arXiv subjects

Yang Ping

Publications and source records attributed to Yang Ping.

5 recordsLinked to original sources

arXiv2026

ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation

Vision-Language-Action (VLA) models and world-action models have emerged as central paradigms for general-purpose robotic intelligence, yet their empirical progress remains constrained by the absence of evaluation protocols that are both physically realistic and diagnostically controlled. Simulator-centric benchmarks provide scale and reproducibility, but cannot fully capture the reality gap induced by perception noise, contact dynamics, latency, calibration error, and hardware constraints. Conversely, real-robot evaluations are often fragmented across platforms, scenes, objects, and scoring rules, making fair comparison and failure attribution difficult. We introduce ManipArena, a standardized real-robot evaluation framework for studying manipulation generalization under matched physical conditions. ManipArena comprises 20 tasks, 10,812 expert trajectories, 13.5M frames, and approximately 188 robot hours across tabletop and mobile manipulation. The framework combines schema-defined task variation, stratified in-domain, visualshift, and semantic-OOD trials, subtask-level partial-credit scoring, three-level language annotations, low-level motor signals, and paired real-to-sim environments reconstructed from physical scenes. Using ManipArena, we evaluate seven tabletop configurations spanning VLA and world-action-model policies. The results show that real-robot conclusions depend not only on architecture, but also on model provenance, fine-tuning regime, data sampling, and annotation granularity. ManipArena thus provides a reproducible and interpretable foundation for diagnosing capability boundaries and failure modes in embodied generalization.

cs.RO↗

arXiv2025

Patch-Discontinuity Mining for Generalized Deepfake Detection

The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic fake facial images, posing serious threats to personal privacy and the integrity of online information. Existing deepfake detection methods often rely on handcrafted forensic cues and complex architectures, achieving strong performance in intra-domain settings but suffering significant degradation when confronted with unseen forgery patterns. In this paper, we propose GenDF, a simple yet effective framework that transfers a powerful large-scale vision model to the deepfake detection task with a compact and neat network design. GenDF incorporates deepfake-specific representation learning to capture discriminative patterns between real and fake facial images, feature space redistribution to mitigate distribution mismatch, and a classification-invariant feature augmentation strategy to enhance generalization without introducing additional trainable parameters. Extensive experiments demonstrate that GenDF achieves state-of-the-art generalization performance in cross-domain and cross-manipulation settings while requiring only 0.28M trainable parameters, validating the effectiveness and efficiency of the proposed framework.

cs.CV↗

arXiv2021

Spectrum Gaussian Processes Based On Tunable Basis Functions

Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity. However, in previous research, those methods always tend to adopt the orthonormal basis functions, such as eigenvectors in the Hilbert space, in the spectrum method, or decoupled orthogonal components in the variational framework. In this paper, inspired by quantum physics, we introduce a novel basis function, which is tunable, local and bounded, to approximate the kernel function in the Gaussian process. There are two adjustable parameters in these functions, which control their orthogonality to each other and limit their boundedness. And we conduct extensive experiments on open-source datasets to testify its performance. Compared to several state-of-the-art methods, it turns out that the proposed method can obtain satisfactory or even better results, especially with poorly chosen kernel functions.

stat.ML↗

arXiv2018

Quinone-based Switches for Candidate Building Blocks of Molecular Junctions with QTAIM and the Stress Tensor

The current work investigates candidate building blocks based on molecular junctions from hydrogen transfer tautomerization in the benzoquinone-like core of an azophenine molecule with QTAIM and the recently-introduced stress tensor trajectory analysis. We find that in particular the stress tensor trajectories are well suited to describe the mechanism of the switching process. The effects of an Fe-dopant atom coordinated to the quinone ring, as well as F and Cl substitution of different ring-hydrogens, are investigated and the new QTAIM and stress tensor analysis is used to draw conclusions on the effectiveness of such molecules as molecular switches in nano-sized electronic circuits. We find that the coordinated Fe-dopant greatly improves the switching properties, both in terms of the tautomerisation barrier that has to be crossed in the switching process and the expected conductance behavior, while the effects of hydrogen substitution are more subtle. The absence of the Fe-dopant atom led to impaired functioning of the switch 'OFF' mechanism as well as coinciding with the formation of closed-shell H---H bond critical points that indicated a strained or electron deficient environment. Our analysis demonstrates promise for future use in design of molecular electronic devices.

physics.chem-ph↗

arXiv2017

The Role of Weak Interactions in Characterizing Peptide Folding Preferences using a QTAIM Interpretation of the Ramachandran Plot ({\phi}-{\psi})

The Ramachandran plot is a potent way to understand structures of biomolecules, however, the original formulation of the Ramachandran plot only considers backbone conformations. We formulate a new interpretation of the original Ramachandran plot ($\phi-\psi$) that can include a description of the weaker interactions including both the hydrogen bonds and H$---$H bonds as a new way to derive insights into the phenomenon of peptide folding. We use QTAIM (quantum theory of atoms in molecules) to interpret the Ramachandran plot. Specifically, we show that QTAIM analysis permits identifying key regions of the Ramachandran plot without the need for massive data sets. A highly non-linear relationship is found between the QTAIM vector-derived interpreted Ramachandran plot and the conventional Ramachandran plot ($\phi-\psi$) demonstrating that this new approach is not a trivial coordinate transformation. An investigation of both the backbone and the weaker bonds within the framework of the QTAIM interpreted Ramachandran plot was found to be in line with physical intuition. The least-preferred directions calculated for the hydrogen bonds and H$---$H bonds were found to coincide with the 'unlikely' regions of the Ramachandran plot.

physics.chem-ph↗