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ABSTRACT High-precision light curves from space-based telescopes and precise astrometry from the Gaia satellite have revolutionized our ability to characterize exoplanet host stars. Asteroseismology has allowed for stellar parameters to be determined to remarkable precision, achieving age uncertainties as low as 10−20  per cent for Sun-like stars. We present an asteroseismic analysis of the naked-eye ($V = 5.78$), G4V star $$\nu ^2$$ Lupi (HD 136352), which hosts three small transiting planets with orbital periods of 11, 27, and 107 d. We used the latest 20-s cadence photometry data from the Transiting Exoplanet Survey Satellite (TESS) to extract stellar oscillations. Comparing these to stellar models, we find that the star has a mass of $$0.83^{+0.04}_{-0.03}$$ (ran) $$\pm 0.07$$ (sys) $$M_\odot$$, a radius of $$1.00^{+0.01}_{-0.02}$$ (ran) $$\pm 0.04$$ (sys) $$R_\odot$$, and an age of $$11.9^{+2.6}_{-1.6}$$ (ran) $$\pm 1.7$$ (sys) Gyr. We also confirm that the star is likely a member of the Galactic thick disc based on its Galactic velocities, consistent with the asteroseismic age. Based on the newly determined stellar parameters, we recalculate the planet parameters. The inner planet has a mass of $$4.55 \pm 0.40$$  $$M_{\oplus }$$ and a radius of $$1.57 \pm 0.04$$  $$R_{\oplus }$$, suggesting the planet is rocky and consisting primarily of silicates without an iron-rich core, consistent with its old age and significant alpha-element enhancement. The two outer planets have masses and radii of $$10.87 \pm 0.62$$  $$M_{\oplus }$$ and $$2.75 \pm 0.06$$  $$R_{\oplus }$$, and $$8.52 \pm 0.90$$  $$M_{\oplus }$$ and $$2.42 \pm 0.08$$  $$R_{\oplus }$$, respectively, suggesting both are sub-Neptune planets with a significant H–He atmosphere. 

Analog integrated circuit (IC) placement is a heavily manual and time-consuming task that has a significant impact on chip quality. Several recent studies apply machine learning (ML) techniques to directly predict the impact of placement on circuit performance or even guide the placement process. However, the significant diversity in analog design topologies can lead to different impacts on performance metrics (e.g., common-mode rejection ratio (CMRR) or offset voltage). Thus, it is unlikely that the same ML model structure will achieve the best performance for all designs and metrics. In addition, customizing ML models for different designs require more tremendous engineering efforts and longer development cycles. In this work, we leverage Neural Architecture Search (NAS) to automatically develop customized neural architectures for different analog circuit designs and metrics. Our proposed NAS methodology supports an unconstrained DAG-based search space containing a wide range of ML operations and topological connections. Our search strategy can efficiently explore this flexible search space and provide every design with the best-customized model to boost the model performance. We make unprejudiced comparisons with the claimed performance of the previous representative work on exactly the same dataset. After fully automated development within only 0.5 days, generated models give 3.61% superior accuracy than the prior art. 

Abstract We consider the problem of estimating multiple change points for a functional data process. There are numerous examples in science and finance in which the process of interest may be subject to some sudden changes in the mean. The process data that are not in a close vicinity of any change point can be analysed by the usual nonparametric smoothing methods. However, the data close to change points and contain the most pertinent information of structural breaks need to be handled with special care. This paper considers a half-kernel approach that addresses the inference of the total number, locations and jump sizes of the changes. Convergence rates and asymptotic distributional results for the proposed procedures are thoroughly investigated. Simulations are conducted to examine the performance of the approach, and a number of real data sets are analysed to provide an illustration. 

Abstract The bright starλSer hosts a hot Neptune with a minimum mass of 13.6M_⊕and a 15.5 day orbit. It also appears to be a solar analog, with a mean rotation period of 25.8 days and surface differential rotation very similar to the Sun. We aim to characterize the fundamental properties of this system and constrain the evolutionary pathway that led to its present configuration. We detect solar-like oscillations in time series photometry from the Transiting Exoplanet Survey Satellite, and we derive precise asteroseismic properties from detailed modeling. We obtain new spectropolarimetric data, and we use them to reconstruct the large-scale magnetic field morphology. We reanalyze the complete time series of chromospheric activity measurements from the Mount Wilson Observatory, and we present new X-ray and ultraviolet observations from the Chandra and Hubble space telescopes. Finally, we use the updated observational constraints to assess the rotational history of the star and estimate the wind braking torque. We conclude that the remaining uncertainty on the stellar age currently prevents an unambiguous interpretation of the properties ofλSer, and that the rate of angular momentum loss appears to be higher than for other stars with a similar Rossby number. Future asteroseismic observations may help to improve the precision of the stellar age.