UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation

LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To address these issues, we present UltraLiDAR, a data-driven framework for scene-level LiDAR completion, LiDAR generation, and LiDAR manipulation. The crux of UltraLiDAR is a compact, discrete representation that encodes the point cloud's geometric structure, is robust to noise, and is easy to manipulate. We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost. Furthermore, by learning a prior over the discrete codebook, we can generate diverse, realistic LiDAR point clouds for self-driving. We evaluate the effectiveness of UltraLiDAR on sparse-to-dense LiDAR completion and LiDAR generation. Experiments show that densifying real-world point clouds with our approach can significantly improve the performance of downstream perception systems. Compared to prior art on LiDAR generation, our approach generates much more realistic point clouds. According to A/B test, over 98.5\% of the time human participants prefer our results over those of previous methods.Comment: CVPR 2023. Project page: https://waabi.ai/ultralidar

Leveraging Trust Relations to Improve Academic Patent Recommendation

Academic patent trading is one of the important ways for university technology transfer. Compared to industry patent trading, academic patent trading suffers from a more serious information asymmetric problem. It needs a recommendation service to help companies identify academic patents that they want to pay. However, existing recommendation approaches have limitations in facilitating academic patent trading in online patent platforms because most of them only consider patent-level characteristics. A high trust degree of a company towards academic patents can alleviate the information asymmetry and encourage trading. This study proposes a novel academic patent recommendation approach with a hybrid strategy, combining citation-based relevance, connectivity, and trustworthiness. An offline experiment is conducted to evaluate the performance of the proposed recommendation approach. The results show that the proposed method performs better than the baseline methods in both accuracy and ranking

Signaling Pathways Associated with Cancer Stem Cells Play a Significant Role in Immunotherapy Resistance

Cancer stem cells (CSCs) are a subpopulation of tumor cells with properties of self-renewal, pluripotency, plasticity, and differentiation, and are associated with various aberrantly stimulated signaling pathways. They are responsible for tumor recurrence, distant metastasis, and drug resistance, thus inducing poor prognosis. Immunotherapy has achieved encouraging results. However, the resistance associated with its clinical application is a persistent problem in clinical and scientific researches. Increasing evidence shows that signaling pathways associated with CSCs mediate immunotherapy resistance. This review highlights the link between them, and focuses on the underlying mechanism so as to provide potential strategies and approaches for the development of new targets against the immune resistance challenge

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

In the era of 5G mobile communication, there has been a significant surge in research focused on unmanned aerial vehicles (UAVs) and mobile edge computing technology. UAVs can serve as intelligent servers in edge computing environments, optimizing their flight trajectories to maximize communication system throughput. Deep reinforcement learning (DRL)-based trajectory optimization algorithms may suffer from poor training performance due to intricate terrain features and inadequate training data. To overcome this limitation, some studies have proposed leveraging federated learning (FL) to mitigate the data isolation problem and expedite convergence. Nevertheless, the efficacy of global FL models can be negatively impacted by the high heterogeneity of local data, which could potentially impede the training process and even compromise the performance of local agents. This work proposes a novel solution to address these challenges, namely personalized federated deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization. PF-DRL aims to develop individualized models for each agent to address the data scarcity issue and mitigate the negative impact of data heterogeneity. Simulation results demonstrate that the proposed algorithm achieves superior training performance with faster convergence rates, and improves service quality compared to other DRL-based approaches

Identifying potential RNAi targets in grain aphid (Sitobion avenae F.) based on transcriptome profiling of its alimentary canal after feeding on wheat plants

BACKGROUND: The grain aphid (Sitobion avenae F.) is a major agricultural pest which causes significant yield losses of wheat in China, Europe and North America annually. Transcriptome profiling of the grain aphid alimentary canal after feeding on wheat plants could provide comprehensive gene expression information involved in feeding, ingestion and digestion. Furthermore, selection of aphid-specific RNAi target genes would be essential for utilizing a plant-mediated RNAi strategy to control aphids via a non-toxic mode of action. However, due to the tiny size of the alimentary canal and lack of genomic information on grain aphid as a whole, selection of the RNAi targets is a challenging task that as far as we are aware, has never been documented previously. RESULTS: In this study, we performed de novo transcriptome assembly and gene expression analyses of the alimentary canals of grain aphids before and after feeding on wheat plants using Illumina RNA sequencing. The transcriptome profiling generated 30,427 unigenes with an average length of 664 bp. Furthermore, comparison of the transcriptomes of alimentary canals of pre- and post feeding grain aphids indicated that 5490 unigenes were differentially expressed, among which, diverse genes and/or pathways were identified and annotated. Based on the RPKM values of these unigenes, 16 of them that were significantly up or down-regulated upon feeding were selected for dsRNA artificial feeding assay. Of these, 5 unigenes led to higher mortality and developmental stunting in an artificial feeding assay due to the down-regulation of the target gene expression. Finally, by adding fluorescently labelled dsRNA into the artificial diet, the spread of fluorescence signal in the whole body tissues of grain aphid was observed. CONCLUSIONS: Comparison of the transcriptome profiles of the alimentary canals of pre- and post-feeding grain aphids on wheat plants provided comprehensive gene expression information that could facilitate our understanding of the molecular mechanisms underlying feeding, ingestion and digestion. Furthermore, five novel and effective potential RNAi target genes were identified in grain aphid for the first time. This finding would provide a fundamental basis for aphid control in wheat through plant mediated RNAi strategy