arXiv subjects

Yifei Tong

Publications and source records attributed to Yifei Tong.

5 recordsLinked to original sources

arXiv2025

Temporal Frictions and Judicial Outcomes: Analyzing the Impact of Time Delays on Criminal Sentencing in Cook County (2020-2024)

This study examines how time delays between criminal offenses and arrests are associated with sentencing outcomes in Cook County, Illinois, during the COVID-19 era. Using administrative court records from 2020 to 2024, the analysis focuses on cases in which arrests did not occur immediately, allowing for systematic variation in procedural delay. The study asks whether longer delays are linked to more severe punishments and whether these associations differ across offense types and institutional contexts during periods of court disruption. The findings indicate that longer delays are consistently associated with harsher sentencing outcomes, even after accounting for demographic characteristics, case complexity, offense category, and pandemic-related disruptions. These associations are particularly pronounced in violent and sexual exploitation cases. While the analysis does not establish causal effects, the consistency of results across multiple empirical approaches suggests that procedural timing is a meaningful feature of judicial decision-making rather than a neutral administrative artifact. By documenting how institutional delays correlate with punishment severity, this study contributes to empirical research on judicial discretion, court efficiency, and inequality in the administration of justice, highlighting the importance of procedural fairness alongside formal legal criteria.

cs.CY↗

arXiv2025

Heard or Halted? Gender, Interruptions, and Emotional Tone in U.S. Supreme Court Oral Arguments

This study examines how interruptions during U.S. Supreme Court oral arguments shape both the semantic content and emotional tone of advocates' speech, with a focus on gendered dynamics in judicial discourse. Using the ConvoKit Supreme Court Corpus (2010-2019), we analyze 12,663 speech chunks from advocate-justice interactions to assess whether interruptions alter the meaning of an advocate's argument and whether interruptions toward female advocates exhibit more negative emotional valence. Semantic shifts are quantified using GloVe-based sentence embeddings, while sentiment is measured through lexicon-based analysis. We find that semantic similarity between pre- and post-interruption speech remains consistently high, suggesting that interruptions do not substantially alter argumentative content. However, interruptions directed at female advocates contain significantly higher levels of negative sentiment. These results deepen empirical understanding of gendered communication in elite institutional settings and demonstrate the value of computational linguistic methods for studying power, discourse, and equity in judicial proceedings.

cs.CL↗

arXiv2025

Non-Linear Determinants of Pedestrian Injury Severity: Evidence from Administrative Data in Great Britain

This study investigates the non-linear determinants of pedestrian injury severity using administrative data from Great Britain's 2023 STATS19 dataset. To address inherent data-quality challenges, including missing information and substantial class imbalance, we employ a rigorous preprocessing pipeline utilizing mode imputation and Synthetic Minority Over-sampling (SMOTE). We utilize non-parametric ensemble methods (Random Forest and XGBoost) to capture complex interactions and heterogeneity often missed by linear models, while Shapley Additive Explanations are employed to ensure interpretability and isolate marginal feature effects. Our analysis reveals that vehicle count, speed limits, lighting, and road surface conditions are the primary predictors of severity, with police attendance and junction characteristics further distinguishing severe collisions. Spatially, while pedestrian risk is concentrated in dense urban Local Authority Districts (LADs), we identify that certain rural LADs experience disproportionately severe outcomes conditional on a collision occurring. These findings underscore the value of combining spatial analysis with interpretable machine learning to guide geographically targeted speed management, infrastructure investment, and enforcement strategies.

cs.CY↗

arXiv2025

ARAP-GS: Drag-driven As-Rigid-As-Possible 3D Gaussian Splatting Editing with Diffusion Prior

Drag-driven editing has become popular among designers for its ability to modify complex geometric structures through simple and intuitive manipulation, allowing users to adjust and reshape content with minimal technical skill. This drag operation has been incorporated into numerous methods to facilitate the editing of 2D images and 3D meshes in design. However, few studies have explored drag-driven editing for the widely-used 3D Gaussian Splatting (3DGS) representation, as deforming 3DGS while preserving shape coherence and visual continuity remains challenging. In this paper, we introduce ARAP-GS, a drag-driven 3DGS editing framework based on As-Rigid-As-Possible (ARAP) deformation. Unlike previous 3DGS editing methods, we are the first to apply ARAP deformation directly to 3D Gaussians, enabling flexible, drag-driven geometric transformations. To preserve scene appearance after deformation, we incorporate an advanced diffusion prior for image super-resolution within our iterative optimization process. This approach enhances visual quality while maintaining multi-view consistency in the edited results. Experiments show that ARAP-GS outperforms current methods across diverse 3D scenes, demonstrating its effectiveness and superiority for drag-driven 3DGS editing. Additionally, our method is highly efficient, requiring only 10 to 20 minutes to edit a scene on a single RTX 3090 GPU.

cs.GR↗

arXiv2025

CAGE-GS: High-fidelity Cage Based 3D Gaussian Splatting Deformation

As 3D Gaussian Splatting (3DGS) gains popularity as a 3D representation of real scenes, enabling user-friendly deformation to create novel scenes while preserving fine details from the original 3DGS has attracted significant research attention. We introduce CAGE-GS, a cage-based 3DGS deformation method that seamlessly aligns a source 3DGS scene with a user-defined target shape. Our approach learns a deformation cage from the target, which guides the geometric transformation of the source scene. While the cages effectively control structural alignment, preserving the textural appearance of 3DGS remains challenging due to the complexity of covariance parameters. To address this, we employ a Jacobian matrix-based strategy to update the covariance parameters of each Gaussian, ensuring texture fidelity post-deformation. Our method is highly flexible, accommodating various target shape representations, including texts, images, point clouds, meshes and 3DGS models. Extensive experiments and ablation studies on both public datasets and newly proposed scenes demonstrate that our method significantly outperforms existing techniques in both efficiency and deformation quality.

cs.GR↗