In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks

The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in edge AI, the time has come to realize intelligence downloading onto edge devices (e.g., smartphones and sensors). To materialize this version, we propose a novel technology in this article, called in-situ model downloading, that aims to achieve transparent and real-time replacement of on-device AI models by downloading from an AI library in the network. Its distinctive feature is the adaptation of downloading to time-varying situations (e.g., application, location, and time), devices' heterogeneous storage-and-computing capacities, and channel states. A key component of the presented framework is a set of techniques that dynamically compress a downloaded model at the depth-level, parameter-level, or bit-level to support adaptive model downloading. We further propose a virtualized 6G network architecture customized for deploying in-situ model downloading with the key feature of a three-tier (edge, local, and central) AI library. Furthermore, experiments are conducted to quantify 6G connectivity requirements and research opportunities pertaining to the proposed technology are discussed.Comment: The paper has been submitted to IEEE for possible publicatio

Intelligent Detection of Road Cracks Based on Improved YOLOv5

With the gradual increase of highway coverage, the frequency of road cracks also increases, which brings a series of security risks. It is necessary to detect road cracks, but the traditional detection method is inefficient and unsafe. In this paper, deep learning is used to detect road cracks, and an improved model BiTrans-YOLOv5 is proposed. We add Swin Transformer to YOLOv5s to replace the original C3 module, and explore the performance of Transformer in the field of road crack detection. We also change the original PANet of YOLOv5s into a bidirectional feature pyramid network (BIFPN), which can detect small targets more accurately. Experiments on the data set Road Damage show that BiTrans-YOLOv5 has improved in Precision, Recall, F1 score and [email protected] compared with YOLOv5s, among which [email protected] has improved by 5.4%. It is proved that BiTrans-YOLOv5 has better performance in road detection projects

VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation

Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single image rather than as a supervised model, which achieves encouraging results in the nonblind deblurring problem. However, since the relationship between images and the network architectures is unclear, it is hard to find a suitable architecture to provide sufficient constraints on the estimated blur kernels and clean images. Also, DIP uses the sparse maximum a posteriori (MAP), which is insufficient to enforce the selection of the recovery image. Recently, variational deep image prior (VDIP) was proposed to impose constraints on both blur kernels and recovery images and take the standard deviation of the image into account during the optimization process by the variational principle. However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large. Therefore, we combine total generalized variational (TGV) regularization with VDIP in this paper to overcome these shortcomings of VDIP. TGV is a flexible regularization that utilizes the characteristics of partial derivatives of varying orders to regularize images at different scales, reducing oil painting artifacts while maintaining sharp edges. The proposed VDIP-TGV effectively recovers image edges and details by supplementing extra gradient information through TGV. Additionally, this model is solved by the alternating direction method of multipliers (ADMM), which effectively combines traditional algorithms and deep learning methods. Experiments show that our proposed VDIP-TGV surpasses various state-of-the-art models quantitatively and qualitatively.Comment: 13 pages, 5 figure

Novel emerging nano-assisted anti-cancer strategies based on the STING pathway

Activation of simulator of interferon genes (STING), which induces the production of proinflammatory factors and immune effector cell activation, is considered a promising strategy for enhanced anti-cancer intervention. However, several obstacles prevent STING signaling in solid tumors, such as delivered molecules’ rapid degradation, restriction to tumor sites, insufficient intracellular concentrations, and low responsivity. Well-designed, multifunctional nano-formulations have emerged as optimized platforms for STING activation. Recently, a variety of nano-formulations have been developed and used in STING activation, thus facilitating immunotherapy in preclinical and clinical stages. Herein, we summarize recent advances in nanotechnology-based delivery, activation, and application strategies, which have advanced various aspects of immunotherapy. Novel STING agonists and their mechanisms in STING-activation-mediated tumor interventions are highlighted herein, to provide a comprehensive overview and discuss future directions for boosting immunotherapy via STING regulation