arXiv subjects

Yang Lu

Publications and source records attributed to Yang Lu.

At least 19 recordsLinked to original sources

arXiv2026

Lone-Pair Stereoactivity Overrides Mass Scaling of Thermal Expansion and Heat Conduction in Non-vander Waals Layered Bismuth Oxychalcogenides

Heavier chalcogens are generally expected to soften isostructural semiconductor lattices, raising thermal expansion and lowering thermal conductivity. Layered bismuth oxychalcogenides, Bi2O2X (X = S, Se, Te), are mixed-anion crystals whose covalent slabs and ionic sheets are bound electrostatically rather than by van der Waals forces and whose Bi3+ 6s^2 lone pairs can become stereochemically active. Despite the rapid development of Bi2O2Se for fin and gate-all-around transistors, its fundamental thermal expansion and heat transport mechanisms remain poorly understood. Here, we show that lone-pair stereoactivity, not chalcogen mass, governs both properties. Path-integral molecular dynamics shows that the volumetric thermal expansion coefficient at 300 K decreases from 8.14 x 10^-5 K^-1 in Bi2O2S to 6.72 x 10^-5 and 6.61 x 10^-5 K^-1 in Bi2O2Se and Bi2O2Te, respectively, despite a 13% larger unit cell in the telluride, whereas substituting the S mass into Bi2O2Se changes its value by only 0.6%. Lone-pair-driven Bi off-centering in orthorhombic Bi2O2S makes b-axis expansion 1.63 times a-axis expansion yet leaves in-plane thermal conduction isotropic within 3%. Phonon transport calculations including four-phonon scattering and Wigner coherence predict a thermal conductivity maximum in Bi2O2Se (1.83 and 0.95 W m^-1 K^-1 in the in-plane and cross-plane directions, respectively, at 300 K) because Bi2O2S is limited by anharmonic scattering and Bi2O2Te by slow cross-plane phonons. Thermoreflectance measurements from 300 to 500 K and in situ X-ray diffraction on Bi2O2Se single crystals agree with these predictions within 6.4%. Phonon Boltzmann transport simulations of 12-nm-wide fins, which run hotter than Fourier diffusion predicts, show that Bi2O2Se minimizes the hotspot temperature and the thermal-mismatch strain at the gate dielectric.

cond-mat.mtrl-sci↗

arXiv2026

ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge

Training large language models (LLMs) in resource-constrained edge networks poses fundamental challenges in computation, communication, data heterogeneity, and privacy. To address these challenges, we propose ELSA (Efficient LLM-Centric Split Aggregation), a distributed fine-tuning framework that integrates split learning (SL) with hierarchical federated learning (HFL) to coordinate computation and model aggregation across heterogeneous clients, edge servers, and the cloud. ELSA introduces three key innovations. First, it employs a task-agnostic, behavior-aware client clustering mechanism that constructs semantic fingerprints from public probe inputs using symmetric Kullback-Leibler (KL) divergence, complemented by prediction-consistency-based trust scoring and latency-aware edge assignment to account for data heterogeneity, client reliability, and communication constraints. Second, it adopts a resource-aware dynamic model partitioning strategy that distributes the LLM across clients and edge servers in three segments, while reserving the cloud for adapter aggregation. This design coordinates computation across heterogeneous nodes while balancing on-device computational demands and global convergence behavior. Third, it integrates computational sketches with semantic subspace orthogonal perturbation (SS-OP) to reduce communication overhead and mitigate privacy leakage during model exchanges. Extensive experiments across diverse NLP tasks demonstrate that ELSA outperforms state-of-the-art baselines in model performance, convergence behavior, and robustness. Further, on-device profiling on NVIDIA RTX 4090 GPUs and Jetson AGX Orin edge devices characterizes the memory footprint, energy consumption, and execution latency of key training stages, providing empirical evidence of ELSA's practical feasibility.

cs.LG↗

arXiv2026

Softplus Affine(1, 1) Processes: Enlarged Stationarity Region, Stationary Unit Roots, and Inference

Softplus affine$(1,1)$ models keep the conditional mean positive while allowing coefficients of either sign. We study them under Poisson, negative binomial, gamma and related conditional distributions. A stationary solution with moments of all positive orders exists whenever the coefficient sum is less than one, strictly enlarging previously known sufficient regions. At the unit root, the coefficient sum equals one. Under an invertibility condition, a stationary solution exists in this case if and only if the intercept is negative. At a stationary unit root, the conditional mean has a power-law tail with an explicit index, and the observations can exhibit long memory. For a fixed softplus scale, the Poisson quasi-maximum likelihood estimator is consistent and asymptotically normal at the usual $\sqrt n$ rate below and at the unit root, with the coefficient sum left unrestricted in estimation. At the unit root, these results require only a finite mean and yield standard Wald inference even when the observations have infinite variance or long memory. Simulations and two applications examine finite-sample inference and sensitivity to the softplus scale.

math.ST↗

arXiv2026

Dual-Waveguide Pinching Antennas for PLS: Parallel Placement or Orthogonal Placement?

Pinching antennas (PAs) represent an emerging flexible-antenna paradigm, where movable PAs arranged along dielectric waveguides enable on-demand customization of wireless channel characteristics. This paper exploits PAs to enhance physical-layer security (PLS) by increasing the channel diversity gap between legitimate users (LUs) and eavesdroppers (Eves). We concentrate on a dual-waveguide PA system, in which each waveguide is equipped with multiple PAs to serve multiple LUs under wiretapping from an Eve. Two waveguide placement schemes are studied: parallel placement and orthogonal placement. To capture practical waveguide propagation effects, we adopt two in-waveguide channel models: in-waveguide phase shifts, and in-waveguide phase shifts and attenuation. We formulate secure sum rate (SSR) and secrecy energy efficiency (SEE) maximization problems, and develop a two-stage optimization framework to tackle these non-convex problems. In the first stage, we design a feasibility-enhanced particle swarm optimization (PSO) algorithm named FeaPSO to search for valid PA positions complying with minimum inter-PA spacing constraints. The second stage leverages successive convex approximation (SCA) to jointly optimize baseband beamforming and artificial noise vectors. Simulation results validate the effectiveness of the proposed two-stage algorithm and confirm that PA adjustment yields substantial gains in both SSR and SEE. Moreover, the simulation outcomes demonstrate the performance benefits brought by orthogonal waveguide placement for PLS enhancement.

eess.SP↗

arXiv2026

Identication and Estimation of Nonsymmetric Determinantal Point Processes

Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonpositive pairwise dependence. Statistical research on nonsymmetric DPPs, which can accommodate positive dependence, remains limited. We study identication and estimation for this class. First, we establish the equivalence of two sucient conditions for a nonsymmetric kernel to dene a valid DPP. Second, we develop the identication theory for nonsymmetric DPPs. Third, we develop cardinality-truncated maximum-likelihood estimation and estimation by averaged stochastic gradient descent. Under suitable identication and regularity conditions, we establish consistency and asymptotic normality for both estimators. Simulations examine identication, the eects of truncation, and the nite-sample performance of the estimators. We illustrate the methods with two cyber applications at markedly dierent sampling frequencies: weekly sector-by-breach data and second-by-second enterprise authentication data with more than ve million set observations.

stat.ML↗

arXiv2026

Drift Inference for Unit-Root Galton-Watson Processes with Immigration

We study inference on the drift of a critical Galton--Watson process with immigration, a count time series with a unit root. Climate change motivates such nonstationary models for weather-related disaster counts. The drift is the expected increase in the count per period. Ordinary least squares is inconsistent for the drift, so we study state-weighted least squares, giving greater weight to observations following small counts, where conditional variance is lower. Under strict recurrence, we apply null-recurrent regenerative limit theory to obtain a polynomial convergence rate and a standard normal studentized limit. Our main contribution is the recurrence boundary, where we establish a logarithmic convergence rate and a parameter-free non-Gaussian studentized limit. Estimating the optimal weights yields the same first-order limiting distribution as knowing them. Simulations show lower root mean squared error than time-weighted least squares with weights $1/t$, and improved state-weighted confidence-interval coverage when the unit root is imposed.

stat.ME↗

arXiv2026

Consistent intercept estimation and inference for unit-root INAR(2) processes

\citet{barczy2014asymptotic} showed that the ordinary least-squares (OLS) estimator of the innovation mean is inconsistent for a unit-root INAR(2) process. We construct a consistent intercept estimator using inverse-time weighted least squares (WLS) and derive its mixed-rate asymptotics: the intercept estimator is asymptotically normal at rate $\sqrt{\log n}$, with a Gaussian limit independent of the autoregressive limits, while the autoregressive estimators retain their OLS rates. We develop an OLS unit-root test calibrated with WLS nuisance estimates. Under the maintained unit root, residual-score Gaussian intervals for the innovation mean and long-run drift remain asymptotically valid after test nonrejection. Simulations show that WLS reduces root mean squared error for both quantities relative to OLS, with further gains from imposing the unit root. An application to two Canadian flood inventories shows that conclusions about persistence depend on the inventory and weighting offset.

math.ST↗

arXiv2026

State Propagation Also Satisfies: A Complex-Valued State-Space Model for Deterministic State Tracking

Despite the dominance of massive language models, leading paradigms like Transformers and Mamba fundamentally falter at continuous deterministic state tracking, suffering from catastrophic out-of-distribution (OOD) collapse when generalizing to extended sequences. To shatter this bottleneck, we present the \textbf{Complex State Propagator (CSP)}, a radically minimalist recurrent paradigm that operates strictly on the complex phase manifold without intermediate output projections, amplitude modulations, or per-step non-linearities. Crucially, we unveil an unprecedented architectural marvel: \textbf{the phase signal survives extreme depth with absolute zero informational attenuation}. By implementing an exact four-quadrant \textbf{Coordinate-to-Phase (C-to-2)} transformation governed by the $\text{atan2}(y, x)$ activation, CSP forces the continuous optimization landscape to seamlessly align with discrete cyclic groups. Remarkably, with a mere \textbf{3-layer hidden topology} trained on short inputs (length 16),CSP demonstrates absolute mathematical purity, achieving a nearly $100\%$ validation accuracy and F1-score when generalized to a $4\times$ prolonged OOD length of 64 on the canonical Mod-3 tracking task. Our work establishes complex-valued, pure-phase propagation not merely as a compact alternative, but as a dominant frontier that beats heavy networks at a fraction of their size.

cs.AI↗

arXiv2026

Decoupling Physical Speed from Path Parameterization in Singularity-Free Guiding Vector Fields

The existing singularity-free guiding vector field (SF-GVF) with an additional virtual coordinate can eliminate singular points (i.e., points where the vector field vanishes) inherent in conventional GVFs and guarantee global convergence of robot trajectories to closed and self-intersecting desired paths. However, the desired speed given by the GVF along the desired path in the original lower-dimensional space cannot be arbitrarily specified but depends on path parameterizations. One possible workaround is to partially normalize the physical projection of the SF-GVF and assign a user-designed speed. However, we show that this workaround may introduce new singularities since the normalization denominator can become zero. To address this issue, we propose a new SF-GVF with prescribed physical speed (PPS). The integral curves of the new SF-GVF converge exponentially to the desired path from any initial condition in the higher-dimensional space (including virtual dimension); more importantly, the robot's physical speed converges to the PPS, while the path-error dynamics remain invariant under regular reparameterizations of the desired path. We further develop a saturated acceleration control law for second-order kinematic models. Finally, comparative simulations and 3D path-following experiments with a quadrotor under different PPS profiles validate the theoretical results and demonstrate the effectiveness of the proposed approach.

cs.RO↗

arXiv2026

Simultaneous Arrival Control for Distributed Multi-Robot Systems with Curvature and Constant-Speed Constraints

The simultaneous arrival of multiple mobile robots at a target point is crucial for cooperation tasks such as cooperative encirclement, disaster relief, and environmental monitoring. Although the simultaneous arrival problem itself is already complex, the problem becomes more challenging when there are constraints on the robot trajectory curvatures and the speeds are required to be constant (possibly different for different robots), and the control law for robots needs to be distributed. These constraints are typical for a multi-robot system consisting of, e.g., fixed-wing UAVs. To address this challenge, this paper proposes a distributed switching control method based on the maximum consensus protocol. By exploiting the geometric properties of Dubins paths along with optimization principles, a virtual time variable is introduced, and a hybrid control law that combines optimal control with saturated proportional control is designed. Under the proposed control law, each robot is driven to approach the maximum virtual time among its neighbors, thereby achieving simultaneous arrival under some mild conditions. Furthermore, we prove that in certain cases the proposed method attains a theoretically optimal arrival time. The approach is scalable and real-time, with low communication overhead. Its effectiveness and robustness are validated through extensive simulations and experiments.

cs.RO↗

arXiv2026

Finite-Time Curvature-Constrained Vector Field for Saturation-Free Motion Planning of Nonholonomic Robots

Accurately steering a robot to a target configuration is fundamental in engineering, yet remains challenging for nonholonomic mobile robots. Vector fields (VFs) provide a natural framework by specifying desired motion directions throughout the workspace and enabling direct integration with feedback control. However, most existing VF-based methods cannot explicitly generate trajectories satisfying curvature constraints. Actuator limits are therefore often enforced by input saturation, which may invalidate stability guarantees and degrade closed-loop performance when not considered in controller design. In addition, these methods usually ensure only asymptotic convergence without an explicit settling-time bound. To address these issues, we propose a generalized motion planning and control framework consisting of a finite-time curvature-constrained vector field (FT-C2VF) and a saturation-free control law. Depending on the motion objective, the framework drives the robot to the target configuration in finite time or through it periodically. First, the FT-C2VF is constructed using complementary gains to achieve finite-time convergence while ensuring that the curvature of its integral curves is continuous, bounded, and monotonically decreasing with the radial ratio. Second, an almost globally C1-smooth, saturation-free controller is developed to track the FT-C2VF without Jacobian information, while keeping all control inputs within prescribed actuator limits. Third, dynamical-systems analysis establishes almost-global finite-time stability of the target equilibrium. Numerical simulations show improved performance over representative VF-based methods, and outdoor experiments on an Ackermann-steered vehicle confirm the effectiveness and robustness of the proposed approach.

cs.RO↗

arXiv2026

A Unified Vision-Language Model for PSMA PET/CT Report Generation, Visual Question Answering, and Lesion Segmentation

Accurate PSMA PET/CT interpretation is central to prostate cancer management, yet existing PET/CT AI models typically address isolated tasks. We propose a unified PSMA PET/CT vision-language model for report generation, visual question answering, and lesion segmentation. The framework adopts an LLaVA-style architecture, comprising a PET/CT vision encoder, an MLP-Mixer projection module, a LoRA-tuned large language model, and a 3D segmentation branch. Training followed a four-stage strategy: vision encoder pretraining, projection-layer alignment, VLM fine-tuning, and final multitask tuning. Language tasks used 5,747 PSMA PET/CT datasets with paired reports, while segmentation used the PSMA subset of AutoPET. The model outperformed PET2REP and a CT-based baseline across standard report-generation metrics, improved performance across VQA question types, and achieved higher Dice and lesion-level overlap F1 than SegAnyPET and nnUNet. These results support the feasibility of a unified framework for structured, interactive, interpretable PSMA PET/CT analysis with voxel-level grounding within a single multitask model architecture.

cs.CV↗

arXiv2026

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering

Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from the ``one-to-many'' nature of video description, where high-quality captions are often penalized for lexical mismatches or valid shifts in visual focus. Furthermore, such assessments are typically one-dimensional, failing to provide a fine-grained analysis of caption quality. To address this, we redefine caption quality through the lens of information fidelity: A caption must maximize the coverage of salient visual information while ensuring strict factuality. We introduce CapQuiz, a novel reference-free benchmark that assesses captions based on their utility in answering human-verified, fine-grained, multiple-choice questions derived from the video. CapQuiz features a hierarchical taxonomy of 10 question types (spanning Descriptive and Inferential categories) across 24 diverse video domains. Extensive experiments demonstrate that CapQuiz correlates significantly better with human judgments than existing metrics and offers interpretable insights into model performance.

cs.CV↗

arXiv2026

Thermal history controls the optoelectronic response of lead halide perovskites through structure and dynamics

Lead halide perovskites are promising optoelectronic materials for photovoltaics, light emission and detection. Their efficiencies in PV now approach the detailed-balance limit, leaving stability as the principal barrier. The intrinsic instabilities studied to date centre on ionic motion within a fixed, homogeneous lattice. Here we identify a further source of intrinsic structural instability, hidden in the lattice dynamics. Mapping caesium, methylammonium and formamidinium-based compositions with Cl, Br, I and mixed X-sites through all accessible phases, using single crystal X-ray and neutron diffuse scattering, machine-learning-assisted molecular dynamics, a phenomenological octahedral tilt model and hyperspectral photoluminescence, we find that nearly every composition hosts equilibrium local structural fluctuations: dynamic nanodomains of correlated octahedral tilts, a few nanometres in size, that locally break the crystallographic symmetry. Three complementary levers control them. The A-site cation sets their symmetry, shape and anisotropy, from sparse, isotropic and tetragonal in formamidinium-based compositions to dense, anisotropic and orthorhombic in nominally cubic caesium-based ones, the most locally disordered we studied. The halide controls the dynamic disorder and the phase-transition sequence. Thermal history is the third: different ramp rates drive nominally identical compositions into distinct crystallographic phases, each with its own hidden local order. In MAPbI3, the heating rate alone changes the photoluminescence quantum efficiency across the phase transition. Because these transitions lie within device operating ranges, from terrestrial thermal cycling to the extremes of space, thermal history may shape the local structure, and hence the optoelectronic response, throughout fabrication and operation, establishing it as a design variable alongside composition.

cond-mat.mtrl-sci↗

arXiv2026

Reachability-Based Capability Confinement for LLM Agents under Indirect Prompt Injection

Large language model agents place outputs from external skills into their execution context, allowing attacker-controlled data to influence later privileged actions. Existing defenses mainly classify untrusted content or authorize proposed operations. They do not directly address how an agent's future authority should change once untrusted data enters its state. We present SkillGuard, a harness-level enforcement layer that treats this event as contamination and restricts future capabilities to disconnect the resulting state from deployer-defined forbidden states. Given sound skill summaries and policies, SkillGuard represents security-relevant transitions with a Skill Impact Graph, specifies admissible control over skill parameters via steerability signatures, and mediates invocations with an inline reference monitor. Following contamination, it computes weighted capability restrictions using binary, fractional, or fractional-flow strategies without auxiliary language-model inference. We evaluate SkillGuard on four AgentDojo suites with two backend LLMs, Gemini 2.5 Flash and Llama3.3-70B, against an LLM-only No Defense baseline and three defenses at different system layers: Spotlighting, CaMeL, and AttriGuard. We construct a compositional attack benchmark in which each attack combines observations individually insufficient to induce target violation and evaluate the same baselines on it. Under AgentDojo's Tool Knowledge attacks, SkillGuard eliminates attack success on three of four suites for both backends and reduces it to 4.8% and 14.3% on Slack. Against compositional attacks, it outperforms every baseline on Llama and matches the strongest baseline on Gemini at higher benign utility. Fractional-flow restriction preserves substantially more capabilities than binary restriction at the same attack success rate. Across both settings, SkillGuard adds no model calls or token overhead.

cs.CR↗

arXiv2026

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents rely on large language models (LLMs) to plan transactions, they inherit the LLM's susceptibility to prompt injection and lack of mechanisms to bind a verifier's approval to the exact transaction ultimately submitted on-chain. We present PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution. PACE introduces typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection. A Solidity smart account enforces PDR signatures on-chain with a measured overhead of 29,826-31,822 gas. We evaluate PACE against six baselines on 40 tasks spanning four attack categories plus benign utility (2,800 trials, 10 seeds). In our deterministic sandbox, PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for the unguarded baseline. Ablation studies identify permissive policy settings (+57.5 pp) and the touched-contract allowlist (+12.5 pp) as the dominant safety components. To test whether the same deterministic floor holds for real model outputs, the artifact additionally provides a three-model live-LLM evaluation over the full task suite with repeated runs. A mainnet-fork harness is included for archive-RPC deployments, but fork results are reported only when the corresponding artifacts are generated. These auxiliary studies are separate from, and never substitute for, the deterministic benchmark. We frame our claims as logic-level safety within a reproducible benchmark rather than deployment-ready DeFi security.

cs.CR↗

arXiv2026

When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling

AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use. When agents exercise this authority on public blockchains through MCP, skills, and tool calling, the consequences of an attack are governed by the blockchain execution layer rather than by conventional software assumptions. This survey argues that four properties of that layer (irreversibility, signing authority, continuous autonomy, and sequence-level composition) qualitatively change the threat model, turning the recoverable failures of generic agent security into a standing, irreversible loss. We organize the fragmented MCP-security literature into an attack-surface taxonomy, then contribute a Web3 risk-mapping matrix that ties each attack class to its amplified impact, the responsible amplifiers, a representative mitigation, and the residual gap. We synthesize defenses, including emerging blockchain-based mechanisms, and find them improving but insufficient: measured protections stop fewer than 30% of attacks, and model-level safety refuses fewer than 3%. We close by positioning the work against adjacent surveys and deriving a research agenda from the matrix's open cells.

cs.CR↗

arXiv2026

Traceable Trust for action-ready artificial intelligence in bioscience

Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structures, design proteins, rank variants, annotate images, recommend strains and optimise experimental conditions. We argue that the decision to use an AI output to guide laboratory action is a key juncture for trustworthy research and should follow a defined, reviewable process. We propose Traceable Trust as a proportionate assessment-and-design framework for this output-to-action boundary. It asks what evidence supports the output, what capability is being claimed, what agency has been delegated, what threshold authorises action, who can override it and how outcomes inform later decisions. We illustrate the framework through three case studies spanning ecosystem resources, project design and laboratory action. Together, the cases show how trust can be documented where AI outputs begin to shape scientific work.

cs.CY↗