arXiv subjects

Shitian Yang

Publications and source records attributed to Shitian Yang.

3 recordsLinked to original sources

arXiv2026

Idea2Prior: Auditable Paper Retrieval and Ranking from Research Ideas

Finding a paper from an informal research idea requires more than matching topic words: relevant work may use different terminology, and its distinguishing mechanism may be absent from the abstract. We present IDEA2PRIOR, an auditable literature-search system built on PaperArena. It combines multi-source discovery, identity verification, facet-based abstract assessment, semantic citation expansion, and joint Expert ranking. A retrieval-only mode ranks up to five papers from a shortlist of at most 60, using complete available abstracts and locally selected PDF evidence from five fixed acquisition attempts, without generating a long-form report. In a historical comparison covering ten queries from each of EASY, MEDIUM, and HARD, aggregate reported Hit@5 is 53.3% for IDEA2PRIOR, 43.3% for ChatGPT web Deep Research, and 36.7% for Gemini web Deep Research under the documented accounting convention. The advantage is concentrated in EASY and MEDIUM; ChatGPT leads on HARD. Selected IDEA2PRIOR runs average 8 minutes 53 seconds, compared with user-reported web averages of 5.2 and 4.7 minutes. A separate nine-query API sample averages $0.0945 per task; this is a historical cost observation, not a matched comparison with web subscription costs. Pairwise Top 5 coverage ranges from 6% to 34%, showing differing returned evidence sets. The system also exposes optional implementation analysis and citation-evolution tools. These results support targeted idea-to-paper retrieval while leaving general web research, forum search, and broader relevance evaluation open.

cs.IR↗

arXiv2026

3DRot: Rediscovering the Missing Primitive for RGB-Based 3D Augmentation

RGB-based 3D tasks, e.g., 3D detection, depth estimation, 3D keypoint estimation, still suffer from scarce, expensive annotations and a thin augmentation toolbox, since many image transforms, including rotations and warps, disrupt geometric consistency. While horizontal flipping and color jitter are standard, rigorous 3D rotation augmentation has surprisingly remained absent from RGB-based pipelines, largely due to the misconception that it requires scene depth or scene reconstruction. In this paper, we introduce 3DRot, a plug-and-play augmentation that rotates and mirrors images about the camera's optical center while synchronously updating RGB images, camera intrinsics, object poses, and 3D annotations to preserve projective geometry, achieving geometry-consistent rotations and reflections without relying on any scene depth. We first validate 3DRot on a classical RGB-based 3D task, monocular 3D detection. On SUN RGB-D, inserting 3DRot into a frozen DINO-X + Cube R-CNN pipeline raises $IoU_{3D}$ from 43.21 to 44.51, cuts rotation error (ROT) from 22.91$^\circ$ to 20.93$^\circ$, and boosts $mAP_{0.5}$ from 35.70 to 38.11; smaller but consistent gains appear on a cross-domain IN10 split. Beyond monocular detection, adding 3DRot on top of the standard BTS augmentation schedule further improves NYU Depth v2 from 0.1783 to 0.1685 in abs-rel (and 0.7472 to 0.7548 in $δ<1.25$), and reduces cross-dataset error on SUN RGB-D. On KITTI, applying the same camera-centric rotations in MVX-Net (LiDAR+RGB) raises moderate 3D AP from about 63.85 to 65.16 while remaining compatible with standard 3D augmentations.

cs.CV↗

arXiv2023

Conditional Flood Fill Method in Logic Synthesis

In the field of Electronic Design Automation (EDA), logic synthesis plays a pivotal role in optimizing hardware resources. Traditional logic synthesis algorithms, such as the Quine-McCluskey method, face challenges in scalability and efficiency, particularly for higher-dimension problems. This paper introduces a novel heuristic algorithm based on Conditional Flood Fill Method aimed at addressing these limitations. Our method employs count-based adjacent element handling and introduces nine new theorems to guide the logic synthesis process. Experimental results validate the efficacy of our approach, showing significant improvements in computational efficiency and scalability compared to existing algorithms. The algorithm holds potential for future advancements in circuit development and Boolean function optimization.

cs.AR↗