LifeWatch – A European e-Science and observatory infrastructure supporting access and use of biodiversity and ecosystem data

There are many promising earth and biodiversity-monitoring projects underway across the globe, but they often operate in information islands, unable easily to share data with others. This is not convenient: It is a barrier to scientists collaborating on complex, cross-disciplinary projects which is an essential nature of biodiversity research. 

LifeWatch (www.lifewatch.eu) is an ESFRI (European Strategy Forum on Research Infrastructures) initiative which has just entered its construction phase. It is aiming at new ways of collaboration, in an open-access research environment to solve complex societal and scientific questions on biodiversity and ecosystems. It installs a range of new services and tools to help the researchers communicate, share data, create models, analyze results, manage projects and organize the community. The power of LifeWatch comes from linking all kinds of biodiversity related databases (e.g. collections, long-term monitoring data) to tools for analysis and modeling, opening entirely new avenues for research with the potential for new targeted data generation. At this level the interface with national data repositories becomes most important, as this opens the opportunity for users to gain advantage from data availability on the European level. LifeWatch will provide common methods to discover, access, and develop available and new data, analytical capabilities, and to catalog everything, to track citation and re-use of data, to annotate, and to keep the system secure. This includes computing tool-kits for researchers: for instance, an interoperable computing environment for statistical analysis, cutting-edge software to manage the workflow in scientific projects, and access to new or existing computing resources. The result: ‘e-laboratories’ or virtual labs, through which researchers distributed across countries, time zones and disciplines can collaborate. With emphasis on the open sharing of data and workflows (and associated provenance information) the infrastructure allows scientists to create e-laboratories across multiple organizations, controlling access where necessary

Meshed Up: Learnt Error Correction in 3D Reconstructions

Dense reconstructions often contain errors that prior work has so far minimised using high quality sensors and regularising the output. Nevertheless, errors still persist. This paper proposes a machine learning technique to identify errors in three dimensional (3D) meshes. Beyond simply identifying errors, our method quantifies both the magnitude and the direction of depth estimate errors when viewing the scene. This enables us to improve the reconstruction accuracy. We train a suitably deep network architecture with two 3D meshes: a high-quality laser reconstruction, and a lower quality stereo image reconstruction. The network predicts the amount of error in the lower quality reconstruction with respect to the high-quality one, having only view the former through its input. We evaluate our approach by correcting two-dimensional (2D) inverse-depth images extracted from the 3D model, and show that our method improves the quality of these depth reconstructions by up to a relative 10% RMSE.Comment: Accepted for the International Conference on Robotics and Automation (ICRA) 201

A Multimodal Hierarchial Approach to Robot Learning by Imitation

In this paper we propose an approach to robot learning by imitation that uses the multimodal inputs of language, vision and motor. In our approach a student robot learns from a teacher robot how to perform three separate behaviours based on these inputs. We considered two neural architectures for performing this robot learning. First, a one-step hierarchial architecture trained with two different learning approaches either based on Kohonen's self-organising map or based on the Helmholtz machine turns out to be inefficient or not capable of performing differentiated behavior. In response we produced a hierarchial architecture that combines both learning approaches to overcome these problems. In doing so the proposed robot system models specific aspects of learning using concepts of the mirror neuron system (Rizzolatti and Arbib, 1998) with regards to demonstration learning

Two-fold Semantic Web service matchmaking – applying ontology mapping for service discovery

Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach

Probing the origin of UX Ori-type variability in the YSO binary CO Ori with VLTI/GRAVITY

The primary star in the young stellar object (YSO) binary CO Ori displays UX Ori-type variability: irregular, high amplitude optical and near-infrared photometric fluctuations where flux minima coincide with polarization maxima. This is attributed to changes in local opacity. In CO Ori A, these variations exhibit a 12.4 yr cycle. Here, we investigate the physical origin of the fluctuating opacity and its periodicity using interferometric observations of CO Ori obtained using VLTI/GRAVITY. Continuum K-band circum-primary and circum-secondary emission are marginally spatially resolved for the first time while Br$\gamma$ emission is detected in the spectrum of the secondary. We estimate a spectral type range for CO Ori B of K2-K5 assuming visual extinction, $A_{\rm{V}}=2$ and a distance of 430 pc. From geometric modelling of the continuum visibilities, the circum-primary emission is consistent with a central point source plus a Gaussian component with a full-width-half-maximum of 2.31$\pm$0.04 milliarcseconds (mas), inclined at 30.2$\pm$2.2$^{\circ}$ and with a major axis position angle of 40$\pm$6$^{\circ}$. This inclination is lower than that reported for the discs of other UX Ori-type stars, providing a first indication that the UX Ori phenomena may arise through fluctuations in circumstellar material exterior to a disc, e.g. in a dusty outflow. An additional wide, symmetric Gaussian component is required to fit the visibilities of CO Ori B, signifying a contribution from scattered light. Finally, closure phases of CO Ori A were used to investigate whether the 12.4 yr periodicity is associated with an undetected third component, as has been previously suggested. We rule out any additional companions contributing more than 3.6% to the K-band flux within ~7.3-20 mas of CO Ori A.Comment: 7 pages, 4 figures, accepted for publication in MNRA

From Imitation to Collusion - A Comment

In oligopoly, imitating the most successful competitor yields very competitive outcomes. This theoretical prediction has been confirmed experimentally by a number of studies. A recent paper by Friedman et al. (2015) qualifies those results in an interesting way: while they replicate the very competitive results for the first 25 to 50 periods, they show that when using a much longer time horizon of 1200 periods, results slowly turn to more and more collusive outcomes. We replicate their result for duopolies. However, with 4 firms none of our oligopolies becomes permanently collusive. Instead, the average quantity always stays above the Cournot-Nash equilibrium quantity. Thus, it seems that “four remain many” even with 1200 periods