321 research outputs found
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
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