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126,618 results on '"Donato A."'

1. Mitochondrial oxidant stress promotes α-synuclein aggregation and spreading in mice with mutated glucocerebrosidase

Author: Pietro La Vitola, Eva M. Szegö, Rita Pinto-Costa, Angela Rollar, Eugenia Harbachova, Anthony HV Schapira, Ayse Ulusoy, and Donato A. Di Monte
Subjects: Neurology. Diseases of the nervous system, RC346-429
Abstract: Abstract In this study, heterozygous expression of a common Parkinson-associated GBA1 variant, the L444P mutation, was found to exacerbate α-synuclein aggregation and spreading in a mouse model of Parkinson-like pathology targeting neurons of the medullary vagal system. These neurons were also shown to become more vulnerable to oxidative and nitrative stress after L444P expression. The latter paralleled neuronal formation of reactive oxygen species and led to a pronounced accumulation of nitrated α-synuclein. A causal relationship linked mutation-induced oxidative/nitrative stress to enhanced α-synuclein aggregation and spreading that could indeed be rescued by neuronal overexpression of mitochondrial superoxide dismutase 2. Further evidence supported a key involvement of mitochondria as sources of reactive oxygen species as well as targets of oxidative and nitrative damage within L444P-expressing neurons. These findings support the conclusion that enhanced vulnerability to mitochondrial oxidative stress should be considered an important mechanism predisposing to pathology conversion in carriers of GBA1 mutations.
Published: 2024
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2. Fight or flight alternative mating tactics may explain the iconic male polymorphism of the European stag beetle

Author: Daniele Giannetti, Enrico Schifani, Enrico Rolli, Emanuele Fior, Benedetta Pasquali, Alessandro Campanaro, and Donato A. Grasso
Subjects: Lucanus cervus, Male-male competition, Behavioral ecology, Intrasexual polymorphism, Flight aggregations, Saproxylic beetles, Medicine, Science
Abstract: Abstract The existence of different male morphologies within a single species is associated with alternative mating tactics across different animal groups, offering textbook examples of evolutionary biology. The European stag beetle Lucanus cervus is a flagship species which has long fascinated naturalists for its charismatic appearance and behavior, with males possessing large mandibles used in ritualized ground fights for females. Males are polymorphic, with larger males possessing disproportionately larger mandibles and being better fighters, while smaller males require less food to develop and may have better chances to escape predation. We discovered an alternative mating tactic that opens a new interpretation on the evolution of the smaller males, based on aggregations of flying males competing to catch flying females in mid-air. Flight aggregations occur earlier in the season and in sites with sparser canopy cover as compared to ground fight aggregations, but most importantly, smaller males tend to prevail due to their greater agility in flight. Competition between males is crucial for reproductive success in this species, and alternative mating tactics involving flight were unknown in beetles, advocating for further research on the ecology and evolution of the iconic male polymorphism of stag beetles.
Published: 2024
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3. Evaluation of the bioenergy potential of agricultural and agroindustrial waste generated in southeastern Mexico

Author: Nathaly A. Díaz Molina, José A. Sosa Olivier, José R. Laines Canepa, Rudy Solis Silvan, and Donato A. Figueiras Jaramillo
Subjects: biomass, calorific value, alternative fuels, waste management, elemental content, Production of electric energy or power. Powerplants. Central stations, TK1001-1841, Renewable energy sources, TJ807-830
Abstract: The generation of large volumes of agricultural and agroindustrial waste in the state of Tabasco represents a significant waste management challenge. We aimed to determine the bioenergy potential of five types of biomasses: Banana rachis, coconut shell, cocoa pod husk, sugarcane bagasse, and palm kernel shell, generated in agricultural and agroindustrial processes. This research involved characterizing and evaluating the energy quality of these biomasses by determining their calorific values and assessing their viability as fuel alternative sources. Additionally, we explored these biomasses' calorific value potential to reduce the inadequate disposal of wastes, reduce environmental impact, and provide alternative uses for these materials, which are typically discarded or have limited added value in the southeast region. The yield of waste generation per amount of production was estimated, with cocoa pod husk biomass and sugarcane bagasse, banana rachis, coconut shell, and palm kernel shell generating 0.685, 0.283, 0.16, 0.135, and 0.0595 kg of biomass per kg of crop, respectively. The bioenergy potential was evaluated through direct measurements using a calorimeter bomb, and indirect measurements using stoichiometric calculations. Four stoichiometric methods based on predictive equations were employed to determine the energy content of the biomasses from their elemental composition (Dulong, Friedl, Channiwala, Boie). The biomasses with the highest calorific values were coconut shell and cocoa pod husk, with values of 16.47 ± 0.24 and 16.02 ± 1.54 MJ/kg, respectively. Moreover, banana rachis had the lowest calorific value at 13.68 ± 3.22 MJ/kg. The calorific values of the sugarcane bagasse and palm kernel shell were 13.91 ± 0.98 and 15.29 ± 1.02, respectively. The factorial experimental design and statistical analysis revealed trends and magnitudes in the evaluation of energy determination methods and types of waste. The predictive equation of Dulong showed the highest similarity to the experimental values, especially for coconut shell (16.02 ± 0.08 MJ/kg). The metal content in biomasses such as palm kernel shell and coconut shell were below the limits established in ISO 17225:2014. Finally, our results indicated that coconut shell has superior characteristics for potential use as an alternative fuel, whereas banana rachis requires exploring alternative utilization options.
Published: 2024
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4. Trail pheromone identification in the ant Crematogaster scutellaris

Author: Florencia Scarano, Daniele Giannetti, Francesco Trenti, Federico Giacomazzi, Jacopo Vigna, Graziano Guella, Donato A. Grasso, and Albrecht Haase
Subjects: Medicine, Science
Abstract: Abstract In this work, we identified the trail pheromone of the ant Crematogaster scutellaris. We combined gas chromatography–mass spectrometry analysis of extracts from the hind tibia, the location of the respective glands, with automated trail following assays. The study found tridecan-2-ol to be the strongest discriminator between hind tibia and other body part extracts. Tridecan-2-ol elicited trail-following behaviour at concentrations of 1 ng/µL. A separation of the enantiomers showed responses to (R)-tridecan-2-ol already at 0.001 ng/µL and only at a 1000-fold higher concentration for (S)-tridecan-2-ol, suggesting that only the R enantiomer is used by C. scutellaris in its natural environment. We also found strong behavioural responses to 2-dodecanol, a substance that was not detectable in the hind tibia extract of C. scutellaris, but which has been reported to be the trail pheromone of the related species C. castanea. We discuss the contribution of these results to the 'dissection and reconstruction' of strategies and mechanisms underlying the social organization of ants.
Published: 2024
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5. Correction: Inflammation promotes synucleinopathy propagation

Author: Tae-Kyung Kim, Eun-Jin Bae, Byung Chul Jung, Minsun Choi, Soo Jean Shin, Sung Jun Park, Jeong Tae Kim, Min Kyo Jung, Ayse Ulusoy, Mi-Young Song, Jun Sung Lee, He-Jin Lee, Donato A. Di Monte, and Seung-Jae Lee
Subjects: Medicine, Biochemistry, QD415-436
Published: 2024
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6. First Record of the Exotic Ant Hypoponera ergatandria in Italy: Indoor Alate Swarms and Stinging Queens

Author: Enrico Schifani, Alex Pezzin, Cristina Castracani, and Donato A. Grasso
Subjects: alien species, Ponerinae, Cryptic species, indoor pests, Zoology, QL1-991, Ecology, QH540-549.5, Natural history (General), QH1-278.5
Abstract: Until recently, Hypoponera ergatandria was commonly misidentified as H. punctatissima, both of which are widespread tramp ant species. Today, H. ergatandria has been recorded in eleven European countries, while most existing H. punctatissima records are doubtful. Its geographic origins, like in the case of H. punctatissima, remain unclear. Here, we present the first record of H. ergatandria in Italy, which also hosts H. punctatissima. We report on a large swarm of alate queens from the basement of a building in the city of Teramo, Central Italy. As reported in Florida under the name H. punctatissima, the species seems capable of producing surprisingly large alate swarms, and alate queens can become a nuisance due to their stinging ability against humans. However, Hypoponera stings are not known to pose any public health threat. Furthermore, H. ergatandria and H. punctatissima are not known to have any ecological impact and are usually confined to a very scattered distribution in Europe.
Published: 2024
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7. Inflammation promotes synucleinopathy propagation

Author: Tae-Kyung Kim, Eun-Jin Bae, Byung Chul Jung, Minsun Choi, Soo Jean Shin, Sung Jun Park, Jeong Tae Kim, Min Kyo Jung, Ayse Ulusoy, Mi-Young Song, Jun Sung Lee, He-Jin Lee, Donato A. Di Monte, and Seung-Jae Lee
Subjects: Medicine, Biochemistry, QD415-436
Abstract: Abstract The clinical progression of neurodegenerative diseases correlates with the spread of proteinopathy in the brain. The current understanding of the mechanism of proteinopathy spread is far from complete. Here, we propose that inflammation is fundamental to proteinopathy spread. A sequence variant of α-synuclein (V40G) was much less capable of fibril formation than wild-type α-synuclein (WT-syn) and, when mixed with WT-syn, interfered with its fibrillation. However, when V40G was injected intracerebrally into mice, it induced aggregate spreading even more effectively than WT-syn. Aggregate spreading was preceded by sustained microgliosis and inflammatory responses, which were more robust with V40G than with WT-syn. Oral administration of an anti-inflammatory agent suppressed aggregate spreading, inflammation, and behavioral deficits in mice. Furthermore, exposure of cells to inflammatory cytokines increased the cell-to-cell propagation of α-synuclein. These results suggest that the inflammatory microenvironment is the major driver of the spread of synucleinopathy in the brain.
Published: 2022
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8. Exercise alters the circadian rhythm of REV-ERB-α and downregulates autophagy-related genes in peripheral and central tissues

Author: Alisson L. da Rocha, Ana P. Pinto, Bruno L. S. Bedo, Gustavo P. Morais, Luciana C. Oliveira, Ruither O. G. Carolino, Jose R. Pauli, Fernando M. Simabuco, Leandro P. de Moura, Eduardo R. Ropelle, Dennys E. Cintra, Donato A. Rivas, and Adelino S. R. da Silva
Subjects: Medicine, Science
Abstract: Abstract The transcriptional repressor REV-ERB-α, encoded by Nuclear Receptor Subfamily 1 Group D Member 1 (Nr1d1), has been considered to play an essential role in the skeletal muscle oxidative capacity adaptation and muscle mass control. Also, this molecule regulates autophagy via the repression of autophagy-related genes both in skeletal muscle and brain regions. Classically, training programs based on endurance or strength characteristics enhance skeletal muscle mass content and/or oxidative capacity, leading to autophagy activation in several tissues. Thus, it seems that REV-ERB-α regulates similar responses induced by exercise. However, how this molecule responds to different exercise models/intensities in different tissues is still unclear. Therefore, the main aim was to characterize the responses of REV-ERB-α and autophagy-related genes to different exercise protocols (endurance/interval run/strength) in distinct tissues (gastrocnemius, soleus and hippocampus). Since REV-ERB-α presents a circadian rhythm, the analyses were performed in a time-course manner. The endurance and strength groups attenuated REV-ERB-α transcriptional response during the time course in gastrocnemius and soleus. Conversely, the interval group enhanced the Nr1d1 expression in the hippocampus. All protocols downregulated the REV-ERB-α protein levels in gastrocnemius following the exercise session with concomitant nuclear exclusion. The major autophagy-related genes presented downregulation after the exercise session in all analyzed tissues. Altogether, these results highlight that REV-ERB-α is extremely sensitive to physical exercise stimuli, including different models and intensities in skeletal muscle and the hippocampus.
Published: 2022
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9. Bioenergetic valorization of Sargassum fluitans in the Mexican Caribbean: The determination of the calorific value and washing mechanism

Author: José A. Sosa Olivier, José R. Laines Canepa, David Guerrero Zarate, Anabel González Díaz, Donato A. Figueiras Jaramillo, Heidi K. Osorio García, and Berenice Evia López
Subjects: alternate fuel, bioenergy, biomass, calorific value, sargassum, seaweed, Production of electric energy or power. Powerplants. Central stations, TK1001-1841, Renewable energy sources, TJ807-830
Abstract: The advent of large volumes of Sargassum sp. on the Mexican Caribbean coast has become an emerging issue for the Mexican population. The most frequent action is harvesting, but a correct treatment or energy recovery strategy is still missing. This work aimed to evaluate the energy potential of Sargassum fluitans, through elemental calculations and direct measurements, considering the effect of its washing. The calorific value determined by the direct method was 9.24 ± 0.28 MJ/kg and 12.64 ± 0.18 MJ/kg for dirty and washed Sargassum, respectively. The washing effect increased the calorific values in Sargassum fluitans by 36.80%. The washing effect increased the calorific values determined by indirect methods, increasing 10.10% and 41.04%, each method, respectively. The content of toxic metals was lower in both materials than that established for non-woody biomass from energy use, concerning the ISO 17225:2014 standard. The unit energy cost of Sargassum fluitans is ＄0.007 and ＄0.011 per MJ for dirty and washed conditions, respectively. Finally, the results of this work indicate that the Sargassum wash provides better characteristics to be considered an alternative fuel option in combustion systems such as thermo-electric plants (based on carbon), sugar mills, and cement kilns with co-processing of solid waste. Attending from a bioenergetic approach, Sargassum's emerging seasonal problem affects the Mexican Caribbean coast.
Published: 2022
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10. Dynamical Cosmological Constant

Author: di Donato, Giuseppe and Pilo, Luigi
Subjects: High Energy Physics - Theory, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology
Abstract: The dynamical realisation of the equation of state $p +\rho =0$ is studied. A non-pathological dynamics for the perturbations of such a system mimicking a dynamical cosmological constant (DCC) requires to go beyond the perfect fluid paradigm. It is shown that an anisotropic stress must be always present. The Hamiltonian of the system in isolation resembles the one of a Pais-Uhlenbeck oscillator and linear stability requires that it cannot be positive definite. The dynamics of linear cosmological perturbations in a DCC dominated Universe is studied in detail showing that when DCC is minimally coupled to gravity no dramatic instability is present. In contrast to what happens in a cosmological constant dominated Universe, the non-relativistic matter contrast is no longer constant and exhibits an oscillator behaviour at small scales while it grows weakly at large scales. In the gravitational waves sector, at small scales, the amplitude is still suppressed as the inverse power of the scale factor while it grows logarithmically at large scales. Also the vector modes propagate, though no growing mode is found., Comment: 29 pages, 10 figures
Published: 2025

11. Nariai spacetime: orbits, scalar self force and Poynting-Robertson-like external force

Author: Bini, Donato and Esposito, Giampiero
Subjects: General Relativity and Quantum Cosmology
Abstract: After studying properties of the Nariai solution, including its geodesics, in spherical and de Sitter coordinates, two kinds of accelerated motion are investigated in detail: either observers at rest with respect to the coordinates, or observers in radial motion. Next, massless scalar perturbations of Nariai spacetime in absence of sources are worked out, and an explicit example out of the black hole context of analytic self-force calculation is obtained. Last, self-force effects are studied as well, together with some variant of the type of Poynting-Robertson external force, and also building a test electromagnetic field and a test gravitational field in Nariai spacetime geometry, Comment: 26 pages, 10 figures
Published: 2025

12. Local ergotropy dynamically witnesses many-body localized phases

Author: Formicola, Francesco, Di Bello, Grazia, De Filippis, Giulio, Cataudella, Vittorio, Farina, Donato, and Perroni, Carmine Antonio
Subjects: Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Statistical Mechanics, Condensed Matter - Strongly Correlated Electrons
Abstract: Many-body localization is a dynamical phenomenon characteristic of strongly interacting and disordered many-body quantum systems which fail to achieve thermal equilibrium. From a quantum information perspective, the fingerprint of this phenomenon is the logarithmic growth of the entanglement entropy over time. We perform intensive numerical simulations, applied to a paradigmatic model system, showing that the local ergotropy, the maximum extractable work via local unitary operations on a small subsystem in the presence of Hamiltonian coupling, dynamically witnesses the change from ergodic to localized phases. Within the many-body localized phase, both the local ergotropy and its quantum fluctuations slowly vary over time with a characteristic logarithmic law analogous to the behaviour of entanglement entropy. This showcases how directly leveraging local control, instead of local observables or entropies analyzed in previous works, provides a thermodynamic marker of localization phenomena based on the locally extractable work., Comment: 6 pages, 4 Figures, End Matter and Supplemental Material
Published: 2025

13. Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition

Author: Clerico, Victoria, Dutta, Anirvan, Falcone, Donato Francesco, Choi, Wooseok, Galetta, Matteo, Stecconi, Tommaso, Horváth, András, Varzandeh, Shokoofeh, Offrein, Bert Jan, Kaboli, Mohsen, and Bragaglia, Valeria
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Tactile hand gesture recognition is a crucial task for user control in the automotive sector, where Human-Machine Interactions (HMI) demand low latency and high energy efficiency. This study addresses the challenges of power-constrained edge training and inference by utilizing analog Resistive Random Access Memory (ReRAM) technology in conjunction with a real tactile hand gesture dataset. By optimizing the input space through a feature engineering strategy, we avoid relying on large-scale crossbar arrays, making the system more suitable for edge deployment. Through realistic hardware-aware simulations that account for device non-idealities derived from experimental data, we demonstrate the functionalities of our analog ReRAM-based analog in-memory computing for on-chip training, utilizing the state-of-the-art Tiki-Taka algorithm. Furthermore, we validate the classification accuracy of approximately 91.4% for post-deployment inference of hand gestures. The results highlight the potential of analog ReRAM technology and crossbar architecture with fully parallelized matrix computations for real-time HMI systems at the Edge., Comment: Accepted in IEEE ISCAS 2025
Published: 2025

14. WIMP Dark Matter Search using a 3.1 tonne $\times$ year Exposure of the XENONnT Experiment

Author: Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Armbruster, S. R., Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fuchs, D., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gao, F., Ghosh, S., Giacomobono, R., Girard, F., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kharbanda, P., Kobayashi, M., Koke, D., Kooshkjalali, K., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Lucchetti, G. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Ravindran, A., Razeto, A., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Stevens, A., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, J., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhao, Y., and Zhong, M.
Subjects: High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract: We report on a search for weakly interacting massive particle (WIMP) dark matter (DM) via elastic DM-xenon-nucleus interactions in the XENONnT experiment. We combine datasets from the first and second science campaigns resulting in a total exposure of $3.1\;\text{tonne}\times\text{year}$. In a blind analysis of nuclear recoil events with energies above $3.8\,\mathrm{keV_{NR}}$, we find no significant excess above background. We set new upper limits on the spin-independent WIMP-nucleon scattering cross-section for WIMP masses above $10\,\mathrm{GeV}/c^2$ with a minimum of $1.7\,\times\,10^{-47}\,\mathrm{cm^2}$ at $90\,\%$ confidence level for a WIMP mass of $30\,\mathrm{GeV}/c^2$. We achieve a best median sensitivity of $1.4\,\times\,10^{-47}\,\mathrm{cm^2}$ for a $41\,\mathrm{GeV}/c^2$ WIMP. Compared to the result from the first XENONnT science dataset, we improve our sensitivity by a factor of up to 1.8., Comment: Limits are included in the submission file
Published: 2025

15. $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering

Author: Boniol, Paul, Tiano, Donato, Bonifati, Angela, and Palpanas, Themis
Subjects: Computer Science - Machine Learning
Abstract: Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In addressing this gap, our work presents $k$-Graph, an unsupervised method explicitly crafted to augment interpretability in time series clustering. Leveraging a graph representation of time series subsequences, $k$-Graph constructs multiple graph representations based on different subsequence lengths. This feature accommodates variable-length time series without requiring users to predetermine subsequence lengths. Our experimental results reveal that $k$-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy, while providing users with meaningful explanations and interpretations of the clustering outcomes.
Published: 2025

16. Optimal Placement of Nature-Based Solutions for Urban Challenges

Author: Pinto, Diego Maria, Russo, Davide Donato, and Sudoso, Antonio M.
Subjects: Mathematics - Optimization and Control
Abstract: Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), such as parks, green walls, roofs, and street trees, offer a promising means to regulate urban temperatures and enhance air quality. However, determining their optimal placement to maximize environmental benefits remains a pressing challenge. Leveraging Operational Research (OR) tools, we propose a Mixed-Integer Linear Programming (MILP) model that integrates multiple factors, including urban challenges, physical constraints, clustering techniques, convolution theory, and fairness considerations. This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces. Through several case study analyses, we demonstrate the effectiveness of our approach in improving environmental and social indicators. This research holds implications for policy and practice, empowering urban planners and policymakers to make informed decisions regarding NBS implementation. Such decisions ensure that investments in urban greening yield maximum environmental, social, and economic benefits.
Published: 2025

17. MERGE$^3$: Efficient Evolutionary Merging on Consumer-grade GPUs

Author: Mencattini, Tommaso, Minut, Adrian Robert, Crisostomi, Donato, Santilli, Andrea, and Rodolà, Emanuele
Subjects: Computer Science - Neural and Evolutionary Computing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: Evolutionary model merging enables the creation of high-performing multi-task models but remains computationally prohibitive for consumer hardware. We introduce MERGE$^3$, an efficient framework that makes evolutionary merging feasible on a single GPU by reducing fitness computation costs 50$\times$ while preserving performance. MERGE$^3$ achieves this by Extracting a reduced dataset for evaluation, Estimating model abilities using Item Response Theory (IRT), and Evolving optimal merges via IRT-based performance estimators. Our method enables state-of-the-art multilingual and cross-lingual merging, transferring knowledge across languages with significantly lower computational overhead. We provide theoretical guarantees and an open-source library, democratizing high-quality model merging., Comment: 19 pages, 13 figures
Published: 2025

18. Studying How Configurations Impact Code Generation in LLMs: the Case of ChatGPT

Author: Donato, Benedetta, Mariani, Leonardo, Micucci, Daniela, and Riganelli, Oliviero
Subjects: Computer Science - Software Engineering
Abstract: Leveraging LLMs for code generation is becoming increasingly common, as tools like ChatGPT can suggest method implementations with minimal input, such as a method signature and brief description. Empirical studies further highlight the effectiveness of LLMs in handling such tasks, demonstrating notable performance in code generation scenarios. However, LLMs are inherently non-deterministic, with their output influenced by parameters such as temperature, which regulates the model's level of creativity, and top-p, which controls the choice of the tokens that shall appear in the output. Despite their significance, the role of these parameters is often overlooked. This paper systematically studies the impact of these parameters, as well as the number of prompt repetitions required to account for non-determinism, in the context of 548 Java methods. We observe significantly different performances across different configurations of ChatGPT, with temperature having a marginal impact compared to the more prominent influence of the top-p parameter. Additionally, we show how creativity can enhance code generation tasks. Finally, we provide concrete recommendations for addressing the non-determinism of the model.
Published: 2025

19. All-in-One Analog AI Accelerator: On-Chip Training and Inference with Conductive-Metal-Oxide/HfOx ReRAM Devices

Author: Falcone, Donato Francesco, Clerico, Victoria, Choi, Wooseok, Stecconi, Tommaso, Horst, Folkert, Begon-Lours, Laura, Galetta, Matteo, La Porta, Antonio, Garg, Nikhil, Alibart, Fabien, Offrein, Bert Jan, and Bragaglia, Valeria
Subjects: Computer Science - Emerging Technologies, Computer Science - Hardware Architecture
Abstract: Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in-memory technology platform-capable of on-chip training, weight retention, and long-term inference acceleration-has yet to be reported. This work presents an all-in-one analog AI accelerator, combining these capabilities to enable energy-efficient, continuously adaptable AI systems. The platform leverages an array of analog filamentary conductive-metal-oxide (CMO)/HfOx resistive switching memory cells (ReRAM) integrated into the back-end-of-line (BEOL). The array demonstrates reliable resistive switching with voltage amplitudes below 1.5 V, compatible with advanced technology nodes. The array's multi-bit capability (over 32 stable states) and low programming noise (down to 10 nS) enable a nearly ideal weight transfer process, more than an order of magnitude better than other memristive technologies. Inference performance is validated through matrix-vector multiplication simulations on a 64x64 array, achieving a root-mean-square error improvement by a factor of 10 at 1 second and 3 at 10 years after programming, compared to state-of-the-art. Training accuracy closely matching the software equivalent is achieved across different datasets. The CMO/HfOx ReRAM technology lays the foundation for efficient analog systems accelerating both inference and training in deep neural networks.
Published: 2025

20. Radon Removal in XENONnT down to the Solar Neutrino Level

Author: Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kharbanda, P., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, D., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., and Zhong, M.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: The XENONnT experiment has achieved an unprecedented reduction of the $^\text{222}$Rn activity concentration within its liquid xenon dual-phase time projection chamber to a level of (0.90$\,\pm\,$0.01$\,$stat.$\,\pm\,$0.07 sys.)$\,\mu$Bq/kg, equivalent to about 1200 $^\text{222}$Rn atoms per cubic meter of liquid xenon. This represents a 15-fold improvement over the $^\text{222}$Rn levels encountered during XENON1T's main science runs and is a factor five lower compared to other currently operational multi-tonne liquid xenon detectors engaged in dark matter searches. This breakthrough enables the pursuit of various rare event searches that lie beyond the confines of the standard model of particle physics, with world-leading sensitivity. The ultra-low $^\text{222}$Rn levels have diminished the radon-induced background rate in the detector to a point where it is for the first time lower than the solar neutrino-induced background, which is poised to become the primary irreducible background in liquid xenon-based detectors.
Published: 2025

21. Humanity's Last Exam

Author: Phan, Long, Gatti, Alice, Han, Ziwen, Li, Nathaniel, Hu, Josephina, Zhang, Hugh, Zhang, Chen Bo Calvin, Shaaban, Mohamed, Ling, John, Shi, Sean, Choi, Michael, Agrawal, Anish, Chopra, Arnav, Khoja, Adam, Kim, Ryan, Ren, Richard, Hausenloy, Jason, Zhang, Oliver, Mazeika, Mantas, Nguyen, Tung, Anderson, Daron, Shah, Imad Ali, Doroshenko, Mikhail, Stokes, Alun Cennyth, Mahmood, Mobeen, Lee, Jaeho, Pokutnyi, Oleksandr, Iskra, Oleg, Wang, Jessica P., Gerbicz, Robert, Levin, John-Clark, Popov, Serguei, Feng, Fiona, Feng, Steven Y., Zhao, Haoran, Yu, Michael, Gangal, Varun, Zou, Chelsea, Wang, Zihan, Kazakov, Mstyslav, Galgon, Geoff, Schmitt, Johannes, Sanchez, Alvaro, Lee, Yongki, Yeadon, Will, Sauers, Scott, Roth, Marc, Agu, Chidozie, Riis, Søren, Giska, Fabian, Utpala, Saiteja, Cheatom, Antrell, Giboney, Zachary, Goshu, Gashaw M., Crowson, Sarah-Jane, Naiya, Mohinder Maheshbhai, Burns, Noah, Finke, Lennart, Cheng, Zerui, Park, Hyunwoo, Fournier-Facio, Francesco, Zampese, Jennifer, Wydallis, John B., Hoerr, Ryan G., Nandor, Mark, Gehrunger, Tim, Cai, Jiaqi, McCarty, Ben, Nam, Jungbae, Taylor, Edwin, Jin, Jun, Loume, Gautier Abou, Cao, Hangrui, Garretson, Alexis C, Sileo, Damien, Ren, Qiuyu, Cojoc, Doru, Arkhipov, Pavel, Qazi, Usman, Bacho, Aras, Li, Lianghui, Motwani, Sumeet, de Witt, Christian Schroeder, Kopylov, Alexei, Veith, Johannes, Singer, Eric, Rissone, Paolo, Jin, Jaehyeok, Shi, Jack Wei Lun, Willcocks, Chris G., Prabhu, Ameya, Tang, Longke, Zhou, Kevin, Santos, Emily de Oliveira, Maksimov, Andrey Pupasov, Vendrow, Edward, Zenitani, Kengo, Robinson, Joshua, Mikov, Aleksandar, Guillod, Julien, Li, Yuqi, Pageler, Ben, Vendrow, Joshua, Kuchkin, Vladyslav, Marion, Pierre, Efremov, Denis, Lynch, Jayson, Liang, Kaiqu, Gritsevskiy, Andrew, Martinez, Dakotah, Crispino, Nick, Zvonkine, Dimitri, Fraga, Natanael Wildner, Soori, Saeed, Press, Ori, Tang, Henry, Salazar, Julian, Green, Sean R., Brüssel, Lina, Twayana, Moon, Dieuleveut, Aymeric, Rogers, T. Ryan, Zhang, Wenjin, Finocchio, Ross, Li, Bikun, Yang, Jinzhou, Rao, Arun, Loiseau, Gabriel, Kalinin, Mikhail, Lukas, Marco, Manolescu, Ciprian, Stambaugh, Nate, Mishra, Subrata, Kamdoum, Ariel Ghislain Kemogne, Hogg, Tad, Jin, Alvin, Bosio, Carlo, Sun, Gongbo, Coppola, Brian P, Heidinger, Haline, Sayous, Rafael, Ivanov, Stefan, Cavanagh, Joseph M, Shen, Jiawei, Imperial, Joseph Marvin, Schwaller, Philippe, Senthilkuma, Shaipranesh, Bran, Andres M, Algaba, Andres, Verbeken, Brecht, Houte, Kelsey Van den, Van Der Sypt, Lynn, Noever, David, Schut, Lisa, Sucholutsky, Ilia, Zheltonozhskii, Evgenii, Yuan, Qiaochu, Lim, Derek, Stanley, Richard, Sivarajan, Shankar, Yang, Tong, Maar, John, Wykowski, Julian, Oller, Martí, Sandlin, Jennifer, Sahu, Anmol, Ardito, Cesare Giulio, Hu, Yuzheng, Dias, Felipe Meneguitti, Kreiman, Tobias, Rawal, Kaivalya, Vilchis, Tobias Garcia, Zu, Yuexuan, Lackner, Martin, Koppel, James, Nguyen, Jeremy, Antonenko, Daniil S., Chern, Steffi, Zhao, Bingchen, Arsene, Pierrot, Ivanov, Sergey, Poświata, Rafał, Wang, Chenguang, Li, Daofeng, Crisostomi, Donato, Dehghan, Ali, Achilleos, Andrea, Ambay, John Arnold, Myklebust, Benjamin, Sen, Archan, Perrella, David, Kaparov, Nurdin, Inlow, Mark H, Zang, Allen, Ramakrishnan, Kalyan, Orel, Daniil, Poritski, Vladislav, Ben-David, Shalev, Berger, Zachary, Whitfill, Parker, Foster, Michael, Munro, Daniel, Ho, Linh, Hava, Dan Bar, Kuchkin, Aleksey, Lauff, Robert, Holmes, David, Sommerhage, Frank, Zhang, Anji, Moat, Richard, Schneider, Keith, Pyda, Daniel, Kazibwe, Zakayo, Singh, Mukhwinder, Clarke, Don, Kim, Dae Hyun, Fish, Sara, Elser, Veit, Vilchis, Victor Efren Guadarrama, Klose, Immo, Demian, Christoph, Anantheswaran, Ujjwala, Zweiger, Adam, Albani, Guglielmo, Li, Jeffery, Daans, Nicolas, Radionov, Maksim, Rozhoň, Václav, Ginis, Vincent, Ma, Ziqiao, Stump, Christian, Platnick, Jacob, Nevirkovets, Volodymyr, Basler, Luke, Piccardo, Marco, Cohen, Niv, Singh, Virendra, Tkadlec, Josef, Rosu, Paul, Goldfarb, Alan, Padlewski, Piotr, Barzowski, Stanislaw, Montgomery, Kyle, Menezes, Aline, Patel, Arkil, Wang, Zixuan, Tucker-Foltz, Jamie, Stade, Jack, Grabb, Declan, Goertzen, Tom, Kazemi, Fereshteh, Milbauer, Jeremiah, Shukla, Abhishek, Elgnainy, Hossam, Labrador, Yan Carlos Leyva, He, Hao, Zhang, Ling, Givré, Alan, Wolff, Hew, Demir, Gözdenur, Aziz, Muhammad Fayez, Kaddar, Younesse, Ängquist, Ivar, Chen, Yanxu, Thornley, Elliott, Zhang, Robin, Pan, Jiayi, Terpin, Antonio, Muennighoff, Niklas, Schoelkopf, Hailey, Zheng, Eric, Carmi, Avishy, Shah, Jainam, Brown, Ethan D. L., Zhu, Kelin, Bartolo, Max, Wheeler, Richard, Ho, Andrew, Barkan, Shaul, Wang, Jiaqi, Stehberger, Martin, Kretov, Egor, Bradshaw, Peter, Heimonen, JP, Sridhar, Kaustubh, Hossain, Zaki, Akov, Ido, Makarychev, Yury, Tam, Joanna, Hoang, Hieu, Cunningham, David M., Goryachev, Vladimir, Patramanis, Demosthenes, Krause, Michael, Redenti, Andrew, Aldous, David, Lai, Jesyin, Coleman, Shannon, Xu, Jiangnan, Lee, Sangwon, Magoulas, Ilias, Zhao, Sandy, Tang, Ning, Cohen, Michael K., Carroll, Micah, Paradise, Orr, Kirchner, Jan Hendrik, Steinerberger, Stefan, Ovchynnikov, Maksym, Matos, Jason O., Shenoy, Adithya, Wang, Michael, Nie, Yuzhou, Giordano, Paolo, Petersen, Philipp, Sztyber-Betley, Anna, Faraboschi, Paolo, Riblet, Robin, Crozier, Jonathan, Halasyamani, Shiv, Pinto, Antonella, Verma, Shreyas, Joshi, Prashant, Meril, Eli, Yong, Zheng-Xin, Tee, Allison, Andréoletti, Jérémy, Weller, Orion, Singhal, Raghav, Zhang, Gang, Ivanov, Alexander, Khoury, Seri, Gustafsson, Nils, Mostaghimi, Hamid, Thaman, Kunvar, Chen, Qijia, Khánh, Tran Quoc, Loader, Jacob, Cavalleri, Stefano, Szlyk, Hannah, Brown, Zachary, Narayan, Himanshu, Roberts, Jonathan, Alley, William, Sun, Kunyang, Stendall, Ryan, Lamparth, Max, Reuel, Anka, Wang, Ting, Xu, Hanmeng, Hernández-Cámara, Pablo, Martin, Freddie, Preu, Thomas, Korbak, Tomek, Abramovitch, Marcus, Williamson, Dominic, Bosio, Ida, Chen, Ziye, Bálint, Biró, Lo, Eve J. Y., Nunes, Maria Inês S., Jiang, Yibo, Bari, M Saiful, Kassani, Peyman, Wang, Zihao, Ansarinejad, Behzad, Sun, Yewen, Durand, Stephane, Douville, Guillaume, Tordera, Daniel, Balabanian, George, Anderson, Earth, Kvistad, Lynna, Moyano, Alejandro José, Milliron, Hsiaoyun, Sakor, Ahmad, Eron, Murat, McAlister, Isaac C., O., Andrew Favre D., Shah, Shailesh, Zhou, Xiaoxiang, Kamalov, Firuz, Clark, Ronald, Abdoli, Sherwin, Santens, Tim, Wang, Harrison K, Chen, Evan, Tomasiello, Alessandro, De Luca, G. Bruno, Looi, Shi-Zhuo, Le, Vinh-Kha, Kolt, Noam, Mündler, Niels, Semler, Avi, Rodman, Emma, Drori, Jacob, Fossum, Carl J, Gloor, Luk, Jagota, Milind, Pradeep, Ronak, Fan, Honglu, Shah, Tej, Eicher, Jonathan, Chen, Michael, Thaman, Kushal, Merrill, William, Firsching, Moritz, Harris, Carter, Ciobâcă, Stefan, Gross, Jason, Pandey, Rohan, Gusev, Ilya, Jones, Adam, Agnihotri, Shashank, Zhelnov, Pavel, Usawasutsakorn, Siranut, Mofayezi, Mohammadreza, Piperski, Alexander, Carauleanu, Marc, Zhang, David K., Dobarskyi, Kostiantyn, Ler, Dylan, Leventov, Roman, Soroko, Ignat, Jansen, Thorben, Creighton, Scott, Lauer, Pascal, Duersch, Joshua, Taamazyan, Vage, Bezzi, Dario, Morak, Wiktor, Ma, Wenjie, Held, William, Huy, Tran Đuc, Xian, Ruicheng, Zebaze, Armel Randy, Mohamed, Mohanad, Leser, Julian Noah, Yuan, Michelle X, Yacar, Laila, Lengler, Johannes, Olszewska, Katarzyna, Shahrtash, Hossein, Oliveira, Edson, Jackson, Joseph W., Gonzalez, Daniel Espinosa, Zou, Andy, Chidambaram, Muthu, Manik, Timothy, Haffenden, Hector, Stander, Dashiell, Dasouqi, Ali, Shen, Alexander, Duc, Emilien, Golshani, Bita, Stap, David, Uzhou, Mikalai, Zhidkovskaya, Alina Borisovna, Lewark, Lukas, Rodriguez, Miguel Orbegozo, Vincze, Mátyás, Wehr, Dustin, Tang, Colin, Phillips, Shaun, Samuele, Fortuna, Muzhen, Jiang, Ekström, Fredrik, Hammon, Angela, Patel, Oam, Farhidi, Faraz, Medley, George, Mohammadzadeh, Forough, Peñaflor, Madellene, Kassahun, Haile, Friedrich, Alena, Sparrow, Claire, Perez, Rayner Hernandez, Sakal, Taom, Dhamane, Omkar, Mirabadi, Ali Khajegili, Hallman, Eric, Okutsu, Kenchi, Battaglia, Mike, Maghsoudimehrabani, Mohammad, Amit, Alon, Hulbert, Dave, Pereira, Roberto, Weber, Simon, Handoko, Peristyy, Anton, Malina, Stephen, Albanie, Samuel, Cai, Will, Mehkary, Mustafa, Aly, Rami, Reidegeld, Frank, Dick, Anna-Katharina, Friday, Cary, Sidhu, Jasdeep, Shapourian, Hassan, Kim, Wanyoung, Costa, Mariana, Gurdogan, Hubeyb, Weber, Brian, Kumar, Harsh, Jiang, Tong, Agarwal, Arunim, Ceconello, Chiara, Vaz, Warren S., Zhuang, Chao, Park, Haon, Tawfeek, Andrew R., Aggarwal, Daattavya, Kirchhof, Michael, Dai, Linjie, Kim, Evan, Ferret, Johan, Wang, Yuzhou, Yan, Minghao, Burdzy, Krzysztof, Zhang, Lixin, Franca, Antonio, Pham, Diana T., Loh, Kang Yong, Jackson, Abram, Gul, Shreen, Chhablani, Gunjan, Du, Zhehang, Cosma, Adrian, Colino, Jesus, White, Colin, Votava, Jacob, Vinnikov, Vladimir, Delaney, Ethan, Spelda, Petr, Stritecky, Vit, Shahid, Syed M., Mourrat, Jean-Christophe, Vetoshkin, Lavr, Sponselee, Koen, Bacho, Renas, de la Rosa, Florencia, Li, Xiuyu, Malod, Guillaume, Lang, Leon, Laurendeau, Julien, Kazakov, Dmitry, Adesanya, Fatimah, Portier, Julien, Hollom, Lawrence, Souza, Victor, Zhou, Yuchen Anna, Degorre, Julien, Yalın, Yiğit, Obikoya, Gbenga Daniel, Arnaboldi, Luca, Rai, Bigi, Filippo, Boscá, M. C., Shumar, Oleg, Bacho, Kaniuar, Clavier, Pierre, Recchia, Gabriel, Popescu, Mara, Shulga, Nikita, Tanwie, Ngefor Mildred, Peskoff, Denis, Lux, Thomas C. H., Rank, Ben, Ni, Colin, Brooks, Matthew, Yakimchyk, Alesia, Huanxu, Liu, Häggström, Olle, Verkama, Emil, Gundlach, Hans, Brito-Santana, Leonor, Amaro, Brian, Vajipey, Vivek, Grover, Rynaa, Fan, Yiyang, Silva, Gabriel Poesia Reis e, Xin, Linwei, Kratish, Yosi, Łucki, Jakub, Li, Wen-Ding, Gopi, Sivakanth, Caciolai, Andrea, Xu, Justin, Scaria, Kevin Joseph, Vargus, Freddie, Habibi, Farzad, Long, Lian, Rodolà, Emanuele, Robins, Jules, Cheng, Vincent, Fruhauff, Tony, Raynor, Brad, Qi, Hao, Jiang, Xi, Segev, Ben, Fan, Jingxuan, Martinson, Sarah, Wang, Erik Y., Hausknecht, Kaylie, Brenner, Michael P., Mao, Mao, Zhang, Xinyu, Avagian, David, Scipio, Eshawn Jessica, Ragoler, Alon, Tan, Justin, Sims, Blake, Plecnik, Rebeka, Kirtland, Aaron, Bodur, Omer Faruk, Shinde, D. P., Adoul, Zahra, Zekry, Mohamed, Karakoc, Ali, Santos, Tania C. B., Shamseldeen, Samir, Karim, Loukmane, Liakhovitskaia, Anna, Resman, Nate, Farina, Nicholas, Gonzalez, Juan Carlos, Maayan, Gabe, Hoback, Sarah, Pena, Rodrigo De Oliveira, Sherman, Glen, Kelley, Elizabeth, Mariji, Hodjat, Pouriamanesh, Rasoul, Wu, Wentao, Mendoza, Sandra, Alarab, Ismail, Cole, Joshua, Ferreira, Danyelle, Johnson, Bryan, Safdari, Mohammad, Dai, Liangti, Arthornthurasuk, Siriphan, Pronin, Alexey, Fan, Jing, Ramirez-Trinidad, Angel, Cartwright, Ashley, Pottmaier, Daphiny, Taheri, Omid, Outevsky, David, Stepanic, Stanley, Perry, Samuel, Askew, Luke, Rodríguez, Raúl Adrián Huerta, Minissi, Ali M. R., Ali, Sam, Lorena, Ricardo, Iyer, Krishnamurthy, Fasiludeen, Arshad Anil, Salauddin, Sk Md, Islam, Murat, Gonzalez, Juan, Ducey, Josh, Somrak, Maja, Mavroudis, Vasilios, Vergo, Eric, Qin, Juehang, Borbás, Benjámin, Chu, Eric, Lindsey, Jack, Radhakrishnan, Anil, Jallon, Antoine, McInnis, I. M. J., Kumar, Pawan, Goswami, Laxman Prasad, Bugas, Daniel, Heydari, Nasser, Jeanplong, Ferenc, Apronti, Archimedes, Galal, Abdallah, Ze-An, Ng, Singh, Ankit, Xavier, Joan of Arc, Agarwal, Kanu Priya, Berkani, Mohammed, Junior, Benedito Alves de Oliveira, Malishev, Dmitry, Remy, Nicolas, Hartman, Taylor D., Tarver, Tim, Mensah, Stephen, Gimenez, Javier, Montecillo, Roselynn Grace, Campbell, Russell, Sharma, Asankhaya, Meer, Khalida, Alapont, Xavier, Patil, Deepakkumar, Maheshwari, Rajat, Dendane, Abdelkader, Shukla, Priti, Bogdanov, Sergei, Möller, Sören, Siddiqi, Muhammad Rehan, Saxena, Prajvi, Gupta, Himanshu, Enyekwe, Innocent, P V, Ragavendran, EL-Wasif, Zienab, Maksapetyan, Aleksandr, Rossbach, Vivien, Harjadi, Chris, Bahaloohoreh, Mohsen, Bian, Song, Lai, John, Uro, Justine Leon, Bateman, Greg, Sayed, Mohamed, Menshawy, Ahmed, Duclosel, Darling, Jain, Yashaswini, Aaron, Ashley, Tiryakioglu, Murat, Siddh, Sheeshram, Krenek, Keith, Hoover, Alex, McGowan, Joseph, Patwardhan, Tejal, Yue, Summer, Wang, Alexandr, and Hendrycks, Dan
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 27 pages, 6 figures
Published: 2025

22. EVolutionary Independent DEtermiNistiC Explanation

Author: Dentamaro, Vincenzo, Giglio, Paolo, Impedovo, Donato, and Pirlo, Giuseppe
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract: The widespread use of artificial intelligence deep neural networks in fields such as medicine and engineering necessitates understanding their decision-making processes. Current explainability methods often produce inconsistent results and struggle to highlight essential signals influencing model inferences. This paper introduces the Evolutionary Independent Deterministic Explanation (EVIDENCE) theory, a novel approach offering a deterministic, model-independent method for extracting significant signals from black-box models. EVIDENCE theory, grounded in robust mathematical formalization, is validated through empirical tests on diverse datasets, including COVID-19 audio diagnostics, Parkinson's disease voice recordings, and the George Tzanetakis music classification dataset (GTZAN). Practical applications of EVIDENCE include improving diagnostic accuracy in healthcare and enhancing audio signal analysis. For instance, in the COVID-19 use case, EVIDENCE-filtered spectrograms fed into a frozen Residual Network with 50 layers improved precision by 32% for positive cases and increased the area under the curve (AUC) by 16% compared to baseline models. For Parkinson's disease classification, EVIDENCE achieved near-perfect precision and sensitivity, with a macro average F1-Score of 0.997. In the GTZAN, EVIDENCE maintained a high AUC of 0.996, demonstrating its efficacy in filtering relevant features for accurate genre classification. EVIDENCE outperformed other Explainable Artificial Intelligence (XAI) methods such as LIME, SHAP, and GradCAM in almost all metrics. These findings indicate that EVIDENCE not only improves classification accuracy but also provides a transparent and reproducible explanation mechanism, crucial for advancing the trustworthiness and applicability of AI systems in real-world settings., Comment: 20 pages, 4 figures
Published: 2025

23. Sensorimotor Control Strategies for Tactile Robotics

Author: Donato, Enrico, Preti, Matteo Lo, Beccai, Lucia, and Falotico, Egidio
Subjects: Computer Science - Robotics
Abstract: How are robots becoming smarter at interacting with their surroundings? Recent advances have reshaped how robots use tactile sensing to perceive and engage with the world. Tactile sensing is a game-changer, allowing robots to embed sensorimotor control strategies to interact with complex environments and skillfully handle heterogeneous objects. Such control frameworks plan contact-driven motions while staying responsive to sudden changes. We review the latest methods for building perception and control systems in tactile robotics while offering practical guidelines for their design and implementation. We also address key challenges to shape the future of intelligent robots., Comment: 39 pages, 8 figures, 1 table
Published: 2025

24. Hamilton-Jacobi equations involving a Caputo time-fractional derivative

Author: Di Donato, Daniela
Subjects: Mathematics - Metric Geometry
Abstract: We prove a representation formula of intrinsic Hopf-Lax type for subsolutions to Hamilton-Jacobi equations involving a Caputo time-fractional derivative.
Published: 2025

25. European first-year university students accept evolution but lack substantial knowledge about it: a standardized European cross-country assessment

Author: Paul Kuschmierz, Anna Beniermann, Alexander Bergmann, Rianne Pinxten, Tuomas Aivelo, Justyna Berniak-Woźny, Gustav Bohlin, Anxela Bugallo-Rodriguez, Pedro Cardia, Bento Filipe Barreiras Pinto Cavadas, Umran Betul Cebesoy, Dragana D. Cvetković, Emilie Demarsy, Mirko S. Đorđević, Szymon M. Drobniak, Liudmyla Dubchak, Radka M. Dvořáková, Jana Fančovičová, Corinne Fortin, Momir Futo, Nicoleta Adriana Geamănă, Niklas Gericke, Donato A. Grasso, Ádám Z. Lendvai, Evangelia Mavrikaki, Andra Meneganzin, Athanasios Mogias, Andrea Möller, Paulo G. Mota, Yamama Naciri, Zoltán Németh, Katarzyna Ożańska-Ponikwia, Silvia Paolucci, Péter László Pap, Maria Petersson, Barbara Pietrzak, Telmo Pievani, Alma Pobric, Juris Porozovs, Giulia Realdon, Xana Sá-Pinto, Uroš B. Savković, Mathieu Sicard, Mircea T. Sofonea, Andrej Sorgo, Alexandru N. Stermin, Ioan Tăușan, Gregor Torkar, Lütfullah Türkmen, Slavica Tutnjević, Anna E. Uitto, Máté Varga, Mirna Varga, Lucia Vazquez-Ben, Constantinos Venetis, Enrique Viguera, Lisa Christine Virtbauer, Albena Vutsova, Inmaculada Yruela, Jelle Zandveld, and Dittmar Graf
Subjects: Evolution, Acceptance, Knowledge, Multilevel modeling, Socioscientific issues, Religious faith, Special aspects of education, LC8-6691, QH359-425
Abstract: Abstract Background Investigations of evolution knowledge and acceptance and their relation are central to evolution education research. Ambiguous results in this field of study demonstrate a variety of measuring issues, for instance differently theorized constructs, or a lack of standardized methods, especially for cross-country comparisons. In particular, meaningful comparisons across European countries, with their varying cultural backgrounds and education systems, are rare, often include only few countries, and lack standardization. To address these deficits, we conducted a standardized European survey, on 9200 first-year university students in 26 European countries utilizing a validated, comprehensive questionnaire, the “Evolution Education Questionnaire”, to assess evolution acceptance and knowledge, as well as influencing factors on evolution acceptance. Results We found that, despite European countries’ different cultural backgrounds and education systems, European first-year university students generally accept evolution. At the same time, they lack substantial knowledge about it, even if they are enrolled in a biology-related study program. Additionally, we developed a multilevel-model that determines religious faith as the main influencing factor in accepting evolution. According to our model, knowledge about evolution and interest in biological topics also increase acceptance of evolution, but to a much lesser extent than religious faith. The effect of age and sex, as well as the country’s affiliation, students’ denomination, and whether or not a student is enrolled in a biology-related university program, is negligible. Conclusions Our findings indicate that, despite all their differences, most of the European education systems for upper secondary education lead to acceptance of evolution at least in university students. It appears that, at least in this sample, the differences in knowledge between countries reflect neither the extent to which school curricula cover evolutionary biology nor the percentage of biology-related students in the country samples. Future studies should investigate the role of different European school curricula, identify particularly problematic or underrepresented evolutionary concepts in biology education, and analyze the role of religious faith when teaching evolution.
Published: 2021
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26. Sociodemographic Predictors of Difficulty in Performing Online Academic Activities among Brazilian Graduate Students in the COVID-19 Pandemic

Author: Corina Milagro Mosqueira Taipe, Larissa Cristina Mazer, Nayara Paula Fernandes Martins Molina, Caíque Rossi Baldassarini, Gabriela Di Donato, Assis Do Carmo Pereira Júnior, Adriana Inocenti Miasso, and Patricia Leila dos Santos
Abstract: The COVID-19 pandemic has brought the challenge of online teaching to graduate courses, without prior preparation for students and teachers. In this context, the aim of the study was to identify sociodemographic predictors of difficulty in carrying out online academic activities by graduate students, by region of Brazil, during the COVID-19 pandemic. An online sociodemographic questionnaire, carried out by the researchers, was answered by 5282 Brazilian graduate students, recruited via institutional e-mail. The chi-square test and multivariate binomial logistic regression were used to analyze the data. There was a significant difference between the regions in terms of difficulty in carrying out online activities, with a predominance in the southeast. The main significant predictors were: age between 40-59 years (Midwest, Southeast), other genders (Southeast); having children (North, Southeast); having no income (North, Northeast, Southeast and South); being black/brown (Midwest, Southeast). Thus, sociodemographic characteristics influenced online graduate education differently according to region, suggesting the need for actions to promote equity in education. It is possible that having children, age and identifying with other genders made it difficult to access and follow online activities.
Published: 2025
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27. IL-6 deletion decreased REV-ERBα protein and influenced autophagy and mitochondrial markers in the skeletal muscle after acute exercise

Author: Ana P. Pinto, Vitor R. Muñoz, Alisson L. da Rocha, Rafael L. Rovina, Gustavo D. Ferrari, Luciane C. Alberici, Fernando M. Simabuco, Giovana R. Teixeira, José R. Pauli, Leandro P. de Moura, Dennys E. Cintra, Eduardo R. Ropelle, Ellen C. Freitas, Donato A. Rivas, and Adelino S. R. da Silva
Subjects: genetic deletion, Nr1d1, autophagic flux, mitochondria, C2C12 cells, pharmacological treatment, Immunologic diseases. Allergy, RC581-607
Abstract: Interleukin 6 (IL-6) acts as a pro and anti-inflammatory cytokine, has an intense correlation with exercise intensity, and activates various pathways such as autophagy and mitochondrial unfolded protein response. Also, IL-6 is interconnected to circadian clock-related inflammation and can be suppressed by the nuclear receptor subfamily 1, group D, member 1 (Nr1d1, protein product REV-ERBα). Since IL-6 is linked to physical exercise-modulated metabolic pathways such as autophagy and mitochondrial metabolism, we investigated the relationship of IL-6 with REV-ERBα in the adaptations of these molecular pathways in response to acute intense physical exercise in skeletal muscle. The present study was divided into three experiments. In the first one, wild-type (WT) and IL-6 knockout (IL-6 KO) mice were divided into three groups: Basal time (Basal; sacrificed before the acute exercise), 1 hour (1hr post-Ex; sacrificed 1 hour after the acute exercise), and 3 hours (3hr post-Ex; sacrificed 3 hours after the acute exercise). In the second experiment, C2C12 cells received IL-6 physiological concentrations or REV-ERBα agonist, SR9009. In the last experiment, WT mice received SR9009 injections. After the protocols, the gastrocnemius muscle or the cells were collected for reverse transcription-quantitative polymerase chain reaction (RTq-PCR) and immunoblotting techniques. In summary, the downregulation of REV-ERBα, autophagic flux, and most mitochondrial genes was verified in the IL-6 KO mice independent of exercise. The WT and IL-6 KO treated with SR9009 showed an upregulation of autophagic genes. C2C12 cells receiving IL-6 did not modulate the Nr1d1 mRNA levels but upregulated the expression of some mitochondrial genes. However, when treated with SR9009, IL-6 and mitochondrial gene expression were upregulated in C2C12 cells. The autophagic flux in C2C12 suggest the participation of REV-ERBα protein in the IL-6-induced autophagy. In conclusion, the present study verified that the adaptations required through physical exercise (increases in mitochondrial content and improvement of autophagy machinery) might be intermediated by an interaction between IL-6 and REVERBα.
Published: 2022
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28. Inhibition of microglial β-glucocerebrosidase hampers the microglia-mediated antioxidant and protective response in neurons

Author: Electra Brunialti, Alessandro Villa, Marianna Mekhaeil, Federica Mornata, Elisabetta Vegeto, Adriana Maggi, Donato A. Di Monte, and Paolo Ciana
Subjects: Parkinson’s disease, Gaucher’s disease, microglia, NFE2L2, Neuroprotection, Neurology. Diseases of the nervous system, RC346-429
Abstract: Abstract Background Homozygotic mutations in the GBA gene cause Gaucher’s disease; moreover, both patients and heterozygotic carriers have been associated with 20- to 30-fold increased risk of developing Parkinson’s disease. In homozygosis, these mutations impair the activity of β-glucocerebrosidase, the enzyme encoded by GBA, and generate a lysosomal disorder in macrophages, which changes morphology towards an engorged phenotype, considered the hallmark of Gaucher’s disease. Notwithstanding the key role of macrophages in this disease, most of the effects in the brain have been attributed to the β-glucocerebrosidase deficit in neurons, while a microglial phenotype for these mutations has never been reported. Methods We applied the bioluminescence imaging technology, immunohistochemistry and gene expression analysis to investigate the consequences of microglial β-glucocerebrosidase inhibition in the brain of reporter mice, in primary neuron/microglia cocultures and in cell lines. The use of primary cells from reporter mice allowed for the first time, to discriminate in cocultures neuronal from microglial responses consequent to the β-glucocerebrosidase inhibition; results were finally confirmed by pharmacological depletion of microglia from the brain of mice. Results Our data demonstrate the existence of a novel neuroprotective mechanism mediated by a direct microglia-to-neuron contact supported by functional actin structures. This cellular contact stimulates the nuclear factor erythroid 2-related factor 2 activity in neurons, a key signal involved in drug detoxification, redox balance, metabolism, autophagy, lysosomal biogenesis, mitochondrial dysfunctions, and neuroinflammation. The central role played by microglia in this neuronal response in vivo was proven by depletion of the lineage in the brain of reporter mice. Pharmacological inhibition of microglial β-glucocerebrosidase was proven to induce morphological changes, to turn on an anti-inflammatory/repairing pathway, and to hinder the microglia ability to activate the nuclear factor erythroid 2-related factor 2 response, thus increasing the neuronal susceptibility to neurotoxins. Conclusion This mechanism provides a possible explanation for the increased risk of neurodegeneration observed in carriers of GBA mutations and suggest novel therapeutic strategies designed to revert the microglial phenotype associated with β-glucocerebrosidase inhibition, aimed at resetting the protective microglia-to-neuron communication.
Published: 2021
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29. Comparison of three reconstruction algorithms for low-dose phase-contrast computed tomography of the breast with synchrotron radiation

Author: Donato, Sandro, Caputo, Simone, Brombal, Luca, Golosio, Bruno, Longo, Renata, Tromba, Giuliana, Agostino, Raffaele G., Greco, Gianluigi, Arhatari, Benedicta D., Hall, Chris, Maksimenko, Anton, Hausermann, Daniel, Lockie, Darren, Fox, Jane, Kumar, Beena, Lewis, Sarah, Brennan, Patrick C., Quiney, Harry M., Taba, Seyedamir Tavakoli, and Gureyev, Timur E.
Subjects: Physics - Medical Physics
Abstract: Three different computed tomography (CT) reconstruction algorithms: Filtered Back Projection (FBP), Unified Tomographic Reconstruction (UTR) and customized Simultaneous Algebraic Reconstruction Technique (cSART), have been systematically compared and evaluated using experimental data from CT scans of ten fresh mastectomy samples collected at the Imaging and Medical beamline of the Australian Synchrotron. All the scans were collected at the mean glandular dose of 2 mGy, using monochromatic X-rays with 32 keV energy, flat-panel detectors with 0.1 mm pixels and 6 meter distance between the rotation stage and the detector. Paganin's phase retrieval method was used in conjunction with all three CT reconstruction algorithms. The reconstructed images were compared in terms of the objective image quality characteristics, including spatial resolution, contrast, signal-to-noise, and contrast-to-noise ratios. The images were also evaluated by seven experienced medical imaging specialists, rating perceptible contrast, sharpness of tissue interfaces, image noise, calcification visibility and overall image quality. Of the three compared algorithms, cSART was clearly superior to UTR and FBP in terms of most measured objective image quality characteristics. At the same time, the results of the subjective quality evaluation consistently favoured the images reconstructed by FBP, followed by UTR, with cSART receiving lower scores on average. We argue that this apparent disagreement between the objective and subjective assessments of image quality can be explained by the importance assigned to image contrast in the subjective assessment, while the signal-to-noise ratio seemed to receive relatively low weighting. This study was conducted in preparation for phase-contrast breast CT imaging of live patients at Australian Synchrotron (Melbourne, Australia)., Comment: 21 pages, 5 figures, 2 tables
Published: 2024

30. Low-Energy Nuclear Recoil Calibration of XENONnT with a $^{88}$YBe Photoneutron Source

Author: XENON Collaboration, Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Ant, D., Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Ch, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-Garc, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kharbanda, P., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Marrod, T., Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Mor, K., Mosbacher, Y., Murra, M., M, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., Ram, D., Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., T, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., and Zhong, M.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: Characterizing low-energy (O(1keV)) nuclear recoils near the detector threshold is one of the major challenges for large direct dark matter detectors. To that end, we have successfully used a Yttrium-Beryllium photoneutron source that emits 152 keV neutrons for the calibration of the light and charge yields of the XENONnT experiment for the first time. After data selection, we accumulated 474 events from 183 hours of exposure with this source. The expected background was $55 \pm 12$ accidental coincidence events, estimated using a dedicated 152 hour background calibration run with a Yttrium-PVC gamma-only source and data-driven modeling. From these calibrations, we extracted the light yield and charge yield for liquid xenon at our field strength of 23 V/cm between 0.5 keV$_{\rm NR}$ and 5.0 keV$_{\rm NR}$ (nuclear recoil energy in keV). This calibration is crucial for accurately measuring the solar $^8$B neutrino coherent elastic neutrino-nucleus scattering and searching for light dark matter particles with masses below 12 GeV/c$^2$.
Published: 2024

31. Melting behavior of CaO at high temperature and pressure: a molecular dynamics study

Author: Menescardi, Francesca, Ceresoli, Davide, and Belmonte, Donato
Subjects: Condensed Matter - Materials Science, Physics - Chemical Physics
Abstract: The thermodynamic behavior of calcium oxide (\ce{CaO}) under high temperature and pressure conditions is critical for understanding the physics of planetary interiors. This study employs molecular dynamics (MD) simulations, including both classical and ab-initio approaches, to investigate the melting behavior of CaO. We calculate the melting temperature of \ce{CaO} by the void-nucleated melting and two-phase coexistence techniques, aiming to resolve discrepancies in experimental data on the melting point, which range from 2843~K to 3223~K in different studies due to the high reactivity and vapor pressure of the substance. The obtained results are $T_f = 3066\pm12$~K and $T_f = 2940\pm65$~K using the void-nucleated melting and the two-phase coexistence method, respectively. Additionally, we calculate the enthalpy of fusion and the high-pressure melting curve, for the first time without making any assumption on the Clapeyron slope. This is extremely important since in experiments the Claperyon slope of the melting curve is estimated from low pressure measurements and the overheating ratio (i.e. $\eta=\frac{T_s}{T_f}-1$, where $T_s$ represents the thermal instability limit corresponding to the homogeneous melting temperature of the solid) is often assumed to be constant in simulations. Our MD results show that $T_s$ increases more rapidly with pressure than $T_f$ and thus that the overheating ratio sensibly depends upon pressure. These findings contribute to the accurate modeling of the CaO phase diagram, which is essential for geochemistry, cosmochemistry, and materials science., Comment: 33 pages, 6 figures, 3 tables
Published: 2024
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32. Proximal Iteration for Nonlinear Adaptive Lasso

Author: Wycoff, Nathan, Singh, Lisa O., Arab, Ali, and Donato, Katharine M.
Subjects: Statistics - Machine Learning, Computer Science - Machine Learning
Abstract: Augmenting a smooth cost function with an $\ell_1$ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal gradient methods. However, one drawback of the $\ell_1$ penalty is bias: nonzero parameters are underestimated in magnitude, motivating techniques such as the Adaptive Lasso which endow each parameter with its own penalty coefficient. But it's not clear how these parameter-specific penalties should be set in complex models. In this article, we study the approach of treating the penalty coefficients as additional decision variables to be learned in a \textit{Maximum a Posteriori} manner, developing a proximal gradient approach to joint optimization of these together with the parameters of any differentiable cost function. Beyond reducing bias in estimates, this procedure can also encourage arbitrary sparsity structure via a prior on the penalty coefficients. We compare our method to implementations of specific sparsity structures for non-Gaussian regression on synthetic and real datasets, finding our more general method to be competitive in terms of both speed and accuracy. We then consider nonlinear models for two case studies: COVID-19 vaccination behavior and international refugee movement, highlighting the applicability of this approach to complex problems and intricate sparsity structures., Comment: Some of these results were previously presented in the Technical Report at arXiv:2211.05089
Published: 2024

33. The neutron veto of the XENONnT experiment: Results with demineralized water

Author: XENON Collaboration, Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morá, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Takeuchi, Y., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., and Zhong, M.
Subjects: Physics - Instrumentation and Detectors, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment
Abstract: Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) tags neutrons via their capture on gadolinium or hydrogen, which release $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the key features and the first results of the XENONnT NV when operated with demineralized water in the initial phase of the experiment. Its efficiency for detecting neutrons is $(82\pm 1)\,\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of $(53\pm 3)\,\%$ for the tagging of WIMP-like neutron signals, inside a tagging time window of $250\,\mathrm{\mu s}$ between TPC and NV, leading to a livetime loss of $1.6\,\%$ during the first science run of XENONnT.
Published: 2024

34. Topological analysis of brain dynamical signals indicates signatures of seizure susceptibility

Author: Lucas, Maxime, Francois, Damien, Mombaerts, Laurent, Donato, Cristina, Skupin, Alexander, and Proverbio, Daniele
Subjects: Physics - Physics and Society, Quantitative Biology - Quantitative Methods
Abstract: Epilepsy is known to drastically alter brain dynamics during seizures (ictal periods), but its effects on background (non-ictal) brain dynamics remain poorly understood. To investigate this, we analyzed an in-house dataset of brain activity recordings from epileptic zebrafish, focusing on two controlled genetic conditions across two fishlines. After using machine learning to segment and label recordings, we applied time-delay embedding and Persistent Homology -- a noise-robust method from Topological Data Analysis (TDA) -- to uncover topological patterns in brain activity. We find that ictal and non-ictal periods can be distinguished based on the topology of their dynamics, independent of genetic condition or fishline, which validates our approach. Remarkably, within a single wild-type fishline, we identified topological differences in non-ictal periods between seizure-prone and seizure-free individuals. These findings suggest that epilepsy leaves detectable topological signatures in brain dynamics even outside of ictal periods. Overall, this study demonstrates the utility of TDA as a quantitative framework to screen for topological markers of epileptic susceptibility, with potential applications across species.
Published: 2024

35. Scalar waves in a Topological Star spacetime: self-force and radiative losses

Author: Bianchi, Massimo, Bini, Donato, and Di Russo, Giorgio
Subjects: General Relativity and Quantum Cosmology
Abstract: We study the radiated energy by a scalar particle moving on a circular orbit (smeared in the extra dimension) in the spacetime of a topological star, extending a previous study [Phys. Rev. D {\bf 110}, 084077 (2024)]. We discuss motion in the presence of self-force effects too., Comment: 17 pages, 5 figures, revtex macros
Published: 2024

36. Task Singular Vectors: Reducing Task Interference in Model Merging

Author: Gargiulo, Antonio Andrea, Crisostomi, Donato, Bucarelli, Maria Sofia, Scardapane, Simone, Silvestri, Fabrizio, and Rodolà, Emanuele
Subjects: Computer Science - Machine Learning, Statistics - Machine Learning, I.5.1, I.4.2, I.2.10
Abstract: Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is susceptible to task interference. In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular value decomposition. In particular, we concentrate on the resulting singular vectors, which we refer to as Task Singular Vectors (TSV). Recognizing that layer task matrices are often low-rank, we propose TSV-Compress (TSV-C), a simple procedure that compresses them to 10% of their original size while retaining 99% of accuracy. We further leverage this low-rank space to define a new measure of task interference based on the interaction of singular vectors from different tasks. Building on these findings, we introduce TSV-Merge (TSV-M), a novel model merging approach that combines compression with interference reduction, significantly outperforming existing methods., Comment: 20 pages, 17 figures, 6 tables; major changes of figures' style, minor fixes, fixed typos
Published: 2024

37. Resistive Fine Granularity Micromegas: Characterization and Performance for Different Spark Protection Resistive Schemes

Author: Alviggi, M., Biglietti, M., Camerlingo, M. T., Della Pietra, M., Di Donato, C., Di Nardo, R., Franchellucci, S., Iengo, P., Iodice, M., Petrucci, F., Sekhniaidze, G., and Sessa, M.
Subjects: Physics - Instrumentation and Detectors
Abstract: The aim of the presented work is the development of single-stage amplification resistive Micro Pattern Gas Detectors (MPGD) based on Micromegas technology with the following characteristics: ability to efficiently operate up to 10 MHz/cm$^2$ counting rate; scalability to large areas; fine granularity readout with small pads of the order of mm$^2$; good spatial and time resolutions (below 100 $\mu$m and 10 ns, respectively). The miniaturization of the readout elements and the optimization of the spark protection system, as well as the stability and robustness under operation, are the primary challenges of the project. Two families of resistive patterns were realized using different techniques: pad-patterned embedded resistors and double-layer of Diamond Like Carbon (DLC) structures foils. Their main difference is that for the embedded resistors the charge is evacuated through independent pads, for double-layer DLC the resistive layers are continuous and uniform and the charge is evacuated through a network of dot-connections, several millimetres apart. Using the DLC technique, a medium-size detector with an active area of 400 cm$^2$ was recently built and tested, with the main results reported in this paper. Additionally, a large module (50x40 cm$^2$ active area), suitable for tiling large systems in future experiments, has been successfully realised and is currently undergoing testing and performance studies. The characterization and performance studies of the detectors were conducted using radioactive sources and an X-rays generator, with the detectors operated with various gas mixtures. A comparison of the results obtained with different resistive layouts and configurations is provided, with a particular focus on the response under high-rate exposure. Key results on tracking and timing performance from test-beam data for the latest constructed medium-size detector are also presented., Comment: submitted to JINST
Published: 2024

38. Search for Light Dark Matter in Low-Energy Ionization Signals from XENONnT

Author: Aprile, E., Aalbers, J., Abe, K., Maouloud, S. Ahmed, Althueser, L., Andrieu, B., Angelino, E., Martin, D. Antón, Arneodo, F., Baudis, L., Bazyk, M., Bellagamba, L., Biondi, R., Bismark, A., Boese, K., Brown, A., Bruno, G., Budnik, R., Cai, C., Capelli, C., Cardoso, J. M. R., Chávez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Eitel, K., Morabit, S. el, Elykov, A., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Ghosh, S., Giacomobono, R., Glade-Beucke, R., Grandi, L., Grigat, J., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Higuera, A., Hils, C., Hoetzsch, L., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., Joerg, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Kobayashi, M., Koke, D., Kopec, A., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, S., Liang, S., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Liu, M., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Merz, J., Messina, M., Michael, A., Miuchi, K., Molinario, A., Moriyama, S., Morå, K., Mosbacher, Y., Murra, M., Müller, J., Ni, K., Oberlack, U., Paetsch, B., Pan, Y., Pellegrini, Q., Peres, R., Peters, C., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qin, J., García, D. Ramírez, Rajado, M., Singh, R., Sanchez, L., Santos, J. M. F. dos, Sarnoff, I., Sartorelli, G., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Shi, S., Shi, J., Silva, M., Simgen, H., Szyszka, C., Takeda, A., Tan, P. -L., Thers, D., Toschi, F., Trinchero, G., Tunnell, C. D., Tönnies, F., Valerius, K., Vecchi, S., Vetter, S., Solar, F. I. Villazon, Volta, G., Weinheimer, C., Weiss, M., Wenz, D., Wittweg, C., Wu, V. H. S., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., and Zhong, M.
Subjects: High Energy Physics - Experiment
Abstract: We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within 10-20\,MeV/$c^2$ and electron absorption of axion-like particles and dark photons for $m_\chi$ below 0.186\,keV/$c^2$., Comment: 9 pages, 5 figures
Published: 2024

39. Boosting thermalization of classical and quantum many-body systems

Author: Chen, Jin-Fu, Rai, Kshiti Sneh, Emonts, Patrick, Farina, Donato, Płodzień, Marcin, Grzybowski, Przemyslaw, Lewenstein, Maciej, and Tura, Jordi
Subjects: Quantum Physics, Condensed Matter - Statistical Mechanics
Abstract: Understanding and optimizing the relaxation dynamics of many-body systems is essential for both foundational studies in quantum thermodynamics, as well as for applications including quantum simulation and quantum computing. Efficient thermal state preparation of a given many-body Hamiltonian depends on the spectrum of Lindbladian defined via jump operators. In this work we provide a systematic framework allowing construction of an optimal Lindbladian resulting in fast thermal state preparation for a considered Hamiltonian. Importantly, the optimal Lindbladian respects the symmetries of the target equilibrium state. We demonstrate the potential of our framework and optimization with the kinetic Ising model and the Lipkin-Meshkov-Glick model, showcasing efficient thermal state preparation., Comment: 17 pages, 4 figures
Published: 2024

40. ATM: Improving Model Merging by Alternating Tuning and Merging

Author: Zhou, Luca, Solombrino, Daniele, Crisostomi, Donato, Bucarelli, Maria Sofia, Silvestri, Fabrizio, and Rodolà, Emanuele
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Model merging has recently emerged as a cost-efficient paradigm for multi-task learning. Among current approaches, task arithmetic stands out for its simplicity and effectiveness. In this paper, we motivate the effectiveness of task vectors by linking them to multi-task gradients. We show that in a single-epoch scenario, task vectors are mathematically equivalent to the gradients obtained via gradient descent in a multi-task setting, and still approximate these gradients in subsequent epochs. Furthermore, we show that task vectors perform optimally when equality is maintained, and their effectiveness is largely driven by the first epoch's gradient. Building on this insight, we propose viewing model merging as a single step in an iterative process that Alternates between Tuning and Merging (ATM). This method acts as a bridge between model merging and multi-task gradient descent, achieving state-of-the-art results with the same data and computational requirements. We extensively evaluate ATM across diverse settings, achieving up to 20% higher accuracy in computer vision and NLP tasks, compared to the best baselines. Finally, we provide both empirical and theoretical support for its effectiveness, demonstrating increased orthogonality between task vectors and proving that ATM minimizes an upper bound on the loss obtained by jointly finetuning all tasks., Comment: Main paper: 10 Pages, 11 figures, 2 tables
Published: 2024

41. Neutrinoless Double Beta Decay Sensitivity of the XLZD Rare Event Observatory

Author: XLZD Collaboration, Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Akerib, D. S., Musalhi, A. K. Al, Alder, F., Althueser, L., Amaral, D. W. P., Amarasinghe, C. S., Ames, A., Andrieu, B., Angelides, N., Angelino, E., Antunovic, B., Aprile, E., Araújo, H. M., Armstrong, J. E., Arthurs, M., Babicz, M., Bajpai, D., Baker, A., Balzer, M., Bang, J., Barberio, E., Bargemann, J. W., Barillier, E., Basharina-Freshville, A., Baudis, L., Bauer, D., Bazyk, M., Beattie, K., Beaupere, N., Bell, N. F., Bellagamba, L., Benson, T., Bhatti, A., Biesiadzinski, T. P., Biondi, R., Biondi, Y., Birch, H. J., Bishop, E., Bismark, A., Boehm, C., Boese, K., Bolotnikov, A., Brás, P., Braun, R., Breskin, A., Brew, C. A. J., Brommer, S., Brown, A., Bruni, G., Budnik, R., Burdin, S., Cai, C., Capelli, C., Carini, G., Carmona-Benitez, M. C., Carter, M., Chauvin, A., Chawla, A., Chen, H., Cherwinka, J. J., Chin, Y. T., Chott, N. I., Chavez, A. P. Cimental, Clark, K., Colijn, A. P., Colling, D. J., Conrad, J., Converse, M. V., Coronel, R., Costanzo, D., Cottle, A., Cox, G., Cuenca-García, J. J., Curran, D., Cussans, D., D'Andrea, V., Garcia, L. C. Daniel, Darlington, I., Dave, S., David, A., Davies, G. J., Decowski, M. P., Deisting, A., Delgaudio, J., Dey, S., Di Donato, C., Di Felice, L., Di Gangi, P., Diglio, S., Ding, C., Dobson, J. E. Y., Doerenkamp, M., Drexlin, G., Druszkiewicz, E., Dunbar, C. L., Eitel, K., Elykov, A., Engel, R., Eriksen, S. R., Fayer, S., Fearon, N. M., Ferella, A. D., Ferrari, C., Fieldhouse, N., Fischer, H., Flaecher, H., Flehmke, T., Flierman, M., Fraser, E. D., Fruth, T. M. A., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gaitskell, R. J., Gallice, N., Galloway, M., Gao, F., Garroum, N., Geffre, A., Genovesi, J., Ghag, C., Ghosh, S., Giacomobono, R., Gibbons, R., Girard, F., Glade-Beucke, R., Glück, F., Gokhale, S., Grandi, L., Green, J., Grigat, J., van der Grinten, M. G. D., Größle, R., Guan, H., Guida, M., Gyorgy, P., Haiston, J. J., Hall, C. R., Hall, T., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M., Hertel, S. A., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Homenides, G. J., Hood, N. F., Horn, M., Huang, D. Q., Hughes, S., Hunt, D., Iacovacci, M., Itow, Y., Jacquet, E., Jakob, J., James, R. S., Joerg, F., Jones, S., Kaboth, A. C., Kahlert, F., Kamaha, A. C., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kemp-Russell, P., Khaitan, D., Kharbanda, P., Kilminster, B., Kim, J., Kirk, R., Kleifges, M., Klute, M., Kobayashi, M., Kodroff, D., Koke, D., Kopec, A., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., von Krosigk, B., Kudryavtsev, V. A., Kuger, F., Kurita, N., Landsman, H., Lang, R. F., Lawes, C., Lee, J., Lehnert, B., Leonard, D. S., Lesko, K. T., Levinson, L., Li, A., Li, I., Li, S., Liang, S., Liang, Z., Lin, J., Lin, Y. -T., Lindemann, S., Linden, S., Lindner, M., Lindote, A., Lippincott, W. H., Liu, K., Loizeau, J., Lombardi, F., Lopes, J. A. M., Lopes, M. I., Lorenzon, W., Loutit, M., Lu, C., Lucchetti, G. M., Luce, T., Luitz, S., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Majewski, P. A., Manalaysay, A., Mancuso, A., Manenti, L., Mannino, R. L., Marignetti, F., Marley, T., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Maupin, C., McCabe, C., McCarthy, M. E., McKinsey, D. N., McLaughlin, J. B., Melchiorre, A., Menéndez, J., Messina, M., Miller, E. H., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Mizrachi, E., Molinario, A., Monteiro, C. M. B., Monzani, M. E., Morå, K., Moriyama, S., Morrison, E., Morteau, E., Mosbacher, Y., Mount, B. J., Müller, J., Murdy, M., Murphy, A. St. J., Murra, M., Naylor, A., Nelson, H. N., Neves, F., Newstead, J. L., Nguyen, A., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Olcina, I., Oliver-Mallory, K. C., Gann, G. D. Orebi, Orpwood, J., Ostrowskiy, I., Ouahada, S., Oyulmaz, K., Paetsch, B., Palladino, K. J., Palmer, J., Pan, Y., Pandurovic, M., Pannifer, N. J., Paramesvaran, S., Patton, S. J., Pellegrini, Q., Penning, B., Pereira, G., Peres, R., Perry, E., Pershing, T., Piastra, F., Pienaar, J., Piepke, A., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qie, Y., Qin, J., Radeka, S., Radeka, V., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Reichenbacher, J., Rhyne, C. A., Richards, A., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Roy, A., Rushton, T., Rynders, D., Saakyan, R., Sanchez, L., Sanchez-Lucas, P., Santone, D., Santos, J. M. F. dos, Sartorelli, G., Sazzad, A. B. M. R., Scaffidi, A., Schnee, R. W., Schreiner, J., Schulte, P., Schulze, H., Eißing, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shaw, S., Shen, W., Sherman, L., Shi, S., Shi, S. Y., Shimada, T., Shutt, T., Silk, J. J., Silva, C., Simgen, H., Sinev, G., Singh, R., Siniscalco, J., Solmaz, M., Solovov, V. N., Song, Z., Sorensen, P., Soria, J., Stanley, O., Steidl, M., Stenhouse, T., Stevens, A., Stifter, K., Sumner, T. J., Takeda, A., Tan, P. -L., Taylor, D. J., Taylor, W. C., Thers, D., Thümmler, T., Tiedt, D. R., Tönnies, F., Tong, Z., Toschi, F., Tovey, D. R., Tranter, J., Trask, M., Trinchero, G., Tripathi, M., Tronstad, D. R., Trotta, R., Tunnell, C. D., Urquijo, P., Usón, A., Utoyama, M., Vaitkus, A. C., Valentino, O., Valerius, K., Vecchi, S., Velan, V., Vetter, S., de Viveiros, L., Volta, G., Vorkapic, D., Wang, A., Wang, J. J., Wang, W., Wang, Y., Waters, D., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Whitis, T. J., Wild, K., Williams, M., Wilson, M., Wilson, S. T., Wittweg, C., Wolf, J., Wolfs, F. L. H., Woodford, S., Woodward, D., Worcester, M., Wright, C. J., Wu, V. H. S., üstling, S. W, Wurm, M., Xia, Q., Xing, Y., Xu, D., Xu, J., Xu, Y., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yeh, M., Yu, B., Zavattini, G., Zha, W., Zhong, M., and Zuber, K.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract: The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures
Published: 2024

42. The XLZD Design Book: Towards the Next-Generation Liquid Xenon Observatory for Dark Matter and Neutrino Physics

Author: XLZD Collaboration, Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Akerib, D. S., Musalhi, A. K. Al, Alder, F., Althueser, L., Amaral, D. W. P., Amarasinghe, C. S., Ames, A., Andrieu, B., Angelides, N., Angelino, E., Antunovic, B., Aprile, E., Araújo, H. M., Armstrong, J. E., Arthurs, M., Babicz, M., Bajpai, D., Baker, A., Balzer, M., Bang, J., Barberio, E., Bargemann, J. W., Barillier, E., Basharina-Freshville, A., Baudis, L., Bauer, D., Bazyk, M., Beattie, K., Beaupere, N., Bell, N. F., Bellagamba, L., Benson, T., Bhatti, A., Biesiadzinski, T. P., Biondi, R., Biondi, Y., Birch, H. J., Bishop, E., Bismark, A., Boehm, C., Boese, K., Bolotnikov, A., Brás, P., Braun, R., Breskin, A., Brew, C. A. J., Brommer, S., Brown, A., Bruni, G., Budnik, R., Burdin, S., Cai, C., Capelli, C., Carini, G., Carmona-Benitez, M. C., Carter, M., Chauvin, A., Chawla, A., Chen, H., Cherwinka, J. J., Chin, Y. T., Chott, N. I., Chavez, A. P. Cimental, Clark, K., Colijn, A. P., Colling, D. J., Conrad, J., Converse, M. V., Coronel, R., Costanzo, D., Cottle, A., Cox, G., Cuenca-García, J. J., Curran, D., Cussans, D., D'Andrea, V., Garcia, L. C. Daniel, Darlington, I., Dave, S., David, A., Davies, G. J., Decowski, M. P., Deisting, A., Delgaudio, J., Dey, S., Di Donato, C., Di Felice, L., Di Gangi, P., Diglio, S., Ding, C., Dobson, J. E. Y., Doerenkamp, M., Drexlin, G., Druszkiewicz, E., Dunbar, C. L., Eitel, K., Elykov, A., Engel, R., Eriksen, S. R., Fayer, S., Fearon, N. M., Ferella, A. D., Ferrari, C., Fieldhouse, N., Fischer, H., Flaecher, H., Flehmke, T., Flierman, M., Fraser, E. D., Fruth, T. M. A., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Gaitskell, R. J., Gallice, N., Galloway, M., Gao, F., Garroum, N., Geffre, A., Genovesi, J., Ghag, C., Ghosh, S., Giacomobono, R., Gibbons, R., Girard, F., Glade-Beucke, R., Glück, F., Gokhale, S., Grandi, L., Green, J., Grigat, J., van der Grinten, M. G. D., Größle, R., Guan, H., Guida, M., Gyorgy, P., Haiston, J. J., Hall, C. R., Hall, T., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Hartigan-O'Connor, E., Haselschwardt, S. J., Hernandez, M., Hertel, S. A., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Homenides, G. J., Hood, N. F., Horn, M., Huang, D. Q., Hughes, S., Hunt, D., Iacovacci, M., Itow, Y., Jacquet, E., Jakob, J., James, R. S., Joerg, F., Jones, S., Kaboth, A. C., Kahlert, F., Kamaha, A. C., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kemp-Russell, P., Khaitan, D., Kharbanda, P., Kilminster, B., Kim, J., Kirk, R., Kleifges, M., Klute, M., Kobayashi, M., Kodroff, D., Koke, D., Kopec, A., Korolkova, E. V., Kraus, H., Kravitz, S., Kreczko, L., von Krosigk, B., Kudryavtsev, V. A., Kuger, F., Kurita, N., Landsman, H., Lang, R. F., Lawes, C., Lee, J., Lehnert, B., Leonard, D. S., Lesko, K. T., Levinson, L., Li, A., Li, I., Li, S., Liang, S., Liang, Z., Lin, J., Lin, Y. -T., Lindemann, S., Linden, S., Lindner, M., Lindote, A., Lippincott, W. H., Liu, K., Loizeau, J., Lombardi, F., Lopes, J. A. M., Lopes, M. I., Lorenzon, W., Loutit, M., Lu, C., Lucchetti, G. M., Luce, T., Luitz, S., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Majewski, P. A., Manalaysay, A., Mancuso, A., Manenti, L., Mannino, R. L., Marignetti, F., Marley, T., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Maupin, C., McCabe, C., McCarthy, M. E., McKinsey, D. N., McLaughlin, J. B., Melchiorre, A., Menéndez, J., Messina, M., Miller, E. H., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Mizrachi, E., Molinario, A., Monteiro, C. M. B., Monzani, M. E., Morå, K., Moriyama, S., Morrison, E., Morteau, E., Mosbacher, Y., Mount, B. J., Müller, J., Murdy, M., Murphy, A. St. J., Murra, M., Naylor, A., Nelson, H. N., Neves, F., Newstead, J. L., Nguyen, A., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Olcina, I., Oliver-Mallory, K. C., Gann, G. D. Orebi, Orpwood, J., Ostrowskiy, I., Ouahada, S., Oyulmaz, K., Paetsch, B., Palladino, K. J., Palmer, J., Pan, Y., Pandurovic, M., Pannifer, N. J., Paramesvaran, S., Patton, S. J., Pellegrini, Q., Penning, B., Pereira, G., Peres, R., Perry, E., Pershing, T., Piastra, F., Pienaar, J., Piepke, A., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qie, Y., Qin, J., Radeka, S., Radeka, V., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Reichenbacher, J., Rhyne, C. A., Richards, A., Rischbieter, G. R. C., Riyat, H. S., Rosero, R., Roy, A., Rushton, T., Rynders, D., Saakyan, R., Sanchez, L., Sanchez-Lucas, P., Santone, D., Santos, J. M. F. dos, Sartorelli, G., Sazzad, A. B. M. R., Scaffidi, A., Schnee, R. W., Schreiner, J., Schulte, P., Schulze, H., Eißing, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shaw, S., Shen, W., Sherman, L., Shi, S., Shi, S. Y., Shimada, T., Shutt, T., Silk, J. J., Silva, C., Simgen, H., Sinev, G., Singh, R., Siniscalco, J., Solmaz, M., Solovov, V. N., Song, Z., Sorensen, P., Soria, J., Stanley, O., Steidl, M., Stenhouse, T., Stevens, A., Stifter, K., Sumner, T. J., Takeda, A., Tan, P. -L., Taylor, D. J., Taylor, W. C., Thers, D., Thümmler, T., Tiedt, D. R., Tönnies, F., Tong, Z., Toschi, F., Tovey, D. R., Tranter, J., Trask, M., Trinchero, G., Tripathi, M., Tronstad, D. R., Trotta, R., Tunnell, C. D., Urquijo, P., Usón, A., Utoyama, M., Vaitkus, A. C., Valentino, O., Valerius, K., Vecchi, S., Velan, V., Vetter, S., de Viveiros, L., Volta, G., Vorkapic, D., Wang, A., Wang, J. J., Wang, W., Wang, Y., Waters, D., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Whitis, T. J., Wild, K., Williams, M., Wilson, M., Wilson, S. T., Wittweg, C., Wolf, J., Wolfs, F. L. H., Woodford, S., Woodward, D., Worcester, M., Wright, C. J., Wu, V. H. S., üstling, S. W, Wurm, M., Xia, Q., Xing, Y., Xu, D., Xu, J., Xu, Y., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yeh, M., Yu, B., Zavattini, G., Zha, W., Zhong, M., and Zuber, K.
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract: This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures
Published: 2024

43. Certifying steady-state properties of open quantum systems

Author: Mortimer, Luke, Farina, Donato, Di Bello, Grazia, Jansen, David, Leitherer, Andreas, Mujal, Pere, and Acín, Antonio
Subjects: Quantum Physics
Abstract: Estimating steady state properties of open quantum systems is a crucial problem in quantum technology. In this work, we show how to derive in a scalable way using semi-definite programming certified bounds on the expectation value of an arbitrary observable of interest on the steady state of Lindbladian dynamics. We illustrate our method on a series of many-body systems, including a one-dimensional chain and a two-dimensional ladder, and benchmark it with state-of-the-art tensor-network approaches. For the tested models, only modest computational effort is needed to obtain certified bounds whose precision is comparable to variational methods, Comment: See also arXiv:2410.07384
Published: 2024

44. Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection

Author: Francesconi, Arianna, di Biase, Lazzaro, Cappetta, Donato, Rebecchi, Fabio, Soda, Paolo, Sicilia, Rosa, and Guarrasi, Valerio
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer's Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD.
Published: 2024

45. Disclosing the catalog pulsars dominating the Galactic positron flux

Author: Orusa, Luca, Manconi, Silvia, Donato, Fiorenza, and Di Mauro, Mattia
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: The cosmic-ray flux of positrons is measured with high precision by the space-borne particle spectrometer AMS-02. The hypothesis that pulsars and their nebulae can significantly contribute to the excess of the AMS-02 positron flux has been consolidated after the observation of a $\gamma$-ray emission at GeV and TeV energies of a few degree size around a few sources, that provide indirect evidence that electron and positron pairs are accelerated to very high energies from these sources. By modeling the emission from pulsars in the ATNF catalog, we find that combinations of positron emission from cataloged pulsars and secondary production can fit the observed AMS-02 data. Our results show that a small number of nearby, middle-aged pulsars, particularly B1055-52, Geminga (J0633+1746), and Monogem (B0656+14), dominate the positron emission, contributing up to 80\% of the flux at energies above 100 GeV. From the fit to the data, we obtain a list of the most important sources for which we recommend multi-wavelength follow-up observations, particularly in the $\gamma$-ray and X-ray bands, to further constrain the injection and diffusion properties of positrons., Comment: 29 pages, 8 figures. Few comments and clarifications added, results unchanged. Matches version published by JCAP
Published: 2024
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46. Model Uncertainty and Missing Data: An Objective Bayesian Perspective

Author: García-Donato, Gonzalo, Castellanos, María Eugenia, Cabras, Stefano, Quirós, Alicia, and Forte, Anabel
Subjects: Statistics - Methodology
Abstract: The interplay between missing data and model uncertainty -- two classic statistical problems -- leads to primary questions that we formally address from an objective Bayesian perspective. For the general regression problem, we discuss the probabilistic justification of Rubin's rules applied to the usual components of Bayesian variable selection, arguing that prior predictive marginals should be central to the pursued methodology. In the regression settings, we explore the conditions of prior distributions that make the missing data mechanism ignorable. Moreover, when comparing multiple linear models, we provide a complete methodology for dealing with special cases, such as variable selection or uncertainty regarding model errors. In numerous simulation experiments, we demonstrate that our method outperforms or equals others, in consistently producing results close to those obtained using the full dataset. In general, the difference increases with the percentage of missing data and the correlation between the variables used for imputation. Finally, we summarize possible directions for future research.
Published: 2024

47. Conservative binary dynamics beyond order $\alpha^5$ in electrodynamics

Author: Bini, Donato and Damour, Thibault
Subjects: General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract: We compute the conservative scattering angle of two classical charged particles at the sixth order in electromagnetic coupling, and at the fourth order in velocity, thereby going beyond the current state of the art [fifth order in coupling, derived by Bern {\it et al.}, Phys. Rev. Lett. \textbf{132}, 251601 (2024)]. Our result is obtained by using the electromagnetic version of the Effective One-Body formalism to transfer information from the exact circular binary-charge solution of Schild [Phys. Rev. \textbf{131}, 2762 (1963)] to the post-Lorentzian expansion of the scattering angle., Comment: 15 pages, 1 figure
Published: 2024

48. Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline

Author: Aalbers, J., Abe, K., Adrover, M., Maouloud, S. Ahmed, Althueser, L., Amaral, D. W. P., Andrieu, B., Angelino, E., Martin, D. Antón, Antunovic, B., Aprile, E., Babicz, M., Bajpai, D., Balzer, M., Barberio, E., Baudis, L., Bazyk, M., Bell, N. F., Bellagamba, L., Biondi, R., Biondi, Y., Bismark, A., Boehm, C., Boese, K., Braun, R., Breskin, A., Brommer, S., Brown, A., Bruni, G., Budnik, R., Cai, C., Capelli, C., Chauvin, A., Chavez, A. P. Cimental, Colijn, A. P., Conrad, J., Cuenca-García, J. J., D'Andrea, V., Garcia, L. C. Daniel, Decowski, M. P., Deisting, A., Di Donato, C., Di Gangi, P., Diglio, S., Doerenkamp, M., Drexlin, G., Eitel, K., Elykov, A., Engel, R., Ferella, A. D., Ferrari, C., Fischer, H., Flehmke, T., Flierman, M., Fujikawa, K., Fulgione, W., Fuselli, C., Gaemers, P., Gaior, R., Galloway, M., Gao, F., Garroum, N., Giacomobono, R., Girard, F., Glade-Beucke, R., Glück, F., Grandi, L., Grigat, J., Größle, R., Guan, H., Guida, M., Gyorgy, P., Hammann, R., Hannen, V., Hansmann-Menzemer, S., Hargittai, N., Higuera, A., Hils, C., Hiraoka, K., Hoetzsch, L., Hoferichter, M., Hood, N. F., Iacovacci, M., Itow, Y., Jakob, J., James, R. S., Joerg, F., Kahlert, F., Kaminaga, Y., Kara, M., Kavrigin, P., Kazama, S., Keller, M., Kharbanda, P., Kilminster, B., Kleifges, M., Klute, M., Kobayashi, M., Koke, D., Kopec, A., von Krosigk, B., Kuger, F., LaCascio, L., Landsman, H., Lang, R. F., Levinson, L., Li, I., Li, A., Li, S., Liang, S., Liang, Z., Lin, Y. -T., Lindemann, S., Lindner, M., Liu, K., Loizeau, J., Lombardi, F., Long, J., Lopes, J. A. M., Lucchetti, G. M., Luce, T., Ma, Y., Macolino, C., Mahlstedt, J., Maier, B., Mancuso, A., Manenti, L., Marignetti, F., Undagoitia, T. Marrodán, Martens, K., Masbou, J., Masson, E., Mastroianni, S., Melchiorre, A., Menéndez, J., Messina, M., Milosovic, B., Milutinovic, S., Miuchi, K., Miyata, R., Molinario, A., Monteiro, C. M. B., Morå, K., Moriyama, S., Morteau, E., Mosbacher, Y., Müller, J., Murra, M., Newstead, J. L., Ni, K., O'Hare, C., Oberlack, U., Obradovic, M., Ostrowskiy, I., Ouahada, S., Paetsch, B., Pan, Y., Pandurovic, M., Pellegrini, Q., Peres, R., Piastra, F., Pienaar, J., Pierre, M., Plante, G., Pollmann, T. R., Principe, L., Qi, J., Qiao, K., Qin, J., Rajado, M., García, D. Ramírez, Ravindran, A., Razeto, A., Sanchez, L., Sanchez-Lucas, P., Sartorelli, G., Scaffidi, A., Schreiner, J., Schulte, P., Eißing, H. Schulze, Schumann, M., Schwenck, A., Schwenk, A., Lavina, L. Scotto, Selvi, M., Semeria, F., Shagin, P., Sharma, S., Shen, W., Shi, S. Y., Shimada, T., Simgen, H., Singh, R., Solmaz, M., Stanley, O., Steidl, M., Stevens, A., Takeda, A., Tan, P. -L., Thers, D., Thümmler, T., Tönnies, F., Toschi, F., Trinchero, G., Trotta, R., Tunnell, C. D., Urquijo, P., Utoyama, M., Valerius, K., Vecchi, S., Vetter, S., Volta, G., Vorkapic, D., Wang, W., Weerman, K. M., Weinheimer, C., Weiss, M., Wenz, D., Wilson, M., Wittweg, C., Wolf, J., Wu, V. H. S., Wüstling, S., Wurm, M., Xing, Y., Xu, D., Xu, Z., Yamashita, M., Yang, L., Ye, J., Yuan, L., Zavattini, G., Zhong, M., and Zuber, K.
Subjects: Physics - Instrumentation and Detectors
Abstract: We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time., Comment: 10 Figures, 3 Tables, 23 Pages (incl. references)
Published: 2024

49. A Comparison of Small Rodent Assemblages after a 20 Year Interval in the Alps

Author: Giulia Ferrari, Dino Scaravelli, Andrea Mustoni, Marco Armanini, Filippo Zibordi, Olivier Devineau, Francesca Cagnacci, Donato A. Grasso, and Federico Ossi
Subjects: assemblage composition, bank vole, Central-Eastern Italian Alps, range limits, small rodents, snow vole, Veterinary medicine, SF600-1100, Zoology, QL1-991
Abstract: Human-induced environmental alterations in the Alps may importantly affect small mammal species, but evidence in this sense is limited. We live-trapped small rodents in the Central-Eastern Italian Alps in three close-by habitat types (rocky scree, alpine grassland, and heath) at 2100 m a.s.l. during summer-fall, in 1997 and 2016. We compared small rodent assemblages through a Redundancy Detrended Analysis (RDA). In both surveys, we detected two specialist species, i.e., the common vole (Microtus arvalis) and the snow vole (Chionomys nivalis), and, unexpectedly, the forest generalist bank vole (Myodes glareolus). In 1997, grassland was mainly occupied by the common vole, while the bank vole and the snow vole were sympatric in the other habitats. In 2016, the snow vole was detected only in the scree, while other species did not show distribution changes. We discuss a series of hypotheses that might have driven the differences observed across decades, among which is a species-specific response to abiotic and biotic environmental alterations, with the alpine habitat specialist moving out of sub-optimal habitats. We encourage further research on this topic, e.g., via long-term longitudinal studies.
Published: 2023
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50. Vegetable Oil and Derivates Hydroprocessing Using Ni as Catalyst for the Production of Hydrocarbons

Author: Lívia C. T. Andrade, Germildo J. Muchave, Samia T. A. Maciel, Isabelly P. da Silva, Gabriel F. da Silva, João M. A. R. Almeida, and Donato A. G. Aranda
Subjects: Chemical engineering, TP155-156
Abstract: The aviation sector has become a considerable market for biofuels since they come from renewable sources and have characteristics that help to reduce pollution. Hydrocarbons production from vegetable oils and their derivates for use in diesel and aviation kerosene are a possible alternative route to reduce fossil fuels. With that in mind, this article aimed to develop nickel catalysts supported on γ-Al2O3, Nb2O5, and zeolites to submit them to the hydroprocessing of vegetable oils and derivatives in the production of hydrocarbons. With soy ester, reactions with the Ni/Al2O3 and Ni/Nb2O5 catalyst showed selectivity of 41.2 and 16.5%, respectively, at a temperature of 300°C and a reaction time of 7 h. Under the same conditions, hydroprocessing reactions for the soybean ester using Ni/Beta and Ni/HY zeolites promoted more excellent conversion (between 80 and 99%) than oxide catalysts and selectivity between 30 and 70% for Ni/Beta and Ni/HY, correspondently. Besides, zeolite catalysts showed high conversion at the higher temperature (340°C) and time (9 h), reaching 100% conversion and hydrocarbons selectivity of 76.8 and 61.9% for zeolite Beta and HY, respectively. Changing the raw material to fatty acids made it possible to notice that zeolite catalysts showed high selectivity reaching 100%. Given the excellent performance of catalysts in hydroprocessing reactions, it is possible to consider them a promising alternative route since they can reduce the production by applying transition metal as a catalyst instead of noble metals used in the industry.
Published: 2022
Full Text: View/download PDF

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