Effect of non-invasive ventilator in combination with tiotropium bromide on pulmonary function and sleep quality of patients with chronic obstructive pulmonary disease complicated with obstructive sleep apnea-hypopnea syndrome

Purpose: To study the influence of non-invasive ventilator and tiotropium bromide on pulmonary function and sleep quality of patients with chronic obstructive pulmonary disease (COPD) combined with obstructive sleep apnea-hypopnea syndrome (OSAHS).Methods: One hundred and twenty patients with COPD-OSAHS were selected and randomly assigned to control group (CG) and treatment group (TG), with 60 subjects in each group. Non-invasive ventilator therapy was used in both groups, based on conventional therapy, while tiotropium bromide was added in TG. Treatment effectiveness in the two groups was evaluated and compared.Results: Total effectiveness was significantly higher in TG than in CG. Post-therapy arterial oxygen saturation (SaO2) and oxygen partial pressure (PaO2) were increased, while partial pressure of carbon dioxide (PaCO2) and lactic acid (Lac) were decreased in both groups (p < 0.05). Post-treatment values of indices of lung function, viz, forced expiratory volume (FEV1), forced vital capacity (FVC) and FEV1/FVC ratio were higher than the corresponding pre-treatment levels, and also values were significantly higher in TG than in CG (p < 0.05). Average sleep time, apnea and hypopnea index (AHI) and mechanical ventilation time of TG were less than those of CG. There were lower levels of Creactive protein (CRP), procalcitonin (PCT) and interleukin-17 (IL-17) in TG than in CG. During the treatment, no obvious adverse reaction was seen in both groups.Conclusion: Non-invasive ventilator, in combination with tiotropium bromide, is more effective in the treatment of COPD-OSAHS than the use of non-invasive ventilator alone. However, further clinical trials are required before its adoption in clinical practice

Power allocation for D2D communications in heterogeneous networks

In this paper, we study power allocation for D2D communications in heterogeneous networks utilizing game theory approach to improve the performance of the whole system. Given D2D's underlay status in the system, Stackelberg game framework is well suited for the situation. In our scheme, macrocell system and femtocell system are considered as two leaders and D2D pairs are considered as the follower, forming a two-leader-one-follower Stackelberg game. The leaders act first, charging some fees from the follower for using the channel and causing interference to jeopardize their communication equality. The follower observes the leaders' behavior and develops its strategy based on the prices offered by the leaders. We analyse the procedure and obtain the Stackeberg equilibrium, which determines the optimal prices for the leaders and optimal transmit power for the follower. In the end, simulations are executed to validate the proposed allocation method, which significantly improves data rate of user equipments. ? 2014 Global IT Research Institute (GIRI).EICPCI-S(ISTP)

Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation

We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object interactions. The dataset includes synchronized egocentric and exocentric images sampled from the recent Assembly101 dataset, in which participants assemble and disassemble take-apart toys. To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset. Our annotation model uses multi-view feature fusion and an iterative refinement scheme, and achieves an average keypoint error of 4.20 mm, which is 85% lower than the error of the original annotations in Assembly101. AssemblyHands provides 3.0M annotated images, including 490K egocentric images, making it the largest existing benchmark dataset for egocentric 3D hand pose estimation. Using this data, we develop a strong single-view baseline of 3D hand pose estimation from egocentric images. Furthermore, we design a novel action classification task to evaluate predicted 3D hand poses. Our study shows that having higher-quality hand poses directly improves the ability to recognize actions.Comment: CVPR 2023. Project page: https://assemblyhands.github.io

A Lifting Relation from Macroscopic Variables to Mesoscopic Variables in Lattice Boltzmann Method: Derivation, Numerical Assessments and Coupling Computations Validation

In this paper, analytic relations between the macroscopic variables and the mesoscopic variables are derived for lattice Boltzmann methods (LBM). The analytic relations are achieved by two different methods for the exchange from velocity fields of finite-type methods to the single particle distribution functions of LBM. The numerical errors of reconstructing the single particle distribution functions and the non-equilibrium distribution function by macroscopic fields are investigated. Results show that their accuracy is better than the existing ones. The proposed reconstruction operator has been used to implement the coupling computations of LBM and macro-numerical methods of FVM. The lid-driven cavity flow is chosen to carry out the coupling computations based on the numerical strategies of domain decomposition methods (DDM). The numerical results show that the proposed lifting relations are accurate and robust

Joint Association of Dietary Pattern and Physical Activity Level with Cardiovascular Disease Risk Factors among Chinese Men: A Cross-Sectional Study

The purpose of this cross-sectional study was to investigate the joint associations of physical activity level (PAL) and dietary patterns in relation to cardiovascular disease (CVD) risk factors among Chinese men. The study population consisted of 13 511 Chinese males aged 18–59 years from the 2002 China National Nutrition and Health Survey. Based on dietary data collected by a food frequency questionnaire, four dietary patterns were identified and labeled as “Green Water” (high consumption of rice, vegetables, seafood, pork, and poultry), “Yellow Earth” (high consumption of wheat flour products and starchy tubers), “New Affluent” (high consumption of animal sourced foods and soybean products), and “Western Adopter” (high consumption of animal sourced foods, cakes, and soft drinks). From the information collected by a 1-year physical activity questionnaire, PAL was calculated and classified into 4 categories: sedentary, low active, active, and very active. As compared with their counterparts from the New Affluent pattern, participants who followed the Green Water pattern had a lower likelihood of abdominal obesity (AO; 50.2%), hypertension (HT; 37.9%), hyperglycemia (HG; 41.5%), elevated triglyceride (ETG; 14.5%), low HDL (LHDL; 39.8%), and metabolic syndrome (MS; 51.9%). When compared to sedentary participants, the odds ratio of participants with very active PAL was 0.62 for AO, 0.85 for HT, 0.71 for HG, 0.76 for ETG, 0.74 for LHDL, and 0.58 for MS. Individuals who followed both very active PAL and the Green Water pattern had a lower likelihood of CVD risk factors (AO: 65.8%, HT: 39.1%, HG: 57.4%, ETG: 35.4%, LHDL: 56.1%, and MS: 75.0%), compared to their counterparts who followed both sedentary PAL and the New Affluent pattern. In addition, adherence to both healthy dietary pattern and very active PAL presented a remarkable potential for CVD risk factor prevention

From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters