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Scanning the sharp metal tip of a scanning tunneling microscope (STM) over a molecule allows tuning the coupling between the tip plasmon and a molecular fluorescence emitter. This allows access to local variations of fluorescence field enhancement and wavelength shifts, which are central parameters for characterizing the plasmon-exciton coupling. Performing the same for phosphorescence with molecular scale resolution remains a significant challenge. In this study, we present the first investigation of phosphorescence from isolated Pt-Phthalocyanine molecules by analyzing tip-enhanced emission spectra in both current-induced and laser-induced phosphorescence. The latter directly monitors singlet-to-triplet state intersystem crossing of a molecule below the tip. The study paves the way to a detailed understanding of triplet excitation pathways and their potential control at sub-molecular length scales. Additionally, the coupling of organic phosphors to plasmonic structures is a promising route for the improving light-emitting diodes., Comment: 39 pages, 12 figures

Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.

The Plug-and-Charge (PnC) process defined by ISO 15118 standardizes automated Electric Vehicle (EV) charging by enabling automatic installation of credentials and use for authentication between EV and Charge Point (CP). However, the current credential installation process is non-uniform, relies on a complex Public Key Infrastructure (PKI), lacks support for fine-grained authorization parameters, and is not very user-friendly. In this paper, we propose a streamlined approach to the initial charging authorization process by leveraging the OAuth Device Authorization Grant and Rich Authorization Requests. The proposed solution reduces technical complexity, simplifies credential installation, introduces flexible authorization constraints (e.g., time- and cost-based), and facilitates payment through OpenID Connect (OIDC). We present a proof-of-concept implementation along with performance evaluations and conduct a symbolic protocol verification using the Tamarin prover. Furthermore, our approach solves the issue of OAuth's cross-device authorization, making it suitable as a formally proven blueprint in contexts beyond EV charging.

Measuring the rheology of liquids typically requires precise control over shear rates and stresses. However, we demonstrate that the features of a power-law fluid can be predicted by simply observing the capillary spreading dynamics of viscous droplets within a wedge-shaped geometry. By considering the influence of capillary and viscous forces within this geometry, we show that the spreading dynamics can be described by a nonlinear diffusion equation. Analytical predictions indicate subdiffusive behavior, establishing a direct relationship between the diffusion exponent and the rheological exponent, which is also corroborated by experimental results. Since this relationship is independent of flow details, it provides robust predictions for the rheological properties of power-law fluids.

In cybersecurity, Intrusion Detection Systems (IDS) serve as a vital defensive layer against adversarial threats. Accurate benchmarking is critical to evaluate and improve IDS effectiveness, yet traditional methodologies face limitations due to their reliance on previously known attack signatures and lack of creativity of automated tests. This paper introduces a novel approach to evaluating IDS through Capture the Flag (CTF) events, specifically designed to uncover weaknesses within IDS. CTFs, known for engaging a diverse community in tackling complex security challenges, offer a dynamic platform for this purpose. Our research investigates the effectiveness of using tailored CTF challenges to identify weaknesses in IDS by integrating them into live CTF competitions. This approach leverages the creativity and technical skills of the CTF community, enhancing both the benchmarking process and the participants' practical security skills. We present a methodology that supports the development of IDS-specific challenges, a scoring system that fosters learning and engagement, and the insights of running such a challenge in a real Jeopardy-style CTF event. Our findings highlight the potential of CTFs as a tool for IDS evaluation, demonstrating the ability to effectively expose vulnerabilities while also providing insights into necessary improvements for future implementations.

Models trained on crowdsourced labels may not reflect broader population views when annotator pools are not representative. Since collecting representative labels is challenging, we propose Population-Aligned Instance Replication (PAIR), a method to address this bias through statistical adjustment. Using a simulation study of hate speech and offensive language detection, we create two types of annotators with different labeling tendencies and generate datasets with varying proportions of the types. Models trained on unbalanced annotator pools show poor calibration compared to those trained on representative data. However, PAIR, which duplicates labels from underrepresented annotator groups to match population proportions, significantly reduces bias without requiring new data collection. These results suggest statistical techniques from survey research can help align model training with target populations even when representative annotator pools are unavailable. We conclude with three practical recommendations for improving training data quality.

A calorimetric detector for minimally disruptive measurements of atomic hydrogen beams is described. The calorimeter measures heat released by the recombination of hydrogen atoms into molecules on a thin wire. As a demonstration, the angular distribution of a beam with a peak intensity of $\approx 10^{16} \,{\rm{atoms}}/{(\rm{cm}^2 \rm{s})}$ is measured by translating the wire across the beam. The data agree well with an analytic model of the beam from the thermal hydrogen atom source. Using the beam shape model, the relative intensity of the beam can be determined to 5% precision or better at any angle.

Fock states of the quantum harmonic oscillator are fundamental to quantum sensing and information processing, serving as key resources for exploiting bosonic degrees of freedom. Here, we prepare high Fock states in a high-overtone bulk acoustic wave resonator (HBAR) by coupling it to a superconducting qubit and applying microwave pulses designed using quantum optimal control. We characterize the experimentally realized states by employing a criterion for genuine quantum non-Gaussianity (QNG) designed to reveal multiphonon contributions. Although energy relaxation and decoherence limit the achievable fidelities, we demonstrate genuine QNG features compatible with Fock state $\vert 6\rangle$, confirming that the prepared states cannot be generated through Gaussian operations on states with up to Fock state $\vert 5\rangle$ contributions. We further investigate the robustness of these QNG features to losses and their utility in sensing displacement amplitudes. In particular, we introduce a hierarchy based on the quantum Fisher information and show that, despite decoherence and measurement imperfections, the prepared states achieve a displacement sensitivity surpassing that of an ideal Fock state $\vert 3\rangle$. Our results have immediate applications in quantum sensing and simulations with HBAR devices.

Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways in which people differ. Here, we propose a lightweight modification to the standard language model transformer architecture - "PsychAdapter" - that uses empirically derived trait-language patterns to generate natural language for specified personality, demographic, and mental health characteristics (with or without prompting). We applied PsychAdapters to modify OpenAI's GPT-2, Google's Gemma, and Meta's Llama 3 and found generated text to reflect the desired traits. For example, expert raters evaluated PsychAdapter's generated text output and found it matched intended trait levels with 87.3% average accuracy for Big Five personalities, and 96.7% for depression and life satisfaction. PsychAdapter is a novel method to introduce psychological behavior patterns into language models at the foundation level, independent of prompting, by influencing every transformer layer. This approach can create chatbots with specific personality profiles, clinical training tools that mirror language associated with psychological conditionals, and machine translations that match an authors reading or education level without taking up LLM context windows. PsychAdapter also allows for the exploration psychological constructs through natural language expression, extending the natural language processing toolkit to study human psychology.

The degrees of freedom of Active Flux are cell averages and point values along the cell boundaries. These latter are shared between neighbouring cells, which gives rise to a globally continuous reconstruction. The semi-discrete Active Flux method uses its degrees of freedom to obtain Finite Difference approximations to the spatial derivatives which are used in the point value update. The averages are updated using a quadrature of the flux and making use of the point values as quadrature points. The integration in time employs standard Runge-Kutta methods. We show that this generalization of the Active Flux method in two and three spatial dimensions is stationarity preserving for linear acoustics on Cartesian grids, and present an analysis of numerical diffusion and stability.

Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing on relationships reflecting causality. In this paper, we improve on the integration of domain knowledge in the analysis of urban blight by introducing four rules for effective modeling of causal domain knowledge. The findings of this study reveal significant deviation from causal modeling guidelines by investigating cognitive maps developed for urban blight analysis. These findings provide valuable insights that will inform future work on urban blight, ultimately enhancing our understanding of urban blight complex interactions., Comment: 16 pages, 4 figures, 2 tables

The rapid advancement of large language models (LLMs) has made it increasingly difficult to distinguish between text written by humans and machines. While watermarks already exist for LLMs, they often lack flexibility, and struggle with attacks such as paraphrasing. To address these issues, we propose a multi-feature method for generating watermarks that combines multiple distinct watermark features into an ensemble watermark. Concretely, we combine acrostica and sensorimotor norms with the established red-green watermark to achieve a 98% detection rate. After a paraphrasing attack the performance remains high with 95% detection rate. The red-green feature alone as baseline achieves a detection rate of 49%. The evaluation of all feature combinations reveals that the ensemble of all three consistently has the highest detection rate across several LLMs and watermark strength settings. Due to the flexibility of combining features in the ensemble, various requirements and trade-offs can be addressed. Additionally, for all ensemble configurations the same detection function can be used without adaptations. This method is particularly of interest to facilitate accountability and prevent societal harm., Comment: 9 pages in the main body. Code is available at http://github.com/CommodoreEU/master-generation. arXiv admin note: substantial text overlap with arXiv:2405.08400

High precision differential Astrometry is the branch of astronomy that evaluates the relative position, distance and motion of celestial objects with respect to the stars present in the field of view. A mission called Theia has been submitted in 2022 for ESA's M7 call for missions, using a diffraction-limited telescope about 1m in diameter and with a field of view of 0.5 degrees, capable of achieving sub-micro-arcsecond angular accuracy, corresponding to 1e-5 pixel on the detector. Such precision makes it possible to study the nature of dark matter in our galaxy and to reveal the architecture of exoplanetary systems close to the Sun, down to the mass of the Earth. The aim of the experimental tests presented in this poster is to improve the TRL of 2 specific aspects: the calibration of new CMOS detectors with very large number of pixels and the calibration of the telescope aberrations.First, a key element of such a space telescope is the focal plane, which must be calibrated spatially with an extreme precision down to the 1e-5 pixel level. Previous work has shown that this is possible with small detector matrices (80x80 px) [1]. The goal is now to check the performances and validate this method with the new very large detectors. Pyxalis, a company based near Grenoble, is developing very large detectors (8000x5000 px) that have a low noise level and high sensitivity. The aim is to characterize and validate this type of detectors in a laboratory demonstration (see poster Pancher et al.), to ensure that the performance achieved meets the required specifications. We present the results of these characterization in this contribution.The telescope stability is also a sensitive issue. Recent work [2] has shown that the reference stars in the field of the telescope can be used as actual metrology sources in order to compute the field distortion function. Our simulations allow to model the optical aberrations with bivariate polynoms. The effects on the calibration accuracy of the degrees of the polynoms, the number of reference stars and the tilt perturbation of the M2 mirror are investigated. This poster will present the latest results obtained on a test bed developed to experimentally study the performances of this new field calibration method.

In this paper, we consider the process of transforming causal domain knowledge into a representation that aligns more closely with guidelines from causal data science. To this end, we introduce two novel tasks related to distilling causal domain knowledge into causal variables and detecting interaction entities using LLMs. We have determined that contemporary LLMs are helpful tools for conducting causal modeling tasks in collaboration with human experts, as they can provide a wider perspective. Specifically, LLMs, such as GPT-4-turbo and Llama3-70b, perform better in distilling causal domain knowledge into causal variables compared to sparse expert models, such as Mixtral-8x22b. On the contrary, sparse expert models such as Mixtral-8x22b stand out as the most effective in identifying interaction entities. Finally, we highlight the dependency between the domain where the entities are generated and the performance of the chosen LLM for causal modeling., Comment: 13 pages, 6 figutrd, 4 tabels

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures; Replaced to match accepted ApJ version. New version contains small editorial changes throughout in response to referee comments, no changes to results

The extraction of causal information from textual data is crucial in the industry for identifying and mitigating potential failures, enhancing process efficiency, prompting quality improvements, and addressing various operational challenges. This paper presents a study on the development of automated methods for causal information extraction from actual industrial documents in the semiconductor manufacturing industry. The study proposes two types of causal information extraction methods, single-stage sequence tagging (SST) and multi-stage sequence tagging (MST), and evaluates their performance using existing documents from a semiconductor manufacturing company, including presentation slides and FMEA (Failure Mode and Effects Analysis) documents. The study also investigates the effect of representation learning on downstream tasks. The presented case study showcases that the proposed MST methods for extracting causal information from industrial documents are suitable for practical applications, especially for semi structured documents such as FMEAs, with a 93\% F1 score. Additionally, MST achieves a 73\% F1 score on texts extracted from presentation slides. Finally, the study highlights the importance of choosing a language model that is more aligned with the domain and in-domain fine-tuning., Comment: 17 pages, 2 figures

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: 1) human agency and oversight, 2) fairness and non-discrimination, 3) transparency and explainability, 4) robustness and accuracy, 5) privacy and security, and 6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to 1) interdisciplinary research, 2) conceptual clarity, 3) context-dependency, 4) dynamics in evolving systems, and 5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions., Comment: Accepted in Frontiers in Big Data and AI, Research Topic: Towards Fair AI for Trustworthy Artificial Intelligence

We show that there exists an algorithm that takes as input two closed, simply connected, topological 4-manifolds and decides whether or not these 4-manifolds are homeomorphic. In particular, we explain in detail how closed, simply connected, topological 4-manifolds can be naturally represented by a Kirby diagram consisting only of 2-handles. This representation is used as input for our algorithm. Along the way, we develop an algorithm to compute the Kirby-Siebenmann invariant of a closed, simply connected, topological 4-manifold from any of its Kirby diagrams and describe an algorithm that decides whether or not two intersection forms are isometric. In a slightly different direction, we discuss the decidability of the stable classification of smooth manifolds with more general fundamental groups. Here we show that there exists an algorithm that takes as input two closed, oriented, smooth 4-manifolds with fundamental groups isomorphic to a finite group with cyclic Sylow 2-subgroup, an infinite cyclic group, or a group of geometric dimension at most 3 (in the latter case we additionally assume that the universal covers of both 4-manifolds are not spin), and decides whether or not these two 4-manifolds are orientation-preserving stably diffeomorphic., Comment: 24 pages, 1 Figure

Probing and manipulating the spatiotemporal dynamics of hot carriers in nanoscale metals is crucial to a plethora of applications ranging from nonlinear nanophotonics to single molecule photochemistry. The direct investigation of these highly non-equilibrium carriers requires the experimental capability of high energy resolution (~ meV) broadband femtosecond spectroscopy. When considering the ultimate limits of atomic scale structures, this capability has remained out of reach until date. Using a two color femtosecond pump-probe spectroscopy, we present here the real-time tracking of hot carrier dynamics in a well-defined plasmonic picocavity, formed in the tunnel junction of a scanning tunneling microscope (STM). The excitation of hot carriers in the picocavity enables ultrafast all optical control over the broadband (~ eV) anti Stokes electronic resonance Raman scattering (ERRS) and the four-wave mixing (FWM) signals generated at the atomic length scale. By mapping the ERRS and FWM signals from a single graphene nanoribbon (GNR), we demonstrate that both signals are more efficiently generated along the edges of the GNR: a manifestation of atomic-scale nonlinear optical microscopy. This demonstration paves the way to the development of novel ultrafast nonlinear picophotonic platforms, affording unique opportunities in a variety of contexts, from the direct investigation of non equilibrium light matter interactions in complex quantum materials, to the development of robust strategies for hot carriers harvesting in single molecules and the next generation of active metasurfaces with deep-sub-wavelength meta-atoms.

The advent of next-generation radio interferometers like the Square Kilometer Array promises to revolutionise our radio astronomy observational capabilities. The unprecedented volume of data these devices generate requires fast and accurate image reconstruction algorithms to solve the ill-posed radio interferometric imaging problem. Most state-of-the-art reconstruction methods lack trustworthy and scalable uncertainty quantification, which is critical for the rigorous scientific interpretation of radio observations. We propose an unsupervised technique based on a conformalized version of a radio-augmented equivariant bootstrapping method, which allows us to quantify uncertainties for fast reconstruction methods. Noticeably, we rely on reconstructions from ultra-fast unrolled algorithms. The proposed method brings more reliable uncertainty estimations to our problem than existing alternatives., Comment: 14 pages, 7 figures. Accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2024

High precision differential astrometry assesses the positions, distances, and motions of celestial objects in relation to the stars. The focal plane of such space telescope must be calibrated with a precision down to the level of 1e-5 pixel in order to be able to detect Earth-like planets in the close vicinity of the Sun. The presented characterization bench is designed to improve the technology readiness level for the following key points: calibration of new detectors with a high number of pixels and correcting the field distortion using stars in the field of view. The first aim of the project concentrates on the characterization of a 46 megapixels sensor from PYXALIS, to assess its typical parameters using an integrating sphere. The next objective intends to map the intra and extra pixel quantum yield of the detector with a precision of 1e-5 pixels and investigate the evolution of the pixel geometry in response to environment fluctuations. To conduct these tests, an optical bench is designed with an LCD screen and a doublet, used as a source that allows directing light to specific groups of pixels. Interferometric calibration of the detector pixel centroid position will be achieved using fibers that illuminate the detector with Young's fringes. To characterize the distortion of the detector, a diaphragm will produce adjustable optical aberrations to be corrected and therefore change the source sensor positional relationship. The final step involves the simulation of a star's field, which will be imaged on the detector to assess optical quality., Comment: 5 pages, 4 figures, conference "ICSO 2024"

When validated neural networks (NNs) are pruned (and retrained) before deployment, it is desirable to prove that the new NN behaves equivalently to the (original) reference NN. To this end, our paper revisits the idea of differential verification which performs reasoning on differences between NNs: On the one hand, our paper proposes a novel abstract domain for differential verification admitting more efficient reasoning about equivalence. On the other hand, we investigate empirically and theoretically which equivalence properties are (not) efficiently solved using differential reasoning. Based on the gained insights, and following a recent line of work on confidence-based verification, we propose a novel equivalence property that is amenable to Differential Verification while providing guarantees for large parts of the input space instead of small-scale guarantees constructed w.r.t. predetermined input points. We implement our approach in a new tool called VeryDiff and perform an extensive evaluation on numerous old and new benchmark families, including new pruned NNs for particle jet classification in the context of CERN's LHC where we observe median speedups >300x over the State-of-the-Art verifier alpha,beta-CROWN., Comment: Accepted at TACAS 2025, 31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems; 47 pages (main paper has 16 pages); 8 figures