Slow Light of an Amplitude Modulated Gaussian Pulse in Cesium Vapor

Slow light of an amplitude modulated Gaussian (AMG) pulse in cesium vapor is demonstrated and studied, as an appropriate amplitude modulation to a single pulse can expand its spectrum and thus increase the utilization efficiency of the bandwidth of a slow light system. In a single-$\Lambda$ type electromagnetically induced transparency (EIT) system, the slowed AMG pulse experiences severe distortion, mainly owing to the frequency dependent transmission of medium. Additionally, due to its spectral distribution, the frequency dependent dispersion of the medium causes simultaneous slow and fast light of different spectral components and thus a certain dispersive distortion of the AMG pulse. Further, a post-processing method is proposed to recover the slowed (distorted) pulse, which indicates that by introducing a linear optical system with a desired gain spectrum we can recover the pulse in an "all-optical" way. Finally, we discuss the limitations during this compensation procedure in detail. Although it is demonstrated in the cesium vapor using EIT, this method should be applicable to a wide range of slow light systems.Comment: 5 pages, 5 figure

Single-Cell Multimodal Prediction via Transformers

The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics. Nevertheless, the proliferation of multimodal single-cell data also introduces tremendous challenges in modeling the complex interactions among different modalities. The recently advanced methods focus on constructing static interaction graphs and applying graph neural networks (GNNs) to learn from multimodal data. However, such static graphs can be suboptimal as they do not take advantage of the downstream task information; meanwhile GNNs also have some inherent limitations when deeply stacking GNN layers. To tackle these issues, in this work, we investigate how to leverage transformers for multimodal single-cell data in an end-to-end manner while exploiting downstream task information. In particular, we propose a scMoFormer framework which can readily incorporate external domain knowledge and model the interactions within each modality and cross modalities. Extensive experiments demonstrate that scMoFormer achieves superior performance on various benchmark datasets. Remarkably, scMoFormer won a Kaggle silver medal with the rank of 24/1221 (Top 2%) without ensemble in a NeurIPS 2022 competition. Our implementation is publicly available at Github.Comment: CIKM 202

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

A Cross-Modal Feature Fusion Model Based on ConvNeXt for RGB-D Semantic Segmentation

Semantic segmentation, as the pixel level classification with dividing an image into multiple blocks based on the similarities and differences of categories (i.e., assigning each pixel in the image to a class label), is an important task in computer vision. Combining RGB and Depth information can improve the performance of semantic segmentation. However, there is still a problem of the way to deeply integrate RGB and Depth. In this paper, we propose a cross-modal feature fusion RGB-D semantic segmentation model based on ConvNeXt, which uses ConvNeXt as the skeleton network and embeds a cross-modal feature fusion module (CMFFM). The CMFFM designs feature channel-wise and spectral-wise fusion, which can realize the deeply feature fusion of RGB and Depth. The in-depth multi-modal feature fusion in multiple stages improves the performance of the model. Experiments are performed on the public dataset of SUN-RGBD, showing the best segmentation by our proposed model ConvNeXt-CMFFM with the highest mIoU score of 53.5% among the nine comparative models. The outstanding performance of ConvNeXt-CMFFM is also achieved on our self-built dataset of RICE-RGBD with the highest mIoU score and pixel accuracy among the three comparative datasets. The ablation experiment on our rice dataset shows that compared with ConvNeXt (without CMFFM), the mIoU score of ConvNext-CMFFM is increased from 71.5% to 74.8% and its pixel accuracy is increased from 86.2% to 88.3%, indicating the effectiveness of the added feature fusion module in improving segmentation performance. This study shows the feasibility of the practical application of the proposed model in agriculture

Cooperative GNSS-RTK Ambiguity Resolution with GNSS, INS, and LiDAR Data for Connected Vehicles

Intelligent vehicles and connected vehicles have garnered more and more attention recently, and both require accurate positions of the vehicles in their operation, which relies on navigation sensors such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), Light Detection And Ranging (LiDAR) and so on. GNSS is the key sensor to obtain high accuracy positions in the navigation system, because GNSS Real Time Kinematic (RTK) with correct ambiguity resolution (AR) can provide centimeter-level absolute position. But AR may fail in the urban occlusion environment because of the limited satellite visibility for single vehicles. The navigation data from multiconnected vehicles can improve the satellite geometry significantly, which is able to help improve the AR, especially in occlusion environment. In this work, the GNSS, INS, and LiDAR data from multiconnected vehicles are jointly processed together to improve the GNSS RTK AR, and to obtain high accuracy positioning results, using a scan-to-map matching algorithm based on an occupancy likelihood map (OLM) for the relative position between the connected vehicles, a Damped Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method with least-squares for a relative AR between the connected vehicles, and a joint RTK algorithm for solving the absolute positioning for the vehicles by involving the relative position and relative ambiguity constraints. The experimental results show that the proposed approach can improve the AR for the connected vehicles with higher ratio values, success rates, and fixed rates, and achieve high-precision cooperative absolute positions compared with traditional GNSS RTK methods, especially in occlusion environments such as below a viaduct

Seed Vigor of Soybean Treated by Corona Discharge Plasma

There is a huge gap between the output and demand of soybean in China. How to improve the seed vigor of soybean has always been a research focus. Low temperature plasma (LTP) is a new green technology, which is widely used in crop seed treatment. Corona plasma is a typical discharge mode of plasma, which can affect the vigor of seeds. The effect of different discharge power on the soybean seed vigor by plasma treatment was experimentally investigated. Plasma discharge characteristic wavelength and spatial distribution were analysed. It shows that the corona discharge spectrum mainly exhibits the strong ultraviolet radiation and 90% of the spectral intensity focused in the center of discharge region. Water absorption and germination index of seeds and the fresh weight of seedlings were used to characterize the specific effects caused by different plasma powers. The results show that plasma treatment has a significant effect on the early stage of germination and can significantly affect the soybean seed vigor and growth. Overdose treatment will cause inhibiting effect. This study provides an experimental basis for the practical agriculture application of corona plasma seed treatment

Design, synthesis, and biological evaluation of benzenesulfonyl chloride-substituted evodiamine derivatives as potential PGAM1 inhibitors

Evodiamine (EVO) is a quinazoline carboline alkaloid isolated from the fruits of the traditional Chinese herb Evodiae fructus. In the present study, we synthesized thirty EVO derivatives (9–38) with various benzenesulfonyl groups by sulfonylation of the amino group of 3-NH-EVO and thirty benzenesulfonyl chloride substituents. The results of the MTT assay showed that most of the compounds had good antitumor activity on H460, PC9, PC9/GR, H1299 and SW620 cancer cells as well as on normal LO2 cells. Among them, compounds 9, 18 and 28 were more potent than other compounds on H460 cell lines with an IC50 value of 9.1 M, 10.5 M and 9.5 M, respectively, even more potent than the positive PGMI-004A with one IC50 of 31.1 M. The enzymatic activity of representative compounds was further evaluated against phosphoglycerate mutase 1 (PGAM1). The results showed that compound 11 with an IC50 of 0.062 μM and compound 34 with an IC50 of 0.059 μM were similar to the positive drug’s IC50 of 0.052 μM. These results indicated that these compounds could be developed into potential PGAM1 inhibitors. In addition, compounds 9, 18, and 28 could induce apoptosis, block the cell cycle at the G2/M stage, lead to bursting of reactive oxygen species, and induce mitochondrial dysfunction. Overall, the present work showed that the benzenesulfonyl chloride-substituted evodiamine derivatives have good antitumor activity against tumor cells and show promise as anticancer agents