pinecone/core-2020-05-10-deduplication huggingface dataset:
The Potential and Challenges of CAD with Equational Constraints for
  SC-Square

Cylindrical algebraic decomposition (CAD) is a core algorithm within Symbolic
Computation, particularly for quantifier elimination over the reals and
polynomial systems solving more generally. It is now finding increased
application as a decision procedure for Satisfiability Modulo Theories (SMT)
solvers when working with non-linear real arithmetic. We discuss the potentials
from increased focus on the logical structure of the input brought by the SMT
applications and SC-Square project, particularly the presence of equational
constraints. We also highlight the challenges for exploiting these: primitivity
restrictions, well-orientedness questions, and the prospect of incrementality.Comment: Accepted into proceedings of MACIS 201
Exponential sum approximations for $t^{-\beta}$

Given $\beta>0$ and $\delta>0$, the function $t^{-\beta}$ may be approximated
for $t$ in a compact interval $[\delta,T]$ by a sum of terms of the form
$we^{-at}$, with parameters $w>0$ and $a>0$. One such an approximation, studied
by Beylkin and Monz\'on, is obtained by applying the trapezoidal rule to an
integral representation of $t^{-\beta}$, after which Prony's method is applied
to reduce the number of terms in the sum with essentially no loss of accuracy.
We review this method, and then describe a similar approach based on an
alternative integral representation. The main difference is that the new
approach achieves much better results before the application of Prony's method;
after applying Prony's method the performance of both is much the same.Comment: 18 pages, 5 figures. I have completely rewritten this paper because
  after uploading the previous version I realised that there is a much better
  approach. Note the change to the title. Have included minor corrections
  following revie
How We Make Sense of the World: Information, Map-Making, and The
  Scientific Narrative

Science is a constructed narrative of the natural world based on information
gathering and its subsequent analysis. In this essay, we develop a novel
approach to the epistemic foundations of the scientific narrative, as based on
our experiential interactions with the natural world. We first review some of
the basic aspects of both Bayesian statistics and Shannon's information theory
as applied to the construction of meaningful conceptualization of the natural
world. This conceptualization is rendered through the maps we construct of the
world based on our limited knowledge of reality. We propose a path from
experience to information and physics based on the notion that information is
experience that induces change in an Epistemic Agent (EA): the change may be
local and focused to a minor aspect of reality or it may be broad and
worldview-changing. We illustrate our approach through an analysis of a measure
of spatial complexity proposed by one of us called Configuration Entropy (CE),
and establish a link between experience at the cognitive level and information
content, showing that the CE is a quantitative measure of how much information
in spatial-complexity the external world hides from an EA.Comment: 28 pages, 1 figure. To appear in Map and Territory - Exploring the
  Foundations of Science, Thought and Reality, ed. by Shyam Wuppuluri, Newton
  da Costa, and Francisco Antonio Doria (Springer, Frontiers Collection
Online hate speech in the European Union : a discourse-analytic perspective

This book reports on research carried out as part of the European Union co-funded C.O.N.T.A.C.T. project which targeted hate speech and hate crime across a number of EU member states. It showcases the bearing that discourse analytic research can have on our understanding of this phenomenon that is a growing global cause for concern.\ud
Although ‘hate speech’ is often incorporated in legal and policy documents, there is no universally accepted definition, which in itself warrants research into how hatred is both expressed and perceived. The research project synthesises discourse analytic and corpus linguistics techniques, and presents its key findings here. The focus is especially on online comments posted in reaction to news items that could trigger discrimination, as well as on the folk perception of online hate speech as revealed through semi-structured interviews with young individuals across the various partner countries.European Commission, DG Justice: Rights, equality and citizenship programme (C.O.N.T.A.C.T.: JUST/2014/RRAC/AG/ HATE/6706)peer-reviewe
Optimized Engagement of Macrophages and Satellite Cells in the Repair and Regeneration of Exercised Muscle

Recurring contraction-relaxation cycles exert a massive mechanical load on muscle fibers. Training adaptation therefore entails the promotion of a series of biological programs aimed at inducing a better stress response but also at optimizing repair processes. Muscle regeneration is controlled by an intricate, tightly coordinated engagement of muscle fibers, satellite cells, macrophages and other cell types. In this review, we discuss some of the recent insights into the regulation of muscle repair and regeneration in exercised muscle, elucidate the role of the peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) in this context, and speculate about potential implications for the treatment of muscle diseases
Smart Tourism Destinations and Higher Tourism Education in Spain. Are We Ready for This New Management Approach?

‘Smart tourism’ and ‘smart tourism destinations’ (STDs) have become commonplace in the research of the interrelationship between tourism, destinations and the latest Information and Communication Technologies (ICTs). However, research has failed hitherto to identify if this evolution towards smartness of tourism is accompanied by a similar process in tourism education to provide the system with prepared human resources once the transformation has been fully completed. This paper aims to fulfil this gap, by taking the case of Spanish public superior education in tourism, to analyse in which degree ICTs, as critical knowledge and skills required within STDs, are included in tourism curricula and how students assess the formation they receive in this regard. The analysis offers several valuable implications for governments in charge of public education design and opens discussion over the possibility to strengthen the technological side of tourism curricula.This research has been carried out within the project “New approaches for tourism destinations planning and management: conceptualization, case studies and problems. Definition of smart tourism destinations models” (CSO2014-59193-R) under the Spanish R&D&I Plan financed by the Ministry of Economy and Competitiveness
Honey Yield Forecast Using Radial Basis Functions

Honey yields are difficult to predict and have been usually
associated with weather conditions. Although some specific meteorological
variables have been associated with honey yields, the reported relationships
concern a specific geographical region of the globe for a given
time frame and cannot be used for different regions, where climate may
behave differently. In this study, Radial Basis Function (RBF) interpolation
models were used to explore the relationships between weather variables
and honey yields. RBF interpolation models can produce excellent
interpolants, even for poorly distributed data points, capable of mimicking
well unknown responses providing reliable surrogates that can
be used either for prediction or to extract relationships between variables.
The selection of the predictors is of the utmost importance and an
automated forward-backward variable screening procedure was tailored
for selecting variables with good predicting ability. Honey forecasts for
Andalusia, the first Spanish autonomous community in honey production,
were obtained using RBF models considering subsets of variables
calculated by the variable screening procedure
Comparison of parallelisation approaches, languages, and compilers for unstructured mesh algorithms on GPUs

Efficiently exploiting GPUs is increasingly essential in scientific computing, as many current and upcoming supercomputers are built using them. To facilitate this, there are a number of programming approaches, such as CUDA, OpenACC and OpenMP 4, supporting different programming languages (mainly C/C++ and Fortran). There are also several compiler suites (clang, nvcc, PGI, XL) each supporting different combinations of languages. In this study, we take a detailed look at some of the currently available options, and carry out a comprehensive analysis and comparison using computational loops and applications from the domain of unstructured mesh computations. Beyond runtimes and performance metrics (GB/s), we explore factors that influence performance such as register counts, occupancy, usage of different memory types, instruction counts, and algorithmic differences. Results of this work show how clang’s CUDA compiler frequently outperform NVIDIA’s nvcc, performance issues with directive-based approaches on complex kernels, and OpenMP 4 support maturing in clang and XL; currently around 10% slower than CUDA
Resilient N-Body Tree Computations with Algorithm-Based Focused Recovery: Model and Performance Analysis

This paper presents a model and performance study for Algorithm-Based Focused Recovery (ABFR) applied to N-body computations, subject to latent errors. We make a detailed comparison with the classical Checkpoint/Restart (CR) approach. While the model applies to general frameworks, the performance study is limited to perfect binary trees, due to the inherent difficulty of the analysis. With ABFR, the crucial parameter is the detection interval, which bounds the error latency. We show that the detection interval has a dramatic impact on the overhead, and that optimally choosing its value leads to significant gains over the CR approach
Original Loop-closure Detection Algorithm for Monocular vSLAM

Vision-based simultaneous localization and mapping (vSLAM) is a
well-established problem in mobile robotics and monocular vSLAM is one of the
most challenging variations of that problem nowadays. In this work we study one
of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure
detection. We analyze the existing methods and propose original algorithm for
loop-closure detection, which is suitable for dense, semi-dense and
feature-based vSLAM methods. We evaluate the algorithm experimentally and show
that it contribute to more accurate mapping while speeding up the monocular
vSLAM pipeline to the extent the latter can be used in real-time for
controlling small multi-rotor vehicle (drone)
Automated Detection of Non-Relevant Posts on the Russian Imageboard
  "2ch": Importance of the Choice of Word Representations

This study considers the problem of automated detection of non-relevant posts
on Web forums and discusses the approach of resolving this problem by
approximation it with the task of detection of semantic relatedness between the
given post and the opening post of the forum discussion thread. The
approximated task could be resolved through learning the supervised classifier
with a composed word embeddings of two posts. Considering that the success in
this task could be quite sensitive to the choice of word representations, we
propose a comparison of the performance of different word embedding models. We
train 7 models (Word2Vec, Glove, Word2Vec-f, Wang2Vec, AdaGram, FastText,
Swivel), evaluate embeddings produced by them on dataset of human judgements
and compare their performance on the task of non-relevant posts detection. To
make the comparison, we propose a dataset of semantic relatedness with posts
from one of the most popular Russian Web forums, imageboard "2ch", which has
challenging lexical and grammatical features.Comment: 6 pages, 1 figure, 1 table, main proceedings of AIST-2017 (Analysis
  of Images, Social Networks, and Texts
Organizing Multimedia Data in Video Surveillance Systems Based on Face
  Verification with Convolutional Neural Networks

In this paper we propose the two-stage approach of organizing information in
video surveillance systems. At first, the faces are detected in each frame and
a video stream is split into sequences of frames with face region of one
person. Secondly, these sequences (tracks) that contain identical faces are
grouped using face verification algorithms and hierarchical agglomerative
clustering. Gender and age are estimated for each cluster (person) in order to
facilitate the usage of the organized video collection. The particular
attention is focused on the aggregation of features extracted from each frame
with the deep convolutional neural networks. The experimental results of the
proposed approach using YTF and IJB-A datasets demonstrated that the most
accurate and fast solution is achieved for matching of normalized average of
feature vectors of all frames in a track.Comment: 8 pages; 1 figure, accepted for publication at AIST1
Stabbing line segments with disks: complexity and approximation
  algorithms

Computational complexity and approximation algorithms are reported for a
problem of stabbing a set of straight line segments with the least cardinality
set of disks of fixed radii $r>0$ where the set of segments forms a straight
line drawing $G=(V,E)$ of a planar graph without edge crossings. Close
geometric problems arise in network security applications. We give strong
NP-hardness of the problem for edge sets of Delaunay triangulations, Gabriel
graphs and other subgraphs (which are often used in network design) for $r\in
[d_{\min},\eta d_{\max}]$ and some constant $\eta$ where $d_{\max}$ and
$d_{\min}$ are Euclidean lengths of the longest and shortest graph edges
respectively. Fast $O(|E|\log|E|)$-time $O(1)$-approximation algorithm is
proposed within the class of straight line drawings of planar graphs for which
the inequality $r\geq \eta d_{\max}$ holds uniformly for some constant
$\eta>0,$ i.e. when lengths of edges of $G$ are uniformly bounded from above by
some linear function of $r.$Comment: 12 pages, 1 appendix, 15 bibliography items, 6th International
  Conference on Analysis of Images, Social Networks and Texts (AIST-2017
Fighting with the Sparsity of Synonymy Dictionaries

Graph-based synset induction methods, such as MaxMax and Watset, induce
synsets by performing a global clustering of a synonymy graph. However, such
methods are sensitive to the structure of the input synonymy graph: sparseness
of the input dictionary can substantially reduce the quality of the extracted
synsets. In this paper, we propose two different approaches designed to
alleviate the incompleteness of the input dictionaries. The first one performs
a pre-processing of the graph by adding missing edges, while the second one
performs a post-processing by merging similar synset clusters. We evaluate
these approaches on two datasets for the Russian language and discuss their
impact on the performance of synset induction methods. Finally, we perform an
extensive error analysis of each approach and discuss prominent alternative
methods for coping with the problem of the sparsity of the synonymy
dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social
  Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science
  (LNCS
Runtime Distributions and Criteria for Restarts

Randomized algorithms sometimes employ a restart strategy. After a certain
number of steps, the current computation is aborted and restarted with a new,
independent random seed. In some cases, this results in an improved overall
expected runtime. This work introduces properties of the underlying runtime
distribution which determine whether restarts are advantageous. The most
commonly used probability distributions admit the use of a scale and a location
parameter. Location parameters shift the density function to the right, while
scale parameters affect the spread of the distribution. It is shown that for
all distributions scale parameters do not influence the usefulness of restarts
and that location parameters only have a limited influence. This result
simplifies the analysis of the usefulness of restarts. The most important
runtime probability distributions are the log-normal, the Weibull, and the
Pareto distribution. In this work, these distributions are analyzed for the
usefulness of restarts. Secondly, a condition for the optimal restart time (if
it exists) is provided. The log-normal, the Weibull, and the generalized Pareto
distribution are analyzed in this respect. Moreover, it is shown that the
optimal restart time is also not influenced by scale parameters and that the
influence of location parameters is only linear
XDOCS: An Application to Index Historical Documents

Dematerialization and digitalization of historical documents are key elements for their availability, preservation and diffusion. Unfortunately, the conversion from handwritten to digitalized documents presents several technical challenges.\ud
The XDOCS project is created with the main goal of making available and extending the usability of historical documents for a great variety of audience, like scholars, institutions and libraries.\ud
In this paper the core elements of XDOCS, i.e. page dewarping and word spotting technique, are described and two new applications, i.e. annotation/indexing and search tool, are presented
Automatic Image Cropping and Selection using Saliency: an Application to Historical Manuscripts

Automatic image cropping techniques are particularly important to improve the visual quality of cropped images and can be applied to a wide range of applications such as photo-editing, image compression, and thumbnail selection. In this paper, we propose a saliency-based image cropping method which produces significant cropped images by only relying on the corresponding saliency maps. Experiments on standard image cropping datasets demonstrate the benefit of the proposed solution with respect to other cropping methods. Moreover, we present an image selection method that can be effectively applied to automatically select the most representative pages of historical manuscripts thus improving the navigation of historical digital libraries
Quark-lepton complementarity model based predictions for
  $\theta_{23}^{PMNS}$ with neutrino mass hierarchy

After the successful investigation and confirmation of non zero
$\theta_{13}^{PMNS}$ by various experiments, we are standing at a square where
we still encounter a number of issues, which are to be settled. In this paper,
we have extended our recent work towards a precise prediction of the
$\theta_{23}^{PMNS}$ mixing angle, taking into account the neutrino mass
hierarchy. We parameterize the non-trivial correlation between quark (CKM) and
lepton (PMNS) mixing matrices in quark-lepton complementarity (QLC) model as
$V_{c}= U_{CKM}. \psi. U_{PMNS}$, where $\psi$ is a diagonal phase matrix.
Monte Carlo simulations are used to estimate the texture of $V_{c}$ and compare
the results with the standard Tri-Bi-Maximal (TBM) and Bi-Maximal(BM)
structures of neutrino mixing matrix. We have predicted the value of
$\theta_{23}^{PMNS} $ for normal and inverted neutrino mass hierarchies. The
value of $\theta_{23}^{PMNS}$ obtained for two cases are about $1.3\sigma$ away
from each other, implying the better precision can give us a strong hint for
the type of neutrino mass hierarchy.Comment: 3 pages, 3 figure
New tensor interaction as the source of the observed CP asymmetry in
  $\tau \to K_{S}\pi\nu_{\tau}$

Babar collaboration has reported an intriguing opposite sign in the
integrated decay rate asymmetry $A_{cp}(\tau \rightarrow K_{S}\pi\nu_{\tau})$
than that of SM prediction from the known K0 - $\bar{K0}$ mixing. Babar's
result deviate from the SM prediction by about 2.7$\sigma$. If the result
stands with higher precision in the future experiments, the observed sign
anomaly in the $A_{cp}(\tau \rightarrow K_{S}\pi\nu_{\tau})$ can most likely
come only from some new physics occurring possibly in both hadronic as well as
leptonic sectors. In this work, we will give an improved analysis of our
previous work where we have illustrated that, while non-standard scalar or
vector/axial-vector interactions will not contribute to the observed asymmetry
in the integrated decay rate, a new tensor interaction can explain the observed
anomaly. Assuming the real part of the new tensorial coupling is negligible
compare to its imaginary part and with $A_{cp}(\tau \rightarrow
K_{S}\pi\nu_{\tau})$ and Br($\tau \rightarrow K_{S}\pi\nu_{\tau}$) as data
points to fit the imaginary part of the NP coupling, we have been able to fit
the result within 1$\sigma$ of the experimental values.Comment: Matches with the XXII DAE High Energy Physics Symposium: Proceedings
  book, Chapter 75; DOI :10.1007/978-3-319-73171-1_75. arXiv admin note:
  substantial text overlap with arXiv:1605.0062
Elements of gamification in virtual learning environments: a systematic review

Information technologies have introduced several changes in teaching and learning environments. In this scenario, the gamification technique emerges as a promissing approach, considering the impact on the students’ motivation and appealing to their participation. This paper describes a systematic review addressing gamification in virtual learning environments (VLE), presenting an overview on how gamification has been applied in these scenarios. This review was based on papers published in highly scored journals in the field of computers in education. The papers were selected according to the gamification theme, and the content was analyzed and a state of the art built, according to the retrieved qualitative data. The results seem to reveal that there are significant gains derived from the adoption of gamification in VLE. However, some gains are not unanimous and it depends on how the elements are applied. This paper also suggests a basis for future work aiming at applying gamification in a VLE.info:eu-repo/semantics/publishedVersio
Assess and enhancing attention in learning activities

The rapid progress of technologies has enabled the development of
innovative environment in learning activities when the student used computer
devices with access to Internet. The goal of this paper is to propose an ambient
intelligent (AmI) system, directed at the teacher that indicates the level of attention
of the students in the class when it requires the use of the computer connected
to the Internet. This AmI system captures, measures, and supervises the
interaction of each student with the computer (or laptop) and indicates the level
of attention of students in the activities proposed by the teacher. When the
teacher has big class, he/she can visualize in real time the level of engagement
of the students in the proposed activities and act accordingly when necessary.
Measurements of attention level are obtained by a proposed model, and user for
training a decision support system that in a real scenario makes recommendations
for the teachers so as to prevent undesirable behaviour and change the
learning styles.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and
FCT – Fundação para a Ciência e Tecnologia within the Project Scope:
UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio
Adaptive Management of Riverine Socio-ecological Systems

If ongoing change in ecosystems and society can render inflexible policies obsolete, then management must dynamically adapt as a counter to perennial uncertainty. This chapter describes a general synthesis of how to make decision-making more adaptive and then explores the barriers to learning in management. We then describe how one such process, known as adaptive management (AM), has been applied in different river basins, on which basis we discuss AM’s strengths and limitations in various resource management contexts