arXiv subjects

Peiheng Li

Publications and source records attributed to Peiheng Li.

3 recordsLinked to original sources

arXiv2026

Compressed Traffic Assignment with the Augmented Lagrangian Method

We consider large-scale traffic assignment problems and develop a path-based compression framework. In particular, we partition paths into major and minor paths according to nominal path flows and a prescribed threshold, and retain the major paths explicitly. For the minor paths, we use reference flows derived from nominal-flow information and represent deviations using a truncated singular value decomposition of the minor path-link incidence matrix. The resulting compressed formulation preserves convexity, and a simple scaling of the nominal minor flows ensures feasibility under changes in demand. To solve the resulting formulation, we use an augmented Lagrangian method with separate penalty parameters for the different constraints and adaptive penalty parameter updates. We conduct computational studies on the Chicago Sketch, Chicago Regional, and Philadelphia networks in two stages. Using nominal demand data, we first study the selection of the compression threshold and rank and the effect of path-pool expansion. The results show substantial dimensionality reduction while maintaining high solution accuracy. We then reuse the nominal representation for perturbed-demand problems. The compressed formulation remains highly accurate and reduces computational time by approximately 58%-77% on the original path pools and 51%-76% on expanded path pools. We also compare it with simpler reduced formulations that either discard the minor paths or fix their flows at the reference values. The results show that fixing the minor-path flows at their reference values already provides high accuracy, while the low-dimensional adjustments can further improve link travel-time accuracy in some cases.

math.OC↗

arXiv2026

Halt the Hallucination: Decoupling Signal and Semantic OOD Detection Based on Cascaded Early Rejection

Efficient and robust Out-of-Distribution (OOD) detection is paramount for safety-critical applications.However, existing methods still execute full-scale inference on low-level statistical noise. This computational mismatch not only incurs resource waste but also induces semantic hallucination, where deep networks forcefully interpret physical anomalies as high-confidence semantic features.To address this, we propose the Cascaded Early Rejection (CER) framework, which realizes hierarchical filtering for anomaly detection via a coarse-to-fine logic.CER comprises two core modules: 1)Structural Energy Sieve (SES), which establishes a non-parametric barrier at the network entry using the Laplacian operator to efficiently intercept physical signal anomalies; and 2) the Semantically-aware Hyperspherical Energy (SHE) detector, which decouples feature magnitude from direction in intermediate layers to identify fine-grained semantic deviations. Experimental results demonstrate that CER not only reduces computational overhead by 32% but also achieves a significant performance leap on the CIFAR-100 benchmark:the average FPR95 drastically decreases from 33.58% to 22.84%, and AUROC improves to 93.97%. Crucially, in real-world scenarios simulating sensor failures, CER exhibits performance far exceeding state-of-the-art methods. As a universal plugin, CER can be seamlessly integrated into various SOTA models to provide performance gains.

cs.CV↗

arXiv2026

VMF-GOS: Geometry-guided virtual Outlier Synthesis for Long-Tailed OOD Detection

Out-of-Distribution (OOD) detection under long-tailed distributions is a highly challenging task because the scarcity of samples in tail classes leads to blurred decision boundaries in the feature space. Current state-of-the-art (sota) methods typically employ Outlier Exposure (OE) strategies, relying on large-scale real external datasets (such as 80 Million Tiny Images) to regularize the feature space. However, this dependence on external data often becomes infeasible in practical deployment due to high data acquisition costs and privacy sensitivity. To this end, we propose a novel data-free framework aimed at completely eliminating reliance on external datasets while maintaining superior detection performance. We introduce a Geometry-guided virtual Outlier Synthesis (GOS) strategy that models statistical properties using the von Mises-Fisher (vMF) distribution on a hypersphere. Specifically, we locate a low-likelihood annulus in the feature space and perform directional sampling of virtual outliers in this region. Simultaneously, we introduce a new Dual-Granularity Semantic Loss (DGS) that utilizes contrastive learning to maximize the distinction between in-distribution (ID) features and these synthesized boundary outliers. Extensive experiments on benchmarks such as CIFAR-LT demonstrate that our method outperforms sota approaches that utilize external real images.

cs.CV↗