arXiv subjects

David Smith

Publications and source records attributed to David Smith.

At least 19 recordsLinked to original sources

arXiv2026

A general extractor for coherent patterns across time-frequency measurements

Scientific discovery increasingly depends on finding meaningful patterns within volumes of sensor data too large for manual inspection. Existing time-frequency event extractors are often engineered or trained on manually labeled examples for each instrument, which can be laborious and difficult to adapt to new environments. To address this, we introduce TokEye, a reference-free method that learns a general criterion for coherent structure from heterogeneous fusion measurements. A model developed under this method, without retraining, can extract signals from various other fields including marine bioacoustics and wideband radar. It recovers 82.5\% of expert-annotated stellarator signals, detects 78\% of dolphin whistles, and identifies radar events with 81.5\% average precision across six benchmarks, while processing full experimental spectrograms in under one second on a GPU. This transfer across unrelated sensors and physical systems reveals common exploitable structure in time-frequency measurements and establishes a scalable foundation for automated scientific signal discovery.

eess.SP↗

arXiv2026

Curvature-Aware Clipping: Mitigating Bias in DPSGD via Approximate Second-Order Information

Differentially Private Stochastic Gradient Descent (DPSGD) has become the de facto standard for training deep neural networks with rigorous privacy guarantees. However, the utility of DPSGD is constrained by gradient clipping bias and DP noise. While existing work has tried to address these issues, the geometric misalignment caused by clipping bias remains a critical challenge. To bridge this gap, we propose a novel framework, Curvature-Aware Differentially Private Stochastic Gradient Descent (CA-DPSGD), which leverages approximate second-order information to construct an optimal, curvature-aware clipping space. CA-DPSGD integrates a rectified estimator for the curvature matrix and is coupled with an anisotropic noise mechanism to ensure privacy guarantees. Extensive empirical experiments demonstrate that our approach outperforms state-of-the-art clipping methods.

cs.LG↗

arXiv2026

The General Quantum Limit for and the Optimization of the Minimum Measurable Frequency Shift in a Laser

We show that, contrary to conventional understanding, the minimum measurable frequency shift (MMFS) for a single-mode ideal laser is determined by a combination of phase diffusion caused by SE and the shot noise caused by the VM. For all practical sensors, the MMFS is found to be given by the geometric mean of the measurement bandwidth and the Schwalow-Townes Linewidth, multiplied by a factor which can be much larger than unity under certain conditions. We determine the optimal value for the MMFS for three different sensing modalities, an unbalanced Mach-Zehnder Interferometer, a passive Fabry-Perot cavity (FPC), and heterodyning with a reference laser, and identify the conditions needed for reaching the optimal value.

quant-ph↗

arXiv2026

Economic Impacts of Climate Change in the United States: Integrating and Harmonizing Evidence from Recent Studies

This paper synthesizes evidence on climate change impacts specific to U.S. populations. We develop an apples-to-apples comparison of econometric studies that empirically estimate the relationship between climate change and gross domestic product (GDP). We demonstrate that with harmonized probabilistic socioeconomic and climate inputs these papers project a narrower and lower range of 2100 GDP losses than what is reported across the published studies, yet the implied U.S.-specific social cost of greenhouse gases (SC-GHG) is still greater than the market-based damage estimates in current enumerative models. We also take a first step in integrating evidence on nonmarket damages with the GDP impacts. Our findings highlight the need for more research addressing the limitations of macroeconomic empirical studies and research on nonmarket and international spillover impacts. Further investigation of how macroeconomic and enumerative approaches can be integrated would enhance the usefulness of both strands of literature to climate policy analysis going forward.

econ.GN↗

arXiv2026

Robustness to Model Uncertainties Drives More Rapid CO2 Emissions Reductions

Evaluating the economic impacts of climate policies is important for designing a response to climate change. One typical approach to assessing mitigation policy options uses integrated climate-economy models to analyze tradeoffs between the costs of reducing greenhouse gas emissions and the benefits of reducing climate damages. However, the uncertainty characterizing these models poses significant challenges for policymakers. We address this difficulty using a robust decision-making framework to evaluate mitigation policy. We show that a shift from a decision framework that maximizes expected outcomes to one that is averse to regret suggests more aggressive emissions reductions. Uncertainties about socioeconomic trajectories and the magnitude and functional form of climate damages create the asymmetric consequences of weak mitigation policy that encourage aggressive emissions reductions and precaution in the face of uncertainty.

econ.GN↗

arXiv2026

A saturation-absorption rubidium magnetometer with multilevel optical Bloch-equation modeling for intermediate-to-high fields

We present SASHMAG (Saturated Absorption Spectroscopy High-field MAGnetometer), an atomic sensor designed for precision magnetic-field measurements in the intermediate-to-high field regime ($>0.2\,\text{T}$) using Rubidium-87 ($^{87}Rb$). The sensor operates in the hyperfine Paschen-Back regime, where the hyperfine and Zeeman interactions decouple, and utilizes counter-propagating pump-probe configuration in Faraday geometry to resolve isolated, Doppler-free Zeeman transitions. To interpret the resulting spectra in this strongly field-dependent regime, we developed a comprehensive multilevel optical Bloch-equation model solved explicitly in the uncoupled $\ket{m_I, m_J}$ basis, capturing state mixing and nonlinear saturation dynamics. This model reproduces measured spectra at sub-Doppler resolution and is consistent with analytical expectations for power broadening and thermal Doppler scaling. Magnetic field estimation is performed using a physics-constrained optimization routine that infers the magnetic field by minimizing the residual between experimentally extracted line centers and calculated transition frequencies from the field-dependent Hamiltonian. We demonstrate magnetic field retrieval from $0.2\,\text{T}$ to $0.4\,\text{T}$ with a precision of $\pm 0.0017 \,\text{T}$). Furthermore, the validated simulation establishes a foundation for generating synthetic training datasets, paving the way for autonomous, Machine Learning-enhanced magnetometry in applications ranging from MRI to fusion reactors.

quant-ph↗

arXiv2026

The Dynamics of Human and AI-Generated Language: How Semantics Fluctuates across Different Timescales

Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack simple, interpretable time-series features that capture how generic versus specific content is distributed over time, and that can be used to compare human and AI-generated speech. We introduce a semantic-timescale analysis pipeline that turns word-level transcripts with timestamps into semantic time-series. For each spoken narrative, we compute (i) semantic specificity using WordNet-based word depth and (ii) contextual similarity using SBERT embeddings and quantify their temporal dependence using autocorrelation-window measures (ACW-0 and related metrics). We then compare original speech to multiple shuffled controls that selectively disrupt lexical identity, temporal order, and word duration. Across human-read autobiographical narratives, TTS readings, and LLM-generated texts rendered with TTS, we find that segments with longer ACW-0 in the semantic time-series tend to contain more generic vocabulary, whereas segments with shorter ACW-0 are enriched in more specific words. These associations are strongly attenuated or abolished when word order and timing are randomized, indicating that ACW-based measures capture non-trivial temporal organization of semantic content beyond static lexical distributions. Our results suggest that ACW-based semantic timescales are a useful family of features for analyzing and comparing the temporal structure of human and AI-generated speech.

cs.CL↗

arXiv2026

WhoSaidIt: Human-LLM Collaborative Annotation for Text-Based Multilingual Speaker-Attribute Classification

Annotating speaker attributes from text is inherently ambiguous, particularly in multilingual settings where demographic and social cues are implicit and culturally variable. We propose a human-large language model (LLM) collaborative re-annotation framework for stabilizing multilingual speaker-attribute labels under practical resource constraints. Starting from a noisy corpus, we use LLMs to surface recurring annotation rationales through iterative interaction with experts, and apply disagreement-focused sampling for targeted re-annotation. Using this framework, we construct WhoSaidIt, a multilingual dataset covering nine speaker-attribute labels. We quantify divergence between original and revised annotations, benchmark recent LLMs, and analyze the effect of explicit rationales on model behavior. Our results reveal substantial cross-lingual differences in annotation decisions and demonstrate both the strengths and limitations of LLMs in speaker-attribute classification.

cs.CL↗

arXiv2026

Yinsen: A low power density HTS tokamak fusion reactor for marine and off-grid applications

Yinsen is a high-temperature-superconducting (HTS) tokamak reactor concept for off-grid applications such as maritime propulsion, remote power, and industrial energy. Rather than pursuing grid-scale power density, the design is anchored to a materials-limited fusion power density of $P_f/S_b=0.7~\mathrm{MW/m^2}$, obtained from a 35 DPA structural limit, a 20-year plant lifetime, 40% utilization, and a geometric damage-peaking correction. The resulting device has a V-4Cr-4Ti vacuum-vessel lifetime of $1040~\mathrm{MW\cdot yr}$, pointing to a minimum useful fusion power of $130~\mathrm{MW}$ and more than $25~\mathrm{MWe}$ net output. Integrated FUSE modeling refines the design into a self-consistent high-field baseline with a shaped 9.29 T, 9.67 MA plasma, while ASTRA transport analysis corroborates a broader operating window above the minimum design point. Divertor power handling is addressed with UEDGE modeling, showing that impurity-seeded detached operation is attainable with neon seeding, reducing peak heat fluxes well below $10~\mathrm{MW/m^2}$. OpenMC neutronics calculations with a double-layered WC/W$_2$B$_5$ shield show that the vacuum vessel is the lifetime-limiting solid structure, while the HTS magnets remain lifetime components: at the 130 MW baseline, total TF nuclear heating is 7.4 kW at 20 K, and the TF fast-neutron limit corresponds to roughly sixteen vacuum-vessel lifetimes. The same neutronics analysis gives $TBR\approx1.1$ with 30% $^6\mathrm{Li}$ enrichment and no dedicated neutron multiplier. Plant-level studies detail a supercritical CO$_2$ balance of plant and pulsed-power operation using a 34 kV medium-voltage backbone and local energy storage. Taken together, these results suggest that a low-power-density HTS tokamak offers a near-term path for relevant FOAK fusion reactors where many remaining challenges between $Q>1$ and economic grid operation are alleviated.

physics.plasm-ph↗

arXiv2026

FPGA-Accelerated Real-Time Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

In this work, we demonstrate the deployment of a hardware-accelerated machine learning (ML) inference system integrated into a real-time processing at the DIII-D tokamak fusion reactor. The team has successfully deployed an AMD/Xilinx KCU1500 field-programmable gate array (FPGA) into the realtime Plasma Control System (PCS) nodes that receives the live Beam Emission Spectroscopy (BES) signal used for Edge Localized Mode (ELM) forecasting. The FPGA hosts a dense neural network using the SLAC Neural Network Library (SNL) that has been trained to infer the likelihood of disruptive ELM conditions. This likelihood then feeds a separate plasma controller that uses Resonant Magnetic Perturbation coils to suppress the predicted disruptive condition. The SNL allows for on-the-fly updates of the neural network weights and biases without requiring full hardware resynthesis for the FPGA. Judicious design of the neural-network architecture can further allow for the hot-swapping of multiple classification tasks to be executed on the single FPGA, significantly enhancing the real-time adaptability of the system for context-aware control strategies that respond in real-time to evolving reactor conditions. These adaptive weights naturally support continuous model refinement and seamless task switching during live experimental operation. This use case is chosen as a high rate signal processing example that can serve as a template for general ML-based reactor diagnostic processing for active reactor control systems. We see this as an essential development for achieving reactor relevant operation in future continuous operation fusion devices.

physics.plasm-ph↗

arXiv2026

Wavelength-Dependent Evolution of Full-Field Transfer Matrices in Photonic Lanterns

A fiber-based photonic lantern can couple an array of single-mode optical fibers to the guided modes of a multimode fiber, with the mapping between the single-mode fibers and guided modes fully described by a complex-valued transfer matrix. Recent experimental studies have reported strong wavelength-dependent evolution of this matrix in non-mode-selective photonic lanterns, yet a quantitative physical explanation for this behavior has not previously been demonstrated. Here, we present direct measurements of the wavelength-dependent encoding transfer matrix of a photonic lantern across the range 1525 nm to 1575 nm using off-axis holographic imaging, enabling high-fidelity recovery of both amplitude and phase. Beyond measurement, we introduce a physically grounded propagation model and numerical simulation that quantitatively reproduces the observed wavelength evolution and provides a unified physical explanation for behavior reported in prior experimental work. The model identifies differential modal phase accumulation in the multimode section as the dominant mechanism governing spectral evolution and shows that increasing the length of the multimode end systematically accelerates the phase evolution of the transfer matrix with wavelength. These results establish a direct and predictive link between photonic lantern geometry and spectral response, providing a design framework for tailoring lanterns either to enhance sensitivity to closely spaced wavelengths or to enforce uniform response over broad bandwidths for spectroscopic and imaging applications.

physics.optics↗

arXiv2026

Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin

Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as CATMuS have shown greater generalizability, their raw outputs remain poorly compatible with most readers and downstream NLP tools, thus creating a usability gap. On the other hand, ATR models trained to produce normalized outputs have been shown to struggle to adapt to new domains and tend to over-normalize and hallucinate. We introduce the task of Pre-Editorial Normalization (PEN), which consists in normalizing graphemic ATR output according to editorial conventions, which has the advantage of keeping an intermediate step with palaeographic fidelity while providing a normalized version for practical usability. We present a new dataset derived from the CoMMA corpus and aligned with digitized Old French and Latin editions using passim. We also produce a manually corrected gold-standard evaluation set. We benchmark this resource using ByT5-based sequence-to-sequence models on normalization and pre-annotation tasks. Our contributions include the formal definition of PEN, a 4.66M-sample silver training corpus, a 1.8k-sample gold evaluation set, and a normalization model achieving a 6.7% CER, substantially outperforming previous models for this task.

cs.CL↗

arXiv2025

FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference

Achieving reliable real-time control of tokamak plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate and timely prediction of edge-localized modes (ELMs), especially in high-confinement regimes such as wide-pedestal quiescent H-mode. We present a hardware-accelerated machine learning (ML) inference system integrated into the RTSTAB processing node of the DIII-D real-time diagnostic and control infrastructure. The system uses an AMD/Xilinx KCU1500 FPGA to enable ultra low latency plasma state classification and ELM forecasting. Input features come from real-time Beam Emission Spectroscopy (BES), and the ML model is implemented as a dense neural network using the SLAC Neural Network Library (SNL). A key capability is SNL dynamic parameter loading, which allows on-the-fly updates of neural network weights and biases without hardware resynthesis. This enables multiple classification tasks on a single FPGA design and supports adaptive control strategies that respond to evolving plasma conditions. By decoupling inference from fixed-weight configurations, the system supports continuous model refinement and seamless task switching during live operation. The SNL-based inference engine is fully integrated with the FPGA in the DIII-D RTSTAB Plasma Control System (PCS), improving ELM avoidance, confinement, and operational stability. These results show the feasibility of embedding dynamically reconfigurable FPGA-based ML inference into real-time fusion diagnostic pipelines, providing a scalable and resilient path toward intelligent and autonomous plasma control in future magnetic confinement fusion devices.

physics.plasm-ph↗

arXiv2025

Enhancing DPSGD via Per-Sample Momentum and Low-Pass Filtering

Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to train deep neural networks with formal privacy guarantees. However, the addition of differential privacy (DP) often degrades model accuracy by introducing both noise and bias. Existing techniques typically address only one of these issues, as reducing DP noise can exacerbate clipping bias and vice-versa. In this paper, we propose a novel method, \emph{DP-PMLF}, which integrates per-sample momentum with a low-pass filtering strategy to simultaneously mitigate DP noise and clipping bias. Our approach uses per-sample momentum to smooth gradient estimates prior to clipping, thereby reducing sampling variance. It further employs a post-processing low-pass filter to attenuate high-frequency DP noise without consuming additional privacy budget. We provide a theoretical analysis demonstrating an improved convergence rate under rigorous DP guarantees, and our empirical evaluations reveal that DP-PMLF significantly enhances the privacy-utility trade-off compared to several state-of-the-art DPSGD variants.

cs.LG↗

arXiv2025

Engineering Social Optimality via Utility Shaping in Non-Cooperative Games under Incomplete Information and Imperfect Monitoring

In this paper, we study decentralized decision-making where agents optimize private objectives under incomplete information and imperfect public monitoring, in a non-cooperative setting. By shaping utilities-embedding shadow prices or Karush-Kuhn-Tucker(KKT)-aligned penalties-we make the stage game an exact-potential game whose unique equilibrium equals the (possibly constrained) social optimum. We characterize the Bayesian equilibrium as a stochastic variational inequality; strong monotonicity follows from a single-inflection compressed/stretched-exponential response combined with convex pricing. We give tracking bounds for damped-gradient and best-response-with-hysteresis updates under a noisy public index, and corresponding steady-state error. The framework accommodates discrete and continuous action sets and composes with slower discrete assignment. Deployable rules include: embed prices/penalties; publish a single public index; tune steps, damping, and dual rates for contraction. Computational experiments cover (i) a multi-tier supply chain and (ii) a non-cooperative agentic-AI compute market of bidding bots. Relative to price-only baselines, utility shaping attains near-centralized welfare, eliminates steady-state constraint/capacity violations when feasible, and accelerates convergence; with quantization, discrete equilibria track continuous ones within the mesh. The blueprint is portable to demand response, cloud/edge scheduling, and transportation pricing and biosecurity/agriculture. Overall, utility shaping plus a public index implements the constrained social optimum with stable equilibria under noise and drift-an operations-research-friendly alternative to heavy messaging or full mechanism design.

cs.GT↗

arXiv2025

ShipwreckFinder: A QGIS Tool for Shipwreck Detection in Multibeam Sonar Data

In this paper, we introduce ShipwreckFinder, an open-source QGIS plugin that detects shipwrecks from multibeam sonar data. Shipwrecks are an important historical marker of maritime history, and can be discovered through manual inspection of bathymetric data. However, this is a time-consuming process and often requires expert analysis. Our proposed tool allows users to automatically preprocess bathymetry data, perform deep learning inference, threshold model outputs, and produce either pixel-wise segmentation masks or bounding boxes of predicted shipwrecks. The backbone of this open-source tool is a deep learning model, which is trained on a variety of shipwreck data from the Great Lakes and the coasts of Ireland. Additionally, we employ synthetic data generation in order to increase the size and diversity of our dataset. We demonstrate superior segmentation performance with our open-source tool and training pipeline as compared to a deep learning-based ArcGIS toolkit and a more classical inverse sinkhole detection method. The open-source tool can be found at https://github.com/umfieldrobotics/ShipwreckFinderQGISPlugin.

cs.CV↗

arXiv2025

Federated Learning with Differential Privacy: An Utility-Enhanced Approach

Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with centralized machine learning algorithms. However, recent studies have shown that federated learning alone does not guarantee privacy, as private data may still be inferred from the uploaded parameters to the central server. In order to successfully avoid data leakage, adopting differential privacy (DP) in the local optimization process or in the local update aggregation process has emerged as two feasible ways for achieving sample-level or user-level privacy guarantees respectively, in federated learning models. However, compared to their non-private equivalents, these approaches suffer from a poor utility. To improve the privacy-utility trade-off, we present a modification to these vanilla differentially private algorithms based on a Haar wavelet transformation step and a novel noise injection scheme that significantly lowers the asymptotic bound of the noise variance. We also present a holistic convergence analysis of our proposed algorithm, showing that our method yields better convergence performance than the vanilla DP algorithms. Numerical experiments on real-world datasets demonstrate that our method outperforms existing approaches in model utility while maintaining the same privacy guarantees.

cs.LG↗

arXiv2025

Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning

Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it a promising approach for privacy-preserving machine learning. However, ensuring differential privacy (DP) in FL presents challenges due to the trade-off between model utility and privacy protection. Clipping gradients before aggregation is a common strategy to limit privacy loss, but selecting an optimal clipping norm is non-trivial, as excessively high values compromise privacy, while overly restrictive clipping degrades model performance. In this work, we propose an adaptive clipping mechanism that dynamically adjusts the clipping norm using a multi-objective optimization framework. By integrating privacy and utility considerations into the optimization objective, our approach balances privacy preservation with model accuracy. We theoretically analyze the convergence properties of our method and demonstrate its effectiveness through extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10 datasets. Our results show that adaptive clipping consistently outperforms fixed-clipping baselines, achieving improved accuracy under the same privacy constraints. This work highlights the potential of dynamic clipping strategies to enhance privacy-utility trade-offs in differentially private federated learning.

cs.LG↗