arXiv subjects

Patrick Kramer

Publications and source records attributed to Patrick Kramer.

8 recordsLinked to original sources

arXiv2026

Causal Inference with High-Dimensional Treatments

In this work, we consider causal inference in various high-dimensional treatment settings, including for single multi-valued treatments and vector treatments with binary or continuous components, when the number of treatments can be comparable to or even larger than the number of observations. These settings bring unique challenges: first, the treatment effects of interest are represented by a high-dimensional vector rather than a scalar; second, positivity violations are often unavoidable; and third, estimation can be based on a smaller effective sample size. We first discuss fundamental limits of estimating effects here, showing that consistent estimation is impossible without further assumptions. We go on to propose novel doubly robust estimators for mean potential outcomes of a high-dimensional single multi-valued treatment. We analyze the proposed estimators under sparsity assumptions, giving finite-sample risk bounds and showing that consistent estimation is possible under these conditions. Moreover, we derive minimax lower bounds in a sparse and structure-agnostic model to characterize optimal rates of convergence and show our risk bounds are unimprovable. We then generalize our proposed estimators as a sparse pseudo-outcome regression framework with constrained regression estimators and error guarantees under sparsity, allowing estimation of generic functionals and different types of high-dimensional treatments. We apply the framework to derive estimators of the mean potential outcomes for high-dimensional vector treatments. Finally, we illustrate the proposed methods through a simulation and an empirical application.

math.ST↗

arXiv2025

Dynamics of a jointly commensurate moir\'e charge density wave

The advent of two-dimensional moir\'e systems has revolutionized the exploration of phenomena arising from strong correlations and nontrivial band topology. Recently, a moir\'e superstructure formed by two coexisting charge density waves (CDWs) with slightly mismatched wavevectors has been realized. These incommensurate CDWs can collectively exhibit commensurability, resulting in the jointly commensurate CDW (JC-CDW) and establishing a new paradigm for controlling moir\'e potential and periodicity. Achieving such functionality, however, hinges on a key open question: how do the amplitude, phase coherence, and periodicity of this order respond to external perturbations? Here, we address this question using a suite of time- and momentum-resolved diffraction and spectroscopic techniques to probe light-induced CDW dynamics in EuTe$_4$. Our time-resolved diffraction measurements distinguish the instantaneous quenching of the JC-CDW amplitude, as verified by time-resolved photoemission spectroscopy, from the much slower evolution of phase fluctuations. Furthermore, while the JC-CDW wavevector remains locked along the CDW direction upon photoexcitation, indicating a preserved moir\'e periodicity, the correlation length of JC-CDW shows an exclusive reduction perpendicular to its wavevector, unveiling the formation of previously unexplored shear-type defects. Together, this multimodal methodology reconstructs the spatiotemporal evolution of the JC-CDW upon excitation. These findings not only highlight the remarkable robustness of JC-CDWs out of equilibrium, but also provide insight into optical manipulation and engineering of moir\'e quantum materials through defect control.

cond-mat.str-el↗

arXiv2025

Explainable AI for Automated User-specific Feedback in Surgical Skill Acquisition

Traditional surgical skill acquisition relies heavily on expert feedback, yet direct access is limited by faculty availability and variability in subjective assessments. While trainees can practice independently, the lack of personalized, objective, and quantitative feedback reduces the effectiveness of self-directed learning. Recent advances in computer vision and machine learning have enabled automated surgical skill assessment, demonstrating the feasibility of automatic competency evaluation. However, it is unclear whether such Artificial Intelligence (AI)-driven feedback can contribute to skill acquisition. Here, we examine the effectiveness of explainable AI (XAI)-generated feedback in surgical training through a human-AI study. We create a simulation-based training framework that utilizes XAI to analyze videos and extract surgical skill proxies related to primitive actions. Our intervention provides automated, user-specific feedback by comparing trainee performance to expert benchmarks and highlighting deviations from optimal execution through understandable proxies for actionable guidance. In a prospective user study with medical students, we compare the impact of XAI-guided feedback against traditional video-based coaching on task outcomes, cognitive load, and trainees' perceptions of AI-assisted learning. Results showed improved cognitive load and confidence post-intervention. While no differences emerged between the two feedback types in reducing performance gaps or practice adjustments, trends in the XAI group revealed desirable effects where participants more closely mimicked expert practice. This work encourages the study of explainable AI in surgical education and the development of data-driven, adaptive feedback mechanisms that could transform learning experiences and competency assessment.

cs.HC↗

arXiv2025

Terahertz-field activation of polar skyrons

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discovered subterahertz collective modes, dubbed 'skyrons', which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. Momentum-resolved time-domain measurements of diffuse scattering revealed an avoided crossing in the dispersion relation of skyrons. We further demonstrated that the amplitude and dispersion of skyrons can be controlled by sample temperature and electric-field bias. Atomistic simulations and dynamical phase-field modeling provided microscopic insights into the three-dimensional crystallographic and polarization dynamics. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

cond-mat.mtrl-sci↗

arXiv2024

Enhancing Sentinel-2 Image Resolution: Evaluating Advanced Techniques based on Convolutional and Generative Neural Networks

This paper investigates the enhancement of spatial resolution in Sentinel-2 bands that contain spectral information using advanced super-resolution techniques by a factor of 2. State-of-the-art CNN models are compared with enhanced GAN approaches in terms of quality and feasibility. Therefore, a representative dataset comprising Sentinel-2 low-resolution images and corresponding high-resolution aerial orthophotos is required. Literature study reveals no feasible dataset for the land type of interest (forests), for which reason an adequate dataset had to be generated in addition, accounting for accurate alignment and image source optimization. The results reveal that while CNN-based approaches produce satisfactory outcomes, they tend to yield blurry images. In contrast, GAN-based models not only provide clear and detailed images, but also demonstrate superior performance in terms of quantitative assessment, underlying the potential of the framework beyond the specific land type investigated.

eess.IV↗

arXiv2024

Multi-Objective Bayesian Active Learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

physics.acc-ph↗

arXiv2024

Observation of polarization density waves in SrTiO3

The nature of the "failed" ferroelectric transition in SrTiO3 has been a long-standing puzzle in condensed matter physics. A compelling explanation is the competition between ferroelectricity and an instability with a mesoscopic modulation of the polarization. These polarization density waves, which should become especially strong near the quantum critical point, break local inversion symmetry and are difficult to probe with conventional x-ray scattering methods. Here we combine a femtosecond x-ray free electron laser (XFEL) with THz coherent control methods to probe inversion symmetry breaking at finite momenta and visualize the instability of the polarization on nanometer lengthscales in SrTiO3. We find polar-acoustic collective modes that are soft particularly at the tens of nanometer lengthscale. These precursor collective excitations provide evidence for the conjectured mesoscopic modulated phase in SrTiO3.

cond-mat.mtrl-sci↗

arXiv2023

Estimating Average Treatment Effects in Regression Discontinuity Designs with Covariates under Minimal Assumptions

We study regression discontinuity designs with the use of additional covariates for estimation of the average treatment effect. We provide a detailed proof of asymptotic normality of the covariate-adjusted estimator under minimal assumptions, which may serve as an accessible text to the mathematics behind regression discontinuity with covariates. In addition, this proof carries at least three benefits. First of all, it allows to draw straightforward consequences concerning the impact of the covariates on the bias and variance of the estimator. In fact, we can provide conditions under which the influence of the covariates on the bias vanishes. Moreover, we show that the variance in the covariate-adjusted case is never worse than in the case of the baseline estimator under a very general invertibility condition. Finally, our approach does not require the existence of potential outcomes, allowing for a sensitivity analysis in case confounding cannot be ruled out, e.g., by a manipulated forcing variable.

math.ST↗