Interactive 3D generation is gaining momentum and capturing extensive attention for its potential to create immersive virtual experiences. However, a critical challenge in current 3D generation technologies lies in achieving real-time interactivity. To address this issue, we introduce WonderTurbo, the first real-time interactive 3D scene generation framework capable of generating novel perspectives of 3D scenes within 0.72 seconds. Specifically, WonderTurbo accelerates both geometric and appearance modeling in 3D scene generation. In terms of geometry, we propose StepSplat, an innovative method that constructs efficient 3D geometric representations through dynamic updates, each taking only 0.26 seconds. Additionally, we design QuickDepth, a lightweight depth completion module that provides consistent depth input for StepSplat, further enhancing geometric accuracy. For appearance modeling, we develop FastPaint, a 2-steps diffusion model tailored for instant inpainting, which focuses on maintaining spatial appearance consistency. Experimental results demonstrate that WonderTurbo achieves a remarkable 15X speedup compared to baseline methods, while preserving excellent spatial consistency and delivering high-quality output.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Towards building online analytical services on big heterogeneous graphs, we study the problem of the multithreading graph aggregation. The purpose is to exploit the thread-level parallelism for accelerating the graph aggregation process, which is both data and computation intensive. We identify the sources of parallelization latency caused by multifarious factors, including data distributions and contentions, uneven workload assignments, logical aggregation plan obstructions, etc. To cope with these problems, we investigate a parallelization solution for graph aggregation with a number of threads packaged as threadblocks, categorize the parallelization latency as the thread-level and threadblock-level latency, and propose a series of optimization techniques for alleviating or eliminating the latency on different levels. The solution supports different aggregate functions, scales up to large number of threads, and scales out to big heterogeneous graphs. Experiments on real datasets show that our solution achieves up to 60x acceleration with 256 threads compared to the non-parallelized solution.

This paper studies the problem of predicting the spectrum efficiency (SE) for massive multiple-input multiple-output (MIMO) empowered 5G networks based on the reference signal received power (RSRP) collected from the drive test (DT). This problem is challenging because there is no precise model between the RSRP and the SE. The SE not only depends on the RSRP, which only captures the statistic of the channel, but also the beamforming strategy of the serving base station (BS) and the interference from the neighboring cells, which are not measured at the 5G client. This paper adopts a model-assisted data-driven approach to develop a machine learning model for the SE prediction. Specifically, a joint interference and SE prediction network is built, demonstrating prediction improvement over pure data-driven neural networks. In addition, a classification-assisted SE prediction network is constructed, which substantially reduces the prediction error at the low SE regime with marginally compromising the total prediction error. It is found that the model-assisted approach generally enhances the SE prediction accuracy by 2% approximately over a purely data-driven approach.

This work studies the event-triggered control problem for networked control systems. The plant is controlled directly by a dynamic local controller, which receives the reference control signal from the remote controller. The measurement signal of the plant and the reference control signal of the remote controller has their separate event trigger, and thus the remote controller and the local controller can decide when to transmit signals on their own. It is proved that with the proposed control approach and the dual event trigger, the closed loop system is globally asymptotically stable, which has been illustrated by simulation results.