TFDet: Target-aware Fusion for RGB-T Pedestrian Detection

Pedestrian detection plays a critical role in computer vision as it contributes to ensuring traffic safety. Existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches have combined thermal images to provide complementary information and have obtained enhanced performances. Nevertheless, few approaches focus on the negative effects of false positives caused by noisy fused feature maps. Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. Our fusion strategy highlights the pedestrian-related features while suppressing unrelated ones, resulting in more discriminative fused features. TFDet achieves state-of-the-art performance on both KAIST and LLVIP benchmarks, with an efficiency comparable to the previous state-of-the-art counterpart. Importantly, TFDet performs remarkably well even under low-light conditions, which is a significant advancement for ensuring road safety. The code will be made publicly available at \url{https://github.com/XueZ-phd/TFDet.git}

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2x uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time

Ethyl 5-cyano-4-[2-(2,4-dichloro­phen­oxy)acetamido]-1-phenyl-1H-pyrrole-3-carboxyl­ate

In the title compound, C22H17Cl2N3O4, the pyrrole ring and the 2,4-dichloro­phenyl group form a dihedral angle of 8.14 (13)°; the phenyl ring is twisted with respect to the pyrrole ring, forming a dihedral angle of 60.77 (14)°. The C=O bond length is 1.213 (3) Å, indicating that the mol­ecule is in the keto form, associated with a –CONH– group, and the amide group adopts the usual trans conformation. The mol­ecule is stabilized by an intra­molecular N—H⋯O hydrogen-bonding inter­action. In the crystal, the stacked mol­ecules exhibit inter­molecular C—H⋯O and C—H⋯N hydrogen-bonding inter­actions

Alendronate prevents angiotensin II-induced collagen I production through geranylgeranylation-dependent RhoA/Rho kinase activation in cardiac fibroblasts

AbstractCollagen I is the main component of extracellular matrix in cardiac fibrosis. Our previous studies have reported inhibition of farnesylpyrophosphate synthase prevents angiotensin II-induced cardiac fibrosis, while the exact molecular mechanism was still unclear. This paper was designed to investigate the effect of alendronate, a farnesylpyrophosphate synthase inhibitor, on regulating angiotensin II-induced collagen I expression in cultured cardiac fibroblasts and to explore the underlying mechanism. By measuring the mRNA and protein levels of collagen I, we found that alendronate prevented angiotensin II-induced collagen I production in a dose-dependent manner. The inhibitory effect on collagen I expression was reversed by geranylgeraniol, and mimicked by inhibitors of RhoA/Rho kinase pathway including C3 exoenzyme and GGTI-286. Thus we suggested geranylgeranylation-dependent RhoA/Rho kinase activation was involved in alendronate-mediated anti-collagen I synthetic effect. Furthermore, we accessed the activation status of RhoA in alendronate-, geranylgeraniol- and GGTI-286-treated cardiac fibroblasts and gave an indirect evidence for RhoA activation via geranylgeranylation. Then we came to the conclusion that in cardiac fibroblasts, alendronate could protect against angiotensin II-induced collagen I synthesis through inhibition of geranylgeranylation and inactivation of RhoA/Rho kinase signaling. Targeting geranylgeranylation and RhoA/Rho kinase signaling will hopefully serve as therapeutic strategies to reduce fibrosis in heart remodeling

Numerical Research on Effects of Splitter Blades to the Influence of Pump as Turbine

Centrifugal pumps can be operated in reverse as small hydropower recovery turbines and are cheaper than bespoke turbines due to their ease of manufacture. Splitter blades technique is one of the techniques used in flow field optimization and performance enhancement of rotating machinery. To understand the effects of splitter blades to the steady and unsteady influence of PAT, numerical research was performed. 3D Navier-Stokes solver CFX was used in the performance prediction and analysis of PAT’s performance. Results show that splitter blades have a positive impact on PAT’s performance. With the increase of splitter blades, its required pressure head is dropped and its efficiency is increased. Unsteady pressure field analysis and comparison show that the unsteady pressure field within PAT is improved when splitter blades are added to impeller flow passage. To verify the accuracy of numerical prediction methods, an open PAT test rig was built at Jiangsu University. The PAT was manufactured and tested. Comparison between experimental and numerical results shows that the discrepancy between numerical and experimental results is acceptable. CFD can be used in the performance prediction and optimization of PAT