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A convex cone is said to be projectionally exposed (p-exposed) if every face arises as a projection of the original cone. It is known that, in dimension at most four, the intersection of two p-exposed cones is again p-exposed. In this paper we construct two p-exposed cones in dimension $5$ whose intersection is not p-exposed. This construction also leads to the first example of an amenable cone that is not projectionally exposed, showing that these properties, which coincide in dimension at most $4$, are distinct in dimension $5$. In order to achieve these goals, we develop a new technique for constructing arbitrarily tight inner convex approximations of compact convex sets with desired facial structure. These inner approximations have the property that all proper faces are extreme points, with the exception of a specific exposed face of the original set., Comment: 21 pages, 2 figures

Non-convex, nonlinear gas network optimization models are used to determine the feasibility of flows on existing networks given constraints on network flows, gas mixing, and pressure loss along pipes. This work improves two existing gas network models: a discrete mixed-integer nonlinear program (MINLP) that uses binary variables to model positive and negative flows, and a continuous nonlinear program (NLP) that implements complementarity constraints with continuous variables. We introduce cuts to expedite the MINLP and we formulate two new pressure loss models that leverage the flow-splitting variables: one that is highly accurate and another that is simpler but less accurate. In computational tests using the global solver BARON our cuts and accurate pressure loss improves: (1) the average run time of the MINLP by a factor of 35, (2) the stability of the MINLP by solving every tested instance within 2.5 minutes (the baseline model timed out on 25% of instances), (3) the stability of the NLP by solving more instances than the baseline. Our simpler pressure loss model further improved run times in the MINLP (by a factor of 48 versus the baseline MINLP), but was unstable in the context of the NLP., Comment: 26 pages, 4 figures

A convex cone $\mathcal{K}$ is said to be homogeneous if its group of automorphisms acts transitively on its relative interior. Important examples of homogeneous cones include symmetric cones and cones of positive semidefinite (PSD) matrices that follow a sparsity pattern given by a homogeneous chordal graph. Our goal in this paper is to elucidate the facial structure of homogeneous cones and make it as transparent as the faces of the PSD matrices. We prove that each face of a homogeneous cone $\mathcal{K}$ is mapped by an automorphism of $\mathcal{K}$ to one of its finitely many so-called principal faces. Furthermore, constructing such an automorphism can be done algorithmically by making use of a generalized Cholesky decomposition. Among other consequences, we give a proof that homogeneous cones are projectionally exposed, which strengthens the previous best result that they are amenable. Using our results, we will carefully analyze the facial structure of cones of PSD matrices satisfying homogeneous chordality and discuss consequences for the corresponding family of PSD completion problems., Comment: 29 pages. Comments welcome

For the first time, we use relativistic mean-field (RMF) approximation with density-dependent couplings, adjusted by the DDME2 parameterization, to investigate the effects of dark matter on supernova remnants. We calculate the nuclear equation of state for nuclear and dark matter separately, under the thermodynamic conditions related to the evolution of supernova remnants. A mirrored model is adopted for dark matter, and its effect on remnant matter is studied using a two-fluid scenario. At each stage of the remnant evolution, we assume that dark and ordinary matter have the same entropy and lepton fraction, and a fixed proportion of dark matter mass fraction is added to the stellar matter to observe its effects on some microscopic and macroscopic properties of the star. We observe that dark matter in the remnant core reduces the remnant's maximum mass, radius, and tidal deformability. Moreover, dark matter heats the remnant matter and alters particle distributions, thereby decreasing its isospin asymmetry and increasing the sound speed through the matter., Comment: 15 pages, 6 figures

Magnetic fields are ubiquitous across different physical systems of current interest; from the early Universe, compact astrophysical objects and heavy-ion collisions to condensed matter systems. A proper treatment of the effects produced by magnetic fields during the dynamical evolution of these systems, can help to understand observables that otherwise show a puzzling behavior. Furthermore, when these fields are comparable to or stronger than \Lambda_QCD, they serve as excellent probes to help elucidate the physics of strongly interacting matter under extreme conditions of temperature and density. In this work we provide a comprehensive review of recent developments on the description of QED and QCD systems where magnetic field driven effects are important. These include the modification of meson static properties such as masses and form factors, the chiral magnetic effect, the description of anomalous transport coefficients, superconductivity in extreme magnetic fields, the properties of neutron stars, the evolution of heavy-ion collisions, as well as effects on the QCD phase diagram. We describe recent theory and phenomenological developments using effective models as well as LQCD methods. The work represents a state-of-the-art review of the field, motivated by presentations and discussions during the "Workshop on Strongly Interacting Matter in Strong Electromagnetic Fields" that took place in the European Centre for Theoretical Studies in Nuclear Physics and Related Areas (ECT*) in the city of Trento, Italy, September 25-29, 2023., Comment: 325 pages-long review of recent topics of interest in the field of magnetic field effects on QED and QCD matter. To be susbmitted to PNPP

We study the effects of hyperons, delta baryons, and quark matter phase transitions on f-mode oscillations in neutron stars. Using the density-dependent relativistic mean-field model (DDME2) for the hadronic phase and the density-dependent quark mass (DDQM) model for the quark phase, we construct hadronic and hybrid equations of state (EoSs) consistent with astrophysical constraints. Including hyperons and delta baryons soften the EoS, reducing maximum masse, while phase transition to the quark matter further softens the EoS, decreasing the speed of sound and hence the maximum mass. f-mode frequencies, calculated using both the Cowling approximation and the general relativistic (GR) frameworks, reveal a significant overestimation by the Cowling method of about 10-30%, with discrepancies decreasing for more massive stars. We derive universal relations connecting the frequencies of the f-mode to the average density, compactness, and tidal deformability, finding significant deviations due to hyperons and delta baryons. Empirical relations for mass-scaled and radius-scaled frequencies are also provided, highlighting the importance of GR calculations for accurate modeling. These findings highlight the potential of gravitational wave asteroseismology to constrain neutron star EoSs and internal structure., Comment: 18 pages, 13 figures, 5 tables

Ram pressure stripped galaxies are rare cases of environmental evolution in action. However, our ability to understand these galaxies is limited by the small number of identified galaxies experiencing ram pressure stripping (RPS). Our aim is to explore the efficacy of citizen science classifications in identifying ram pressure stripped galaxies, and use this to aid in motivating new samples of ram pressure stripped candidates. We compile a sample of over 200 known ram pressure stripped galaxies from existing literature, with morphological classifications obtained from Galaxy Zoo. We compare these galaxies with magnitude and redshift-matched comparison cluster and field galaxies. Additionally, we create a sample of SDSS cluster galaxies, with morphological classifications similar to known ram pressure stripped galaxies, and compare the fraction of potential new RPS candidates against control samples. We find that ram pressure stripped galaxies exhibit a higher proportion of odd and irregular morphological classifications compared to field and cluster comparison samples. This trend is particularly pronounced in galaxies displaying strong optical ram pressure stripping features, but absent from galaxies with only radio tails. We find that SDSS galaxies with Galaxy Zoo classifications consistent with the known RPS galaxies have a higher fraction of visible ram pressure stripping features ($19\%$) compared with other cluster galaxies ($12\%$) when classified by experts. We identify 101 new ram pressure stripping candidate galaxies through these expert classifications. We demonstrate that indirect morphological classifications from citizen science projects can increase the efficiency in which new stripping candidates are found. Projects such as Galaxy Zoo can aid in the identification of ram pressure stripped galaxies that are key to understanding galaxy evolution in clusters., Comment: 20 pages, 11 figures, accepted for publication in Astronomy and Astrophysics

Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with a classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si$_{11}$ with 4 Al (4Al@Si$_{11}$) and the results indicate the QAL method is able to find the optimum 4Al@Si$_{11}$ structure. The aim of this work is to present the QAL method -- formulated in a noise-free quantum computing framework -- for automatic structural determination of doped nanoparticles and materials defects., Comment: 22 pages,6 figures

Artificial Intelligence (AI) has driven innovations and created new opportunities across various sectors. However, leveraging domain-specific knowledge often requires automated tools to design and configure models effectively. In the case of Deep Neural Networks (DNNs), researchers and practitioners usually resort to Neural Architecture Search (NAS) approaches, which are resource- and time-intensive, requiring the training and evaluation of numerous candidate architectures. This raises sustainability concerns, particularly due to the high energy demands involved, creating a paradox: the pursuit of the most effective model can undermine sustainability goals. To mitigate this issue, zero-cost proxies have emerged as a promising alternative. These proxies estimate a model's performance without the need for full training, offering a more efficient approach. This paper addresses the challenges of model evaluation by automatically designing zero-cost proxies to assess DNNs efficiently. Our method begins with a randomly generated set of zero-cost proxies, which are evolved and tested using the NATS-Bench benchmark. We assess the proxies' effectiveness using both randomly sampled and stratified subsets of the search space, ensuring they can differentiate between low- and high-performing networks and enhance generalizability. Results show our method outperforms existing approaches on the stratified sampling strategy, achieving strong correlations with ground truth performance, including a Kendall correlation of 0.89 on CIFAR-10 and 0.77 on CIFAR-100 with NATS-Bench-SSS and a Kendall correlation of 0.78 on CIFAR-10 and 0.71 on CIFAR-100 with NATS-Bench-TSS., Comment: Submitted to CVPR 2025

Phase-shaped electron energy-loss spectroscopy (PSEELS) measures the scattering probability of structured free electron beams by a target. Over the last decade, it was shown that this scheme can be employed to emulate polarized optical spectroscopies with electrons, and therefore to transpose macroscopic optical concepts - such as dichroism - down to the deep sub-wavelength scale. In this work, we theoretically demonstrate that PSEELS can, in fact, go way further than mimicking optics and enables to map a plethora of so far inaccessible nano-optical quantities such as the electric quadrupolar momentum., Comment: 7 pages, 2 figures, 2 tables for main text; 22 pages, 1 table for supplementary informations; 29 pages, 2 figures, 3 tables in total

By considering realistic equations of state (EoSs) to describe the ordinary matter of the stellar crust, in this study, we explore the effect of a dark energy core, made of Chaplygin Dark Fluid (CDF), on neutron stars (NSs). To accomplish this purpose, we solve the stellar structure equations and investigate the impact of the CDF parameters on the several macroscopic properties of NSs such as mass-radius ($M-R$) relation, and tidal deformabilities of a single star and of a binary system, the latter being of great importance when analyzing gravitational-wave signals coming from the merger of such compact objects. We also present an analysis of the radial oscillation modes for the rapid phase transition, with the aim of distinguishing regions consisting of dynamically stable stars from those of unstable ones. Specifically, our outcomes reveal that an increase in the energy density jump (controlled by a parameter $\alpha$) leads to an increase in the radial stability of the NS with a CDF core. Furthermore, our theoretical results are consistent with the observational $M-R$ measurements of millisecond pulsars from NICER data and tidal deformability constraints from the GW170817 event., Comment: 12 pages, 7 figures and 3 tables

This work delves into the study of flavor invariants and, in special, invariants capable of detecting CP (Charge-Parity) violation. Through the mathematical tool of the Hilbert series, we systematically enumerate and explore flavor invariants that are unchanged under weak basis transformations. After reviewing the Hilbert series and the flavor invariants of the SM quark sector, we apply the tool of Hilbert series to the SM extended by a singlet vector-like quark (VLQ) of down-type. The introduction of these hypothetical particles leads to a simple extension of the SM that can be motivated by many problems, including the need for new sources of CP violation to explain the observed matter-antimatter asymmetry in the universe. We were successful in calculating the Hilbert series for the VLQ extension in the mass basis of the VLQ, where the spurion transformations are simpler. Based on the Hilbert series, we build and enumerate the basic flavor invariants with which all invariants can be constructed. For a generic basis, where the spurion transformations involve a larger group, we could only get the first few terms of the Hilbert series., Comment: Master thesis presented at Federal University of ABC (UFABC), Brazil; supervised by C. C. Nishi. 122 pages, 2 figures, 8 tables, PDFLaTeX

We prove a Baum-Bott type residual formula for one-dimensional holomorphic foliations, with isolated singularities, and logarithmic along free divisors. More precisely, this provides a Baum--Bott theorem for a foliated triple $(X,\mathscr{F}, D)$, where $ \mathscr{F}$ is a foliation by curves and $D $ is a free divisor on a complex manifold $X$. From the local point of view, we show that the log Baum--Bott residues are a generalization of the Aleksandrov logarithmic index for vector fields with isolated singularities on hypersurfaces. We also show how these new indices are related to Poincar\'e's Problem for foliations by curves. In the case of foliated surfaces, we show that the differences between the logarithmic residues and Baum--Bott indices along invariant curves can be expressed in terms of the GSV and Camacho--Sad indices. We also obtain a Baum--Bott type formula for singular varieties via log resolutions. Finally, we prove a weak global version of the Lipman--Zariski conjecture for compact algebraic surfaces., Comment: 31 pages

The way we look at the sky is connected to the cosmological paradigm embraced by the society we live in. On the other hand, several astronomical concepts reinforce the idea of a common humanity. Yet, scientific outreach is frequenty reaching out only to a specific part of the world population, often excluding people living in extreme social vulnerability, victims of violence and prejudice, fighting for their lives and for the right of living according to their traditions. We present two outreach projects, developed in Brazil, funded by the Office of Astronomy for Development (OAD) of the International Astronomical Union (IAU), i.e. 'Under Other Skies' and 'OruMbya', which tackle the importance of ethno-astronomy, and the collaboration with leaders and cultural agents of marginalised communities. We also describe an educational project born in the favela of Cantagalo Pav\~ao Pava\~azinho (PPG), in Rio de Janeiro, during the COVID19 pandemic, which started a collaboration with local educators and artists to offer classes of astronomy and English language to children in the favela, Comment: 13 pages, 7 figures, conference proceedings

We carry out a stability and convergence analysis for the fully discrete scheme obtained by combining a finite or virtual element spatial discretization with the upwind-discontinuous Galerkin time-stepping applied to the time-dependent advection-diffusion equation. A space-time streamline-upwind Petrov-Galerkin term is used to stabilize the method. More precisely, we show that the method is inf-sup stable with constant independent of the diffusion coefficient, which ensures the robustness of the method in the convection- and diffusion-dominated regimes. Moreover, we prove optimal convergence rates in both regimes for the error in the energy norm. An important feature of the presented analysis is the control in the full $L^2(0,T;L^2(\Omega))$ norm without the need of introducing an artificial reaction term in the model. We finally present some numerical experiments in $(3 + 1)$-dimensions that validate our theoretical results.

Recent spacecraft mission concepts propose larger payloads that have lighter, less rigid structures. For large lightweight structures, the natural frequencies of their vibration modes may fall within the attitude controller bandwidth, threatening the stability and settling time of the controller and compromising performance. This work tackles this issue by proposing an attitude control design paradigm of distributing momentum actuators throughout the structure to have more control authority over vibration modes. The issue of jitter disturbances introduced by these actuators is addressed by expanding the bandwidth of the attitude controller to suppress excess vibrations. Numerical simulation results show that, at the expense of more control action, a distributed configuration can achieve lower settling times and reduce structural deformation compared to a more standard centralized configuration., Comment: 10 pages, 9 figures

In this work we study characteristic classes of possibly singular varieties embedded as a closed subvariety of a nonsingular variety. In special, we express the Schwartz-MacPherson class in terms of the $\mu$-class and Chern class of the sheaves of logarithmic and multi-logarithmic differential forms. As an application we show an expression for Euler characteristic of a complement of a singular variety.

Hourglass modes, characterized by zigzag particle and stress distributions, are a common numerical instability encountered when simulating solid materials with updated Lagrangian smoother particle hydrodynamics (ULSPH). While recent solutions have effectively addressed this issue in elastic materials using an essentially non-hourglass formulation, extending these solutions to plastic materials with more complex constitutive equations has proven challenging due to the need to express shear forces in the form of a velocity Laplacian. To address this, a generalized non-hourglass formulation is proposed within the ULSPH framework, suitable for both elastic and plastic materials. Specifically, a penalty force is introduced into the momentum equation to resolve the disparity between the linearly predicted and actual velocities of neighboring particle pairs, thereby mitigating the hourglass issue. The stability, convergence, and accuracy of the proposed method are validated through a series of classical elastic and plastic cases, with a dual-criterion time-stepping scheme to improve computational efficiency. The results show that the present method not only matches or even surpasses the performance of the recent essentially non-hourglass formulation in elastic cases but also performs well in plastic scenarios., Comment: 42 pages 31 figures

Ionic liquids have been suggested as new engineering fluids, namely in the area of heat transfer, as alternatives to current biphenyl and diphenyl oxide, alkylated aromatics and dimethyl polysiloxane oils, which degrade above 200 {\deg}C and pose some environmental problems. Recently, we have proposed 1-ethyl-3-methylimidazolium methanesulfonate, [$C_{2}mim$][$CH_{3}SO_{3}$], as a new heat transfer fluid, because of its thermophysical and toxicological properties. However, there are some interesting points raised in this work, namely the possibility of the existence of liquid metastability below the melting point (303 K) or second order-disorder transitions ($\lambda$-type) before reaching the calorimetric freezing point. This paper analyses in more detail this zone of the phase diagram of the pure fluid, by reporting accurate thermal-conductivity measurements between 278 and 355 K with an estimated uncertainty of 2% at a 95% confidence level. A new value of the melting temperature is also reported, $T_{melt}$ = 307.8 $\pm$ 1 K. Results obtained support liquid metastability behaviour in the solid-phase region and permit the use of this ionic liquid at a heat transfer fluid at temperatures below its melting point. Thermal conductivity models based on Bridgman theory and estimation formulas were also used in this work, failing to predict the experimental data within its uncertainty.

Ionic liquids have proved to be excellent heat transfer fluids and alternatives to common HTFs used in industries for heat exchangers and other heat transfer equipment. However, its industrial utilization depends on the cost per kg of its production, to be competitive for industrial applications with biphenyl and diphenyl oxide, alkylated aromatics, and dimethyl polysiloxane oils, which degrade above 200 {\deg}C and possess some environmental problems. The efficiency of a heat transfer fluid depends on the fundamental thermophysical properties influencing convective heat transfer (density, heat capacity, thermal conductivity, and viscosity), as these properties are necessary to calculate the heat transfer coefficients for different heat exchanger geometries. In Part 1, the thermophysical properties of pure 1-ethyl-3-methylimidazolium methanesulfonate $[C_{2}mim][CH_{3}SO_{3}]$ (CAS no. 145022-45-3), (ECOENG 110), produced by BASF, under the trade name of Basionics ST35, with an assay $\geq$97% with $\leq$0.5% water and $\leq$2% chloride ($Cl^{-}$), were presented, for temperatures slightly below room temperature and up to 355 K. In this paper, we report the thermophysical properties of mixtures of [C2mim][CH3SO3] with water, in the whole concentration range, at $P$ = 0.1 MPa. The properties measured were density and speed of sound (293.15 < $T$/K < 343.15), viscosity, electrical and thermal conductivities, refractive index (293.15 < $T$/K < 353.15), and infinite dilution diffusion coefficient of the ionic liquid in water (298.15 K).

In this work we explore the possible scenario of strange stars admixed with fermionic or bosonic dark matter. For the description of the ``visible'' sector, we use a specific phenomenological quark model that takes into account in-medium effects for the quark masses through a suitable baryonic density dependence, in which the free parameters are chosen from the analysis of the stellar matter stability window (parametrizations presenting lower energy per baryon than iron nuclei). In the case of the dark sector, we investigate the predictions of fermionic and bosonic models. In the former we consider a spin $1/2$ dark particle, and the latter is described by a dark scalar meson. Both models present a repulsive vector interaction, particularly important for the bosonic model since it helps avoiding the collapse of the star due to the lack of the degeneracy pressure. Our results point out to possible stable strange stars configurations in agreement with data from PSR J0030+0451, PSR J0740+6620, NICER and HESS J1731-347. As another feature, we find stars with dark matter halo configurations for lower values of the dark particle mass., Comment: More references added. Some figures updated

In this paper we investigate the arising of non-hermitian phase transitions on quantum torus surfaces. We consider a single fermion whose dynamics is governed by the Dirac equation confined to move on a quantum torus surface. The effects of the geometry are take into account by using the tetrad formalism and the spin connection. The Dirac equation gives rise to two coupled first-order differential equations for each spinor component. The eigenvalues and eigenfunctions for each spinor component are computed numerically and the non-hermitian phase transitions are investigated in terms of the geometric features of the torus and the magnitude of the imaginary component of the mass., Comment: 6 pages, 5 figures, two columns

The widespread application of Artificial Intelligence (AI) techniques has significantly influenced the development of new therapeutic agents. These computational methods can be used to design and predict the properties of generated molecules. Multi-target Drug Discovery (MTDD) is an emerging paradigm for discovering drugs against complex disorders that do not respond well to more traditional target-specific treatments, such as central nervous system, immune system, and cardiovascular diseases. Still, there is yet to be an established benchmark suite for assessing the effectiveness of AI tools for designing multi-target compounds. Standardized benchmarks allow for comparing existing techniques and promote rapid research progress. Hence, this work proposes an evaluation framework for molecule generation techniques in MTDD scenarios, considering brain diseases as a case study. Our methodology involves using large language models to select the appropriate molecular targets, gathering and preprocessing the bioassay datasets, training quantitative structure-activity relationship models to predict target modulation, and assessing other essential drug-likeness properties for implementing the benchmarks. Additionally, this work will assess the performance of four deep generative models and evolutionary algorithms over our benchmark suite. In our findings, both evolutionary algorithms and generative models can achieve competitive results across the proposed benchmarks., Comment: 8 pages, 1 figure, published in 2024 IEEE Congress on Evolutionary Computation (CEC)

Ram pressure stripping is perhaps the most efficient mechanism for removing gas and quenching galaxies in dense environments as they move through the intergalactic medium. Extreme examples of on-going ram pressure stripping are known as jellyfish galaxies, characterized by a tail of stripped material that can be directly observed in multiple wavelengths. Using the largest homogeneous broad-band optical jellyfish candidate sample in local clusters known to date, we measure the angle between the direction of the tails visible in the galaxies, and the direction towards the host cluster center. We find that $33\%$ of the galaxy tails point away from the cluster center, $18\%$ point towards the cluster center, and $49\%$ point elsewhere. Moreover, we find stronger signatures of ram pressure stripping happening on galaxies with a tail pointing away and towards the cluster center, and larger velocity dispersion profiles for galaxies with tails pointing away. These results are consistent with a scenario where ram pressure stripping has a stronger effect for galaxies following radial orbits on first infall. The results also suggest that in many cases, radially infalling galaxies are able to retain their tails after pericenter and continue to experience significant on-going ram pressure stripping. We further constrain the lifespan of the optical tails from the moment they first appear to the moment they disappear, by comparing the observed tail directions with matched N-body simulations through Bayesian parameter estimation. We obtain that galaxy tails appear for the first time at $\sim 1.16$ R$_{200}$ and disappear $\sim660$ Myr after pericenter., Comment: 19 pages, 17 figures

As LHC's Run3 unfolds, we peer deeper into the couplings of the 125GeV Higgs ($h_{125}$) and also test for extra scalars. Even before such extra scalars are found, one can probe them by using the fact that theories with multiple Higgs doublets allow for substantial deviations of the couplings of $h_{125}$ from their SM values. Indeed, the observed couplings already place stringent limits on such theories. We investigate the curious possibility that $h_{125}$ couples to the top quark mostly as a scalar, while it couples to the bottom quark mostly as a pseudoscalar. This possibility was allowed by 2017 data for the so-called C2HDM; a two Higgs doublet model with a single source of explicit CP violation. It was shown recently that this possibility disappears when using the full experimental data of 2024. Here we discuss a three Higgs doublet with explicit CP violation (C3HDM), and show that the curious CP-even/CP-odd $t$/$b$ possibility is partly resuscitated, prompting further experimental exploration of this prospect., Comment: 33 pages, 14 figures

The meeting of artificial intelligence (AI) and quantum computing is already a reality; quantum machine learning (QML) promises the design of better regression models. In this work, we extend our previous studies of materials discovery using classical active learning (AL), which showed remarkable economy of data, to explore the use of quantum algorithms within the AL framework (QAL) as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses quantum support vector regressor (QSVR) or a quantum Gaussian process regressor (QGPR) with various quantum kernels and different feature maps. Data sets include perovskite properties (piezoelectric coefficient, band gap, energy storage) and the structure optimization of a doped nanoparticle (3Al@Si11) chosen to compare with classical AL results. Our results revealed that the QAL method improved the searches in most cases, but not all, seemingly correlated with the roughness of the data. QAL has the potential of finding optimum solutions, within chemical space, in materials science and elsewhere in chemistry., Comment: 30 pages, 6 figures

We introduce the notion of Karamata regular operators, which is a notion of regularity that is suitable for obtaining concrete convergence rates for common fixed point problems. This provides a broad framework that includes, but goes beyond, H\"olderian error bounds and H\"older regular operators. By concrete, we mean that the rates we obtain are explicitly expressed in terms of a function of the iteration number $k$ instead, of say, a function of the iterate $x^k$. While it is well-known that under H\"olderian-like assumptions many algorithms converge linearly/sublinearly (depending on the exponent), little it is known when the underlying problem data does not satisfy H\"olderian assumptions, which may happen if a problem involves exponentials and logarithms. Our main innovation is the usage of the theory of regularly varying functions which we showcase by obtaining concrete convergence rates for quasi-cylic algorithms in non-H\"olderian settings. This includes certain rates that are neither sublinear nor linear but sit somewhere in-between, including a case where the rate is expressed via the Lambert W function. Finally, we connect our discussion to o-minimal geometry and show that definable operators in any o-minimal structure are always Karamata regular., Comment: 44 pages, comments welcome

We discuss the problem of projecting a point onto an arbitrary hyperbolicity cone from both theoretical and numerical perspectives. While hyperbolicity cones are furnished with a generalization of the notion of eigenvalues, obtaining closed form expressions for the projection operator as in the case of semidefinite matrices is an elusive endeavour. To address that we propose a Frank-Wolfe method to handle this task and, more generally, strongly convex optimization over closed convex cones. One of our innovations is that the Frank-Wolfe method is actually applied to the dual problem and, by doing so, subproblems can be solved in closed-form using minimum eigenvalue functions and conjugate vectors. To test the validity of our proposed approach, we present numerical experiments where we check the performance of alternative approaches including interior point methods and an earlier accelerated gradient method proposed by Renegar. We also show numerical examples where the hyperbolic polynomial has millions of monomials. Finally, we also discuss the problem of projecting onto p-cones which, although not hyperbolicity cones in general, are still amenable to our techniques., Comment: 41 pages, some typo fixes. Comments welcome

Adversarial examples, designed to trick Artificial Neural Networks (ANNs) into producing wrong outputs, highlight vulnerabilities in these models. Exploring these weaknesses is crucial for developing defenses, and so, we propose a method to assess the adversarial robustness of image-classifying ANNs. The t-distributed Stochastic Neighbor Embedding (t-SNE) technique is used for visual inspection, and a metric, which compares the clean and perturbed embeddings, helps pinpoint weak spots in the layers. Analyzing two ANNs on CIFAR-10, one designed by humans and another via NeuroEvolution, we found that differences between clean and perturbed representations emerge early on, in the feature extraction layers, affecting subsequent classification. The findings with our metric are supported by the visual analysis of the t-SNE maps.

In the present paper, we show a simple method to obtain fittings for the surface and curvature tensions. The method uses the nuclear mass of a spherical fully ionized atom and a simple expression for the binding energy such that a least square fit is found when confronted with the Atomic Mass Evaluation (AME) 2020. The fittings are then used to evaluate the pasta phase free energy per particle, which is confronted with the one obtained with a Thomas-Fermi fitting. The results are very encouraging and suggest that this recipe can be safely used whenever the surface and curvature tensions are necessary., Comment: 7 pages, 8 figures