Automatic Estimation of Modulation Transfer Functions

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available

The dynamics of financial crises and the risk to defend the exchange rate

Despite major recent advance in the literature on financial crises, the key role of central banks in financial crises is still not well understood. Our aim is to contribute to a better understanding of the dynamics of financial crises by explicitly modeling the strategic options of both traders and central banks. We analyze a global game in which both speculative traders and the central bank face imperfect information. In case of an attack, the central bank basically faces three alternatives. It can either give in to the speculative attack or it can try to defend its exchange rate regime. If it chooses to defend its currency, the defense can be successful or not. Under rational behavior, the strength of the realized defensive measures - in contrast to the potential defense - in general does not monotonously increase with the fundamental state. In accordance with stylized facts for emerging markets, immediate devaluations are associated with costs in terms of higher (imported) inflation, successful interventions are followed by sluggish growth due to the underlying restrictive monetary policy while unsuccessful interventions typically result in both high inflation and a recession. Taken together, intervention is risky. If a central bank chooses to defend its currency it can avoid the costs of a devaluation in case the defense is successful. However, if it fails it faces the even higher costs of an (unsuccessful) defense and a devaluation, i.e. higher inflation and lower growth

The Dynamics of Financial Crises and the Risk to Defend the Exchange Rate

Despite major recent advance in the literature on financial crises, the key role of central banks in the dynamics of financial crises are still not well understood. Our aim is to contribute to a better understanding of the dynamics of financial crises by explicitly modeling the strategic options of both traders and central banks. We analyze a global game in which both speculative traders and the central bank face imperfect information. In case of an attack, the central bank basically faces three alternatives. It can either give in to the speculative attack or it can try to defend its exchange rate regime. If it chooses to defend its currency, the defense can be successful or not. In accordance with stylized facts for emerging markets, immediate devaluations are associated with costs in terms of higher (imported) inflation, successful interventions are followed by sluggish growth due to the underlying restrictive monetary policy while unsuccessful interventions typically result in both high inflation and a recession. Taken together, intervention is risky. If a central bank chooses to defend its currency it can avoid the costs of a devaluation in case the defense is successful. However, if it fails it faces the even higher costs of an (unsuccessful) defense and a devaluation, i.e. higher inflation and lower growth. In our global game approach, the strength of the realized defensive measures - in contrast to the potential defense - in general does not monotonously increase with the fundamental state. Thus global games attack models need to take into account the difference between the fundamentals themselves .i.e. the strength of the status quo or the defensive potential .and the optimal central bank reaction to an attack, i.e. the realized defensive measures

A methodology based on quality gates for certifiable AI in medicine: towards a reliable application of metrics in machine learning

As of now, intelligent technologies experience a rapid growth. For a reliable adoption of those new and powerful systems into day-to-day life, especially with respect to high-risk settings such as medicine, technical means to realize legal requirements correctly, are indispensible. Our proposed methodology comprises an approach to translate such partly more abstract concepts into concrete instructions - it is based on Quality Gates along the intelligent system’s complete life cycle, which are composed of use-case adapted Criteria that need to be addressed with respect to certification. Also, the underlying philosophy regarding stakeholder inclusion, domain embedding and risk analysis is illustrated. In the present paper, the Quality Gate Metrics is outlined for the application of machine learning performance metrics focused on binary classification

Git workflow for active learning - a development methodology proposal for data-centric AI projects

As soon as Artificial Intelligence (AI) projects grow from small feasibility studies to mature projects, developers and data scientists face new challenges, such as collaboration with other developers, versioning data, or traceability of model metrics and other resulting artifacts. This paper suggests a data-centric AI project with an Active Learning (AL) loop from a developer perspective and presents ”Git Workflow for AL”: A methodology proposal to guide teams on how to structure a project and solve implementation challenges. We introduce principles for data, code, as well as automation, and present a new branching workflow. The evaluation shows that the proposed method is an enabler for fulfilling established best practices

Data-Flow Based Model Analysis

The concept of (meta) modeling combines an intuitive way of formalizing the structure of an application domain with a high expressiveness that makes it suitable for a wide variety of use cases and has therefore become an integral part of many areas in computer science. While the definition of modeling languages through the use of meta models, e.g. in Unified Modeling Language (UML), is a well-understood process, their validation and the extraction of behavioral information is still a challenge. In this paper we present a novel approach for dynamic model analysis along with several fields of application. Examining the propagation of information along the edges and nodes of the model graph allows to extend and simplify the definition of semantic constraints in comparison to the capabilities offered by e.g. the Object Constraint Language. Performing a flow-based analysis also enables the simulation of dynamic behavior, thus providing an "abstract interpretation"-like analysis method for the modeling domain