tri-modal attention, which can help our model to achieve better performance while significantly reducing the computation and memory cost. Our code will be publicly available

Extraction and Purification of a Lectin from Red Kidney Bean and Preliminary Immune Function Studies of the Lectin and Four Chinese Herbal Polysaccharides

Reversed micelles were used to extract lectin from red kidney beans and factors affecting reverse micellar systems (pH value, ionic strength and extraction time) were studied. The optimal conditions were extraction at pH 4–6, back extraction at pH 9–11, ion strength at 0.15 M NaCl, extraction for 4–6 minutes and back extraction for 8 minutes. The reverse micellar system was compared with traditional extraction methods and demonstrated to be a time-saving method for the extraction of red kidney bean lectin. Mitogenic activity of the lectin was reasonably good compared with commercial phytohemagglutinin (extracted from Phaseolus vulgaris) Mitogenic properties of the lectin were enhanced when four Chinese herbal polysaccharides were applied concurrently, among which 50 μg/mL Astragalus mongholicus polysaccharides (APS) with 12.5 μg/mL red kidney bean lectin yielded the highest mitogenic activity and 100 mg/kg/bw APS with 12.5 mg/kg/bw red kidney bean lectin elevated mouse nonspecific immunity

Is ChatGPT Equipped with Emotional Dialogue Capabilities?

This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emotional dialogue understanding and generation through a series of experiments on several downstream tasks. Our findings indicate that while ChatGPT's performance on emotional dialogue understanding may still lag behind that of supervised models, it exhibits promising results in generating emotional responses. Furthermore, the study suggests potential avenues for future research directions

Mitochondrial EF4 links respiratory dysfunction and cytoplasmic translation in Caenorhabditis elegans

AbstractHow animals coordinate cellular bioenergetics in response to stress conditions is an essential question related to aging, obesity and cancer. Elongation factor 4 (EF4/LEPA) is a highly conserved protein that promotes protein synthesis under stress conditions, whereas its function in metazoans remains unknown. Here, we show that, in Caenorhabditis elegans, the mitochondria-localized CeEF4 (referred to as mtEF4) affects mitochondrial functions, especially at low temperature (15°C). At worms' optimum growing temperature (20°C), mtef4 deletion leads to self-brood size reduction, growth delay and mitochondrial dysfunction. Transcriptomic analyses show that mtef4 deletion induces retrograde pathways, including mitochondrial biogenesis and cytoplasmic translation reorganization. At low temperature (15°C), mtef4 deletion reduces mitochondrial translation and disrupts the assembly of respiratory chain supercomplexes containing complex IV. These observations are indicative of the important roles of mtEF4 in mitochondrial functions and adaptation to stressful conditions

An Early Evaluation of GPT-4V(ision)

In this paper, we evaluate different abilities of GPT-4V including visual understanding, language understanding, visual puzzle solving, and understanding of other modalities such as depth, thermal, video, and audio. To estimate GPT-4V's performance, we manually construct 656 test instances and carefully evaluate the results of GPT-4V. The highlights of our findings are as follows: (1) GPT-4V exhibits impressive performance on English visual-centric benchmarks but fails to recognize simple Chinese texts in the images; (2) GPT-4V shows inconsistent refusal behavior when answering questions related to sensitive traits such as gender, race, and age; (3) GPT-4V obtains worse results than GPT-4 (API) on language understanding tasks including general language understanding benchmarks and visual commonsense knowledge evaluation benchmarks; (4) Few-shot prompting can improve GPT-4V's performance on both visual understanding and language understanding; (5) GPT-4V struggles to find the nuances between two similar images and solve the easy math picture puzzles; (6) GPT-4V shows non-trivial performance on the tasks of similar modalities to image, such as video and thermal. Our experimental results reveal the ability and limitations of GPT-4V and we hope our paper can provide some insights into the application and research of GPT-4V.Comment: Technical Report. Data are available at https://github.com/albertwy/GPT-4V-Evaluatio