Non-empirical shape dynamics of heavy nuclei with multi-task deep learning

A microscopic description of nuclear fission represents one of the most challenging problems in nuclear theory. While phenomenological coordinates, such as multipole moments, have often been employed to describe fission, it is not obvious whether these parameters fully reflect the shape dynamics of interest. We here propose a novel method to extract collective coordinates, which are free from phenomenology, based on multi-task deep learning in conjunction with a density functional theory (DFT). To this end, we first introduce randomly generated external fields to a Skyrme-EDF and construct a set of nuclear number densities and binding energies for deformed states of ${}^{236}$U around the ground state. By training a neural network on such dataset with a combination of an autoencoder and supervised learning, we successfully identify a two-dimensional latent variables that accurately reproduce both the energies and the densities of the original Skyrme-EDF calculations, within a mean absolute error of 113 keV for the energies. In contrast, when multipole moments are used as latent variables for training in constructing the decoders, we find that the training data for the binding energies are reproduced only within 2 MeV. This implies that conventional multipole moments do not provide fully adequate variables for a shape dynamics of heavy nuclei.Comment: 15 pages, 11 figure

Nonempirical shape dynamics of heavy nuclei with multitask deep learning

A microscopic description of nuclear fission represents one of the most challenging problems in nuclear theory. While phenomenological coordinates, such as multipole moments, have often been employed to describe fission, it is not obvious whether these parameters fully reflect the shape dynamics of interest. We here propose a novel method to extract collective coordinates, which are free from phenomenology, based on multitask deep learning in conjunction with density functional theory (DFT). To this end, we first introduce randomly generated external fields to a Skyrme energy density functional (EDF) and construct a set of nuclear number densities and binding energies for deformed states of ²³⁶U around the ground state. By training a neural network on such a dataset with a combination of an autoencoder and supervised learning, we successfully identify a two-dimensional latent variables that accurately reproduce both the energies and the densities of the original Skyrme-EDF calculations, within a mean absolute error of 113 keV for the energies. In contrast, when multipole moments are used as latent variables for training in constructing the decoders, we find that the training data for the binding energies are reproduced only within 2 MeV. This implies that conventional multipole moments do not provide fully adequate variables for a shape dynamics of heavy nuclei

Applications of the dynamical generator coordinate method to quadrupole excitations

We apply the dynamical generator coordinate method (DGCM) with a conjugate momentum to a nuclear collective excitation. To this end, we first discuss how to construct a numerically workable scheme of the DGCM for a general one-body operator. We then apply the DGCM to the quadrupole vibration of $^{16}$O using the Gogny D1S interaction. We show that both the ground state energy and the excitation energies are lowered as compared to the conventional GCM with the same number of basis functions. We also compute the sum rule values for the quadrupole and monopole operators, and show that the DGCM yields more consistent results than the conventional GCM to the values from the double commutator. These results imply that the conjugate momentum is an important and relevant degree of freedom in collective motions.Comment: 9 pages, 4 figure

Analysis of a Skyrme energy density functional with deep learning

Over the past decade, machine learning has been successfully applied in various fields of science. In this study, we employ a deep learning method to analyze a Skyrme energy density functional (Skyrme-EDF), that is a Kohn-Sham type functional commonly used in nuclear physics. Our goal is to construct an orbital-free functional that reproduces the results of the Skyrme-EDF. To this end, we first compute energies and densities of a nucleus with the Skyrme Kohn-Sham + Bardeen-Cooper-Schrieffer method by introducing a set of external fields. Those are then used as training data for deep learning to construct a functional which depends only on the density distribution. Applying this scheme to the $^{24}$Mg nucleus with two distinct random external fields, we successfully obtain a new functional which reproduces the binding energy of the original Skyrme-EDF with an accuracy of about 0.04 MeV. The rate at which the neural network outputs the energy for a given density is about $10^5$--$10^6$ times faster than the Kohn-Sham scheme, demonstrating a promising potential for applications to heavy and superheavy nuclei, including the dynamics of fission.Comment: 16 pages, 9 figure

Generator coordinate method with a conjugate momentum: application to the particle number projection

We discuss an extension of the generator coordinate method (GCM) by taking simultaneously a collective coordinate and its conjugate momentum as generator coordinates. To this end, we follow the idea of the dynamical GCM (DGCM) proposed by Goeke and Reinhard. We first show that the DGCM method can be regarded as an extension of the double projection method for the center of mass motion. As an application of DGCM, we then investigate the particle number projection, for which we not only carry out an integral over the gauge angle as in the usual particle number projection but also take a linear superposition of BCS states which have different mean particle numbers. We show that the ground state energy is significantly lowered by such effect, especially for magic nuclei for which the pairing gap is zero in the BCS approximation. This suggests that the present method makes a good alternative to the variation after projection (VAP) method, as the method is much simpler than the VAP.Comment: 9 pages, 4 figure

Analysis of a Skyrme energy density functional with deep learning

Over the past decade, machine learning has been successfully applied in various fields of science. In this study, we employ a deep learning method to analyze a Skyrme energy density functional (Skyrme-EDF), which is a Kohn-Sham type functional commonly used in nuclear physics. Our goal is to construct an orbital-free functional that reproduces the results of the Skyrme-EDF. To this end, we first compute energies and densities of a nucleus with the Skyrme Kohn-Sham + Bardeen-Cooper-Schrieffer method by introducing a set of external fields. Those are then used as training data for deep learning to construct a functional which depends only on the density distribution. Applying this scheme to the ²⁴Mg nucleus with two distinct random external fields, we successfully obtain a new functional which reproduces the binding energy of the original Skyrme-EDF with an accuracy of about 0.04 MeV. The rate at which the neural network outputs the energy for a given density is about 10⁵–10⁶ times faster than the Kohn-Sham scheme, demonstrating a promising potential for applications to heavy and superheavy nuclei, including the dynamics of fission

The distribution of blood eosinophil levels in a Japanese COPD clinical trial database and in the rest of the world.

Background: Blood eosinophil measurements may help to guide physicians on the use of inhaled corticosteroids (ICS) for patients with chronic obstructive pulmonary disease (COPD). Emerging data suggest that COPD patients with higher blood eosinophil counts may be at higher risk of exacerbations and more likely to benefit from combined ICS/long-acting beta2-agonist (LABA) treatment than therapy with a LABA alone. This analysis describes the distribution of blood eosinophil count at baseline in Japanese COPD patients in comparison with non-Japanese COPD patients. Methods: A post hoc analysis of eosinophil distribution by percentage and absolute cell count was performed across 12 Phase II-IV COPD clinical studies (seven Japanese studies [N=848 available absolute eosinophil counts] and five global studies [N=5,397 available eosinophil counts] that included 246 Japanese patients resident in Japan with available counts). Blood eosinophil distributions were assessed at baseline, before blinded treatment assignment. Findings: Among Japanese patients, the median (interquartile range) absolute eosinophil count was 170 cells/mm3(100-280 cells/mm3). Overall, 612/1,094 Japanese patients (56%) had an absolute eosinophil count ≥150 cells/mm3and 902/1,304 Japanese patients (69%) had a percentage eosinophil ≥2%. Among non-Japanese patients, these values were 160 (100-250) cells/mm3, 2,842/5,151 patients (55%), and 2,937/5,155 patients (57%), respectively. The eosinophil distribution among Japanese patients was similar to that among non-Japanese patients. Within multi-country studies with similar inclusion criteria, the eosinophil count was numerically lower in Japanese compared with non-Japanese patients (median 120 vs 160 cells/mm3). Interpretation: The eosinophil distribution in Japanese patients seems comparable to that of non-Japanese patients; although within multi-country studies, there was a slightly lower median eosinophil count for Japanese patients compared with non-Japanese patients. These findings suggest that blood eosinophil data from global studies are of relevance in Japan

A Prospective Cohort Study to Assess Obstructive Respiratory Disease Phenotypes and Endotypes in Japan: The TRAIT Study Design.

Background: Asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO) are complex and heterogeneous diseases that share clinical characteristics (phenotypes) and molecular mechanisms (endotypes). Whilst physicians make clinical decisions on diagnostic groups, for some such as ACO there is no commonly accepted criteria. An alternative approach is to evaluate phenotypes and endotypes that are considered to respond well to a specific type of treatment ("treatable traits") rather than diagnostic labels. Purpose: The prospective, longitudinal, and observational TRAIT study will evaluate disease characteristics, including both phenotypes and endotypes, in relation to the presentation of obstructive respiratory disease characteristics in patients diagnosed with asthma, COPD, or ACO in Japan, with the aim of further understanding the clinical benefit of a treatable traits-based approach. Patients and Methods: A total of 1500 participants will be enrolled into three cohorts according to their treating physician's diagnosis of asthma, COPD, or ACO at screening. Part 1 of the study will involve cross-sectional phenotyping and endotyping at study enrollment. Part 2 of the study will evaluate the progression of clinical characteristics, biomarker profiles, and treatment over a 3-year follow-up period. The follow-up will involve three annual study visits and three telephone calls scheduled at 6-month intervals. A substudy involving 50 participants from the asthma cohort (in which the ratio will be approximately 1:1 including 25 participants with a smoking history of ≥10 pack-years and 25 participants with no smoking history), 100 participants from the ACO cohort, and 100 participants from the COPD cohort will evaluate disease phenotypes using inspiratory and expiratory computed tomography scans. Conclusion: TRAIT will describe clinical characteristics of patients with obstructive respiratory diseases to better understand potential differences and similarities between clinical diagnoses, which will support the improvement of personalized treatment strategies

Different regulation of cigarette smoke induced inflammation in upper versus lower airways

Background: Cigarette smoke (CS) is known to initiate a cascade of mediator release and accumulation of immune and inflammatory cells in the lower airways. We investigated and compared the effects of CS on upper and lower airways, in a mouse model of subacute and chronic CS exposure. Methods: C57BL/6 mice were whole-body exposed to mainstream CS or air, for 2, 4 and 24 weeks. Bronchoalveolar lavage fluid (BAL) was obtained and tissue cryosections from nasal turbinates were stained for neutrophils and T cells. Furthermore, we evaluated GCP-2, KC, MCP-1, MIP-3 alpha, RORc, IL-17, FoxP3, and TGF-beta 1 in nasal turbinates and lungs by RT-PCR. Results: In both upper and lower airways, subacute CS-exposure induced the expression of GCP-2, MCP-1, MIP-3a and resulted in a neutrophilic influx. However, after chronic CS-exposure, there was a significant downregulation of inflammation in the upper airways, while on the contrary, lower airway inflammation remained present. Whereas nasal FoxP3 mRNA levels already increased after 2 weeks, lung FoxP3 mRNA increased only after 4 weeks, suggesting that mechanisms to suppress inflammation occur earlier and are more efficient in nose than in lungs. Conclusions: Altogether, these data demonstrate that CS induced inflammation may be differently regulated in the upper versus lower airways in mice. Furthermore, these data may help to identify new therapeutic targets in this disease model