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Researcher profile

David Schneider

David Schneider contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer VisionArtificial Intelligenceastro-ph.SRastro-ph.HEeess.IVMachine LearningMathematical SoftwareProgramming LanguagesRobotics

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Published work

9 published item(s)

preprint2026arXiv

IMPACT-HOI: Supervisory Control for Onset-Anchored Partial HOI Event Construction

We present IMPACT-HOI, a mixed-initiative framework for annotating egocentric procedural video by constructing structured event graphs for Human-Object Interactions (HOI), motivated by the need for high-quality structured supervision for learning robot manipulation from human demonstration. IMPACT-HOI frames this task as the incremental resolution of a partially specified, onset-anchored event state. A trust-calibrated controller selects among direct queries, human-confirmed suggestions, and conservative completions based on empirical annotator behavior and evidence quality. A risk-bounded execution protocol, utilizing atomic rollback, ensures that human-confirmed decisions are preserved against conflicting automated updates. A user study with 9 participants shows a 13.5% reduction in manual annotation actions, a 46.67% event match rate, and zero confirmed-field violations under the studied protocol. The code will be made publicly available at https://github.com/541741106/IMPACT_HOI.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to reactive tools. Each correction is treated as an isolated edit, limiting reuse of information on annotator uncertainty and model reliability. We introduce IMPACT-Scribe, a correction-driven framework for dense labeling that uses each correction to improve future human-machine collaboration. IMPACT-Scribe combines uncertainty-aware boundary scribble supervision, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation. Experiments and a human study show that this closed-loop design improves labeling quality per effort, enhances boundary accuracy, and fosters better human-machine interaction over time. The code will be made publicly available at https://github.com/BanzQians/IMPACT_AS.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization

Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise--a common issue in real-world datasets--has been largely overlooked. Label noise can mislead model optimization, thereby exacerbating the challenges of open-set recognition in novel domains. In this study, we take the first step towards addressing Open-Set Domain Generalization under Noisy Labels (OSDG-NL) by constructing dedicated benchmarks derived from widely used OSDG datasets, including PACS and DigitsDG. We evaluate baseline approaches by integrating techniques from both label denoising and OSDG methodologies, highlighting the limitations of existing strategies in handling label noise effectively. To address these limitations, we propose HyProMeta, a novel framework that integrates hyperbolic category prototypes for label noise-aware meta-learning alongside a learnable new-category agnostic prompt designed to enhance generalization to unseen classes. Our extensive experiments demonstrate the superior performance of HyProMeta compared to state-of-the-art methods across the newly established benchmarks. The source code of this work is released at https://github.com/KPeng9510/HyProMeta.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding

Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly limited body visibility and changing illumination. Multimodal recognition mitigates a number of such issues: prediction outcomes of different sensors complement each other due to different modality-specific strengths and weaknesses. While several late fusion methods have been considered in previously published frameworks, they constantly feature different architecture backbones and building blocks making it very hard to isolate the role of the chosen late fusion strategy itself. This paper presents an empirical evaluation of different paradigms for decision-level late fusion in video-based driver observation. We compare seven different mechanisms for joining the results of single-modal classifiers which have been both popular, (e.g. score averaging) and not yet considered (e.g. rank-level fusion) in the context of driver observation evaluating them based on different criteria and benchmark settings. This is the first systematic study of strategies for fusing outcomes of multimodal predictors inside the vehicles, conducted with the goal to provide guidance for fusion scheme selection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Discovery of a double detonation thermonuclear supernova progenitor

We present the discovery of a new double detonation progenitor system consisting of a hot subdwarf B (sdB) binary with a white dwarf companion with an P=76.34179(2) min orbital period. Spectroscopic observations are consistent with an sdB star during helium core burning residing on the extreme horizontal branch. Chimera light curves are dominated by ellipsoidal deformation of the sdB star and a weak eclipse of the companion white dwarf. Combining spectroscopic and light curve fits we find a low mass sdB star, $M_{\rm sdB}=0.383\pm0.028$ M$_\odot$ with a massive white dwarf companion, $M_{\rm WD}=0.725\pm0.026$ M$_\odot$. From the eclipses we find a blackbody temperature for the white dwarf of 26,800 K resulting in a cooling age of $\approx$25 Myrs whereas our MESA model predicts an sdB age of $\approx$170 Myrs. We conclude that the sdB formed first through stable mass transfer followed by a common envelope which led to the formation of the white dwarf companion $\approx$25 Myrs ago. Using the MESA stellar evolutionary code we find that the sdB star will start mass transfer in $\approx$6 Myrs and in $\approx$60 Myrs the white dwarf will reach a total mass of $0.92$ M$_\odot$ with a thick helium layer of $0.17$ M$_\odot$. This will lead to a detonation that will likely destroy the white dwarf in a peculiar thermonuclear supernova. PTF1 2238+7430 is only the second confirmed candidate for a double detonation thermonuclear supernova. Using both systems we estimate that at least $\approx$1% of white dwarf thermonuclear supernovae originate from sdB+WD binaries with thick helium layers, consistent with the small number of observed peculiar thermonuclear explosions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates

Driver observation models are rarely deployed under perfect conditions. In practice, illumination, camera placement and type differ from the ones present during training and unforeseen behaviours may occur at any time. While observing the human behind the steering wheel leads to more intuitive human-vehicle-interaction and safer driving, it requires recognition algorithms which do not only predict the correct driver state, but also determine their prediction quality through realistic and interpretable confidence measures. Reliable uncertainty estimates are crucial for building trust and are a serious obstacle for deploying activity recognition networks in real driving systems. In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality. To correct this misalignment between the confidence values and the actual uncertainty, we consider two strategies. First, we enhance two activity recognition models often used for driver observation with temperature scaling-an off-the-shelf method for confidence calibration in image classification. Then, we introduce Calibrated Action Recognition with Input Guidance (CARING)-a novel approach leveraging an additional neural network to learn scaling the confidences depending on the video representation. Extensive experiments on the Drive&Act dataset demonstrate that both strategies drastically improve the quality of model confidences, while our CARING model out-performs both, the original architectures and their temperature scaling enhancement, leading to best uncertainty estimates.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

preCICE v2: A Sustainable and User-Friendly Coupling Library

preCICE is a free/open-source coupling library. It enables creating partitioned multi-physics simulations by gluing together separate software packages. This paper summarizes the development efforts in preCICE of the past five years. During this time span, we have turned the software from a working prototype -- sophisticated numerical coupling methods and scalability on ten thousands of compute cores -- to a sustainable and user-friendly software project with a steadily-growing community. Today, we know through forum discussions, conferences, workshops, and publications of more than 100 research groups using preCICE. We cover the fundamentals of the software alongside a performance and accuracy analysis of different data mapping methods. Afterwards, we describe ready-to-use integration with widely-used external simulation software packages, tests and continuous integration from unit to system level, and community building measures, drawing an overview of the current preCICE ecosystem.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A new class of Roche lobe-filling hot subdwarf binaries

We present the discovery of the second binary with a Roche lobe-filling hot subdwarf transferring mass to a white dwarf (WD) companion. This 56 minute binary was discovered using data from the Zwicky Transient Facility. Spectroscopic observations reveal an He-sdOB star with an effective temperature of $T_{\rm eff}=33,700\pm1000$ K and a surface gravity of $log(g)=5.54\pm0.11$. The GTC+HiPERCAM light curve is dominated by the ellipsoidal deformation of the He-sdOB star and shows an eclipse of the He-sdOB by an accretion disk as well as a weak eclipse of the WD. We infer a He-sdOB mass of $M_{\rm sdOB}=0.41\pm0.04$ M$_\odot$ and a WD mass of $M_{\rm WD}=0.68\pm0.05$ M$_\odot$. The weak eclipses imply a WD black-body temperature of $63,000\pm10,000$ K and a radius $R_{\rm WD}=0.0148\pm0.0020$ M$_\odot$ as expected for a WD of such high temperature. The He-sdOB star is likely undergoing hydrogen shell burning and will continue transferring mass for $\approx1$ Myrs at a rate of $10^{-9} M_\odot {\rm yr}^{-1}$ which is consistent with the high WD temperature. The hot subdwarf will then turn into a WD and the system will merge in $\approx30$ Myrs. We suggest that Galactic reddening could bias discoveries towards preferentially finding Roche lobe-filling systems during the short-lived shell burning phase. Studies using reddening corrected samples should reveal a large population of helium core-burning hot subdwarfs with $T_{\rm eff}\approx25,000$ K in binaries of 60-90 minutes with WDs. Though not yet in contact, these binaries would eventually come into contact through gravitational wave emission and explode as a sub-luminous thermonuclear supernova or evolve into a massive single WD.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Performance of Bytecode Interpreters in Prolog

The semantics and the recursive execution model of Prolog make it very natural to express language interpreters in form of AST (Abstract Syntax Tree) interpreters where the execution follows the tree representation of a program. An alternative implementation technique is that of bytecode interpreters. These interpreters transform the program into a compact and linear representation before evaluating it and are generally considered to be faster and to make better use of resources. In this paper, we discuss different ways to express the control flow of interpreters in Prolog and present several implementations of AST and bytecode interpreters. On a simple language designed for this purpose, we evaluate whether techniques best known from imperative languages are applicable in Prolog and how well they perform. Our ultimate goal is to assess which interpreter design in Prolog is the most efficient, as we intend to apply these results to a more complex language. However, we believe the analysis in this paper to be of more general interest.

0 citations0 reviewsNo code linkedOpen access