Interaction networks for the identification of boosted H→bb‾H\to b\overline{b} decays

We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. Describing the jet shower as a combination of particle-to-particle and particle-to-vertex interactions, the model is trained to learn a jet representation on which the classification problem is optimized. The algorithm is trained on simulated samples of realistic LHC collisions, released by the CMS Collaboration on the CERN Open Data Portal. The interaction network achieves a drastic improvement in the identification performance with respect to state-of-the-art algorithms.Comment: 20 pages, 8 figures, 6 tables, version published in PR

Optimal Design of a Five-phase External Rotor Permanent Magnet Machine for Convey Application

This paper proposes the design and development of a five-phase external rotor permanent magnet synchronous machine (PMSM) which is used for direct drive convey application. Firstly, the slot/pole combination of fraction slot concentrated winding (FSCW) is selected according to four criteria, which are the least common multiple (LCM) and the greatest common divisor (GCD), the winding factor and the MMF distribution; secondly, based on the design requirement, an analytical model of the proposed machine topology is built, and the initial machine parameters are then obtained; thirdly, the machine is optimized by combing the finite element model and the Kriging model, and the final optimal results are compared to the initial one. Detail design principles and performance characteristics of the proposed machine topology are presented and validated with finite element models

JEDI-net: a jet identification algorithm based on interaction networks

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.Comment: To appear in Neural Network

Deaminase-Independent Inhibition of Parvoviruses by the APOBEC3A Cytidine Deaminase

The APOBEC3 proteins form a multigene family of cytidine deaminases with inhibitory activity against viruses and retrotransposons. In contrast to APOBEC3G (A3G), APOBEC3A (A3A) has no effect on lentiviruses but dramatically inhibits replication of the parvovirus adeno-associated virus (AAV). To study the contribution of deaminase activity to the antiviral activity of A3A, we performed a comprehensive mutational analysis of A3A. By mutation of non-conserved residues, we found that regions outside of the catalytic active site contribute to both deaminase and antiviral activities. Using A3A point mutants and A3A/A3G chimeras, we show that deaminase activity is not required for inhibition of recombinant AAV production. We also found that deaminase-deficient A3A mutants block replication of both wild-type AAV and the autonomous parvovirus minute virus of mice (MVM). In addition, we identify specific residues of A3A that confer activity against AAV when substituted into A3G. In summary, our results demonstrate that deaminase activity is not necessary for the antiviral activity of A3A against parvoviruses

Somatic cell type specific gene transfer reveals a tumor-promoting function for p21Waf1/Cip1

How proteins participate in tumorigenesis can be obscured by their multifunctional nature. For example, depending on the cellular context, the cdk inhibitors can affect cell proliferation, cell motility, apoptosis, receptor tyrosine kinase signaling, and transcription. Thus, to determine how a protein contributes to tumorigenesis, we need to evaluate which functions are required in the developing tumor. Here we demonstrate that the RCAS/TvA system, originally developed to introduce oncogenes into somatic cells of mice, can be adapted to allow us to define the contribution that different functional domains make to tumor development. Studying the development of growth-factor-induced oligodendroglioma, we identified a critical role for the Cy elements in p21, and we showed that cyclin D1T286A, which accumulates in the nucleus of p21-deficient cells and binds to cdk4, could bypass the requirement for p21 during tumor development. These genetic results suggest that p21 acts through the cyclin D1–cdk4 complex to support tumor growth, and establish the utility of using a somatic cell modeling system for defining the contribution proteins make to tumor development

The Apache Point Observatory Galactic Evolution Experiment (APOGEE)

The Apache Point Observatory Galactic Evolution Experiment (APOGEE), one of the programs in the Sloan Digital Sky Survey III (SDSS-III), has now completed its systematic, homogeneous spectroscopic survey sampling all major populations of the Milky Way. After a three-year observing campaign on the Sloan 2.5 m Telescope, APOGEE has collected a half million high-resolution (R ~ 22,500), high signal-to-noise ratio (>100), infrared (1.51–1.70 μm) spectra for 146,000 stars, with time series information via repeat visits to most of these stars. This paper describes the motivations for the survey and its overall design—hardware, field placement, target selection, operations—and gives an overview of these aspects as well as the data reduction, analysis, and products. An index is also given to the complement of technical papers that describe various critical survey components in detail. Finally, we discuss the achieved survey performance and illustrate the variety of potential uses of the data products by way of a number of science demonstrations, which span from time series analysis of stellar spectral variations and radial velocity variations from stellar companions, to spatial maps of kinematics, metallicity, and abundance patterns across the Galaxy and as a function of age, to new views of the interstellar medium, the chemistry of star clusters, and the discovery of rare stellar species. As part of SDSS-III Data Release 12 and later releases, all of the APOGEE data products are publicly available

Protection from ultraviolet damage and photocarcinogenesis by vitamin d compounds

© Springer Nature Switzerland AG 2020. Exposure of skin cells to UV radiation results in DNA damage, which if inadequately repaired, may cause mutations. UV-induced DNA damage and reactive oxygen and nitrogen species also cause local and systemic suppression of the adaptive immune system. Together, these changes underpin the development of skin tumours. The hormone derived from vitamin D, calcitriol (1,25-dihydroxyvitamin D3) and other related compounds, working via the vitamin D receptor and at least in part through endoplasmic reticulum protein 57 (ERp57), reduce cyclobutane pyrimidine dimers and oxidative DNA damage in keratinocytes and other skin cell types after UV. Calcitriol and related compounds enhance DNA repair in keratinocytes, in part through decreased reactive oxygen species, increased p53 expression and/or activation, increased repair proteins and increased energy availability in the cell when calcitriol is present after UV exposure. There is mitochondrial damage in keratinocytes after UV. In the presence of calcitriol, but not vehicle, glycolysis is increased after UV, along with increased energy-conserving autophagy and changes consistent with enhanced mitophagy. Reduced DNA damage and reduced ROS/RNS should help reduce UV-induced immune suppression. Reduced UV immune suppression is observed after topical treatment with calcitriol and related compounds in hairless mice. These protective effects of calcitriol and related compounds presumably contribute to the observed reduction in skin tumour formation in mice after chronic exposure to UV followed by topical post-irradiation treatment with calcitriol and some, though not all, related compounds

The Generation R Study: design and cohort update 2010

The Generation R Study is a population-based prospective cohort study from fetal life until young adulthood. The study is designed to identify early environmental and genetic causes of normal and abnormal growth, development and health during fetal life, childhood and adulthood. The study focuses on four primary areas of research: (1) growth and physical development; (2) behavioural and cognitive development; (3) diseases in childhood; and (4) health and healthcare for pregnant women and children. In total, 9,778 mothers with a delivery date from April 2002 until January 2006 were enrolled in the study. General follow-up rates until the age of 4 years exceed 75%. Data collection in mothers, fathers and preschool children included questionnaires, detailed physical and ultrasound examinations, behavioural observations, and biological samples. A genome wide association screen is available in the participating children. Regular detailed hands on assessment are performed from the age of 5 years onwards. Eventually, results forthcoming from the Generation R Study have to contribute to the development of strategies for optimizing health and healthcare for pregnant women and children