Reconnaissance study of ground-water levels in the Havana lowlands area

"May 1995.""Contract report 582.""Prepared for Imperial Valley Water Authority [and] Division of Water Resources, IDOT.

Experimental study of partitioning of trace elements and rare earth elements between immiscible silicate liquids-titanite-zircon at the atmospheric pressure

第2回極域科学シンポジウム/第31回極域地学シンポジウム 11月17日（木） 国立極地研究所　2階大会議室前フロ

A star-forming galaxy at z= 5.78 in the Chandra Deep Field South

We report the discovery of a luminous z = 5.78 star-forming galaxy in the Chandra Deep Field South. This galaxy was selected as an ‘i-drop’ from the GOODS public survey imaging with the Hubble Space Telescope/Advanced Camera for Surveys (object 3 in the work of Stanway, Bunker & McMahon 2003). The large colour of (i′−z′)AB = 1.6 indicated a spectral break consistent with the Lyman α forest absorption shortward of Lyman α at z≈ 6. The galaxy is very compact (marginally resolved with ACS with a half-light radius of 0.08 arcsec, so rhl 5. Our spectroscopic redshift for this object confirms the validity of the i′-drop technique of Stanway et al. to select star-forming galaxies atz≈ 6

Investigating the effects of inter-annual weather variation (1968- 2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term experiments

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops

Partitioning of trace elements and rare earth elements between titanite and major rock-forming minerals in eclogite and clinopyroxenite from the northeastern Shandong Peninsula, eastern China

第2回極域科学シンポジウム/第31回極域地学シンポジウム 11月17日（木） 国立極地研究所　2階大会議

Rayleigh noise mitigation in DWDM LR-PONs using carrier suppressed subcarrier-amplitude modulated phase shift keying

We demonstrate a novel Rayleigh interferometric noise mitigation scheme for applications in carrier-distributed dense wavelength division multiplexed (DWDM) passive optical networks at 10 Gbit/s using carrier suppressed subcarrier-amplitude modulated phase shift keying modulation. The required optical signal to Rayleigh noise ratio is reduced by 12 dB, while achieving excellent tolerance to dispersion, subcarrier frequency and drive amplitude variations

The Color-Magnitude Relation in Coma: Clues to the Age and Metallicity of Cluster Populations

We have observed three fields of the Coma cluster of galaxies with a narrow band (modified Stromgren) filter system. Observed galaxies include 31 in the vicinity of NGC 4889, 48 near NGC 4874, and 60 near NGC 4839 complete to M_5500=-18 in all three subclusters. Spectrophotometric classification finds all three subclusters of Coma to be dominated by red, E type (ellipticals/S0's) galaxies with a mean blue fraction, f_B, of 0.10. The blue fraction increases to fainter luminosities, possible remnants of dwarf starburst population or the effects of dynamical friction removing bright, blue galaxies from the cluster population by mergers. We find the color-magnitude (CM) relation to be well defined and linear over the range of M_5500=-13 to -22. After calibration to multi-metallicity models, bright ellipticals are found to have luminosity weighted mean [Fe/H] values between -0.5 and +0.5, whereas low luminosity ellipticals have [Fe/H] values ranging from -2 to solar. The lack of CM relation in our continuum color suggests that a systematic age effect cancels the metallicity effects in this bandpass. This is confirmed with our age index which finds a weak correlation between luminosity and mean stellar age in ellipticals such that the stellar populations of bright ellipticals are 2 to 3 Gyrs younger than low luminosity ellipticals.Comment: 26 pages AAS LaTeX, 6 figures, accepted for publication in A

GANs and Closures: Micro-Macro Consistency in Multiscale Modeling

Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery. These problems are often multiscale in nature: they can be described in terms of low-dimensional effective free energy surfaces parametrized by a small number of "slow" reaction coordinates; the remaining "fast" degrees of freedom populate an equilibrium measure on the reaction coordinate values. Sampling procedures for such problems are used to estimate effective free energy differences as well as ensemble averages with respect to the conditional equilibrium distributions; these latter averages lead to closures for effective reduced dynamic models. Over the years, enhanced sampling techniques coupled with molecular simulation have been developed. An intriguing analogy arises with the field of Machine Learning (ML), where Generative Adversarial Networks can produce high dimensional samples from low dimensional probability distributions. This sample generation returns plausible high dimensional space realizations of a model state, from information about its low-dimensional representation. In this work, we present an approach that couples physics-based simulations and biasing methods for sampling conditional distributions with ML-based conditional generative adversarial networks for the same task. The "coarse descriptors" on which we condition the fine scale realizations can either be known a priori, or learned through nonlinear dimensionality reduction. We suggest that this may bring out the best features of both approaches: we demonstrate that a framework that couples cGANs with physics-based enhanced sampling techniques can improve multiscale SDE dynamical systems sampling, and even shows promise for systems of increasing complexity.Comment: 21 pages, 10 figures, 3 table