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Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system's decisions, such as domain experts and decision subjects. To address this, we present the "XAI Novice Question Bank," an extension of the XAI Question Bank containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task-based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants' confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants' prior perceptions of the systems' risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system's deployment, while those who perceived low risks rather asked about the system's operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences., Comment: Main text: 26 pages, 4 figures. Supplementary material is provided

In this report we explore the application of the Lagrange-Newton method to the SAM (smoothing-and-mapping) problem in mobile robotics. In Lagrange-Newton SAM, the angular component of each pose vector is expressed by orientation vectors and treated through Lagrange constraints. This is different from the typical Gauss-Newton approach where variations need to be mapped back and forth between Euclidean space and a manifold suitable for rotational components. We derive equations for five different types of measurements between robot poses: translation, distance, and rotation from odometry in the plane, as well as home-vector angle and compass angle from visual homing. We demonstrate the feasibility of the Lagrange-Newton approach for a simple example related to a cleaning robot scenario.

Interpretability tools that offer explanations in the form of a dialogue have demonstrated their efficacy in enhancing users' understanding (Slack et al., 2023; Shen et al., 2023), as one-off explanations may fall short in providing sufficient information to the user. Current solutions for dialogue-based explanations, however, often require external tools and modules and are not easily transferable to tasks they were not designed for. With LLMCheckup, we present an easily accessible tool that allows users to chat with any state-of-the-art large language model (LLM) about its behavior. We enable LLMs to generate explanations and perform user intent recognition without fine-tuning, by connecting them with a broad spectrum of Explainable AI (XAI) methods, including white-box explainability tools such as feature attributions, and self-explanations (e.g., for rationale generation). LLM-based (self-)explanations are presented as an interactive dialogue that supports follow-up questions and generates suggestions. LLMCheckupprovides tutorials for operations available in the system, catering to individuals with varying levels of expertise in XAI and supporting multiple input modalities. We introduce a new parsing strategy that substantially enhances the user intent recognition accuracy of the LLM. Finally, we showcase LLMCheckup for the tasks of fact checking and commonsense question answering., Comment: Accepted to NAACL 2024 HCI+NLP workshop; camera-ready version

The rise of serverless introduced a new class of scalable, elastic and highly available parallel workers in the cloud. Many systems and applications benefit from offloading computations and parallel tasks to dynamically allocated resources. However, the developers of C++ applications found it difficult to integrate functions due to complex deployment, lack of compatibility between client and cloud environments, and loosely typed input and output data. To enable single-source and efficient serverless acceleration in C++, we introduce Cppless, an end-to-end framework for implementing serverless functions which handles the creation, deployment, and invocation of functions. Cppless is built on top of LLVM and requires only two compiler extensions to automatically extract C++ function objects and deploy them to the cloud. We demonstrate that offloading parallel computations from a C++ application to serverless workers can provide up to 30x speedup, requiring only minor code modifications and costing less than one cent per computation.

In the search for scalable, fault-tolerant quantum computing, distributed quantum computers are promising candidates. These systems can be realized in large-scale quantum networks or condensed onto a single chip with closely situated nodes. We present a framework for numerical simulations of a memory channel using the distributed toric surface code, where each data qubit of the code is part of a separate node, and the error-detection performance depends on the quality of four-qubit Greenberger-Horne-Zeilinger (GHZ) states generated between the nodes. We quantitatively investigate the effect of memory decoherence and evaluate the advantage of GHZ creation protocols tailored to the level of decoherence. We do this by applying our framework for the particular case of color centers in diamond, employing models developed from experimental characterization of nitrogen-vacancy centers. For diamond color centers, coherence times during entanglement generation are orders of magnitude lower than coherence times of idling qubits. These coherence times represent a limiting factor for applications, but previous surface code simulations did not treat them as such. Introducing limiting coherence times as a prominent noise factor makes it imperative to integrate realistic operation times into simulations and incorporate strategies for operation scheduling. Our model predicts error probability thresholds for gate and measurement reduced by at least a factor of three compared to prior work with more idealized noise models. We also find a threshold of $4\cdot10^2$ in the ratio between the entanglement generation and the decoherence rates, setting a benchmark for experimental progress., Comment: 24 pages, 13 figures

We present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.10$< z <$1.13, which is the largest sample of supernovae from any single survey and increases the number of known $z>0.5$ supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are $\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=$0.017 in a flat $\Lambda$CDM model, and $(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=$(0.082, 0.152) in a flat $w$CDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time., Comment: 39 pages, 19 figures; Submitted to ApJ; companion paper Dark Energy Collaboration et al. on consecutive arxiv number 2401.02929

We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SNe in the redshift range $0.100.5$ SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning $0.025

We investigate the phases and phase transitions of the disordered Haldane model in the presence of on-site disorder. We use the real-space Chern marker and transfer matrices to extract critical exponents over a broad range of parameters. The disorder-driven transitions are consistent with the plateau transitions in the Integer Quantum Hall Effect (IQHE), in conformity with recent simulations of disordered Dirac fermions. Our numerical findings are compatible with an additional line of mass-driven transitions with a continuously varying correlation length exponent. The values interpolate between free Dirac fermions and the IQHE with increasing disorder strength. We also show that the fluctuations of the Chern marker exhibit a power-law divergence in the vicinity of both sets of transitions, yielding another varying exponent. We discuss the interpretation of these results., Comment: 23 pages, 48 figures

Because of their high photon flux, X-ray free-electron lasers (FEL) allow to resolve the structure of individual nanoparticles via coherent diffractive imaging (CDI) within a single X-ray pulse. Since the inevitable rapid destruction of the sample limits the achievable resolution, a thorough understanding of the spatiotemporal evolution of matter on the nanoscale following the irradiation is crucial. We present a technique to track X-ray induced structural changes in time and space by recording two consecutive diffraction patterns of the same single, free-flying nanoparticle, acquired separately on two large-area detectors opposite to each other, thus examining both the initial and evolved particle structure. We demonstrate the method at the extreme ultraviolet (XUV) and soft X-ray Free-electron LASer in Hamburg (FLASH), investigating xenon clusters as model systems. By splitting a single XUV pulse, two diffraction patterns from the same particle can be obtained. For focus intensities of about $2\cdot10^{12}\,\text{W/cm}^2$ we observe still largely intact clusters even at the longest delays of up to 650 picoseconds of the second pulse, indicating that in the highly absorbing systems the damage remains confined to one side of the cluster. Instead, in case of five times higher flux, the diffraction patterns show clear signatures of disintegration, namely increased diameters and density fluctuations in the fragmenting clusters. Future improvements to the accessible range of dynamics and time resolution of the approach are discussed.

Sequential model synchronisation is the task of propagating changes from one model to another correlated one to restore consistency. It is challenging to perform this propagation in a least-changing way that avoids unnecessary deletions (which might cause information loss). From a theoretical point of view, so-called short-cut (SC) rules have been developed that enable provably correct propagation of changes while avoiding information loss. However, to be able to react to every possible change, an infinite set of such rules might be necessary. Practically, only small sets of pre-computed basic SC rules have been used, severely restricting the kind of changes that can be propagated without loss of information. In this work, we close that gap by developing an approach to compute more complex required SC rules on-the-fly during synchronisation. These higher-order SC rules allow us to cope with more complex scenarios when multiple changes must be handled in one step. We implemented our approach in the model transformation tool eMoflon. An evaluation shows that the overhead of computing higher-order SC rules on-the-fly is tolerable and at times even improves the overall performance. Above that, completely new scenarios can be dealt with without the loss of information.

We propose a notion of multi-scale stability conditions with the goal of providing a smooth compactification of the quotient of the space of projectivized Bridgeland stability conditions by the group of autoequivalence. For the case of the 3CY category associated with the $A_n$-quiver this goal is achieved by defining a topology and complex structure that relies on a plumbing construction. We compare this compactification to the multi-scale compactification of quadratic differentials and briefly indicate why even for the Kronecker quiver this notion needs refinement to provide a full compactification., Comment: Final version to appear in Forum of Mathematics, Sigma

This paper introduces panoptica, a versatile and performance-optimized package designed for computing instance-wise segmentation quality metrics from 2D and 3D segmentation maps. panoptica addresses the limitations of existing metrics and provides a modular framework that complements the original intersection over union-based panoptic quality with other metrics, such as the distance metric Average Symmetric Surface Distance. The package is open-source, implemented in Python, and accompanied by comprehensive documentation and tutorials. panoptica employs a three-step metrics computation process to cover diverse use cases. The efficacy of panoptica is demonstrated on various real-world biomedical datasets, where an instance-wise evaluation is instrumental for an accurate representation of the underlying clinical task. Overall, we envision panoptica as a valuable tool facilitating in-depth evaluation of segmentation methods., Comment: 15 pages, 6 figures, 3 tables

Photo-assisted tunneling in scanning tunneling microscopy has attracted considerable interest to combine sub-picosecond and sub-nanometer resolutions. The illumination of a junction with visible or infrared light, however, induces thermal expansion of the tip and the sample, which strongly affects the measurements. Employing free-space THz pulses instead of visible light has been proposed to solve these thermal issues while providing photo-induced currents of similar magnitude. Here we compared the impact of illuminating the same tunneling junction, reaching comparable photo-induced current, with red light and with THz radiations. Our data provide a clear and direct evidence of thermal expansion with red light-illumination, while such thermal effects are negligible with THz radiations.

We report on a lightwave-driven scanning tunneling microscope based on a home-built microscope and a compact, commercial, and cost-effective terahertz-generation unit with a repetition rate of 100 MHz. The measurements are performed in ultrahigh vacuum at temperatures between 10 K and 300 K. The cross-correlation of the pump and probe pulses indicate a temporal resolution on the order of a picosecond. In terms of spatial resolution, CO molecules, step edges and atomically resolved terraces are readily observed in terahertz images, with sometimes better contrast than in the topographic and (DC) current channels. The utilization of a compact, turn-key terahertz-generation system requires only limited experience with optics and terahertz generation, which may facilitate the deployment of the technique to further research groups.

Methods: Observing the large pc-scale Stokes I mm dust continuum emission with the IRAM 30m telescope and the intermediate-scale (<0.1pc) polarized submm dust emission with the Submillimeter Array toward a sample of 20 high-mass star-forming regions allows us to quantify the dependence of the fragmentation behaviour of these regions depending on the density and magnetic field structures. Results: We infer density distributions n~r^{-p} of the regions with typical power-law slopes p around ~1.5. There is no obvious correlation between the power-law slopes of the density structures on larger clump scales (~1pc) and the number of fragments on smaller core scales (<0.1pc). Comparing the large-scale single-dish density profiles to those derived earlier from interferometric observations at smaller spatial scales, we find that the smaller-scale power-law slopes are steeper, typically around ~2.0. The flattening toward larger scales is consistent with the star-forming regions being embedded in larger cloud structures that do not decrease in density away from a particular core. Regarding the magnetic field, for several regions it appears aligned with filamentary structures leading toward the densest central cores. Furthermore, we find different polarization structures with some regions exhibiting central polarization holes whereas other regions show polarized emission also toward the central peak positions. Nevertheless, the polarized intensities are inversely related to the Stokes I intensities. We estimate magnetic field strengths between ~0.2 and ~4.5mG, and we find no clear correlation between magnetic field strength and the fragmentation level of the regions. Comparison of the turbulent to magnetic energies shows that they are of roughly equal importance in this sample. The mass-to-flux ratios range between ~2 and ~7, consistent with collapsing star-forming regions., Comment: Accepted for Astronomy & Astrophysics, 14 pages, 14 figures plus appendices, also download option at https://www2.mpia-hd.mpg.de/homes/beuther/papers.html

Various fields, including medical and human interaction robots, gain advantages from the development of bioinspired soft actuators. Many recently developed grippers are pneumatics that require external pressure supply systems, thereby limiting the autonomy of these robots. This necessitates the development of scalable and efficient on-board pressure generation systems. While conventional air compression systems are hard to miniaturize, thermopneumatic systems that joule-heat a transducer material to generate pressure present a promising alternative. However, the transducer materials of previously reported thermopneumatic systems demonstrate high heat capacities and limited surface area resulting in long response times and low operation frequencies. This study presents a thermopneumatic pressure generator using aerographene, a highly porous (>99.99 %) network of interconnected graphene microtubes, as lightweight and low heat capacity transducer material. An aerographene pressurizer module (AGPM) can pressurize a reservoir of 4.2 cm3 to about ~140 mbar in 50 ms. Periodic operation of the AGPM for 10 s at 0.66 Hz can further increase the pressure in the reservoir to ~360 mbar. It is demonstrated that multiple AGPMs can be operated parallelly or in series for improved performance. For example, three parallelly operated AGPMs can generate pressure pulses of ~215 mbar. Connecting AGPMs in series increases the maximum pressure achievable by the system. It is shown that three AGPMs working in series can pressurize the reservoir to ~2000 mbar in about 2.5 min. The AGPM's minimalistic design can be easily adapted to circuit boards, making the concept a promising fit for the on-board pressure supply of soft robots., Comment: Author Affiliation: Functional Nanomaterials, Department of Materials Science, Kiel University, Germany; Corresponding Authors: Dr.-Ing. Fabian Sch\"utt (fas@tf.uni-kiel.de), Prof. Dr. Rainer Adelung (ra@tf.uni-kiel.de)

Dark field X-ray microscopy (DXFM) can visualize microstructural distortions in bulk crystals. Using the femtosecond X-ray pulses generated by X-ray free-electron lasers (XFEL), DFXM can achieve sub-{\mu}m spatial resolution and <100 fs time resolution simultaneously. In this paper, we demonstrate ultrafast DFXM measurements at the European XFEL to visualize an optically-driven longitudinal strain wave propagating through a diamond single crystal. We also present two DFXM scanning modalities that are new to the XFEL sources: spatially 3D and 2D axial-strain scans with sub-{\mu}m spatial resolution. With this progress in XFEL-based DFXM, we discuss new opportunities to study multi-timescale spatio-temporal dynamics of microstructures.