Search

Fusion excitation functions for $^{19}$O and $^{19}$F + $^{12}$C were simultaneously measured along with $^{20}$O + $^{12}$C using the active-target detector MuSIC@Indiana. Examination of the cross-section just above the barrier reveals a significant suppression of fusion for the $^{19}$O + $^{12}$C system at E$_{c.m.}$~12 MeV. This suppression may be due to the formation of a transient $^{18}$O-n-$^{12}$C molecule-like complex favored at a particular angular momentum. Mean-field level models are used as a reference to understand the measured fusion excitation functions., Comment: 4 figures; supplementary file

The physical and orbital parameters of Trans-Neptunian Objects (TNOs) provide valuable information about the Solar System's formation and evolution. In particular, the characterization of binaries provides insights into the formation mechanisms that may be playing a role at such large distances from the Sun. Studies show two distinct populations, and (38628) Huya occupies an intermediate position between the unequal-size binaries and those with components of roughly equal sizes. In this work, we predicted and observed three stellar occultation events by Huya. Huya and its satellite - S/2012 (38628) 1 - were detected during occultations in March 2021 and again in June 2023. Additionally, an attempt to detect Huya in February 2023 resulted in an additional single-chord detection of the secondary. A spherical body with a minimum diameter of D = 165 km can explain the three single-chord observations and provide a lower limit for the satellite size. The astrometry of Huya's system, as derived from the occultations and supplemented by observations from the Hubble Space Telescope and Keck Observatory, provided constraints on the satellite orbit and the mass of the system. Therefore, assuming the secondary is in an equatorial orbit around the primary, the limb fitting was constrained by the satellite orbit position angle. The system density, calculated by summing the most precise measurement of Huya's volume to the spherical satellite average volume, is $\rho_{1}$ = 1073 $\pm$ 66 kg m$^{-3}$. The density that the object would have assuming a Maclaurin equilibrium shape with a rotational period of 6.725 $\pm$ 0.01 hours is $\rho_{2}$ = 768 $\pm$ 42 kg m$^{-3}$. This difference rules out the Maclaurin equilibrium assumption for the main body shape., Comment: Manuscript accepted by Planetary Science Journal (PSJ)

SLAM is a foundational technique with broad applications in robotics and AR/VR. SLAM simulations evaluate new concepts, but testing on resource-constrained devices, such as VR HMDs, faces challenges: high computational cost and restricted sensor data access. This work proposes a sparse framework using mesh geometry projections as features, which improves efficiency and circumvents direct sensor data access, advancing SLAM research as we demonstrate in VR and through numerical evaluation., Comment: Accepted to ENPT XR at IEEE VR 2025

We introduce a new representation for 3D molecules based on their continuous atomic density fields. Using this representation, we propose a new model based on walk-jump sampling for unconditional 3D molecule generation in the continuous space using neural fields. Our model, FuncMol, encodes molecular fields into latent codes using a conditional neural field, samples noisy codes from a Gaussian-smoothed distribution with Langevin MCMC (walk), denoises these samples in a single step (jump), and finally decodes them into molecular fields. FuncMol performs all-atom generation of 3D molecules without assumptions on the molecular structure and scales well with the size of molecules, unlike most approaches. Our method achieves competitive results on drug-like molecules and easily scales to macro-cyclic peptides, with at least one order of magnitude faster sampling. The code is available at https://github.com/prescient-design/funcmol., Comment: NeurIPS 2024

A common assumption in the literature on information diffusion is that populations are homogeneous regarding individuals' information acquisition and propagation process: Individuals update their informed and actively communicating state either through imitation (simple contagion) or peer influence (complex contagion). Here, we study the impact of the mixing and placement of individuals with different update processes on how information cascades in social networks. We consider Simple Spreaders, which take information from a random neighbor and communicate it, and Threshold-based Spreaders, which require a threshold number of active neighbors to change their state to active communication. Even though, in a population made exclusively of Simple Spreaders, information reaches all elements of any (connected) network, we show that, when Simple and Threshold-based Spreaders coexist and occupy random positions in a social network, the number of Simple Spreaders systematically amplifies the cascades only in degree heterogeneous networks (exponential and scale-free). In random and modular structures, this cascading effect originated by Simple Spreaders only exists above a critical mass of these individuals. In contrast, when Threshold-based Spreaders are assorted preferentially in the nodes with a higher degree, the cascading effect of Simple Spreaders vanishes, and the spread of information is drastically impaired. Overall, the study highlights the significance of the strategic placement of different roles in networked structures, with Simple Spreaders driving widespread cascades in heterogeneous networks and Threshold-based Spreaders playing a critical regulatory role in information spread with a tunable effect based on the threshold value., Comment: 12 pages, 5 figures

This paper presents a study of the radiation hardness of the hadronic Tile Calorimeter of the ATLAS experiment in the LHC Run 2. Both the plastic scintillators constituting the detector active media and the wavelength-shifting optical fibres collecting the scintillation light into the photodetector readout are elements susceptible to radiation damage. The dedicated calibration and monitoring systems of the detector (caesium radioactive sources, laser and minimum bias integrator) allow to assess the response of these optical components. Data collected with these systems between 2015 and 2018 are analysed to measure the degradation of the optical instrumentation across Run 2. Moreover, a simulation of the total ionising dose in the calorimeter is employed to study and model the degradation profile as a function of the exposure conditions, both integrated dose and dose rate. The measurement of the relative light output loss in Run 2 is presented and extrapolations to future scenarios are drawn based on current data. The impact of radiation damage on the cell response uniformity is also analysed., Comment: 31 pages in total, 18 figures, 2 tables, submitted to JINST

In this work, we introduce the minimal set of leptoquarks into the 3-3-1 model with right-handed neutrinos, capable of generating radiative masses for active neutrinos. As a main consequence, the standard neutrinos acquire small Majorana masses at the one-loop level, while right-handed (sterile) neutrinos obtain small Majorana masses at the two-loop level, naturally making them light particles as well. Additionally, we discuss the viability of this scenario and several other interesting phenomenological consequences, including its impact on $B$-meson physics and rare Higgs decays, both of which are also induced by the leptoquarks.

The axioms of Quantum Mechanics require that the hamiltonian of any closed system is self-adjoint, so that energy levels are real and time evolution preserves probability. On the other hand, non-hermitian hamiltonians with ${\cal{PT}}$-symmetry can have both real spectra and unitary time evolution. In this paper, we study in detail a pair of quantum oscillators coupled by an imaginary bilinear term, both in quantum mechanics and in quantum field theory. We discuss explicitly how such hamiltonians lead to perfectly sound physical theories with real spectra and unitary time evolution, in spite of their non-hermiticity. We also analyze two-point correlation functions and their associated K\"allen-Lehmann representation. In particular, we discuss the intimate relation between positivity violation of the spectral functions and the non-observability of operators in a given correlation function. Finally, we conjecture that positivity violation of some spectral functions of the theory could be a generic sign of the existence of complex pairs of energy eigenvalues (i.e., a ${\cal{PT}}$-broken phase) somewhere in its parameter space., Comment: 15 pages, no figures

We explore, for the first time, {\textit{neutral-current}} events at long-baseline experiments to constrain vector and axial-vector neutrino non-standard interactions (NSI) with quarks. We leverage the flavor dependence of NSIs to perform an oscillation analysis in the neutral-current channel. We first introduce a framework to parametrize the effect of NSI on the cross section. Then, as an example, we analyze NOvA neutral-current data which provides significantly improved constraints on the axial-vector NSI parameters $\varepsilon_{\mu\mu}^A,~\varepsilon_{\tau\tau}^A$ and $\varepsilon_{e\mu}^A$. This is highly complementary to constraints from SNO data, which, differently from long-baseline neutral current data, is not sensitive to isospin conserving NSIs $\varepsilon^A_u=\varepsilon^A_d$. Additionally, we disfavor large values of the diagonal vectorial NSI $\varepsilon_{\mu\mu}^V$ and \varepsilon_{\tau\tau}^V$ which originate from the LMA-Dark solution. We also highlight the complementarity between NSI searches at oscillation experiments using charged current and neutral current channels.

Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to generate predictive maps of stable regions surrounding a hypothetical planet. The approach can also be extended to planet-satellite systems, planetary ring systems, and other similar configurations. A dataset was generated using 10^5 numerical simulations, each incorporating nine orbital features for the planet and a test particle in a star-planet-test particle system. The simulations were classified as stable or unstable based on stability criteria, requiring particles to remain stable over a timespan equivalent to 10,000 orbital periods of the planet. Various ML algorithms were tested and fine-tuned through hyperparameter optimization to determine the most effective predictive model. Tree-based algorithms showed comparable accuracy in performance. The best-performing model, using the Extreme Gradient Boosting (XGBoost) algorithm, achieved an accuracy of 98.48%, with 94% recall and precision for stable particles and 99% for unstable particles. ML algorithms significantly reduce the computational time required for three-body simulations, operating approximately 100,000 times faster than traditional numerical methods. Predictive models can generate entire stability maps in less than a second, compared to the days required by numerical simulations. The results from the trained ML models will be made accessible through a public web interface, enabling broader scientific applications., Comment: 12 pages, 13 figures, Accepted for publication in Astronomy & Astrophysics

We perform a global analysis of most up-to-date solar neutrino data and KamLAND reactor antineutrino data in the framework of the 3+1 sterile neutrino mixing scenario (invoked to explain the results of the Gallium source experiments) with the aim of quantifying the dependence of the (in)compatibility of the required mixing with assumptions on the initial fluxes. The analysis of solar data is performed in two alternative ways: using the flux predicted by the latest standard solar models, and in a model independent approach where the solar fluxes are also determined by the fit. The dependence on the normalization of the capture rate in the solar Gallium experiments is also quantified. Similarly, in the KamLAND analysis we consider both the case where the reactor flux normalization is assumed to be known \textit{a priori}, as well as a normalization free case which relies solely on available neutrino data. Using a parameter goodness of fit test, we find that in most cases the compatibility between Gallium and solar+KamLAND data only occur at the $3\sigma$ level or higher. We also discuss the implications of enforcing better compatibility by tweaking the mechanism for the energy production in the Sun.

In this work we propose a general nonmonotone line-search method for nonconvex multi\-objective optimization problems with convex constraints. At the $k$th iteration, the degree of nonmonotonicity is controlled by a vector $\nu_{k}$ with nonnegative components. Different choices for $\nu_{k}$ lead to different nonmonotone step-size rules. Assuming that the sequence $\left\{\nu_{k}\right\}_{k\geq 0}$ is summable, and that the $i$th objective function has H\"older continuous gradient with smoothness parameter $\theta_i \in(0,1]$, we show that the proposed method takes no more than $\mathcal{O}\left(\epsilon^{-\left(1+\frac{1}{\theta_{\min}}\right)}\right)$ iterations to find a $\epsilon$-approximate Pareto critical point for a problem with $m$ objectives and $\theta_{\min}= \min_{i=1,\dots, m} \{\theta_i\}$. In particular, this complexity bound applies to the methods proposed by Drummond and Iusem (Comput. Optim. Appl. 28: 5--29, 2004), by Fazzio and Schuverdt (Optim. Lett. 13: 1365--1379, 2019), and by Mita, Fukuda and Yamashita (J. Glob. Optim. 75: 63--90, 2019). The generality of our approach also allows the development of new methods for multiobjective optimization. As an example, we propose a new nonmonotone step-size rule inspired by the Metropolis criterion. Preliminary numerical results illustrate the benefit of nonmonotone line searches and suggest that our new rule is particularly suitable for multiobjective problems in which at least one of the objectives has many non-global local minimizers.

Typically, point cloud encoders allocate a similar bitrate for geometry and attributes (usually RGB color components) information coding. This paper reports a quality study considering different coding bitrate tradeoff between geometry and attributes. A set of five point clouds, representing different characteristics and types of content was encoded with the MPEG standard Geometry Point Cloud Compression (G-PCC), using octree to encode geometry information, and both the Region Adaptive Hierarchical Transform and the Prediction Lifting transform for attributes. Furthermore, the JPEG Pleno Point Cloud Verification Model was also tested. Five different attributes/geometry bitrate tradeoffs were considered, notably 70%/30%, 60%/40%, 50%/50%, 40%/60%, 30%/70%. Three point cloud objective metrics were selected to assess the quality of the reconstructed point clouds, notably the PSNR YUV, the Point Cloud Quality Metric, and GraphSIM. Furthermore, for each encoder, the Bjonteegaard Deltas were computed for each tradeoff, using the 50%/50% tradeoff as a reference. The reported results indicate that using a higher bitrate allocation for attribute encoding usually yields slightly better results.

We present an updated global analysis of neutrino oscillation data as of September 2024. The parameters $\theta_{12}$, $\theta_{13}$, $\Delta m^2_{21}$, and $|\Delta m^2_{3\ell}|$ ($\ell = 1,2$) are well-determined with relative precision at $3\sigma$ of about 13\%, 8\%, 15\%, and 6\%, respectively. The third mixing angle $\theta_{23}$ still suffers from the octant ambiguity, with no clear indication of whether it is larger or smaller than $45^\circ$. The determination of the leptonic CP phase $\delta_{CP}$ depends on the neutrino mass ordering: for normal ordering the global fit is consistent with CP conservation within $1\sigma$, whereas for inverted ordering CP-violating values of $\delta_{CP}$ around $270^\circ$ are favored against CP conservation at more than $3.6\sigma$. While the present data has in principle $2.5$--$3\sigma$ sensitivity to the neutrino mass ordering, there are different tendencies in the global data that reduce the discrimination power: T2K and NOvA appearance data individually favor normal ordering, but they are more consistent with each other for inverted ordering. Conversely, the joint determination of $|\Delta m^2_{3\ell}|$ from global disappearance data prefers normal ordering. Altogether, the global fit including long-baseline, reactor and IceCube atmospheric data results into an almost equally good fit for both orderings. Only when the $\chi^2$ table for atmospheric neutrino data from Super-Kamiokande is added to our $\chi^2$, the global fit prefers normal ordering with $\Delta\chi^2 = 6.1$. We provide also updated ranges and correlations for the effective parameters sensitive to the absolute neutrino mass from $\beta$-decay, neutrinoless double-beta decay, and cosmology.

Theories based on the Ricci and the trace of the energy-momentum tensor, or the short name Ricci-trace-based (RTB) theories, represent a gravitational approach based on the Ricci scalar $R$, and the trace of the energy-momentum tensor $g^{\mu\nu}T_{\mu\nu} = T$. This theory fits into gravitational models of the type $f(R, T) = R + f(T)$, where $f(T)$ is an arbitrary function of $T$. In this study, we explore a cosmological scenario within the context of RTB models, investigating in detail the cosmological consequences of the coupling between matter and geometry. In order to address this issue, we propose a toy model in which the non-relativistic cosmological neutrino transition plays a role in the cosmic evolution since the effective total energy-momentum tensor trace is affected in this process. We raise questions about the coupling of neutrinos with geometry during this transition, providing a detailed analysis of how RTB gravity deals with this phenomenon and the impact of neutrinos on cosmological dynamics. In summary, we show that the coupling of cosmological neutrinos with $T$ dependent cosmologies is severely challenged.

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from S\~ao Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80\% compared to an exact method, while achieving optimal solutions.

When microspheres are illuminated by tightly focused vortex beams, they can be trapped in a non-equilibrium steady state where they orbit around the optical axis. By using the Mie-Debye theory for optical tweezers, we demonstrate that the orbital period strongly depends on the particle's chirality index. Taking advantage of such sensitivity, we put forth a method to experimentally characterize with high precision the chiroptical response of individual optically trapped particles. The method allows for an enhanced precision at least one order of magnitude larger than that of similar existing enantioselective approaches. It is particularly suited to probe the chiroptical response of individual particles, for which light-chiral matter interactions are typically weak.

In recent years, deep learning techniques have been used to develop sign language recognition systems, potentially serving as a communication tool for millions of hearing-impaired individuals worldwide. However, there are inherent challenges in creating such systems. Firstly, it is important to consider as many linguistic parameters as possible in gesture execution to avoid ambiguity between words. Moreover, to facilitate the real-world adoption of the created solution, it is essential to ensure that the chosen technology is realistic, avoiding expensive, intrusive, or low-mobility sensors, as well as very complex deep learning architectures that impose high computational requirements. Based on this, our work aims to propose an efficient sign language recognition system that utilizes low-cost sensors and techniques. To this end, an object detection model was trained specifically for detecting the interpreter's face and hands, ensuring focus on the most relevant regions of the image and generating inputs with higher semantic value for the classifier. Additionally, we introduced a novel approach to obtain features representing hand location and movement by leveraging spatial information derived from centroid positions of bounding boxes, thereby enhancing sign discrimination. The results demonstrate the efficiency of our handcrafted features, increasing accuracy by 7.96% on the AUTSL dataset, while adding fewer than 700 thousand parameters and incurring less than 10 milliseconds of additional inference time. These findings highlight the potential of our technique to strike a favorable balance between computational cost and accuracy, making it a promising approach for practical sign language recognition applications., Comment: 28 pages, 12 figures, submitted to Image and Vision Computing Journal

We theoretically investigate the effects of criticality and multifractal states in a one-dimensional Aubry-Andre-Harper model coupled to electromagnetic cavities. We focus on two specific cases where the phonon frequencies are $\omega_{0}=1$ and $\omega_{0}=2$, respectively. Phase transitions are analyzed using both the average and minimum inverse participation ratio to identify metallic, fractal, and insulating states. We provide numerical evidence to show that the presence of the optical cavity induces a critical, intermediate phase in between the extended and localized phases, hence drastically modifying the traditional transport phase diagram of the Aubry-Andre-Harper model, in which critical states can only exist at the well-defined metal-insulator critical point. We also investigate the probability distribution of the inverse participation ratio and conduct a multifractal analysis to characterize the nature of the critical phase, in which we show that extended, localized, and fractal eigenstates coexist. Altogether our findings reveal the pivotal role that the coupling to electromagnetic cavities plays in tailoring critical transport phenomena at the microscopic level of the eigenstates., Comment: 10 pages, 10 Figures

We discuss the exclusive lepton flavor violating (LFV) decays modes based on $b\to d\ell_i\ell_j$ and $b\to s\ell_i\ell_j$ by considering the ground state mesons and baryons. After spelling out the expressions for such decay rates in a low energy effective theory which includes generic contributions arising from physics beyond the Standard Model (BSM), we show that the experimental bounds on meson decays can be used to bound the corresponding modes involving baryons. We find, for example, $\mathcal{B}(\Lambda_b\to \Lambda\mu\tau)\lesssim 4\times 10^{-5}$. We also consider two specific models and constrain the relevant LFV couplings by using the low energy observables. In the first model we assume the Higgs mediated LFV and find the resulting decay rates to be too small to be experimentally detectable. We also emphasize that the regions favored by the bounds $\mathcal{B}(h\to\mu\tau)^\mathrm{Atlas}$ and $\mathcal{B}(h\to e\tau)^\mathrm{Atlas}$ are not compatible with $\mathcal{B}(\mu\to e\gamma)^\mathrm{MEG}$ to $1\sigma$. In the second model we assume LFV mediated by a heavy $Z'$ boson and find that the corresponding $b$-hadron branching fractions can be $\mathcal{O}(10^{-6})$, thus possibly within experimental reach at LHCb and Belle~II.

The increasing need to analyse large volumes of data has led to the development of Symbolic Data Analysis as a promising field to tackle the data challenges of our time. New data types, such as interval-valued data, have brought fresh theoretical and methodological problems to be solved. In this paper, we derive explicit formulas for computing the Mallows' distance, also known as $L_2$ Wasserstein distance, between two \textit{p}-dimensional intervals, using information regarding the distribution of the microdata. We establish this distance as a Mahalanobis' distance between two 2\textit{p}-dimensional vectors. Our comprehensive analysis leads to the generalisation of the definitions of the expected value and covariance matrix of an interval-valued random vector. These novel results bring theoretical support and interpretability to state-of-the-art contributions. Additionally, we discuss real examples that illustrate how we can model different levels of available information on the microdata, leading to proper estimates of the measures of location and association.

We present NEBULA, the first latent 3D generative model for scalable generation of large molecular libraries around a seed compound of interest. Such libraries are crucial for scientific discovery, but it remains challenging to generate large numbers of high quality samples efficiently. 3D-voxel-based methods have recently shown great promise for generating high quality samples de novo from random noise (Pinheiro et al., 2023). However, sampling in 3D-voxel space is computationally expensive and use in library generation is prohibitively slow. Here, we instead perform neural empirical Bayes sampling (Saremi & Hyvarinen, 2019) in the learned latent space of a vector-quantized variational autoencoder. NEBULA generates large molecular libraries nearly an order of magnitude faster than existing methods without sacrificing sample quality. Moreover, NEBULA generalizes better to unseen drug-like molecules, as demonstrated on two public datasets and multiple recently released drugs. We expect the approach herein to be highly enabling for machine learning-based drug discovery. The code is available at https://github.com/prescient-design/nebula

We investigate Eigen's model for the evolution of the genetic code of microorganisms using a novel method based on population dynamics analysis. This model, for a given number of offspring, determines long-term survival as a function of the "genetic" information length and copy error probability. There exists a maximum threshold for the quantity of information that can be consistently preserved through the process of evolution within a population of perfectly replicating sequences, meaning no errors are allowed. With our formula, we expand upon the traditional error threshold formula of Eigen's theory and introduce a new expression for general cases where the self-reproduction process allows up to any integer number of copying errors per digit per replication step., Comment: 5 pages, 1 figure

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.

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform., Comment: 14 pages, 13 figures

This paper deals with the control of a kind of turbulent flows. We consider a simplified k-e model with distributed controls, locally supported in space. We proof that the system is partially locally null-controllable, in the sense that the velocity field can be driven exactly to zero if the initial state is small enough. The proof relies on an argument where we have concatenated several techniques: fixed-point formulation, linearization, energy and Carleman estimates, local inversion, etc. Ths result can be viewed as a nontrivial step towards the control of turbulent fluids., Comment: Partial null controllability for Nonlinear parabolic PDEs using Carleman estimates in Turbulence models

Random forests are among the most famous algorithms for solving classification problems, in particular for large-scale data sets. Considering a set of labeled points and several decision trees, the method takes the majority vote to classify a new given point. In some scenarios, however, labels are only accessible for a proper subset of the given points. Moreover, this subset can be non-representative, e.g., due to collection bias. Semi-supervised learning considers the setting of labeled and unlabeled data and often improves the reliability of the results. In addition, it can be possible to obtain additional information about class sizes from undisclosed sources. We propose a mixed-integer linear optimization model for computing a semi-supervised random forest that covers the setting of labeled and unlabeled data points as well as the overall number of points in each class for a binary classification. Since the solution time rapidly grows as the number of variables increases, we present some problem-tailored preprocessing techniques and an intuitive branching rule. Our numerical results show that our approach leads to a better accuracy and a better Matthews correlation coefficient for biased samples compared to random forests by majority vote, even if only few labeled points are available., Comment: 16 pages,3 figures. arXiv admin note: text overlap with arXiv:2401.09848, arXiv:2303.12532

The magnetic moment of the muon can receive significant two-loop contributions from a light pseudoscalar. Notably, the spectrum of scalars of 3-3-1 models include one pseudoscalar. However this scalar spectrum inevitably gives rise to flavor-changing neutral current (FCNC) processes. In this study, we examine, within the 3-3-1 model with right-handed neutrinos, whether such spectrum of scalars can account for the anomalous magnetic moment of the muon, considering the constraints imposed by $B$-meson decays, meson mixing, and invisible Higgs decays. Our principal finding reveals that a pseudoscalar with mass around 66 GeV and $\tan \beta =58$ can account for the $g-2$ anomaly without conflicting with flavor physics.

We quantify the effects of light spin-zero particles with pseudoscalar couplings to leptons and scalar couplings to nucleons on the evolution of solar neutrinos. In this scenario the matter potential sourced by the nucleons in the Sun's matter gives rise to spin precession of the relativistic neutrino ensemble. As such the effects in the solar observables are different if neutrinos are Dirac or Majorana particles. For Dirac neutrinos the spin-flavour precession results into left-handed neutrino to right-handed neutrino (i.e., active-sterile) oscillations, while for Majorana neutrinos it results into left-handed neutrino to right-handed antineutrino (i.e., active-active) oscillations. In both cases this leads to distortions in the solar neutrino spectrum which we use to derive constraints on the allowed values of the mediator mass and couplings via a global analysis of the solar neutrino data. In addition for Majorana neutrinos spin-flavour precession results into a potentially observable flux of solar electron antineutrinos at the Earth which we quantify and constrain with the existing bounds from Borexino and KamLAND., Comment: 22 pages, 5 figures, 1 table. Matches published version

We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures. Our approach represents molecules as 3D atomic density grids and leverages a 3D voxel-denoising network for learning and generation. We extend the neural empirical Bayes formalism (Saremi & Hyvarinen, 2019) to the conditional setting and generate structure-conditioned molecules with a two-step procedure: (i) sample noisy molecules from the Gaussian-smoothed conditional distribution with underdamped Langevin MCMC using the learned score function and (ii) estimate clean molecules from the noisy samples with single-step denoising. Compared to the current state of the art, our model is simpler to train, significantly faster to sample from, and achieves better results on extensive in silico benchmarks -- the generated molecules are more diverse, exhibit fewer steric clashes, and bind with higher affinity to protein pockets. The code is available at https://github.com/genentech/voxbind/.

The estimation of relative motion between spacecraft increasingly relies on feature-matching computer vision, which feeds data into a recursive filtering algorithm. Kalman filters, although efficient in noise compensation, demand extensive tuning of system and noise models. This paper introduces FlexKalmanNet, a novel modular framework that bridges this gap by integrating a deep fully connected neural network with Kalman filter-based motion estimation algorithms. FlexKalmanNet's core innovation is its ability to learn any Kalman filter parameter directly from measurement data, coupled with the flexibility to utilize various Kalman filter variants. This is achieved through a notable design decision to outsource the sequential computation from the neural network to the Kalman filter variant, enabling a purely feedforward neural network architecture. This architecture, proficient at handling complex, nonlinear features without the dependency on recurrent network modules, captures global data patterns more effectively. Empirical evaluation using data from NASA's Astrobee simulation environment focuses on learning unknown parameters of an Extended Kalman filter for spacecraft pose and twist estimation. The results demonstrate FlexKalmanNet's rapid training convergence, high accuracy, and superior performance against manually tuned Extended Kalman filters.

In this work we study, both theoretically and experimentally, the tomography of the spatial structure of light. We consider a a spatially structured light mode that is specified by coefficients with respect to some basis, such as the Laguerre-Gaussian or Hermite-Gaussian modes. Our aim is to retrieve these coefficients given only spatially resolved intensity measurements. We explore a mathematical analogy between this problem and quantum state tomography, which allows us to readily utilize many techniques of this well established field. In order to demonstrate the robustness of our approach, we apply it to several different experimental scenarios: intense light beams in a variety of basis, the photocount regime, and beams obstructed by obstacles. The method is also extremely fast, being able to perform the tomography of a 6-dimensional state at around 50$\mu$s. The work here presented is expected to help the development of characterization techniques in the field of structured light and in its application for both classical and quantum information protocols.

Microbial contamination poses a threat to both the preservation of library and archival collections and the health of staff and users. This study investigated the microbial communities and potential health risks associated with the UNESCO-classified Norwegian Sea Trade Archive (NSTA) collection exhibiting visible microbial colonization and staff health concerns. Dust samples from book surfaces and the storage environment were analysed using culturing methods, qPCR, Next Generation Sequencing, and mycotoxin, cytotoxicity and azole resistance assays. Penicillium sp., Aspergillus sp., and Cladosporium sp. were the most common fungi identified, with some potentially toxic species like Stachybotrys sp., Toxicladosporium sp. and Aspergillus section Fumigati. Fungal resistance to azoles was not detected. Only one mycotoxin, sterigmatocystin, was found in a heavily contaminated book. Dust extracts from books exhibited moderate to high cytotoxicity on human lung cells, suggesting a potential respiratory risk. The collection had higher contamination levels compared to the storage environment, likely due to improved storage conditions. Even though, overall low contamination levels were obtained, which might be underestimated due to the presence of salt (from cod preservation) that could have interfered with the analyses. This study underlines the importance of monitoring microbial communities and implementing proper storage measures to safeguard cultural heritage and staff well-being., Comment: 17 pages, 7 figures, 2 tables

Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection and, consequently, early treatment. Any development for detection or perdition this kind of cancer is important for a better healthy life. Many studies focus on a model with high accuracy in cancer prediction, but sometimes accuracy alone may not always be a reliable metric. This study implies an investigative approach to studying the performance of different machine learning algorithms based on boosting to predict breast cancer focusing on the recall metric. Boosting machine learning algorithms has been proven to be an effective tool for detecting medical diseases. The dataset of the University of California, Irvine (UCI) repository has been utilized to train and test the model classifier that contains their attributes. The main objective of this study is to use state-of-the-art boosting algorithms such as AdaBoost, XGBoost, CatBoost and LightGBM to predict and diagnose breast cancer and to find the most effective metric regarding recall, ROC-AUC, and confusion matrix. Furthermore, our study is the first to use these four boosting algorithms with Optuna, a library for hyperparameter optimization, and the SHAP method to improve the interpretability of our model, which can be used as a support to identify and predict breast cancer. We were able to improve AUC or recall for all the models and reduce the False Negative for AdaBoost and LigthGBM the final AUC were more than 99.41\% for all models., Comment: 9 pages, 16 figures

We study scalar cosmological perturbations in $f(R, T)$ modified gravity theories being $T$ the trace of the energy-momentum tensor. We provide detailed equations for the matter energy density contrast. We solve then numerically to promote a comparison with available large scale structure (LSS) formation observational data on $f \sigma_8$ and also addressing the $S_8$ tension. We identify $f(R,T)$ models that lead either to growth enhancement or suppression. Since recent results in the literature indicate a preference for the latter feature, this type of analysis is quite useful to select viable modifications of gravity. We studied class of such $f(R,T)$ models are either ruled out or severely restricted., Comment: V3 matches published verion in Phys. Dark Univ. This version also revisits the conclusion of our previous work in Phys. Dark Univ. 43 (2024) 101401

We investigate theoretically and experimentally the optical second harmonic generation (SHG) with a twisted Gaussian Schell model (TGSM) beam as the fundamental field. We use Type-II phase matching and analyze the cross spectral density (CSD) of the SHG output beam when the input fundamental is prepared with a TGSM structure. We analyze two synthetization methods for preparing the TGSM fundamental beam and we find that for one method the SHG is also a TGSM beam. For the other method, we find that the SHG is not a TGSM beam and presents an anomalous CSD possessing a dip instead of a peak in the transverse spatial structure. Moreover, we show that the dip depth is directly related to the twisted phase parameter, being absent for a non twisted GSM beam. Our results show that the SHG from a fundamental TGSM beam can result in a doubled frequency TGSM or in a non-TGSM beam depending on the synthetization method.

Palms play an outsized role in tropical forests and are important resources for humans and wildlife. A central question in tropical ecosystems is understanding palm distribution and abundance. However, accurately identifying and localizing palms in geospatial imagery presents significant challenges due to dense vegetation, overlapping canopies, and variable lighting conditions in mixed-forest landscapes. Addressing this, we introduce PalmProbNet, a probabilistic approach utilizing transfer learning to analyze high-resolution UAV-derived orthomosaic imagery, enabling the detection of palm trees within the dense canopy of the Ecuadorian Rainforest. This approach represents a substantial advancement in automated palm detection, effectively pinpointing palm presence and locality in mixed tropical rainforests. Our process begins by generating an orthomosaic image from UAV images, from which we extract and label palm and non-palm image patches in two distinct sizes. These patches are then used to train models with an identical architecture, consisting of an unaltered pre-trained ResNet-18 and a Multilayer Perceptron (MLP) with specifically trained parameters. Subsequently, PalmProbNet employs a sliding window technique on the landscape orthomosaic, using both small and large window sizes to generate a probability heatmap. This heatmap effectively visualizes the distribution of palms, showcasing the scalability and adaptability of our approach in various forest densities. Despite the challenging terrain, our method demonstrated remarkable performance, achieving an accuracy of 97.32% and a Cohen's kappa of 94.59% in testing., Comment: 11 pages, 6 figures, and 1 table, to appear in ACMSE 2024