Story-to-Motion: Synthesizing Infinite and Controllable Character Animation from Long Text

Generating natural human motion from a story has the potential to transform the landscape of animation, gaming, and film industries. A new and challenging task, Story-to-Motion, arises when characters are required to move to various locations and perform specific motions based on a long text description. This task demands a fusion of low-level control (trajectories) and high-level control (motion semantics). Previous works in character control and text-to-motion have addressed related aspects, yet a comprehensive solution remains elusive: character control methods do not handle text description, whereas text-to-motion methods lack position constraints and often produce unstable motions. In light of these limitations, we propose a novel system that generates controllable, infinitely long motions and trajectories aligned with the input text. (1) We leverage contemporary Large Language Models to act as a text-driven motion scheduler to extract a series of (text, position, duration) pairs from long text. (2) We develop a text-driven motion retrieval scheme that incorporates motion matching with motion semantic and trajectory constraints. (3) We design a progressive mask transformer that addresses common artifacts in the transition motion such as unnatural pose and foot sliding. Beyond its pioneering role as the first comprehensive solution for Story-to-Motion, our system undergoes evaluation across three distinct sub-tasks: trajectory following, temporal action composition, and motion blending, where it outperforms previous state-of-the-art motion synthesis methods across the board. Homepage: https://story2motion.github.io/.Comment: 8 pages, 6 figure

Exploring Mean Annual Precipitation Values (2003–2012) in a Specific Area (36°N–43°N, 113°E–120°E) Using Meteorological, Elevational, and the Nearest Distance to Coastline Variables

Gathering very accurate spatially explicit data related to the distribution of mean annual precipitation is required when laying the groundwork for the prevention and mitigation of water-related disasters. In this study, four Bayesian maximum entropy (BME) models were compared to estimate the spatial distribution of mean annual precipitation of the selected areas. Meteorological data from 48 meteorological stations were used, and spatial correlations between three meteorological factors and two topological factors were analyzed to improve the mapping results including annual precipitation, average temperature, average water vapor pressure, elevation, and distance to coastline. Some missing annual precipitation data were estimated based on their historical probability distribution and were assimilated as soft data in the BME method. Based on this, the univariate BME, multivariate BME, univariate BME with soft data, and multivariate BME with soft data analysis methods were compared. The estimation accuracy was assessed by cross-validation with the mean error (ME), mean absolute error (MAE), and root mean square error (RMSE). The results showed that multivariate BME with soft data outperformed the other methods, indicating that adding the spatial correlations between multivariate factors and soft data can help improve the estimation performance

Modification and application of sports rehabilitation materials based on conjugated materials

Existing elastic band materials for sports rehabilitation equipment have some deficiencies in strength, flexibility and durability, and need to be further improved. Therefore, the aim of this paper is to modify elastic bands using a conjugated material, carbon nanotubes, to improve the strength, flexibility and durability of elastic bands. In this paper, conjugated carbon nanotubes were prepared, and their elastic bands were strengthened and toughened by solvent, dispersant and functionalizer respectively under tensile testing machine and scanning electron microscope. Then the application effect of elastic band modified by conjugated materials in exercise rehabilitation was analyzed experimentally. The experimental results show that the strength of the elastic bands modified with carbon nanotubes is in the optimal range for sports rehabilitation, and the elongation at break of the test elastic band toughness index was also higher than that before modification, all of which were more than 90%. The recovery time of the elastic band after modification was long; the elastic retention rate was high, and the deformation was not easy. The satisfaction rate of different grades of elastic bands after modification was particularly high, which was not less than 95%. The research and application of elastic band modification based on conjugated material carbon nanotubes is very important for training and treatment in sports rehabilitation, which can provide better support and stability

Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM

This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method

A Practical Cross-Device Federated Learning Framework over 5G Networks

The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising the privacy. However, limited by the capacity of wireless data transmission, the employment of federated learning on mobile devices has been making slow progress in practical. The development and commercialization of the 5th generation (5G) mobile networks has shed some light on this. In this paper, we analyze the challenges of existing federated learning schemes for mobile devices and propose a novel cross-device federated learning framework, which utilizes the anonymous communication technology and ring signature to protect the privacy of participants while reducing the computation overhead of mobile devices participating in FL. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL. We also give a case study of autonomous driving. Finally, we present the performance evaluation of the proposed scheme and discuss some open issues in federated learning.Comment: This paper has been accepted by IEEE Wireless Communication

PKC-induced Sensitization of Ca2+-dependent Exocytosis Is Mediated by Reducing the Ca2+ Cooperativity in Pituitary Gonadotropes

The highly cooperative nature of Ca2+-dependent exocytosis is very important for the precise regulation of transmitter release. It is not known whether the number of binding sites on the Ca2+ sensor can be modulated or not. We have previously reported that protein kinase C (PKC) activation sensitizes the Ca2+ sensor for exocytosis in pituitary gonadotropes. To further unravel the underlying mechanism of how the Ca2+ sensor is modulated by protein phosphorylation, we have performed kinetic modeling of the exocytotic burst and investigated how the kinetic parameters of Ca2+-triggered fusion are affected by PKC activation. We propose that PKC sensitizes exocytosis by reducing the number of calcium binding sites on the Ca2+ sensor (from three to two) without significantly altering the Ca2+-binding kinetics. The reduction in the number of Ca2+-binding steps lowers the threshold for release and up-regulates release of fusion-competent vesicles distant from Ca2+ channels

IoT and Wearable Devices-Enhanced Information Provision of AR Glasses: A Multi-Modal Analysis in Aviation Industry

While Augmented Reality (AR) glasses are now instrumental in industries for delivering work-related information, the current one-size-fits-all information provision of AR glasses fails to cater to diverse workers’ needs and environmental conditions. We propose a framework for harnessing Internet of thing (IoT) and wearable technology to improve the adaptability and customization of information provision by AR. As a preliminary exploration, this short paper develops a multi-modal data processing system for work performance classification in the aviation industry. Using machine learning algorithms for multi-modal feature extraction and classifier construction, this framework provides a more objective and consistent evaluation of work performance compared to single-modal approaches. The proposed analytics architecture can provide valuable insights for other industries struggling to implement IoT and mixed reality

PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter

The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The generator formulates the answer based on the documents retrieved by the retriever. Incorporating Large Language Models (LLMs) as generators is beneficial due to their advanced QA capabilities, but they are typically too large to be fine-tuned with budget constraints while some of them are only accessible via APIs. To tackle this issue and further improve ReQA performance, we propose a trainable Pluggable Reward-Driven Contextual Adapter (PRCA), keeping the generator as a black box. Positioned between the retriever and generator in a Pluggable manner, PRCA refines the retrieved information by operating in a token-autoregressive strategy via maximizing rewards of the reinforcement learning phase. Our experiments validate PRCA's effectiveness in enhancing ReQA performance on three datasets by up to 20% improvement to fit black-box LLMs into existing frameworks, demonstrating its considerable potential in the LLMs era.Comment: Accepted by the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. (EMNLP2023