153 research outputs found
Effect of rPMS on N-type calcium channel in rats with neuropathic pain
Purpose: To investigate the effect of repetitive peripheral magnetic stimulation (rPMS) on N-type calcium channel of rats with neuropathic pain (NP).
Methods: Thirty-two Sprague-Dawley (SD) rats were randomized into control, mock surgical, model, and rPMS groups. For the model and rPMS groups, rat NP models were made based on chronic constriction injury (CCI) model from January 2018 to June 2019; the mock surgical group was treated to expose the sciatic nerve, while the control group received no treatment.
Results: Compared to the control group, the model group demonstrated a prominent increase in spontaneous pain-like behaviors, threshold of claw withdrawal in reaction to mechanical stimulation, substance P, glutamic acid, calcitonin gene-related peptide (CGRP), and calcium current, with a decrease in paw withdrawal thermal latency (PwTL) (p < 0.05). In comparison to the model group, alleviated spontaneous pain-like behaviors, reduced threshold of claw withdrawal in reaction to mechanical stimulation, substance P, glutamic acid, CGRP, and calcium current rPMS, with increased PwTL were observed in the rPMS group (p < 0.05).
Conclusion: rPMS alleviates NP syndromes and inhibits the activity of N-type calcium channel in rats. This finding provides a theoretical basis and reference for the clinical application of rPMS in the treatment of NP.
Keywords: Repetitive peripheral magnetic stimulation (rPMS); Neuropathic pain; N-type calcium channel; Paw withdrawal thermal latenc
Optimized Merge Sort on Modern Commodity Multi-core CPUs
Sorting is a kind of widely used basic algorithms. As the high performance computing devices are increasingly common, more and more modern commodity machines have the capability of parallel concurrent computing. A new implementation of sorting algorithms is proposed to harness the power of newer SIMD operations and multi-core computing provided by modern CPUs. The algorithm is hybrid by optimized bitonic sorting network and multi-way merge. New SIMD instructions provided by modern CPUs are used in the bitonic network implementation, which adopted a different method to arrange data so that the number of SIMD operations is reduced. Balanced binary trees are used in multi-way merge, which is also different with former implementations. Efforts are also paid on minimizing data moving in memory since merge sort is a kind of memory-bound application. The performance evaluation shows that the proposed algorithm is twice as fast as the sort function in C++ standard library when only single thread is used. It also outperforms radix sort implemented in Boost library
Supervised local descriptor learning for human action recognition
Local features have been widely used in computer vision tasks, e.g., human action recognition, but it tends to be an extremely challenging task to deal with large-scale local features of high dimensionality with redundant information. In this paper, we propose a novel fully supervised local descriptor learning algorithm called discriminative embedding method based on the image-to-class distance (I2CDDE) to learn compact but highly discriminative local feature descriptors for more accurate and efficient action recognition. By leveraging the advantages of the I2C distance, the proposed I2CDDE incorporates class labels to enable fully supervised learning of local feature descriptors, which achieves highly discriminative but compact local descriptors. The objective of our I2CDDE is to minimize the I2C distances from samples to their corresponding classes while maximizing the I2C distances to the other classes in the low-dimensional space. To further improve the performance, we propose incorporating a manifold regularization based on the graph Laplacian into the objective function, which can enhance the smoothness of the embedding by extracting the local intrinsic geometrical structure. The proposed I2CDDE for the first time achieves fully supervised learning of local feature descriptors. It significantly improves the performance of I2C-based methods by increasing the discriminative ability of local features while greatly reducing the computational burden by dimensionality reduction to handle large-scale data. We apply the proposed I2CDDE algorithm to human action recognition on four widely used benchmark datasets. The results have shown that I2CDDE can significantly improve I2C-based classifiers and achieves state-of-the-art performance
Collaborative Authentication for 6G Networks: An Edge Intelligence based Autonomous Approach
The conventional device authentication of wireless networks usually relies on
a security server and centralized process, leading to long latency and risk of
single-point of failure. While these challenges might be mitigated by
collaborative authentication schemes, their performance remains limited by the
rigidity of data collection and aggregated result. They also tend to ignore
attacker localization in the collaborative authentication process. To overcome
these challenges, a novel collaborative authentication scheme is proposed,
where multiple edge devices act as cooperative peers to assist the service
provider in distributively authenticating its users by estimating their
received signal strength indicator (RSSI) and mobility trajectory (TRA). More
explicitly, a distributed learning-based collaborative authentication algorithm
is conceived, where the cooperative peers update their authentication models
locally, thus the network congestion and response time remain low. Moreover, a
situation-aware secure group update algorithm is proposed for autonomously
refreshing the set of cooperative peers in the dynamic environment. We also
develop an algorithm for localizing a malicious user by the cooperative peers
once it is identified. The simulation results demonstrate that the proposed
scheme is eminently suitable for both indoor and outdoor communication
scenarios, and outperforms some existing benchmark schemes
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing
By leveraging the concept of mobile edge computing (MEC), massive amount of
data generated by a large number of Internet of Things (IoT) devices could be
offloaded to MEC server at the edge of wireless network for further
computational intensive processing. However, due to the resource constraint of
IoT devices and wireless network, both the communications and computation
resources need to be allocated and scheduled efficiently for better system
performance. In this paper, we propose a joint computation offloading and
multi-user scheduling algorithm for IoT edge computing system to minimize the
long-term average weighted sum of delay and power consumption under stochastic
traffic arrival. We formulate the dynamic optimization problem as an
infinite-horizon average-reward continuous-time Markov decision process (CTMDP)
model. One critical challenge in solving this MDP problem for the multi-user
resource control is the curse-of-dimensionality problem, where the state space
of the MDP model and the computation complexity increase exponentially with the
growing number of users or IoT devices. In order to overcome this challenge, we
use the deep reinforcement learning (RL) techniques and propose a neural
network architecture to approximate the value functions for the post-decision
system states. The designed algorithm to solve the CTMDP problem supports
semi-distributed auction-based implementation, where the IoT devices submit
bids to the BS to make the resource control decisions centrally. Simulation
results show that the proposed algorithm provides significant performance
improvement over the baseline algorithms, and also outperforms the RL
algorithms based on other neural network architectures
Representation Disparity-aware Distillation for 3D Object Detection
In this paper, we focus on developing knowledge distillation (KD) for compact
3D detectors. We observe that off-the-shelf KD methods manifest their efficacy
only when the teacher model and student counterpart share similar intermediate
feature representations. This might explain why they are less effective in
building extreme-compact 3D detectors where significant representation
disparity arises due primarily to the intrinsic sparsity and irregularity in 3D
point clouds. This paper presents a novel representation disparity-aware
distillation (RDD) method to address the representation disparity issue and
reduce performance gap between compact students and over-parameterized
teachers. This is accomplished by building our RDD from an innovative
perspective of information bottleneck (IB), which can effectively minimize the
disparity of proposal region pairs from student and teacher in features and
logits. Extensive experiments are performed to demonstrate the superiority of
our RDD over existing KD methods. For example, our RDD increases mAP of
CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher
performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with
arXiv:2205.15156 by other author
DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs
Neural architecture search (NAS) proves to be among the effective approaches
for many tasks by generating an application-adaptive neural architecture, which
is still challenged by high computational cost and memory consumption. At the
same time, 1-bit convolutional neural networks (CNNs) with binary weights and
activations show their potential for resource-limited embedded devices. One
natural approach is to use 1-bit CNNs to reduce the computation and memory cost
of NAS by taking advantage of the strengths of each in a unified framework,
while searching the 1-bit CNNs is more challenging due to the more complicated
processes involved. In this paper, we introduce Discrepant Child-Parent Neural
Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs, based on a new
framework of searching the 1-bit model (Child) under the supervision of a
real-valued model (Parent). Particularly, we first utilize a Parent model to
calculate a tangent direction, based on which the tangent propagation method is
introduced to search the optimized 1-bit Child. We further observe a coupling
relationship between the weights and architecture parameters existing in such
differentiable frameworks. To address the issue, we propose a decoupled
optimization method to search an optimized architecture. Extensive experiments
demonstrate that our DCP-NAS achieves much better results than prior arts on
both CIFAR-10 and ImageNet datasets. In particular, the backbones achieved by
our DCP-NAS achieve strong generalization performance on person
re-identification and object detection.Comment: Accepted by International Journal of Computer Visio
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