Transformer-based 3D Object Detection for Autonomous Driving

Object detection is one of the most important directions in computer vision, where the task is to find out the targets of interest in an image and determine their location and class. With the development of deep learning in recent years, object detection has formed a wide range of applications in various fields, such as intelligent robotics and autonomous driving. And with the expansion of application scenarios, the object detection task has been extended for temporal and 3D spacial. In the temporal dimension, image data in most application scenarios are acquired as video streams, so video-based object detection tasks can perform information fusion in the temporal dimension. In the spatial dimension it is difficult to accurately restore the 3D spatial position of an object because of the lack of depth information in the image. Point cloud data is the data of points with the information of object’s 3D position acquired by LIDAR, which is good to make up the problem of image data without depth information. This has led to the study of object detection based on point cloud data. The strong demand of autonomous driving in the industry has led to vigorous interest in 3D object detection and resulted in many excellent 3D object detection algorithms. However, the vast majority of algorithms only model single-frame data, ignoring the temporal clue in video sequence. In this work, we propose a new transformer, called Temporal-Channel Transformer (TCTR), to model the temporal-channel domain and spatial-wise relationships for video object detecting from Lidar data. As the special design of this transformer, the information encoded in the encoder is different from that in the decoder. The encoder encodes temporal-channel information of multiple frames while the decoder decodes the spatial-wise information for the current frame in a voxel-wise manner. Specifically, the temporal-channel encoder of the transformer is designed to encode the information of different channels and frames by utilizing the correlation among features from different channels and frames. On the other hand, the spatial decoder of the transformer decodes the information for each location of the current frame. Before conducting the object detection with detection head, a gate mechanism is further deployed for re-calibrating the features of current frame, which filters out the object-irrelevant information by repetitively refining the representation of target frame along with the up-sampling process. Experimental results reveal that TCTR achieves the state-of-the-art performance in grid voxel-based 3D object detection on the nuScenes benchmark. Our work wants to advance the accuracy of 3D object detection as well as inspire more research on point cloud time-series dat

Methionine is a metabolic dependency of tumor-initiating cells

Understanding cellular metabolism holds immense potential for developing new classes of therapeutics that target metabolic pathways in cancer. Metabolic pathways are altered in bulk neoplastic cells in comparison to normal tissues. However, carcinoma cells within tumors are heterogeneous, and tumor-initiating cells (TICs) are important therapeutic targets that have remained metabolically uncharacterized. To understand their metabolic alterations, we performed metabolomics and metabolite tracing analyses, which revealed that TICs have highly elevated methionine cycle activity and transmethylation rates that are driven by MAT2A. High methionine cycle activity causes methionine consumption to far outstrip its regeneration, leading to addiction to exogenous methionine. Pharmacological inhibition of the methionine cycle, even transiently, is sufficient to cripple the tumor-initiating capability of these cells. Methionine cycle flux specifically influences the epigenetic state of cancer cells and drives tumor initiation. Methionine cycle enzymes are also enriched in other tumor types, and MAT2A expression impinges upon the sensitivity of certain cancer cells to therapeutic inhibition.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Medical Research Council (NMRC)National Research Foundation (NRF)This research is supported by the National Research Foundation, Singapore (NRF-NRFF2015-04), the National Medical Research Council, Singapore (LCG17MAY004; NMRC/OFIRG/0064/2017; NMRC/TCR/007- NCC/2013; OFYIRG16nov013), the Agency for Science, Research and Technology, Singapore (1331AEG071; 334I00053; SPF 2012/001), and the Singapore Ministry of Education under its Research Centers of Excellence initiative