Task-Oriented Communication for Edge Video Analytics

With the development of artificial intelligence (AI) techniques and the increasing popularity of camera-equipped devices, many edge video analytics applications are emerging, calling for the deployment of computation-intensive AI models at the network edge. Edge inference is a promising solution to move the computation-intensive workloads from low-end devices to a powerful edge server for video analytics, but the device-server communications will remain a bottleneck due to the limited bandwidth. This paper proposes a task-oriented communication framework for edge video analytics, where multiple devices collect the visual sensory data and transmit the informative features to an edge server for processing. To enable low-latency inference, this framework removes video redundancy in spatial and temporal domains and transmits minimal information that is essential for the downstream task, rather than reconstructing the videos at the edge server. Specifically, it extracts compact task-relevant features based on the deterministic information bottleneck (IB) principle, which characterizes a tradeoff between the informativeness of the features and the communication cost. As the features of consecutive frames are temporally correlated, we propose a temporal entropy model (TEM) to reduce the bitrate by taking the previous features as side information in feature encoding. To further improve the inference performance, we build a spatial-temporal fusion module at the server to integrate features of the current and previous frames for joint inference. Extensive experiments on video analytics tasks evidence that the proposed framework effectively encodes task-relevant information of video data and achieves a better rate-performance tradeoff than existing methods

LDMIC: Learning-based Distributed Multi-view Image Coding

Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as residual information. This demands collaboration among cameras and enforces the epipolar geometric constraint between different views, which makes it challenging to deploy these methods in distributed camera systems with randomly overlapping fields of view. Meanwhile, distributed source coding theory indicates that efficient data compression of correlated sources can be achieved by independent encoding and joint decoding, which motivates us to design a learning-based distributed multi-view image coding (LDMIC) framework. With independent encoders, LDMIC introduces a simple yet effective joint context transfer module based on the cross-attention mechanism at the decoder to effectively capture the global inter-view correlations, which is insensitive to the geometric relationships between images. Experimental results show that LDMIC significantly outperforms both traditional and learning-based MIC methods while enjoying fast encoding speed. Code will be released at https://github.com/Xinjie-Q/LDMIC.Comment: Accepted by ICLR 202

Effect of blocking Ras signaling pathway with K-Ras siRNA on apoptosis in esophageal squamous carcinoma cells

AbstractObjectiveTo study the effect of RNAi silencing of the K-Ras gene on Ras signal pathway activity in EC9706 esophageal cancer cells.MethodsEC9706 cells were treated in the following six groups: blank group (no transfection), negative control group (transfection no-carrier), transfection group (transfected with pSilencer-siK-ras), taxol chemotherapy group, taxol chemotherapy plus no-carrier group, taxol chemotherapy plus transfection group. Immunocytochemistry, Reverse transcription-polymerase chain reaction and western blotting were used to analyze the expression of MAPK1 (mitogen-activated protein kinases 1) and cyclin D1 in response to siRNA (small interfering RNA) transfection and taxol treatment.ResultsK-Ras (K-Ras gene) siRNA transfection of EC9706 esophageal squamous carcinoma cells decreased the expression of K-Ras, MAPK1 and cyclin D1 at the mRNA and protein level. Reverse transcription-polymerase chain reaction indicated that the expression levels of MAPK1 and cyclin D1 mRNAs were significantly lower in the transfection group than in the blank group (P<0.05). Western blotting showed that 72 h after EC9706 cell transfection, the expression levels of MAPK1 and cyclin D1 proteins had decreased in all groups, and the expression levels in the transfection group were significantly inhibited as compared with the blank group. Apoptosis increased significantly in the transfection group or after addition of taxol as compared with the blank group and the no-carrier group. The degree of apoptosis in the taxol plus transfection group was more severe.ConclusionApoptosis increased significantly in EC9706 esophageal carcinoma cells after siRNA-mediated inhibition of Ras signaling, with the most obvious increase observed in the transfection plus taxol chemotherapy group. Ras knockdown therefore increased cellular sensitivity to the chemotherapeutic agent, taxol. Ras knockdown also down-regulated the expression of the downstream genes, MAPK1 and cyclin D1, thus inhibiting the growth, proliferation and metabolism of esophageal cancer cells

Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce

Clinical phenotype and genotype of children with GABAA receptor α1 subunit gene-related epilepsy

ObjectiveThis study aimed to summarize the clinical phenotype and genotype of children with epilepsy caused by GABRA1 gene variants.MethodsEight epilepsy patients, who were admitted to Qilu Hospital of Shandong University from 2015 to 2021, were enrolled in the study. GABRA1 gene variants were detected by whole-exome sequencing. Epilepsy clinical manifestations, electroencephalography, neuroimaging characteristics and treatment methods were retrospectively analyzed.ResultsAmong the eight patients, four were males and four were females. Epilepsy onset age was between 3 and 8 months of age. Two patients had a family history of epilepsy. Six cases were de novo variants, and two were hereditary variants. Two children carried the same pathogenic variants, and five carried novel pathogenic variants that had not been reported internationally. The types of seizures were diverse, including focal seizures in five cases, generalized tonic-clonic seizures in five cases, and spasms in two cases. Electroencephalography of seven cases showed abnormal background rhythms, and six cases showed abnormal discharge during the interictal period. No obvious abnormalities were found on magnetic resonance imaging in five cases. All eight children had different degrees of developmental retardation.ConclusionDe novo pathogenic variants in GABRA1 are more common than inherited pathogenic variants, and most epilepsy symptoms begin in the first year of life, manifesting with a variety of seizure types and developmental delays. Conventional treatment usually involves one or more drugs; although drug treatment can control seizures in some cases, cognitive and developmental deficits often exist. The five newly discovered pathogenic variants enrich the GABRA1 gene pathogenic variant spectrum