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At their best, K-12 public school systems can be engines of social and economic mobility. Unfortunately, schools in lower-income districts -- whose students have the greatest academic needs -- often receive less funding than their counterparts in more affluent districts. Discussions about closing these funding gaps usually zoom all the way out to the state level or all the way down to the district level. But a big part of the problem lies in how funding is distributed across districts in the same metro area -- and in state policies that allow wealthy communities to raise and keep large amounts of local revenue exclusively for their own schools. "Leveling the Landscape: An Analysis of K-12 Funding Inequities Within Metro Areas" takes a closer look at the scale and sources of education funding within 123 large metro areas in 38 states, focusing on funding disparities among districts serving the same region. Key takeaways include: (1) a majority of public school students (62%) live in large metro areas with more than five districts -- a level of fragmentation that makes funding disparities more likely; (2) within fragmented metro areas, wealthy districts often generate much more local funding per student than less affluent districts; (3) state policies, despite their progressive tilt, rarely bridge this gap -- and often don't even come close; (4) in 49 of the 123 large metro areas we examined, school districts in affluent areas receive the most funding per pupil; (5) closing the state and local funding gap between districts within the metro areas we examined would cost $26 billion in additional state funding per year; and (6) more ambitious policies can greatly reduce or even eliminate funding disparities. The report also explores policy tools state leaders can use to ensure all districts within the same metro area at the very least receive similar funding per student and ideally, set the stage for even greater levels of per-pupil funding to flow to higher-needs districts.

This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to develop diverse models from a single well-trained checkpoint, facilitating the training of a model family. This involves producing multiple outputs from a single input, fusing them into a final output, and estimating uncertainty based on output disagreements. Implemented using U-Net and UNETR models for segmentation and synthesis tasks, this approach was tested on CT body segmentation and MR-CT synthesis datasets. It achieved a mean Dice coefficient of 0.814 in segmentation and a Mean Absolute Error of 88.17 HU in synthesis, improved from 89.43 HU by pruning. Additionally, the framework was evaluated under corruption and undersampling, maintaining correlation between uncertainty and error, which highlights its robustness. These results suggest that the proposed approach not only maintains the performance of well-trained models but also enhances interpretability through effective uncertainty estimation, applicable to both convolutional and transformer models in a range of imaging tasks., Comment: 16 pages, 12 figures, 5 tables

Retrieval-Augmented Generation (RAG) systems enhance text generation by incorporating external knowledge but often struggle when retrieving context across different text modalities due to semantic gaps. We introduce a generalized projection-based method, inspired by adapter modules in transfer learning, that efficiently bridges these gaps between various text types, such as programming code and pseudocode, or English and French sentences. Our approach emphasizes speed, accuracy, and data efficiency, requiring minimal resources for training and inference. By aligning embeddings from heterogeneous text modalities into a unified space through a lightweight projection network, our model significantly outperforms traditional retrieval methods like the Okapi BM25 algorithm and models like Dense Passage Retrieval (DPR), while approaching the accuracy of Sentence Transformers. Extensive evaluations demonstrate the effectiveness and generalizability of our method across different tasks, highlighting its potential for real-time, resource-constrained applications., Comment: 18 pages, 3 figures

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

We study the evolution of populations of binary stars within massive cluster-forming regions. We simulate the formation of young massive star clusters within giant molecular clouds with masses ranging from 2 x 10$^{4}$ to 3.2 x 10$^{5}$ M$_{\odot}$. We use Torch, which couples stellar dynamics, magnetohydrodynamics, star and binary formation, stellar evolution, and stellar feedback through the AMUSE framework. We find that the binary fraction decreases during cluster formation at all molecular cloud masses. The binaries' orbital properties also change, with stronger and quicker changes in denser, more massive clouds. Most of the changes we see can be attributed to the disruption of binaries wider than 100 au, although the close binary fraction also decreases in the densest cluster-forming region. The binary fraction for O stars remains above 90%, but exchanges and dynamical hardening are ubiquitous, indicating that O stars undergo frequent few-body interactions early during the cluster formation process. Changes to the populations of binaries are a by-product of hierarchical cluster assembly: most changes to the binary population take place when the star formation rate is high and there are frequent mergers between sub-clusters in the cluster-forming region. A universal primordial binary distribution based on observed inner companions in the Galactic field is consistent with the binary populations of young clusters with resolved stellar populations, and the scatter between clusters of similar masses could be explained by differences in their formation history., Comment: 15 pages, 8 figures, 3 tables. Submitted to ApJ

Mass segregation is seen in many star clusters, but whether massive stars form in the center of a cluster or migrate there dynamically is still debated. N-body simulations have shown that early dynamical mass segregation is possible when sub-clusters merge to form a dense core with a small crossing time. However, the effect of gas dynamics on both the formation and dynamics of the stars could inhibit the formation of the dense core. We aim to study the dynamical mass segregation of star cluster models that include gas dynamics and self-consistently form stars from the dense substructure in the gas. Our models use the Torch framework, which is based on AMUSE and includes stellar and magnetized gas dynamics, as well as stellar evolution and feedback from radiation, stellar winds, and supernovae. Our models consist of three star clusters forming from initial turbulent spherical clouds of mass $10^{4,5,6}\rm~M_\odot$ and radius $11.7\rm~pc$ that have final stellar masses of $3.6\times10^3\rm~M_\odot$, $6.5\times10^4\rm~M_\odot$, and $8.9\times10^5\rm~M_\odot$, respectively. There is no primordial mass segregation in the model by construction. All three clusters become dynamically mass segregated at early times via collapse confirming that this mechanism occurs within sub-clusters forming directly out of the dense substructure in the gas. The dynamics of the embedded gas and stellar feedback do not inhibit the collapse of the cluster. We find that each model cluster becomes mass segregated within $2~$Myr of the onset of star formation, reaching the levels observed in young clusters in the Milky Way. However, we note that the exact values are highly time-variable during these early phases of evolution. Massive stars that segregate to the center during core collapse are likely to be dynamically ejected, a process that can decrease the overall level of mass segregation again., Comment: Submitted to A&A. 8 pages, 4 figures

Van-der-Waals materials have been shown to support numerous exotic polaritonic phenomena originating from their layered structures and associated vibrational and electronic properties. This includes emergent polaritonic phenomena, including hyperbolicity and exciton-polariton formation. However, many van-der-Waals materials' unique properties are most prominent at cryogenic temperatures. This presents a particular challenge for polaritonics research, as reliable optical constant data is required for understanding light-matter coupling. For infrared polaritonics (3-100um), the small size of exfoliated flakes makes conventional ellipsometry impossible. This paper presents a cryogenic Fourier transform infrared microscope design constructed entirely from off-the-shelf components and fitting procedures for determining optical constants. We use this microscope to present the first temperature-dependent characterization of the optical properties of hexagonal boron nitride grown with isotopically pure boron. We show that Fabry Perot-type resonances close to the transverse optical phonon show the key temperature-dependent tuning of several parameters. Our full analysis of the infrared dielectric function shows small but significant tuning of the optical constants, which is highly consistent with Raman data from the literature. We then use this dielectric data to perform and analyze the polariton propagation properties, which agree extremely well with published cryogenic scattering-type nearfield microscopy results. In addition to the insights gained into hyperbolic polaritons in hBN, our paper represents a transferable framework for characterizing exfoliated infrared polaritonic materials and other infrared devices. This could accelerate discoveries in other material systems, especially those that are spatially inhomogeneous or cannot be prepared as large single crystals.

Code summary generation is the task of writing natural language descriptions of a section of source code. Recent advances in Large Language Models (LLMs) and other AI-based technologies have helped make automatic code summarization a reality. However, the summaries these approaches write tend to focus on a narrow area of code. The results are summaries that explain what that function does internally, but lack a description of why the function exists or its purpose in the broader context of the program. In this paper, we present an approach for including this context in recent LLM-based code summarization. The input to our approach is a Java method and that project in which that method exists. The output is a succinct English description of why the method exists in the project. The core of our approach is a 350m parameter language model we train, which can be run locally to ensure privacy. We train the model in two steps. First we distill knowledge about code summarization from a large model, then we fine-tune the model using data from a study of human programmer who were asked to write code summaries. We find that our approach outperforms GPT-4 on this task., Comment: 21 pages, 5 figures, preprint under review

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

Context: The Milky Way's history of recent disturbances is vividly demonstrated by a structure in the vertical phase-space distribution known as the Gaia phase spiral. A one-armed phase spiral has been seen widely across the Milky Way disc, while a two-armed one has only been observed in the solar neighbourhood. Aims: This study aims to determine the properties of the two-armed phase spiral and to put it in a Galactic context, with the ultimate goal of understanding the structure and history of the Milky Way disc. Methods: The Gaia DR3 data is used to trace and characterise the two-armed phase spiral. Special focus is put on the phase spiral's spatial distribution, rotational behaviour, and chemical characteristics. To quantify the properties of the phase spiral we use a model that fits a spiral pattern to the phase space distribution of the stars. Results: We find that the two-armed phase spiral is detectable only within a narrow range of galactocentric distances and angular momenta in the solar neighbourhood, $R = 8 \pm 0.5 $ kpc, $L_Z = 1450 \pm 50$ kpc km s$^{-1}$. Outside this region, the phase spiral is one-armed. The two-armed phase spiral rotates with the phase angle, like the one-armed phase spiral, and changes axis ratio with phase angle. Additionally, stars within the phase-space overdensity caused by the two-armed phase spiral pattern have slightly higher mean metallicity than stars in the underdense regions of the pattern at equivalent galactocentric distances, angular momenta, and vertical orbit extents. Conclusions: The two-armed phase spiral rotates with phase angle and its effect can be seen in metallicity, like the one-armed phase spiral. However, the limited range over which it can be found, and its variation in shape are quite different from the one-armed version, suggesting it is a much more localised phenomenon in the Galactic disc., Comment: Submitted to A&A

This study focuses on inverting time-domain airborne electromagnetic data in 2D by training a neural-network to understand the relationship between data and conductivity, thereby removing the need for expensive forward modeling during the inversion process. Instead the forward modeling is completed in the training stage, where training models are built before calculating 3D forward modeling training data. The method relies on training data being similar to the field dataset of choice, therefore, the field data was first inverted in 1D to get an idea of the expected conductivity distribution. With this information, $ 10,000 $ training models were built with similar conductivity ranges, and the research shows that this provided enough information for the network to produce realistic 2D inversion models over an aquifer-bearing region in California. Once the training was completed, the actual inversion time took only a matter of seconds on a generic laptop, which means that if future data was collected in this region it could be inverted in near real-time. Better results are expected by increasing the number of training models and eventually the goal is to extend the method to 3D inversion., Comment: 4 pages, 5 figures, conference submission

Estimating the density of a distribution from samples is a fundamental problem in statistics. In many practical settings, the Wasserstein distance is an appropriate error metric for density estimation. For example, when estimating population densities in a geographic region, a small Wasserstein distance means that the estimate is able to capture roughly where the population mass is. In this work we study differentially private density estimation in the Wasserstein distance. We design and analyze instance-optimal algorithms for this problem that can adapt to easy instances. For distributions $P$ over $\mathbb{R}$, we consider a strong notion of instance-optimality: an algorithm that uniformly achieves the instance-optimal estimation rate is competitive with an algorithm that is told that the distribution is either $P$ or $Q_P$ for some distribution $Q_P$ whose probability density function (pdf) is within a factor of 2 of the pdf of $P$. For distributions over $\mathbb{R}^2$, we use a different notion of instance optimality. We say that an algorithm is instance-optimal if it is competitive with an algorithm that is given a constant-factor multiplicative approximation of the density of the distribution. We characterize the instance-optimal estimation rates in both these settings and show that they are uniformly achievable (up to polylogarithmic factors). Our approach for $\mathbb{R}^2$ extends to arbitrary metric spaces as it goes via hierarchically separated trees. As a special case our results lead to instance-optimal private learning in TV distance for discrete distributions.

In the context of global gyrokinetic simulations of turbulence using a Particle-In-Cell framework, verifying the delta-f assumption with a fixed background distribution becomes challenging when determining quasi-steady state profiles corresponding to given sources over long time scales, where plasma profiles can evolve significantly. The advantage of low relative sampling noise afforded by the delta-f scheme is shown to be retained by considering the background as a time-evolving Maxwellian with time-dependent density and temperature profiles. Implementation of this adaptive scheme to simulate electrostatic collisionless flux-driven turbulence in tokamak plasmas show small and non-increasing sampling noise levels, which would otherwise increase indefinitely with a stationary background scheme. The adaptive scheme furthermore allows one to reach numerically converged results of quasi-steady state with much lower marker numbers.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with XY Java programmers, where each programmer generated summaries for 40 methods from five large Java projects over five one-hour sessions. We used eye-tracking equipment to map the visual attention of programmers while they wrote the summaries. We also rate the quality of each summary. We found eye-gaze patterns and metrics that define common behaviors between programmer attention during context-aware code summarization. Specifically, we found that programmers need to read significantly (p<0.01) fewer words and make significantly fewer revisits to words (p\textless0.03) as they summarize more methods during a session, while maintaining the quality of summaries. We also found that the amount of source code a participant looks at correlates with a higher quality summary, but this trend follows a bell-shaped curve, such that after a threshold reading more source code leads to a significant decrease (p<0.01) in the quality of summaries. We also gathered insight into the type of methods in the project that provide the most contextual information for code summarization based on programmer attention. Specifically, we observed that programmers spent a majority of their time looking at methods inside the same class as the target method to be summarized. Surprisingly, we found that programmers spent significantly less time looking at methods in the call graph of the target method. We discuss how our empirical observations may aid future studies towards modeling programmer attention and improving context-aware automatic source code summarization., Comment: 10 pages, 4 figures, 4 tables. this is a pre-print submitted to IEEE Transactions on Software Engineering for review

The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how developers validate and repair code generated by Copilot and examines the impact of code provenance awareness during these processes. We conducted a lab study with 28 participants, who were tasked with validating and repairing Copilot-generated code in three software projects. Participants were randomly divided into two groups: one informed about the provenance of LLM-generated code and the other not. We collected data on IDE interactions, eye-tracking, cognitive workload assessments, and conducted semi-structured interviews. Our results indicate that, without explicit information, developers often fail to identify the LLM origin of the code. Developers generally employ similar validation and repair strategies for LLM-generated code, but exhibit behaviors such as frequent switching between code and comments, different attentional focus, and a tendency to delete and rewrite code. Being aware of the code's provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload. These findings enhance our understanding of how developers interact with LLM-generated code and carry implications for designing tools that facilitate effective human-LLM collaboration in software development.

Two main mechanisms have classically been proposed for the formation of runaway stars. In the binary supernova scenario (BSS), a massive star in a binary explodes as a supernova, ejecting its companion. In the dynamical ejection scenario, a star is ejected during a strong dynamical encounter between multiple stars. We propose a third mechanism for the formation of runaway stars: the subcluster ejection scenario (SCES), where a subset of stars from an infalling subcluster is ejected out of the cluster via a tidal interaction with the contracting gravitational potential of the assembling cluster. We demonstrate the SCES in a star-by-star simulation of the formation of a young massive cluster from a $10^6\rm~M_\odot$ gas cloud using the Torch framework. This star cluster forms hierarchically through a sequence of subcluster mergers determined by the initial turbulent, spherical conditions of the gas. We find that these mergers drive the formation of runaway stars in our model. Late-forming subclusters fall into the central potential, where they are tidally disrupted, forming tidal tails of runaway stars that are distributed highly anisotropically. Runaways formed in the same SCES have similar ages, velocities, and ejection directions. Surveying observations, we identify several SCES candidate groups with anisotropic ejection directions. The SCES is capable of producing runaway binaries: two wide dynamical binaries in infalling subclusters were tightened through ejection. This allows for another velocity kick via subsequent via a subsequent BSS ejection. An SCES-BSS ejection is a possible avenue for the creation of hypervelocity stars unbound to the Galaxy. We expect nonspherical initial gas distributions to increase the number of calculated runaway stars. The observation of groups of runaway stars formed via the SCES can thus reveal the assembly history of their natal clusters., Comment: 11 pages, 10 figures. Accepted for publication in A&A. Abstract abridged for arXiv

We determine the influence of the Milky Way's warp on the kinematics of stars across the disc, and therefore measure its precession rate and line of nodes under different assumptions. We do this by applying Jeans' first equation to a model of a rigidly precessing warp. The predictions of these models are fit to the average vertical velocities of stars with measured line-of-sight velocities in Gaia DR3 data. We test models in which the warp's line of nodes and precession speed are fixed, and models in which they are allowed to vary linearly with radius. We also test models in which the velocity of stars radially in the disc is included in Jeans' equation. The kinematic data is best fit by models with a line of nodes that is 40 degrees offset from the Sun's Galactic azimuth, significantly leading the line of nodes found from the positions of stars. These models have a warp precession speed of around 13 km/s/kpc in the direction of Galactic rotation, close to other recent estimates. We find that including the velocity of stars radially in the disc in our kinematic model leads to a significantly worse fit to the data, and implausible warp parameters. We conclude that the Milky Way's warp appears to be rapidly precessing, but the structure and kinematics of the warped disc are not consistent within the approximation of a fixed, precessing, warp shape. This implies that the Milky Way's warp is dynamically evolving, which is a challenge to models of the warp's creation, and must be considered in the context of other known disturbances of the disc., Comment: Explainer video available at https://www.youtube.com/watch?v=8Wl81JC7l7k. A&A accepted. 13 pages, 10 figures

Toroidal Alfv\'en eigenmodes (TAEs) can transport fusion-born energetic particles out of the plasma volume, thereby decreasing plasma self-heating efficiency and possibly damaging reactor walls. Therefore, understanding TAE destabilisation and identifying saturation mechanisms is crucial to achieving burning plasma. While TAEs have been studies extensively in the past using kinetic-MHD codes, here a fully gyrokinetic study is employed which allows for additional physics. In the case studied, the primary drive mechanism is identified as the resonance between the magnetic drifts and the TAE, and this is seen to be disrupted by equilibrium flow shear which can stabilize the mode by rotating it in the the poloidal plane. It is found that zonal flows do not play a significant role in the saturation of these TAEs, and there are no saturation mechanisms present in the local gyrokinetic picture that are able to saturate the mode at physically relevant transport levels in the case of TAE-only turbulence. Instead, we confirm that the global profile flattening of fast-ion density is the key saturation mechanism. The nonlinear excitation of TAE travelling along the electron diamagnetic direction and its beating with the ion diamagnetic TAE, resulting in large amplitude oscillations that may help detect TAEs more easily in tokamaks, is also reported.

Given two smooth manifolds with tangent subbundle distributions, an embedding is Pfaffian if its differential sends the distribution on the source into the distribution on the target. In this paper, we consider the question of existence of Pfaffian embeddings in the specific case where the source is a (2,3,5)-manifold, the target is the 7-dimensional space of isotropic 2-planes in a 6-dimensional symplectic vector space, and the Pfaffian condition is that the derived 3-distribution on the source be mapped into the natural 4-distribution on the target. This is one of the simpler non-trivial cases of the general question on existence of Pfaffian embeddings, but already the answer here requires solution of an interesting differential equation. It turns out that a generic (2,3,5)-manifold does not embed, the first obstruction being the fact that a Pfaffian embeddable (2,3,5)-manifold necessarily has a double root for its Cartan quartic at each point. We determine a complete characterization of embeddable (2,3,5)-manifolds in terms of their associated Cartan geometries, which characterization depends on higher order (non-harmonic) curvature as well., Comment: 34 pages; Comments welcome

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Social technologies are the systems, interfaces, features, infrastructures, and architectures that allow people to interact with each other online. These technologies dramatically shape the fabric of our everyday lives, from the information we consume to the people we interact with to the foundations of our culture and politics. While the benefits of social technologies are well documented, the harms, too, have cast a long shadow. To address widespread problems like harassment, disinformation, information access, and mental health concerns, we need to rethink the foundations of how social technologies are designed, sustained, and governed. This report is based on discussions at the Computing Community Consortium Workshop, The Future of Research on Social Technologies, that was held November 2-3, 2023 in Washington, DC. The visioning workshop came together to focus on two questions. What should we know about social technologies, and what is needed to get there? The workshop brought together over 50 information and computer scientists, social scientists, communication and journalism scholars, and policy experts. We used a discussion format, with one day of guiding topics and a second day using an unconference model where participants created discussion topics. The interdisciplinary group of attendees discussed gaps in existing scholarship and the methods, resources, access, and collective effort needed to address those gaps. We also discussed approaches for translating scholarship for various audiences including citizens, funders, educators, industry professionals, and policymakers. This report presents a synthesis of major themes during our discussions. The themes presented are not a summary of what we know already, they are an exploration of what we do not know enough about, and what we should spend more effort and investment on in the coming years.

We present KRATOS, a comprehensive suite of 28 open access pure N-body simulations of isolated and interacting LMC-like galaxies, to study the formation of substructures in their disc after the interaction with an SMC-mass galaxy. The primary objective of this paper is to provide theoretical models that help interpreting the formation of general structures of an LMC-like galaxy under various tidal interaction scenarios. This is the first paper of a series that will be dedicated to the analysis of this complex interaction. Simulations are grouped in 11 sets of at most three configurations each containing: (1) a control model of an isolated LMC-like galaxy; (2) a model that contains the interaction with an SMC-mass galaxy, and; (3) the most realistic configuration where both an SMC-mass and MW-mass galaxies may interact with the LMC-like galaxy. In each simulation, we analyse the orbital history between the three galaxies and examine the morphological and kinematic features of the LMC-like disc galaxy throughout the interaction. This includes investigating the disc scale height and velocity maps. When a bar develops, our analysis involves characterising its strength, length, off-centeredness and pattern speed. The diverse outcomes found in the KRATOS simulations, including the presence of bars, warped discs, or various spiral arm shapes (along with the high spatial, temporal, and mass resolution used), demonstrate their capability to explore a range of LMC-like galaxy morphologies. Those directly correspond to distinct disc kinematic maps, making them well-suited for a first-order interpretation of the LMC's kinematic maps. From the simulations we note that tidal interactions can: boost the disc scale height; both destroy and create bars, and; naturally explain the off-center stellar bars. The bar length and pattern speed of long-lived bars are not appreciably altered by the interaction., Comment: Visit https://www.oscarjimenezarranz.com/visualizations for additional figures and animations of the KRATOS suite

Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized anatomical regions. Recently ex vivo (postmortem) imaging of the brain has opened-up avenues to study brain structure at sub-millimeter ultra high-resolution revealing details not possible to observe with in vivo MRI. Unfortunately, there has been limited methodological development in ex vivo MRI primarily due to lack of datasets and limited centers with such imaging resources. Therefore, in this work, we present one-of-its-kind dataset of 82 ex vivo T2w whole brain hemispheres MRI at 0.3 mm isotropic resolution spanning Alzheimer's disease and related dementias. We adapted and developed a fast and easy-to-use automated surface-based pipeline to parcellate, for the first time, ultra high-resolution ex vivo brain tissue at the native subject space resolution using the Desikan-Killiany-Tourville (DKT) brain atlas. This allows us to perform vertex-wise analysis in the template space and thereby link morphometry measures with pathology measurements derived from histology. We will open-source our dataset docker container, Jupyter notebooks for ready-to-use out-of-the-box set of tools and command line options to advance ex vivo MRI clinical brain imaging research on the project webpage.

While orbital analysis studies were so far mainly focused on the Galactic halo, it is possible now to do these studies in the heavily obscured region close to the Galactic Centre. We aim to do a detailed orbital analysis of stars located in the nuclear stellar disc (NSD) of the Milky Way allowing us to trace the dynamical history of this structure. We integrated orbits of the observed stars in a non-axisymmetric potential. We used a Fourier transform to estimate the orbital frequencies. We compared two orbital classifications, one made by eye and the other with an algorithm, in order to identify the main orbital families. We also compared the Lyapunov and the frequency drift techniques to estimate the chaoticity of the orbits. We identified several orbital families as chaotic, $z$-tube, $x$-tube, banana, fish, saucer, pretzel, 5:4, and 5:6 orbits. As expected for stars located in a NSD, the large majority of orbits are identified as $z$-tubes (or as a sub-family of $z$-tubes). Since the latter are parented by $x_{2}$ orbits, this result supports the contribution of the bar (in which $x_{2}$ orbits are dominant in the inner region) in the formation of the NSD. Moreover, most of the chaotic orbits are found to be contaminants from the bar or bulge which would confirm the predicted contamination from the most recent NSD models. Based on a detailed orbital analysis, we were able to classify orbits into various families, most of which are parented by $x_{2}$-type orbits, which are dominant in the inner part of the bar., Comment: 19 pages, 25 figures, accepted in A&A

This paper addresses the problem of checking invariant properties for a large class of symbolic transition systems, defined by a combination of SMT theories and quantifiers. State variables can be functions from an uninterpreted sort (finite, but unbounded) to an interpreted sort, such as the the integers under the theory of linear arithmetic. This formalism is very expressive and can be used for modeling parameterized systems, array-manipulating programs, and more. We propose two algorithms for finding universal inductive invariants for such systems. The first algorithm combines an IC3-style loop with a form of implicit predicate abstraction to construct an invariant in an incremental manner. The second algorithm constructs an under-approximation of the original problem, and searches for a formula which is an inductive invariant for this case; then, the invariant is generalized to the original case, and checked with a portfolio of techniques. We have implemented the two algorithms and conducted an extensive experimental evaluation, considering various benchmarks and different tools from the literature. As far as we know, our method is the first capable of handling in a large class of systems in a uniform way. The experiment shows that both algorithms are competitive with the state of the art.

Recent language models have demonstrated proficiency in summarizing source code. However, as in many other domains of machine learning, language models of code lack sufficient explainability. Informally, we lack a formulaic or intuitive understanding of what and how models learn from code. Explainability of language models can be partially provided if, as the models learn to produce higher-quality code summaries, they also align in deeming the same code parts important as those identified by human programmers. In this paper, we report negative results from our investigation of explainability of language models in code summarization through the lens of human comprehension. We measure human focus on code using eye-tracking metrics such as fixation counts and duration in code summarization tasks. To approximate language model focus, we employ a state-of-the-art model-agnostic, black-box, perturbation-based approach, SHAP (SHapley Additive exPlanations), to identify which code tokens influence that generation of summaries. Using these settings, we find no statistically significant relationship between language models' focus and human programmers' attention. Furthermore, alignment between model and human foci in this setting does not seem to dictate the quality of the LLM-generated summaries. Our study highlights an inability to align human focus with SHAP-based model focus measures. This result calls for future investigation of multiple open questions for explainable language models for code summarization and software engineering tasks in general, including the training mechanisms of language models for code, whether there is an alignment between human and model attention on code, whether human attention can improve the development of language models, and what other model focus measures are appropriate for improving explainability.

Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examining and comprehending code. In this paper, we develop a method for incorporating human attention into machine attention to enhance neural code summarization. To facilitate this incorporation and vindicate this hypothesis, we introduce EyeTrans, which consists of three steps: (1) we conduct an extensive eye-tracking human study to collect and pre-analyze data for model training, (2) we devise a data-centric approach to integrate human attention with machine attention in the Transformer architecture, and (3) we conduct comprehensive experiments on two code summarization tasks to demonstrate the effectiveness of incorporating human attention into Transformers. Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance, demonstrating the substantial benefits of this combination. We further explore performance in terms of robustness and efficiency by creating challenging summarization scenarios in which EyeTrans exhibits interesting properties. We also visualize the attention map to depict the simplifying effect of machine attention in the Transformer by incorporating human attention. This work has the potential to propel AI research in software engineering by introducing more human-centered approaches and data.

In this work, we use the global electromagnetic and electrostatic gyro kinetic approaches to investigate the effects of zonal flows forced-driven by Alfv\'en modes due to their excitation by energetic particles (EPs), on the dynamics of ITG (Ion temperature gradient) instabilities. The equilibrium of the 92416 JET tokamak shot is considered. The linear and nonlinear Alfv\'en modes dynamics, as well as the zonal flow dynamics, are investigated and their respective radial structures and saturation levels are reported. ITG dynamics in the presence of the zonal flows excited by these Alfv\'en modes are also investigated. We find that, the zonal flows forced-driven by Alfv\'en modes can significantly impact the ITG dynamics. A zonal flow amplitude scan reveals the existence of an inverse relation between the zonal flow amplitude and the ITG growth rate. These results show that, forced-driven zonal flows can be an important indirect part of turbulence mitigation due to the injection of energetic particles., Comment: 13 pages, 10 figures