Deep Dimension Reduction for Supervised Representation Learning

The success of deep supervised learning depends on its automatic data representation abilities. Among all the characteristics of an ideal representation for high-dimensional complex data, information preservation, low dimensionality and disentanglement are the most essential ones. In this work, we propose a deep dimension reduction (DDR) approach to achieving a good data representation with these characteristics for supervised learning. At the population level, we formulate the ideal representation learning task as finding a nonlinear dimension reduction map that minimizes the sum of losses characterizing conditional independence and disentanglement. We estimate the target map at the sample level nonparametrically with deep neural networks. We derive a bound on the excess risk of the deep nonparametric estimator. The proposed method is validated via comprehensive numerical experiments and real data analysis in the context of regression and classification

SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation

Recently, self-supervised monocular depth estimation has gained popularity with numerous applications in autonomous driving and robotics. However, existing solutions primarily seek to estimate depth from immediate visual features, and struggle to recover fine-grained scene details with limited generalization. In this paper, we introduce SQLdepth, a novel approach that can effectively learn fine-grained scene structures from motion. In SQLdepth, we propose a novel Self Query Layer (SQL) to build a self-cost volume and infer depth from it, rather than inferring depth from feature maps. The self-cost volume implicitly captures the intrinsic geometry of the scene within a single frame. Each individual slice of the volume signifies the relative distances between points and objects within a latent space. Ultimately, this volume is compressed to the depth map via a novel decoding approach. Experimental results on KITTI and Cityscapes show that our method attains remarkable state-of-the-art performance (AbsRel = $0.082$ on KITTI, $0.052$ on KITTI with improved ground-truth and $0.106$ on Cityscapes), achieves $9.9\%$, $5.5\%$ and $4.5\%$ error reduction from the previous best. In addition, our approach showcases reduced training complexity, computational efficiency, improved generalization, and the ability to recover fine-grained scene details. Moreover, the self-supervised pre-trained and metric fine-tuned SQLdepth can surpass existing supervised methods by significant margins (AbsRel = $0.043$, $14\%$ error reduction). self-matching-oriented relative distance querying in SQL improves the robustness and zero-shot generalization capability of SQLdepth. Code and the pre-trained weights will be publicly available. Code is available at \href{https://github.com/hisfog/SQLdepth-Impl}{https://github.com/hisfog/SQLdepth-Impl}.Comment: 14 pages, 9 figure

PERFORMANCE COMPARISON OF SYNGAS METHANATION ON FLUIDIZED AND FIXED BED REACTORS

The performance was compared on Syngas Completely Methanation at atmospheric pressure on fluidized and fixed bed reactors. From space-time yield of CH4, coke content and hot spots of bed temperature, fluidized bed technology was demonstrated to be more applicable to Syngas Completely Methanation. Characterization results showed that different carbon deposition forms were presented on the two operation modes

Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks

The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks. In this paper, we investigate the UL/DL decoupled access performance in cellular vehicle-to-everything (C-V2X). We propose a unified analytical framework for the UL/DL decoupled access in C-V2X from the perspective of spectral efficiency (SE). By modeling the UL/DL decoupled access C-V2X as a Cox process and leveraging the stochastic geometry, we obtain the joint association probability, the UL/DL distance distributions to serving base stations and the SE for the UL/DL decoupled access in C-V2X networks with different association cases. We conduct extensive Monte Carlo simulations to verify the accuracy of the proposed unified analytical framework, and the results show a better system average SE of UL/DL decoupled access in C-V2X.Comment: 6pagaes,5 figures, globecom 202

Protection effect of sanguinarine on whole-body exposure of X radiation in BALB/c mice

To investigate the effects of sanguinarine (SAN) on acute radiation induced injury in mice, 45 mice were randomly divided into control, 10 Gy and SAN+10 Gy groups. Mice in the 10 Gy and SAN+10 Gy groups were exposed to single X-ray radiation with an accumulated dose of 10 Gy. Mice in the SAN+10 Gy group were administered intraperitoneally with 2.5 mg/kg body weight of SAN before radiation. Five days after radiation exposure, 5 mice from each group were sacrificed and samples of the small intestine, lung, spleen and liver were fixed for histopathological examinations. Compared with the 10 Gy group, radiation sickness was obviously delayed or attenuated in the SAN+10 Gy group. Survival analysis showed a significant difference between 2 radiation groups (PPara investigar os efeitos da sanguinarina (SAN) em lesões induzidas em ratos por radiação aguda, 45 ratos foram aleatoriamente divididos em grupo controle, grupo 10 Gy e grupo SAN+10 Gy. Os ratos dos grupos 10 Gy e SAN+10 Gy foram expostos à radiação de raio-X simples com uma dose acumulada de 10 Gy. Aos ratos do grupo SAN+10 Gy administraram-se, intraperitonealmente, 2.5 mg/kg de peso de SAN antes da radiação. Aos 5 dias de exposição à radiação, sacrificaram-se 5 ratos de cada grupo e retiraram-se amostras do intestino delgado, pulmões, baço e fígado para exames histopatológicos. Comparando com o grupo 10 Gy, a doença por radiação foi claramente atrasada e atenuada no grupo SAN+10 Gy. A análise de sobrevivência mostrou diferença significativa entre os dois grupos de radiação (