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Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 73 records · Page 4Linked to original sources
arXiv2026

Probabilistic Reachable-Action Verification of Visuomotor Policies via Set-Based Training

Reachability analysis for visuomotor policies is difficult because large visual encoders make end-to-end set propagation computationally expensive and excessively conservative. We therefore freeze the visual encoder and confine set propagation to a low-dimensional interface between it and the downstream policy, with the interface set calibrated from held-out camera-pose perturbations. Propagating this set through the policy with zonotopes yields a terminal output-enclosure width that set-based training optimizes directly. During evaluation, camera-pose perturbations are sampled from the prescribed distribution, and rollout-level split conformal calibration converts the resulting action-deviation scores into a probabilistic reachable-action radius with finite-sample coverage. In controlled manipulation experiments, set-based training reduces this radius while preserving closed-loop task capability, and matched behavior-only, observational-consistency, and pointwise-adversarial controls all leave a larger radius.

cs.RO↗
arXiv2026

A lab-on-a-silicon-chip platform for all-electrical antibiotic susceptibility tests with a sample-to-results time within 20 minutes

Rapid antibiotic susceptibility tests (ASTs) are essential for quick selection of effective drugs to treat bacterial infections at an early stage. However, the most widely used phenotypic ASTs in clinical practice often require 24 - 48 hours of pre-culture enrichment and 4 - 20 hours of testing. They are too slow to guide therapy, even with the most rapid protocol. Here, we report a lab-on-a-silicon chip (LOSC) system, which integrates arrays of silicon nanowire field-effect transistor (SiNWFET) sensors with high-throughput cell-collection microfluidics for rapid ASTs. The microfluidics concentrate bacteria into picoliter-scale chambers within minutes, eliminating the need for pre-cultivation. Embedded SiNWFETs sensitively track antibiotic-induced metabolic pH shifts, enabling AST by detecting rapid metabolic inhibition in susceptible bacteria. Using an unbuffered culturing medium, LOSC achieves sample-to-result times within 20 minutes for clinically isolated E. coli strains. With its electrical readout and compact design, LOSC offers a low-cost, rapid, and portable AST solution for point-of-care diagnostics.

physics.ins-det↗
arXiv2026

Quarkyonic Stars with Strangeness

We propose an extension of the quarkyonic matter framework that includes $u$, $d$, and $s$ quarks and the full baryon octet. Within this extended framework, we impose beta-equilibrium between baryons and leptons, while determining the quark fractions from the constituent quark contents of baryons. The hadronic sector of octet baryons is described by a recently developed density, momentum and isospin dependent effective interaction based on the N3LO Skyrme pseudopotential, whereas quarks and leptons are treated as free particles. We find that the quarkyonic mechanism can obviously reduce the critical density for hyperon appearance in neutron stars due to the fact that the nucleons are displaced to higher momentum states in quarkyonic matter and their chemical potentials rise accordingly. Furthermore, the quarkyonic mechanism can significantly stiffen the equation of state of hyperon star matter and thereby enhance the hyperon star maximum mass, thus helping to mitigate the hyperon puzzle.

nucl-th↗
arXiv2026

WEKP-PLP: A Wireless Environment Knowledge Pool-Enhanced Path Loss Prediction Framework for Shore-to-Ship Communication

Accurate shore-to-ship path loss prediction is essential for maritime mobile communication systems, but it remains challenging because dynamic sea surface conditions can alter reflection paths and multipath effects, introducing uncertainty into the observed path loss. This paper proposes a wireless environment knowledge pool-enhanced path loss prediction (WEKP-PLP) method for point prediction and interval characterization of shore-to-ship path loss. The proposed method first constructs a wireless environment knowledge pool (WEKP) from ray tracing (RT) simulations under different wind speeds, temperatures, salinities, frequencies, antenna heights, and propagation distances. The WEKP learns the mapping from environmental and system parameters to path loss residual quantiles and provides a prior that contains both the median prediction and the associated prediction interval. To adapt this prior to real scenarios, a small number of measurement samples are used to learn the residual between the WEKP median prediction and the measured path loss through Gaussian process regression (GPR). The experimental results show that the WEKP-PLP provides accurate point prediction and compact intervals with reliable coverage. The proposed method achieves an MAE of 1.03 dB and an RMSE of 1.54 dB. The ablation results further confirm that both the WEKP prior and the residual correction using a small number of measurement samples are necessary for accurate and reliable shore-to-ship path loss prediction.

eess.SP↗
arXiv2026

Support Thresholds, Not Algorithms, Limit Rare-Association Recovery in Co-Purchase Networks

The support threshold of the Apriori algorithm involves a trade-off in conducting market basket analysis: the associations that occur frequently are noted with high threshold; however, the low ones lead to generating the large amount of rules. The paper compares five methods for co-purchase edge filtration on two grocery datasets: i.e., Instacart (3.2 million baskets) and Dunnhumby (208 thousand baskets), including Apriori, Apriori + lift post-filtering, top-$K$ ranking based on lift, and two methods based on networks, noise-corrected (NC) and disparity filter (DF). The top-$K$ method ensures the maximum average lift, while the NC achieves similar lift level by means of a single value of the significance parameter ($α$). These two methods recover substantially more rare high-lift associations than Apriori (80-100% against 22-28%). NC and top-$K$ select meaningfully different edges (18-29% non-overlapping): NC retains statistically validated pairs, while top-$K$ retains rare pairs with high lift but low statistical significance. A rolling-origin holdout evaluation shows that top-$K$ edges recur at higher rates at every split, but NC edges are ~12 pp more likely to remain statistically significant in the held-out network.

cs.LG↗
arXiv2026

Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement

Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correlations from semantically irrelevant neighbors contaminate node embeddings. This challenge is most acute for nodes near class boundaries in the embedding space, where amplified structural noise blurs decision boundaries and destabilizes predictions. Existing robust GNN methods largely treat all nodes uniformly, ignoring boundary vulnerabilities. In this paper, to improve classification performance, we tackle graph structural disentanglement by identifying boundary-region entanglement as the primary bottleneck and propose Boundary Embedding Shaping (BES), an adaptive contrastive learning GNN plug-in module that selectively suppresses spurious structural noise at decision boundaries with minimal model parameter perturbation. Extensive experiments demonstrate that BES consistently improves boundary discrimination and outperforms existing leading methods. Notably, BES boosts GCN performance by an average of 3.3% in node classification (up to 5.0% on WikiCS) and achieves superior accuracy in link prediction.

cs.LG↗
arXiv2026

Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling

Tokens are the fundamental units of computation in modern autoregressive models, and generation length directly influences both inference cost and reasoning performance. Despite its importance, existing approaches model length primarily at the coarse sequence level. We introduce the Length Value Model (LenVM), a token-level framework that estimates the remaining generation length at every decoding step. By formulating length modeling as a value estimation problem and assigning a constant negative reward to each generated token, LenVM predicts a bounded, discounted return that is a monotone proxy for the remaining generation horizon. This value formulation provides annotation-free, dense, unbiased, and scalable supervision. Experiments on LLMs and VLMs show that LenVM supports exact control, continuous performance--efficiency steering, length prediction, and interpretation. On LIFEBench-token, it raises the exact-length score of Qwen2.5-7B-Instruct from $30.9$ to $64.8$ in one pass and reaches $83.6$ when combined with LCG. Near $200$ tokens on GSM8K, LenVM retains about $63\%$ Pass@1 versus $6\%$ under a hard token budget. Across other settings, LenVM improves matched-length quality over prompting and EOS calibration, while its length-prediction accuracy improves with scale and exceeds specialized predictors. Its token-level values also identify markers of shifts between longer and shorter trajectories. These results establish generation length as an effective scalable token-level value signal for control, prediction, and future value-based training.

cs.CL↗
arXiv2026

Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market

We present an interpretable machine learning pipeline to decompose cross-sectional equity return predictability into auditable factor contributions. We apply an XGBoost model with TreeSHAP attribution and conduct stress testing on 3,632 Chinese A-share stocks from 2009 until 2019. On prediction, using 60-month rolling windows over 55 months of out-of-sample data, XGBoost obtains a mean AUC of 0.547 (rank IC = 0.119) and +2.38%/month (Newey-West t = 5.94; annualized Sharpe 2.23) long-short spread for the top vs bottom quintiles. This alpha is persistent after adjusting for the Carhart four-factor model (+2.31%/month; t = 7.48). On interpretation, SHAP decomposition indicates that behavioral signals (turnover and momentum) account for 58.2% of predictive attribution compared to 10.7% for valuation ratios, on average, across 50 industry groups. Ablation analysis serves to cross-validate this ranking and provides evidence that SHAP and ablation diverge in a manner that highlights feature substitutability structure that is largely invisible to either method used in isolation.

cs.LG↗
arXiv2026

Deformations and second-order rigidity of polytopes

We study deformations of polytopes that preserve edge lengths and face coplanarities. This gives rise to a notion of rigidity, for which we develop a second-order theory together with an effective algorithm for testing second-order rigidity symbolically. The strength of these new tools is demonstrated on several polytope classes, including the previously intractable regular dodecahedron, which we find to be rigid. We also find that a single tested polytope evades our techniques and will therefore provide a simple test case for future developments of tools of even higher order. This paper also contains a study of so-called edge-length perturbations. We point out connections between rigidity and the ability to realize polytopes with slightly perturbed edge lengths. To explore these connections in practice, we dedicate a section to another case study of the regular dodecahedron. We investigate the local behavior of its realization space with a view towards edge-length perturbations, singularities and generic global rigidity.

math.CO↗
arXiv2026

Interpretable vs Learned Encoders for High-Cardinality Fraud Detection

A total of seven categorical encoding methods were tested on the IEEE-CIS fraud benchmark dataset (590,540 records, 3.5% positives, 8 high-cardinality columns). The encoders were evaluated using a stratified 5-fold cross-validation (CV) with three repetitions. Five of the encoders had identical frozen LightGBM learners in the downstream phase, allowing for controlled comparisons of their performance to each other. CatBoost and TabNet were included as comparisons across paradigms using different learners. The entity embeddings produced the highest AUC-ROC (0.9612), with a statistically significant tie with that of CatBoost (0.9602) and statistically superior to tier group encoding (0.9548), whereas target encoding was only 0.0023 worse than tier group encoding and the auditor-friendly tier boundaries were maintained. Off-the-shelf TabNet did not outperform tree-based pipelines and collapsed under data scarcity. On AUC-PR, CatBoost leads (0.822 vs. 0.793); no encoder dominated both metrics. Per-column analysis confirmed the embedding advantage arises from joint multi-column representation.

cs.LG↗
arXiv2026

Reachability Analysis With Probabilistic Zonotopes: Learning Realized Disturbances and Refining Aleatory Uncertainty

This paper develops a data-driven reachability framework for linear systems whose disturbances are modeled by probabilistic zonotopes (PZs), combining bounded deterministic and Gaussian stochastic components. In contrast to methods that require a precisely known disturbance model (either purely deterministic or purely stochastic), we assume only a conservative prior PZ and refine it from data. The framework separates two uncertainty sources: realized disturbances, which act along the collected trajectory and govern the size of the data-consistent model set, and aleatory disturbances, which enter as future additive uncertainty during reachable-set propagation; both shape the reachable sets, but through different mechanisms. Refinement exploits prior system knowledge together with trajectory-consistency constraints induced by the data, which impose affine couplings between deterministic and Gaussian latent variables. We accordingly develop a constrained-PZ calculus that absorbs the stochastic part of these constraints into an equivalent representation, removes infeasible latent directions, and reduces stochastic covariance, together with identification-aware fusion rules for combining heterogeneous constrained-PZ descriptions. The refined realized-disturbance proxies then serve as scenarios in a linear program that learns the smallest translated and scaled copy of the prior disturbance set that contains all proxy confidence sets while remaining nested in the prior. The resulting deterministic, high-probability reachable sets carry formal containment guarantees with substantially reduced conservatism, and numerical examples confirm that the pipeline tightens both the data-consistent model set and the propagated reachable sets.

eess.SY↗
arXiv2026

Perturbation Effects on Robustness and Individual Fairness

Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings. However, existing evaluation protocols typically assess these dimensions in isolation, thereby obscuring critical failure modes. To bridge this gap, we formalize Robust Individual Fairness (RIF): under semantic-preserving (truth-condition-preserving) perturbations, predictions should remain both correct with respect to the ground truth and invariant across semantically equivalent individuals. To surface RIF violations in practice, we introduce RIFair, a black-box adversarial framework that leverages a decoupled perturbation strategy to construct semantically preserved yet unrobust and/or unfair instance pairs. Experiments across multiple model architectures and real-world textual datasets show that robustness-only or fairness-only metrics often miss Robust Biased and Unrobust Fair behaviors. RIFair}reliably exposes these hidden vulnerabilities, supporting RIF as a necessary criterion for trustworthy model assessment. The experimental code is publicly available at https://github.com/Xuran-LI/RIFair.

cs.LG↗
arXiv2026

MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination

Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant focus of defense. In this work, we propose a deep learning defense known as multi-source adversarial perturbations elimination (MAPE) to counter diverse transferable attacks. MAPE comprises the single-source adversarial perturbation elimination (SAPE) mechanism and the pre-trained models probabilistic scheduling algorithm (PPSA). SAPE utilizes a thoughtfully designed channel-attention U-Net as the defense model and employs adversarial examples generated by a pre-trained model (e.g., ResNet) for its training, thereby enabling the elimination of known adversarial perturbations. PPSA introduces model difference quantification and negative momentum to strategically schedule multiple pre-trained models, thereby maximizing the differences among adversarial examples during the defense model's training and enhancing its robustness in eliminating adversarial perturbations. MAPE effectively eliminates adversarial perturbations in various adversarial examples, providing a robust defense against attacks from different substitute models. In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1\% on CIFAR-10 and over 71.5\% on Mini-ImageNet, demonstrating state-of-the-art performance.

cs.CV↗
arXiv2026

Efficient and Trainable Language Model Test-Time Scaling via Local Branch Routing

Test-time scaling improves language-model reasoning, but existing approaches often face a difficult trade-off: long chain-of-thought sampling remains single-threaded, while sentence- or solution-level search can be computationally expensive and hard to train end-to-end. We introduce Local Branch Routing (LBR), a token-level test-time scaling framework that expands a small local lookahead tree, forwards all sampled branches through the language model, and uses a lightweight router to select the depth-1 subtree to commit. By routing over the hidden states of candidate local futures, LBR allows each token decision to use evidence beyond the root next-token distribution while avoiding full solution-level search. The resulting prune-shift-grow decoding process preserves discrete branch identities and defines a tractable tree-trajectory likelihood: newly grown nodes are counted when first sampled, and router decisions are assigned explicit probabilities. This enables end-to-end reinforcement learning with verifiable rewards, jointly optimizing the base model and router under the same likelihood-ratio principle as discrete-token RLVR. On synthetic hierarchical-planning tasks, LBR shows that post-candidate hidden states provide useful routing evidence. On mathematical reasoning benchmarks, LBR improves both Pass@1 and Pass@32 over discrete chain-of-thought, vanilla discrete-token RLVR, and RL-compatible soft-token branching baselines. These results suggest that lightweight local branching offers an efficient, trainable, and discrete form of language-model test-time scaling.

cs.CL↗
arXiv2026

M-EPDet: Real-Time Real-Bogus Classification and Transient Candidate Judgement for the EP-WXT Pipeline via Multi-Modal Data

The Wide-field X-ray Telescope (WXT) onboard the Einstein Probe (EP) produces a large post-detection candidate stream in which genuine astrophysical sources coexist with instrumental artifacts and Cosmic Ray events. We present M-EPDet, a three-step post-detection framework for real-time candidate vetting in EP-WXT lobster-eye Micro-pore Optics (MPO) data. The framework combines a ResNet-based Arm filter, a dual-branch temporal-spectral Cosmic Ray filter, and a background-aware Bayesian Blocks module for single-exposure variability screening. Using on-orbit EP-WXT observations, we report decoupled metrics for the cascading system. M-EPDet achieves a Real-Bogus Recall of 98.31\% ($98.53\% \times 99.78\%$) for genuine astrophysical sources, together with rejection rates of 92.99\% for instrumental artifacts and 98.18\% for Cosmic Ray events. In the final step, the Bayesian Blocks module flags 0.75\% of the post-filtration observations, corresponding to a 99.25\% reduction in candidate volume. The system is deployed in the EP-WXT pipeline as a lightweight real-time service, reducing the manual-inspection burden in candidate vetting.

astro-ph.IM↗
arXiv2026

MVG-KAN: Multi-View Geo-Wind Guided KAN for PM$_{2.5}$ Forecasting

Accurate short-term PM$_{2.5}$ forecasting is important for public health protection, air-quality early warning, and urban environmental management. However, PM$_{2.5}$ variation is driven by multiple coupled factors, including stable periodic changes induced by human activities and meteorological regularity, station-specific short-term concentration evolution, and meteorology-driven pollutant dispersion among monitoring stations. Existing spatio-temporal forecasting methods may capture station relationships to some extent, but distance-only, correlation-based, or purely adaptive graphs are often insufficient to comprehensively represent these heterogeneous factors, especially wind-direction-dependent pollutant transport. To address this problem, we propose a Multi-View Geo-Wind Guided KAN model for PM$_{2.5}$ forecasting, named \textbf{MVG-KAN}, which models station-level PM$_{2.5}$ evolution from three complementary views: local periodic regularity, station-wise residual temporal dynamics, and meteorological-environment-guided spatial dispersion. Specifically, the periodic-residual forecasting backbone first separates stable daily and weekly patterns from non-periodic residual variations. A Geo-Wind Graph is constructed by combining geographic distance decay with wind-direction- and wind-speed-aware transport, providing a lightweight physically motivated directed spatial prior for residual propagation among stations. In addition, a temporal Kolmogorov-Arnold network (TKAN) residual head is then introduced to learn station-wise nonlinear autoregressive correction from de-periodized PM$_{2.5}$ residuals and historical multi-pollutant sequences, thereby enhancing the modeling of local residual inertia and pollutant co-variation.

cs.AI↗
arXiv2026

A note on simple-minded systems and weakly simple-minded systems over self-injective algebras

Let A be a self-injective algebra over an algebraically closed field. We study the relationship between simple-minded systems and weakly simple-minded systems in A-stmod. We present a necessary and sufficient condition for an orthogonal system to be a simple-minded system over domestic Brauer graph algebras. As a byproduct, we construct a class of simple-minded systems over 2-domestic Brauer graph algebras.

math.RT↗
arXiv2026

A relativistic mechanism for the enhanced isovector spin-orbit interaction suggested by parity-violating electron scattering experiments

Recent high-precision parity-violating electron scattering (PVES) measurements on $^{208}$Pb (PREX-II) and $^{48}$Ca (CREX) reveal a tension in their simultaneous description within modern nuclear energy density functionals (EDFs). Analyses of these data suggest that an enhanced isovector spin-orbit interaction may help account for both measurements, but its relativistic origin in covariant density functional theory remains to be clarified. We show that, within the framework of a covariant density-dependent point-coupling EDF, an enhanced isovector tensor coupling can naturally induce such a strong isovector spin-orbit interaction. This mechanism provides a promising route toward a simultaneous description of the PREX-II and CREX results while preserving a reasonable description of finite nuclei and nuclear matter. PVES on $^{48}$Ca thus provides a sensitive probe of the covariant isovector tensor interaction.

nucl-th↗