235 research outputs found
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
An Inductive Study from China
This study aims to put forward a new concept in charismatic leadership
theory: source of leader charisma (SLC). Using an inductive approach, we
identified the various dimensions of SLC in the Chinese context, and found that
SLC comprises of charismatic personality and charismatic behaviors.
Charismatic personality consists of three dimensions: high morality, outstanding
talents, and attractive characteristics. Charismatic behavior also includes three
dimensions: visional inspiration, character development, and morale stimulation.
Finally, we developed a primary model to explore the mechanism by which the
SLCs are attributed to charisma by follower. Our findings in the present study
contribute to new evidence that charismatic leadership theory may transcend
cultural boundaries
DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models
Image-based fashion design with AI techniques has attracted increasing
attention in recent years. We focus on a new fashion design task, where we aim
to transfer a reference appearance image onto a clothing image while preserving
the structure of the clothing image. It is a challenging task since there are
no reference images available for the newly designed output fashion images.
Although diffusion-based image translation or neural style transfer (NST) has
enabled flexible style transfer, it is often difficult to maintain the original
structure of the image realistically during the reverse diffusion, especially
when the referenced appearance image greatly differs from the common clothing
appearance. To tackle this issue, we present a novel diffusion model-based
unsupervised structure-aware transfer method to semantically generate new
clothes from a given clothing image and a reference appearance image. In
specific, we decouple the foreground clothing with automatically generated
semantic masks by conditioned labels. And the mask is further used as guidance
in the denoising process to preserve the structure information. Moreover, we
use the pre-trained vision Transformer (ViT) for both appearance and structure
guidance. Our experimental results show that the proposed method outperforms
state-of-the-art baseline models, generating more realistic images in the
fashion design task. Code and demo can be found at
https://github.com/Rem105-210/DiffFashion
In-situ synthesis of single-atom Ir by utilizing metal-organic frameworks: An acid-resistant catalyst for hydrogenation of levulinic acid to γ-valerolactone
The hydrogenation of levulinic acid (LA) to gamma-valerolactone (GVL) is a key reaction for the production of renewable chemicals and fuels, wherein acid-resistant and robust catalysts are highly desired for practical usage. Herein, an ultra-stable 0.6 wt% Ir@ZrO2@C single-atom catalyst was prepared via an in-situ synthesis approach during the assembly of UiO-66, followed by confined pyrolysis. The Ir@ZrO2@C offered not only a quantitative LA conversion and an excellent GVL selectivity (>99%), but also an unprecedented stability during recycling runs under harsh conditions (at T= 453 K, P-H2 = 40 bar in pH = 3 or pH =1 aqueous solution). By thorough spectroscopy characterizations, a well-defined structure of atomically dispersed Ir delta+ atoms onto nano-tetragonal ZrO2 confined in the amorphous carbon was identified for the Ir@ZrO2@C. The strong metal-support interaction and the confinement of the amorphous carbon account for the ultra-stability of the Ir@ZrO2@C. (C) 2019 Elsevier Inc. All rights reserved
What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery
Training control policies in simulation is more appealing than on real robots
directly, as it allows for exploring diverse states in a safe and efficient
manner. Yet, robot simulators inevitably exhibit disparities from the real
world, yielding inaccuracies that manifest as the simulation-to-real gap.
Existing literature has proposed to close this gap by actively modifying
specific simulator parameters to align the simulated data with real-world
observations. However, the set of tunable parameters is usually manually
selected to reduce the search space in a case-by-case manner, which is hard to
scale up for complex systems and requires extensive domain knowledge. To
address the scalability issue and automate the parameter-tuning process, we
introduce an approach that aligns the simulator with the real world by
discovering the causal relationship between the environment parameters and the
sim-to-real gap. Concretely, our method learns a differentiable mapping from
the environment parameters to the differences between simulated and real-world
robot-object trajectories. This mapping is governed by a simultaneously-learned
causal graph to help prune the search space of parameters, provide better
interpretability, and improve generalization. We perform experiments to achieve
both sim-to-sim and sim-to-real transfer, and show that our method has
significant improvements in trajectory alignment and task success rate over
strong baselines in a challenging manipulation task
Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles
Self-driving vehicles have their own intelligence to drive on open roads.
However, vehicle managers, e.g., government or industrial companies, still need
a way to tell these self-driving vehicles what behaviors are encouraged or
forbidden. Unlike human drivers, current self-driving vehicles cannot
understand the traffic laws, thus rely on the programmers manually writing the
corresponding principles into the driving systems. It would be less efficient
and hard to adapt some temporary traffic laws, especially when the vehicles use
data-driven decision-making algorithms. Besides, current self-driving vehicle
systems rarely take traffic law modification into consideration. This work aims
to design a road traffic law adaptive decision-making method. The
decision-making algorithm is designed based on reinforcement learning, in which
the traffic rules are usually implicitly coded in deep neural networks. The
main idea is to supply the adaptability to traffic laws of self-driving
vehicles by a law-adaptive backup policy. In this work, the natural
language-based traffic laws are first translated into a logical expression by
the Linear Temporal Logic method. Then, the system will try to monitor in
advance whether the self-driving vehicle may break the traffic laws by
designing a long-term RL action space. Finally, a sample-based planning method
will re-plan the trajectory when the vehicle may break the traffic rules. The
method is validated in a Beijing Winter Olympic Lane scenario and an overtaking
case, built in CARLA simulator. The results show that by adopting this method,
the self-driving vehicles can comply with new issued or updated traffic laws
effectively. This method helps self-driving vehicles governed by digital
traffic laws, which is necessary for the wide adoption of autonomous driving
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