arXiv subjects

Xinzhu Wang

Publications and source records attributed to Xinzhu Wang.

7 recordsLinked to original sources

arXiv2026

Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement

We introduce the \textbf{Concept Field} of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score its agreement with the field by $ζ$, the mean absolute z-distance between the observed delta and the field's local Gaussian estimate. The score is black-box (no model internals), corpus-attributable (every score traces to nearby corpus sentences), and admits a probabilistically motivated interpretation under a local Gaussian approximation. We support the computation with the introduction of a \textbf{Vector Sequence Database (VSDB)} that stores embeddings together with sequence-position and next-delta metadata. We evaluate this approach on two large-scale settings: hallucination-style groundedness detection over the U.S. Code of Federal Regulations, and novelty detection over Project Gutenberg, where we show Concept Fields achieve strong selective classification performance under a grounded / ungrounded / unsure triage policy. Unlike retrieval-centric baselines, the resulting coverage-risk behavior is similar across both domains, supporting a degree of cross-domain stability for the standardized deviation score. We also sketch how divergence and curl of the Concept Field, computed on dense clusters, surface qualitatively meaningful semantic patterns (logic sources, sinks, and implicit topics), which we offer as hypothesis-generating rather than as a quantitative result. Concept Fields provide a fast, lightweight, and interpretable signal for groundedness and novelty, complementary to LLM-as-judge and white-box detectors.

cs.CL↗

arXiv2026

Deep-learning jet flavor tagging for precision hadronic Higgs measurements at future $e^+e^-$ Higgs factories

Precise measurements of Higgs decays into quarks and gluons are essential for probing the Yukawa couplings of the Higgs boson and testing the flavor structure of the Standard Model. We investigate the process $e^+e^- \to ZH$ at $\sqrt{s}=240~\mathrm{GeV}$ at a future $e^+e^-$ Higgs factory, taking the CEPC design as a benchmark. The analysis focuses on events with $Z\toν\barν$ and hadronic Higgs decays $H\to b\bar{b}$, $c\bar{c}$, $s\bar{s}$ and $gg$. Jet flavor is identified using state-of-the-art particle-level deep neural network taggers (ParticleNet, Particle Transformer and More-Interaction Particle Transformer), whose per-jet outputs are combined with global event observables in a two-stage analysis employing XGBoost classifiers to separate the four Higgs decay modes from the dominant two- and four-fermion Standard Model backgrounds. Assuming an integrated luminosity of $20~\mathrm{ab}^{-1}$, we obtain projected relative precision on $σ(ZH)\times\mathrm{Br}(H\to X)$ of 0.17% for $X=b\bar{b}$, 1.06% for $c\bar{c}$, 0.50% for $gg$ and 68% for $s\bar{s}$. Compared with the CEPC published results, the precisions for $H\to c\bar{c}$ and $H\to gg$ are improved by about 43% and 29%, respectively. For $H\to s\bar{s}$ we present a quantitative sensitivity estimation corresponding to a statistical significance of about $1.5σ$. These results highlight the potential of deep-learning-based jet flavor tagging for precision studies of Higgs decays at future $e^+e^-$ Higgs factories.

hep-ph↗

arXiv2025

HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

Accurate, real-time object detection on resource-constrained hardware is critical for anomaly-behavior monitoring. We introduce HGO-YOLO, a lightweight detector that combines GhostHGNetv2 with an optimized parameter-sharing head (OptiConvDetect) to deliver an outstanding accuracy-efficiency trade-off. By embedding GhostConv into the HGNetv2 backbone with multi-scale residual fusion, the receptive field is enlarged while redundant computation is reduced by 50%. OptiConvDetect shares a partial-convolution layer for the classification and regression branches, cutting detection-head FLOPs by 41% without accuracy loss. On three anomaly datasets (fall, fight, smoke), HGO-YOLO attains 87.4% mAP@0.5 and 81.1% recall at 56 FPS on a single CPU with just 4.3 GFLOPs and 4.6 MB-surpassing YOLOv8n by +3.0% mAP, -51.7% FLOPs, and 1.7* speed. Real-world tests on a Jetson Orin Nano further confirm a stable throughput gain of 42 FPS.

cs.CV↗

arXiv2025

Machine learning algorithms to predict stroke in China based on causal inference of time series analysis

Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P < 0.01). Among all the models, the Gradient Boosting model demonstrated the highest performance and stability. Model explanation and feature importance analysis generated model interpretation that illustrated significant contributors associated with risks of stroke. Conclusions and Relevance: This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. The research results indicate that dynamic causal inference features have important value in predicting stroke risk, especially in capturing the impact of changes in health status over time on stroke risk. By further optimizing the model and introducing more variables, this study provides theoretical basis and practical guidance for future stroke prevention and intervention strategies.

q-bio.QM↗

arXiv2025

A unified model for the origins of spongiform degeneration and other neuropathological features in prion diseases

Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes to spongiform degeneration. To assess the significance of this phenotype for the etiology of prion diseases, we explored whether it can be functionally linked to other neuropathological hallmarks observed in these diseases, as this would indicate it to be a central event. Having surveyed the neuropathological record and other distant literature niches, we propose a model in which pathogenic forms of the prion protein poison raft domains, including essential Na+, K+-ATPases (NKAs) embedded within them, thereby triggering an ER-centered cellular rescue program coordinated by the unfolded protein response (UPR). The execution of this program stalls general protein synthesis, causing the deterioration of synaptic spines. As the disease progresses, cells selectively increase sterol biosynthesis, along with ribosome and ER biogenesis. These adaptive rescue attempts cause morphological changes to the ER which manifest as ER dilation or ER hypertrophy in a manner that is influenced by Ca2+ influx into the cell. The nuclear-to-cytoplasmic transport of mRNAs and tRNAs interrupts in late stage disease, thereby depriving ribosomes of supplies and inducing them to aggregate into a paracrystalline form. In support of this model, we share previously reported data, whose features are consistent with the interpretation that 1) the phenotype of ER dilation is observed in major prion diseases, 2) varicose tubules and oval bodies represent ER hypertrophy, and 3) virus-like dense particles are paracrystalline aggregates of inactive ribosomes.

q-bio.NC↗

arXiv2013

Wetting Boundary Conditions in Phase-Field-Based Simulation of Binary Fluids: Some Comparative Studies and New Development

We studied several wetting boundary conditions (WBCs) in the simulation of binary fluids based on phase-field theory. Five WBCs, three belonging to the surface energy (SE) formulation using the linear, cubic and sine functions (denoted as LinSE, CubSE and SinSE), the fourth using a geometric formulation (Geom), and the fifth using a characteristic interpolation (CI), were compared with each other through the study of several problems: (1) the static contact angle of a drop; (2) a Poiseuille flow-driven liquid column; (3) a wettability gradient (WG)-driven liquid column; (4) drop dewetting. It was found that while all WBCs can predict the static contact angle fairly accurately, they may affect the simulation outcomes of dynamic problems differently, depending on the driving mechanism. For the flow-driven problem, to use different WBCs had almost no effect on the flow characteristics over a large scale. But for other capillarity-driven problems, the WBC had some noticeable effects. For the WG-driven liquid column, Geom gave the most consistent prediction between the drop velocity and dynamic contact angles, and LinSE delivered the poorest prediction in this aspect. Except for Geom, the dynamic contact angle differed from the prescribed (static) one when other WBCs were used. For drop dewetting, Geom led to the most violent drop motion whereas CubSE caused the weakest motion; the initial contact line velocity was also found to be dependent on the WBC. For several problems, CubSE and SinSE gave almost the same results, and those by Geom and CI were close as well, possibly due to similar consideration in their design. Besides various comparisons, a new implementation that may be used for all WBCs was proposed to mimic the wall energy relaxation and control the degree of slip. This new procedure made it possible to allow the simulations to match experimental measurements well.

physics.flu-dyn↗

arXiv2013

Numerical Study of Drop Motion on a Surface with Wettability Gradient and Contact Angle Hysteresis

In this work, the motion of a 2-D drop on a surface with given wettability gradient is studied numerically by a hybrid lattice-Boltzmann finite-difference method using the multiple-relaxation-time collision model. We incorporate the geometric wetting boundary condition that allows accurate implementation of a contact angle hysteresis model. The method is first validated through three benchmark tests, including the layered Poiseuille flow with a viscosity contrast, the motion of a liquid column in a channel with specified wettability gradient and the force balance for a static drop attached to a surface with hysteresis subject to a body force. Then, simulations of a drop on a wall with given wettability gradient are performed under different conditions. The effects of the Reynolds number, the viscosity ratio, the wettability gradient, as well as the contact angle hysteresis on the drop motion are investigated in detail. It is found that the capillary number of the drop in steady state is significantly affected by the viscosity ratio, the magnitudes of the wettability gradient and the contact angle hysteresis, whereas it only shows very weak dependence on the Reynolds number.

physics.flu-dyn↗