arXiv subjects

Yanhua Liu

Publications and source records attributed to Yanhua Liu.

5 recordsLinked to original sources

arXiv2026

AdaRare: Telemetry-Guided Joint Profile Control for Greybox Fuzzing

Greybox fuzzers combine interacting queue, mutation, dictionary, energy, and comparison-solving control surfaces, while prior adaptive systems typically optimize other decision objects or control layers. We present AdaRare, an AFL++ extension that coordinates five internal actuation mechanisms as one bounded in-process profile updated every 5,000 ms. Completed-window, action-induced telemetry feeds an arm-local recency-weighted linear scorer and a profile-conditioned controller target. The scorer borrows the algebraic structure of disjoint LinUCB, but serves as a closed-loop profile-ranking mechanism rather than a calibrated contextual-bandit action-value estimator or statistical confidence bound. Across three sequential repeated-trial phases, Main provides broad integrated-system evidence: AdaRare has higher median edge coverage than vanilla AFL++ on all eight targets, with five Holm-significant comparisons. In the strongest matched result, Full AdaRare has higher median edge coverage than CmpLog-matched AFL++ on all five follow-up targets, with four Holm-significant comparisons. Batch A finds higher medians for telemetry-guided selection than fixed-context, random, and round-robin schedules in all 15 target-control comparisons, with 13 Holm-significant comparisons. The experiments do not establish independent No-A6-versus-Shadow or scarcity-bundle effects; A6 evidence is target-dependent and weakens under batch-wide correction. In an unmatched firmware case study, AdaRare-generated inputs exposed five distinct memory-corruption findings, each reproduced in a separate environment and later assigned a CVE identifier. Controller-boundary compute P99 medians are below 6.5 ms for a five-second window; complete-boundary P99 medians including synchronous logging are below 14.7 ms. These measurements characterize boundary latency, not total system overhead.

cs.CR↗

arXiv2026

Rank-Two Frobenius-Linearized Normal Forms and Orthoderivative Dual Coordinates in Quadratic APN Maps

We classify binary-linear two-term Frobenius-linearized operators $L(Y)=AY^\sigma+BY$ on $K^3$, where $K$ is a finite extension of $\mathbb{F}_2$ and $\sigma$ is a fixed nontrivial Frobenius automorphism of $K$ with fixed field $\mathbb{F}_2$. Under a coefficient-rank and binary-kernel condition, if $A$ and $B$ both have $K$-rank two and $L$ has a one-dimensional kernel over $\mathbb{F}_2$, then invertible $K$-linear input and output changes reduce $L$, for this fixed $\sigma$, to the canonical model $(\alpha,\beta,\gamma)\mapsto(\alpha^\sigma+\alpha,\beta^\sigma,\gamma)$. The proof constructs the coordinate frames from the two coefficient-kernel directions and the binary kernel. In these coordinates, the first dual output row is exactly the unique nonzero trace-adjoint normal, with an exact $K$-valued normalization. For pure $\sigma$-quadratic almost perfect nonlinear maps, this identifies the orthoderivative by $\pi_F(X)^T F(X)=1$; in odd extension degree it also yields permutation behavior and a bijection from the projective plane to its dual. The triprojective construction of Gologlu and Kolsch and the cubic norm-twist construction of Li, Zhou, Li, and Qu provide two realizations arising from different algebraic constructions. The triprojective case further admits a determinant factorization and a complete dual frame, whereas the norm-twist realization shows that the pure-map consequences do not follow from the operator theorem alone. A natural Gold representation has coefficient-rank pair $(3,3)$, delimiting the rank-two subclass. The normal form also supplies exact extension-field labels for known component-radical and Walsh-support relations.

cs.CR↗

arXiv2026

Pulse Shape Discrimination Algorithms: Survey and Benchmark

This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly sixty methods into statistical (time-domain, frequency-domain, neural network-based) and prior-knowledge (machine learning, deep learning) paradigms. We implement and evaluate all algorithms on two standardized datasets: an unlabeled set from a 241Am-9Be source and a time-of-flight labeled set from a 238Pu-9Be source, using metrics including Figure of Merit (FOM), F1-score, ROC-AUC, and inter-method correlations. Our analysis reveals that deep learning models, particularly Multi-Layer Perceptrons (MLPs) and hybrid approaches combining statistical features with neural regression, often outperform traditional methods. We discuss architectural suitabilities, the limitations of FOM, alternative evaluation metrics, and performance across energy thresholds. Accompanying this work, we release an open-source toolbox in Python and MATLAB, along with the datasets, to promote reproducibility and advance PSD research.

cs.LG↗

arXiv2023

$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/

physics.geo-ph↗

arXiv2022

Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks

Monitoring changes inside a reservoir in real time is crucial for the success of CO2 injection and long-term storage. Machine learning (ML) is well-suited for real-time CO2 monitoring because of its computational efficiency. However, most existing applications of ML yield only one prediction (i.e., the expectation) for a given input, which may not properly reflect the distribution of the testing data, if it has a shift with respect to that of the training data. The Simultaneous Quantile Regression (SQR) method can estimate the entire conditional distribution of the target variable of a neural network via pinball loss. Here, we incorporate this technique into seismic inversion for purposes of CO2 monitoring. The uncertainty map is then calculated pixel by pixel from a particular prediction interval around the median. We also propose a novel data-augmentation method by sampling the uncertainty to further improve prediction accuracy. The developed methodology is tested on synthetic Kimberlina data, which are created by the Department of Energy and based on a CO2 capture and sequestration (CCS) project in California. The results prove that the proposed network can estimate the subsurface velocity rapidly and with sufficient resolution. Furthermore, the computed uncertainty quantifies the prediction accuracy. The method remains robust even if the testing data are distorted due to problems in the field data acquisition. Another test demonstrates the effectiveness of the developed data-augmentation method in increasing the spatial resolution of the estimated velocity field and in reducing the prediction error.

physics.geo-ph↗