78 research outputs found
Intestinal Electric Stimulation Accelerates Whole Gut Transit and Promotes Fat Excrement in Conscious Rats
*_Introduction:_* Intestinal electric stimulation (IES) is proposed as a potential tool for the treatment of morbid obesity. Our previous study showed that IES with one pair of electrodes accelerated intestinal transit and decreased fat absorption in a segment of the jejunum in the anesthetized rats. The aims of this study were to assess the effects of IES on the whole gut transit and fat absorption in conscious rats, to examine the effects of multi-channel IES, and to explore the cholinergic mechanism behind the effects of IES. 
*_Methods:_* Thirty-eight male rats implanted with serosal electrodes were randomized into five groups: control without IES, 2/3 channel IES with short pulses, atropine and atropine plus IES. The whole gut transit and fat remained and emptied from the gut were analyzed after continuous 2-hour IES. 
*_Results:_* Two and three channel IES significantly accelerated phenol red (marker used for transit) excretion (ANOVA, P < 0.001). No significant difference was found between two and three channel IES. Two channel IES significantly increased the excretion of fat (P < 0.05). Atropine significantly blocked the accelerated transit induced by IES (ANOVA, P < 0.001). Correlation was found between the percentage of phenol red and fat retained in the whole gut (r = 0.497, P < 0.01). 
*_Conclusions:_* IES accelerates whole gut transit and promotes fat excrement in conscious rats, and these effects are mediated through the cholinergic nerves. These findings are in support of the concept that IES may be a promising treatment option for obesity
Cost-Efficient Computation Offloading and Service Chain Caching in LEO Satellite Networks
The ever-increasing demand for ubiquitous, continuous, and high-quality
services poses a great challenge to the traditional terrestrial network. To
mitigate this problem, the mobile-edge-computing-enhanced low earth orbit (LEO)
satellite network, which provides both communication connectivity and on-board
processing services, has emerged as an effective method. The main issue in LEO
satellites includes finding the optimal locations to host network functions
(NFs) and then making offloading decisions. In this article, we jointly
consider the problem of service chain caching and computation offloading to
minimize the overall cost, which consists of task latency and energy
consumption. In particular, the collaboration among satellites, the network
resource limitations, and the specific operation order of NFs in service chains
are taken into account. Then, the problem is formulated and linearized as an
integer linear programming model. Moreover, to accelerate the solution, we
provide a greedy algorithm with cubic time complexity. Numerical investigations
demonstrate the effectiveness of the proposed scheme, which can reduce the
overall cost by around 20% compared to the nominal case where NFs are served in
data centers.Comment: 10 pages, 3 figure
Gait Recognition
Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, humanācomputer interaction and so on. In this chapter, we introduce the stateāofātheāart gait recognition techniques, which include 3Dābased and 2Dābased methods, in the first part. And considering the advantages of 3Dābased methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be viewāinvariant, in the third part. And some gaitābased applications are introduced
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Hashing is an effective technique to address the large-scale recommendation
problem, due to its high computation and storage efficiency on calculating the
user preferences on items. However, existing hashing-based recommendation
methods still suffer from two important problems: 1) Their recommendation
process mainly relies on the user-item interactions and single specific content
feature. When the interaction history or the content feature is unavailable
(the cold-start problem), their performance will be seriously deteriorated. 2)
Existing methods learn the hash codes with relaxed optimization or adopt
discrete coordinate descent to directly solve binary hash codes, which results
in significant quantization loss or consumes considerable computation time. In
this paper, we propose a fast cold-start recommendation method, called
Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these
problems. Specifically, a low-rank self-weighted multi-feature fusion module is
designed to adaptively project the multiple content features into binary yet
informative hash codes by fully exploiting their complementarity. Additionally,
we develop a fast discrete optimization algorithm to directly compute the
binary hash codes with simple operations. Experiments on two public
recommendation datasets demonstrate that MFDCF outperforms the
state-of-the-arts on various aspects
GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework
Many gait recognition methods first partition the human gait into N-parts and
then combine them to establish part-based feature representations. Their gait
recognition performance is often affected by partitioning strategies, which are
empirically chosen in different datasets. However, we observe that strips as
the basic component of parts are agnostic against different partitioning
strategies. Motivated by this observation, we present a strip-based multi-level
gait recognition network, named GaitStrip, to extract comprehensive gait
information at different levels. To be specific, our high-level branch explores
the context of gait sequences and our low-level one focuses on detailed posture
changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the
strip-based feature representations by directly taking each strip of the human
body as the basic unit. Moreover, we propose a novel multi-branch structure,
called Enhanced Convolution Module (ECM), to extract different representations
of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the
Frame-Level feature extractor (FL) and SPB, and has two obvious advantages:
First, each branch focuses on a specific representation, which can be used to
improve the robustness of the network. Specifically, ST aims to extract
spatial-temporal features of gait sequences, while FL is used to generate the
feature representation of each frame. Second, the parameters of the ECM can be
reduced in test by introducing a structural re-parameterization technique.
Extensive experimental results demonstrate that our GaitStrip achieves
state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202
Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead
Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area
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