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1. Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic

Author: Alyafeai, Zaid, Pieler, Michael, Teufel, Hannah, Tow, Jonathan, Bellagente, Marco, Phung, Duy, Pinnaparaju, Nikhil, Adithyan, Reshinth, Rocha, Paulo, Zhuravinskyi, Maksym, and Riquelme, Carlos
Subjects: Computer Science - Computation and Language
Abstract: Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more LLMs have incorporated a larger proportion of multilingual text to represent low-resource languages. In Arabic NLP, several Arabic-centric LLMs have shown remarkable results on multiple benchmarks in the past two years. However, most Arabic LLMs have more than 7 billion parameters, which increases their hardware requirements and inference latency, when compared to smaller LLMs. This paper introduces Arabic Stable LM 1.6B in a base and chat version as a small but powerful Arabic-centric LLM. Our Arabic Stable LM 1.6B chat model achieves impressive results on several benchmarks beating multiple models with up to 8x the parameters. In addition, we show the benefit of mixing in synthetic instruction tuning data by augmenting our fine-tuning data with a large synthetic dialogue dataset.
Published: 2024

2. Deformed AdS/QCD, mesonic mass spectra, and DCE: Still a margin for heavier resonances

Author: da Rocha, Roldao and Silva, Pedro H. O.
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract: The mass spectra of light-flavor mesons are analyzed in a deformed AdS/QCD soft-wall model, driven by four distinct anomalous 5-dimensional mass corrections of the scalar field, coupled to Einstein--Hilbert gravity, from the QCD running coupling. Using the differential configurational entropy (DCE) underlying the families of pseudoscalar, axial-vector, scalar, and vector mesons, the mass spectra of heavier meson resonances with radial quantum numbers beyond the ones already in the summary table of Particle Data Group (PDG) are then estimated. This protocol merges AdS/QCD and experimental data in PDG through Regge-like trajectories. Some of the estimated meson resonances may be identified as further candidates omitted from the summary table in PDG., Comment: 36 pages, 10 figs., to appear in PRD
Published: 2024

3. Coherent deflection of atomic samples and positional mesoscopic superpositions

Author: da Silva, L. F. Alves, Rocha, L. M. R., and Moussa, M. H. Y.
Subjects: Quantum Physics, Physics - Atomic Physics, Physics - Optics
Abstract: We present a protocol based on the interplay between superradiance and superabsorption to achieve the coherent deflection of an atomic sample due to the momentum transfer from the atoms to a cavity field. The coherent character of this momentum transfer, causing the atomic sample to deflect as a whole, follows from the collective nature of the atomic superradiant pulse and its superabsorption by the cavity field. The protocol is then used for the construction of positional mesoscopic atomic superpositions.
Published: 2024

4. Triple Evolution Pathways to Black Hole Low-Mass X-ray Binaries: Insights from V404 Cygni

Author: Shariat, Cheyanne, Naoz, Smadar, El-Badry, Kareem, Rocha, Kyle Akira, Kalogera, Vicky, Stephan, Alexander P., Burdge, Kevin B., and Angelo, Isabel
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: A recent discovery shows that V404 Cygni, a prototypical black hole low-mass X-ray binary (BH-LMXB) is a hierarchical triple: the BH and donor star are orbited by a $1.2$ M$_{\odot}$ tertiary at a distance of at least $3500$ au. Motivated by this system, we evolve a grid of $\sim50,000$ triple star systems, spanning a broad range of initial orbits. Our calculations employ {\tt MESA} stellar evolution models, using {\tt POSYDON}, and self-consistently track the effects of eccentric Kozai-Lidov (EKL) oscillations, mass loss, tides, and BH natal kicks. In our simulations, the progenitors of V404 Cygni-like systems have initial outer separations of $1000 - 10000$ au and inner separations of $\sim100$ au, such that they avoid Roche lobe overflow most of the time. Later on, EKL oscillations drive the inner binary to high eccentricities until tides shrink the orbit and mass transfer begins. Notably, such systems only form in simulations with very weak black hole natal kicks ($\lesssim 5\,{\rm km\,s^{-1}}$) because stronger kicks unbind the tertiaries. Our simulations also predict a population of BH-LMXB triples that form via the classical common-envelope channel, when the BH progenitor does overflow its Roche lobe. The formation rate for this channel is also higher in triples than in isolated binaries because early EKL oscillations cause inner binaries with a wider range of initial separations to enter and survive a common envelope. Our calculations demonstrate that at least some stellar BHs form with extremely weak kicks, and that triple evolution is a significant formation channel for BH-LMXBs., Comment: Submitted to ApJ, comments are welcome
Published: 2024

5. Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties

Author: Riccius, Leon, Rocha, Iuri B. C. M., Bierkens, Joris, Kekkonen, Hanne, and van der Meer, Frans P.
Subjects: Physics - Computational Physics, Statistics - Applications, Statistics - Machine Learning, 74S60
Abstract: Recent advancements in Markov chain Monte Carlo (MCMC) sampling and surrogate modelling have significantly enhanced the feasibility of Bayesian analysis across engineering fields. However, the selection and integration of surrogate models and cutting-edge MCMC algorithms, often depend on ad-hoc decisions. A systematic assessment of their combined influence on analytical accuracy and efficiency is notably lacking. The present work offers a comprehensive comparative study, employing a scalable case study in computational mechanics focused on the inference of spatially varying material parameters, that sheds light on the impact of methodological choices for surrogate modelling and sampling. We show that a priori training of the surrogate model introduces large errors in the posterior estimation even in low to moderate dimensions. We introduce a simple active learning strategy based on the path of the MCMC algorithm that is superior to all a priori trained models, and determine its training data requirements. We demonstrate that the choice of the MCMC algorithm has only a small influence on the amount of training data but no significant influence on the accuracy of the resulting surrogate model. Further, we show that the accuracy of the posterior estimation largely depends on the surrogate model, but not even a tailored surrogate guarantees convergence of the MCMC.Finally, we identify the forward model as the bottleneck in the inference process, not the MCMC algorithm. While related works focus on employing advanced MCMC algorithms, we demonstrate that the training data requirements render the surrogate modelling approach infeasible before the benefits of these gradient-based MCMC algorithms on cheap models can be reaped., Comment: 28 pages, 14 figures
Published: 2024

6. Irregularly Sampled Time Series Interpolation for Detailed Binary Evolution Simulations

Author: Srivastava, Philipp M., Demir, Ugur, Katsaggelos, Aggelos, Kalogera, Vicky, Teng, Elizabeth, Fragos, Tassos, Andrews, Jeff J., Bavera, Simone S., Briel, Max, Gossage, Seth, Kovlakas, Konstantinos, Kruckow, Matthias U., Liotine, Camille, Rocha, Kyle A., Sun, Meng, Xing, Zepei, and Zapartas, Emmanouil
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Modeling of large populations of binary stellar systems is an intergral part of a many areas of astrophysics, from radio pulsars and supernovae to X-ray binaries, gamma-ray bursts, and gravitational-wave mergers. Binary population synthesis codes that employ self-consistently the most advanced physics treatment available for stellar interiors and their evolution and are at the same time computationally tractable have started to emerge only recently. One element that is still missing from these codes is the ability to generate the complete time evolution of binaries with arbitrary initial conditions using pre-computed three-dimensional grids of binary sequences. Here we present a highly interpretable method, from binary evolution track interpolation. Our method implements simulation generation from irregularly sampled time series. Our results indicate that this method is appropriate for applications within binary population synthesis and computational astrophysics with time-dependent simulations in general. Furthermore we point out and offer solutions to the difficulty surrounding evaluating performance of signals exhibiting extreme morphologies akin to discontinuities., Comment: 15 pages, 11 figures, Submitted to ApJ
Published: 2024

7. POSYDON Version 2: Population Synthesis with Detailed Binary-Evolution Simulations across a Cosmological Range of Metallicities

Author: Andrews, Jeff J., Bavera, Simone S., Briel, Max, Chattaraj, Abhishek, Dotter, Aaron, Fragos, Tassos, Gallegos-Garcia, Monica, Gossage, Seth, Kalogera, Vicky, Kasdagli, Eirini, Katsaggelos, Aggelos, Kimball, Chase, Kovlakas, Konstantinos, Kruckow, Matthias U., Liotine, Camille, Misra, Devina, Rocha, Kyle A., Souropanis, Dimitris, Srivastava, Philipp M., Sun, Meng, Teng, Elizabeth, Xing, Zepei, Zapartas, Emmanouil, and Zevin, Michael
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: Whether considering rare astrophysical events on cosmological scales or unresolved stellar populations, accurate models must account for the integrated contribution from the entire history of star formation upon which that population is built. Here, we describe the second version of POSYDON, an open-source binary population synthesis code based on extensive grids of detailed binary evolution models computed using the MESA code, which follows both stars' structures as a binary system evolves through its complete evolution from the zero-age main sequence, through multiple phases of mass transfer and supernovae, to their death as compact objects. To generate synthetic binary populations, POSYDON uses advanced methods to interpolate between our large, densely spaced grids of simulated binaries. In our updated version of POSYDON, we account for the evolution of stellar binaries across a cosmological range of metallicities, extending from $10^{-4}\,Z_{\odot}$ to $2\,Z_{\odot}$, including grids specifically focused on the Small and Large Magellanic Clouds ($0.2\,Z_{\odot}$ and $0.45\,Z_{\odot}$). In addition to describing our model grids and detailing our methodology, we outline several improvements to POSYDON. These include the incorporation of single stars in stellar populations, a treatment for stellar mergers, and a careful modeling of "reverse-mass transferring" binaries, in which an once-accreting star later becomes a donor star. Our simulations are focused on binaries with at least one high-mass component, such as those that host neutron stars and black holes, and we provide post-processing methods to account for the cosmological evolution of metallicity and star formation as well as rate calculations for gravitational wave events, gamma-ray bursts, and other transients., Comment: 48 pages, 26 figures, submitted to AAS Journals
Published: 2024

8. Partial generalized crossed products, Brauer groups and a comparison of seven-term exact sequences

Author: Dokuchaev, Mikhailo, Pinedo, Hector, and Rocha, Itailma
Subjects: Mathematics - Rings and Algebras
Abstract: Given a unital partial action $\alpha $ of a group $G$ on a commutative ring $R$ we denote by $ {\bf PicS} _{R^{\alpha}}(R) $ the Picard monoid of the isomorphism classes of partially invertible $R$-bimodules, which are central over the subring $R^{\alpha} \subseteq R$ of $\alpha$-invariant elements, and consider a specific unital partial representation $\Theta : G \to {\bf PicS} _{R^{\alpha}}(R), $ along with the abelian group $\mathcal {C}(\Theta/R)$ of the isomorphism classes of partial generalized crossed products related to $\Theta,$ which already showed their importance in obtaining a partial action analogue of the Chase-Harrison-Rosenberg seven-term exact sequence. We give a description of $\mathcal {C}(\Theta/R)$ in terms partial generalized products of the form $\mathcal D(f \Theta)$ where $f$ is partial $1$-cocycle of $G$ with values in a submonoid of $ {\bf PicS}_{R^{\alpha}}(R).$ Assuming that $G$ is finite and that $R^{\alpha} \subseteq R$ is a partial Galois extension, we prove that any Azumaya $R^\alpha$-algebra, containing $R$ as a maximal commutative subalgebra, is isomorphic to a partial generalized crossed product. Furthermore, we show that the relative Brauer group $\mathcal B(R/R^\alpha)$ can be seen as a quotient of $\mathcal {C}(\Theta/R)$ by a subgroup isomorphic to the Picard group of $R.$ Finally, we prove that the analogue of the Chase-Harrison-Rosenberg sequence, obtained earlier for partial Galois extensions of commutative rings, can be derived from a recent seven-term exact sequence established in a non-commutative setting.
Published: 2024

9. Probing Water-Electrified Electrode interfaces: Insights from Au and Pd

Author: Arvelos, Graciele M., Fernández-Serra, Marivi, Rocha, Alexandre R., and Pedroza, Luana S.
Subjects: Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract: The water/electrode interface under an applied bias potential is a challenging out-of-equilibrium phenomenon, which is difficult to accurately model at the atomic scale. In this study, we employ a combined approach of Density Functional Theory (DFT) and non-equilibrium Green's function (NEGF) methods to analyze the influence of an external bias on the properties of water adsorbed on Au(111) and Pd(111) metallic electrodes. Our results demonstrate that while both Au and Pd-electrodes induce qualitatively similar structural responses in adsorbed water molecules, the quantitative differences are substantial, driven by the distinct nature of water-metal bonding. Our findings underscore the necessity of quantum-mechanical modeling for accurately describing electrochemical interfaces.
Published: 2024

10. Bibliometrics effects of a new item-by-item classification system based on reference reclassification

Author: Pena-Rocha, Marcos, Gomez-Crisostomo, Maria Rocio, Guerrero-Bote, Vicente Pablo, and de Moya-Anegon, Felix
Subjects: Computer Science - Digital Libraries
Abstract: This study presents a comparative analysis between two scientific document classification systems. The first system employs the Scopus journal-based assignment method, adapted to a fractional model, while the second system uses an item-by-item system based on reclassified references according to the origin of the citers. The study's results are divided into three different sections: the first involves comparisons at the Scopus area level, the second examines comparisons at the category level, and the third tests various bibliometric indicators to identify the variations between the two systems. Highlighting the characteristics of the paper level system, it offers a reduction in the number of categories to which each document is assigned, achieving higher values of single-category assignment compared to the All Science Journal Classification (ASJC). When reclassifying areas and categories, the paper level system tends to accentuate differences at the extreme values, increasing the size of the largest categories and reducing that of the smallest ones. Moreover, the paper-by-paper system provides more homogeneous distributions in normalised impacts and adjusts values related to excellence more uniformly.
Published: 2024

11. Tidal Love numbers of static black holes in anti-de Sitter

Author: Franzin, Edgardo, Frassino, Antonia M., and Rocha, Jorge V.
Subjects: High Energy Physics - Theory
Abstract: Tidal Love numbers of anti-de Sitter black holes are understood as linear response coefficients governing how the holographically dual plasma polarizes when the geometry of the space, in which the plasma lives, is deformed. So far, this picture has been applied only to black branes with plane wave perturbations. We fill the gap in the literature by performing the computation of tidal Love numbers for the four-dimensional Schwarzschild solution in global anti-de Sitter, which is dual to a conformal plasma on $S^2$. We conclude about the effect of the bulk gravitational perturbations on the boundary metric and stress tensor, responsible for the geometric polarization. The computation of the tidal Love numbers is performed in both Regge-Wheeler gauge and the Kodama-Ishibashi gauge-invariant approach. We spell out how to convert the tidal Love numbers determined in these two formalisms and find perfect agreement. We also relate the Kodama- Ishibashi formalism with the Kovtun-Starinets approach, which is particularly well suited for the holographic analysis of black branes. This allows us to compare with the tidal Love number results for black branes in anti-de Sitter, also finding agreement in the relevant regime., Comment: 28 pages total (7 appendices over 7 pages), 5 figures
Published: 2024

12. Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training

Author: Pieler, Michael, Bellagente, Marco, Teufel, Hannah, Phung, Duy, Cooper, Nathan, Tow, Jonathan, Rocha, Paulo, Adithyan, Reshinth, Alyafeai, Zaid, Pinnaparaju, Nikhil, Zhuravinskyi, Maksym, and Riquelme, Carlos
Subjects: Computer Science - Computation and Language
Abstract: Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data. We build upon previous work by replicating existing results on C4 and extending them with our optimized rephrasing pipeline to the English, German, Italian, and Spanish Oscar subsets of CulturaX. Our pipeline leads to increased performance on standard evaluation benchmarks in both the mono- and multilingual setup. In addition, we provide a detailed study of our pipeline, investigating the choice of the base dataset and LLM for the rephrasing, as well as the relationship between the model size and the performance after pre-training. By exploring data with different perceived quality levels, we show that gains decrease with higher quality. Furthermore, we find the difference in performance between model families to be bigger than between different model sizes. This highlights the necessity for detailed tests before choosing an LLM to rephrase large amounts of data. Moreover, we investigate the effect of pre-training with synthetic data on supervised fine-tuning. Here, we find increasing but inconclusive results that highly depend on the used benchmark. These results (again) highlight the need for better benchmarking setups. In summary, we show that rephrasing multilingual and low-quality data is a very promising direction to extend LLM pre-training data., Comment: 21 pages, 4 figures, 12 tables
Published: 2024

13. Holonomic $A\mathcal{V}$-modules for the affine space

Author: Billig, Yuly and Rocha, Henrique
Subjects: Mathematics - Representation Theory, Mathematics - Rings and Algebras, 17B10, 17B66
Abstract: We study the growth of representations of the Lie algebra of vector fields on the affine space that admit a compatible action of the polynomial algebra. We establish the Bernstein inequality for these representations, enabling us to focus on modules with minimal growth, known as holonomic modules. We show that simple holonomic modules are isomorphic to the tensor product of a holonomic module over the Weyl algebra and a finite-dimensional $\mathfrak{gl}_n$-module. We also prove that holonomic modules have a finite length and that the representation map associated with a holonomic module is a differential operator. Finally, we present examples illustrating our results.
Published: 2024

14. Economic Integration of Africa in the 21st Century: Complex Network and Panel Regression Analysis

Author: Choramo, Tekilu Tadesse, Abafita, Jemal, Gandica, Yerali, and Rocha, Luis E C
Subjects: Economics - General Economics, Physics - Physics and Society
Abstract: Global and regional integration has grown significantly in recent decades, boosting intra-African trade and positively impacting national economies through trade diversification and sustainable development. However, existing measures of economic integration often fail to capture the complex interactions among trading partners. This study addresses this gap by using complex network analysis and dynamic panel regression techniques to identify factors driving economic integration in Africa, based on data from 2002 to 2019. The results show that economic development, institutional quality, regional trade agreements, human capital, FDI, and infrastructure positively influence a country's position in the African trade network. Conversely, trade costs, the global financial crisis, and regional overlapping memberships negatively affect network based integration. Our findings suggest that enhancing a country's connectivity in the African trade network involves identifying key economic and institutional factors of trade partners and strategically focusing on continent-wide agreements rather than just regional ones to boost economic growth., Comment: Comments welcome
Published: 2024

15. Mass-gap Black Holes in Coalescing Neutron Star Black Hole Binaries

Author: Xing, Zepei, Kalogera, Vicky, Fragos, Tassos, Andrews, Jeff J., Bavera, Simone S., Briel, Max, Gossage, Seth, Kovlakas, Konstantinos, Kruckow, Matthias U., Rocha, Kyle A., Sun, Meng, Srivastava, Philipp M., and Zapartas, Emmanouil
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: The existence of a mass gap of $3-5\,M_{\odot}$ between the heaviest neutron stars (NSs) and the lightest black holes (BHs), inferred from the BH mass distribution in low mass X-ray binaries (LMXBs), has been suggested for decades. The recently reported gravitational-wave source GW230529 has been confidently identified as a NSBH merger, with the BH mass falling within this lower mass gap. This detection provides strong evidence against the existence of the latter and introduces new implications for the coalescing NSBH population, including a revised BH mass distribution and an updated local merger rate. In this study, we employ POSYDON, a binary population synthesis code that integrates detailed single- and binary-star models, to investigate coalescing NSBH binaries formed through isolated binary evolution. In particular, we focus on the BH mass distribution of the intrinsic NSBH merger population. We find that, with a high common-envelope efficiency of $\alpha_{\rm{CE}} =2 $, the BH masses in NSBH mergers concentrate in the lower mass gap, aligning more closely with observations. However, after accounting for the constraints of the selection bias against mass-gap BHs in LMXBs, which suggests that the maximum NS birth mass is below $\simeq 2\,M_{\odot}$, we find that introducing a high $\alpha_{\rm{CE}}$ is not required to match observations. Additionally, we explore the impact of core-collapse supernova kicks. Finally, we present the property distributions of observable NSBH mergers from our simulation and find that they match well with the observations. We find that the fraction of electromagnetic counterparts in observable populations is $\approx 4-30\%$, depending on different NS equations of state. Future detections of coalescing NSBH binaries would provide invaluable insights into SN mechanisms, common envelope evolution, and NS physics., Comment: 9 pages, 4 figures. Comments are welcome!
Published: 2024

16. ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics

Author: Viswanath, Siddharth, Bhaskar, Dhananjay, Johnson, David R., Rocha, Joao Felipe, Castro, Egbert, Grady, Jackson D., Grigas, Alex T., Perlmutter, Michael A., O'Hern, Corey S., and Krishnaswamy, Smita
Subjects: Computer Science - Machine Learning, Physics - Chemical Physics, Quantitative Biology - Biomolecules, Quantitative Biology - Quantitative Methods
Abstract: Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations., Comment: Accepted as a short paper at the 5th Molecular Machine Learning Conference (MoML 2024)
Published: 2024

17. Causality and instability in wave propagation in random time-varying media

Author: Pierrat, Romain, Rocha, Julia, and Carminati, Rémi
Subjects: Physics - Optics, Condensed Matter - Disordered Systems and Neural Networks
Abstract: We develop a theoretical model to investigate wave propagation in media with random time-varying properties, where temporal fluctuations lead to complex scattering dynamics. Focusing on the ensemble-averaged field, we derive an exact expression for the average Green's function in the presence of finite temporal disorder, and extend our analysis to the thermodynamic limit. In contrast to spatial disorder, causality prevents recurrent scattering, allowing us to achieve a non-perturbative solution. We identify two distinct propagation regimes: one where the field oscillates with constant amplitude, and another where it grows exponentially at long times. Our findings offer new insights into wave dynamics in temporally disordered media, with potential applications in time-varying metamaterials, dynamic sensing, and imaging in turbulent or chaotic environments.
Published: 2024

18. The Formation of Black Holes in Non-interacting, Isolated Binaries. Gaia Black Holes as Calibrators of Stellar Winds From Massive Stars

Author: Kruckow, Matthias U., Andrews, Jeff J., Fragos, Tassos, Holl, Berry, Bavera, Simone S., Briel, Max, Gossage, Seth, Kovlakas, Konstantinos, Rocha, Kyle A., Sun, Meng, Srivastava, Philipp M., Xing, Zepei, and Zapartas, Emmanouil
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Context. The black holes discovered using Gaia, especially Gaia BH1 and BH2, have low mass companions of solar-like metallicity in wide orbits. For standard isolated binary evolution formation channels including interactions such an extreme mass ratio is unexpected; especially in orbits of hundreds to thousands of days. Aims. Here, we investigate a non-interacting formation path for isolated binaries to explain the formation of Gaia BH1 and BH2. Methods. We use single star models computed with MESA to constrain the main characteristics of possible progenitors of long-period black hole binaries like Gaia BH1 and BH2. Then, we incorporate these model grids into the binary population synthesis code POSYDON, to explore whether the formation of the observed binaries at solar metallicity is indeed possible. Results. We find that winds of massive stars ($\gtrsim 80\,M_\odot$), especially during the Wolf-Rayet phase, tend to cause a plateau in the initial stellar mass to final black hole mass relation (at about $13\,M_\odot$ in our default wind prescription). However, stellar winds at earlier evolutionary phases are also important at high metallicity, as they prevent the most massive stars from expanding ($<100\,R_\odot$) and filling their Roche lobe. Consequently, the strength of the applied winds affects the range of the final black hole masses in non-interacting binaries, making it possible to form systems similar to Gaia BH1 and BH2. Conclusions. We deduce that wide binaries with a black hole and a low mass companion can form at high metallicity without binary interactions. There could be hundreds of such systems in the Milky Way. The mass of the black hole in binaries evolved through the non-interacting channel can potentially provide insights into the wind strength during the progenitors evolution., Comment: 8+4 pages, 6+6 figures, resubmitted to A&A
Published: 2024
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19. Search for gravitational waves emitted from SN 2023ixf

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. 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R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19th, during the LIGO-Virgo-KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered $\sim 14\%$ of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz where we assume the GW emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy $1 \times 10^{-5} M_{\odot} c^2$ and luminosity $4 \times 10^{-5} M_{\odot} c^2/\text{s}$ for a source emitting at 50 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as $1.04$, at frequencies above $1200$ Hz, surpassing results from SN 2019ejj., Comment: Main paper: 6 pages, 4 figures and 1 table. Total with appendices: 20 pages, 4 figures, and 1 table
Published: 2024

20. Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure

Author: Cruz-Martinez, Juan, Jansen, Aaron, van Oord, Gijs, Rabemananjara, Tanjona R., Rocha, Carlos M. R., Rojo, Juan, and Stegeman, Roy
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Computational Physics
Abstract: Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. These hyperparameters must be determined separately from the model parameters such as network weights, and are often fixed by ad-hoc methods or by manual inspection of the results. An algorithmic, objective determination of hyperparameters demands the introduction of dedicated target metrics, different from those adopted for the model training. Here we present a new approach to the automated determination of hyperparameters in deep learning models based on statistical estimators constructed from an ensemble of models sampling the underlying probability distribution in model space. This strategy requires the simultaneous parallel training of up to several hundreds of models and can be effectively implemented by deploying hardware accelerators such as GPUs. As a proof-of-concept, we apply this method to the determination of the partonic substructure of the proton within the NNPDF framework and demonstrate the robustness of the resultant model uncertainty estimates. The new GPU-optimised NNPDF code results in a speed-up of up to two orders of magnitude, a stabilisation of the memory requirements, and a reduction in energy consumption of up to 90% as compared to sequential CPU-based model training. While focusing on proton structure, our method is fully general and is applicable to any deep learning problem relying on hyperparameter optimisation for an ensemble of models., Comment: 27 pages, 7 figures
Published: 2024

21. Structural, mechanical, and electronic properties of single graphyne layers based on a 2D biphenylene network

Author: Rêgo, Mateus Silva, Santos, Mário Rocha dos, Júnior, Marcelo Lopes Pereira, Girão, Eduardo Costa, Meunier, Vincent, and Silva, Paloma Vieira
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Computational Physics
Abstract: Graphene is a promising material for the development of applications in nanoelectronic devices, but the lack of a band gap necessitates the search for ways to tune its electronic properties. In addition to doping, defects, and nanoribbons, a more radical alternative is the development of 2D forms with structures that are in clear departure from the honeycomb lattice, such as graphynes, with the distinctive property of involving carbon atoms with both hybridizations sp and sp2. The density and details of how the acetylenic links are distributed allow for a variety of electronic signatures. Here we propose a graphyne system based on the recently synthesized biphenylene monolayer. We demonstrate that this system features highly localized states with a spin-polarized semiconducting configuration. We study its stability and show that the system's structural details directly influence its highly anisotropic electronic properties. Finally, we show that the symmetry of the frontier states can be further tuned by modulating the size of the acetylenic chains forming the system., Comment: 14 pages, 7 figures
Published: 2024

22. Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC

Author: Arganda, Ernesto, Carena, Marcela, Rios, Martín de los, Perez, Andres D., Rocha, Duncan, Seoane, Rosa M. Sandá, and Wagner, Carlos E. M.
Subjects: High Energy Physics - Phenomenology, Computer Science - Machine Learning, High Energy Physics - Experiment
Abstract: The search for weakly interacting matter particles (WIMPs) is one of the main objectives of the High Luminosity Large Hadron Collider (HL-LHC). In this work we use Machine-Learning (ML) techniques to explore WIMP radiative decays into a Dark Matter (DM) candidate in a supersymmetric framework. The minimal supersymmetric WIMP sector includes the lightest neutralino that can provide the observed DM relic density through its co-annihilation with the second lightest neutralino and lightest chargino. Moreover, the direct DM detection cross section rates fulfill current experimental bounds and provide discovery targets for the same region of model parameters in which the radiative decay of the second lightest neutralino into a photon and the lightest neutralino is enhanced. This strongly motivates the search for radiatively decaying neutralinos which, however, suffers from strong backgrounds. We investigate the LHC reach in the search for these radiatively decaying particles by means of cut-based and ML methods and estimate its discovery potential in this well-motivated, new physics scenario., Comment: 24 pages + references, 8 figures, 3 tables, 3 appendices. Version updated with published LZ results, appendix reserved for preliminary LZ results deleted
Published: 2024

23. Physically Recurrent Neural Networks for Computational Homogenization of Composite Materials with Microscale Debonding

Author: Kovács, N., Maia, M. A., Rocha, I. B. C. M., Furtado, C., Camanho, P. P., and van der Meer, F. P.
Subjects: Mathematics - Numerical Analysis
Abstract: The growing use of composite materials in engineering applications has accelerated the demand for computational methods to accurately predict their complex behavior. Multiscale modeling based on computational homogenization is a potentially powerful approach for this purpose, but its widespread adoption is prevented by its excessive computational costs. A popular approach to address this computational bottleneck is using surrogate models, which have been used to successfully predict a wide range of constitutive behaviors. However, applications involving microscale damage and fracture remain largely unexplored. This work aims to extend a recent surrogate modeling approach, the Physically Recurrent Neural Network (PRNN), to include the effect of debonding at the fiber-matrix interface while capturing path-dependent behavior. The core idea of the PRNN is to implement the exact material models from the micromodel into one of the layers of the network. In this work, additional material points with a cohesive zone model are integrated within the network, along with the bulk points associated to the fibers and/or matrix. The limitations of the existing architecture are discussed and taken into account for the development of novel architectures that better represent the stress homogenization procedure. In the proposed layout, the history variables of cohesive points act as extra latent features that help determine the local strains of bulk points. Different architectures are evaluated starting with small training datasets. To maximize the predictive accuracy and extrapolation capabilities of the network, various configurations of bulk and cohesive points are explored, along with different training dataset types and sizes.
Published: 2024

24. Compact object populations over cosmic time I. BOSSA: a Binary Object environment-Sensitive Sampling Algorithm

Author: de Sá, L. M., Bernardo, A., Rocha, L. S., Bachega, R. R. A., and Horvath, J. E.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Binary population synthesis (BPS) is an essential tool for extracting information about massive binary evolution from gravitational-wave (GW) detections of compact object mergers. It has been successfully used to constrain the most likely permutations of evolution models among hundreds of alternatives, while initial condition models, in contrast, have not yet received the same level of attention. Here, we introduce BOSSA, a detailed initial sampling code including a set of 192 initial condition permutations for BPS that capture both "invariant" and "varying" models, the latter accounting for a possible metallicity- and star formation rate (SFR)-dependence of the initial mass function (IMF); as well as correlations between the initial primary mass, orbital period, mass ratio and eccentricity of binaries. We include 24 metallicity-specific cosmic star formation history (cSFH) models and propose two alternate models for the mass-dependent binary fraction. We build a detailed pipeline for time-evolving BPS, such that each binary has well-defined initial conditions, and we are able to distinguish the contributions from populations of different ages. We discuss the meaning of the IMF for binaries and introduce a refined initial sampling procedure for component masses. We also discuss the treatment of higher-order multiple systems when normalizing a binary sample. In particular, we argue for how a consistent interpretation of the IMF implies that this is not the distribution from which any set of component masses should be independently drawn, and show how the individual IMF of primaries and companions is expected to deviate from the full IMF., Comment: 23 pages, 16 figures, 2 tables. Submitted to MNRAS. Repository (under construction) at https://github.com/lmdesa/BOSSA
Published: 2024
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25. Emulators for stellar profiles in binary population modeling

Author: Teng, Elizabeth, Demir, Ugur, Doctor, Zoheyr, Srivastava, Philipp M., Lalvani, Shamal, Kalogera, Vicky, Katsaggelos, Aggelos, Andrews, Jeff J., Bavera, Simone S., Briel, Max M., Gossage, Seth, Kovlakas, Konstantinos, Kruckow, Matthias U., Rocha, Kyle Akira, Sun, Meng, Xing, Zepei, and Zapartas, Emmanouil
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning
Abstract: Knowledge about the internal physical structure of stars is crucial to understanding their evolution. The novel binary population synthesis code POSYDON includes a module for interpolating the stellar and binary properties of any system at the end of binary MESA evolution based on a pre-computed set of models. In this work, we present a new emulation method for predicting stellar profiles, i.e., the internal stellar structure along the radial axis, using machine learning techniques. We use principal component analysis for dimensionality reduction and fully-connected feed-forward neural networks for making predictions. We find accuracy to be comparable to that of nearest neighbor approximation, with a strong advantage in terms of memory and storage efficiency. By delivering more information about the evolution of stellar internal structure, these emulators will enable faster simulations of higher physical fidelity with large-scale simulations of binary star population synthesis possible with POSYDON and other population synthesis codes., Comment: 11 pages, 10 figures. Submitted to Astronomy and Computing
Published: 2024

26. A search using GEO600 for gravitational waves coincident with fast radio bursts from SGR 1935+2154

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abouelfettouh, I., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Agarwal, D., Agathos, M., Abchouyeh, M. Aghaei, Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Argianas, L., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Attadio, F., Aubin, F., AultONeal, K., Avallone, G., Azrad, D., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Baier, J. G., Baiotti, L., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartoletti, A. M., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bates, D. E., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Baynard II, P. A., Bazzan, M., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belardinelli, D., Bell, A. S., Benedetto, V., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bhowmick, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blagg, L. A., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bolliand, A., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonino, A., Bonnand, R., Booker, P., Borchers, A., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Boudon, A., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brandt, J., Braun, I., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brockmueller, E., Brooks, A. F., Brown, B. C., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cáceres-Barbosa, V., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakraborty, P., Subrahmanya, S. Chalathadka, Chan, J. C. L., Chan, M., Chandra, K., Chang, R. -J., Chao, S., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, Y., Chen, Yanbei, Chen, Yitian, Cheng, H. P., Chessa, P., Cheung, H. T., Cheung, S. Y., Chiadini, F., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chugh, P., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciolfi, R., Clara, F., Clark, J. A., Clarke, J., Clarke, T. A., Clearwater, P., Clesse, S., Coccia, E., Codazzo, E., Cohadon, P. -F., Colace, S., Colleoni, M., Collette, C. G., Collins, J., Colloms, S., Colombo, A., Colpi, M., Compton, C. M., Connolly, G., Conti, L., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Crook, S., Crouch, R., Csizmazia, J., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. 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K., Rinaldi, S., Rittmeyer, J., Robertson, C., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S. K., Roy, S., Rozza, D., Ruggi, P., Ruhama, N., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Sah, M. R., Saha, S. S., Saha, S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Santoliquido, F., Saravanan, T. R., Sarin, N., Sasaoka, S., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, Y., Sauter, O., Savage, R. L., Sawada, T., Sawant, H. L., Sayah, S., Scacco, V., Schaetzl, D., Scheel, M., Schiebelbein, A., Schiworski, M. G., Schmidt, P., Schmidt, S., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schouteden, K., Schulte, B. W., Schutz, B. F., Schwartz, E., Scialpi, M., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Serra, M., Servignat, G., Sevrin, A., Shaffer, T., Shah, U. S., Shaikh, M. A., Shao, L., Sharma, A. K., Sharma, P., Sharma-Chaudhary, S., Shaw, M. R., Shawhan, P., Shcheblanov, N. S., Sheridan, E., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singh, S., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Smith, W. J., Soldateschi, J., Somiya, K., Song, I., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Southgate, A., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Spoon, J. B., Sprague, C. A., Srivastava, A. K., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suleiman, L., Sullivan, K. D., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Suzuki, T., Suzuki, Y., Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takase, T., Takatani, K., Takeda, H., Takeshita, K., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tang, D., Tanioka, S., Tanner, D. B., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Themann, H., Theodoropoulos, A., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, A., Tiwari, P., Tiwari, S., Tiwari, V., Todd, M. R., Toivonen, A. M., Toland, K., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tong-Yu, C., Toriyama, A., Toropov, N., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trevor, M., Tringali, M. C., Tripathee, A., Troian, G., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, T. T. L., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Uchiyama, T., Udall, R. P., Uehara, T., Uematsu, M., Ueno, K., Ueno, S., Undheim, V., Ushiba, T., Vacatello, M., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valencia, J., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., Vandra, K., van Haevermaet, H., van Heijningen, J. V., Van Hove, P., VanKeuren, M., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varghese, J. J., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venikoudis, S., Venneberg, J., Verdier, P., Verkindt, D., Verma, B., Verma, P., Verma, Y., Vermeulen, S. M., Vetrano, F., Veutro, A., Vibhute, A. M., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Vilkha, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vives, A., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Wajid, A., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Wang, Z., Waratkar, G., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watarai, D., Wayt, K. E., Weaver, B. R., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Wilkin, A. T., Willadsen, D. J., Willetts, K., Williams, D., Williams, M. J., Williams, N. S., Willis, J. L., Willke, B., Wils, M., Winterflood, J., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wolfe, N. E., Wong, H. T., Wong, H. W. Y., Wong, I. C. F., Wright, J. L., Wright, M., Wu, C., Wu, D. S., Wu, H., Wuchner, E., Wysocki, D. M., Xu, V. A., Xu, Y., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yan, T., Yang, F. W., Yang, F., Yang, K. Z., Yang, Y., Yarbrough, Z., Yasui, H., Yeh, S. -W., Yelikar, A. B., Yin, X., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuan, S., Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zeoli, M., Zerrad, M., Zevin, M., Zhang, A. C., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. -J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by CHIME/FRB and STARE2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations' O3 observing run. Here we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts $\leq$ 1 s) we derive 50\% (90\%) upper limits of $10^{48}$ ($10^{49}$) erg for GWs at 300 Hz and $10^{49}$ ($10^{50}$) erg at 2 kHz, and constrain the GW-to-radio energy ratio to $\leq 10^{14} - 10^{16}$. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs., Comment: 15 pages of text including references, 4 figures, 5 tables
Published: 2024

27. JWST-IPA: Chemical Inventory and Spatial Mapping of Ices in the Protostar HOPS370 -- Evidence for an Opacity Hole and Thermal Processing of Ices

Author: Tyagi, Himanshu, P., Manoj, Narang, Mayank, Megeath, S T., Rocha, Will Robson M., Brunken, Nashanty, Rubinstein, Adam E., Gutermuth, Robert A., Evans, Neal J., van Dishoeck, Ewine, Federman, Sam, Watson, Dan M., Neufeld, David A., Anglada, Guillem, Beuther, Henrik, Garatti, Alessio Caratti o, Looney, Leslie W., Nazari, Pooneh, Osorio, Mayra, Stanke, Thomas, Yang, Yao-Lun, Bourke, Tyler L., Fischer, William J., Furlan, Elise, Green, Joel D., Habel, Nolan, Klaassen, Pamela, Karnath, Nicole, Linz, Hendrik, Muzzerolle, James, Tobin, John J., Atnagulov, Prabhani, Rahatgaonkar, Rohan, Sheehan, Patrick D., Slavicinska, Katerina, Stutz, Amelia M., Tychoniec, Łukasz, and Wolk, Scott J.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The composition of protoplanetary disks, and hence the initial conditions of planet formation, may be strongly influenced by the infall and thermal processing of material during the protostellar phase. Composition of dust and ice in protostellar envelopes, shaped by energetic processes driven by the protostar, serves as the fundamental building material for planets and complex organic molecules. As part of the JWST GO program, "Investigating Protostellar Accretion" (IPA), we observed an intermediate-mass protostar HOPS 370 (OMC2-FIR3) using NIRSpec/IFU and MIRI/MRS. This study presents the gas and ice phase chemical inventory revealed with the JWST in the spectral range of $\sim$2.9 to 28 $\mu$m and explores the spatial variation of volatile ice species in the protostellar envelope. We find evidence for thermal processing of ice species throughout the inner envelope. We present the first high-spatial resolution ($\sim 80$ au) maps of key volatile ice species H$_{2}$O, CO$_{2}$, $^{13}$CO$_2$, CO, and OCN$^-$, which reveal a highly structured and inhomogeneous density distribution of the protostellar envelope, with a deficiency of ice column density that coincides with the jet/outflow shocked knots. Further, we observe high relative crystallinity of H$_{2}$O ice around the shocked knot seen in the H$_2$ and OH wind/outflow, which can be explained by a lack of outer colder material in the envelope along the line of sight due to the irregular structure of the envelope. These observations show clear evidence of thermal processing of the ices in the inner envelope, close to the outflow cavity walls, heated by the luminous protostar., Comment: Submitted to ApJ. Main text:16 pages with 11 figures
Published: 2024

28. The effect of mass loss in models of red supergiants in the Small Magellanic Cloud

Author: Zapartas, E., de Wit, S., Antoniadis, K., Muñoz-Sanchez, G., Souropanis, D., Bonanos, A. Z., Maravelias, G., Kovlakas, K., Kruckow, M. U., Fragos, T., Andrews, J. J., Bavera, S. S., Briel, M., Gossage, S., Kasdagli, E., Rocha, K. A., Sun, M., Srivastava, P. M., and Xing, Z.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - High Energy Astrophysical Phenomena
Abstract: The rate and mechanism of mass loss of red supergiants (RSGs) remain poorly understood, especially at low metallicities. Motivated by the new empirical prescription by Yang et al. 2023, based on the largest and most complete sample in the Small Magellanic Cloud, we investigate the impact of different popular and recent RSG mass-loss prescriptions that span a range of RSG mass-loss rates on the evolution and observable properties of single massive stars. Our results show that higher mass-loss rates result in earlier envelope stripping and shorter RSG lifetimes, particularly for the more luminous stars, leading to a steeper luminosity function and predicting hotter final positions for the SN progenitors. None of the considered mass-loss prescriptions is fully consistent with all observational constraints, highlighting ongoing uncertainties in deriving and modeling RSGs mass loss. The mass-loss rates suggested by Kee et al. predict rapid envelope stripping, inconsistent with the observed population of luminous RSGs and SN progenitor detections, while the models implementing the commonly used de Jager et al. and the recent Beasor et al. prescriptions overestimate the number of luminous RSGs. While the increased mass-loss rates for luminous RSGs predicted by Yang et al. lead to better agreement with the observed RSG luminosity function, naturally reproducing the updated Humphreys-Davidson limit, they also produce luminous yellow supergiant progenitors not detected in nearby supernovae. We also estimate that binary interactions tend to slightly increase the formation of luminous RSGs due to mass accretion or merging. Our study examines the impact of RSG mass loss during the late stages of massive stars, highlighting the significance of using comprehensive observational data, exploring the uncertainties involved, and considering the effects of binary-induced or episodic mass loss., Comment: submitted in A&A
Published: 2024

29. A Taxonomy of Collectible Card Games from a Game-Playing AI Perspective

Author: Vieira, Ronaldo e Silva, Tavares, Anderson Rocha, and Chaimowicz, Luiz
Subjects: Computer Science - Artificial Intelligence
Abstract: Collectible card games are challenging, widely played games that have received increasing attention from the AI research community in recent years. Despite important breakthroughs, the field still poses many unresolved challenges. This work aims to help further research on the genre by proposing a taxonomy of collectible card games by analyzing their rules, mechanics, and game modes from the perspective of game-playing AI research. To achieve this, we studied a set of popular games and provided a thorough discussion about their characteristics., Comment: 16 pages, accepted at the International Conference on Entertainment Computing (ICEC) 2024
Published: 2024
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30. Minimally Intrusive Access Management to Content Delivery Networks based on Performance Models and Access Patterns

Author: Rodrigues, Lenise M. V., Menasché, Daniel Sadoc, Serra, Arthur, and Rocha, Antonio A. de Aragão
Subjects: Computer Science - Networking and Internet Architecture, Computer Science - Cryptography and Security
Abstract: This paper presents an approach to managing access to Content Delivery Networks (CDNs), focusing on combating the misuse of tokens through performance analysis and statistical access patterns. In particular, we explore the impact of token sharing on the content delivery infrastructure, proposing the definition of acceptable request limits to detect and block abnormal accesses. Additionally, we introduce countermeasures against piracy, such as degrading the quality of service for pirate users to discourage them from illegal sharing, and using queuing models to quantify system performance in different piracy scenarios. Adopting these measures can improve the consistency and efficiency of CDN access and cost management, protecting the infrastructure and the legitimate user experience., Comment: The International Symposium on Cyber Security, Cryptology and Machine Learning (CSCML 2024)
Published: 2024

31. Interactive Event Sifting using Bayesian Graph Neural Networks

Author: Nascimento, José, Jacobs, Nathan, and Rocha, Anderson
Subjects: Computer Science - Machine Learning, Computer Science - Social and Information Networks
Abstract: Forensic analysts often use social media imagery and texts to understand important events. A primary challenge is the initial sifting of irrelevant posts. This work introduces an interactive process for training an event-centric, learning-based multimodal classification model that automates sanitization. We propose a method based on Bayesian Graph Neural Networks (BGNNs) and evaluate active learning and pseudo-labeling formulations to reduce the number of posts the analyst must manually annotate. Our results indicate that BGNNs are useful for social-media data sifting for forensics investigations of events of interest, the value of active learning and pseudo-labeling varies based on the setting, and incorporating unlabelled data from other events improves performance., Comment: Accepted in IEEE International Workshop on Information Forensics and Security - WIFS 2024, Rome, Italy
Published: 2024

32. Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation

Author: Yang, Jing and Rocha, Anderson
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Computational methods to aid journalists in the task often require adapting a model to specific domains and generating explanations. However, most automated fact-checking methods rely on three-class datasets, which do not accurately reflect real-world misinformation. Moreover, fact-checking explanations are often generated based on text summarization of evidence, failing to address the relationship between the claim and the evidence. To address these issues, we extend the self-rationalization method--typically used in natural language inference (NLI) tasks--to fact verification. We propose a label-adaptive learning approach: first, we fine-tune a model to learn veracity prediction with annotated labels (step-1 model). Then, we fine-tune the step-1 model again to learn self-rationalization, using the same data and additional annotated explanations. Our results show that our label-adaptive approach improves veracity prediction by more than ten percentage points (Macro F1) on both the PubHealth and AVeriTec datasets, outperforming the GPT-4 model. Furthermore, to address the high cost of explanation annotation, we generated 64 synthetic explanations from three large language models: GPT-4-turbo, GPT-3.5-turbo, and Llama-3-8B and few-shot fine-tune our step-1 model. The few-shot synthetic explanation fine-tuned model performed comparably to the fully fine-tuned self-rationalization model, demonstrating the potential of low-budget learning with synthetic data. Our label-adaptive self-rationalization approach presents a promising direction for future research on real-world explainable fact-checking with different labeling schemes., Comment: Paper accepted in the 16th IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS) 2024
Published: 2024

33. Development of a Digital Front-End for Electrooculography Circuits to Facilitate Digital Communication in Individuals with Communicative and Motor Disabilities

Author: da Costa, Andre Heid Rocha and O'Keeffe, Keiran Robert
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: This project developed a cost-effective, digital-viable front-end for electrooculography (EOG) circuits aimed at enabling communication for individuals with Locked-in Syndrome (LIS) and Amyotrophic Lateral Sclerosis (ALS). Using the TL072 operational amplifier, the system amplifies weak EOG signals and processes them through an Arduino Uno for real-time monitoring. The circuit includes preamplification, filtering between 0.1 Hz and 30 Hz, and final amplification stages, achieving accurate eye movement tracking with a 256 Hz sampling rate. The approach to this was described in detail, with a comparison drawn between the theoretical expectations of our circuit design and its viability in contrast to the actual values measured. Our readings aimed to create an interface that optimized max-gaze angle readings by outputting a maximum reading at values above the baseline theory of our amplification circuit. From this, we measured the latency between the serial output and action, analyzing video recordings of such readings. The Latency value read reached around 20ms, which is within the tolerance for proper communication and did not seriously affect the readings. Beyond this, challenges such as noise interference (with an SNR of 1.07 dB) remain despite achieving reliable signal amplification. This was during a test of the analog functionality of this circuit. However, its limitations mean that future improvements will focus on reducing environmental interference, optimizing electrode placement, applying a novel detection algorithm to optimize communication applications, and enhancing signal clarity to make the system more effective for real-world applications., Comment: 24 pages, 6 figures
Published: 2024

34. JWST Observations of Young protoStars (JOYS). Overview of gaseous molecular emission and absorption in low-mass protostars

Author: van Gelder, M. L., Francis, L., van Dishoeck, E. F., Tychoniec, Ł., Ray, T. P., Beuther, H., Garatti, A. Caratti o, Chen, Y., Devaraj, R., Gieser, C., Justtanont, K., Kavanagh, P. J., Nazari, P., Reyes, S., Rocha, W. R. M., Slavicinska, K., Güdel, M., Henning, Th., Lagage, P. -O., and Wright, G.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The MIRI-MRS instrument onboard JWST allows for probing the molecular gas composition at mid-IR wavelengths at unprecedented resolution and sensitivity. It is important to study these features in low-mass embedded protostellar systems since the formation of planets is thought to start in this phase. We present JWST/MIRI-MRS data of 18 low-mass protostellar systems in the JOYS program, focusing on gas-phase molecular lines in spectra extracted from the central protostellar positions. Besides H2, the most commonly detected molecules are H2O, CO2, CO, and OH. Other molecules such as 13CO2, C2H2, 13CCH, HCN, C4H2, CH4, and SO2 are detected only toward at most three of the sources. The JOYS data also yield the surprising detection of SiO gas toward two sources (BHR71-IRS1, L1448-mm) and for the first time CS and NH3 at mid-IR wavelengths toward a low-mass protostar (B1-c). The temperatures derived for the majority of the molecules are 100-300 K, much lower than what is typically derived toward more evolved Class II sources (>500 K). Toward three sources (e.g., TMC1-W), hot (~1000 K) H2O is detected, indicative of the presence of hot molecular gas in the embedded disks, but such warm emission from other molecules is absent. The agreement in abundance ratios with respect to H2O between ice and gas point toward ice sublimation in a hot core for a few sources (e.g., B1-c) whereas their disagreement and velocity offsets hint at high-temperature (shocked) conditions toward other sources (e.g., L1448-mm, BHR71-IRS1). The typical temperatures of the gas-phase molecules of 100-300 K are consistent with both ice sublimation in hot cores as well as high-temperature gas phase chemistry. Molecular features originating from the inner embedded disks are not commonly detected, likely because they are too extincted even at mid-IR wavelengths by small not-settled dust grains in upper layers of the disk., Comment: 25 pages, 11 figures, accepted for publication in A&A
Published: 2024

35. Compact object populations over cosmic time II. Compact object merger rates and masses over redshift from varying initial conditions

Author: de Sá, L. M., Rocha, L. S., Bernardo, A., Bachega, R. R. A., and Horvath, J. E.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics
Abstract: We perform a first study of the impact of varying two components of the initial conditions in binary population synthesis of compact binary mergers - the initial mass function, which is made metallicity- and star formation rate-dependent, and the orbital parameter (orbital period, mass ratio and eccentricity) distributions, which are assumed to be correlated - within a larger grid of initial condition models also including alternatives for the primary mass-dependent binary fraction and the metallicity-specific cosmic star formation history. We generate the initial populations with the sampling code BOSSA and evolve them with the rapid population synthesis code COMPAS. We find strong suggestions that the main role of initial conditions models is to set the relative weights of key features defined by the evolution models. In the two models we compare, black hole-black hole (BHBH) mergers are the most strongly affected, which we connect to a shift from the common envelope to the stable Roche lobe overflow formation channels with decreasing redshift. We also characterize variations in the black hole-neutron star (BHNS) and neutron star-neutron star (NSNS) final parameter distributions. We obtain the merger rate evolution for BHBH, BHNS and NSNS mergers up to $z=10$, and find a variation by a factor of $\sim50-60$ in the local BHBH and BHNS merger rates, suggesting a more important contribution from initial conditions than previously thought, and calling for a complete exploration of the initial conditions model permutations., Comment: 28 pages, 14 figures, 3 tables. Accepted for publication in MNRAS. Code/data to be made publicly available
Published: 2024
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36. Generic Multicast (Extended Version)

Author: Bolina, José Augusto, Sutra, Pierre, Rocha, Douglas Antunes, and Camargos, Lasaro
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing, C.2.4
Abstract: Communication primitives play a central role in modern computing. They offer a panel of reliability and ordering guarantees for messages, enabling the implementation of complex distributed interactions. In particular, atomic broadcast is a pivotal abstraction for implementing fault-tolerant distributed services. This primitive allows disseminating messages across the system in a total order. There are two group communication primitives closely related to atomic broadcast. Atomic multicast permits targeting a subset of participants, possibly stricter than the whole system. Generic broadcast leverages the semantics of messages to order them only where necessary (that is when they conflict). In this paper, we propose to combine all these primitives into a single, more general one, called generic multicast. We formally specify the guarantees offered by generic multicast and present efficient algorithms. Compared to prior works, our solutions offer appealing properties in terms of time and space complexity. In particular, when a run is conflict-free, that is no two messages conflict, a message is delivered after at most three message delays., Comment: 12 pages, 2 figures
Published: 2024
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37. Exploring Influences on ICT Adoption in German Schools: A UTAUT-Based Structural Equation Model

Author: Daniel Petri Rocha, Arne Bewersdorff, and Claudia Nerdel
Abstract: The 2018 International Computer and Information Literacy Study (ICILS) provides a comprehensive dataset with contextual information on German school environments. The data comes from responses by eighth-grade teachers and principals to a questionnaire about Information and Communication Technology (ICT) for teaching. We performed an exploratory re-analysis examining what influences teachers' adoption and use of ICT by employing six factors -- Social Influence, Effort Expectancy, Performance Expectancy, Facilitating Conditions, Behavioural Intention, and Use Behaviour -- from the Unified Theory of Acceptance and Use of Technology (UTAUT). We measured relationships between these constructs with a Structural Equation Model (SEM) consisting of 15 indicators provided by the ICILS study. The goodness of fit measures indicated confirmation of the model (RMSEA = 0.05, SRMR = 0.05, CFI = 0.93, TLI = 0.91). Our findings show that teachers' attitudes toward ICT and the school administration's support affected how ICT was applied in the classroom.
Published: 2024

38. Problem-Solving in a Real-Life Context: An Approach during the Learning of Inequalities

Author: Helena Rocha, Floriano Viseu, and Sara Matos
Abstract: This study was conducted while 9th grade students learn to solve inequalities and seeks to understand their approach to solving problems with a real-life context. Specifically, the aim is to understand: (1) What are the main characteristics of the students' approaches to the proposed problems? (2) What is the impact of the real context on the students' resolutions? A qualitative and interpretative methodology is adopted, based on case studies, with data collected through documentary collection and audio recording of discussions between a pair of students while solving problems. The main conclusions suggest a trend to approach problems without establishing immediate connections with what was being done in the classroom, with students' decisions being essentially guided by criteria of simplicity. The real context of the problems seems to have the potential to develop in students a more integrated mathematics, focused on understanding and not so much on the repetition of mechanical and meaning-independent procedures. The students' familiarization with the context in question is one of the aspects highlighted by this study.
Published: 2024

39. Suitable mouse model to study dynamics of West Nile virus infection in 'Culex quinquefasciatus' mosquitoes

Author: Baldon, Livia, de Mendonca, Silvana, Santos, Ellen, Marcal, Bruno, de Freitas, Amanda Cupertino, Rezende, Fernanda, Moreira, Rafaela, Sousa, Viviane, Comini, Sara, Lima, Mariana, Ferreira, Flavia, de Almeida, Joao Paulo, Silva, Emanuele, Amadou, Siad, Rocha, Marcele, Leite, Thiago, Todjro, Yaovi, de Carvalho, Camila, Santos, Viviane, Giovanetti, Marta, Alcantara, Luiz, Moreira, Luciano A, and Ferreira, Alvaro
Published: 2024

40. Pre-exposure prophylaxis to prevent hematophagous bat-mediated rabies outbreaks in remote Amazon communities: Lessons from a pilot for public health policy

Author: Rocha, Felipe, Vargas, Alexander, Nogueira de Abreu, Elke Maria, Augusto Pompei, Julio Cesar, Natal Vigilato, Marco Antonio, Lima, Daniel Magalhaes, Vianna, Raphael Schneider, Cosivi, Ottorino, Recuenco, Sergio E, Costa, Wagner Augusto, Hardt, Luciana, Correa Scheffer Ferreira, Karin, Santos Cunha Neto, Rene dos, Chaves, Luciana Botelho, da Silva, Andrea de Cassia Rodrigues, Begot, Alberto Lopes, Azevedo Andrade, Jorge Alberto, Marcos, Weber, Rocha, Silene Manrique, Lima Junior, Francisco Edilson Ferreira, and Wada, Marcelo Yoshito
Published: 2024

41. One health approach to toxoplasmosis: Owner and dog seropositivity as spatial indicators of risk areas for acquired, gestational and congenital transmission

Author: Sohn-Hausner, Natacha, Correa, Ricardo Guedes, Kmetiuk, Louise Bach, da Silva, Evelyn Cristine, de Moraes, Gustavo Nunes, Langoni, Helio, dos Santos Rocha, Gabrielle, and Biondo, Alexander Welker
Published: 2024

42. Randomized Controlled Trial to Evaluate the Efficacy of the SALFOAM 3% Method Compared to the Conventional Polidocanol Foam Method (Tessari Method) for Treating Lower Limb Varicose Veins (SALFOAM 3%)

Author: Lidiane Aparecida Ferreira Rocha and Lidiane Rocha, MD
Published: 2024

43. In Situ Solid-State Dewetting of Ag-Au-Pd Alloy: From Macro- to Nanoscale.

Author: Lyu, Peifen, Matusalem, Filipe, Deniz, Ece, Rocha, Alexandre, and Leite, Marina
Subjects: Ag, Au, Pd, color filters, in situ ellipsometry, metasurfaces, nanoparticles, solid-state dewetting
Abstract: Metal alloy nanostructures represent a promising platform for next-generation nanophotonic devices, surpassing the limitations of pure metals by offering additional buttons for tailoring their optical properties by compositional variations. While alloyed nanoparticles hold great potential, their scalability and underexplored optical behavior still limit their application. Here, we establish a systematic approach to quantifying the unique optical behavior of the AgAuPd ternary system while providing a direct comparison with its pure constituent metals. Computationally, we analyze their electronic structure and uncover the transition of Pd d states to Pd/Ag hybridized s states in the bulk form, explaining the similar optical properties observed between Pd and AgAuPd. Experimentally, we fabricate pure metal and fully alloyed nanoparticles through solid-state dewetting, a scalable method. During the process, we trace the optical transition in the systems from the initial thin film stage to the final nanoparticle stage with in situ ellipsometry. We reveal the interplay between optical properties influenced by chemical interdiffusion and localized surface plasmon resonance arising from morphological changes with ex situ surface characterizations. Additionally, we analytically implement a metallic layer derived from the ternary system in a trilayer device, resulting in a single-time and irreversible color filter, to demonstrate an application encompassing a lithography-free and cost-effective route for nanophotonic devices.
Published: 2024

44. Recurrent evolution and selection shape structural diversity at the amylase locus.

Author: Bolognini, Davide, Halgren, Alma, Lou, Runyang, Raveane, Alessandro, Rocha, Joana, Guarracino, Andrea, Soranzo, Nicole, Chin, Chen-Shan, Garrison, Erik, and Sudmant, Peter
Subjects: Humans, Agriculture, Amylases, Evolution, Molecular, Gene Dosage, Gene Duplication, Genetic Loci, Genome, Human, Haplotypes, History, Ancient, Mutation Rate, Polymorphism, Single Nucleotide, Selection, Genetic, Hunting, Gene Deletion, DNA, Ancient
Abstract: The adoption of agriculture triggered a rapid shift towards starch-rich diets in human populations1. Amylase genes facilitate starch digestion, and increased amylase copy number has been observed in some modern human populations with high-starch intake2, although evidence of recent selection is lacking3,4. Here, using 94 long-read haplotype-resolved assemblies and short-read data from approximately 5,600 contemporary and ancient humans, we resolve the diversity and evolutionary history of structural variation at the amylase locus. We find that amylase genes have higher copy numbers in agricultural populations than in fishing, hunting and pastoral populations. We identify 28 distinct amylase structural architectures and demonstrate that nearly identical structures have arisen recurrently on different haplotype backgrounds throughout recent human history. AMY1 and AMY2A genes each underwent multiple duplication/deletion events with mutation rates up to more than 10,000-fold the single-nucleotide polymorphism mutation rate, whereas AMY2B gene duplications share a single origin. Using a pangenome-based approach, we infer structural haplotypes across thousands of humans identifying extensively duplicated haplotypes at higher frequency in modern agricultural populations. Leveraging 533 ancient human genomes, we find that duplication-containing haplotypes (with more gene copies than the ancestral haplotype) have rapidly increased in frequency over the past 12,000 years in West Eurasians, suggestive of positive selection. Together, our study highlights the potential effects of the agricultural revolution on human genomes and the importance of structural variation in human adaptation.
Published: 2024

45. Explainable Artifacts for Synthetic Western Blot Source Attribution

Author: Cardenuto, João Phillipe, Mandelli, Sara, Moreira, Daniel, Bestagini, Paolo, Delp, Edward, and Rocha, Anderson
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image)., Comment: Accepted in IEEE International Workshop on Information Forensics and Security - WIFS 2024, Rome, Italy
Published: 2024

46. The hypothetical track-length fitting algorithm for energy measurement in liquid argon TPCs

Author: DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Alex, N. S., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., 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., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., 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., 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, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., 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., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., 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, Chakraborty, K., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Choi, G., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., 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., Dallavalle, R., 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., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., 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., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, 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., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., Gago, A. M., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., 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, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., 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., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A. L., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Jung, K. Y., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., 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., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., Li, J. -Y, Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., 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, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. 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., Mineev, O., Minotti, A., Miralles, L., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., 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, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., 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., 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., Nandakumar, R., Naples, D., Narita, S., 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., 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., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, 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., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paixão, L. G. Porto, Potekhin, M., Potenza, R., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rahaman, U., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Razakamiandra, R. F., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Restrepo, Diego, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., 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., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., 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, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. 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., Timm, S. C., Tiras, E., Tishchenko, V., Tiwari, S., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., 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., Turnberg, S., 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., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, 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, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., 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., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., 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., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.
Published: 2024

47. Deformations of the AdS-Schwarzschild black brane and the shear viscosity of the quark-gluon plasma

Author: da Rocha, Roldao
Subjects: High Energy Physics - Theory, Nuclear Theory
Abstract: Deformations of the AdS$_5$-Schwarzschild black brane, implemented in the AdS/CFT membrane paradigm, are scrutinized in the dual viscous hydrodynamic infrared limit. The latest experimental data analyses, regarding the shear viscosity-to-entropy density ratio of the quark-gluon plasma produced by heavy-ion collisions at the LHC and RHIC, are shown to constrain these deformations severely. Although corroborating with the robustness of the standard AdS$_5$-Schwarzschild black brane against deformations, there is still a margin for mild deformations which may carry 2-loop quantum corrections to gravity, whose implications to the strongly-coupled dual field theory are addressed and discussed., Comment: 42 pages, 8 figures
Published: 2024
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48. JOYS+ study of solid state $^{12}$C/$^{13}$C isotope ratios in protostellar envelopes: Observations of CO and CO$_2$ ice with JWST

Author: Brunken, N. G. C., van Dishoeck, E. F., Slavicinska, K., Gouellec, V. J. M. le, Rocha, W. R. M., Francis, L., Tychoniec, L., van Gelder, M. L., Navarro, M. G., Boogert, A. C. A., Kavanagh, P. J., Nazari, P., Greene, T., Ressler, M. E., and Majumdar, L.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The carbon isotope ratio is a powerful tool for studying the evolution of stellar systems. Recent detections of CO isotopologues in disks and exoplanet atmospheres pointed towards significant fractionation in these systems. In order to understand the evolution of this quantity, it is crucial to trace the isotope abundance from stellar nurseries to planetary systems. During the protostellar stage the multiple vibrational modes of CO$_2$ and CO ice provide a unique opportunity to examine the carbon isotope ratio in the solid state. Now with the sensitivity of the \textit{James Webb Space Telescope}, these absorption features have become accessible at high S/N in Solar-mass systems. We quantify the $^{12}$CO$_2$/$^{13}$CO$_2$ and the $^{12}$CO/$^{13}$CO isotope ratios in 17 class 0/I low mass protostars from the $^{12}$CO$_2$ combination modes (2.70 ${\mu}$m and 2.77 ${\mu}$m), the $^{12}$CO$_2$ stretching mode (4.27 ${\mu}$m), the $^{13}$CO$_2$ stretching mode (4.39 ${\mu}$m), the $^{12}$CO$_2$ bending mode (15.2 ${\mu}$m), the $^{12}$CO stretching mode (4.67 ${\mu}$m) and the $^{13}$CO stretching mode (4.78 ${\mu}$m) using JWST observations. We also report a detection of the $^{12}$CO overtone mode at 2.35 ${\mu}$m. The $^{12}$CO$_2$/$^{13}$CO$_2$ ratios are in agreement and we find mean ratios of 85 $\pm$ 23, 76 $\pm$ 12 and 97 $\pm$ 17 for the 2.70 ${\mu}$m, 4.27 ${\mu}$m and the 15.2 ${\mu}$m bands, respectively. The main source of uncertainty stem from the error on the band strengths. The $^{12}$CO/$^{13}$CO ratios derived from the 4.67 ${\mu}$m bands are consistent, albeit elevated with respect to the $^{12}$CO$_2$/$^{13}$CO$_2$ ratios and we find a mean ratio of 165 $\pm$ 52. These findings indicate that ices leave the pre-stellar stage with elevated carbon isotope ratios relative to the interstellar medium and that fractionation becomes significant during the later stages.
Published: 2024
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49. Critical Node Detection in Temporal Social Networks, Based on Global and Semi-local Centrality Measures

Author: Farahi, Zahra, Kamandi, Ali, Abedian, Rooholah, and Rocha, Luis Enrique Correa
Subjects: Physics - Physics and Society, Computer Science - Social and Information Networks
Abstract: Nodes that play strategic roles in networks are called critical or influential nodes. For example, in an epidemic, we can control the infection spread by isolating critical nodes; in marketing, we can use certain nodes as the initial spreaders aiming to reach the largest part of the network, or they can be selected for removal in targeted attacks to maximise the fragmentation of the network. In this study, we focus on critical node detection in temporal networks. We propose three new measures to identify the critical nodes in temporal networks: the temporal supracycle ratio, temporal semi-local integration, and temporal semi-local centrality. We analyse the performance of these measures based on their effect on the SIR epidemic model in three scenarios: isolating the influential nodes when an epidemic happens, using the influential nodes as seeds of the epidemic, or removing them to analyse the robustness of the network. We compare the results with existing centrality measures, particularly temporal betweenness, temporal centrality, and temporal degree deviation. The results show that the introduced measures help identify influential nodes more accurately. The proposed methods can be used to detect nodes that need to be isolated to reduce the spread of an epidemic or as initial nodes to speedup dissemination of information., Comment: Comments and criticisms are welcomed
Published: 2024

50. Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries

Author: de Carvalho, Andre, Bonidia, Robson, Kong, Jude Dzevela, Dauhajre, Mariana, Struchiner, Claudio, Goedert, Guilherme, Stadler, Peter F., Walter, Maria Emilia, Sanches, Danilo, Day, Troy, Castro, Marcia, Edmunds, John, Colome-Hidalgo, Manuel, Morban, Demian Arturo Herrera, Franco, Edian F., Ugarte-Gil, Cesar, Espinoza-Lopez, Patricia, Carrasco-Escobar, Gabriel, and Rocha, Ulisses
Subjects: Computer Science - Artificial Intelligence
Abstract: Infectious diseases, transmitted directly or indirectly, are among the leading causes of epidemics and pandemics. Consequently, several open challenges exist in predicting epidemic outbreaks, detecting variants, tracing contacts, discovering new drugs, and fighting misinformation. Artificial Intelligence (AI) can provide tools to deal with these scenarios, demonstrating promising results in the fight against the COVID-19 pandemic. AI is becoming increasingly integrated into various aspects of society. However, ensuring that AI benefits are distributed equitably and that they are used responsibly is crucial. Multiple countries are creating regulations to address these concerns, but the borderless nature of AI requires global cooperation to define regulatory and guideline consensus. Considering this, The Global South AI for Pandemic & Epidemic Preparedness & Response Network (AI4PEP) has developed an initiative comprising 16 projects across 16 countries in the Global South, seeking to strengthen equitable and responsive public health systems that leverage Southern-led responsible AI solutions to improve prevention, preparedness, and response to emerging and re-emerging infectious disease outbreaks. This opinion introduces our branches in Latin American and Caribbean (LAC) countries and discusses AI governance in LAC in the light of biotechnology. Our network in LAC has high potential to help fight infectious diseases, particularly in low- and middle-income countries, generating opportunities for the widespread use of AI techniques to improve the health and well-being of their communities.
Published: 2024

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