Haoran Ma

Yuhe Tian, Kun Zhang, Haoran Ma et al.

While large language models (LLMs) have advanced CT report generation, existing methods typically encode 3D volumes holistically, failing to distinguish informative cues from redundant anatomical background. Inspired by radiological cognitive subtraction, we propose Differential Visual Prompting (DiffVP), which conditions report generation on explicit, high-level semantic scan-to-reference differences rather than solely on absolute visual features. DiffVP employs a hierarchical difference extractor to capture complementary global and local semantic discrepancies into a shared latent space, along with a difference-to-prompt generator that transforms these signals into learnable visual prefix tokens for LLM conditioning. These difference prompts serve as structured conditioning signals that implicitly suppress invariant anatomy while amplifying diagnostically relevant visual evidence, thereby facilitating accurate report generation without explicit lesion localization. On two large-scale benchmarks, DiffVP consistently outperforms prior methods, improving the average BLEU-1-4 by +10.98 and +4.36, respectively, and further boosts clinical efficacy on RadGenome-ChestCT (F1 score 0.421). All codes will be released at https://github.com/ArielTYH/DiffVP/.

Haoran Ma, Yan Zhang, Pengyuan Liu et al.

The Attention Restoration Theory (ART) presents a theoretical framework with four essential indicators (being away, extent, fascinating, and compatibility) for comprehending urban and natural restoration quality. However, previous studies relied on non-sequential data and non-spatial dependent methods, which overlooks the impact of spatial structure defined here as the positional relationships between scene entities on restoration quality. The past methods also make it challenging to measure restoration quality on an urban scale. In this work, a spatial-dependent graph neural networks (GNNs) approach is proposed to reveal the relation between spatial structure and restoration quality on an urban scale. Specifically, we constructed two different types of graphs at the street and city levels. The street-level graphs, using sequential street view images (SVIs) of road segments to capture position relationships between entities, were used to represent spatial structure. The city-level graph, modeling the topological relationships of roads as non-Euclidean data structures and embedding urban features (including Perception-features, Spatial-features, and Socioeconomic-features), was used to measure restoration quality. The results demonstrate that: 1) spatial-dependent GNNs model outperforms traditional methods (Acc = 0.735, F1 = 0.732); 2) spatial structure portrayed through sequential SVIs data significantly influences restoration quality; 3) spaces with the same restoration quality exhibited distinct spatial structures patterns. This study clarifies the association between spatial structure and restoration quality, providing a new perspective to improve urban well-being in the future.

Haoran Ma

Stable Diffusion model has been extensively employed in the study of archi-tectural image generation, but there is still an opportunity to enhance in terms of the controllability of the generated image content. A multi-network combined text-to-building facade image generating method is proposed in this work. We first fine-tuned the Stable Diffusion model on the CMP Fa-cades dataset using the LoRA (Low-Rank Adaptation) approach, then we ap-ply the ControlNet model to further control the output. Finally, we contrast-ed the facade generating outcomes under various architectural style text con-tents and control strategies. The results demonstrate that the LoRA training approach significantly decreases the possibility of fine-tuning the Stable Dif-fusion large model, and the addition of the ControlNet model increases the controllability of the creation of text to building facade images. This pro-vides a foundation for subsequent studies on the generation of architectural images.

Haoran Ma, Dongdong Wu

In the past decade, using Street View images and machine learning to measure human perception has become a mainstream research approach in urban science. However, this approach using only image-shallow information makes it difficult to comprehensively understand the deep semantic features of human perception of a scene. In this study, we proposed a new framework based on a pre-train natural language model to understand the relationship between human perception and the sense of a scene. Firstly, Place Pulse 2.0 was used as our base dataset, which contains a variety of human-perceived labels, namely, beautiful, safe, wealthy, depressing, boring, and lively. An image captioning network was used to extract the description information of each street view image. Secondly, a pre-trained BERT model was finetuning and added a regression function for six human perceptual dimensions. Furthermore, we compared the performance of five traditional regression methods with our approach and conducted a migration experiment in Hong Kong. Our results show that human perception scoring by deep semantic features performed better than previous studies by machine learning methods with shallow features. The use of deep scene semantic features provides new ideas for subsequent human perception research, as well as better explanatory power in the face of spatial heterogeneity.

Lingfei Kong, Haoran Ma

This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability. A Bayesian physics-informed neural network is developed by combining Gompertz growth dynamics with low-dimensional Bayesian inference in the log-volume domain. The framework employs a two-stage inference strategy combining maximum a posteriori (MAP) estimation and Hamiltonian Monte Carlo (HMC) sampling to estimate posterior predictive distributions and uncertainty intervals. The method was evaluated on longitudinal data from the National Lung Screening Trial (30 patients). Results show that the model captures heterogeneous tumor growth patterns while maintaining reasonable prediction accuracy under limited observations. Compared with deterministic modeling approaches, the proposed approach additionally provides calibrated uncertainty estimates. The inferred posterior parameter correlations were consistent with expected biological growth behavior. The proposed framework achieved a cohort-level log-space RMSE of approximately 0.20 together with well-calibrated 95% credible interval coverage across 30 patients. These findings suggest that Bayesian physics-informed modeling may be useful for uncertainty-aware tumor growth assessment when only limited longitudinal follow-up scans are available.

Tianyu Wu, Yu Yao, Zhenting Qi et al.

Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffusion-based parallel drafters such as DFlash predict the full B-token block in one forward pass, enabling deeper drafters and longer accepted blocks. However, existing multi-token drafter objectives often use fixed position-dependent weighting schedules, such as head-dependent weights or block-position decays, which do not adapt as the positions limiting acceptance change during training. To address this, we derive per-position training weights from a differentiable surrogate of expected accepted draft length, matching the weight of each position to its log-probability gradient contribution. The resulting loss, D-PACE (Dynamic Position-Aware Cross-Entropy), shifts training signal toward positions that currently limit acceptance as the drafter improves. Across six benchmarks, two Qwen3-4B draft depths, two decoding temperatures, and two additional target models, D-PACE consistently improves both wall-clock speedup and average emitted length, with 2.3\% measured training-time overhead and no changes to the drafter architecture or inference procedure.

Zihan Liang, Jiahao Sun, Haoran Ma

Despite the remarkable capabilities of text-to-image (T2I) generation models, real-world applications often demand fine-grained, iterative image editing that existing methods struggle to provide. Key challenges include granular instruction understanding, robust context preservation during modifications, and the lack of intelligent feedback mechanisms for iterative refinement. This paper introduces RefineEdit-Agent, a novel, training-free intelligent agent framework designed to address these limitations by enabling complex, iterative, and context-aware image editing. RefineEdit-Agent leverages the powerful planning capabilities of Large Language Models (LLMs) and the advanced visual understanding and evaluation prowess of Vision-Language Large Models (LVLMs) within a closed-loop system. Our framework comprises an LVLM-driven instruction parser and scene understanding module, a multi-level LLM-driven editing planner for goal decomposition, tool selection, and sequence generation, an iterative image editing module, and a crucial LVLM-driven feedback and evaluation loop. To rigorously evaluate RefineEdit-Agent, we propose LongBench-T2I-Edit, a new benchmark featuring 500 initial images with complex, multi-turn editing instructions across nine visual dimensions. Extensive experiments demonstrate that RefineEdit-Agent significantly outperforms state-of-the-art baselines, achieving an average score of 3.67 on LongBench-T2I-Edit, compared to 2.29 for Direct Re-Prompting, 2.91 for InstructPix2Pix, 3.16 for GLIGEN-based Edit, and 3.39 for ControlNet-XL. Ablation studies, human evaluations, and analyses of iterative refinement, backbone choices, tool usage, and robustness to instruction complexity further validate the efficacy of our agentic design in delivering superior edit fidelity and context preservation.

Haoran Ma, Kaihan Zhang, Jiannan Cai

Heat exposure significantly influences pedestrian routing behaviors. Existing methods such as agent-based modeling (ABM) and empirical measurements fail to account for individual physiological variations and environmental perception mechanisms under thermal stress. This results in a lack of human-centred, heat-adaptive routing suggestions. To address these limitations, we propose a novel Vision Language Model (VLM)-driven Persona-Perception-Planning-Memory (PPPM) framework that integrating street view imagery and urban network topology to simulate heat-adaptive pedestrian routing. Through structured prompt engineering on Gemini-2.0 model, eight distinct heat-sensitive personas were created to model mobility behaviors during heat exposure, with empirical validation through questionnaire survey. Results demonstrate that simulation outputs effectively capture inter-persona variations, achieving high significant congruence with observed route preferences and highlighting differences in the factors driving agents decisions. Our framework is highly cost-effective, with simulations costing 0.006USD and taking 47.81s per route. This Artificial Intelligence-Generated Content (AIGC) methodology advances urban climate adaptation research by enabling high-resolution simulation of thermal-responsive mobility patterns, providing actionable insights for climate-resilient urban planning.

Haoran Ma, Benyamin Ghojogh, Maria N. Samad et al.

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection. The proposed method is a novel hybrid of isolation forest and Mondrian forest which are existing methods for batch anomaly detection and online random forest, respectively. iMondrian forest takes the idea of isolation, using the depth of a node in a tree, and implements it in the Mondrian forest structure. The result is a new data structure which can accept streaming data in an online manner while being used for anomaly detection. Our experiments show that iMondrian forest mostly performs better than isolation forest in batch settings and has better or comparable performance against other batch and online anomaly detection methods.