Rendering high-resolution photorealistic images in real time is challenging for video games and emerging virtual reality headsets. Thus, fovea sampled image reconstruction and super-resolution technologies become more and more crucial. However, most current methods process foveated reconstruction and super-resolution separately, which is slow. To address this issue, we propose a novel multi-scale spatiotemporal kernel prediction network for real-time foveated rendering that can perform sparse peripheral region reconstruction and supersampling simultaneously, resulting in a substantial reduction in rendering computation without visually noticeable quality degradation. Thanks to the multiscale kernel prediction architecture, different levels of details can be effectively preserved. Furthermore, we introduce an effective motion vector mask to explicitly identify occluded regions, which can help to use historical information more effectively. Our network runs in real time and achieves superior image quality and better inter-frame stability than existing methods.

With technological advancements, virtual reality (VR), once limited to high-end professional applications, is rapidly expanding into entertainment and broader consumer domains. However, the inherent contradiction between mobile hardware computing power and the demand for high-resolution, high-refresh-rate rendering has intensified, leading to critical bottlenecks, including frame latency and power overload, which constrain large-scale applications of VR systems. This study systematically analyzes four key technologies for efficient VR rendering: (1) foveated rendering, which dynamically reduces rendering precision in peripheral regions based on the physiological characteristics of the human visual system (HVS), thereby significantly decreasing graphics computation load; (2) stereo rendering, optimized through consistent stereo rendering acceleration algorithms; (3) cloud rendering, utilizing object-based decomposition and illumination-based decomposition for distributed resource scheduling; and (4) low-power rendering, integrating parameter-optimized rendering, super-resolution technology, and frame-generation technology to enhance mobile energy efficiency. Through a systematic review of the core principles and optimization approaches of these technologies, this study establishes research benchmarks for developing efficient VR systems that achieve high fidelity and low latency while providing further theoretical support for the engineering implementation and industrial advancement of VR rendering technologies.

MOTIVATION: Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity. RESULTS: Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest. AVAILABILITY AND IMPLEMENTATION: https://github.com/song0309/asGNN/.

Artificial Intelligence of Things (AIoT) applications have advanced rapidly. However, most of them are inherently vulnerable to security threats and may be the source of spoofing attacks, and meanwhile, in AIoT systems, the transfer of real-time data from terminals and the cloud strains network bandwidth. To defend against attacks, save network forwarding resources, and relieve authentication pressure on the receiver end, it is essential to verify the source identity of AIoT terminals on the forwarding path. In this article, we propose PDIV, a probabilistic and distributed identity validation solution for AIoT applications. In the framework of PDIV, honest forwarders can verify the authenticity of the source identity of packets with a certain probability and filter spoofed packets to prevent them from spreading, reduce end-to-end network latency, and increase throughput as much as possible. Additionally, PDIV, a blockchain-based system that uses Merkle Patricia Trie (MPT) on the blockchain, makes it practical and efficient to realize distributed storage and verification of identity information. Moreover, we theorize about the tradeoff between PDIV network performance and detection effectiveness, as well as how PDIV enables defenses against different attacks, such as spoofing and Distributed Denial-of-Service attacks. Furthermore, we implement PDIV on network simulator version 3 (NS3) and evaluate its performance. The simulation results demonstrate that PDIV can prevent the spread of spoofed packets effectively and PDIV works better than currently available blockchain-based public-key infrastructure (PKI) approaches in terms of network latency.

Internet of Things (IoT) devices have achieved rapid development but most of them are vulnerable to spoofing attacks and spoofing-related attacks. It is crucial to verify source identity at the near-source end to defend against attacks, save network forwarding resources, and relieve the authentication pressure on the receiver end. In this paper, we propose Smart-PKI, a blockchain-based distributed identity validation scheme for IoT Devices. In the architecture of Smart-PKI, near-source forwarders can verify the authenticity of the source identity of packets and can filter spoofed packets. Besides, we apply Merkle Patricia Trie (MPT) to the Smart-PKI blockchain to enable lightweight blockchain copy storage and efficient retrieval and verification of identity information on forwarders. Meanwhile, Smart-PKI proposes an identity restoration mechanism and enables solutions for the attacks caused by public and private key compromise. Furthermore, we implement Smart-PKI on Network Simulator Version 3 (NS3) and evaluate its performance against reflection denial-of-service (DDoS) attacks. The simulation results demonstrate the effectiveness and efficiency of Smart-PKI and it outperforms existing blockchain-based PKI solutions for IoT devices in terms of network latency for verifying certificates.

Wireless networks are vulnerable to many attacks due to its dynamic environments. Unrestricted forwarding of packets from untrusted sources in wireless networks has caused many serious security threats and a great waste of network resources. This paper presents RepuFilter, a probabilistic packet filtering scheme based on trust evaluation, which enables a shared data plane to provide security services for wireless networks. RepuFilter proposes a dynamic trust evaluation model based on the transitivity of trust evaluation between network users, and applies the model to packet filtering. In the framework of RepuFilter, forwarders verify packets with a certain probability, and discard packets from untrusted sources, to prevent the packets from being spread, and reduce inspection expenses as much as possible. We implement RepuFilter and evaluate its performance based on Network Simulator Version 3 (NS3). Simulation results prove that RepuFilter can resist the spread of untrusted packets more effectively than the classic and latest trust models and RepuFilter can meet current network performance requirements,