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Developers require accurate descriptions of REpresentational State Transfer (REST) Application Programming Interfaces (APIs) for a successful interaction between web services. The OpenAPI Specification (OAS) has become the de facto standard for documenting REST APIs. Manually creating an OpenAPI description is time-consuming and error-prone, and therefore several approaches were proposed to automatically generate them from bytecode or runtime information. In this paper, we first study three state-of-the-art approaches, Respector, Prophet, and springdoc-openapi, and present and discuss their shortcomings. Next, we introduce AutoOAS, our approach addressing these shortcomings to generate accurate OpenAPI descriptions. It detects exposed REST endpoint paths, corresponding HTTP methods, HTTP response codes, and the data models of request parameters and responses directly from Java source code. We evaluated AutoOAS on seven real-world Spring Boot projects and compared its performance with the three state-of-the-art approaches. Based on a manually created ground truth, AutoOAS achieved the highest precision and recall when identifying REST endpoint paths, HTTP methods, parameters, and responses. It outperformed the second-best approach, Respector, with a 39% higher precision and 35% higher recall when identifying parameters and a 29% higher precision and 11% higher recall when identifying responses. Furthermore, AutoOAS is the only approach that handles configuration profiles, and it provided the most accurate and detailed description of the data models that were used in the REST APIs.

Dust-obscured galaxies (DOGs) are enshrouded by dust, and many are believed to host accreting supermassive black holes (SMBHs), which makes them unique objects for probing the coevolution of galaxies and SMBHs. We select and characterize DOGs in the $13\,deg^2$ XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS), leveraging the superb multiwavelength data from X-rays to radio. We select 3738 DOGs at $z\approx1.6-2.1$ in XMM-SERVS, while maintaining good data quality without introducing significant bias. This represents the largest DOG sample with thorough multiwavelength source characterization. Spectral energy distribution (SED) modeling shows DOGs are a heterogeneous population consisting of both normal galaxies and active galactic nuclei (AGNs). Our DOGs are massive ($\log M_\bigstar/M_\odot\approx10.7-11.3$), 174 are detected in X-rays, and they are generally radio-quiet systems. X-ray detected DOGs are luminous and are moderately to heavily obscured in X-rays. Stacking analyses for the X-ray undetected DOGs show highly significant average detections. Critically, we compare DOGs with matched galaxy populations. DOGs have similar AGN fractions compared with typical galaxy populations. X-ray detected DOGs have higher $M_\bigstar$ and higher X-ray obscuration, but they are not more star-forming than typical X-ray AGNs. The results potentially challenge the relevance of the merger-driven galaxy-SMBH coevolution framework for X-ray detected DOGs., Comment: 35 pages, 19 figures. Accepted for publication in ApJ

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

Machine-generated data is a valuable resource for training Artificial Intelligence algorithms, evaluating rare workflows, and sharing data under stricter data legislations. The challenge is to generate data that is accurate and private. Current statistical and deep learning methods struggle with large data volumes, are prone to hallucinating scenarios incompatible with reality, and seldom quantify privacy meaningfully. Here we introduce Genomator, a logic solving approach (SAT solving), which efficiently produces private and realistic representations of the original data. We demonstrate the method on genomic data, which arguably is the most complex and private information. Synthetic genomes hold great potential for balancing underrepresented populations in medical research and advancing global data exchange. We benchmark Genomator against state-of-the-art methodologies (Markov generation, Restricted Boltzmann Machine, Generative Adversarial Network and Conditional Restricted Boltzmann Machines), demonstrating an 84-93% accuracy improvement and 95-98% higher privacy. Genomator is also 1000-1600 times more efficient, making it the only tested method that scales to whole genomes. We show the universal trade-off between privacy and accuracy, and use Genomator's tuning capability to cater to all applications along the spectrum, from provable private representations of sensitive cohorts, to datasets with indistinguishable pharmacogenomic profiles. Demonstrating the production-scale generation of tuneable synthetic data can increase trust and pave the way into the clinic.

Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables Large Language Models to be applied to securely shared cohorts via the GA4GH Beacon protocol. By simply "asking" Beacon, actionable insights can be gained, analyzed and made publication-ready.

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

We report results of a search for nuclear recoils induced by weakly interacting massive particle (WIMP) dark matter using the LUX-ZEPLIN (LZ) two-phase xenon time projection chamber. This analysis uses a total exposure of $4.2\pm0.1$ tonne-years from 280 live days of LZ operation, of which $3.3\pm0.1$ tonne-years and 220 live days are new. A technique to actively tag background electronic recoils from $^{214}$Pb $\beta$ decays is featured for the first time. Enhanced electron-ion recombination is observed in two-neutrino double electron capture decays of $^{124}$Xe, representing a noteworthy new background. After removal of artificial signal-like events injected into the data set to mitigate analyzer bias, we find no evidence for an excess over expected backgrounds. World-leading constraints are placed on spin-independent (SI) and spin-dependent WIMP-nucleon cross sections for masses $\geq$9 GeV/$c^2$. The strongest SI exclusion set is $2.1\times10^{-48}$ cm$^{2}$ at the 90% confidence level at a mass of 36 GeV/$c^2$, and the best SI median sensitivity achieved is $5.0\times10^{-48}$ cm$^{2}$ for a mass of 40 GeV/$c^2$.

The thematic fit estimation task measures the compatibility between a predicate (typically a verb), an argument (typically a noun phrase), and a specific semantic role assigned to the argument. Previous state-of-the-art work has focused on modeling thematic fit through distributional or neural models of event representation, trained in a supervised fashion with indirect labels. In this work, we assess whether pre-trained autoregressive LLMs possess consistent, expressible knowledge about thematic fit. We evaluate both closed and open state-of-the-art LLMs on several psycholinguistic datasets, along three axes: (1) Reasoning Form: multi-step logical reasoning (chain-of-thought prompting) vs. simple prompting. (2) Input Form: providing context (generated sentences) vs. raw tuples . (3) Output Form: categorical vs. numeric. Our results show that chain-of-thought reasoning is more effective on datasets with self-explanatory semantic role labels, especially Location. Generated sentences helped only in few settings, and lowered results in many others. Predefined categorical (compared to numeric) output raised GPT's results across the board with few exceptions, but lowered Llama's. We saw that semantically incoherent generated sentences, which the models lack the ability to consistently filter out, hurt reasoning and overall performance too. Our GPT-powered methods set new state-of-the-art on all tested datasets., Comment: 15 pages, 3 figures

The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, potentially leading to distrust in human-machine interactions. To address this issue, we introduce a novel dataset of 1,015 tissue microarray core images, annotated by an international group of 54 pathologists. The annotations provide detailed localized pattern descriptions for Gleason grading in line with international guidelines. Utilizing this dataset, we develop an inherently explainable AI system based on a U-Net architecture that provides predictions leveraging pathologists' terminology. This approach circumvents post-hoc explainability methods while maintaining or exceeding the performance of methods trained directly for Gleason pattern segmentation (Dice score: 0.713 $\pm$ 0.003 trained on explanations vs. 0.691 $\pm$ 0.010 trained on Gleason patterns). By employing soft labels during training, we capture the intrinsic uncertainty in the data, yielding strong results in Gleason pattern segmentation even in the context of high interobserver variability. With the release of this dataset, we aim to encourage further research into segmentation in medical tasks with high levels of subjectivity and to advance the understanding of pathologists' reasoning processes., Comment: 58 pages, 15 figures (incl. supplementary)

We present a quantum computational framework for SU(2) lattice gauge theory, leveraging continuous variables instead of discrete qubits to represent the infinite-dimensional Hilbert space of the gauge fields. We consider a ladder as well as a two-dimensional grid of plaquettes, detailing the use of gauge fixing to reduce the degrees of freedom and simplify the Hamiltonian. We demonstrate how the system dynamics, ground states, and energy gaps can be computed using the continuous-variable approach to quantum computing. Our results indicate that it is feasible to study non-Abelian gauge theories with continuous variables, providing new avenues for understanding the real-time dynamics of quantum field theories., Comment: 26 pages

All-solid-state nanoscale devices capable of efficiently controlling a heat flow are crucial for advanced thermal management technologies. Here we predict a magnon-driven magnetothermal resistance (mMTR) effect in multilayers of ferromagnets and normal metals, i.e. a thermal resistance that varies when switching between parallel and antiparallel magnetization orientations of the ferromagnetic layers, even in the absence of conduction electrons in the ferromagnets. The mMTR arises from an interfacial temperature drop caused by magnon spin accumulations and can be engineered by the layer thicknesses, spin diffusion lengths, and spin conductances. The mMTR predicted here enables magnetothermal switching in insulator-based systems; we already predict large mMTR ratios up to 40$\%$ for superlattices of the electrically insulating magnet yttrium iron garnet and elemental metals.

Discovery of the X(3872) meson in 2003 ignited intense interest in exotic (neither $q\bar{q}$ nor $qqq$) hadrons, but a $c\bar{c}$ interpretation of this state was difficult to exclude. An unequivocal exotic was discovered in the $Z_c(3900)^+$ meson -- a charged charmonium-like state. A variety of models of exotic structure have been advanced but consensus is elusive. The grand lesson from heavy quarkonia was that heavy quarks bring clarity. Thus, the recently reported triplet of all-charm tetraquark candidates -- $X(6600)$, $X(6900)$, and $X(7100)$ -- decaying to $J/\psi\,J/\psi$ is a great boon, promising important insights. We review some history of exotics, chronicle the road to prospective all-charm tetraquarks, discuss in some detail the divergent modeling of $J/\psi\,J/\psi$ structures, and offer some inferences about them. These states form a Regge trajectory and appear to be a family of radial excitations. A reported, but unexplained, threshold excess could hint at a fourth family member. We close with a brief look at a step beyond: all-bottom tetraquarks., Comment: Submitted to Chinese Physics Letter

Human-robot interaction through mixed reality (MR) technologies enables novel, intuitive interfaces to control robots in remote operations. Such interfaces facilitate operations in hazardous environments, where human presence is risky, yet human oversight remains crucial. Potential environments include disaster response scenarios and areas with high radiation or toxic chemicals. In this paper we present an interface system projecting a 3D representation of a scanned room as a scaled-down 'dollhouse' hologram, allowing users to select and manipulate objects using a straightforward drag-and-drop interface. We then translate these drag-and-drop user commands into real-time robot actions based on the recent Spot-Compose framework. The Unity-based application provides an interactive tutorial and a user-friendly experience, ensuring ease of use. Through comprehensive end-to-end testing, we validate the system's capability in executing pick-and-place tasks and a complementary user study affirms the interface's intuitive controls. Our findings highlight the advantages of this interface in improving user experience and operational efficiency. This work lays the groundwork for a robust framework that advances the potential for seamless human-robot collaboration in diverse applications. Paper website: https://holospot.github.io/, Comment: 6 pages, 8 figures, submitted to ICRA 2025

Simulating open quantum systems, which interact with external environments, presents significant challenges on noisy intermediate-scale quantum (NISQ) devices due to limited qubit resources and noise. In this paper, we propose an efficient framework for simulating open quantum systems on NISQ hardware by leveraging a time-perturbative Kraus operator representation of the system's dynamics. Our approach avoids the computationally expensive Trotterization method and exploits the Lindblad master equation to represent time evolution in a compact form, particularly for systems satisfying specific commutation relations. We demonstrate the efficiency of our method by simulating quantum channels, such as the continuous-time Pauli channel and damped harmonic oscillators, on NISQ devices, including IonQ Harmony and Quantinuum H1-1. Additionally, we introduce hardware-agnostic error mitigation techniques, including Pauli channel fitting and quantum depolarizing channel inversion, to enhance the fidelity of quantum simulations. Our results show strong agreement between the simulations on real quantum hardware and exact solutions, highlighting the potential of Kraus-based methods for scalable and accurate simulation of open quantum systems on NISQ devices. This framework opens pathways for simulating more complex systems under realistic conditions in the near term., Comment: 18 pages, 12 figures

CDF-S XT1 and XT2 are considered two "canonical" extragalactic fast X-ray transients (FXTs). In this work, we report new constraints on both FXTs, based on recent JWST NIRCam and MIRI photometry, as well as NIRspec spectroscopy for CDF-S XT2 that allow us to improve our understanding of their distances, energetics, and host galaxy properties compared to the pre-JWST era. We use the available HST and JWST archival data to determine the host properties and constrain the energetics of each FXT based on spectral energy distribution (SED) photometric fitting. The host of CDF-S XT1 is now constrained to lie at $z_{phot}{=}2.76_{-0.13}^{+0.21}$, implying a host absolute magnitude $M_{R}=-19.14$ mag, stellar mass $M_{*}$=2.8e8 $M_\odot$, and star formation rate SFR=0.62 $M_\odot$~yr$^{-1}$. These properties lie at the upper end of previous estimates, leaving CDF-S XT1 with a peak X-ray luminosity of $L_{X,peak}$=2.8e47 erg s$^{-1}$. We argue that the best progenitor scenario for XT1 is a low-luminosity gamma-ray burst (GRB), although we do not fully rule out a proto-magnetar association or a jetted tidal disruption event involving a white dwarf and an intermediate-massive black hole. In the case of CDF-S XT2, JWST imaging reveals a new highly obscured component of the host galaxy, previously missed by HST, while NIRspec spectroscopy securely places the host at $z_{spec}{=}3.4598{\pm}0.0022$. The new redshift implies a host with $M_{R}=-21.76$ mag, $M_{*}$=5.5e10 $M_\odot$, SFR=160 $M_\odot$ yr$^{-1}$, and FXT $L_{X,peak}$=1.4e47 erg s$^{-1}$. The revised energetics, similarity to X-ray flash event light curves, small host offset, and high host SFR favor a low-luminosity collapsar progenitor for CDF-S XT2. While these HST and JWST observations shed light on the host galaxies of XT1 and XT2, and by extension, on the nature of FXTs, a unique explanation for both sources remains elusive., Comment: The manuscript was submitted to Astronomy & Astrophysics in August 2024

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic., Comment: Accepted version; not Version of Record

The difficulty of anonymizing text data hinders the development and deployment of NLP in high-stakes domains that involve private data, such as healthcare and social services. Poorly anonymized sensitive data cannot be easily shared with annotators or external researchers, nor can it be used to train public models. In this work, we explore the feasibility of using synthetic data generated from differentially private language models in place of real data to facilitate the development of NLP in these domains without compromising privacy. In contrast to prior work, we generate synthetic data for real high-stakes domains, and we propose and conduct use-inspired evaluations to assess data quality. Our results show that prior simplistic evaluations have failed to highlight utility, privacy, and fairness issues in the synthetic data. Overall, our work underscores the need for further improvements to synthetic data generation for it to be a viable way to enable privacy-preserving data sharing., Comment: Accepted to EMNLP 2024 (Findings)

Decoupling charge and heat transport is essential for optimizing thermoelectric materials. Strategies to inhibit lattice-driven heat transport, however, also compromise carrier mobility, limiting the performance of most thermoelectrics, including Fe$_2$VAl Heusler compounds. Here, we demonstrate an innovative approach, which bypasses this tradeoff: via liquid-phase sintering, we incorporate the archetypal topological insulator Bi$_{1-x}$Sb$_{x}$ between Fe$_2$V$_{0.95}$Ta$_{0.1}$Al$_{0.95}$ grains. Structural investigations alongside extensive thermoelectric and magneto-transport measurements reveal distinct modifications in the microstructure, and a reduced lattice thermal conductivity and enhanced carrier mobility are simultaneously found. This yields a huge performance boost $-$ far beyond the effective-medium limit $-$ and results in one of the highest figure of merits among both half- and full-Heusler compounds, $z\approx 1.6\times 10^{-3}\,$K$^{-1}$ ($zT\approx 0.5$) at 295 K. Our findings highlight the potential of secondary phases to decouple charge and heat transport and call for more advanced theoretical studies of multiphase composites.

Hard X-ray-selected samples of Active Galactic Nuclei (AGN) provide one of the cleanest views of supermassive black hole accretion, but are biased against objects obscured by Compton-thick gas column densities of $N_{\rm H}$ $>$ 10$^{24}$ cm$^{-2}$. To tackle this issue, we present the NuSTAR Local AGN $N_{\rm H}$ Distribution Survey (NuLANDS)$-$a legacy sample of 122 nearby ($z$ $<$ 0.044) AGN primarily selected to have warm infrared colors from IRAS between 25$-$60 $\mu$m. We show that optically classified type 1 and 2 AGN in NuLANDS are indistinguishable in terms of optical [OIII] line flux and mid-to-far infrared AGN continuum bolometric indicators, as expected from an isotropically selected AGN sample, while type 2 AGN are deficient in terms of their observed hard X-ray flux. By testing many X-ray spectroscopic models, we show the measured line-of-sight column density varies on average by $\sim$ 1.4 orders of magnitude depending on the obscurer geometry. To circumvent such issues we propagate the uncertainties per source into the parent column density distribution, finding a directly measured Compton-thick fraction of 35 $\pm$ 9%. By construction, our sample will miss sources affected by severe narrow-line reddening, and thus segregates sources dominated by small-scale nuclear obscuration from large-scale host-galaxy obscuration. This bias implies an even higher intrinsic obscured AGN fraction may be possible, although tests for additional biases arising from our infrared selection find no strong effects on the measured column-density distribution. NuLANDS thus holds potential as an optimized sample for future follow-up with current and next-generation instruments aiming to study the local AGN population in an isotropic manner., Comment: Accepted for publication in ApJ. 50 pages (78 including appendix and bibliography), 21 figures

Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, and X-rays are commonly used for its diagnosis due to their cost-effectiveness. Magnetic Resonance Imaging (MRI), on the other hand, offers detailed soft tissue visualization and has become a valuable supplementary diagnostic tool for KOA. Unfortunately, the high cost and limited accessibility of MRI hinders its widespread use, leaving many patients with KOA to rely solely on X-ray imaging. In this study, we introduce a novel diffusion-based Xray2MRI model capable of generating pseudo-MRI volumes from a single X-ray image. In addition to using X-rays as conditional input, our model integrates target depth, KOA probability distribution, and image intensity distribution modules to guide the synthesis process, ensuring that the generated corresponding slices accurately correspond to the anatomical structures. Experimental results demonstrate that by integrating information from X-rays with additional input data, our proposed approach is capable of generating pseudo-MRI sequences that approximate real MRI scans. In addition, by increasing the number of inference steps, the model achieves effective interpolation, which further improves the continuity and smoothness of the generated MRI sequences, representing a promising first attempt at cost-effective medical imaging solutions. This study is available on https://zwang78.github.io/.

Neutron scattering characterization of materials allows for the study of entanglement and microscopic structure, but is inefficient to simulate classically for comparison to theoretical models and predictions. However, quantum processors, notably analog quantum simulators, have the potential to offer an unprecedented, efficient method of Hamiltonian simulation by evolving a state in real time to compute phase transitions, dynamical properties, and entanglement witnesses. Here, we present a method for simulating neutron scattering on QuEra's Aquila processor by measuring the dynamic structure factor (DSF) for the prototypical example of the critical transverse field Ising chain, and propose a method for error mitigation. We provide numerical simulations and experimental results for the performance of the procedure on the hardware, up to a chain of length $L=25$. Additionally, the DSF result is used to compute the quantum Fisher information (QFI) density, where we confirm bipartite entanglement in the system experimentally., Comment: 13 pages, 10 figures

Tidal disruption events (TDEs) could be an important growth channel for massive black holes in dwarf galaxies. Theoretical work suggests that the observed active galactic nuclei (AGNs) in dwarf galaxies are predominantly TDE-powered. To assess this claim, we perform variability analyses on the dwarf-hosted AGNs detected in the $7$ Ms Chandra Deep Field-South (CDF-S) survey, with observations spanning $\approx 16$ years. Based on the spectral energy distribution (SED) modeling with X-CIGALE, we select AGNs hosted by dwarf galaxies (stellar mass below $10^{10}\ M_\odot$). We focus on X-ray sources with full-band detections, leading to a sample of $78$ AGNs (0.122 $\leq$ $z$ $\leq$ 3.515). We fit the X-ray light curves with a canonical TDE model of $t^{-5/3}$ and a constant model. If the former outperforms the latter in fitting quality for a source, we consider the source as a potential TDE. We identify five potential TDEs, constituting a small fraction of our sample. Using true- and false-positive rates obtained from fitting models to simulated light curves, we perform Bayesian analysis to obtain the posterior of the TDE fraction for our sample. The posterior peaks close to zero ($2.56\%$), and we obtain a $2$-$\sigma$ upper limit of $9.80\%$. Therefore, our result indicates that the observed AGNs in dwarf galaxies are not predominantly powered by TDEs., Comment: 16 pages, 10 figures, accepted for publication in ApJ

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

Modern high performance computers are massively parallel; for many PDE applications spatial parallelism saturates long before the computer's capability is reached. Parallel-in-time methods enable further speedup beyond spatial saturation by solving multiple timesteps simultaneously to expose additional parallelism. ParaDiag is a particular approach to parallel-in-time based on preconditioning the simultaneous timestep system with a perturbation that allows block diagonalisation via a Fourier transform in time. In this article, we introduce asQ, a new library for implementing ParaDiag parallel-in-time methods, with a focus on applications in the geosciences, especially weather and climate. asQ is built on Firedrake, a library for the automated solution of finite element models, and the PETSc library of scalable linear and nonlinear solvers. This enables asQ to build ParaDiag solvers for general finite element models and provide a range of solution strategies, making testing a wide array of problems straightforward. We use a quasi-Newton formulation that encompasses a range of ParaDiag methods, and expose building blocks for constructing more complex methods. The performance and flexibility of asQ is demonstrated on a hierarchy of linear and nonlinear atmospheric flow models. We show that ParaDiag can offer promising speedups and that asQ is a productive testbed for further developing these methods., Comment: 30 pages, 15 figures. To be submitted to journal

Background: The integration of multi-stain histopathology images through deep learning poses a significant challenge in digital histopathology. Current multi-modal approaches struggle with data heterogeneity and missing data. This study aims to overcome these limitations by developing a novel transformer model for multi-stain integration that can handle missing data during training as well as inference. Methods: We propose UNICORN (UNiversal modality Integration Network for CORonary classificatioN) a multi-modal transformer capable of processing multi-stain histopathology for atherosclerosis severity class prediction. The architecture comprises a two-stage, end-to-end trainable model with specialized modules utilizing transformer self-attention blocks. The initial stage employs domain-specific expert modules to extract features from each modality. In the subsequent stage, an aggregation expert module integrates these features by learning the interactions between the different data modalities. Results: Evaluation was performed using a multi-class dataset of atherosclerotic lesions from the Munich Cardiovascular Studies Biobank (MISSION), using over 4,000 paired multi-stain whole slide images (WSIs) from 170 deceased individuals on 7 prespecified segments of the coronary tree, each stained according to four histopathological protocols. UNICORN achieved a classification accuracy of 0.67, outperforming other state-of-the-art models. The model effectively identifies relevant tissue phenotypes across stainings and implicitly models disease progression. Conclusion: Our proposed multi-modal transformer model addresses key challenges in medical data analysis, including data heterogeneity and missing modalities. Explainability and the model's effectiveness in predicting atherosclerosis progression underscores its potential for broader applications in medical research.

Verbalization of robot experience, i.e., summarization of and question answering about a robot's past, is a crucial ability for improving human-robot interaction. Previous works applied rule-based systems or fine-tuned deep models to verbalize short (several-minute-long) streams of episodic data, limiting generalization and transferability. In our work, we apply large pretrained models to tackle this task with zero or few examples, and specifically focus on verbalizing life-long experiences. For this, we derive a tree-like data structure from episodic memory (EM), with lower levels representing raw perception and proprioception data, and higher levels abstracting events to natural language concepts. Given such a hierarchical representation built from the experience stream, we apply a large language model as an agent to interactively search the EM given a user's query, dynamically expanding (initially collapsed) tree nodes to find the relevant information. The approach keeps computational costs low even when scaling to months of robot experience data. We evaluate our method on simulated household robot data, human egocentric videos, and real-world robot recordings, demonstrating its flexibility and scalability., Comment: Code, data and demo videos at https://hierarchical-emv.github.io

We develop and investigate a general theory of representations of second-order functionals, based on a notion of a right comodule for a monad on the category of containers. We show how the notion of comodule representability naturally subsumes classic representations of continuous functionals with well-founded trees. We find other kinds of representations by varying the monad, the comodule, and in some cases the underlying category of containers. Examples include uniformly continuous or finitely supported functionals, functionals querying their arguments precisely once, or at most once, functionals interacting with an ambient environment through computational effects, as well as functionals trivially representing themselves. Many of these rely on our construction of a monad on containers from a monad on shapes and a weak Mendler-style monad algebra on the universe for positions. We show that comodule representability on the category of propositional containers, which have positions valued in a universe of propositions, is closely related to instance reducibility in constructive mathematics, and through it to Weihrauch reducibility in computability theory.

We analyze the Spitzer spectra of 140 active galactic nuclei (AGN) detected in the hard X-rays (14-195 keV) by the Burst Alert Telescope (BAT) on board Swift. This sample allows us to probe several orders of magnitude in black hole masses ($10^6-10^9 M_{\odot}$), Eddington ratios ($10^{-3}-1$), X-ray luminosities ($10^{42}-10^{45}\rm\,erg\,s^{-1}$), and X-ray column densities ($10^{20}-10^{24}\rm\,cm^{-2}$). The AGN emission is expected to be the dominant source of ionizing photons with energies $\gtrsim50$ eV, and therefore high-ionization mid-infrared (MIR) emission lines such as [Ne V] 14.32, 24.32 $\mu$m and [O IV] 25.89 $\mu$m are predicted to be good proxies of AGN activity, and robust against obscuration effects. We find high detection rates ($\gtrsim85-90$ per cent) for the mid-infrared coronal emission lines in our AGN sample. The luminosities of these lines are correlated with the 14-150 keV luminosity (with a typical scatter of $\sigma \sim 0.4-0.5$ dex), strongly indicating that the mid-infrared coronal line emission is driven by AGN activity. Interestingly, we find that the coronal lines are more tightly correlated to the bolometric luminosity ($\sigma \sim 0.2-0.3$ dex), calculated from careful analysis of the spectral energy distribution, than to the X-ray luminosity. We find that the relationship between the coronal line strengths and $L_{14-150\rm\,keV}$ is independent of black hole mass, Eddington ratio and X-ray column density. This confirms that the mid-infrared coronal lines can be used as unbiased tracers of the AGN power for X-ray luminosities in the $10^{42}-10^{45}\rm\,erg\,s^{-1}$ range., Comment: ApJ in press

We investigate the kinematics and dynamics of the molecular and ionized gas in the host galaxies of three Palomar-Green quasars at low redshifts, benefiting from the archival millimeter-wave interferometric and optical integral field unit data. We study the kinematics of both cold molecular and hot ionized gas by analyzing the CO and H$\alpha$ data cubes, and construct the mass distributions of our sample through gas dynamics, utilizing a priori knowledge regarding the galaxy light distribution. We find no systematic offset between the stellar mass derived from our dynamical method and that from the broad-band photometry and mass-to-light ratio, suggesting the consistency of both methods. We then study the kinetic pressure and the weight of the interstellar medium using our dynamical mass model. By studying the relationship between kinetic pressure and gravitational pressure of the quasar host galaxies, we find an equivalence in the hydrostatic equilibrium states of ISM in the quasar host galaxies, similar to the result of gas equilibrium in normal star-forming galaxies, suggesting minimal quasar feedback. Regarding non-circular motion as indicative of quasar-driven outflows, we observe an exceptionally low coupling efficiency between molecular gas outflow and AGN bolometric luminosities. These results demonstrate the marginal influence of the central engine on the properties of cold molecular gas in quasar host galaxies., Comment: 27 pages, 12 figures, accepted for publication in ApJ

We develop a gauge invariant, Loop-String-Hadron (LSH) based representation of SU(2) Yang-Mills theory defined on a general graph consisting of vertices and half-links. Inspired by weak coupling studies, we apply this technique to maximal tree gauge fixing. This allows us to develop a fully gauge fixed representation of the theory in terms of LSH quantum numbers. We explicitly show how the quantum numbers in this formulation directly relate to the variables in the magnetic description. In doing so, we will also explain in detail the way that the Kogut-Susskind formulation, prepotentials, and point splitting, work for general graphs. In the appendix of this work we provide a self-contained exposition of the mathematical details of Hamiltonian pure gauge theories defined on general graphs., Comment: 23 pages, 37 pages of appendices, 27 Figures, 1 Table; v2: Added references, corrected typos

Covert channel attacks have been continuously studied as severe threats to modern computing systems. Software-based covert channels are a typically hard-to-detect branch of these attacks, since they leverage virtual resources to establish illegitimate communication between malicious actors. In this work, we present MeMoir: a novel software-driven covert channel that, for the first time, utilizes memory usage as the medium for the channel. We implemented the new covert channel on two real-world platforms with different architectures: a general-purpose Intel x86-64-based desktop computer and an ARM64-based embedded system. Our results show that our new architecture- and hardware-agnostic covert channel is effective and achieves moderate transmission rates with very low error. Moreover, we present a real use-case for our attack where we were able to communicate information from a Hyper-V virtualized enviroment to a Windows 11 host system. In addition, we implement a machine learning-based detector that can predict whether an attack is present in the system with an accuracy of more than 95% with low false positive and false negative rates by monitoring the use of system memory. Finally, we introduce a noise-based countermeasure that effectively mitigates the attack while inducing a low power overhead in the system compared to other normal applications.

Through its affordances, immersive virtual reality (VR) offers a means to apply embodied and external cognition from the physical realm to solving analytical problems that are typically only conceptual. We present an example of executing a structured analysis following the tenets of cognitive systems engineering to derive immersive affordances applicable to a difficult analytical problem, in our case, reverse engineering (RE) binary programs. We conducted a basic cognitive task analysis of the problem to reveal features of its cognitive model and their associated fundamental cognitive phenomena, and then we mapped those concepts to immersive affordances associated with those concepts. We implemented a subset of those affordances in a VR system facilitating discovery of features of a binary program. Feedback from RE practitioners drove the initial development of the system and we are preparing for a formal effectiveness study to inform the direction of future research., Comment: 5 pages, 4 figures

We present one of the largest multiwavelength studies of simultaneous optical-to-X-ray spectral energy distributions (SEDs) of unobscured active galactic nuclei (AGN) in the local Universe. Using a representative sample of hard-X-ray-selected AGN from the 70-month Swift/BAT catalog, with optical/UV photometric data from Swift/UVOT and X-ray spectral data from Swift/XRT, we constructed broadband SEDs of 236 nearby AGN (0.001

We introduce Clerical, a programming language for exact real-number computation that combines first-order imperative-style programming with a limit operator for computation of real numbers as limits of Cauchy sequences. We address the semidecidability of the linear ordering of the reals by incorporating nondeterministic guarded choice, through which decisions based on partial comparison operations on reals can be patched together to give total programs. The interplay between mutable state, nondeterminism, and computation of limits is controlled by the requirement that expressions computing limits and guards modify only local state. We devise a domain-theoretic denotational semantics that uses a variant of Plotkin powerdomain construction tailored to our specific version of nondeterminism. We formulate a Hoare-style specification logic, show that it is sound for the denotational semantics, and illustrate the setup by implementing and proving correct a program for computation of $\pi$ as the least positive zero of $\sin$. The modular character of Clerical allows us to compose the program from smaller parts, each of which is shown to be correct on its own. We provide a proof-of-concept OCaml implementation of Clerical, and formally verify parts of the development, notably the soundness of specification logic, in the Coq proof assistant.

Despite increasing research efforts on household robotics, robots intended for deployment in domestic settings still struggle with more complex tasks such as interacting with functional elements like drawers or light switches, largely due to limited task-specific understanding and interaction capabilities. These tasks require not only detection and pose estimation but also an understanding of the affordances these elements provide. To address these challenges and enhance robotic scene understanding, we introduce SpotLight: A comprehensive framework for robotic interaction with functional elements, specifically light switches. Furthermore, this framework enables robots to improve their environmental understanding through interaction. Leveraging VLM-based affordance prediction to estimate motion primitives for light switch interaction, we achieve up to 84% operation success in real world experiments. We further introduce a specialized dataset containing 715 images as well as a custom detection model for light switch detection. We demonstrate how the framework can facilitate robot learning through physical interaction by having the robot explore the environment and discover previously unknown relationships in a scene graph representation. Lastly, we propose an extension to the framework to accommodate other functional interactions such as swing doors, showcasing its flexibility. Videos and Code: timengelbracht.github.io/SpotLight/, Comment: timengelbracht.github.io/SpotLight/

We demonstrate how to construct a fully gauge-fixed lattice Hamiltonian for a pure SU(2) gauge theory. Our work extends upon previous work, where a formulation of an SU(2) lattice gauge theory was developed that is efficient to simulate at all values of the gauge coupling. That formulation utilized maximal-tree gauge, where all local gauge symmetries are fixed and a residual global gauge symmetry remains. By using the geometric picture of an SU(2) lattice gauge theory as a system of rotating rods, we demonstrate how to fix the remaining global gauge symmetry. In particular, the quantum numbers associated with total charge can be isolated by rotating between the lab and body frames using the three Euler angles. The Hilbert space in this new `sequestered' basis partitions cleanly into sectors with differing total angular momentum, which makes gauge-fixing to a particular total charge sector trivial, particularly for the charge-zero sector. In addition to this sequestered basis inheriting the property of being efficient at all values of the coupling, we show that, despite the global nature of the final gauge-fixing procedure, this Hamiltonian can be simulated using quantum resources scaling only polynomially with the lattice volume., Comment: 24 pages, 1 figure, 3 tables, 3 appendices

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided., Comment: Accepted to IECON 2024

Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization., Comment: 45 pages, 17 figures

This study investigates the 5$f$-electron-conduction electron hybridization process in the heavy fermion superconductor UPd$_2$Al$_3$ using a combination of angle-resolved photoemission spectroscopy (ARPES) and time-resolved photoemission spectroscopy (tr-PES). ARPES measurements reveal the formation of a hybridization gap at a temperature of approximately 75 K, which becomes more pronounced as the temperature decreases. Notably, the persistence of a flat U 5$f$ band at temperatures well above the hybridization onset challenges conventional understanding. Our findings demonstrate a non-monotonic temperature dependence of the quasiparticle relaxation time, with an anomalous decrease at 20 K, suggesting complex electronic and magnetic interactions. These findings provide detailed insights into the 5$f$-electron hybridization process in UPd$_2$Al$_3$, with significant implications for the understanding of heavy fermion superconductivity and the role of 5$f$-electron hybridization in uranium-based materials., Comment: 5 pages, 4 figures

Aerial manipulation combines the versatility and speed of flying platforms with the functional capabilities of mobile manipulation, which presents significant challenges due to the need for precise localization and control. Traditionally, researchers have relied on offboard perception systems, which are limited to expensive and impractical specially equipped indoor environments. In this work, we introduce a novel platform for autonomous aerial manipulation that exclusively utilizes onboard perception systems. Our platform can perform aerial manipulation in various indoor and outdoor environments without depending on external perception systems. Our experimental results demonstrate the platform's ability to autonomously grasp various objects in diverse settings. This advancement significantly improves the scalability and practicality of aerial manipulation applications by eliminating the need for costly tracking solutions. To accelerate future research, we open source our ROS 2 software stack and custom hardware design, making our contributions accessible to the broader research community., Comment: Project website: https://sites.google.com/view/open-source-soft-platform/open-source-soft-robotic-platform

Explicitly utilizing the sparsity of the electronic structure problem, fragmentation methods have been heavily researched for decades with great success, pushing the limits of ab initio quantum chemistry ever further. Recently, this set of methods was expanded to include a fundamentally different approach called excitonic renormalization, providing promising initial results. It builds a supersystem Hamiltonian in a second-quantized-like representation from transition-density tensors of isolated fragments, contracted with biorthogonalized molecular integrals. This makes the method fully modular in terms of the quantum chemical methods applied to each fragment and enables massive truncation of the state-space required. Proof-of-principle tests have previously shown that solving for the ground state of an excitonically renormalized Hamiltonian can efficiently scale to hundreds of fragments, but the ad hoc approach that was used to build the Hamiltonian in those tests was not scalable to larger fragments. On the other hand, initial tests of the originally proposed fully modular Hamiltonian build, presented here, have shown the accuracy to be poor on account of its non-Hermitian character. In this study we bridge the gap between these with an operator expansion that is shown to converge rapidly, tending towards a Hermitian Hamiltonian while retaining the modularity, yielding an accurate, scalable method. The accuracy of the Hamiltonian is tested here for a beryllium dimer. At distances near the equilibrium point and longer, the zeroth-order method is comparable to CCSD(T), and the first-order method to FCI. Deviations occurring at shorter bond distances are discussed along with approaches to scaling up to larger fragments., Comment: 18 pages, 2 figures

The application of machine learning to medical ultrasound videos of the heart, i.e., echocardiography, has recently gained traction with the availability of large public datasets. Traditional supervised tasks, such as ejection fraction regression, are now making way for approaches focusing more on the latent structure of data distributions, as well as generative methods. We propose a model trained exclusively by knowledge distillation, either on real or synthetical data, involving retrieving masks suggested by a teacher model. We achieve state-of-the-art (SOTA) values on the task of identifying end-diastolic and end-systolic frames. By training the model only on synthetic data, it reaches segmentation capabilities close to the performance when trained on real data with a significantly reduced number of weights. A comparison with the 5 main existing methods shows that our method outperforms the others in most cases. We also present a new evaluation method that does not require human annotation and instead relies on a large auxiliary model. We show that this method produces scores consistent with those obtained from human annotations. Relying on the integrated knowledge from a vast amount of records, this method overcomes certain inherent limitations of human annotator labeling. Code: https://github.com/GregoirePetit/EchoDFKD

The tetragonal $4f$-electron intermetallic ErB$_4$ is characterized by strong Ising anisotropy along the tetragonal $c$ axis. The magnetic moments on the erbium sites can be mapped onto a Shastry-Sutherland (SSL) lattice resulting in geometrical frustration. At zero magnetic field ErB$_4$ exhibits collinear columnar antiferromagnetic (CAFM) order below $T_\text{N} = 15.4$ K. In the presence of a magnetic field parallel to the $c$ axis, ErB$_4$ exhibits a plateau at $1/2$ of the saturation magnetization $M_\text{S}$, which arises at a spin flip transition at $H_1$ $=$ 1.9 T. Fractional magnetization plateaus and other exotic spin phases are a well-established characteristic feature of frustrated spin systems. Monte Carlo simulations propose that ErB$_4$ is an ideal candidate to feature a spin supersolid phase in close vicinity of $H_1$ between the CAFM and $M/M_\text{S}=1/2$ plateau (HP) phase. Here we combine single-crystal neutron diffraction and inelastic neutron scattering to study the magnetic phase diagram and the crystal electric field (CEF) ground state of ErB$_4$. Our measurements as a function of magnetic field find no signature of the spin supersolid phase but allow us to determine the magnetic structure of the HP phase to be of the uuud type consistent with an Ising material. The magnetic moment $\mu_{\mathrm{CEF}}$ $=$ 8.96 $\mu_B$ expected from the CEF configuration determined by our inelastic neutron scattering measurements is also consistent with the ordered moment observed in neutron diffraction showing that the moments are fully ordered and close to the Er$^{3+}$ free ion moment (9.6 $\mu_B$).

Linking the fundamental physics of band structure and scattering theory with macroscopic features such as measurable bulk thermoelectric transport properties is indispensable to a thorough understanding of transport phenomena and ensures more targeted and efficient experimental research. Here, we introduce SeeBand, a highly efficient and interactive fitting tool based on Boltzmann transport theory. A fully integrated user interface and visualization tool enable real-time comparison and connection between the electronic band structure (EBS) and microscopic transport properties. It allows simultaneous analysis of data for the Seebeck coefficient $S$, resistivity $\rho$ and Hall coefficient $R_\text{H}$ to identify suitable EBS models and extract the underlying microscopic material parameters and additional information from the model. Crucially, the EBS can be obtained by directly fitting the temperature-dependent properties of a single sample, which goes beyond previous approaches that look into doping dependencies. Finally, the combination of neural-network-assisted initial guesses and an efficient subsequent fitting routine allows for a rapid processing of big datasets, facilitating high-throughput analyses to identify underlying, yet undiscovered dependencies, thereby guiding material design.

Reparametrization invariant Sobolev metrics on spaces of regular curves have been shown to be of importance in the field of mathematical shape analysis. For practical applications, one usually discretizes the space of smooth curves and considers the induced Riemannian metric on a finite dimensional approximation space. Surprisingly, the theoretical properties of the corresponding finite dimensional Riemannian manifolds have not yet been studied in detail, which is the content of the present article. Our main theorem concerns metric and geodesic completeness and mirrors the results of the infinite dimensional setting as obtained by Bruveris, Michor and Mumford., Comment: 22 pages, 6 figures

Block Encoding (BE) is a crucial subroutine in many modern quantum algorithms, including those with near-optimal scaling for simulating quantum many-body systems, which often rely on Quantum Signal Processing (QSP). Currently, the primary methods for constructing BEs are the Linear Combination of Unitaries (LCU) and the sparse oracle approach. In this work, we demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU), can themselves be efficiently utilized for BE implementation. Specifically, we present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons, an essential ingredient in simulations of high-energy physics. We also introduce a straightforward approach to BE based on the exact implementation of Linear Operators Via Exponentiation and LCU (LOVE-LCU). We find that, while using QSVT for BE results in the best asymptotic gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to $\lesssim11$ qubits, highlighting the importance of concrete circuit constructions over mere comparisons of asymptotic scalings. Using LOVE-LCU to implement the BE, we simulate the time evolution of single-site and two-site systems in the lattice $\varphi^4$ theory using the Generalized QSP algorithm and compare the gate counts to those required for Trotter simulation., Comment: 20 pages, 16 Figures, 2 Tables, 2 Appendices

Understanding energetic electron precipitation is crucial for accurate space weather modeling and forecasting, impacting the Earth's upper atmosphere and human infrastructure. This study presents a low-cost, low-mass, and low-power solution for high-fidelity analysis of electron precipitation events by measuring the resulting bremsstrahlung X-ray emissions. Specifically, we report on results from the flight of a radiation detector payload based on a silicon pixel read-out Timepix detector technology, and its successful utilization onboard a `burster' weather balloon. We launched this payload during the May 2024 superstorm, capturing high-resolution measurements of both background galactic cosmic ray radiation as well as storm-time energetic electron precipitation. We further developed particle and radiation detection algorithms to separate bremsstrahlung X-rays from other particle species in the pixel-resolved trajectories as seen in the Timepix detector. The measurements revealed a distinctive four-peak structure in X-ray flux, corresponding to periodic four-minute-long bursts of energetic electron precipitation between 21:20 and 21:40 UT. This precipitation was also observed by a riometer station close to the balloon launch path, further validating balloon measurements and the developed X-ray identification algorithm. The clear periodic structure of the measured precipitation is likely caused by modulation of the electron losses from the radiation belt by harmonic Pc5 ULF waves, observed contemporaneously on the ground. The study underscores the potential of compact, low-cost payloads for advancing our understanding of space weather. Specifically, we envision a potential use of such Timepix-based detectors in space science, for example on sounding rockets or nano-, micro-, and small satellite platforms.

As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders. One particular concern in music recommendation is artist gender fairness. Recent work has shown that the gender imbalance in the sector translates to the output of music recommender systems, creating a feedback loop that can reinforce gender biases over time. In this work, we examine that feedback loop to study whether algorithmic strategies or user behavior are a greater contributor to ongoing improvement (or loss) in fairness as models are repeatedly re-trained on new user feedback data. We simulate user interaction and re-training to investigate the effects of ranking strategies and user choice models on gender fairness metrics. We find re-ranking strategies have a greater effect than user choice models on recommendation fairness over time., Comment: 6 pages, 3 figures, conference short paper, to be published at RecSys 2024

The Gromov-Wasserstein (GW) distance is a powerful tool for comparing metric measure spaces which has found broad applications in data science and machine learning. Driven by the need to analyze datasets whose objects have increasingly complex structure (such as node and edge-attributed graphs), several variants of GW distance have been introduced in the recent literature. With a view toward establishing a general framework for the theory of GW-like distances, this paper considers a vast generalization of the notion of a metric measure space: for an arbitrary metric space $Z$, we define a $Z$-network to be a measure space endowed with a kernel valued in $Z$. We introduce a method for comparing $Z$-networks by defining a generalization of GW distance, which we refer to as $Z$-Gromov-Wasserstein ($Z$-GW) distance. This construction subsumes many previously known metrics and offers a unified approach to understanding their shared properties. This paper demonstrates that the $Z$-GW distance defines a metric on the space of $Z$-networks which retains desirable properties of $Z$, such as separability, completeness, and geodesicity. Many of these properties were unknown for existing variants of GW distance that fall under our framework. Our focus is on foundational theory, but our results also include computable lower bounds and approximations of the distance which will be useful for practical applications., Comment: V2: Added a new result on contractibility and fixed small errors