Portfolio saliency and ministerial turnover: Dynamics in Scandinavian postwar cabinets

© 2013 The Author(s) Scandinavian Political Studies © 2013 Nordic Political Science Association. This is the accepted version of the following article: Hansen, M. E., Klemmensen, R., Hobolt, S. B. and Bäck, H. (2013), Portfolio Saliency and Ministerial Turnover: Dynamics in Scandinavian Postwar Cabinets. Scandinavian Political Studies, 36: 227–248, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1467-9477.12004/abstract.Why do certain ministers remain in their post for years while others have their time in office cut short? Drawing on the broader literature on portfolio allocation, this article argues that the saliency of individual portfolios shapes ministerial turnover. The main argument is that ministerial dismissals are less likely to occur the higher the saliency attributed to the ministerial portfolio since ministers appointed to important posts are more likely to have been through extensive screening before appointment. Importantly, it is also posited in the article that the effect of portfolio salience is conditioned by government approval ratings: when government ratings are on the decline, prime ministers are less likely to reshuffle or fire important ministers than when approval ratings are improving. To test these claims, Cox proportional hazards models are applied to a new dataset on ministerial turnover in Scandinavia during the postwar period. The results strongly support the proposition that portfolio saliency matters for ministerial survival, and that this effect is moderated by government popularity

Stand type affects fluxes of volatile organic compounds from the forest floor in hemiboreal and boreal climates

The forest floor is a significant contributor to the stand-scale fluxes of biogenic volatile organic compounds. In this study, the effect of tree species (Scots pine vs. Norway spruce) on forest floor fluxes of volatile organic compounds (VOC) was compared in boreal and hemiboreal climates.Peer reviewe

Application of quantum-inspired generative models to small molecular datasets

Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions point toward generative modeling as a promising direction to realize the first examples of real-world quantum advantages from these technologies. A few empirical studies also demonstrate such potential, especially when considering quantum-inspired models based on tensor networks. In this work, we apply tensor-network-based generative models to the problem of molecular discovery. In our approach, we utilize two small molecular datasets: a subset of $4989$ molecules from the QM9 dataset and a small in-house dataset of $516$ validated antioxidants from TotalEnergies. We compare several tensor network models against a generative adversarial network using different sample-based metrics, which reflect their learning performances on each task, and multiobjective performances using $3$ relevant molecular metrics per task. We also combined the output of the models and demonstrate empirically that such a combination can be beneficial, advocating for the unification of classical and quantum(-inspired) generative learning.Comment: First versio

Towards Self-Adaptive Efficient Global Optimization

Algorithms and the Foundations of Software technolog

Neural network design: learning from Neural Architecture Search

Neural Architecture Search (NAS) aims to optimize deep neural networks' architecture for better accuracy or smaller computational cost and has recently gained more research interests. Despite various successful approaches proposed to solve the NAS task, the landscape of it, along with its properties, are rarely investigated. In this paper, we argue for the necessity of studying the landscape property thereof and propose to use the so-called Exploratory Landscape Analysis (ELA) techniques for this goal. Taking a broad set of designs of the deep convolutional network, we conduct extensive experimentation to obtain their performance. Based on our analysis of the experimental results, we observed high similarities between well-performing architecture designs, which is then used to significantly narrow the search space to improve the efficiency of any NAS algorithm. Moreover, we extract the ELA features over the NAS landscapes on three common image classification data sets, MNIST, Fashion, and CIFAR-10, which shows that the NAS landscape can be distinguished for those three data sets. Also, when comparing to the ELA features of the well-known Black-Box optimization Benchmarking (BBOB) problem set, we found out that the NAS landscapes surprisingly form a new problem class on its own, which can be separated from all 24 BBOB problems. Given this interesting observation, we, therefore, state the importance of further investigation on selecting an efficient optimizer for the NAS landscape as well as the necessity of augmenting the current benchmark problem set.Algorithms and the Foundations of Software technolog

Explorative data analysis of time series based algorithm features of CMA-ES variants

Algorithms and the Foundations of Software technolog

Living with diabetes: An exploratory study of illness representation and medication adherence in Ghana

Background: Compared to other chronic conditions, non-adherence to medication in diabetes patients is very high. This study explores the relationship between illness representation and medication adherence in diabetes patients in Ghana. Method: A total of 196 type 2 diabetes patients purposively and conveniently sampled from a tertiary hospital in Ghana responded to the Revised Illness Perception Questionnaire (IPQ-R) and the Medication Adherence Report Scale (MARS-5). The Pearson Moment Product correlation and the hierarchical multiple regression statistical tools were used to analyse the data. Results: Illness consequence and emotional representation were negatively related to medication adherence, while personal control positively accounted for significant variance in medication adherence. However, none of the selected key demographic variables (i.e. age, illness duration, gender, religion and education) independently accounted for any significant variance in medication adherence. Conclusion: Diabetes has a telling consequence on patients’ life; the patient can do something to control diabetes; and the negative emotional representations concerning the disease have a significant influence on the degree of medication adherence by the patients. This observation has implications for the management and treatment plan of diabetes

Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm

As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of parameters are being tweaked to optimize a cost function out of the quantum circuit output. One of these algorithms, the Quantum Approximate Optimization Algorithm stands out as a promising approach to tackling combinatorial problems. However, finding the appropriate parameters is a difficult task. Although QAOA exhibits concentration properties, they can depend on instances characteristics that may not be easy to identify, but may nonetheless offer useful information to find good parameters. In this work, we study unsupervised Machine Learning approaches for setting these parameters without optimization. We perform clustering with the angle values but also instances encodings (using instance features or the output of a variational graph autoencoder), and compare different approaches. These angle-finding strategies can be used to reduce calls to quantum circuits when leveraging QAOA as a subroutine. We showcase them within Recursive-QAOA up to depth 3 where the number of QAOA parameters used per iteration is limited to 3, achieving a median approximation ratio of 0.94 for MaxCut over 200 Erdős-Rényi graphs. We obtain similar performances to the case where we extensively optimize the angles, hence saving numerous circuit calls.Algorithms and the Foundations of Software technolog

Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysis

Algorithms and the Foundations of Software technolog