The Effects of Attention on Consumption in Restrained and Non-Restrained Eaters

According to the cognitive capacity theory of attention, individuals have only a limited availability of cognitive resources. Previous research has shown that restrained eaters (i.e., those who typically restrict their intake for weight control) expend a considerable amount of cognitive energy regulating their food intake. As a result, they tend to overeat when these cognitive resources are depleted by engaging in a cognitive task because there are fewer resources available to focus on inhibiting food intake. The purpose of the present study was to test this hypothesis to determine whether the difficulty of a task affected restrained eaters\u27 consumption of a palatable food. We exposed restrained (n=30) and non-restrained (n=23) eaters either to a relatively easy or difficult cognitive computer task. As participants responded to the computer task with their dominant hand, they were exposed to a bowl of chocolate which was placed beside the computer within easy reach of their non-dominant hand. Results indicated that restrained eaters ate significantly less than non-restrained eaters in the heavy cognitive load task, whereas in the light cognitive load task the restrained and non-restrained groups ate similar amounts of chocolate. Thus, contrary to the findings of other studies, restrained eaters were able to continue to control their food intake when exposed to a difficult cognitive task. However, in the easy task food intake may have been disinhibited due to feelings of boredom. These results highlight the importance of future research to further assess models that attempt to explain the effects of boredom on eating behavior

»Morpheus, Gabel, Schere, Licht« – Aktuelle Haftungsfragen zu Rechtsverletzungen durch Kinder im Internet

Die Klausur greift die gegenwärtige Entwicklung der Recht- sprechung zur Haftung von Eltern für Rechtsverstöße ihres Kindes im Internet auf. Von den Bearbeitern wird sowohl deliktsrechtliches Einzelwissen als auch die Fähigkeit ver- langt, mit den weniger bekannten Vorschriften des Urheber- rechtsgesetzes methodisch sicher umzugehen

Photon propagation in a discrete fiber network: An interplay of coherence and losses

We study light propagation in a photonic system that shows stepwise evolution in a discretized environment. It resembles a discrete-time version of photonic waveguide arrays or quantum walks. By introducing controlled photon losses to our experimental setup, we observe unexpected effects like sub-exponential energy decay and formation of complex fractal patterns. This demonstrates that the interplay of linear losses, discreteness and energy gradients leads to genuinely new coherent phenomena in classical and quantum optical experiments. Moreover, the influence of decoherence is investigated.Comment: To appear in PR

Transfer of a quantum state from a photonic qubit to a gate-defined quantum dot

Interconnecting well-functioning, scalable stationary qubits and photonic qubits could substantially advance quantum communication applications and serve to link future quantum processors. Here, we present two protocols for transferring the state of a photonic qubit to a single-spin and to a two-spin qubit hosted in gate-defined quantum dots (GDQD). Both protocols are based on using a localized exciton as intermediary between the photonic and the spin qubit. We use effective Hamiltonian models to describe the hybrid systems formed by the the exciton and the GDQDs and apply simple but realistic noise models to analyze the viability of the proposed protocols. Using realistic parameters, we find that the protocols can be completed with a success probability ranging between 85-97%

DESIGNING FOR TRUST: Futures of Digital Financial Experiences Beyond the Smartphone Era

The amount a customer trusts a product is a major indicator of product success. Trust is a core component of overall customer experience, but it is poorly defined, rarely measured, and never explicitly designed for. This masters research project explores how customers develop trust with new digital products broadly, and uncovers what organizations can do to develop more trust with their customers in the short term. This masters research project set out to answer two questions: “How might organizations design for trust?” and “How might customers trust digital financial products in the future?” A design thinking approach was used to develop this work which presents a novel Trust Adoption Cycle (TAC) model. The TAC model describes how trust can be demonstrated to customers and when to demonstrate it to them. A 2x2 scenario matrix foresight technique is used to develop four unique visions of how personal financial services might feel to customers in the future. In these speculative future scenarios, the different ways in which customers develop trust with financial providers is considered The key findings are relevant for digital financial industry leaders who are interested in launching widely adopted digital products that maximize long-term customer value. They are: 1) customer trust is developed as a result of demonstrating trustworthiness in a specific cycle, starting with integrity, then competence, and then reliability, and 2) in the future, this cycle by which customers develop trust in the product is developed may change depending on the degree to which customers want to be engaged in their financial decision-making

Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia

Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN. Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN. Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified. Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features

Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks

In this work, we propose a new model for aspect-based sentiment analysis. In contrast to previous approaches, we jointly model the detection of aspects and the classification of their polarity in an end-to-end trainable neural network. We conduct experiments with different neural architectures and word representations on the recent GermEval 2017 dataset. We were able to show considerable performance gains by using the joint modeling approach in all settings compared to pipeline approaches. The combination of a convolutional neural network and fasttext embeddings outperformed the best submission of the shared task in 2017, establishing a new state of the art.Comment: EMNLP 201

Quantum Algorithms for Solving Ordinary Differential Equations via Classical Integration Methods

Identifying computational tasks suitable for (future) quantum computers is an active field of research. Here we explore utilizing quantum computers for the purpose of solving differential equations. We consider two approaches: (i) basis encoding and fixed-point arithmetic on a digital quantum computer, and (ii) representing and solving high-order Runge-Kutta methods as optimization problems on quantum annealers. As realizations applied to two-dimensional linear ordinary differential equations, we devise and simulate corresponding digital quantum circuits, and implement and run a 6$^{\mathrm{th}}$ order Gauss-Legendre collocation method on a D-Wave 2000Q system, showing good agreement with the reference solution. We find that the quantum annealing approach exhibits the largest potential for high-order implicit integration methods. As promising future scenario, the digital arithmetic method could be employed as an "oracle" within quantum search algorithms for inverse problems