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Self-organized criticality is a dynamical system property where, without external tuning, a system naturally evolves towards its critical state, characterized by scale-invariant patterns and power-law distributions. In this paper, we explored a self-organized critical dynamic on the Sierpinski carpet lattice, a scale-invariant structure whose dimension is defined as a power-law with a non-integer exponent, i.e. a fractal. To achieve this, we proposed an Ising-BCP (bond-correlated percolation) model as the foundation for investigating critical dynamics. Within this framework, we outlined a feedback mechanism for critical self-organization and followed an algorithm for its numerical implementation. The results obtained from the algorithm demonstrated enhanced efficiency when driving the Sierpinski carpet towards critical self-organization compared to a two-dimensional lattice. This efficiency was attributed to the iterative construction of the lattice and the distribution of spins within it. The key outcome of our findings is a novel dependence of self-organized criticality on topology for this particular model, which may have several applications in fields regarding information transmission.

Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet. In order to show how CIA can be used to search for an optimal augmentation pipeline of training data, we study human object detection in a data constrained scenario, using YOLOv8n on COCO and Flickr30k datasets. We have recorded significant improvement using CIA-generated images, approaching the performances obtained when doubling the amount of real images in the dataset. Our findings suggest that our modular framework can significantly enhance object detection systems, and make it possible for future research to be done on data-constrained scenarios. The framework is available at: github.com/multitel-ai/CIA.

Binary black holes are the most abundant source of gravitational-wave observations. Gravitational-wave observatories in the next decade will require tremendous increases in the accuracy of numerical waveforms modeling binary black holes, compared to today's state of the art. One approach to achieving the required accuracy is using spectral-type methods that scale to many processors. Using the SpECTRE numerical-relativity code, we present the first simulations of a binary black hole inspiral, merger, and ringdown using discontinuous Galerkin methods. The efficiency of discontinuous Galerkin methods allows us to evolve the binary through ~18 orbits at reasonable computational cost. We then use SpECTRE's Cauchy Characteristic Evolution (CCE) code to extract the gravitational waves at future null infinity. The open-source nature of SpECTRE means this is the first time a spectral-type method for simulating binary black hole evolutions is available to the entire numerical-relativity community., Comment: 32 pages, 12 figures, 28 ancillary input files

We present the Cosmic Piano, an innovative outreach tool designed to detect muons from air showers produced by cosmic rays colliding with the atmosphere. The Cosmic Piano consists of scintillator-based modules with avalanche photodiodes (APD) readout, which produce a musical note and a flash of light whenever a muon passes through one of them and triggers its electronics. The Cosmic Piano has been showcased at various high-profile events, including the Montreux Jazz Festival and the EuroScience Open Forum, seamlessly fusing scientific phenomena with live music. Building on its success, we have developed a new, comprehensive Masterclass session aimed at engaging high school and university students. This session includes analysing both simulated cosmic ray events and real data from the Cosmic Piano. The Masterclass provides participants with a hands-on experience that mirrors the experimental procedures used in major physics experiments, such as those conducted at CERN with the Large Hadron Collider (LHC) collision data. This initiative offers a unique opportunity for students to engage with an active experiment, bridging the gap between outreach and serious scientific research., Comment: 6 pages, 6 figures

Radio surveys typically sample extragalactic sources in higher redshift regimes than is typical for optical surveys, resulting in many radio sources not having a detected optical counterpart. Over the next decade the Legacy Survey of Space and Time (LSST) will be performing the deepest ($i < 26.4\,$mag) wide-area optical survey to date increasing the fraction of radio sources for which we have optical data. In this Research Note we use the Hyper Suprime-Cam survey to analyse how the fraction of radio sources in the Very Large Array Sky Survey (VLASS) with optical detections varies as a function of $i$-band magnitude and extrapolate to predict the number of optical counterparts we expect LSST to detect. Assuming a final VLASS point source depth of $S_{3\,\text{GHz}}\lesssim350\,\mu$Jy, we expect LSST to identify optical counterparts to $\sim 10^6$ radio sources in VLASS., Comment: 3 pages, 1 figure

We determine the temperature fluctuations associated with the Higgs boson $p_T$ spectrum through the derivation of the string tension distribution corresponding to the QCD-based Hagedorn function, frequently used to fit the transverse momentum distribution (TMD). The identified string tension fluctuations are heavy tailed, behaving similarly to the $q$-Gaussian distribution. After the convolution with the Schwinger mechanism, both approaches correctly describe the entire TMD. This approach is the onset for the nonthermal description of the particle production in ultrarelativistic pp collisions. By analyzing the data of pp collisions at $\sqrt{s} =13$ TeV, we found that the average temperature associated with the Higgs boson differential cross section is around 85 times greater than the estimated value for the charged particle TMD. Our results show that the Higgs boson production exhibits the largest deviation from the thermal description., Comment: 7 pages, 3 figures, accepted for publication in PRC

The coupling between antiferromagnetic spins and infrared-active phonons in solids is responsible for many intriguing phenomena and is a field of intense research with extensive potential applications in the modern devices based on antiferromagnetic spintronics and phononics. Insulating rutile antiferromagnetic crystal CoF$_2$ is one of the model materials for studying nonlinear magnetophononics due to the strong spin-lattice coupling as a result of the orbitally degenerate ground state of Co$^{2+}$ ions manifested in the plethora of static and induced piezomagnetic effects. Here we report results on the complete infrared spectroscopy study of lattice and magnetic dynamics in CoF$_2$ in a wide temperature range and their careful analysis. We observed that infrared-active phonons demonstrate frequency shifts at the antiferromagnetic ordering. Furthermore, using first-principles calculations, we examined the lattice dynamics and disclosed that these frequency shifts are rather due to the spin-phonon coupling than geometrical lattice effects. Next we found that the low-frequency dielectric permittivity demonstrates distinct changes at the antiferromagnetic ordering due to the spontaneous magnetodielectric effect caused by the behavior of infrared-active phonons. In addition, we have observed magnetic excitations in the infrared spectra and identified their magnetodipole origin. To strengthen our conclusions, we analyze the theoretical phonon-magnon coupling overall phonons at the $\Gamma$ point. We conclude that the largest effect comes from the $A_{1g}$ and $B_{2g}$ Raman-active modes. As such, our results establish a solid basis for further investigations and more deeper understanding of the coupling of phonons with spins and magnetic excitations in antiferromagnets., Comment: 17 pages, 11 figures, 5 tables

Understanding complex phenomena often requires analyzing high-dimensional data to uncover emergent properties that arise from multifactorial interactions. Here, we present EMUSES (Emerging-properties Mapping Using Spatial Embedding Statistics), an innovative approach employing Uniform Manifold Approximation and Projection (UMAP) to create high-dimensional embeddings that reveal latent structures within data. EMUSES facilitates the exploration and prediction of emergent properties by statistically analyzing these latent spaces. Using three distinct datasets--a handwritten digits dataset from the National Institute of Standards and Technology (NIST, E. Alpaydin, 1998), the Chicago Face Database (Ma et al., 2015), and brain disconnection data post-stroke (Talozzi et al., 2023)--we demonstrate EMUSES' effectiveness in detecting and interpreting emergent properties. Our method not only predicts outcomes with high accuracy but also provides clear visualizations and statistical insights into the underlying interactions within the data. By bridging the gap between predictive accuracy and interpretability, EMUSES offers researchers a powerful tool to understand the multifactorial origins of complex phenomena., Comment: 14 pages, 3 figures

We report the first detection of coherent elastic neutrino-nucleus scattering (CEvNS) on germanium, measured at the Spallation Neutron Source at Oak Ridge National Laboratory. The Ge-Mini detector of the COHERENT collaboration employs large-mass, low-noise, high-purity germanium spectrometers, enabling excellent energy resolution, and an analysis threshold of 1.5 keV electron-equivalent ionization energy. We observe a on-beam excess of 20.6$_{+7.1}^{-6.3}$ counts with a total exposure of 10.22 GWhkg and we reject the no-CEvNS hypothesis with 3.9 sigma significance. The result agrees with the predicted standard model of particle physics signal rate within 2 sigma., Comment: 7 pages, 5 figures

Multimodal artificial intelligence (AI) systems have the potential to enhance clinical decision-making by interpreting various types of medical data. However, the effectiveness of these models across all medical fields is uncertain. Each discipline presents unique challenges that need to be addressed for optimal performance. This complexity is further increased when attempting to integrate different fields into a single model. Here, we introduce an alternative approach to multimodal medical AI that utilizes the generalist capabilities of a large language model (LLM) as a central reasoning engine. This engine autonomously coordinates and deploys a set of specialized medical AI tools. These tools include text, radiology and histopathology image interpretation, genomic data processing, web searches, and document retrieval from medical guidelines. We validate our system across a series of clinical oncology scenarios that closely resemble typical patient care workflows. We show that the system has a high capability in employing appropriate tools (97%), drawing correct conclusions (93.6%), and providing complete (94%), and helpful (89.2%) recommendations for individual patient cases while consistently referencing relevant literature (82.5%) upon instruction. This work provides evidence that LLMs can effectively plan and execute domain-specific models to retrieve or synthesize new information when used as autonomous agents. This enables them to function as specialist, patient-tailored clinical assistants. It also simplifies regulatory compliance by allowing each component tool to be individually validated and approved. We believe, that our work can serve as a proof-of-concept for more advanced LLM-agents in the medical domain., Comment: 91 pages, 2 Figures

Research in cultural evolution aims at providing causal explanations for the change of culture over time. Over the past decades, this field has generated an important body of knowledge, using experimental, historical, and computational methods. While computational models have been very successful at generating testable hypotheses about the effects of several factors, such as population structure or transmission biases, some phenomena have so far been more complex to capture using agent-based and formal models. This is in particular the case for the effect of the transformations of social information induced by evolved cognitive mechanisms. We here propose that leveraging the capacity of Large Language Models (LLMs) to mimic human behavior may be fruitful to address this gap. On top of being an useful approximation of human cultural dynamics, multi-agents models featuring generative agents are also important to study for their own sake. Indeed, as artificial agents are bound to participate more and more to the evolution of culture, it is crucial to better understand the dynamics of machine-generated cultural evolution. We here present a framework for simulating cultural evolution in populations of LLMs, allowing the manipulation of variables known to be important in cultural evolution, such as network structure, personality, and the way social information is aggregated and transformed. The software we developed for conducting these simulations is open-source and features an intuitive user-interface, which we hope will help to build bridges between the fields of cultural evolution and generative artificial intelligence., Comment: 17 pages, 20 figures. Open-source code available at https://github.com/jeremyperez2/LLM-Culture

We investigate the transverse momentum distribution (TMD) statistics from three different theoretical approaches. In particular, we explore the framework used for string models, wherein the particle production is given by the Schwinger mechanism. The thermal distribution arises from the Gaussian fluctuations of the string tension. The hard part of the TMD can be reproduced by considering heavy tailed string tension fluctuations, for instance, the Tsallis $q$-Gaussian function, giving rise to a confluent hypergeometric function that fits the entire experimental TMD data. We also discuss the QCD-based Hagerdon function, another family of fitting functions frequently used to describe the spectrum. We analyze the experimental data of minimum bias pp collisions reported by the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) experiments (from $\sqrt{s}=0.2$ TeV to $\sqrt{s}=13$ TeV). We extracted the corresponding temperature by studying the behavior of the spectra at low transverse momentum values. For the three approaches, we compute all moments, highlighting the average, variance, and kurtosis. Finally, we compute the Shannon entropy and the heat capacity through the entropy derivative with respect to the temperature. We found that the $q$-Gaussian string tension fluctuations lead to a monotonically increasing heat capacity as a function of the center of mass energy, which is also observed for the Hagedorn fitting function. This behavior is consistent with the experimental observation that the temperature slowly rises with increments of the collision energy., Comment: 12 pages, 10 figures. Accepted for publication in Physical Review C

In this technical report, a new formulation for embedding a neural network into an optimization model is described. This formulation does not require binary variables to properly compute the output of the neural network for specific types of problems. Preliminary experiments show that this reformulation resulted in faster computation times when solving a proposed showcase model, in which non-linearity is necessary to be computed. This is in comparison with the classic formulation and off-the-shelf tools of commercial solvers.

The ALICE collaboration is proposing a completely new detector, ALICE 3, for operation during the LHC Runs 5 and 6. One of the ALICE~3 subsystems is the Muon IDentifier detector (MID), which has to be optimised to be efficient for the reconstruction of $J/\psi$ at rest (muons down to $p_{\rm T}\approx1.5$ GeV/$c$) for $|\eta|<1.3$. Given the modest particle flux expected in the MID of a few Hz/cm$^2$, technologies like plastic scintillator bars ($\approx1$ m length) equipped with wavelength-shifting fibers and silicon photomultiplier readout, and lightweight Multi-Wire Proportional Chambers (MWPCs) are under investigation. To this end, different plastic scintillator paddles and MWPCs were studied at the CERN T10 test beam facility. This paper reports on the performance of the scintillator prototypes tested at different beam momenta (from 0.5 GeV/$c$ up to 6 GeV/$c$) and positions (horizontal, vertical, and angular scans). The MWPCs were tested at different momenta (from 0.5 GeV/$c$ to 10 GeV/$c$) and beam intensities, their efficiency and position resolutions were verified beyond the particle rates expected with the MID in ALICE 3., Comment: 11 pages, 8 figures, to appear in JINST

The ClearMind project aims to develop a TOF-PET position-sensitive detection module optimized for time and spatial resolutions and detection efficiency. For this, we use a 59 mm $\times$ 59 mm $\times$ 5 mm monolithic PbWO$_4$ (PWO) crystal, which is encapsulated within a commercial Micro-Channel Plate Photomultiplier tube MAPMT253 with a bialkali photocathode directly deposited on the crystal. We report the proof of concept of the directly deposited of a bialkali photocathode on a PWO crystal and its stability over time. The full calibration of the ClearMind photodetector module in the single-photoelectron regime is described. We measured a time resolution of 70 ps FWHM using a 20 ps pulsed laser. We present the performance of the prototype used in coincidence with a 3 $\times$ 3 $\times$ 3 mm$^3$ LYSO:Ca,Ce crystal readout by a SiPM. We obtained a coincidence time resolution of 350 ps FWHM, a spatial resolution of 4 to 5 mm, and a detection efficiency of 28 %, consistent with Monte Carlo simulations of the ClearMind detector module., Comment: 23 pages, 24 figures

We consider the potential for a 10-kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model. Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several beyond-the-standard-model scenarios such as neutrino non-standard interactions and accelerator-produced dark matter. This detector's performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of CsI nuclei and detection of neutrinos from a core-collapse supernova.

The performance of the proposed MATHUSLA detector as an instrument for studying the physics of cosmic rays by measuring extensive air showers is presented. The MATHUSLA detector is designed to observe and study the decay of long-lived particles produced at the pp interaction point of the CMS detector at CERN during the HL-LHC data-taking period. The proposed MATHUSLA detector will be composed of many layers of long scintillating bars that cannot measure more than one hit per bar and correctly report the hit coordinate in case of multiple hits. This study shows that adding a layer of RPC detectors with both analogue and digital readout significantly enhances the capabilities of MATHUSLA to measure the local densities and arrival times of charged particles at the front of air showers. We discuss open issues in cosmic-ray physics that the proposed MATHUSLA detector with an additional layer of RPC detectors could address and conclude by comparing with other air-shower facilities that measure cosmic rays in the PeV energy range., Comment: 64 pages, 58 figures

We compute the bulk, $\zeta$, and shear, $\eta$, viscosity over entropy density, $s$, for the QCD matter formed in small collision systems at LHC. We consider a scenario of the String Percolation Model by proposing a global form of the color reduction factor that describes both the thermodynamic limit and its maximum deviation due to small-bounded effects. Our method involves estimations at vanishing baryon-chemical potential, assuming local equilibrium for string clusters in the initial state. To compute $\eta/s$, we employed a kinetic approach that accounts QCD states as an ideal gas of partons, while $\zeta/s$ is computed by using two different approaches: a simple kinetic formula and the causal dissipative relativistic fluid dynamics formulation. Our results align with Lattice QCD computations and Bayesian methods and are consistent with holographic conjecture bounds. Furthermore, our findings support the notion of a strongly interacting medium, similar to that observed in nuclear collisions, albeit with a phase transition occurring outside the thermodynamic limit., Comment: 12 pages, 8 figures

The classical Chern correspondence states that a choice of Hermitian metric on a holomorphic vector bundle determines uniquely a unitary 'Chern connection'. This basic principle in Hermitian geometry, later generalized to the theory of holomorphic principal bundles, provides one of the most fundamental ingredients in modern gauge theory, via its applications to the Donaldson-Uhlenbeck-Yau Theorem. In this work we study a generalization of the Chern correspondence in the context of higher gauge theory, where the structure group of the bundle is categorified. For this, we define connective structures on a multiplicative gerbe and propose a natural notion of complexification for an important class of 2-groups. Using this, we put forward a new notion of higher connection which is well-suited for describing holomorphic principal 2-bundles for these 2-groups, and establish a Chern correspondence in this way. As an upshot of our construction, we unify two previous notions of higher connections in the literature, namely those of adjusted connections and of trivializations of Chern-Simons 2-gerbes with connection., Comment: 48 pages

This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters. These encompass the elliptic curve coefficients, prime number, generator point, group order, and cofactor. The study provides insights into which of the bio-inspired algorithms yields better optimization results for ECC configurations, examining performances under the same fitness function. This function incorporates methods to ensure robust ECC parameters, including assessing for singular or anomalous curves and applying Pollard's rho attack and Hasse's theorem for optimization precision. The optimized parameters generated by GA and PSO are tested in a simulated e-commerce environment, contrasting with well-known curves like secp256k1 during the transmission of order messages using Elliptic Curve-Diffie Hellman (ECDH) and Hash-based Message Authentication Code (HMAC). Focusing on traditional computing in the pre-quantum era, this research highlights the efficacy of GA and PSO in ECC optimization, with implications for enhancing cybersecurity in third-party e-commerce integrations. We recommend the immediate consideration of these findings before quantum computing's widespread adoption., Comment: 14 pages

When a system deviates from equilibrium, it is possible to manipulate and control it to drive it towards equilibrium within a finite time $t_f$, even reducing its natural relaxation time scale $\tau_{relax}$. Although numerous theoretical and experimental studies have explored these shortcut protocols, few have yielded analytical results for the probability distribution of work, heat and entropy production. In this paper, we propose a two-step protocol that captures the essential characteristics of more general protocols and has analytical solution for the relevant thermodynamic probability distributions. Additionally, we demonstrate that for very short protocol duration $t_f\ll \tau_{relax}$, all protocols exhibit universal behavior in their shape and for the ratio of probability distribution functions of positive and negative work, heat and the entropy production., Comment: 16 pages, 13 figures

Description of the data release 10.13139/OLCF/1969085 (https://doi.ccs.ornl.gov/ui/doi/426) from the measurements of the CsI[Na] response to low energy nuclear recoils by the COHERENT collaboration. The release corresponds to the results published in "D. Akimov et al 2022 JINST 17 P10034". We share the data in the form of raw ADC waveforms, provide benchmark values, and share plots to enhance the transparency and reproducibility of our results. This document describes the contents of the data release as well as guidance on the use of the data.