LinK: Linear Kernel for LiDAR-based 3D Perception

Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity. Previous work has taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by introducing block-shared weights. However, to reduce the feature variations within a block, it only employs modest block size and fails to achieve larger kernels like the 21x21x21. To address this issue, we propose a new method, called LinK, to achieve a wider-range perception receptive field in a convolution-like manner with two core designs. The first is to replace the static kernel matrix with a linear kernel generator, which adaptively provides weights only for non-empty voxels. The second is to reuse the pre-computed aggregation results in the overlapped blocks to reduce computation complexity. The proposed method successfully enables each voxel to perceive context within a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D object detection and 3D semantic segmentation, demonstrate the effectiveness of our method. Notably, we rank 1st on the public leaderboard of the 3D detection benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based backbone into the basic detector, CenterPoint. We also boost the strong segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is available at https://github.com/MCG-NJU/LinK.Comment: Accepted to CVPR202

Nanosheets-in-nanotube Co3O4-carbon array design enables stable Li-ion storage

Carbon composite products with different structures have been developed and used as anode for lithium-ion batteries due to the superior elasticity of carbon, which can keep the morphology integrity of the electrode materials in the process of the multiple cycles. Herein, a novel structure of nanosheets-in-nanotube Co 3 O 4 /carbon arrays is fabricated by the method of modified chemical vapor deposition (CVD). The carbon nanotube (CNT) layer acting as an outside coater can efficiently prevent the electrode from fragmentation and consequently ensure its shape integrity. The specific structure shows the ultra-stable cycle life (850 mAh g −1 after 200 cycles at 0.5C) and high rate capability (694 mAh g −1 at 2C). The favorable electrochemical properties are contributed to the combination of the wrapped elastic carbon and the enclosed Co 3 O 4 nanosheets in the lithiation process, which is confirmed by an in situ transmission electron microscope

MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association

Identifying accurate associations between miRNAs and diseases is beneficial for diagnosis and treatment of human diseases. It is especially important to develop an efficient method to detect the association between miRNA and disease. Traditional experimental method has high precision, but its process is complicated and time-consuming. Various computational methods have been developed to uncover potential associations based on an assumption that similar miRNAs are always related to similar diseases. In this paper, we propose an accurate method, MDA-SKF, to uncover potential miRNA-disease associations. We first extract three miRNA similarity kernels (miRNA functional similarity, miRNA sequence similarity, Hamming profile similarity for miRNA) and three disease similarity kernels (disease semantic similarity, disease functional similarity, Hamming profile similarity for disease) in two subspaces, respectively. Then, due to limitations that some initial information may be lost in the process and some noises may be exist in integrated similarity kernel, we propose a novel Similarity Kernel Fusion (SKF) method to integrate multiple similarity kernels. Finally, we utilize the Laplacian Regularized Least Squares (LapRLS) method on the integrated kernel to find potential associations. MDA-SKF is evaluated by three evaluation methods, including global leave-one-out cross validation (LOOCV) and local LOOCV and 5-fold cross validation (CV), and achieves AUCs of 0.9576, 0.8356, and 0.9557, respectively. Compared with existing seven methods, MDA-SKF has outstanding performance on global LOOCV and 5-fold. We also test case studies to further analyze the performance of MDA-SKF on 32 diseases. Furthermore, 3200 candidate associations are obtained and a majority of them can be confirmed. It demonstrates that MDA-SKF is an accurate and efficient computational tool for guiding traditional experiments

Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P-values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment

Discovering Cancer Subtypes via an Accurate Fusion Strategy on Multiple Profile Data

Discovering cancer subtypes is useful for guiding clinical treatment of multiple cancers. Progressive profile technologies for tissue have accumulated diverse types of data. Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. In this paper, we collect multiple profiles of data for five cancers on The Cancer Genome Atlas (TCGA). Then, we construct three similarity kernels for all patients of the same cancer by gene expression, miRNA expression and isoform expression data. We also propose a novel unsupervised multiple kernel fusion method, Similarity Kernel Fusion (SKF), in order to integrate three similarity kernels into one combined kernel. Finally, we make use of spectral clustering on the integrated kernel to predict cancer subtypes. In the experimental results, the P-values from the Cox regression model and survival curve analysis can be used to evaluate the performance of predicted subtypes on three datasets. Our kernel fusion method, SKF, has outstanding performance compared with single kernel and other multiple kernel fusion strategies. It demonstrates that our method can accurately identify more accurate subtypes on various kinds of cancers. Our cancer subtype prediction method can identify essential genes and biomarkers for disease diagnosis and prognosis, and we also discuss the possible side effects of therapies and treatment

Phase boundary engineering of metal-organic-framework-derived carbonaceous nickel selenides for sodium-ion batteries

Abstract: Sodium-ion batteries (SIBs) are promising power sources due to the low cost and abundance of battery-grade sodium resources, while practical SIBs suffer from intrinsically sluggish diffusion kinetics and severe volume changes of electrode materials. Metal-organic framework (MOFs) derived carbonaceous metal compound offer promising applications in electrode materials due to their tailorable composition, nanostructure, chemical and physical properties. Here, we fabricated hierarchical MOF-derived carbonaceous nickel selenides with bi-phase composition for enhanced sodium storage capability. As MOF formation time increases, the pyrolyzed and selenized products gradually transform from a single-phase Ni3Se4 into bi-phase NiSex then single-phase NiSe2, with concomitant morphological evolution from solid spheres into hierarchical urchin-like yolk-shell structures. As SIBs anodes, bi-phase NiSex@C/CNT-10h (10 h of hydrothermal synthesis time) exhibits a high specific capacity of 387.1 mAh/g at 0.1 A/g, long cycling stability of 306.3 mAh/g at a moderately high current density of 1 A/g after 2,000 cycles. Computational simulation further proves the lattice mismatch at the phase boundary facilitates more interstitial space for sodium storage. Our understanding of the phase boundary engineering of transformed MOFs and their morphological evolution is conducive to fabricate novel composites/hybrids for applications in batteries, catalysis, sensors, and environmental remediation