Search

We present flux measurements of Uranus observed at phase angles of 43.9{\deg}, 44.0{\deg}, and 52.4{\deg} by the Multispectral Visible Imaging Camera (MVIC) on the New Horizons spacecraft during 2023, 2010, and 2019, respectively. New Horizons imaged Uranus at a distance of about 24-70 AU (2023) in four color filters, with bandpasses of 400-550 nm, 540-700 nm, 780-975 nm, and 860-910 nm. High-phase-angle observations are of interest for studying the energy balance of Uranus, constraining the atmospheric scattering behavior, and understanding the planet as an analog for ice giant exoplanets. The new observations from New Horizons provide access to a wider wavelength range and different season compared to previous observations from both Voyager spacecraft. We performed aperture photometry on the New Horizons observations of Uranus to obtain its brightness in each photometric band. The photometry suggests that Uranus may be darker than predicted by a Lambertian phase curve in the Blue and Red filters. Comparison to simultaneous low-phase Hubble WFC3 and ground-based community-led observations indicates a lack of large-scale features at full-phase that would introduce variation in the rotational light curve. The New Horizons reflectance in the Blue (492 nm) and Red (624 nm) filters does not exhibit statistically significant variation and is consistent with the expected error bars. These results place new constraints on the atmospheric model of Uranus and its reflectivity. The observations are analogous to those from future exoplanet direct-imaging missions, which will capture unresolved images of exoplanets at partial phases. These results will serve as a "ground-truth" with which to interpret exo-ice giant data., Comment: Accepted for publication in the Planetary Science Journal, 11 pages, 6 figures

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 \AA, with a spectral resolution of 6.0 \AA\ (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database., Comment: 17 pages, 9 figures, 5 tables. Accepted for publication in A&A (replacement after minor language edits)

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments., Comment: Note: To access the list of contributions, please follow the "HTML" link that can be found on the arXiv page

To detect ultra-high-energy neutrinos, experiments such as ARA and RNO-G target the radio emission these particles induce when cascading in the ice, using deep antennas in South Pole or in Greenland. One of the main backgrounds for such signals is the radio emission generated by cosmic-ray showers, either directly in the ice, or in the air and transmitted to the ice, which can both reach the deep antennas. The first detection of cosmic rays with deep antennas would thus validate this detection principle and allow us to calibrate the detectors. FAERIE, the Framework for the simulation of Air shower Emission of Radio for in-Ice Experiments, is a numerical tool that couples both CoREAS and GEANT4 Monte-Carlo codes to simulate the radio emission from cosmic-ray showers deep in the ice. Using this code, we will investigate cosmic-ray radio signatures and the possible implications on the design of a cosmic-ray veto., Comment: 8 pages, 5 figures. Proceeding of the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA)

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks. The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging. Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution. The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions., Comment: Accept at NeurIPS 2024 Dataset and Benchmark Track

The butterfly diagram of the solar cycle exhibits a poleward migration of the diffuse magnetic field resulting from the decay of trailing sunspots. It is one component of what is sometimes referred to as the "rush to the poles". We investigate under which conditions the rush to the poles can be reproduced in flux-transport Babcock-Leighton dynamo models. We identify three main ways to reproduce it: a flux emergence probability that decreases rapidly with latitude; a threshold in subsurface toroidal field strength between slow and fast emergence; and an emergence rate based on magnetic buoyancy. We find that all three mechanisms lead to solar-like butterfly diagrams, but which present notable differences between them. The shape of the butterfly diagram is very sensitive to model parameters for the threshold prescription, while most models incorporating magnetic buoyancy converge to very similar butterfly diagrams, with butterfly wings widths of $\lesssim\pm 30^\circ$, in very good agreement with observations. With turbulent diffusivities above $35~\text{km}^2/\text{s}$ but below about $40~\text{km}^2/\text{s}$, buoyancy models are strikingly solar-like. The threshold and magnetic buoyancy prescriptions make the models non-linear and as such can saturate the dynamo through latitudinal quenching. The period of the models involving buoyancy is independent of the source term amplitude, but emergence loss increases it by $\simeq 60\%$. For the rush to the poles to be visible, a mechanism suppressing (enhancing) emergences at high (low) latitudes must operate. It is not sufficient that the toroidal field be stored at low latitudes for emergences to be limited to low latitudes. From these models we infer that the Sun is not in the advection-dominated regime, but also not in the diffusion-dominated regime. The cycle period is set through a balance between advection, diffusion and flux emergence., Comment: 12 pages, 17 figures, accepted for publication in A&A

Soft robots pose difficulties in terms of control, requiring novel strategies to effectively manipulate their compliant structures. Model-based approaches face challenges due to the high dimensionality and nonlinearities such as hysteresis effects. In contrast, learning-based approaches provide nonlinear models of different soft robots based only on measured data. In this paper, recurrent neural networks (RNNs) predict the behavior of an articulated soft robot (ASR) with five degrees of freedom (DoF). RNNs based on gated recurrent units (GRUs) are compared to the more commonly used long short-term memory (LSTM) networks and show better accuracy. The recurrence enables the capture of hysteresis effects that are inherent in soft robots due to viscoelasticity or friction but cannot be captured by simple feedforward networks. The data-driven model is used within a nonlinear model predictive control (NMPC), whereby the correct handling of the RNN's hidden states is focused. A training approach is presented that allows measured values to be utilized in each control cycle. This enables accurate predictions of short horizons based on sensor data, which is crucial for closed-loop NMPC. The proposed learning-based NMPC enables trajectory tracking with an average error of 1.2deg in experiments with the pneumatic five-DoF ASR., Comment: Accepted for publication in IEEE Robotics and Automation Letters (RA-L) 2024

The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irrespective of the objectives and data modalities used to train these models. Representational similarity is generally measured for individual datasets and is not necessarily consistent across datasets. Thus, one may wonder whether this convergence of model representations is confounded by the datasets commonly used in machine learning. Here, we propose a systematic way to measure how representational similarity between models varies with the set of stimuli used to construct the representations. We find that the objective function is the most crucial factor in determining the consistency of representational similarities across datasets. Specifically, self-supervised vision models learn representations whose relative pairwise similarities generalize better from one dataset to another compared to those of image classification or image-text models. Moreover, the correspondence between representational similarities and the models' task behavior is dataset-dependent, being most strongly pronounced for single-domain datasets. Our work provides a framework for systematically measuring similarities of model representations across datasets and linking those similarities to differences in task behavior., Comment: 26 pages

In this paper, we examine roots of graph polynomials where those roots can be considered as structural graph measures. More precisely, we prove analytical results for the roots of certain modified graph polynomials and also discuss numerical results. As polynomials, we use, e.g., the Hosoya, the Schultz, and the Gutman polynomial which belong to an interesting family of degree-distance-based graph polynomials; they constitute so-called counting polynomials with non-negative integers as coefficients and the roots of their modified versions have been used to characterize the topology of graphs. Our results can be applied for the quantitative characterization of graphs. Besides analytical results, we also investigate other properties of those measures such as their degeneracy which is an undesired aspect of graph measures. It turns out that the measures representing roots of graph polynomials possess high discrimination power on exhaustively generated trees, which outperforms standard versions of these indices. Furthermore, a new measure is introduced that allows us to compare different topological indices in terms of structure sensitivity and abruptness.

In this work, we describe a computational model for categories and functors. The categories that are handled by this model are locally finitely presentable categories which can be "sufficiently finitely" described to a computer. As an application of this model, we introduce a criterion to show whether a functor, as described in the model, is a left adjoint. The verification of this criterion can be partially automatised, if not fully in some cases, as witnessed by an implementation. While this work is focused on computational aspects, it is relevant to the broader categorical community, as it presents a new way to check whether functors are left adjoints and outlines a new computational methodology in category theory.

In a previous publication [J. Chem. Phys., 161, 044105 (2024)], it has been shown that Rothe's method can be used to solve the time-dependent Schr\"odinger equation (TDSE) for the hydrogen atom in a strong laser field using time-dependent Gaussian wave packets. Here, we generalize these results, showing that Rothe's method can propagate arbitrary numbers of thawed, complex-valued, explicitly correlated Gaussian functions (ECGs) with dense correlation matrices for systems with varying dimensionality. We consider the multidimensional Henon-Heiles potential, and show that the dynamics can be quantitatively reproduced using only 30 Gaussians in 2D, and that accurate spectra can be obtained using 20 Gaussians in 2D and 30 to 40 Gaussians in 3D and 4D. Thus, the relevant multidimensional dynamics can be described at high quality using only a small number of ECGs that give a very compact representation of the wave function. This efficient representation, along with the demonstrated ability of Rothe's method to propagate Gaussian wave packets in strong fields and ECGs in complex potentials, paves the way for accurate molecular dynamics calculations beyond the Born-Oppenheimer approximation in strong fields., Comment: 15 pages, 11 figures

MXenes stand out from other 2D materials because they combine very good electrical conductivity with hydrophilicity, allowing cost-effective processing as thin films. Therefore, there is a high fundamental interest in unraveling the electronic transport mechanisms at stake in multilayers of the most conducting MXene, Ti$_3$C$_2$T$_x$. Although weak localization (WL) has been proposed as the dominating low-temperature (LT) transport mechanism in Ti$_3$C$_2$T$_x$ thin films, there have been few attempts to model it quantitatively. In this paper, the role of important structural parameters -- thickness, interflake coupling, defects -- on the dimensionality of the LT transport mechanisms in spin-coated Ti$_3$C$_2$T$_x$ thin films is investigated through LT and magnetic field dependent resistivity measurements. A dimensional crossover from 2D to 3D WL is clearly evidenced when the film thickness exceeds the dephasing length $l_\phi$, which is in the $50-100\,\mathrm{nm}$ range. 2D WL can be restored by weakening the coupling between adjacent flakes, the intrinsic thickness of which is lower than $l_\phi$, hence acting as parallel 2D conductors. Alternatively, $l_\phi$ can be reduced down to the $10\,\mathrm{nm}$ range by defects. Our results clearly emphasize the ability of WL quantitative study to give deep insights in the physics of electron transport in MXene thin films.

We investigate the use of an atomic Fabry-Perot interferometer (FPI) with a pulsed non-interacting Bose-Einstein condensate (BEC) source as a space-based acceleration sensor. We derive an analytic approximation for the device's transmission under a uniform acceleration, which we use to compute the device's attainable acceleration sensitivity using the classical Fisher information. In the ideal case of a high-finesse FPI and an infinitely narrow momentum width atomic source, we find that when the total length of the device is constrained to small values, the atomic FPI can achieve greater acceleration sensitivity than a Mach-Zender (MZ) interferometer of equivalent total device length. Under the more realistic case of a finite momentum width atomic source, We identify the ideal cavity length that gives the best sensitivity. Although the MZ interferometer now offers enhanced sensitivity within currently-achievable experimental parameter regimes, our analysis demonstrates that the atomic FPI holds potential as a promising alternative in the future, provided that narrow momentum width atomic sources can be engineered., Comment: 17 pages, 9 figures

Despite significant advancements in report generation methods, a critical limitation remains: the lack of interpretability in the generated text. This paper introduces an innovative approach to enhance the explainability of text generated by report generation models. Our method employs cyclic text manipulation and visual comparison to identify and elucidate the features in the original content that influence the generated text. By manipulating the generated reports and producing corresponding images, we create a comparative framework that highlights key attributes and their impact on the text generation process. This approach not only identifies the image features aligned to the generated text but also improves transparency but also provides deeper insights into the decision-making mechanisms of the report generation models. Our findings demonstrate the potential of this method to significantly enhance the interpretability and transparency of AI-generated reports.

We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing. In particular, we propose Clifford Frame Attention (CFA), an extension of the invariant point attention (IPA) architecture from AlphaFold2, in which the backbone residue frames and geometric features are represented in the projective geometric algebra. This enables to construct geometrically expressive messages between residues, including higher order terms, using the bilinear operations of the algebra. We evaluate our architecture by incorporating it into the framework of FrameFlow, a state-of-the-art flow matching model for protein backbone generation. The proposed model achieves high designability, diversity and novelty, while also sampling protein backbones that follow the statistical distribution of secondary structure elements found in naturally occurring proteins, a property so far only insufficiently achieved by many state-of-the-art generative models., Comment: To be published in proceedings of NeurIPS 2024

Recent research suggests that the failures of Vision-Language Models (VLMs) at visual reasoning often stem from erroneous agreements -- when semantically distinct images are ambiguously encoded by the CLIP image encoder into embeddings with high cosine similarity. In this paper, we show that erroneous agreements are not always the main culprit, as Multimodal Large Language Models (MLLMs) can still extract distinct information from them. For instance, when distinguishing objects on the left vs right in the What'sUp benchmark, the CLIP image embeddings of the left/right pairs have an average cosine similarity $>0.99$, and CLIP performs at random chance; but LLaVA-1.5-7B, which uses the same CLIP image encoder, achieves nearly $100\%$ accuracy. We find that the extractable information in CLIP image embeddings is likely obscured by CLIP's inadequate vision-language alignment: Its matching score learned by the contrastive objective might not capture all diverse image-text correspondences. We also study the MMVP benchmark, on which prior work has shown that LLaVA-1.5 cannot distinguish image pairs with high cosine similarity. We observe a performance gain brought by attending more to visual input through an alternative decoding algorithm. Further, the accuracy significantly increases if the model can take both images as input to emphasize their nuanced differences. Both findings indicate that LLaVA-1.5 did not utilize extracted visual information sufficiently. In conclusion, our findings suggest that while improving image encoders could benefit VLMs, there is still room to enhance models with a fixed image encoder by applying better strategies for extracting and utilizing visual information., Comment: 18 pages, 4 figures

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

General-domain knowledge bases (KB), in particular the "big three" -- Wikidata, Yago and DBpedia -- are the backbone of many intelligent applications. While these three have seen steady development, comprehensive KB construction at large has seen few fresh attempts. In this work, we propose to build a large general-domain KB entirely from a large language model (LLM). We demonstrate the feasibility of large-scale KB construction from LLMs, while highlighting specific challenges arising around entity recognition, entity and property canonicalization, and taxonomy construction. As a prototype, we use GPT-4o-mini to construct GPTKB, which contains 105 million triples for more than 2.9 million entities, at a cost 100x less than previous KBC projects. Our work is a landmark for two fields: For NLP, for the first time, it provides \textit{constructive} insights into the knowledge (or beliefs) of LLMs. For the Semantic Web, it shows novel ways forward for the long-standing challenge of general-domain KB construction. GPTKB is accessible at http://gptkb.org., Comment: 11 pages, 4 tables

In this work, we study $\gamma$-discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework directly integrates dynamic programming with (any) policy gradient method, explicitly leveraging the Markovian property of the environment. DynPG dynamically adjusts the problem horizon during training, decomposing the original infinite-horizon MDP into a sequence of contextual bandit problems. By iteratively solving these contextual bandits, DynPG converges to the stationary optimal policy of the infinite-horizon MDP. To demonstrate the power of DynPG, we establish its non-asymptotic global convergence rate under the tabular softmax parametrization, focusing on the dependencies on salient but essential parameters of the MDP. By combining classical arguments from dynamic programming with more recent convergence arguments of policy gradient schemes, we prove that softmax DynPG scales polynomially in the effective horizon $(1-\gamma)^{-1}$. Our findings contrast recent exponential lower bound examples for vanilla policy gradient., Comment: 46 pages, 4 figures

In transition metal dichalcogenides a plethora of emergent states arise from competing electron-electron and electron-phonon interactions. Among these, the non-volatile metallic 'hidden' state of 1T-TaS2 can be induced from its insulating equilibrium charge-density wave ground state using either optical or electrical pulses. Here we report in-operando micro-beam X-ray diffraction, fluorescence, and concurrent transport measurements, allowing us to spatially image the non-thermal hidden state induced by electrical switching of a 1T-TaS2 device. Our findings reveal that the electrically and optically switched hidden states are structurally equivalent. Additionally, we observe a bulk switching channel extending beyond the intergap space to partially underneath the electrodes, suggesting that the non-equilibrium phase is caused by a combination of charge flow and lattice response. Besides identifying strain propagation as an important factor for non-thermal switching of layered materials, our results illustrate the power of non-destructive, three-dimensional X-ray imaging for studying phase-change materials and devices.

Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-related behaviors and contexts, contributing significantly to digital phenotyping, a process where data from digital interactions is analyzed to infer behaviors and assess mental health. Traditional methods process raw sensor data into information features for statistical and machine learning analyses. In this paper, we introduce a novel approach that systematically converts smartphone-collected data into structured, chronological narratives. The AWARE Narrator translates quantitative smartphone sensing data into English language descriptions, forming comprehensive narratives of an individual's activities. We apply the framework to the data collected from university students over a week, demonstrating the potential of utilizing the narratives to summarize individual behavior, and analyzing psychological states by leveraging large language models.

We show that the monodromoy group of Nikulin-type orbifolds is maximal and classify finite order symplectic automorphisms up to deformation in terms of their action on the second integral cohomology group.

Background: Transitioning from hospital to home presents significant challenges, particularly for patients aged 75 and above. The 'Your Care Needs You' (YCNY) intervention, evaluated through a cluster randomised controlled trial (cRCT), aims to empower these patients by providing essential information and skills to prepare them for being at home. This study evaluates the cost-effectiveness of the YCNY intervention compared to standard care within the UK National Health Service (NHS) over the trial's duration (90 days post-discharge). Methods: The economic evaluation utilised individual patient-level data from the YCNY cRCT. The analysis was conducted from the perspective of the NHS and Personal Social Services. The analysis calculated the adjusted mean differences in costs and Quality-Adjusted Life Years (QALYs) between the YCNY intervention and control groups. In our base case, Multilevel Mixed-Effects Generalised Linear Models (MME-GLM) were employed to account for the hierarchical data structure. Additional alternative scenarios and a probabilistic sensitivity analysis were conducted to assess the robustness of the findings. Results: The base case results indicated the YCNY intervention reduced costs ({\pounds}269) and improved outcomes (QALY gain of 0.0057). The net health benefit (NHB) of the YCNY intervention using a cost-effectiveness threshold of {\pounds}15.000/QALY was 0.0246. These findings suggest the YCNY intervention dominates usual care, with a high probability (89%) of the YCNY intervention being cost-effective at a {\pounds}15,000 per QALY threshold. The results remained robust across several alternative scenario analyses. Discussion: The YCNY intervention could represent a promising and potentially cost-effective approach for enhancing the safety and experience of older adults transitioning from hospital to home in the UK.

Generative modelling of multi-user datasets has become prominent in science and engineering. Generating a data point for a given user requires employing user information, and conventional generative models, including variational autoencoders (VAEs), often ignore that. This paper introduces GUIDE-VAE, a novel conditional generative model that leverages user embeddings to generate user-guided data. By allowing the model to benefit from shared patterns across users, GUIDE-VAE enhances performance in multi-user settings, even under significant data imbalance. In addition to integrating user information, GUIDE-VAE incorporates a pattern dictionary-based covariance composition (PDCC) to improve the realism of generated samples by capturing complex feature dependencies. While user embeddings drive performance gains, PDCC addresses common issues such as noise and over-smoothing typically seen in VAEs. The proposed GUIDE-VAE was evaluated on a multi-user smart meter dataset characterized by substantial data imbalance across users. Quantitative results show that GUIDE-VAE performs effectively in both synthetic data generation and missing record imputation tasks, while qualitative evaluations reveal that GUIDE-VAE produces more plausible and less noisy data. These results establish GUIDE-VAE as a promising tool for controlled, realistic data generation in multi-user datasets, with potential applications across various domains requiring user-informed modelling.

Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries, leading to high inter-observer variability and time-intensive manual annotation. To tackle this challenge, we propose DiffSeg, a novel weakly supervised semantic segmentation (WSSS) method that uses image-level annotations to generate pixel-level fibrosis segmentation, reducing the need for fine-grained manual labeling. Additionally, our DiffSeg incorporates a diffusion-based generative model to synthesize HRCT images with different levels of fibrosis from healthy slices, enabling the generation of the fibrosis-injected slices and their paired fibrosis location. Experiments indicate that our method significantly improves the accuracy of pseudo masks generated by existing WSSS methods, greatly reducing the complexity of manual labeling and enhancing the consistency of the generated masks.

Planets form from the same cloud of molecular gas and dust as their host stars. Confirming if planetary bodies acquire the same refractory element composition as their natal disc during formation, and how efficiently volatile elements are incorporated into growing planets, is key to linking the poorly constrained interior composition of rocky exoplanets to the observationally-constrained composition of their host star. Such comparisons also afford insight into the planet formation process. This work compares planetary composition with host-star composition using observations of a white dwarf that has accreted planetary material and its F-type star wide binary companion as a reference for the composition of the natal molecular gas and dust. Spectroscopic analysis reveals abundances of Fe, Mg, Si, Ca, and Ti in both stars. We use the white dwarf measurements to estimate the composition of the exoplanetary material and the F-type companion to constrain the composition of the material the planet formed from. Comparing planetary material to the composition of its natal cloud, our results reveal that the planetary material is depleted in moderate refractories (Mg, Si, Fe) relative to the refractory material (Ca, Ti). Grouping elements based on their condensation temperatures is key to linking stellar and planetary compositions. Fractionation during formation or subsequent planetary evolution leads to the depletion of moderate refractories from the planetary material accreted by the white dwarf. This signature, as seen for bulk Earth, will likely be present in the composition of many exoplanets relative to their host-stars., Comment: 14 pages (without including appendix). 12 Figures. Accepted for publication in A&A

We study multiparty entanglement signals, which are functions of a quantum state that are non-zero only when the state has multiparty entanglement. We consider known signals of three- and four-party entanglement, and propose new signals for four- and higher-party entanglement. We make some remarks on their general properties, but mainly focus on using holographic states in AdS$_3/$CFT$_2$ as a test case to explore their properties. For both the AdS vacuum and multiboundary wormhole states, we find that the multiparty entanglement signals are generically non-zero and of order one in units of the central charge of the dual CFT, revealing substantial multiparty entanglement. In the large-horizon limit of multiboundary wormhole states, however, the signals for three or more parties become small indicating the short-range nature of entanglement., Comment: 57 pages, 20 figures

We study the problem of matching markets with ties, where one side of the market does not necessarily have strict preferences over members at its other side. For example, workers do not always have strict preferences over jobs, students can give the same ranking for different schools and more. In particular, assume w.l.o.g. that workers' preferences are determined by their utility from being matched to each job, which might admit ties. Notably, in contrast to classical two-sided markets with strict preferences, there is no longer a single stable matching that simultaneously maximizes the utility for all workers. We aim to guarantee each worker the largest possible share from the utility in her best possible stable matching. We call the ratio between the worker's best possible stable utility and its assigned utility the \emph{Optimal Stable Share} (OSS)-ratio. We first prove that distributions over stable matchings cannot guarantee an OSS-ratio that is sublinear in the number of workers. Instead, randomizing over possibly non-stable matchings, we show how to achieve a tight logarithmic OSS-ratio. Then, we analyze the case where the real utility is not necessarily known and can only be approximated. In particular, we provide an algorithm that guarantees a similar fraction of the utility compared to the best possible utility. Finally, we move to a bandit setting, where we select a matching at each round and only observe the utilities for matches we perform. We show how to utilize our results for approximate utilities to gracefully interpolate between problems without ties and problems with statistical ties (small suboptimality gaps).

The design of starshades, i.e. external occulters for stellar coronography, relies on the fast and precise computation of their associated diffraction patterns of incoming plane waves in the telescope aperture plane. We present here a method based on a polygonal approximation of the occulter's shape, that allows fast computation of their diffraction patterns in the Fresnel approximation, without aliasing artefacts. It is competitive with respect to methods based on direct 2D Fourier transforms, or Boundary Diffraction Wave algorithms., Comment: 8 pages, 4 figures

Reverberation may severely degrade the quality of speech signals recorded using microphones in a room. For compact microphone arrays, the choice of the reference microphone for multi-microphone dereverberation typically does not have a large influence on the dereverberation performance. In contrast, when the microphones are spatially distributed, the choice of the reference microphone may significantly contribute to the dereverberation performance. In this paper, we propose to perform reference microphone selection for the weighted prediction error (WPE) dereverberation algorithm based on the normalized $\ell_p$-norm of the dereverberated output signal. Experimental results for different source positions in a reverberant laboratory show that the proposed method yields a better dereverberation performance than reference microphone selection based on the early-to-late reverberation ratio or signal power.

Solar and stellar externally occulted coronagraphs share similar concepts, but are actually very different because of geometric characteristics. Solar occulters were first developed with a simple geometric model of diffraction perpendicular to the occulter edges. We apply this mere approach to starshades, and introduce a simple shifted circular integral of the occulter which allows to illustrate the influence of the number of petals on the extent of the deep central dark zone. We illustrate the reasons for the presence of an internal coronagraph in the solar case and its absence in the exoplanet case., Comment: 8 pages, 10 figures

The behaviour and stability of soft and biological matter depend significantly on electrostatic interactions, as particles such as proteins and colloids acquire a charge when dispersed in an electrolytic solution. A typical simplification used to understand bulk phenomena involving electrostatic interactions is the isotropy of the charge on the particles. However, whether arising naturally or by synthesis, charge distributions are often inhomogeneous, leading to an intricate particle-particle interaction landscape and complex assembly phenomena. The fundamental complexity of these interactions gives rise to models based on distinct assumptions and varying degrees of simplifications which can blur the line between genuine physical behaviour and artefacts arising from the choice of a particular electrostatic model. Building upon the widely-used linearized Poisson-Boltzmann theory, we propose a theoretical framework that -- by bridging different models -- provides a robust DLVO-like description of electrostatic interactions between inhomogeneously charged particles. By matching solely the {\em single-particle} properties of two different mean-field models, we find a quantitative agreement between the {\em pair interaction energies} over a wide range of system parameters. Our work identifies a strategy to merge different models of inhomogeneously charged particles and paves the way to a reliable, accurate, and computationally affordable description of their interactions., Comment: 10 pages, 5 figures (main) plus 7 pages, 2 figures (supplemental)

Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the server and keeping private data local. Nevertheless, recent research has revealed that the security of Federated Learning is compromised, as private ground truth data can be recovered through a gradient inversion technique known as Deep Leakage. While these attacks are crafted with a focus on applications in Federated Learning, they generally are not evaluated in realistic scenarios. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses within a realistic Federated context. By implementing a unified benchmark that encompasses multiple state-of-the-art Deep Leakage techniques and various defense strategies, our framework facilitates the evaluation and comparison of the efficacy of these methods across different datasets and training states. This work highlights a crucial trade-off between privacy and model accuracy in Federated Learning and aims to advance the understanding of security challenges in decentralized machine learning systems, stimulate future research, and enhance reproducibility in evaluating Deep Leakage attacks and defenses., Comment: 9 pages

This paper introduces WASHtsApp, a WhatsApp-based chatbot designed to educate rural African communities on clean water access, sanitation, and hygiene (WASH) principles. WASHtsApp leverages a Retrieval-Augmented Generation (RAG) approach to address the limitations of previous approaches with limited reach or missing contextualization. The paper details the development process, employing Design Science Research Methodology. The evaluation consisted of two phases: content validation by four WASH experts and community validation by potential users. Content validation confirmed WASHtsApp's ability to provide accurate and relevant WASH-related information. Community validation indicated high user acceptance and perceived usefulness of the chatbot. The paper concludes by discussing the potential for further development, including incorporating local languages and user data analysis for targeted interventions. It also proposes future research cycles focused on wider deployment and leveraging user data for educational purposes., Comment: Working Paper

CONTEXT: Assurance Cases (ACs) are prepared to argue that the system's desired quality attributes (e.g., safety or security) are satisfied. While there is strong adoption of ACs, practitioners are often left asking an important question: are we confident that the claims made by the case are true? While many confidence assessment methods (CAMs) exist, little is known about the use of these methods in practice OBJECTIVE: Develop an understanding of the current state of practice for AC confidence assessment: what methods are used in practice and what barriers exist for their use? METHOD: Structured interviews were performed with practitioners with experience contributing to real-world ACs. Open-coding was performed on transcripts. A description of the current state of AC practice and future considerations for researchers was synthesized from the results. RESULTS: A total of n = 19 practitioners were interviewed. The most common CAMs were (peer-)review of ACs, dialectic reasoning ("defeaters"), and comparing against checklists. Participants preferred qualitative methods and expressed concerns about quantitative CAMs. Barriers to using CAMs included additional work, inadequate guidance, subjectivity and interpretation of results, and trustworthiness of methods. CONCLUSION: While many CAMs are described in the literature there is a gap between the proposed methods and needs of practitioners. Researchers working in this area should consider the need to: connect CAMs to established practices, use CAMs to communicate with interest holders, crystallize the details of CAM application, curate accessible guidance, and confirm that methods are trustworthy.

The origin of the tight scaling relation between the mass of supermassive black holes (SMBHs; $M_{\rm BH}$) and their host-galaxy properties remains unclear. Active galactic nuclei (AGNs) probe phases of ongoing SMBH growth and offer the only opportunity to measure $M_{\rm BH}$ beyond the local Universe. However, determining AGN host galaxy stellar velocity dispersion $\sigma_\star$, and their galaxy dynamical masses $M_{\rm dyn}$, is complicated by AGN contamination, aperture effects and different host galaxy morphologies. We select a sample of AGNs for which $M_{\rm BH}$ has been independently determined to high accuracy by state-of-the-art techniques: dynamical modeling of the reverberation signal and spatially resolving the broad-line region with VLTI/GRAVITY. Using IFU observations, we spatially map the host galaxy stellar kinematics across the galaxy and bulge effective radii. We find that that the dynamically hot component of galaxy disks correlates with $M_{\rm BH}$; however, the correlations are tightest for aperture-integrated $\sigma_\star$ measured across the bulge. Accounting for the different $M_{\rm BH}$ distributions, we demonstrate - for the first time - that AGNs follow the same $M_{\rm BH}$-$\sigma_\star$ and $M_{\rm BH}$-$M_{\rm bulge, dyn}$ relations as quiescent galaxies. We confirm that the classical approach of determining the virial factor as sample-average, yielding ${\rm log }f= 0.65 \pm 0.18$, is consistent with the average $f$ from individual measurements. The similarity between the underlying scaling relations of AGNs and quiescent galaxies implies that the current AGN phase is too short to have altered BH masses on a population level. These results strengthen the local calibration of $f$ for measuring single-epoch $M_{\rm BH}$ in the distant Universe., Comment: Resubmitted to ApJ after initial positive referee report. 40 pages, 19 figures, 6 tables

Gatemons are superconducting qubits similar to transmons except that the Josephson element consists of a gate-tunable semiconducting weak link. Here, we report the realization of a gatemon device obtained by fabricating a microwave circuit in aluminium on a SiGe heterostructure embedding a Ge quantum well. Owing to the superconducting proximity effect, the high-mobility two-dimensional hole gas confined in this well provides a gate-tunable superconducting weak link between two Al contacts. We perform Rabi oscillation and Ramsey interference measurements, demonstrate the gate-voltage dependence of the qubit frequency, and measure the qubit anharmonicity. The reported proof-of-concept reproduces the results of a very recent work [Sagi et al., Nat. Commun. 15, 6400 (2024)] using similar SiGe heterostructures thereby validating a novel platform for the development of gatemons and parity-protected $\cos(2\phi)$ qubits.

When learning an input-output mapping from very few examples, is it better to first infer a latent function that explains the examples, or is it better to directly predict new test outputs, e.g. using a neural network? We study this question on ARC, a highly diverse dataset of abstract reasoning tasks. We train neural models for induction (inferring latent functions) and transduction (directly predicting the test output for a given test input). Our models are trained on synthetic data generated by prompting LLMs to produce Python code specifying a function to be inferred, plus a stochastic subroutine for generating inputs to that function. We find inductive and transductive models solve very different problems, despite training on the same problems, and despite sharing the same neural architecture.

Measuring the Hubble constant (H$_0$) is one of the most important missions in astronomy. Nevertheless, recent studies exhibit differences between the employed methods. Fast radio bursts (FRBs) are coherent radio transients with large dispersion measures (DM) with a duration of milliseconds. DM$_{\rm IGM}$, DM in the intergalactic medium (IGM), could open a new avenue for probing H$_0$. However, it has been challenging to separate DM contributions from different components (i.e., the IGM and the host galaxy plasma), and this hampers the accurate measurements of DM$_{\rm IGM}$ and hence H$_0$. We adopted a method to overcome this problem by using the temporal scattering of the FRB pulses due to the propagation effect through the host galaxy plasma (scattering time). The scattering-inferred DM in a host galaxy improves the estimate of DM$_{\rm IGM}$, which in turn leads to a better constraint on H$_0$. In previous studies, a certain value or distribution has conventionally been assumed of the dispersion measure in host galaxies (DM$_{\rm h}$). We compared this method with ours by generating 100 mock FRBs, and we found that our method reduces the systematic (statistical) error of H$_0$ by 9.1% (1%) compared to the previous method. We applied our method to 30 localized FRB sources with both scattering and spectroscopic redshift measurements to constrain H$_0$. Our result is H$_0$=74$_{-7.2}^{+7.5}$ km s$^{-1}$ Mpc$^{-1}$, where the central value prefers the value obtained from local measurements over the cosmic microwave background. We also measured DM$_{\rm h}$ with a median value of $103^{+68}_{-48}$ pc cm$^{-3}$. The reduction in systematic error is comparable to the Hubble tension ($\sim10$%). Combined with the fact that more localized FRBs will become available, our result indicates that our method can be used to address the Hubble tension using future FRB samples., Comment: 13 pages, 13 figures, Accepted for publication in A&A, for the oral presentation in the conference (ASROC 2023), see https://www.youtube.com/watch?v=1umtxDVN-Ro&t=44s

Frustrated magnets can elude the paradigm of conventional symmetry breaking and instead exhibit signatures of emergent symmetries at low temperatures. Such symmetries arise from "accidental" degeneracies within the ground state manifold and have been explored in a number of disparate models, in both two and three dimensions. Here we report the systematic construction of a family of classical spin models that, for a wide variety of lattice geometries with triangular motifs in one, two and three spatial dimensions, such as the kagome or hyperkagome lattices, exhibit an emergent, continuous U(1) symmetry. This is particularly surprising because the underlying Hamiltonian actually has very little symmetry - a bond-directional, off-diagonal exchange model inspired by the microscopics of spin-orbit entangled materials (the $\Gamma^{\prime}$-model). The construction thus allows for a systematic study of the interplay between the emergent continuous U(1) symmetry and the underlying discrete Hamiltonian symmetries in different lattices across different spatial dimensions. We discuss the impact of thermal and quantum fluctuations in lifting the accidental ground state degeneracy via the thermal and quantum order-by-disorder mechanisms, and how spatial dimensionality and lattice symmetries play a crucial role in shaping the physics of the model. Complementary Monte Carlo simulations, for representative one-, two-, and three-dimensional lattice geometries, provide a complete account of the thermodynamics and confirm our analytical expectations., Comment: 14 pages, 14 figures

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

Under some technical assumptions, and building on joint work with Bezrukavnikov, we prove a multiplicity formula for indecomposable tilting perverse sheaves on affine flag varieties, with coefficients in a field of characteristic $p$, in terms of $p$-Kazhdan--Lusztig polynomials. Under the same assumptions, we also explain the construction of a "degrading functor" relating mixed modular perverse sheaves (as defined in joint work with Achar) on such varieties to ordinary perverse sheaves., Comment: 34 pages. arXiv admin note: substantial text overlap with arXiv:2206.03738

The measurement and description of the charge-carrier lifetime (tauc) is crucial for the wide-ranging applications of lead-halide perovskites. We present time-resolved microwave-detected photoconductivity decay (TRMCD) measurements and a detailed analysis of the possible recombination mechanisms including trap-assisted, radiative, and Auger recombination. We prove that performing injection-dependent measurement is crucial in identifying the recombination mechanism. We present temperature and injection level dependent measurements in CsPbBr_3, which is an inorganic lead-halide perovskite. In this material, we observe the dominance of charge-carrier trapping, which results in ultra-long charge-carrier lifetimes. Although charge trapping can limit the effectiveness of materials in photovoltaic applications, it also offers significant advantages for various alternative uses, including delayed and persistent photodetection, charge-trap memory, afterglow light-emitting diodes, quantum information storage, and photocatalytic activity.

Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI. While significant progress has been made in anticipating the future actions of individual agents, prior work has largely overlooked a key aspect of real-world human activity -- interactions. To address this gap in human-like forecasting within multi-agent environments, we present the Hierarchical Memory-Aware Transformer (HiMemFormer), a transformer-based model for online multi-agent action anticipation. HiMemFormer integrates and distributes global memory that captures joint historical information across all agents through a transformer framework, with a hierarchical local memory decoder that interprets agent-specific features based on these global representations using a coarse-to-fine strategy. In contrast to previous approaches, HiMemFormer uniquely hierarchically applies the global context with agent-specific preferences to avoid noisy or redundant information in multi-agent action anticipation. Extensive experiments on various multi-agent scenarios demonstrate the significant performance of HiMemFormer, compared with other state-of-the-art methods.

Accurately predicting blood glucose (BG) levels of ICU patients is critical, as both hypoglycemia (BG < 70 mg/dL) and hyperglycemia (BG > 180 mg/dL) are associated with increased morbidity and mortality. We develop the Multi-source Irregular Time-Series Transformer (MITST), a novel machine learning-based model to forecast the next BG level, classifying it into hypoglycemia, hyperglycemia, or euglycemia (70-180 mg/dL). The irregularity and complexity of Electronic Health Record (EHR) data, spanning multiple heterogeneous clinical sources like lab results, medications, and vital signs, pose significant challenges for prediction tasks. MITST addresses these using hierarchical Transformer architectures, which include a feature-level, a timestamp-level, and a source-level Transformer. This design captures fine-grained temporal dynamics and allows learning-based data integration instead of traditional predefined aggregation. In a large-scale evaluation using the eICU database (200,859 ICU stays across 208 hospitals), MITST achieves an average improvement of 1.7% (p < 0.001) in AUROC and 1.8% (p < 0.001) in AUPRC over a state-of-the-art baseline. For hypoglycemia, MITST achieves an AUROC of 0.915 and an AUPRC of 0.247, both significantly higher than the baseline's AUROC of 0.862 and AUPRC of 0.208 (p < 0.001). The flexible architecture of MITST allows seamless integration of new data sources without retraining the entire model, enhancing its adaptability in clinical decision support. Although this study focuses on predicting BG levels, MITST can easily be extended to other critical event prediction tasks in ICU settings, offering a robust solution for analyzing complex, multi-source, irregular time-series data., Comment: 18 pages, 7 figures

Multiplex Imaging (MI) enables the simultaneous visualization of multiple biological markers in separate imaging channels at subcellular resolution, providing valuable insights into cell-type heterogeneity and spatial organization. However, current computational pipelines rely on cell segmentation algorithms, which require laborious fine-tuning and can introduce downstream errors due to inaccurate single-cell representations. We propose a segmentation-free deep learning approach that leverages grouped convolutions to learn interpretable embedded features from each imaging channel, enabling robust cell-type identification without manual feature selection. Validated on an Imaging Mass Cytometry dataset of 1.8 million cells from neuroblastoma patients, our method enables the accurate identification of known cell types, showcasing its scalability and suitability for high-dimensional MI data., Comment: 5 Pages, 5 Figures, Submitted to ISBI 2025

Statistical learning in high-dimensional spaces is challenging without a strong underlying data structure. Recent advances with foundational models suggest that text and image data contain such hidden structures, which help mitigate the curse of dimensionality. Inspired by results from nonparametric statistics, we hypothesize that this phenomenon can be partially explained in terms of decomposition of complex tasks into simpler subtasks. In this paper, we present a controlled experimental framework to test whether neural networks can indeed exploit such ``hidden factorial structures.'' We find that they do leverage these latent patterns to learn discrete distributions more efficiently, and derive scaling laws linking model sizes, hidden factorizations, and accuracy. We also study the interplay between our structural assumptions and the models' capacity for generalization.

In this study, we introduce FEET, a standardized protocol designed to guide the development and benchmarking of foundation models. While numerous benchmark datasets exist for evaluating these models, we propose a structured evaluation protocol across three distinct scenarios to gain a comprehensive understanding of their practical performance. We define three primary use cases: frozen embeddings, few-shot embeddings, and fully fine-tuned embeddings. Each scenario is detailed and illustrated through two case studies: one in sentiment analysis and another in the medical domain, demonstrating how these evaluations provide a thorough assessment of foundation models' effectiveness in research applications. We recommend this protocol as a standard for future research aimed at advancing representation learning models., Comment: Findings paper presented at Machine Learning for Health (ML4H) symposium 2024, December 15-16, 2024, Vancouver, Canada, 11 pages

Image Quality of MRI brain scans is strongly influenced by within scanner head movements and the resulting image artifacts alter derived measures like brain volume and cortical thickness. Automated image quality assessment is key to controlling for confounding effects of poor image quality. In this study, we systematically test for the influence of image quality on univariate statistics and machine learning classification. We analyzed group effects of sex/gender on local brain volume and made predictions of sex/gender using logistic regression, while correcting for brain size. From three large publicly available datasets, two age and sex-balanced samples were derived to test the generalizability of the effect for pooled sample sizes of n=760 and n=1094. Results of the Bonferroni corrected t-tests over 3747 gray matter features showed a strong influence of low-quality data on the ability to find significant sex/gender differences for the smaller sample. Increasing sample size and more so image quality showed a stark increase in detecting significant effects in univariate group comparisons. For the classification of sex/gender using logistic regression, both increasing sample size and image quality had a marginal effect on the Area under the Receiver Operating Characteristic Curve for most datasets and subsamples. Our results suggest a more stringent quality control for univariate approaches than for multivariate classification with a leaning towards higher quality for classical group statistics and bigger sample sizes for machine learning applications in neuroimaging.