arXiv subjects

Teng Wu

Publications and source records attributed to Teng Wu.

At least 19 recordsLinked to original sources

arXiv2026

Network Availability Enhancement in Low-Altitude HetNets: A Cross-Layer Design Perspective

This paper proposes a computing-communication resource interchange method to enhance network availability (NA) in low-altitude heterogeneous networks (LA-HetNets). In these networks, communication resource conflicts and imbalances, caused by extreme heterogeneity (diverse mobility, mixed delays, and hybrid transmission), and cross-regional traffic, reduce reliability and lead to unavailability. Restoring NA requires additional communication resources, yet dynamic cross-regional scheduling is limited, making locally redundant computing resources an alternative to reduce communication resource overhead. While computing resources address medium access control (MAC)-layer unreliability, physical (PHY)-layer functionalities still rely on communication resources. Thus, it remains unclear whether increasing computing resources alone can achieve target NA, especially under greater heterogeneity. We elaborate on the impact of heterogeneity on NA and show that expanding computing resources alone cannot meet target NA under high heterogeneity, as NA degrades sharply due to increased communication capability demands. To overcome this, we propose a cross-layer optimization method enabling computing-communication resource interchange to address both MAC- and PHY-layer unreliability. By reducing processing delays with computing resources while ensuring MAC-layer reliability, our method extends PHY-layer transmission delay and expands communication resources. Simulations demonstrate our approach's superiority in achieving target NA under greater heterogeneity, revealing that computing-communication resource interchange fulfills expanding communication capability demands more effectively than conventional resource overhead reduction.

cs.NI↗

arXiv2026

Synergy between laser linewidth and frequency chirp in mesospheric magnetometry based on the sodium laser guide star

Mesospheric sodium magnetometry with a laser guide star measures the geomagnetic field near 90~km. Its sensitivity hinges on laser linewidth and chirp, yet prior work optimized these two parameters only separately. We use velocity-resolved density-matrix simulations of Larmor-synchronous pulsed Na D$_2$ pumping to scan both parameters jointly. Linewidth and chirp exhibit a synergy: when chirping carries the recoil-mitigation role, the return flux stays within 1\% of its peak across linewidths of 2--10~MHz. The flux-optimal chirp is $0.19~\mathrm{MHz/\upmu s}$, one fifth of the continuous-wave rate; this synergy guides high-sensitivity mesospheric magnetometer design.

physics.app-ph↗

arXiv2026

Search for a solar-bound axion halo using the Global Network of Optical Magnetometers for Exotic physics searches

We report on a search for a gravitationally bound solar axion halo using data from the Global Network of Optical Magnetometers for Exotic physics searches (GNOME), a worldwide array of magnetically shielded atomic magnetometers with sensitivity to exotic spin couplings. Motivated by recent theoretical work suggesting that self-interacting ultralight axions can be captured by the Sun's gravitational field and thermalize into the ground state, we develop a signal model for the pseudo-magnetic fields generated by axion-proton gradient couplings in such a halo. The analysis focuses on the fifth GNOME Science Run (69 days, 12 stations), employing a cross-correlation pipeline with time-shifted daily modulation templates to search for the global, direction-dependent, monochromatic signal expected from a solar axion halo. No statistically significant candidate signals are observed. We set 95% confidence-level upper limits on the amplitude of the axion-induced pseudo-magnetic field over the frequency range $\approx 0.05-20$ Hz, translating to constraints on the linear and quadratic axion-proton couplings for halo densities predicted by gravitational capture models and for the maximum overdensities allowed by planetary ephemerides. In the quadratic coupling case, our limits surpass existing astrophysical bounds by over two orders of magnitude across much of the accessible parameter space.

physics.atom-ph↗

arXiv2026

Collaborative Disagreement Resolution for Scalable Oversight

Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: models are incentivized to be persuasive to the judge, which may not always align with epistemic honesty. In this work, we propose an alternative paradigm: disagreement resolution, which reframes the interaction mechanism from adversarial debate to collaborative truth seeking. Drawing on principles from human mediation and conflict resolution, where mediators facilitate dialogue to help disputing parties reach consensus rather than adjudicating between them, we design an automated pipeline that adapts these strategies to AI oversight. Unlike standard debate where models argue for fixed positions, our pipeline directs models to collaboratively identify points of disagreement, examine the evidence for conflicting claims, and converge toward consensus or isolate the specific ''crux'' of their disagreement. We find that Disagreement Resolution consistently helps non-expert models identify the truth, achieving 62.1% judging accuracy compared to 49.2% for standard debate. Our results provide encouraging empirical evidence for rethinking the scalable oversight protocol from adversarial persuasion to collaborative truth-seeking.

cs.CY↗

arXiv2026

Spectral convergence of sum-of-Gaussians tensor neural networks for many-electron Schrödinger equation

We present an improved version of the sum-of-Gaussians tensor neural network (SOG-TNN) architecture for solving many-electron Schrödinger equation for one-dimensional soft-Coulomb systems. Model reduction techniques are introduced to reduce the number of tensor-factorized bases under the SOG approximation of the kernel. The Slater determinant ansatz is employed so that the anti-symmetric property of the wave function can be strictly preserved. Numerical results show that the SOG-TNN achieves high accuracy with remarkably small basis sizes. Robust spectral convergence with respect to the basis size is also observed, consistently characterized by a mixed algebraic-exponential model for the error decay. These findings validate that the SOG-TNN architecture provides an ultra-efficient and low-rank representation of complex multi-electron wave functions, shedding light on high-fidelity quantum calculations in larger-scale many-electron systems.

physics.chem-ph↗

arXiv2026

Sum-of-Gaussians tensor neural networks for high-dimensional Schrödinger equation

We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the solution to overcome the curse of dimensionality associated with high-dimensional integration. To handle the Coulomb interaction, we introduce an SOG decomposition to approximate the interaction kernel such that it is dimensionally separable, leading to a tensor representation with rapid convergence. We further develop a range-splitting scheme that partitions the Gaussian terms into short-, long-, and mid-range components. They are treated with the asymptotic expansion, the low-rank Chebyshev expansion, and the model reduction with singular-value decomposition, respectively, significantly reducing the number of two-dimensional integrals in computing electron-electron interactions. The SOG decomposition well resolves the computational challenge due to the singularity of the Coulomb interaction, leading to an efficient algorithm for the high-dimensional problem under the TNN framework. Numerical results demonstrate the outstanding performance of the new method, revealing that the SOG-TNN is a promising way for accurately tackling quantum systems.

physics.comp-ph↗

arXiv2026

Availability of Aerial Heterogeneous Networks for Reliable Emergency Communications

We investigate network availability (NA) in aerial heterogeneous networks (AHetNets) for effective emergency rescue, where diverse delay-constrained communication services must be provided to user equipments (UEs) with varying mobility. The heterogeneity in delay constraints and UE mobility introduces resource allocation conflicts and imbalances, which undermine communication reliability and challenge NA. Although unified resource allocation (URA) can mitigate these issues, it remains unclear whether NA can be sustained under such diverse conditions. To address this, we derive expressions for the lower bound (LB) on NA in AHetNets under URA. Our analysis reveals that extended heterogeneity significantly degrades the LB due to resource limitations-even when the heterogeneity stems from additional services under less stringent delay constraints (LSDC) or from UEs with lower mobility. To overcome this degradation, we formulate and solve a joint optimization problem for the number of UEs sharing time-frequency resources ($K$) and pilot length ($ξ$), aiming to enhance the LB by improving spatial, frequency, and temporal resource efficiency. Simulation results validate our analysis and demonstrate that jointly optimizing $K$ and $ξ$ enables AHetNets to achieve the target NA under greater heterogeneity, outperforming existing resource allocation policies.

eess.SP↗

arXiv2025

BLADE: Benchmarking Language Model Agents for Data-Driven Science

Data-driven scientific discovery requires the iterative integration of scientific domain knowledge, statistical expertise, and an understanding of data semantics to make nuanced analytical decisions, e.g., about which variables, transformations, and statistical models to consider. LM-based agents equipped with planning, memory, and code execution capabilities have the potential to support data-driven science. However, evaluating agents on such open-ended tasks is challenging due to multiple valid approaches, partially correct steps, and different ways to express the same decisions. To address these challenges, we present BLADE, a benchmark to automatically evaluate agents' multifaceted approaches to open-ended research questions. BLADE consists of 12 datasets and research questions drawn from existing scientific literature, with ground truth collected from independent analyses by expert data scientists and researchers. To automatically evaluate agent responses, we developed corresponding computational methods to match different representations of analyses to this ground truth. Though language models possess considerable world knowledge, our evaluation shows that they are often limited to basic analyses. However, agents capable of interacting with the underlying data demonstrate improved, but still non-optimal, diversity in their analytical decision making. Our work enables the evaluation of agents for data-driven science and provides researchers deeper insights into agents' analysis approaches.

cs.CL↗

arXiv2025

Response of Comagnetometer to Exotic Spin-Dependent Couplings

Comagnetometers offer unique advantages in measuring exotic spin-dependent interactions by suppressing magnetic noise. Understanding the frequency response to exotic interactions is essential for evaluating and improving the sensitivity of comagnetometers, as the signals often exhibit distinct spectral features. We conduct theoretical and experimental studies on the frequency response of a differential comagnetometer. We derive analytical expressions for the frequency response of the differential comagnetometer to both magnetic field and exotic couplings, along with the conversion relationship between them. Based on this, we revise the method for determining the axion-nucleon coupling strength with comagnetometers. Furthermore, we introduce a light-shift-based calibration protocol to verify the response to exotic interactions. This work helps predict the sensitivity of comagnetometers in detecting exotic fields and advances the exploration of dark matter.

hep-ph↗

arXiv2025

Large Processor Chip Model

Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on manual expertise, fragmented optimization across software and hardware layers, and high costs associated with exploring expansive design spaces. While automated methods leveraging optimization algorithms and machine learning have improved efficiency, they remain constrained by a single-stage focus, limited data availability, and a lack of comprehensive human domain knowledge. The emergence of large language models offers transformative opportunities for the design of computer system architecture. By leveraging the capabilities of LLMs in areas such as code generation, data analysis, and performance modeling, the traditional manual design process can be transitioned to a machine-based automated design approach. To harness this potential, we present the Large Processor Chip Model (LPCM), an LLM-driven framework aimed at achieving end-to-end automated computer architecture design. The LPCM is structured into three levels: Human-Centric; Agent-Orchestrated; and Model-Governed. This paper utilizes 3D Gaussian Splatting as a representative workload and employs the concept of software-hardware collaborative design to examine the implementation of the LPCM at Level 1, demonstrating the effectiveness of the proposed approach. Furthermore, this paper provides an in-depth discussion on the pathway to implementing Level 2 and Level 3 of the LPCM, along with an analysis of the existing challenges.

cs.AR↗

arXiv2025

A Novel Dynamic Bias-Correction Framework for Hurricane Risk Assessment under Climate Change

Conventional hurricane track generation methods typically depend on biased outputs from Global Climate Models (GCMs), which undermines their accuracy in the context of climate change. We present a novel dynamic bias correction framework that adaptively corrects biases in GCM outputs. Our approach employs machine learning to predict evolving GCM biases, allowing dynamic corrections that account for changing climate conditions. By combining dimensionality reduction with data-driven surrogate modeling, we capture the system's underlying dynamics to produce realistic spatial distributions of environmental parameters under future scenarios. Using the empirical Weibull plotting approach, we calculate return periods for wind speed and rainfall across coastal cities. Our results reveal significant differences in projected risks with and without dynamic bias correction, emphasizing the increased threat to critical infrastructure in hurricane-prone regions. This work highlights the necessity of adaptive techniques for accurately assessing future climate impacts, offering a critical advancement in hurricane risk modeling and resilience planning.

physics.ao-ph↗

arXiv2025

Solving Schrödinger Equation Using Tensor Neural Network

In this paper, we introduce a novel approach to solve the many-body Schrodinger equation by the tensor neural network. Based on the tensor product structure, we can do the direct numerical integration by using fixed quadrature points for the functions constructed by the tensor neural network within tolerable computational complexity. Especially, we design several types of efficient numerical methods to treat the variable-coupled Coulomb potentials with high accuracy. The corresponding machine learning method is built for solving many-body Schrodinger equation. Some numerical examples are provided to validate the accuracy and efficiency of the proposed algorithms.

physics.comp-ph↗

arXiv2024

Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks

Coastal regions in North America face major threats from storm surges caused by hurricanes and nor'easters. Traditional numerical models, while accurate, are computationally expensive, limiting their practicality for real-time predictions. Recently, deep learning techniques have been developed for efficient simulation of time-dependent storm surge. To resolve the small scales of storm surge in both time and space over a long duration and a large area, these simulations typically need to employ oversized neural networks that struggle with the accumulation of prediction errors over successive time steps. To address these challenges, this study introduces a hierarchical deep neural network (HDNN) combined with a convolutional autoencoder (CAE) to accurately and efficiently predict storm surge time series. The CAE reduces the dimensionality of storm surge data, streamlining the learning process. HDNNs then map storm parameters to the low-dimensional representation of storm surge, allowing for sequential predictions across different time scales. Specifically, the current-level neural network is utilized to predict future states with a relatively large time step, which are passed as inputs to the next-level neural network for smaller time-step predictions. This process continues sequentially for all time steps. The results from different-level neural networks across various time steps are then stacked to acquire the entire time series of storm surge. The simulated low-dimensional representations are finally decoded back into storm surge time series. The proposed model was trained and tested using synthetic data from the North Atlantic Comprehensive Coastal Study. Results demonstrate its excellent performance to effectively handle high-dimensional surge data while mitigating the accumulation of prediction errors over time, making it a promising tool for advancing storm surge prediction.

physics.ao-ph↗

arXiv2024

An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset

Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar stereo disparity estimation in the computer vision community. DL-based methods depend heavily on the quality and quantity of training datasets. However, generating ground-truth disparity maps for real scenes remains a challenging task in the photogrammetry community. To address this challenge, we propose a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images to produce a large and diverse dataset for six aerial datasets across four different areas and two areas with different resolution images. We also introduce a LiDAR-to-image co-registration refinement to the framework that takes special precautions regarding occlusions and refrains from disparity interpolation to avoid precision loss. Evaluating 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations, which are deeply investigated in dataset shift, GANet performs best with identical training and testing data, and PSMNet shows robustness across different datasets, and we proposed the best strategy for training with a limit dataset. We will also provide the dataset and training models; more information can be found at https://github.com/whuwuteng/Aerial_Stereo_Dataset.

cs.CV↗

arXiv2024

Exotic Spin-dependent Energy-level Shift Noise Induced by Thermal Motion

Searching for exotic spin-dependent interactions that beyond the standard model has been of interest for past decades and is crucial for unraveling the mysteries of the universe. Previous laboratory searches primarily focus on searching for either static or modulated energy-level shifts caused by exotic spin-dependent interactions. Here, we introduce a theoretical model based on thermal motion of particles, providing another efficient way to search for exotic spin-dependent interactions. The theoretical model indicates that as the exotic spin-dependent interactions are related with the relative displacements and velocities of atoms, atoms undergoing thermal motion would experience a fluctuating energy-level shift induced by the exotic interactions. Moreover, the resulting exotic energy-level shift noise could be sensed by high-sensitivity instruments. By using the model and taking the high-sensitivity atomic magnetometer as an example, we set the most stringent laboratory experiment constraints on eight different kinds of exotic spin- and velocity-dependent interactions, with five of which at the force range below 1 cm have not been covered previously. Furthermore, this theoretical model can be easily applied in other fields of quantum sensing, such as atomic clocks, atom interferometers and NV-diamond sensors, to further improve the laboratory constraints on exotic spin-dependent interactions.

physics.atom-ph↗

arXiv2023

Change-point Inference for High-dimensional Heteroscedastic Data

We propose a bootstrap-based test to detect a mean shift in a sequence of high-dimensional observations with unknown time-varying heteroscedasticity. The proposed test builds on the U-statistic based approach in Wang et al. (2022), targets a dense alternative, and adopts a wild bootstrap procedure to generate critical values. The bootstrap-based test is free of tuning parameters and is capable of accommodating unconditional time varying heteroscedasticity in the high-dimensional observations, as demonstrated in our theory and simulations. Theoretically, we justify the bootstrap consistency by using the recently proposed unconditional approach in Bucher and Kojadinovic (2019). Extensions to testing for multiple change-points and estimation using wild binary segmentation are also presented. Numerical simulations demonstrate the robustness of the proposed testing and estimation procedures with respect to different kinds of time-varying heteroscedasticity.

stat.ME↗

arXiv2023

Mobile Mapping Mesh Change Detection and Update

Mobile mapping, in particular, Mobile Lidar Scanning (MLS) is increasingly widespread to monitor and map urban scenes at city scale with unprecedented resolution and accuracy. The resulting point cloud sampling of the scene geometry can be meshed in order to create a continuous representation for different applications: visualization, simulation, navigation, etc. Because of the highly dynamic nature of these urban scenes, long term mapping should rely on frequent map updates. A trivial solution is to simply replace old data with newer data each time a new acquisition is made. However it has two drawbacks: 1) the old data may be of higher quality (resolution, precision) than the new and 2) the coverage of the scene might be different in various acquisitions, including varying occlusions. In this paper, we propose a fully automatic pipeline to address these two issues by formulating the problem of merging meshes with different quality, coverage and acquisition time. Our method is based on a combined distance and visibility based change detection, a time series analysis to assess the sustainability of changes, a mesh mosaicking based on a global boolean optimization and finally a stitching of the resulting mesh pieces boundaries with triangle strips. Finally, our method is demonstrated on Robotcar and Stereopolis datasets.

cs.CV↗

arXiv2021

IFC models for (semi)automating common planning checks for building permits

To support building permit issuing with automatic digital tools, the reuse of models produced by designers would make the process quicker and more objective. However, current studies and pilots often leave a gap with respect to the models as actually provided by architects, having varying quality and content. In this study, rather than taking a top down approach, we started from the available data and made the necessary inferences, which gave the opportunity to tackle basic and common issues often preventing smooth automatic processing. Specific characteristics of the IFC models were outlined and a tool was developed to extract the necessary information from them to check representative regulations. While the case study is specific in location, regulations and input models, the type of issues encountered are a generally applicable example for automated code compliance checking. This represents a solid base for future works towards the automation of building permits issuing.

cs.CY↗