Generation of Coherent X-Ray Radiation Through Modulation Compression

In this paper, we propose a scheme to generate tunable coherent X-ray radiation for future light source applications. This scheme uses an energy chirped electron beam, a laser modulator, a laser chirper and two bunch compressors to generate a prebunched kilo-Ampere current electron beam from a few tens Ampere electron beam out of a linac. The initial modulation energy wavelength can be compressed by a factor of $1+h_b R_{56}^a$ in phase space, where $h_b$ is the energy bunch length chirp introduced by the laser chirper, $R_{56}^a$ is the momentum compaction factor of the first bunch compressor. As an illustration, we present an example to generate more than 400 MW, 170 attoseconds pulse, 1 nm coherent X-ray radiation using a 60 Ampere electron beam out of the linac and 200 nm laser seed. Both the final wavelength and the radiation pulse length in the proposed scheme are tunable by adjusting the compression factor and the laser parameters

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

deepDriver: Predicting Cancer Driver Genes Based on Somatic Mutations Using Deep Convolutional Neural Networks

With the advances in high-throughput technologies, millions of somatic mutations have been reported in the past decade. Identifying driver genes with oncogenic mutations from these data is a critical and challenging problem. Many computational methods have been proposed to predict driver genes. Among them, machine learning-based methods usually train a classifier with representations that concatenate various types of features extracted from different kinds of data. Although successful, simply concatenating different types of features may not be the best way to fuse these data. We notice that a few types of data characterize the similarities of genes, to better integrate them with other data and improve the accuracy of driver gene prediction, in this study, a deep learning-based method (deepDriver) is proposed by performing convolution on mutation-based features of genes and their neighbors in the similarity networks. The method allows the convolutional neural network to learn information within mutation data and similarity networks simultaneously, which enhances the prediction of driver genes. deepDriver achieves AUC scores of 0.984 and 0.976 on breast cancer and colorectal cancer, which are superior to the competing algorithms. Further evaluations of the top 10 predictions also demonstrate that deepDriver is valuable for predicting new driver genes

Twisted Parafermions

A new type of nonlocal currents (quasi-particles), which we call twisted parafermions, and its corresponding twisted $Z$-algebra are found. The system consists of one spin-1 bosonic field and six nonlocal fields of fractional spins. Jacobi-type identities for the twisted parafermions are derived, and a new conformal field theory is constructed from these currents. As an application, a parafermionic representation of the twisted affine current algebra $A^{(2)}_2$ is given.Comment: RevTex 5 pages; Cosmetic changes, to appear in Phys.Lett.

7-(1,3-Dioxolan-2-ylmeth­yl)-1,3-di­methyl­purine-2,6(1H,3H)-dione trichloro­acetic acid solvate

In the title compound, C11H14N4O4·C2HCl3O2, the dioxolane ring adopts an envelope conformation. Doxophylline [7-(1,3-dioxolan-2-yl-meth­yl)-1,3-dimethyl-3,7-dihydro-1H-purine-2,6-dione] and trichloro­acetic acid mol­ecules are linked by O—H⋯N and C—H⋯O hydrogen bonds