arXiv subjects

Jie Wang

Publications and source records attributed to Jie Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

On $L^p$ bounds for the Littlewood-Paley $g_α$ function with fractional poisson kernel

Let $0<α\leq1$, and let $g_α$ be the Littlewood-Paley function associated with the fractional Poisson kernel, which reduces to the classical $g$-function when $α=1$. We establish dimension-free $L^p$ bounds for $g_α$ for every $1<p<\infty$ and identify its exact $L^2$ norm as $\sqrt{α/2}$. We further determine the precise asymptotic behavior of the weak-type $(1,1)$ constant as $n\to\infty$, yielding quantitative upper and lower bounds in terms of the dimension.

math.FA↗

arXiv2026

Cosserat Modeling of Trimmed Helicoid Soft Arms with a Separated-Section Constitutive Law

Cosserat rod models for soft robots usually construct sectional stiffness by summing material properties over a common cross-section. This assumption becomes inaccurate for trimmed helicoid arms, where load-bearing helix domains are separated and connected only through sparse fused crossings. This paper formulates a separated-section constitutive law that evaluates each helix domain in its local frame and pulls its constitutive response back to the backbone, yielding an effective backbone stiffness. Sparse-fusion mechanics captures the additional compliance caused by relative motion between neighboring domains and determines channel-wise reduction profiles $η_c(s/L)$ for bending, torsion, and extension. The resulting effective sectional stiffness is strongly anisotropic: bending and extension are reduced by about one order of magnitude, whereas torsion remains close to the effective backbone stiffness. The resulting sectional law is embedded in a geometrically exact dynamic Cosserat model with GVS discretization and routed-tendon actuation. Across 103 measured configurations, the three datasets give pooled normalized position errors of $7.7 \ \%$, $6.7 \ \%$, and $7.8 \ \%$, while each full-arm solve requires approximately $0.3 \ \mathrm{s}$ on one CPU core (Intel Xeon, Cascade Lake, $2.8 \mathrm{GHz}$), enabling rapid model-based planning, state and load estimation, and morphology--control co-design for architected soft robots.

cs.RO↗

arXiv2026

NanoZone: Scalable, Efficient, and Secure Memory Protection for Arm CCA

Arm Confidential Compute Architecture (CCA) provides hardware primitives for confidential computing, but its standard CVM-based deployment model still isolates software only at the VM granularity. This leaves two critical gaps: the guest OS remains in the Trusted Computing Base, and applications lack any intra-process isolation boundary, exposing secrets to vulnerabilities such as Heartbleed. While intra-address-space isolation offers a theoretical solution, realizing it under an adversarial OS faces a fundamental scalability-efficiency trade-off: existing hardware primitives are either strictly limited in domain capacity or incur prohibitive privileged-switching latency. To bridge this gap, we propose NanoZone, a novel isolation architecture built on Arm CCA primitives. Rather than extending the realm-world software stack, NanoZone protects normal-world processes and enforces fine-grained intra-process isolation from a minimal root-world monitor. NanoZone unifies the speed of user-space permission switching with the capacity of physical address space isolation to achieve effectively unlimited domain scalability. To mask the latency of privileged transitions, it employs a locality-aware scheduling policy that maximizes execution residency within the fast user-level tier. To defeat an adversarial OS, NanoZone anchors its root of trust in the root world, offloading critical isolation enforcement away from the untrusted kernel. Against intra-process adversaries, it further leverages hardware-assisted Code-Pointer Integrity (CPI) to prevent domain-switching abuse. We prototyped NanoZone on Arm's official emulator and on physical hardware. Evaluation on real-world server applications shows that, compared to process-level isolation systems, our fine-grained protection preserves about 95% of their throughput.

cs.CR↗

arXiv2026

RoboCafé in the Open: Interaction Continuity in Long-Term Public Human-Robot Interaction

As robots remain in public spaces over extended periods, they must maintain interaction continuity by preserving and correctly applying context as people, encounters, and circumstances change. To study interaction continuity in long-term public human-robot interactions, we developed RoboCafé, an autonomous conversational coffee robot designed to support repeated interactions through task-aware dialogue, real-time multimodal perception, and memory of prior encounters. We deployed RoboCafé for 12 days in a university building, where it received 148 orders. The deployment involved repeat customers, passersby, changing groups, and back-to-back orders that repeatedly crossed the boundaries assumed by the system's order-centered interaction model. We found that successful interaction continuity requires a robot to determine who is currently present, which prior context belongs to whom, where interactions begin and end, and whether its representation of an interaction matches what is occurring in the physical world. From these observations, we derive four system design requirements for maintaining interaction continuity in longitudinal public human-robot interactions: contextual interaction state, persistent person grounding, explicit interaction life-cycle management, and interaction observability.

cs.RO↗

arXiv2026

RadarVox: Radar-Audio Multimodal Cocktail-Party Speech Separation with Speaker-Aware Cross-Modal Matching

In embodied voice interaction, cocktail-party speech perception requires both speech separation and speaker attribution across machine-generated and human speech sources. However, conventional audio-only blind source separation remains permutation ambiguous, making the correspondence between separated streams and physical speakers unclear. This paper presents RadarVox, a radar-audio multimodal benchmark for identity-aware cocktail-party speech separation. RadarVox provides acoustic mixtures and source-level radar displacement signals from loudspeaker-emitted and human speech, enabling source-aware speaker assignment. FMCW radar captures laryngeal mechanical motion, providing speaker-specific spatial-motion cues unavailable to a single-channel microphone. We inject radar-derived priors into a DPRNN separator via gated fusion and learn a speaker-aware cross-modal matcher to associate unordered speech streams with radar-observed speakers. Experiments on multi-speaker mixtures show that radar cues provide complementary benefits, achieving scale-invariant signal-to-distortion ratio (SI-SDR) values of 9.75 dB and 7.13 dB in two- and three-speaker scenarios, respectively. More importantly, the proposed method improves ordered SI-SDR by more than 13 dB over audio-only methods while achieving over 80\% speaker assignment accuracy.

eess.AS↗

arXiv2026

Supernova origin of galactic turbulence revealed by superbubbles

Supernovae (SNe) are among the leading candidates for powering galactic-scale turbulence. SNe drive expanding shells of neutral atomic hydrogen (HI) known as superbubbles. Due to the lack of a sensitive, dynamically complete, galaxy-wide census, superbubbles have not been used to quantify the galactic-scale turbulent energy budget. Here we present a combined Five-hundred-meter Aperture Spherical radio Telescope (FAST) and Jansky Very Large Array HI survey of the Andromeda galaxy (M31), the nearest giant spiral, with superior sensitivity and dynamical coverage. We identify 118 superbubbles across the entire disk of M31 with dynamical ages up to 40 Myr, consistent with the expected duration of SN activity in a star cluster and extending the age coverage well beyond previous surveys. Inferred from these superbubbles, the kinetic energy injection rates ($10^{49}$--$10^{51.5}$ erg kpc$^{-3}$ Myr$^{-1}$) from SNe closely match the turbulence dissipation rates derived independently from the same data, in both magnitude and spatial distribution. These results demonstrate that clustered SN feedback is sufficient to sustain galactic-scale turbulence, which shapes disk structure and influences galaxy evolution.

astro-ph.GA↗

arXiv2026

Heat kernel on Ricci shrinker metric measure spaces

As a metric measure space possessing positive Bakry-Émery curvature, a Ricci shrinker with potential function $f$ admits a heat kernel $H_f$ under the weighted volume measure $e^{-f}dv$. In this paper, we study $H_f$ systematically and establish a series of fundamental estimates. Moreover, we clarify the equivalence between $H_f$ and the spacetime heat kernel $H(x,t;y,s)$ under Ricci flow induced by a Ricci shrinker. Inspired by this relation, we extend corresponding analysis tools of Ricci flows to Ricci shrinker metric measure spaces, such as Nash entropy and reduced distance. As a direct application, we prove that $\left|\nabla R\right|=o(f^{3/2})$ implies $R=o(f)$.

math.DG↗

arXiv2026

Nilpotent Jacobian maps in dimension three and stable tameness in block extensions

We study polynomial maps in three variables with nilpotent Jacobian over a field of characteristic zero. The proposed classification reduces maps with linearly independent components to a family determined by a univariate polynomial evaluated at a quadratic coordinate. The geometric part of the argument produces two algebraically dependent constant linear combinations of the components. A derivation argument then yields the normal form over the original field. We obtain explicit polynomial inverses and tame factorizations. We then study higher-dimensional maps in which all but the last three components depend on three variables. For this class, we give separate normal forms for the two three-variable blocks and deduce stable tameness from the residue-field criterion of Berson, van den Essen, and Wright.

math.AG↗

arXiv2026

Lower central dark matter densities in nearby galaxies than predicted by simulations

Baryonic feedback in hydrodynamical simulations is typically invoked to alleviate the core--cusp problem in dwarf galaxies. Yet baryonic processes also induce adiabatic contraction of dark matter, producing overly steep density profiles and excessively high dark matter fractions in the inner regions of massive galaxies. The dark matter distribution of galaxies across a wide stellar-mass range is therefore a critical test for such simulations, but a comprehensive benchmark has remained absent. Here, we consistently measure the dark matter distribution from galaxy centres out to radii of 20--50 kpc for 136 nearby galaxies that together span the local mass--size relation over the stellar mass interval $10^9$--$10^{11.5}\,M_{\odot}$. We identify central regions with lower dark matter densities relative to $Λ$CDM simulation expectations---whose extent grows from about 10 kpc to $>50$ kpc as stellar mass increases from $10^{10} M_{\odot}$ to $10^{11.5} M_{\odot}$. Although their physical origin remains unclear, these low--dark matter regions are clearly indicated by the data. Our results provide an important observational benchmark for future hydrodynamical simulations that explore alternative dark matter models and feedback processes.

astro-ph.GA↗

arXiv2026

Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition

As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perception but typically relies on centralized training, where multi-source SAR data are uploaded to a single server, raising privacy and security concerns. Federated learning (FL) provides an emerging computational intelligence paradigm for SAR image target recognition, enabling cross-site collaboration while preserving local data privacy. However, FL confronts critical security risks, where malicious clients can exploit SAR's multiplicative speckle noise to conceal backdoor triggers, severely challenging the robustness of the computational intelligence model. To address this challenge, we propose NADAFD, a noise-aware and dynamically adaptive federated defense framework that integrates frequency-domain, spatial-domain, and client-behavior analyses to counter SAR-specific backdoor threats. Specifically, we introduce a frequency-domain collaborative inversion mechanism to expose cross-client spectral inconsistencies indicative of hidden backdoor triggers. We further design a noise-aware adversarial training strategy that embeds $Γ$-distributed speckle characteristics into mask-guided adversarial sample generation to enhance robustness against both backdoor attacks and SAR speckle noise. In addition, we present a dynamic health assessment module that tracks client update behaviors across training rounds and adaptively adjusts aggregation weights to mitigate evolving malicious contributions. Experiments on MSTAR and OpenSARShip datasets demonstrate that NADAFD achieves higher accuracy on clean test samples and a lower backdoor attack success rate on triggered inputs than existing federated backdoor defenses for SAR target recognition.

cs.CR↗

arXiv2026

Coupled Scaling: A Representational Accessibility Framework for Neural Scaling Laws

We ask when two learning systems trained on the same task under a common resource protocol should share a scaling rate and when their rates should differ. Coupled Scaling answers this through representational accessibility: the task-relevant geometry that a specified architecture-optimization system can reach and how that geometry is acquired as resources grow. In an orthogonal model, unsupported target energy sets the asymptotic floor, while unacquired supported energy sets the finite-budget residual. When acquisition can skip high-value directions, the largest fully acquired prefix no longer determines a unique exponent. For target powers $a_j \asymp j^{-b}$, $b>1$, and prefix log-growth rate $0<ρ\leq1$, the sharp attainable interval is $[ρ(b-1),\min\{b-1,ρb\}]$, and every rate in this interval is realized by a fixed acquisition order on a common support. A rank-window condition identifies when the product law $α=ρ(b-1)$ applies, while a fixed-kernel specialization expresses the same task-system dependence through the task-weighted spectral tail. We then audit released capability trajectories to diagnose measurement and comparison effects and propose a staged test in which independently measured geometry predicts held-out loss, scaling rates, and cross-task reversals.

cs.LG↗

arXiv2026

Gaussian-trigonometric functional link artificial neural network: design and analysis

This paper proposes a Gaussian function-based trigonometric functional link artificial neural network (GTFLN) filter for linear-in-the-parameters nonlinear filtering. Compared with the adaptive exponential TFLN (AETFLN) filter, the GTFLN filter provides smooth and localized basis functions with reduced computational complexity, where modeling advantages are theoretically established through the smoothness, reproducing kernel Hilbert space, approximation error, and operator theory properties. To maximize the modeling performance, an optimized scaling parameter for the GTFLN filter is derived, yielding the optimized GTFLN (OGTFLN) filter. Least mean square (LMS) adaptation is applied to the GTFLN and OGTFLN filters for nonlinear system identification, resulting in the GTFLMS and OGTFLMS algorithms, respectively. Moreover, the theoretical steady-state excess mean-square error of the GTFLN filter is analyzed. Simulations validate the effectiveness of the theoretical analysis and demonstrate the improved performance of the GTFLN and OGTFLN filters over the linear-in-the-parameters benchmarks in nonlinear system identification and nonlinear acoustic echo cancellation. Based on the GTFLN filter, the filtered-g LMS (FgLMS) algorithm is proposed for nonlinear active noise control. Simulations demonstrate improved stability and noise reduction performance compared to the benchmarks.

eess.SP↗

arXiv2026

Quaternionic Sums of Squares, Moments, and Quaternionic Polynomial Optimization

We develop a moment-quaternionic-sum-of-squares (Moment--QSOS) framework for global polynomial optimization over the standard Hamilton quaternions. The main difficulty is the hybrid algebraic structure of scalar quaternionic variables: multiplication is noncommutative, while scalar evaluations satisfy additional polynomial identities inherited from the four commuting real coordinates of each quaternion. To capture this structure, we introduce quaternionic sums of squares together with real and quaternionic scalar-identity ideals. We establish Archimedean Positivstellensätze for real-coefficient quaternionic polynomials and for objectives with quaternionic middle coefficients and real-coefficient constraints; a counterexample shows that the latter constraint hypothesis cannot, in general, be removed for the proposed pure-word certificate cone. On the dual side, we solve the corresponding full real-valued and quaternion-valued moment problems through Riesz--Haviland-type characterizations, and we prove a flat-extension theorem for truncated quaternion-valued moments under an explicit scalar-consistency condition. These results yield convergent Moment--QSOS hierarchies and finite-exactness criteria based on flatness. For quaternionic quadratically constrained quadratic programs (QCQPs), we further prove that the first-order quaternionic moment relaxation has the same optimal value as the first-order relaxation of the equivalent real QCQP. Numerical experiments on quadratic, quartic, and sparse problems, together with applications to quaternion-based maximum margin feature extraction and rotation synchronization, show that the quaternionic formulation attains the same relaxation values as the tested real SOS formulations while requiring substantially shorter computation times in the reported instances.

math.OC↗

arXiv2026

Temporal Residual Neural Radiance Fields for Monocular Video Dynamic Human Body Reconstruction

In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes has been achieved in recent years by a single multilayer perceptron (MLP) in a number of approaches. However, MLPs have capacity limitations, requiring substantial training time and computational resources for dynamic scene reconstruction. And the quality of reconstruction is significantly constrained. This paper proposes a method for effectively processing complex spatiotemporal signals in dynamic scene human 3D modeling. The proposed method uses Temporal Residual Neural Radiance Fields to achieve novel view rendering and new pose synthesis of human bodies.To address the problem of representing temporal signals in video sequences, we construct a temporal residual field which is not related to the MLP architecture. Secondly, to improve reconstruction efficiency, we propose an integrated approach that reduces trainable parameters and accelerates rendering, thereby enhancing the network's feature representation capability. Finally, we design a multi-dimensional loss function to accurately measure the loss between predicted and actual spatial pixel values. The experimental results show that our proposed approach improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) accuracy metrics compared to the latest representative methods. It maintains similar accuracy to Anim-NeRF and Neural Body while achieving a nearly 780-fold increase in time efficiency.

cs.CV↗

arXiv2026

PrivateHub: Contrastive Diffusion Model for Private Sensor-Intensive Environment Data Generation

Sensor-intensive environments enable many intelligent services by inferring user applications from heterogeneous data streams. However, not all applications should be exposed: users want some activities to stay private. This creates a tension between inferring applications for useful services and preventing unwanted inference. Existing approaches such as differential privacy and rule-based filtering protect individual streams but cannot address the privacy risk from cross-sensor inference. We introduce Privatehub, which uses contrastive learning within a diffusion model to generate synthetic multi-sensor streams that keep non-private applications detectable while concealing private ones. Privatehub has two stages: App-Conditioned Pre-training (ACP), which conditions the model on multi-sensor data with application embeddings, and App-Aware Fine-tuning (AAF), which separates private from non-private data via contrastive learning. We also define a threat model for the multi-sensor sharing setting. Experiments on three real-world multi-sensor datasets show Privatehub lowers private-application accuracy by 40 to 50\% without hurting non-private performance, and stays robust when the attacker retrains on the synthetic data.

cs.CR↗

arXiv2026

Variable Selection for Feature-Based Newsvendor

Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand uncertainty. However, high-dimensional feature sets often hinder interpretability and inflate data collection and implementation costs. This paper studies variable selection for the feature-based newsvendor problem under a hard cardinality constraint on the number of selected features. We formulate the resulting $\ell_0$-constrained empirical newsvendor problem with $\ell_2$-regularization, establish its computational hardness, and develop a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation. To enable scalability beyond exact optimization, we develop a randomized-rounding algorithm with a bi-criteria guarantee and a greedy heuristic. Statistically, we provide theoretical analysis of the resulting sparse policy estimator, including finite-sample estimation error, out-of-sample risk bounds, and support recovery guarantees. Extensive experiments on both synthetic and real data illustrate the computational and statistical trade-offs among various baselines. Our results demonstrate that the proposed variable selection framework achieves competitive out-of-sample operational costs while using substantially fewer covariates.

stat.ML↗

arXiv2026

EGT-KG: Evidence-Grounded Typed KG Retrieval for Practical Scientific QA with Small Language Models

For emerging scientific research domains, local Small Language Models (SLMs) are becoming more attractive, as they offer stronger privacy control and more stable deployment pipelines than Large Language Models. However, in practice, scientific question-answering on SLMs often operates under inevitable constraints: small literature collections, fragmented evidence, limited context window and reasoning abilities. We propose the Evidence-Grounded Typed Knowledge Graph (EGT-KG), a retrieval framework to improve information retrieval with local SLMs. We assessed three question-answering settings: a vanilla Retrieval-Augmented Generation (RAG) workflow and two EGT-KG workflows: an automatically generated relation schema (AS) and an expert-defined relation schema (ES). Our experiments were evaluated with a six-dimensional evaluation framework (S3CRF: Soundness, Correctness, Completeness, Conciseness, Relevance, Fluency) on a Biopolymer-bound Soil Composite literature benchmark, showing that EGT-KG outperforms the vanilla RAG method in most settings, with the best improvement from llama3:8b: a Final Score of 70.37 (+14.67%) and 68.82 (+12.14%) by AS/ES EGT-KG variants.

cs.AI↗

arXiv2026

H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use of distributed data. However, conventional two-tier FL architectures are poorly suited to the hierarchical and heterogeneous nature of real-world IoT systems. Hierarchical Federated Learning (HFL) introduces multi-layer aggregation to better match IoT environments, but still suffers from communication inefficiencies and performance limitations caused by large data transfers, non-IID data distributions, and uneven device participation. These challenges hinder the realization of low-latency and high-accuracy training in practical IoT deployments. To address these limitations, we propose H-FedSN for practical IoT environments. H-FedSN leverages a binary mask mechanism with shared and personalized layers to reduce communication overhead by creating a sparse network without altering original weights. To tackle data heterogeneity and imbalanced device distribution, H-FedSN incorporates personalized layers for local data adaptation and employs Bayesian aggregation with cumulative Beta distribution updates at edge and cloud levels, effectively balancing contributions from diverse client groups. Experiments on three real-world IoT datasets and MNIST under non-IID conditions show that H-FedSN reduces communication costs by up to 477 times compared to baseline methods while maintaining high accuracy, making it well-suited for hierarchical FL in IoT deployments.

cs.LG↗