pinecone/core-2020-05-10-deduplication huggingface dataset:
Large scale monocular vision-only mapping from a fixed-wing sUAS

This paper presents the application of a monocular visual SLAMon a fixed-wing small Unmanned Aerial System (sUAS) capable of simultaneous estimation of aircraft pose and scene structure. We demonstrate the robustness of unconstrained vision alone in producing reliable pose estimates of a sUAS, at altitude. It is ultimately capable of online state estimation feedback for aircraft control and next-best-view estimation for complete map coverage without the use of additional sensors.We explore some of the challenges of visual SLAM from a sUAS including dealing with planar structure, distant scenes and noisy observations. The developed techniques are applied on vision data gathered from a fast-moving fixed-wing radio control aircraft flown over a 1×1km rural area at an altitude of 20-100m.We present both raw Structure from Motion results and a SLAM solution that includes FAB-MAP based loop-closures and graph-optimised pose. Timing information is also presented to demonstrate near online capabilities. We compare the accuracy of the 6-DOF pose estimates to an off-the-shelfGPS aided INS over a 1.7kmtrajectory.We also present output 3D reconstructions of the observed scene structure and texture that demonstrates future applications in autonomous monitoring and surveying
Featureless visual processing for SLAM in changing outdoor environments

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in\ud
these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the\ud
SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather\ud
to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to\ud
only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability\ud
of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
An Efficient OpenMP Loop Scheduler for Irregular Applications on Large-Scale NUMA Machines

International audienceNowadays shared memory HPC platforms expose a large number of cores organized in a hierarchical way. Parallel application programmers strug- gle to express more and more fine-grain parallelism and to ensure locality on such NUMA platforms. Independent loops stand as a natural source of paral- lelism. Parallel environments like OpenMP provide ways of parallelizing them efficiently, but the achieved performance is closely related to the choice of pa- rameters like the granularity of work or the loop scheduler. Considering that both can depend on the target computer, the input data and the loop workload, the application programmer most of the time fails at designing both portable and ef- ficient implementations. We propose in this paper a new OpenMP loop scheduler, called adaptive, that dynamically adapts the granularity of work considering the underlying system state. Our scheduler is able to perform dynamic load balancing while taking memory affinity into account on NUMA architectures. Results show that adaptive outperforms state-of-the-art OpenMP loop schedulers on memory- bound irregular applications, while obtaining performance comparable to static on parallel loops with a regular workload
Statistical machine translation of subtitles: From OpenSubtitles to TED

In this paper, we describe how the differences between subtitle corpora, OpenSubtitles and TED, influence machine translation quality. In particular, we investigate whether statistical machine translation systems built on their basis can be used interchangeably. Our results show that OpenSubtiles and TED contain very different kinds of subtitles that warrant a subclassification of the genre. In addition, we have taken a closer look at the translation of questions as a sentence type with special word order. Interestingly, we found the BLEU scores for questions to be higher than for random sentences
Efficient Baseline-free Sampling in Parameter Exploring Policy
  Gradients: Super Symmetric PGPE

Policy Gradient methods that explore directly in parameter space are among
the most effective and robust direct policy search methods and have drawn a lot
of attention lately. The basic method from this field, Policy Gradients with
Parameter-based Exploration, uses two samples that are symmetric around the
current hypothesis to circumvent misleading reward in \emph{asymmetrical}
reward distributed problems gathered with the usual baseline approach. The
exploration parameters are still updated by a baseline approach - leaving the
exploration prone to asymmetric reward distributions. In this paper we will
show how the exploration parameters can be sampled quasi symmetric despite
having limited instead of free parameters for exploration. We give a
transformation approximation to get quasi symmetric samples with respect to the
exploration without changing the overall sampling distribution. Finally we will
demonstrate that sampling symmetrically also for the exploration parameters is
superior in needs of samples and robustness than the original sampling
approach.Comment: Artificial Neural Networks and Machine Learning - ICANN 2013 Springer
  Berlin Heidelberg 2013. 130-13
A tensor-based population value decomposition to explain rectal toxicity after prostate cancer radiotherapy.

International audienceIn prostate cancer radiotherapy the association between the dose distribution and the occurrence of undesirable side-effects is yet to be revealed. In this work a method to perform population analysis by comparing the dose distributions is proposed. The method is a tensor-based approach that generalises an existing method for 2D images and allows for the highlighting of over irradiated zones correlated with rectal bleeding after prostate cancer radiotherapy. Thus, the aim is to contribute to the elucidation of the dose patterns correlated with rectal toxicity. The method was applied to a cohort of 63 patients and it was able to build up a dose pattern characterizing the difference between patients presenting rectal bleeding after prostate cancer radiotherapy and those who did not
Genetic clustering on the hippocampal surface for genome-wide association studies

Imaging genetics aims to discover how variants in the human genome influence brain measures derived from images. Genome-wide association scans (GWAS) can screen the genome for common differences in our DNA that relate to brain measures. In small samples, GWAS has low power as individual gene effects are weak and one must also correct for multiple comparisons across the genome and the image. Here we extend recent work on genetic clustering of images, to analyze surface-based models of anatomy using GWAS. We performed spherical harmonic analysis of hippocampal surfaces, automatically extracted from brain MRI scans of 1254 subjects. We clustered hippocampal surface regions with common genetic influences by examining genetic correlations (rg) between the normalized deformation values at all pairs of surface points. Using genetic correlations to cluster surface measures, we were able to boost effect sizes for genetic associations, compared to clustering with traditional phenotypic correlations using Pearson\u27s r
Comparing fbeta-optimal with distance based merge functions

Merge functions informally combine information from a certain universe into a solution over that same universe. This typically results in a, preferably optimal, summarization. In previous research, merge functions over sets have been looked into extensively. A specic case concerns sets that allow elements to appear more than once, multisets. In this paper we compare two types of merge functions over multisets against each other. We examine both general properties as practical usability in a real world application
A Possibilistic Logic Approach to Conditional Preference Queries

The paper presents a new approach to deal with database preference queries, where preferences are represented in the style of possibilistic logic, using symbolic weights. The symbolic weights may be processed without the need of a numerical assignment of priority. Still, it is possible to introduce a partial ordering among the symbolic weights if necessary. On this basis, four methods that have an increasing discriminating power for ranking the answers to conjunctive queries, are proposed. The approach is compared to different lines of research in preference queries including skyline-based methods and fuzzy set-based queries. With the four proposed ranking methods the first group of best answers is made of non dominated items. The purely qualitative nature of the approach avoids the commensurability requirement of elementary evaluations underlying the fuzzy logic methods
Bipolar querying of valid-time intervals subject to uncertainty

Databases model parts of reality by containing data representing properties of real-world objects or concepts. Often, some of these properties are time-related. Thus, databases often contain data representing time-related information. However, as they may be produced by humans, such data or information may contain imperfections like uncertainties. An important purpose of databases is to allow their data to be queried, to allow access to the information these data represent. Users may do this using queries, in which they describe their preferences concerning the data they are (not) interested in. Because users may have both positive and negative such preferences, they may want to query databases in a bipolar way. Such preferences may also have a temporal nature, but, traditionally, temporal query conditions are handled specifically. In this paper, a novel technique is presented to query a valid-time relation containing uncertain valid-time data in a bipolar way, which allows the query to have a single bipolar temporal query condition
Understanding functional resonance through a federation of models : preliminary findings of an avionics case study

FRAM has been proposed as a method for the analysis of complex socio-technical systems, which may be able to overcome the limitations of traditional methods that focus on simple cause and effect relationships. FRAM on its own may be most useful for modeling the system at a high level of abstraction. There is less evidence about its utility for modeling interactions at greater levels of detail. We applied different modeling approaches to investigate situations that may give rise to functional resonance in an avionics case study. FRAM was used to model higher-level dependencies, HAMSTERS was used to provide a deeper understanding of human functions, and ICO-Petshop was used to model technical system functions. The paper describes preliminary results of the application of this federation of models, and highlights potential benefits as well as challenges that may have to be overcome
Testing Autonomous Robot Control Software Using Procedural Content Generation

We present a novel approach for reducing manual effort when testing autonomous robot control algorithms. We use procedural content generation, as developed for the film and video game industries, to create a diverse range of test situations. We execute these in the Player/Stage robot simulator and automatically rate them for their safety significance using an event-based scoring system. Situations exhibiting dangerous behaviour will score highly, and are thus flagged for the attention of a safety engineer. This process removes the time-consuming tasks of hand-crafting and monitoring situations while testing an autonomous robot control algorithm. We present a case study of the proposed approach – we generated 500 randomised situations, and our prototype tool simulated and rated them. We have analysed the three highest rated situations in depth, and this analysis revealed weaknesses in the smoothed nearness-diagram control algorithm
Does Malware Detection Improve With Diverse AntiVirus Products? An Empirical Study

We present results of an empirical study to evaluate the detection ca-pability of diverse AntiVirus products (AVs). We used malware samples collected in a geographically distributed honeypot deployment in several different countries and organizations. The malware was collected in August 2012: the results are relevant to recent and current threats observed in the internet. We sent these malware to 42 AVs available from the VirusTotal service to evaluate the benefits in detection from using more than one AV. We then compare these findings with similar ones performed in the past to evaluate diversity with AVs. In general we found that the new findings are consistent with previous ones, despite some differences. This study provides additional evidence that detection capabilities are improved by diversity with AVs
Overview of INEX 2013

INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data (Social Book Search track); searching structured or semantic data (Linked Data track); and focused retrieval (Snippet Retrieval and Tweet Contextualization tracks). INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with (other activities in) CLEF and for the second time ran our work shop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results
A Proposal for New Evaluation Metrics and Result Visualization Technique for Sentiment Analysis Tasks

Proceedings of: 4th International Conference of the CLEF Initiative (CLEF 2013). 4th International Conference of the CLEF Initiative (CLEF 2013). Valencia, Spain,
September 23-26, 2013.In this paper we propound the use of a number of entropybased metrics and a visualization tool for the intrinsic evaluation of Sentiment and Reputation Analysis tasks. We provide a theoretical justification for their use and discuss how they complement other accuracybased metrics. We apply the proposed techniques to the analysis of TASS-SEPLN and RepLab 2012 results and show how the metric is effective for system comparison purposes, for system development and postmortem evaluation.FJVA and JCdA are supported by EU FP7 project LiMoSINe (contract 288024). CPM has been partially supported by the Spanish Government-Comisión Interministerial de Ciencia y Tecnología project TEC2011-26807 for this paper.Publicad
Detecting Epileptic Regions Based on Global Brain Connectivity Patterns

We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.MIT/Lincoln Laboratory CollaborationNational Alliance for Medical Image Computing (U.S.) (grant (NIH NIBIB NAMIC U54-EB005149))Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (NIH NIBIB NAC P41-EB-015902)National Science Foundation (U.S.) (NSF CAREER Grant 0642971
Iterative Closest Curve: a Framework for Curvilinear Structure Registration Application to 2D/3D Coronary Arteries Registration

International audienceTreatment coronary arteries endovascular involves catheter navigation through patient vasculature. The projective angiography guidance is limited in the case of chronic total occlusion where occluded vessel can not be seen. Integrating standard preoperative CT angiography information with live fluoroscopic images addresses this limitation but requires alignment of both modalities. This article proposes a structure-based registration method that intrinsically preserves both the geometrical and topological coherencies of the vascular centrelines to be registered, by the means of a dedicated curve-to-curve distance pairs of closest curves are identified, while pairing their points. Preliminary experiments demonstrate that the proposed approach performs better than the standard Iterative Closest Point method giving a wider attraction basin and improved accuracy
Bayesian Atlas Estimation for the Variability Analysis of Shape Complexes

International audienceIn this paper we propose a Bayesian framework for multi-object atlas estimation based on the metric of currents which permits to deal with both curves and surfaces without relying on point correspondence. This approach aims to study brain morphometry as a whole and not as a set of different components, focusing mainly on the shape and relative position of different anatomical structures which is fundamental in neuro-anatomical studies. We propose a generic algorithm to estimate templates of sets of curves (fiber bundles) and closed surfaces (sub-cortical structures) which have the same " form " (topology) of the shapes present in the population. This atlas construction method is based on a Bayesian framework which brings to two main improvements with respect to previous shape based methods. First, it allows to estimate from the data set a parameter specific to each object which was previously fixed by the user: the trade-off between data-term and regularity of deformations. In a multi-object analysis these parameters balance the contributions of the different objects and the need for an automatic estimation is even more crucial. Second, the covariance matrix of the deformation parameters is estimated during the atlas construction in a way which is less sensitive to the outliers of the population
Analysing the Impact of Built-In and External Social Tools in a MOOC on Educational Technologies

Proocedings of: 8th European Conference on Technology Enhanced Learning: Scaling Up Learning for Sustained Impact (EC-TEL 2013). Paphos, Cyprus, September 17-21, 2013MOOCs have been a disruptive educational trend in the last months. Some MOOCs just replicate traditional teaching pedagogies, adding multimedia elements like video lectures. Others go beyond, trying to engage the massive number of participants by promoting discussions and relying on their contributions to the course. MOOC platforms usually provide some built-in social tools for this purpose, although instructors or participants may suggest others to foster discussions and crowdsourcing. This paper analyses the impact of two built-in (Q&A and forum) and three external social tools (Facebook, Twitter and MentorMob) in a MOOC on educational technologies. Most of the participants agreed on the importance of social tools to be in touch with their partners and share information related to the course, the forum being the one preferred. Furthermore, the lessons learned from the enactment of this MOOC employing social tools are summarized so that others may benefit from them.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2009/TIC-1650, and the postdoctoral fellowship Alianza 4 Universidades.Publicad
Answering confucius: The reason why we complicate

Learning is a level-progressing process. In any field of study, one must master basic concepts to understand more complex ones. Thus, it is important that during the learning process learners are presented and challenged with knowledge which they are able to comprehend (not a level below, not a level too high). In this work we focus on language learners. By gradually improving (complicating) texts, readers are challenged to learn new vocabulary. To achieve such goals, in this paper we propose and evaluate the 'complicator' that translates given sentences to a chosen level of higher degree of difficulty. The 'complicator' is based on natural language processing and information retrieval approaches that perform lexical replacements. 30 native English speakers participated in a user study evaluating our methods on an expert-tailored dataset of children books. Results show that our tool can be of great utility for language learners who are willing to improve their vocabulary. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40814-4_45.TERENCEEC/FP
ARLearn - Open source mobile application platform for learning

Börner, D., Ternier, S., Klemke, R., Schmitz, B., Kalz, M., Tabuenca, B., & Specht, M. (2013). ARLearn - Open source mobile application platform for learning. In D. Hernández-Leo et al. (Eds.), Scaling up Learning for Sustained Impact. Proceedings of the 8th European Conference on Technology Enhanced Learning (EC-TEL 2013), LNCS 8095 (pp. 536-539). Berlin Heidelberg, Germany: Springer.
[The final publication is available at link.springer.com/chapter/10.1007/978-3-642-40814-4_52]The paper presents and outlines the demonstration of an open source mobile application platform for designing, supporting, and evaluating mobile learning scenarios that make use of media artefacts in a specific context. The platform contains a web-based authoring environment, cross-platform mobile applications to run the scenarios, as well as tools to monitor progress and results.
Besides exploring the pedagogical background, the paper describes the conceptual implementation as well as the technical infrastructure and lists the requirements for demonstrating the platform and all its components