Haoran Ma

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.

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, 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.