pinecone/core-2020-05-10-deduplication huggingface dataset:
Visual attention model with a novel learning strategy and its application to target detection from SAR images

The selective visual attention mechanism in human visual system helps human to act efficiently when dealing with massive visual information. Over the last two decades, biologically inspired attention model has drawn lots of research attention and many models have been proposed. However, the top-down cues in human brain are still not fully understood, which makes top-down models not biologically plausible. This paper proposes an attention model containing both the bottom-up stage and top-down stage for the target detection from SAR (Synthetic Aperture Radar) images. The bottom-up stage is based on the biologically-inspired Itti model and is modified by taking fully into account the characteristic of SAR images. The top-down stage contains a novel learning strategy to make the full use of prior information. It is an extension of the bottom-up process and more biologically plausible. The experiments in this research aim to detect vehicles in different scenes to validate the proposed model by comparing with the well-known CFAR (constant false alarm rate) algorithm
Benchmarking Fast Data Platforms for the Aadhaar Biometric Database

Aadhaar is the world's largest biometric database with a billion records,
being compiled as an identity platform to deliver social services to residents
of India.Aadhaar processes streams of biometric data as residents are enrolled
and updated.Besides 1 million enrolments and updates per day,up to 100 million
daily biometric authentications are expected during delivery of various public
services.These form critical Big Data applications,with large volumes and high
velocity of data.Comment: 9 pages,Preprint:To appear in Seventh Workshop on Big Data
  Benchmarking (WBDB) proceedings (LNCS
ALOJA: A benchmarking and predictive platform for big data performance analysis

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectivenessof Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system's cost-performance1.\ud
This article describes the evolution of the project's focus and research\ud
lines from over a year of continuously benchmarking Hadoop under dif-\ud
ferent configuration and deployments options, presents results, and dis\ud
cusses the motivation both technical and market-based of such changes.\ud
During this time, ALOJA's target has evolved from a previous low-level\ud
profiling of Hadoop runtime, passing through extensive benchmarking\ud
and evaluation of a large body of results via aggregation, to currently\ud
leveraging Predictive Analytics (PA) techniques. Modeling benchmark\ud
executions allow us to estimate the results of new or untested configu-\ud
rations or hardware set-ups automatically, by learning techniques from\ud
past observations saving in benchmarking time and costs.This work is partially supported the BSC-Microsoft Research Centre, the Span-\ud
ish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
On Mixing in Pairwise Markov Random Fields with Application to Social
  Networks

We consider pairwise Markov random fields which have a number of important
applications in statistical physics, image processing and machine learning such
as Ising model and labeling problem to name a couple. Our own motivation comes
from the need to produce synthetic models for social networks with attributes.
First, we give conditions for rapid mixing of the associated Glauber dynamics
and consider interesting particular cases. Then, for pairwise Markov random
fields with submodular energy functions we construct monotone perfect
simulation
Behind the price: on the role of agent's reflexivity in financial market
  microstructure

In this chapter we review some recent results on the dynamics of price
formation in financial markets and its relations with the efficient market
hypothesis. Specifically, we present the limit order book mechanism for markets
and we introduce the concepts of market impact and order flow, presenting their
recently discovered empirical properties and discussing some possible
interpretation in terms of agent's strategies. Our analysis confirms that
quantitative analysis of data is crucial to validate qualitative hypothesis on
investors' behavior in the regulated environment of order placement and to
connect these micro-structural behaviors to the properties of the collective
dynamics of the system as a whole, such for instance market efficiency. Finally
we discuss the relation between some of the described properties and the theory
of reflexivity proposing that in the process of price formation positive and
negative feedback loops between the cognitive and manipulative function of
agents are present.Comment: 12 pages, 1 figur
Engineering Support Systems for Industrial Machines and Plants

In the business of industrial machines and plants, rapid and detailed estimates for planning installation, replacement of equipment, or maintenance work are key requirements for meeting the demands for greater reliability, lower costs and for maintaining safe and secure operation. These demands have been addressed by developing technology driven by IT. When replacing equipment at complex building or plants with high equipment density, the existing state of the installation locations and transportation routes for old and new equipment need to be properly measured. We have met this need by developing parts recognition technology based on 3D measurement, and by developing high-speed calculation technology of optimal routes for installation parts. This chapter provides an overview of these development projects with some real business application results
MARL-Ped+Hitmap: Towards Improving Agent-Based Simulations with Distributed Arrays

Producción CientíficaMulti-agent systems allow the modelling of complex, heterogeneous, and distributed systems in a realistic way. MARL-Ped is a multi-agent system tool, based on the MPI standard, for the simulation of different scenarios of pedestrians who autonomously learn the best behavior by Reinforcement Learning. MARL-Ped uses one MPI process for each agent by design, with a fixed fine-grain granularity. This requirement limits the performance of the simulations for a restricted number of processors that is lesser than the number of agents. On the other hand, Hitmap is a library to ease the programming of parallel applications based on distributed arrays. It includes abstractions for the automatic partition and mapping of arrays at runtime with arbitrary granularity, as well as functionalities to build flexible communication patterns that transparently adapt to the data partitions. In this work, we present the methodology and techniques of granularity selection in Hitmap, applied to the simulations of agent systems. As a first approximation, we use the MARL-Ped multi-agent pedestrian simulation software as a case of study for intra-node cases. Hitmap allows to transparently map agents to processes, reducing oversubscription and intra-node communication overheads. The evaluation results show significant advantages when using Hitmap, increasing the flexibility, performance, and agent-number scalability for a fixed number of processing elements, allowing a better exploitation of isolated nodes.Spanish MINECO and the EU ERDF program under grants HomProg-HetSys TIN2014-58876-P, TIN2015-66972-C5-5-R, CAPAP-H5 network TIN2014-53522-REDT, and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS)
Comparative Analysis of OpenACC Compilers

Producción CientíficaOpenACC has been on development for a few years now. The OpenACC 2.5 specification was recently made public and there are some initiatives for developing full implementations of the standard to make use of accelerator capabilities. There is much to be done yet, but currently, OpenACC for GPUs is reaching a good maturity level in various implementations of the standard, using CUDA and OpenCL as backends. Nvidia is investing in this project and they have released an OpenACC Toolkit, including the PGI Compiler. There are, however, more developments out there. In this work, we analyze different available OpenACC compilers that have been developed by companies or universities during the last years. We check their performance and maturity, keeping in mind that OpenACC is designed to be used without extensive knowledge about parallel programming. Our results show that the compilers are on their way to a reasonable maturity, presenting different strengths and weaknesses.MICINN (Spain) and ERDF program of the European Union: HomProg-HetSys project (TIN2014-58876-P), and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS)
The Vitali Covering Theorem in the Weihrauch Lattice

We study the uniform computational content of the Vitali Covering Theorem for
intervals using the tool of Weihrauch reducibility. We show that a more
detailed picture emerges than what a related study by Giusto, Brown, and
Simpson has revealed in the setting of reverse mathematics. In particular,
different formulations of the Vitali Covering Theorem turn out to have
different uniform computational content. These versions are either computable
or closely related to uniform variants of Weak Weak K\H{o}nig's Lemma.Comment: 13 page
A note on the differences of computably enumerable reals

We show that given any non-computable left-c.e. real α there exists a left-c.e. real β such that α≠β+γ for all left-c.e. reals and all right-c.e. reals γ. The proof is non-uniform, the dichotomy being whether the given real α is Martin-Loef random or not. It follows that given any universal machine U, there is another universal machine V such that the halting probability of U is not a translation of the halting probability of V by a left-c.e. real. We do not know if there is a uniform proof of this fact
Bitonic st-orderings for Upward Planar Graphs

Canonical orderings serve as the basis for many incremental planar drawing algorithms. All these techniques, however, have in common that they are limited to undirected graphs. While st-orderings do extend to directed graphs, especially planar st-graphs, they do not offer the same properties as canonical orderings. In this work we extend the so called bitonic st-orderings to directed graphs. We fully characterize planar st-graphs that admit such an ordering and provide a linear-time algorithm for recognition and ordering. If for a graph no bitonic st-ordering exists, we show how to find in linear time a minimum set of edges to split such that the resulting graph admits one. With this new technique we are able to draw every upward planar graph on n vertices by using at most one bend per edge, at most n - 3 bends in total and within quadratic area
Crossing Minimization in Storyline Visualization

A storyline visualization is a layout that represents the temporal dynamics of social interactions along time by the convergence of chronological lines. Among the criteria oriented at improving aesthetics and legibility of a representation of this type, a small number of line crossings is the hardest to achieve. We model the crossing minimization in the storyline visualization problem as a multi-layer crossing minimization problem with tree constraints. Our algorithm can compute a layout with the minimum number of crossings of the chronological lines. Computational results demonstrate that it can solve instances with more than 100 interactions and with more than 100 chronological lines to optimality
Learning Grasps in a Synergy-based Framework

In this work, a supervised learning strategy has been applied in conjunction with a control strategy to provide anthropomorphic hand-arm systems with autonomous grasping capabilities. Both learning and control algorithms have been developed in a synergy-basedframework in order to address issues related to high dimension of the configuration space, that typically characterizes robotic hands and arms with humanlike kinematics. An experimental setup has been built to learn hand-arm motion from humans during reaching and grasping tasks. Then, a Neural Network (NN) has been realized to generalize the grasps learned by imitation. Since the NN approximates the relationship between the object characteristics and the grasp configuration of the hand-arm system, a synergy-based control strategy has been applied to overcome planning errors. The reach-to-grasp strategy has been tested on a setup constituted by the KUKA LWR 4+Arm and the SCHUNK 5-Finger Hand
"On-the-spot Training" for Terrain Classification in Autonomous Air-Ground Collaborative Teams

We consider the problem of performing rapid training of a terrain classifier in the context of a collaborative robotic search and rescue system. Our system uses a vision-based flying robot to guide a ground robot through unknown terrain to a goal location by building a map of terrain class and elevation. However, due to the unknown environments present in search and rescue scenarios, our system requires a terrain classifier that can be trained and deployed quickly, based on data collected on the spot. We investigate the relationship of training set size and complexity on training time and accuracy, for both feature-based and convolutional neural network classifiers in this scenario. Our goal is to minimize the deployment time of the classifier in our terrain mapping system within acceptable classification accuracy tolerances. So we are not concerned with training a classifier that generalizes well, only one that works well for this particular environment. We demonstrate that we can launch our aerial robot, gather data, train a classifier, and begin building a terrain map after only 60 seconds of flight
Learning functional argument mappings for hierarchical tasks from situation specific explanations

Hierarchical tasks learnt from situation specific explanations are typically limited in how well they generalise to situations beyond the explanation provided. To address this we present an approach to learning functional argument mappings for enabling task generalisation regardless of explanation specificity. These functional argument mappings allow subtasks within a hierarchical task to utilise both arguments provided to the parent task, as well as domain knowledge, to generalise to novel situations. We validate this approach with a number of scenarios in which the agent learns generalised tasks from situation specific explanations, and show that these tasks provide equal performance when compared to tasks learnt from generalisable explanations
The RAGE Advanced Game Technologies Repository for Supporting Applied Game Development

This paper describes the structural architecture of the RAGE repository, which is a unique and dedicated infrastructure that provides access to a wide variety of advanced technologies (RAGE software assets) for applied game development. These software assets are reusable across a wide diversity of game engines, game platforms and programming languages. The RAGE repository allows applied game developers and studios to search for software assets for inclusion in applied games. The repository is designed as an asset life-cycle management system for defining, publishing, updating, searching and packaging for distribution of these assets. The RAGE repository provides storage space for assets and their artefacts. It will be embedded in a social platform for networking among asset developers and other users. A dedicated Asset Repository Manager provides the main functionality of the repository and its integration with other systems. Tools supporting the Asset Manager  are presented and discussed. When the RAGE repository is in full operation, applied game developers will be able to easily enhance the quality of their games by including advanced game technology assets.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187.  This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains
Compressing web Geodata for real-time environmental applications

The advent of connected mobile devices has caused an unprecedented availability of geo-referenced user-generated content, which can be exploited for environment monitoring. In particular, Augmented Reality (AR) mobile applications can be designed to enable citizens collect observations, by overlaying relevant meta-data on their current view. This class of applications rely on multiple meta-data, which must be properly compressed for transmission and real-time usage. This paper presents a two-stage approach for the compression of Digital Elevation Model (DEM) data and geographic entities for a mountain environment monitoring mobile AR application. The proposed method is generic and could be applied to other types of geographical data
Creating new learning environment to foster enrollment in engineering programs

A discovery adventure! This is the program that has been developed by the Education Research Team of COPEC – Science and Education Research Council – a K12 School program for a city, with the goal of providing better and effective knowledge for young students, especially those who do not think about to enter a University. The main goal is to encourage more bright young minds to pursue careers in engineering or technology, by providing K12 students, from public schools of a municipality, knowledge about science and research methodology in a way that it will remain as a life practice. It fits into the counties’ necessity to improve competitiveness in technology growth, which has implications in workforce development, as well as in science and technology development. The main characteristic of this project is the possibility to apply new and innovative approaches, which provide teenagers students the ability to develop concepts and theories to solve and understand scientiﬁc and nonscientiﬁc problems and, consequently, ﬁnd solutions for those problems.This work has been financed by FEDER funds through the Competitivity Factors
Operational Programme - COMPETE: POCI-01-0145-FEDER-007136 and POCI-01-
0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the
Project Scope: UID/CEC/00319/2013
Network-Guided Biomarker Discovery

Identifying measurable genetic indicators (or biomarkers) of a specific
condition of a biological system is a key element of precision medicine. Indeed
it allows to tailor diagnostic, prognostic and treatment choice to individual
characteristics of a patient. In machine learning terms, biomarker discovery
can be framed as a feature selection problem on whole-genome data sets.
However, classical feature selection methods are usually underpowered to
process these data sets, which contain orders of magnitude more features than
samples. This can be addressed by making the assumption that genetic features
that are linked on a biological network are more likely to work jointly towards
explaining the phenotype of interest. We review here three families of methods
for feature selection that integrate prior knowledge in the form of networks.Comment: 18 pages, 1 figure, published in LNCS 9605 (Machine Learning for
  Health Informatics
Deep learning trends for focal brain pathology segmentation in MRI

Segmentation of focal (localized) brain pathologies such as brain tumors and
brain lesions caused by multiple sclerosis and ischemic strokes are necessary
for medical diagnosis, surgical planning and disease development as well as
other applications such as tractography. Over the years, attempts have been
made to automate this process for both clinical and research reasons. In this
regard, machine learning methods have long been a focus of attention. Over the
past two years, the medical imaging field has seen a rise in the use of a
particular branch of machine learning commonly known as deep learning. In the
non-medical computer vision world, deep learning based methods have obtained
state-of-the-art results on many datasets. Recent studies in computer aided
diagnostics have shown deep learning methods (and especially convolutional
neural networks - CNN) to yield promising results. In this chapter, we provide
a survey of CNN methods applied to medical imaging with a focus on brain
pathology segmentation. In particular, we discuss their characteristic
peculiarities and their specific configuration and adjustments that are best
suited to segment medical images. We also underline the intrinsic differences
deep learning methods have with other machine learning methods.Comment: Published in Machine Learning for Health Informatic
Vaccination against fungal diseases: lessons from Candida albicans

The advances in medicine have achieved great benefits by improving or even eliminating various debilitating diseases or malignancies, expanding life expectancy. However, it has also originated the development of a compromised population susceptible to opportunistic diseases. A global major concern is the emergence and spread of life-threatening invasive infections in immunocompromised patients, in which the opportunistic fungal infections have greatly increased in the last decades. The high mortality rates associated with these infections, which remain as high as 40%, are due to the limited therapeutic options and the emergence of drug-resistant fungi, but also due to the lack of efficient early diagnosis. Consequently, these facts led to the opinion that new approaches are needed to improve the outcome of these patients, such as immunopreventive strategies that could even be combined with standard antifungal treatment. In view of the proven effectiveness of various antibacterial and antiviral vaccines in preventing the respective diseases, several works have been developed to induce protective immunity against fungal infections as well. The better understanding of how the immune system works against fungal pathogens has made possible to explore immunomodulatory strategies that can protect both immunocompetent and immunocompromised hosts and generate memory. Recently, two fungal vaccines against Candida have advanced through clinical trials. However, there are still many challenges in the development of an efficient vaccine against invasive fungal infections. We will provide an update on the progress made in immunization against fungal infections, reviewing host-fungi interactions, antigens, and adjuvants exploited in vaccine strategies, and discuss concerns that need to be overcome to further advance in the area of fungal vaccines.(undefined)info:eu-repo/semantics/publishedVersio
Finite-Difference Time-Domain Simulation for Three-dimensional Polarized
  Light Imaging

Three-dimensional Polarized Light Imaging (3D-PLI) is a promising technique
to reconstruct the nerve fiber architecture of human post-mortem brains from
birefringence measurements of histological brain sections with micrometer
resolution. To better understand how the reconstructed fiber orientations are
related to the underlying fiber structure, numerical simulations are employed.
Here, we present two complementary simulation approaches that reproduce the
entire 3D-PLI analysis: First, we give a short review on a simulation approach
that uses the Jones matrix calculus to model the birefringent myelin sheaths.
Afterwards, we introduce a more sophisticated simulation tool: a 3D Maxwell
solver based on a Finite-Difference Time-Domain algorithm that simulates the
propagation of the electromagnetic light wave through the brain tissue. We
demonstrate that the Maxwell solver is a valuable tool to better understand the
interaction of polarized light with brain tissue and to enhance the accuracy of
the fiber orientations extracted by 3D-PLI.Comment: 13 pages, 5 figure