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How turbulent convective fluctuations organise to form large-scale structures in planetary atmospheres remains a question that eludes quantitative answers. The assumption that this process is the result of an inverse cascade was suggested half a century ago in two-dimensional fluids, but its applicability to atmospheric and oceanic flows remains heavily debated, hampering our understanding of the energy balance in planetary systems. We show with direct numerical simulations of spatial resolutions of 122882 $\times$ 384 points that rotating and stratified flows can support a bidirectional cascade of energy, in three dimensions, with a ratio of Rossby to Froude numbers comparable to that of the Earth's atmosphere. Our results establish that in dry atmospheres spontaneous order can arise via an inverse cascade to the largest spatial scales.

Chimeric antigen receptor (CAR) T cell therapy is revolutionizing the treatment of blood cancers. Mathematical models that can predict the effectiveness of immunotherapies such as CAR T are of increasing interest due to their ability to reduce the number of experiments performed and to guide the theoretical development of new therapeutic strategies. {Following this rationale, we propose the use of control-oriented models to guide the augmentation of CAR T therapy with synthetic gene circuitry. Here we present an initial investigation where we adapt a previously developed CAR T model for control-oriented purposes. We then explore the impact of realistic alternative activation methods as control inputs to ensure effective tumor clearance.

Aims. Recently, both the presence of multiple stellar chemo-kinematic components and rotation in the Sculptor dwarf spheroidal galaxy have been put into question. Therefore, we re-examine the chemo-kinematic properties of this galaxy making use of the best spectroscopic data-set available containing both line-of-sight velocities and metallicities of individual stars. Methods. We carry out a detailed, quantitative analysis on the recent spectroscopic data-set from Tolstoy et al. (2023) that contains high precision velocities and metallicities for 1339 members of Sculptor. In particular, we assess whether Sculptor is best represented by a single stellar population with a negative metallicity gradient or by the super-position of two or more components with different mean metallicity, spatial distribution and kinematic properties. For this analysis, we also include the incompleteness of the spectroscopic data-set. Results. We find that Sculptor is better described by a two-populations model than by a single-population model with a metallicity gradient. Moreover, given the assumptions of the current modeling, we find evidence of a third population, composed of few stars, that is more extended and metal-poor than the two other populations. This very metal-poor group of stars shows a shift of around 15 km/s in its average l.o.s. velocity (vlos) with respect to the rest of the galaxy. We discuss several possible origins for this new population, finding a minor merger as the most likely one. We also find a vlos gradient of 4.0 +1.5 -1.5 km s-1 deg-1 but its statistical evidence is inconclusive and, moreover, its detection is partially driven by the group of stars with off-set velocities., Comment: 17 pages, 9 figures, Accepted for publication in Astronomy & Astrophysics

The dwarf spheroidal galaxies (dSphs) satellites of the Milky Way (MW) are nearby astrophysical laboratories to study the nature of dark matter (DM). We present some properties of the DM halos of the three classical dSphs Fornax, Sculptor and Leo I, obtained using dynamical models based on distribution functions depending on the action integrals. In particular, we report accurate estimates of their central DM density rho150 (measured at a distance of 150 pc from the galaxy centre), which is relevant for galaxy formation studies and for models of self-interacting DM, and their DM annihilation J-factor and decay D-factor, which are key tools for indirect DM detection experiments. Among these three galaxies, Fornax has the highest J- and D- factors (but the lowest rho150), while Leo I has the highest rho150 (but the lowest J- and D- factors)., Comment: 4 pages, 1 figure, to appear in the Proceedings of the XXXII IAU General Assembly held in Cape Town in August 2024 (Focus Meeting 9: Measures of luminous and dark matter in galaxies across time)

We propose a model that provides a simultaneous solution to the doublet-triplet splitting problem of grand unified theories, the electroweak hierarchy problem and the strong CP problem. The mechanism is based on the dynamics of two axion-like particles that would crunch the universe at the time of the QCD phase transition if triplets were light or had a VEV or if doublets were heavy or did not have a VEV. The only trace left at low energies are these two axion-like particles. They are weakly coupled to the Standard Model and could be detected at upcoming axion experiments or by a combination of neutron EDM measurements and the astrophysical detection of fuzzy dark matter., Comment: 40 pages, 14 figures

This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device., Comment: 1 page, 1 figure, Proceedings of the IEEE 20th International Conference on Body Sensor Networks (BSN), October 2024

We address our attention to the numerical time discretization of stochastic Poisson systems via Poisson integrators. The aim of the investigation regards the backward error analysis of such integrators to reveal their ability of being structure-preserving, for long times of integration. In particular, we first provide stochastic modified equations suitable for such integrators and then we rigorously study them to prove accurate estimates on the long-term numerical error along the dynamics generated by stochastic Poisson integrators, with reference to the preservation of the random Hamiltonian conserved along the exact flow of the approximating Wong-Zakai Poisson system. Finally, selected numerical experiments confirm the effectiveness of the theoretical analysis.

We present a cooperative protocol to charge quantum spin networks up to the highest-energy configuration, in terms of the network's magnetization. The charging protocol leverages spin-spin interactions and the crossing of a phase transition's critical point. Exploiting collective dynamics of the spin network, the cooperative protocol guarantees a precision advantage over any local charging protocol and leads to fluctuations (standard deviation) of the magnetization that scale as $1/N$, with $N$ being the number of spins in the network, i.e., the size of the spin battery. These findings mirror the Heisenberg limit for precision for parameter estimation in quantum metrology. We test our protocol on the D-Wave's Advantage quantum processing unit by charging sub-lattices with sizes ranging from $40$ to $5\,612$ spins, achieving the maximum magnetization and reaching a scalable charging precision beyond the standard quantum limit of $1/\sqrt{N}$., Comment: 13 pages, 10 figures

In this paper we prove the existence of solutions to the quantum Euler equations on $\mathbb{T}^d$, $d\geqslant 2$, with almost constant mass density, displaying energy transfers to high Fourier modes and polynomially fast-in-time growth of Sobolev norms above the finite-energy level. These solutions are uniformly far from vacuum, suggesting that weak turbulence in quantum hydrodynamics is not necessarily related to the occurrence of vortex structures. In view of possible connections with instability mechanisms for the classical compressible Euler equations, we also keep track of the dependence on the semiclassical parameter, showing that, at high regularity, the time at which the Sobolev norm inflations occur is uniform when approaching the semiclassical limit. Our construction relies on a novel result of Sobolev instability for the plane waves of the cubic nonlinear Schr\"odinger equation (NLS), which is connected to the quantum Euler equations through the Madelung transform}. More precisely, we show the existence of smooth solutions to NLS, which are small-amplitude perturbations of a plane wave and undergo a polynomially fast $H^s$-norm inflation for $s>1$. The proof is based on a partial Birkhoff normal form procedure, involving the normalization of non-homogeneous Hamiltonian terms.

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$\Phi$NE to study the performance of this detector, exploring the possibility to build a dedicated setup., Comment: 10 pages, 3 figures, eQCD conference

We provide a compact expression for the generating function of correlators involving an arbitrary number of bosonic open string DDF states. The explicit correlators for $M$ DDF states can then be obtained by differentiating this generating function with respect to the DDF polarization tensors. The generating function depends on single and double complex integrals centred around the punctures corresponding to the insertion points of the DDF vertices on the real axis in the upper half-plane. We have explicitly evaluated these integrals for an arbitrary number of DDF states. To check our results, we computed some massive scalars and spin-2 amplitudes for $M=3$ and $M=4$, verifying that these amplitudes have the form expected from Lorentz invariance. Moreover, for the first excited levels, we also show that the DDF amplitudes with generic polarizations can be reassembled into Lorentz-covariant amplitudes, with emergent covariant polarizations that match the expressions obtained by comparing the string states in both the DDF and covariant formalisms., Comment: Latex, 83 pages

We introduce a dynamic spatiotemporal volatility model that extends traditional approaches by incorporating spatial, temporal, and spatiotemporal spillover effects, along with volatility-specific observed and latent factors. The model offers a more general network interpretation, making it applicable for studying various types of network spillovers. The primary innovation lies in incorporating volatility-specific latent factors into the dynamic spatiotemporal volatility model. Using Bayesian estimation via the Markov Chain Monte Carlo (MCMC) method, the model offers a robust framework for analyzing the spatial, temporal, and spatiotemporal effects of a log-squared outcome variable on its volatility. We recommend using the deviance information criterion (DIC) and a regularized Bayesian MCMC method to select the number of relevant factors in the model. The model's flexibility is demonstrated through two applications: a spatiotemporal model applied to the U.S. housing market and another applied to financial stock market networks, both highlighting the model's ability to capture varying degrees of interconnectedness. In both applications, we find strong spatial/network interactions with relatively stronger spillover effects in the stock market.

In recent years, nine small near-Earth asteroids were discovered a few hours before the collision with the Earth: these are about one meter in diameter objects that have all disintegrated in the atmosphere, generating bright fireballs without causing damage. In some cases, several meteorites have been recovered. In cases like these, it is not always possible to triangulate the fireball generated by the asteroid's fall to circumscribe the strewn field position. For this reason, it can be important to compute a strewn field "ab initio", i.e. propagating the asteroid's trajectory in the atmosphere starting from the initial conditions obtained directly from the heliocentric orbit, coupled with some reasonable hypothesis about the mean strength and the mass of the fragments to "sample" the strewn field. By adopting a simple fragmentation model coupled with a real atmospheric profile, useful results can be obtained to locate the strewn field, as we will show for the recent falls of asteroids 2024 BX1, 2023 CX1 and 2008 TC3. It was possible to locate the strewn field of our study cases with an uncertainty of the order of one kilometre with the mean strength in the range 0.5-5 MPa and the mass of the possible final fragments in the 1 g - 1 kg range. We have also verified that a pancake phase after fragmentation is unnecessary to locate the strewn field for a small asteroid fall., Comment: Paper accepted for publication in Icarus, 32 pages, 16 figures

The transport of matter by turbulent flows plays an important role, in particular in a geophysical context. Here, we study the relative movement of groups of two (pairs) and four (tetrahedra) Lagrangian particles using direct numerical simulations of the stably-stratified Boussinsesq equations, with Brunt-V\"ais\"al\"a frequency $N$ and Coriolis parameter $f$. We cover regimes close to homogeneous isotropic turbulence, to flows dominated by stratification and rotation, keeping fixed the ratio $N/f = 5$. The flows studied are anisotropic, so the relative motion between two particles depends not only on the initial separation between the particles, but also on their orientation with respect to the vertical axis. In all cases considered, we demonstrate that the relative particle motion differs depending on whether dispersion is considered forward or backwards in time, although the asymmetry becomes less pronounced when stratification and rotation increase. On the other hand, the strong fluctuations in the dispersion between two particles become more extreme when $N$ and $f$ increase. We also find evidence for the formation of shear layers, which become more pronounced as $N$ and $f$ become larger. Finally, we show that the irreversibility on the dispersion of a set of particles forming initially a regular tetrahedron becomes weaker when the influence of stratification and rotation increase, a property that we relate to that of the perceived rate-of-strain tensor.

The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$\Phi$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics., Comment: 15 pages, 9 figures, 3 tables

Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization, coding, and translation. In some cases, the results provided by LLMs are on par with those of human experts. These models' most disruptive innovation is their ability to perform tasks via zero-shot or few-shot prompting. This capability has been successfully exploited to train instructed LLMs, where reinforcement learning with human feedback is used to guide the model to follow the user's requests directly. In this paper, we investigate the ability of instructed LLMs to improve conversational search effectiveness by rewriting user questions in a conversational setting. We study which prompts provide the most informative rewritten utterances that lead to the best retrieval performance. Reproducible experiments are conducted on publicly-available TREC CAST datasets. The results show that rewriting conversational utterances with instructed LLMs achieves significant improvements of up to 25.2% in MRR, 31.7% in Precision@1, 27% in NDCG@3, and 11.5% in Recall@500 over state-of-the-art techniques.

Nonadiabatic coupling between electrons and molecular motion at metal surfaces leads to energy dissipation and dynamical steering effects during chemical surface dynamics. We present a theoretical approach to the scattering of molecules from metal surfaces that incorporates all nonadiabatic and quantum nuclear effects due to the coupling of the molecular degrees of freedom to the electrons in the metal. This is achieved with the hierarchical equations of motion (HEOM) approach combined with a matrix product state representation in twin space. The method is applied to the scattering of nitric oxide from Au(111), for which strongly nonadiabatic energy loss during scattering has been experimentally observed, thus presenting a significant theoretical challenge. Since the HEOM approach treats the molecule-surface coupling exactly, it captures the interplay between nonadiabatic and quantum nuclear effects. Finally, the data obtained by the HEOM approach is used as a rigorous benchmark to assess various mixed quantum-classical methods, from which we derive insights into the mechanisms of energy dissipation and the suitable working regimes of each method.

This study introduces a novel frequency-based data-driven controller for adaptive optics, using power spectral density for optimization while ensuring stability criteria. It addresses disturbance rejection, command amplitude constraints and system transfer functions through convex optimization to obtain an optimal control in an infinite input response filter form. Evaluated within the SAXO+ project, it demonstrates efficacy under diverse atmospheric conditions and operational scenarios. The proposed controller is tested in both standard and disentangled adaptive optics schemes, showcasing its adaptability and performance. Experimental validation is conducted using the COMPASS simulation tool, affirming the controller's promise for enhancing adaptive optics systems in real-world applications.

Access to the potential energy Hessian enables determination of the Gibbs free energy, and certain approaches to transition state search and optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst Project (OCP) machine learned potentials (MLPs) determine the Hessian with great success (58 cm$^{-1}$ mean absolute error (MAE)) for intermediates adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models for the aforementioned applications. The top performing model, with a simple offset correction, gives good estimations of the vibrational entropy contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The ability to leverage models to capture the translational entropy was also explored. It was determined that 94% of randomly sampled systems had a translational entropy greater than 0.1 eV at 300 K. This underscores the need to go beyond the harmonic approximation to consider the entropy introduced by adsorbate translation, which increases with temperature. Lastly, we used MLP determined Hessian information for transition state search and found we were able to reduce the number of unconverged systems by 65% to 93% overall convergence, improving on the baseline established by CatTSunami., Comment: 43 pages, 8 figures

The exceptional magnetic, optical and phononic properties of Yttrium Iron Garnet (YIG) make it unique for spin-wave based and photonic applications. Yet, nanostructuring crystalline YIG and manipulating its magnetism in a non-destructive way is an outstanding challenge, and so far mostly limited to two-dimensional capabilities. Here, we show that irradiation of single-crystal YIG films with a focused UV laser drives a stable, giant enhancement of the perpendicular magnetic anisotropy, preserving the crystalline quality. This modulation is highly confined at the nanoscale in both the lateral and vertical directions, and its extension within the volume can be finely tuned with a continuous depth-control. By harnessing these three-dimensional anisotropy profiles, we demonstrate a large tuning of the spin-wave band structure, volume spatial localization, and non-reciprocity, realizing proof-of-principle 3D magnonic crystals. This straightforward, single-step, laser nanofabrication of three-dimensional magnetic systems based on crystalline YIG thin films opens the way to design novel functions in magnonic and magneto-optic devices., Comment: 17 pages, 5 figures

The price elasticity of demand can be estimated from observational data using instrumental variables (IV). However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal time graphs can simplify IV identification and help select consistent estimators. To do so, we propose to first model the equilibrium condition by an unobserved confounder, deriving a directed acyclic graph (DAG) while maintaining the assumption of a simultaneous determination of prices and quantities. We then exploit recent advances in graphical inference to derive valid IV estimators, including estimators that achieve consistency by simultaneously estimating nuisance effects. We further argue that observing significant differences between the estimates of presumably valid estimators can help to reject false model assumptions, thereby improving our understanding of underlying economic dynamics. We apply this approach to the German electricity market, estimating the price elasticity of demand on simulated and real-world data. The findings underscore the importance of accounting for structural autocorrelation in IV-based analysis.

Assessing the severity of stenoses in coronary angiography is critical to the patient's health, as coronary stenosis is an underlying factor in heart failure. Current practice for grading coronary lesions, i.e. fractional flow reserve (FFR) or instantaneous wave-free ratio (iFR), suffers from several drawbacks, including time, cost and invasiveness, alongside potential interobserver variability. In this context, some deep learning methods have emerged to assist cardiologists in automating the estimation of FFR/iFR values. Despite the effectiveness of these methods, their reliance on large datasets is challenging due to the distributed nature of sensitive medical data. Federated learning addresses this challenge by aggregating knowledge from multiple nodes to improve model generalization, while preserving data privacy. We propose the first federated detection transformer approach, FeDETR, to assess stenosis severity in angiography videos based on FFR/iFR values estimation. In our approach, each node trains a detection transformer (DETR) on its local dataset, with the central server federating the backbone part of the network. The proposed method is trained and evaluated on a dataset collected from five hospitals, consisting of 1001 angiographic examinations, and its performance is compared with state-of-the-art federated learning methods., Comment: 9 pages, 9 figures, Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14366. Springer, Cham

Solar irradiance variations across various timescales, from minutes to centuries, represents a potential natural driver of past regional and global climate cold phases. To accurately assess the Sun's effect on climate, particularly during periods of exceptionally low solar activity known as grand minima, an accurate reconstruction of solar forcing is essential. While direct measurements of Total Solar Irradiance (TSI) only began in the late 1970s with the advent of space radiometers, indirect evidence from various historical proxies suggests that the Sun's magnetic activity has undergone possible significant fluctuations over much longer timescales. Employing diverse and independent methods for TSI reconstruction is essential to gaining a comprehensive understanding of this issue. This study employs a semi-empirical model to reconstruct TSI over the past millennium. Our approach uses an estimated open solar magnetic field ($F_{o}$), derived from cosmogenic isotope data, as a proxy for solar activity. We reconstruct the cyclic variations of TSI, due to the solar surface magnetic features, by correlating $F_{o}$ with the parameter of active region functional form. Instead, we obtain the long-term TSI trend by applying the Empirical Mode Decomposition (EMD) algorithm to the reconstructed $F_{o}$ to filter out the 11-year and 22-year solar variability. We prepare a reconstructed TSI record, spanning 971 to 2020 CE. The estimated departure from modern TSI values occurred during the Sp\"{o}rer Minimum (around 1400 CE), with a decrease of approximately 2.3 $W m^{-2}$. A slightly smaller decline of 2.2 $W m^{-2}$ is reported during the Maunder Minimum, between 1645 and 1715 CE.

The widespread use of Large Language Models (LLMs) in society creates new information security challenges for developers, organizations, and end-users alike. LLMs are trained on large volumes of data, and their susceptibility to reveal the exact contents of the source training datasets poses security and safety risks. Although current alignment procedures restrict common risky behaviors, they do not completely prevent LLMs from leaking data. Prior work demonstrated that LLMs may be tricked into divulging training data by using out-of-distribution queries or adversarial techniques. In this paper, we demonstrate a simple, query-based decompositional method to extract news articles from two frontier LLMs. We use instruction decomposition techniques to incrementally extract fragments of training data. Out of 3723 New York Times articles, we extract at least one verbatim sentence from 73 articles, and over 20% of verbatim sentences from 6 articles. Our analysis demonstrates that this method successfully induces the LLM to generate texts that are reliable reproductions of news articles, meaning that they likely originate from the source training dataset. This method is simple, generalizable, and does not fine-tune or change the production model. If replicable at scale, this training data extraction methodology could expose new LLM security and safety vulnerabilities, including privacy risks and unauthorized data leaks. These implications require careful consideration from model development to its end-use.

In this manuscript, I present an analysis on the performance of OpenAI O1-preview model in solving random K-SAT instances for K$\in {2,3,4}$ as a function of $\alpha=M/N$ where $M$ is the number of clauses and $N$ is the number of variables of the satisfiable problem. I show that the model can call an external SAT solver to solve the instances, rather than solving them directly. Despite using external solvers, the model reports incorrect assignments as output. Moreover, I propose and present an analysis to quantify whether the OpenAI O1-preview model demonstrates a spark of intelligence or merely makes random guesses when outputting an assignment for a Boolean satisfiability problem.

Many treatments are non-randomly assigned, continuous in nature, and exhibit heterogeneous effects even at identical treatment intensities. Taken together, these characteristics pose significant challenges for identifying causal effects, as no existing estimator can provide an unbiased estimate of the average causal dose-response function. To address this gap, we introduce the Clustered Dose-Response Function (Cl-DRF), a novel estimator designed to discern the continuous causal relationships between treatment intensity and the dependent variable across different subgroups. This approach leverages both theoretical and data-driven sources of heterogeneity and operates under relaxed versions of the conditional independence and positivity assumptions, which are required to be met only within each identified subgroup. To demonstrate the capabilities of the Cl-DRF estimator, we present both simulation evidence and an empirical application examining the impact of European Cohesion funds on economic growth.

This paper investigates the implementation of microwave and mm-wave integrated circuits for control and readout of electron/hole spin qubits, as elementary building blocks for future emerging quantum computing technologies. In particular, it summarizes the most relevant readout and control techniques of electron/hole spin qubits, addresses the feasibility and reports some preliminary simulation results of two blocks: transimpedance amplifier (TIA) and pulse generator (PG). The TIA exhibits a transimpedance gain of 108.5 dB Ohm over a -3dB bandwidth of 18 GHz, with input-referred noise current spectral density of 0.89 pA/root(Hz) at 10 GHz. The PG provides a mm-wave sinusoidal pulse with a minimum duration time of 20 ps., Comment: The final version of this draft has been appearing in the Proceedings of ISCAS 2020 available on IEEE Explore Digital Library

X-ray computed microtomography ({\mu}-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples. These images enable clinicians to examine internal anatomy and gain insights into the disease or anatomical morphology. However, extracting relevant information from 3D images requires semantic segmentation of the regions of interest, which is usually done manually and results time-consuming and tedious. In this work, we propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus. The framework employs an optimized 2D CNN architecture that can infer a 3D segmentation of the sample, avoiding the high computational cost of a 3D CNN architecture. We tackle the challenges of handling large and high-resoluted image data (over a thousand pixels in each dimension) and a small training database (only three samples) by proposing a standard protocol for data normalization and processing. Moreover, we investigate how the noise, contrast, and spatial resolution of the sample and the training of the architecture are affected by the reconstruction technique, which depends on the number of input images. Experiments show that our framework significantly reduces the time required to segment new samples, allowing a faster microtomography analysis of the Carassius auratus heart shape. Furthermore, our framework can work with any bio-image (biological and medical) from {\mu}-CT with high-resolution and small dataset size