Low-temperature ordered phases of the spin-12\frac{1}{2} XXZ chain system Cs2_2CoCl4_4

In this study the magnetic order of the spin-1/2 XXZ chain system Cs$_2$CoCl$_4$ in a temperature range from 50 mK to 0.5 K and in applied magnetic fields up to 3.5 T is investigated by high-resolution measurements of the thermal expansion and the specific heat. Applying magnetic fields along a or c suppresses $T_\textrm{N}$ completely at about 2.1 T. In addition, we find an adjacent intermediate phase before the magnetization saturates close to 2.5 T. For magnetic fields applied along b, a surprisingly rich phase diagram arises. Two additional transitions are observed at critical fields $\mu_0 H_{SF1}\simeq 0.25$ T and $\mu_0 H_{SF2}\simeq 0.7$ T, which we propose to arise from a two-stage spin-flop transition.Comment: 10 pages, 10 figure

IT outsourcing and firm productivity : eliminating bias from selective missingness in the dependent variable

Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing-at-random and uses imputation methods, or even listwise deletion. This approach is justified if item non-response does not depend on the potentially missing variables’ realization. However, assuming missing-at-random may introduce bias if non-response is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach to deal with selective item nonresponse in the model’s dependent variable. Our approach is based on instrumental variables that affect selection only through potential outcomes. In addition, we allow for endogenous regressors. We establish identification of the structural parameter and propose a simple two-step estimation procedure for it. Our estimator is consistent and robust against biases that would prevail when assuming missingness at random. We implement the estimation procedure using firm-level survey data and a binary instrumental variable to estimate the effect of outsourcing on productivity

Topologically Consistent Models for Efficient Big Geo-Spatio-Temporal Data Distribution

Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment

The spin-12\frac{1}{2} XXZ chain system Cs2_2CoCl4_4 in a transverse magnetic field

Comparing high-resolution specific heat and thermal expansion measurements to exact finite-size diagonalization, we demonstrate that Cs$_2$CoCl$_4$ for a magnetic field along the crystallographic b axis realizes the spin-$\frac{1}{2}$ XXZ chain in a transverse field. Exploiting both thermal as well as virtual excitations of higher crystal field states, we find that the spin chain is in the XY-limit with an anisotropy $J_z/J_\perp \approx 0.12$ substantially smaller than previously believed. A spin-flop Ising quantum phase transition occurs at a critical field of $\mu_0 H_b^{\rm cr} \approx 2$ T before around 3.5 T the description in terms of an effective spin-$\frac{1}{2}$ chain becomes inapplicable.Comment: 5 pages, 3 figure

Modeling plasticity and dysplasia of pancreatic ductal organoids derived from human pluripotent stem cells

Personalized in vitro models for dysplasia and carcinogenesis in the pancreas have been constrained by insufficient differentiation of human pluripotent stem cells (hPSCs) into the exocrine pancreatic lineage. Here, we differentiate hPSCs into pancreatic duct-like organoids (PDLOs) with morphological, transcriptional, proteomic, and functional characteristics of human pancreatic ducts, further maturing upon transplantation into mice. PDLOs are generated from hPSCs inducibly expressing oncogenic GNAS, KRAS, or KRAS with genetic covariance of lost CDKN2A and from induced hPSCs derived from a McCune-Albright patient. Each oncogene causes a specific growth, structural, and molecular phenotype in vitro. While transplanted PDLOs with oncogenic KRAS alone form heterogenous dysplastic lesions or cancer, KRAS with CDKN2A loss develop dedifferentiated pancreatic ductal adenocarcinomas. In contrast, transplanted PDLOs with mutant GNAS lead to intraductal papillary mucinous neoplasia-like structures. Conclusively, PDLOs enable in vitro and in vivo studies of pancreatic plasticity, dysplasia, and cancer formation from a genetically defined background

IMPROVING DATA QUALITY AND MANAGEMENT FOR REMOTE SENSING ANALYSIS: USE-CASES AND EMERGING RESEARCH QUESTIONS

During the last decades satellite remote sensing has become an emerging technology producing big data for various application fields every day. However, data quality checking as well as the long-time management of data and models are still issues to be improved. They are indispensable to guarantee smooth data integration and the reproducibility of data analysis such as carried out by machine learning models. In this paper we clarify the emerging need of improving data quality and the management of data and models in a geospatial database management system before and during data analysis. In different use cases various processes of data preparation and quality checking, integration of data across different scales and references systems, efficient data and model management, and advanced data analysis are presented in detail. Motivated by these use cases we then discuss emerging research questions concerning data preparation and data quality checking, data management, model management and data integration. Finally conclusions drawn from the paper are presented and an outlook on future research work is given