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This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

In an attempt to further characterise the near-Earth asteroid (NEA) population we present 38 new light-curves acquired between September 2020 and November 2023 for NEAs (7335) 1989 JA, (7822) 1991 CS, (154244) 2002 KL6 and (159402) 1999 AP10, obtained from observations taken at the Teide Observatory (Tenerife, Spain). With these new observations along with archival data, we computed their first shape models and spin solutions by applying the light curve inversion method. The obtained rotation periods are in good agreement with those reported in previous works, with improved uncertainties. Additionally, besides the constant period models for (7335) 1989 JA, (7822) 1991 CS and (159402) 1999 AP10, our results for (154244) 2002 KL6 suggest that it could be affected by a Yarkovsky-O'Keefe-Radzievskii-Paddack acceleration with a value of $\upsilon \simeq -7 \times 10^{-9}$ rad d$^{-2}$. This would be one of the first detections of this effect slowing down an asteroid.

Vehicular mobility underscores the need for collaborative misbehavior detection at the vehicular edge. However, locally trained misbehavior detection models are susceptible to adversarial attacks that aim to deliberately influence learning outcomes. In this paper, we introduce a deep reinforcement learning-based approach that employs transfer learning for collaborative misbehavior detection among roadside units (RSUs). In the presence of label-flipping and policy induction attacks, we perform selective knowledge transfer from trustworthy source RSUs to foster relevant expertise in misbehavior detection and avoid negative knowledge sharing from adversary-influenced RSUs. The performance of our proposed scheme is demonstrated with evaluations over a diverse set of misbehavior detection scenarios using an open-source dataset. Experimental results show that our approach significantly reduces the training time at the target RSU and achieves superior detection performance compared to the baseline scheme with tabula rasa learning. Enhanced robustness and generalizability can also be attained, by effectively detecting previously unseen and partially observable misbehavior attacks.

In this paper we study a principal-agent problem in continuous time with multiple lump-sum payments (contracts) paid at different deterministic times. We reduce the non-zero sum Stackelberg game between the principal and agent to a standard stochastic optimal control problem. We apply our result to a benchmark model for which we investigate how different inputs (payment frequencies, payments' distribution, discounting factors, agent's reservation utility) affect the principal's value and agent's optimal compensations.

A one-dimensional cellular automaton $\tau : A^\mathbb{Z} \to A^\mathbb{Z}$ is a transformation of the full shift defined via a finite neighborhood $S \subset \mathbb{Z}$ and a local function $\mu : A^S \to A$. We study the family of cellular automata whose finite neighborhood $S$ is an interval containing $0$, and there exists a pattern $p \in A^S$ satisfying that $\mu(z) = z(0)$ if and only if $z \neq p$; this means that these cellular automata have a unique \emph{active transition}. Despite its simplicity, this family presents interesting and subtle problems, as the behavior of the cellular automaton completely depends on the structure of $p$. We show that every cellular automaton $\tau$ with a unique active transition $p \in A^S$ is either idempotent or strictly almost equicontinuous, and we completely characterize each one of these situations in terms of $p$. In essence, the idempotence of $\tau$ depends on the existence of a certain subpattern of $p$ with a translational symmetry., Comment: 13 pages

Wavefront reconstruction is a critical component in various optical systems, including adaptive optics, interferometry, and phase contrast imaging. Traditional reconstruction methods often employ either the Cartesian (pixel) basis or the Zernike polynomial basis. While the Cartesian basis is adept at capturing high-frequency features, it is susceptible to overfitting and inefficiencies due to the high number of degrees of freedom. The Zernike basis efficiently represents common optical aberrations but struggles with complex or non-standard wavefronts such as optical vortices, Bessel beams, or wavefronts with sharp discontinuities. This paper introduces a novel approach to wavefront reconstruction using an over-complete phase dictionary combined with sparse representation techniques. By constructing a dictionary that includes a diverse set of basis functions - ranging from Zernike polynomials to specialized functions representing optical vortices and other complex modes - we enable a more flexible and efficient representation of complex wavefronts. Furthermore, a trainable affine transform is implemented to account for misalignment. Utilizing principles from compressed sensing and sparse coding, we enforce sparsity in the coefficient space to avoid overfitting and enhance robustness to noise.

Biological transport networks are highly optimized structures that ensure power-efficient distribution of fluids across various domains, including animal vasculature and plant venation. Theoretically, these networks can be described as space-embedded graphs, and rich structures that align well with observations emerge from optimizing their hydrodynamic energy dissipation. Studies on these models typically use regular grids and focus solely on edge width optimization. Here, we present a generalization of the hydrodynamic graph model which permits additional optimization of node positioning. We achieve this by defining sink regions, accounting for the energy dissipation of delivery within these areas, and optimizing by means of differentiable physics. In the context of leaf venation patterns, our method results in organic networks that adapt to irregularities of boundaries and node misalignment, as well as overall improved efficiency. We study the dependency of the emergent network structures on the capillary delivery conductivity and identify a phase transition in which the network collapses below a critical threshold. Our findings provide insights into the early formation of biological systems and the efficient construction of transport networks., Comment: Supplementary Information included as Appendix

We use the projected clustering of quasars in the Gaia-unWISE quasar catalog, Quaia, and its cross-correlation with CMB lensing data from Planck, to measure the large-scale turnover of the matter power spectrum, associated with the size of the horizon at the epoch of matter-radiation equality. The turnover is detected with a significance of between $2.3$ and $3.1\sigma$, depending on the method used to quantify it. From this measurement, the equality scale is determined at the $\sim20\%$ level. Using the turnover scale as a standard ruler alone (discarding information from the large-scale curvature of the power spectrum), in combination with supernova data through an inverse distance ladder approach, we measure the current expansion rate to be $H_0=62.7\pm17.2\,{\rm km}\,{\rm s}^{-1}\,{\rm Mpc}^{-1}$. The addition of information coming from the power spectrum curvature approximately halves the standard ruler uncertainty. Our measurement in combination with calibrated supernovae from Pantheon$+$ and SH0ES constrains the CMB temperature to be $T_{\rm CMB}=3.10^{+0.48}_{-0.36}\,{\rm K}$, independently of CMB data. Alternatively, assuming the value of $T_{\rm CMB}$ from COBE-FIRAS, we can constrain the effective number of relativistic species in the early Universe to be $N_{\rm eff}=3.0^{+5.8}_{-2.9}$., Comment: 18 pages, 14 figures. Comments welcome

We prove endpoint and sparse-like bounds for Bergman projectors on nonhomogeneous, radial trees $X$ that model manifolds with possibly unbounded geometry. The natural Bergman measures on $X$ may fail to be doubling, and even locally doubling, with respect to the right metric in our setting. Weighted consequences of our sparse domination results are also considered, and are in line with the known results in the disk. Our endpoint results are partly a consequence of a new Calder\'on-Zygmund theory for discrete, non-locally doubling metric spaces., Comment: 38 pages

We present a canonical model of spherical gravity with covariant corrections motivated by loop quantum gravity. The effective Hamiltonian defines univocally a family of geometries that generalizes the Lema\^itre-Tolman-Bondi spacetimes, and they can be matched to the vacuum of the theory across a timelike hypersurface comoving with the flow of matter. Such is precisely the complete spacetime picture of a spherical star subject to its own gravitational pull. The singularity gets replaced with a spacelike boundary in the trapped region of spacetime, where the curvature remains finite, and the area of the orbits of the spherical symmetry group attains its infimum. Observers falling into the black hole are doomed to travel forever towards this boundary without ever reaching it. The theory also predicts the formation of stable black-hole remnants of Planckian mass.

We present new observations from CHEOPS and TESS to clarify the architecture of the planetary system hosted by the old Galactic thick disk star TOI-561. Our global analysis, which also includes previously published photometric and radial velocity data, incontrovertibly proves that TOI-561 is hosting at least four transiting planets with periods of 0.44 days (TOI-561 b), 10.8 days (TOI-561 c), 25.7 days (TOI-561 d), and 77.1 days (TOI-561 e) and a fifth non-transiting candidate, TOI-561f with a period of 433 days. The precise characterisation of TOI-561's orbital architecture is interesting since old and metal-poor thick disk stars are less likely to host ultra-short period Super-Earths like TOI-561 b. The new period of planet -e is consistent with the value obtained using radial velocity alone and is now known to be $77.14399\pm0.00025$ days, thanks to the new CHEOPS and TESS transits. The new data allowed us to improve its radius ($R_p = 2.517 \pm 0.045 R_{\oplus}$ from 5$\%$ to 2$\%$ precision) and mass ($M_p = 12.4 \pm 1.4 M_{\oplus}$) estimates, implying a density of $\rho_p = 0.778 \pm 0.097 \rho_{\oplus}$. Thanks to recent TESS observations and the focused CHEOPS visit of the transit of TOI-561 e, a good candidate for exomoon searches, the planet's period is finally constrained, allowing us to predict transit times through 2030 with 20-minute accuracy. We present an updated version of the internal structure of the four transiting planets. We finally performed a detailed stability analysis, which confirmed the long-term stability of the outer planet TOI-561 f., Comment: 13 pages, 10 Figures. Accepted on MNRAS. Updated the author list

Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: "probably you will like this film", "almost certainly you will like this song", etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields., Comment: 9 pages, 7 figures

Security and RAS are two non-functional requirements under focus for current systems developed for the computing continuum. Due to the increased number of interconnected computer systems across the continuum, security becomes especially pervasive at all levels, from the smallest edge device to the high-performance cloud at the other end. Similarly, RAS (Reliability, Availability, and Serviceability) ensures the robustness of a system towards hardware defects. Namely, making them reliable, with high availability and design for easy service. In this paper and as a result of the Vitamin-V EU project, the authors detail the comprehensive approach to malware and hardware attack detection; as well as, the RAS features envisioned for future systems across the computing continuum.

H. Gl\"ockner and G. A. Willis have recently shown that locally pro-p contraction groups are nilpotent. The proof hinges on a fixed-point result: if the local field $\mathbb{F}_{p}(\!(t)\!)$ acts on its $d$-th power $\mathbb{F}_{p}(\!(t)\!)^{d}$ additively, continuously, and in an appropriately equivariant manner, then the action has a non-zero fixed point. We provide a short proof of this theorem.

Diagnosing and treating skin diseases require advanced visual skills across multiple domains and the ability to synthesize information from various imaging modalities. Current deep learning models, while effective at specific tasks such as diagnosing skin cancer from dermoscopic images, fall short in addressing the complex, multimodal demands of clinical practice. Here, we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on a dataset of over 2 million real-world images of skin diseases, sourced from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse datasets covering a range of clinical tasks, including skin cancer screening, phenotype assessment and risk stratification, diagnosis of neoplastic and inflammatory skin diseases, skin lesion segmentation, change monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models even when using only 5-10% of labeled data. PanDerm's clinical utility was demonstrated through reader studies in real-world clinical settings across multiple imaging modalities. It outperformed clinicians by 10.2% in early-stage melanoma detection accuracy and enhanced clinicians' multiclass skin cancer diagnostic accuracy by 11% in a collaborative human-AI setting. Additionally, PanDerm demonstrated robust performance across diverse demographic factors, including different body locations, age groups, genders, and skin tones. The strong results in benchmark evaluations and real-world clinical scenarios suggest that PanDerm could enhance the management of skin diseases and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of AI support in healthcare., Comment: 56 pages; Technical report

The Radcliffe Wave has only recently been recognised as a about 3 kpc long coherent gas structure encompassing most of the star forming regions in the solar vicinity. Since its discovery, it has been mainly studied from the perspective of dynamics, but a detailed chemical study is necessary to understand its nature and the composition of the natal clouds that gave rise to it. In this paper we used some of the connected young open clusters (age $\lesssim$ 100 Myr) as tracers of the molecular clouds. We performed high-resolution spectroscopy with GIARPS at the TNG of 53 stars that are bona fide members of seven clusters located at different positions along the Radcliffe Wave. We provide radial velocities and atmospheric parameters for all of them. For a subsample consisting of 41 FGK stars we also studied the chromospheric activity and the content of Li, from which we inferred the age of the parent clusters. These values agree with the evolutionary ages reported in the literature. For these FGK stars we determined the chemical abundances for 25 species. Pleiades, ASCC 16 and NGC 7058 exhibit a solar metallicity while Melotte 20, ASCC 19, NGC 2232, and Roslund 6 show a slightly subsolar value ($\approx-$0.1 dex). On average, the clusters show a chemical composition compatible with that of the Sun, especially for $\alpha$- and Fe-peak elements. Neutron-capture elements, on the other hand, present a slight overabundance of about 0.2 dex, specially barium. Finally, considering also ASCC 123, studied by our group in a previous research, we infer a correlation between the chemical composition and the age or position of the clusters along the Wave, demonstrating their physical connection within an inhomogeneous mixing scenario., Comment: 37 pages, 19 figures and 15 tables. Accepted for publication in Astronomy and Astrophysics

Climate change is negatively impacting the world's biodiversity. To build automated systems to monitor these negative biodiversity impacts, large-scale, volunteer-collected datasets like iNaturalist are built from community-identified, natural imagery. However, such volunteer-based data are opportunistic and lack a structured sampling strategy, resulting in geographic, temporal, observation quality, and socioeconomic, biases that stymie uptake of these models for downstream biodiversity monitoring tasks. Here we introduce DivShift North American West Coast (DivShift-NAWC), a curated dataset of almost 8 million iNaturalist plant images across the western coast of North America, for exploring the effects of these biases on deep learning model performance. We compare model performance across four known biases and observe that they indeed confound model performance. We suggest practical strategies for curating datasets to train deep learning models for monitoring climate change's impacts on the world's biodiversity., Comment: Accepted to NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But how can we evaluate this hypothesis? In this paper, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them. The criteria focus on the extent to which the LLM's behavior is preserved, the degree of localization of this behavior, and whether the circuit is minimal. We apply these tests to six circuits described in the research literature. We find that synthetic circuits -- circuits that are hard-coded in the model -- align with the idealized properties. Circuits discovered in Transformer models satisfy the criteria to varying degrees. To facilitate future empirical studies of circuits, we created the \textit{circuitry} package, a wrapper around the \textit{TransformerLens} library, which abstracts away lower-level manipulations of hooks and activations. The software is available at \url{https://github.com/blei-lab/circuitry}., Comment: Code available here: https://github.com/blei-lab/circuitry

This paper introduces a novel approach that integrates future closest point predictions into the distance constraints of a collision avoidance controller, leveraging convex hulls with closest point distance calculations. By addressing abrupt shifts in closest points, this method effectively reduces collision risks and enhances controller performance. Applied to an Image Guided Therapy robot and validated through simulations and user experiments, the framework demonstrates improved distance prediction accuracy, smoother trajectories, and safer navigation near obstacles.

The classical solution to the Helmholtz wave equation in spherical coordinates is well known and has found many important applications in wave propagation, scattering, and imaging in optics and acoustics. The separable solution is comprised of spherical Bessel functions in the radial direction and spherical harmonics in the angular directions. The nature of the spherical Bessel functions includes a long asymptotic oscillatory tail at large radii, not conducive to applications where a tight concentration of wave amplitude around a ring is desired, for example in toroidal configurations. However, we have found that certain practical bandpass spectral shapes, centered around a peak frequency, can create a superposition of spherical Bessel functions that effectively concentrate the wave amplitude around a defined ring at the time instant of coherent addition, avoiding the long tail asymptotic oscillations of the single frequency solution. Theoretical solutions are shown for different bandpass spectra applied to the spherical Bessel functions, along with numerical solutions of transient wave propagation using practical hemispherical source shapes. These findings introduce a framework by which ring or toroidal concentrated waves can be produced with a simple bandpass superposition applied to hemispherical source shapes and with reference to the classical solutions in spherical coordinates., Comment: 18 pages; 10 figures

Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical method of constructing CBF approximations that is easy to implement and robust to disturbances via the estimation of a value function. We demonstrate the effectiveness of our method in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of RPCBF in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. The project page can be found at https://oswinso.xyz/rpcbf., Comment: Submitted to ICRA 2025. The project page can be found at https://oswinso.xyz/rpcbf

In this note we study the well-posedness of two systems of Burgers type arising in nonlinear wave interactions. The first model describes the interaction of a Burger's bore with the classical Korteweg-de Vries equation while the second exemplify the interaction of weak sound waves and entropy waves with small amplitudes. For the former, we show the local existence and uniqueness of solutions in Sobolev spaces and Wiener-type spaces. For the latter, we provide an elementary proof of finite time singularity., Comment: 11 pages

In this paper, we introduce an $L^p$-operator algebraic analogue of Hilbert C*-modules. We initiate the theory of concrete $L^p$-modules, their morphisms, and basic constructions such as countable direct sums and tensor products. We then define $L^p$-correspondences together with their Fock representations and the $L^p$-operator algebras generated by these. We present evidence that well-known $L^p$-operator algebras can be constructed from $L^p$-correspondences via covariant Fock representations. In particular, for $p \in (1,\infty)$ and $q$ its H\"older conjugate, we show that the $L^p$-module $(\ell_d^p, \ell_d^q)$ gives rise to an $L^p$-correspondence over $\Bbb{C}$ whose $L^p$-operator algebra is isometrically isomorphic to $\mathcal{O}_d^p$, the $L^p$-analogue of the Cuntz algebra $\mathcal{O}_d$ introduced by N.C. Phillips in 2012. As a second example, we fix a nondegenerate $L^p$-operator algebra $A$ with a bicontractive approximate identity together with an isometric automorphism $\varphi_A \in \mathrm{Aut}(A)$. We then show that there is a contractive map from the crossed product $F^p(\Bbb{Z}, A, \varphi_A)$ to the $L^p$-operator algebra generated by the covariant Fock representation of the $L^p$-correspondence $(A, A, \varphi_A)$., Comment: AMSLaTeX; 54 pages

Rank collapse, a phenomenon where embedding vectors in sequence models rapidly converge to a uniform token or equilibrium state, has recently gained attention in the deep learning literature. This phenomenon leads to reduced expressivity and potential training instabilities due to vanishing gradients. Empirical evidence suggests that architectural components like skip connections, LayerNorm, and MultiLayer Perceptrons (MLPs) play critical roles in mitigating rank collapse. While this issue is well-documented for transformers, alternative sequence models, such as State Space Models (SSMs), which have recently gained prominence, have not been thoroughly examined for similar vulnerabilities. This paper extends the theory of rank collapse from transformers to SSMs using a unifying framework that captures both architectures. We study how a parametrized version of the classic skip connection component, which we call \emph{lambda-skip connections}, provides guarantees for rank collapse prevention. Through analytical results, we present a sufficient condition to guarantee prevention of rank collapse across all the aforementioned architectures. We also study the necessity of this condition via ablation studies and analytical examples. To our knowledge, this is the first study that provides a general guarantee to prevent rank collapse, and that investigates rank collapse in the context of SSMs, offering valuable understanding for both theoreticians and practitioners. Finally, we validate our findings with experiments demonstrating the crucial role of architectural components such as skip connections and gating mechanisms in preventing rank collapse.

The aim of these notes is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of various topics in machine learning., Comment: 254 pages

This paper presents the development and application of an AI-based method for particle track identification using scintillating fibres read out with imaging sensors. We propose a variational autoencoder (VAE) to efficiently filter and identify frames containing signal from the substantial data generated by SPAD array sensors. Our VAE model, trained on purely background frames, demonstrated a high capability to distinguish frames containing particle tracks from background noise. The performance of the VAE-based anomaly detection was validated with experimental data, demonstrating the method's ability to efficiently identify relevant events with rapid processing time, suggesting a solid prospect for deployment as a fast inference tool on hardware for real-time anomaly detection. This work highlights the potential of combining advanced sensor technology with machine learning techniques to enhance particle detection and tracking., Comment: 19 pages, 9 figures

The structure of the normal modes of vibration of rotationally invariant $n$-vortices in the Ginzburg-Landau/Abelian Higgs model is completely unveiled for any value of the coupling constant., Comment: 5 pages, 4 figures

Dealing with boundary conditions in Smoothed Particle Hydrodynamics (SPH) poses significant difficulties, indeed being one of the SPHERIC Grand Challenges. In particular, wall boundary conditions have been pivotal in SPH model development since it evolved from astrophysics to more generic fluid dynamics simulations. Despite considerable attention from researchers and numerous publications dedicated to formulating and assessing wall boundary conditions, few of them have addressed the crucial aspect of energy conservation. This work introduces a novel boundary condition designed with energy conservation as a primary consideration, effectively extending the unconditional stability of SPH to problems involving wall boundary conditions. The result is formulated within the framework of the Boundary Integrals technique. The proposal is tested on a number of cases: normal impact against a wall, adiabatic oscillations of a piston, dam break, and the water landing of a spacecraft., Comment: 35 pages, 25 figures. SPH solver released as free software (see https://gitlab.com/sanguinariojoe/aquagpusph). Accepted for publication on "Computers & Fluids"

The present paper is concerned with the existence of traveling wave solutions of the asymptotic model, derived by the authors in a previous work, to approximate the unidirectional evolution of a collision-free plasma in a magnetic field. First, using bifurcation theory, we can rigorously prove the existence of periodic traveling waves of small amplitude. Furthermore, our analysis also evidences the existence of different type of traveling waves. To this end, we present a second approach based on the analysis of the differential system satisfied by the traveling-wave profiles, the existence of equilibria, and the identification of associated homo-clinic and periodic orbits around them. The study makes use of linearization techniques and numerical computations to show the existence of different types of traveling-wave solutions, with monotone and non-monotone behaviour and different regularity, as well as periodic traveling waves., Comment: 27 pages

We investigate the effects of the interaction between time-inhomogeneous damping, non-local diffusion, and noise on classical solutions to the Camassa--Holm equations that incorporate these additional factors. Initially, a local-in-time theory is developed, covering the existence, uniqueness, and a blow-up criterion under relatively general conditions for these interacting factors. Subsequently, we identify different conditions on the interactions between damping, diffusion, and noise that enable the global regularity and long-time behaviour of classical solutions. Notably, we demonstrate the existence of an evolution system of measures, generalising the concept of invariant measures to the time-inhomogeneous system.

We present a generalisation of the standard pseudo-$C_\ell$ approach for power spectrum estimation to the case of spin-$s$ fields weighted by a general positive-definite weight matrix that couples the different spin components of the field (e.g. $Q$ and $U$ maps in CMB polarisation analyses, or $\gamma_1$ and $\gamma_2$ shear components in weak lensing). Relevant use cases are, for example, data with significantly anisotropic noise properties, or situations in which different masks must be applied to the different field components. The weight matrix map is separated into a spin-0 part, which corresponds to the "mask" in the standard pseudo-$C_\ell$ approach, and a spin-$2s$ part sourced solely by the anisotropic elements of the matrix, leading to additional coupling between angular scales and $E/B$ modes. The general expressions for the mode-coupling coefficients involving the power spectra of these anisotropic weight components are derived and validated. The generalised algorithm is as computationally efficient as the standard approach. We implement the method in the public code NaMaster., Comment: Comments welcome

We deploy the Hartree-Fock approximation for all-heavy quark hadrons, including quarkonium, baryons, tetraquarks, pentaquarks, dibaryons and up to the 12-body dibaryon-antidibaryon which completely fill the $1s$ orbital, in a unified manner, with the spinless LO Coulomb interaction and beyond. After treating the $c$ and $b$ quarks in various combinations, we delve a bit longer on $t$-quark bound states. We extend the negative result of Kuchiev, Flambaum and Shuryak on the 12-body topball to now include the NLO QCD potential. We find that none of the examined multitop states should have binding energy exceeding their width in the Standard Model. Additional new-physics interactions can then be constrained by experimental searches for these bound states, assuming they are not detected, at a level somewhat less stringent than standing HEFT bounds, but with different systematics., Comment: 39 pages, 7 figures, 16 tables

Surrogate endpoints are often used in place of expensive, delayed, or rare true endpoints in clinical trials. However, regulatory authorities require thorough evaluation to accept these surrogate endpoints as reliable substitutes. One evaluation approach is the information-theoretic causal inference framework, which quantifies surrogacy using the individual causal association (ICA). Like most causal inference methods, this approach relies on models that are only partially identifiable. For continuous outcomes, a normal model is often used. Based on theoretical elements and a Monte Carlo procedure we studied the impact of model misspecification across two scenarios: 1) the true model is based on a multivariate t-distribution, and 2) the true model is based on a multivariate log-normal distribution. In the first scenario, the misspecification has a negligible impact on the results, while in the second, it has a significant impact when the misspecification is detectable using the observed data. Finally, we analyzed two data sets using the normal model and several D-vine copula models that were indistinguishable from the normal model based on the data at hand. We observed that the results may vary when different models are used.

Hydrogen is one of the most promising candidates for clean energy production. Photoelectrochemical devices look promising for the decomposition of the water molecule into 2H$_2$ + O$_2$. Oxynitrides, like the solid solution Sr$_{1-x}$Ca$_x$O$_2$N, are good candidates due to the low band gap that lies into the maximum zone of the solar radiation spectrum. A necessary condition for the photoelectrochemical process to work without a bias voltage is that the minimum of the semiconductor conduction band must be more positive than the reduction potential H$^+$ to H$_2$, whereas the maximum of the semiconductor valence band must be more negative than the oxidation potential of H$_2$O to O$_2$. Thus, band alignment studies in interfaces of semiconductors with water become of great importance. They present several subtleties, as different or simplistic modelling will result in few decimes of eV difference that would lead to a wrong prediction in the alignment. So, to find a trustful method is desirable. In this work, first principles calculations based on density functional theory (DFT) in the all electron and the pseudopotential approaches have been performed for the analysis of the band alignment in Sr$_{1-x}$Ca$_x$O$_2$N /H$_2$O interfaces. A detailed study of the modelling of the surface, supercells, and interface with water molecules was done. Experimental data were taken for the structures and photoelectrochemical behaviour and well reproduced by the methodology implemented. The analysis carried out led to theoretical results compatible with the experimental results: the calculations show that the Sr$_{1-x}$Ca$_x$O$_2$N /H$_2$O is suitable for photoelectrochemical applications, and the partial substitution of Sr by Ca enables the gap and alignment tuning thus enhancing the complex performance.

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the relevance of our model in realistic scenarios. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.

Background Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies. New method We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding the nuclei of non-neuronal cell types. Results We have tested the algorithm on stacks of images from rat neocortex, in a complex scenario (large stacks of images, uneven staining, and three different channels to visualize different cellular markers). It was able to provide a good identification ratio of neuronal nuclei and a 3D segmentation. Comparison with Existing Methods: Many automatic tools are in fact currently available, but different methods yield different cell count estimations, even in the same brain regions, due to differences in the labeling and imaging techniques, as well as in the algorithms used to detect cells. Moreover, some of the available automated software methods have provided estimations of cell numbers that have been reported to be inaccurate or inconsistent after evaluation by neuroanatomists. Conclusions It is critical to have a tool for automatic segmentation that allows discrimination between neurons, glial cells and perivascular cells. It would greatly speed up a task that is currently performed manually and would allow the cell counting to be systematic, avoiding human bias. Furthermore, the resulting 3D reconstructions of different cell types can be used to generate models of the spatial distribution of cells.

Most of the matter in the universe is in the form of dark matter. However, dark matter has never been observed to interact with normal matter in laboratory experiments. ALPs -- ultralight axionlike particles -- are a class of dark matter models that are consistent with astrophysical constraints, and produce measurable signatures in the form of oscillating violations of discrete symmetries in nuclei. We report the first results from a search for oscillating parity-odd time-reversal-odd nuclear moments of $^{153}$Eu ions in a crystal, which leads to an experimental constraint on the ALP-gluon coupling strength across a wide band of ALP masses.

The teaching innovation project SpaceRaceEdu: development of an educational multiplayer video game for self-study and self-assessment has been carried out under the INNOVA call of the Autonomous University of Madrid during the 2022-2023 academic year. In this project, a functional prototype of SpaceRaceEdu has been developed: a multiplayer video game with a social and educational nature, which can be used both by teachers as a training and evaluation activity and by students as a tool for study and evaluation. In SpaceRaceEdu, several student teams try to launch a rocket before everyone else. To meet this objective, they must gather a series of resources by going through a scenario and answering questions of different types. The teachers can introduce these questions according to the contents of their subject. The videogame balances competition and cooperation to promote participation and learning. Competition occurs between teams who strive to answer all their questions correctly before their rivals. In contrast, cooperation occurs between students on the same team who can organize and support each other to be more effective., Comment: 8 pages, in Spanish language, 3 figures

In this paper methods for deforming scalar field theories on Euclidean target spaces, in which new field theories are constructed so that solutions are known, are generalized to the context of Sigma models. In particular, deformations between Sigma models on the plane and on the sphere are considered. Three different examples are presented, where the change in the structure of the kink variety and the energy of the deformed kinks during this procedure are studied., Comment: 23 pages, 20 figures

Magnetotactic bacteria (MTB) are of significant interest for biophysical applications, particularly in cancer treatment. The biomineralized magnetosomes produced by these bacteria are high-quality magnetic nanoparticles that form chains through a highly reproducible natural process. Specifically, Magnetovibrio blakemorei and Magnetospirillum gryphiswaldense exhibit distinct magnetosome morphologies: truncated hexa-octahedral and truncated octahedral shapes, respectively. Despite having identical compositions (magnetite, Fe3O4) and comparable dimensions, their effective uniaxial anisotropies differ significantly, with M. blakemorei showing ~25 kJ/m^3 and M. gryphiswaldense ~11 kJ/m^3 at 300K. This variation presents a unique opportunity to explore the role of different anisotropy contributions in the magnetic responses of magnetite-based nanoparticles. This study systematically investigates these responses by examining static magnetization as a function of temperature (M vs. T, 5 mT) and magnetic field (M vs. H, up to 1 T). Above the Verwey transition temperature (110 K), the effective anisotropy is dominated by shape anisotropy, notably increasing coercivity for M. blakemorei by up to two-fold compared to M. gryphiswaldense. Below this temperature, the effective uniaxial anisotropy increases non-monotonically, significantly altering magnetic behavior. Our simulations based on dynamic Stoner-Wohlfarth models indicate that below the Verwey temperature, a uniaxial magnetocrystalline contribution emerges, peaking at ~22-24 kJ/m^3 at 5 K, values close to those of bulk magnetite. This demonstrates the profound impact of anisotropic properties on the magnetic behaviors and applications of magnetite-based nanoparticles and highlights the exceptional utility of magnetosomes as ideal model systems for studying the complex interplay of anisotropies in magnetite-based nanoparticles.

Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one view is absent. We propose ContRastive Image-remote Sensing Pre-training (CRISP)$\unicode{x2014}$a new pre-training task for ground-level and aerial image representation learning of the natural world$\unicode{x2014}$and introduce Nature Multi-View (NMV), a dataset of natural world imagery including $>3$ million ground-level and aerial image pairs for over 6,000 plant taxa across the ecologically diverse state of California. The NMV dataset and accompanying material are available at hf.co/datasets/andyvhuynh/NatureMultiView., Comment: Accepted to ECCV 2024

Densities ($\rho$), speeds of sound ($c$), relative permittivities at 1 MHz ($\varepsilon_{\text{r}}$) and refractive indices at the sodium D-line ($n_{\text{D}}$) at $T$ = (293.15 K to 303.15 K) and $p$ = 0.1 MPa are reported for binary liquid mixtures alkan-1-ol + benzylamine. Methanol, propan-1-ol and pentan-1-ol are the alkan-1-ols studied in this work. The values of the excess molar volume ($V_{\text{m}}^{\text{E}}$), excess isentropic compressibility ($\kappa_S^{\text{E}}$), excess speed of sound ($c^{\text{E}}$), excess refractive index ($n_{\text{D}}^{\text{E}}$), excess relative permittivity ($\varepsilon_{\text{r}}^{\text{E}}$) and its temperature derivative ${(\partial{\varepsilon_{\text{r}}}/\partial T)}_p$ are calculated, and they are adjusted to Redlich-Kister polynomials. The $V_{\text{m}}^{\text{E}}$ values are negative, indicating a predominance of the solvation between unlike molecules and structural effects. $\varepsilon_{\text{r}}^{\text{E}}$ values indicate a positive contribution from the creation of (alkan-1-ol)-benzylamine interactions, and the positive value for the methanol mixture emphasises the importance of solvation. Calculations on excess molar refractions point out to weaker dispersive interactions than in the ideal mixture, which may be explained by the mentioned solvation effects. The Kirkwood-Fr\"ohlich model has been applied to the mixtures, and the Kirkwood correlation factors suggest an important relative weight, especially in the methanol system, of linear-like molecules in the solutions, which is in accordance with the positive contribution of the formed multimers to $\varepsilon_{\text{r}}^{\text{E}}$ due to their good effective response to the electric field., Comment: arXiv admin note: substantial text overlap with arXiv:2409.17926

In this paper, as a continuation of [Contreras-Astorga A., Escobar-Ruiz A. M. and Linares R., \textit{Phys. Scr.} {\bf99} 025223 (2024)] the one-dimensional quasi-exactly solvable (QES) sextic potential $V^{\rm(qes)}(x) = \frac{1}{2}(\nu\, x^{6} + 2\, \nu\, \mu\,x^{4} + \left[\mu^2-(4N+3)\nu \right]\, x^{2})$ is considered. In the cases $N=0,\frac{1}{4},\,\frac{1}{2},\,\frac{7}{10}$ the WKB correction $\gamma=\gamma(N,n)$ is calculated for the first lowest 50 states $n\in [0,\,50]$ using highly accurate data obtained by the Lagrange Mesh Method. Closed analytical approximations for both $\gamma$ and the energy $E=E(N,n)$ of the system are constructed. They provide a reasonably relative accuracy $|\Delta|$ with upper bound $\lesssim 10^{-3}$ for all the values of $(N,n)$ studied. Also, it is shown that the QES Morse potential is shape invariant characterized by a hidden $\mathfrak{sl}_2(\mathbb{R})$ Lie algebra and vanishing WKB correction $\gamma=0$., Comment: 12 pages, 12 figures

Densities and speeds of sound at a temperature $T$ = 298.15 K, relative permittivities at 1 MHz and refractive indices at the sodium D-line at $T$ = (293.15 K to 303.15 K), all at a pressure p = 0.1 MPa, are reported for liquid mixtures methanol, propan-1-ol or pentan-1-ol + aniline. Excess molar volume ($V_{\text{m}}^{\text{E}}$), excess isentropic compressibility, excess speed of sound, excess refractive index, excess relative permittivity ($\varepsilon_{\text{r}}^{\text{E}}$) and its temperature derivative are calculated and fitted to Redlich-Kister polynomials. The agreement among the reported data and other literature sources is analysed by comparing $V_{\text{m}}^{\text{E}}$, $n_{\text{D}}^{\text{E}}$, $\varepsilon_{\text{r}}^{\text{E}}$ and the deviation of the speed of sound from mole-fraction linearity. The positive excess molar internal energies at constant volume ($U_{\text{m,}V}^{\text{E}}$) show the dominance of the breaking of interactions between like molecules in the energy balance on mixing, particularly the breaking of strong dipolar interactions between aniline molecules. This contribution is also dominant for the $\varepsilon_{\text{r}}^{\text{E}}$ values, as they are negative and decrease with the length of the alkan-1-ol chain. Calculations on the concentration-concentration structure factor are consistent with these statements, revealing homocoordination in the studied systems. The $V_{\text{m}}^{\text{E}}$ are negative, which together with the positive $U_{\text{m,}V}^{\text{E}}$ indicate the existence of important structural effects in the studied mixtures. The application of the Kirkwood-Fr\"ohlich model shows that the average relative orientation of neighbouring dipoles is similar in the mixtures methanol + aniline or + pyridine, in spite of the different character of the predominant interactions in the latter mixture (heterocoordination).

Mixtures involving nitrobenzene and hydrocarbons, or 1-alkanols and 1-nitroalkane, or nitrobenzene have been investigated based on a whole set of thermophysical properties available in the literature: excess molar functions (enthalpies, entropies, isobaric heat capacities, and volumes), vapour-liquid and liquid-liquid equilibria, permittivities or dynamic viscosities. In addition, the mixtures have been studied by means of the DISQUAC, ERAS, and UNIFAC models, and the concentration-concentration structure factor. The corresponding DISQUAC and ERAS interaction parameters are reported. In alkane mixtures, dipolar interactions between 1-nitroalkane molecules are weakened when the size of the polar compound increases, accordingly with the relative variation of their effective dipolar moment. Dipolar interactions are stronger in nitrobenzene solutions than in those containing the smaller 1-nitropropane, although both nitroalkanes have very similar effective dipole moment (aromaticity effect). Systems with 1-alkanols are characterized by dipolar interactions between like molecules which sharply increases when the alkanol size increases. Simultaneously, interactions between unlike molecules become weaker, as the OH group is then more sterically hindered. Interactions between unlike molecules are stronger in systems with nitromethane than in nitrobenzene solutions. The replacement of nitromethane by nitroethane in systems with a given 1-alkanol leads to strengthen those effects related with the alcohol self-association. Permittivity data and results on Kirkwood correlation factors show that the addition of 1-alkanol to a nitroalkane leads to cooperative effects, which increase the dipolar polarization of the solution, in such way that the destruction of the existing structure in pure liquids is partially counterbalanced. This effect is less important when longer 1-alkanols are involved.

Hot Jupiters are giant planets subject to intense stellar radiation. The physical and chemical properties of their atmosphere makes them the most amenable targets for the atmospheric characterization. In this paper we analyze the photometry collected during the secondary eclipses of the hot Jupiter WASP-3 b by CHEOPS, TESS and Spitzer. Our aim is to characterize the atmosphere of the planet by measuring the secondary eclipse depth in several passbands and constrain the planetary dayside spectrum. Our update of the stellar and planetary properties is consistent with previous works. The analysis of the occultations returns an eclipse depth of 92+-21 ppm in the CHEOPS passband, 83+-27 ppm for TESS and >2000 ppm in the IRAC 1-2-4 Spitzer passbands. Using the eclipse depths in the Spitzer bands we propose a set of likely emission spectra which constrain the emission contribution in the \cheops and TESS passbands to approximately a few dozens of parts per million. This allowed us to measure a geometric albedo of 0.21+-0.07 in the CHEOPS passband, while the TESS data lead to a 95\% upper limit of $\sim$0.2. WASP-3 b belongs to the group of ultra-hot Jupiters which are characterized by low Bond albedo (<0.3+-0.1), as predicted by different atmospheric models. On the other hand, it unexpectedly seems to efficiently recirculate the absorbed stellar energy, unlike similar highly irradiated planets. To explain this inconsistency, we propose that other energy recirculation mechanisms may be at play other than advection (for example, dissociation and recombination of H_2). Another possibility is that the observations in different bandpasses probe different atmospheric layers, making the atmospheric analysis difficult without an appropriate modeling of the thermal emission spectrum of WASP-3 b, which is not feasible with the limited spectroscopic data available to date.

The liquid-liquid equilibrium (LLE) curves have been determined for the 2-hydroxyl-benzaldehyde (salicylaldehyde, SAC) + CH$_3$(CH$_2$)$_n$CH$_3$ mixtures ($n$ = 5,6,7,8,9). The equilibrium temperatures were determined observing, by means of a laser scattering technique, the turbidity produced on cooling when a second phase takes place. All the systems show an upper critical solution temperature, which linearly increases with $n$. Intermolecular effects have been investigated in alkanol + benzaldehyde systems using data from the literature. Interactions in 1-alkanol mixtures are mainly of dipolar type. The corresponding excess molar enthalpies, $H_{\text{m}}^{\text{E}}$, are large and positive, which reveals that interactions between like molecules are dominant. Interactions between unlike molecules are stronger for the methanol-containing system. For the other mixtures, the enthalpy of the (1-alkanol)-benzaldehyde interactions remains more or less constant. At 298.15 K and equimolar composition, the replacement of a linear polar solvent by the isomeric aromatic one leads to increased $H_{\text{m}}^{\text{E}}$ values in systems with a given 1-alkanol. The phenol + benzaldehyde system shows strongly negative deviations from the Raoult's law. Proximity effects have been examined in SAC + hydrocarbon mixtures. Alkane-containing systems are essentially characterized by dipolar interactions, while dispersive interactions are prevalent in the solution with benzene. All the mixtures have been treated in terms of DISQUAC. The interaction parameters for the OH/CHO contacts and for the SAC/aromatic and SAC/alkane contacts have been reported. DISQUAC provides a correct description of the thermodynamic properties considered. In the case of SAC systems, this is done by defining a new specific group HO-C-C-CHO for salicylaldehyde.

The coexistence curves of liquid-liquid equilibrium (LLE) for the mixtures: phenylacetonitrile + heptane, + octane, + nonane, + cyclooctane, or + 2,2,4-trimethylpentane and for 3-phenylpropionitrile + heptane, or + octane are reported. Aromatic nitrile + alkane, + aromatic hydrocarbon or + 1 alkanol systems are investigated using a set of thermophysical properties: phase equilibria (solid-liquid, SLE, vapour-liquid, VLE and LLE), excess molar functions, enthalpies ($H_{\text{m}}^{\text{E}}$), isochoric internal energies, isobaric heat capacities ($C_{p \text{m}}^{\text{E}}$) and volumes ($V_{\text{m}}^{\text{E}}$), and the Kirkwood correlation factor. Due to proximity effects between the phenyl and the CN groups, dipolar interactions between molecules of aromatic nitriles are stronger than those between molecules of isomeric linear nitriles. Dipolar interactions become weaker in the order: 3-phenylpropionitrile > phenylacetonitrile > benzonitrile. Benzonitrile + aromatic hydrocarbon mixtures are characterized by dispersive interactions and structural effects. The latter are more important in systems with phenylacetonitrile. Structural effects are also present in benzonitrile + n-alkane, or + 1-alkanol + mixtures. The systems mentioned above have been studied using DISQUAC. Interaction parameters for contacts where the CN group in aromatic nitriles participates are given and DISQUAC results on excess properties and phase equilibria are discussed. 1-Alkanol + benzonitrile mixtures are also investigated by means of the ERAS model. ERAS represents well $H_{\text{m}}^{\text{E}}$ of these systems. The $V_{\text{m}}^{\text{E}}$ curves of solutions with longer 1-alkanols are more poorly described, which has been explained in terms of the existence of structural effects.

Interactions and structure of organic carbonate + alkane, and 1-alkanol + organic carbonate mixtures have been investigated by means of a set of molar excess functions: enthalpies, volumes, isobaric heat capacities, or entropies; and considering internal pressure, liquid-liquid equilibria or permittivity data. In addition, the mentioned systems have been studied using the Flory model and the concentration-concentration structure factor formalism. The mixtures under consideration are characterized by dipolar interactions and by homocoordination (that is, by interactions between like molecules). In systems with a given solvent, dipolar interactions are weakened in the order: propylene carbonate (PC) > dimethyl carbonate (DMC) > diethyl carbonate (DEC). Comparison of mixtures containing DMC or DEC with those involving 2-propanone or 3-pentanone shows that dipolar interactions are not determined merely by values of the dipole moment, but they also depend on the group size. The enthalpies of the alkanol-carbonate interactions have been evaluated from calorimetric data. They are stronger in DMC solutions and become weaker when the alcohol size increases in mixtures with a given carbonate. Application of the Flory model to 43 systems of the type 1-alkanol + carbonate provides a mean relative standard deviation for excess molar enthalpy equal to 0.107. Results reveal that orientational effects decrease in the order DEC > PC > DMC. Orientational effects are particularly relevant in methanol or ethanol + DEC mixtures. Interestingly, the mentioned effects are weaker in 1-alkanol + DMC mixtures than in DMC + alkane systems. A similar trend is observed in DEC solutions when the considered alcohol is longer than ethanol.