Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github.io/Occ3D/.Comment: Accepted to NeurIPS 202

Prognosis signature for predicting the survival and immunotherapy response in esophageal carcinoma based on cellular senescence-related genes

BackgroundCellular senescence occurs throughout life and can play beneficial roles in a variety of physiological processes, including embryonic development, tissue repair, and tumor suppression. However, the relationship between cellular senescence-related genes (CSRGs) and immunotherapy in esophageal carcinoma (ECa) remains poorly defined.MethodsThe data set used in the analysis was retrieved from TCGA (Research Resource Identifier (RRID): SCR_003193), GEO (RRID: SCR_005012), and CellAge databases. Data processing, statistical analysis, and diagram formation were conducted in R software (RRID: SCR_001905) and GraphPad Prism (RRID: SCR_002798). Based on CSRGs, we used the TCGA database to construct a prognostic signature for ECa and then validated it in the GEO database. The predictive efficiency of the signature was evaluated using receiver operating characteristic (ROC) curves, Cox regression analysis, nomogram, and calibration curves. According to the median risk score derived from CSRGs, patients with ECa were divided into high- and low-risk groups. Immune infiltration and immunotherapy were also analyzed between the two risk groups. Finally, the hub genes of the differences between the two risk groups were identified by the STRING (RRID: SCR_005223) database and Cytoscape (RRID: SCR_003032) software.ResultsA six-gene risk signature (DEK, RUNX1, SMARCA4, SREBF1, TERT, and TOP1) was constructed in the TCGA database. Patients in the high-risk group had a worse overall survival (OS) was disclosed by survival analysis. As expected, the signature presented equally prognostic significance in the GSE53624 cohort. Next, the Area Under ROC Curve (AUC=0.854) and multivariate Cox regression analysis (HR=3.381, 2.073-5.514, P<0.001) also proved that the risk signature has a high predictive ability. Furthermore, we can more accurately predict the prognosis of patients with ECa by nomogram constructed by risk score. The result of the TIDE algorithm showed that ECa patients in the high-risk group had a greater possibility of immune escape. At last, a total of ten hub genes (APOA1, MUC5AC, GC, APOA4, AMBP, FABP1, APOA2, SOX2, MUC8, MUC17) between two risk groups with the highest interaction degrees were identified. By further analysis, four hub genes (APOA4, AMBP, FABP1, and APOA2) were related to the survival differences of ECa.ConclusionsOur study reveals comprehensive clues that a novel signature based on CSRGs may provide reliable prognosis prediction and insight into new therapy for patients with ECa

Leveraging Large Language Models for Pre-trained Recommender Systems

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.Comment: 13 pages, 4 figure

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.Comment: 12 pages, 6 figure

Study on closing and cracking stress calculation method of fractured rock

Determining the characteristic stress intensity according to the rock stress-strain curve is significant significance for rock engineering. Nowadays, there are relatively mature methods for determining peak stress and damage stress. However, the crack volume strain method, axial strain method, and strain response method are more subjective to determine rock’s closure stress and initiation stress. The closure rock stress and crack initiation stress refined value method are proposed based on these methods, which are based on the discreteness of the rock stress and strain point. Through optimizing the stress characteristics by an objective function (variance function), the subjectivity of artificial observation is avoided; Based on the trend of rock stress-strain curve, an empirical method for determining rock closure stress and crack initiation stress is proposed. The test results indicate that the two proposed methods that calculate closure rock stress and crack initiation stress can obtain reasonable results. These methods can be used as a supplement to the characteristics of the rock stress determination method and can be used in actual engineering

On “Officer Professional Military Education Policy” and its enlightenment

"Officer Professional Military Education Policy" is an important guide for M army. This paper aims to deeply explore the core content of its military education and its development trend, and draw useful experience from it, so as to promote the innovation of military education and provide reference for training new military talents with international competitiveness