We present GravLensX, an innovative method for rendering black holes with gravitational lensing effects using neural networks. The methodology involves training neural networks to fit the spacetime around black holes and then employing these trained models to generate the path of light rays affected by gravitational lensing. This enables efficient and scalable simulations of black holes with optically thin accretion disks, significantly decreasing the time required for rendering compared to traditional methods. We validate our approach through extensive rendering of multiple black hole systems with superposed Kerr metric, demonstrating its capability to produce accurate visualizations with significantly $15\times$ reduced computational time. Our findings suggest that neural networks offer a promising alternative for rendering complex astrophysical phenomena, potentially paving a new path to astronomical visualization.

Urban daily activity patterns play an important role in fields such as urban planning and traffic management, while the powerful data generation and reasoning capabilities of LLMs (Large Language Models) have sparked a surge of interest in recent years, with applications in various domains. Inspired by their natural language processing and pattern recognition functionalities, we attempted to utilize LLMs to simulate the activity patterns of people of different ages and occupations in metropolitan areas (Tokyo). By leveraging pre-processed Person Trip data, we employed 3 methods to test the ability of LLMs to generate urban daily activity data. The results were evaluated based on metrics such as rationality, diversity, and error rates. Results indicate that the fine-tuned LLaMA-3 model is capable of accurately simulating the activity distribution patterns of metropolitan populations. Among the prompt-based strategies, the few-shot approach yielded the best performance. Although designing the instruction for prompts and post-processing the data required considerable time, the few-shot prompt strategy proved to be an effective option for this task.

Human mobility generation plays a critical role in urban transportation planning. Existing human mobility generation models often fall short of understanding travelers' demographics and integrating multimodal information, including activity purposes, destination choices and transport mode preferences. Recently, mobility generation models leveraging Large Language Models (LLMs) have gained significant attention, while they are limited in directly reproducing spatial information in human mobility profiles. To address these challenges, this paper proposes the Mobility Generative Language Model (MobGLM), a novel approach for generating synthetic human mobility data to support urban planning, transport management, energy consumption and epidemic control. MobGLM addresses these limitations by capturing the complex relationships between agents' mobility patterns and individual demographics. By incorporating personal information, activity types, locations and traffic modes as encoders, MobGLM uniquely identifies and replicates features of human mobility. Our framework is evaluated using a large, real-world mobility dataset and benchmarked against state-of-the-art personal mobility generation techniques. The results demonstrate the effectiveness of MobGLM in producing accurate and reliable synthetic mobility data, highlighting its potential applications in various urban mobility contexts.

Recently, the community has witnessed numerous datasets built for developing and testing state estimators. However, for some applications such as aerial transportation or search-and-rescue, the contact force or other disturbance must be perceived for robust planning and control, which is beyond the capacity of these datasets. This paper introduces a Visual-Inertial-Dynamical (VID) dataset, not only focusing on traditional six degrees of freedom (6-DOF) pose estimation but also providing dynamical characteristics of the flight platform for external force perception or dynamics-aided estimation. The VID dataset contains hardware synchronized imagery and inertial measurements, with accurate ground truth trajectories for evaluating common visual-inertial estimators. Moreover, the proposed dataset highlights rotor speed and motor current measurements, control inputs, and ground truth 6-axis force data to evaluate external force estimation. To the best of our knowledge, the proposed VID dataset is the first public dataset containing visual-inertial and complete dynamical information in the real world for pose and external force evaluation. The dataset1and related files2are open-sourced.

Recently, quadrotors are gaining significant attention in aerial transportation and delivery. In these scenarios, an accurate estimation of the external force is as essential as the six degree-of-freedom (DoF) pose since it is of vital importance for planning and control of the vehicle. To this end, we propose a tightly-coupled Visual-Inertial-Dynamics (VID) system that simultaneously estimates the external force applied to the quadrotor along with the six DoF pose. Our method builds on the state-of-the-art optimization-based Visual-Inertial system [1], with a novel deduction of the dynamics and external force factor extended from VIMO [2]. Utilizing the proposed dynamics and external force factor, our estimator robustly and accurately estimates the external force even when it varies widely. Moreover, since we explicitly consider the influence of the external force, when compared with VIMO [2] and VINS-Mono [1], our method shows comparable and superior pose accuracy, even when the external force ranges from neglectable to significant. The robustness and effectiveness of the proposed method are validated by extensive real-world experiments and application scenario simulation. We will release an open-source package of this method along with datasets with ground truth force measurements for the reference of the community.