One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection.

Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network.

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants.

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Self-assembled CsPbBr3 quantum dots with wavelength-tunable photoluminescence for efficient active jamming.

CsPbBr3 perovskite quantum dots (QDs) show great potential in various applications due to their size-dependent and excellent optoelectronic properties. However, it is still challenging to synthesize size-tunable CsPbBr3 QDs with purple emission. Herein, CsPbBr3 nanospheres (NS) with purple emission (432 nm) and wavelength-tunable photoluminescence were synthesized using a two-step recrystallization method for the first time. A nanocube (NC) strategy resulting from CsPbBr3 nanosphere self-assembly via polar solvent-induced surface ligand mismatch was proposed. The self-assembly process endows the QDs with wavelength-tunable photoluminescence ranging from 432 to 518 nm. The significant reduction in defects during self-assembly was confirmed by transient optical spectroscopy measurements, photoluminescence quantum yields (PLQY), and the disappearance of tail bands in the long-wavelength region of the photoluminescence (PL) spectrum. This theory demonstrated that the decrease in high defect surfaces and increase in specific surface area were the reasons for the decline in defects. Most importantly, these QDs could be used for the active jamming of optical imaging systems based on charged-coupled devices (CCDs), including laser imaging radar and low light level (LLL) night vision systems. QDs significantly increase the mean square error (MSE) of the image, while the detection rate of the target by the artificial intelligence algorithm decreased by 95.17%. The wide wavelength tunable emission caused by structural changes makes it arduous for silicon-based detectors to avoid the interference of QDs by adding filters or by other means.

F-Seq2: improving the feature density based peak caller with dynamic statistics.

Genomic and epigenomic features are captured at a genome-wide level by using high-throughput sequencing (HTS) technologies. Peak calling delineates features identified in HTS experiments, such as open chromatin regions and transcription factor binding sites, by comparing the observed read distributions to a random expectation. Since its introduction, F-Seq has been widely used and shown to be the most sensitive and accurate peak caller for DNase I hypersensitive site (DNase-seq) data. However, the first release (F-Seq1) has two key limitations: lack of support for user-input control datasets, and poor test statistic reporting. These constrain its ability to capture systematic and experimental biases inherent to the background distributions in peak prediction, and to subsequently rank predicted peaks by confidence. To address these limitations, we present F-Seq2, which combines kernel density estimation and a dynamic 'continuous' Poisson test to account for local biases and accurately rank candidate peaks. The output of F-Seq2 is suitable for irreproducible discovery rate analysis as test statistics are calculated for individual candidate summits, allowing direct comparison of predictions across replicates. These improvements significantly boost the performance of F-Seq2 for ATAC-seq and ChIP-seq datasets, outperforming competing peak callers used by the ENCODE Consortium in terms of precision and recall.