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[en] Although the Transiting Exoplanet Survey Satellite (TESS) primary mission observed the northern and southern ecliptic hemispheres, generally avoiding the ecliptic, and the Kepler space telescope during the K2 mission could only observe near the ecliptic, many of the K2 fields extend far enough from the ecliptic plane that sections overlap with TESS fields. Using photometric observations from both K2 and TESS, combined with archival spectroscopic observations, we globally modeled four known planetary systems discovered by K2 that were observed in the first year of the primary TESS mission. Specifically, we provide updated ephemerides and system parameters for K2-114 b, K2-167 b, K2-237 b, and K2-261 b. These were some of the first K2 planets to be observed by TESS in the first year and include three Jovian sized planets and a sub-Neptune with orbital periods less than 12 days. In each case, the updated ephemeris significantly reduces the uncertainty in prediction of future times of transit, which is valuable for planning observations with the James Webb Space Telescope and other future facilities. The TESS extended mission is expected to observe about half of the K2 fields, providing the opportunity to perform this type of analysis on a larger number of systems.

[en] From our own experience, we know that there is a gap to bridge between the scientists focused on basic material research and their counterparts in a close-to-application community focused on identifying and solving final technological and engineering challenges. In this review, we try to provide an easy-to-grasp introduction to the field of memory technology for materials scientists. An understanding of the big picture is vital, so we first provide an overview of the development and architecture of memories as part of a computer and call attention to some basic limitations that all memories are subject to. As any new technology has to compete with mature existing solutions on the market, today’s mainstream memories are explained, and the need for future solutions is highlighted. The most prominent contenders in the field of emerging memories are introduced and major challenges on their way to commercialization are elucidated. Based on these discussions, we derive some predictions for the memory market to conclude the paper. (review)

[en] Planar graphene metalens has demonstrated advantages of ultrathin thickness (200 nm), high focusing resolution (343 nm) and efficiency (>32%) and robust mechanical strength and flexibility. However, diffraction-limited imaging with such a graphene metalens has not been realized, which holds the key to designing practical integrated imaging systems. In this work, the imaging rule for graphene metalenses is first derived and theoretically verified by using the Rayleigh-Sommerfeld diffraction theory to simulate the imaging performance of the 200 nm ultrathin graphene metalens. The imaging rule is applicable to graphene metalenses in different immersion media, including water or oil. Based on the theoretical prediction, high-resolution imaging using the graphene metalens with diffraction-limited resolution (500 nm) is demonstrated for the first time. This work opens the possibility for graphene metalenses to be applied in particle tracking, microfluidic chips and biomedical devices. (paper)

[en] The electrical response of an electrolytic cell to an external ac excitation is analysed by solving the equations of the Poisson–Nernst–Planck (PNP) continuum model for two ions (ambipolar) and one mobile ion diffusive systems. The theoretical predictions of the ambipolar system, formed by positive ions of mobility μ _p and negative ions of mobility μ _m, are investigated in the limit in which one of the mobilities goes to zero. The analysis reveals that these predictions correspond to the ones arising from the one mobile ion diffusive system only in the frequency range ω ≫ ω _D μ _m/μ _p, in which ω _D is the Debye’s frequency. For very low frequencies, it shows that the physical system formed by two mobile ions, one of which has a very low mobility, is clearly distinct from the physical system in which just one of the ions is mobile. We argue that apparent deviations of the experimental spectra from the predictions of the PNP model in the low frequency region, usually interpreted as an interfacial property, may be connected with the difference in the diffusion coefficients of cations and anions. (paper)

[en] Recently, the first Ca-bearing molecule discovered in space, calcium isocyanide, CaNC, has been detected in the IRC+10216 circumstellar envelope. Related to CaNC are closed-shell stable molecules of [C, Ca, H, N] composition. One of the isomers with this composition is hydrocalcium isocyanide, HCaNC, which belongs to the hydrometal isocyanide/cyanide group of compounds, among which hydromagnesium isocyanide, HMgNC, is, to date, the only one detected in space. In this work, we have performed a study of the [C, Ca, H, N] isomers that are systems of possible interstellar interest by using quantum theoretical methodologies. The analysis of the relative energies predicts three low-lying isomers: c-HCa–NC (¹A′), hydrocalcium isocyanide, HCaNC (¹Σ), and hydrocalcium cyanide, HCaCN (¹Σ). At the composite level, HCaNC (¹Σ) and HCaCN (¹Σ) were located 0.87 and 4.84 kcal mol⁻¹, respectively, above the c-HCa–NC (¹Σ) isomer. Interconversion processes between these isomers have been studied to analyze their stability. Isomer HCaNC is characterized as a transition state and thus directly isomerizes to the c-HCa–NC minimum. The isomerization process of HCaCN → c-HCa–NC shows a small barrier of about 0.76 kcal mol⁻¹ (∼382 K) above HCaCN, at the highest level of theory employed in this work, suggesting that HCaCN could easily isomerize into the most stable isomer, c-HCa–NC. Our results predict two low-lying isomers, namely, c-HCa–NC (¹A′) and hydrocalcium cyanide, HCaCN (¹Σ), as possible candidates for experimental or radioastronomical detection. For these isomers, we provide predictions for their vibrational and rotational spectroscopic parameters that could aid in their eventual characterization in the laboratory or in space.

[en] We explore connections between brightest cluster galaxies (BCGs) and their host clusters. We first construct a HeCS-omnibus cluster sample including 227 galaxy clusters within 0.02 < z < 0.30; the total number of spectroscopic members from MMT/Hectospec and SDSS observations is 52325. Taking advantage of the large spectroscopic sample, we compute physical properties of the clusters including the dynamical mass and cluster velocity dispersion (σ _cl). We also measure the central stellar velocity dispersion of the BCGs (${\mathrm{\sigma <!--\; ??\; -->}}_{\ast <!--\; ???\; -->,\mathrm{B}\mathrm{C}\mathrm{G}\mathrm{s}}$) to examine the relation between BCG velocity dispersion and cluster velocity dispersion for the first time. The observed relation between BCG velocity dispersion and the cluster velocity dispersion is remarkably tight. Interestingly, the ${\mathrm{\sigma <!--\; ??\; -->}}_{\ast <!--\; ???\; -->,\mathrm{B}\mathrm{C}\mathrm{G}}/{\mathrm{\sigma <!--\; ??\; -->}}_{\mathrm{c}\mathrm{l}}$ ratio decreases as a function of σ _cl unlike the prediction from the numerical simulation of Dolag et al. The trend in ${\mathrm{\sigma <!--\; ??\; -->}}_{\ast <!--\; ???\; -->,\mathrm{B}\mathrm{C}\mathrm{G}}/{\mathrm{\sigma <!--\; ??\; -->}}_{\mathrm{c}\mathrm{l}}$ suggests that BCG formation is more efficient in lower mass halos.

[en] To automatically reconstruct the prior austenite grains from as-quenched martensitic structure, we applied a deep learning algorithm to recognize the prior austenite grains boundaries hidden in the martensitic matrix. The FC-DenseNet architecture based on FCN (fully convolutional networks) was used to train the martensite and ground truth label of the prior austenite grain boundaries. The original martensite structures and prior austenite grain boundaries were prepared using different chemical etching solutions. The initial PAGS detection rate was as low as 37.1%, which is not suitable for quantifying the basic properties of the microstructure such as grain size or grain boundary area. By changing the weight factor of the neural net loss function and increasing the size of the data set, the detection rate was improved up to 56.1%. However, even when the detection rate reached 50% or more, the quality of the reconstructed PAGS was not comparable to the analytically calculated results based on EBSD measurements and crystallographic orientation relationships. The prior austenite grain size data sets were obtained from martensite samples via the FC-DenseNet method, and had a linear correlation with the mechanical properties measured in the same samples. In order to improve the accuracy of the detection rate using neural networks, it is necessary to increase the number of neural networks and data sets.

[en] The recommendation is now part of our daily life. As the years pass by, companies collect more and more information about the users of their platforms. One question which could arise is: are the data collected useful for better predictions? In this paper, we investigate the performance impact on adding geographical positions on the performance of the prediction of users’ behavior using an existing diffusion-based recommender system. We show how we can improve the accuracy of the diffusion algorithm using the geographical position of users. The accuracy of the improved algorithm is compared with the state of art similar recommender algorithms. Moreover, we design a general framework to infer the position location of users based on the position of their activities. (paper: interdisciplinary statistical mechanics)

[en] A crucial challenge to successful flare prediction is forecasting periods that transition between “flare-quiet” and “flare-active.” Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016–2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth–Sun line.