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Graph Neural Networks (GNNs) are routinely used in molecular physics, social sciences, and economics to model many-body interactions in graph-like systems. However, GNNs are inherently local and can suffer from information flow bottlenecks. This is particularly problematic when modeling large molecular systems, where dispersion forces and local electric field variations drive collective structural changes. Existing solutions face challenges related to computational cost and scalability. We introduce RANGE, a model-agnostic framework that employs an attention-based aggregation-broadcast mechanism that significantly reduces oversquashing effects, and achieves remarkable accuracy in capturing long-range interactions at a negligible computational cost. Notably, RANGE is the first virtual-node message-passing implementation to integrate attention with positional encodings and regularization to dynamically expand virtual representations. This work lays the foundation for next-generation of machine-learned force fields, offering accurate and efficient modeling of long-range interactions for simulating large molecular systems.

Responsible disclosure limitation is an iterative exercise in risk assessment and mitigation. From time to time, as disclosure risks grow and evolve and as data users' needs change, agencies must consider redesigning the disclosure avoidance system(s) they use. Discussions about candidate systems often conflate inherent features of those systems with implementation decisions independent of those systems. For example, a system's ability to calibrate the strength of protection to suit the underlying disclosure risk of the data (e.g., by varying suppression thresholds), is a worthwhile feature regardless of the independent decision about how much protection is actually necessary. Having a principled discussion of candidate disclosure avoidance systems requires a framework for distinguishing these inherent features of the systems from the implementation decisions that need to be made independent of the system selected. For statistical agencies, this framework must also reflect the applied nature of these systems, acknowledging that candidate systems need to be adaptable to requirements stemming from the legal, scientific, resource, and stakeholder environments within which they would be operating. This paper proposes such a framework. No approach will be perfectly adaptable to every potential system requirement. Because the selection of some methodologies over others may constrain the resulting systems' efficiency and flexibility to adapt to particular statistical product specifications, data user needs, or disclosure risks, agencies may approach these choices in an iterative fashion, adapting system requirements, product specifications, and implementation parameters as necessary to ensure the resulting quality of the statistical product.

A dedicated search for upward-going air showers at zenith angles exceeding $110^\circ$ and energies $E>0.1$ EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two "anomalous" radio pulses observed by the ANITA flights I and III which appear inconsistent with the Standard Model of particle physics. Using simulations of both regular cosmic ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1-33] EeV. One event has been found in the search period between 1 Jan 2004 and 31 Dec 2018, consistent with an expected background of $0.27 \pm 0.12$ events from mis-reconstructed cosmic ray showers. This translates to an upper bound on the integral flux of $(7.2 \pm 0.2) \times 10^{-21}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ and $(3.6 \pm 0.2) \times 10^{-20}$ cm$^{-2}$ sr$^{-1}$ y$^{-1}$ for an $E^{-1}$ and $E^{-2}$ spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an $E^{-3}$ spectrum and over 8.1 events for a conservative $E^{-5}$ spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers., Comment: 10+5 Pages of Manuscript plus Supplemental Material, 3+8 figures, Accepted for publication in Physical Review Letters

Diffuse photons of energy above 0.1 PeV, produced through the interactions between cosmic rays and either interstellar matter or background radiation fields, are powerful tracers of the distribution of cosmic rays in the Galaxy. Furthermore, the measurement of a diffuse photon flux would be an important probe to test models of super-heavy dark matter decaying into gamma-rays. In this work, we search for a diffuse photon flux in the energy range between 50 PeV and 200 PeV using data from the Pierre Auger Observatory. For the first time, we combine the air-shower measurements from a 2 km$^2$ surface array consisting of 19 water-Cherenkov surface detectors, spaced at 433 m, with the muon measurements from an array of buried scintillators placed in the same area. Using 15 months of data, collected while the array was still under construction, we derive upper limits to the integral photon flux ranging from 13.3 to 13.8 km$^{-2}$ sr$^{-1}$ yr$^{-1}$ above tens of PeV. We extend the Pierre Auger Observatory photon search program towards lower energies, covering more than three decades of cosmic-ray energy. This work lays the foundation for future diffuse photon searches: with the data from the next 10 years of operation of the Observatory, this limit is expected to improve by a factor of $\sim$20., Comment: Submitted to JCAP

Phonon decoherence determines the characteristic timescales over which coherent lattice vibrations decay, making it a crucial process for understanding the non-equilibrium dynamics of crystal lattices after excitation by a pump pulse. Here, we report a theoretical and computational investigation of the origin of phonon decoherence within a first-principles many-body framework. We derive quantum kinetic equations for the dynamics of coherent phonons by explicitly accounting for dissipation processes induced by electron-phonon and phonon-phonon interactions. The decoherence rate and frequency renormalization are formulated in terms of the non-equilibrium phonon self energy, providing a framework amenable for ab initio calculations. To validate this approach, we conduct a first-principles study of phonon decoherence for the elemental semimetals antimony and bismuth. The robust agreement with available temperature- and fluence-dependent experimental data confirms the accuracy of our theoretical and computational framework. More generally, our findings reveal that either electron-phonon and phonon-phonon coupling can prevail in determining the decoherence time, depending on the temperature and driving conditions. Overall, this work fills a critical gap in the theoretical understanding of phonon decoherence, providing a predictive framework for determining the timescales of light-induced structural dynamics in driven solids.

JUNO is a huge neutrino detector that will use 20 kton of organic liquid scintillator as its detection medium. The scintillator is a mixture of linear alkyl benzene (LAB), 2.5 g/L of 2,5-diphenyloxazole (PPO) and 3 mg/L of 1,4-Bis(2-methylstyryl)benzene (Bis-MSB). The main goal of JUNO is to determine the Neutrino Mass Ordering [1, 2, 3]. In order to achieve this purpose, good energy and position reconstruction is required, hence a complete understanding of the optical characteristics of the liquid scintillator is mandatory. In this paper we present the measurements on the JUNO scintillator emission spectrum, absorption length and fluorescence time distribution performed respectively with a spectrofluorimeter, a spectrophotometer and a custom made setup

The exploration of ultrafast phenomena is a frontier of condensed matter research, where the interplay of theory, computation, and experiment is unveiling new opportunities for understanding and engineering quantum materials. With the advent of advanced experimental techniques and computational tools, it has become possible to probe and manipulate nonequilibrium processes at unprecedented temporal and spatial resolutions, providing insights into the dynamical behavior of matter under extreme conditions. These capabilities have the potential to revolutionize fields ranging from optoelectronics and quantum information to catalysis and energy storage. This Roadmap captures the collective progress and vision of leading researchers, addressing challenges and opportunities across key areas of ultrafast science. Contributions in this Roadmap span the development of ab initio methods for time-resolved spectroscopy, the dynamics of driven correlated systems, the engineering of materials in optical cavities, and the adoption of FAIR principles for data sharing and analysis. Together, these efforts highlight the interdisciplinary nature of ultrafast research and its reliance on cutting-edge methodologies, including quantum electrodynamical density-functional theory, correlated electronic structure methods, nonequilibrium Green's function approaches, quantum and ab initio simulations.

Quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successful for solving combinatorial optimization problems (COPs). It has been observed that the optimal variational parameters obtained from one instance of a COP can be transferred to another instance, producing sufficiently satisfactory solutions for the latter. In this context, a suitable method for further improving the solution is to fine-tune a subset of the transferred parameters. We numerically explore the role of optimizing individual QAOA layers in improving the approximate solution of the Max-Cut problem after parameter transfer. We also investigate the trade-off between a good approximation and the required optimization time when optimizing transferred QAOA parameters. These studies show that optimizing a subset of layers can be more effective at a lower time-cost compared to optimizing all layers., Comment: 8 pages, 6 figures. Comments are welcome

The impact of cell segregation and margination in blood disorders on microcirculatory hemodynamics within bifurcated vessels are physiologically significant, yet poorly understood. This study presents a comprehensive computational investigation of red blood cell (RBC) suspension dynamics, with a focus on a model of sickle cell disease (SCD) as an example of a disorder associated with subpopulations of aberrant RBCs. The findings reveal how cell margination influences cellular partitioning and distributions as well as vessel wall shear stress (WSS) at vascular bifurcations. Normal RBCs, which migrate toward the channel center, exhibit the Zweifach-Fung effect, preferentially entering high-flow-rate branches. In contrast, sickle cells, which marginate near the vessel wall, demonstrate an anti-Zweifach-Fung effect, favoring lower-flow-rate branches due to their position within the cell-free layer (CFL). The upstream segregation of cells remains downstream through the bifurcation, where sickle cells accumulate along the outer branch walls. This accumulation of sickle cells increases the frequency of high WSS events via direct physical interactions, particularly on the outer side of high-velocity branches, potentially contributing to the vascular damage and endothelial disruption observed in many disorders that affect RBCs. In geometrically asymmetric bifurcations, cells preferentially enter branches with larger radii, underscoring the influence of geometric complexity on microcirculatory blood flow. These findings provide insights into microvascular hemodynamics in SCD and other blood disorders.

In healthcare, the integration of multimodal data is pivotal for developing comprehensive diagnostic and predictive models. However, managing missing data remains a significant challenge in real-world applications. We introduce MARIA (Multimodal Attention Resilient to Incomplete datA), a novel transformer-based deep learning model designed to address these challenges through an intermediate fusion strategy. Unlike conventional approaches that depend on imputation, MARIA utilizes a masked self-attention mechanism, which processes only the available data without generating synthetic values. This approach enables it to effectively handle incomplete datasets, enhancing robustness and minimizing biases introduced by imputation methods. We evaluated MARIA against 10 state-of-the-art machine learning and deep learning models across 8 diagnostic and prognostic tasks. The results demonstrate that MARIA outperforms existing methods in terms of performance and resilience to varying levels of data incompleteness, underscoring its potential for critical healthcare applications.

The aim of this paper is to discuss the notion of Wieferich primes in the context of Drinfeld modules. Our main result is a surprising connection between the proprety of a monic irreducible polynomial $\mathfrak p$ to be Wieferich and the $\mathfrak p$-adic valuation of special $L$-values of Drinfeld modules. This generalizes a theorem of Thakur for the Carlitz module.We also study statistical distributions of Wieferich primes, proving in particular that a place of degree $d$ is Wieferich with the expected probability $q^{-d}$ when we average over large enough sets of Drinfeld modules.

The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices., Comment: Accepted in 2024 IEEE INTERNATIONAL WORKSHOP ON Metrology for Agriculture and Forestry

Cosmic dust plays a crucial role in the evolution of galaxies, significantly influencing star formation and the interstellar medium. However, in active galactic nuclei (AGN), the role and origin of dust remain poorly understood. High-resolution X-ray spectroscopy is a powerful tool for probing the properties of dust in AGN. NGC 6860, an X-ray bright type-1 quasar, is an ideal target for investigating the connection between dust and winds in AGN. It exhibits reddening and X-ray absorption by both dust and winds. By modeling high-resolution X-ray spectra from XMM-Newton and Chandra observations, we determine the properties of dust and outflows in this AGN. Our analysis finds four photoionized components, outflowing with velocities of 50-300 km/s. The first two are relatively highly ionized with logxi = 3.4 and logxi = 2.9. The results of our photoionization modeling suggest that these two components are thermally unstable. The third component is ionized, with logxi = 2.3 and is located further away from the central black hole. The fourth component is less ionized, and is possibly located in the host galaxy. The application of dust models enables us to probe the abundance and location of the dust in NGC 6860. Our findings suggest that dust absorption and reddening originates from the extended narrow-line region and its host galaxy., Comment: Accepted for publication in the Astrophysical Journal (ApJ)

The paper presents a pilot exploration of the construction, management and analysis of a multimodal corpus. Through a three-layer annotation that provides orthographic, prosodic, and gestural transcriptions, the Gest-IT resource allows to investigate the variation of gesture-making patterns in conversations between sighted people and people with visual impairment. After discussing the transcription methods and technical procedures employed in our study, we propose a unified CoNLL-U corpus and indicate our future steps

Given a finite extension $K/F$ of degree $r$ of a finite field $F$, we enumerate all selfdual skew cyclic codes in the Ore quotient ring $K[X;\text{Frob}]/(X^{rk}-1)$ for any positive integer $k$ coprime to the characteristic $p$ (separable case). We also provide an enumeration algorithm when $k$ is a power of $p$ (purely inseparable case), at the cost of some redundancies. Our approach is based on an explicit bijection between skew cyclic codes, on the one hand, and certain families of $F$-linear subspaces of some extensions of $K$. Finally, we report on an implementation in SageMath.

The behavior of many complex systems, from nanostructured materials to animal colonies, is governed by local transitions that, while involving a restricted number of interacting units, may generate collective cascade phenomena. Tracking such local events and understanding how they emerge and propagate throughout these systems represent often a challenge. Common strategies monitor specific parameters, tailored ad hoc to describe certain systems, over time. However, such approaches typically require prior knowledge of the underpinning physics and are poorly transferable to different systems. Here we present LEAP, a general, transferable, agnostic analysis approach that can reveal precious information on the physics of a variety of complex dynamical systems simply starting from the trajectory of their constitutive units. Built on a bivariate combination of two abstract descriptors, LENS and {\tau}SOAP, the LEAP analysis allows (i) detecting the emergence of local fluctuations in simulation or experimentally-acquired trajectories of any type of multicomponent system, (ii) classifying fluctuations into categories, and (iii) correlating them in space and time. We demonstrate how LEAP, just building on the abstract concepts of local fluctuations and their spatiotemporal correlation, efficiently reveals precious insights on the emergence and propagation of local and collective phenomena in a variety of complex dynamical systems ranging from the atomic- to the microscopic-scale. Given its abstract character, we expect that LEAP will offer an important tool to understand and predict the behavior of systems whose physics is unknown a priori, as well as to revisit a variety of known complex physical phenomena under a new perspective.

We develop formal privacy mechanisms for releasing statistics from data with many outlying values, such as income data. These mechanisms ensure that a per-record differential privacy guarantee degrades slowly in the protected records' influence on the statistics being released. Formal privacy mechanisms generally add randomness, or "noise," to published statistics. If a noisy statistic's distribution changes little with the addition or deletion of a single record in the underlying dataset, an attacker looking at this statistic will find it plausible that any particular record was present or absent, preserving the records' privacy. More influential records -- those whose addition or deletion would change the statistics' distribution more -- typically suffer greater privacy loss. The per-record differential privacy framework quantifies these record-specific privacy guarantees, but existing mechanisms let these guarantees degrade rapidly (linearly or quadratically) with influence. While this may be acceptable in cases with some moderately influential records, it results in unacceptably high privacy losses when records' influence varies widely, as is common in economic data. We develop mechanisms with privacy guarantees that instead degrade as slowly as logarithmically with influence. These mechanisms allow for the accurate, unbiased release of statistics, while providing meaningful protection for highly influential records. As an example, we consider the private release of sums of unbounded establishment data such as payroll, where our mechanisms extend meaningful privacy protection even to very large establishments. We evaluate these mechanisms empirically and demonstrate their utility.

We investigate the influence of vibrational screening on the excitonic and optical properties of solids based on first-principles electronic-structure calculations. We solve the Bethe-Salpeter equation - the state-of-the-art description of excitons - by explicitly accounting for phonon-assisted screening effects in the screened Coulomb interaction. In the examples of the polar semiconductors ZnS, MgO, and GaN, the exciton binding energies at the absorption onset are found to be renormalized by a few tens of meV. Similar effects are also found for higher-lying unbound electron-hole pairs, leading to red-shifts of the absorption peaks by up to 50 meV. Our analysis reveals that vibrational screening is dictated by long-range Fr\"ohlich coupling involving polar longitudinal optical phonons, whereas the remaining vibrational degrees of freedom are negligible. Overall, by elucidating the influence of phonon screening on the excitonic states and absorption spectra of these selected ionic semiconductors, this study contributes to advancing the ab initio methodology and the fundamental understanding of exciton-phonon coupling in solids.

Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. We experimentally demonstrate this new approach, reaching an improvement in the relevant figure of merit by a factor of up to 5. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies., Comment: 12 pages including supplementary, 6 figures

Among all the physical platforms for the realization of a Quantum Processing Unit (QPU), neutral atom devices are emerging as one of the main players. Their scalability, long coherence times, and the absence of manufacturing errors make them a viable candidate.. Here, we use a binary classifier model whose training is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implemented on a neutral atom QPU. In particular, we test it on a Credit Card Fraud (CCF) dataset. We propose several versions of the model, including exploiting the model in ensemble learning schemes. We show that one of our proposed versions seems to achieve higher performance and lower errors, validating our claims by comparing the most popular Machine Learning (ML) models with QUBO SVM models trained with ideal, noisy simulations and even via a real QPU. In addition, the data obtained via real QPU extend up to 24 atoms, confirming the model's noise robustness. We also show, by means of numerical simulations, how a certain amount of noise leads surprisingly to enhanced results. Our results represent a further step towards new quantum ML algorithms running on neutral atom QPUs for cybersecurity applications., Comment: 28 pages, 9 figures, 1 table

Multi-arm trials are gaining interest in practice given the statistical and logistical advantages that they can offer. The standard approach is to use a fixed (throughout the trial) allocation ratio, but there is a call for making it adaptive and skewing the allocation of patients towards better performing arms. However, among other challenges, it is well-known that these approaches might suffer from lower statistical power. We present a response-adaptive design for continuous endpoints which explicitly allows to control the trade-off between the number of patients allocated to the 'optimal' arm and the statistical power. Such a balance is achieved through the calibration of a tuning parameter, and we explore various strategies to effectively select it. The proposed criterion is based on a context-dependent information measure which gives a greater weight to those treatment arms which have characteristics close to a pre-specified clinical target. We also introduce a simulation-based hypothesis testing procedure which focuses on selecting the target arm, discussing strategies to effectively control the type-I error rate. The potential advantage of the proposed criterion over currently used alternatives is evaluated in simulations, and its practical implementation is illustrated in the context of early Phase IIa proof-of-concept oncology clinical trials.

In this work, we propose a simple yet effective method to tackle the problem of imbalanced multi-class semantic segmentation in deep learning systems. One of the key properties for a good training set is the balancing among the classes. When the input distribution is heavily imbalanced in the number of instances, the learning process could be hindered or difficult to carry on. To this end, we propose a Dynamic Label Injection (DLI) algorithm to impose a uniform distribution in the input batch. Our algorithm computes the current batch defect distribution and re-balances it by transferring defects using a combination of Poisson-based seamless image cloning and cut-paste techniques. A thorough experimental section on the Magnetic Tiles dataset shows better results of DLI compared to other balancing loss approaches also in the challenging weakly-supervised setup. The code is available at https://github.com/covisionlab/dynamic-label-injection.git, Comment: ECCV 2024 VISION Workshop

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV., Comment: Minor typo in table 4 corrected

Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stands out for its ability to effectively combine modality-specific features during the learning process. This systematic review aims to comprehensively analyze and formalize current intermediate fusion methods in biomedical applications. We investigate the techniques employed, the challenges faced, and potential future directions for advancing intermediate fusion methods. Additionally, we introduce a structured notation to enhance the understanding and application of these methods beyond the biomedical domain. Our findings are intended to support researchers, healthcare professionals, and the broader deep learning community in developing more sophisticated and insightful multimodal models. Through this review, we aim to provide a foundational framework for future research and practical applications in the dynamic field of MDL.

Coherent phonons, light-induced coherent lattice vibrations in solids, provide a powerful route to engineer structural and electronic degrees of freedom using light. In this manuscript, we formulate an ab initio theory of the displacive excitation of coherent phonons (DECP), the primary mechanism for light-induced structural control in semimetals. Our study - based on the ab initio simulations of the ultrafast electron and coherent-phonon dynamics in presence of electron-phonon interactions - establishes a predictive computational framework for describing the emergence of light-induced structural changes and the ensuing transient band-structure renormalization arising from the DECP mechanism. We validate this framework via a combined theoretical and experimental investigation of coherent phonons in the elemental semimetal antimony. Via a Fourier analysis of time- and angle-resolved photoemission spectroscopy (tr-ARPES) measurements, we retrieve information about transient spectral features and quasiparticle renormalization arising from the coherent A1g phonon as a function of momentum, energy, time, and fluence. The qualitative and quantitative agreement between experiment and theory corroborates the first-principles approach formulated in this study. Besides advancing the fundamental understanding of electron-phonon interactions mediated by coherent phonons, this study opens new opportunities for predictively engineering structural and electronic degrees of freedom in semimetals via the DECP mechanism.

Handling missing values in tabular datasets presents a significant challenge in training and testing artificial intelligence models, an issue usually addressed using imputation techniques. Here we introduce "Not Another Imputation Method" (NAIM), a novel transformer-based model specifically designed to address this issue without the need for traditional imputation techniques. NAIM employs feature-specific embeddings and a masked self-attention mechanism that effectively learns from available data, thus avoiding the necessity to impute missing values. Additionally, a novel regularization technique is introduced to enhance the model's generalization capability from incomplete data. We extensively evaluated NAIM on 5 publicly available tabular datasets, demonstrating its superior performance over 6 state-of-the-art machine learning models and 4 deep learning models, each paired with 3 different imputation techniques when necessary. The results highlight the efficacy of NAIM in improving predictive performance and resilience in the presence of missing data. To facilitate further research and practical application in handling missing data without traditional imputation methods, we made the code for NAIM available at https://github.com/cosbidev/NAIM., Comment: 12 pages, 8 figures

Ultra-high-energy cosmic rays are known to be mainly of extragalactic origin, and their propagation is limited by energy losses, so their arrival directions are expected to correlate with the large-scale structure of the local Universe. In this work, we investigate the possible presence of intermediate-scale excesses in the flux of the most energetic cosmic rays from the direction of the supergalactic plane region using events with energies above 20 EeV recorded with the surface detector array of the Pierre Auger Observatory up to 31 December 2022, with a total exposure of 135,000 km^2 sr yr. The strongest indication for an excess that we find, with a post-trial significance of 3.1{\sigma}, is in the Centaurus region, as in our previous reports, and it extends down to lower energies than previously studied. We do not find any strong hints of excesses from any other region of the supergalactic plane at the same angular scale. In particular, our results do not confirm the reports by the Telescope Array collaboration of excesses from two regions in the Northern Hemisphere at the edge of the field of view of the Pierre Auger Observatory. With a comparable exposure, our results in those regions are in good agreement with the expectations from an isotropic distribution., Comment: submitted to ApJ

In the History of Ideas, a succession of philosophical and scientific achievements, concerning the concept of space and its dimensionality, were essential to contribute, after a long period, to the theoretical possibility of thinking physical time with more than one dimension. Meanwhile, such a progress brought with it the expectation that one can either understand the role of dimensionality in the World or disclose how certain physical phenomena depend on it. Some of these issues are sketched throughout the text, as well as those remarkable moments in the History of Science where important contributions were made in order to give a satisfactory answer to the following inescapable question: -- How many dimensions does time have?, Comment: 22 pp. arXiv admin note: substantial text overlap with arXiv:2101.12167