Multi-Cell, Multi-Channel Scheduling with Probabilistic Per-Packet Real-Time Guarantee

For mission-critical sensing and control applications such as those to be enabled by 5G Ultra-Reliable, Low-Latency Communications (URLLC), it is critical to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks with implicit deadline constraints, but they have not considered real-world complexities such as inter-cell interference and multiple communication channels. Towards ensuring PPRC in multi-cell, multi-channel wireless networks, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}. The LDP algorithm is suitable for distributed implementation, and it ensures probabilistic per-packet real-time guarantee for multi-cell, multi-channel networks with general deadline constraints. We also address the associated challenge of the schedulability test of PPRC traffic. In particular, we propose the concept of \emph{feasible set} and identify a closed-form sufficient condition for the schedulability of PPRC traffic. We propose a distributed algorithm for the schedulability test, and the algorithm includes a procedure for finding the minimum sum work density of feasible sets which is of interest by itself. We also identify a necessary condition for the schedulability of PPRC traffic, and use numerical studies to understand a lower bound on the approximation ratio of the LDP algorithm. We experimentally study the properties of the LDP algorithm and observe that the PPRC traffic supportable by the LDP algorithm is significantly higher than that of a state-of-the-art algorithm

Performances Comparison between Real-Time Auto-Tuning PID and Conventional PID Controller for a Dairy Industrial Evaporation Process Control

In this study, an industrial milk evaporation process is introduced and a mathematical model of a multi-effect falling film evaporator is developed using MATLAB/Simulink to evaluate the performance of the controller. A real-time closed-loop auto-tuning PID controller is presented as a candidate control strategy for the evaporation process. The simulation results controlled by autotuning and conventional PID are compared and discussed and the performance improvement by the auto-tuning PID controller is illustrated

The Motivation of Capital-giving in Crowdfunding Market: A Self-determination Theory Perspective

How to promote crowd-funding results successfully are crucial to crowdfunding platforms and crowdfunding projects. The results of crowd-funding projects are determined by investors’ subjective behavior, which is triggered by some certain motivations. However, for different investors, the motivation toward a speculative behavior may be different. Thus, it is very necessary to explore and analyze the composition of the motivations behind each investor’s decision. In this paper, we identify different motivation modes mainly influenced by the project description, which will be beneficial to identify the investment intention of each investor. Based on the self-determination theory, we first create the corpus targeting different motives by means of the text mining method. Then, we classify the project description and project investment options. Last, we conduct an econometric model to examine the effect of investor’s motives on crowd-funding results based on the real dataset from Indiegogo Platform

A Model-Driven Method for Quality Reviews Detection: An Ensemble Model of Feature Selection

With the rapid growth of e-commerce and user-generated content online, the increasing product online reviews have significant influence on both buyers and sellers. However, among the thousands of online reviews, only the reviews of high-quality matters to the market, thus quality reviews detection rises in response to the requirement of retrieving authentic feedbacks from consumers. In this paper, a state-of-the-art ensemble model, gradient boosting decision trees (GBDT), is applied to select useful features for quality evaluation of online reviews. Firstly, four types of features are extracted based on information adoption theory. Then, the GBDT model is adopted to select useful features for quality reviews detection. At last, comparative experiments are conducted through online reviews of searching goods, based on two baseline models such as Decision Tree and Logistic Regression, and the results show that GBDT model achieves a better performance in detecting reviews of high-quality. This research indicates that product attributes, reviewer characteristics and objectiveness of reviews are key ingredients in high quality reviews

Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms

: Management of poultry farms in China mostly relies on manual labor. Since such a large amount of valuable data for the production process either are saved incomplete or saved only as paper documents, making it very difficult for data retrieve, processing and analysis. An integrated cloud-based data management system (CDMS) was proposed in this study, in which the asynchronous data transmission, distributed file system, and wireless network technology were used for information collection, management and sharing in large-scale egg production. The cloud-based platform can provide information technology infrastructures for different farms. The CDMS can also allocate the computing resources and storage space based on demand. A real-time data acquisition software was developed, which allowed farm management staff to submit reports through website or smartphone, enabled digitization of production data. The use of asynchronous transfer in the system can avoid potential data loss during the transmission between farms and the remote cloud data center. All the valid historical data of poultry farms can be stored to the remote cloud data center, and then eliminates the need for large server clusters on the farms. Users with proper identification can access the online data portal of the system through a browser or an APP from anywhere worldwide

Variational Denoising Network: Toward Blind Noise Modeling and Removal

Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising.Comment: 11 pages, 4 figure

MicroRNA-140-5p inhibits cellular proliferation, migration and invasion by downregulating AKT/STAT3/NF-κB pathway in breast carcinoma cells

MicroRNA-140-5p (miR-140-5p) plays a pivotal role in human cancers. However, its role and molecular mechanisms in breast carcinoma are not fully explored. Using miR-140-5p transfected breast cancer cell line MDA-MB-231, several in vitro experiments were performed and described in this paper. They consist of the cell proliferation assay, wound healing assay, transwell assay, colony formation assays and qRTPCR. Expression levels of target proteins were determined using western blotting. In addition, experiments on animal models were performed to study the possible role of miR-140-5p in tumorigenesis of breast carcinoma cells. The induction of experimental breast tumor in mice model was achieved through the incorporation of MDA-MB-231 tumor cells subcutaneously into the middle left side of the mice. The results showed that miR-140-5p up-regulation significantly suppresses proliferation, cellular invasion and migration of breast carcinoma cells. Furthermore, miR-140-5p up-regulation stops breast cancer cells at G0/G1 phase. The results of the animal model indicated that up-regulation of miR-140-5p suppresses its tumorigenic ability. Moreover, we also found that miR-140-5p up-regulation reduces the phosphorylation level of STAT3, p65, and AKT. In addition, miR-140-5p overexpression significantly decreases CDK2 expression while increasing E-cadherin expression level. These data revealed that miR-140-5p suppressed tumor progression of breast carcinoma cells through inhibition of the AKT/STAT3/NF-κB pathway. Taken the present study results together, we can conclude that miR-140-5p may act as a novel target in microRNA-targeting anticancer strategy for the treatment of breast cancer

A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification

Cloth-changing person reidentification (ReID) is a newly emerging research topic that aims to retrieve pedestrians whose clothes are changed. Since the human appearance with different clothes exhibits large variations, it is very difficult for existing approaches to extract discriminative and robust feature representations. Current works mainly focus on body shape or contour sketches, but the human semantic information and the potential consistency of pedestrian features before and after changing clothes are not fully explored or are ignored. To solve these issues, in this work, a novel semantic-aware attention and visual shielding network for cloth-changing person ReID (abbreviated as SAVS) is proposed where the key idea is to shield clues related to the appearance of clothes and only focus on visual semantic information that is not sensitive to view/posture changes. Specifically, a visual semantic encoder is first employed to locate the human body and clothing regions based on human semantic segmentation information. Then, a human semantic attention module (HSA) is proposed to highlight the human semantic information and reweight the visual feature map. In addition, a visual clothes shielding module (VCS) is also designed to extract a more robust feature representation for the cloth-changing task by covering the clothing regions and focusing the model on the visual semantic information unrelated to the clothes. Most importantly, these two modules are jointly explored in an end-to-end unified framework. Extensive experiments demonstrate that the proposed method can significantly outperform state-of-the-art methods, and more robust features can be extracted for cloth-changing persons. Compared with FSAM (published in CVPR 2021), this method can achieve improvements of 32.7% (16.5%) and 14.9% (-) on the LTCC and PRCC datasets in terms of mAP (rank-1), respectively.Comment: arXiv admin note: text overlap with arXiv:2108.0452