Text-to-video diffusion models have made significant progress. However, there is still a lack of dedicated research on generating fire scene videos with physical realism and visual fidelity. To address this gap, we propose text-to-video fire (T2VFire) scene generation. T2VFire uses GPT-4o as the core engine, which is integrated with an external fire-related knowledge base and a retrieval-augmented generation (RAG) mechanism that can be dynamically updated based on prompts. With the support of this knowledge, the system first expands the user's initial text description and generates a keyframe image. Then, through iterative prompt optimization, it guides a pretrained video diffusion model to generate fire scene videos with physical consistency. Experimental results show that T2VFire improves upon the physical consistency and visual realism of fire scene videos generated by current video generation models. This method provides a solid foundation for future smart firefighting and digital twin systems in building fire safety management.

Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers. However, a significant challenge hindering their widespread adoption in both scientific and industrial domains is the lack of understanding about their prediction uncertainties, particularly in scenarios that involve critical decision making. To address this limitation, we propose a method that integrates efficient and precise uncertainty quantification into a deep learning-based surrogate model. Our method, termed Latent Evolution of PDEs with Uncertainty Quantification (LE-PDE-UQ), endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems. LE-PDE-UQ leverages latent vectors within a latent space to evolve both the system's state and its corresponding uncertainty estimation. The latent vectors are decoded to provide predictions for the system's state as well as estimates of its uncertainty. In extensive experiments, we demonstrate the accurate uncertainty quantification performance of our approach, surpassing that of strong baselines including deep ensembles, Bayesian neural network layers, and dropout. Our method excels at propagating uncertainty over extended auto-regressive rollouts, making it suitable for scenarios involving long-term predictions. Our code is available at: https://github.com/AI4Science-WestlakeU/le-pde-uq.

Timely fire alarms are crucial as they can save lives and avoid major economic losses. However, due to the complexity of the structure, the current mainstream DETR-based fire detection models are problematic in terms of practicality because they require large amounts of memory and long inference times. Meanwhile, high-quality fire detection datasets are very scarce, severely limiting the performance of the algorithms. To address these challenges and improve accuracy in complex fire environments, first, we introduce a dataset quality enhancement framework based on diffusion modele (DDPM) to improve the quality of low-quality fire alarm datasets. Second, we propose a novel Deformable-DETR-based fire detection framework (FTA-DETR). Among the innovative optimizations of FTA-DETR, first, we introduce a trainable matrix in the encoder to compute features, which reduces the computational burden of the encoder, highlights compelling features, and significantly reduces the training time. Second, we improve the encoding block by alternately updating high-level and low-level features, greatly reducing the amount of feature computation required for effective detection. This encoder structure is compatible with any state-of-the-art transformer decoder. Next, to accommodate the multi-scale nature of fires and different environmental complexities, we modify the loss function to WiouV3, which not only speeds up the convergence of the model but also improves the performance. Finally, we smoothly combine FTA-DETR with an acceleration engine like TensorRT to improve inference speed with little loss of accuracy. Compared to other deep learning methods, our approach greatly simplifies the detection process and establishes an end-to-end detection framework. Experiments demonstrate the high quality of datasets generated by the diffusion model-based dataset quality enhancement framework, and these datasets greatly improve the mAP of FTA-DETR (by 2.42% overall). In addition, FTA-DETR outperforms almost all fire detection frameworks in terms of detection accuracy and immunity to interference due to all YOLO-series and DETR-series algorithms in terms of inference speed on a high-performance graphics card A100, with an FPS as fast as 211, and runs stably on small embedded platforms like the Jetson Orin Nano, which has very little computational power, with FPS reaches 76. Overall, the algorithm proposed in this paper outperforms existing mainstream fire detection algorithms across various performance metrics. This superiority is clearly demonstrated in the test results on public datasets: our algorithm achieved an accuracy of 98.32% on the Mivia dataset, a recall rate of 98% on the BoWFire dataset, and an impressive precision of 99.55% on the FireNet dataset. The code is available at https://github.com/wanggoat/FTA-detr.

Researcher has been trying to optimize the method for evaluating presence in virtual reality (VR) to address variability and uncertainty in quick evaluations using questionnaires. We recommend using the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model to calculated the iGroup Presence Questionnaire to measure users’ overall presence in VR prototyping, instead of the weighted sum approach. The effects of two presence factors (scene density and motion trajectory technology) on a self-developed VR swimming virtual system were tested using the TOPSIS model with 20 participants each 12 tasks in a user experiment. The results were compared using two different weighting methods, fuzzy hierarchical analysis and uniform weighting methods. TOPSIS had a narrower range of data within the 95% confidence interval and a significantly lower coefficient of variation (CV). This indicates enhanced precision in evaluating presence and can be used to compare different technique setting of virtual systems.