Practical Distributed Video Coding in Packet Lossy Channels

Improving error resilience of video communications over packet lossy channels is an important and tough task. We present a framework to optimize the quality of video communications based on distributed video coding (DVC) in practical packet lossy network scenarios. The peculiar characteristics of DVC indeed require a number of adaptations to take full advantage of its intrinsic robustness when dealing with data losses of typical real packet networks. This work proposes a new packetization scheme, an investigation of the best error-correcting codes to use in a noisy environment, a practical rate-allocation mechanism, which minimizes decoder feedback, and an improved side-information generation and reconstruction function. Performance comparisons are presented with respect to a conventional packet video communication using H.264/advanced video coding (AVC). Although currently the H.264/AVC rate-distortion performance in case of no loss is better than state-of-the-art DVC schemes, under practical packet lossy conditions, the proposed techniques provide better performance with respect to an H.264/AVC-based system, especially at high packet loss rates. Thus the error resilience of the proposed DVC scheme is superior to the one provided by H.264/AVC, especially in the case of transmission over packet lossy networks

Positive and unlabeled learning for user behavior analysis based on mobile internet traffic data

With the rapid development of wireless communication and mobile Internet, mobile phone becomes ubiquitous and functions as a versatile and smart system, on which people frequently interact with various mobile applications (Apps). Understanding human behaviors using mobile phone is significant for mobile system developers, for human-centered system optimization and better service provisioning. In this paper, we focus on mobile user behavior analysis and prediction based on mobile Internet traffic data. Real traffic flow data is collected from the public network of Internet Service Providers (ISPs), by high-performance network traffic monitors.We construct User-App bipartite network to represent the traffic interaction pattern between users and App servers. After mining the explicit and implicit features from User-App bipartite network, we propose two positive and unlabeled learning (PU learning) methods, including Spy-based PU learning and K-means-based PU learning, for App usage prediction and mobile video traffic identification. We firstly use the traffic flow data of QQ, a very famous messaging and social media application possessing high market share in China, as the experimental dataset for App usage prediction task. Then we use the traffic flow data from six popular Apps, including video intensive Apps (Youku, Baofeng, LeTV, Tudou) and other Apps (Meituan, Apple), as the experimental dataset for mobile video traffic identification task. Experimental results show that our proposed PU learning methods perform well in both tasks

A New Regularized Matrix Discriminant Analysis (R-MDA) Enabled Human-Centered EEG Monitoring Systems

The wider use of wearable devices for electroencephalogram (EEG) data capturing providesa very useful way for the monitoring and self-management of human health. However, the large volumesof data with high dimensions cause computational complexity in EEG data processing and pose a greatchallenge to the use of wearable EEG devices in healthcare. This paper proposes a new approach to extract thestructural information of EEG data and tackle the curse of dimensionality of the EEG data. A set of methodsfor dimensionality reduction (DR)-like linear discriminant analysis (LDA) and their improved methodshave been developed for EEG processing in the literature. However, the existing LDA-related methodssuffer from the singularity problem or expensive computational cost, and none of existing methods takeinto consideration the structure of the projection matrix, which is crucial for the extraction of the structuralinformation of the EEG data. In this paper, a new method called a regularized matrix discriminant analysis(R-MDA) is proposed for EEG feature representation and DR. In the R-MDA, the EEG data are representedas a data matrix, and projection vectors are reshaped to be a set of projection matrices stacking together. Byreformulating the LDA as a least-square formulation and imposing specified constraint on each projectionmatrix, the new R-MDA has been constructed to effectively reduce EEG dimensions and capturing thestructural information of the EEG data. Experimental results demonstrate that this new R-MDA outperformsthe existing LDA-related methods, including achieving improved accuracy with significant DR of the EEGdata. This offers an effective way to enable wearable EEG devices be applicable in human-centered healthmonitorin

From Eyes to Face Synthesis: a New Approach for Human-Centered Smart Surveillance

With the popularity of surveillance cameras and the development of deep learning, significant progress has been made in the field of smart surveillance. Face recognition is one of the most important yet challenging tasks in human-centered smart surveillance, especially in public security, criminal investigation and anti-terrorism, and so on. Although, the state-of-the-art algorithms for face recognition have achieved dramatically improved results and have been widely applied in authentication scenario, the occlusion problem on face is still one of the critical issues for personal identification in smart surveillance, especially in the occasion of terrorist searching and identification. To address this issue, this paper proposed a new approach for eyes-to-face synthesis and personal identification for human-centered smart surveillance. An end-toend network based on conditional generative adversarial networks (GAN) is designed to generate the face information based only on the available data of eyes region. To obtain photorealistic faces and identitypreserving information, a synthesis loss function based on feature loss, GAN loss, and total variation loss is proposed to guide the training process. Both the subject and objective experimental results demonstrated that the proposed method can preserve the identity based on eyes-only data, and provide a potential solution for the identification of person even in the case of face occlusion

Transcriptomics and Network Pharmacology Reveal the Protective Effect of Chaiqin Chengqi Decoction on Obesity-Related Alcohol-Induced Acute Pancreatitis via Oxidative Stress and PI3K/Akt Signaling Pathway

Obesity-related acute pancreatitis (AP) is characterized by increasing prevalence worldwide and worse clinical outcomes compared to AP of other etiologies. Chaiqin chengqi decoction (CQCQD), a Chinese herbal formula, has long been used for the clinical management of AP but its therapeutic actions and the underlying mechanisms have not been fully elucidated. This study has investigated the pharmacological mechanisms of CQCQD in a novel mouse model of obesity-related alcohol-induced AP (OA-AP). The mouse OA-AP model was induced by a high-fat diet for 12 weeks and subsequently two intraperitoneal injections of ethanol, CQCQD was administered 2 h after the first injection of ethanol. The severity of OA-AP was assessed and correlated with changes in transcriptomic profiles and network pharmacology in the pancreatic and adipose tissues, and further docking analysis modeled the interactions between compounds of CQCQD and their key targets. The results showed that CQCQD significantly reduced pancreatic necrosis, alleviated systemic inflammation, and decreased the parameters associated with multi-organ dysfunction. Transcriptomics and network pharmacology analysis, as well as further experimental validation, have shown that CQCQD induced Nrf2/HO-1 antioxidant protein response and decreased Akt phosphorylation in the pancreatic and adipose tissues. In vitro, CQCQD protected freshly isolated pancreatic acinar cells from H2O2-elicited oxidative stress and necrotic cell death. The docking results of AKT1 and the active compounds related to AKT1 in CQCQD showed high binding affinity. In conclusion, CQCQD ameliorates the severity of OA-AP by activating of the antioxidant protein response and down-regulating of the PI3K/Akt signaling pathway in the pancreas and visceral adipose tissue