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1,006,326 results on '"Gabriel AS"'

1. Diretriz de Avaliação Cardiovascular Perioperatória da Sociedade Brasileira de Cardiologia – 2024

Author

Danielle Menosi Gualandro, Luciana Savoy Fornari, Bruno Caramelli, Alexandre Antonio Cunha Abizaid, Brenno Rizerio Gomes, Caio de Assis Moura Tavares, Caio Julio Cesar dos Santos Fernandes, Carisi Anne Polanczyk, Carlos Jardim, Carolina Leticia Zilli Vieira, Claudio Pinho, Daniela Calderaro, Dirk Schreen, Fabiana Goulart Marcondes-Braga, Fábio de Souza, Francisco Akira Malta Cardozo, Flavio Tarasoutchi, Gabriel Assis Lopes Carmo, Gabriel Kanhouche, José Jayme Galvão de Lima, Luciana Dornfeld Bichuette, Luciana Sacilotto, Luciano Ferreira Drager, Luciano Janussi Vacanti, Luis Henrique Wolff Gowdak, Marcelo Luiz Campos Vieira, Marcelo Luiz Floriano Melo Martins, Márcio Silva Miguel Lima, Marcos Pita Lottenberg, Márlon Juliano Romero Aliberti, Mauricio Felippi de Sá Marchi, Milena Ribeiro Paixão, Mucio Tavares de Oliveira Junior, Pai Ching Yu, Patricia Ramos Cury, Pedro Silvio Farsky, Ranna Santos Pessoa, Rinaldo Focaccia Siciliano, Tarso Augusto Duenhas Accorsi, Vinícius Machado Correia, and Wilson Mathias Junior

Subjects
Diseases of the circulatory (Cardiovascular) system, RC666-701
Published
2024
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2. Examining teachers’ perceptions of the impact of government of Ghana’s wi-fi technology program on teaching practices: an empirical study from the senior high schools in the cape coast metropolis

Author

Richard Kyere Asomah, Douglas Darko Agyei, Gabriel Assamah, and Kwaku Darko Amponsah

Subjects
Wi-fi technology, teacher’s learning experiences, online delivery of education, school’s organization and culture, teacher’s reaction, teacher’s use of wi-fi technology, Education (General), L7-991
Abstract

AbstractThe study discusses the impact of a nationwide deployment of Wi-Fi technology in some Senior High Technical Vocational Schools (SHTVs) in Ghana, using the first four levels of Guskey’s framework. A stratified sampling approach was used to select 119 teachers as participants in the study. An embedded mixed method research design was employed to collect data using a questionnaire with both close-ended and open-ended items. The findings revealed that the deployment of Wi-Fi technology impacted positively on the teachers’ learning experiences and was positively perceived by the teachers in facilitating teaching and learning practices. However, the study also revealed some deficits in reliability, trust, and connectivity associated with the Wi-Fi technology, highlighting the need to explore factors that maximize the output of technological initiatives. The study identifies the school’s organization and the teachers’ learning experiences as two key predictors in maximizing the use of Wi-Fi technology in educational establishments. The study recommends equipping teachers with the requisite competencies in the use of Wi-Fi technologies through professional development programs, training, and the enactment of ICT curriculum-based policies in schools. These policies and support will promote and enhance the effective utility of Wi-Fi technology among teachers, enabling them to shift from traditional to more technologically inclined student-centred learning.

Published
2024
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3. Avaliação de biomarcadores inflamatórios em artérias pulmonares de transplantados cardíacos chagásicos e não chagásicos

Author

Marcus Vinícius de Paula da Silva, Ildernandes Vieira Alves, Antonielle Rodrigues Pereira Alves, Virginia Soares Lemos, Gabriel Assis Lopes do Carmo, Fábio o Morato de Castilho, and Cláudio Léo Gelape

Subjects
Transplante Cardíaco, Hipertensão Arterial Pulmonar, Biomarcadores Inflamatórios, Specialties of internal medicine, RC581-951, Special situations and conditions, RC952-1245, Surgery, RD1-811
Abstract

O transplante cardíaco (TxC) é opção terapêutica considerada em pacientes com insuficiência cardíaca (IC) avançada e refratária ao tratamento otimizado, de acordo com diretrizes nacionais e internacionais. A IC é uma doença progressiva que afeta milhões de pessoas. O paciente com IC avançada frequentemente desenvolve hipertensão arterial pulmonar (HAP). A HAP é definida como uma condição em que a pressão média da artéria pulmonar é superior a 20 mmHg. Em relação à HAP, reconhece-se que a inflamação é um aspecto importante no seu desenvolvimento. Objetivo: Neste contexto, o objetivo desse trabalho foi avaliar a relação entre o processo inflamatório e o desenvolvimento de hipertensão arterial pulmonar em pacientes submetido ao TxC. Métodos: Os níveis das interleucinas IL-6, IL-1β e TNF-α foram obtidos por ELISA, associados das contagens de CD68 e de neutrófilos CD66b pela técnica de imunofluorescência, em fragmentos das artérias pulmonares de pacientes portadores de cardiomiopatia chagásica ou não submetidos ao TxC. Resultados: O estudo revelou que não existe diferença estatisticamente significativa (p ≥ 0,05) entre os pacientes receptores chagásicos e os receptores não-chagásicos no que diz respeito às variáveis clínicas, ecocardiográficas, manométricas e aos marcadores inflamatórios. Conclusão: Esses dados sugerem a presença de um perfil pró-inflamatório nos pacientes transplantados cardíaco independente dos níveis aferidos de pressão na artéria pulmonar.

Published
2024

4. Modeling Borehole Resistivity Logs with Exponentially Graded Multilayered Soil Inversion for Electrical Engineering Applications

Author

Ricardo Paramo, Eduardo Faleiro, Jorge Moreno, and Gabriel Asensio

Subjects
resistivity logging, inversion procedure, exponentially graded multilayered soils, soil modeling, grounding systems calculations, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

In this paper, we present the application of a special soil resistivity model, the Exponentially Graded Multilayered Soil model, to the interpretation of Borehole Resistivity Logging surveys. The model, which was recently introduced by the authors, offers an alternative to the interpretation provided by constant resistivity layer models. The model is applied to synthetic soil surveys as well as to a real survey that is not satisfactorily interpreted using conventional models. This model aids in the design and calculation of grounding systems in electrical engineering, providing an alternative to calculations performed by traditional layered models.

Published
2024
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5. A Simple Procedure to Obtain the Grounding Resistance Measurement of Very Large and Urban Electrodes by a Modified Fall-of-Potential Method

Author

Jorge Moreno, Pascual Simón, Eduardo Faleiro, Daniel García, Gregorio Denche, and Gabriel Asensio

Subjects
very large and urban grids, modified fall-of-potential method, corrected unitary grounding resistance, grounding electrode status, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

The measurement of the grounding resistance of grounding grids in large installations as well as grounding electrodes in urban areas is addressed in this article. The resistance value is obtained using a three-pin array by measuring the fall-of-potential on the ground surface. The resistance measured by this method is adjusted to its true value using a correction factor that aligns the measured resistance with the actual value. The proposed measurement method obtains correct values of the grounding resistance even when the auxiliary and potential electrodes of the tree-pin array are close to the electrode to be measured. Thus, it can be applied to large electrodes as well as electrodes in urban areas. Several simulated examples are used to illustrate the method, and some real cases with field measurements are presented for a final validation of the method.

Published
2024
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6. Clinical characteristics and outcomes in COVID-19 in kidney transplant recipients: a propensity score matched cohort study

Author

Polianna Delfino-Pereira, Vanessa das Graças José Ventura, Magda Carvalho Pires, Daniela Ponce, Gabriel Assis Lopes do Carmo, Lilian Pires de Freitas do Carmo, Bruno Barbosa Miranda de Paiva, Alexandre Vargas Schwarzbold, Angélica Gomides dos Reis Gomes, Bruno Mateus de Castro, Carísi Anne Polanczyk, Christiane Corrêa Rodrigues Cimini, Daniela Antunes de Lima, Fabiano Carvalho de Sousa, Frederico Bartolazzi, Giovanna Grunewald Vietta, Heloisa Reniers Vianna, José Miguel Chatkin, Karen Brasil Ruschel, Luciane Kopittke, Luís César de Castro, Marcelo Carneiro, Priscilla Pereira dos Reis, and Milena Soriano Marcolino

Subjects
COVID-19, kidney transplantation, chronic kidney disease, dialysis, clinical characteristics, outcomes, Medicine (General), R5-920
Abstract

Patients with chronic kidney disease (CKD), especially those on dialysis or who have received a kidney transplant (KT), are considered more vulnerable to severe COVID-19. This susceptibility is attributed to advanced age, a higher frequency of comorbidities, and the chronic immunosuppressed state, which may exacerbate their susceptibility to severe outcomes. Therefore, our study aimed to compare the clinical characteristics and outcomes of COVID-19 in KT patients with those on chronic dialysis and non-CKD patients in a propensity score-matched cohort study. This multicentric retrospective cohort included adult COVID-19 laboratory-confirmed patients admitted from March/2020 to July/2022, from 43 Brazilian hospitals. The primary outcome was in-hospital mortality. Propensity score analysis matched KT recipients with controls - patients on chronic dialysis and those without CKD (within 0.25 standard deviations of the logit of the propensity score) - according to age, sex, number of comorbidities, and admission year. This study included 555 patients: 163 KT, 146 on chronic dialysis, and 249 non-CKD patients (median age 57 years, 55.2% women). With regards to clinical outcomes, chronic dialysis patients had a higher prevalence of acute heart failure, compared to KT recipients, furthermore, both groups presented high in-hospital mortality, 34.0 and 28.1%, for KT and chronic dialysis patients, respectively. When comparing KT and non-CKD patients, the first group had a higher incidence of in-hospital dialysis (26.4% vs. 8.8%, p

Published
2024
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7. COVID-19 Computed tomography patterns in renal replacement therapy patients

Author

Gabriel Assis Lopes do Carmo, Mariana Paiva Oliveira, Anna Luiza Lino Campos, Bráulio Roberto Gonçalves Marinho Couto, Lilian Pires de Freitas do Carmo, Tiago Lemos Cerqueira, Camila Alencar Monteiro de Souza, Yan Lopes Goll, Vitor Santos de Souza, Mariana Oliveira Guimarães Vieira, Pedro Alves Soares Vaz de Castro, Pedro Augusto Botelho Lemos, and Ana Cristina Simões e Silva

Subjects
Computed tomography, COVID-19, Dialysis, Emergency department, End stage kidney disease, Diseases of the genitourinary system. Urology, RC870-923
Abstract

ABSTRACT Introduction: Lung diseases are common in patients with end stage kidney disease (ESKD), making differential diagnosis with COVID-19 a challenge. This study describes pulmonary chest tomography (CT) findings in hospitalized ESKD patients on renal replacement therapy (RRT) with clinical suspicion of COVID-19. Methods: ESKD individuals referred to emergency department older than 18 years with clinical suspicion of COVID-19 were recruited. Epidemiological baseline clinical information was extracted from electronic health records. Pulmonary CT was classified as typical, indeterminate, atypical or negative. We then compared the CT findings of positive and negative COVID-19 patients. Results: We recruited 109 patients (62.3% COVID-19-positive) between March and December 2020, mean age 60 ± 12.5 years, 43% female. The most common etiology of ESKD was diabetes. Median time on dialysis was 36 months, interquartile range = 12–84. The most common pulmonary lesion on CT was ground glass opacities. Typical CT pattern was more common in COVID-19 patients (40 (61%) vs 0 (0%) in non-COVID-19 patients, p < 0.001). Sensitivity was 60.61% (40/66) and specificity was 100% (40/40). Positive predictive value and negative predictive value were 100% and 62.3%, respectively. Atypical CT pattern was more frequent in COVID-19-negative patients (9 (14%) vs 24 (56%) in COVID-19-positive, p < 0.001), while the indeterminate pattern was similar in both groups (13 (20%) vs 6 (14%), p = 0.606), and negative pattern was more common in COVID-19-negative patients (4 (6%) vs 12 (28%), p = 0.002). Conclusions: In hospitalized ESKD patients on RRT, atypical chest CT pattern cannot adequately rule out the diagnosis of COVID-19.

Published
2024
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8. Prediction of measles patients using machine learning classifiers: a comparative study

Author

Robert Gyebi, Gabriel Asare Okyere, Emmanuel Kwaku Nakua, Franklin Aseidu-Bekoe, Jane Serwaa Akoto Nti, Emmanuel Owusu Ansah, and Felix Agyemang Opoku

Subjects
Measles, Prediction, Machine learning, Classification techniques, Prediction accuracy, Science
Abstract

Abstract Background Measles has high primary reproductive number, extremely infectious and ranked second to malaria in terms of disease burden in Ghana. Owing to the disease’s high infectious rate, making early diagnosis based on an accurate system can help limit the spread of the disease. Studies have been conducted to derive models to serve as preliminary tools for early detection. However, these derived models are based on traditional methods, which may be limited in terms of model sensitivity and prediction power. This study focuses on comparing the performance of five machine learning classification techniques with a traditional method for predicting measles patients in Ghana. The study was an analytical cross-sectional design of suspected measles cases in Ghana. Results The performance of six classifiers were compared and the random forest (RF) model demonstrated better performance among other models. The RF model achieved the highest sensitivity (0.88) specificity (0.96), ROC (0.92) and total accuracy (0.92). Conclusions Our findings showed that, despite all the six methods had good performance in classifying measles patients, the RF model outperformed all the other classifiers in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting measles in the early stage.

Published
2023
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9. Amerindian ancestry proportion as a risk factor for inflammatory bowel diseases: results from a Latin American Andean cohort

Author

Tamara Pérez-Jeldres, Fabien Magne, Gabriel Ascui, Danilo Alvares, Matias Orellana, Manuel Alvarez-Lobos, Cristian Hernandez-Rocha, Lorena Azocar, Nataly Aguilar, Alberto Espino, Ricardo Estela, Sergio Escobar, Alejandra Zazueta, Pablo Baez, Verónica Silva, Andres De La Vega, Elizabeth Arriagada, Carolina Pavez-Ovalle, Alejandro Díaz-Asencio, Dante Travisany, Juan Francisco Miquel, Eduardo J. Villablanca, Mitchell Kronenberg, and María Leonor Bustamante

Subjects
inflammatory bowel disease, single nucleotide polymorphism (SNP), ancestry, Latin American, genetics, Medicine (General), R5-920
Abstract

Background and aimsLatin American populations remain underrepresented in genetic studies of inflammatory bowel diseases (IBDs). Most genetic association studies of IBD rely on Caucasian, African, and Asian individuals. These associations have yet to be evaluated in detail in the Andean region of South America. We explored the contribution of IBD-reported genetic risk variants to a Chilean cohort and the ancestry contribution to IBD in this cohort.MethodsA total of 192 Chilean IBD patients were genotyped using Illumina's Global Screening Array. Genotype data were combined with similar information from 3,147 Chilean controls. The proportions of Aymara, African, European, and Mapuche ancestries were estimated using the software ADMIXTURE. We calculated the odds ratios (ORs) and 95% confidence intervals (CIs) for gender, age, and ancestry proportions. We also explored associations with previously reported IBD-risk variants independently and in conjunction with genetic ancestry.ResultsThe first and third quartiles of the proportion of Mapuche ancestry in IBD patients were 24.7 and 34.2%, respectively, and the corresponding OR was 2.30 (95%CI 1.52–3.48) for the lowest vs. the highest group. Only one variant (rs7210086) of the 180 reported IBD-risk SNPs was associated with IBD risk in the Chilean cohort (adjusted P = 0.01). This variant is related to myeloid cells.ConclusionThe type and proportion of Native American ancestry in Chileans seem to be associated with IBD risk. Variants associated with IBD risk in this Andean region were related to myeloid cells and the innate immune response.

Published
2023
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10. How Artificial Intelligence (AI) Impacts Inclusive Education

Author

Gabriel Julien

Abstract

Since artificial intelligence (AI) is extensively used in the field of education, it presents various opportunities in teaching and learning. In education, AI is chiefly used to impart knowledge, stimulate comprehension, heighten intelligence, and is treasured as support in learning. It is also instrumental in empowering and inspiring students. The emergence of AI also has a positive impact on inclusive education. Because these technologies can potentially transform teaching and learning, it is important to understand how they can be used to further support inclusive education. This non-empirical research analyzed some advantages and challenges of incorporating AI in teaching and learning and highlighted how it influences inclusion. Based on the literature, some of the advantages of AI are improved performance of students, motivation, and encouragement of students. This review also identified some challenges associated with the use of AI for inclusive education, such as technological difficulties, poor connectivity, pedagogical issues, and limitations of the database. Some recommendations are proposed to address these challenges. This research can assist educators, parents, students, government officials, and policymakers in making proper decisions on the effective use of AI and inclusion.

Published
2024

11. The Politics of Social Policy in Sub-Saharan Africa: A Configurational Approach to Fee-Free Policies at the High School Level

Author

Gabriel Asante

Subjects
History of scholarship and learning. The humanities, AZ20-999, Social Sciences
Abstract

Governments in Sub-Saharan Africa (SSA) are expanding access to high school education, which is a perpetuation of the previous focus on basic education. This study applies qualitative comparative analysis (QCA) using data from seven countries from 2010 to 2020 to examine the potential conditions of fee-free policies at the high school level. Five potential conditions are analyzed. They include the regime type, electoral competition, ideological lineage, economic conditions, and social context. The findings indicate the significant influence of electoral competition and a high level of lower secondary school enrolment for the adoption of fee-free policies. The absence of electoral competition leads to a lack of fee-free policy. The paper explains how elections, one indicator of representative democracy, motivate political leaders to initiate social policies. Additionally, the study challenges the relevance of two important explanations for expansionary social policy in the literature—the partisan theory of policy outcomes, and the economy.

Published
2023
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12. Primordial power spectrum from an objective collapse mechanism: The simplest case

Author

Ocampo, Martin Miguel, Palermo, Octavio, León, Gabriel, and Bengochea, Gabriel R.

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Quantum Physics
Abstract

In this work we analyzed the physical origin of the primordial inhomogeneities during the inflation era. The proposed framework is based, on the one hand, on semiclassical gravity, in which only the matter fields are quantized and not the spacetime metric. Secondly, we incorporate an objective collapse mechanism based on the Continuous Spontaneous Localization (CSL) model, and we apply it to the wavefunction associated with the inflaton field. This is introduced due to the close relation between cosmology and the so-called ``measurement problem'' in Quantum Mechanics. In particular, in order to break the homogeneity and isotropy of the initial Bunch-Davies vacuum, and thus obtain the inhomogeneities observed today, the theory requires something akin to a ``measurement'' (in the traditional sense of Quantum Mechanics). This is because the linear evolution driven by Schr\"odinger's equation does not break any initial symmetry. The collapse mechanism given by the CSL model provides a satisfactory mechanism for breaking the initial symmetries of the Bunch-Davies vacuum. The novel aspect in this work is that the constructed CSL model arises from the simplest choices for the collapse parameter and operator. From these considerations, we obtain a primordial spectrum that has the same distinctive features as the standard one, which is consistent with the observations from the Cosmic Microwave Background., Comment: 9 pages. Final version. Published in BAAA 65

Published
2024

13. Gravitational Polarizability of Schwarzschild Black Holes

Author

Vidal, Gabriel, Dantas, Gabriel M., Sturani, Riccardo, and Menezes, Gabriel

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology, High Energy Physics - Theory
Abstract

The linear response of a Schwarzschild black hole to an external quadrupolar perturbation is studied in analogy to a mechanical electrodynamical system, with the goal to describe the gravitational polarizability. Its causality properties imply dispersion relations that relate fluctuation and dissipative properties. We review and combine results obtained via the Regge-Wheeler equation on one side and a perturbative, worldline effective field theory description on the other, obtaining a consistent description of the dispersion relations for the gravitational polarizability of a Schwarzschild black hole. We find that the classical part of the 2-point correlation function of the black hole multipole depends on the boundary conditions of the space-time the black hole is immersed in, which is relevant for the dispersion relations considered., Comment: 21 pages, 3 figures

Published
2024

14. Optical memory in a MoSe$_2$/Clinochlore device

Author

Ames, Alessandra, Sousa, Frederico B., Souza, Gabriel A. D., de Oliveira, Raphaela, Silva, Igor R. F., Rodrigues, Gabriel L., Watanabe, Kenji, Taniguchi, Takashi, Marques, Gilmar E., Barcelos, Ingrid D., Cadore, Alisson R., López-Richard, Victor, and Teodoro, Marcio D.

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Two-dimensional heterostructures have been crucial in advancing optoelectronic devices utilizing van der Waals materials. Semiconducting transition metal dichalcogenide monolayers, known for their unique optical properties, offer extensive possibilities for light-emitting devices. Recently, a memory-driven optical device, termed a Mem-emitter, was proposed using these monolayers atop dielectric substrates. The successful realization of such devices heavily depends on selecting the optimal substrate. Here, we report a pronounced memory effect in a MoSe$_2$/clinochlore device, evidenced by electric hysteresis in the intensity and energy of MoSe$_2$ monolayer emissions. This demonstrates both population-driven and transition-rate-driven Mem-emitter abilities. Our theoretical approach correlates these memory effects with internal state variables of the substrate, emphasizing that clinochlore layered structure is crucial for a robust and rich memory response. This work introduces a novel two-dimensional device with promising applications in memory functionalities, highlighting the importance of alternative insulators in fabricating van der Waals heterostructures.

Published
2024

15. Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques

Author

da Silva, Davide Clode, Bernardes, Marina Musse, Ceretta, Nathalia Giacomini, de Souza, Gabriel Vaz, Silva, Gabriel Fonseca, Bordini, Rafael Heitor, and Musse, Soraia Raupp

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

Machine learning has significantly advanced healthcare by aiding in disease prevention and treatment identification. However, accessing patient data can be challenging due to privacy concerns and strict regulations. Generating synthetic, realistic data offers a potential solution for overcoming these limitations, and recent studies suggest that fine-tuning foundation models can produce such data effectively. In this study, we explore the potential of foundation models for generating realistic medical images, particularly chest x-rays, and assess how their performance improves with fine-tuning. We propose using a Latent Diffusion Model, starting with a pre-trained foundation model and refining it through various configurations. Additionally, we performed experiments with input from a medical professional to assess the realism of the images produced by each trained model.

Published
2024

16. Introducing Technologies into National Large-Scale Testing: Are We Ready?

Author

Gabriel Cipriano and Susana Da Cruz Martins

Abstract

In recent decades, the potential benefits of introducing technologies into large-scale tests have been much discussed. Yet the path to effective technology use on large-scale testing has fallen short of expectations, especially when these tests have medium or high stakes for students. After a temporary cancelation of external assessment of learning due to the COVID19 pandemic, the Portuguese government is taking a step forward. With a gradual implementation until 2025, the reintroduction of external assessment of learning in the Portuguese education system foresees all national assessment tests and all national examinations in digital format with the implementation of the "External Assessment Dematerialization Project" (DAVE). However, assessment reforms, such as DAVE, raise concerns within school communities. To identify and analyze these concerns, we conducted 32 semi-structured interviews with head teachers from mainland Portugal. To understand to what extent these concerns were considered and covered in DAVE's design and implementation, a supplementary interview was conducted with the president of the Portuguese institute responsible for DAVE. In addition, a survey with 2 673 teachers was carried out to find out their degree of agreement with the implementation of DAVE. Results show that DAVE raises many concerns among head teachers, and it is not fully accepted by the teachers.

Published
2024

18. Cost projection of combined cycle power plants equipped with post-combustion carbon capture

Author

Pablo R. Díaz-Herrera, Ascención Romero-Martínez, and Gabriel Ascanio

Subjects
post-combustion, natural gas combined cycle, cost projection, learning rate, experience curves, second-law efficiency, General Works
Abstract

This work aims to analyze the cost projection of natural gas combined cycles (NGCC) with post-combustion carbon capture (PCC) technology for two promising power plant configurations, namely: conventional NGCC and exhaust gas recirculation (EGR). A thermo-economic analysis was performed considering the second-law efficiency for the CO2 separation process (η2nd) and the CO2 avoided cost (CAC) as main indicators. Several critical variables influencing the overall cost of the plant were considered, such as the work required for solvent regeneration (Wregen), technology maturity, learning rate, carbon tax credit, and carbon capture level (85%, 90%, and 95%). A hybrid method combining engineering-economic and experience-curve approaches was used to estimate the costs of Nth-of-a-kind (NOAK) plants. The results showed that NOAK plants could potentially decrease the levelized cost of electricity (LCOE) by 10%–11%, and the CAC by 21%–23%, compared with first-of-a-kind (FOAK) plants. EGR at 85% capture level showed the best economic performance among the study cases evaluated, with a CAC equal to $102.5/tCO2. At an 85% capture level, the CAC for the conventional NOAK NGCC plant is $104.1/tCO2; maintaining this same CAC value, the carbon capture rate could increase from 85% to 90.8% if EGR configuration is implemented. Finally, from the findings of this research, it is concluded that the CAC for NOAK plants is expected to be, in the best scenario, as low as $69/tCO2. Therefore, these plants might need at least a similar carbon tax value to ensure their operation during their useful life.

Published
2022
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19. The positive externality of education on crime: Insights from Sub-Saharan Africa

Author

Gabriel Asante and Attila Bartha

Subjects
upper secondary education, property crime, violent crime, social support, cost elimination, sub-saharan africa, Social Sciences
Abstract

Although researchers have investigated the association between education and crime, few studies have studied Sub-Saharan Africa, which has the lowest rate of youth enrolled in high school. Notwithstanding, some countries are paying attention to high school education, whereby specific policies often termed “cost elimination” are designed to facilitate free education. At the micro-to-micro level, it is argued that enrolling and completing high school reduces the rate of criminal engagement. Against this backdrop, we investigate the effect of high school enrolment on the crime rate using macro-to-macro-level panel data about Sub-Saharan Africa countries from 2003 to 2018. Using theft and homicide rates as proxies for property and violent crime, respectively, our results show that an increase in enrolment has a significant negative effect on property crime. We find no evidence of a significant effect on violent crime. When addressing endogeneity bias using cost elimination as an instrument for enrolment, we find that the magnitude of the negative effect on the rate of theft is significantly greater and robust than the baseline estimates. These findings support the assumption that interventions that support access to education improve social structures and have the additional benefit of reducing the rate of theft, giving credence to social support theory. In a region where a majority of theft is committed by youth without a high school education, policymakers need to make concerted efforts to raise participation in high school as one of the means of reducing crime, rather than focusing exclusively on crime control.

Published
2022
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20. Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation

Author

Le, Huy, Gabriel, Miroslav, Hoang, Tai, Neumann, Gerhard, and Vien, Ngo Anh

Subjects
Computer Science - Robotics
Abstract

Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance exploration through a two-fold approach within a hybrid framework that tackles both discrete and continuous action spaces. First, we model the continuous motion parameter policy as a diffusion model, and second, we incorporate this into a maximum entropy reinforcement learning framework that unifies both the discrete and continuous components. The discrete action space, such as contact point selection, is optimized through Q-value function maximization, while the continuous part is guided by a diffusion-based policy. This hybrid approach leads to a principled objective, where the maximum entropy term is derived as a lower bound using structured variational inference. We propose the Hybrid Diffusion Policy algorithm (HyDo) and evaluate its performance on both simulation and zero-shot sim2real tasks. Our results show that HyDo encourages more diverse behavior policies, leading to significantly improved success rates across tasks - for example, increasing from 53% to 72% on a real-world 6D pose alignment task. Project page: https://leh2rng.github.io/hydo, Comment: 8 pages

Published
2024

21. The NANOGrav 15 year Data Set: Removing pulsars one by one from the pulsar timing array

Author

Agazie, Gabriella, Anumarlapudi, Akash, Archibald, Anne M., Arzoumanian, Zaven, Baier, Jeremy G., Baker, Paul T., Becsy, Bence, Blecha, Laura, Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, Crowter, Kathryn, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Garver-Daniels, Nate, Gentile, Peter A., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Guertin, Lydia, Gultekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Jones, Megan L., Kaiser, Andrew R., Kaplan, David L., Kelley, Luke Zoltan, Kerr, Matthew, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Lorimer, Duncan R., Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., McMann, Natasha, Meyers, Bradley W., Meyers, Patrick M., Middleton, Hannah, Mingarelli, Chiara M. F., Mitridate, Andrea, Moore, Christopher J., Ng, Cherry, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Perera, Benetge B. P., Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Romano, Joseph D., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmiedekamp, Ann, Schmiedekamp, Carl, Schmitz, Kai, Shapiro-Albert, Brent J., Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Stovall, Kevin, Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, Vecchio, Alberto, Vigeland, Sarah J., Wahl, Haley M., Witt, Caitlin A., Wright, David, and Young, Olivia

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

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

Published
2024

22. K-GBS3FCM -- KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means

Author

Santos, Gabriel, Julia, Rita, and Nascimento, Marcelo

Subjects
Computer Science - Machine Learning
Abstract

Clustering data using prior domain knowledge, starting from a partially labeled set, has recently been widely investigated. Often referred to as semi-supervised clustering, this approach leverages labeled data to enhance clustering accuracy. To maximize algorithm performance, it is crucial to ensure the safety of this prior knowledge. Methods addressing this concern are termed safe semi-supervised clustering (S3C) algorithms. This paper introduces the KNN graph-based safety-aware semi-supervised fuzzy c-means algorithm (K-GBS3FCM), which dynamically assesses neighborhood relationships between labeled and unlabeled data using the K-Nearest Neighbors (KNN) algorithm. This approach aims to optimize the use of labeled data while minimizing the adverse effects of incorrect labels. Additionally, it is proposed a mechanism that adjusts the influence of labeled data on unlabeled ones through regularization parameters and the average safety degree. Experimental results on multiple benchmark datasets demonstrate that the graph-based approach effectively leverages prior knowledge to enhance clustering accuracy. The proposed method was significantly superior in 64% of the 56 test configurations, obtaining higher levels of clustering accuracy when compared to other semi-supervised and traditional unsupervised methods. This research highlights the potential of integrating graph-based approaches, such as KNN, with established techniques to develop advanced clustering algorithms, offering significant applications in fields that rely on both labeled and unlabeled data for more effective clustering., Comment: 10 pages

Published
2024

23. Engineering spectro-temporal light states with physics-trained deep learning

Author

Liu, Shilong, Virally, Stéphane, Demontigny, Gabriel, Cusson, Patrick, and Seletskiy, Denis V.

Subjects
Physics - Optics, Nonlinear Sciences - Pattern Formation and Solitons, Physics - Classical Physics, Quantum Physics
Abstract

Frequency synthesis and spectro-temporal control of optical wave packets are central to ultrafast science, with supercontinuum (SC) generation standing as one remarkable example. Through passive manipulation, femtosecond (fs) pulses from nJ-level lasers can be transformed into octave-spanning spectra, supporting few-cycle pulse outputs when coupled with external pulse compressors. While strategies such as machine learning have been applied to control the SC's central wavelength and bandwidth, their success has been limited by the nonlinearities and strong sensitivity to measurement noise. Here, we propose and demonstrate how a physics-trained convolutional neural network (P-CNN) can circumvent such challenges, showing few-fold speedups over the direct approaches. We highlight three key advancements enabled by the P-CNN approach: (i) on-demand control over spectral features of SC, (ii) direct generation of sub-3-cycle pulses from the highly nonlinear fiber, and (iii) the production of high-order solitons, capturing distinct "breather" dynamics in both spectral and temporal domains. This approach heralds a new era of arbitrary spectro-temporal state engineering, with transformative implications for ultrafast and quantum science., Comment: Welcome to place your comments

Published
2024

24. The constraining power of the Marked Power Spectrum: an analytical study

Author

Marinucci, Marco, Jung, Gabriel, Liguori, Michele, Ravenni, Andrea, Spezzati, Francesco, Andrews, Adam, Baldi, Marco, Coulton, William R., Karagiannis, Dionysios, Villaescusa-Navarro, Francisco, and Wandlet, Benjamin

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present the first comprehensive analytical study of the marked power spectrum's sensitivity to primordial non-Gaussianity (PNG) of the non-local type. We extend previous effective field theory frameworks to incorporate PNG, developing a complete theoretical model that we validate against the Quijote simulation suite. Through a systematic Fisher analysis, we compare the constraining power of the marked power spectrum against traditional approaches combining the power spectrum and bispectrum (P+B). We explore different choices of mark parameters to evaluate their impact on parameter constraints, particularly focusing on equilateral and orthogonal PNG as well as neutrino masses. Our analysis shows that while marking up underdense regions yields optimal constraints in the low shot-noise regime, the marked power spectrum's performance for discrete tracers with BOSS-like number densities does not surpass that of P+B analysis at mildly non-linear scales ($k \lesssim 0.25 \,h/\text{Mpc}$). However, the marked approach offers several practical advantages, including simpler estimation procedures and potentially more manageable systematic effects. Our theoretical framework reveals how the marked power spectrum incorporates higher-order correlation information through terms resembling tree-level bispectra and power spectrum convolutions. This work establishes a robust foundation for applying marked statistics to future large-volume surveys., Comment: 18 pages, 7 figures

Published
2024

25. ScAlN-on-SiC Ku-Band Solidly-Mounted Bidimensional Mode Resonators

Author

Colombo, Luca, Spagnuolo, Luca, Saha, Kapil, Giribaldi, Gabriel, Simeoni, Pietro, and Rinaldi, Matteo

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This letter reports on Solidly-Mounted Bidimensional Mode Resonators (S2MRs) based on 30% Scandium-doped Aluminum Nitride (ScAlN) on Silicon Carbide (SiC), operating near 16 GHz. Experimental results demonstrate mechanical quality factors (Qm) as high as 380, electromechanical coupling coefficients (kt2) of 4.5%, an overall Figure of Merit (FOM = Qmkt2) exceeding 17, and power handling greater than 20 dBm for devices closely matched to 50 ohm. To the best of the authors' knowledge, S2MRs exhibit the highest Key Performance Indicators (KPIs) among solidly mounted resonators in the Ku band, paving the way for the integration of nanoacoustic devices on fast substrates with high-power electronics, tailored for military and harsh environment applications., Comment: Submitted to IEEE EDL

Published
2024

26. CHANCES, The Chilean Cluster Galaxy Evolution Survey: selection and initial characterization of clusters and superclusters

Author

Sifón, Cristóbal, Finoguenov, Alexis, Haines, Christopher P., Jaffé, Yara, Amrutha, B. M., Demarco, Ricardo, Lima, E. V. R., Lima-Dias, Ciria, Méndez-Hernández, Hugo, Merluzzi, Paola, Monachesi, Antonela, Teixeira, Gabriel S. M., Tejos, Nicolas, Araya-Araya, Pablo, Argudo-Fernández, Maria, Baier-Soto, Raúl, Bilton, Lawrence E., Bom, C. R., Calderón, Juan Pablo, Cassarà, Letizia P., Comparat, Johan, Courtois, H. M., D'Ago, Giuseppe, Dupuy, Alexandra, Fritz, Alexander, Haack, Rodrigo F., Herpich, Fabio R., Ibar, E., Kuchner, Ulrike, Lopes, Amanda R., Lopez, Sebastian, Lösch, Elismar, McGee, Sean, de Oliveira, C. Mendes, Morelli, Lorenzo, Moretti, Alessia, Pallero, Diego, Piraino-Cerda, Franco, Pompei, Emanuela, Rescigno, U., Smith, Rory, Castelli, Analía V. Smith, Sodré Jr, Laerte, and Tempel, Elmo

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A

Published
2024

27. The NANOGrav 15 yr Data Set: Harmonic Analysis of the Pulsar Angular Correlations

Author

Agazie, Gabriella, Baier, Jeremy G., Baker, Paul T., Becsy, Bence, Blecha, Laura, Boddy, Kimberly K., Brazier, Adam, Brook, Paul R., Burke-Spolaor, Sarah, Burnette, Rand, Casey-Clyde, J. Andrew, Charisi, Maria, Chatterjee, Shami, Cohen, Tyler, Cordes, James M., Cornish, Neil J., Crawford, Fronefield, Cromartie, H. Thankful, DeCesar, Megan E., Demorest, Paul B., Deng, Heling, Dey, Lankeswar, Dolch, Timothy, Ferrara, Elizabeth C., Fiore, William, Fonseca, Emmanuel, Freedman, Gabriel E., Gardiner, Emiko C., Gersbach, Kyle A., Glaser, Joseph, Good, Deborah C., Gultekin, Kayhan, Hazboun, Jeffrey S., Jennings, Ross J., Johnson, Aaron D., Kaplan, David L., Kelley, Luke Zoltan, Key, Joey S., Laal, Nima, Lam, Michael T., Lamb, William G., Larsen, Bjorn, Lazio, T. Joseph W., Lewandowska, Natalia, Liu, Tingting, Luo, Jing, Lynch, Ryan S., Ma, Chung-Pei, Madison, Dustin R., McEwen, Alexander, McKee, James W., McLaughlin, Maura A., Meyers, Patrick M., Mingarelli, Chiara M. F., Mitridate, Andrea, Nay, Jonathan, Nice, David J., Ocker, Stella Koch, Olum, Ken D., Pennucci, Timothy T., Petrov, Polina, Pol, Nihan S., Radovan, Henri A., Ransom, Scott M., Ray, Paul S., Runnoe, Jessie C., Saffer, Alexander, Sardesai, Shashwat C., Schmitz, Kai, Siemens, Xavier, Simon, Joseph, Siwek, Magdalena S., Smith, Tristan L., Fiscella, Sophia V. Sosa, Stairs, Ingrid H., Stinebring, Daniel R., Susobhanan, Abhimanyu, Swiggum, Joseph K., Taylor, Jacob, Taylor, Stephen R., Turner, Jacob E., Unal, Caner, Vallisneri, Michele, van Haasteren, Rutger, Verbiest, Joris, Vigeland, Sarah J., Witt, Caitlin A., Wright, David, and Young, Olivia

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies, General Relativity and Quantum Cosmology
Abstract

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations., Comment: 15 pages, 6 figures

Published
2024

28. The impact of recovery rate heterogeneity in achieving herd immunity

Author

Turinici, Gabriel

Subjects
Quantitative Biology - Populations and Evolution, Mathematics - Dynamical Systems
Abstract

Herd immunity is a critical concept in epidemiology, describing a threshold at which a sufficient proportion of a population is immune, either through infection or vaccination, thereby preventing sustained transmission of a pathogen. In the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to study infectious disease dynamics, the achievement of herd immunity depends on key parameters, including the transmission rate ($\beta$) and the recovery rate ($\gamma$), where $\gamma$ represents the inverse of the mean infectious period. While the transmission rate has received substantial attention, recent studies have underscored the significant role of $\gamma$ in determining the timing and sustainability of herd immunity. Additionally, it is becoming increasingly evident that assuming $\gamma$ as a constant parameter might oversimplify the dynamics, as variations in recovery times can reflect diverse biological, social, and healthcare-related factors. In this paper, we investigate how heterogeneity in the recovery rate affects herd immunity. We show empirically that the mean of the recovery rate is not a reliable metric for determining the achievement of herd immunity. Furthermore, we provide a theoretical result demonstrating that it is instead the mean recovery time, which is the mean of the inverse $1/\gamma$ of the recovery rate that is critical in deciding whether herd immunity is achievable within the SIR framework. A similar result is proved for the SEIR model. These insights have significant implications for public health interventions and theoretical modeling of epidemic dynamics.

Published
2024

29. A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection

Author

Chua, Gabriel, Chan, Shing Yee, and Khoo, Shaun

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning, 68T50, I.2.7
Abstract

Large Language Models are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impracticality of requiring real-world data that is not available in pre-production. In this paper, we introduce a flexible, data-free guardrail development methodology that addresses these challenges. By thoroughly defining the problem space qualitatively and passing this to an LLM to generate diverse prompts, we construct a synthetic dataset to benchmark and train off-topic guardrails that outperform heuristic approaches. Additionally, by framing the task as classifying whether the user prompt is relevant with respect to the system prompt, our guardrails effectively generalize to other misuse categories, including jailbreak and harmful prompts. Lastly, we further contribute to the field by open-sourcing both the synthetic dataset and the off-topic guardrail models, providing valuable resources for developing guardrails in pre-production environments and supporting future research and development in LLM safety., Comment: 8 pages, 5 figures

Published
2024

30. A critical non-homogeneous heat equation with weighted source

Author

Iagar, Razvan Gabriel and Sánchez, Ariel

Subjects
Mathematics - Analysis of PDEs
Abstract

Some qualitative properties of radially symmetric solutions to the non-homogeneous heat equation with critical density and weighted source $$ |x|^{-2}\partial_tu=\Delta u+|x|^{\sigma}u^p, \quad (x,t)\in\mathbb{R}^N\times(0,T), $$ are obtained, in the range of exponents $p>1$, $\sigma\ge-2$. More precisely, we establish conditions fulfilled by the initial data in order for the solutions to either blow-up in finite time or decay to zero as $t\to\infty$ and, in the latter case, we also deduce decay rates and large time behavior. In the limiting case $\sigma=-2$ we prove the existence of non-trivial, non-negative solutions, in stark contrast to the homogeneous case. A transformation to a generalized Fisher-KPP equation is derived and employed in order to deduce these properties.

Published
2024

31. Qualitative properties of solutions to a generalized Fisher-KPP equation

Author

Iagar, Razvan Gabriel and Sánchez, Ariel

Subjects
Mathematics - Analysis of PDEs
Abstract

The following Fisher-KPP type equation $$ u_t=Ku_{xx}-Bu^q+Au^p, \quad (x,t)\in\real\times(0,\infty), $$ with $p>q>0$ and $A$, $B$, $K$ positive coefficients, is considered. For both $p>q>1$ and $p>1$, $q=1$, we construct stationary solutions, establish their behavior as $|x|\to\infty$ and prove that they are separatrices between solutions decreasing to zero in infinite time and solutions presenting blow-up in finite time. We also establish decay rates for the solutions that decay to zero as $t\to\infty$.

Published
2024

32. Underlying Core Inflation with Multiple Regimes

Author

Rodriguez-Rondon, Gabriel

Subjects
Economics - General Economics, Statistics - Applications, Statistics - Methodology
Abstract

This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based on Markov switching. The structural breaks approach can eliminate revisions for past regimes, though it functions as an offline indicator, as real-time detection of breaks is not feasible with this method. On the other hand, the Markov switching approach can reduce revisions while being useful in real time, making it a simple and robust core inflation indicator suitable for real-time monitoring and as a short-term guide for monetary policy. Additionally, this approach allows us to estimate the probability of being in different inflationary regimes. To demonstrate the effectiveness of these indicators, we apply them to Canadian price data. To compare the real-time performance of the Markov switching approach to the benchmark model without regime-switching, we assess their abilities to forecast headline inflation and minimize revisions. We find that the Markov switching model delivers superior predictive accuracy and significantly reduces revisions during periods of substantial inflation changes. Hence, our findings suggest that accounting for time-varying factors and parameters enhances inflation signal accuracy and reduces data requirements, especially following sudden economic shifts.

Published
2024

33. Measuring the expansion velocities of broad-line Ic supernovae: An investigation of neglected sources of error in two popular methods

Author

Finneran, Gabriel and Martin-Carrillo, Antonio

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The velocities of Ic-BL supernovae can be determined using two techniques (spline fitting and template fitting), sometimes resulting in different velocities for the same event. This work compares and contrasts both methods, identifying sources of error which are not accounted for by most authors and quantifying their impact on the final velocity measurement. Finally, it identifies the cause of velocity discrepancies for events measured using both methods. We quantified the impact of pre-smoothing the spectra prior to use of both methods using two well-sampled cases. To identify the source of velocity discrepancies, two cases were measured and directly compared. Additional sources of error for template fitting arise due to the choice of phase of the template spectrum ($\sim$1000 km/s) and smoothing of the input spectrum ($\sim$500 km/s). The impact of phase shifts is minimised at peak time. The spline fitting method tends to underestimate uncertainties by around 1000 km/s. This method can also be impacted by fine tuning of the smoothing parameters ($\sim$500-1000 km/s). Optimum smoothing parameters for different cases are presented along with suggestions for best practice. Direct comparison of both methods showed that velocity discrepancies are not always present, debunking the claim that the template fitting method always handles blending better than spline fitting. Spline fitting seems to struggle to handle blending only in cases where the Fe II features are superimposed on a red continuum, which biases the minimum of this feature towards the bluest line of the triplet, creating an artificially higher velocity. This situation may be relatively rare among Ic-BLs, based on typical temperature evolution. Both methods can be applied under certain circumstances with similar results. The morphology of the velocity evolution of an SN appears to be the same regardless of the method used., Comment: Submitted to A&A

Published
2024

34. Generalized Fefferman-Graham gauge and boundary Weyl structures

Author

Arenas-Henriquez, Gabriel, Diaz, Felipe, and Rivera-Betancour, David

Subjects
High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract

In the framework of AdS/CFT correspondence, the Fefferman--Graham (FG) gauge offers a useful way to express asymptotically anti-de Sitter spaces, allowing a clear identification of their boundary structure. A known feature of this approach is that choosing a particular conformal representative for the boundary metric breaks explicitly the boundary scaling symmetry. Recent developments have shown that it is possible to generalize the FG gauge to restore boundary Weyl invariance by adopting the Weyl--Fefferman--Graham gauge. In this paper, we focus on three-dimensional gravity and study the emergence of a boundary Weyl structure when considering the most general AdS boundary conditions introduced by Grumiller and Riegler. We extend the holographic renormalization scheme to incorporate Weyl covariant quantities, identifying new subleading divergences appearing at the boundary. To address these, we introduce a new codimension-two counterterm, or corner term, that ensures the finiteness of the gravitational action. From here, we construct the quantum-generating functional, the holographic stress tensor, and compute the corresponding Weyl anomaly, showing that the latter is now expressed in a full Weyl covariant way. Finally, we discuss explicit applications to holographic integrable models and accelerating black holes. For the latter, we show that the new corner term plays a crucial role in the computation of the Euclidean on-shell action., Comment: 33 pages. v2: typos corrected, references added

Published
2024

35. Machine Learning for Arbitrary Single-Qubit Rotations on an Embedded Device

Author

Bhat, Madhav Narayan, Russo, Marco, Carloni, Luca P., Di Guglielmo, Giuseppe, Fahim, Farah, Li, Andy C. Y., and Perdue, Gabriel N.

Subjects
Quantum Physics, Computer Science - Emerging Technologies
Abstract

Here we present a technique for using machine learning (ML) for single-qubit gate synthesis on field programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first bootstrap a model based on simulation with access to the full statevector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.

Published
2024

36. On Halin's end-degree Conjecture and $\omega$-strong cardinals

Author

Fernandes, Gabriel and Gaspar, Michel

Subjects
Mathematics - Logic, Mathematics - Combinatorics, 05C63, 03E05, 03E35
Abstract

We prove new instances of \emph{Halin's end degree conjecture} within $\mathrm{ZFC}$. In particular, we prove that there is a proper class of cardinals $\kappa$ for which Haliln's end-degree conjecture holds. This answers two questions posed by Geschke, Kurkofka, Melcher and Pitz in 2023. Furthermore, we comment on the relationship between Halin's conjecture and the \emph{Singular Cardinal Hypothesis}, deriving consistency strength from failures of the former. We also show that Halin's conjecture fails on finite intervals of successors of singular cardinals in Meremovich's model., Comment: 12 pages

Published
2024

37. Additional Tests for TV 3.0

Author

Peixoto, Eduardo, Freitas, Pedro Garcia, Farias, Mylene Christine Queiroz, Medeiros, Edil, Menezes, Gabriel Correia Lima da Cunha e, and da Costa, André Henrique Macedo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In 2023 we have conducted extensive experiments on subjective video quality for the TV 3.0 project at University of Bras\'ilia. A full report on these tests is available at the F\'orum SBTVD website . These tests have evaluated the H.266/VVC codec and a hybrid codec formed by the H.266/VVC and the LCEVC (Low Complexity Enhancement Video Coding) with different resolutions, ranging from 720p to 4K. This report contains the results of additional tests performed for TV 3.0 performed at University of Bras\'ilia. This new experiment consists of two new Video Under Tests (VUTs), one with the H.266/VVC codec at 4K resolution, and the other with the H.266/VVC+LCEVC codec at 4K resolution. In this new test, both codecs have the same GOP size (120 frames) and use the same VVC encoder (MainConcept live encoder). This new experiment follows the same experimental protocol as the previous experiments, in order to be fully comparable to the reported results. This document details the results of the new experiments., Comment: 73 pages

Published
2024

38. Optimization Algorithm for Inventory Management on Classical, Quantum and Quantum-Hybrid Hardware

Author

Fernandes, Gabriel P. L. M., Fonseca, Matheus S., Valério, Amanda G., Ricardo, Alexandre C., Carpio, Nicolás A. C., Bezerra, Paulo C. C., and Villas-Boas, Celso J.

Subjects
Quantum Physics
Abstract

Among the challenges of efficiently managing a factory, inventory management is essential for minimizing operational costs and delivery times. In this paper, we focus on optimizing item allocation in warehouses that use gravity flow racks, which are designed for First In, First Out (FIFO) logistics but present challenges due to the need for frequent item reinsertions during picking operations. We introduce a novel strategy formulated as a QUBO problem, suitable for classical, quantum, and hybrid hardware implementations. By leveraging advances in Adiabatic Quantum Computing and Quantum Annealing, we demonstrate the effectiveness of our strategy through simulations and real-world scenarios. The results highlight the potential of quantum-hybrid approaches to significantly enhance operational efficiency in warehouse management.

Published
2024

39. Distributed Asynchronous Time-Varying Quadratic Programming with Asynchronous Objective Sampling

Author

Behrendt, Gabriel, Bell, Zachary I., and Hale, Matthew

Subjects
Mathematics - Optimization and Control
Abstract

We present a distributed algorithm to track the fixed points of time-varying quadratic programs when agents can (i) sample their objective function asynchronously, (ii) compute new iterates asynchronously, and (iii) communicate asynchronously. We show that even for a time-varying strongly convex quadratic program, asynchronous sampling of objectives can cause agents to minimize a certain form of nonconvex "aggregate" objective function. Then, we show that by minimizing the aggregate objective, agents still track the solution of the original quadratic program to within an error ball dependent upon (i) agents' tracking error when solving the aggregate problem, and (ii) the time variation of the original quadratic objective. Lastly, we show that this work generalizes existing work, in the sense that synchronizing the agents' behaviors recovers existing convergence results (up to constants). Simulations show the robustness of our algorithm to asynchrony.

Published
2024

40. Velocity evolution of broad-line Ic supernovae with and without gamma-ray bursts

Author

Finneran, Gabriel, Cotter, Laura, and Martin-Carrillo, Antonio

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

There are more than 60 broad-line Ic (Ic-BL) supernovae (SNe) which are associated with a long Gamma-ray Burst (GRB). A large population of `ordinary' Ic-BLs for which no GRB component is detected also exists. On average, the expansion velocities of GRB-associated Ic-BLs exceed those of ordinary Ic-BLs. This work presents the largest spectroscopic sample of Ic-BL SNe with and without GRBs to date. The goal of this work is to investigate how the expansion velocities evolve in cases where an ultra-relativistic jet has been launched (GRB-SN cases) and compare these to Ic-BL SNe without a GRB detection. We measured the expansion velocities of the Fe II, Si II and Ca II lines observed in the spectra of Ic-BL SNe using a spline fitting method. We fit the expansion velocity evolution with single and broken power-laws. The expansion velocities of the Fe II and Si II features reveal considerable overlap between the two populations. It is not clear that GRB-associated supernovae expand more rapidly. Broken power-law evolution appears to be more common for the Si II feature, which always follows a shallow-steep decay, while the broken power-law Fe II decays are predominantly steep-shallow. The power-law indices for both samples were compared for both Fe II and Si II, and suggest that GRB-SNe decline at a similar rate to non-GRB Ic-BL supernovae. Neither the velocities nor their evolution can be used to distinguish between Ic-BLs with and without GRBs. Expansion velocities consistent with broken power-law evolution may indicate the presence of two velocity components, which may be evidence for a jet in some of these explosions. However, it is not possible to rule in or out the presence of a jet in any Ic-BL supernova purely based on the velocities. These results suggest that GRB-SNe and Ic-BLs are drawn from the same underlying population of events., Comment: 39 pages, 22 figures, 9 tables. Submitted to A&A

Published
2024

41. ezyMRI: How to build an MRI machine from scratch -- Experience from a four-day hackathon

Author

Huang, Shaoying, Algarín, José Miguel, Alonso, Joseba, R, Anieyrudh, Borreguero, Jose, Bschorr, Fabian, Cassidy, Paul, Choo, Wei Ming, Corcos, David, Guallart-Naval, Teresa, Han, Heng Jing, Igwe, Kay Chioma, Kang, Jacob, Li, Joe, Littin, Sebastian, Liu, Jie, Rodriguez, Gonzalo Gabriel, Solomon, Eddy, Tan, Li-Kuo, Tian, Rui, Webb, Andrew, Weber, Susanna, Xiao, Dan, Xu, Minxuan, Yu, Wenwei, Zhang, Zhiyong, Zinghini, Isabelle, and Blümich, Bernhard

Subjects
Physics - Medical Physics
Abstract

Nuclear magnetic resonance instruments are becoming available to the do-it-yourself community. The challenges encountered in the endeavor to build a magnetic resonance imaging instrument from scratch were confronted in a four-day hackathon at Singapore University of Technology and Design in spring 2024. One day was devoted to educational lectures and three days to system construction and testing. Seventy young researchers from all parts of the world formed six teams focusing on magnet, gradient coil, RF coil, console, system integration, and design, which together produced a working MRI instrument in three days. The different steps, encountered challenges, and their solutions are reported., Comment: 49 pages, 23 figures, comments welcome (this paper is meant to be useful to people constructing their own MRI systems)

Published
2024

42. First evidence for direct CP violation in beauty to charmonium decays

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment
Abstract

The $C\!P$ asymmetry and branching fraction of the CKM-suppressed decay $B^+\!\to J\mskip -3mu/\mskip -2mu\psi\,\pi^+$ are precisely measured relative to the favoured decay $B^+\!\to J\mskip -3mu/\mskip -2mu\psi\,K^+$, using a sample of proton-proton collision data corresponding to an integrated luminosity of $5.4~\mathrm{fb}^{-1}$ recorded at center-of-mass energy of $13~\mathrm{TeV}$ during 2016--2018. The results of the $C\!P$ asymmetry difference and branching fraction ratio are \begin{align*} \Delta\mathcal{A}^{C\!P} &\equiv \mathcal{A}^{C\!P}(B^+ \to J\mskip -3mu/\mskip -2mu\psi\,\pi^+) - \mathcal{A}^{C\!P}(B^+ \to J\mskip -3mu/\mskip -2mu\psi\,K^+) = (1.29 \pm 0.49 \pm 0.08) \times 10^{-2}, \end{align*} \begin{equation*} \mathcal{R}_{\pi/K} \equiv \frac{\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu\psi\,\pi^+)}{\mathcal{B}(B^+ \!\to J\mskip -3mu/\mskip -2mu\psi\,K^+)} = (3.852 \pm 0.022 \pm 0.018) \times 10^{-2}. \end{equation*} where the first uncertainties are statistical and the second systematic. A combination with previous LHCb results based on data collected at $7$ and $8~\mathrm{TeV}$ in 2011 and 2012 yields $\Delta\mathcal{A}^{C\!P} = (1.42 \pm 0.43 \pm 0.08) \times 10^{-2}$ and $\mathcal{R}_{\pi/K} = (3.846 \pm 0.018 \pm 0.018) \times 10^{-2}$. The combined $\Delta\mathcal{A}^{C\!P}$ value deviates from zero by 3.2 standard deviations, providing the first evidence for direct $C\!P$ violation in the amplitudes of beauty decays to charmonium final states., Comment: 18 pages, 2 figures, no conference or journal information All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1623/ (LHCb public pages)

Published
2024

43. Careless Whisper: Exploiting Stealthy End-to-End Leakage in Mobile Instant Messengers

Author

Gegenhuber, Gabriel K., Günther, Maximilian, Maier, Markus, Judmayer, Aljosha, Holzbauer, Florian, Frenzel, Philipp É., and Ullrich, Johanna

Subjects
Computer Science - Cryptography and Security, Computer Science - Networking and Internet Architecture
Abstract

With over 3 billion users globally, mobile instant messaging apps have become indispensable for both personal and professional communication. Besides plain messaging, many services implement additional features such as delivery and read receipts informing a user when a message has successfully reached its target. This paper highlights that delivery receipts can pose significant privacy risks to users. We use specifically crafted messages that trigger delivery receipts allowing any user to be pinged without their knowledge or consent. By using this technique at high frequency, we demonstrate how an attacker could extract private information such as the online and activity status of a victim, e.g., screen on/off. Moreover, we can infer the number of currently active user devices and their operating system, as well as launch resource exhaustion attacks, such as draining a user's battery or data allowance, all without generating any notification on the target side. Due to the widespread adoption of vulnerable messengers (WhatsApp and Signal) and the fact that any user can be targeted simply by knowing their phone number, we argue for a design change to address this issue.

Published
2024

44. Enhanced FIWARE-Based Architecture for Cyberphysical Systems With Tiny Machine Learning and Machine Learning Operations: A Case Study on Urban Mobility Systems

Author

Conde, Javier, Munoz-Arcentales, Andrés, Alonso, Álvaro, Salvachúa, Joaquín, and Huecas, Gabriel

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Networking and Internet Architecture
Abstract

The rise of AI and the Internet of Things is accelerating the digital transformation of society. Mobility computing presents specific barriers due to its real-time requirements, decentralization, and connectivity through wireless networks. New research on edge computing and tiny machine learning (tinyML) explores the execution of AI models on low-performance devices to address these issues. However, there are not many studies proposing agnostic architectures that manage the entire lifecycle of intelligent cyberphysical systems. This article extends a previous architecture based on FIWARE software components to implement the machine learning operations flow, enabling the management of the entire tinyML lifecycle in cyberphysical systems. We also provide a use case to showcase how to implement the FIWARE architecture through a complete example of a smart traffic system. We conclude that the FIWARE ecosystem constitutes a real reference option for developing tinyML and edge computing in cyberphysical systems.

Published
2024
Full Text
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45. Heterostructure and Interfacial Engineering for Low-Resistance Contacts to Ultra-Wide Bandgap AlGaN

Author

Zhu, Yinxuan, Allerman, Andrew A., Joishi, Chandan, Pratt, Jonathan, Xavier, Agnes Maneesha Dominic Merwin, Ortiz, Gabriel Calderon, Klein, Brianna A., Armstrong, Andrew, Hwang, Jinwoo, and Rajan, Siddharth

Subjects
Condensed Matter - Materials Science
Abstract

We report on the heterostructure and interfacial engineering of metalorganic chemical vapor deposition (MOCVD) grown reverse-graded contacts to ultra-wide bandgap AlGaN. A record low contact resistivity of 1.4 x 10-6 Ohm.cm2 was reported on an Al0.82Ga0.18N metal semiconductor field effect transistor (MESFET) by compositionally grading the contact layer from Al0.85Ga0.15N to Al0.14Ga0.86N with degenerate doping and proper interfacial engineering considering bandgap-narrowing-induced band offset between channel and contact layer. This represents orders-of-magnitude of lower contact resistivity than that obtained in similar MOCVD-grown structures. A detailed, layer-by-layer analysis of the reverse graded contact and TCAD simulation of the bandgap narrowing effect highlighted that the reverse graded contact layer itself is extremely conductive and interfacial resistance due to bandgap-narrowing-induced barrier between contact and channel dominates the contact resistance.

Published
2024

46. Constraints on the photon polarisation in $b \to s \gamma$ transitions using $B_s^0 \rightarrow \phi e^+e^-$ decays

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment
Abstract

An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $\phi$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions., Comment: 21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/ (LHCb public pages)

Published
2024

47. A Hard-Label Cryptanalytic Extraction of Non-Fully Connected Deep Neural Networks using Side-Channel Attacks

Author

Coqueret, Benoit, Carbone, Mathieu, Sentieys, Olivier, and Zaid, Gabriel

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

During the past decade, Deep Neural Networks (DNNs) proved their value on a large variety of subjects. However despite their high value and public accessibility, the protection of the intellectual property of DNNs is still an issue and an emerging research field. Recent works have successfully extracted fully-connected DNNs using cryptanalytic methods in hard-label settings, proving that it was possible to copy a DNN with high fidelity, i.e., high similitude in the output predictions. However, the current cryptanalytic attacks cannot target complex, i.e., not fully connected, DNNs and are limited to special cases of neurons present in deep networks. In this work, we introduce a new end-to-end attack framework designed for model extraction of embedded DNNs with high fidelity. We describe a new black-box side-channel attack which splits the DNN in several linear parts for which we can perform cryptanalytic extraction and retrieve the weights in hard-label settings. With this method, we are able to adapt cryptanalytic extraction, for the first time, to non-fully connected DNNs, while maintaining a high fidelity. We validate our contributions by targeting several architectures implemented on a microcontroller unit, including a Multi-Layer Perceptron (MLP) of 1.7 million parameters and a shortened MobileNetv1. Our framework successfully extracts all of these DNNs with high fidelity (88.4% for the MobileNetv1 and 93.2% for the MLP). Furthermore, we use the stolen model to generate adversarial examples and achieve close to white-box performance on the victim's model (95.8% and 96.7% transfer rate).

Published
2024

48. Establishing and Evaluating Trustworthy AI: Overview and Research Challenges

Author

Kowald, Dominik, Scher, Sebastian, Pammer-Schindler, Viktoria, Müllner, Peter, Waxnegger, Kerstin, Demelius, Lea, Fessl, Angela, Toller, Maximilian, Estrada, Inti Gabriel Mendoza, Simic, Ilija, Sabol, Vedran, Truegler, Andreas, Veas, Eduardo, Kern, Roman, Nad, Tomislav, and Kopeinik, Simone

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: 1) human agency and oversight, 2) fairness and non-discrimination, 3) transparency and explainability, 4) robustness and accuracy, 5) privacy and security, and 6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to 1) interdisciplinary research, 2) conceptual clarity, 3) context-dependency, 4) dynamics in evolving systems, and 5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions., Comment: Accepted in Frontiers in Big Data and AI, Research Topic: Towards Fair AI for Trustworthy Artificial Intelligence

Published
2024

49. The Last Arecibo Message

Author

Palencia-Torres, Kelby D., Quiñones-Martínez, César F., Sepúlveda, Javier A. García, Gabriel, Luis R. Rivera, Roubert, Lizmarie Mateo, Pérez, Germán Vázquez, and Méndez, Abel

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Arecibo Message was a brief binary-encoded communication transmitted into space from the Arecibo Observatory on November 16, 1974, intended to demonstrate human technological prowess. In late 2018, to commemorate the 45th anniversary of this message, the Arecibo Observatory initiated the New Arecibo Message competition. Following a series of challenges, our Boriken Voyagers team was recognized as the winner of the competition in August 2020. Although the primary objective of the competition was to conceptualize rather than transmit a message, the collapse of the Arecibo Telescope in December 2020 precluded any subsequent transmission efforts. Therefore, to commemorate the 50th anniversary of the Arecibo Message, this paper presents the Last Arecibo Message, as originally developed for the Arecibo Telescope. If the original message says "we are a form of life reaching out to connect", our message says "we are ready to explore the universe together." The prospect of transmitting this or a similar message remains an open question., Comment: 12 pages, 4 figures. Additional content is available at https://phl.upr.edu/message/

Published
2024

50. Measurement of $\phi(1020)$ meson production in fixed-target $\textit{p}$Ne collisions at $\sqrt{s_{NN}}$ = 68.5 GeV

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M. C., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. 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D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. 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Subjects
High Energy Physics - Experiment, Nuclear Experiment
Abstract

The first measurement of $\phi(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $\phi(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $\phi(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8

Published
2024

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