Contributions in Merlin

Donald Trump’s surprise election shifted expectations: corporate taxes would be lower and trade policies more restrictive. Relative stock prices responded appropriately. High-tax firms and those with large deferred tax liabilities (DTLs) gained; those with significant deferred tax assets from net operating loss carryforwards (NOL DTAs) lost. Domestically focused companies fared better than internationally oriented firms. A price contribution analysis shows that easily assessed consequences (DTLs, NOL DTAs, tax rates) were priced faster than more complex issues (net DTLs, foreign exposure). In sum, the analysis demonstrates that expectations about tax rates greatly impact firm values.

With the growing adoption of IoT and sensor technologies, an enormous amount of data is being produced at a very rapid pace and in different application domains. This sensor data consists mostly of live data streams containing sensor observations, generated in a distributed fashion by multiple heterogeneous infrastructures with minimal or no interoperability. RDF streams emerged as a model to represent data streams, and RDF Stream Processing (RSP) refers to a set of technologies to process such data. RSP research has produced several successful results and scientific output, but it can be evidenced that in most of the cases the Web dimension is marginal or missing. It also noticeable the lack of proper infrastructures to enable the exchange of RDF streams over heterogeneous and different types of RSP systems, whose features may vary from data generation to querying, and from reasoning to visualisation. This article defines a set of requirements related to the creation of a web of RDF stream processors. These requirements are then used to analyse the current state of the art, and to build a novel proposal, WeSP, which addresses these concerns.

Fueled by enthusiasm of volunteers, government subsidies, and open data legislation, the Web of Data (WoD) has enjoyed a phenomenal growth. Commercial data, however, has been stuck in proprietary silos, as the monetization strategy for sharing data in the WoD is unclear. This is in contrast to the traditional web where advertisement fueled a lot of the growth. This raises the question how the WoD can (i) maintain its success when government subsidies disappear and (ii) convince commercial entities to share their wealth of data. In this talk based on a paper, we propose a marketplace for decentralized data follow- ing basic WoD principles. Our approach allows a customer to buy data from different, decentralized providers in a transparent way. As such, our marketplace presents a first step towards an economically viable WoD beyond subsidies.

We provide a rationale for imposing counter-cyclical capital ratios on banks. In our simple model, bankers cannot pledge the entire future revenues to investors, which limits borrowing in good and bad times. Complete markets do not sufficiently stabilize credit fluctuations, as banks allocate too much borrowing capacity to good states and too little to bad states. As a consequence, bank credit, output, capital prices or wages are excessively volatile. Imposing a (stricter) capital ratio in good states corrects the misallocation of the borrowing capacity, increases expected output and can be beneficial to all agents in the economy. Although in our economy, all agents are risk-neutral, counter-cyclical capital ratios are an effective stabilization tool. To ensure this effectiveness, capital ratios have to be based on ex ante equity capital, as classical capital ratios can be bypassed.

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For robotics and augmented reality systems operating in large and dynamic environments, place recognition and tracking using vision represent very challenging tasks. Additionally, when these systems need to reliably operate for very long time periods, such as months or years, further challenges are introduced by severe environmental changes, that can significantly alter the visual appearance of a scene. Thus, to unlock long term, large scale visual place recognition, it is necessary to develop new methodologies for improving localization under difficult conditions. As shown in previous work, gains in robustness can be achieved by exploiting the 3D structural information of a scene. The latter, extracted from image sequences, carries in fact more discriminative clues than individual images only. In this paper, we propose to represent a scene’s structure with semi-dense point clouds, due to their highly informative power, and the simplicity of their generation through mature visual odometry and SLAM systems. Then we cast place recognition as an instance of pose retrieval and evaluate several techniques, including recent learning based approaches, to produce discriminative descriptors of semi-dense point clouds. Our proposed methodology, evaluated on the recently published and challenging Oxford Robotcar Dataset, shows to outperform image-based place recognition, with improvements up to 30% in precision across strong appearance changes. To the best of our knowledge, we are the first to propose place recognition in semi-dense maps.