Contributions in Merlin

L'industrie des fonds de placement suisse se porte comme un charme. Les capitaux venant de l’étranger augmentent, ainsi qu’il ressort de la «journée annuelle de l’asset management» tenue vendredi. Mais elle peine à obtenir un accès au marché européen et à démontrer son rôle positif envers le climat

The multi-modal and interrelated nature of lifelog data makes it well suited for graph-based representations. In this paper, we present the second iteration of LifeGraph, a Knowledge Graph for Lifelog Data, initially introduced during the 3rd Lifelog Search Challenge in 2020. This second iteration incorporates several lessons learned from the previous version. While the actual graph has undergone only small changes, the mechanisms by which it is traversed during querying as well as the underlying storage system which performs the traversal have been changed. The means for query formulation have also been slightly extended in capability and made more efficient and intuitive. All these changes have the aim of improving result quality and reducing query time.

A common method to promote behavior change, particularly in contexts related to collective action, is to reference a social norm and ask people to comply with it. We argue that such appeals will be more effective when they couch the norm as an invitation to work with others toward a common goal. In six experiments, we found that working-together normative appeals, which invited people to “join in” and “do it together,” increased interest in (Experiments 1, 4, and 5) and actual charitable giving (Experiment 2); reduced paper-towel use in public restrooms (Experiment 3); and increased interest in reducing personal carbon emissions (Experiment 6). By contrast, normative-information appeals, which included the same normative information but no reference to working together, did not affect interest or behavior. Mediation analyses suggest that working-together normative appeals were more effective because they fostered a feeling in participants that they were working together with others, which increased motivation, while inducing less social pressure, which undermined effectiveness. Results show how the very collective nature of collective action problems can be leveraged to promote personal behavior change and help solve societal problems.

Healthy aging is in part dependent upon people's willingness and ability to mobilize the effort necessary to support behaviors that promote health and well-being. People may have the best information relating to health along with the best intentions to stay healthy (e.g., health-related goals), but positive outcomes will ultimately be dependent upon them actually investing the necessary effort toward using this information to achieve their goals. In addition, the influences on effort mobilization may vary as a function of physical, psychological, and social changes experienced by the individual across the life span. Building on the overall theme of this special issue, we explore the relationships between motivation, effort mobilization, and healthy aging. We begin by characterizing the relationship between motivation and effort, and identify the factors that influence effort mobilization. We then consider the factors associated specifically with aging that may influence effort mobilization (e.g., changes in cardiovascular and neural mechanisms) and, ultimately, the health and well-being of older adults. Finally, distinguishing between those influential factors that are modifiable versus intractable, we identify ways to structure situations and beliefs to optimize mobilization in support of healthy aging.

Estimates of the effect of foetal health shocks may suffer from survivorship bias. The foetal origins literature seemingly agrees that survivorship bias is innocuous in the sense that it induces a bias towards zero. Arguably, however, selective mortality can imply a bias away from zero. In the case of the 1918 flu pandemic, a suppressed immune system may have been protective against the most severe consequences of infection. We use historical birth records from the maternity hospital of Bern, Switzerland, to evaluate this possibility. Our results suggest that a careful consideration of survivorship bias is imperative for the evaluation of the 1918 flu pandemic and other foetal health shocks.

Banks need a lot of information from their customers in order to offer them the best possible services and solutions. The Hypothekarbank Lenzburg and their employees, especially customer consultants, experienced difficulties in this information collecting process in the past. Therefore, in this thesis, three new digital prototypes were created in Figma and designed to facilitate the information collection from bank customers. The prototypes focus on usability, usefulness and further on disturbing the consultant-customer meeting as little as possible. In evaluation, customer consultants were impressed by the features the prototypes have to offer and by the simplicity in which they are designed. Additionally, users made some improvement suggestions for further development.

Fuzzing describes the fully automatic testing of software for bugs. While fuzzing has become more popular in recent times, the process of setting up a fuzzer for learning purposes is complicated and the output of the fuzzer is hard to understand. This report presents a Fuzzing Playground that gives the user the possibility to easily and quickly start a fuzzing process and see what is happening under the hood. This is being done by running the fuzzing process in-browser and by having precompiled fuzzing targets, ready for the user to pick. The output visualizes the processes of the fuzzer and presents the user with the real fuzzed data.

The objective of the thesis is to investigate a one-paramenter family of risk measures called Conditional Drawdown at Risk (CDaR) and its application to Equal Risk Budgeting Portfolios and Machine Learning techniques for Asset Allocation. We first introduce and develop a theoretical framework for the Conditional Drawdown at Risk. The next step will be thorough study of the properties of Equal Risk Contribution and their drawbacks, that will lead to the formulation of an optimization problem based on the CDaR. The Hierarchical Clustering approach applied to portfolio allocation has been of interest for many years has it seemed to tackle what was commonly known as the Markowitz' curse. More recent studies implement techniques to improve the performance of this strategy by computing beforehand the optimal number of clusters and adopting different linkage methods. The use of a tail risk measure, like CDaR, instead of the volatility, defined as the standard deviation, will be tested in this context too. We produce a series of strategies based both Equal Risk Contribution and Hierarchical Clustering algorithms that we will backtest against traditional portfolios, namely Minimum Variance, Maximum Sharpe Ratio and Equally Weighted. The statistical significance of the risk adjusted performance of the proposed strategies will need to be evaluated in order to understand if outperfomances were produced by chance. Finally we will try to reconcile our finding within the modern portfolio theory framework.