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The advancement of the Event Horizon Telescope has enabled the study of relativistic jets in active galactic nuclei down to sub-parsec linear scales even at high redshift. Quasi-simultaneous multifrequency observations provide insights into the physical conditions in compact regions and allow testing accretion theories. Initially we aimed at measuring the magnetic field strength close to the central supermassive black hole in NRAO 530 (1730-130) by studying frequency-dependent opacity of the jet matter, Faraday rotation and the spectral index in the mm-radio bands. NRAO 530 was observed quasi-simultaneously at 15, 22, 43, 86, and 227 GHz at four different very long baseline interferometer (VLBI) networks. By the means of imaging and model-fitting, we aligned the images, taken at different frequencies. We explored opacity along the jet and distribution of the linearly polarized emission in it. Our findings reveal that the jet of NRAO 530 at 86 and 227 GHz is transparent down to its origin, with 70 mJy emission detected at 227 GHz potentially originating from the accretion disk. The magnetic field strength near the black hole, estimated at $5r_\mathrm{g}$, is $3\times10^3-3\times10^4$ G (depending on the central black hole mass). These values represent some of the highest magnetic field strengths reported for active galaxies. We also report the first ever VLBI measurement of the Faraday rotation at 43-227 GHz, which reveals rotation measure values as high as -48000 rad/m2 consistent with higher particle density and stronger magnetic fields at the jet's outset. The complex shape of the jet in NRAO 530 is in line with the expected behavior of a precessing jet, with a period estimated to be around $6\pm4$~years., Comment: 18 pages, accepted to A&A

The current neutrino experiments provide an opportunity for testing the inverse see-saw mechanism through charged lepton flavor violating processes and neutrinoless double beta decay. Motivated by this, in this paper we study the $\mathbf{S}_{3}\otimes \mathbf{Z}_{2}$ discrete symmetry in the $B-L$ gauge model where the active light neutrino mass matrix comes from the aforementioned mechanism. In this framework, the effect of complex vacuum expectation values of the Higgs doublets on the fermion masses is explored and, under certain assumptions on the Yukawa couplings, we find that the neutrino mixing is controlled by the Cobimaximal pattern, but a sizeable deviation from the charged lepton sector breaks the well known predictions on the atmospheric angle ($45^{\circ}$) and the Dirac CP-violating phase ($-90^{\circ}$). In addition, due to the presence of heavy neutrinos at the $TeV$ scale, charged lepton flavor violation (CLFV) and neutrinoless double beta decay get notable contributions. Analytical formulae for these observables are obtained, and then a numerical calculation allows to fit quite well the lepton mixing for the normal and inverted hierarchies, however, the branching ratios decay values for CLFV disfavors the latter one. Along with this, the region of parameter space for the $m_{ee}$ effective neutrino mass lies below the GERDA bounds for both the normal and inverted hierarchies. On the other hand, with a particular benchmark, the quark mass matrices are found to have textures that allow to fit with great accuracy the CKM mixing matrix., Comment: 37 pages, 10 figures, 3 tables

Differential dynamic microscopy (DDM) typically relies on movies containing hundreds or thousands of frames to accurately quantify motion in soft matter systems. Using movies much shorter in duration produces noisier and less accurate results. This limits the applicability of DDM to situations where the dynamics are stationary over extended times. Here, we investigate a method to denoise the DDM process, particularly suited to when a limited number of imaging frames are available or when dynamics are quickly evolving in time. We use a convolutional neural network encoder-decoder (CNN-ED) model to reduce the noise in the intermediate scattering function that is computed via DDM. We demonstrate this approach of combining machine learning and DDM on samples containing diffusing micron-sized colloidal particles. We quantify how the particles' diffusivities change over time as the fluid they are suspended in gels. We also quantify how the diffusivity of particles varies with position in a sample containing a viscosity gradient. These test cases demonstrate how studies of non-equilibrium dynamics and high-throughput screens could benefit from a method to denoise the outputs of DDM.

This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constrained settings. Utilizing EfficientViT and MobileViT as teacher models, this framework enables lightweight student models, particularly ResNet8 and ResNet16, to surpass 90% in accuracy, precision, and recall, adhering to the stringent confidence thresholds necessary for reliable classification tasks. Unlike conventional KD methods that rely on static weight distribution, our adaptive weighting mechanism responds to each teacher model's confidence, allowing student models to prioritize more credible sources of knowledge dynamically. Remarkably, ResNet8 delivers substantial efficiency gains, achieving a 97.5% reduction in parameters, a 96.7% decrease in FLOPs, an 86.2% cut in power consumption, and a 63.5% increase in inference speed over MobileViT. This significant optimization of complexity and resource demands establishes ResNet8 as an optimal candidate for EO tasks, combining robust performance with feasibility in deployment. The confidence-based, adaptable KD approach underscores the potential of dynamic distillation strategies to yield high-performing, resource-efficient models tailored for satellite-based EO applications. The reproducible code is accessible on our GitHub repository., Comment: Under revisions

In this paper, we extend the museum pass problem to incorporate the market structure. To be more precise, we consider that museums are organized into several pass programs or consortia. Within this framework, we propose four allocation mechanisms based on the market structure and the principles of proportionality and egalitarianism. All these mechanisms satisfy different reasonable properties related to fairness and stability which serve to axiomatically characterize them.

Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components include conditional expectations, conditional choice probabilities, or policy functions, which might be flexibly estimated using Machine Learning tools. This paper presents a characterization of locally debiased moments for regular models defined by general semiparametric CMRs with possibly different conditioning variables. These moments are appealing as they are known to be less affected by first-step bias. Additionally, we study their existence and relevance. Such results apply to a broad class of smooth functionals of finite and infinite dimensional parameters that do not necessarily appear in the CMRs. As a leading application of our theory, we characterize debiased machine learning for settings of treatment effects with endogeneity, giving necessary and sufficient conditions. We present a large class of relevant debiased moments in this context. We then propose the Compliance Machine Learning Estimator (CML), based on a practically convenient orthogonal relevant moment. We show that the resulting estimand can be written as a convex combination of conditional local average treatment effects (LATE). Altogether, CML enjoys three appealing properties in the LATE framework: (1) local robustness to first-stage estimation, (2) an estimand that can be identified under a minimal relevance condition, and (3) a meaningful causal interpretation. Our numerical experimentation shows satisfactory relative performance of such an estimator. Finally, we revisit the Oregon Health Insurance Experiment, analyzed by Finkelstein et al. (2012). We find that the use of machine learning and CML suggest larger positive effects on health care utilization than previously determined.

Earth observation (EO) systems are essential for mapping, catastrophe monitoring, and resource management, but they have trouble processing and sending large amounts of EO data efficiently, especially for specialized applications like agriculture and real-time disaster response. This paper presents a novel framework for semantic communication in EO satellite networks, aimed at enhancing data transmission efficiency and system performance through cognitive processing techniques. The proposed system leverages Discrete Task-Oriented Joint Source-Channel Coding (DT-JSCC) and Semantic Data Augmentation (SA) integrate cognitive semantic processing with inter-satellite links, enabling efficient analysis and transmission of multispectral imagery for improved object detection, pattern recognition, and real-time decision-making. Cognitive Semantic Augmentation (CSA) is introduced to enhance a system's capability to process and transmit semantic information, improving feature prioritization, consistency, and adaptation to changing communication and application needs. The end-to-end architecture is designed for next-generation satellite networks, such as those supporting 6G, demonstrating significant improvements in fewer communication rounds and better accuracy over federated learning., Comment: 8 Pages, 5 figures, Magazine. arXiv admin note: substantial text overlap with arXiv:2409.15246

Scallop-shell stars, a recently discovered class of young M dwarfs, show complex optical light curves that are characterized by periodic dips as well as other features that are stable over tens to hundreds of rotation cycles. The origin of these features is not well-understood. 2MASS J05082729$-$2101444 is a $\sim$25 Myr old scallop-shell star that was identified using TESS data; it has a photometric period of 6.73h that has been attributed to rotation. Of the $\sim$50 recently confirmed scallop-shell stars, it is one of the few detected at radio frequencies between 1 and 8 GHz. We observed this rare system with the upgraded Giant Meterwave Radio Telescope at 575--720 MHz, covering 88% of the photometric period in each of the two observations scheduled almost a month apart in 2023. We detected $\sim$millijansky emission from the target in both epochs, with a significant circular polarization fraction: $|V/I|\sim$20--50%. The 3.5-min phase-folded light curves reveal unique variability in circular polarization, showing an $\sim$hour-long helicity reversal in both epochs, similar in amplitude, length, and (possibly) phase. These results suggest two emission components: The first is a persistent, moderately polarized component possibly ascribable to gyro-synchrotron emission driven by centrifugal breakout events. The second is a highly polarized, short burst-like component, likely due to an electron cyclotron maser (ECM), indicative of auroral emission and potentially responsible for the helicity reversal. To explain this, we discuss the different origins of the plasma responsible for the radio emission, including the possibility that the occulting material is acting as a plasma source. Future coordinated multifrequency radio and optical observations can further constrain the underlying scenario, as well as the magnetic geometry of the system, if we assume an ECM-like auroral emission., Comment: 9 pages, 5 figures, 1 table, Accepted for publication in Astronomy & Astrophysics Letters

After over three decades of unsuccessful attempts, we report the first detection of molecular gas emission in Malin 1, the largest spiral galaxy observed to date, and one of the most iconic giant low surface brightness galaxies. Using ALMA, we detect significant $^{12}$CO(J=1-0) emission in the galaxy's central region and tentatively identify CO emission across three regions on the disc. These observations allow for a better estimate of the H$_2$ mass and molecular gas mass surface density, both of which are remarkably low given the galaxy's scale. By integrating data on its HI mass, we derive a very low molecular-to-atomic gas mass ratio. Overall, our results highlight the minimal presence of molecular gas in Malin 1, contrasting sharply with its extensive, homogeneous atomic gas reservoir. For the first time, we position Malin 1 on the Kennicutt-Schmidt (K-S) diagram, where it falls below the main sequence for normal spirals, consistent with previous upper limits but now with more accurate figures. These findings are crucial for constraining our understanding of star formation processes in environments characterized by extremely low molecular gas densities and for refining models of galaxy formation, thereby improving predictions concerning the formation, evolution, and distribution of these giant, elusive galaxies., Comment: Accepted for publication in The Astrophysical Journal Letters. Published

We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the use of text gradients from a reflection and optimization engine to derive these action principles. To adapt action principles to specific task requirements, we propose a new optimization framework, Reflective Principle Optimization (RPO). After execution, RPO employs a reflector to critique current action principles and an optimizer to update them accordingly. We develop the RPO framework under two scenarios: Reward-RPO, which uses environmental rewards for reflection, and Self-RPO, which conducts self-reflection without external rewards. Additionally, two RPO methods, RPO-Traj and RPO-Batch, is introduced to adapt to different settings. Experimental results across four environments demonstrate that the PRAct agent, leveraging the RPO framework, effectively learns and applies action principles to enhance performance., Comment: Accepted to SIG CoNLL 2024

This paper presents a model for a vehicle routing problem in which customer demands are stochastic and vehicles are divided into compartments. The problem is motivated by the needs of certain agricultural cooperatives that produce various types of livestock food. The vehicles and their compartments have different capacities, and each compartment can only contain one type of feed. Additionally, certain farms can only be accessed by specific vehicles, and there may be urgency constraints. To solve the problem, a two-step heuristic algorithm is proposed. First, a constructive heuristic is applied, followed by an improvement phase based on iterated tabu search. The designed algorithm is tested on several instances, including an analysis of real-world datasets where the results are compared with those provided by the model. Furthermore, multiple benchmark instances are created for this problem and an extensive simulation study is conducted. Results are presented for different model parameters, and it is shown that, despite the problems' complexity, the algorithm performs efficiently. Finally, the proposed heuristic is compared to existing solution algorithms for similar problems using benchmark instances from the literature, achieving competitive results., Comment: 25 pages, 8 figures

We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for videos, particularly designed to efficiently capture temporal information over multiple frames. BLIP-3-Video takes advantage of the 'temporal encoder' in addition to the conventional visual tokenizer, which maps a sequence of tokens over multiple frames into a compact set of visual tokens. This enables BLIP3-Video to use much fewer visual tokens than its competing models (e.g., 32 vs. 4608 tokens). We explore different types of temporal encoders, including learnable spatio-temporal pooling as well as sequential models like Token Turing Machines. We experimentally confirm that BLIP-3-Video obtains video question-answering accuracies comparable to much larger state-of-the-art models (e.g., 34B), while being much smaller (i.e., 4B) and more efficient by using fewer visual tokens. The project website is at https://www.salesforceairesearch.com/opensource/xGen-MM-Vid/index.html

Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fairness. Multiple-choice question and answer (QA) datasets derived from national medical examinations have long served as valuable evaluation tools, but existing datasets are largely text-only and available in a limited subset of languages and countries. To address these challenges, we present WorldMedQA-V, an updated multilingual, multimodal benchmarking dataset designed to evaluate VLMs in healthcare. WorldMedQA-V includes 568 labeled multiple-choice QAs paired with 568 medical images from four countries (Brazil, Israel, Japan, and Spain), covering original languages and validated English translations by native clinicians, respectively. Baseline performance for common open- and closed-source models are provided in the local language and English translations, and with and without images provided to the model. The WorldMedQA-V benchmark aims to better match AI systems to the diverse healthcare environments in which they are deployed, fostering more equitable, effective, and representative applications., Comment: submitted for review, total of 14 pages

A major feature of efflux pumps is multidrug polyspecificity, which to date still eludes some of the traditional dogmas of drug binding within protein science. We propose a combinatorial approach to explore, in the vast sequence space of polypeptide chains, the neighbourhood of some superfamilies of efflux pumps. Creating new functional structures as permutations of existing ones has the potential to unveil some of the hidden mechanisms behind the functionality of efflux pumps., Comment: 3 pages

The process of collecting blood from donors and making it available for transfusion requires a complex series of operations involving multiple actors and resources at each step. Ensuring hospitals receive adequate and safe blood for transfusion is a common challenge across low- and middle-income countries, but is rarely addressed from a system level. This paper presents the first use of discrete event simulation to study the blood system in Kenya and to explore the effect of variations and perturbations at different steps of the system on meeting patient blood demand. A process map of the Kenyan blood system was developed to capture critical steps from blood donation to transfusion using interviews with blood bank, hospital, and laboratory personnel at four public hospitals across three counties in Kenya. The blood system was simulated starting with blood collection, a blood bank where blood is tested and stored before it is issued, a major hospital attached to the blood bank, and several smaller hospitals served by the same blood bank. Values for supply-side parameters were based mainly on expert opinion; demand-side parameters were based on data from blood requisitions made in hospital wards, and dispatch of blood from the hospital laboratory. Illustrative examples demonstrate how the model can be used to explore the impacts of changes in blood collection (e.g., prioritising different donor types), blood demand (e.g., differing clinical case mix), and blood distribution (e.g., restocking strategies) on meeting demand at patient level. The model can reveal potential process impediments in the blood system and aid in choosing strategies for improving blood collection, distribution or use. Such a systems approach allows for interventions at different steps in the blood continuum to be tested on blood availability for different patients presenting at diverse hospitals across the country., Comment: 38 pages, 8 figures

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

The miniaturisation of transistors is approaching its limits due to challenges in heat management and information transfer speed. To overcome these obstacles, emerging technologies such as spintronics are being developed, which leverage the electron's spin in addition to its charge. Local phenomena at interfaces or structural defects will greatly influence the efficiency of spin-based devices, making the ability to study and control spin wave propagation at the nano- and atomic scales a key challenge. The development of high-spatial-resolution tools to probe spin waves, also called magnons, at relevant lengthscales is thus essential to understand how their properties are affected by such local features. Here, we show the first experimental detection of bulk magnons at the nanoscale using scanning transmission electron microscopy. By employing high-resolution electron energy loss spectroscopy with hybrid-pixel direct electron detectors optimized for low acceleration voltages, we successfully overcome the challenges posed by weak signals and identify magnon excitations in a thin NiO nanocrystal. Advanced inelastic electron scattering simulations corroborate our findings. These results open new avenues for detecting magnons, exploring their dispersions and their modifications arising from nanoscale structural or chemical defects. This marks an important milestone in magnonics and presents exciting opportunities for the future development of spintronic devices.

We analyze truncated series generated as divergent formal solutions of non-linear ordinary differential equations. Motivating the study is a specific non-linear, first-order differential equation, which is the basis of the resurgent formulation of renormalized perturbation theory in quantum field theory. We use the Borel-Pad\'e approximant and classical analysis to determine the analytic structure of the solution using the first few terms of its asymptotic series. Afterward, we build an approximant, consistent with the resurgent properties of the equation. The procedure gives an approximate expression for the Borel-Ecalle resummation of the solution useful for practical applications. Connections with other physical applications are also discussed., Comment: 24 pages, preprint based on a version to appear in Mathematics

In this article, the Hausdorff dimension is estimated using the box-counting dimension and the information dimension. It is shown that the former is an upper bound for the Hausdorff dimension, while the latter is a lower bound for the box-counting dimension, and that the information dimension is an upper bound for the Hausdorff dimension. Additionally, the dimension of the Henon attractor is estimated using the box-counting dimension., Comment: 14 pages, in Spanish language

In this work, we will study a numerical method that allows finding an approximation of the exact solution for a in-situ combustion model using the nonlinear mixed complementary method, which is a variation of the Newtons method for solving nonlinear systems based on an implicit finite difference scheme and a nonlinear algorithm mixed complementarity, FDA-MNCP. The method has the advantage of provide a global convergence in relation to the finite difference method and method of Newton that only has local convergence. The theory is applied to model in-situ combustion, which can be rewritten in the form of mixed complementarity also we do a comparison with the FDA-NCP method, Comment: 17 Pages

In this paper, we prove that the trajectories of unreduced $\phi$-simple Chaplygin kinetic systems are reparametrizations of horizontal geodesics with respect to a modified Riemannian metric. Furthermore, our proof is constructive and these Riemannian metrics, which are not unique, are obtained explicitly in interesting examples. We also extend these results to $\phi$-simple Chaplygin mechanical systems (not necessarily kinetic).

Earth Observation (EO) systems are crucial for cartography, disaster surveillance, and resource administration. Nonetheless, they encounter considerable obstacles in the processing and transmission of extensive data, especially in specialized domains such as precision agriculture and real-time disaster response. Earth observation satellites, outfitted with remote sensing technology, gather data from onboard sensors and IoT-enabled terrestrial objects, delivering important information remotely. Domain-adapted Large Language Models (LLMs) provide a solution by enabling the integration of raw and processed EO data. Through domain adaptation, LLMs improve the assimilation and analysis of many data sources, tackling the intricacies of specialized datasets in agriculture and disaster response. This data synthesis, directed by LLMs, enhances the precision and pertinence of conveyed information. This study provides a thorough examination of using semantic inference and deep learning for sophisticated EO systems. It presents an innovative architecture for semantic communication in EO satellite networks, designed to improve data transmission efficiency using semantic processing methodologies. Recent advancements in onboard processing technologies enable dependable, adaptable, and energy-efficient data management in orbit. These improvements guarantee reliable performance in adverse space circumstances using radiation-hardened and reconfigurable technology. Collectively, these advancements enable next-generation satellite missions with improved processing capabilities, crucial for operational flexibility and real-time decision-making in 6G satellite communication., Comment: 17 pages, 7 figures, Journal

The Optional Public Goods Game is a three-strategy game in which an individual can play as a cooperator or defector or decide not to participate. Despite its simplicity, this model can effectively represent many human social dilemmas, such as those found in the use of public services, environmental concerns, or other activities related to society. In this contribution, we present a comprehensive analysis of the conditions under which spontaneous, sustained cooperation emerges and the characteristics of these cooperative states. Through simulations, we demonstrate the conditions leading to the coexistence of the three strategies in a steady equilibrium or the alternate dominance of each strategy in a rock-paper-scissors fashion. The results identify each of the possible scenarios in terms of two key parameters: the multiplication rate of the public good game (reward) and the size of the group of potential players. We also discuss other details of the game that may influence the appearance of cycles, along with relevant characteristics of these cycles, such as the prevalence of cooperation., Comment: 8 pages, 6 figures

Quantum linear optics is a promising candidate for obtaining a quantum computational advantage. However, linear optics without post-selection is not powerful enough to produce any quantum state from a given input state. This limits its utility since some computations require entangled resources that are difficult to prepare. Thus, we need a deeper understanding of linear optical state preparation. In this work, we give a recipe to derive conserved quantities in the evolution of arbitrary states along any possible passive linear interferometer. We obtain the invariants by projecting the density operator of the state onto the Lie algebra of passive linear optical Hamiltonians. The conservation of the invariants gives necessary conditions for exact and approximate state preparation with passive linear optics: if input and output states have different invariants, it will be impossible to design a linear optical experiment that evolves one into the other or a sufficiently close one. Therefore, the invariants allow us to narrow the search when trying to prepare entangled states useful for quantum information, like Bell or NOON states, from easy-to-prepare states, like Fock states. We conclude that future exact and approximate state preparation methods will need to take into account the necessary conditions given by our invariants to weed out impossible linear optical evolutions., Comment: 10 pages, 2 figures. Comments welcome!

In this paper we use abstract bifurcation theory for Fredholm operators of index zero to deal with periodic even solutions of the one-dimensional equation $\mathcal{L}u=\lambda u+|u|^{p}$, where $\mathcal{L}$ is a nonlocal pseudodifferential operator defined as a Fourier multiplier and $\lambda$ is the bifurcation parameter. Our general setting includes the fractional Laplacian $\mathcal{L}\equiv(-\Delta)^{s}$ and sharpens the results obtained for this operator to date. As a direct application, we establish the existence of traveling waves for general nonlocal dispersive equations for some velocity ranges.

This study focuses on identifying the most effective pre-trained model for land use classification in onboard satellite processing, emphasizing achieving high accuracy, computational efficiency, and robustness against noisy data conditions commonly encountered during satellite-based inference. Through extensive experimentation, we compare the performance of traditional CNN-based, ResNet-based, and various pre-trained vision Transformer models. Our findings demonstrate that pre-trained Vision Transformer (ViT) models, particularly MobileViTV2 and EfficientViT-M2, outperform models trained from scratch in terms of accuracy and efficiency. These models achieve high performance with reduced computational requirements and exhibit greater resilience during inference under noisy conditions. While MobileViTV2 has excelled on clean validation data, EfficientViT-M2 has proved more robust when handling noise, making it the most suitable model for onboard satellite EO tasks. Our experimental results demonstrate that EfficientViT-M2 is the optimal choice for reliable and efficient RS-IC in satellite operations, achieving 98.76 % of accuracy, precision, and recall. Precisely, EfficientViT-M2 delivers the highest performance across all metrics, excels in training efficiency (1,000s) and inference time (10s), and demonstrates greater robustness (overall robustness score of 0.79). Consequently, EfficientViT-M2 consumes 63.93 % less power than MobileViTV2 (79.23 W) and 73.26 % less power than SwinTransformer (108.90 W). This highlights its significant advantage in energy efficiency.

Autonomous agents powered by large language models (LLMs) have attracted significant research interest. However, the open-source community faces many challenges in developing specialized models for agent tasks, driven by the scarcity of high-quality agent datasets and the absence of standard protocols in this area. We introduce and publicly release xLAM, a series of large action models designed for AI agent tasks. The xLAM series includes five models with both dense and mixture-of-expert architectures, ranging from 1B to 8x22B parameters, trained using a scalable, flexible pipeline that unifies, augments, and synthesizes diverse datasets to enhance AI agents' generalizability and performance across varied environments. Our experimental results demonstrate that xLAM consistently delivers exceptional performance across multiple agent ability benchmarks, notably securing the 1st position on the Berkeley Function-Calling Leaderboard, outperforming GPT-4, Claude-3, and many other models in terms of tool use. By releasing the xLAM series, we aim to advance the performance of open-source LLMs for autonomous AI agents, potentially accelerating progress and democratizing access to high-performance models for agent tasks. Models are available at https://huggingface.co/collections/Salesforce/xlam-models-65f00e2a0a63bbcd1c2dade4, Comment: Technical report for the Salesforce xLAM model series

We revisit the problem of describing creep in heterogeneous materials by an effective temperature by considering more realistic (and complex) non-mean-field elastic redistribution kernels. We show first, from theoretical considerations, that, if elastic stress redistribution and memory effects are neglected, the average creep failure time follows an Arrhenius expression with an effective temperature explicitly increasing with the quenched heterogeneity. Using a thermally activated progressive damage model of compressive failure, we show that this holds true when taking into account elastic interactions and memory effects, however with an effective temperature $T_{eff}$ depending as well on the nature of the (non-democratic) elastic interaction kernel. We observe that the variability of creep lifetimes, for given external conditions of load and temperature, is roughly proportional to the mean lifetime, therefore depends as well on $T$, on quenched heterogeneity, and the elastic kernel. Finally, we discuss the implications of this effective temperature effect on the interpretation of macroscopic creep tests to estimate an activation volume at the microscale.

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.

One of the main obstacles for the signal extraction of the three point correlation function using photometric surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST), will be the prohibitive computation time required for dealing with a vast quantity of sources. Brute force algorithms, which naively scales as $\mathcal{O}(N^3)$ with the number of objects, can be further improved with tree methods but not enough to deal with large scale correlations of Rubin's data. However, a harmonic basis decomposition of these higher order statistics reduces the time dramatically, to scale as a two-point correlation function with the number of objects, so that the signal can be extracted in a reasonable amount of time. In this work, we aim to develop the framework to use these expansions within the Limber approximation for scalar (or spin-0) fields, such as galaxy counts, weak lensing convergence or aperture masses. We develop an estimator to extract the signal from catalogs and different phenomenological and theoretical models for its description. The latter includes halo model and standard perturbation theory, to which we add a simple effective field theory prescription based on the short range of non-locality of cosmic fields, significantly improving the agreement with simulated data. In parallel to the modeling of the signal, we develop a code that can efficiently calculate three points correlations of more than 200 million data points (a full sky simulation with Nside=4096) in $\sim$40 minutes on a single high-performance computing node, enabling a feasible analysis for the upcoming LSST data., Comment: 31 pages, 10 figures

Reinforcement learning is a subfield of machine learning that is having a huge impact in the different conventional disciplines, including physical sciences. Here, we show how reinforcement learning methods can be applied to solve optimization problems in the context of radiative heat transfer. We illustrate their use with the optimization of the near-field radiative heat transfer between multilayer hyperbolic metamaterials. Specifically, we show how this problem can be formulated in the language of reinforcement learning and tackled with a variety of algorithms. We show that these algorithms allow us to find solutions that outperform those obtained using physical intuition. Overall, our work shows the power and potential of reinforcement learning methods for the investigation of a wide variety of problems in the context of radiative heat transfer and related topics., Comment: 21 pages, 9 figures

In this paper, we introduce the notion of a multiplicative unimodularity for a coisotropic Poisson homogeneous space. Then, we discuss the unimodularity and the multiplicative unimodularity for these spaces and the existence of an invariant volume form for explicit Hamiltonian systems on such spaces. Several interesting examples illustrating the theoretical results are also presented., Comment: 36 pages

We present xGen-VideoSyn-1, a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. Building on recent advancements, such as OpenAI's Sora, we explore the latent diffusion model (LDM) architecture and introduce a video variational autoencoder (VidVAE). VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens and the computational demands associated with generating long-sequence videos. To further address the computational costs, we propose a divide-and-merge strategy that maintains temporal consistency across video segments. Our Diffusion Transformer (DiT) model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios. We have devised a data processing pipeline from the very beginning and collected over 13M high-quality video-text pairs. The pipeline includes multiple steps such as clipping, text detection, motion estimation, aesthetics scoring, and dense captioning based on our in-house video-LLM model. Training the VidVAE and DiT models required approximately 40 and 642 H100 days, respectively. Our model supports over 14-second 720p video generation in an end-to-end way and demonstrates competitive performance against state-of-the-art T2V models., Comment: Accepted by ECCV24 AI4VA

This report introduces xGen-MM (also known as BLIP-3), a framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. xGen-MM, short for xGen-MultiModal, expands the Salesforce xGen initiative on foundation AI models. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks. Our pre-trained base model exhibits strong in-context learning capabilities and the instruction-tuned model demonstrates competitive performance among open-source LMMs with similar model sizes. In addition, we introduce a safety-tuned model with DPO, aiming to mitigate harmful behaviors such as hallucinations and improve safety. We open-source our models, curated large-scale datasets, and our fine-tuning codebase to facilitate further advancements in LMM research. Associated resources will be available on our project page above.

In this paper, optimal linear precoding for the multibeam geostationary earth orbit (GEO) satellite with the multi-user (MU) multiple-input-multiple-output (MIMO) downlink scenario is addressed. Multiple-user interference is one of the major issues faced by the satellites serving the multiple users operating at the common time-frequency resource block in the downlink channel. To mitigate this issue, the optimal linear precoders are implemented at the gateways (GWs). The precoding computation is performed by utilizing the channel state information obtained at user terminals (UTs). The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. The efficacy of the proposed algorithm is validated using the In-Lab experiment for 16X16 precoding with multi-beam satellite for transmitting and receiving the precoded data with digital video broadcasting satellite-second generation extension (DVB- S2X) standard for the GW and the UTs. The software defined radio platforms are employed for emulating the GWs, UTs, and satellite links. The validation is supported by comparing the proposed optimal linear precoder with full frequency reuse (FFR), and minimum mean square error (MMSE) schemes. The experimental results demonstrate that with the optimal linear precoders it is possible to successfully cancel the inter-user interference in the simulated satellite FFR link. Thus, optimal linear precoding brings gains in terms of enhanced signal-to-noise-and-interference ratio, and increased system throughput and spectral efficiency.

In recent years, the continued detection of complex organic molecules of prebiotic interest has refueled the interest on a panspermic origin of life. The prebiotic molecule glyceraldehyde is proposed to be formed from (Z)-1,2-ethenediol, a molecule recently detected towards the G+0.693-0.027 molecular cloud at the galactic center. In this work, we computationally simulate the formation of (Z)-1,2-ethenediol from vinyl alcohol on the surface of amorphous solid water in a two-step synthesis involving a OH addition and a H abstraction reaction. In total, we considered all reaction possibilities of the 1,1 and 1,2-OH addition to vinyl alcohol followed by H-abstraction or H-addition reactions on the resulting radicals. The combination of these reactions is capable of explaining the formation of (Z)-1,2-ethenediol provided a suprathermal diffusion of OH. We also conclude that our proposed formation pathway is not selective and also yields other abstraction and addition products. Key in our findings is the connection between the adsorption modes of the reactants and intermediates and the stereoselectivity of the reactions., Comment: This version is the unedited accepted version in the Astrophysical Journal

In the context of the left-right symmetric model, we study the interplay of neutrinoless double beta ($0\nu\beta\beta$) decay, parity-violating M{\o}ller scattering, and high-energy colliders, resulting from the Yukawa interaction of the right-handed doubly-charged scalar to electrons, which could evade the severe constraints from charged lepton flavor violation. The $0\nu\beta\beta$ decay amplitude receives additional contributions from right-handed sterile neutrinos. The half-life, calculated in the effective field theory (EFT) framework, allows for an improved description of the contributions involving non-zero mixing between left- and right-handed $W$ bosons and those arising from exchanging a light right-handed neutrino. We find that the relative sensitivities between the low-energy (or high-precision) and high-energy experiments are affected by the left-right mixing. On the other hand, our results show how the interplay of collider and low-energy searches provides a manner to explore regions that are inaccessible to $0\nu\beta\beta$ decay experiments., Comment: 12 pages, 7 figures

We present solutions to the Einstein-Klein Gordon system representing boson stars in the slow rotation approximation. By considering slow rotation we are able to reduce the number of equations yielding a system of ordinary differential equations that is conveniently solved numerically without the need of expensive computational resources. We find sequences of solutions and describe some of their physical properties such as, total mass, angular momentum and compactness. We also consider the dynamics of particles (geodesics) in the resulting spacetime. A detailed comparison with fully rotating boson stars (non-linear treatment) is performed by showing the region of validity of the slow-rotation approximation., Comment: 21 pages, 12 figures

In quantum field theories, field redefinitions are often employed to remove redundant operators in the Lagrangian, making calculations simpler and physics more evident. This technique requires some care regarding, among other things, the choice of observables, the range of applicability, and the appearance and disappearance of solutions of the equations of motion (EOM). Many of these issues can already be studied at the classical level, which is the focus of this work. We highlight the importance of selecting appropriate observables and initial/boundary conditions to ensure the physical invariance of solutions. A classical analogue to the Lehmann-Symanzik-Zimmermann (LSZ) formula is presented, confirming that some observables remain independent of field variables without tracking redefinitions. Additionally, we address, with an example, the limitations of non-invertible field redefinitions, particularly with non-perturbative objects like solitons, and discuss their implications for classical and quantum field theories., Comment: 29 pages, 2 figures

Neutrino masses and dark matter (DM) might have a common origin. The scotogenic model can be considered the proto-type model realizing this idea, but many other variants exist. In this paper we explore the phenomemology of a particular DM neutrino mass model, containing a triplet scalar. We calculate the relic density and check for constraints from direct detection experiments. The parameter space of the model, allowed by these constraints, contains typically a long-lived or quasi-stable doubly charged scalar, that can be searched for at the LHC. We reinterpret existing searches to derive limits on the masses of the scalars of the model and estimate future sensitivities in the high-luminosity phase of the LHC. The searches we discuss can serve to constrain also many other 1-loop neutrino mass models., Comment: 22 pages, 7 figures, 1 table

Given a graph $G$, a mutual-visibility coloring of $G$ is introduced as follows. We color two vertices $x,y\in V(G)$ with a same color, if there is a shortest $x,y$-path whose internal vertices have different colors than $x,y$. The smallest number of colors needed in a mutual-visibility coloring of $G$ is the mutual-visibility chromatic number of $G$, which is denoted $\chi_{\mu}(G)$. Relationships between $\chi_{\mu}(G)$ and its two parent ones, the chromatic number and the mutual-visibility number, are presented. Graphs of diameter two are considered, and in particular the asymptotic growth of the mutual-visibility number of the Cartesian product of complete graphs is determined. A greedy algorithm that finds a mutual-visibility coloring is designed and several possible scenarios on its efficiency are discussed. Several bounds are given in terms of other graph parameters such as the diameter, the order, the maximum degree, the degree of regularity of regular graphs, and/or the mutual-visibility number. For the corona products it is proved that the value of its mutual-visibility chromatic number depends on that of the first factor of the product. Graphs $G$ for which $\chi_{\mu}(G)=2$ are also considered., Comment: 19 pages, 3 figures

Knowledge graphs (KGs) have been successfully applied to the analysis of complex scientific and technological domains, with automatic KG generation methods typically building upon relation extraction models capturing fine-grained relations between domain entities in text. While these relations are fully applicable across scientific areas, existing models are trained on few domain-specific datasets such as SciERC and do not perform well on new target domains. In this paper, we experiment with leveraging in-context learning capabilities of Large Language Models to perform schema-constrained data annotation, collecting in-domain training instances for a Transformer-based relation extraction model deployed on titles and abstracts of research papers in the Architecture, Construction, Engineering and Operations (AECO) domain. By assessing the performance gain with respect to a baseline Deep Learning architecture trained on off-domain data, we show that by using a few-shot learning strategy with structured prompts and only minimal expert annotation the presented approach can potentially support domain adaptation of a science KG generation model.

Quantum conductors attached to metallic reservoirs have been demonstrated to overcome the thermodynamic uncertainty relation (TUR), a trade-off relation between the amount of dissipation and the absence of charge and heat current fluctuations. Here, we report large TUR violations when superconducting reservoirs replace metallic ones. The coexistence of different transport processes, namely (multiple) Andreev reflection, where electrons and their retro-reflected holes create Cooper pairs, in addition to the normal quasiparticle transport is identified as the source for such TUR breakdowns. The large TUR violation is a remarkable advantage for building low dissipative and highly stable quantum thermal machines., Comment: 6 pages, 2 figures

We examine the prospect of observing genuine lepton number violating (LNV) signals at hadron colliders in the context of the Type II seesaw mechanism. The model features smoking gun signals involving same-sign di-leptons and jets that may be the primary observable channel in certain regions of the parameter space. The flavour composition of final-state charged leptons in the minimal model is related to the origin of neutrino masses and is correlated with other rare processes, such as neutrinoless double beta decay. We review existing collider limits and provide sensitivity estimates from LNV signals at upcoming runs of the LHC, for zero and non-zero mass splittings between the scalar triplet components., Comment: 7 pages, 7 figures

Functional markers become a more frequent tool in medical diagnosis. In this paper, we aim to define an index allowing to discriminate between populations when the observations are functional data belonging to a Hilbert space. We discuss some of the problems arising when estimating optimal directions defined to maximize the area under the curve of a projection index and we construct the corresponding ROC curve. We also go one step forward and consider the case of possibly different covariance operators, for which we recommend a quadratic discrimination rule. Consistency results are derived for both linear and quadratic indexes, under mild conditions. The results of our numerical experiments allow to see the advantages of the quadratic rule when the populations have different covariance operators. We also illustrate the considered methods on a real data set.