Number conserving particle-hole RPA for superfluid nuclei

TheAuthor(s) - .Published by Elsevier B.V. "This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).Funded by SCOAP"We present a number conserving particle-hole RPA theory for collective excitations in the transition from normal to superfluid nuclei. The method derives from an RPA theory developed long ago in quantum chemistry using antisymmetric geminal powers, or equivalently number projected HFB states, as reference states. We show within a minimal model of pairing plus monopole interactions that the number conserving particle-hole RPA excitations evolve smoothly across the superfluid phase transition close to the exact results, contrary to particle-hole RPA in the normal phase and quasiparticle RPA in the superfluid phase that require a change of basis at the broken symmetry point. The new formalism can be applied in a straightforward manner to study particle-hole excitations on top of a number projected HFB state.Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Spain) FQM-160 and FQM-370Fondo Europeo de Desarrollo Regional (ERDF), ref. SOMM17/6105/UGRMinisterio de Ciencia, Innovación y Universidades and the ERDF under Projects No. FIS2015-63770-P, FIS2017-88410-P and PGC2018-094180-B-I00CEAFMC and Universidad de Huelva High Performance Computer (HPC@UHU) funded by FEDER/MINECO project UNHU-15CE-284

Number conserving particle-hole RPA for superfluid nuclei

TheAuthor(s) - .Published by Elsevier B.V. "This is an open access article under the CCBY license (http://creativecommons.org/licenses/by/4.0/).Funded by SCOAP"We present a number conserving particle-hole RPA theory for collective excitations in the transition from normal to superfluid nuclei. The method derives from an RPA theory developed long ago in quantum chemistry using antisymmetric geminal powers, or equivalently number projected HFB states, as reference states. We show within a minimal model of pairing plus monopole interactions that the number conserving particle-hole RPA excitations evolve smoothly across the superfluid phase transition close to the exact results, contrary to particle-hole RPA in the normal phase and quasiparticle RPA in the superfluid phase that require a change of basis at the broken symmetry point. The new formalism can be applied in a straightforward manner to study particle-hole excitations on top of a number projected HFB state.Consejería de Economía, Conocimiento, Empresas y Universidad de la Junta de Andalucía (Spain) FQM-160 and FQM-370Fondo Europeo de Desarrollo Regional (ERDF), ref. SOMM17/6105/UGRMinisterio de Ciencia, Innovación y Universidades and the ERDF under Projects No. FIS2015-63770-P, FIS2017-88410-P and PGC2018-094180-B-I00CEAFMC and Universidad de Huelva High Performance Computer (HPC@UHU) funded by FEDER/MINECO project UNHU-15CE-284

Connection between decoherence and excited state quantum phase transitions

In this work we explore the relationship between an excited state quantum phase transition (ESQPT) and the phenomenon of quantum decoherence. For this purpose, we study how the decoherence is affected by the presence of a continuous ESQPT in the environment. This one is modeled as a two level boson system described by a Lipkin Hamiltonian. We will show that the decoherence of the system is maximal when the environment undergoes a continuous ESQPT

A Methodology based on Rebalancing Techniques to measure and improve Fairness in Artificial Intelligence algorithms

Artificial Intelligence (AI) has become one of the key drivers for the next decade. As important decisions are increasingly supported or directly made by AI systems, concerns regarding the rationale and fairness in their outputs are becoming more and more prominent nowadays. Following the recent interest in fairer predictions, several metrics for measuring fairness have been proposed, leading to different objectives which may need to be addressed in different fashion. In this paper, we propose (i) a methodology for analyzing and improving fairness in AI predictions by selecting sensitive attributes that should be protected; (ii) We analyze how the most common rebalance approaches affect the fairness of AI predictions and how they compare to the alternatives of removing or creating separate classifiers for each group within a protected attribute. Finally, (iii) our methodology generates a set of tables that can be easily computed for choosing the best alternative in each particular case. The main advantage of our methodology is that it allows AI practitioners to measure and improve fairness in AI algorithms in a systematic way. In order to check our proposal, we have properly applied it to the COMPAS dataset, which has been widely demonstrated to be biased by several previous studies.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), funded by Spanish Ministry of Science and Innovation and the BALLADEER (PROMETEO/2021/088) projects, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana)

An extended Agassi model: Algebraic structure, phase diagram, and large size limit

The Agassi model (Agassi 1968 Nucl. Phys. A 116 49) is a schematic two-level model that involves pairing and monopole-monopole interactions. It is, therefore, an extension of the well known Lipkin-Meshkov-Glick model (Lipkin et al 1965 Nucl. Phys. 62 188). In this paper we review the algebraic formulation of an extension of the Agassi model as well as its bosonic realization through the Schwinger representation. Moreover, a mean-field approximation for the model is presented and its phase diagram discussed. Finally, a 1/j analysis, with j proportional to the degeneracy of each level, is worked out to obtain the thermodynamic limit of the ground state energy and some order parameters from the exact Hamiltonian diagonalization for finite - j.Ministerio de Economía y Competitividad FIS2017-88410- 88410-P, FIS2014-53448-C2-2-P, FIS2015-63770-PJunta de Andalucía FQM-160, FQM- 37

Impact of COVID-19 pandemic in surgical training of Junior Residents in general surgery

Background: The pandemic caused by SARS-CoV2 has had a huge impact on our health system. Due to both cancellation of elective surgery and restructuring in departments at most medical centers, surgical residents face a potential training deficit in their specialty. This study aims to objectively analyze and quantify the impact of the pandemic on the surgical activity of residents, in the setting of emergency and elective surgery, to assess whether this period has really supposed a training deficit. Material and methods: A descriptive analysis is proposed, comparing the number of procedures performed by residents of our department during the year prior to the pandemic and during the pandemic, clustering them into different subgroups. Results: The results give an optimistic outlook. In the first place, in elective surgery, despite the lower procedures performed in absolute numbers, the proportional participation of residents in the scheduled surgeries increased in all the subgroups analyzed, finding statistically significant differences and finally approaching the total number of procedures in both periods, without relevant differences in the comparison. As for emergency surgery, residents also increased their proportional participation in most subgroups, in this case reaching more total procedures, even in absolute numbers. Conclusion: Therefore, the results seem to indicate that the teaching effort made by staff surgeons of the department has managed to palliate, in most of the subgroups analyzed, the decrease in surgical activity that the pandemic has produced, so, at least in the area of surgical practice, the impact of the pandemic has probably been reduced comparing to previous expectations

Habitat association of Orthoptera in El Cimatario National Park, Querétaro, México

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Construcción de un modelo Scoring de Probabilidad: el caso de la empresa SEGUMAR S.A.

The objective of this work is the construction of a probability credit scoring model in order to minimize the risk of default on payment of the customer portfolio, for which dependent (customer good and bad) and independent (characteristic of customer) variables were used to provide a correct analysis to determine whether or not the company grants a loan. The descriptive methodology and quantitative and qualitative approaches were applied taking as primary sources the data of the customer portfolio of the company SEGUMAR S.A. The database consists of the information of 100 people applying for a loan and is included in the measurement of 7 variables for each person. Each applicant is classified into one of two possible categories, "good customer" (70 cases) or "bad customer" (30 cases).  A credit scoring rule was developed to determine whether a new applicant is a "Good" or "Bad" customer, based on the values of one or more explanatory variables resulting from the final model. This study evaluated the characteristics that customers have at the time of requesting a loan and that according to the characteristics of each customer it is possible to make predictions, classify them as a good customer or a bad customer. In the results obtained from the Logit model it can be concluded that the selected variables that were applied in the model gave us a 76% success rate that allows us to classify each of our customers as a good customer or bad customer in our model.El objetivo del presente trabajo es la construcción de un modelo credit scoring de probabilidad con la finalidad de minimizar el riesgo de incumplimiento de pago de la cartera de clientes, para lo que se utilizó variables dependientes (cliente “bueno o malo”) y como independientes (características de los clientes) para proporcionar un análisis correcto para determinar si la empresa concede o no un crédito. Se aplicó la metodología descriptiva y enfoques cuantitativos y cualitativos tomando como fuentes primarias los datos de la cartera de clientes de la empresa SEGUMAR S.A. La base de datos consiste de la información de 100 personas solicitantes de un crédito y se incluye en la medición de 7 variables para cada persona. Cada solicitante se clasifica en una de dos categorías posibles, "buen cliente" (70 casos) o "mal cliente" (30 casos).  Se desarrolló una regla de credit scoring para determinar si un nuevo solicitante es “Bueno” o “Malo” cliente, basándose en los valores de una o más variables explicativas resultantes del modelo final. Este estudio evaluó las características que tienen los clientes al momento de pedir un crédito y según las características de cada cliente se puede realizar predicciones, clasificarlos como un buen o un mal cliente. En los resultados obtenidos del modelo Logit se puede concluir que las variables seleccionadas que se aplicaron en el modelo arrojaron un 76% de éxito que nos permite clasificar a cada uno de nuestros clientes como un buen cliente o mal cliente en nuestro modelo

A Hartree-Bose mean-field approximation for IBM-3

A Hartree-Bose mean-field approximation for the IBM-3 is presented. A Hartree-Bose transformation from the spherical to the deformed bosons with charge-dependent parameters is proposed which allows bosonic pair correlations and includes higher angular momentum bosons. The formalism contains previously proposed IBM-2 and IBM-3 intrinsic states as particular limits.Dirección General de Investigación Científica y Técnica PB 95/0123, PB95-0533European Commission CI1*-CT94-007

Hartree-Bose mean-field approximation for the interacting boson model (IBM-3)

A Hartree-Bose mean-field approximation for the IBM-3 is presented. A Hartree-Bose transformation from spherical to deformed bosons with charge-dependent parameters is proposed which allows bosonic pair correlations and includes higher angular momentum bosons. The formalism contains previously proposed IBM-2 and IBM-3 intrinsic states as particular limits.DGICYT PB95/0123 PB95–0533Comisión Europea CI1*-CT94-007