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Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems.

Linear complementary pairs (LCPs) of codes have been studied since they were introduced in the context of discussing mitigation measures against possible hardware attacks to integrated circuits. Since the security parameters for LCPs of codes are defined from the (Hamming) distance and the dual distance of the codes in the pair, and the additional algebraic structure of skew constacyclic codes provides tools for studying the the dual and the distance of a code, we study the properties of LCPs of skew constacyclic codes. As a result, we give a characterization for those pairs, as well as multiple results that lead to constructing pairs with designed security parameters. We extend skew BCH codes to a constacyclic context and show that an LCP of codes can be immediately constructed from a skew BCH constacyclic code. Additionally, we describe a Hamming weight-preserving automorphism group in the set of skew constacyclic codes, which can be used for constructing LCPs of codes., Comment: 25 pages, 0 figures

We introduce Spiral, a third-order integration algorithm for the rotational motion of extended bodies. It requires only one force calculation per time step, does not require quaternion normalization at each time step, and can be formulated for both leapfrog and synchronous integration schemes, making it compatible with many particle simulation codes. The stability and precision of Spiral exceed those of state-of-the-art algorithms currently used in popular DEM codes such as Yade, MercuryDPM, LIGGGHTS, PFC, and more, at only slightly higher computational cost. Also, beyond DEM, we see potential applications in all numerical simulations that involve the 3D rotation of extended bodies., Comment: 25 pages, 9 figures

We present the discovery of TOI-1420b, an exceptionally low-density ($\rho = 0.08\pm0.02$ g cm$^{-3}$) transiting planet in a $P = 6.96$ day orbit around a late G dwarf star. Using transit observations from TESS, LCOGT, OPM, Whitin, Wendelstein, OAUV, Ca l'Ou, and KeplerCam along with radial velocity observations from HARPS-N and NEID, we find that the planet has a radius of $R_p$ = 11.9 $\pm$ 0.3 $R_\Earth$ and a mass of $M_p$ = 25.1 $\pm$ 3.8 $M_\Earth$. TOI-1420b is the largest-known planet with a mass less than $50M_\Earth$, indicating that it contains a sizeable envelope of hydrogen and helium. We determine TOI-1420b's envelope mass fraction to be $f_{env} = 82^{+7}_{-6}\%$, suggesting that runaway gas accretion occurred when its core was at most $4-5\times$ the mass of the Earth. TOI-1420b is similar to the planet WASP-107b in mass, radius, density, and orbital period, so a comparison of these two systems may help reveal the origins of close-in low-density planets. With an atmospheric scale height of 1950 km, a transmission spectroscopy metric of 580, and a predicted Rossiter-McLaughlin amplitude of about $17$ m s$^{-1}$, TOI-1420b is an excellent target for future atmospheric and dynamical characterization.

JWST has ushered in an era of unprecedented ability to characterize exoplanetary atmospheres. While there are over 5,000 confirmed planets, more than 4,000 TESS planet candidates are still unconfirmed and many of the best planets for atmospheric characterization may remain to be identified. We present a sample of TESS planets and planet candidates that we identify as "best-in-class" for transmission and emission spectroscopy with JWST. These targets are sorted into bins across equilibrium temperature $T_{\mathrm{eq}}$ and planetary radius $R{_\mathrm{p}}$ and are ranked by transmission and emission spectroscopy metric (TSM and ESM, respectively) within each bin. In forming our target sample, we perform cuts for expected signal size and stellar brightness, to remove sub-optimal targets for JWST. Of the 194 targets in the resulting sample, 103 are unconfirmed TESS planet candidates, also known as TESS Objects of Interest (TOIs). We perform vetting and statistical validation analyses on these 103 targets to determine which are likely planets and which are likely false positives, incorporating ground-based follow-up from the TESS Follow-up Observation Program (TFOP) to aid the vetting and validation process. We statistically validate 23 TOIs, marginally validate 33 TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and leave the dispositions for 4 TOIs as inconclusive. 14 of the 103 TOIs were confirmed independently over the course of our analysis. We provide our final best-in-class sample as a community resource for future JWST proposals and observations. We intend for this work to motivate formal confirmation and mass measurements of each validated planet and encourage more detailed analysis of individual targets by the community., Comment: Submitted to AJ. Machine-readable versions of Tables 2 and 3 are included. 40 pages, 7 figures, 3 tables

This paper aims to shed some more light on one of the best known phenomena in the field of physics, the Doppler effect, in particular, on its classical version. Although, as mentioned, it is a phenomenon already described more than 150 years ago, the general case, even considering constant velocities, continues to be the subject of very recent bibliographical works which either show it incompletely or start from a not entirely accurate description of it. Here, general expressions of both frequency shift and aberration are obtained by geometrical description and, more interestingly, by describing the phase of the wave in the reference frame in which the propagating medium is at rest. The coherence between the two points of view permits to contrast the validity of the proposed results, and allows for a generalization of the resulting expressions for any reference frame, even those where the propagating medium is not still., Comment: 7 pages, 10 figures

In nature, limbless locomotion is adopted by a wide range of organisms at various length scales. Interestingly, undulatory, crawling and inching/looping gait constitutes a fundamental class of limbless locomotion and is often observed in many species such as caterpillars, earthworms, leeches, larvae, and \emph{C. elegans}, to name a few. In this work, we developed a computationally efficient 3D Finite Element (FE) based unified framework for the locomotion of limbless organisms on soft substrates. Muscle activity is simulated with a multiplicative decomposition of deformation gradient, which allows mimicking a broad range of locomotion patterns in 3D solids on frictional substrates. In particular, a two-field FE formulation based on positions and velocities is proposed. Governing partial differential equations are transformed into equivalent time-continuous differential-algebraic equations (DAEs). Next, the optimal locomotion strategies are studied in the framework of optimal control theory. We resort to adjoint-based methods and deduce the first-order optimality conditions, that yield a system of DAEs with two-point end conditions. Hidden symplectic structure and Symplectic Euler time integration of optimality conditions have been discussed. The resulting discrete first-order optimality conditions form a non-linear programming problem that is solved efficiently with the Forward Backwards Sweep Method. Finally, some numerical examples are provided to demonstrate the comprehensiveness of the proposed computational framework and investigate the energy-efficient optimal limbless locomotion strategy out of distinct locomotion patterns adopted by limbless organisms.

Deep learning approaches for jet tagging in high-energy physics are characterized as black boxes that process a large amount of information from which it is difficult to extract key distinctive observables. In this proceeding, we present an alternative to deep learning approaches, Boost Invariant Polynomials, which enables direct analysis of simple analytic expressions representing the most important features in a given task. Further, we show how this approach provides an extremely low dimensional classifier with a minimum set of features representing %effective discriminating physically relevant observables and how it consequently speeds up the algorithm execution, with relatively close performance to the algorithm using the full information.

Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quantum mechanics, quantum chromodynamics, molecular dynamics, computer vision, and imaging. In this paper, we present a general Equivariant Neural Network architecture capable of respecting the symmetries of the finite-dimensional representations of any reductive Lie Group G. Our approach generalizes the successful ACE and MACE architectures for atomistic point clouds to any data equivariant to a reductive Lie group action. We also introduce the lie-nn software library, which provides all the necessary tools to develop and implement such general G-equivariant neural networks. It implements routines for the reduction of generic tensor products of representations into irreducible representations, making it easy to apply our architecture to a wide range of problems and groups. The generality and performance of our approach are demonstrated by applying it to the tasks of top quark decay tagging (Lorentz group) and shape recognition (orthogonal group).

Vertex models have been extensively used for simulating the evolution of multicellular systems, and have given rise to important global properties concerning their macroscopic rheology or jamming transitions. These models are based on the definition of an energy functional, which fully determines the cellular response and conclusions. While two-dimensional vertex models have been widely employed, three-dimensional models are far more scarce, mainly due to the large amount of configurations that they may adopt and the complex geometrical transitions they undergo. We here investigate the shape of single and two-cells configurations as a function of the energy terms, and we study the dependence of the final shape on the model parameters: namely the exponent of the term penalising cell-cell adhesion and surface contractility. In single cell analysis, we deduce analytically the radius and limit values of the contractility for linear and quadratic surface energy terms, in 2D and 3D. In two-cells systems, we deduce the evolution of the aspect ratio. While in functionals with linear surface terms yield the same aspect ratio in 2D and 3D, the configurations when using quadratic surface terms are distinct. We relate our results with well-known solutions from capillarity theory, and verify our analytical findings with a three-dimensional vertex model., Comment: Article contents have been updated and modified substantially

Optimal Control Problems consist on the optimisation of an objective functional subjected to a set of Ordinary Differential Equations. In this work, we consider the effects on the stability of the numerical solution when this optimisation is discretised in time. In particular, we analyse a OCP with a quadratic functional and linear ODE, discretised with Mid-point and implicit Euler. We show that the numerical stability and the presence of numerical oscillations depends not only on the time-step size, but also on the parameters of the objective functional, which measures the amount of control input. Finally, we also show with an illustrative example that these results also carry over non-linear optimal control problems

$Q_\beta$ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the $\beta$-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify uncertainty and atomic number as the most relevant characteristics to predict $Q_\beta$ energies. Furthermore, physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.

NASA's Transiting Exoplanet Survey Satellite (TESS) mission promises to improve our understanding of hot Jupiters by providing an all-sky, magnitude-limited sample of transiting hot Jupiters suitable for population studies. Assembling such a sample requires confirming hundreds of planet candidates with additional follow-up observations. Here, we present twenty hot Jupiters that were detected using TESS data and confirmed to be planets through photometric, spectroscopic, and imaging observations coordinated by the TESS Follow-up Observing Program (TFOP). These twenty planets have orbital periods shorter than 7 days and orbit relatively bright FGK stars ($10.9 < G < 13.0$). Most of the planets are comparable in mass to Jupiter, although there are four planets with masses less than that of Saturn. TOI-3976 b, the longest period planet in our sample ($P = 6.6$ days), may be on a moderately eccentric orbit ($e = 0.18\pm0.06$), while observations of the other targets are consistent with them being on circular orbits. We measured the projected stellar obliquity of TOI-1937A b, a hot Jupiter on a 22.4 hour orbit with the Rossiter-McLaughlin effect, finding the planet's orbit to be well-aligned with the stellar spin axis ($|\lambda| = 4.0\pm3.5^\circ$). We also investigated the possibility that TOI-1937 is a member of the NGC 2516 open cluster, but ultimately found the evidence for cluster membership to be ambiguous. These objects are part of a larger effort to build a complete sample of hot Jupiters to be used for future demographic and detailed characterization work., Comment: 67 pages, 11 tables, 13 figures, 2 figure sets. Resubmitted to ApJS after revisions

Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally inefficient and lack interpretability. This is especially the case with jet tagging algorithms, where computational efficiency is crucial considering the large amounts of data produced by modern particle detectors. In this work, we present a novel, versatile and transparent framework for jet representation; invariant to Lorentz group boosts, which achieves high accuracy on jet tagging benchmarks while being orders of magnitudes faster to train and evaluate than other modern approaches for both supervised and unsupervised schemes.

In this work, we have assumed special structures for the charged and neutral mass matrices in the lepton sector, inspired by structures for the up and down quark mass matrices that result by assuming a certain number of symmetrical zeros in their entries named texture zeros. A prediction of the lepton mixing matrix results from the rotation matrices that diagonalize the mass matrices in the neutral and charged lepton sectors. The use of texture zeros reduces the number of spurious parameters to the minimal ones needed to explain observations i.e. charged lepton masses and neutrino oscillation parameters. Specifically, we have considered the case of five texture zeros and we have confronted the resulting lepton mixing matrices with current measurements in the neutrino sector. Finally, sensitivities to the independent parameters in the mixing predicted by the nonequivalent forms were studied using simulated events at the DUNE neutrino oscillation experiment. We have found that DUNE is sensitive to nonzero CP violation allowed in the models., Comment: 30 pages, 11 figures, 12 tables, v2: minor typos corrected, matches version accepted for publication in PRD

We report the discovery of ten short-period giant planets (TOI-2193A b, TOI-2207 b, TOI-2236 b, TOI-2421 b, TOI-2567 b, TOI-2570 b, TOI-3331 b, TOI-3540A b, TOI-3693 b, TOI-4137 b). All of the planets were identified as planet candidates based on periodic flux dips observed by NASA's Transiting Exoplanet Survey Satellite (TESS). The signals were confirmed to be from transiting planets using ground-based time-series photometry, high angular resolution imaging, and high-resolution spectroscopy coordinated with the TESS Follow-up Observing Program. The ten newly discovered planets orbit relatively bright F and G stars ($G < 12.5$,~$T_\mathrm{eff}$ between 4800 and 6200 K). The planets' orbital periods range from 2 to 10~days, and their masses range from 0.2 to 2.2 Jupiter masses. TOI-2421 b is notable for being a Saturn-mass planet and TOI-2567 b for being a ``sub-Saturn'', with masses of $0.322\pm 0.073$ and $0.195\pm 0.030$ Jupiter masses, respectively. In most cases, we have little information about the orbital eccentricities. Two exceptions are TOI-2207 b, which has an 8-day period and a detectably eccentric orbit ($e = 0.17\pm0.05$), and TOI-3693 b, a 9-day planet for which we can set an upper limit of $e < 0.052$. The ten planets described here are the first new planets resulting from an effort to use TESS data to unify and expand on the work of previous ground-based transit surveys in order to create a large and statistically useful sample of hot Jupiters., Comment: 44 pages, 15 tables, 21 figures; revised version submitted to AJ

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt., Comment: 19 pages, 7 tables, 6 figures