Probing the qudit depolarizing channel

For the quantum depolarizing channel with any finite dimension, we compare three schemes for channel identification: unentangled probes, probes maximally entangled with an external ancilla, and maximally entangled probe pairs. This comparison includes cases where the ancilla is itself depolarizing and where the probe is circulated back through the channel before measurement. Compared on the basis of (quantum Fisher) information gained per channel use, we find broadly that entanglement with an ancilla dominates the other two schemes, but only if entanglement is cheap relative to the cost per channel use and only if the external ancilla is well shielded from depolarization. We arrive at these results by a relatively simple analytical means. A separate, more complicated analysis for partially entangled probes shows for the qudit depolarizing channel that any amount of probe entanglement is advantageous and that the greatest advantage comes with maximal entanglement

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We pro- pose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis aects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more eficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting, Markov chain Monte Carlo.

Forecasting Value-at-Risk Using Nonlinear Regression Quantiles and the Intra-day Range

Value-at-Risk (VaR) is commonly used for financial risk measurement. It has recently become even more important, especially during the 2008-09 global financial crisis. We propose some novel nonlinear threshold conditional autoregressive VaR (CAViaR) models that incorporate intra-day price ranges. Model estimation and inference are performed using the Bayesian approach via the link with the Skewed-Laplace distribution. We examine how a range of risk models perform during the 2008-09 financial crisis, and evaluate how the crisis affects the performance of risk models via forecasting VaR. Empirical analysis is conducted on five Asia-Pacific Economic Cooperation stock market indices as well as two exchange rate series. We examine violation rates, back-testing criteria, market risk charges and quantile loss function values to measure and assess the forecasting performance of a variety of risk models. The proposed threshold CAViaR model, incorporating range information, is shown to forecast VaR more efficiently than other models, across the series considered, which should be useful for financial practitioners.Value-at-Risk; CAViaR model; Skewed-Laplace distribution; intra-day range; backtesting; Markov chain Monte Carlo

Ordered and linked chordal graphs

A graph G is called k-ordered if for every sequence of k distinct vertices there is a cycle traversing these vertices in the given order. In the present paper we consider two novel generalizations of this concept, k-vertex-edge-ordered and strongly k-vertex-edge-ordered. We prove the following results for a chordal graph G: (a) G is (2k-3)-connected if and only if it is k-vertex-edge-ordered (k ≥ 3). (b) G is (2k-1)-connected if and only if it is strongly k-vertex-edge-ordered (k ≥ 2). (c) G is k-linked if and only if it is (2k-1)-connected

Quantum Energy Teleportation between Spin Particles in a Gibbs State

Energy in a multipartite quantum system appears from an operational perspective to be distributed to some extent non-locally because of correlations extant among the system's components. This non-locality allows users to transfer, in effect, locally accessible energy between sites of different system components by LOCC (local operations and classical communication). Quantum energy teleportation is a three-step LOCC protocol, accomplished without an external energy carrier, for effectively transferring energy between two physically separated, but correlated, sites. We apply this LOCC teleportation protocol to a model Heisenberg spin particle pair initially in a quantum thermal Gibbs state, making temperature an explicit parameter. We find in this setting that energy teleportation is possible at any temperature, even at temperatures above the threshold where the particles' entanglement vanishes. This shows for Gibbs spin states that entanglement is not fundamentally necessary for energy teleportation; correlation other than entanglement can suffice. Dissonance---quantum correlation in separable states---is in this regard shown to be a quantum resource for energy teleportation, more dissonance being consistently associated with greater energy yield. We compare energy teleportation from particle A to B in Gibbs states with direct local energy extraction by a general quantum operation on B and find a temperature threshold below which energy extraction by a local operation is impossible. This threshold delineates essentially two regimes: a high temperature regime where entanglement vanishes and the teleportation generated by other quantum correlations yields only vanishingly little energy relative to local extraction and a second low-temperature teleportation regime where energy is available at B only by teleportation

E-Learning in family medicine education : faculty support in a community clerkship ; an evaluation

E-Learning soll im Rahmen der allgemeinmedizinischen Ausbildung von Medizinstudierenden erprobt werden. Ein zielgruppenspezifisches, multimodulares Online-Angebot begleitet Medizinstudenten des 10. Semesters während ihres dezentralen Praktikums in hausärztlichen Praxen. Folgende Lehrziele werden angestrebt: (1) Einführung in das E-Learning, (2) Klinische Allgemeinmedizin - Online-Modul, (3) Chronic Care Online-Modul, (4) Online-Bewerbung. Die systematische Evaluation zeigt, dass E-Learning die Kommunikation der Studierenden untereinander und mit der universitären Lehreinheit während des Praktikum fördert. Auf der Grundlage der in diesem Pilotversuch gewonnenen Erfahrungen erscheint die Kombination mit Präsenzunterricht (Blended Learning) eine vielversprechende Option für die allgemeinmedizinische Ausbildung zu sein.E-learning was planned as a test for medical students within their curriculum of family medicine. A multi-modular onlineoffer specific to the target group accompanies the 10th term medical students during their peripheral practical courses in family practices. Teaching objectives are as follows: (1) Introduction into e-learning, (2) clinical general medicine - onlinemodule, (3) chronic care online-module, (4) online-application. The systematic evaluation shows that e-learning promotes the communication of students both among themselves and with the university during their practical courses. On the basis of the experiences from this pilot test the combination with blended learning seems to be a promising option for medical education

PERSONAL DATA PROTECTION RULES! GUIDELINES FOR PRIVACY-FRIENDLY SMART ENERGY SERVICES

Privacy-friendly processing of personal data is proving to be increasingly challenging in today’s energy systems as the amount of data grows. Smart energy services provide value creation and co-creation by processing sensible user data collected from smart meters, smart home devices, storage systems, and renewable energy plants. To address this challenge, we analyze key topics and develop design requirements and design principles for privacy-friendly personal data processing in smart energy services. We identify these key topics through expert interviews, text-mining, and topic modelling techniques based on 149 publications. Following this, we derive our design requirements and principles and evaluate these with experts and an applicability check with three real-world smart energy services. Based on our results and findings, we establish a further research agenda consisting of five specific research directions