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Scientists across disciplines often use data from the internet to conduct research, generating valuable insights about human behavior. However, as generative AI relying on massive text corpora becomes increasingly valuable, platforms have greatly restricted access to data through official channels. As a result, researchers will likely engage in more web scraping to collect data, introducing new challenges and concerns for researchers. This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers, examining the legal, ethical, institutional, and scientific factors that researchers should consider when scraping the web. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner. We aim to equip researchers with the relevant information to mitigate risks and maximize the impact of their research amidst this evolving data access landscape.

We propose Counterfactual Analysis Quadratic Unconstrained Binary Optimization (CAQUBO) to solve QUBO problems for feature selection in recommender systems. CAQUBO leverages counterfactual analysis to measure the impact of individual features and feature combinations on model performance and employs the measurements to construct the coefficient matrix for a quantum annealer to select the optimal feature combinations for recommender systems, thereby improving their final recommendation performance. By establishing explicit connections between features and the recommendation performance, the proposed approach demonstrates superior performance compared to the state-of-the-art quantum annealing methods. Extensive experiments indicate that integrating quantum computing with counterfactual analysis holds great promise for addressing these challenges.

Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limitations, such as high false positive rates as well as difficulties with handling variable-sized graphs and non-trivial temporal dynamics. To address this, we propose a technique where temporal dependencies are explicitly modelled via time series analysis of a large set of pertinent graph features, followed by using residuals to remove the dependencies. Extreme Value Theory is then used to robustly model and classify any remaining extremes, aiming to produce low false positives rates. Comparative evaluations on a multitude of graph instances show that the proposed approach obtains considerably better accuracy than TensorSplat and Laplacian Anomaly Detection., Comment: extended and revised version of arXiv:2210.07407

$\Lambda$CDM cosmology predicts the hierarchical formation of galaxies which build up mass by merger events and accreting smaller systems. The stellar halo of the Milky Way has proven to be useful a tool for tracing this accretion history. However, most of this work has focused on the outer halo where dynamical times are large and the dynamical properties of accreted systems are preserved. In this work, we investigate the inner galaxy regime, where dynamical times are relatively small and systems are generally completely phase-mixed. Using the FIRE-2 and Auriga cosmological zoom-in simulation suites of Milky Way-mass galaxies, we find the stellar density profiles along the minor axis (perpendicular to the galactic disk) within the NFW scale radii (R$\approx$15 kpc) are best described as an exponential disk with scale height <0.3 kpc and a power law component with slope $\alpha\approx$-4. The stellar density amplitude and slope for the power law component is not significantly correlated with metrics of the galaxy's accretion history. Instead, we find the stellar profiles strongly correlate with the dark matter profile. Across simulation suites, the galaxies studied in this work have a stellar to dark matter mass ratio that decreases as $1/r^2$ along the minor axis., Comment: 29 pages, 13 figures, 1 table, Submitted to ApJ, comments welcome!

Algorithmic recourse provides actions to individuals who have been adversely affected by automated decision-making and helps them achieve a desired outcome. Knowing the recourse, however, does not guarantee that users would implement it perfectly, either due to environmental variability or personal choices. Recourse generation should thus anticipate its sub-optimal or noisy implementation. While several approaches have constructed recourse that accounts for robustness to small perturbation (i.e., noisy recourse implementation), they assume an entire recourse to be implemented in a single step and thus apply one-off uniform noise to it. Such assumption is unrealistic since recourse often includes multiple sequential steps which becomes harder to implement and subject to more noise. In this work, we consider recourse under plausible noise that adapts to the local data geometry and accumulates at every step of the way. We frame this problem as a Markov Decision Process and demonstrate that the distribution of our plausible noise satisfies the Markov property. We then propose the RObust SEquential (ROSE) recourse generator to output a sequence of steps that will lead to the desired outcome even under imperfect implementation. Given our plausible modelling of sub-optimal human actions and greater recourse robustness to accumulated uncertainty, ROSE can grant users higher chances of success under low recourse costs. Empirical evaluation shows our algorithm manages the inherent trade-off between recourse robustness and costs more effectively while ensuring its low sparsity and fast computation.

Combining Chandra, ALMA, EVLA, and Hubble Space Telescope archival data and newly acquired APO/DIS spectroscopy, we detect a double-lobed 17~kpc X-ray emission with plumes oriented approximately perpendicular and parallel to the galactic plane of the massive lenticular galaxy NGC\,5084 at 0.3--2.0~keV. We detect a highly inclined ($i=71.2^{+1.8\circ}_{-1.7}$), molecular circumnuclear disk ($D=304^{+10}_{-11}$ pc) in the core of the galaxy rotating (V$^{\rm (2-1) CO}_{\rm rot}=242.7^{+9.6}_{-6.4}$ km s$^{-1}$) in a direction perpendicular to that of the galactic disk, implying a total mass of $\log_{10}\left( \frac{M_{\rm BH}}{M_{\odot}} \right) = 7.66^{+0.21}_{-0.15}$ for NGC\,5084's supermassive black hole. Archival EVLA radio observations at 6 cm and 20 cm reveal two symmetric radio lobes aligned with the galactic plane, extending to a distance of $\overline{R}=4.6\pm0.6$ kpc from the core, oriented with the polar axis of the circumnuclear disk. The spectral energy distribution lacks strong emission lines in the optical range. Three formation scenarios are considered to explain these multi-wavelength archival observations: 1) AGN re-orientation caused by accretion of surrounding material, 2) AGN-driven hot gas outflow directed along the galactic minor axis, or 3) a starburst / supernovae driven outflow at the core of the galaxy. This discovery is enabled by new imaging analysis tools including \SAUNAS\ (Selective Amplification of Ultra Noisy Astronomical Signal), demonstrating the abundance of information still to be exploited in the vast and growing astronomical archives., Comment: Submitted to ApJ in May 21st 2024, in revision. 26 pages, 14 figures

In this paper, we employ a Bayesian approach to assess the reliability of a critical component in the Mars Sample Return program, focusing on the Earth Entry System's risk of containment not assured upon reentry. Our study uses Gaussian Process modeling under a Bayesian regime to analyze the Earth Entry System's resilience against operational stress. This Bayesian framework allows for a detailed probabilistic evaluation of the risk of containment not assured, indicating the feasibility of meeting the mission's stringent safety goal of 0.999999 probability of success. The findings underscore the effectiveness of Bayesian methods for complex uncertainty quantification analyses of computer simulations, providing valuable insights for computational reliability analysis in a risk-averse setting.

Citizen science offers an opportunity for ordinary people, known as citizen scientists or citizen astronomers in the context of astronomy, to contribute to scientific research. The Pan-African Citizen Science e-Lab (PACS e-Lab) was founded to promote and engage the African public in citizen science and soft astronomy research to advance space research and exploration and enhance space education and outreach. PACS e-Lab, in collaboration with several international astronomy research, education, and outreach organizations, currently runs several projects including but not limited to asteroid search, exoplanet photometry, research writing for peer-reviewed publications, astrophoto visual development, and Amateur Radio contact with astronauts aboard the International Space Station (ARISS). Despite several challenges, the group has engaged over 600 Africans from more than 40 countries and is working towards covering the entire continent in the future. PACS e-Lab's development efforts resonate with seven United Nations Sustainable Development Goals (UN-SDGs)., Comment: 32 pages,17 figures, 2 Tables

Asteroid search is a global effort for planetary defense. The International Astronomical Search Collaboration (IASC) is the leading global educational outreach program that provides high-quality astronomical datasets to citizen scientists to discover asteroids. Since December 4, 2020, the Pan-African Citizen Science e-Lab (PACS e-Lab) has been IASC's biggest partner on the continent in recruiting and training citizen scientists in asteroid search endeavors. Over 30 asteroids have been discovered by 60 citizen scientists. About 595 citizen scientists from over 40 countries have been engaged in the project up to the time of composing this literature. The group is set to expand its endeavors to the rest of the continent and aims to engage thousands of citizen scientists., Comment: 16 pages, 10 figures, 5 Tables

We construct time-evolving gravitational potential models for a Milky Way-mass galaxy from the FIRE-2 suite of cosmological-baryonic simulations using basis function expansions. These models capture the angular variation with spherical harmonics for the halo and azimuthal harmonics for the disk, and the radial or meridional plane variation with splines. We fit low-order expansions (4 angular/harmonic terms) to the galaxy's potential for each snapshot, spaced roughly 25 Myr apart, over the last 4 Gyr of its evolution, then extract the forces at discrete times and interpolate them between adjacent snapshots for forward orbit integration. Our method reconstructs the forces felt by simulation particles with high fidelity, with 95% of both stars and dark matter, outside of self-gravitating subhalos, exhibiting errors $\leq4\%$ in both the disk and the halo. Imposing symmetry on the model systematically increases these errors, particularly for disk particles, which show greater sensitivity to imposed symmetries. The majority of orbits recovered using the models exhibit positional errors $\leq10\%$ for 2-3 orbital periods, with higher errors for orbits that spend more time near the galactic center. Approximate integrals of motion are retrieved with high accuracy even with a larger potential sampling interval of 200 Myr. After 4 Gyr of integration, 43% and 70% of orbits have total energy and angular momentum errors within 10%, respectively. Consequently, there is higher reliability in orbital shape parameters such as pericenters and apocenters, with errors $\sim10\%$ even after multiple orbital periods. These techniques have diverse applications, including studying satellite disruption in cosmological contexts., Comment: 28 pages, 13 figures, and 4 tables. Submitted to APJ

The thermal and magnetic histories of planetesimals provide unique insights into the formation and evolution of Earth's building blocks. These histories can be gleaned from meteorites by using numerical models to translate measured properties into planetesimal behaviour. In this paper, we present a new 1D planetesimal thermal evolution and dynamo generation model. This magnetic field generation model is the first of a differentiated, mantled planetesimal that includes both mantle convection and non-eutectic core solidification. We have improved fundamental aspects of mantle heat transport by including a more detailed viscosity model and stagnant lid convection parametrisations consistent with internal heating. We have also added radiogenic heating from $^{60}Fe$ in the metallic Fe-FeS core. Additionally, we implement a combined thermal and compositional buoyancy flux, as well as the latest magnetic field scaling laws to predict magnetic field strengths during the planetesimal's thermal evolution until core solidification is complete. We illustrate the consequences of our model changes with an example run for a 500 km radius planetesimal. These effects include more rapid erosion of core thermal stratification and longer duration of mantle convection compared to previous studies. The additional buoyancy from core solidification has a marginal effect on dynamo strength, but for some initial core sulfur contents it can prevent cessation of the dynamo when mantle convection ends. Our model can be used to investigate the effects of individual parameters on dynamo generation and constrain properties of specific meteorite parent bodies. Combined, these updates mean this model can predict the most reliable and complete magnetic field history for a planetesimal to date, so is a valuable tool for deciphering planetesimal behaviour from meteorite properties., Comment: 38 pages, 12 figures, 3 tables. Accepted for publication in Icarus. Supplementary material is a pdf in the TeX source file

While many previous studies have indicated that encouraging a growth mindset can improve student learning outcomes, this conclusion's applicability to college-level astronomy classrooms remains poorly understood owing to the variation in students' overall and domain-specific learning attitudes. To address this, we surveyed undergraduate students in an introductory astronomy class about their attitudes towards learning astronomy over the course of five semesters. Overall, students felt an affinity for astronomy, felt moderately competent, perceived astronomy to be intermediate in terms of difficulty, and agreed strongly with standard statements reflecting a "growth mindset," i.e., the belief that intelligence is malleable rather than fixed from birth. Their responses were stable over the course of the semester and did not appear to depend strongly on student demographics. The unexpected start of the COVID-19 pandemic and the associated shift to all-virtual learning correlated with a drop in their affinity for astronomy, a small decrease in their perceived competence, and an increase in the perceived difficulty of the topic. Their overall learning mindset showed negligible change during this time, emphasizing the stability of their belief in a growth mindset as compared to other measured learning attitudes. However, more nuanced questions about their behaviors and interpretations in the classroom, about how they felt "in the moment", and about what factors were most important for their success in the class revealed significantly lower alignment with a growth mindset. This suggests that while introductory astronomy students may believe that they have a growth mindset, this mindset is not necessarily reflected in their self-reported classroom behaviors or measured responses to actual learning challenges., Comment: 14 pages, 2 figures, 10 tables; accepted for publication in the journal Physical Review Physics Education Research (PRPER)

Measurements of the accelerations of stars enabled by time-series extreme-precision spectroscopic observations, from pulsar timing, and from eclipsing binary stars in the Solar Neighborhood offer insights into the mass distribution of the Milky Way that do not rely on traditional equilibrium modeling. Given the measured accelerations, we can determine a total mass density, and from this, by accounting for the mass in stars, gas, and dust, we can infer the amount of dark matter. Leveraging the FIRE-2 simulations of Milky Way-mass galaxies, we compare vertical acceleration profiles between cold dark matter (CDM) and self-interacting dark matter (SIDM) with constant cross-section of 1 cm$^2$ g$^{-1}$ across three halos with diverse assembly histories. Notably, significant asymmetries in vertical acceleration profiles near the midplane at fixed radii are observed in both CDM and SIDM, particularly in halos recently affected by mergers with satellites of Sagittarius/SMC-like masses or greater. These asymmetries offer a unique window into exploring the merger history of a galaxy. We show that SIDM halos consistently exhibit higher local stellar and dark matter densities and steeper vertical acceleration gradients, up to 30% steeper near the Solar Neighborhood. SIDM halos also manifest a more oblate halo shape in the Solar Neighborhood. Furthermore, enhanced precision in acceleration measurements and larger datasets promise to provide better constraints on the local dark matter density, complementing our understanding from kinematic analysis of their distribution within galaxies., Comment: 14 pages, 5 figures, and 2 tables. Submitted to APJ

Jetted Active Galactic Nuclei (AGN) hosting extended photoionized nebulae provide us with a unique view of the timescales associated with AGN activity. Here, we present a new Green Bean galaxy (RGB1) at $z=0.304458\pm0.000007$ with large scale jet-induced radio emission. The Spectral Energy Distributions (SEDs) of the radio components show steep spectral indices ($\alpha=-0.85$ to $-0.92$ for the extended regions, and $\alpha=-1.02$ for the faint radio core), and spectral age modeling of the extended radio emission indicates that the lobes are $>$6 Myrs old. It is unclear whether the jet is active, or remnant with an off-time of 2-3 Myr. Several detached clouds lie around the host galaxy up to 37.8 kpc away from the nucleus, and their ionization profile indicates a decline ($\sim$2 dex) in the AGN ionizing photon production over the past $\sim$0.15 Myr. Furthermore, we measure a blue shift for one of the clouds that is spatially coincident with the path of the radio jet. The cloud is likely illuminated by the photoionizing AGN, and potentially underwent an interaction with the relativistic jet. Our multiwavelength analysis suggests that RGB1 was in a phase of jet production prior to the radiatively efficient accretion phase traced by the detached cloud emission. It is unclear whether RGB1 transitioned into a low-excitation radio galaxy or an inactive galaxy over the past $\sim$0.15 Myr, or whether the extended radio and optical emission trace distinct accretion phases that occurred in succession., Comment: 23 pages, 10 figures, accepted for publication in ApJ

It has recently been demonstrated that pretraining backbones in a self-supervised manner generally provides better fine-tuned polyp segmentation performance, and that models with ViT-B backbones typically perform better than models with ResNet50 backbones. In this paper, we extend this recent work to consider generalisability. I.e., we assess the performance of models on a different dataset to that used for fine-tuning, accounting for variation in network architecture and pretraining pipeline (algorithm and dataset). This reveals how well models with different pretrained backbones generalise to data of a somewhat different distribution to the training data, which will likely arise in deployment due to different cameras and demographics of patients, amongst other factors. We observe that the previous findings, regarding pretraining pipelines for polyp segmentation, hold true when considering generalisability. However, our results imply that models with ResNet50 backbones typically generalise better, despite being outperformed by models with ViT-B backbones in evaluation on the test set from the same dataset used for fine-tuning.

Accreting in the first few million years (Ma) of the Solar System, planetesimals record conditions in the protoplanetary disc and are the remnants of planetary formation processes. The meteorite paleomagnetic record carries key insights into the thermal history of planetesimals and their extent of differentiation. The current paradigm splits the meteorite paleomagnetic record into three magnetic field generation epochs: an early nebula field ($\lesssim$5 Ma after CAI formation), followed by thermal dynamos ($\sim$5-34 Ma after CAI formation), then a gap in dynamo generation, before the onset of core solidification and compositional dynamos. These epochs have been defined using current thermal evolution and dynamo generation models of planetesimals. Here, we demonstrate these epochs are not as distinct as previously thought based on refined thermal evolution models that include more realistic parametrisations for mantle convection, non-eutectic core solidification, and radiogenic $^{60}Fe$ in the core. We find thermal dynamos can start earlier and last longer. Inclusion of appreciable $^{60}Fe$ in the core brings forward the onset of dynamo generation to $\sim$1-2 Ma after CAI formation, which overlaps with the existence of the nebula field. The second epoch of dynamo generation begins prior to the onset of core solidification, suggesting this epoch is not purely compositionally driven. Planetesimal radius is the dominant control on the strength and duration of dynamo generation, and the choice of reference viscosity can widen the gap between epochs of dynamo generation from 0-200 Ma. Overall, variations in planetesimal properties lead to more variable timings of different planetesimal magnetic field generation mechanisms than previously thought. This alters the information we can glean from the meteorite paleomagnetic record about the early Solar System., Comment: 29 pages, 8 figures. Accepted for publication in Earth and Planetary Science Letters

Using properties of diffusion according to a quantum heat kernel constructed as an expectation over classical heat kernels on $S^1$, we probe the non-manifold-like nature of quantized space in a model of (1+1)-dimensional quantum gravity. By computing the mean squared displacement of a diffusing particle, we find that diffusion is anomalous, behaving similarly to that on a porous substrate, network, or fractal over short distances. The walk dimension of the path for a particle diffusing in quantized space is calculated to have an infimum of 4, rising to arbitrarily large values depending on a parameter labeling the choice of factor ordering in the quantum Hamiltonian for our model and figuring in the asymptotic behavior of the wavefunction used to construct the quantum heat kernel. Additionally, we derive an expansion for return probability of a diffusing particle, whose modifications from the classical power-series form depend on the factor-ordering parameter.

Approximate k-Nearest Neighbour (ANN) methods are often used for mining information and aiding machine learning on large scale high-dimensional datasets. ANN methods typically differ in the index structure used for accelerating searches, resulting in various recall/runtime trade-off points. For applications with static datasets, runtime constraints and dataset properties can be used to empirically select an ANN method with suitable operating characteristics. However, for applications with dynamic datasets, which are subject to frequent online changes (like addition of new samples), there is currently no consensus as to which ANN methods are most suitable. Traditional evaluation approaches do not consider the computational costs of updating the index structure, as well as the rate and size of index updates. To address this, we empirically evaluate 5 popular ANN methods on two main applications (online data collection and online feature learning) while taking into account these considerations. Two dynamic datasets are used, derived from the SIFT1M dataset with 1 million samples and the DEEP1B dataset with 1 billion samples. The results indicate that the often used k-d trees method is not suitable on dynamic datasets as it is slower than a straightforward baseline exhaustive search method. For online data collection, the Hierarchical Navigable Small World Graphs method achieves a consistent speedup over baseline across a wide range of recall rates. For online feature learning, the Scalable Nearest Neighbours method is faster than baseline for recall rates below 75%.

Understanding and controlling the electrical properties of solution-processed 2D materials is key to further printed electronics progress. Here we demonstrate that the thermolysis of the aromatic intercalants utilized in nanosheet exfoliation for graphene laminates opens the route to achieving high intrinsic mobility and simultaneously controlling doping type ($n$- and $p$-) and concentration over a wide range. We establish that the intra-flake mobility is high by observing a linear magnetoresistance of such solution-processed graphene laminates and using it to devolve the inter-flake tunneling and intra-layer magnetotransport. Consequently, we determine the temperature dependences of the inter- and intra-layer characteristics, which both appear to be dominated by phonon-assisted processes at temperature $T>$20 Kelvin. In particular, we identify the efficiency of phonon-assisted tunneling as the main limiting factor for electrical conductivity in graphene laminates at room temperature. We also demonstrate a thermoelectric sensitivity of around 50 ${\mu}$V K$^{-1}$ in a solution-processed metal-free graphene-based thermocouple.

The Nancy Grace Roman Space Telescope (Roman), NASA's next flagship observatory, has significant mission time to be spent on surveys for general astrophysics in addition to its three core community surveys. We considered what types of observations outside the core surveys would most benefit from early definition, given 700 hours of mission time in the first two years of Roman's operation. We recommend that a survey of the Galactic plane be defined early, based on the broad range of stakeholders for such a survey, the added scientific value of a first pass to obtain a baseline for proper motions complementary to Gaia's, and the significant potential synergies with ground-based surveys, notably the Legacy Survey of Space and Time (LSST) on Rubin. We also found strong motivation to follow a community definition process for ultra-deep observations with Roman., Comment: Final report of the committee convened by NASA to assess community proposals for early definition science with Roman

This paper is a draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White. In this chapter, we consider generative information retrieval evaluation from two distinct but interrelated perspectives. First, large language models (LLMs) themselves are rapidly becoming tools for evaluation, with current research indicating that LLMs may be superior to crowdsource workers and other paid assessors on basic relevance judgement tasks. We review past and ongoing related research, including speculation on the future of shared task initiatives, such as TREC, and a discussion on the continuing need for human assessments. Second, we consider the evaluation of emerging LLM-based generative information retrieval (GenIR) systems, including retrieval augmented generation (RAG) systems. We consider approaches that focus both on the end-to-end evaluation of GenIR systems and on the evaluation of a retrieval component as an element in a RAG system. Going forward, we expect the evaluation of GenIR systems to be at least partially based on LLM-based assessment, creating an apparent circularity, with a system seemingly evaluating its own output. We resolve this apparent circularity in two ways: 1) by viewing LLM-based assessment as a form of "slow search", where a slower IR system is used for evaluation and training of a faster production IR system; and 2) by recognizing a continuing need to ground evaluation in human assessment, even if the characteristics of that human assessment must change., Comment: Draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement., Comment: 27 pages

Constraining molecules in simulations (such as with constant bond lengths and/or angles) reduces their degrees of freedom (DoF), which in turn affects temperature calculations in those simulations. When local temperatures are measured, e.g. from a set of atoms in a subvolume or from velocities in one Cartesian direction, the result can appear to unphysically violate equipartition of the kinetic energy if the local DoF are not correctly calculated. Here we determine how to correctly calculate local temperatures from arbitrary Cartesian component kinetic energies, accounting for general geometric constraints, by self-consistently evaluating the DoF of atoms subjected to those constraints. The method is validated on a variety of test systems, including systems subject to a temperature gradient and those confined between walls. It is also shown to provide a sensitive test for the breakdown of kinetic energy equipartition caused by the approximate nature of numerical integration or insufficient equilibration times. As a practical demonstration, we show that kinetic energy equipartition between C and H atoms connected by rigid bonds can be violated even at the commonly-used time step of 2 fs, and that this equipartition violation appears to usefully indicate configurational overheating., Comment: 14 pages of main text and 4 pages of Supporting Information. This version includes more details and examples

While the operationalisation of high-level AI ethics principles into practical AI/ML systems has made progress, there is still a theory-practice gap in managing tensions between the underlying AI ethics aspects. We cover five approaches for addressing the tensions via trade-offs, ranging from rudimentary to complex. The approaches differ in the types of considered context, scope, methods for measuring contexts, and degree of justification. None of the approaches is likely to be appropriate for all organisations, systems, or applications. To address this, we propose a framework which consists of: (i) proactive identification of tensions, (ii) prioritisation and weighting of ethics aspects, (iii) justification and documentation of trade-off decisions. The proposed framework aims to facilitate the implementation of well-rounded AI/ML systems that are appropriate for potential regulatory requirements.

Solutions to vision tasks in gastrointestinal endoscopy (GIE) conventionally use image encoders pretrained in a supervised manner with ImageNet-1k as backbones. However, the use of modern self-supervised pretraining algorithms and a recent dataset of 100k unlabelled GIE images (Hyperkvasir-unlabelled) may allow for improvements. In this work, we study the fine-tuned performance of models with ResNet50 and ViT-B backbones pretrained in self-supervised and supervised manners with ImageNet-1k and Hyperkvasir-unlabelled (self-supervised only) in a range of GIE vision tasks. In addition to identifying the most suitable pretraining pipeline and backbone architecture for each task, out of those considered, our results suggest three general principles. Firstly, that self-supervised pretraining generally produces more suitable backbones for GIE vision tasks than supervised pretraining. Secondly, that self-supervised pretraining with ImageNet-1k is typically more suitable than pretraining with Hyperkvasir-unlabelled, with the notable exception of monocular depth estimation in colonoscopy. Thirdly, that ViT-Bs are more suitable in polyp segmentation and monocular depth estimation in colonoscopy, ResNet50s are more suitable in polyp detection, and both architectures perform similarly in anatomical landmark recognition and pathological finding characterisation. We hope this work draws attention to the complexity of pretraining for GIE vision tasks, informs this development of more suitable approaches than the convention, and inspires further research on this topic to help advance this development. Code available: \underline{github.com/ESandML/SSL4GIE}

In this perspective we consider how modern statistical mechanics and response theory can be applied to understand the response of polar molecules to an applied electric field and the fluctuations in these systems. Results that are consistent with thermodynamics and physical expectations are derived, as well as a new fluctuation relation that is tested in molecular simulations. It is demonstrated that a deterministic approach leads to distribution functions that continually evolve, even when system properties have relaxed, which has implications for treatment of nonequilibrium steady states., Comment: 19 pages and 2 ancillary files (animations)

We revisit Ito's (\cite{I1989}) natural extension of the Farey tent map, which generates all regular continued fraction convergents and mediants of a given irrational. With a slight shift in perspective on the order in which these convergents and mediants arise, this natural extension is shown to provide an elegant and powerful tool in the metric theory of continued fractions. A wealth of old and new results -- including limiting distributions of approximation coefficients, analogues of a theorem of Legendre and their refinements, and a generalisation of L\'evy's Theorem to subsequences of convergents and mediants -- are presented as corollaries within this unifying theory., Comment: 35 pages, 8 figures. Proofs added to Corollary 5.3 and Theorem 5.18. Figures 7 and 8 corrected. Other minor edits following referee suggestions

Use of edge computing in application of Computer Vision (CV) is an active field of research. Today, most CV applications make use of Convolutional Neural Networks (CNNs) to inference on and interpret video data. These edge devices are responsible for several CV related tasks, such as gathering, processing and enhancing, inferencing on, and displaying video data. Due to ease of reconfiguration, computation on FPGA fabric is used to achieve such complex computation tasks. Xilinx provides the PYNQ environment as a user-friendly interface that facilitates in Hardware/Software system integration. However, to the best of authors' knowledge there is no end-to-end framework available for the PYNQ environment that allows Hardware/Software system integration and deployment of CNNs for real-time input feed from High Definition Multimedia Interface (HDMI) input to HDMI output, along with insertion of customized hardware IPs. In this work we propose an integration of rea\textbf{L}-time image \textbf{E}nancement IP with \textbf{A}I inferencing engine in the \textbf{P}ynq environment (\textbf{LEAP}), that integrates HDMI, AI acceleration, image enhancement in the PYNQ environment for Xilinx's Microprocessor on Chip (MPSoC) platform. We evaluate our methodology with two well know CNN models, Resnet50 and YOLOv3. To validate our proposed methodology, LEAP, a simple image enhancement algorithm, histogram equalization, is designed and integrated in the FPGA fabric along with Xilinx's Deep Processing Unit (DPU). Our results show successful implementation of end-to-end integration using completely open source information., Comment: 6 pages, 4 figures, 3 tables

Neural Networks (NNs) have become indispensable for applications of Computer Vision (CV) and their use has been ever-growing. NNs are commonly trained for long periods of time on datasets like ImageNet and COCO that have been carefully created to represent common "real-world" environments. When deployed in the field, such as applications of autonomous vehicles, NNs can encounter adverse scenarios that degrade performance. Using image enhancements algorithms to enhance images before being inferenced on a NN model poses an intriguing alternative to retraining, however, published literature on the effectiveness of this solution is scarce. To fill this knowledge gap, we provide a case study on two popular image enhancement algorithms, Histogram Equalization (HE) and Retinex (RX). We simulate four types of adverse scenarios an autonomous vehicle could encounter, dark, over exposed, foggy, and dark & rainy weather conditions. We evaluate the effectiveness of HE and RX using several well established models:, Resnet, GoogleLeNet, YOLO, and a Vision Transformer., Comment: 5 pages, 8 figures

We find ourselves on the brink of an exciting era in observational astrophysics, driven by groundbreaking facilities like JWST, Euclid, Rubin, Roman, SKA, or ELT. Simultaneously, computational astrophysics has shown significant strides, yielding highly realistic galaxy formation simulations, thanks to both hardware and software enhancements. Bridging the gap between simulations and observations has become paramount for meaningful comparisons. We introduce py-ananke, a Python pipeline designed to generate synthetic resolved stellar surveys from cosmological simulations, adaptable to various instruments. Building upon its predecessor, ananke by Sanderson et al. 2020 (arXiv:1806.10564), which produced Gaia DR2 mock star surveys, the py-ananke package offers a user-friendly "plug & play" experience. The pipeline employs cutting-edge phase-space density estimation and initial mass function sampling to convert particle data into synthetic stars, while interpolating pre-computed stellar isochrone tracks for photometry. Additionally, it includes modules for estimating interstellar reddening, dust-induced extinctions, and for quantifying errors through dedicated modeling approaches. py-ananke promises to serve as a vital bridge between computational astrophysics and observational astronomy, facilitating preparations and making scientific predictions for the next generation of telescopes., Comment: 12 pages (6 pages of bibliography) and 1 figure. Software repository at https://github.com/athob/py-ananke

Understanding the physical mechanism behind the formation of a co-rotating thin plane of satellite galaxies, like the one observed around the Milky Way (MW), has been challenging. The perturbations induced by a massive satellite galaxy, like the Large Magellanic Cloud (LMC) provide valuable insight into this problem. The LMC induces an apparent co-rotating motion in the outer halo by displacing the inner regions of the halo with respect to the outer halo. Using the Latte suite of FIRE-2 cosmological simulations of MW-mass galaxies, we confirm that the apparent motion of the outer halo induced by the infall of a massive satellite changes the observed distribution of orbital poles of outer-halo tracers, including satellites. We quantify the changes in the distribution of orbital poles using the two-point angular correlation function and find that all satellites induce changes. However, the most massive satellites with pericentric passages between 30-100kpc induce the largest changes. The best LMC-like satellite analog shows the largest change in orbital pole distribution. The dispersion of orbital poles decreases by 20{\deg} during the first two pericentric passages. Even when excluding the satellites brought in with the LMC-like satellite, there is clustering of orbital poles. These results suggest that in the MW, the recent pericentric passage of the LMC should have changed the observed distribution of orbital poles of all other satellites. Therefore, studies of kinematically-coherent planes of satellites that seek to place the MW in a cosmological context should account for the existence of a massive satellite like the LMC., Comment: 20 pages, 10 figures. ApJ submitted, Comments are welcome

The charge density wave (CDW) is a condensate that often forms in layered materials. It is known to carry electric current \emph{en masse}, but the transport mechanism remains poorly understood at the microscopic level. Its quantum nature is revealed by several lines of evidence. Experiments often show lack of CDW displacement when biased just below the threshold for nonlinear transport, indicating the CDW never reaches the critical point for classical depinning. Quantum behavior is also revealed by oscillations of period $h/2e$ in CDW conductance vs. magnetic flux, sometimes accompanied by telegraph-like switching, in $\text{TaS}_3$ rings above 77 K. Here we discuss further evidence for quantum CDW electron transport. We find that, for temperatures ranging from 9 to 474 K, CDW current-voltage plots of three trichalcogenide materials agree almost precisely with a modified Zener-tunneling curve and with time-correlated soliton tunneling model simulations. In our model we treat the Schr\"{o}dinger equation as an emergent classical equation that describes fluidic Josephson-like coupling of paired electrons between evolving topological states. We find that an extension of this \lq classically robust' quantum picture explains both the $h/2e$ magnetoconductance oscillations and switching behavior in CDW rings. We consider potential applications for thermally robust quantum information processing systems.

The Milky Way's (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the "last major merger." Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor collided with the MW proto-disk 8-11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the MW disk within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space, because the morphology of debris depends on how long it has had to phase mix. The recently-identified phase-space folds in Gaia DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations at late times. Roughly 20\% of the stars in the prograde local stellar halo are associated with the observed caustics. Based on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1--2 Gyr ago. We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data 1--2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the "last major merger" did not collide with the MW proto-disk at early times, as is thought for the GSE, but instead collided with the MW disk within the last few Gyr, consistent with the body of work surrounding the VRM.

A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coordination since the thief's travelling speed depends on the knapsack's weight and the order of visiting cities affects the order of item collection. Existing TTP solvers deal with city selection and item selection separately, keeping decisions for one type unchanged while dealing with the other type. This separation essentially means very poor coordination between two types of decision. In this paper, we first show that a simple local search based coordination approach does not work in TTP. Then, to address the aforementioned problems, we propose a human designed coordination heuristic that makes changes to collection plans during exploration of cyclic tours. We further propose another human designed coordination heuristic that explicitly exploits the cyclic tours in item selections during collection plan exploration. Lastly, we propose a machine learning based coordination heuristic that captures characteristics of the two human designed coordination heuristics. Our proposed coordination based approaches help our TTP solver significantly outperform existing state-of-the-art TTP solvers on a set of benchmark problems. Our solver is named Cooperation Coordination (CoCo) and its source code is available from https://github.com/majid75/CoCo, Comment: expanded and revised version of arXiv:1911.03124

This work combines spectroscopic and photometric data of the polluted white dwarf WD0141-675 which has a now retracted astrometric super-Jupiter candidate and investigates the most promising ways to confirm Gaia astrometric planetary candidates and obtain follow-up data. Obtaining precise radial velocity measurement for white dwarfs is challenging due to their intrinsic faint magnitudes, lack of spectral absorption lines, and broad spectral features. However, dedicated radial velocity campaigns are capable of confirming close in giant exoplanets (a few M$_{\textrm{Jup}}$) around polluted white dwarfs, where additional metal lines aid radial velocity measurements. Infrared emission from these giant exoplanets is shown to be detectable with JWST MIRI and will provide constraints on the formation of the planet. Using the initial Gaia astrometric solution for WD0141-675 as a case study, if there were a planet with a 33.65 d period or less with a nearly edge on orbit, 1) ground-based radial velocity monitoring limits the mass to $<$ 15.4 M$_{\textrm{Jup}}$, and 2) space-based infrared photometry shows a lack of infrared excess and in a cloud-free planetary cooling scenario, a sub-stellar companion would have to be $<$ 16 M$_{\textrm{Jup}}$ and be older than 3.7 Gyr. These results demonstrate how radial velocities and infrared photometry can probe the mass of the objects producing some of the astrometric signals, and rule out parts of the brown dwarf and planet mass parameter space. Therefore, combining astrometric data with spectroscopic and photometric data is crucial to both confirm, and characterise astrometric planet candidates around white dwarfs., Comment: Accepted in MNRAS

This chapter presents the potential of interoperability and standardised data publication for cultural heritage resources, with a focus on community-driven approaches and web standards for usability. The Linked Open Usable Data (LOUD) design principles, which rely on JSON-LD as lingua franca, serve as the foundation. We begin by exploring the significant advances made by the International Image Interoperability Framework (IIIF) in promoting interoperability for image-based resources. The principles and practices of IIIF have paved the way for Linked Art, which expands the use of linked data by demonstrating how it can easily facilitate the integration and sharing of semantic cultural heritage data across portals and institutions. To provide a practical demonstration of the concepts discussed, the chapter highlights the implementation of LUX, the Yale Collections Discovery platform. LUX serves as a compelling case study for the use of linked data at scale, demonstrating the real-world application of automated enrichment in the cultural heritage domain. Rooted in empirical study, the analysis presented in this chapter delves into the broader context of community practices and semantic interoperability. By examining the collaborative efforts and integration of diverse cultural heritage resources, the research sheds light on the potential benefits and challenges associated with LOUD., Comment: This is the preprint version of a chapter submitted to be included in the book "Decoding Cultural Heritage: a critical dissection and taxonomy of human creativity through digital tools", to be published by Springer Nature. The chapter is currently undergoing peer review for potential inclusion in the book

The physical mechanisms responsible for bar formation and destruction in galaxies remain a subject of debate. While we have gained valuable insight into how bars form and evolve from isolated idealized simulations, in the cosmological domain, galactic bars evolve in complex environments with mergers, gas accretion events, in presence of turbulent Inter Stellar Medium (ISM) with multiple star formation episodes, in addition to coupling to their host galaxies' dark matter halos. We investigate bar formation in 13 Milky Way-mass galaxies from the FIRE-2 (Feedback in Realistic Environments) cosmological zoom-in simulations. 8 of the 13 simulated galaxies form bars at some point during their history: three from tidal interactions and five from internal evolution of the disk. The bars in FIRE-2 are generally shorter than the corotation radius (mean bar radius $\sim 1.53$ kpc), have a wide range of pattern speeds (36--97 km s$^{-1}$kpc$^{-1}$), and live for a wide range of dynamical times (2--160 bar rotations). We find that bar formation in FIRE-2 galaxies is influenced by satellite interactions and the stellar-to-dark matter mass ratio in the inner galaxy, but neither is a sufficient condition for bar formation. Bar formation is more likely to occur, and the bars formed are stronger and longer-lived, if the disks are kinematically cold; galaxies with high central gas fractions and/or vigorous star formation, on the other hand, tend to form weaker bars. In the case of the FIRE-2 galaxies these properties combine to produce ellipsoidal bars with strengths $A_2/A_0 \sim$ 0.1--0.2., Comment: 39 pages (including Appendix), 25 figures, accepted in The Astrophysical Journal

Dark Matter (DM) subhalos are predicted to perturb stellar streams; stream morphologies and dynamics can constrain the mass distribution of subhalos. Using FIRE-2 simulations of Milky Way-mass galaxies, we show that presence of a Large Magellanic Cloud (LMC)--analog significantly changes stream-subhalo encounter rates. Three key factors drive these changes. First, the LMC--analog brings in many subhalos, increasing encounter rates for streams near the massive satellite by up to 20--40%. Second, the LMC--analog displaces the host from its center-of-mass (inducing reflex motion), causing a north-south asymmetry in the density and radial velocity distribution of subhalos. This asymmetry results in encounter rates varying by 50--70% across the sky at the same distance. Finally, the LMC--mass satellite induces a density wake in the host's DM halo, further boosting the encounter rates near the LMC--analog. We also explore the influence of stream orbital properties, finding a 50% increase in encounters for streams moving retrograde to the LMC--analog's orbit in the opposite hemisphere. The dependence of encounter rates on stream location and orbit has important implications for where to search for new streams with spurs and gaps in the Milky Way., Comment: 28 pages, 17 figures, 3 tables, Accepted in APJ