arXiv subjects

Yu Chen

Publications and source records attributed to Yu Chen.

At least 145 records · Page 8Linked to original sources

arXiv2026

Securing AI Agents in Cyber-Physical Systems: A Survey of Environmental Interactions, Deepfake Threats, and Defenses

The increasing integration of AI agents into cyber-physical systems (CPS) introduces new security risks that extend beyond traditional cyber or physical threat models. Recent advances in generative AI enable deepfake and semantic manipulation attacks that can compromise agent perception, reasoning, and interaction with the physical environment, while emerging protocols such as the Model Context Protocol (MCP) further expand the attack surface through dynamic tool use and cross-domain context sharing. This survey provides a comprehensive review of security threats targeting AI agents in CPS, with a particular focus on environmental interactions, deepfake-driven attacks, and MCP-mediated vulnerabilities. We organize the literature using the SENTINEL framework, a lifecycle-aware methodology that integrates threat characterization, feasibility analysis under CPS constraints, defense selection, and continuous validation. Through an end-to-end case study grounded in a real-world smart grid deployment, we quantitatively illustrate how timing, noise, and false-positive costs constrain deployable defenses, and why detection mechanisms alone are insufficient as decision authorities in safety-critical CPS. The survey highlights the role of provenance- and physics-grounded trust mechanisms and defense-in-depth architectures, and outlines open challenges toward trustworthy AI-enabled CPS.

cs.CR↗

arXiv2026

Beyond the Needle's Illusion: Decoupled Evaluation of Evidence Access and Use under Semantic Interference at 326M-Token Scale

Long-context LLM agents must access the right evidence from large environments and use it faithfully. However, the popular Needle-in-a-Haystack (NIAH) evaluation mostly measures benign span localization. The needle is near-unique, and the haystack is largely irrelevant. We introduce EverMemBench-S (EMB-S), an adversarial NIAH-style benchmark built on a 326M-token MemoryBank. While the full MemoryBank spans 326M tokens for retrieval-based (RAG) evaluation, we evaluate native long-context models only at scales that fit within each model's context window (up to 1M tokens in this work) to ensure a fair comparison. EMB-S pairs queries with collision-tested near-miss hard negatives and gold evidence sets spanning one or more documents, validated via human screening and LLM verification. We also propose a decoupled diagnostic protocol that reports evidence access (document-ID localization) separately from end-to-end QA quality under full-context prompting. This enables consistent diagnosis for both native long-context prompting and retrieval pipelines. Across a reference-corpus ladder from domain-isolated 64K contexts to a globally shared 326M-token environment, we observe a clear reality gap. Systems that saturate benign NIAH degrade sharply in evidence access under semantic interference. These results indicate that semantic discrimination, not context length alone, is the dominant bottleneck for long-context memory at scale.

cs.CL↗

arXiv2026

Analytic Incremental Learning For Sound Source Localization With Imbalance Rectification

Sound source localization (SSL) demonstrates remarkable results in controlled settings but struggles in real-world deployment due to dual imbalance challenges: intra-task imbalance arising from long-tailed direction-of-arrival (DoA) distributions, and inter-task imbalance induced by cross-task skews and overlaps. These often lead to catastrophic forgetting, significantly degrading the localization accuracy. To mitigate these issues, we propose a unified framework with two key innovations. Specifically, we design a GCC-PHAT-based data augmentation (GDA) method that leverages peak characteristics to alleviate intra-task distribution skews. We also propose an Analytic dynamic imbalance rectifier (ADIR) with task-adaption regularization, which enables analytic updates that adapt to inter-task dynamics. On the SSLR benchmark, our proposal achieves state-of-the-art (SoTA) results of 89.0% accuracy, 5.3° mean absolute error, and 1.6 backward transfer, demonstrating robustness to evolving imbalances without exemplar storage.

cs.SD↗

arXiv2026

TUN: Detecting Significant Points in Persistence Diagrams with Deep Learning

Persistence diagrams (PDs) provide a powerful tool for understanding the topology of the underlying shape of a point cloud. However, identifying which points in PDs encode genuine signals remains challenging. This challenge directly hinders the practical adoption of topological data analysis in many applications, where automated and reliable interpretation of persistence diagrams is essential for downstream decision-making. In this paper, we study automatic significance detection for one-dimensional persistence diagrams. Specifically, we propose Topology Understanding Net (TUN), a multi-modal network that combines enhanced PD descriptors with self-attention, a PointNet-style point cloud encoder, learned fusion, and per-point classification, alongside stable preprocessing and imbalance-aware training. It provides an automated and effective solution for identifying significant points in PDs, which are critical for downstream applications. Experiments show that TUN outperforms classic methods in detecting significant points in PDs, illustrating its effectiveness in real-world applications.

cs.CV↗

arXiv2026

FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation

Accurate segmentation of cervical structures in transvaginal ultrasound (TVS) is critical for assessing the risk of spontaneous preterm birth (PTB), yet the scarcity of labeled data limits the performance of supervised learning approaches. This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation, hosted at ISBI 2025. FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images. Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2. The challenge attracted 10 teams with 82 participants submitting innovative solutions. The best-performing methods for each individual metric achieved 90.26\% mDSC, 38.88 mHD, and 32.85 ms RT, respectively. FUGC establishes a standardized benchmark for cervical segmentation, demonstrates the efficacy of semi-supervised methods with limited labeled data, and provides a foundation for AI-assisted clinical PTB risk assessment.

eess.IV↗

arXiv2026

Design, waterproofing, and mass production of the 3-inch PMT frontend system of JUNO

Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines the design and mass production processes for the high-voltage divider, the cable and connector, as well as the waterproof potting of the PMT bases. The results of the acceptance tests of all the integrated PMTs are also presented.

physics.ins-det↗

arXiv2026

QSteed: A Resource-Virtualized and Hardware-Aware Quantum Compilation Framework for Real Quantum Computing Processors

As quantum computing systems continue to scale up and become more clustered, efficiently compiling user quantum programs into high fidelity executable sequences on real hardware remains a key challenge for current quantum compilation systems. In this study, we introduce a system software framework that integrates resource virtualization and hardware aware compilation for real quantum computing processors, termed QSteed. QSteed virtualizes quantum processors through a four layer abstraction hierarchy comprising the Real Quantum Processing Unit (QPU), Standard QPU (StdQPU), Substructure of the QPU (SubQPU), and Virtual QPU (VQPU). These abstractions, together with calibration data, device topology, and noise descriptors, are maintained in a dedicated database to enable unified and fine grained management across superconducting quantum platforms. At run time, the modular compiler queries the database to match each incoming circuit with the most suitable VQPU, after which it confines layout, routing, gate resynthesis, and noise adaptive optimizations to that virtual subregion. The complete stack has been deployed on the Quafu superconducting cluster, where experimental runs confirm the correctness of the virtualization model and the efficacy of the compiler without requiring modifications to user code. By integrating resource virtualization with a select-then-compile workflow, QSteed demonstrates a robust architecture for compiling programs on noisy superconducting processors. This architectural approach offers a promising path towards efficient compilation needs across various superconducting quantum computing platforms in the noisy intermediate scale quantum (NISQ) era.

quant-ph↗

arXiv2026

Beyond Visual Realism: Toward Reliable Financial Time Series Generation

Generative models for financial time series often create data that look realistic and even reproduce stylized facts such as fat tails or volatility clustering. However, these apparent successes break down under trading backtests: models like GANs or WGAN-GP frequently collapse, yielding extreme and unrealistic results that make the synthetic data unusable in practice. We identify the root cause in the neglect of financial asymmetry and rare tail events, which strongly affect market risk but are often overlooked by objectives focusing on distribution matching. To address this, we introduce the Stylized Facts Alignment GAN (SFAG), which converts key stylized facts into differentiable structural constraints and jointly optimizes them with adversarial loss. This multi-constraint design ensures that generated series remain aligned with market dynamics not only in plots but also in backtesting. Experiments on the Shanghai Composite Index (2004--2024) show that while baseline GANs produce unstable and implausible trading outcomes, SFAG generates synthetic data that preserve stylized facts and support robust momentum strategy performance. Our results highlight that structure-preserving objectives are essential to bridge the gap between superficial realism and practical usability in financial generative modeling.

q-fin.ST↗

arXiv2026

Intrinsic Negative-U Centers in Freestanding LaAlO3/SrTiO3 Micro-membranes

The LaAlO3/SrTiO3 (LAO/STO) interface hosts a rich range of electronic phenomena, including unconventional electron pairing that in quantum dots gives rise to a negative effective charging energy U. Here, we show freestanding LAO/STO micro-membranes naturally hosting negative-U centers, where lateral confinement arises intrinsically, rather than from engineered nanostructures. These centers coexist with gate-tunable superconductivity and can remain stable upon thermal cycling from millikelvin temperatures to room temperature. Transport is in excellent agreement with calculations based on a negative-U Anderson model, and electrostatic simulations indicate characteristic center sizes of 20-80 nm. Our findings suggest that negative-U centers may arise from the intrinsic interfacial inhomogeneity typical of LAO/STO, and should therefore be considered a general feature of the LAO/STO interface. This could have important consequences for the microwave response of interfacial superconducting devices.

cond-mat.mtrl-sci↗

arXiv2026

From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards

Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hallucinations can yield conclusions that do not reliably align with their rationale. Thus, we propose "Result -> Justify", which constrains the output communication to present a conclusion before its structured justification. We introduce SEF (Structured Explainability Framework), operationalizing professional conventions (e.g., CREAC, BLUF) via six metrics for structure and grounding. Experiments across four tasks in three domains validate this approach: all six metrics correlate with correctness (r=0.20-0.42; p<0.001), and SEF achieves 83.9% accuracy (+5.3 over CoT). These results suggest structured justification can improve verifiability and may also improve reliability.

cs.AI↗

arXiv2026

Hybrid Disclination Skin-topological Effects in Non-Hermitian Circuits

The bulk-disclination correspondence (BDC) is a fundamental concept in Hermitian systems that has been widely applied to predict disclination states. Recently, disclination states have also been observed and experimentally verified in non-Hermitian systems with C6 lattice symmetry, where gain and loss are introduced to induce non-Hermiticity. In this Letter, we propose a non-Hermitian two-dimensional (2D) Su-Schrieffer-Heeger (SSH) disclination model with skin-topological (ST) disclination states, and calculate its biorthogonal Zak phase. Together with the real-space disclination index, we predict the emergence of disclination states in a C4-symmetric non-Hermitian lattice and the corresponding fractional charge. We also generalize the symmetry indicator within the biorthogonal framework to predict the anomalous filling near the disclination core. Experimentally, the model is implemented on a nonreciprocal circuit platform, where we analyze the impedance matrix characterized by complex eigenfrequencies and directly observe the ST disclination states. Our work further extends the bulk-disclination correspondence to the non-Hermitian realm.

cond-mat.mtrl-sci↗

arXiv2025

Non-(strong, geometrically) ergodicity criteria for discrete time Markov chains on general state

For discrete-time Markov chains on general state spaces, we establish criteria for non-ergodicity and non-strong ergodicity, and derive sufficient conditions for non-geometric ergodicity via the theory of minimal nonnegative solutions. Our criteria are formulated based on the existence of solutions to inequalities involving the chain's one-step transition kernel. Meanwhile, these practical criteria are applied to a type of examples, which can effectively characterize the non-ergodicity and non-strong ergodicity of a specific class of single birth (death) processes.

math.PR↗

arXiv2025

Activations as Features: Probing LLMs for Generalizable Essay Scoring Representations

Automated essay scoring (AES) is a challenging task in cross-prompt settings due to the diversity of scoring criteria. While previous studies have focused on the output of large language models (LLMs) to improve scoring accuracy, we believe activations from intermediate layers may also provide valuable information. To explore this possibility, we evaluated the discriminative power of LLMs' activations in cross-prompt essay scoring task. Specifically, we used activations to fit probes and further analyzed the effects of different models and input content of LLMs on this discriminative power. By computing the directions of essays across various trait dimensions under different prompts, we analyzed the variation in evaluation perspectives of large language models concerning essay types and traits. Results show that the activations possess strong discriminative power in evaluating essay quality and that LLMs can adapt their evaluation perspectives to different traits and essay types, effectively handling the diversity of scoring criteria in cross-prompt settings.

cs.CL↗

arXiv2025

Single-molecule Scale Nuclear Magnetic Resonance Spectroscopy using a Robust Near-Infrared Spin Sensor

Nuclear magnetic resonance (NMR) at the single-molecule level with atomic resolution holds transformative potential for structural biology and surface chemistry. Near-surface solid-state spin sensors with optical readout ability offer a promising pathway toward this goal. However, their extreme proximity to target molecules demands exceptional robustness against surface-induced perturbations. Furthermore, life science applications require these sensors to operate in biocompatible spectral ranges that minimize photodamage. In this work, we demonstrate that the PL6 quantum defect in 4H silicon carbide (4H-SiC) can serve as a robust near-infrared spin sensor. This sensor operates at tissue-transparent wavelengths and exhibits exceptional near-surface stability even at depth of 2 nm. Using shallow PL6 centers, we achieve nanoscale NMR detection of proton ($\mathrm{^{1}H}$) spins in immersion oil and fluorine ($\mathrm{^{19}F}$) spins in Fomblin, attaining a detection volume of $\mathrm{(3~nm)^3}$ and a sensitivity reaching the requirement for single-proton spin detection. This work establishes 4H-SiC quantum sensors as a compelling platform for nanoscale magnetic resonance, with promising applications in probing low-dimensional water phases, protein folding dynamics, and molecular interactions.

quant-ph↗

arXiv2025

Intelligent Image Search Algorithms Fusing Visual Large Models

Fine-grained image retrieval, which aims to find images containing specific object components and assess their detailed states, is critical in fields like security and industrial inspection. However, conventional methods face significant limitations: manual features (e.g., SIFT) lack robustness; deep learning-based detectors (e.g., YOLO) can identify component presence but cannot perform state-specific retrieval or zero-shot search; Visual Large Models (VLMs) offer semantic and zero-shot capabilities but suffer from poor spatial grounding and high computational cost, making them inefficient for direct retrieval. To bridge these gaps, this paper proposes DetVLM, a novel intelligent image search framework that synergistically fuses object detection with VLMs. The framework pioneers a search-enhancement paradigm via a two-stage pipeline: a YOLO detector first conducts efficient, high-recall component-level screening to determine component presence; then, a VLM acts as a recall-enhancement unit, performing secondary verification for components missed by the detector. This architecture directly enables two advanced capabilities: 1) State Search: Guided by task-specific prompts, the VLM refines results by verifying component existence and executing sophisticated state judgments (e.g., "sun visor lowered"), allowing retrieval based on component state. 2) Zero-shot Search: The framework leverages the VLM's inherent zero-shot capability to recognize and retrieve images containing unseen components or attributes (e.g., "driver wearing a mask") without any task-specific training. Experiments on a vehicle component dataset show DetVLM achieves a state-of-the-art overall retrieval accuracy of 94.82\%, significantly outperforming detection-only baselines. It also attains 94.95\% accuracy in zero-shot search for driver mask-wearing and over 90\% average accuracy in state search tasks.

cs.CV↗

arXiv2025

Posterior Collapse as a Phase Transition in Variational Autoencoders

We investigate the phenomenon of posterior collapse in variational autoencoders (VAEs) from the perspective of statistical physics, and reveal that it constitutes a phase transition governed jointly by data structure and model hyper-parameters. By analyzing the stability of the trivial solution associated with posterior collapse, we identify a critical hyper-parameter threshold. In particular, we derive an explicit criterion for the onset of collapse: posterior collapse occurs when the decoder variance exceeds the largest eigenvalue of the data covariance matrix. This critical boundary, separating meaningful latent inference from collapse, is characterized by a discontinuity in the KL divergence between the approximate posterior and the prior distribution, where the KL divergence and its derivatives exhibit clear non-analytic behavior. We validate this critical behavior on both synthetic and real-world datasets, confirming the existence of a phase transition. The experimental results align well with our theoretical predictions, demonstrating the robustness of our collapse criterion across various VAE architectures. Our stability-based analysis demonstrate that posterior collapse is not merely an optimization failure, but rather an emerging phase transition arising from the interplay between data structure and variational constraints. This perspective offers new insights into the trainability and representational capacity of deep generative models.

cs.LG↗

arXiv2025

Conceptual Design of the Muonium-to-Antimuonium Conversion Experiment (MACE)

The spontaneous conversion of muonium to antimuonium is one of the interesting charged lepton flavor violation phenomena offering a sensitive probe of potential new physics and serving as a tool to constrain the parameter space beyond the Standard Model. The Muonium-to-Antimuonium Conversion Experiment (MACE) is designed to utilize a high-intensity muon beam, a Michel electron magnetic spectrometer, a positron transport system, and a positron detection system, to either discover or constrain this rare process with a conversion probability of $\mathcal{O}(10^{-13})$. This article presents an overview of the theoretical framework as well as a detailed description of the experimental design for the search for muonium-to-antimuonium conversion.

hep-ex↗

arXiv2025

Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection

Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually limit the latent factor dimension to around K=5 due to concerns that larger K can degrade performance. To overcome this challenge, we propose a scalable framework that couples a high-dimensional CAE with an uncertainty-aware factor selection procedure. We employ three models for quantile prediction: zero-shot Chronos, a pretrained time-series foundation model (ZS-Chronos), gradient-boosted quantile regression trees using XGBoost and RAPIDS (Q-Boost), and an I.I.D bootstrap-based sample mean model (IID-BS). For each model, we rank factors by forecast uncertainty and retain the top-k most predictable factors for portfolio construction, where k denotes the selected subset of factors. This pruning strategy delivers substantial gains in risk-adjusted performance across all forecasting models. Furthermore, due to each model's uncorrelated predictions, a performance-weighted ensemble consistently outperforms individual models with higher Sharpe, Sortino, and Omega ratios.

q-fin.PM↗