pinecone/core-2020-05-10-deduplication huggingface dataset:
Morphogenetic self-organization of collective movement without directional sensing

In this paper, we present a morphogenetic approach to self-organized collective movement of a swarm. We assume that the robots (agents) do not have global knowledge of the environment and can communicate only locally with other robots. In addition, we assume that the robots are not able to perform directional sensing. To self-organize such systems, we adopt here a simplified diffusion mechanism inspired from biological morphogenesis. A guidance mechanism is proposed based on the history of morphogen concentrations. The division of labor is achieved by type differentiation to allocate different tasks to different type of robots. Simulations are run to show the efficiency of the proposed algorithm. The robustness of the algorithm is demonstrated by introducing an obstacle into the environment and removing a subset of robots from the swarm. © 2014 Springer International Publishing
Adaptive swarm robot region coverage using gene regulatory networks

This paper proposes a morphogenetic pattern formation approach for collective systems to cover a desired region for target entrapment. This has been achieved by combining a two-layer hierarchical gene regulatory network (H-GRN) with a region-based shape control strategy. The upper layer of the H-GRN is for pattern generation that provides a desired region for entrapping targets generated from local sensory inputs of detected targets. This pattern is represented by a set of arc segments, which allow us to form entrapping shape constraints with the minimum information that can be easily used by the lower layer of the H-GRN. The lower layer is for region-based shape control consisting of two steps: guiding all robots into the desired region designated by the upper layer, and maintaining a specified minimum distance between each robot and its neighbouring robots. Numerical simulations have been performed for scenarios containing either static and moving targets to validate the feasibility and benefits of the proposed approach. © 2014 Springer International Publishing
Monte Carlo localization for teach-and-repeat feature-based navigation

This work presents a combination of a teach-and-replay visual navigation and Monte Carlo localization methods. It improves a reliable teach-and-replay navigation method by replacing its dependency on precise dead-reckoning by introducing Monte Carlo localization to determine robot position along the learned path. In consequence, the navigation method becomes robust to dead-reckoning errors, can be started from at any point in the map and can deal with the `kidnapped robot' problem. Furthermore, the robot is localized with MCL only along the taught path, i.e. in one dimension, which does not require a high number of particles and significantly reduces the computational cost.\ud
Thus, the combination of MCL and teach-and-replay navigation mitigates the disadvantages of both methods. The method was tested using a P3-AT ground robot and a Parrot AR.Drone aerial robot over a long indoor corridor. Experiments show the validity of the approach and establish a solid base for continuing this work
"The fridge door is open" :
                    temporal verification of a robotic assistant's behaviours

Robotic assistants are being designed to help, or work with, humans in a variety of situations from assistance within domestic situations, through medical care, to industrial settings. Whilst robots have been used in industry for some time they are often limited in terms of their range of movement or range of tasks. A new generation of robotic assistants have more freedom to move, and are able to autonomously make decisions and decide between alternatives. For people to adopt such robots they will have to be shown to be both safe and trustworthy. In this paper we focus on formal verification of a set of rules that have been developed to control the Care-O-bot, a robotic assistant located in a typical domestic environment. In particular, we apply model-checking, an automated and exhaustive algorithmic technique, to check whether formal temporal properties are satisfied on all the possible behaviours of the system. We prove a number of properties relating to robot behaviours, their priority and interruptibility, helping to support both safety and trustworthiness of robot behaviours
Atlas-Based Under-Segmentation

We study the widespread, but rarely discussed, tendency of atlas-based segmentation to under-segment the organs of interest. Commonly used error measures do not distinguish between under- and over-segmentation, contributing to the problem. We explicitly quantify over- and under-segmentation in several typical examples and present a new hypothesis for the cause. We provide evidence that segmenting only one organ of interest and merging all surrounding structures into one label creates bias towards background in the label estimates suggested by the atlas. We propose a generative model that corrects for this effect by learning the background structures from the data. Inference in the model separates the background into distinct structures and consequently improves the segmentation accuracy. Our experiments demonstrate a clear improvement in several applications.National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902
A New 2.5D Representation for Lymph Node Detection using Random Sets of
  Deep Convolutional Neural Network Observations

Automated Lymph Node (LN) detection is an important clinical diagnostic task
but very challenging due to the low contrast of surrounding structures in
Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely
distributed locations. State-of-the-art studies show the performance range of
52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1
FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this
paper, we first operate a preliminary candidate generation stage, towards 100%
sensitivity at the cost of high FP levels (40 per patient), to harvest volumes
of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by
resampling 2D reformatted orthogonal views N times, via scale, random
translations, and rotations with respect to the VOI centroid coordinates. These
random views are then used to train a deep Convolutional Neural Network (CNN)
classifier. In testing, the CNN is employed to assign LN probabilities for all
N random views that can be simply averaged (as a set) to compute the final
classification probability per VOI. We validate the approach on two datasets:
90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs.
We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in
mediastinum and abdomen respectively, which drastically improves over the
previous state-of-the-art work.Comment: This article will be presented at MICCAI (Medical Image Computing and
  Computer-Assisted Interventions) 201
Information Nonanticipative Rate Distortion Function and Its
  Applications

This paper investigates applications of nonanticipative Rate Distortion
Function (RDF) in a) zero-delay Joint Source-Channel Coding (JSCC) design based
on average and excess distortion probability, b) in bounding the Optimal
Performance Theoretically Attainable (OPTA) by noncausal and causal codes, and
computing the Rate Loss (RL) of zero-delay and causal codes with respect to
noncausal codes. These applications are described using two running examples,
the Binary Symmetric Markov Source with parameter p, (BSMS(p)) and the
multidimensional partially observed Gaussian-Markov source. For the
multidimensional Gaussian-Markov source with square error distortion, the
solution of the nonanticipative RDF is derived, its operational meaning using
JSCC design via a noisy coding theorem is shown by providing the optimal
encoding-decoding scheme over a vector Gaussian channel, and the RL of causal
and zero-delay codes with respect to noncausal codes is computed.
  For the BSMS(p) with Hamming distortion, the solution of the nonanticipative
RDF is derived, the RL of causal codes with respect to noncausal codes is
computed, and an uncoded noisy coding theorem based on excess distortion
probability is shown. The information nonanticipative RDF is shown to be
equivalent to the nonanticipatory epsilon-entropy, which corresponds to the
classical RDF with an additional causality or nonanticipative condition imposed
on the optimal reproduction conditional distribution.Comment: 34 pages, 12 figures, part of this paper was accepted for publication
  in IEEE International Symposium on Information Theory (ISIT), 2014 and in
  book Coordination Control of Distributed Systems of series Lecture Notes in
  Control and Information Sciences, 201
MHD Equilibria and Triggers for Prominence Eruption

Magneto-hydrodynamic (MHD) simulations of the emergence of twisted magnetic
flux tubes from the solar interior into the corona are discussed to illustrate
how twisted and sheared coronal magnetic structures (with free magnetic
energy), capable of driving filament eruptions, can form in the corona in
emerging active regions. Several basic mechanisms that can disrupt the
quasi-equilibrium coronal structures and trigger the release of the stored free
magnetic energy are discussed. These include both ideal processes such as the
onset of the helical kink instability and the torus instability of a twisted
coronal flux rope structure and the non-ideal process of the onset of fast
magnetic reconnections in current sheets. Representative MHD simulations of the
non-linear evolution involving these mechanisms are presented.Comment: 26 pages, 10 figures, Chapter 12 of Solar Prominences, Astrophysics
  and Space Science Library, Volume 415. ISBN 978-3-319-10415-7. Springer
  International Publishing Switzerland, 2015, p. 29
Coronal cavities: observations and implications for the magnetic
  environment of prominences

Dark and elliptical, coronal cavities yield important clues to the magnetic
structures that cradle prominences, and to the forces that ultimately lead to
their eruption. We review observational analyses of cavity morphology, thermal
properties (density and temperature), line-of-sight and plane-of-sky flows,
substructure including hot cores and central voids, linear polarization
signatures, and observational precursors and predictors of eruption. We discuss
a magnetohydrodynamic interpretation of these observations which argues that
the cavity is a magnetic flux rope, and pose a set of open questions for
further study
Formation and large-scale patterns of filament channels and filaments

2015ASSL..415..355MThe properties and large-scale patterns of filament channels and filaments are considered. Initially, the global formation locations of filament channels and filaments are discussed, along with their hemispheric pattern. Next, observations of the formation of filament channels and filaments are described where two opposing views are considered. Finally, the wide range of models that have been constructed to consider the formation of filament channels and filaments over long time-scales are described, along with the origin of the hemispheric pattern of filaments.Postprin
Derivations and Observations of Prominence Bulk Motions and Mass

In this chapter we review observations and techniques for measuring both bulk
flows in prominences and prominence mass. Measuring these quantities is
essential to development and testing of models discussed throughout this book.
Prominence flows are complex and various, ranging from the relatively linear
flows along prominence spines to the complex, turbulent patterns exhibited by
hedgerow prominences. Techniques for measuring flows include time slice and
optical flow techniques used for motions in the plane of the sky and the use of
spectral line profiles to determine Doppler velocities along the line of sight.
Prominence mass measurement is chiefly done via continuum absorption
measurements, but mass has also been estimated using cloud modeling and white
light measurements.Comment: in Solar Prominences, eds. J.-C. Vial and O. Engvold, Springer, p.
  79, 201
Online Gamers Classification using K-means

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_22In order to achieve flow and increase player retention, it is important
that games di culty matches player skills. Being able to evaluate how people play a game is a crucial component for detecting gamers strategies in videogames.
One of the main problems in player strategy detection is whether attributes selected to define strategies correctly detect the actions of the player. In this paper, we will study a Real Time Strategy (RTS) game. In RTS the participants make use of units and structures to secure areas of a map and/or destroy the opponents resources. We will extract real-time information about the players strategies at
several gameplays through a Web Platform. After gathering enough information, the model will be evaluated in terms of unsupervised learning (concretely, KMeans).
Finally, we will study the similitude between several gameplays where
players use di erent strategies
SACOC: A spectral-based ACO clustering algorithm

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-10422-5_20Proceedings of Eight International Symposium on Intelligent Distributed Computing held in Madrid in September 3-5, 2014The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach.Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest—an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experimentsmeasure the accuracy of the algorithm for both synthetic datasets and realworld datasets extracted from the UCI Machine Learning Repository.This work has been partly supported by: Spanish Ministry of Science and Education
under project TIN2010-19872 and Savier an Airbus Defense & Space project (FUAM-
076914 and FUAM-076915)
A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models

Proceedings of: 8th International Symposium on Intelligent Distributed Computing (IDC'2014). Madrid, September 3-5, 2014Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach.We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.Publicad
A survey of social web mining applications for disease outbreak detection

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-10422-5_36Social Web Media is one of the most important sources of big data to extract and acquire new knowledge. Social Networks have become an important environmentwhere users provide information of their preferences and relationships. This information can be used to measure the influence of ideas and the society opinions in real time, being very useful on several fields and research areas such as marketing campaigns, financial prediction or public healthcare among others. Recently, the research on artificial intelligence techniques applied to develop technologies allowing monitoring web data sources for detecting public health events has emerged as a new relevant discipline called Epidemic Intelligence. Epidemic Intelligence Systems are nowadays widely used by public health organizations like monitoring mechanisms for early detection of disease outbreaks to reduce the impact of epidemics. This paper presents a survey on current data mining applications and web systems based on web data for public healthcare over the last years. It tries to take special attention to machine learning and data mining techniques and how they have been applied to these web data to extract collective knowledge from TwitterThis work was supported by Spanish Ministry of Science and Education under Project Code TIN2010-19872 and Savier Project (Airbus Defence & Space, FUAM-076915
How musical selection impacts the performance of the interaction with the computer

In this busy society of ours people push their limits to work
better and more in order to remain competitive with their peers. Nonetheless,
working longer hours does not necessarily improves productivity nor
performance. In order to prevent the negative consequences of this increasing
trend, the evolution of performance throughout the day of work
should be more closely monitored. This could avoid undesirable states or
even breakdowns, which have social and economical implications. In this
work we measure user performance through their interaction with the
computer. We monitor its evolution during a day of work and how di erent
types of music may increase or decrease its natural daily degradation.
We conclude that the relationship between types of music and its e ects
is not universal and depends, among other things, on the musical pro le
of the individual. A prototype for a distributed music recommendation
service is presented that suggests musics at an individual and group level,
based on user musical pro les and objectives.This work is part-funded by ERDF - European Regional Development Fund
through the COMPETE Programme (operational programme for competitiveness)
and by National Funds through the FCT - Fundação para a Ciência e a
Tecnologia (Portuguese Foundation for Science and Technology) within project
FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012)
Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers

Nowadays, our lifestyle can lead to a scatter of focus, especially when we
attend to several tasks in parallel or have to filter the important information from all
the remaining one. In the context of a computer this usually means interacting with
several applications simultaneously. Over the day, this significant demand on our
brain results in the emergence of fatigue, making an individual more prone to distractions.
Good management of the working time and effort invested in each task, as
well as the effect of breaks at work, can result in better performance and better mental
health, delaying the effects of fatigue. This paper presents a non-intrusive and
non-invasive method for measuring distraction and fatigue in an individual and in a
group of people. The main aim is to allow team managers to better understand the
state of their collaborators, thus preparing them to take better decisions concerning
their management.This work is part-funded by ERDF - European Regional Development Fund
through the COMPETE Programme (operational programme for competitiveness) and by National
Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation
for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEISII/
1386/2012).
Reference
Beyond Consistency and Substitutability

Elimination of inconsistent values in instances of the constraint satisfaction problem (CSP) conserves all solutions. Elimination of substitutable values conserves at least one solution. We show that certain values which are neither inconsistent nor substitutable can also be deleted while conserving at least one solution. This allows us to state novel rules for the elimination of values in binary CSP. From a practical point of view, we show that one such rule can be applied in the same asymptotic time complexity as neighbourhood substitution but is strictly stronger.  An alternative to the elimination of values from domains is the elimination of variables. We give novel satisfiability-preserving variable elimination operations. In each case we show that if the instance is satisfiable, then a solution to the original instance can always be recovered in low-order polynomial time from a solution to the reduced instance
The StockingCost Constraint

Many production planning problems call for the minimiza- tion of stocking/storage costs. This paper introduces a new global con- straint StockingCost([X1,...,Xn],[d1,...,dn],H,c) that holds when each item Xi is produced on or before its due date di, the capacity c of the machine is respected, and H is an upper bound on the stocking cost. We propose a linear time algorithm to achieve bound consistency on the StockingCost constraint. On a version of the Discrete Lot Sizing Problem, we demonstrate experimentally the pruning and time efficiency of our algorithm compared to other state-of-the-art approaches
Incremental QBF Solving

We consider the problem of incrementally solving a sequence of quantified
Boolean formulae (QBF). Incremental solving aims at using information learned
from one formula in the process of solving the next formulae in the sequence.
Based on a general overview of the problem and related challenges, we present
an approach to incremental QBF solving which is application-independent and
hence applicable to QBF encodings of arbitrary problems. We implemented this
approach in our incremental search-based QBF solver DepQBF and report on
implementation details. Experimental results illustrate the potential benefits
of incremental solving in QBF-based workflows.Comment: revision (camera-ready, to appear in the proceedings of CP 2014,
  LNCS, Springer
Incremental Cardinality Constraints for MaxSAT

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean
Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a
succession of SAT solver calls to reach an optimum solution making extensive
use of cardinality constraints. Many of these algorithms are non-incremental in
nature, i.e. at each iteration the formula is rebuilt and no knowledge is
reused from one iteration to another. In this paper, we exploit the knowledge
acquired across iterations using novel schemes to use cardinality constraints
in an incremental fashion. We integrate these schemes with several MaxSAT
algorithms. Our experimental results show a significant performance boost for
these algo- rithms as compared to their non-incremental counterparts. These
results suggest that incremental cardinality constraints could be beneficial
for other constraint solving domains.Comment: 18 pages, 4 figures, 1 table. Final version published in Principles
  and Practice of Constraint Programming (CP) 201