Secure server-aided data sharing clique with attestation

National Research Foundation (NRF) Singapor

Infrared Image Super-Resolution via Progressive Compact Distillation Network

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance

Infrared Image Super-Resolution via Progressive Compact Distillation Network

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance

Atractylodin attenuates lipopolysaccharide-induced acute lung injury by inhibiting NLRP3 inflammasome and TLR4 pathways

Acute lung injury (ALI) arises from uncontrolled pulmonary inflammation with high mortality rates. Atractylodin (Atr) is a polyethylene alkynes and has been reported to possess anti-inflammation effect. Thus, we aimed to investigate the protective effect of Atr on lipopolysaccharide (LPS)-induced inflammatory responses ALI. The results indicated that Atr treatment not only significantly attenuated LPS-stimulated histopathological changes but also lessened the myeloperoxidase (MPO) activity, the wet-to-dry weight ratio of the lungs, protein leakage and infiltration of inflammatory cells. Moreover, Atr inhibited the tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-1β and monocyte chemoattractant protein (MCP)-1 secretion in BALF. Further study demonstrated that such inhibitory effects of Atr were due to suppression of nucleotide-binding domain-(NOD-) like receptor protein 3 (NLRP3) inflammasome and toll like receptor 4 (TLR4) activation, likely contributing to its anti-inflammatory effects. Collectively, these findings suggest that Atr may be an effective candidate for alleviating LPS-induced inflammatory responses. Keywords: Atractylodin, Inflammation, Acute lung injury, NLRP3, TLR

Effect of Shear Keys on the Quasi-Isolated Behavior of Small-to-Medium-Span Girder Bridges

Small-to-medium-span girder bridges equipped with shear keys play a significant role in the Chinese highway bridge system. However, shear key failure was observed during the 2008 Wenchuan earthquake, which resulted in excessive superstructure displacements and even catastrophic span collapse. For this, six refined bridges were investigated for the quasi-isolated behaviors under different shear key strengths by using the Pushover and IDA methods. Results indicate that the bridges exhibit two distinct damage states upon the shear key strengths. The shear key failure and bearing sliding create a natural quasi-isolated mechanism, with the following damage sequence: shear key failure → bearing sliding → pier undamaged or slight damage. Quasi-isolated behavior leads to higher displacement demands for beams, especially when the peak ground acceleration (PGA) exceeds 0.45 g. By selecting suitable shear key strength, below 9% for 20 m piers and 30% for 10 m piers, quasi-isolated damage is expected to occur in bridges. The study offers a fresh perspective on the concept of seismic design for highway girder bridges in China

Radiated Two-Stage method for LTE MIMO User Equipment Performance Evaluation

Two-stage method for long-term-evolution (LTE) multiple-input-multiple-output (MIMO) wireless user equipment (UE) performance evaluation is one of the methods proposed for standard organizations. However, the conducted two-stage method has been challenged for its lack of support for \u27over-the-air\u27 (OTA) as well as for its negligence of the self-interference in the device under test (DUT) in the throughput test. Self-interference in DUT such as cell phones could significantly reduce receiver sensitivity, thus, if not properly included in the test setup, could affect the test accuracy. In order to solve the problems, a radiated two-stage (RTS) test method for LTE MIMO UE test is presented in this paper. By applying an invert calibration matrix to the input signal of the throughput test, the proposed method performs OTA second-stage test, which eliminates the problems of connecting an RF cable directly to the DUT receiver. The RTS OTA MIMO test method can be executed in a standard single-input-single-output anechoic chamber, reduces overall system cost, and offers high reliability and repeatability. Meanwhile, the measurement provides extensive subcomponent-level performance information and makes it an ideal solution for both research and development (R&D) and certification test