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This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 18 teams whose submissions are documented in 26 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia., Comment: IWSLT 2024; 59 pages

Evolved cool stars have three distinct evolutionary status: shell Hydrogen-burning (RGB), core Helium and shell Hydrogen burning (RC), and double shell burning (AGB). Asteroseismology can distinguish between the RC and the other status, but distinguishing RGB and AGB has been difficult seismically and spectroscopically. The precise boundaries of different status in the HR diagram have also been difficult to establish. In this article, we present a comprehensive catalog of asteroseismic evolutionary status, RGB and RC, for evolved red giants in the Kepler field. We carefully examine boundary cases to define the lower edge of the RC phase in radius and surface gravity. We also test different published asteroseisemic methods claiming to distinguish AGB and RGB stars against a sample where AGB candidates were selected using a spectrocopic identification method. We used six different seismic techniques to distinguish RC and RGB stars, and tested two proposed methods for distinguishing AGB and RGB stars. These status were compared with those inferred from spectroscopy. We present consensus evolutionary status for 18,784 stars out of the 30,337 red giants present in the Kepler data, including 11,516 stars with APOGEE spectra available. The agreement between seismic and spectroscopic classification is excellent for distinguishing RC stars, agreeing at the 94% level. Most disagreements can be traced to uncertainties in spectroscopic parameters, but some are caused by blends with background stars. We find a sharp lower boundary in surface gravity at log(g) = 2.99+/-0.01 for the RC and discuss the implications. We demonstrate that asteroseismic tools for distinguishing AGB and RGB stars are consistent with spectroscopic evolutionary status at near the RC but that the agreement between the different methods decreases rapidly as the star evolves., Comment: Article submitted to Astronomy&Astrophysics, 16 pages, 11 figures

Existing LLM agent systems typically select actions from a fixed and predefined set at every step. While this approach is effective in closed, narrowly-scoped environments, we argue that it presents two major challenges when deploying LLM agents in real-world scenarios: (1) selecting from a fixed set of actions significantly restricts the planning and acting capabilities of LLM agents, and (2) this approach requires substantial human effort to enumerate and implement all possible actions, which becomes impractical in complex environments with a vast number of potential actions. In this work, we propose an LLM agent framework that enables the dynamic creation and composition of actions in an online manner. In this framework, the agent interacts with the environment by generating and executing programs written in a general-purpose programming language at each step. Furthermore, generated actions are accumulated over time for future reuse. Our extensive experiments on the GAIA benchmark demonstrate that this framework offers significantly greater flexibility and outperforms previous methods. Notably, it allows an LLM agent to recover in scenarios where no relevant action exists in the predefined set or when existing actions fail due to unforeseen edge cases. At the time of writing, we hold the top position on the GAIA public leaderboard. Our code can be found in \href{https://github.com/adobe-research/dynasaur}{https://github.com/adobe-research/dynasaur}., Comment: 15 pages, 8 figures

The scale and diversity of demonstration data required for imitation learning is a significant challenge. We present EgoMimic, a full-stack framework which scales manipulation via human embodiment data, specifically egocentric human videos paired with 3D hand tracking. EgoMimic achieves this through: (1) a system to capture human embodiment data using the ergonomic Project Aria glasses, (2) a low-cost bimanual manipulator that minimizes the kinematic gap to human data, (3) cross-domain data alignment techniques, and (4) an imitation learning architecture that co-trains on human and robot data. Compared to prior works that only extract high-level intent from human videos, our approach treats human and robot data equally as embodied demonstration data and learns a unified policy from both data sources. EgoMimic achieves significant improvement on a diverse set of long-horizon, single-arm and bimanual manipulation tasks over state-of-the-art imitation learning methods and enables generalization to entirely new scenes. Finally, we show a favorable scaling trend for EgoMimic, where adding 1 hour of additional hand data is significantly more valuable than 1 hour of additional robot data. Videos and additional information can be found at https://egomimic.github.io/

Solar magnetic activity follows regular cycles of about 11 years with an inversion of polarity in the poles every 22 years. This changing surface magnetism impacts the properties of the acoustic modes. The acoustic mode frequency shifts are a good proxy of the magnetic cycle. In this Letter we investigate solar magnetic activity cycles 23 and 24 through the evolution of the frequency shifts of low-degree modes (l= 0, 1, and 2) in three frequency bands. These bands probe properties between 74 and 1575 km beneath the surface. The analysis was carried out using observations from the space instrument Global Oscillations at Low Frequency and the ground-based Birmingham Solar Oscillations Network and Global Oscillation Network Group. The frequency shifts of radial modes suggest that changes in the magnetic field amplitude and configuration likely occur near the Sun's surface rather than near its core. The maximum shifts of solar cycle 24 occurred earlier at mid and high latitudes (relative to the equator) and about 1550 km beneath the photosphere. At this depth but near the equator, this maximum aligns with the surface activity but has a stronger magnitude. At around 74 km deep, the behaviour near the equator mirrors the behaviour at the surface, while at higher latitudes, it matches the strength of cycle 23., Comment: Letter accepted in Astronomy and Astrophysics, 10 pages, 5 figures

We evaluate the impact of the rotation on the stochastic excitation of acoustic (p) modes in solar-like pulsators. First, we derive the forced wave equation taking rotation into account and we compute the source terms, which inject energy into the oscillations. We make use of the Rotating Mixing Length Theory (R-MLT) to assess how the convective root mean square velocities are modified by the Coriolis acceleration. Finally, we use the stellar structure and evolution code MESA combined with the stellar pulsation code GYRE to show that the resulting modes amplitudes are inhibited by rotation., Comment: 3 pages, 1 figures, Proceeding of SF2A annual conference

The applications of generative AI have become extremely impressive, and the interplay between users and AI is even more so. Current human-AI interaction literature has taken a broad look at how humans interact with generative AI, but it lacks specificity regarding the user interface designs and patterns used to create these applications. Therefore, we present a survey that comprehensively presents taxonomies of how a human interacts with AI and the user interaction patterns designed to meet the needs of a variety of relevant use cases. We focus primarily on user-guided interactions, surveying interactions that are initiated by the user and do not include any implicit signals given by the user. With this survey, we aim to create a compendium of different user-interaction patterns that can be used as a reference for designers and developers alike. In doing so, we also strive to lower the entry barrier for those attempting to learn more about the design of generative AI applications.

Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long sequences due to quadratic computational complexity in training and linearly scaling memory costs during inference. Recent State Space Models (SSMs) such as Mamba offer alternatives with constant memory usage, but they underperform in tasks requiring extensive in-context retrieval. We introduce Taipan, a novel hybrid architecture that combines Mamba-2 with Selective Attention Layers (SALs). These SALs identify tokens requiring long-range interactions, remove less important features, and then augment their representations using the attention module. This approach balances Mamba's efficiency with Transformer-like performance in memory-intensive tasks. By constraining the attention budget, Taipan extends accurate predictions to context lengths of up to 1 million tokens while preserving computational efficiency. Our experiments demonstrate Taipan's superior performance across various scales and tasks, offering a promising solution for efficient long-context language modeling.

With the new discoveries enabled thanks to the recent space missions, stellar physics is going through a revolution. However, these discoveries opened the door to many new questions that require more observations. The European Space Agency's Human and Robotic Exploration programme provides an excellent opportunity to push forward the limits of our knowledge and better understand stellar structure and dynamics evolution. Long-term observations, Ultra-Violet observations, and a stellar imager are a few highlights of proposed missions for late-type stars that will enhance the already planned space missions., Comment: 15 pages, invited review as part of the European Space Agency SciSpacE science community white paper series, published in npj Microgravity; doi: 10.1038/s41526-024-00431-2

The classical `decision problem' asks whether a given formula of first-order logic is satisfiable. In this work we consider an extension of this problem to regular first-order theories, i.e. (infinite) regular sets of formulae. Building on the beautiful classification of syntactic classes as decidable or undecidable for the classical decision problem, we show that some classes (the EPR and Gurevich classes) which are decidable in the classical setting are undecidable for regular theories; on the other hand for each we show a subclass which remains decidable in our setting, leaving a complete classification as a challenge for future work. Finally, we observe that our problem generalises prior work on verification of uninterpreted programs, and give a semantic class of existential formulae for which the problem is decidable.

Document structure editing involves manipulating localized textual, visual, and layout components in document images based on the user's requests. Past works have shown that multimodal grounding of user requests in the document image and identifying the accurate structural components and their associated attributes remain key challenges for this task. To address these, we introduce the DocEdit-v2, a novel framework that performs end-to-end document editing by leveraging Large Multimodal Models (LMMs). It consists of three novel components: (1) Doc2Command, which simultaneously localizes edit regions of interest (RoI) and disambiguates user edit requests into edit commands; (2) LLM-based Command Reformulation prompting to tailor edit commands originally intended for specialized software into edit instructions suitable for generalist LMMs. (3) Moreover, DocEdit-v2 processes these outputs via Large Multimodal Models like GPT-4V and Gemini, to parse the document layout, execute edits on grounded Region of Interest (RoI), and generate the edited document image. Extensive experiments on the DocEdit dataset show that DocEdit-v2 significantly outperforms strong baselines on edit command generation (2-33%), RoI bounding box detection (12-31%), and overall document editing (1-12\%) tasks., Comment: EMNLP 2024 (Main)

The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope that the ideas and perspectives are a source of inspiration. Structure: Black Hole Evaporation - 50 Years by William Unruh The Stability Problem for Extremal Black Holes by Mihalis Dafermos The Entropy of Black Holes by Robert M. Wald The Non-linear Regime of Gravity by Luis Lehner Black Holes Galore in D > 4 by Roberto Emparan Same as Ever: Looking for (In)variants in the Black Holes Landscape by Carlos A. R. Herdeiro Black Holes, Cauchy Horizons, and Mass Inflation by Matt Visser The Backreaction Problem for Black Holes in Semiclassical Gravity by Adrian del Rio Black Holes Beyond General Relativity by Enrico Barausse and Jutta Kunz Black Holes as Laboratories: Searching for Ultralight Fields by Richard Brito Primordial Black Holes from Inflation by Misao Sasaki Tests of General Relativity with Future Detectors by Emanuele Berti Black Holes as Laboratories: Tests of General Relativity by Ruth Gregory and Samaya Nissanke Simulating Black Hole Imposters by Frans Pretorius Black Hole Spectroscopy: Status Report by Gregorio Carullo VLBI as a Precision Strong Gravity Instrument by Paul Tiede Testing the nature of compact objects and the black hole paradigm by Mariafelicia De Laurentis and Paolo Pani Some Thoughts about Black Holes in Asymptotic Safety by Alessia Platania Black Hole Evaporation in Loop Quantum Gravity by Abhay Ashtekar How the Black Hole Puzzles are Resolved in String Theory by Samir D. Mathur Quantum Black Holes: From Regularization to Information Paradoxes by Niayesh Afshordi and Stefano Liberati, Comment: 221 pages, 21 contributions

Yangian-type differential operators are shown to constrain Feynman integrals beyond the restriction to integrable graphs. In particular, we prove that all position-space Feynman diagrams at tree level feature a Yangian level-one momentum symmetry as long as their external coordinates are distinct. This symmetry is traced back to a set of more elementary bilocal operators, which annihilate the integrals. In dual momentum space, the considered Feynman graphs represent multi-loop integrals without `loops of loops', generalizing for instance the family of so-called train track or train track network diagrams. The extension of these results to integrals with massive propagators on the boundary of the Feynman graph is established. When specializing to the dual conformal case, where propagator powers sum up to the spacetime dimension at each position-space vertex, the symmetry extends to the full dual conformal Yangian. Hence, our findings represent a generalization of the statements on the Yangian symmetry of Feynman integrals beyond integrability and reveal its origin lying in a set of more elementary bilocal annihilators. Previous applications of the Yangian suggest to employ the resulting differential equations for bootstrapping multi-loop integrals beyond the dual conformal case. The considered bilocal constraints on Feynman integrals resemble the definition of conformal partial waves via Casimir operators, but are based on a different algebraic structure., Comment: 34 pages, v2: references added, typo corrected

During the last decade, our understanding of stellar physics and evolution has undergone a tremendous revolution thanks to asteroseismology. Space missions such as CoRoT, \kep, K2, and TESS have already been observing millions of stars providing high-precision photometric data. With these data, it is possible to study the convection of stars through the convective background in the power spectrum density of the light curves. The properties of the convective background or granulation has been shown to be correlated to the surface gravity of the stars. In addition, when we have enough resolution (so long enough observations) and a high signal-to-noise ratio (SNR), the individual modes can be characterized in particular to study the internal rotational splittings and magnetic field of stars. Finally, the surface magnetic activity also impacts the amplitude and hence detection of the acoustic modes. This effect can be seen as a double-edged sword. Indeed, modes can be studied to look for magnetic activity changes. However, this also means that for stars too magnetically active, modes can be suppressed, preventing us from detecting them. In this talk, I will present some highlights on what asteroseismology has allowed us to better understand the convection, rotation, and magnetism of solar-like stars while opening doors to many more questions., Comment: 8 pages; Proceedings of the IAU Symposium 365 - Dynamics of Solar and Stellar Convection Zones and Atmospheres, 2023 August 21-25, Yerevan, Armenia

We present the ground state energy spectra of two-flavored heavy dibaryons in the spin-singlet channel. In particular, the ground state masses of $\Omega_{llQ}\Omega_{llQ}, \Omega_{QlQ}\Omega_{llQ}$ and $\Omega_{QlQ}\Omega_{QlQ}$ states are computed and compared with their respective lowest non-interacting energy levels, where the flavor $Q \in (c,b)$ denotes charm and bottom quarks, and the other flavor $l \in {s,c,b}$ represents strange, charm and bottom, respectively. Considering their valence quark structures, these hadrons could be thought of as the heavy flavor analogues of spin-singlet nucleon-nucleon states. The gauge configurations employed in this study are HISQ ensembles with $N_f = 2+1+1$ flavors, generated by the MILC collaboration, at four lattice spacings, namely $a=0.1207, 0.0888, 0.0582$ and $0.0448$ fm. The aforementioned states are also computed at different quark masses, between $m_s \le m_l \le m_b$, including at unphysical heavy quark masses, to explore the quark mass dependence of any possible binding. For the dibaryon states $\Omega_{bbc}\Omega_{bbc}, \Omega_{ccb}\Omega_{ccb}$ and $\Omega_{ccb} \Omega_{bbc}$, we find a clear evidence of an energy level below their respective non-interacting energy levels. In addition, for these dibaryons at quark masses $m_c < m_l \le m_b$, a trend is found where the gap, between the lowest energy levels and the respective lowest non-interacting levels, increases as the quark mass $m_l$ increases. We also study the heavy quark spin-symmetry and its breaking for these heavy dibaryons., Comment: 15 pages, 7 figures

In this paper, we consider the problem of automatically monitoring linearizability. Here, one observes an execution of a concurrent program that interacts with a concurrent object and determines if the execution witnesses the violation of linearizability with respect to the sequential specification of the underlying data structure of the concurrent object. This problem has been extensively studied in the past for read-write registers, and both tight upper and lower bounds have been proposed in this case. While this problem has also been studied for the case of other prominent data structures such as stacks and queues, we find that these results are either not extensive or in some cases incorrect. In this paper, we study the problem under the restriction where values inserted in the data types are distinct (in the execution observed). We then show that under such a restriction, the linearizability problem is solvable in polynomial time for these data types. Beyond theoretical soundness and completeness, the algorithms proposed are empirically proven to outperform all state-of-the-art linearizability monitors.

In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used ten independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from Gaia $L$ and spectroscopic $T_{\rm eff}$. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the spectroscopic and asteroseismic measurements used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in \nmax, \dnu, mass, radius and age of 0.6\%, 0.6\%, 3.8\%, 1.8\%, and 11.1\% respectively. We provide more limited data for 1,624 additional stars which either have lower quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of $9.14 \pm 0.05 ({\rm ran}) \pm 0.9 ({\rm sys})$ Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the usage of multiple methods, calibration to a fundamental scale, and the usage of stellar models to interpret frequency spacings., Comment: 43 pages, 25 figures, submitted ApJSupp. Comments welcome. Data tables available on request from pinsonneault.1@osu.edu

A single material can exist in different states, with solids, liquids, and gases being the most familiar examples. In materials, these states can exhibit periodic structures spanning from atomic to macroscopic length scales. The conventional wisdom is that a low-symmetry periodic structure transitions into a high-symmetry structure as temperature increases beyond the critical point, which is defined by a thermal limit. In this work, we demonstrate an unforeseen emergence of low-symmetry modulated structures with a reentrant phase in nanoflakes of two-dimensional TaCo2Te2 far beyond their thermal limit, using in-situ heating transmission electron microscopy. We contend that entropy can drive the reappearance of structural modulations, consistent with predicted dynamic structural instabilities in undistorted TaCo2Te2, and further supported by Raman measurements. These findings not only reveal unexpected phase transitions in a crystalline material but also present a new pathway for creating novel ordered phases in low-dimensional systems., Comment: 36 pages, 19 figures

We report on the discovery and spectroscopic confirmation of TOI-2458 b, a transiting mini-Neptune around an F-type star leaving the main-sequence with a mass of $M_\star=1.05 \pm 0.03$ M$_{\odot}$, a radius of $R_\star=1.31 \pm 0.03$ R$_{\odot}$, an effective temperature of $T_{\rm eff}=6005\pm50$ K, and a metallicity of $-0.10\pm0.05$ dex. By combining TESS photometry with high-resolution spectra acquired with the HARPS spectrograph, we found that the transiting planet has an orbital period of $\sim$3.74 days, a mass of $M_p=13.31\pm0.99$ M$_{\oplus}$ and a radius of $R_p=2.83\pm0.20$ R$_{\oplus}$. The host star TOI-2458 shows a short activity cycle of $\sim$54 days revealed in the HARPS S-index time series. We took the opportunity to investigate other F stars showing activity cycle periods comparable to that of TOI-2458 and found that they have shorter rotation periods than would be expected based on the gyrochronology predictions. In addition, we determined TOI-2458's stellar inclination angle to be $i_\star\,=\,10.6_{-10.6}^{+13.3}$ degrees. We discuss that both phenomena (fast stellar rotation and planet orbit inclination) could be explained by in situ formation of a hot Jupiter interior to TOI-2458 b. It is plausible that this hot Jupiter was recently engulfed by the star. Analysis of HARPS spectra has identified the presence of another planet with a period of $P\,=\,16.55\pm0.06$ days and a minimum mass of $M_p \sin i=10.22\pm1.90$ M$_{\oplus}$., Comment: 22 pages, 17 figures, submitted to Astronomy & Astrophysics

In an effort to understand the presence of super-virial gas detected in the Milky Way, we present our findings from isolated galaxy simulations of Milky Way-like systems using GIZMO with the FIRE-2 (Feedback In Realistic Environments) stellar feedback model. It unveils the presence of a significant super-virial temperature ( $T>6\times10^6$K) gas component within 20 kpc from the galactic center. This super-virial gas has a mass of $1-2\times10^7$ ${\rm M_\odot}$ and is found close to the disk, where typical gas densities are $0.004-0.01 \, \rm cm^{-3}$. We find that some of the virial gas ($T\sim10^6$K) forms a rotating hot inflow, where gravitational energy is converted to heat mainly via compressive heating. This process causes gas infalling close to the rotation axis to reach super-virial temperatures just before cooling and joining the disk. Stellar feedback heating accounts for less than 1% of the super-virial gas, indicating its minimal influence despite expectations. Even in scenarios with no stellar feedback effects considered, abundant super-virial gas persists, highlighting the dominance of alternative heating mechanisms. We also show that cosmic rays do not have a significant effect on heating the gas to a super-virial temperature. Our study illuminates the intricate dynamics of hot virial and super-virial gas surrounding Milky Way-like galaxies, emphasizing the prominent role of infall-driven and compressive heating processes in shaping thermal evolution., Comment: Submitted to ApJ, 14 pages, 8 figures, 1 table

We present a lattice calculation of dibaryons composed of single-flavor quarks with either charm or strange quark mass. We utilize a set of lattice QCD ensembles with $N_f=2+1+1$ dynamical HISQ fields, two spatial volumes, and four different lattice spacings generated by the MILC collaboration. By using an overlap action for the valence quark propagators, we calculate the ground state energies of dibaryons in $S = 0$ and $S = 2$ spin channels. By analyzing the energy difference of the ground state of the dibaryon with respect to the relevant threshold, we provide insights into the interactions involved in different spin channels at the charm and the strange quark masses., Comment: 9 pages, 4 figures, 1 table, contribution to the 41st International Symposium on Lattice Field Theory (Lattice 2024), July 28th - August 3rd, 2024, University of Liverpool

Selecting an automatic metric that best emulates human annotators is often non-trivial, because there is no clear definition of "best emulates." A meta-metric is required to compare the human judgments to the automatic metric scores, and metric rankings depend on the choice of meta-metric. We propose Soft Pairwise Accuracy (SPA), a new meta-metric that builds on Pairwise Accuracy (PA) but incorporates the statistical significance of both the human judgments and the metric scores. We show that SPA is more stable than PA with respect to changes in the number of systems/segments used for evaluation. We also show that PA can only assign a small set of distinct output values to metrics, and this results in many metrics being artificially assigned the exact same PA score. We demonstrate that SPA fixes this issue. Finally, we show that SPA is more discriminative than PA, producing more statistically significant comparisons between metrics. SPA was selected as the official system-level metric for the 2024 WMT Metrics Shared Task., Comment: Accepted at WMT 2024

The field of text-to-3D content generation has made significant progress in generating realistic 3D objects, with existing methodologies like Score Distillation Sampling (SDS) offering promising guidance. However, these methods often encounter the "Janus" problem-multi-face ambiguities due to imprecise guidance. Additionally, while recent advancements in 3D gaussian splitting have shown its efficacy in representing 3D volumes, optimization of this representation remains largely unexplored. This paper introduces a unified framework for text-to-3D content generation that addresses these critical gaps. Our approach utilizes multi-view guidance to iteratively form the structure of the 3D model, progressively enhancing detail and accuracy. We also introduce a novel densification algorithm that aligns gaussians close to the surface, optimizing the structural integrity and fidelity of the generated models. Extensive experiments validate our approach, demonstrating that it produces high-quality visual outputs with minimal time cost. Notably, our method achieves high-quality results within half an hour of training, offering a substantial efficiency gain over most existing methods, which require hours of training time to achieve comparable results., Comment: 13 pages, 10 figures

In this work, we present a novel approach for reconstructing shape point clouds using planar sparse cross-sections with the help of generative modeling. We present unique challenges pertaining to the representation and reconstruction in this problem setting. Most methods in the classical literature lack the ability to generalize based on object class and employ complex mathematical machinery to reconstruct reliable surfaces. We present a simple learnable approach to generate a large number of points from a small number of input cross-sections over a large dataset. We use a compact parametric polyline representation using adaptive splitting to represent the cross-sections and perform learning using a Graph Neural Network to reconstruct the underlying shape in an adaptive manner reducing the dependence on the number of cross-sections provided.

The presence of the $\approx 10^6$ K gas in the circumgalactic medium of the Milky Way has been well established. However, the location and the origin of the newly discovered hot gas at `super-virial' temperatures of $\approx 10^7$ K have been puzzling. This hot gas has been detected in both absorption and emission; here we focus on the emitting gas only. We show that both the `virial' and the `super-virial' temperature gas as observed in \emph{emission} occupy disk-like extraplanar regions, in addition to the diffuse virial temperature gas filling the halo of the Milky Way. We perform idealized hydrodynamical simulations to show that the $\approx 10^7$ K emitting gas is likely to be produced by stellar feedback in and around the Galactic disk. We further show that the emitting gas at both super-virial and virial temperatures in the extraplanar regions is metal enriched and is not in hydrostatic equilibrium with the halo but is continuously evolving., Comment: 11 pages, accepted for publication in the Astrophysical Journal (ApJ)

Diffusion-based video generation models have made significant strides, producing outputs with improved visual fidelity, temporal coherence, and user control. These advancements hold great promise for improving surgical education by enabling more realistic, diverse, and interactive simulation environments. In this study, we introduce SurGen, a text-guided diffusion model tailored for surgical video synthesis. SurGen produces videos with the highest resolution and longest duration among existing surgical video generation models. We validate the visual and temporal quality of the outputs using standard image and video generation metrics. Additionally, we assess their alignment to the corresponding text prompts through a deep learning classifier trained on surgical data. Our results demonstrate the potential of diffusion models to serve as valuable educational tools for surgical trainees.

Existing datasets for tabular question answering typically focus exclusively on text within cells. However, real-world data is inherently multimodal, often blending images such as symbols, faces, icons, patterns, and charts with textual content in tables. With the evolution of AI models capable of multimodal reasoning, it is pertinent to assess their efficacy in handling such structured data. This study investigates whether current AI models can perform knowledge-aware reasoning on multimodal structured data. We explore their ability to reason on tables that integrate both images and text, introducing MMTabQA, a new dataset designed for this purpose. Our experiments highlight substantial challenges for current AI models in effectively integrating and interpreting multiple text and image inputs, understanding visual context, and comparing visual content across images. These findings establish our dataset as a robust benchmark for advancing AI's comprehension and capabilities in analyzing multimodal structured data.

Precise space-based photometry from the Transiting Exoplanet Survey Satellite results in a huge number of exoplanetary candidates. However, the masses of these objects are unknown and must be determined by ground-based spectroscopic follow-up observations, frequently revealing the companions to be low-mass stars rather than exoplanets. We present the first orbital and stellar parameter solutions for five such eclipsing binary-star systems using radial-velocity follow-up measurements together with spectral-energy-distribution solutions. TOI-416 and TOI-1143 are totally eclipsing F+M star systems with well-determined secondary masses, radii, and temperatures. TOI-416 is a circular system with an F6 primary and a secondary with a mass of $M_2={0.131(8)}{M_\odot}$. TOI-1143 consists of an F6 primary with an $M_2={0.142(3)}{M_\odot}$ secondary on an eccentric orbit with a third companion. With respect to the other systems, TOI-1153 shows ellipsoidal variations, TOI-1615 contains a pulsating primary, and TOI-1788 has a spotted primary, while all have moderate mass ratios of 0.2-0.4. However, these systems are in a grazing configuration, which limits their full description. The parameters of TOI-416B and TOI-1143B are suitable for the calibration of the radius-mass relation for dwarf stars., Comment: A&A accepted on 06/06/2024

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

We report the discovery and characterization of a small planet, TOI-1408 c, on a 2.2-day orbit located interior to a previously known hot Jupiter, TOI-1408 b ($P=4.42$ d, $M=1.86\pm0.02\,M_\mathrm{Jup}$, $R=2.4\pm0.5\,R_\mathrm{Jup}$) that exhibits grazing transits. The two planets are near 2:1 period commensurability, resulting in significant transit timing variations (TTVs) for both planets and transit duration variations (TDVs) for the inner planet. The TTV amplitude for TOI-1408 c is 15% of the planet's orbital period, marking the largest TTV amplitude relative to the orbital period measured to date. Photodynamical modeling of ground-based radial velocity (RV) observations and transit light curves obtained with the Transiting Exoplanet Survey Satellite (TESS) and ground-based facilities leads to an inner planet radius of $2.22\pm0.06\,R_\oplus$ and mass of $7.6\pm0.2\,M_\oplus$ that locates the planet into the Sub-Neptune regime. The proximity to the 2:1 period commensurability leads to the libration of the resonant argument of the inner planet. The RV measurements support the existence of a third body with an orbital period of several thousand days. This discovery places the system among the rare systems featuring a hot Jupiter accompanied by an inner low-mass planet., Comment: Accepted to ApJL, 17 pages, 6 figures, 4 tables

Hot, \logT\ $\sim$ 7.5, gas was recently discovered in the Milky Way in extragalactic sightlines. In order to determine its location, here we present sightlines to Galactic X-ray binaries (XRBs) passing through the Interstellar Medium (ISM). In this pilot study we investigate absorption features of \SXVI, \SiXIV, and \NeX\ in the spectra of three XRBs, namely 4U 1735-44, 4U 1820-30, and Cyg X-2, using Chandra High Energy Transmission Grating archival observations. We do not detect any of these lines. {We determine the 2$\sigma$ upper limit for the equivalent widths of the undetected absorption lines and the column densities of the corresponding ions.} We note that the 2$\sigma$ upper limits for \SXVI\ \Ka\ and \SiXIV\ \Ka\ are an order of magnitude smaller than those previously detected in the extragalactic sightlines. Our finding suggests that if any gas at \logT\ $>7$ is present in the Galactic ISM, it is unlikely to be ubiquitous. This is an important result because it implies that \SXVI, \SiXIV\ {and \NeX\ }absorption detected in extragalactic sightlines is not from the ISM, but is likely from a hot gas phase in the extraplanar region beyond the ISM or in the extended CGM.

Our study focuses on characterizing the highly ionized gas within the Milky Way's (MW) Circumgalactic Medium (CGM) that gives rise to ionic transitions in the X-ray band 2 - 25 \AA. Utilizing stacked \Chandra/\ACISS\ \MEG\ and \LETG\ spectra toward QSO sightlines, we employ the self-consistent hybrid ionization code PHASE to model our data. The stacked spectra are optimally described by three distinct gas phase components: a \warm\ (\logT\ $\sim$ 5.5), \warmhot\ (\logT\ $\sim 6$), and \hot\ (\logT\ $\sim$ 7.5) components. These findings confirm the presence of the \hot\ component in the MW's CGM indicating its coexistence with a \warm\ and a \warmhot\ gas phases. We find this \hot\ component to be homogeneous in temperature but inhomogeneous in column density. The gas in the \hot\ component requires over-abundances relative to solar to be consistent with the Dispersion Measure (DM) from the Galactic halo reported in the literature. {For the hot phase we estimated a DM = $55.1^{+29.9}_{-23.7}$ pc cm$^{-3}$}. We conclude that this phase is either enriched in Oxygen, Silicon, and Sulfur, or has metallicity {over 6} times solar value, or a combination of both. We do not detect Fe L-shell absorption lines, implying O/Fe $\geq$ 4. The non-solar abundance ratios found in the super-virial gas component in the Galactic halo suggest that this phase arises from Galactic feedback.

We present a recent calculation of the quark and gluon contributions to the angular momentum of a composite spin -$1/2$ state in QCD. The state we consider is a quark dressed with a gluon, and we use the two-component framework in light-front Hamiltonian QCD. We compare the results from different decompositions available in the literature. We also present the angular momentum distributions., Comment: Prepared for 31 st International Workshop on Deep Inelastic Scattering and Related Phenomena, Grenoble, France, April 8-12, 2024

The Planetary Transits and Oscillations of stars mission (PLATO) will allow us to measure surface rotation and monitor photometric activity of tens of thousands of main sequence solar-type and subgiant stars. This paper is the first of a series dedicated to the preparation of the analysis of stellar surface rotation and photospheric activity with the near-future PLATO data. We describe in this work the strategy that will be implemented in the PLATO pipeline to measure stellar surface rotation, photometric activity, and long-term modulations. The algorithms are applied on both noise-free and noisy simulations of solar-type stars, which include activity cycles, latitudinal differential rotation, and spot evolution. PLATO simulated systematics are included in the noisy light curves. We show that surface rotation periods can be recovered with confidence for most of the stars with only six months of observations and that the {recovery rate} of the analysis significantly improves as additional observations are collected. This means that the first PLATO data release will already provide a substantial set of measurements for this quantity, with a significant refinement on their quality as the instrument obtains longer light curves. Measuring the Schwabe-like magnetic activity cycle during the mission will require that the same field be observed over a significant timescale (more than four years). Nevertheless, PLATO will provide a vast and robust sample of solar-type stars with constraints on the activity-cycle length. Such a sample is lacking from previous missions dedicated to space photometry., Comment: 19 pages, 16 figures, accepted for publication in A&A

Multimodal foundation models that can holistically process text alongside images, video, audio, and other sensory modalities are increasingly used in a variety of real-world applications. However, it is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains. In this paper, we introduce Holistic Evaluation of Multimodal Models (HEMM) to systematically evaluate the capabilities of multimodal foundation models across a set of 3 dimensions: basic skills, information flow, and real-world use cases. Basic multimodal skills are internal abilities required to solve problems, such as learning interactions across modalities, fine-grained alignment, multi-step reasoning, and the ability to handle external knowledge. Information flow studies how multimodal content changes during a task through querying, translation, editing, and fusion. Use cases span domain-specific challenges introduced in real-world multimedia, affective computing, natural sciences, healthcare, and human-computer interaction applications. Through comprehensive experiments across the 30 tasks in HEMM, we (1) identify key dataset dimensions (e.g., basic skills, information flows, and use cases) that pose challenges to today's models, and (2) distill performance trends regarding how different modeling dimensions (e.g., scale, pre-training data, multimodal alignment, pre-training, and instruction tuning objectives) influence performance. Our conclusions regarding challenging multimodal interactions, use cases, and tasks requiring reasoning and external knowledge, the benefits of data and model scale, and the impacts of instruction tuning yield actionable insights for future work in multimodal foundation models., Comment: Code available at https://github.com/pliang279/HEMM

In this work, we present the results of a detailed X-ray spectral analysis of the brightest AGNs detected in the XMM-Newton 1.75 Ms Ultra Narrow Deep Field. We analyzed 23 AGNs that have a luminosity range of $\sim 10^{42} - 10^{46}\, \rm{erg}\, \rm{s}^{-1}$ in the $2 - 10\, \rm{keV}$ energy band, redshifts up to 2.66, and $\sim 10,000$ X-ray photon counts in the $0.3 - 10\, \rm{keV}$ energy band. Our analysis confirms the Iwasawa-Taniguchi effect, an anti-correlation between the X-ray luminosity ($L_x$) and the Fe-k$\alpha$ Equivalent Width ($EW_{Fe}$) possibly associated with the decreasing of the torus covering factor as the AGN luminosity increases. We investigated the relationship among black hole mass ($M_{BH}$), $L_x$, and X-ray variability, quantified by the Normalized Excess Variance ($\sigma^2_{rms}$). Our analysis suggest an anti-correlation in both $M_{BH} - \sigma^2_{rms}$ and $L_x- \sigma^2_{rms}$ relations. The first is described as $\sigma^2_{rms} \propto M^{-0.26 \pm 0.05}_{BH}$, while the second presents a similar trend with $\sigma^2_{rms} \propto L_{x}^{-0.31 \pm 0.04}$. These results support the idea that the luminosity-variability anti-correlation is a byproduct of an intrinsic relationship between the BH mass and the X-ray variability, through the size of the emitting region. Finally, we found a strong correlation among the Eddington ratio ($\lambda_{Edd}$), the hard X-ray photon index ($\Gamma$), and the illumination factor $\log(A)$, which is related to the ratio between the number of Compton scattered photons and the number of seed photons. The $\log(\lambda_{Edd})-\Gamma-\log(A)$ plane could arise naturally from the connection between the accretion flow and the hot corona., Comment: 16 pages, 14 figures, to be published in MNRAS

Immunohistochemistry (IHC) analysis is a well-accepted and widely used method for molecular subtyping, a procedure for prognosis and targeted therapy of breast carcinoma, the most common type of tumor affecting women. There are four molecular biomarkers namely progesterone receptor (PR), estrogen receptor (ER), antigen Ki67, and human epidermal growth factor receptor 2 (HER2) whose assessment is needed under IHC procedure to decide prognosis as well as predictors of response to therapy. However, IHC scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility, high subjectivity, and often incorrect scoring in low-score cases. In this paper, we present, a deep learning-based semi-supervised trained, fully automatic, decision support system (DSS) for IHC scoring of invasive ductal carcinoma. Our system automatically detects the tumor region removing artifacts and scores based on Allred standard. The system is developed using 3 million pathologist-annotated image patches from 300 slides, fifty thousand in-house cell annotations, and forty thousand pixels marking of HER2 membrane. We have conducted multicentric trials at four centers with three different types of digital scanners in terms of percentage agreement with doctors. And achieved agreements of 95, 92, 88 and 82 percent for Ki67, HER2, ER, and PR stain categories, respectively. In addition to overall accuracy, we found that there is 5 percent of cases where pathologist have changed their score in favor of algorithm score while reviewing with detailed algorithmic analysis. Our approach could improve the accuracy of IHC scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. Our system is highly modular. The proposed algorithm modules can be used to develop DSS for other cancer types.

While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. This paper delves into this advanced domain, proposing a novel approach called DocSynthv2 through the development of a simple yet effective autoregressive structured model. Our model, distinct in its integration of both layout and textual cues, marks a step beyond existing layout-generation approaches. By focusing on the relationship between the structural elements and the textual content within documents, we aim to generate cohesive and contextually relevant documents without any reliance on visual components. Through experimental studies on our curated benchmark for the new task, we demonstrate the ability of our model combining layout and textual information in enhancing the generation quality and relevance of documents, opening new pathways for research in document creation and automated design. Our findings emphasize the effectiveness of autoregressive models in handling complex document generation tasks., Comment: Spotlight (Oral) Acceptance to CVPR 2024 Workshop for Graphic Design Understanding and Generation (GDUG)

This paper presents Neural Visibility Field (NVF), a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region, resulting in increased uncertainty in the synthesized views. To address this, we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently, NVF naturally assigns higher uncertainty to unobserved regions, aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping, outperforming existing methods., Comment: Accepted to CVPR 2024. More details can be found at https://sites.google.com/view/nvf-cvpr24/

Digital pathology and microscopy image analysis are widely employed in the segmentation of digitally scanned IHC slides, primarily to identify cancer and pinpoint regions of interest (ROI) indicative of tumor presence. However, current ROI segmentation models are either stain-specific or suffer from the issues of stain and scanner variance due to different staining protocols or modalities across multiple labs. Also, tissues like Ductal Carcinoma in Situ (DCIS), acini, etc. are often classified as Tumors due to their structural similarities and color compositions. In this paper, we proposed a novel convolutional neural network (CNN) based Multi-class Tissue Segmentation model for histopathology whole-slide Breast slides which classify tumors and segments other tissue regions such as Ducts, acini, DCIS, Squamous epithelium, Blood Vessels, Necrosis, etc. as a separate class. Our unique pixel-aligned non-linear merge across spatial resolutions empowers models with both local and global fields of view for accurate detection of various classes. Our proposed model is also able to separate bad regions such as folds, artifacts, blurry regions, bubbles, etc. from tissue regions using multi-level context from different resolutions of WSI. Multi-phase iterative training with context-aware augmentation and increasing noise was used to efficiently train a multi-stain generic model with partial and noisy annotations from 513 slides. Our training pipeline used 12 million patches generated using context-aware augmentations which made our model stain and scanner invariant across data sources. To extrapolate stain and scanner invariance, our model was evaluated on 23000 patches which were for a completely new stain (Hematoxylin and Eosin) from a completely new scanner (Motic) from a different lab. The mean IOU was 0.72 which is on par with model performance on other data sources and scanners.

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.

As the permeability of AI systems in interpersonal domains like the home expands, their technical capabilities of generating explanations are required to be aligned with user expectations for transparency and reasoning. This paper presents insights from our ongoing work in understanding the effectiveness of explanations in Conversational AI systems for older adults aging in place and their family caregivers. We argue that in collaborative and multi-user environments like the home, AI systems will make recommendations based on a host of information sources to generate explanations. These sources may be more or less salient based on user mental models of the system and the specific task. We highlight the need for cross technological collaboration between AI systems and other available sources of information in the home to generate multiple explanations for a single user query. Through example scenarios in a caregiving home setting, this paper provides an initial framework for categorizing these sources and informing a potential design space for AI explanations surrounding everyday tasks in the home.

In this paper of a research based project, using Bidirectional Long Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection (FSD) model which can be used for Movie Highlight Generation Systems (MHGS) based on deep learning and Neural Networks . Movies usually have Fight Scenes to keep the audience amazed. For trailer generation, or any other application of Highlight generation, it is very tidious to first identify all such scenes manually and then compile them to generate a highlight serving the purpose. Our proposed FSD system utilises temporal characteristics of the movie scenes and thus is capable to automatically identify fight scenes. Thereby helping in the effective production of captivating movie highlights. We observe that the proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough Forests which being 92% accurate and is significantly higher than 3D CNN which features an accuracy of 65%.

We present spectropolarimetric observations of an active region recorded simultaneously in the H$\alpha$ Ca II 8662 {\AA} lines. The sunspot exhibits multiple structures, including a lightbridge and a region where Ca II 8662 {\AA} line core is in emission. Correspondingly, the H$\alpha$ line core image displays brightening in the emission region, with the spectral profiles showing elevated line cores. The stratification of the line-of-sight magnetic field is inferred through non-LTE multiline inversions of the Ca II 8662 {\AA} line and the weak field approximation over the H$\alpha$ line. The field strength inferred from the H$\alpha$ line core is consistently smaller than that inferred from inversions at $\log \tau_{500}$ = $-$4.5. However, the study finds no correlation between the WFA over the core of the H$\alpha$ line and that inferred from inversions at $\log \tau_{500}$ = $-$4.5. In regions exhibiting emission features, the morphology of the magnetic field at $\log \tau_{500}$ = $-$4.5 resembles that at $\log \tau_{500}$ = $-$1, with slightly higher or comparable field strengths. The magnetic field morphology inferred from the core of the H$\alpha$ line is also similar to that inferred from the full spectral range of the H$\alpha$ line in the emission region. The field strength inferred in the lightbridge at $\log \tau_{500}$ = $-$1 is smaller than the surrounding umbral regions and comparable at $\log \tau_{500}$ = $-$4.5. Similarly, the field strength inferred in the lightbridge from the WFA over the H$\alpha$ line appears lower compared to the surrounding umbral regions., Comment: accepted to be published in ApJ on 4th June, 2024, 14 pages, 7 figures

The integration of magnetic nanoparticles within fibrillar structures represents an interesting avenue for the remotely controlled release of therapeutic agents. This work presents a novel drug release platform based on electrospun magnetic fibers (EMFs) combining drugs, magnetic nanoparticles (MNPs) and mesoporous silica nanoparticles (MSNs) for controlled drug delivery via alternating magnetic fields (AMF). The platform was demonstrated to be versatile and effective for hydrophilic ketorolac (KET) and hydrophobic curcumin (CUR) encapsulation and the major response observed for AMF-triggered release was reached using drug-loaded MSNs within the fibers, providing fine control over drug release patterns. The EMFs exhibited excellent inductive heating capabilities, showing a temperature increase up to 8 {\deg}C within a 5 min AMF pulse. The system is shown to be promising for applications like transdermal pain management, oncological drug delivery, tissue engineering, and wound healing, enabling precise control over drug release in both spatial and temporal dimensions. The findings of this study offer valuable insights into the development of the next generation of smart drug delivery systems, based in multifunctional materials that can be remotely regulated and potentially revolutionize the field of nanomedicine.

Quantum machine learning requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. In this work, we present density quantum neural networks, a learning model incorporating randomisation over a set of trainable unitaries. These models generalise quantum neural networks using parameterised quantum circuits, and allow a trade-off between expressibility and efficient trainability, particularly on quantum hardware. We demonstrate the flexibility of the formalism by applying it to two recently proposed model families. The first are commuting-block quantum neural networks (QNNs) which are efficiently trainable but may be limited in expressibility. The second are orthogonal (Hamming-weight preserving) quantum neural networks which provide well-defined and interpretable transformations on data but are challenging to train at scale on quantum devices. Density commuting QNNs improve capacity with minimal gradient complexity overhead, and density orthogonal neural networks admit a quadratic-to-constant gradient query advantage with minimal to no performance loss. We conduct numerical experiments on synthetic translationally invariant data and MNIST image data with hyperparameter optimisation to support our findings. Finally, we discuss the connection to post-variational quantum neural networks, measurement-based quantum machine learning and the dropout mechanism., Comment: 17 pages main text, 9 pages appendices. 9 figures

The RSNA Abdominal Traumatic Injury CT (RATIC) dataset is the largest publicly available collection of adult abdominal CT studies annotated for traumatic injuries. This dataset includes 4,274 studies from 23 institutions across 14 countries. The dataset is freely available for non-commercial use via Kaggle at https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection. Created for the RSNA 2023 Abdominal Trauma Detection competition, the dataset encourages the development of advanced machine learning models for detecting abdominal injuries on CT scans. The dataset encompasses detection and classification of traumatic injuries across multiple organs, including the liver, spleen, kidneys, bowel, and mesentery. Annotations were created by expert radiologists from the American Society of Emergency Radiology (ASER) and Society of Abdominal Radiology (SAR). The dataset is annotated at multiple levels, including the presence of injuries in three solid organs with injury grading, image-level annotations for active extravasations and bowel injury, and voxelwise segmentations of each of the potentially injured organs. With the release of this dataset, we hope to facilitate research and development in machine learning and abdominal trauma that can lead to improved patient care and outcomes., Comment: 40 pages, 2 figures, 3 tables