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Polynomial reproduction plays a relevant role in deriving error estimates for various approximation schemes. Local reproduction in a quasi-uniform setting is a significant factor in the estimation of error and the assessment of stability but for some computationally relevant schemes, such as Rescaled Localized Radial Basis Functions (RL-RBF), it becomes a limitation. To facilitate the study of a greater variety of approximation methods in a unified and efficient manner, this work proposes a framework based on fast decaying polynomial reproduction: we do not restrict to compactly supported basis functions, but we allow the basis function decay to infinity as a function of the separation distance. Implementing fast decaying polynomial reproduction provides stable and convergent methods, that can be smooth when approximating by moving least squares otherwise very efficient in the case of linear programming problems. All the results presented in this paper concerning the rate of convergence, the Lebesgue constant, the smoothness of the approximant, and the compactness of the support have been verified numerically, even in the multivariate setting.

This work proposes to model the space environment as a stochastic dynamic network where each node is a group of objects of a given class, or species, and their relationship is represented by stochastic links. A set of stochastic dynamic equations, governing the evolution of the network, are derived from the network structure and topology. It will be shown that the proposed system of stochastic dynamic equations well reproduces existing results on the evolution of the space environment. The analysis of the structure of the network and relationships among node can help to understand which species of objects and orbit regimes are more critical and affect the most the future evolution of the space environment. In analogy with ecological networks, we develop a theory of the carrying capacity of space based on the stability of equilibria of the network dynamics. Some examples are presented starting from the current population of resident objects and different launch traffic forecast models. It will be shown how the proposed network model can be used to study the effect of the adoption of different policies on the execution of collision avoidance and post mission disposal manoeuvres., Comment: 40 pages, 28 figures, 8 tables

Shapley values have seen widespread use in machine learning as a way to explain model predictions and estimate the importance of covariates. Accurately explaining models is critical in real-world models to both aid in decision making and to infer the properties of the true data-generating process (DGP). In this paper, we demonstrate that while model-based Shapley values might be accurate explainers of model predictions, machine learning models themselves are often poor explainers of the DGP even if the model is highly accurate. Particularly in the presence of interrelated or noisy variables, the output of a highly predictive model may fail to account for these relationships. This implies explanations of a trained model's behavior may fail to provide meaningful insight into the DGP. In this paper we introduce a novel variable importance algorithm, Shapley Marginal Surplus for Strong Models, that samples the space of possible models to come up with an inferential measure of feature importance. We compare this method to other popular feature importance methods, both Shapley-based and non-Shapley based, and demonstrate significant outperformance in inferential capabilities relative to other methods.

Quasi-stationary Mean Field Games models consider agents who base their strategies on current information without forecasting future states. In this paper we address the first-order quasi-stationary Mean Field Games system, which involves an ergodic Hamilton-Jacobi equation and an evolutive continuity equation. Our approach relies on weak KAM theory. We introduce assumptions on the Hamiltonian and coupling cost to ensure continuity of the Peierls barrier and the Aubry set over time. These assumptions, though restrictive, cover interesting cases such as perturbed mechanical Hamiltonians.

The aim of the present work is a comparative study of different persistence kernels applied to various classification problems. After some necessary preliminaries on homology and persistence diagrams, we introduce five different kernels that are then used to compare their performances of classification on various datasets. We also provide the Python codes for the reproducibility of results., Comment: 23 pages, 13 figures

Asteroids with diameters less than about 5 km have complex histories because they are small enough for radiative torques, YORP, to be a notable factor in their evolution. (152830) Dinkinesh is a small asteroid orbiting the Sun near the inner edge of the Main Asteroid Belt with a heliocentric semimajor axis of 2.19 AU; its S type spectrum is typical of bodies in this part of the Main Belt. Here we report observations by the Lucy spacecraft as it passed within 431 km of Dinkinesh. Lucy revealed Dinkinesh, which has an effective diameter of only $\sim$720 m, to be unexpectedly complex. Of particular note is the presence of a prominent longitudinal trough overlain by a substantial equatorial ridge, and the discovery of the first confirmed contact binary satellite, now named (152830) Dinkinesh I Selam. Selam consists of two near-equal sized lobes with diameters of $\sim$210 m and $\sim$230 m. It orbits Dinkinesh at a distance of 3.1 km with an orbital period of about 52.7 hr, and is tidally locked. The dynamical state, angular momentum, and geomorphologic observations of the system lead us to infer that the ridge and trough of Dinkinesh are probably the result of mass failure resulting from spin-up by YORP followed by the partial reaccretion of the shed material. Selam probably accreted from material shed by this event., Comment: 25 pages, 5 figures

Necessary optimality conditions in Lagrangian form and the augmented Lagrangian framework are extended to mixed-integer nonlinear optimization, without any convexity assumptions. Building upon a recently developed notion of local optimality for problems with polyhedral and integrality constraints, a characterization of local minimizers and critical points is given for problems including also nonlinear constraints. This approach lays the foundations for developing affordable sequential minimization algorithms with convergence guarantees to critical points from arbitrary initializations. A primal-dual perspective, a local saddle point property, and the dual relationships with the proximal point algorithm are also advanced in the presence of integer variables., Comment: 18 pages, added motivating example

Focusing on minimization problems with structured objective function and smooth constraints, we present a flexible technique that combines the beneficial regularization effects of (exact) penalty and interior-point methods. Working in the fully nonconvex setting, a pure barrier approach requires careful steps when approaching the infeasible set, thus hindering convergence. We show how a tight integration with a penalty scheme overcomes such conservatism, does not require a strictly feasible starting point, and thus accommodates equality constraints. The crucial advancement that allows us to invoke generic (possibly accelerated) subsolvers is a marginalization step: closely related to a conjugacy operation, this step effectively merges (exact) penalty and barrier into a smooth, full domain functional object. When the penalty exactness takes effect, the generated subproblems do not suffer the ill-conditioning typical of barrier methods, nor do they exhibit the nonsmoothness of exact penalty terms. We provide a theoretical characterization of the algorithm and its asymptotic properties, deriving convergence results for fully nonconvex problems. Stronger conclusions are available for the convex setting, where optimality can be guaranteed. Illustrative examples and numerical simulations demonstrate the wide range of problems our theory and algorithm are able to cover.

Automatic methods for generating and gathering linguistic data have proven effective for fine-tuning Language Models (LMs) in languages less resourced than English. Still, while there has been emphasis on data quantity, less attention has been given to its quality. In this work, we investigate the impact of human intervention on machine-generated data when fine-tuning dialogical models. In particular, we study (1) whether post-edited dialogues exhibit higher perceived quality compared to the originals that were automatically generated; (2) whether fine-tuning with post-edited dialogues results in noticeable differences in the generated outputs; and (3) whether post-edited dialogues influence the outcomes when considering the parameter size of the LMs. To this end we created HED-IT, a large-scale dataset where machine-generated dialogues are paired with the version post-edited by humans. Using both the edited and unedited portions of HED-IT, we fine-tuned three different sizes of an LM. Results from both human and automatic evaluation show that the different quality of training data is clearly perceived and it has an impact also on the models trained on such data. Additionally, our findings indicate that larger models are less sensitive to data quality, whereas this has a crucial impact on smaller models. These results enhance our comprehension of the impact of human intervention on training data in the development of high-quality LMs.

We provide a representation of the weak solution of the continuity equation on the Heisenberg group $\mathbb H^1$ with periodic data (the periodicity is suitably adapted to the group law). This solution is the push forward of a measure concentrated on the flux associated with the drift of the continuity equation. Furthermore, we shall use this interpretation for proving that weak solutions to first order Mean Field Games on $\mathbb H^1$ are also mild solutions., Comment: arXiv admin note: substantial text overlap with arXiv:2010.09279

We tackle the question of whether Large Language Models (LLMs), viewed as dynamical systems with state evolving in the embedding space of symbolic tokens, are observable. That is, whether there exist multiple 'mental' state trajectories that yield the same sequence of generated tokens, or sequences that belong to the same Nerode equivalence class ('meaning'). If not observable, mental state trajectories ('experiences') evoked by an input ('perception') or by feedback from the model's own state ('thoughts') could remain self-contained and evolve unbeknown to the user while being potentially accessible to the model provider. Such "self-contained experiences evoked by perception or thought" are akin to what the American Psychological Association (APA) defines as 'feelings'. Beyond the lexical curiosity, we show that current LLMs implemented by autoregressive Transformers cannot have 'feelings' according to this definition: The set of state trajectories indistinguishable from the tokenized output is a singleton. But if there are 'system prompts' not visible to the user, then the set of indistinguishable trajectories becomes non-trivial, and there can be multiple state trajectories that yield the same verbalized output. We prove these claims analytically, and show examples of modifications to standard LLMs that engender such 'feelings.' Our analysis sheds light on possible designs that would enable a model to perform non-trivial computation that is not visible to the user, as well as on controls that the provider of services using the model could take to prevent unintended behavior.

Context. A necessary ingredient in understanding the star formation history of a young cluster is knowledge of the extinction towards the region. This has typically been done by making use of the colour-difference method with photometry, or similar methods utilising the colour-colour diagram. Aims. The colour-excess can be independently determined by studying the decrements of the recombination lines produced by the nebular gas. Having access to many recombination lines from the same spectral series removes the need of adopting an extinction curve. Methods. Using the Micro-Shutter Assembly (MSA) on board the Near InfraRed Spectrograph (NIRSpec), multi-object spectroscopy was performed, yielding 600 nebular spectra from the Galactic massive star formation region NGC 3603. The recombination line intensity ratios were used to determine independent values of colour-excess. Results. The extinction characteristics of NGC 3603 are similar to other Galactic HII regions like Orion, as well as starburst regions such 30 Doradus in the Large Magellanic Cloud, in that we find a relatively large value of total-to-selective extinction of 4.8 $\pm$ 1.06, larger than the Galactic average of 3.1. We find a typical value for the colour-excess of 0.64 $\pm$ 0.27, significantly lower than values determined in previous studies. We also present a stacked nebular spectrum with a typical continuum S/N = 70. This spectrum highlights the recombination lines of the HII region, several s-process elements such as Kr III and Se IV, and molecular H2 emission lines. Conclusions. Using ratios of hydrogen recombination lines, we calculated the total-to-selective extinction, colour-excess and visual extinction for > 200 lines of sight across NGC 3603. An extinction curve with a total-to-selective extinction of 4.8 $\pm$ 1.06 was found, corresponding to a colour-excess of 0.64 $\pm$ 0.27.

We compute the flux of relic neutrino background (R$\nu$B) up-scattered by ultra-high-energy (UHE) cosmic rays (CRs) in clusters that act as CR-reservoirs. The long trapping times of UHECRs make this flux larger than that of R$\nu$B up-scattered by UHECRs on their way to Earth, which we also compute. We find that IceCube excludes R$\nu$B weighted overdensities larger than $10^{10}$ in clusters, and that PUEO, RNO-G, GRAND and IceCube-Gen2 will test values down to $10^{8}$. Our treatment incorporates the momentum transfer dependence of the neutrino-nucleus cross section, deep inelastic scattering, a mixed UHECR composition, and flavour information on the up-scattered R$\nu$B fluxes for both cases of neutrino mass spectrum with normal and inverted ordering, providing new handles to possibly disentangle the up-scattered R$\nu$B from cosmogenic neutrinos., Comment: 5 pages + appendices, 5 figures, 1 table

In this work, we study the Kantorovich variant of max-min neural network operators, in which the operator kernel is defined in terms of sigmoidal functions. Our main aim is to demonstrate the $L^{p}$-convergence of these nonlinear operators for $1\leq p<\infty$, which makes it possible to obtain approximation results for functions that are not necessarily continuous. In addition, we will derive quantitative estimates for the rate of approximation in the $L^{p}$-norm. We will provide some explicit examples, studying the approximation of discontinuous functions with the max-min operator, and varying additionally the underlying sigmoidal function of the kernel. Further, we numerically compare the $L^{p}$-approximation error with the respective error of the Kantorovich variants of other popular neural network operators. As a final application, we show that the Kantorovich variant has advantages compared to the sampling variant of the max-min operator and Kantorovich variant of the max-product operator when it comes to approximate noisy functions as for instance biomedical ECG signals., Comment: 23 pages, 6 figures

We present a JWST imaging survey with NIRCam and MIRI of NGC 346, the brightest star-forming region in the Small Magellanic Cloud (SMC). By combining aperture and point spread function (PSF) photometry of eleven wavelength bands across these two instruments, we have detected more than 200,000 unique sources. Using near-infrared (IR) color analysis, we observe various evolved and young populations, including 196 young stellar objects (YSOs) and pre-main sequence stars suitable for forthcoming spectroscopic studies. We expand upon this work, creating mid-IR color-magnitude diagrams and determining color cuts to identify 833 reddened sources which are YSO candidates. We observe that these candidate sources are spatially associated with regions of dusty, filamentary nebulosity. Furthermore, we fit model YSO spectral energy distributions (SEDs) to a selection of sources with detections across all of our MIRI bands. We classify with a high degree of confidence 23 YSOs in this sample and estimate their radii, bolometric temperatures, luminosities, and masses. We detect YSOs approaching 1 solar mass, the lowest-mass extragalactic YSOs confirmed to date.

JWST/NIRCam obtained high angular-resolution (0.05-0.1''), deep near-infrared 1--5 micron imaging of Supernova (SN) 1987A taken 35 years after the explosion. In the NIRCam images, we identify: 1) faint H2 crescents, which are emissions located between the ejecta and the equatorial ring, 2) a bar, which is a substructure of the ejecta, and 3) the bright 3-5 micron continuum emission exterior to the equatorial ring. The emission of the remnant in the NIRCam 1-2.3 micron images is mostly due to line emission, which is mostly emitted in the ejecta and in the hot spots within the equatorial ring. In contrast, the NIRCam 3-5 micron images are dominated by continuum emission. In the ejecta, the continuum is due to dust, obscuring the centre of the ejecta. In contrast, in the ring and exterior to the ring, synchrotron emission contributes a substantial fraction to the continuum. Dust emission contributes to the continuum at outer spots and diffuse emission exterior to the ring, but little within the ring. This shows that dust cooling and destruction time scales are shorter than the synchrotron cooling time scale, and the time scale of hydrogen recombination in the ring is even longer than the synchrotron cooling time scale. With the advent of high sensitivity and high angular resolution images provided by JWST/NIRCam, our observations of SN 1987A demonstrate that NIRCam opens up a window to study particle-acceleration and shock physics in unprecedented details, probed by near-infrared synchrotron emission, building a precise picture of how a SN evolves., Comment: Accepted for publication in MNRAS; 18 pages

We present a detailed study of the centre of NGC4654, a Milky Way-like spiral galaxy in the Virgo cluster that has been reported to host a double stellar nucleus, thus promising a rare view of ongoing star cluster infall into a galaxy nucleus. Analysing JWST NIRSpec integral-field spectroscopic data and Hubble Space Telescope imaging of the inner 330 $\times$ 330 pc, we find that the nucleus harbours in fact three massive star clusters. Maps of infrared emission lines from NIRSpec show different morphologies for the ionised and molecular gas components. The emission from molecular hydrogen gas is concentrated at the NSC location, while emission from hydrogen recombination lines is more extended beyond the central cluster. The velocity fields of both gas and stars indicate that the three clusters are part of a complicated dynamical system, with the NSC having an elevated velocity dispersion in line with its high stellar mass. To investigate the stellar populations of the three clusters in more detail, we use surface brightness modelling to measure their fluxes from ultraviolet to mid-infrared wavelengths and fit their spectral energy distributions (SEDs). Two of the clusters are UV-bright and well described by single stellar populations with young ages ($\sim$ 3 and 5 Myr) and low masses ($M_\ast \sim 4 \times 10^{4} - 10^{5} M_\odot$), whereas the central cluster is much more massive ($3 \times 10^7 M_\odot$), and cannot be fitted by a single stellar population. Instead, we find that a minor young population ($\sim$ 1 Myr) embedded in a dominant old population ($\sim$ 8 Gyr) is needed to explain its SED. Given its complex composition and the close proximity of two young star clusters that are likely to merge with it within a few hundred million years, we consider NGC4654 a unique laboratory to study NSC growth from both in-situ star formation and the infall of star clusters., Comment: Main text: 13 pages, 8 figures, accepted for publication in A&A

Improvement and adoption of generative machine learning models is rapidly accelerating, as exemplified by the popularity of LLMs (Large Language Models) for text, and diffusion models for image generation. As generative models become widespread, data they generate is incorporated into shared content through the public web. This opens the question of what happens when data generated by a model is fed back to the model in subsequent training campaigns. This is a question about the stability of the training process, whether the distribution of publicly accessible content, which we refer to as "knowledge", remains stable or collapses. Small scale empirical experiments reported in the literature show that this closed-loop training process is prone to degenerating. Models may start producing gibberish data, or sample from only a small subset of the desired data distribution (a phenomenon referred to as mode collapse). So far there has been only limited theoretical understanding of this process, in part due to the complexity of the deep networks underlying these generative models. The aim of this paper is to provide insights into this process (that we refer to as "generative closed-loop learning") by studying the learning dynamics of generative models that are fed back their own produced content in addition to their original training dataset. The sampling of many of these models can be controlled via a "temperature" parameter. Using dynamical systems tools, we show that, unless a sufficient amount of external data is introduced at each iteration, any non-trivial temperature leads the model to asymptotically degenerate. In fact, either the generative distribution collapses to a small set of outputs or becomes uniform over a large set of outputs.

Optimization algorithms have a rich and fundamental relationship with ordinary differential equations given by its continuous-time limit. When the cost function varies with time -- typically in response to a dynamically changing environment -- online optimization becomes a continuous-time trajectory tracking problem. To accommodate these time variations, one typically requires some inherent knowledge about their nature such as a time derivative. In this paper, we propose a novel construction and analysis of a continuous-time derivative estimation scheme based on "dirty-derivatives", and show how it naturally interfaces with continuous-time optimization algorithms using the language of ISS (Input-to-State Stability). More generally, we show how a simple Lyapunov redesign technique leads to provable suboptimality guarantees when composing this estimator with any well-behaved optimization algorithm for time-varying costs.

Interactions with virtual assistants typically start with a predefined trigger phrase followed by the user command. To make interactions with the assistant more intuitive, we explore whether it is feasible to drop the requirement that users must begin each command with a trigger phrase. We explore this task in three ways: First, we train classifiers using only acoustic information obtained from the audio waveform. Second, we take the decoder outputs of an automatic speech recognition (ASR) system, such as 1-best hypotheses, as input features to a large language model (LLM). Finally, we explore a multimodal system that combines acoustic and lexical features, as well as ASR decoder signals in an LLM. Using multimodal information yields relative equal-error-rate improvements over text-only and audio-only models of up to 39% and 61%. Increasing the size of the LLM and training with low-rank adaption leads to further relative EER reductions of up to 18% on our dataset., Comment: arXiv admin note: text overlap with arXiv:2312.03632

In this letter we present the first systematic spectroscopic measurements of the Near-InfraRed (NIR) hydrogen recombination lines Paschen alpha ($Pa_{\alpha}$ $\lambda = 1.875 \mu m$) and Brackett beta ($Br_{\beta}$ $\lambda = 2.626 \mu m$), produced by pre-main-sequence (PMS) stars. These stars, consisting of T Tauri and Herbig AeBe stars are located in the massive Galactic star-formation region NGC 3603. The measurements were obtained from JWST NIRSpec, using multi-object spectroscopy (MOS) mode. Utilising existing empirical relations between $L_{acc}$ and $L_{Br_{\gamma}}$, we have used our new measurements to formulate, for the first time, an empirical relationship between the accretion luminosity $L_{acc}$ of the stars, and the line luminosities $L_{line}$ of both $Pa_{\alpha}$ and $Br_{\beta}$. These relationships are: $\log_{10}( \frac{L_{acc}}{L_{\odot}}) = 1.42 (\pm 0.18) \times \log_{10}( \frac{L_{Pa_{\alpha}}}{L_{\odot}}) + 3.33 (\pm 0.42)$ and $\log_{10}( \frac{L_{acc}}{L_{\odot}}) = 1.47 (\pm 0.18) \times \log_{10}( \frac{L_{Br_{\beta}}}{L_{\odot}}) + 4.60 (\pm 0.57)$. These new relationships are key for roughly estimating the accretion rates for large samples of PMS stars with JWST.

Models involving hybrid systems are versatile in their application, but difficult to handle and optimize efficiently due to their combinatorial nature. This work presents a method to cope with hybrid optimal control problems which, in contrast to decomposition techniques, does not require relaxing the integrality constraints. Based on the discretize-then-optimize approach, our scheme addresses mixed-integer nonlinear problems under mild assumptions. The proposed numerical algorithm builds upon the augmented Lagrangian framework, whose subproblems are handled using successive mixed-integer linearizations with trust regions. We validate the performance of the numerical routine with extensive investigations using several hybrid optimal control problems from different fields of application. Promising preliminary results are presented for a motion planning task with hysteresis and drag, a Lotka-Volterra fishing problem, and a facility location design problem., Comment: 12 pages, 6 figures. This work has been submitted to the IEEE for possible publication

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. However, its assessment is by and large qualitative and subjective, focusing on visually identified, age-specific gestures. Here, we follow an alternative approach, predicting infants' neurodevelopmental maturation based on data-driven evaluation of individual motor patterns. We utilize 3D video recordings of infants processed with pose-estimation to extract spatio-temporal series of anatomical landmarks, and apply adaptive graph convolutional networks to predict the actual age. We show that our data-driven approach achieves improvement over traditional machine learning baselines based on manually engineered features., Comment: 10 pages, 3 figures. Code repository available via https://github.com/deinal/infant-aagcn

We examine over 68,000 refereed publications based on data from 25 missions in the ESA Science Programme and 11 additional missions in which ESA is involved as a junior partner. The publications cover the fields of astronomy, planetary science, and heliophysics and are spread over almost 50 years, spanning the period between the year a mission was launched and the end of 2021. We study the number of papers as a function of time and the evolution of several metrics, including citations and other indices. We also investigate the geographical distribution of the authors, and for ESA Member States we correlate the various indices with the level of financial contribution of the individual countries to the ESA Science Programme. We find that in general the involvement of the scientific communities in the various Member States follows the distribution expected from the countries' gross domestic products, with communities in some field and countries, both large and small, being particularly effective at turning data into scientific discoveries. We also analyse the differences between papers written by investigators directly involved in the provision of the payloads or in the definition of the scientific projects and those written by other scientists not directly involved in the process. We find that the latter, the so-called "archival papers", represent more than 50\,\% of the literature based on data from ESA Space Science missions, and have a similar impact on the literature in the respective fields, as judged by the number of citations. This highlights the importance of sharing and preserving the scientific data produced by the missions., Comment: To be submitted to Springer for publication in the ISSI Scientific Reports series

The emission of the pre-equilibrium particles during nuclear collisions at moderate beam energies is still an open question. This influences the properties of the compound nucleus but also changes the interpretation of the quasi-fission process. A systematic analysis of the data obtained by the FAZIA collaboration during a recent experiment with a neutron rich projectile is presented. The full range of charged particles detected in the experiment is within the limit of isotopic resolution of the FAZIA detector. Quasi-projectile (QP) fragments were detected in majority thanks to the forward angular acceptance of the experimental setup which was confirmed by introducing cuts based on the HIPSE event generator calculations. The main goal was to compare the experimental results with the HIPSE simulations after introducing these cuts to investigate the influence of the n-rich entrance channel on the QP fragment properties. More specifically, the lowering of N/Z of QP fragments with beam energy was found to be present since the initial phase of the reaction. Thus, pre-equilibrium emissions might be a possible candidate to explain such an effect., Comment: 10 pages, 10 figures

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged particle coincidence techniques. The exploitation, for the first time in a similar experiment, of a bidimensional map of the coincidence efficiency allows to reach an unitary value and, consequently, to strongly reduce sources of systematic uncertainties. The present results suggest a value of the radiative branching ratio of $\Gamma_{rad}/\Gamma_{tot}=4.2(6)\cdot10^{-4}$. This finding helps to resolve the tension between recent data published in the literature.

The Extreme Universe Space Observatory on a Super Pressure Balloon 1 (EUSO-SPB1) was launched in 2017 April from Wanaka, New Zealand. The plan of this mission of opportunity on a NASA super pressure balloon test flight was to circle the southern hemisphere. The primary scientific goal was to make the first observations of ultra-high-energy cosmic-ray extensive air showers (EASs) by looking down on the atmosphere with an ultraviolet (UV) fluorescence telescope from suborbital altitude (33~km). After 12~days and 4~hours aloft, the flight was terminated prematurely in the Pacific Ocean. Before the flight, the instrument was tested extensively in the West Desert of Utah, USA, with UV point sources and lasers. The test results indicated that the instrument had sensitivity to EASs of approximately 3 EeV. Simulations of the telescope system, telescope on time, and realized flight trajectory predicted an observation of about 1 event assuming clear sky conditions. The effects of high clouds were estimated to reduce this value by approximately a factor of 2. A manual search and a machine-learning-based search did not find any EAS signals in these data. Here we review the EUSO-SPB1 instrument and flight and the EAS search., Comment: 18 pages, 19 figures

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data., Comment: Significant revisions