Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework

Existing deep learning approaches for travel mode choice modeling fail to inform modelers about their prediction uncertainty. Even when facing scenarios that are out of the distribution of training data, which implies high prediction uncertainty, these approaches still provide deterministic answers, potentially leading to misguidance. To address this limitation, this study introduces the concept of uncertainty from the field of explainable artificial intelligence into travel mode choice modeling. We propose a Bayesian neural network-based travel mode prediction model (BTMP) that quantifies the uncertainty of travel mode predictions, enabling the model itself to "know" and "tell" what it doesn't know. With BTMP, we further propose an uncertainty-guided active survey framework, which dynamically formulates survey questions representing travel mode choice scenarios with high prediction uncertainty. Through iterative collection of responses to these dynamically tailored survey questions, BTMP is iteratively trained to achieve the desired accuracy faster with fewer questions, thereby reducing survey costs. Experimental validation using synthetic datasets confirms the effectiveness of BTMP in quantifying prediction uncertainty. Furthermore, experiments, utilizing both synthetic and real-world data, demonstrate that the BTMP model, trained with the uncertainty-guided active survey framework, requires 20% to 50% fewer survey responses to match the performance of the model trained on randomly collected survey data. Overall, the proposed BTMP model and active survey framework innovatively incorporate uncertainty quantification into travel mode choice modeling, providing model users with essential insights into prediction reliability while optimizing data collection for deep learning model training in a cost-efficient manner

Control Strategy and Simulation of the Regenerative Braking of an Electric Vehicle Based on an Electromechanical Brake

The electromechanical brake (EMB) has very broad prospects for application in the automotive industry, especially in small- and medium-sized vehicles. To extend the endurance range of pure electric vehicles, a regenerative braking control strategy combined with an electromechanical brake model is designed that divides the braking modes according to the braking intensity and controls the regenerative braking force based on fuzzy theory. Considering a front-wheel-drive pure electric vehicle equipped with a floating clamp disc electromechanical brake as the research object, a structural form of electromechanical brake is proposed and a mathematical model of the electromechanical brake is built. Combined with the relevant influencing factors, the regenerative braking force is limited to a certain extent, and the simulation models of the electromechanical brake and the regenerative braking force distribution control strategy are built in MATLAB/Simulink. Co-simulation in MATLAB and AVL CRUISE software is conducted. The simulation results demonstrate that the braking energy recovery rate of the whole vehicle with the fuzzy control strategy put forward in this paper is 28.9% under mild braking and 34.11% under moderate braking. The control method substantially increases the energy utilization rate

Research on Design Optimization and Simulation of Regenerative Braking Control Strategy for Pure Electric Vehicle Based on EMB Systems

The benefits of electromechanical braking (EMB) systems are short response time, high braking efficiency, ease of assembly and easy integration with other electronic control systems. Therefore, a model of an EMB system is developed based on which the braking stability, braking efficiency, and the regenerative braking energy recovery in electric vehicles are investigated. Electric vehicles can effectively increase their driving range by using a rational regenerative braking control strategy. Firstly, a fuzzy regenerative braking control strategy is developed for comparison, and an optimized regenerative braking control strategy is designed based on the NSGA-II algorithm. The technique for order preference by similarity to ideal solution (TOPSIS) is used to comprehensively evaluate the Pareto optimal solution set and to select an optimal solution for the optimization problem. Secondly, a Takagi-Sugeno fuzzy neural network is trained with the optimized discrete data, and then the braking force distribution controller is obtained. Simulink and AVL CRUISE are used to simulate the control strategy. The simulation results for variable intensity braking conditions and cyclic conditions NEDC, FTP75, and CLTC-P show that the optimized control strategy outperforms the fuzzy control strategy in braking stability and braking energy recovery

Convolutional Hierarchical Attention Network for Query-Focused Video Summarization

Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate queryfocused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.Comment: Accepted by AAAI 2020 Conferenc

Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design

Graph neural networks (GNNs) have shown significant accuracy improvements in a variety of graph learning domains, sparking considerable research interest. To translate these accuracy improvements into practical applications, it is essential to develop high-performance and efficient hardware acceleration for GNN models. However, designing GNN accelerators faces two fundamental challenges: the high bandwidth requirement of GNN models and the diversity of GNN models. Previous works have addressed the first challenge by using more expensive memory interfaces to achieve higher bandwidth. For the second challenge, existing works either support specific GNN models or have generic designs with poor hardware utilization. In this work, we tackle both challenges simultaneously. First, we identify a new type of partition-level operator fusion, which we utilize to internally reduce the high bandwidth requirement of GNNs. Next, we introduce partition-level multi-threading to schedule the concurrent processing of graph partitions, utilizing different hardware resources. To further reduce the extra on-chip memory required by multi-threading, we propose fine-grained graph partitioning to generate denser graph partitions. Importantly, these three methods make no assumptions about the targeted GNN models, addressing the challenge of model variety. We implement these methods in a framework called SwitchBlade, consisting of a compiler, a graph partitioner, and a hardware accelerator. Our evaluation demonstrates that SwitchBlade achieves an average speedup of $1.85\times$ and energy savings of $19.03\times$ compared to the NVIDIA V100 GPU. Additionally, SwitchBlade delivers performance comparable to state-of-the-art specialized accelerators

Cross-Cultural Semantic Differences in Emoji Usage on Social Media Platforms

Digital communication through social media platforms has fundamentally transformed how individuals express emotions and convey meaning across cultural boundaries. This research investigates the semantic variations in emoji usage patterns among different cultural groups on major social media platforms including Twitter, Instagram, and Facebook. Through comprehensive analysis of 2.3 million emoji-containing posts collected from 15 distinct cultural regions, we examine how cultural background influences emoji interpretation and usage frequency. Our methodology employs advanced natural language processing techniques combined with cross-cultural sentiment analysis to identify significant semantic divergences. The study reveals substantial variations in emoji semantic interpretation across cultures, with Western users demonstrating higher frequency of positive emotion emojis compared to Eastern cultures who exhibit more contextual and subtle emotional expressions. Platform-specific adaptations show distinct patterns where Instagram users across all cultures tend toward visual storytelling emojis while Twitter users prefer reaction-based emotional expressions. These findings provide crucial insights for improving cross-cultural communication design in global social media platforms and contribute to the broader understanding of digital cultural linguistics. The research establishes foundational frameworks for developing culturally-aware emoji recommendation systems and enhancing international digital communication effectiveness

Label-Free Liver Tumor Segmentation

We demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two intriguing advantages: (I) realistic in shape and texture, which even medical professionals can confuse with real tumors; (II) effective for training AI models, which can perform liver tumor segmentation similarly to the model trained on real tumors -- this result is exciting because no existing work, using synthetic tumors only, has thus far reached a similar or even close performance to real tumors. This result also implies that manual efforts for annotating tumors voxel by voxel (which took years to create) can be significantly reduced in the future. Moreover, our synthetic tumors can automatically generate many examples of small (or even tiny) synthetic tumors and have the potential to improve the success rate of detecting small liver tumors, which is critical for detecting the early stages of cancer. In addition to enriching the training data, our synthesizing strategy also enables us to rigorously assess the AI robustness.Comment: CVPR 202

Prevalence of Metabolic Syndrome Among the Adult Population in Western China and the Association With Socioeconomic and Individual Factors: Four Cross-Sectional Studies

Objectives: This study explored the prevalence of and individual influencing factors for metabolic syndrome (MS) as well as associated socioeconomic factors and regional aggregation. Design: Four cross-sectional surveys were analysed for trends in MS and associations with socioeconomic and individual factors through multilevel logistic regression analyses. The risk associated with nutrient intake was also assessed through a dietary survey in 2015. Setting: From 2010 to 2018, 8-15 counties/districts of West China were included. Participants: A total of 28 274 adults were included in the prevalence analysis. A total of 23 708 adults were used to analyse the related factors. Results: The overall prevalence of MS ranged from 21.4% to 27.8% over the 8 years, remaining basically stable within the 95% CI. Our study found that the urbanisation rate and hospital beds per 1000 people were positively associated with MS, and the number of doctors in healthcare institutions per 1000 persons was negatively associated with MS. The ORs for females, people with college education and higher and unmarried or single people were 1.49, 0.67 and 0.51, respectively (p\u3c0.05). The ORs of people who smoked at least 20 cigarettes/day, ate more than 100 g of red meat/day, consumed fruit or vegetable juice and drank carbonated soft drinks less than weekly were 1.10, 1.16, 1.19-1.27 and 0.81-0.84, respectively. The ORs rose with increasing sedentary time and decreased with higher physical activity. Conclusion: The high burden of MS, unreasonable proportions of energy and micronutrient intake and low percentage of high levels of physical activity were the major challenges to public health in western China. Improving the human resources component of medical services, such as the number of doctors, increasing the availability of public sports facilities and E-health tools and improving individual dietary quality and education might help prevent MS