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This study explores the potential of Large Language Models (LLMs) to generate artificial surveys, with a focus on personal mobility preferences in Germany. By leveraging LLMs for synthetic data creation, we aim to address the limitations of traditional survey methods, such as high costs, inefficiency and scalability challenges. A novel approach incorporating "Personas" - combinations of demographic and behavioural attributes - is introduced and compared to five other synthetic survey methods, which vary in their use of real-world data and methodological complexity. The MiD 2017 dataset, a comprehensive mobility survey in Germany, serves as a benchmark to assess the alignment of synthetic data with real-world patterns. The results demonstrate that LLMs can effectively capture complex dependencies between demographic attributes and preferences while offering flexibility to explore hypothetical scenarios. This approach presents valuable opportunities for transportation planning and social science research, enabling scalable, cost-efficient and privacy-preserving data generation.

We investigate the gravitational wave emission from extreme mass ratio inspirals, key targets for the upcoming space-based detector LISA, considering the scenario where the lighter black hole in the binary is endowed by a long-lived, time-dependent scalar field configuration, known as a scalar wig. We develop a formalism to compute scalar perturbations for extreme mass ratio inspirals on circular orbits around Schwarzschild and Kerr black holes, and apply this framework to compute additional fluxes induced by the scalar wig as well as their dependence on the scalar field properties. Our computation provides strong indications that in this scenario the presence of the scalar field does not significantly affect the orbital motion and the gravitational waveform., Comment: 11 pages, 1 figure

We study the gravitational perturbations of black holes in quadratic gravity, in which the Einstein-Hilbert term is supplemented by quadratic terms in the curvature tensor. In this class of theories, the Schwarzschild solution can coexist with modified black hole solutions, and both families are radially stable in a wide region of the parameter space. Here we study non-radial perturbations of both families of static, spherically symmetric black holes, computing the quasi-normal modes with axial parity and finding strong numerical evidence for the stability of these solutions under axial perturbations. The perturbation equations describe the propagation of a massless and a massive spin-two fields. We show that the Schwarzschild solution admits the same quasi-normal modes as in general relativity, together with new classes of modes corresponding to the massive spin-two degrees of freedom. The spectrum of the modified black hole solution has the same structure, but all modes are different from those of general relativity. We argue that both classes of modes can be excited in physical processes, suggesting that a characteristic signature of this theory is the presence of massive spin-two modes in the gravitational ringdown, even when the stationary solution is the same as in general relativity., Comment: 15 pages, 9 figures

We present a new empirical framework modeling the metallicity and star-formation history (SFH) dependence of X-ray luminous ($L > 10^{36}$ ergs s$^{-1}$) point-source population luminosity functions (XLFs) in normal galaxies. We expect the X-ray point-source populations are dominated by X-ray binaries (XRBs), with contributions from supernova remnants near the low luminosity end of our observations. Our framework is calibrated using the collective statistical power of 3,731 X-ray detected point-sources within 88 Chandra-observed galaxies at $D <$ 40 Mpc that span broad ranges of metallicity ($Z \approx$ 0.03-2 $Z_\odot$), SFH, and morphology (dwarf irregulars, late-types, and early-types). Our best-fitting models indicate that the XLF normalization per unit stellar mass declines by $\approx$2-3 dex from 10 Myr to 10 Gyr, with a slower age decline for low-metallicity populations. The shape of the XLF for luminous X-ray sources ($L < 10^{38}$ ergs s$^{-1}$) significantly steepens with increasing age and metallicity, while the lower-luminosity XLF appears to flatten with increasing age. Integration of our models provide predictions for X-ray scaling relations that agree very well with past results presented in the literature, including, e.g., the $L_{\rm X}$-SFR-$Z$ relation for high-mass XRBs (HMXBs) in young stellar populations as well as the $L_{\rm X}/M_\star$ ratio observed in early-type galaxies that harbor old populations of low-mass XRBs (LMXBs). The model framework and data sets presented in this paper further provide unique benchmarks that can be used for calibrating binary population synthesis models., Comment: Accepted for publication in ApJS; extended figures/materials available at https://lehmer.uark.edu/downloads/ ; python SED fitting code Lightning available at https://github.com/ebmonson/lightningpy

Large Language Models (LLMs) have gained prominence in the AI landscape due to their exceptional performance. Thus, it is essential to gain a better understanding of their capabilities and limitations, among others in terms of nonmonotonic reasoning. This paper proposes a benchmark that corresponds to various defeasible rule-based reasoning patterns. We modified an existing benchmark for defeasible logic reasoners by translating defeasible rules into text suitable for LLMs. We conducted preliminary experiments on nonmonotonic rule-based reasoning using ChatGPT and compared it with reasoning patterns defined by defeasible logic., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Many online services running in datacenters are implemented using a microservice software architecture characterized by strict latency requirements. Consequently, this popular software paradigm is increasingly used for the performance evaluation of server systems. Due to the scale and complexity of datacenters, the evaluation of server optimization techniques is usually done on a smaller scale using a client-server model. Although the experimental details of the server side are excessively described in most publications, the client side is often ignored. This paper identifies the hardware configuration of the client side as an important source of performance variation that can affect the accuracy and the correctness of the conclusions of a study that analyzes the performance of microservices. This is partially attributed to the strict latency requirements of microservices and the small scale of the experimental environment. In this work we present, using a widely used online-service, several examples where the accuracy and the trends of the conclusions differ based on the configuration of the client-side. At the same time we show that the experimental evaluation time can be significantly affected by the hardware configuration of the client. All these provoke the discussion of the right way to configure the experimental environment for assessing the performance of microservices.

As the complexities of urban environments continue to grow, the modelling of transportation systems become increasingly challenging. This paper explores the application of advanced Graph Neural Network (GNN) architectures as surrogate models for strategic transport planning. Building upon a prior work that laid the foundation with graph convolution networks (GCN), our study delves into the comparative analysis of established GCN with the more expressive Graph Attention Network (GAT). Additionally, we propose a novel GAT variant (namely GATv3) to address over-smoothing issues in graph-based models. Our investigation also includes the exploration of a hybrid model combining both GCN and GAT architectures, aiming to investigate the performance of the mixture. The three models are applied to various experiments to understand their limits. We analyse hierarchical regression setups, combining classification and regression tasks, and introduce fine-grained classification with a proposal of a method to convert outputs to precise values. Results reveal the superior performance of the new GAT in classification tasks. To the best of the authors' knowledge, this is the first GAT model in literature to achieve larger depths. Surprisingly, the fine-grained classification task demonstrates the GCN's unexpected dominance with additional training data. This shows that synthetic data generators can increase the training data, without overfitting issues whilst improving model performance. In conclusion, this research advances GNN based surrogate modelling, providing insights for refining GNN architectures. The findings open avenues for investigating the potential of the newly proposed GAT architecture and the modelling setups for other transportation problems.

Using hard (E>10 keV) X-ray observations with NuSTAR, we are able to differentiate between accretion states, and thus compact object types, of neutron stars and black holes in X-ray binaries (XRBs) in M31, our nearest Milky Way-type neighbor. Using ten moderate-depth (20-50 ks) observations of the disk of M31 covering a total of ~0.45 deg$^{2}$, we detect 20 sources at 2$\sigma$ in the 4-25 keV band pass, 14 of which we consider to be XRB candidates. This complements an existing deeper (100-400 ks) survey covering ~0.2 deg$^{2}$ of the bulge and the northeastern disk. We make tentative classifications of 9 of these sources with the use of diagnostic color-intensity and color-color diagrams, which separate sources into various neutron star and black hole regimes, identifying 3 black holes and 6 neutron stars. In addition, we create X-ray luminosity functions for both the full (4-25 keV) and hard (12-25 keV) band, as well as sub-populations of the full band based on compact object type and association with globular clusters. Our best fit globular cluster XLF is shallower than the field XLF, and preliminary BH and NS XLFs suggest a difference in shape based on compact object type. We find that the cumulative disk XLFs in the full and hard band are best fit by power laws with indices of 1.32 and 1.28 respectively. This is consistent with models of the Milky Way XLF from Grimm et al. (2002), Voss & Ajello (2010), and Doroshenko et al. (2014)., Comment: 19 pages, 6 Figures, Published in ApJ

Alternative theories of gravity may be put to the test by making use of gravitational wave observations. Scalar-tensor theories provide a relatively simple framework that allows for compact object solutions with characteristics different from those of their general relativity counterparts. In shift-symmetric theories, black hole hair may arise when a linear coupling with the Gauss-Bonnet (GB) invariant is introduced. The effect of this coupling on the black hole quasinormal modes (QNMs) has been shown to be small, therefore limiting the observational interest in the theory. In general, however, one expects that additional shift-symmetric terms in the Lagrangian may be relevant, and indeed, it has been shown that they can have a nontrivial impact on the mass and scalar charge of the stationary solutions. In this work we explore the effect that various such terms have on the axial and polar QNMs of the hairy black hole solutions., Comment: 14 pages, 6 figures, 2 tables

Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces in NAS often aren't diverse enough to include such transformations a priori. Instead, for NAS to provide greater potential for fundamental design shifts, we need a novel expressive search space design which is built from more fundamental operations. To this end, we introduce einspace, a search space based on a parameterised probabilistic context-free grammar. Our space is versatile, supporting architectures of various sizes and complexities, while also containing diverse network operations which allow it to model convolutions, attention components and more. It contains many existing competitive architectures, and provides flexibility for discovering new ones. Using this search space, we perform experiments to find novel architectures as well as improvements on existing ones on the diverse Unseen NAS datasets. We show that competitive architectures can be obtained by searching from scratch, and we consistently find large improvements when initialising the search with strong baselines. We believe that this work is an important advancement towards a transformative NAS paradigm where search space expressivity and strategic search initialisation play key roles., Comment: NeurIPS 2024. Project page at https://linusericsson.github.io/einspace/

Gravitational wave observations can test the validity of General Relativity (GR) in the strong field regime. Certain classes of scalar-tensor theories indeed predict that compact objects can exhibit significant deviations from their GR counterparts. Here we explore the quasinormal modes of axial perturbations in spherically symmetric black holes in three such classes: (i) dilatonic black holes with an additional scalar-Ricci coupling (EdRGB), (ii) spontaneously scalarized black holes (EsRGB) with a quadratic coupling to the Gauss-Bonnet invariant and the Ricci scalar, (iii) spontaneously scalarized black holes with a quadratic and a quartic coupling to the Gauss-Bonnet invariant., Comment: 12 pages, 7 figures

Despite recent advances in video action recognition achieving strong performance on existing benchmarks, these models often lack robustness when faced with natural distribution shifts between training and test data. We propose two novel evaluation methods to assess model resilience to such distribution disparity. One method uses two different datasets collected from different sources and uses one for training and validation, and the other for testing. More precisely, we created dataset splits of HMDB-51 or UCF-101 for training, and Kinetics-400 for testing, using the subset of the classes that are overlapping in both train and test datasets. The other proposed method extracts the feature mean of each class from the target evaluation dataset's training data (i.e. class prototype) and estimates test video prediction as a cosine similarity score between each sample to the class prototypes of each target class. This procedure does not alter model weights using the target dataset and it does not require aligning overlapping classes of two different datasets, thus is a very efficient method to test the model robustness to distribution shifts without prior knowledge of the target distribution. We address the robustness problem by adversarial augmentation training - generating augmented views of videos that are "hard" for the classification model by applying gradient ascent on the augmentation parameters - as well as "curriculum" scheduling the strength of the video augmentations. We experimentally demonstrate the superior performance of the proposed adversarial augmentation approach over baselines across three state-of-the-art action recognition models - TSM, Video Swin Transformer, and Uniformer. The presented work provides critical insight into model robustness to distribution shifts and presents effective techniques to enhance video action recognition performance in a real-world deployment.

The perturbative treatment of realistic quantum field theories, such as quantum electrodynamics, requires the use of mathematical idealizations in the approximation series for scattering amplitudes. Such mathematical idealisations are necessary to derive empirically relevant models from the theory. Mathematical idealizations can be either controlled or uncontrolled, depending on whether current scientific knowledge can explain whether the effects of the idealization are negligible or not. Drawing upon negative formal results in asymptotic analysis (failure of Borel summability) and renormalization group theory (failure of asymptotic safety), we argue that the mathematical idealizations applied in perturbative quantum electrodynamics should be understood as uncontrolled. This, in turn, leads to the problematic conclusion that such theories do not have theoretical models in the standard understanding of this term. The existence of unquestionable empirically successful theories without theoretical models has significant implications both for our understanding of the theory-model relationship in physics and the concept of empirical adequacy.

We present a new five-epoch Chandra X-ray Observatory monitoring survey of the nearby spiral galaxy M33 which probes X-ray variability with time sampling between two weeks and four months. We characterize the X-ray variability of 55 bright point sources outside of the nucleus, many of which are expected to be high-mass X-ray binaries (HMXBs). We detect eight new candidate transients not detected in previous X-ray catalogs of M33 and discuss their possible nature. The final catalog includes 26 known HMXB candidates identified in the literature. We extend the baseline of the X-ray light curves up to 21 years by including archival X-ray observations of these sources. We compare the detection and non-detection epochs of the sources to suites of simulated source duty cycles and infer that most of our detected sources have duty cycles > 30%. We find only four sources whose detection patterns are consistent with having duty cycles below 30%. This large fraction of sources with high duty cycles is unexpected for a population of HMXBs, thus more frequent X-ray monitoring will likely reveal many more low duty cycle HMXBs in M33., Comment: Accepted for publication in ApJ. 31 pages, 14 figures