Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild

Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit function has shown encouraging results on high-quality 3D shape reconstruction, it is still very challenging to generalize to cluttered and partially observable LiDAR data. In this paper, we propose to leverage the continuity in video data. We introduce a novel and unified framework which utilizes a neural implicit function to simultaneously track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF model (i.e., an instantiation of the implicit function) in the video online, iteratively improving the shape reconstruction while in return improving the tracking, and vice versa. We experiment with both Waymo and KITTI datasets and show significant improvements over state-of-the-art methods for both tracking and shape reconstruction tasks. Our project page is at https://jianglongye.com/implicit-tracking .Comment: Accepted to RA-L 2022 & IROS 2022. Project page: https://jianglongye.com/implicit-trackin

Structured sparse model based feature selection and classification for hyperspectral imagery

Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large num-ber of feature variables exist but the amount of training sam-ples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A lin-ear sparse logistic regression model is proposed to combine feature selection and pixel classification into a regularized op-timization problem with the constraint of sparsity. To explore the structured features, three-dimensional discrete wavelet transform (3D-DWT) is employed, which processes the hy-perspectral data cube as a whole tensor instead of adapting the data to a vector or matrix. This allows more effective capturing of the spatial and spectral structure. The structure of the 3D-DWT features is imposed on the sparse model by group LASSO which selects the features on the group level. The advantages of our method are validated on the real hyperspectral data

Research progress on the effect of traditional Chinese medicine on the activation of PRRs-mediated NF-κB signaling pathway to inhibit influenza pneumonia

Influenza pneumonia has challenged public health and social development. One of the hallmarks of severe influenza pneumonia is overproduction of pro-inflammatory cytokines and chemokines, which result from the continuous activation of intracellular signaling pathways, such as the NF-κB pathway, mediated by the interplay between viruses and host pattern recognition receptors (PRRs). It has been reported that traditional Chinese medicines (TCMs) can not only inhibit viral replication and inflammatory responses but also affect the expression of key components of PRRs and NF-κB signaling pathways. However, whether the antiviral and anti-inflammatory roles of TCM are related with its effects on NF-κB signaling pathway activated by PRRs remains unclear. Here, we reviewed the mechanism of PRRs-mediated activation of NF-κB signaling pathway following influenza virus infection and summarized the influence of anti-influenza TCMs on inflammatory responses and the PRRs/NF-κB signaling pathway, so as to provide better understanding of the mode of action of TCMs in the treatment of influenza pneumonia

Scientific Opportunities with an X-ray Free-Electron Laser Oscillator

An X-ray free-electron laser oscillator (XFELO) is a new type of hard X-ray source that would produce fully coherent pulses with meV bandwidth and stable intensity. The XFELO complements existing sources based on self-amplified spontaneous emission (SASE) from high-gain X-ray free-electron lasers (XFEL) that produce ultra-short pulses with broad-band chaotic spectra. This report is based on discussions of scientific opportunities enabled by an XFELO during a workshop held at SLAC on June 29 - July 1, 2016Comment: 21 pages, 12 figure

Fast decoupled state estimation for distribution networks considering branch ampere measurements

Fast decoupled state estimation (FDSE) is proposed for distribution networks, with fast convergence and high efficiency. Conventionally, branch current magnitude measurements cannot be incorporated into FDSE models; however, in this paper, branch ampere measurements are reformulated as active and reactive branch loss measurements and directly formulated in the proposed FDSE model. Using the complex per unit normalization technique and special chosen state variables, the performance of this FDSE can be guaranteed when it is applied to distribution networks. Numerical tests on seven different distribution networks show that this method outperforms Newton type solutions and is a promising method for practical application

Structured sparse model based feature selection and classification for hyperspectral imagery

Sparse modeling is a powerful framework for data analysis and processing. It is especially useful for high-dimensional regression and classification problems in which a large number of feature variables exist but the amount of training samples is limited. In this paper, we address the problems of feature description, feature selection and classifier design for hyperspectral images using structured sparse models. A linear sparse logistic regression model is proposed to combine feature selection and pixel classification into a regularized optimization problem with the constraint of sparsity. To explore the structured features, three-dimensional discrete wavelet transform (3D-DWT) is employed, which processes the hyperspectral data cube as a whole tensor instead of adapting the data to a vector or matrix. This allows more effective capturing of the spatial and spectral structure. The structure of the 3D-DWT features is imposed on the sparse model by group LASSO which selects the features on the group level. The advantages of our method are validated on the real hyperspectral data

Multiscale Analysis of the Strength Deterioration of Loess under the Action of Drying and Wetting Cycles

To study the strength degradation mechanism of compacted loess during dry-wet cycles, 0–5 dry-wet cycles tests and many triaxial compression tests were carried out on loess with an optimal moisture content. During the dry-wet cycles, the loess samples were analyzed by nuclear magnetic resonance and scanning electron microscopy. Studies have shown that at the macro level, with increasing numbers of wet and dry cycles and increasing cycle amplitude, the cohesive force and internal friction angle of the loess decrease, and the shear strength of the loess deteriorates significantly. At the micro level, with the number of wet and dry cycles increasing, the connection between particles changes from surface-to-surface contacts to point-to-point or point-to-surface contacts. The edges and corners of the particles decrease, the roundness increases, the large pores gradually decrease, the small pores gradually increase, and the fractal dimension gradually increases. In terms of microscopic view, the NMR test shows that with increasing numbers of dry-wet cycles, the T2 peak curve increases, the curve width increases slightly, the peak area gradually increases, and the porosity increases. From the macroscopic, mesoscopic, and microscopic multiscale analysis, the structure of loess is degraded under the action of dry and wet cycles; the strength of the loess is degraded significantly after 0 to 3 cycles and then gradually stabilizes. These research results can provide a certain reference value for the management of loess collapse geological disasters in semiarid climates