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1. EuroBERT: Scaling Multilingual Encoders for European Languages

Author

Boizard, Nicolas, Gisserot-Boukhlef, Hippolyte, Alves, Duarte M., Martins, André, Hammal, Ayoub, Corro, Caio, Hudelot, Céline, Malherbe, Emmanuel, Malaboeuf, Etienne, Jourdan, Fanny, Hautreux, Gabriel, Alves, João, El-Haddad, Kevin, Faysse, Manuel, Peyrard, Maxime, Guerreiro, Nuno M., Fernandes, Patrick, Rei, Ricardo, and Colombo, Pierre

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models. Despite their wide applicability, encoders have been recently overshadowed by advances in generative decoder-only models. However, many innovations driving this progress are not inherently tied to decoders. In this paper, we revisit the development of multilingual encoders through the lens of these advances, and introduce EuroBERT, a family of multilingual encoders covering European and widely spoken global languages. Our models outperform existing alternatives across a diverse range of tasks, spanning multilingual capabilities, mathematics, and coding, and natively supporting sequences of up to 8,192 tokens. We also examine the design decisions behind EuroBERT, offering insights into our dataset composition and training pipeline. We publicly release the EuroBERT models, including intermediate training checkpoints, together with our training framework., Comment: 26 pages, 6 figures, 11 tables

Published
2025

2. On partial representations of pointed Hopf algebras

Author

Neto, Arthur Rezende Alves and Alves, Marcelo Muniz

Subjects
Mathematics - Rings and Algebras, Mathematics - Representation Theory, 16T05, 16S40, 16S35
Abstract

Partial representations of Hopf algebras were motivated by the theory of partial representations of groups. Alves, Batista e Vercruysse introduced partial representations of a Hopf algebra and showed that, as in the case of partial groups actions, a partial $H$-action on an algebra $A$ leads to a partial representation on the algebra of linear endomorphisms of $A$, and a left module $M$ over the partial smash product of $A$ by $H$ carries also a partial representation of $H$ on its algebra of linear endomorphisms. Moreover, partial representations of $H$ correspond to left modules over a Hopf algebroid $H_{par}$. It is known from a result by Dokuchaev, Exel and Piccione that when $H$ is the algebra of a finite group $G$, then $H_{par}$ is isomorphic to the algebra of a finite groupoid determined by $G$. In this work we show that if $H$ is a pointed Hopf algebra with finite group $G$ of grouplikes then $H_{par}$ can be written as a direct sum of unital ideals indexed by the components of the same groupoid associated to the group $G$.

Published
2025

5. Measurement of inclusive and differential cross sections for W+W− production in proton-proton collisions at s = 13.6 TeV

Author

Hayrapetyan, A, Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Chatterjee, S, Damanakis, K, Dragicevic, M, Hussain, PS, Jeitler, M, Krammer, N, Li, A, Liko, D, Mikulec, I, Schieck, J, Schöfbeck, R, Schwarz, D, Sonawane, M, Waltenberger, W, Wulz, C-E, Janssen, T, Van Laer, T, Van Mechelen, P, Breugelmans, N, D'Hondt, J, Dansana, S, De Moor, A, Delcourt, M, Heyen, F, Lowette, S, Makarenko, I, Müller, D, Tavernier, S, Tytgat, M, Van Onsem, GP, Van Putte, S, Vannerom, D, Bilin, B, Clerbaux, B, Das, AK, De Lentdecker, G, Evard, H, Favart, L, Gianneios, P, Jaramillo, J, Khalilzadeh, A, Khan, FA, Lee, K, Mahdavikhorrami, M, Malara, A, Paredes, S, Shahzad, MA, Thomas, L, Bemden, M Vanden, Vander Velde, C, Vanlaer, P, De Coen, M, Dobur, D, Gokbulut, G, Hong, Y, Knolle, J, Lambrecht, L, Marckx, D, Amarilo, K Mota, Samalan, A, Skovpen, K, Van Den Bossche, N, van der Linden, J, Wezenbeek, L, Benecke, A, Bethani, A, Bruno, G, Caputo, C, De Jeneret, J De Favereau, Delaere, C, Donertas, IS, Giammanco, A, Guzel, AO, Jain, Lemaitre, V, Lidrych, J, Mastrapasqua, P, Tran, TT, Wertz, S, Alves, GA, Pereira, M Alves Gallo, Coelho, E, Silva, G Correia, Hensel, C, De Oliveira, T Menezes, Herrera, C Mora, Moraes, A, Teles, P Rebello, Soeiro, M, Pereira, A Vilela, Júnior, WL Aldá, Filho, M Barroso Ferreira, Malbouisson, H Brandao, Carvalho, W, and Chinellato, J

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Mathematical Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Nuclear & Particles Physics, Mathematical sciences, Physical sciences
Abstract

Measurements at s=13.6TeV of the opposite-sign W boson pair production cross section in proton-proton collisions are presented. The data used in this study were collected with the CMS detector at the CERN LHC in 2022, and correspond to an integrated luminosity of 34.8fb−1. Events are selected by requiring one electron and one muon of opposite charge. A maximum likelihood fit is performed on signal- and background-enriched data categories defined by the flavor and charge of the leptons, the number of jets, and number of jets originating from b quarks. The overall sensitivity is significantly better than that of previous results with a similar integrated luminosity. The improvement comes from a more refined control of experimental uncertainties and an improved fit strategy. An inclusive W+W− production cross section of 125.7±5.6 pb is measured, in agreement with standard model predictions. Cross sections are also reported in a fiducial region close to that of the detector acceptance, both inclusively and differentially, as a function of the jet multiplicity in the event. For the first time in proton-proton collisions, WW events with zero, one, and at least two jets are studied simultaneously and compared with recent theoretical predictions.

Published
2025

6. Constraints on the Higgs boson self-coupling from the combination of single and double Higgs boson production in proton-proton collisions at s = 13 TeV

Author

Hayrapetyan, A, Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Chatterjee, S, Damanakis, K, Dragicevic, M, Hussain, PS, Jeitler, M, Krammer, N, Li, A, Liko, D, Mikulec, I, Schieck, J, Schöfbeck, R, Schwarz, D, Sonawane, M, Templ, S, Waltenberger, W, Wulz, C-E, Darwish, MR, Janssen, T, Van Laer, T, Van Mechelen, P, Breugelmans, N, D'Hondt, J, Dansana, S, De Moor, A, Delcourt, M, Heyen, F, Lowette, S, Makarenko, I, Müller, D, Tavernier, S, Tytgat, M, Van Onsem, GP, Van Putte, S, Vannerom, D, Bilin, B, Clerbaux, B, Das, AK, De Lentdecker, G, Evard, H, Favart, L, Gianneios, P, Jaramillo, J, Khalilzadeh, A, Khan, FA, Lee, K, Mahdavikhorrami, M, Malara, A, Paredes, S, Shahzad, MA, Thomas, L, Bemden, M Vanden, Vander Velde, C, Vanlaer, P, De Coen, M, Dobur, D, Gokbulut, G, Hong, Y, Knolle, J, Lambrecht, L, Marckx, D, Amarilo, K Mota, Samalan, A, Skovpen, K, Van Den Bossche, N, van der Linden, J, Wezenbeek, L, Benecke, A, Bethani, A, Bruno, G, Caputo, C, De Jeneret, J De Favereau, Delaere, C, Donertas, IS, Giammanco, A, Guzel, AO, Jain, Lemaitre, V, Lidrych, J, Mastrapasqua, P, Tran, TT, Wertz, S, Alves, GA, Pereira, M Alves Gallo, Coelho, E, Silva, G Correia, Hensel, C, De Oliveira, T Menezes, Moraes, A, Teles, P Rebello, Soeiro, M, Pereira, A Vilela, Júnior, WL Aldá, Filho, M Barroso Ferreira, Malbouisson, H Brandao, and Carvalho, W

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Mathematical Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Nuclear & Particles Physics, Mathematical sciences, Physical sciences
Abstract

The Higgs boson (H) trilinear self-coupling, λ3, is constrained via its measured properties and limits on the HH pair production using the proton-proton collision data collected by the CMS experiment at s=13TeV. The combination of event categories enriched in single-H and HH events is used to measure κλ, defined as the value of λ3 normalized to its standard model prediction, while simultaneously constraining the Higgs boson couplings to fermions and vector bosons. Values of κλ outside the interval −1.2

Published
2025

7. Girth and groomed radius of jets recoiling against isolated photons in lead-lead and proton-proton collisions at s NN = 5.02  TeV

Author

Hayrapetyan, A, Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Chatterjee, S, Damanakis, K, Dragicevic, M, Hussain, PS, Jeitler, M, Krammer, N, Li, A, Liko, D, Mikulec, I, Schieck, J, Schöfbeck, R, Schwarz, D, Sonawane, M, Templ, S, Waltenberger, W, Wulz, C-E, Darwish, MR, Janssen, T, Van Mechelen, P, Bols, ES, D'Hondt, J, Dansana, S, De Moor, A, Delcourt, M, Lowette, S, Makarenko, I, Müller, D, Tavernier, S, Tytgat, M, Van Onsem, GP, Van Putte, S, Vannerom, D, Clerbaux, B, Das, AK, De Lentdecker, G, Evard, H, Favart, L, Gianneios, P, Hohov, D, Jaramillo, J, Khalilzadeh, A, Khan, FA, Lee, K, Mahdavikhorrami, M, Malara, A, Paredes, S, Thomas, L, Bemden, M Vanden, Vander Velde, C, Vanlaer, P, De Coen, M, Dobur, D, Hong, Y, Knolle, J, Lambrecht, L, Mestdach, G, Amarilo, K Mota, Rendón, C, Samalan, A, Skovpen, K, Van Den Bossche, N, van der Linden, J, Wezenbeek, L, Benecke, A, Bethani, A, Bruno, G, Caputo, C, Delaere, C, Donertas, IS, Giammanco, A, Jain, Lemaitre, V, Lidrych, J, Mastrapasqua, P, Tran, TT, Wertz, S, Alves, GA, Coelho, E, Hensel, C, De Oliveira, T Menezes, Moraes, A, Teles, P Rebello, Soeiro, M, Júnior, WL Aldá, Pereira, M Alves Gallo, Filho, M Barroso Ferreira, Malbouisson, H Brandao, Carvalho, W, Chinellato, J, Da Costa, EM, Da Silveira, GG, De Jesus Damiao, D, De Souza, S Fonseca, De Souza, R Gomes, and Martins, J

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Mathematical Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Nuclear & Particles Physics, Mathematical sciences, Physical sciences
Abstract

This Letter presents the first measurements of the groomed jet radius Rg and the jet girth g in events with an isolated photon recoiling against a jet in lead-lead (PbPb) and proton-proton (pp) collisions at the LHC at a nucleon-nucleon center-of-mass energy of 5.02 TeV. The observables Rg and g provide a quantitative measure of how narrow or broad a jet is. The analysis uses PbPb and pp data samples with integrated luminosities of 1.7 nb−1 and 301 pb−1, respectively, collected with the CMS experiment in 2018 and 2017. Events are required to have a photon with transverse momentum pTγ>100 GeV and at least one jet back-to-back in azimuth with respect to the photon and with transverse momentum pTjet such that pTjet/pTγ>0.4. The measured Rg and g distributions are unfolded to the particle level, which facilitates the comparison between the PbPb and pp results and with theoretical predictions. It is found that jets with pTjet/pTγ>0.8, i.e., those that closely balance the photon pTγ, are narrower in PbPb than in pp collisions. Relaxing the selection to include jets with pTjet/pTγ>0.4 reduces the narrowing of the angular structure of jets in PbPb relative to the pp reference. This shows that selection bias effects associated with jet energy loss play an important role in the interpretation of jet substructure measurements.

Published
2025

8. Search for bottom quark associated production of the standard model Higgs boson in final states with leptons in proton-proton collisions at s = 13 TeV

Author

Hayrapetyan, A, Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Chatterjee, S, Damanakis, K, Dragicevic, M, Hussain, PS, Jeitler, M, Krammer, N, Li, A, Liko, D, Mikulec, I, Schieck, J, Schöfbeck, R, Schwarz, D, Sonawane, M, Waltenberger, W, Wulz, C-E, Janssen, T, Van Laer, T, Van Mechelen, P, Breugelmans, N, D'Hondt, J, Dansana, S, De Moor, A, Delcourt, M, Heyen, F, Lowette, S, Makarenko, I, Müller, D, Tavernier, S, Tytgat, M, Van Onsem, GP, Van Putte, S, Vannerom, D, Bilin, B, Clerbaux, B, Das, AK, De Lentdecker, G, Evard, H, Favart, L, Gianneios, P, Jaramillo, J, Khalilzadeh, A, Khan, FA, Lee, K, Mahdavikhorrami, M, Malara, A, Paredes, S, Shahzad, MA, Thomas, L, Bemden, M Vanden, Vander Velde, C, Vanlaer, P, De Coen, M, Dobur, D, Gokbulut, G, Hong, Y, Knolle, J, Lambrecht, L, Marckx, D, Amarilo, K Mota, Skovpen, K, Van Den Bossche, N, van der Linden, J, Wezenbeek, L, Benecke, A, Bethani, A, Bruno, G, Caputo, C, De Jeneret, J De Favereau, Delaere, C, Donertas, IS, Giammanco, A, Guzel, AO, Jain, Lemaitre, V, Lidrych, J, Mastrapasqua, P, Tran, TT, Wertz, S, Alves, GA, Pereira, M Alves Gallo, Coelho, E, Silva, G Correia, Hensel, C, De Oliveira, T Menezes, Herrera, C Mora, Moraes, A, Teles, P Rebello, Soeiro, M, Pereira, A Vilela, Júnior, WL Aldá, Filho, M Barroso Ferreira, Malbouisson, H Brandao, Carvalho, W, Chinellato, J, and Da Costa, EM

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, CMS, Higgs, tau, Search, Bottom, Mathematical Physics, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Nuclear & Particles Physics, Mathematical sciences, Physical sciences
Abstract

This Letter presents the first search for bottom quark associated production of the standard model Higgs boson, in final states with leptons. Higgs boson decays to pairs of tau leptons and pairs of leptonically decaying W bosons are considered. The search is performed using data collected from 2016 to 2018 by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138fb−1. Upper limits at the 95% confidence level are placed on the signal strength for Higgs boson production in association with bottom quarks; the observed (expected) upper limit is 3.7 (6.1) times the standard model prediction.

Published
2025

9. Search for charged-lepton flavor violation in the production and decay of top quarks using trilepton final states in proton-proton collisions at s=13 TeV

Author

Hayrapetyan, A, Tumasyan, A, Adam, W, Andrejkovic, JW, Bergauer, T, Chatterjee, S, Damanakis, K, Dragicevic, M, Del Valle, A Escalante, Hussain, PS, Jeitler, M, Krammer, N, Liko, D, Mikulec, I, Schieck, J, Schöfbeck, R, Schwarz, D, Sonawane, M, Templ, S, Waltenberger, W, Wulz, C-E, Darwish, MR, Janssen, T, Van Mechelen, P, Bols, ES, D’Hondt, J, Dansana, S, De Moor, A, Delcourt, M, Faham, H El, Lowette, S, Makarenko, I, Müller, D, Sahasransu, AR, Tavernier, S, Tytgat, M, Van Putte, S, Vannerom, D, Clerbaux, B, De Lentdecker, G, Favart, L, Hohov, D, Jaramillo, J, Khalilzadeh, A, Lee, K, Mahdavikhorrami, M, Malara, A, Paredes, S, Pétré, L, Postiau, N, Thomas, L, Bemden, M Vanden, Vander Velde, C, Vanlaer, P, De Coen, M, Dobur, D, Hong, Y, Knolle, J, Lambrecht, L, Mestdach, G, Rendón, C, Samalan, A, Skovpen, K, Van Den Bossche, N, Wezenbeek, L, Benecke, A, Bruno, G, Caputo, C, Delaere, C, Donertas, IS, Giammanco, A, Jaffel, K, Jain, Lemaitre, V, Lidrych, J, Mastrapasqua, P, Mondal, K, Tran, TT, Wertz, S, Alves, GA, Coelho, E, Hensel, C, De Oliveira, T Menezes, Moraes, A, Teles, P Rebello, Soeiro, M, Júnior, WL Aldá, Pereira, M Alves Gallo, Filho, M Barroso Ferreira, Malbouisson, H Brandao, Carvalho, W, Chinellato, J, Da Costa, EM, Da Silveira, GG, De Jesus Damiao, D, De Souza, S Fonseca, Martins, J, Herrera, C Mora, Amarilo, K Mota, and Mundim, L

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Mathematical Physics, Mathematical Sciences, Physical Sciences
Abstract

A search is performed for charged-lepton flavor violating processes in top quark ((Formula presented)) production and decay. The data were collected by the CMS experiment from proton-proton collisions at a center-of-mass energy of 13 TeV and correspond to an integrated luminosity of (Formula presented). The selected events are required to contain one opposite-sign electron-muon pair, a third charged lepton (electron or muon), and at least one jet of which no more than one is associated with a bottom quark. Boosted decision trees are used to distinguish signal from background, exploiting differences in the kinematics of the final states particles. The data are consistent with the standard model expectation. Upper limits at 95% confidence level are placed in the context of effective field theory on the Wilson coefficients, which range between (Formula presented) depending on the flavor of the associated light quark and the Lorentz structure of the interaction. These limits are converted to upper limits on branching fractions involving up (charm) quarks, (Formula presented) ((Formula presented)), of (Formula presented), (Formula presented), and (Formula presented) for tensorlike, vectorlike, and scalarlike interactions, respectively.

Published
2025

10. Cosmology with second and third-order shear statistics for the Dark Energy Survey: Methods and simulated analysis

Author

Gomes, R. C. H., Sugiyama, S., Jain, B., Jarvis, M., Anbajagane, D., Gatti, M., Gebauer, D., Gong, Z., Halder, A., Marques, G. A., Pandey, S., Marshall, J. L., Allam, S., Alves, O., Andrade-Oliveira, F., Bacon, D., Blazek, J., Bocquet, S., Brooks, D., Rosell, A. Carnero, Carretero, J., da Costa, L. N., Doel, P., Doux, C., Everett, S., Flaugher, B., Frieman, J., García-Bellido, J., Gaztanaga, E., Gruen, D., Gruendl, R. A., Gutierrez, G., Herner, K., Hinton, S. R., Hollowood, D. L., Honscheid, K., Huterer, D., James, D. J., Jeffrey, N., Mena-Fernández, J., Miquel, R., Muir, J., Ogando, R. L. C., Pereira, M. E. S., Pieres, A., Malagón, A. A. Plazas, Samuroff, S., Sanchez, E., Cid, D. Sanchez, Santiago, B., Sevilla-Noarbe, I., Smith, M., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Vikram, V., Weaverdyck, N., and Weller, J.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a new pipeline designed for the robust inference of cosmological parameters using both second- and third-order shear statistics. We build a theoretical model for rapid evaluation of three-point correlations using our fastnc code and integrate it into the CosmoSIS framework. We measure the two-point functions $\xi_{\pm}$ and the full configuration-dependent three-point shear correlation functions across all auto- and cross-redshift bins. We compress the three-point functions into the mass aperture statistic $\langle M_{\rm ap}^3\rangle$ for a set of 796 simulated shear maps designed to model the Dark Energy Survey (DES) Year 3 data. We estimate from it the full covariance matrix and model the effects of intrinsic alignments, shear calibration biases and photometric redshift uncertainties. We apply scale cuts to minimize the contamination from the baryonic signal as modeled through hydrodynamical simulations. We find a significant improvement of $83\%$ on the Figure of Merit in the $\Omega_{\rm m}$-$S_8$ plane when we add the $\langle M_{\rm ap}^3\rangle$ data to the $\xi_{\pm}$ information. We present our findings for all relevant cosmological and systematic uncertainty parameters and discuss the complementarity of third-order and second-order statistics., Comment: 25 pages, 15 figures

Published
2025

11. Extension of the creep tide theory to exoplanet systems with high stellar obliquity. The dynamic tide of CoRoT-3b

Author

Folonier, Hugo, Ferraz-Mello, Sylvio, and Alves-Silva, Raphael

Subjects
Astrophysics - Earth and Planetary Astrophysics, 70F15
Abstract

This paper extends the creep tide theory to exoplanetary systems with significant obliquities. The extended theory allows us to obtain the stellar and planetary hydrodynamic equilibrium tides and the evolution of the rotational state of the bodies. The dynamic ellipsoidal figure of equilibrium of the body is calculated taking into account that its reaction to external forces is delayed by its viscosity. The derived equations are used to determine the motion of the tidal bulge of the planetary companion CoRoT-3b (a brown dwarf) and its host star. We show how the tides deform the figure of the companion and how its tidal bulge moves close to the substellar meridian from one hemisphere to another. The stellar lag is mostly positive and is braking the star's rotation., Comment: 29 pages, 11 figures

Published
2025

12. Friedman vs P\'olya

Author

Alves, Raphael and Rosales, Rafael A.

Subjects
Mathematics - Probability, Primary: 60F15, Secondary: 60G42, 60J05
Abstract

Suppose an urn contains initially any number of balls of two colours. One ball is drawn randomly and then put back with $\alpha$ balls of the same colour and $\beta$ balls of the opposite colour. Both cases, $\beta=0$ and $\beta>0$ are well known and correspond respectively to P\'olya's and Friedman's replacement schemes. We consider a mixture of both: with probability $p\in(0,1]$ balls are replaced according to Friedman's recipe and with probability $1-p$ to the one by P\'olya. Independently of the initial urn composition and independently of $\alpha$, $\beta$, and the value of $p>0$, we show that the proportion of balls of one colour converges almost surely to $\frac12$. The latter is the limit behaviour obtained by using Friedman's scheme alone, i.e. when $p=1$. Our result follows by adapting an argument due to D. S. Ornstein., Comment: 4 pages, 1 figure

Published
2025

13. The Solar System's passage through the Radcliffe wave during the middle Miocene

Author

Maconi, E., Alves, J., Swiggum, C., Ratzenböck, S., Großschedl, J., Köhler, P., Miret-Roig, N., Meingast, S., Konietzka, R., Zucker, C., Goodman, A., Lombardi, M., Knorr, G., Lohmann, G., Forbes, J. C., Burkert, A., and Opher, M.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Earth and Planetary Astrophysics, Physics - Geophysics
Abstract

Context. As the Solar System orbits the Milky Way, it encounters various Galactic environments, including dense regions of the interstellar medium (ISM). These encounters can compress the heliosphere, exposing parts of the Solar System to the ISM, while also increasing the influx of interstellar dust into the Solar System and Earth's atmosphere. The discovery of new Galactic structures, such as the Radcliffe wave, raises the question of whether the Sun has encountered any of them. Aims. The present study investigates the potential passage of the Solar System through the Radcliffe wave gas structure over the past 30 million years (Myr). Methods. We used a sample of 56 high-quality, young ($\leq$ 30 Myr) open clusters associated with a region of interest of the Radcliffe wave to trace its motion back and investigate a potential crossing with the Solar System's past orbit. Results. We find that the Solar System's trajectory intersected the Radcliffe wave in the Orion region. We have constrained the timing of this event to between 18.2 and 11.5 Myr ago, with the closest approach occurring between 14.8 and 12.4 Myr ago. Notably, this period coincides with the Middle Miocene climate transition on Earth, providing an interdisciplinary link with paleoclimatology. The potential impact of the crossing of the Radcliffe wave on the climate on Earth is estimated. This crossing could also lead to anomalies in radionuclide abundances, which is an important research topic in the field of geology and nuclear astrophysics.

Published
2025
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14. Extra-Dimensional de Broglie-Bohm Quantum Cosmology

Author

Alves-Júnior, F. A. P., Lemos, A. S., and Brito, F. A.

Subjects
General Relativity and Quantum Cosmology
Abstract

In this work, we explore the de Broglie-Bohm Quantum Cosmology for a stiff matter, $p = \rho$, anisotropic n-dimensional Universe. One begins by considering a Gaussian wave function for the Universe, which depends on the momenta parameters $q_1$ and $q_2$ , in addition to the dispersion parameters $\sigma_1$ and $\sigma_2$. Our solutions show that the extra dimensions are stabilized through a dynamical compactification mechanism within the quantum cosmology framework. In this case, we find two distinct configurations for the dynamics of the extra dimensions. The first configuration features larger extra dimensions at the bounce, which subsequently undergo compactification to a smaller size. In contrast, the second configuration exhibits a smaller extra dimension at the bounce, evolving toward a larger, finite, and stabilized value. We also address the particular five-dimensional case where the Wheeler-DeWitt equation degenerates.

Published
2025

15. UV photodetectors and field-effect transistors based on $\beta$-Ga$_2$O$_3$ nanomembranes produced by ion-beam-assisted exfoliation

Author

Pedro, Miguel Cardoso, Esteves, Duarte Magalhães, Pereira, Daniela Rodrigues, Alves, Luís Cerqueira, Bouhafs, Chamseddine, Lorenz, Katharina, and Peres, Marco

Subjects
Condensed Matter - Materials Science
Abstract

$\beta$-Ga$_{2}$O$_{3}$ nanomembranes, obtained by ion-beam-assisted exfoliation, are used in the fabrication of simple metal-semiconductor-metal (MSM) structures, that are tested as photodetectors (PD) and field-effect transistors (FET). Ti/Au contacts to the membrane are found to be rectifying. However, through thermal treatment in a nitrogen atmosphere for one minute at 500 {\deg}C, it is possible to modify this junction to have an ohmic behavior. An MSM PD is studied, reaching a high responsivity of 2.6$\times$10$^{4}$ A/W and a detectivity of 2.4$\times$10$^{14}$ Jones, under 245 nm wavelength illumination, and an applied voltage of 40 V. In order to better understand the behavior of the two junctions, in particular the iono/photocurrent mechanisms, an ion microprobe system is used to assess the response of these PD when excitation is localized in the different regions of the device. Finally, a depletion-mode FET is obtained, with an on/off current ratio of 7.7$\times$10$^{7}$ in the linear regime, at a drain-to-source voltage of 5 V, and with a threshold voltage around $-$3 V. The success in obtaining FET, and most notably, MSM photodetectors, while using a simple device structure, indicates a great potential of the nanomembranes produced by ion-beam-assisted exfoliation for the development of high-performance devices., Comment: 23 pages, 9 figures, 2 tables

Published
2025

16. Investigating Issues that Lead to Code Technical Debt in Machine Learning Systems

Author

Ximenes, Rodrigo, Alves, Antonio Pedro Santos, Escovedo, Tatiana, Spinola, Rodrigo, and Kalinowski, Marcos

Subjects
Computer Science - Software Engineering
Abstract

[Context] Technical debt (TD) in machine learning (ML) systems, much like its counterpart in software engineering (SE), holds the potential to lead to future rework, posing risks to productivity, quality, and team morale. Despite growing attention to TD in SE, the understanding of ML-specific code-related TD remains underexplored. [Objective] This paper aims to identify and discuss the relevance of code-related issues that lead to TD in ML code throughout the ML workflow. [Method] The study first compiled a list of 34 potential issues contributing to TD in ML code by examining the phases of the ML workflow, their typical associated activities, and problem types. This list was refined through two focus group sessions involving nine experienced ML professionals, where each issue was assessed based on its occurrence contributing to TD in ML code and its relevance. [Results] The list of issues contributing to TD in the source code of ML systems was refined from 34 to 30, with 24 of these issues considered highly relevant. The data pre-processing phase was the most critical, with 14 issues considered highly relevant. Shortcuts in code related to typical pre-processing tasks (e.g., handling missing values, outliers, inconsistencies, scaling, rebalancing, and feature selection) often result in "patch fixes" rather than sustainable solutions, leading to the accumulation of TD and increasing maintenance costs. Relevant issues were also found in the data collection, model creation and training, and model evaluation phases. [Conclusion] We have made the final list of issues available to the community and believe it will help raise awareness about issues that need to be addressed throughout the ML workflow to reduce TD and improve the maintainability of ML code., Comment: To appear in the 2025 4th IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2025

Published
2025

17. KM3NeT Constraint on Lorentz-Violating Superluminal Neutrino Velocity

Author

KM3NeT Collaboration, Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Argüelles, C., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kamp, N., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., Wen, A. Y., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Lorentz invariance is a fundamental symmetry of spacetime and foundational to modern physics. One of its most important consequences is the constancy of the speed of light. This invariance, together with the geometry of spacetime, implies that no particle can move faster than the speed of light. In this article, we present the most stringent neutrino-based test of this prediction, using the highest energy neutrino ever detected to date, KM3-230213A. The arrival of this event, with an energy of $220^{+570}_{-110}\,\text{PeV}$, sets a constraint on $\delta \equiv c_\nu^2-1 < 4\times10^{-22}$.

Published
2025

18. Unified Multivariate Ordinal Model for analysis of sensory attributes

Author

Melo, Janaína Marques e, Alves, João César Reis, Palma, Gabriel Rodrigues, de Freitas, Sílvia Maria, and de Lara, Idemauro Antonio Rodrigues

Subjects
Statistics - Methodology
Abstract

Experiments involving sensory analysis of foods and beverages are beneficial for selecting healthy products and assessing the preferences of potential consumers. They are generally planned in incomplete blocks, and their attributes, such as aroma, colour, and flavour, are evaluated using a 9-point hedonic scale, characterising an ordinal variable response. Also, the generalised logit model with random effects for panellists is one of the appropriate models to relate the multivariate response to the covariates. This study aims to present a method for analysing sensory attributes through a unified multivariate model. Due to the nature of the variable, each separate model already corresponds to a multivariate analysis, so our proposal would incorporate a complete analysis with solely one model. This proposal is based on multivariate methods for categorical data and maximum likelihood theory. Our method was evaluated through a simulation study, in which we consider three distinct formulations with two attributes to represent various formulation selection scenarios via mixed discrete models. The simulated results demonstrated overall concordance rates exceeding 80\% for the unified model compared to the separate models. Moreover, as motivation is presented, a study of 13 prebiotic beverages based on cashew nut almonds added to grape juice, with 130 potential consumers. The attributes evaluated were overall impression, aroma, Body, sweetness and flavour, using a 9-point hedonic scale. The selected unified model considering all attributes was the non-proportional odds mixed-effect model. According to this model, the prebiotic beverage formulations most likely to be accepted were: 8\% sugar and 40\% grape juice ($F_4$), 6\% sugar and 44\% grape juice ($F_6$), and 9\% sugar and 30\% grape juice ($F_{13}$). The unified analysis and computational time showed the advantages of this proposal., Comment: 22 pages

Published
2025

19. Exploring Novel 2D Analogues of Goldene: Electronic, Mechanical, and Optical Properties of Silverene and Copperene

Author

Santos, Emanuel J. A. dos, Alves, Rodrigo A. F., Dias, Alexandre C., Junior, Marcelo L. Pereira, Galvão, Douglas S., and Junior, Luiz A. Ribeiro

Subjects
Condensed Matter - Materials Science, 00-xx, J.2, I.6
Abstract

Two-dimensional (2D) materials have garnered significant attention due to their unique properties and broad application potential. Building on the success of goldene, a monolayer lattice of gold atoms, we explore its proposed silver and copper analogs, silverene and copperene, using density functional theory calculations. Our findings reveal that silverene and copperene are energetically stable, with formation energies of -2.3 eV/atom and -3.1 eV/atom, closely matching goldene's -2.9 eV/atom. Phonon dispersion and ab initio molecular dynamics simulations confirm their structural and dynamical stability at room temperature, showing no bond breaking or structural reconfiguration. Mechanical analyses indicate isotropy, with Young's moduli of 73 N/m, 44 N/m, and 59 N/m for goldene, silverene, and copperene, respectively, alongside Poisson's ratios of 0.46, 0.42, and 0.41. These results suggest comparable rigidity and deformation characteristics. Electronic band structure analysis highlights their metallic nature, with variations in the band profiles at negative energy levels. Despite their metallic character, these materials exhibit optical properties akin to semiconductors, pointing to potential applications in optoelectronics., Comment: 24 pages and six figures

Published
2025

20. How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Author

Landreman, Matt, Choi, Jong Youl, Alves, Caio, Balaprakash, Prasanna, Churchill, R. Michael, Conlin, Rory, and Roberg-Clark, Gareth

Subjects
Physics - Plasma Physics, Computer Science - Machine Learning
Abstract

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyze this dependence using multiple machine learning methods and a dataset of > 200,000 nonlinear simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimized and randomly generated stellarator equilibria. At fixed gradients, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection, and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find many previously published proxies do correlate well with both the heat flux and stability boundary.

Published
2025

21. Enhancing catalyst activity of two-dimensional C$_4$N$_2$ through doping for the hydrogen evolution reaction

Author

Ipaves, Bruno, Justo, João F., de Almeida, James M., Assali, Lucy V. C., and Autreto, Pedro Alves da Silva

Subjects
Condensed Matter - Materials Science, Physics - Computational Physics
Abstract

This study investigates the structural, electronic, and catalytic properties of pristine and doped C$_4$N$_2$ nanosheets as potential electrocatalysts for the hydrogen evolution reaction. The pristine C$_{36}$N$_{18}$ nanosheets exhibit limited HER activity, primarily due to high positive Gibbs free energies ($>$ 2.2 eV). To enhance catalytic performance, doping with B, Si, or P at the nitrogen site was explored. Among these systems, B-doped C$_{36}$N$_{17}$ nanosheets exhibit the most promising catalytic activity, with a Gibbs free energy close to zero ($\approx -0.2$ eV), indicating efficient hydrogen adsorption. Band structure, projected density of states, charge density, and Bader charge analyses reveal significant changes in the electronic environment due to doping. While stacking configurations (AA$'$A$''$ and ABC) have minimal effect on catalytic performance, doping -- particularly with B -- substantially alters the electronic structure, optimizing hydrogen adsorption and facilitating efficient HER. These findings suggest that B-doped C$_{36}$N$_{17}$ nanosheets could serve as efficient cocatalysts when combined with metallic materials, offering a promising approach to enhance catalytic efficiency in electrocatalytic and photocatalytic applications.

Published
2025

22. The ultra-high-energy event KM3-230213A within the global neutrino landscape

Author

KM3NeT Collaboration, Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Argüelles, C., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaš, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kamp, N., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaš, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., Wen, A. Y., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

On February 13th, 2023, the KM3NeT/ARCA telescope detected a neutrino candidate with an estimated energy in the hundreds of PeVs. In this article, the observation of this ultra-high-energy neutrino is discussed in light of null observations above tens of PeV from the IceCube and Pierre Auger observatories. Performing a joint fit of all experiments under the assumption of an isotropic $E^{-2}$ flux, the best-fit single-flavour flux normalisation is $E^2 \Phi^{\rm 1f}_{\nu + \bar \nu} = 7.5 \times 10^{-10}~{\rm GeV cm^{-2} s^{-1} sr^{-1}}$ in the 90% energy range of the KM3NeT event. Furthermore, the ultra-high-energy data are then fit together with the IceCube measurements at lower energies, either with a single power law or with a broken power law, allowing for the presence of a new component in the spectrum. The joint fit including non-observations by other experiments in the ultra-high-energy region shows a slight preference for a break in the PeV regime if the ``High-Energy Starting Events'' sample is included, and no such preference for the other two IceCube samples investigated. A stronger preference for a break appears if only the KM3NeT data is considered in the ultra-high-energy region, though the flux resulting from such a fit would be inconsistent with null observations from IceCube and Pierre Auger. In all cases, the observed tension between KM3NeT and other datasets is of the order of $2.5\sigma-3\sigma$, and increased statistics are required to resolve this apparent tension and better characterise the neutrino landscape at ultra-high energies., Comment: 11 pages, 4 figures

Published
2025

23. Bistatic Micro-Doppler Analysis of a Vertical Takeoff and Landing (VTOL) Drone in ICAS Framework

Author

Costa, Heraldo Cesar Alves, Myint, Saw James, Andrich, Carsten, Giehl, Sebastian W., Novotny, Dieter, Beuster, Julia, Schneider, Christian, and Thomä, Reiner S.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

Integrated Communication and Sensing (ICAS) is a key technology that enables sensing functionalities within the next-generation mobile communication (6G). Joint design and optimization of both functionalities could allow coexistence, therefore it advances toward joint signal processing and using the same hardware platform and common spectrum. Contributing to ICAS sensing, this paper presents the measurement and analysis of the micro-Doppler signature of Vertical Takeoff and Landing (VTOL) drones. Measurement is performed with an OFDM-like communication signal and bistatic constellation, which is a typical case in ICAS scenarios. This work shows that micro-Doppler signatures can be used to precisely distinguish flight modes, such as take-off, landing, hovering, transition, and cruising., Comment: First version of the work accepted to the GeMiC 2025 conference

Published
2025

24. On the Potential Galactic Origin of the Ultra-High-Energy Event KM3-230213A

Author

Adriani, O., Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Berti, E., Bertin, V., Betti, P., Biagi, S., Boettcher, M., Bonanno, D., Bottai, S., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Janik, O., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Keegans, J., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Mori, N., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Pacini, L., Paesani, D., González, J. Palacios, Papalashvili, G., Papini, P., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Petropavlova, M., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Robinson, J., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scaringella, M., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Myhr, P. A. Sevle, Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Starodubtsev, O., Stavropoulos, D., Štekl, I., Stocco, D., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vannuccini, E., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The KM3NeT observatory detected the most energetic neutrino candidate ever observed, with an energy between 72 PeV and 2.6 EeV at the 90% confidence level. The observed neutrino is likely of cosmic origin. In this article, it is investigated if the neutrino could have been produced within the Milky Way. Considering the low fluxes of the Galactic diffuse emission at these energies, the lack of a nearby potential Galactic particle accelerator in the direction of the event and the difficulty to accelerate particles to such high energies in Galactic systems, we conclude that if the event is indeed cosmic, it is most likely of extragalactic origin.

Published
2025

25. Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning

Author

DUNE Collaboration, Abud, A. Abed, Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alemanno, F., Alex, N. S., Allison, K., Alrashed, M., Alton, A., Alvarez, R., Alves, T., Aman, A., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anjarazafy, F., Antic, D., Antoniassi, M., Antonova, M., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Gómez, D. Ávila, Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Baigarashev, D., Balasubramanian, S., Balboni, A., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Basu, D., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, B., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolini, E., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bezawada, Y., Bezerra, A. T., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogart, B., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Booth, A., Boran, F., Merlo, R. Borges, Bostan, N., Botogoske, G., Bottino, B., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M. B., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chalifour, M., Chappell, A., Chatterjee, A., Chauhan, B., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen, Z., Cherdack, D., Chhibra, S. S., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conrad, J. M., Convery, M., Conway, K., Copello, S., Cova, P., Cox, C., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., De Jong, P., Sanchez, P. Del Amo, De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Di Silvestre, V., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Dolan, S., Dolce, M., Dolek, F., Dolinski, M. J., Domenici, D., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Emark, W., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Felix, J., Feng, Y., Fernandez-Martinez, E., da Silva, M. Ferreira, Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fucci, M., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M, Galizzi, F., Gallagher, H., Galli, M., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghosh, A., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonzalez-Diaz, D., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Haegel, L., Hagaman, L., Hahn, A., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Handley, M., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hawkins, S., Hays, J., He, M., Heavey, A., Heeger, K. M., Heindel, A., Heise, J., Hellmuth, P., Henderson, L., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Zink, J. Hoefken, Hoff, J., Holin, A., Holvey, T., Hong, C., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Hosokawa, R., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Huang, X., Hulcher, Z., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Oliveira, M. Ismerio, Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kalikulov, O., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasetti, S. P., Kashur, L., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Keloth, R., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., Kim, S., King, B., King, M., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kordosky, M., Kosc, T., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kroupova, T., Kubota, S., Kubu, M., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Larkin, J., Lasorak, P., Last, D., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Miotto, G. Lehmann, Lehnert, R., Leitner, M., Lemoine, H., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lima, R., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Luo, X., Luppi, E., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Malige, A., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Manzanillas-Velez, L., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, Martinez-Casales, M., López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Mastbaum, A., Masud, M., Matichard, F., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mayhew, F., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., McNulty, C., Meazza, L., Meddage, V. C. N., Mehmood, M., Mehta, B., Mehta, P., Mei, F., Melas, P., Mellet, L., Mena, O., Mendez, H., Méndez, D. P., Mendonca, A. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mladenov, D., Mocioiu, I., Mogan, A., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, M., Moore, Z., Moreno, D., Moreno-Granados, G., Moreno-Palacios, O., Morescalchi, L., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mukhamejanov, Y., Mukhamejanova, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Naples, D., Narita, S., Nava, J., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nielsen, A., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panareo, M., Panda, P., Pandey, V., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papadopoulou, A., Papaleo, R., Papoulias, D., Paramesvaran, S., Parke, S., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Camargo, G. Patiño, Paton, J. L., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paul, J., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Peake, A., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pierini, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Pletcher, K., Plows, K., Pollack, C., Pollmann, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Pozzato, M., Pradhan, R., Prakash, T., Prest, M., Psihas, F., Pugnere, D., Pullia, D., Qian, X., Queen, J., Raaf, J. L., Rabelhofer, M., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahe, A., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralaikoto, M. A., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Randriamanampisoa, S. S., Rappoldi, A., Raselli, G., Rath, T., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Renner, E., Repetto, S., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rikalo, A., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Robert, A., Roberts, A., Robles, E., Rocha, J. L. Rocabado, Roda, M., Rodrigues, M. J. O., Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Roy, N., Roy, P., Rubbia, C., Rudik, D., Ruggeri, A., Ferreira, G. Ruiz, Rushiya, K., Russell, B., Sacerdoti, S., Saduyev, N., Sahoo, S. K., Sahu, N., Sakhiyev, S., Sala, P., Salmoria, G., Samanta, S., Samios, N., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sanders, D. A., Sanfilippo, S., Santoro, D., Saoulidou, N., Sapienza, P., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Schwartz, S., Segade, A., Segreto, E., Senise, C. R., Sensenig, J., Seppela, D., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shmakov, A., Shoemaker, I., Shooltz, D., Shrock, R., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Nunes, M. Soares, Sobel, H., Soderberg, M., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Soto-Oton, J., Sousa, A., Soustruznik, K., Correia, D. Souza, Spinella, F., Spitz, J., Spooner, N. J. C., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stewart, J., Stillwell, B., Stock, J., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Sweeney, C., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Thorpe, C., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Muñoz, D. Torres, Torti, M., Tortola, M., Torun, Y., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valdiviesso, G. A., Vale, V., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Auccalla, T. Vargas, Oliva, D. Vargas, Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Vences, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizarreta, R., Hernandez, A. P. Vizcaya, Vlachos, S., Vorobyev, G., Vuong, Q., Waldron, A. V., Wallach, M., Walsh, J., Walton, T., Wan, L., Wang, B., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wieler, F., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wresilo, K., Wrobel, M., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Wynne, B. M., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, T., Yankelevich, A., Yates, L., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zettlemoyer, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.

Subjects
High Energy Physics - Experiment
Abstract

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Published
2025

26. Ideas and Requirements for the Global Cosmic-Ray Observatory (GCOS)

Author

Ahlers, Markus, Allekotte, Ingo, Alvarez-Muniz, Jaime, Anastasi, Gioacchino Alex, Anchordoqui, Luis, Anjos, Rita de Cassia Dos, Balakrishnan, Hari Haran, Batista, Rafael Alves, Bellido, Jose, Bertaina, Mario, Bhatnagar, Sonali, Billoir, Pierre, Bismark, Kathrin, Bister, Teresa, Bohacova, Martina, Bonifazi, Carla, Bradfield, Fraser, Castellina, Antonella, Cazon, Lorenzo, Cheminant, Kevin Almeida, Coleman, Alan, Convenga, Fabio, Veberič, Darko, Dasgupta, Paramita, Daumiller, Kai, Dawson, Bruce, Deval, Luca, Engel, Ralph, Eser, Johannes, Fang, Ke, Farrar, Glennys R., Fedynitch, Anatoli, Fenu, Francesco, Fitoussi, Thomas, Flaggs, Benjamin, Fodran, Tomas, Fujii, Toshihiro, Fujita, Keitaro, Garzelli, Maria Vittoria, Globus, Noemie, Goksu, Hazal, Gou, Quanbu, Hahn, Steffen, Hariharan, Balakrishnan, Haungs, Andreas, Higuchi, Ryo, Hnatyk, Bohdan, Hörandel, Jörg, Huege, Tim, Ikeda, Daisuke, Ikkatai, Yuko, Mariş, Ioana, Isar, Gina, James, Robin, Carvalho Jr, Washington, Kaderi, Yunos El, Kadler, Matthias, Kampert, Karl-Heinz, Kang, Donghwa, Khakurdikar, Abha, Kido, Eiji, Kleifges, Matthias, Koirala, Ramesh, Kong, Chuizheng, Koyama, C., Krizmanic, John, Kulshrestha, Shivam, Kungel, Viktoria, Leszczyńska, Agnieszka, Liu, Ruoyu, Luce, Quentin, Marchenko, Volodymyr, Mariazzi, Analisa, di Matteo, Armando, Matthews, John N., Mayotte, Eric, Mazur, Peter, Meli, Athina, Menjo, Hiroaki, Montanet, François, Müller, Ana Laura, Murase, Kohta, Muzio, Marco, Nellen, Lukas, Niechciol, Marcus, Nitz, David, Nonaka, Toshiyuki, Ogio, Shoichi, Ohira, Yutaka, Oikonomou, Foteini, Olinto, Angela V, Oshima, Hitoshi, Oueslati, Rami, Paudel, Ek Narayan, Paul, Thomas, Pawlowsky, Jannis, Payeras, Allan Machado, Pelgrims, Vincent, Perrone, Lorenzo, Pont, Bjarni, Porcelli, Alessio, Rautenberg, Julian, Riehn, Felix, Risse, Markus, Roth, Markus, Saftoiu, Alexandra, Sako, Takashi, Sakurai, Shunsuke, Salamida, Francesco, Sánchez, Juan Antonio Aguilar, Santangelo, Andrea, Santos, Eva, Sarazin, Fred, Schäfer, Christoph, Scherini, Viviana, Schieler, Harald, Schmidt, David, Schoorlemmer, Harm, Schroeder, Frank, Sergijenko, Olga, Shin, H. S., Soldin, Dennis, Suarez-Duran, Mauricio, Takahashi, Kaoru, Takeda, Masahiro, Tameda, Yuichiro, Tkachenko, Olena, Tomida, Takayuki, Travnicek, Petr, Unger, Michael, Urban, Federico, Venters, Tonia, Verzi, Valerio, Vicha, Jakub, van Vliet, Arjen, Watson, Alan A., Yushkov, Alexey, Zapparrata, Orazio, and Zhang, Pengfei

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract

After a successful kick-off meeting in 2021. two workshops in 2022 and 2023 on the future Global Cosmic-Ray Observatory (GCOS) focused mainly on a straw man design of the detector and science possibilities for astro- and particle physics. About 100 participants gathered for in-person and hybrid panel discussions. In this report, we summarize these discussions, present a preliminary straw-man design for GCOS and collect short write-ups of the flash talks given during the focus sessions., Comment: 48 pages, 27 figures

Published
2025

27. Gaussian Models to Non-Gaussian Realms of Quantum Photonic Simulators

Author

Wayo, Dennis Delali Kwesi, Dias, Rodrigo Alves, Ganji, Masoud Darvish, Saporetti, Camila Martins, and Goliatt, Leonardo

Subjects
Quantum Physics
Abstract

Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.

Published
2025

28. A JWST Project on 47 Tucanae: Kinematics, energy equipartition and anisotropy of multiple populations

Author

Ziliotto, T., Milone, A. P., Cordoni, G., Aros, F. I., Vesperini, E., Lee, J. W., Bellini, A., Libralato, M., Dondoglio, E., Tailo, M., Livernois, A., Legnardi, M. V., Mastrobuono-Battisti, A., Lagioia, E., Bortolan, E., Muratore, F., Marino, A. F., Alves-Brito, A., and Renzini, A.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Recent work with JWST has demonstrated its capability to identify and chemically characterize multiple populations in globular clusters down to the H-burning limit. In this study, we explore the kinematics of multiple populations in the globular cluster 47 Tucanae by combining data from JWST, HST, and Gaia. We analyzed velocity dispersion and anisotropy profiles from the cluster center out to $\sim$10$R_h$. Our findings indicate that while 1G stars are isotropic, 2G stars are significantly radially anisotropic. These results align with the predictions of simulations of the dynamical evolution of clusters where 2G stars are initially more centrally concentrated than 1G stars. Furthermore, we subdivided the 2G population into two subpopulations: $2G_A$ and $2G_B$, with the latter being more chemically extreme. We compared their dynamical profiles and found no significant differences. For the first time, we measured the degree of energy equipartition among the multiple populations of 47 Tucanae. Overall, within the analyzed radial range ($\sim$2-4$R_h$), both populations exhibit a low degree of energy equipartition. The most significant differences between 1G and 2G stars are observed in the tangential velocity component, where 2G stars are characterized by a stronger degree of energy equipartition than 1G stars. In the radial component, the behavior of 1G and 2G stars is more variable, with differences largely dependent on radius. Finally, our analysis reveals that the ratio of rotational velocity to velocity dispersion is larger for the 2G population, while 1G stars exhibit higher skewness in their tangential proper motions, providing further evidence of differences in the kinematic properties of the 1G and 2G populations.

Published
2025

29. Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach

Author

Marques, Eduardo Santos de Oliveira, Pinto, Arthur Caio Vargas, Alves, Kaike Sa Teles Rocha, and de Aguiar, Eduardo Pestana

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Real-world data contain uncertainty and variations that can be correlated to external variables, known as randomness. An alternative cause of randomness is chaos, which can be an important component of chaotic time series. One of the existing methods to deal with this type of data is the use of the evolving Fuzzy Systems (eFSs), which have been proven to be a powerful class of models for time series forecasting, due to their autonomy to handle the data and highly complex problems in real-world applications. However, due to its working structure, type-2 fuzzy sets can outperform type-1 fuzzy sets for highly uncertain scenarios. We then propose ePL-KRLS-FSM+, an enhanced class of evolving fuzzy modeling approach that combines participatory learning (PL), a kernel recursive least squares method (KRLS), type-2 fuzzy logic and data transformation into fuzzy sets (FSs). This improvement allows to create and measure type-2 fuzzy sets for better handling uncertainties in the data, generating a model that can predict chaotic data with increased accuracy. The model is evaluated using two complex datasets: the chaotic time series Mackey-Glass delay differential equation with different degrees of chaos, and the main stock index of the Taiwan Capitalization Weighted Stock Index - TAIEX. Model performance is compared to related state-of-the-art rule-based eFS models and classical approaches and is analyzed in terms of error metrics, runtime and the number of final rules. Forecasting results show that the proposed model is competitive and performs consistently compared with type-1 models, also outperforming other forecasting methods by showing the lowest error metrics and number of final rules.

Published
2025

30. Resilient UAV Trajectory Planning via Few-Shot Meta-Offline Reinforcement Learning

Author

Eldeeb, Eslam and Alves, Hirley

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Reinforcement learning (RL) has been a promising essence in future 5G-beyond and 6G systems. Its main advantage lies in its robust model-free decision-making in complex and large-dimension wireless environments. However, most existing RL frameworks rely on online interaction with the environment, which might not be feasible due to safety and cost concerns. Another problem with online RL is the lack of scalability of the designed algorithm with dynamic or new environments. This work proposes a novel, resilient, few-shot meta-offline RL algorithm combining offline RL using conservative Q-learning (CQL) and meta-learning using model-agnostic meta-learning (MAML). The proposed algorithm can train RL models using static offline datasets without any online interaction with the environments. In addition, with the aid of MAML, the proposed model can be scaled up to new unseen environments. We showcase the proposed algorithm for optimizing an unmanned aerial vehicle (UAV) 's trajectory and scheduling policy to minimize the age-of-information (AoI) and transmission power of limited-power devices. Numerical results show that the proposed few-shot meta-offline RL algorithm converges faster than baseline schemes, such as deep Q-networks and CQL. In addition, it is the only algorithm that can achieve optimal joint AoI and transmission power using an offline dataset with few shots of data points and is resilient to network failures due to unprecedented environmental changes.

Published
2025

31. Optimizing Impulsive Releases in Species Competition Models

Author

Alves, Jéssica C. S., Oliva, Sergio M., and Schaerer, Christian E.

Subjects
Mathematics - Optimization and Control, Quantitative Biology - Populations and Evolution, 92D25 (primary), 49N25, 34C60 (secondary)
Abstract

This study focuses on optimizing species release $S_2$ to control species population $S_1$ through impulsive release strategies. We investigate the conditions required to remove species $S_1$, which is equivalent to the establishment of $S_2$. The research includes a theoretical analysis that examines the positivity, existence, and uniqueness of solutions and the conditions for the global stability of the $S_1$-free solution. In addition, we formulate an optimal control problem to maximize the effectiveness of $S_2$ releases, manage the population of $S_1$, and minimize the costs associated with this intervention strategy. Numerical simulations are conducted to validate the proposed theories and allow visualization of population dynamics under various releases scenarios., Comment: 30 pages and 40 figures

Published
2025

32. Study of tau neutrinos and non-unitary neutrino mixing with the first six detection units of KM3NeT/ORCA

Author

KM3NeT Collaboration, Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lema^itre, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Paesani, D., González, J. Palacios, Papalashvili, G., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poirè, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Romanov, A., Ros, E., Šaina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Sevle, P., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Štekl, I., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.

Subjects
High Energy Physics - Experiment
Abstract

Oscillations of atmospheric muon and electron neutrinos produce tau neutrinos with energies in the GeV range, which can be observed by the ORCA detector of the KM3NeT neutrino telescope in the Mediterranean Sea. First measurements with ORCA6, an early subarray corresponding to about 5$\%$ of the final detector, are presented. A sample of 5828 neutrino candidates has been selected from the analysed exposure of 433 kton-years. The $\nu_\tau$ normalisation, defined as the ratio between the number of observed and expected tau neutrino events, is measured to be $S_\tau = 0.48^{+0.5}_{-0.33}$. This translates into a $\nu_\tau$ charged-current cross section measurement of $\sigma_{\tau}^{\text{meas}} = (2.5 ^{+2.6}_{-1.8}) \times 10^{-38}$ cm$^{2}$ nucleon$^{-1}$ at the median $\nu_\tau$ energy of 20.3 GeV. The result is consistent with the measurements of other experiments. In addition, the current limit on the non-unitarity parameter affecting the $\tau$-row of the neutrino mixing matrix was improved, with $\alpha_{33}>$ 0.95 at the 95$\%$ confidence level., Comment: 25 pages, 8 figures

Published
2025

33. A Microcanonical Inflection Point Analysis via Parametric Curves and its Relation to the Zeros of the Partition Function

Author

Rocha, Julio Cesar Siqueira, Dias, Rodrigo Alves, and da Costa, Bismarck Vaz

Subjects
Condensed Matter - Statistical Mechanics
Abstract

In statistical physics, phase transitions are arguably among the most extensively studied phenomena. In the computational approach to this field, the development of algorithms capable of estimating entropy across the entire energy spectrum in a single execution has highlighted the efficacy of microcanonical inflection point analysis, while Fisher zeros technique has re-emerged as a powerful methodology for investigating these phenomena. This paper presents an alternative protocol for analyzing phase transitions based on parametric microcanonical curves. We also provide a clear demonstration of the relation of the linear pattern of the Fisher's zeros on the complex inverse temperature map (a circle in the complex $x=e^{-\beta \varepsilon}$ map) with the order of the transition, showing that the specific heat is inversely related to the distance between the zeros. We study various model systems, including the Lennard-Jones cluster, the Ising, the XY, and the Zeeman models, illustrating the characterization of first-order, second-order, and Berezinskii-Kosterlitz-Thouless (BKT) transitions, respectively. By examining the behavior of thermodynamic quantities such as entropy and its derivatives in the microcanonical ensemble, we identify key features - such as loops and discontinuities in parametric curves - which signal phase transitions' presence and nature. We are confident that this approach can facilitate the classification of phase transitions across various physical systems., Comment: 13 pages, 11 figures

Published
2025

34. Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data

Author

Paucar-Escalante, Jesus, da Silva, Matheus Alves, Sanches, Bruno De Lima, Soriano-Vargas, Aurea, Moriyama, Laura Silveira, and Colombini, Esther Luna

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners., Comment: 28 pages, 9 figures, 3 tables, Journal Artificial Intelligence In Medicine

Published
2025

35. SDM Optical Systems with MMSE Equalizers: Information Rates and Performance Monitoring

Author

Zischler, Lucas Alves and Mello, Darli A. A.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

The information rate of coupled space-division multiplexing (SDM) transmission systems is impaired by the stochastic effects of mode-dependent gain (MDG) and mode-dependent loss (MDL), turning it into a random variable and reducing its average value. In systems operating with minimum mean squared error (MMSE) equalizers and no channel-state information (CSI), co-channel interference further reduces the instantaneous and average information rates. Analytical solutions for the average information rate in MDG- and MDL-impaired systems under strong coupling have been presented in early studies assuming ideal maximum-likelihood (ML) equalization. However, to the best of our knowledge, a solution encompassing co-channel interference under MMSE equalization has not been presented yet. In this work, we derive statistical models for the MMSE equalizer coefficients and develop analytical solutions for the post-filtering information rate. We also use these statistical models and analytical solutions to carry out MDG and signal-to-noise ratio (SNR) monitoring in coupled SDM systems. The derived analytical solutions and monitoring techniques are validated by Monte-Carlo simulations, exhibiting a suitable accuracy within practical operational values., Comment: Submitted to Journal of Lightwave Technology

Published
2025

36. Analysis of the navigation of magnetic microrobots through cerebral bifurcations

Author

Alves, Pedro G., Pinto, Maria, Moreira, Rosa, Sivakumaran, Derick, Landers, Fabian C., Guix, Maria, Nelson, Bradley J., Flouris, Andreas D., Pané, Salvador, Puigmartí-Luis, Josep, and Mayor, Tiago Sotto

Subjects
Mathematics - Numerical Analysis, Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control, Physics - Biological Physics
Abstract

Local administration of thrombolytics in ischemic stroke could accelerate clot lysis and the ensuing reperfusion while minimizing the side effects of systemic administration. Medical microrobots could be injected into the bloodstream and magnetically navigated to the clot for administering the drugs directly to the target. The magnetic manipulation required to navigate medical microrobots will depend on various parameters such as the microrobots size, the blood velocity, and the imposed magnetic field gradients. Numerical simulation was used to study the motion of magnetically controlled microrobots flowing through representative cerebral bifurcations, for predicting the magnetic gradients required to navigate the microrobots from the injection point until the target location. Upon thorough validation of the model against several independent analytical and experimental results, the model was used to generate maps and a predictive equation providing quantitative information on the required magnetic gradients, for different scenarios. The developed maps and predictive equation are crucial to inform the design, operation and optimization of magnetic navigation systems for healthcare applications.

Published
2025

37. A closer look at LTT 9779b: The ESPRESSO endeavour to pierce the atmospheric veil

Author

Reyes, R. Ramírez, Jenkins, James S., Sedaghati, Elyar, Seidel, J. V., Pavlenko, Yakiv, Palle, E., López-Morales, Mercedes, Alves, Douglas, Vines, José, R, Pablo A. Peña, Díaz, Matías R., and Rojo, Patricio

Subjects
Astrophysics - Earth and Planetary Astrophysics
Abstract

The proliferation of exoplanet discoveries in exotic environments like the Neptune desert challenges our understanding of planetary atmospheres under intense irradiation. The unexpected discovery of LTT9779 b, an ultra-hot Neptune within this desert, offers a prime opportunity for atmospheric studies. We build on prior observations of LTT9779 b from TESS, Spitzer, and CHEOPS, incorporating new VLT/ESPRESSO data to probe its atmospheric dynamics. Preliminary analyses suggest a metal-rich atmosphere and a high day-side geometric albedo, possibly indicating silicate clouds. Minimal atmospheric escape is observed, contrasting existing models of planetary evolution under extreme irradiation. We obtained the transmission spectrum of LTT9779 b between 0.4 and 0.78 microns with ESPRESSO, addressing systematics across three transits. Our analysis focused on the sodium doublet and H-alpha, using cross-correlation with models containing Na, K, FeH, TiO, and VO. No significant atmospheric signal was detected, with metallicity limits set at [Fe/H] $\geq$ 2.25 ($\geq$ 180 times solar). The non-detection aligns with a high-metallicity, cloud-free model, implying a high mean molecular weight and reduced atmospheric scale height. We interpret this as evidence for a metal-rich atmosphere with suppressed spectral features, possibly due to high-altitude clouds or hazes. These findings are consistent with JWST observations, supporting the hypothesis of metal-rich atmospheres obscured by aerosols in extreme environments., Comment: 12 pages, 11 figures, 3 tables, Accepted for publication in Astronomy and Astrophysics

Published
2025
Full Text
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38. Multi-Agent Meta-Offline Reinforcement Learning for Timely UAV Path Planning and Data Collection

Author

Eldeeb, Eslam and Alves, Hirley

Subjects
Computer Science - Multiagent Systems
Abstract

Multi-agent reinforcement learning (MARL) has been widely adopted in high-performance computing and complex data-driven decision-making in the wireless domain. However, conventional MARL schemes face many obstacles in real-world scenarios. First, most MARL algorithms are online, which might be unsafe and impractical. Second, MARL algorithms are environment-specific, meaning network configuration changes require model retraining. This letter proposes a novel meta-offline MARL algorithm that combines conservative Q-learning (CQL) and model agnostic meta-learning (MAML). CQL enables offline training by leveraging pre-collected datasets, while MAML ensures scalability and adaptability to dynamic network configurations and objectives. We propose two algorithm variants: independent training (M-I-MARL) and centralized training decentralized execution (M-CTDE-MARL). Simulation results show that the proposed algorithm outperforms conventional schemes, especially the CTDE approach that achieves 50 % faster convergence in dynamic scenarios than the benchmarks. The proposed framework enhances scalability, robustness, and adaptability in wireless communication systems by optimizing UAV trajectories and scheduling policies.

Published
2025

39. New scenarios and trends in non-traditional laboratories from 2000 to 2020

Author

Fernandez, Ricardo M., Garcia-Loro, Felix, Alves, Gustavo, Lopez-Rey, Africa, Meier, Russ, and Castro, Manuel

Subjects
Computer Science - Computers and Society, Electrical Engineering and Systems Science - Systems and Control
Abstract

For educational institutions in STEM areas, the provision of practical learning scenarios is, traditionally, a major concern. In the 21st century, the explosion of ICTs, as well as the universalization of low-cost hardware, have allowed the proliferation of technical solutions for any field; in the case of experimentation, encouraging the emergence and proliferation of non-traditional experimentation platforms. This movement has resulted in enriched practical environments, with wider adaptability for both students and teachers. In this paper, the evolution of scholar production has been analyzed at the global level from 2000 to 2020. Current and emerging experimentation scenarios have been identified, specifying the scope and boundaries between them., Comment: Transaction on learning Technologies, 20 pages

Published
2025
Full Text
View/download PDF

40. Humanity's Last Exam

Author

Phan, Long, Gatti, Alice, Han, Ziwen, Li, Nathaniel, Hu, Josephina, Zhang, Hugh, Zhang, Chen Bo Calvin, Shaaban, Mohamed, Ling, John, Shi, Sean, Choi, Michael, Agrawal, Anish, Chopra, Arnav, Khoja, Adam, Kim, Ryan, Ren, Richard, Hausenloy, Jason, Zhang, Oliver, Mazeika, Mantas, Nguyen, Tung, Anderson, Daron, Shah, Imad Ali, Doroshenko, Mikhail, Stokes, Alun Cennyth, Mahmood, Mobeen, Lee, Jaeho, Pokutnyi, Oleksandr, Iskra, Oleg, Wang, Jessica P., Gerbicz, Robert, Levin, John-Clark, Popov, Serguei, Feng, Fiona, Feng, Steven Y., Zhao, Haoran, Yu, Michael, Gangal, Varun, Zou, Chelsea, Wang, Zihan, Kazakov, Mstyslav, Galgon, Geoff, Schmitt, Johannes, Sanchez, Alvaro, Lee, Yongki, Yeadon, Will, Sauers, Scott, Roth, Marc, Agu, Chidozie, Riis, Søren, Giska, Fabian, Utpala, Saiteja, Cheatom, Antrell, Giboney, Zachary, Goshu, Gashaw M., Crowson, Sarah-Jane, Naiya, Mohinder Maheshbhai, Burns, Noah, Finke, Lennart, Cheng, Zerui, Park, Hyunwoo, Fournier-Facio, Francesco, Zampese, Jennifer, Wydallis, John B., Hoerr, Ryan G., Nandor, Mark, Gehrunger, Tim, Cai, Jiaqi, McCarty, Ben, Nam, Jungbae, Taylor, Edwin, Jin, Jun, Loume, Gautier Abou, Cao, Hangrui, Garretson, Alexis C, Sileo, Damien, Ren, Qiuyu, Cojoc, Doru, Arkhipov, Pavel, Qazi, Usman, Bacho, Aras, Li, Lianghui, Motwani, Sumeet, de Witt, Christian Schroeder, Kopylov, Alexei, Veith, Johannes, Singer, Eric, Rissone, Paolo, Jin, Jaehyeok, Shi, Jack Wei Lun, Willcocks, Chris G., Prabhu, Ameya, Tang, Longke, Zhou, Kevin, Santos, Emily de Oliveira, Maksimov, Andrey Pupasov, Vendrow, Edward, Zenitani, Kengo, Robinson, Joshua, Mikov, Aleksandar, Guillod, Julien, Li, Yuqi, Pageler, Ben, Vendrow, Joshua, Kuchkin, Vladyslav, Marion, Pierre, Efremov, Denis, Lynch, Jayson, Liang, Kaiqu, Gritsevskiy, Andrew, Martinez, Dakotah, Crispino, Nick, Zvonkine, Dimitri, Fraga, Natanael Wildner, Soori, Saeed, Press, Ori, Tang, Henry, Salazar, Julian, Green, Sean R., Brüssel, Lina, Twayana, Moon, Dieuleveut, Aymeric, Rogers, T. Ryan, Zhang, Wenjin, Finocchio, Ross, Li, Bikun, Yang, Jinzhou, Rao, Arun, Loiseau, Gabriel, Kalinin, Mikhail, Lukas, Marco, Manolescu, Ciprian, Stambaugh, Nate, Mishra, Subrata, Kamdoum, Ariel Ghislain Kemogne, Hogg, Tad, Jin, Alvin, Bosio, Carlo, Sun, Gongbo, Coppola, Brian P, Heidinger, Haline, Sayous, Rafael, Ivanov, Stefan, Cavanagh, Joseph M, Shen, Jiawei, Imperial, Joseph Marvin, Schwaller, Philippe, Senthilkuma, Shaipranesh, Bran, Andres M, Algaba, Andres, Verbeken, Brecht, Houte, Kelsey Van den, Van Der Sypt, Lynn, Noever, David, Schut, Lisa, Sucholutsky, Ilia, Zheltonozhskii, Evgenii, Yuan, Qiaochu, Lim, Derek, Stanley, Richard, Sivarajan, Shankar, Yang, Tong, Maar, John, Wykowski, Julian, Oller, Martí, Sandlin, Jennifer, Sahu, Anmol, Ardito, Cesare Giulio, Hu, Yuzheng, Dias, Felipe Meneguitti, Kreiman, Tobias, Rawal, Kaivalya, Vilchis, Tobias Garcia, Zu, Yuexuan, Lackner, Martin, Koppel, James, Nguyen, Jeremy, Antonenko, Daniil S., Chern, Steffi, Zhao, Bingchen, Arsene, Pierrot, Ivanov, Sergey, Poświata, Rafał, Wang, Chenguang, Li, Daofeng, Crisostomi, Donato, Dehghan, Ali, Achilleos, Andrea, Ambay, John Arnold, Myklebust, Benjamin, Sen, Archan, Perrella, David, Kaparov, Nurdin, Inlow, Mark H, Zang, Allen, Ramakrishnan, Kalyan, Orel, Daniil, Poritski, Vladislav, Ben-David, Shalev, Berger, Zachary, Whitfill, Parker, Foster, Michael, Munro, Daniel, Ho, Linh, Hava, Dan Bar, Kuchkin, Aleksey, Lauff, Robert, Holmes, David, Sommerhage, Frank, Zhang, Anji, Moat, Richard, Schneider, Keith, Pyda, Daniel, Kazibwe, Zakayo, Singh, Mukhwinder, Clarke, Don, Kim, Dae Hyun, Fish, Sara, Elser, Veit, Vilchis, Victor Efren Guadarrama, Klose, Immo, Demian, Christoph, Anantheswaran, Ujjwala, Zweiger, Adam, Albani, Guglielmo, Li, Jeffery, Daans, Nicolas, Radionov, Maksim, Rozhoň, Václav, Ginis, Vincent, Ma, Ziqiao, Stump, Christian, Platnick, Jacob, Nevirkovets, Volodymyr, Basler, Luke, Piccardo, Marco, Cohen, Niv, Singh, Virendra, Tkadlec, Josef, Rosu, Paul, Goldfarb, Alan, Padlewski, Piotr, Barzowski, Stanislaw, Montgomery, Kyle, Menezes, Aline, Patel, Arkil, Wang, Zixuan, Tucker-Foltz, Jamie, Stade, Jack, Grabb, Declan, Goertzen, Tom, Kazemi, Fereshteh, Milbauer, Jeremiah, Shukla, Abhishek, Elgnainy, Hossam, Labrador, Yan Carlos Leyva, He, Hao, Zhang, Ling, Givré, Alan, Wolff, Hew, Demir, Gözdenur, Aziz, Muhammad Fayez, Kaddar, Younesse, Ängquist, Ivar, Chen, Yanxu, Thornley, Elliott, Zhang, Robin, Pan, Jiayi, Terpin, Antonio, Muennighoff, Niklas, Schoelkopf, Hailey, Zheng, Eric, Carmi, Avishy, Shah, Jainam, Brown, Ethan D. L., Zhu, Kelin, Bartolo, Max, Wheeler, Richard, Ho, Andrew, Barkan, Shaul, Wang, Jiaqi, Stehberger, Martin, Kretov, Egor, Bradshaw, Peter, Heimonen, JP, Sridhar, Kaustubh, Hossain, Zaki, Akov, Ido, Makarychev, Yury, Tam, Joanna, Hoang, Hieu, Cunningham, David M., Goryachev, Vladimir, Patramanis, Demosthenes, Krause, Michael, Redenti, Andrew, Aldous, David, Lai, Jesyin, Coleman, Shannon, Xu, Jiangnan, Lee, Sangwon, Magoulas, Ilias, Zhao, Sandy, Tang, Ning, Cohen, Michael K., Carroll, Micah, Paradise, Orr, Kirchner, Jan Hendrik, Steinerberger, Stefan, Ovchynnikov, Maksym, Matos, Jason O., Shenoy, Adithya, Wang, Michael, Nie, Yuzhou, Giordano, Paolo, Petersen, Philipp, Sztyber-Betley, Anna, Faraboschi, Paolo, Riblet, Robin, Crozier, Jonathan, Halasyamani, Shiv, Pinto, Antonella, Verma, Shreyas, Joshi, Prashant, Meril, Eli, Yong, Zheng-Xin, Tee, Allison, Andréoletti, Jérémy, Weller, Orion, Singhal, Raghav, Zhang, Gang, Ivanov, Alexander, Khoury, Seri, Gustafsson, Nils, Mostaghimi, Hamid, Thaman, Kunvar, Chen, Qijia, Khánh, Tran Quoc, Loader, Jacob, Cavalleri, Stefano, Szlyk, Hannah, Brown, Zachary, Narayan, Himanshu, Roberts, Jonathan, Alley, William, Sun, Kunyang, Stendall, Ryan, Lamparth, Max, Reuel, Anka, Wang, Ting, Xu, Hanmeng, Hernández-Cámara, Pablo, Martin, Freddie, Preu, Thomas, Korbak, Tomek, Abramovitch, Marcus, Williamson, Dominic, Bosio, Ida, Chen, Ziye, Bálint, Biró, Lo, Eve J. Y., Nunes, Maria Inês S., Jiang, Yibo, Bari, M Saiful, Kassani, Peyman, Wang, Zihao, Ansarinejad, Behzad, Sun, Yewen, Durand, Stephane, Douville, Guillaume, Tordera, Daniel, Balabanian, George, Anderson, Earth, Kvistad, Lynna, Moyano, Alejandro José, Milliron, Hsiaoyun, Sakor, Ahmad, Eron, Murat, McAlister, Isaac C., O., Andrew Favre D., Shah, Shailesh, Zhou, Xiaoxiang, Kamalov, Firuz, Clark, Ronald, Abdoli, Sherwin, Santens, Tim, Wang, Harrison K, Chen, Evan, Tomasiello, Alessandro, De Luca, G. Bruno, Looi, Shi-Zhuo, Le, Vinh-Kha, Kolt, Noam, Mündler, Niels, Semler, Avi, Rodman, Emma, Drori, Jacob, Fossum, Carl J, Gloor, Luk, Jagota, Milind, Pradeep, Ronak, Fan, Honglu, Shah, Tej, Eicher, Jonathan, Chen, Michael, Thaman, Kushal, Merrill, William, Firsching, Moritz, Harris, Carter, Ciobâcă, Stefan, Gross, Jason, Pandey, Rohan, Gusev, Ilya, Jones, Adam, Agnihotri, Shashank, Zhelnov, Pavel, Usawasutsakorn, Siranut, Mofayezi, Mohammadreza, Piperski, Alexander, Carauleanu, Marc, Zhang, David K., Dobarskyi, Kostiantyn, Ler, Dylan, Leventov, Roman, Soroko, Ignat, Jansen, Thorben, Creighton, Scott, Lauer, Pascal, Duersch, Joshua, Taamazyan, Vage, Bezzi, Dario, Morak, Wiktor, Ma, Wenjie, Held, William, Huy, Tran Đuc, Xian, Ruicheng, Zebaze, Armel Randy, Mohamed, Mohanad, Leser, Julian Noah, Yuan, Michelle X, Yacar, Laila, Lengler, Johannes, Olszewska, Katarzyna, Shahrtash, Hossein, Oliveira, Edson, Jackson, Joseph W., Gonzalez, Daniel Espinosa, Zou, Andy, Chidambaram, Muthu, Manik, Timothy, Haffenden, Hector, Stander, Dashiell, Dasouqi, Ali, Shen, Alexander, Duc, Emilien, Golshani, Bita, Stap, David, Uzhou, Mikalai, Zhidkovskaya, Alina Borisovna, Lewark, Lukas, Rodriguez, Miguel Orbegozo, Vincze, Mátyás, Wehr, Dustin, Tang, Colin, Phillips, Shaun, Samuele, Fortuna, Muzhen, Jiang, Ekström, Fredrik, Hammon, Angela, Patel, Oam, Farhidi, Faraz, Medley, George, Mohammadzadeh, Forough, Peñaflor, Madellene, Kassahun, Haile, Friedrich, Alena, Sparrow, Claire, Perez, Rayner Hernandez, Sakal, Taom, Dhamane, Omkar, Mirabadi, Ali Khajegili, Hallman, Eric, Okutsu, Kenchi, Battaglia, Mike, Maghsoudimehrabani, Mohammad, Amit, Alon, Hulbert, Dave, Pereira, Roberto, Weber, Simon, Handoko, Peristyy, Anton, Malina, Stephen, Albanie, Samuel, Cai, Will, Mehkary, Mustafa, Aly, Rami, Reidegeld, Frank, Dick, Anna-Katharina, Friday, Cary, Sidhu, Jasdeep, Shapourian, Hassan, Kim, Wanyoung, Costa, Mariana, Gurdogan, Hubeyb, Weber, Brian, Kumar, Harsh, Jiang, Tong, Agarwal, Arunim, Ceconello, Chiara, Vaz, Warren S., Zhuang, Chao, Park, Haon, Tawfeek, Andrew R., Aggarwal, Daattavya, Kirchhof, Michael, Dai, Linjie, Kim, Evan, Ferret, Johan, Wang, Yuzhou, Yan, Minghao, Burdzy, Krzysztof, Zhang, Lixin, Franca, Antonio, Pham, Diana T., Loh, Kang Yong, Jackson, Abram, Gul, Shreen, Chhablani, Gunjan, Du, Zhehang, Cosma, Adrian, Colino, Jesus, White, Colin, Votava, Jacob, Vinnikov, Vladimir, Delaney, Ethan, Spelda, Petr, Stritecky, Vit, Shahid, Syed M., Mourrat, Jean-Christophe, Vetoshkin, Lavr, Sponselee, Koen, Bacho, Renas, de la Rosa, Florencia, Li, Xiuyu, Malod, Guillaume, Lang, Leon, Laurendeau, Julien, Kazakov, Dmitry, Adesanya, Fatimah, Portier, Julien, Hollom, Lawrence, Souza, Victor, Zhou, Yuchen Anna, Degorre, Julien, Yalın, Yiğit, Obikoya, Gbenga Daniel, Arnaboldi, Luca, Rai, Bigi, Filippo, Boscá, M. C., Shumar, Oleg, Bacho, Kaniuar, Clavier, Pierre, Recchia, Gabriel, Popescu, Mara, Shulga, Nikita, Tanwie, Ngefor Mildred, Peskoff, Denis, Lux, Thomas C. H., Rank, Ben, Ni, Colin, Brooks, Matthew, Yakimchyk, Alesia, Huanxu, Liu, Häggström, Olle, Verkama, Emil, Gundlach, Hans, Brito-Santana, Leonor, Amaro, Brian, Vajipey, Vivek, Grover, Rynaa, Fan, Yiyang, Silva, Gabriel Poesia Reis e, Xin, Linwei, Kratish, Yosi, Łucki, Jakub, Li, Wen-Ding, Gopi, Sivakanth, Caciolai, Andrea, Xu, Justin, Scaria, Kevin Joseph, Vargus, Freddie, Habibi, Farzad, Long, Lian, Rodolà, Emanuele, Robins, Jules, Cheng, Vincent, Fruhauff, Tony, Raynor, Brad, Qi, Hao, Jiang, Xi, Segev, Ben, Fan, Jingxuan, Martinson, Sarah, Wang, Erik Y., Hausknecht, Kaylie, Brenner, Michael P., Mao, Mao, Zhang, Xinyu, Avagian, David, Scipio, Eshawn Jessica, Ragoler, Alon, Tan, Justin, Sims, Blake, Plecnik, Rebeka, Kirtland, Aaron, Bodur, Omer Faruk, Shinde, D. P., Adoul, Zahra, Zekry, Mohamed, Karakoc, Ali, Santos, Tania C. B., Shamseldeen, Samir, Karim, Loukmane, Liakhovitskaia, Anna, Resman, Nate, Farina, Nicholas, Gonzalez, Juan Carlos, Maayan, Gabe, Hoback, Sarah, Pena, Rodrigo De Oliveira, Sherman, Glen, Kelley, Elizabeth, Mariji, Hodjat, Pouriamanesh, Rasoul, Wu, Wentao, Mendoza, Sandra, Alarab, Ismail, Cole, Joshua, Ferreira, Danyelle, Johnson, Bryan, Safdari, Mohammad, Dai, Liangti, Arthornthurasuk, Siriphan, Pronin, Alexey, Fan, Jing, Ramirez-Trinidad, Angel, Cartwright, Ashley, Pottmaier, Daphiny, Taheri, Omid, Outevsky, David, Stepanic, Stanley, Perry, Samuel, Askew, Luke, Rodríguez, Raúl Adrián Huerta, Minissi, Ali M. R., Ali, Sam, Lorena, Ricardo, Iyer, Krishnamurthy, Fasiludeen, Arshad Anil, Salauddin, Sk Md, Islam, Murat, Gonzalez, Juan, Ducey, Josh, Somrak, Maja, Mavroudis, Vasilios, Vergo, Eric, Qin, Juehang, Borbás, Benjámin, Chu, Eric, Lindsey, Jack, Radhakrishnan, Anil, Jallon, Antoine, McInnis, I. M. J., Kumar, Pawan, Goswami, Laxman Prasad, Bugas, Daniel, Heydari, Nasser, Jeanplong, Ferenc, Apronti, Archimedes, Galal, Abdallah, Ze-An, Ng, Singh, Ankit, Xavier, Joan of Arc, Agarwal, Kanu Priya, Berkani, Mohammed, Junior, Benedito Alves de Oliveira, Malishev, Dmitry, Remy, Nicolas, Hartman, Taylor D., Tarver, Tim, Mensah, Stephen, Gimenez, Javier, Montecillo, Roselynn Grace, Campbell, Russell, Sharma, Asankhaya, Meer, Khalida, Alapont, Xavier, Patil, Deepakkumar, Maheshwari, Rajat, Dendane, Abdelkader, Shukla, Priti, Bogdanov, Sergei, Möller, Sören, Siddiqi, Muhammad Rehan, Saxena, Prajvi, Gupta, Himanshu, Enyekwe, Innocent, P V, Ragavendran, EL-Wasif, Zienab, Maksapetyan, Aleksandr, Rossbach, Vivien, Harjadi, Chris, Bahaloohoreh, Mohsen, Bian, Song, Lai, John, Uro, Justine Leon, Bateman, Greg, Sayed, Mohamed, Menshawy, Ahmed, Duclosel, Darling, Jain, Yashaswini, Aaron, Ashley, Tiryakioglu, Murat, Siddh, Sheeshram, Krenek, Keith, Hoover, Alex, McGowan, Joseph, Patwardhan, Tejal, Yue, Summer, Wang, Alexandr, and Hendrycks, Dan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures

Published
2025

41. Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks

Author

Sarathchandra, Sankani, Eldeeb, Eslam, Shehab, Mohammad, Alves, Hirley, Mikhaylov, Konstantin, and Alouini, Mohamed-Slim

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall., Comment: 10 pages, 8 figures

Published
2025

42. HP2 Survey V. Ophiuchus: Filament formation in a dispersing cloud complex

Author

Alves, João, Lombardi, Marco, and Lada, Charles

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We search for potential ``birthmarks'' left from the formation of filamentary molecular clouds in the Ophiuchus complex. We use high dynamic-range column density and temperature maps derived from \textit{Herschel}, \textit{Planck}, and \textit{2MASS/NICEST} extinction data. We find two distinct types of filaments based on their orientation relative to nearby massive stars: radial (R-type) and tangential (T-type). R-type filaments exhibit decreasing mass profiles away from massive stars, while T-type filaments show flat but structured profiles. We propose a scenario where both filament types originate from the dynamic interplay of compression and stretching forces exerted by a fast outflow emanating from the OB association. The two formation mechanisms leave distinct observable ``birthmarks'' (namely, filament orientation, mass distribution, and star formation location) on each filament type. Our results illustrate a complex phase in molecular cloud evolution with two simultaneous yet contrasting processes: the formation of filaments and stars via the dispersal of residual gas from a previous massive star formation event. Our approach highlights the importance of taking into account the wider context of a star-forming complex, rather than concentrating exclusively on particular subregions., Comment: Accepted for publication in Astronomy & Astrophysics (A&A)

Published
2025

43. Valuing science and the teaching of Physics through theatrical communication

Author

Junior, Ederaldo Bueno de Macedo, Filho, Edemar Benedetti, and de Souza, James Alves

Subjects
Physics - Physics Education, Physics - Physics and Society
Abstract

Scientific communication inside and outside the classroom is the main means for providing an adequate understanding of how science and technological innovation relate to society. In order to achieve this goal, it is important to explore new didactic instruments for teaching science in general. The intersection between science and arts has contributed to make science accessible, relevant and interesting to diverse audiences with the potential to provide scientific content, significant artistic and entertainment values. Here, we use theatrical communication to value science and the teaching of Physics through an original script of a play in which concepts and phenomena related to astronomy are addressed. Using an artistic and casual language, we adapt the proposed scientific concepts to the contextual and instrumental nature of theater to demonstrate its educational value and its potential to transform science into entertainment. Our proposal allowed students being engaged in a highly motivating and collaborative environment, providing them the opportunity to recognize, analyze and imagine explanations and performances to communicate science to the general public in a creative and alternative way., Comment: 49 pages, in Portuguese language, 4 figures

Published
2025

44. Encouraging the teaching of science through composting

Author

Muniz, Michele Cristina, da Silva, Emanuele Vitoria, Barrile, João Pedro Ribeiro, Porto, Raquel Martins, and de Souza, James Alves

Subjects
Physics - Physics Education, Physics - Physics and Society
Abstract

The integration of environmental education into the school curriculum can be inspiring for students to assimilate scientific concepts and methods presented in the Science subjects in a contextualized and interdisciplinary way. To carry out such integration in the classroom it is important that a link between teacher training courses and practical activities on the subject during the training of the student teachers exist, to provide them conditions for testing hypotheses, pedagogical methodologies and evaluate their feasibility, considering real limitations of the school environment. The Institutional Program of Scholarships for Teaching Initiation (PIBID - Programa Institucional de Bolsas de Inicia\c{c}\~ao \`a Doc\^encia) is a Brazilian government policy developed to improve the training of student teachers which provides an ideal scenario for this. In this work, students from PIBID carried out this proposal through the windrow composting process. This was developed at the school considering the selective separation of waste, the construction of compost bins, the introduction and discussion of scientific concepts and experimentation, by monitoring the process. In addition to the beneficial experience for the training of student teachers, we show that the proposal is promising for developing student's skills such as scientific literacy and protagonism, and also the establishment of a school composting program., Comment: 22 pages, in Portuguese language, 4 figures

Published
2025

45. Metarecycling in Physics Education in Basic Education within the Context of the UN Sustainable Development Goals through Technological Research

Author

de Souza, James Alves, Batista, Michel Corci, and Ferreira, Marcello

Subjects
Physics - Physics Education, Physics - Physics and Society
Abstract

The rapid advancement of digital technologies in the first quarter of the 21st century has introduced significant transformations in various fields, such as communication, healthcare, and education. However, it has also led to an increase in the use and disposal of electronic devices, resulting in environmental challenges related to Waste Electrical and Electronic Equipment (WEEE), also known as e-waste. This phenomenon is observed in schools, where the integration and renewal of equipment have become essential for the development and implementation of new teaching strategies. Based on a technological research project, we present how students from a public school in S\~ao Paulo's countryside conducted e-waste reuse processes, applying the principles of metarecycling and physics knowledge to build a portable battery (power bank) and a smartphone charger powered by a dynamo attached to a bicycle. The appropriation of the relationships between science, technology, and social aspects was facilitated by validating the chargers through characterization tests of the charging time provided by the power bank and the bicycle-installed device under riding conditions. Educational actions within the community, involving concepts of sustainability, clean energy, and health benefits through physical exercise, were guided by the United Nations Sustainable Development Goals (SDGs)., Comment: 15 pages, in Portuguese language, 10 figures

Published
2025

46. An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management

Author

Eldeeb, Eslam and Alves, Hirley

Subjects
Computer Science - Multiagent Systems, Computer Science - Machine Learning
Abstract

Offline multi-agent reinforcement learning (MARL) addresses key limitations of online MARL, such as safety concerns, expensive data collection, extended training intervals, and high signaling overhead caused by online interactions with the environment. In this work, we propose an offline MARL algorithm for radio resource management (RRM), focusing on optimizing scheduling policies for multiple access points (APs) to jointly maximize the sum and tail rates of user equipment (UEs). We evaluate three training paradigms: centralized, independent, and centralized training with decentralized execution (CTDE). Our simulation results demonstrate that the proposed offline MARL framework outperforms conventional baseline approaches, achieving over a 15\% improvement in a weighted combination of sum and tail rates. Additionally, the CTDE framework strikes an effective balance, reducing the computational complexity of centralized methods while addressing the inefficiencies of independent training. These results underscore the potential of offline MARL to deliver scalable, robust, and efficient solutions for resource management in dynamic wireless networks.

Published
2025

47. Probing invisible neutrino decay with the first six detection units of KM3NeT/ORCA

Author

Aiello, S., Albert, A., Alhebsi, A. R., Alshamsi, M., Garre, S. Alves, Ambrosone, A., Ameli, F., Andre, M., Aphecetche, L., Ardid, M., Ardid, S., Aublin, J., Badaracco, F., Bailly-Salins, L., Bardačová, Z., Baret, B., Bariego-Quintana, A., Becherini, Y., Bendahman, M., Gualandi, F. Benfenati, Benhassi, M., Bennani, M., Benoit, D. M., Berbee, E., Bertin, V., Biagi, S., Boettcher, M., Bonanno, D., Bouasla, A. B., Boumaaza, J., Bouta, M., Bouwhuis, M., Bozza, C., Bozza, R. M., Brânzaş, H., Bretaudeau, F., Breuhaus, M., Bruijn, R., Brunner, J., Bruno, R., Buis, E., Buompane, R., Busto, J., Caiffi, B., Calvo, D., Capone, A., Carenini, F., Carretero, V., Cartraud, T., Castaldi, P., Cecchini, V., Celli, S., Cerisy, L., Chabab, M., Chen, A., Cherubini, S., Chiarusi, T., Circella, M., Clark, R., Cocimano, R., Coelho, J. A. B., Coleiro, A., Condorelli, A., Coniglione, R., Coyle, P., Creusot, A., Cuttone, G., Dallier, R., De Benedittis, A., De Wasseige, G., Decoene, V., Deguire, P., Del Rosso, I., Di Mauro, L. S., Di Palma, I., Díaz, A. F., Diego-Tortosa, D., Distefano, C., Domi, A., Donzaud, C., Dornic, D., Drakopoulou, E., Drouhin, D., Ducoin, J. -G., Duverne, P., Dvornický, R., Eberl, T., Eckerová, E., Eddymaoui, A., van Eeden, T., Eff, M., van Eijk, D., Bojaddaini, I. El, Hedri, S. El, Mentawi, S. El, Ellajosyula, V., Enzenhöfer, A., Ferrara, G., Filipović, M. D., Filippini, F., Franciotti, D., Fusco, L. A., Gagliardini, S., Gal, T., Méndez, J. García, Soto, A. Garcia, Oliver, C. Gatius, Geißelbrecht, N., Genton, E., Ghaddari, H., Gialanella, L., Gibson, B. K., Giorgio, E., Goos, I., Goswami, P., Gozzini, S. R., Gracia, R., Guidi, C., Guillon, B., Gutiérrez, M., Haack, C., van Haren, H., Heijboer, A., Hennig, L., Hernández-Rey, J. J., Idrissi, A., Ibnsalih, W. Idrissi, Illuminati, G., Joly, D., de Jong, M., de Jong, P., Jung, B. J., Kalaczyński, P., Kikvadze, V., Kistauri, G., Kopper, C., Kouchner, A., Kovalev, Y. Y., Krupa, L., Kueviakoe, V., Kulikovskiy, V., Kvatadze, R., Labalme, M., Lahmann, R., Lamoureux, M., Larosa, G., Lastoria, C., Lazar, J., Lazo, A., Stum, S. Le, Lehaut, G., Lemaître, V., Leonora, E., Lessing, N., Levi, G., Clark, M. Lindsey, Longhitano, F., Magnani, F., Majumdar, J., Malerba, L., Mamedov, F., Manfreda, A., Manousakis, A., Marconi, M., Margiotta, A., Marinelli, A., Markou, C., Martin, L., Mastrodicasa, M., Mastroianni, S., Mauro, J., Mehta, K. C. K., Meskar, A., Miele, G., Migliozzi, P., Migneco, E., Mitsou, M. L., Mollo, C. M., Morales-Gallegos, L., Moussa, A., Mateo, I. Mozun, Muller, R., Musone, M. R., Musumeci, M., Navas, S., Nayerhoda, A., Nicolau, C. A., Nkosi, B., Fearraigh, B. Ó, Oliviero, V., Orlando, A., Oukacha, E., Paesani, D., González, J. Palacios, Papalashvili, G., Parisi, V., Parmar, A., Gomez, E. J. Pastor, Pastore, C., Păun, A. M., Păvălaş, G. E., Martínez, S. Peña, Perrin-Terrin, M., Pestel, V., Pestes, R., Piattelli, P., Plavin, A., Poiré, C., Popa, V., Pradier, T., Prado, J., Pulvirenti, S., Quiroz-Rangel, C. A., Randazzo, N., Ratnani, A., Razzaque, S., Rea, I. C., Real, D., Riccobene, G., Romanov, A., Ros, E., Săina, A., Greus, F. Salesa, Samtleben, D. F. E., Losa, A. Sánchez, Sanfilippo, S., Sanguineti, M., Santonocito, D., Sapienza, P., Scarnera, M., Schnabel, J., Schumann, J., Schutte, H. M., Seneca, J., Sennan, N., Sevle, P., Sgura, I., Shanidze, R., Sharma, A., Shitov, Y., Šimkovic, F., Simonelli, A., Sinopoulou, A., Spisso, B., Spurio, M., Stavropoulos, D., Štekl, I., Taiuti, M., Takadze, G., Tayalati, Y., Thiersen, H., Thoudam, S., Melo, I. Tosta e, Trocmé, B., Tsourapis, V., Tudorache, A., Tzamariudaki, E., Ukleja, A., Vacheret, A., Valsecchi, V., Van Elewyck, V., Vannoye, G., Vasileiadis, G., de Sola, F. Vazquez, Veutro, A., Viola, S., Vivolo, D., van Vliet, A., de Wolf, E., Lhenry-Yvon, I., Zavatarelli, S., Zegarelli, A., Zito, D., Zornoza, J. D., Zúñiga, J., and Zywucka, N.

Subjects
High Energy Physics - Experiment
Abstract

In the era of precision measurements of neutrino oscillation parameters, it is necessary for experiments to disentangle discrepancies that may indicate physics beyond the Standard Model in the neutrino sector. KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study the oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on the initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photosensors. A high-purity neutrino sample was extracted during 2020 and 2021, corresponding to an exposure of 433 kton-years. This sample is analysed following a binned log-likelihood approach to search for invisible neutrino decay, in a three-flavour neutrino oscillation scenario, where the third neutrino mass state $\nu_3$ decays into an invisible state, e.g. a sterile neutrino. The resulting best fit of the invisible neutrino decay parameter is $\alpha_3 = 0.92^{+1.08}_{-0.57}\times 10^{-4}~\mathrm{eV^2}$, corresponding to a scenario with $\theta_{23}$ in the second octant and normal neutrino mass ordering. The results are consistent with the Standard Model, within a $2.1\,\sigma$ interval., Comment: 20 pages, 8 figures

Published
2025

48. The TW Hydrae Association is a cluster chain of Sco-Cen

Author

Miret-Roig, N., Alves, J., Ratzenböck, S., Galli, P. A. B., Bouy, H., Figueras, F., Großschedl, J., Meingast, S., Posch, L., Rottensteiner, A., Swiggum, C., and Wagner, N.

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

The TW Hydrae Association (TWA) is a young local association (YLA) about 50 pc from the Sun, offering a unique opportunity to study star and planet formation processes in detail. We characterized TWA's location, kinematics, and age, investigating its origin within the Scorpius-Centaurus (Sco-Cen) OB association. Using Gaia DR3 astrometric data and precise ground-based radial velocities, we identified substructures within TWA, tentatively dividing them into TWA-a and TWA-b. Sco-Cen's massive cluster $\sigma$ Cen (15 Myr, 1,805 members) may have influenced TWA's formation. The alignment of $\sigma$ Cen, TWA-a, and TWA-b in 3D positions, velocities, and ages resembles patterns in regions such as Corona Australis, suggesting that TWA is part of a cluster chain from sequential star formation induced by massive stars in Sco-Cen. TWA's elongation in the opposite direction to that produced by Galactic differential rotation indicates its shape is still influenced by its formation processes and will dissipate in less than 50 Myr due to Galactic forces. These findings unveil the nature of YLAs and low-mass clusters in a new light. We propose that clusters such as $\epsilon$ Chamaeleontis, $\eta$ Chamaeleontis, and TWA were forged by stellar feedback from massive stars in Sco-Cen, while others--such as $\beta$ Pictoris, Carina, Columba, and Tucana-Horologium--are older and formed differently. Remarkably, all these YLAs and Sco-Cen are part of the $\alpha$ Persei cluster family, a vast kiloparsec-scale star formation event active over the past 60 Myr. This suggests that YLAs are the smallest stellar structures emerging from major star formation episodes and should be common in the Milky Way. Crucially, their formation in regions with intense stellar feedback may have influenced planet formation in these systems., Comment: Accepted for publication in A&A

Published
2025

49. Clinically Ready Magnetic Microrobots for Targeted Therapies

Author

Landers, Fabian C., Hertle, Lukas, Pustovalov, Vitaly, Sivakumaran, Derick, Brinkmann, Oliver, Meiners, Kirstin, Theiler, Pascal, Gantenbein, Valentin, Veciana, Andrea, Mattmann, Michael, Riss, Silas, Gervasoni, Simone, Chautems, Christophe, Ye, Hao, Sevim, Semih, Flouris, Andreas D., Puigmartí-Luis, Josep, Mayor, Tiago Sotto, Alves, Pedro, Lühmann, Tessa, Chen, Xiangzhong, Ochsenbein, Nicole, Moehrlen, Ueli, Gruber, Philipp, Weisskopf, Miriam, Boehler, Quentin, Pané, Salvador, and Nelson, Bradley J.

Subjects
Computer Science - Robotics, Condensed Matter - Materials Science, Electrical Engineering and Systems Science - Systems and Control, Physics - Applied Physics, Physics - Biological Physics, Physics - Medical Physics
Abstract

Systemic drug administration often causes off-target effects limiting the efficacy of advanced therapies. Targeted drug delivery approaches increase local drug concentrations at the diseased site while minimizing systemic drug exposure. We present a magnetically guided microrobotic drug delivery system capable of precise navigation under physiological conditions. This platform integrates a clinical electromagnetic navigation system, a custom-designed release catheter, and a dissolvable capsule for accurate therapeutic delivery. In vitro tests showed precise navigation in human vasculature models, and in vivo experiments confirmed tracking under fluoroscopy and successful navigation in large animal models. The microrobot balances magnetic material concentration, contrast agent loading, and therapeutic drug capacity, enabling effective hosting of therapeutics despite the integration complexity of its components, offering a promising solution for precise targeted drug delivery.

Published
2025

50. High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

Author

Wagner, Fabien H, Dalagnol, Ricardo, Carter, Griffin, Hirye, Mayumi CM, Gill, Shivraj, Takougoum, Le Bienfaiteur Sagang, Favrichon, Samuel, Keller, Michael, Ometto, Jean PHB, Alves, Lorena, Creze, Cynthia, George-Chacon, Stephanie P, Li, Shuang, Liu, Zhihua, Mullissa, Adugna, Yang, Yan, Santos, Erone G, Worden, Sarah R, Brandt, Martin, Ciais, Philippe, Hagen, Stephen C, and Saatchi, Sassan

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.8
Abstract

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the mean tree canopy height in the Amazon forest from Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with their corresponding Planet NICFI images. Predictions of tree heights on the validation sample exhibited a mean error of 3.68 m and showed relatively low systematic bias across the entire range of tree heights present in the Amazon forest. Our model successfully estimated canopy heights up to 40-50 m without much saturation, outperforming existing canopy height products from global models in this region. We determined that the Amazon forest has an average canopy height of ~22 m. Events such as logging or deforestation could be detected from changes in tree height, and encouraging results were obtained to monitor the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height for old and regenerating Amazon forests using Planet NICFI imagery., Comment: will be submitted to the journal Remote Sensing of Environment in February 2025

Published
2025

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