pinecone/core-2020-05-10-deduplication huggingface dataset:
The Economy as a Complex Spatial System

This collected volume gives a concise account of the most rel-evant scientific results of the COST Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", a four-year project supported by COST (European Cooperation in Science and Technology). It is divided into three parts reflecting the different perspectives under which complex spatial economic systems have been studied: (i) the Macro perspective looks at the interactions among international or regional trading partners; (ii) the Meso perspective considers the functioning of (financial, labour) markets as social network\ud
structures; and, finally, (iii) the Micro perspective focuses on the strategic choices of single firms and households. This Volume points also at open issues to be addressed in future research
Emerging Trade Patterns in a 3-Region Linear NEG Model: Three Examples

This chapter draws attention to a specific feature of a NEG model that uses linear (and not iso-elastic) demand functions, namely its ability to account for zero trade. Thus, it represents a suitable framework to study how changes in parameters that are typical for NEG models, such as trade costs and regional market size, not only shape the regional distribution of economic activity, but at the same time determine the emergence of additional trade links between formerly autarkic regions. We survey some related papers and present a three-region framework that potentially nests many possible trade patterns. To focus the analysis, we study in more detail three specific trade patterns frequently found in the EU trade network. We start with three autarkic regions; then we introduce the possibility that two regions trade with each other; and, finally, we allow for one region trading with the other two, but the latter are still not trading with each other. We find a surprising plethora of long-run equilibria each involving a specific regional distribution of economic activity and a specific pattern of trade links. We show how a reduction in trade costs shapes simultaneously industry location and the configuration of the trade network
Citizen Science for Observing and Understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges. / An erratum to this publication is available online at  https://doi.org/10.1007/978-3-319-65633-5_19
Efficient Online Timed Pattern Matching by Automata-Based Skipping

The timed pattern matching problem is an actively studied topic because of
its relevance in monitoring of real-time systems. There one is given a log $w$
and a specification $\mathcal{A}$ (given by a timed word and a timed automaton
in this paper), and one wishes to return the set of intervals for which the log
$w$, when restricted to the interval, satisfies the specification
$\mathcal{A}$. In our previous work we presented an efficient timed pattern
matching algorithm: it adopts a skipping mechanism inspired by the classic
Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem
of online timed pattern matching, towards embedded applications where it is
vital to process a vast amount of incoming data in a timely manner.
Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching
algorithm---a recent variant of the BM algorithm---and extend it to timed
pattern matching. Our experiments indicate the efficiency of our FJS-type
algorithm in online and offline timed pattern matching
Enriching Existing Test Collections with OXPath

Extending TREC-style test collections by incorporating external resources is
a time consuming and challenging task. Making use of freely available web data
requires technical skills to work with APIs or to create a web scraping program
specifically tailored to the task at hand. We present a light-weight
alternative that employs the web data extraction language OXPath to harvest
data to be added to an existing test collection from web resources. We
demonstrate this by creating an extended version of GIRT4 called GIRT4-XT with
additional metadata fields harvested via OXPath from the social sciences portal
Sowiport. This allows the re-use of this collection for other evaluation
purposes like bibliometrics-enhanced retrieval. The demonstrated method can be
applied to a variety of similar scenarios and is not limited to extending
existing collections but can also be used to create completely new ones with
little effort.Comment: Experimental IR Meets Multilinguality, Multimodality, and Interaction
  - 8th International Conference of the CLEF Association, CLEF 2017, Dublin,
  Ireland, September 11-14, 201
CLEF 2017 Microblog Cultural Contextualization Lab Overview

MC2 CLEF 2017 lab deals with how cultural context of a microblog affects its social impact at large. This involves microblog search, classification, filtering, language recognition, localization, entity extraction,  linking  open  data,  and  summarization.  Regular  Lab  participants have access to the private massive multilingual microblog stream of The Festival Galleries project. Festivals have a large presence on social media. The resulting mircroblog stream and related URLs is appropriate to experiment advanced social media search and mining methods. A collection of 70,000,000 microblogs over 18 months dealing with cultural events in all languages has been released to test multilingual content analysis and microblog search. For content analysis topics were in any language and results were expected in four languages: English, Spanish, French, and Portuguese. For microblog search topics were in four languages: Arabic, English, French and Spanish, and results were expected in any language
IRIT-QFR: IRIT Query Feature Resource

In this paper, we present a resource that consists of query features associated with TREC adhoc collections. We developed two types of query features: linguistics features that can be calculated from the query itself, prior to any search although some are collection-dependent and post-retrieval features that imply the query has been evaluated over the target collection. This paper presents the two types of features that we have estimated as well as their variants, and the resource produced. The total number of features with their variants that we have estimated is 258 where the number of pre-retrieval and post-retrieval features are 81 and 171, respectively. We also present the first analysis of this data that shows that some features are more relevant than others in IR applications. Finally, we present a few applications in which these resources could be used although the idea of making them available is to foster new usages for IR
Query expansion for sentence retrieval using pseudo relevance feedback and word embedding

This study investigates the use of query expansion (QE) methods in sentence retrieval for non-factoid queries to address the query-document term mismatch problem. Two alternative QE approaches: i) pseudo relevance feedback (PRF), using Robertson term selection, and ii) word embeddings (WE) of query words, are explored. Experiments are carried out on the WebAP data set developed using the\ud
TREC GOV2 collection. Experimental results using P@10, NDCG@10 and MRR show that QE using PRF achieves a statistically significant improvement over baseline retrieval models, but that while WE also improves over the baseline, this is not statistically significant. A method combining PRF and WE expansion performs consistently better than using only the PRF method
Considerations about Continuous Experimentation for Resource-Constrained
  Platforms in Self-Driving Vehicles

Autonomous vehicles are slowly becoming reality thanks to the efforts of many
academic and industrial organizations. Due to the complexity of the software
powering these systems and the dynamicity of the development processes, an
architectural solution capable of supporting long-term evolution and
maintenance is required.
  Continuous Experimentation (CE) is an already increasingly adopted practice
in software-intensive web-based software systems to steadily improve them over
time. CE allows organizations to steer the development efforts by basing
decisions on data collected about the system in its field of application.
Despite the advantages of Continuous Experimentation, this practice is only
rarely adopted in cyber-physical systems and in the automotive domain. Reasons
for this include the strict safety constraints and the computational
capabilities needed from the target systems.
  In this work, a concept for using Continuous Experimentation for
resource-constrained platforms like a self-driving vehicle is outlined.Comment: Copyright 2017 Springer. Paper submitted and accepted at the 11th
  European Conference on Software Architecture. 8 pages, 1 figure. Published in
  Lecture Notes in Computer Science vol 10475 (Springer),
  https://link.springer.com/chapter/10.1007/978-3-319-65831-5_
Affective Classication of Gaming Activities Coming From RPG Gaming Sessions

Each human activity involves feelings and subjective emotions: different people will perform and sense the same task with different outcomes and experience; to understand this experience, concepts like Flow or Boredom must be investigated using objective data provided by methods like electroencephalography. This work carries on the analysis of EEG data coming from brain-computer interface and videogame "Neverwinter Nights 2": we propose an experimental methodology comparing results coming from different off-the-shelf machine learning techniques, employed on the gaming activities, to check if each affective state corresponds to the hypothesis xed in their formal design guidelines
Certified Reduced Basis Method for Affinely Parametric Isogeometric Analysis NURBS Approximation

In this work we apply reduced basis methods for parametric PDEs to an isogeometric formulation based on NURBS. We propose an integrated and complete work pipeline from CAD to parametrization of domain geometry, then from full order to certified reduced basis solution. IsoGeometric Analysis (IGA), as well as reduced basis methods for parametric PDEs growing research themes in scientific computing and computational mechanics. Their combination enhances the solution of some class of problems, especially the ones characterized by parametrized geometries. This work shows that it is also possible for some class of problems to deal with affine geometrical parametrization combined with a NURBS IGA formulation. In this work we show a certification of accuracy and a complete integration between IGA formulation and parametric certified greedy RB formulation by introducing two numerical examples in heat transfer with different parametrization
Enterprise social network success: Evidences from a multinational corporation

In a globalized world, where companies operate across different locations and work becomes increasingly complex, collaboration in a diversity of ways is required among employees to perform tasks more effectively. Following a case study methodology that involved six interviews across three different country locations, this research addresses the phenomenon of Enterprise Social Networks (ESN) in a multinational corporation with a focus on the assessment of ESN success. The findings show that the company, while trying to assess the success of Yammer, the freemium social networking service at use, has mainly relied on analytics tools to measure usage through indicators such as the total number of users. However, the extent to which ESN is used does not provide a complete picture of ESN success. Business value from that ESN usage is another dimension to be considered to assess success. Therefore, the study of specific ESN usage scenarios that are perceived to have a trackable impact on business results can be used to assess ESN business value on top of ESN usage to fully understand ESN success.COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013info:eu-repo/semantics/publishedVersio
Combining stochastic constraint optimization and probabilistic programming: From knowledge compilation to constraint solving

We show that a number of problems in Artificial Intelligence can be seen as Stochastic Constraint Optimization Problems (SCOPs): problems that have both a stochastic and a constraint optimization component. We argue that these problems can be modeled in a new language, SC-ProbLog, that combines a generic Probabilistic Logic Programming (PLP) language, ProbLog, with stochastic constraint optimization. We propose a toolchain for effectively solving these SC-ProbLog programs, which consists of two stages. In the first stage, decision diagrams are com- piled for the underlying distributions. These diagrams are converted into models that are solved using Mixed Integer Programming or Constraint Programming solvers in the second stage. We show that, to yield linear constraints, decision diagrams need to be compiled in a specific form. We introduce a new method for compiling small Sentential Decision Diagrams in this form. We evaluate the effectiveness of several variations of this toolchain on test cases in viral marketing and bioinformatics.status: publishe
Subtropical Satisfiability

Quantifier-free nonlinear arithmetic (QF_NRA) appears in many applications of
satisfiability modulo theories solving (SMT). Accordingly, efficient reasoning
for corresponding constraints in SMT theory solvers is highly relevant. We
propose a new incomplete but efficient and terminating method to identify
satisfiable instances. The method is derived from the subtropical method
recently introduced in the context of symbolic computation for computing real
zeros of single very large multivariate polynomials. Our method takes as input
conjunctions of strict polynomial inequalities, which represent more than 40%
of the QF_NRA section of the SMT-LIB library of benchmarks. The method takes an
abstraction of polynomials as exponent vectors over the natural numbers tagged
with the signs of the corresponding coefficients. It then uses, in turn, SMT to
solve linear problems over the reals to heuristically find suitable points that
translate back to satisfying points for the original problem. Systematic
experiments on the SMT-LIB demonstrate that our method is not a sufficiently
strong decision procedure by itself but a valuable heuristic to use within a
portfolio of techniques.Comment: Accepted into Proc. FROCOS 201
Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep
neural networks have increased the accuracy of automatic segmentation
significantly, however these models tend to generalise poorly to different
imaging modalities than those for which they have been designed, thereby
limiting their applications. For example, a network architecture initially
designed for brain parcellation of monomodal T1 MRI can not be easily
translated into an efficient tumour segmentation network that jointly utilises
T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable
multimodal deep learning architecture using new nested structures that
explicitly leverage deep features within or across modalities. This aims at
making the early layers of the architecture structured and sparse so that the
final architecture becomes scalable to the number of modalities. We evaluate
the scalable architecture for brain tumour segmentation and give evidence of
its regularisation effect compared to the conventional concatenation approach.Comment: Paper accepted at MICCAI 201
Transfer Learning for Domain Adaptation in MRI: Application in Brain
  Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis
and treatment. However, variations in MRI acquisition protocols result in
different appearances of normal and diseased tissue in the images.
Convolutional neural networks (CNNs), which have shown to be successful in many
medical image analysis tasks, are typically sensitive to the variations in
imaging protocols. Therefore, in many cases, networks trained on data acquired
with one MRI protocol, do not perform satisfactorily on data acquired with
different protocols. This limits the use of models trained with large annotated
legacy datasets on a new dataset with a different domain which is often a
recurring situation in clinical settings. In this study, we aim to answer the
following central questions regarding domain adaptation in medical image
analysis: Given a fitted legacy model, 1) How much data from the new domain is
required for a decent adaptation of the original network?; and, 2) What portion
of the pre-trained model parameters should be retrained given a certain number
of the new domain training samples? To address these questions, we conducted
extensive experiments in white matter hyperintensity segmentation task. We
trained a CNN on legacy MR images of brain and evaluated the performance of the
domain-adapted network on the same task with images from a different domain. We
then compared the performance of the model to the surrogate scenarios where
either the same trained network is used or a new network is trained from
scratch on the new dataset.The domain-adapted network tuned only by two
training examples achieved a Dice score of 0.63 substantially outperforming a
similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure
Discriminative Localization in CNNs for Weakly-Supervised Segmentation
  of Pulmonary Nodules

Automated detection and segmentation of pulmonary nodules on lung computed
tomography (CT) scans can facilitate early lung cancer diagnosis. Existing
supervised approaches for automated nodule segmentation on CT scans require
voxel-based annotations for training, which are labor- and time-consuming to
obtain. In this work, we propose a weakly-supervised method that generates
accurate voxel-level nodule segmentation trained with image-level labels only.
By adapting a convolutional neural network (CNN) trained for image
classification, our proposed method learns discriminative regions from the
activation maps of convolution units at different scales, and identifies the
true nodule location with a novel candidate-screening framework. Experimental
results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised
nodule segmentation framework achieves competitive performance compared to a
fully-supervised CNN-based segmentation method
Progressive and Multi-Path Holistically Nested Neural Networks for
  Pathological Lung Segmentation from CT Images

Pathological lung segmentation (PLS) is an important, yet challenging,
medical image application due to the wide variability of pathological lung
appearance and shape. Because PLS is often a pre-requisite for other imaging
analytics, methodological simplicity and generality are key factors in
usability. Along those lines, we present a bottom-up deep-learning based
approach that is expressive enough to handle variations in appearance, while
remaining unaffected by any variations in shape. We incorporate the deeply
supervised learning framework, but enhance it with a simple, yet effective,
progressive multi-path scheme, which more reliably merges outputs from
different network stages. The result is a deep model able to produce finer
detailed masks, which we call progressive holistically-nested networks
(P-HNNs). Using extensive cross-validation, our method is tested on
multi-institutional datasets comprising 929 CT scans (848 publicly available),
of pathological lungs, reporting mean dice scores of 0.985 and demonstrating
significant qualitative and quantitative improvements over state-of-the art
approaches.Comment: 8 Pages, 4 figures, MICCAI 200
Statistical learning of spatiotemporal patterns from longitudinal
  manifold-valued networks

We introduce a mixed-effects model to learn spatiotempo-ral patterns on a
network by considering longitudinal measures distributed on a fixed graph. The
data come from repeated observations of subjects at different time points which
take the form of measurement maps distributed on a graph such as an image or a
mesh. The model learns a typical group-average trajectory characterizing the
propagation of measurement changes across the graph nodes. The subject-specific
trajectories are defined via spatial and temporal transformations of the
group-average scenario, thus estimating the variability of spatiotemporal
patterns within the group. To estimate population and individual model
parameters, we adapted a stochastic version of the Expectation-Maximization
algorithm, the MCMC-SAEM. The model is used to describe the propagation of
cortical atrophy during the course of Alzheimer's Disease. Model parameters
show the variability of this average pattern of atrophy in terms of
trajectories across brain regions, age at disease onset and pace of
propagation. We show that the personalization of this model yields accurate
prediction of maps of cortical thickness in patients
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer

Mammography screening for early detection of breast lesions currently suffers
from high amounts of false positive findings, which result in unnecessary
invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
of these false-positive findings prior to biopsy. Current approaches estimate
tissue properties by means of quantitative parameters taken from generative,
biophysical models fit to the q-space encoded signal under certain assumptions
regarding noise and spatial homogeneity. This process is prone to fitting
instability and partial information loss due to model simplicity. We reveal
unexplored potentials of the signal by integrating all data processing
components into a convolutional neural network (CNN) architecture that is
designed to propagate clinical target information down to the raw input images.
This approach enables simultaneous and target-specific optimization of image
normalization, signal exploitation, global representation learning and
classification. Using a multicentric data set of 222 patients, we demonstrate
that our approach significantly improves clinical decision making with respect
to the current state of the art.Comment: Accepted conference paper at MICCAI 201