"i have a feeling trump will win..................": Forecasting Winners and Losers from User Predictions on Twitter

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome:"Leonardo DiCaprio will win Best Actor" vs. "Leonardo DiCaprio may win" or "No way Leonardo wins!". Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users' explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts' predictions and retrospectively identify surprise outcomes.Comment: Accepted at EMNLP 2017 (long paper

Duplicate Detection in Probabilistic Data

Collected data often contains uncertainties. Probabilistic databases have been proposed to manage uncertain data. To combine data from multiple autonomous probabilistic databases, an integration of probabilistic data has to be performed. Until now, however, data integration approaches have focused on the integration of certain source data (relational or XML). There is no work on the integration of uncertain (esp. probabilistic) source data so far. In this paper, we present a first step towards a concise consolidation of probabilistic data. We focus on duplicate detection as a representative and essential step in an integration process. We present techniques for identifying multiple probabilistic representations of the same real-world entities. Furthermore, for increasing the efficiency of the duplicate detection process we introduce search space reduction methods adapted to probabilistic data

On the Lie-algebraic origin of metric 3-algebras

Since the pioneering work of Bagger-Lambert and Gustavsson, there has been a proliferation of three-dimensional superconformal Chern-Simons theories whose main ingredient is a metric 3-algebra. On the other hand, many of these theories have been shown to allow for a reformulation in terms of standard gauge theory coupled to matter, where the 3-algebra does not appear explicitly. In this paper we reconcile these two sets of results by pointing out the Lie-algebraic origin of some metric 3-algebras, including those which have already appeared in three-dimensional superconformal Chern-Simons theories. More precisely, we show that the real 3-algebras of Cherkis-Saemann, which include the metric Lie 3-algebras as a special case, and the hermitian 3-algebras of Bagger-Lambert can be constructed from pairs consisting of a metric real Lie algebra and a faithful (real or complex, respectively) unitary representation. This construction generalises and we will see how to construct many kinds of metric 3-algebras from pairs consisting of a real metric Lie algebra and a faithful (real, complex or quaternionic) unitary representation. In the real case, these 3-algebras are precisely the Cherkis-Saemann algebras, which are then completely characterised in terms of this data. In the complex and quaternionic cases, they constitute generalisations of the Bagger-Lambert hermitian 3-algebras and anti-Lie triple systems, respectively, which underlie N=6 and N=5 superconformal Chern-Simons theories, respectively. In the process we rederive the relation between certain types of complex 3-algebras and metric Lie superalgebras.Comment: 29 pages (v4: really final version to appear in CMP. Example 7 has been improved.

Mission-driven entrepreneurship in ecosystems for sustainable systems change

De wereld is in verandering, we leven in een tijd van transitie. De schaal van de economische, sociale, ecologische en bestuurlijke uitdagingen die voor ons staan, is ongeëvenaard. De bevolking groeit in de richting van 9 miljard, de verstedelijking blijft toenemen, de risico’s van klimaatverandering nemen toe en de daarmee gepaarde energie-, water- en voedselveiligheid. Daarom volstaat vandaag de dag ‘business as usual’ niet meer. Dit promotieonderzoek baseert zich op de veronderstelling dat veel van de gangbare organiseerprincipes, zoals hiërarchie en strikte functie-verdeling, niet goed passen bij veel van de huidige maatschappelijke vraagstukken. Vanuit deze veronderstelling hebben we een vijflagenmodel ontwikkeld, welke gebaseerd is op de principes van systeemdenken. In dit proefschrift was speciale aandacht voor het meso-niveau (het ecosysteem), de onmisbare schakel die het micro-niveau (individuele missie-gedreven ondernemers) en het macro-niveau (duurzame systeemverandering) met elkaar verbindt. Dit proefschrift laat zien dat missie-gedreven ondernemers inspirerende change agents zijn in het systeem met hun onderneming als vehikel voor verandering. Voor grootschaligere systeemverandering naar een meer duurzame economie is het echter nodig samen te werken in ecosystemen. Het idee hierbij is dat er een vergaande samenwerking ontstaat tussen uiteenlopende organisaties, zowel uit de private als de publieke sector, zowel grote bedrijven en kleinere ondernemers. Deze organisaties overschrijden daarmee de grenzen van de traditionele industrieën en zijn in plaats daarvan georganiseerd rond een specifiek vraagstuk of thema. Samenwerken in ecosystemen gaat echter niet vanzelf, er zijn specifieke kennis en vaardigheden nodig, zoals systeemdenken, netwerkbenaderingen binnen en tussen organisaties, en het leiden van meerpartijenoverleg.In this time of systems change entrepreneurs can contribute to finding solutions for challenges the world faces. Focusing on technology is not sufficient, when we do not address organizing principles to change the way of doing business and stimulating innovation. This PhD research has been carried out with the assumption that current and still dominant organizing principles based on hierarchies in business and society are inadequate or even counter-effective in achieving a more sustainable economy. Thereto, this dissertation introduces a five-layered conceptual model based on systems thinking that offers a guidance to identify and analyze the organizing principles needed for sustainable systems change. The main aim of this research was to obtain in-depth insights into how this conceptual model works in practice, with an emphasis on the meso-level (ecosystem) and how this level connects the micro-level (mission-driven entrepreneur) and macro-level (sustainable systems change). The research has found that the ecosystem indeed plays an important role in leveraging the initiatives of mission-driven entrepreneurs for sustainable systems change. New skills and knowledge are needed in order to effectively apply organizational principles, such as deep collaboration and networking, to working in ecosystems. This dissertation shows that mission-driven entrepreneurs are inspiring change agents in the system with their company as a vehicle for change. However, for more systemic change needed for a sustainable economy, it is necessary to work together in ecosystems. The idea here is that there will be a far-reaching collaboration between various organizations, both from the private and the public sector, both large companies and smaller enterprises. These organizations therewith transcend the boundaries of traditional industries and are instead organized around a specific issue or theme. However, collaboration in ecosystems does not happen by itself; specific knowledge and skills are needed, such as systems thinking, network approaches, and leading multiparty dialogues

Catalogue of cataclysmic binaries, low-mass X-ray binaries and related objects (Seventh edition)

The catalogue lists coordinates, apparent magnitudes, orbital parameters, and stellar parameters of the components and other characteristc properties of 472 cataclysmic binaries, 71 low-mass X-ray binaries and 113 related objects with known or suspected orbital periods together with a comprehensive selection of the relevant recent literature. In addition, the catalogue contains a list of references to published finding charts for 635 of the 656 objects, and a cross-reference list of alias object designations. Literature published before 1 January 2003 has, as far as possible, been taken into account. All data can be accessed via the dedicated catalogue webpage at http://www.mpa-garching.mpg.de/RKcat/ and http://physics.open.ac.uk/RKcat/ and at CDS via anonymous ftp to cdsarc.u-strasbg.fr (30.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/404/301. We will update the information given on the catalogue webpage regularly, initially every six months

The minimum period problem in cataclysmic variables

We investigate if consequential angular momentum losses (CAML) or an intrinsic deformation of the donor star in CVs could increase the CV bounce period from the canonical theoretical value ~65 min to the observed value $P_{min} \approx77$ min, and if a variation of these effects in a CV population could wash out the theoretically predicted accumulation of systems near the minimum period (the period spike). We are able to construct suitably mixed CV model populations that a statisticial test cannot rule out as the parent population of the observed CV sample. However, the goodness of fit is never convincing, and always slightly worse than for a simple, flat period distribution. Generally, the goodness of fit is much improved if all CVs are assumed to form at long orbital periods. The weighting suggested by King, Schenker & Hameury (2002) does not constitute an improvment if a realistically shaped input period distribution is used. Put your abstract here.Comment: 10 pages, Latex, 13 postscript figures, Accepted for publication in MNRA

On the use of self-organizing maps to accelerate vector quantization

Self-organizing maps (SOM) are widely used for their topology preservation property: neighboring input vectors are quantified (or classified) either on the same location or on neighbor ones on a predefined grid. SOM are also widely used for their more classical vector quantization property. We show in this paper that using SOM instead of the more classical Simple Competitive Learning (SCL) algorithm drastically increases the speed of convergence of the vector quantization process. This fact is demonstrated through extensive simulations on artificial and real examples, with specific SOM (fixed and decreasing neighborhoods) and SCL algorithms.Comment: A la suite de la conference ESANN 199

Numerical stability of mass transfer driven by Roche lobe overflow in close binaries

Numerical computation of the time evolution of the mass transfer rate in a close binary can be and, in particular, has been a computational challenge. Using a simple physical model to calculate the mass transfer rate, we show that for a simple explicit iteration scheme the mass transfer rate is numerically unstable unless the time steps are sufficiently small. In general, more sophisticated explicit algorithms do not provide any significant improvement since this instability is a direct result of time discretization. For a typical binary evolution, computation of the mass transfer rate as a smooth function of time limits the maximum tolerable time step and thereby sets the minimum total computational effort required for an evolutionary computation. By methods of ``Controlling Chaos'' it can be shown that a specific implicit iteration scheme, based on Newton's method, is the most promising solution for the problem.Comment: 6 pages, LaTeX, two eps figures, Astronomy and Astrophysics, accepte