462 research outputs found
Establishment of the prediction equations of 1RM skeletal muscle strength in 60- to 75-year-old Chinese men and women
The purpose of this study was to establish the one-repetition maximum (1RM) prediction equations of biceps curl, bench press, and squat from the submaximal skeletal muscle strength of 4-10RM or 11-15RM in older adults. The first group of 109 participants aged 60-75 years was recruited to measure their 1RM, 4-10RM, and 11-15RM of the three exercises. The 1RM prediction equations were developed by multiple regression analyses. A second group of participants with the similar physical characteristics to the first group was used to evaluate the equations. The actual measured 1RM of the second group correlated significantly to the predicted 1RM obtained from the equations (r values were from 0.633 to 0.985), and standard error of estimate ranged from 1.08 to 5.88. Therefore, the equations can be utilized to predict 1RM from submaximal skeletal muscle strength accurately for older adults
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
Pre-trained language models (PLMs) have been widely used to underpin various
downstream tasks. However, the adversarial attack task has found that PLMs are
vulnerable to small perturbations. Mainstream methods adopt a detached
two-stage framework to attack without considering the subsequent influence of
substitution at each step. In this paper, we formally model the adversarial
attack task on PLMs as a sequential decision-making problem, where the whole
attack process is sequential with two decision-making problems, i.e., word
finder and word substitution. Considering the attack process can only receive
the final state without any direct intermediate signals, we propose to use
reinforcement learning to find an appropriate sequential attack path to
generate adversaries, named SDM-Attack. Extensive experimental results show
that SDM-Attack achieves the highest attack success rate with a comparable
modification rate and semantic similarity to attack fine-tuned BERT.
Furthermore, our analyses demonstrate the generalization and transferability of
SDM-Attack. The code is available at https://github.com/fduxuan/SDM-Attack
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
DDT: Dual-branch Deformable Transformer for Image Denoising
Transformer is beneficial for image denoising tasks since it can model
long-range dependencies to overcome the limitations presented by inductive
convolutional biases. However, directly applying the transformer structure to
remove noise is challenging because its complexity grows quadratically with the
spatial resolution. In this paper, we propose an efficient Dual-branch
Deformable Transformer (DDT) denoising network which captures both local and
global interactions in parallel. We divide features with a fixed patch size and
a fixed number of patches in local and global branches, respectively. In
addition, we apply deformable attention operation in both branches, which helps
the network focus on more important regions and further reduces computational
complexity. We conduct extensive experiments on real-world and synthetic
denoising tasks, and the proposed DDT achieves state-of-the-art performance
with significantly fewer computational costs.Comment: The code is avaliable at: https://github.com/Merenguelkl/DD
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