Behaviour of the potentials due to strangeness degree of freedom in ΛΛ6_{\Lambda\Lambda}^6He hypernucleus

Fully correlated study of $_{\Lambda\Lambda} ^6$He hypernucleus has been performed with two- and three- baryon potentials. For the S=-2 sector, various simulations of Nijmegen $\Lambda\Lambda$ potential models have been used. We investigate the role of every strength of the strange sector potentials on the energy breakdown and present a crystal clear understanding of their interplay. Consistency between $_\Lambda ^5$He and $_{\Lambda\Lambda}^6$He depends on the $\Lambda N$ space-exchange strength only. Investigation limits the strength of simulated Nijmegen $\Lambda\Lambda$ potential models. The study is a step forward to determine all the strengths, to reslove A=5 anomaly and to search for $_{\Lambda\Lambda}^4$H in an authentic way.Comment: 4 pages, 1 figur

Latent Gaussian processes for distribution estimation of multivariate categorical data

Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.YG is supported by the Google European fellowship in Machine Learning.This is the final version of the article. It first appeared from Microtome Publishing via http://jmlr.org/proceedings/papers/v37/gala15.htm

Deep Bayesian Active Learning with Image Data

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, active learning (AL) methods generally rely on being able to learn and update models from small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many AL acquisition functions rely on model uncertainty, yet deep learning methods rarely represent such model uncertainty. In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way. We develop an active learning framework for high dimensional data, a task which has been extremely challenging so far, with very sparse existing literature. Taking advantage of specialised models such as Bayesian convolutional neural networks, we demonstrate our active learning techniques with image data, obtaining a significant improvement on existing active learning approaches. We demonstrate this on both the MNIST dataset, as well as for skin cancer diagnosis from lesion images (ISIC2016 task).Alan Turing Institute Grant EP/N510129/1 EPSRC Grant EP/N014162/1 Qualcom

Surface and near surface geochemical surveying of a CO2 injection pilot: application study to the French Pyrenean foreland (Rousse CCS pilot)

The geochemical monitoring of the Rousse injection pilot, operated by TOTAL Exploration Pproduction France, is presented over a 5- years long time period. The monitoring consisted in the acquisition at regular frequency of soil gas concentrations and fluxes at selected sampling points, coupled with the study of the geochemical parameters evolution of a perched aquifer overlying the storage reservoir through a dedicated 85 m depth borehole. Baseline data were acquired between September 2008 and December 2009 then the monitoring shifted to the surveying of the pilot during the operating phase. This second phase ended in March 2013. Data acquired during these two phases are presented and discussed

The Exchange of Tritiated Water Between Zn-A Zeolitevand the Surrounding Water

Zeolites are considered as adsorbents for tritiated waste water. The sorption capacity of 4A zeolite is increased, and the speed of tritium release, as THO, decreased if sodium ions in 4A zeolite are replaced with small divalent cations. The exchange kinetics THO/H20 between Zn-A zeolite containing tritiated water and the surrounding water was investigated at 30-60 De, using zinc-exchanged 4A zeolite beads of 0.8 and 1.6 mm radius. The kinetics was controlled by the diffusion of water in the solid particle and interpreted in terrns of the Boyd - Reichenberg diffusion model. Effective self-diffusion coefficients of water in the zeolite beads were obtained and the kinetic data are discussed from the point of view of using zeolites as sorbents and storage material for tritiated waste water

Object-X: The Brightest Mid-IR Point Source in M33

We discuss the nature of the brightest mid-IR point source (which we dub Object X) in the nearby galaxy M33. Although multi-wavelength data on this object have existed in the literature for some time, it has not previously been recognized as the most luminous mid-IR object in M33 because it is entirely unremarkable in both optical and near-IR light. In the Local Group Galaxies Survey, Object X is a faint red source visible in VRI and H-alpha but not U or B. It was easily seen at JHK_s in the 2MASS survey. It is the brightest point source in all four Spitzer IRAC bands and is also visible in the MIPS 24-micron band. Its bolometric luminosity is 5x10^5 L_sun. The source is optically variable on short time scales (tens of days) and is also slightly variable in the mid-IR, indicating that it is a star. Archival photographic plates (from 1949 and 1991) show no optical source, so the star has been obscured for at least half a century. Its properties are similar to those of the Galactic OH/IR star IRC+10420 which has a complex dusty circumstellar structure resulting from episodic low velocity mass ejections. We propose that Object X is a M>30 M_sun evolved star obscured in its own dust ejected during episodic mass loss events over at least half a century. It may emerge from its current ultra-short evolutionary phase as a hotter post-RSG star analogous to M33 Var A. The existence and rarity of such objects can be an important probe of a very brief yet eventful stellar evolutionary phase.Comment: 20 pages, 10 figures. Accepted for publication by Ap

Derivation of species-specific hybridization-like knowledge out of cross-species hybridization results

BACKGROUND: One of the approaches for conducting genomics research in organisms without extant microarray platforms is to profile their expression patterns by using Cross-Species Hybridization (CSH). Several different studies using spotted microarray and CSH produced contradicting conclusions in the ability of CSH to reflect biological processes described by species-specific hybridization (SSH). RESULTS: We used a tomato-spotted cDNA microarray to examine the ability of CSH to reflect SSH data. Potato RNA was hybridized to spotted cDNA tomato and potato microarrays to generate CSH and SSH data, respectively. Difficulties arose in obtaining transcriptomic data from CSH that reflected those obtained from SSH. Nevertheless, once the data was filtered for those corresponding to matching probe sets, by restricting proper cutoffs of probe homology, the CSH transcriptome data showed improved reflection of those of the SSH. CONCLUSIONS: This study evaluated the relative performance of CSH compared to SSH, and proposes methods to ensure that CSH closely reflects the biological process analyzed by SSH

Relationship between solid state structure and solution stability of copper(ii)-hydroxypyridinecarboxylate complexes

The complementary solid state/solution studies of the systematic series of bioactive ligands 3-hydroxy- 1-methyl-4-pyridinecarboxylate (L1), 3-hydroxy-1,2,6-trimethyl-4-pyridinecarboxylate (L2), 4-hydroxy-1- methyl-3-pyridinecarboxylate (L3), 4-hydroxy-1,6-dimethyl-3-pyridinecarboxylate (L4), 4-hydroxy-1-(2- hydroxyethyl)-6-methyl-3-pyridinecarboxylate (L5) and 4-hydroxy-1-(2-carboxyethyl)-6-methyl-3- pyridinecarboxylate (L6) with copper(II) have been performed in order to design efficient chelating drugs for the treatment of metal overloading conditions. Single crystals of [Cu(L1)2(H2O)]3H2O (1) (monomer) with axial water coordination, [Cu2(L2)4]6H2O (2) and [Cu2(L3)4]4H2O (3) (cyclic dimers), where pyridinolato and carboxylato oxygens, respectively, act as linkers between adjacent copper complexes, [Cu(L4)2]n3H2O (4) (1D polymer) and [Cu3(L5)6]18H2O (5) (trimer), constructed using two square-pyramidal and one elongated octahedral Cu(II) complexes have been determined by SXRD. The bidentate coordination mode of the ligands has been found preferentially with cis arrangements in 1 and 2 and trans arrangements in 3\u20135. The solution speciation and complex stability of aqueous solutions have been studied by pH-dependent electron paramagnetic resonance spectroscopy resulting in the detection of solely monomeric [CuL]+ and [CuL2] complexes. The stability order obtained for the [CuL]+ complexes could be correlated with the deprotonation constants of their hydroxyl group (log bLH) reflecting that the higher acidity increases the complex stability in the order L2 o L1 E L6 o L4 E L5 o L3. This stability order elucidates the different axial linkers in the cyclic dimers 2 and 3. DFT quantum-chemical calculations support the effect of the electron distribution on the established stability order

Structural Properties, Order-Disorder Phenomena and Phase Stability of Orotic Acid Crystal Forms

Orotic acid (OTA) is reported to exist in the anhydrous (AH), monohydrate (Hy1) and dimethylsulfoxide monosolvate (SDMSO) forms. In this study we investigate the (de)hydration/desolvation behavior, aiming at an understanding of the elusive structural features of anhydrous OTA by a combination of experimental and computational techniques, namely, thermal analytical methods, gravimetric moisture (de)sorption studies, water activity measurements, X-ray powder diffraction, spectroscopy (vibrational, solid-state NMR), crystal energy landscape and chemical shift calculations. The Hy1 is a highly stable hydrate, which dissociates above 135°C and loses only a small part of the water when stored over desiccants (25°C) for more than one year. In Hy1, orotic acid and water molecules are linked by strong hydrogen bonds in nearly perfectly planar arranged stacked layers. The layers are spaced by 3.1 Å and not linked via hydrogen-bonds. Upon dehydration the X-ray powder diffraction and solid-state NMR peaks become broader indicating some disorder in the anhydrous form. The Hy1 stacking reflection (122) is maintained, suggesting that the OTA molecules are still arranged in stacked layers in the dehydration product. Desolvation of SDMSO, a non-layer structure, results in the same AH phase as observed upon dehydrating Hy1. Depending on the desolvation conditions different levels of order-disorder of layers present in anhydrous OTA are observed, which is also suggested by the computed low energy crystal structures. These structures provide models for stacking faults as intergrowth of different layers is possible. The variability in anhydrate crystals is of practical concern as it affects the moisture dependent stability of AH with respect to hydration