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This study presents a Bayesian hierarchical model for analyzing spatially correlated functional data and handling irregularly spaced observations. The model uses Bernstein polynomial (BP) bases combined with autoregressive random effects, allowing for nuanced modeling of spatial correlations between sites and dependencies of observations within curves. Moreover, the proposed procedure introduces a distinct structure for the random effect component compared to previous works. Simulation studies conducted under various challenging scenarios verify the model's robustness, demonstrating its capacity to accurately recover spatially dependent curves and predict observations at unmonitored locations. The model's performance is further supported by its application to real-world data, specifically PM$_{10}$ particulate matter measurements from a monitoring network in Mexico City. This application is of practical importance, as particles can penetrate the respiratory system and aggravate various health conditions. The model effectively predicts concentrations at unmonitored sites, with uncertainty estimates that reflect spatial variability across the domain. This new methodology provides a flexible framework for the FDA in spatial contexts and addresses challenges in analyzing irregular domains with potential applications in environmental monitoring.

The assumption of a flat universe that follows the cosmological principle, i.e., that the universe is statistically homogeneous and isotropic at large scales, comprises one of the core foundations of the standard cosmological model -- namely, the $\Lambda$CDM paradigm. Nevertheless, it has been seldom tested in the literature. In this work, we assess the validity of this hypothesis by reconstructing the cosmic curvature with currently available observations, such as Type Ia Supernova and Cosmic Chronometers. We do so by means of null tests, given by consistency relations within the standard model scenario, using a non-parametric method -- which allows us to circumvent prior assumptions on the underlying cosmology. We find no statistically significant departure from the cosmological principle and null curvature in our analysis. In addition, we show that future cosmological observations, such as those expected from Hubble parameter measurements from redshift surveys, along with gravitational wave observations as standard sirens, will be able to significantly reduce the uncertainties of current reconstructions., Comment: 7 pages, 3 figures and 1 appendix

Context. Spectroscopic surveys like APOGEE, GALAH, and LAMOST have significantly advanced our understanding of the Milky Way by providing extensive stellar parameters and chemical abundances. Complementing these, photometric surveys with narrow/medium-band filters, such as the Southern Photometric Local Universe Survey (S-PLUS), offer the potential to estimate stellar parameters and abundances for a much larger number of stars. Aims. This work develops methodologies to extract stellar atmospheric parameters and selected chemical abundances from S-PLUS photometric data, which spans ~3000 square degrees using seven narrowband and five broadband filters. Methods. Using 66 S-PLUS colors, we estimated parameters based on training samples from LAMOST, APOGEE, and GALAH, applying Cost-Sensitive Neural Networks (NN) and Random Forests (RF). We tested for spurious correlations by including abundances not covered by the S-PLUS filters and evaluated NN and RF performance, with NN consistently outperforming RF. Including Teff and log g as features improved accuracy by ~3%. We retained only parameters with a goodness-of-fit above 50%. Results. Our approach provides reliable estimates of fundamental parameters (Teff, log g, [Fe/H]) and abundance ratios such as [{\alpha}/Fe], [Al/Fe], [C/Fe], [Li/Fe], and [Mg/Fe] for ~5 million stars, with goodness-of-fit >60%. Additional ratios like [Cu/Fe], [O/Fe], and [Si/Fe] were derived but are less accurate. Validation using star clusters, TESS, and J-PLUS data confirmed the robustness of our methodology. Conclusions. By leveraging S-PLUS photometry and machine learning, we present a cost-effective alternative to high-resolution spectroscopy for deriving stellar parameters and abundances, enabling insights into Milky Way stellar populations and supporting future classification efforts., Comment: 23 pages, 14 Figures

Using a mix of analytical and numerical methods, we construct new rotating, charged "hairy" black hole solutions of $D=5$, ${\cal N}=8$ gauged supergravity that are dual, via the AdS/CFT correspondence, to thermal states in $D=4$, ${\cal N}=4$ SYM at finite chemical and angular potential, thereby complementing and extending the results of [arXiv:1005.1287, arXiv:1806.01849, arXiv:1809.04084]. These solutions uplift to asymptotically AdS$_5 \times S^5$ solutions of Type IIB supergravity with equal angular momenta along AdS$_5$ ($J=J_1=J_2$) and $S^5$ ($Q=Q_1=Q_2=Q_3$). As we lower the mass $E$ at fixed $Q$ and $J$, the known Cveti\v{c}-L\"u-Pope (CLP) black holes are unstable to scalar condensation and the hairy black holes constructed here emerge as novel solutions associated to the instability. In the region of phase space where the CLP and hairy black holes coexist, the hairy black holes dominate the microcanonical ensemble and, therefore, describe a new thermodynamic phase of SYM. The hairy black holes extend beyond the CLP extremality surface all the way to the BPS surface, defined by $E = 3 Q + 2 J / L$. Through a combination of analytical and numerical techniques, we argue that the BPS limit of the hairy black holes is a singular, horizonless solution, and $not$ a new two-parameter family of BPS black holes that extend the known one-parameter Gutowski-Reall (GR) black hole solution, in contradiction with the conjectures of [arXiv:1005.1287, arXiv:1806.01849]. To further support our conclusions, we perform a near-horizon analysis of the BPS equations and argue that they do not admit any regular solutions with an horizon., Comment: 71 pages, 18 figures

This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VIO framework is incorporating a GAN-based pre-processing module and a hybrid CNN-LSTM module for precise pose estimation, using visibility-enhanced underwater images and raw IMU data. Accurate pose estimation is paramount for various underwater robotics and exploration applications. However, underwater visibility is often compromised by suspended particles and attenuation effects, rendering visual-inertial pose estimation a formidable challenge. DU-VIO aims to overcome these limitations by effectively removing visual disturbances from raw image data, enhancing the quality of image features used for pose estimation. We demonstrate the effectiveness of DU-VIO by calculating RMSE scores for translation and rotation vectors in comparison to their reference values. These scores are then compared to those of a base model using a modified AQUALOC Dataset. This study's significance lies in its potential to revolutionize underwater robotics and exploration. DU-VIO offers a robust solution to the persistent challenge of underwater visibility, significantly improving the accuracy of pose estimation. This research contributes valuable insights and tools for advancing underwater technology, with far-reaching implications for scientific research, environmental monitoring, and industrial applications.

This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, leveraging spiking neural network architectures, promises sophisticated artificial intelligence with significantly reduced computational requirements and power consumption, emulating human brain operational principles. Despite documented neuromorphic technology applications in various robotic domains, its utilization in marine robotics remains largely unexplored. Thus, this article proposes a unified framework for integrating neuromorphic technologies for perception, pose estimation, and haptic-guided conditional control of underwater vehicles, customized to specific user-defined objectives. This conceptual framework stands to revolutionize underwater robotics, enhancing efficiency and autonomy while reducing energy consumption. By enabling greater adaptability and robustness, this advancement could facilitate applications such as underwater exploration, environmental monitoring, and infrastructure maintenance, thereby contributing to significant progress in marine science and technology.

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A

This article introduces a novel approach to the mathematical development of Ordinary Least Squares and Neural Network regression models, diverging from traditional methods in current Machine Learning literature. By leveraging Tensor Analysis and fundamental matrix computations, the theoretical foundations of both models are meticulously detailed and extended to their complete algorithmic forms. The study culminates in the presentation of three algorithms, including a streamlined version of the Backpropagation Algorithm for Neural Networks, illustrating the benefits of this new mathematical approach., Comment: 16 pages, 3 algorithms

The symmetrized Asymptotic Mean Value Laplacian $\tilde{\Delta}$, obtained as limit of approximating operators $\tilde{\Delta}_r$, is an extension of the classical Euclidean Laplace operator to the realm of metric measure spaces. We show that, as $r \downarrow 0$, the operators $\tilde{\Delta}_r$ eventually admit isolated eigenvalues defined via min-max procedure on any compact locally Ahlfors regular metric measure space. Then we prove $L^2$ and spectral convergence of $\tilde{\Delta}_r$ to the Laplace--Beltrami operator of a compact Riemannian manifold, imposing Neumann conditions when the manifold has a non-empty boundary., Comment: 35 pages, all comments welcome

We demonstrate the effect of pulse shaping in momentum selective atomic Bragg diffraction. We compare temporal square pulses, which produce sidelobes in momentum space, with other shapes which can produce more nearly square momentum distributions. We produce pulses that simultaneously address two sets of velocity classes and demonstrate that we can control the differential phase imprinted on them in a way that is insensitive to laser phase fluctuations. Our work marks a significant step forward in testing Bell inequalities using massive particles entangled in momentum.

Quantum mechanics started out as a theory to describe the smallest scales of energy in Nature. After hundred years of development it is now routinely employed to describe, for example, quantum computers with thousands of qubits. This tremendous progress turns the debate of foundational questions into a technological imperative. In what follows we introduce a model of a quantum measurement process that consistently includes the impact of having access only to finite resources when describing a macroscopic system, like a measurement apparatus. Leveraging modern tools from equilibration of closed systems and typicality, we show how the collapse can be seen as an effective description of a closed dynamics, of which we do not know all its details. Our model is then exploited to address the ``Wigner Friend Scenario'', and we observe that an agreement is reached when both Wigner and his friend acknowledge their finite resources perspective and describe the measurement process accordingly., Comment: 7+2 pages, 2 figures. Comments are welcome

Many systems, classical or quantum, closed or open, exhibit universal statistical properties. Exciton-polariton condensates, being intrinsically driven-dissipative, offer a promising platform for observing non-equilibrium universal features. By conducting extensive numerical simulations of an incoherently pumped and interacting condensate coupled to an exciton reservoir we show that the effective nonlinearity of the condensate phase dynamics can be finely adjusted across a broad range, by varying the exciton-polariton interaction strength, allowing one to probe three main universal regimes with parameters accessible in current experiments: the weakly nonlinear Edwards-Wilkinson (EW) regime, where the phase fluctuations dominate, but the phase profile does not become rough, the strongly non-linear Kardar-Parisi-Zhang regime, where the condensate phase fluctuations grow in a superdiffusive manner leading to roughening of the phase, and a vortex-dominated phase emerging at stronger interactions, where both density and phase dynamics play significant roles. Our results provide a unified picture of the phase diagram of 2d exciton-polariton condensates under incoherent pumping, and shed light on recent experimental and numerical observations., Comment: 17 pages, 10 figures

This paper presents near-infrared spectropolarimetric and velocimetric observations of the young planet-hosting T Tauri star PDS 70, collected with SPIRou at the 3.6m Canada-France-Hawaii Telescope from 2020 to 2024. Clear Zeeman signatures from magnetic fields at the surface of PDS 70 are detected in our data set of 40 circularly polarized spectra. Longitudinal fields inferred from Zeeman signatures, ranging from -116 to 176 G, are modulated on a timescale of 3.008$\pm$0.006 d, confirming that this is the rotation period of PDS 70. Applying Zeeman-Doppler imaging to subsets of unpolarized and circularly polarised line profiles, we show that PDS 70 hosts low-contrast brightness spots and a large-scale magnetic field in its photosphere, featuring in particular a dipole component of strength 200-420 G that evolves on a timescale of months. From the broadening of spectral lines, we also infer that PDS 70 hosts a small-scale field of 2.51$\pm$0.12 kG. Radial velocities derived from unpolarized line profiles are rotationally modulated as well, and exhibit additional longer-term chromatic variability, most likely attributable to magnetic activity rather than to a close-in giant planet (with a 3sigma upper limit on its minimum mass of ~4 Mjup at a distance of ~0.2 au). We finally confirm that accretion occurs at the surface of PDS 70, generating modulated red-shifted absorption in the 1083.3-nm He i triplet, and show that the large-scale magnetic field, often strong enough to disrupt the inner accretion disc up to the corotation radius, weakens as the star gets fainter and redder (as in 2022), suggesting that dust from the disc more easily penetrates the stellar magnetosphere in such phases., Comment: MNRAS in press (20 pages, 12 figures, 5 tables)

Traditional natural disaster response involves significant coordinated teamwork where speed and efficiency are key. Nonetheless, human limitations can delay critical actions and inadvertently increase human and economic losses. Agentic Large Vision Language Models (LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-economic impact, particularly by improving resilience and resource access in underdeveloped regions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to automate tasks in post-disaster management, including on-site assessment, emergency alerts, resource allocation, and recovery planning. By coordinating four specialised LVLM agents with GPT-4 as the core model, DisasTeller autonomously implements disaster response activities, reducing human execution time and optimising resource distribution. Our evaluations through both LVLMs and humans demonstrate DisasTeller's effectiveness in streamlining disaster response. This framework not only supports expert teams but also simplifies access to disaster management processes for non-experts, bridging the gap between traditional response methods and LVLM-driven efficiency., Comment: 13 pages, 4 figures

This paper aims to review the radical role of AI and deep learning in various functional areas of the business, such as marketing, finance, operations, human resources and customer service. Thus, based on the overview of the latest research and practices focusing on AI technologies in different industries, the possibilities of improving organizational efficiency by personalized AI for making decisions based on big data and personalizing clients' interactions with organizations are presented and discussed. Several operational issues, ethical concerns, and regulatory concerns have also been discussed in the review of the literature. Moreover, it covers material applications in the healthcare sector, the retail and manufacturing industry, agriculture and farming, and finance before considering possible future developments and themes for further investigation. Drawing from this revolutionary ethnographic review, organizations aiming to implement strategic and responsible optimization benefit from detailed guides., Comment: 8 pages

We report on the reactions $\psi(3770)\to \bar{\Omega}^+ \bar{K} \Xi $ and $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*(1530)~(\Xi^*(1530)\to \pi\Xi $), and calculate the mass distributions $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi)}$ and $\frac{{\rm d}\Gamma}{{\rm d}M_\text{inv}(\bar{K}\Xi^*)}$, respectively. We obtain clear peaks corresponding to the $\Omega(2012)$. From the decay of $\psi(3770)\to \bar{\Omega}^+ \bar{K}\Xi^*$, we also get a second, broader, peak around $2035\,\rm MeV$, which comes from the $\Omega(2012)$ decay to $\bar{K}\Xi^*$. This second peak is closely tied to the molecular picture of the $\Omega(2012)$ with the $\bar{K}\Xi^*(1530)$ and $\eta\Omega$ components. Its observation would provide a boost to the molecular picture of the $\Omega(2012)$., Comment: 6 pages, 5 figures;V2: new reference and footnote added

Objective: Scientific publications play a crucial role in uncovering insights, testing novel drugs, and shaping healthcare policies. Accessing the quality of publications requires evaluating their Risk of Bias (RoB), a process typically conducted by human reviewers. In this study, we introduce a new dataset for machine reading comprehension and RoB assessment and present RoBIn (Risk of Bias Inference), an innovative model crafted to automate such evaluation. The model employs a dual-task approach, extracting evidence from a given context and assessing the RoB based on the gathered evidence. Methods: We use data from the Cochrane Database of Systematic Reviews (CDSR) as ground truth to label open-access clinical trial publications from PubMed. This process enabled us to develop training and test datasets specifically for machine reading comprehension and RoB inference. Additionally, we created extractive (RoBInExt) and generative (RoBInGen) Transformer-based approaches to extract relevant evidence and classify the RoB effectively. Results: RoBIn is evaluated across various settings and benchmarked against state-of-the-art methods for RoB inference, including large language models in multiple scenarios. In most cases, the best-performing RoBIn variant surpasses traditional machine learning and LLM-based approaches, achieving an ROC AUC of 0.83. Conclusion: Based on the evidence extracted from clinical trial reports, RoBIn performs a binary classification to decide whether the trial is at a low RoB or a high/unclear RoB. We found that both RoBInGen and RoBInExt are robust and have the best results in many settings.

Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum error-correcting codes. We present new $n$-dimensional toric quantum codes, where $n\geq 5$, which are featured by lattice codes and apply the proposed quantum interleaving method to such new $n$-dimensional toric quantum codes. Through the application of this method to these novel $n$-dimensional toric quantum codes we derive new $n$-dimensional quantum burst-error-correcting codes. Consequently, $n$-dimensional toric quantum codes and burst-error-correcting quantum codes are provided offering both a good code rate and a significant coding gain when it comes to toric quantum codes. Another important consequence from the presented $n$-dimensional toric quantum codes is that if the Golomb and Welch conjecture in \cite{perfcodes} regarding the Lee sphere in $n$ dimensions for the respective close packings holds true, then it follows that these $n$-dimensional toric quantum codes are the only possible ones to be obtained from lattice codes. Moreover, such a methodology can be applied for burst error correction in cases involving localized errors, quantum data storage and quantum channels with memory., Comment: This manuscript is the proposed generalization of the work New Three and Four-Dimensional Toric and Burst-Error-Correcting Quantum Codes

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside industry R&D labs. In this work, we pair high-performance computing resources including Frontier supercomputer, America's first exascale system, and high-resolution optical RS data to pretrain billion-scale FMs. Our study assesses performance of different pretrained variants of vision Transformers across image classification, semantic segmentation and object detection benchmarks, which highlight the importance of data scaling for effective model scaling. Moreover, we discuss construction of a novel TIU pretraining dataset, model initialization, with data and pretrained models intended for public release. By discussing technical challenges and details often lacking in the related literature, this work is intended to offer best practices to the geospatial community toward efficient training and benchmarking of larger FMs.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

We explore a protocol that efficiently charges multiple open quantum batteries in parallel using a single charger. This protocol shows super-extensive charging through collective coupling of the charger and the battery to the same thermal reservoir. When applied to multiple quantum batteries, each coupled to different thermal reservoirs, the energy cannot be efficiently transferred from the charger to the battery via collective dissipation alone. We show that the counter-intuitive act of incorporating both dissipation and incoherent collective pumping on the charger enables efficient parallel charging of many quantum batteries., Comment: 5 pages, 2 figures

Architecting mechanisms of damage in metamaterials by leveraging lattice topology and geometry poses a vital yet complex challenge, essential for engineering desirable mechanical responses. Of these metamaterials, Maxwell lattices, which are on the verge of mechanical stability, offer significant potential for advanced functionality. By leveraging their robust topological features, they enable precise control of effective elastic properties, manipulation of stress localisation and delocalisation across specific domains, and targeted global damage that follows local fracture events. In this work, we identify topology and geometry-dependent parameters that establish a simple, yet precise, framework for designing the behavior of non-idealized Maxwell lattices and their damage processes. Using a beam element simulator, we systematically explore the underlying phenomenology to demonstrate how this framework can guide or arrest damage in lattices, both with and without domain walls and additional boundary constraints. Our approach uncovers a robust way to manipulate the mechanisms of damage and the path they follow in metamaterials, with further insight into crack arrest, diversion, and shielding.

It is shown how spin one vector matter fields can be coupled to a Yang-Mills theory. Such matter fields are defined as belonging to a representation $R$ of this Yang-Mills gauge algebra $\mathfrak{g}$. It is also required that these fields together with the original gauge fields be the gauge fields of an embedding total gauge algebra $\mathfrak{g}_{\rm tot}$. The existence of a physically consistent Yang-Mills action for the total algebra is finally required. These conditions are rather restrictive, as shown in some examples: non-trivial solutions may or may not exist depending on the choice of the original algebra $\mathfrak{g}$ and of the representation $R$. The case of the initial algebra $\mathfrak{g}$ = $\mathfrak{u}(1)\oplus\mathfrak{su}(2)$ is treated in more detail., Comment: Latex, 21 pages, 2 ancillary files: "Example_G=U1xSU2.nb" and "Example_G=U1xSU2.pdf". Added references, added commentaries in the Introduction and the Conclusion, small changes in the text, results unchanged

Liquid crystals are known for their optical birefringence, a property that gives rise to intricate patterns and colors when viewed in a microscope between crossed polarisers. Resulting images are rich in geometric patterns and serve as valuable fingerprints of the liquid crystal's intrinsic properties. By using machine learning techniques, it is possible to extract from the images information about, e.g., liquid crystal elastic constants, the scalar order parameter, local orientation of the director, etc. Machine learning can also be employed to identify phase transitions and classify different liquid crystalline phases and topological defects. In addition to well studied singular defects such as point or line disclinations, liquid crystals can also host non-singular solitonic defects such as skyrmions, hopfions, and torons. The solitons, with their localised and stable configurations, offer an alternative view into material properties and behaviour of liquid crystals. In this study, we demonstrate that the optical signatures of skyrmions can be utilised effectively in machine learning to predict important system parameters. Our method focuses specifically on the skyrmion-localised regions, reducing significantly the computational cost. By training convolutional neural networks on simulated polarised optical microscopy images of liquid crystal skyrmions, we showcase the ability of trained networks to accurately predict several selected parameters such as the free energy, cholesteric pitch, and strength of applied electric fields. This study highlights the importance of localized topologically arrested order parameter configurations for materials characterisation research empowered by state-of-the-art data science methods, and may pave the way for the development of advanced skyrmion-based applications.

Recent years witnessed remarkable progress in computational histopathology, largely fueled by deep learning. This brought the clinical adoption of deep learning-based tools within reach, promising significant benefits to healthcare, offering a valuable second opinion on diagnoses, streamlining complex tasks, and mitigating the risks of inconsistency and bias in clinical decisions. However, a well-known challenge is that deep learning models may contain up to billions of parameters; supervising their training effectively would require vast labeled datasets to achieve reliable generalization and noise resilience. In medical imaging, particularly histopathology, amassing such extensive labeled data collections places additional demands on clinicians and incurs higher costs, which hinders the art's progress. Addressing this challenge, researchers devised various strategies for leveraging deep learning with limited data and annotation availability. In this paper, we present a comprehensive review of deep learning applications in histopathology, with a focus on the challenges posed by data scarcity over the past decade. We systematically categorize and compare various approaches, evaluate their distinct contributions using benchmarking tables, and highlight their respective advantages and limitations. Additionally, we address gaps in existing reviews and identify underexplored research opportunities, underscoring the potential for future advancements in this field., Comment: 36 pages

Hydroelectricity accounted for roughly 66% of the total generation in Brazil in 2023 and addressed most of the intermittency of wind and solar generation. Thus, one of the most important steps in the operation planning of this country is the forecast of the natural inflow energy (NIE) time series, an approximation of the energetic value of the water inflows. To manage water resources over time, the Brazilian system operator performs long-term forecasts for the NIE to assess the water values through long-term hydrothermal planning models, which are then used to define the short-term merit order in day-ahead scheduling. Therefore, monitoring optimistic bias in NIE forecasts is crucial to prevent an optimistic view of future system conditions and subsequent riskier storage policies. In this article, we investigate and showcase strong evidence of an optimistic bias in the official NIE forecasts, with predicted values consistently exceeding the observations over the past 12 years in the two main subsystems (Southeast and Northeast). Rolling window out-of-sample tests conducted with real data demonstrate that the official forecast model exhibits a statistically significant bias of 6%, 13%, 18%, and 23% for 1, 6, 12, and 24 steps ahead in the Southeast subsystem, and 19%, 57%, 80%, and 108% in the Northeast.

Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature. In this paper, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population., Comment: 30 pages, 8 figures

We bridge sheaves of rings over a topological space with common meadows (algebraic structures where the inverse for multiplication is a total operation). More specifically, we show that the subclass of pre-meadows with $\mathbf{a}$, coming from the lattice of open sets of a topological space $X$, and presheaves over $X$ are the same structure. Furthermore, we provide a construction that, given a sheaf of rings $\mathcal{F}$ on $X$ produces a common meadow as a disjoint union of elements of the form $\mathcal{F}(U)$ indexed over the open subsets of $X$. We also establish a correspondence between the process of going from a presheaf to a sheaf (called sheafification) and the process of going from a pre-meadow with $\mathbf{a}$ to a common meadow.

Major Depressive Disorder and anxiety disorders affect millions globally, contributing significantly to the burden of mental health issues. Early screening is crucial for effective intervention, as timely identification of mental health issues can significantly improve treatment outcomes. Artificial intelligence (AI) can be valuable for improving the screening of mental disorders, enabling early intervention and better treatment outcomes. AI-driven screening can leverage the analysis of multiple data sources, including facial features in digital images. However, existing methods often rely on controlled environments or specialized equipment, limiting their broad applicability. This study explores the potential of AI models for ubiquitous depression-anxiety screening given face-centric selfies. The investigation focuses on high-risk pregnant patients, a population that is particularly vulnerable to mental health issues. To cope with limited training data resulting from our clinical setup, pre-trained models were utilized in two different approaches: fine-tuning convolutional neural networks (CNNs) originally designed for facial expression recognition and employing vision-language models (VLMs) for zero-shot analysis of facial expressions. Experimental results indicate that the proposed VLM-based method significantly outperforms CNNs, achieving an accuracy of 77.6% and an F1-score of 56.0%. Although there is significant room for improvement, the results suggest that VLMs can be a promising approach for mental health screening, especially in scenarios with limited data.

Solitons in liquid crystals are spatially localised stable configuration of the liquid crystal orientational order parameter that exhibit emergent particle-like properties such as mutual interaction, translational motion and reconfigurable self-asembling behaviour in oscillating electric fields. Understanding solitons' out-of-equilibrium interactions remains a significant challenge. In this study, we present a minimalist model capable to address this challenge. We derive analytically elastic multipole interaction potentials between two solitons assuming two dimensional LC configurations. We determine the soliton's elastic dipole and quadrupole moments based on the equilibrium director textures obtained as minimizers of the Frank-Oseen free energy functional. Incorporating the time dependence of the soliton's multipole moments in response to electric field oscillations, we carry out particle-based simulations that demonstrate the formation of soliton chains and two dimensional clusters by varying the amplitude and/or the frequency of the electric field, in agreement with experiments. The presented formalism may serve as a benchmark model for future theoretical developments incorporating, e.g. higher order elastic multipoles or/and many body interactions.

Robot-assisted ultrasound scanning promises to advance autonomous and accessible medical imaging. However, ensuring patient safety and compliant human-robot interaction (HRI) during probe contact poses a significant challenge. Most existing systems either have high mechanical stiffness or are compliant but lack sufficient force and precision. This paper presents a novel single-degree-of-freedom end-effector for safe and accurate robotic ultrasound imaging, using a quasi-direct drive actuator to achieve both passive mechanical compliance and precise active force regulation, even during motion. The end-effector demonstrates an effective force control bandwidth of 100 Hz and can apply forces ranging from 2.5N to 15N. To validate the end-effector's performance, we developed a novel ex vivo actuating platform, enabling compliance testing of the end-effector on simulated abdominal breathing and sudden patient movements. Experiments demonstrate that the end-effector can maintain consistent probe contact during simulated respiratory motion at 2.5N, 5N, 10N, and 15N, with an average force tracking RMS error of 0.83N compared to 4.70N on a UR3e robot arm using conventional force control. This system represents the first compliant ultrasound end-effector tested on a tissue platform simulating dynamic movement. The proposed solution provides a novel approach for designing and evaluating compliant robotic ultrasound systems, advancing the path for more compliant and patient-friendly robotic ultrasound systems in clinical settings.

This work presents the design, fabrication, and experimental validation of a refractive diamond axicon for X-ray beam shaping. The diamond axicon was developed to overcome the limitations of polymer-based axicons particularly for application in Transmission X-ray Microscopy (TXM) systems, offering superior mechanical strength, thermal stability, and radiation resistance, making it ideal for synchrotron applications. The axicon was fabricated using femtosecond laser ablation and tested at 11 keV under various coherence conditions. Results demonstrated that the axicon efficiently transformed the X-ray beam into a ring-shaped profile with over 80% transmission. Simulations confirmed the experimental findings and highlighted the potential for further improvements. This work paves the way for the use of diamond axicons in next-generation synchrotron facilities, with future efforts focusing on optimizing fabrication and testing the axicon in full TXM systems.

Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmography (PPG) is more practical due to its affordability and widespread use in wearable devices. However, state-of-the-art sleep staging methods often require prolonged continuous signal acquisition, making them impractical for wearable devices due to high energy consumption. Shorter signal acquisitions are more feasible but less accurate. Our work proposes an adapted sleep staging model based on top-performing state-of-the-art methods and evaluates its performance with different PPG segment sizes. We concatenate 30-second PPG segments over 15-minute intervals to leverage longer segment contexts. This approach achieved an accuracy of 0.75, a Cohen's Kappa of 0.60, an F1-Weighted score of 0.74, and an F1-Macro score of 0.60. Although reducing segment size decreased sensitivity for deep and REM stages, our strategy outperformed single 30-second window methods, particularly for these stages., Comment: 11 pages, 5 figures, 1 table

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.

This work proposes a parametric probabilistic approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed approach is validated using fatigue life experiments available in the literature.

We have investigated the Raman spectrum and excitonic effects of the novel two-dimensional Ta$_2$Ni$_3$Te$_5$ structure. The monolayer is an indirect band gap semiconductor with an electronic band gap value of 0.09 eV and 0.38 eV, determined using GGA-PBE and HSE06 exchange-correlation functionals, respectively. Since this structure is energetically, dynamically, and mechanically stable, it could be synthesized as a free-standing material. We identify ten Raman and ten infrared active modes for various laser energies, including those commonly used in Raman spectroscopy experiments. It was also observed that the contribution of Ni atoms is minimal in most Raman vibrational modes. In contrast, most infrared vibrational modes do not involve the vibration of the Ta atoms. As far as the optical properties are concerned, this monolayer shows a robust linear anisotropy, an exciton binding energy of 287 meV, and also presents a high reflectivity in the ultraviolet region, which is more intense for linear light polarization along the x-direction.

The rolling tachyon is a non-canonical scalar field model well motivated in string theory which naturally predicts variations of the fine-structure constant. Such variations can in principle lead to interesting observable consequences, but they can also lead to extremely tight constraints on these kinds of models. In this work we subject the rolling tachyon model evolving in a variety of potentials to current data and show that most cosmologically interesting evolutions are already strongly excluded. We find $|1+w_0| < 10^{-3}$ from cosmological data and $|1+w_0| < 10^{-9}$ from fine-structure data, leaving the rolling tachyon to either play a role almost entirely equivalent to a cosmological constant or that of a test field. We also find that in most of the allowed parameter space the field evolves very slowly, allowing its evolution to be approximated as an equivalent canonical scalar field., Comment: 31 pages, 19 figures, 1 table. Published in JCAP - [v2: Added figure 13, extended the conclusion, and corrected a few typos]

The development of electric vehicles has led to a growing need for more efficient and environmentally friendly batteries. As a result, there is significant interest in researching new materials and techniques to enhance battery efficiency. One such material being explored is bismuth ferrite (BiFeO$_3$ or BFO), a perovskite with versatile properties. Researchers are particularly intrigued by the potential to control its antiferromagnetic magnetization using magnetic or electric fields. Here, a comprehensive analysis of BFO was conducted, with a focus on its behavior when subjected to oscillating magnetic fields. The research revealed that BFO is sensitive to the frequency and shape of these magnetic fields, leading to the discovery of a new effect related to the transmission of electromagnetic signals on its surface. This effect resulted in a significant increase in the power of the electromagnetic signal, representing a major technological breakthrough. According to the findings, this gain in power has not been observed in any system of this kind before. The study also demonstrated that BFO has the ability to detect magnetic fields through electrical output signals and vice versa, which is crucial for assessing the state and efficiency of batteries, thus contributing to significant advancements in energy storage technology.

We analyze the latest LHCb data on the $\pi^+\pi^-$ spectrum in the isospin-violating $X(3872)~\to~J/\psi \pi^+\pi^-$ decay, employing a model-independent approach based on dispersion theory to deal with the $\pi\pi$ final state interactions. Additionally, the isospin breaking effects are properly introduced, allowing for reliable and accurate extraction of the ratio, $R_X$, between the $X(3872)$ couplings to the $J/\psi \rho$ and $J/\psi \omega$ channels from the data. We find very good agreement with the LHCb data for the whole range of the $\pi^+\pi^-$ invariant mass, and $R_X$ is determined to be $0.19\pm 0.02$, significantly smaller than what was determined earlier. Using this value, we make predictions for the $\pi^+\pi^0\pi^-$ mass distribution in the $X(3872)~\to~J/\psi \pi^+\pi^0\pi^-$ process, which is currently accessible by the BESIII Collaboration, and update a prediction for the pole positions of the isovector partner states of the $X(3872)$, $W_{c1}$, with $I(J^{PC})=1(1^{++})$., Comment: 9 pages, 1 table and 2 figures

We explore the phase diagram of the Mott metal-insulator transition (MIT), focusing on the effects of particle-hole asymmetry (PHA) in the single-band Hubbard model. Our dynamical mean-field theory (DMFT) study reveals that the introduction of PHA in the model significantly influences the critical temperature ($T_c$) and interaction strength ($U_c$), as well as the size of the co-existence region of metallic and insulating phases at low temperatures. Specifically, as the system is moved away from particle-hole symmetry, $T_c$ decreases and $U_c$ increases, indicating a suppression of the insulating phase and the strengthening of the metallic behavior. Additionally, the first-order transition line between metallic and insulating phases is better defined in the model with PHA, leading to a reduced co-existence region at $T