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This paper presents a comprehensive study on a novel multilayer surface plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in liquid samples with exceptional precision and efficiency. Leveraging the Kretschmann configuration, the proposed design integrates advanced two-dimensional materials, including black phosphorus (BP) and transition metal dichalcogenides (TMDs), to significantly enhance the performance metrics of the sensor. Key innovations include the optimization of sensitivity through precise material layering, minimization of full-width at half-maximum (FWHM) to improve signal resolution, and maximization of the figure of merit (FoM) for superior detection accuracy. Numerical simulations are employed to validate the structural and functional enhancements of the biosensor. The results demonstrate improved interaction between the evanescent field and the analyte, enabling detection at trace concentrations with higher specificity. This biosensor is poised to contribute to advancements in biochemical sensing, environmental monitoring, and other critical applications requiring high-sensitivity toxin detection.

In the age of data-driven decision making, preserving privacy while providing personalized experiences has become paramount. Personalized Federated Learning (PFL) offers a promising framework by decentralizing the learning process, thus ensuring data privacy and reducing reliance on centralized data repositories. However, the integration of advanced Artificial Intelligence (AI) techniques within PFL remains underexplored. This paper proposes a novel approach that enhances PFL with cutting-edge AI methodologies including adaptive optimization, transfer learning, and differential privacy. We present a model that not only boosts the performance of individual client models but also ensures robust privacy-preserving mechanisms and efficient resource utilization across heterogeneous networks. Empirical results demonstrate significant improvements in model accuracy and personalization, along with stringent privacy adherence, as compared to conventional federated learning models. This work paves the way for a new era of truly personalized and privacy-conscious AI systems, offering significant implications for industries requiring compliance with stringent data protection regulations., Comment: arXiv admin note: substantial text overlap with arXiv:2501.16758

Excitonic insulators represent a unique quantum phase of matter, providing a rich ground for studying exotic quantum bosonic states. Strongly coupled electron-hole bilayers, which host stable dipolar exciton fluids with an exciton density that can be adjusted electrostatically, offer an ideal platform to investigate correlated excitonic insulators. Based on electron-hole bilayers made of MoSe2/hBN/WSe2 heterostructures, here we study the behavior of excitonic insulators in a perpendicular magnetic field. We report the observation of excitonic quantum oscillations in both Coulomb drag signals and electrical resistance at low to medium magnetic fields. Under a strong magnetic field, we identify multiple quantum phase transitions between the excitonic insulator phase and the bilayer quantum Hall insulator phase. These findings underscore the interplay between the electron-hole interactions and Landau level quantization that opens new possibilities for exploring quantum phenomena in composite bosonic insulators.

We present the TRansient Image Processing Pipeline (TRIPP), a transient and variable source detection pipeline that employs both difference imaging and light curve analysis techniques for astronomical data. Additionally, we demonstrate TRIPP's rapid analysis capability by detecting transient candidates in near-real time. TRIPP was tested using image data of the supernova SN2023ixf and from the Local Galactic Transient Survey (LGTS) collected by the Las Cumbres Observatory's (LCO) network of 0.4 m telescopes. To verify the methods employed by TRIPP, we compare our results to published findings on the photometry of SN2023ixf. Additionally, we report the ability of TRIPP to detect transient signals from optical Search for Extra Terrestrial Intelligence (SETI) sources., Comment: 11 pages, 10 figures, 2 tables

Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (DPMs). The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p < 0.05). This performance also generalizes well to previously unseen datasets. For skeletal muscle radiodensity predictions, using defaced images results in significantly weaker Spearman's rank correlation coefficients compared to using original images (p < 10-4). For shin muscle, the correlation is statistically significant (p < 0.05) when using original images but not statistically significant (p > 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information.

A dynamic treatment regime (DTR) is an approach to delivering precision medicine that uses patient characteristics to guide treatment decisions for optimal health outcomes. Numerous methods have been proposed for DTR estimation, including dynamic weighted ordinary least squares (dWOLS), a regression-based approach that affords double robustness to model misspecification within an easy to implement analytical framework. Initially, the dWOLS approach was developed under the assumptions of continuous outcomes and binary treatment decisions. Motivated by clinical research, subsequent theoretical advancements have extended the dWOLS framework to address binary, continuous and multicategory treatments across various outcome types, including binary, continuous, and survival-type. However, certain scenarios remain unexplored. This paper summarizes the last ten years of extension and application of the dWOLS method, providing a comprehensive and detailed review of the original dWOLS method and its extensions, as well as highlighting its diverse practical applications. We also explore studies that have addressed challenges associated with dWOLS implementation, such as model validation, variable selection, and handling measurement errors. Using simulated data, we present numerical illustrations along with step-by-step implementations in the \texttt{R} environment to facilitate a deeper understanding of dWOLS-based DTR estimation methodologies.

The goal of offline model-based optimization (MBO) is to propose new designs that maximize a reward function given only an offline dataset. However, an important desiderata is to also propose a diverse set of final candidates that capture many optimal and near-optimal design configurations. We propose Diversity in Adversarial Model-based Optimization (DynAMO) as a novel method to introduce design diversity as an explicit objective into any MBO problem. Our key insight is to formulate diversity as a distribution matching problem where the distribution of generated designs captures the inherent diversity contained within the offline dataset. Extensive experiments spanning multiple scientific domains show that DynAMO can be used with common optimization methods to significantly improve the diversity of proposed designs while still discovering high-quality candidates.

We show how to construct a sequence of isomorphisms between two descriptions of the fundamental group of a compact Riemann surface., Comment: 19 pages. Comments welcome

Many modern wireless devices with accurate positioning needs also have access to vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determining the precise location of the wireless device. Specifically, vision data can be used to estimate distances between the target (where the sensors are mounted) and nearby landmarks. However, a significant challenge in positioning using these measurements is the inability to uniquely identify which specific landmark is visible in the data. For instance, when the target is located close to a lamppost, it becomes challenging to precisely identify the specific lamppost (among several in the region) that is near the target. This work proposes a new framework for target localization using range measurements to multiple proximate landmarks. The geometric constraints introduced by these measurements are utilized to narrow down candidate landmark combinations corresponding to the range measurements and, consequently, the target's location on a map. By modeling landmarks as a marked Poisson point process (PPP), we show that three noise-free range measurements are sufficient to uniquely determine the correct combination of landmarks in a two-dimensional plane. For noisy measurements, we provide a mathematical characterization of the probability of correctly identifying the observed landmark combination based on a novel joint distribution of key random variables. Our results demonstrate that the landmark combination can be identified using ranges, even when individual landmarks are visually indistinguishable., Comment: 14 pages

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projectionbased methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for crosslingual named entity recognition in lowresource languages., Comment: Accepted at NoDaLiDa/Baltic-HLT 2025

Context: The recently discovered spherical eROSITA bubbles arise up to a latitude of $\pm$80\deg-85\deg in the X-ray regime of the Milky Way halo. Similar to the $\gamma$-ray Fermi bubbles, they evolve around the Galactic center, making a common origin plausible. However, the driving mechanism and evolution of both bubbles are still under debate. Aims: We want to investigate whether hydrodynamic energy injections at the Galactic center, such as e.g. tidal disruption events (TDEs), could have inflated both bubbles. The supermassive black hole Sagittarius A* is expected to tidally disrupt a star every 10-100 kyr, potentially leading to an outflow from the central region that drives a shock propagating into the Galactic halo due to its vertically declining density distribution, ultimately forming a superbubble blown out of the disk similar to the eROSITA and Fermi bubbles. Methods: We model TDEs in the Galaxy using three-dimensional hydrodynamical simulations, considering different Milky Way mass models and TDE rates. We then generate synthetic X-ray maps and compare them to observations. Results: Our simulation results of a $\beta$-model Milky Way halo show that superbubbles, blown for 16 Myr by regular energy injections at the Galactic center that occur every 100 kyr, can have a shape, shell stability, size, and evolution time similar to estimates for the eROSITA bubbles, and an overall structure reminiscent to the Fermi bubbles. The $\gamma$-rays in our model would stem from cosmic ray interactions at the contact discontinuity, where they were previously accelerated by first order Fermi acceleration at in situ shocks. Conclusions: Regular TDEs in the past 10-20 Myr near the Galactic center could have driven an outflow resulting in both, the X-ray emission of the eROSITA bubbles and the $\gamma$-ray emission of the Fermi bubbles., Comment: 13 pages, 7 figures, accepted for publication in Astronomy & Astrophysics (A&A)

LP 791-18 d is a temperate Earth-sized planet orbiting a late M dwarf, surrounded by an interior super-Earth (LP 791-18 b, $R_P$ = 1.2 $R_{\oplus}$, $P=0.95$ days) and an exterior sub-Neptune (LP 791-18 c, $R_P$ = 2.5 $R_{\oplus}$, $P=4.99$ days). Dynamical interactions between LP 791-18 d and c produce transit timing variations (TTVs) that can be used to constrain the planet masses and eccentricities. These interactions can also force a non-zero eccentricity for LP 791-18 d, which raises its internal temperature through tidal heating and could drive volcanic outgassing. We present three new transit observations of LP 791-18 c with Palomar/WIRC, including the most precise TTV measurements ($<$ 6 seconds) of this planet to date. We fit these times with a TTV model to obtain updated constraints on the mass, eccentricity, and tidal heat flux of LP 791-18 d. We reduce the mass uncertainty by more than a factor of two ($M_d$ = 0.91 $\pm$ 0.19 $M_{\oplus}$). We perform an updated fit assuming tidally damped free eccentricities and find $e_d = 0.0011^{+0.0010}_{-0.0008}$ and $e_c = 0.0001 \pm 0.0001$, consistent with circular orbits. We find that the observed TTVs are not sensitive to $e \leq$ $\sim$0.01. Without a tidally damped eccentricity prior, $e_d = 0.056^{+0.015}_{-0.014}$, much higher than the eccentricity predicted by n-body simulations incorporating the effects of dynamical excitation and tidal damping. We predict the timing of upcoming JWST secondary eclipse observations for LP 791-18 d, which could tightly constrain the eccentricity and tidal quality factor of this Earth-sized exoplanet., Comment: 14 pages, 8 figures, 4 tables, submitted to The Astronomical Journal

Phases with topological order exhibit further complexity in the presence of global symmetries: States with the same topological order are distinguished by how their anyonic excitations transform under these symmetries, leading to a classification in terms of symmetry-enriched topological phases. In this work, we develop a generic scheme to study an analogous situation for three-dimensional fracton phases by means of isometric tensor network states (isoTNS) with finite bond dimension, which allow us to tune between wavefunctions of different symmetry fractionalization. We focus on the X-Cube model, a paradigmatic fracton model hosting two types of excitations: lineons, which are mobile in a single direction only, and fractons that are completely immobile as individual particles. By deforming the local tensors that describe the ground state of the fixed point model, we find a family of exact wavefunctions for which the symmetry fractionalization under an anti-unitary symmetry on both types of excitations is directly visible. These wavefunctions have non-vanishing correlation lengths and are non-stabilizer states. At the critical points between the phases, power-law correlations are supported in certain spatial directions. Furthermore, based on the isoTNS description of the wavefunction, we determine a linear-depth quantum circuit to sequentially realize these states on a quantum processor, including a holographic scheme for which a pair of two-dimensional qubit arrays suffices to encode the three-dimensional state using measurements. Our approach provides a construction to enrich phases with exotic topological or fracton order based on the language of tensor networks and offers a tractable route to implement and characterize fracton order with quantum processors., Comment: 22 pages, 10 figures

We study magneto-transport through topological insulator nanowires shaped in the form of a constriction, as can be obtained by etching techniques. The magnetic field is coaxial, potentially turning the nanowire into a magneto-chiral junction. We show in a detailed analytical and numerical study that two main transport regimes emerge, depending on the central narrow region being short or long as compared to the magnetic length at the junction entrance and exit. In both cases the central region hosts Dirac-particle-in-a-box states due to magnetic confinement, whose conductance properties are strongly influenced by Landau levels at the ends of the constriction. Notably, in the low-energy regime only chiral states with a specific handedness can transport charge across the junction. Based on these properties and general symmetry considerations we argue that the shaped nanowire should exhibit strong magneto-chiral non-reciprocal transport beyond linear response. We employ a numerical tight-binding implementation of an effective 2D model on a non-homogeneous grid, capable of simulating samples of realistic sizes, and test its soundness against full simulations for scaled-down 3D topological insulator wires., Comment: 10 pages, 10 figures

We present initial results on Hessian-free force-gradient integrators for lattice field theories. Integrators of this framework promise to provide substantial performance enhancements, particularly for larger lattice volumes where higher-order integrators demonstrate greater efficiency. Numerical results demonstrate the superior efficiency of the proposed integrators compared to commonly employed non-gradient schemes, particularly due to enhanced stability properties. It is anticipated that the advantages of the Hessian-free framework will become even more pronounced in nested integration approaches and for smaller fermion masses, where the numerical stability properties of the integrators become increasingly important., Comment: Contribution to Proceedings of the 41st International Symposium on Lattice Field Theory (LATTICE2024)

Structural anisotropy in layered two-dimensional materials can lead to highly anisotropic optical absorption which, in turn, can profoundly a^ect their phonon modes. These e^ects include lattice orientation-dependent and excitation energy-dependent mode intensities that can enable new phononic and optoelectronic applications. Here, we report anomalous Raman spectra in single-crystalline AgCrP$_2$Se$_6$, a layered antiferromagnetic material. Density functional theory calculations and experimental measurements reveal several unique features in the Raman spectra of bulk and exfoliated AgCrP$_2$Se$_6$ crystals including three helical vibrational modes. These modes exhibit large Raman optical activities (circular intensity di^erences) in bulk AgCrP$_2$Se$_6$, which progressively decrease with thickness. We also observe strong excitation energy dependent peak intensities as well as a decrease in anti-Stokes peak intensities at room temperature with increasing excitation energy, resulting in an apparent cooling by up to 220 K. All of these anomalies in bulk and exfoliated flakes are attributed to the unique ABC layer stacking structure of AgCrP$_2$Se$_6$ and to the smaller unit cell volume that causes hybridization between the Se and Ag/Cr electron densities, resulting in charge transfer and strongly a^ecting the electron-phonon coupling. This work thus positions AgCrP$_2$Se$_6$ as an exciting new 2D material for optical and phononic applications., Comment: 22 pages, 6 figures

Intercurrent events, common in clinical trials and observational studies, affect the existence or interpretation of final outcomes. Principal stratification addresses these challenges by defining local average treatment effects within latent subpopulations, but often relies on restrictive assumptions such as monotonicity and counterfactual intermediate independence. To address these limitations, we propose a unified semiparametric framework for principal stratification analysis leveraging a margin-free, conditional odds ratio sensitivity parameter. Under principal ignorability, we derive nonparametric identification formulas and develop efficient estimation methods, including a conditionally doubly robust parametric estimator and a de-biased machine learning estimator with data-adaptive nuisance estimators. Simulations show that incorrectly assuming monotonicity can often lead to suboptimal inference, while specifying non-trivial odds ratio sensitivity parameter can enable approximately valid inference under monotonicity. We apply our methods to a critical care trial and further suggest a semiparametric sensitivity analysis approach under violation of principal ignorability.

As the industrial and commercial use of Federated Learning (FL) has expanded, so has the need for optimized algorithms. In settings were FL clients' data is non-independently and identically distributed (non-IID) and with highly heterogeneous distributions, the baseline FL approach seems to fall short. To tackle this issue, recent studies, have looked into personalized FL (PFL) which relaxes the implicit single-model constraint and allows for multiple hypotheses to be learned from the data or local models. Among the personalized FL approaches, cluster-based solutions (CFL) are particularly interesting whenever it is clear -through domain knowledge -that the clients can be separated into groups. In this paper, we study recent works on CFL, proposing: i) a classification of CFL solutions for personalization; ii) a structured review of literature iii) a review of alternative use cases for CFL. CCS Concepts: $\bullet$ General and reference $\rightarrow$ Surveys and overviews; $\bullet$ Computing methodologies $\rightarrow$ Machine learning; $\bullet$ Information systems $\rightarrow$ Clustering; $\bullet$ Security and privacy $\rightarrow$ Privacy-preserving protocols.

Decentralized physical infrastructure networks (DePINs) are an emerging vertical within "Web3" replacing the traditional method that physical infrastructures are constructed. Yet, the boundaries between DePIN and traditional method of building crowd-sourced infrastructures such as citizen science initiatives or other Web3 verticals are not always so clear cut. In this work, we systematically analyze the differences between DePIN and other Web2 and Web3 verticals. For this, the study proposes a novel decision tree for classifying systems as DePIN. This tree is informed by prior studies and differentiates DePIN from related concepts using criteria such as the presence of a three-sided market, token-based incentives for supply, and the requirement for physical asset placement in those systems. The paper demonstrates the application of the decision tree to various blockchain systems, including Helium and Bitcoin, showcasing its practical utility in differentiating DePIN systems. This research offers significant contributions towards establishing a more objective and systematic approach to identifying and categorizing DePIN systems. It lays the groundwork for creating a comprehensive and unbiased database of DePIN systems, which will inform future research and development within this emerging sector.

We propose a novel deep learning method which combines classical regularization with data augmentation for estimating myelin water fraction (MWF) in the brain via biexponential analysis. Our aim is to design an accurate deep learning technique for analysis of signals arising in magnetic resonance relaxometry. In particular, we study the biexponential model, one of the signal models used for MWF estimation. We greatly extend our previous work on \emph{input layer regularization (ILR)} in several ways. We now incorporate optimal regularization parameter selection via a dedicated neural network or generalized cross validation (GCV) on a signal-by-signal, or pixel-by-pixel, basis to form the augmented input signal, and now incorporate estimation of MWF, rather than just exponential time constants, into the analysis. On synthetically generated data, our proposed deep learning architecture outperformed both classical methods and a conventional multi-layer perceptron. On in vivo brain data, our architecture again outperformed other comparison methods, with GCV proving to be somewhat superior to a NN for regularization parameter selection. Thus, ILR improves estimation of MWF within the biexponential model. In addition, classical methods such as GCV may be combined with deep learning to optimize MWF imaging in the human brain.

We propose a new method in indefinite-horizon settings for estimating optimal dynamic treatment regimes for time-to-event outcomes. This method allows patients to have different numbers of treatment stages and is constructed using generalized survival random forests to maximize mean survival time. We use summarized history and data pooling, preventing data from growing in dimension as a patient's decision points increase. The algorithm operates through model re-fitting, resulting in a single model optimized for all patients and all stages. We derive theoretical properties of the estimator such as consistency of the estimator and value function and characterize the number of refitting iterations needed. We also conduct a simulation study of patients with a flexible number of treatment stages to examine finite-sample performance of the estimator. Finally, we illustrate use of the algorithm using administrative insurance claims data for pediatric Crohn's disease patients., Comment: 24 pages, 7 figures

Abrupt and unexpected terminations of software are termed as software crashes. They can be challenging to analyze. Finding the root cause requires extensive manual effort and expertise to connect information sources like stack traces, source code, and logs. Typical approaches to fault localization require either test failures or source code. Crashes occurring in production environments, such as that of SAP HANA, provide solely crash logs and stack traces. We present a novel approach to localize faults based only on the stack trace information and no additional runtime information, by fine-tuning large language models (LLMs). We address complex cases where the root cause of a crash differs from the technical cause, and is not located in the innermost frame of the stack trace. As the number of historic crashes is insufficient to fine-tune LLMs, we augment our dataset by leveraging code mutators to inject synthetic crashes into the code base. By fine-tuning on 64,369 crashes resulting from 4.1 million mutations of the HANA code base, we can correctly predict the root cause location of a crash with an accuracy of 66.9\% while baselines only achieve 12.6% and 10.6%. We substantiate the generalizability of our approach by evaluating on two additional open-source databases, SQLite and DuckDB, achieving accuracies of 63% and 74%, respectively. Across all our experiments, fine-tuning consistently outperformed prompting non-finetuned LLMs for localizing faults in our datasets., Comment: Accepted at ICST 2025

We report our progress in combining a two-level sampling algorithm with distillation techniques for calculations of disconnected diagrams in quenched QCD. The simulations are performed on a single ensemble at $\beta=6.0$ and volume $V=16^3\times 64$, and at a pion mass of $m_\pi\approx 760~\mathrm{MeV}$., Comment: 10 pages, 4 figures, talk presented at the 41st International Symposium on Lattice Field Theory (Lattice2024), July 28th - August 3rd, 2024, Liverpool, UK

The growing demands of ultra-reliable and low-latency communication (URLLC) in 5G networks necessitate enhanced resilience mechanisms to address user plane failures caused by outages, hardware defects, or software bugs. An important aspect for achieving ultra-reliable communication is the redundant transmission of packets, as also highlighted in 3GPP Release 18. This paper explores leveraging the Packet Replication, Elimination, and Ordering Function (PREOF) to achieve 1+1 path protection within 5G environments. By extending existing 5G components with mechanisms for path redundancy and offloading the reordering mechanism to external servers, the proposed approach ensures ultra-low latency and minimal packet loss in case of a failure. A conceptual integration of redundant paths and programmable elements is presented, with considerations for deployment in existing 5G infrastructures and the trade-offs of latency versus enhanced traffic engineering. Future work aims to evaluate practical implementations using P4-based hardware and offloading technologies like DPDK and eBPF.

Precise, unbiased measurements of extragalactic background anisotropies require careful treatment of systematic effects in fluctuation-based, broad-band intensity mapping measurements. In this paper we detail improvements in methodology for the Cosmic Infrared Background ExpeRiment (CIBER), concentrating on flat field errors and source masking errors. In order to bypass the use of field differences, which mitigate flat field errors but reduce sensitivity, we characterize and correct for the flat field on pseudo-power spectra, which includes both additive and multiplicative biases. To more effectively mask point sources at 1.1 $\mu$m and 1.8 $\mu$m, we develop a technique for predicting masking catalogs that utilizes optical and NIR photometry through random forest regression. This allows us to mask over two Vega magnitudes deeper than the completeness limits of 2MASS alone, with errors in the shot noise power remaining below $<10\%$ at all masking depths considered. Through detailed simulations of CIBER observations, we validate our formalism and demonstrate unbiased recovery of the sky fluctuations on realistic mocks. We demonstrate that residual flat field errors comprise $<20\%$ of the final CIBER power spectrum uncertainty with this methodology., Comment: 29 pages, 17 figures. Submitted to ApJ

We present new anisotropy measurements in the near-infrared (NIR) for angular multipoles $300<\ell<10^5$ using imaging data at 1.1 $\mu$m and 1.8 $\mu$m from the fourth flight of the Cosmic Infrared Background ExpeRiment (CIBER). Using improved analysis methods and higher quality fourth flight data, we detect surface brightness fluctuations on scales $\ell<2000$ with CIBER auto-power spectra at $\sim14\sigma$ and 18$\sigma$ for 1.1 and 1.8 $\mu$m, respectively, and at $\sim10\sigma$ in cross-power spectra. The CIBER measurements pass internal consistency tests and represent a $5-10\times$ improvement in power spectrum sensitivity on several-arcminute scales relative to that of existing studies. Through cross-correlations with tracers of diffuse galactic light (DGL), we determine that scattered DGL contributes $<10\%$ to the observed fluctuation power at high confidence. On scales $\theta > 5'$, the CIBER auto- and cross-power spectra exceed predictions for integrated galactic light (IGL) and integrated stellar light (ISL) by over an order of magnitude, and are inconsistent with our baseline IGL+ISL+DGL model at high significance. We cross-correlate two of the CIBER fields with 3.6 $\mu$m and 4.5 $\mu$m mosaics from the Spitzer Deep Wide-Field Survey and find similar evidence for departures from Poisson noise in Spitzer-internal power spectra and CIBER $\times$ Spitzer cross-power spectra. A multi-wavelength analysis indicates that the auto-power of the fluctuations at low-$\ell$ is bluer than the Poisson noise from IGL and ISL; however, for $1' <\theta < 10'$, the cross-correlation coefficient $r_{\ell}$ of nearly all band combinations decreases with increasing $\theta$, disfavoring astrophysical explanations that invoke a single correlated sky component., Comment: 41 pages, 32 figures. Submitted to ApJ

Understanding the precise interaction mechanisms between quantum systems and their environment is crucial for advancing stable quantum technologies, designing reliable experimental frameworks, and building accurate models of real-world phenomena. However, simulating open quantum systems, which feature complex non-unitary dynamics, poses significant computational challenges that require innovative methods to overcome. In this work, we introduce the tensor jump method (TJM), a scalable, embarrassingly parallel algorithm for stochastically simulating large-scale open quantum systems, specifically Markovian dynamics captured by Lindbladians. This method is built on three core principles where, in particular, we extend the Monte Carlo wave function (MCWF) method to matrix product states, use a dynamic time-dependent variational principle (TDVP) to significantly reduce errors during time evolution, and introduce what we call a sampling MPS to drastically reduce the dependence on the simulation's time step size. We demonstrate that this method scales more effectively than previous methods and ensures convergence to the Lindbladian solution independent of system size, which we show both rigorously and numerically. Finally, we provide evidence of its utility by simulating Lindbladian dynamics of XXX Heisenberg models up to a thousand spins using a consumer-grade CPU. This work represents a significant step forward in the simulation of large-scale open quantum systems, with the potential to enable discoveries across various domains of quantum physics, particularly those where the environment plays a fundamental role, and to both dequantize and facilitate the development of more stable quantum hardware., Comment: 24 pages, 13 figures, 1 table (includes Methods and Appendix)

Improved algorithms for computing (partial and full) exterior algebraic shifts of hypergraphs and simplicial complexes are presented. The main benefit is in positive characteristic. Experiments with an implementation in OSCAR are reported.

Pathologically altered iron levels, detected using iron-sensitive MRI techniques such as quantitative susceptibility mapping (QSM), are observed in neurological disorders such as multiple sclerosis (MS) and may play a crucial role in disease pathophysiology. However, brain iron changes occur slowly, even in neurological diseases, and can be influenced by physiological factors such as diet. Therefore, novel analysis methods are needed to improve sensitivity to disease-related iron changes as compared to conventional region-based analysis methods. This study introduces IRONMAP, Iron Network Mapping and Analysis Protocol, which is a novel network-based analysis method to evaluate over-time changes in magnetic susceptibility. With this novel methodology, we analyzed short-term (<1 year) longitudinal QSM data from a cohort of individuals with MS (pwMS) and healthy controls (HCs) and assessed disease-related network patterns, comparing the new approach to a conventional per-region rate-of-change method. IRONMAP analysis was able to detect over-time, MS-related brain iron abnormalities that were undetectable using the rate-of-change approach. IRONMAP was applicable on the per-subject level, improving binary classification of pwMS vs HCs compared to rate-of-change data alone (areas under the curve: 0.773 vs 0.636, p = 0.024). Further analysis revealed that the observed IRONMAP-derived HC network structure closely aligned with simulated networks based on healthy aging-related susceptibility data, suggesting that disruptions in normal aging-related iron changes may contribute to the network differences seen in pwMS. IRONMAP is generalizable to any neurological disease, including Alzheimer's disease and Parkinson's disease, and may allow for study of brain iron abnormalities over shorter timeframes than previously possible.

The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by the nations attending the AI Safety Summit in Bletchley, UK. Thirty nations, the UN, the OECD, and the EU each nominated a representative to the report's Expert Advisory Panel. A total of 100 AI experts contributed, representing diverse perspectives and disciplines. Led by the report's Chair, these independent experts collectively had full discretion over the report's content.

One of the primary objectives of the SPHEREx mission is to understand the origin of molecules such as H2O, CO2, and other volatile compounds at the early stages of planetary system formation. Because the vast majority of these compounds -- typically exceeding 95% -- exist in the solid phase rather than the gaseous phase in the systems of concern here, the observing strategy planned to characterize them is slightly unusual. Specifically, SPHEREx will target highly obscured sources throughout the Milky Way, and observe the species of concern in absorption against background illumination. SPHEREx spectrophotometry will yield ice column density measurements for millions of obscured Milky Way sources of all ages and types. By correlating those column densities with source ages, the SPHEREx mission will shed light on whether those molecules were formed in situ along with their nascent stellar systems, or whether instead they formed elsewhere and were introduced into those systems after their formation. To that end, this work describes version 7$.$1 of the SPHEREx Target List of Ice Sources (SPLICES) for the community. It contains about 8$.$6 million objects brighter than W2~12 Vega mag over much of the sky, principally within a broad strip running the length of the Milky Way midplane, but also within high-latitude molecular clouds and even the Magellanic Clouds., Comment: Published by ApJ. 21 pages, 6 figures. This article documents the original version of SPLICES (7.1). The current version as well as the complete catalog is publicly available along with release notes documenting all additions and changes at the NASA/IPAC Infrared Science Archive (IRSA) at this URL: https://irsa.ipac.caltech.edu/data/SPHEREx/SPLICES/

With the growing complexity in architecture and the size of large-scale computing systems, monitoring and analyzing system behavior and events has become daunting. Monitoring data amounting to terabytes per day are collected by sensors housed in these massive systems at multiple fidelity levels and varying temporal resolutions. In this work, we develop an incremental version of multiresolution dynamic mode decomposition (mrDMD), which converts high-dimensional data to spatial-temporal patterns at varied frequency ranges. Our incremental implementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable information in the massive environment log dataset, which is then visually aligned with the processed hardware and job log datasets through our generalizable rack visualization using D3 visualization integrated into the Jupyter Notebook interface. We demonstrate the efficacy of our approach with two use scenarios on a real-world dataset from a Cray XC40 supercomputer, Theta.

We formulate the $(n,k)$ Coset Monogamy Game, in which two players must extract complementary information of unequal size ($k$ bits vs. $n-k$ bits) from a random coset state without communicating. The complementary information takes the form of random Pauli-X and Pauli-Z errors on subspace states. Our game generalizes those considered in previous works that deal with the case of equal information size $(k=\frac{n}{2})$. We prove a convex upper bound of the information-theoretic winning rate of the $(n,k)$ Coset Monogamy Game in terms of the subspace rate $R=\frac{k}{n}\in [0,1]$. This bound improves upon previous results for the case of $R=\frac{1}{2}$. We also prove the achievability of an optimal winning probability upper bound for the class of unentangled strategies of the $(n,k)$ Coset Monogamy Game., Comment: Submitted to the ISIT 2025 Conference

Pure as well as Ag- and Au-substituted YbInCu$_4$ single crystals were structurally and chemically characterized and investigated by means of heat capacity, magnetization, resistivity and ultrasonic measurements. We studied the influence of different compositions of the initial melt as well as of Au and Ag substitutions on the valence change and investigated whether this change occurs via a first-order phase transition or via crossover. We constructed a phase diagram of YbInCu$_4$ as a function of various substitutions and show that the position of the critical endpoint of the valence transition depends on the substituent and on the conditions under which the samples were grown. Multiple thermal cycles through the first-order transition lead to a significant modification of the physical properties which clearly demonstrated the influence of defects in substituted YbInCu$_4$.

B-gallium oxide (B-Ga2O3) is of high interest for power electronics because of its unique combination of melt growth, epitaxial growth, n-type dopability, ultrawide bandgap, and high critical field. Optimization of crystal growth processes to promote beneficial defects and suppress harmful ones requires accurate quantitative modelling of both native and impurity defects. Here we quantitatively model defect concentrations as a function of bulk crystal growth conditions and demonstrate the necessity of including effects such as bandgap temperature dependence, chemical potentials from thermochemistry, and defect vibrational entropy in modelling based on defect formation energies computed by density functional theory (DFT) with hybrid functionals. Without these contributions, grossly-erroneous and misleading predictions arise, e.g. that n-type doping attempts would be fully compensated by Ga vacancies. Including these effects reproduces the experimental facts that melt-grown Sn-doped B-Ga2O3 crystals are conductive with small compensation while annealing the same crystals in O2 at intermediate temperatures renders them insulating. To accomplish this modeling, we developed a comprehensive modelling framework (KROGER) based on calculated defect formation energies and flexible thermodynamic conditions. These capabilities allow KROGER to capture full and partial defect equilibria amongst native defects and impurities occurring during specific semiconductor growth or fabrication processes. We use KROGER to model 873 charge-states of 259 defects involving 19 elements in conditions representing bulk crystal growth by edge-fed growth (EFG) and annealing in O2. Our methodology is transferrable to a wide range of materials beyond B-Ga2O3. Integration of thermodynamic and first-principles modelling of point defects provides insight into optimization of point defect populations in growth and processing., Comment: 39 pages, 6 figures, references

This study demonstrates that two-dimensional talc, a naturally abundant mineral, supports hyperbolic phonon-polaritons (HPhPs) at mid-infrared wavelengths, thus offering a low-cost alternative to synthetic polaritonic materials. Using scattering scanning near-field optical microscopy (s-SNOM) and synchrotron infrared nano spectroscopy (SINS), we reveal tunable HPhP modes in talc flakes of a long lifetime. These results highlight the potential of natural 2D talc crystals to constituting an effective platform for establishing scalable optoelectronic and photonic devices., Comment: 8 pages, 4 figures

Plasma velocity distribution functions (VDFs) constitute a fundamental observation of numerous operational and future missions. An efficient parameterization of VDFs is crucial for (1) preserving enough information to investigate macroscopic moments along with kinetic effects, (2) producing smooth distributions whereby it is possible to perform derivatives in phase space to support numerical solvers, and (3) economic data management and its storage. Previous studies have used spherical harmonics as an efficient basis for representing electron VDFs. In this paper, we present a novel algorithm targeted towards decomposing ion VDFs measured by electrostatic analyzers onboard Magnetospheric Multiscale Mission (MMS) and Solar Orbiter (SolO) spacecrafts. We use Slepian functions, custom-designed bases providing compact support in phase space, initially developed in information theory and later used for terrestrial and planetary applications. In this paper, we choose well-studied, well-measured, and complex intervals from MMS and SolO containing a range of simpler gyrotropic and agyrotropic distributions to benchmark the robustness of our reconstruction method. We demonstrate the advantages of using Slepian functions over spherical harmonics for solar wind plasma distributions. We also demonstrate that our choice of basis representation efficiently preserves phase space complexities of a 3D agyrotropic distribution function. This algorithm shown in this study will be extended to Parker Solar Probe and future missions such as Helioswarm., Comment: 15 pages, 7 figures (submitted to The Astrophysical Journal); comments welcome

The development of new network protocols and solutions requires extendable switches. With software-defined networking (SDN), the control plane and data plane are separated and the control plane can be programmed. Data plane programming further enables the programmability of network devices with domain-specific programming languages, like Programming Protocol-independent Packet Processors (P4). One P4-programmable hardware target is the Intel Tofino switching ASIC. The Tofino can be configured with shell scripts or a Python library. Both are limited in their capabilities and usability. This work introduces the Rust Barefoot Runtime (RBFRT), a Rust-based control plane library for the Tofino. The RBFRT provides an easy-to-use interface while being fast and memory-safe. In our evaluation, we achieved a speedup factor of 1.33 for single-entry configuration and 60 for batch configuration compared to the Python library.

Conversational AI agents are commonly applied within single-user, turn-taking scenarios. The interaction mechanics of these scenarios are trivial: when the user enters a message, the AI agent produces a response. However, the interaction dynamics are more complex within group settings. How should an agent behave in these settings? We report on two experiments aimed at uncovering users' experiences of an AI agent's participation within a group, in the context of group ideation (brainstorming). In the first study, participants benefited from and preferred having the AI agent in the group, but participants disliked when the agent seemed to dominate the conversation and they desired various controls over its interactive behaviors. In the second study, we created functional controls over the agent's behavior, operable by group members, to validate their utility and probe for additional requirements. Integrating our findings across both studies, we developed a taxonomy of controls for when, what, and where a conversational AI agent in a group should respond, who can control its behavior, and how those controls are specified and implemented. Our taxonomy is intended to aid AI creators to think through important considerations in the design of mixed-initiative conversational agents., Comment: 31 pages, 7 figures. In Proceedings of the 30th International Conference on Intelligent User Interfaces (IUI '25), March 24-27, 2025, Cagliari, Italy

We present the third phase of the largest high-frequency, high-resolution imaging survey of 231 nearby, hard X-ray selected AGN, with a very high $98 \pm 1\%$ detection fraction. This survey presents VLA 22 GHz radio observations with 1" spatial resolution covering over $6$ orders of magnitude in radio luminosity in nearby AGN that span $\sim4$ orders of magnitude in black hole mass and X-ray luminosity. We identify three different radio morphologies: $44 \pm 3\%$ (102/231) are compact or unresolved, $46 \pm 3\%$ (106/231) show an extended structure (star formation, possible one-sided jets, etc.), and $8 \pm 2\%$ (19/231) have a biconical or two-sided jet-like morphology. The remaining $2 \pm 1\%$ (4/231) sources are non-detections. The radio-to-X-ray luminosity ratios of the Swift-BAT AGN ($\text{L}_R/\text{L}_{14-195 \text{keV}} \sim 10^{-5.5}$ and $\text{L}_R/\text{L}_{2-10 \text{keV}} \sim 10^{-5}$) with a scatter of $\sim0.5$ dex are similar to that of coronally active stars ($\text{L}_R/\text{L}_X \sim 10^{-5}$). For most targets, extended emission in radio-quiet objects is broadly consistent with the expectation for star formation from previous FIR observations, once the contribution from the radio core has been subtracted. Our sample represents nearby analogs of distant AGN at the peak of black hole growth, and thus the high detection fraction in our work has important implications for future high frequency AGN radio surveys with the next generation VLA (ngVLA) or Square Kilometre Array (SKA), both of which should detect large fractions of more distant AGN., Comment: 26 pages, 8 figures, 4tables. Accepted for publication in ApJ

We introduce a novel method for generating 360{\deg} panoramas from text prompts or images. Our approach leverages recent advances in 3D generation by employing multi-view diffusion models to jointly synthesize the six faces of a cubemap. Unlike previous methods that rely on processing equirectangular projections or autoregressive generation, our method treats each face as a standard perspective image, simplifying the generation process and enabling the use of existing multi-view diffusion models. We demonstrate that these models can be adapted to produce high-quality cubemaps without requiring correspondence-aware attention layers. Our model allows for fine-grained text control, generates high resolution panorama images and generalizes well beyond its training set, whilst achieving state-of-the-art results, both qualitatively and quantitatively. Project page: https://cubediff.github.io/, Comment: Accepted at ICLR 2025

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.

The P4-based traffic generator (P4TG) is a hardware-based traffic generator (TG) running on the Intel Tofino 1 ASIC. The TG can generate up to 1 Tb/s of traffic and directly measures rates, packet loss, and other metrics in the data plane. Many researchers and industrial partners have used it since its publication in 2023 and new features have been requested to be incorporated into P4TG. In this work, we provide an overview of the recently added features of P4TG. These enhancements include new traffic generation capabilities including IPv6 and segment routing v6 (SRv6) support and various encapsulation protocols such as VLAN, QinQ, VxLAN, and MPLS. Further, P4TG is ported to the Intel Tofino 2 platform enabling a generation capability of up to 4 Tb/s. The enhancements to P4TG also provide an improved user experience facilitating automated testing based on RFC 2544, report generation, and visualization.

We provide a review of our current knowledge of galaxies throughout the first billion years of cosmic history. This field has undergone a transformation in the last two years following the launch of $\textit{JWST}$, and we aim to deliver an observational overview of what we have learned about $z\gtrsim 5$ galaxies. We introduce the latest selection methods of high redshift galaxies and describe new measurements of the census of continuum-selected and dusty star forming galaxies at $z\gtrsim 5$. We discuss new measurements of the UV luminosity function at $z\gtrsim 10$ and associated implications for early star formation. We then summarize what is being learned about the physical properties of early galaxies, with up-to-date discussions of the sizes, masses, ages, metallicities, abundance patterns, UV colors, dust properties, and ionizing sources in $z\gtrsim 5$ galaxies. We review observational evidence for bursty star formation histories and describe prospects for characterizing the duty cycle with future observations. We provide a brief overview of the insight being gained through new detections of AGN in early galaxies. Finally we introduce the latest constraints on the contribution of galaxies to reionziation and discuss how $\textit{JWST}$ measurements of Ly$\alpha$ emission offer the potential to probe the earliest stages of the process. This review is meant to provide a broad introduction to those new to the observational study of very high redshift galaxies., Comment: 49 pages, 12 figures; Submitted to Encyclopedia of Astrophysics, November 2024

We conduct the first systematic survey of a total of eleven optical coronal lines in the spectra of a large sample of low redshift (z < 0.8) Type 1 quasars observed by the Sloan Digital Sky Survey (SDSS). We find that strong coronal line emission is rare in SDSS even in Type 1 quasars; only 885 out of 19,508 (4.5%) galaxies show at least one coronal line, with higher ionization potential lines ($>100$eV) being even rarer. The [Ne V] $\lambda$3426 line, which constitutes the majority of detections, is strongly correlated with the bolometric luminosity. These findings suggest that the optical coronal lines are significantly suppressed in the majority of local AGNs, possibly as a result of the presence of dust in the emitting regions. We find that the incidence of ionized outflows is significantly higher in coronal line emitters compared with non-coronal line emitters, possibly suggesting that dust destruction in outflows enhances coronal line emission in AGNs. Many coronal lines show line profiles that are broader than those of narrow lines, and are blue-shifted relative the lower ionization potential lines, suggesting outflows in the highly ionized gas. Given the limited number of detections, we do not find any statistically significant trends of detection statistics, or line ratios with black hole mass, Eddington ratio, or AGN bolometric luminosity. The catalog is publicly available and can provide a useful database of the coronal line properties of low redshift quasars that can be compared to the growing number of high-z AGNs discovered by JWST., Comment: Accepted for publication in ApJS

Two major milestones on the road to the full complexity dichotomy for finite-domain constraint satisfaction problems were Bulatov's proof of the dichotomy for conservative templates, and the structural dichotomy for smooth digraphs of algebraic length 1 due to Barto, Kozik, and Niven. We lift the combined scenario to the infinite, and prove that any smooth digraph of algebraic length 1 pp-constructs, together with pairs of orbits of an oligomorphic subgroup of its automorphism group, every finite structure -- and hence its conservative graph-colouring problem is NP-hard -- unless the digraph has a pseudo-loop, i.e. an edge within an orbit. We thereby overcome, for the first time, previous obstacles to lifting structural results for digraphs in this context from finite to $\omega$-categorical structures; the strongest lifting results hitherto not going beyond a generalisation of the Hell-Ne\v{s}et\v{r}il theorem for undirected graphs. As a consequence, we obtain a new algebraic invariant of arbitrary $\omega$-categorical structures enriched by pairs of orbits which fail to pp-construct some finite structure., Comment: 27 pages

This study develops a Bayesian semi-parametric model to examine the longitudinal growth and appetite phenotypes in children from the GUSTO cohort, with a focus on understanding the relationship between eating behaviours and growth outcomes over time. While eating behaviours, such as picky eating, have been shown to influence future weight and obesity risk, their developmental patterns and associations with growth trajectories remain under-explored. This work addresses these gaps by modelling longitudinal data, including both growth metrics (e.g., BMI) and eating behaviours (e.g., Child Eating Behaviour Questionnaire, CEBQ), across multiple time points. We extend the Partial Credit Model, commonly used for questionnaire data analysis, to accommodate repeated measurements and incorporate covariates. The growth outcomes are modelled using flexible splines regression. The two components of the model are linked through a shared Bayesian nonparametric prior distribution, specifically a Normalized Generalized Gamma process, allowing to identify clinically relevant subgroups. This joint modelling approach offers a more nuanced understanding of how early eating behaviours relate to growth patterns and developmental outcomes, providing insights into childhood obesity risk.

This article presents a collaborative research effort aimed at developing a novel six-degree-of-freedom (6-DOF) motion platform for the empirical characterization of hydrodynamic forces crucial for the control and stability of surface and subsurface vehicles. Traditional experimental methods, such as the Planar Motion Mechanism (PMM), are limited by the number of simultaneously articulated DOFs and are limited to single-frequency testing, making such systems impractical for resolving frequency-dependent added mass or damping matrices. The 6 DOF platform, termed a hexapod, overcomes these limitations by offering enhanced maneuverability and the ability to test broad-banded frequency spectra in multiple degrees of freedom in a single experiment.

We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant rates over long periods. Production of patents and publications doubles every ten years, by contrast with the growth of computing resources driven by Moore's Law, roughly a doubling every two years. We argue that the input of AI researchers is also crucial and its contribution can be objectively estimated. Consequently, we give a simple argument that explains the 5:1 relation between these two rates. We then discuss the application of this argument to different output measures and compare our analyses with predictions based on machine learning scaling laws proposed in existing literature. Our quantitative framework facilitates understanding, predicting, and modulating the development of these important technologies.

The advances in Vision-Language models (VLMs) offer exciting opportunities for robotic applications involving image geo-localization, the problem of identifying the geo-coordinates of a place based on visual data only. Recent research works have focused on using a VLM as embeddings extractor for geo-localization, however, the most sophisticated VLMs may only be available as black boxes that are accessible through an API, and come with a number of limitations: there is no access to training data, model features and gradients; retraining is not possible; the number of predictions may be limited by the API; training on model outputs is often prohibited; and queries are open-ended. The utilization of a VLM as a stand-alone, zero-shot geo-localization system using a single text-based prompt is largely unexplored. To bridge this gap, this paper undertakes the first systematic study, to the best of our knowledge, to investigate the potential of some of the state-of-the-art VLMs as stand-alone, zero-shot geo-localization systems in a black-box setting with realistic constraints. We consider three main scenarios for this thorough investigation: a) fixed text-based prompt; b) semantically-equivalent text-based prompts; and c) semantically-equivalent query images. We also take into account the auto-regressive and probabilistic generation process of the VLMs when investigating their utility for geo-localization task by using model consistency as a metric in addition to traditional accuracy. Our work provides new insights in the capabilities of different VLMs for the above-mentioned scenarios., Comment: Submitted to IROS 2025