pinecone/core-2020-05-10-deduplication huggingface dataset:
Open clusters: probes of galaxy evolution and bench tests of stellar
  models

Open clusters are the only example of single-age, single initial chemical
composition populations in the Galaxy, and they play an important role in the
study of the formation and evolution of the Galactic disk. In addition, they
have been traditionally employed to test theoretical stellar evolution models.
A brief review of constraints/tests of white dwarf models/progenitors, and
rotating star models based on Galactic open clusters' observations is
presented, introducing also recent contributions of asteroseismic analyses.Comment: Proc. of the workshop "Asteroseismology of stellar populations in the
  Milky Way" (Sesto, 22-26 July 2013), Astrophysics and Space Science
  Proceedings, (eds. A. Miglio, L. Girardi, P. Eggenberger, J. Montalban
Exploiting the open clusters in the Kepler and CoRoT fields

The open clusters in the Kepler and CoRoT fields potentially provide tight
constraints for tests of stellar models and observational methods because they
allow a combination of complementary methods. We are in the process of identi-
fying and measuring parameters for detached eclipsing binaries (dEBs) in the
open clusters in the Kepler and CoRoT fields. We make use of measurements of
dEBs in the clusters to test the accuracy of asteroseismic scaling relations
for mass. We are able to provide strong indications that the asteroseismic
scaling relations over- estimate the stellar mass, but we are not yet able to
distinguish between different proposed corrections from the literature. We
argue how our ongoing measurements of more dEBs in more clusters, complemented
by dEBs in the field, should be able to break the degeneracy. We also briefly
describe how we can identify cluster stars that have evolved through
non-standard evolution by making use of ensemble asteroseismology.Comment: Proc. of the workshop "Asteroseismology of stellar populations in the
  Milky Way" (Sesto, 22-26 July 2013), Astrophysics and Space Science
  Proceedings, (eds. A. Miglio, L. Girardi, P. Eggenberger, J. Montalban
Photometric stellar parameters for asteroseismology and Galactic studies

Asteroseismology has the capability of delivering stellar properties which
would otherwise be inaccessible, such as radii, masses and thus ages of stars.
When coupling this information with classical determinations of stellar
parameters, such as metallicities, effective temperatures and angular
diameters, powerful new diagnostics for both stellar and Galactic studies can
be obtained. I review how different photometric systems and filters carry
important information on classical stellar parameters, the accuracy at which
those parameters can be derived, and summarize some of the calibrations
available in the literature for late-type stars. Recent efforts in combining
classical and asteroseismic parameters are discussed, and the unique- ness of
their intertwine is highlighted.Comment: Proc. of the workshop "Asteroseismology of stellar populations in the
  Milky Way" (Sesto, 22-26 July 2013), Astrophysics and Space Science
  Proceedings, (eds. A. Miglio, L. Girardi, P. Eggenberger, J. Montalban
Spectroscopic constraints for low-mass asteroseismic targets

A full exploitation of the observations provided by the CoRoT and Kepler
missions depends on our ability to complement these data with accurate
effective temperatures and chemical abundances. We review in this contribution
the major efforts that have been undertaken to characterise late-type, seismic
targets based on spectra gathered as part of the ground-based, follow-up
campaigns. A specific feature of the spectroscopic studies of these stars is
that the gravity can be advantageously fixed to the more accurate value derived
from the pulsation spectrum. We describe the impact that such an approach has
on the estimation of Teff and [Fe/H]. The relevance of red-giant seismic
targets for studies of internal mixing processes and stellar populations in our
Galaxy is also briefly discussed.Comment: To appear in 'Asteroseismology of Stellar Populations in the Milky
  Way', Astrophysics and Space Science Proceedings (8 pages, 3 colour figures
Preliminary Evaluation of the Kepler Input Catalog Extinction Model
  Using Stellar Temperatures

The Kepler Input Catalog (KIC) provides reddening estimates for its stars,
based on the assumption of a simple exponential dusty screen. This project
focuses on evaluating and improving these reddening estimates for the KIC's
giant stars, for which extinction is a much more significant concern than for
the nearby dwarf stars. We aim to improve the calibration (and thus
consistency) amongst various photometric and spectroscopic temperatures of
stars in the Kepler field by removing systematics due to incorrect extinction
assumptions. The revised extinction estimates may then be used to derive
improved stellar and planetary properties. We plan to eventually use the large
number of KIC stars as probes into the structure and properties of the Galactic
ISM.Comment: Proc. of the workshop "Asteroseismology of stellar populations in the
  Milky Way" (Sesto, 22-26 July 2013), Astrophysics and Space Science
  Proceedings, (eds. A. Miglio, L. Girardi, P. Eggenberger, J. Montalban
IK-FA, a new heuristic inverse kinematics solver using firefly algorithm

In this paper, a heuristic method based on Firefly Algorithm is proposed for inverse kinematics problems in articulated robotics. The proposal is called, IK-FA. Solving inverse kinematics, IK, consists in finding a set of joint-positions allowing a specific point of the system to achieve a target position. In IK-FA, the Fireflies positions are assumed to be a possible solution for joints elementary motions. For a robotic system with a known forward kinematic model, IK-Fireflies, is used to generate iteratively a set of joint motions, then the forward kinematic model of the system is used to compute the relative Cartesian positions of a specific end-segment, and to compare it to the needed target position. This is a heuristic approach for solving inverse kinematics without computing the inverse model. IK-FA tends to minimize the distance to a target position, the fitness function could be established as the distance between the obtained forward positions and the desired one, it is subject to minimization. In this paper IK-FA is tested over a 3 links articulated planar system, the evaluation is based on statistical analysis of the convergence and the solution quality for 100 tests. The impact of key FA parameters is also investigated with a focus on the impact of the number of fireflies, the impact of the maximum iteration number and also the impact of (a, ß, ¿, d) parameters. For a given set of valuable parameters, the heuristic converges to a static fitness value within a fix maximum number of iterations. IK-FA has a fair convergence time, for the tested configuration, the average was about 2.3394 × 10-3 seconds with a position error fitness around 3.116 × 10-8 for 100 tests. The algorithm showed also evidence of robustness over the target position, since for all conducted tests with a random target position IK-FA achieved a solution with a position error lower or equal to 5.4722 × 10-9.Peer ReviewedPostprint (author's final draft
Peri-implant infections of oral biofilm etiology

Biofilms are complex microbial communities that grow on various surfaces in nature. The oral micobiota tend to form polymicrobial biofilms, particularly on the hard mineralized surfaces of teeth, which may impact on oral health and disease. They can cause inflammation of the adjacent tooth-supporting (periodontal) tissues, leading to destructive periodontal disease and tooth loss. The emergence of osseointegrated dental implants as a restorative treatment option for replacing missing teeth has also brought along new artificial surfaces within the oral cavity, on which oral bacteria can form biofilms. As in the case of natural teeth, biofilms on implant surfaces may also trigger infection and cause inflammatory destruction of the peri-implant tissue (i.e. peri-implantitis). While there are strong similarities in the composition of the mixed microbial flora between periodontal and peri-implant infections, there are also a few distinctive differences. The immunological events underlying the pathogenesis of peri-implant infections are qualitatively similar, yet more extensive, compared to periodontal infections, resulting in a faster progression of tissue destruction. This chapter summarizes the current knowledge on the microbiology and immunology of peri-implant infections, including findings from the peri-implant crevicular fluid, the inflammatory exudate of the peri-implant tissue. Moreover, it discusses the diagnosis and current approaches for the treatment of oral infections
Cut-elimination, substitution and normalisation

Date of Acceptance: 01/2015We present a proof (of the main parts of which there is a formal version, checked with the Isabelle proof assistant) that, for a G3-style calculus covering all of intuitionistic zero-order logic, with an associated term calculus, and with a particular strongly normalising and confluent system of cut-reduction rules, every reduction step has, as its natural deduction translation, a sequence of zero or more reduction steps (detour reductions, permutation reductions or simplifications). This complements and (we believe) clarifies earlier work by (e.g.) Zucker and Pottinger on a question raised in 1971 by Kreisel.PostprintPeer reviewe
Advances in Extreme Learning Machines

Nowadays, due to advances in technology, data is generated at an incredible pace, resulting in large data sets of ever-increasing size and dimensionality. Therefore, it is important to have efficient computational methods and machine learning algorithms that can handle such large data sets, such that they may be analyzed in reasonable time. One particular approach that has gained popularity in recent years is the Extreme Learning Machine (ELM), which is the name given to neural networks that employ randomization in their hidden layer, and that can be trained efficiently. This dissertation introduces several machine learning methods based on Extreme Learning Machines (ELMs) aimed at dealing with the challenges that modern data sets pose. The contributions follow three main directions.
   Firstly, ensemble approaches based on ELM are developed, which adapt to context and can scale to large data. Due to their stochastic nature, different ELMs tend to make different mistakes when modeling data. This independence of their errors makes them good candidates for combining them in an ensemble model, which averages out these errors and results in a more accurate model. Adaptivity to a changing environment is introduced by adapting the linear combination of the models based on accuracy of the individual models over time. Scalability is achieved by exploiting the modularity of the ensemble model, and evaluating the models in parallel on multiple processor cores and graphics processor units. Secondly, the dissertation develops variable selection approaches based on ELM and Delta Test, that result in more accurate and efficient models. Scalability of variable selection using Delta Test is again achieved by accelerating it on GPU. Furthermore, a new variable selection method based on ELM is introduced, and shown to be a competitive alternative to other variable selection methods. Besides explicit variable selection methods, also a new weight scheme based on binary/ternary weights is developed for ELM. This weight scheme is shown to perform implicit variable selection, and results in increased robustness and accuracy at no increase in computational cost. Finally, the dissertation develops training algorithms for ELM that allow for a flexible trade-off between accuracy and computational time. The Compressive ELM is introduced, which allows for training the ELM in a reduced feature space. By selecting the dimension of the feature space, the practitioner can trade off accuracy for speed as required.
   Overall, the resulting collection of proposed methods provides an efficient, accurate and flexible framework for solving large-scale supervised learning problems. The proposed methods are not limited to the particular types of ELMs and contexts in which they have been tested, and can easily be incorporated in new contexts and models
Recent Topics of Research around the YAGO Knowledge Base

A knowledge base (KB) is a formal collection of knowledge about the world. In this paper, we explain how the YAGO KB is constructed. We also summarize our contributions to different aspects of KB management in general. One of these aspects is rule mining, i.e., the identification of patterns such as spouse(x,y) ∧ livesIn(x,z) ⇒livesIn(y,z). Another aspect is the incompleteness of KBs. We propose to integrate data from Web Services into the KB in order to fill the gaps. Further, we show how the overlap between existing KBs can be used to align them, both in terms of instances and in terms of the schema. Finally, we show how KBs can be protected by watermarking
Investigation of Alzheimer's Disease EEG Frequency Components with Lempel-Ziv Complexity

. This pilot study applied Lempel-Ziv Complexity (LZC) to 22 resting EEG signals, collected using the 10-20 international system, from 11 patients with Alzheimer’s disease (AD) and 11 age-matched controls. This allowed for frequency band analysis as the EEG signals were first prefiltered with a third order Hamming window in the ranges F to F+WHz with both F and W equal to 1-30Hz respectively. Control subjects were found to have a greater signal complexity than AD patients with statistically significant bands seen at various ranges in all 16 electrodes. The maximum statistical significance (Student’s t test, p<0.01) was increased over the findings with traditional signal filtering techniques allowing the whole range, with a maximum significance of p=3.50e-6 at electrode T4 between 7-18Hz. Electrode F4 also showed significantly high statistically significant differences. The maximum accuracy, both controls and AD patients correctly identified, found with Receiver Operating Characteristic Curves was 95.45% (21 of 22 subjects correctly classified) at T4 (7-18Hz and 7-20Hz), Fp2 (8-32Hz) and F4 (6-21Hz), which is significantly more accurate than the most accurate methods previously applied to this dataset. The beta band (13-30Hz) was found to be most influential in separating the two test groups in this study with the best range suggested to be 5-26Hz, combining traditional theta, alpha and beta bands. These findings suggest pre-filtering has a significant effect on method outcomes and can be successfully tailored to improve the statistical effectiveness of LZC at distinguishing between these two EEG groups. However, more testing is required to investigate the effectiveness at distinguishing other signal dynamics
Making Meaningful Musical Experiences Accessible Using the iPad

In this chapter we report on our experiences using ubiquitous computing devices to introduce music-based creative activities into an Australian school. The use of music applications on mobile tablet computers (iPads) made these activities accessible to students with a limited musical background and practicable in a general-purpose classroom setting. The activities were designed to be meaningful and contribute toward personal re-silience in the students. We describe the approach to meeting these objectives and discuss results of the project. The chapter includes an overview of the ongoing project including its aims, objectives and utilization of mobile technologies and software with generative and networkable capabilities. Two theoretical frameworks informed the research design; the meaningful engagement matrix and personal resilience. We describe these frameworks and how they inform the activity planning. We report on the activities undertaken to date and share results from ques-tionnaires, interviews, musical outcomes, and observation.Full Tex
Organising LTL Monitors over Distributed Systems with a Global Clock

International audienceUsers wanting to monitor distributed systems often prefer to abstract away the architecture of the system, allowing them to directly specify correct-ness properties on the global system behaviour. To support this abstraction, a compilation of the properties would not only involve the typical choice of monitoring algorithm, but also the organisation of submonitors across the component network. Existing approaches, considered in the context of LTL properties over distributed systems with a global clock, include the so-called orchestration and migration approaches. In the orchestration approach, a central monitor receives the events from all subsystems. In the migration approach, LTL formulae transfer themselves across subsystems to gather local information. We propose a third way of organising submonitors: choreography — where monitors are orgnized as a tree across the distributed system, and each child feeds intermediate results to its parent. We formalise this approach, proving its cor-rectness and worst case performance, and report on an empirical investigation comparing the three approaches on several concerns of decentralised monitoring
Factors influencing polychronous group sustainability as a model of working memory

Several computational models have been designed to help our understanding of the conditions under which persistent activity can be sustained in cortical circuits during working memory tasks. Here we focus on one such model that has shown promise, that uses polychronization and short term synaptic dynamics to achieve this reverberation, and explore it with respect to different physiological parameters in the brain, including size of the network, number of synaptic connections, small-world connectivity, maximum axonal conduction delays, and type of cells (excitatory or inhibitory). We show that excitation and axonal conduction delays greatly affect the sustainability of spatio-temporal patterns of spikes called polychronous groups. © 2014 Springer International Publishing Switzerland
Classifying spike patterns by reward-modulated STDP

Reward-modulated learning rules for spiking neural networks have emerged, that have been demonstrated to solve a wide range of reinforcement learning tasks. Despite this, little work has aimed to classify spike patterns by the timing of output spikes. Here, we apply a rewardmaximising learning rule to teach a spiking neural network to classify input patterns by the latency of output spikes. Furthermore, we compare the performance of two escape rate functions that drive output spiking activity: the Arrhenius & Current (A&C) model and Exponential (EXP) model. We find A&C consistently outperforms EXP, and especially in terms of the time taken to converge in learning. We also show that jittering input patterns with a low noise amplitude leads to an improvement in learning, by reducing the variation in the performance. © 2014 Springer International Publishing Switzerland
Fiber bundle segmentation using spectral embedding and supervised learning

Diffusion-weighted imaging and tractography offer a unique approach to probe the microarchitecture of brain tissue noninvasively. Whole brain tractography, however, produces an unstructured set of fiber trajectories, whereas clinical applications often demand targeted tracking of specific bundles. This work presents a novel, hybrid approach to fiber bundle segmentation, using spectral embedding and supervised learning. Training data of 20 healthy subjects is labeled with a parcellation-based method, and used to train support vector machine and random forest classifiers. Cross-validation was used to avoid overfitting. Results on testing data of 5 independent subjects show a clear improvement over unsupervised methods. Moreover, estimating the label probabilities allows to reduce the effect of outliers.status: publishe
Atlas-guided global tractography: imposing a prior on the local track orientation

Since its introduction over a decade ago, diffusion tractography has come a long way from local, deterministic methods, over probabilistic approaches, towards global tractography. Yet, the development of tractography methods has been largely focused on single subject data, and very little on cross-population analysis and inter-subject variability. In this work, we extend global tractography with a prior on the local track orientation distribution (TOD), derived from 20 normal subjects. The proposed method is evaluated in 5 independent subjects. Results show that adding such prior regularizes the reconstructed track distribution, although registration errors can induce local artefacts. We conclude that atlas-guided global tractography can improve the fibre reconstruction and ultimately detect and quantify inter-subject differences in tractography.status: publishe
Immersive multi-user decision training games with ARLearn

Serious gaming approaches so far focus mainly on skill development, motivational aspects or providing immersive learning situations. Little work has been reported to foster awareness and decision competencies in complex deci-sion situations involving incomplete information and multiple stakeholders. We address this issue exploring the technical requirements and possibilities to de-sign games for such situations in three case studies: a hostage taking situation, a multi-stakeholder logistics case, and a health-care related emergency case. To implement the games, we use a multi-user enabled mobile game development platform (ARLearn). We describe the underlying real world situations and edu-cational challenges and analyse how these are reflected in the ARLearn games realized. Based on these cases we propose a way to increase the immersiveness of mobile learning games.SALOM
DDART, a Dynamic Dashboard for Collection, Analysis and Visualization of Activity and Reporting Traces

International audienceMost Learning Management Systems (LMS) focus on providing rich learning materials to the learners but rarely offer possibilities to monitor and analyze their learning processes. However metacognitive strategies argue that learners can improve their learning performances by monitoring their activities, especially in the context of Project-Based Learning (PBL). Our work consists in designing tools to support metacognitive strategies. In this paper, a framework and a dynamic dashboard for PBL situations are proposed to help learners to collect, analyze and visualize the meaningful traces of their activities by themselves. The proposed framework integrates activity traces (interactions with the LMS) and reporting traces (semi-structured sentences written by the learners). The dynamic dashboard supports learners in the creation customizable indicators through a user-friendly interface
An evaluation framework for data competitions in TEL

This paper presents a study describing the development of an Evaluation Framework (EF) for data competitions in TEL. The study applies the Group Concept Method (GCM) to empirically depict criteria and their indicators for evaluating software applications in TEL. A statistical analysis including multidimensional scaling and hierarchical clustering on the GCM data identified the following six evaluation criteria: 1.Educational Innovation, 2.Usability, 3.Data, 4.Performance, 5.Privacy, and 6.Audience. Each of them was operationalized through a set of indicators. The resulting Evaluation Framework (EF) incorporating these criteria was applied to the first data competition of the LinkedUp project. The EF was consequently improved using the results from reviewers' interviews, which were analysed qualitatively and quantitatively. The outcome of these efforts is a comprehensive EF that can be used for TEL data competitions and for the evaluation of TEL tools in general. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11200-8_6.EC/FP7/LinkedUpEC/FP7/DURAAR