Maximizing the divergence from a hierarchical model of quantum states

We study many-party correlations quantified in terms of the Umegaki relative entropy (divergence) from a Gibbs family known as a hierarchical model. We derive these quantities from the maximum-entropy principle which was used earlier to define the closely related irreducible correlation. We point out differences between quantum states and probability vectors which exist in hierarchical models, in the divergence from a hierarchical model and in local maximizers of this divergence. The differences are, respectively, missing factorization, discontinuity and reduction of uncertainty. We discuss global maximizers of the mutual information of separable qubit states.Comment: 18 pages, 1 figure, v2: improved exposition, v3: less typo

Entropy Distance: New Quantum Phenomena

We study a curve of Gibbsian families of complex 3x3-matrices and point out new features, absent in commutative finite-dimensional algebras: a discontinuous maximum-entropy inference, a discontinuous entropy distance and non-exposed faces of the mean value set. We analyze these problems from various aspects including convex geometry, topology and information geometry. This research is motivated by a theory of info-max principles, where we contribute by computing first order optimality conditions of the entropy distance.Comment: 34 pages, 5 figure

Fouling and cleaning synergy in ultrafiltration membrane systems -- chemical cleaning after filtration of spent sulphite liquor

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Tensorial Curvature Measures in Integral Geometry

The tensorial curvature measures are tensor-valued generalizations of the curvature measures of convex bodies. On convex polytopes, there exist further generalizations some of which also have continuous extensions to arbitrary convex bodies. We prove complete sets of kinematic and Crofton formulae for the (generalized) tensorial curvature measures. By globalization of these integral geometric formulae, we derive complete sets of the corresponding formulae for the total tensorial curvature measures, the well-known Minkowski tensors

Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices

The prediction of groundwater nitrate concentration\u27s response to geo-environmental and human-influenced factors is essential to better restore groundwater quality and improve land use management practices. In this paper, we regionalize groundwater nitrate concentration using different machine learning methods (Random forest (RF), unimodal 2D and 3D convolutional neural networks (CNN), and multi-stream early and late fusion 2D-CNNs) so that the nitrate situation in unobserved areas can be predicted. CNNs take into account not only the nitrate values of the grid cells of the observation wells but also the values around them. This has the added benefit of allowing them to learn directly about the influence of the surroundings. The predictive performance of the models was tested on a dataset from a pilot region in Germany, and the results show that, in general, all the machine learning models, after a Bayesian optimization hyperparameter search and training, achieve good spatial predictive performance compared to previous studies based on Kriging and numerical models. Based on the mean absolute error (MAE), the random forest model and the 2DCNN late fusion model performed best with an MAE (STD) of 9.55 (0.367) mg/l, R2 = 0.43 and 10.32 (0.27) mg/l, R2 = 0.27, respectively. The 3DCNN with an MAE (STD) of 11.66 (0.21) mg/l and largest resources consumption is the worst performing model. Feature importance learning from the models was used in conjunction with partial dependency analysis of the most important features to gain greater insight into the major factors explaining the nitrate spatial variability. Large uncertainties in nitrate prediction have been shown in previous studies. Therefore, the models were extended to quantify uncertainty using prediction intervals (PIs) derived from bootstrapping. Knowledge of uncertainty helps the water manager reduce risk and plan more reliably

Power Hardware-in-the-Loop Test Bench for Permanent Magnet Synchronous Machines based on a Parallel Hybrid Converter

This paper presents a Power Hardware-in-the-Loop (PHIL) emulation test bench for emulating highly utilized perma-nent magnet synchronous machines (PMSM). The output stage of the PHIL is a Cascaded H-bridge based Parallel Hybrid Converter (PHC) with a 17-level output voltage and an effective switching frequency of 1 MHz. The nonlinear machine is emu-lated with a sampling frequency of 5 MHz and is implemented on a field programmable gate array (FPGA) using Matlab/Simulink\u27s HDL Coder. For this purpose, the time-discretized model equations of a PMSM and the PHIL test bench are derived and their mapping into an HDL code-generable and fully fixed-point transformed model in Simulink is described. To enable the high model sampling rate of 5 MHz, it is optimized for a low clock cycle count and the nonlinear relations between the machine currents and flux linkages are stored in lookup tables (LUT). The measurements are carried out in steady-state operation as well as for highly dynamic current and rotor speed steps. They demonstrate the excellent performance of the presented PHIL test bench, which even perfectly reproduces the current ripple of the modeled PMSM

814-1 Variations of Segmental Endothelium Dependent and Endothelium Independent Vasomotor Tone in the Long Term Follow Up After Cardiac Transplantation (Qualitative Changes in Endothelial Function)

To assess segmental vasoconstrictor and dilator responses in patients after cardiac transplantation (CT) without obvious angiographic disease we infused the endothelium dependent vasodilator acetylcholine (ACH) and the endothelium independent vasodilator SIN-1 sequentially into the left coronary artery. ACH infusions always preceded SIN-1 infusions. Responses of 156 nonstenotic coronary segments (LAD and CX) were investigated in 26 patients (P). Group 1: l0 P 11.3±3 months after CT. Group 2: 16 P 52±11 months after CT. Five different responses to ACH followed by SIN-1 were observed: A) dilation followed by no change (fully preserved endothelium dependent function). B) dilation followed by further dilation. C) no change followed by dilation. D) constriction followed by dilation (defective endogenous NO-release and intact vascular smooth muscle function). EI constriction followed by constriction (defective endogenous NO-release and defective vascular smooth muscle function).ResultsDifferent segmental reaction types in both groups.proximal LADdistal LADGroup 1:20% A, 20% B. 30% C. 30% D50% C. 50% DGroup 2:19% C. 69% 0.12% E12% C. 81% D.7% EConclusion(1) In only 20% of patients 1 year after CT the endothelium dependent vasodilation is completely preserved. In 40% of the patients 1 year after CT the endothelium shows segmental heterogeneity in response to ACH [absence of endothelium dependent vasodilation (type A and B) in the distal segment]. (2) In the long term course after CT more than 80% of P have defective endothelial function in proximal and distal segments [absence of endothelium dependent vasodilation (type A and B) in the proximal and distal segment]. Moreover approximately 10% have defective vascular smooth muscle function. Functional assessment of endothelial integrity in P after CT shows time dependent qualitative differences between proXimal and distal coronary segment