arXiv subjects

Liu Peng

Publications and source records attributed to Liu Peng.

6 recordsLinked to original sources

arXiv2026

EEG Benchmarking Needs a Task Specification Layer: NeuroDoc for Rulebook-Guided, Executable Benchmark Construction

Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared task specification layer that can turn heterogeneous recordings into reusable benchmark units. Existing standards organize files, metadata, and provenance, but they do not specify EEG tasks under a common language and rulebook, leaving critical task semantics scattered across papers, code, and manual interpretation. We investigate whether heterogeneous public EEG datasets can be standardized through a structured task specification language paired with a shared rulebook. Our methodology represents each benchmark entry as a task document synchronized with an executable task kernel, with the rulebook defining task fields, evidence requirements, document-kernel alignment, review states, and machine-checkable constraints. Using this methodology, we release a community-reviewed EEG benchmark corpus centered on 53 completed and reviewed entries with 245 task definitions spanning diverse paradigms, and we introduce NeuroDoc and NeuroAudit as the operational support layer for rulebook-guided drafting, upgrading, review, amendment, and release management. We further examine whether the resulting benchmark units can be instantiated in a shared downstream setting across four EEG foundation model backbones, providing execution-based evidence for reusable, auditable, and executable EEG benchmarking infrastructure.

cs.LG↗

arXiv2026

Mud-Standoff Effect Correction Based on Open-Short Calibration and Resistivity Consistency-Constrained Iterative Inversion for Oil-Based Mud Imagers

We propose a mud-standoff effect correction method and a set of approximate apparent resistivity inversion methods suitable for oil-based mud micro-resistivity imaging logging. To calibrate the influence of the mud layer on electrode measurement signals, this study integrates the Open-Short calibration(OSC) method with the three-layer impedance model of the oil-based mud resistivity imager. By treating the electrode and the mud layer as an integrated whole and simulating the open/short-circuit states via the finite element method, the independent extraction of the mud layer impedance signal is achieved, and the formation impedance signal is separated from the total impedance. For fast inversion of formation resistivity, standoff thickness (mud layer thickness), and relative permittivity of formation, a resistivity consistency-constrained iterative inversion method is further proposed. In this method, the formation impedance is first converted into resistivity, and then the consistency residual of the resistivity at different frequencies is used as the objective function to invert the approximate apparent resistivity of the formation through an iterative optimization algorithm. The effectiveness of the finite element-simulated OSC method, the objective function construction scheme, and the consistency inversion method is verified through both numerical models and an example of field data.

physics.geo-ph↗

arXiv2025

Robustness of Probabilistic Models to Low-Quality Data: A Multi-Perspective Analysis

A systematic, comparative investigation into the effects of low-quality data reveals a stark spectrum of robustness across modern probabilistic models. We find that autoregressive language models, from token prediction to sequence-to-sequence tasks, are remarkably resilient (for GPT-2, test NLL increases modestly from 2.87 to 3.59 despite 50% token corruption). By contrast, under the same levels of data corruption, class-conditional diffusion models degrade catastrophically (image-label consistency plummets by 56.81% relative to baseline), while classifiers show a moderate impact that diminishes with dataset scale. To explain these discrepancies, we analyze the results through a multi-perspective lens, integrating information theory, PAC learning, and gradient dynamics. These analyses suggest that robustness is heavily influenced by two key principles: the richness of conditioning information, which constrains the learning problem, and the absolute information content of the training data, which allows the signal from correct information to dominate statistical noise.

cs.AI↗

arXiv2019

Differentiated context-aware hook placement for different owners' smartphones

A hook is a piece of code. It checks user privacy policy before some sensitive operations happen. We propose an automated solution named Prihook for hook placement in the Android Framework. Addressing specific context-aware user privacy concerns, the hook placement in Prihook is personalized. Specifically, we design User Privacy Preference Table (UPPT) to help a user express his privacy concerns. And we leverage machine learning to discover a Potential Method Set (consisting of Sensor Data Access Methods and Sensor Control Methods) from which we can select a particular subset to put hooks. We propose a mapping from words in the UPPT lexicon to methods in the Potential Method Set. With this mapping, Prihook is able to (a) select a specific set of methods; and (b) generate and place hooks automatically. We test Prihook separately on 6 typical UPPTs representing 6 kinds of resource-sensitive UPPTs, and no user privacy violation is found. The experimental results show that the hooks placed by PriHook have small runtime overhead.

cs.SE↗

arXiv2013

Beam test of a one-dimensional position sensitive chamber on synchrotron radiation

One-dimensional single-wire chamber was developed to provide high position resolution for powder diffraction experiments with synchrotron radiation. A diffraction test using the sample of SiO2 has been accomplished at 1W2B laboratory of Beijing Synchrotron Radiation Source. The data of beam test were analyzed and some diffraction angles were obtained. The experimental results were in good agreement with standard data from ICDD powder diffraction file. The precision of diffraction angles was 1% to 4.7%. Most of relative errors between measured values of diffraction angles and existing data were less than 1%. As for the detector, the best position resolution in the test was 138 um (sigma value) with an X-ray tube. Finally, discussions of the results were given. The major factor that affected the precision of measurement was deviation from the flat structure of detector. The effect was analyzed and it came to a conclusion that it would be the optimal measurement scheme when the distance between the powder sample and detector was from 400 mm to 600 mm.

physics.ins-det↗

arXiv2011

An evolving network model with modular growth

In this paper, we propose an evolving network model growing fast in units of module, based on the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes, and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing ratio of the number of inner-module edges and the number of inter-module edges. Based on the mean field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module.

physics.soc-ph↗