Transport Properties of an Interacting Quantum Dot with a Non-Uniform Magnetization

We study the influence of the non-homogeneity of a magnetization field on the behaviour of interacting electrons in a quantum dot. In particular we investigate the magnetotransport properties when the dot is weakly coupled to two ferromagnetic leads. We take into account the interactions in the quantum dot non-perturbatively. For a magnetization which varies slowly on the scale of the Fermi wave length, the non-homogeneity effect is described by a gauge potential that can be treated perturbatively.Comment: 6 pages, to be published in EP

Tunnelling density of states at Coulomb blockade peaks

We calculate the tunnelling density of states (TDoS) for a quantum dot in the Coulomb blockade regime, using a functional integral representation with allowing correctly for the charge quantisation. We show that in addition to the well-known gap in the TDoS in the Coulomb-blockade valleys, there is a suppression of the TDoS at the peaks. We show that such a suppression is necessary in order to get the correct result for the peak of the differential conductance through an almost close quantum dot.Comment: 6 pages, 2 figure

Current-induced interactions of multiple domain walls in magnetic quantum wires

We show that an applied charge current in a magnetic nanowire containing domain walls (DWs) results in an interaction between DWs mediated by spin-dependent interferences of the scattered carriers. The energy and torque associated with this interaction show an oscillatory behaviour as a function of the mutual DWs orientations and separations, thus affecting the DWs' arrangements and shapes. Based on the derived DWs interaction energy and torque we calculate DW dynamics and uncover potential applications of interacting DWs as a tunable nano-mechanical oscillator. We also discuss the effect of impurities on the DW interaction.Comment: Published as Phys. Rev. B 79, 174422 (2009

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201

Spin and Charge Correlations in Quantum Dots: An Exact Solution

The inclusion of charging and spin-exchange interactions within the Universal Hamiltonian description of quantum dots is challenging as it leads to a non-Abelian action. Here we present an {\it exact} analytical solution of the probem, in particular, in the vicinity of the Stoner instabilty point. We calculate several observables, including the tunneling density of states (TDOS) and the spin susceptibility. Near the instability point the TDOS exhibits a non-monotonous behavior as function of the tunneling energy, even at temperatures higher than the exchange energy. Our approach is generalizable to a broad set of observables, including the a.c. susceptibility and the absorption spectrum for anisotropic spin interaction. Our results could be tested in nearly ferromagnetic materials.Comment: JETPL class, 6 pages, 2 figure

Superconductivity in monolayer and few-layer graphene: II. Topological edge states and Chern numbers

We study the emergence of electronic edge states in superconducting (SC) monolayer, bilayer, and trilayer graphene for both spin-singlet and spin-triplet SC order parameters. We focus mostly on the gapped chiral $p+ip'$- and $d+id'$-wave SC states that show a non-zero Chern number and a corresponding number of edge states. For the $p+ip'$-wave state, we observe a rich Chern phase diagram when tuning the chemical potential and the SC order parameter amplitudes, which depends strongly on the number of layers and their stacking, and is also modified by trigonal warping. At small parameter values we observe a region whose Chern number is unique to rhombohedrally stacked graphene, and is independent of the number of layers. Our results can be understood in relation not only to the SC order parameter winding as expected, but also to the normal state band structure. This observation establishes the importance of the normal state characteristics for understanding the topology in SC graphene systems

\u3cem\u3eIn Situ\u3c/em\u3e Nanomechanical Testing in Focused Ion Beam and Scanning Electron Microscopes

The recent interest in size-dependent deformation of micro- and nanoscale materials has paralleled both technological miniaturization and advancements in imaging and small-scale mechanical testing methods. Here we describe a quantitative in situ nanomechanical testing approach adapted to a dualbeam focused ion beam and scanning electron microscope. A transducer based on a three-plate capacitor system is used for high-fidelity force and displacement measurements. Specimen manipulation, transfer, and alignment are performed using a manipulator, independently controlled positioners, and the focused ion beam. Gripping of specimens is achieved using electron-beam assisted Pt-organic deposition. Local strain measurements are obtained using digital image correlation of electron images taken during testing. Examples showing results for tensile testing of single-crystalline metallic nanowires and compression of nanoporous Au pillars will be presented in the context of size effects on mechanical behavior and highlight some of the challenges of conducting nanomechanical testing in vacuum environments

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

Background: Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. Methods: We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. Results: Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype. Conclusions: Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping