pinecone/core-2020-05-10-deduplication huggingface dataset:
A Coq-based Implementation of Tarski's Mereogeometry

International audienceDuring the last decade, the domain of Qualitative Spatial Reasoning, has known a renewal of interest for mereogeometry, a theory that has been initiated by Tarski. Mereogeometry relies on mereology, the Lesniewski's theory of parts and wholes that is further extended with geometrical primitives and appropriate definitions. However, most approaches (i) depart from the original Lesniewski's mereology which does not assume usual sets as a basis, (ii) restrict the logical power of mereology to a mere theory of part-whole relations and (iii) require the introduction of a connection relation. Moreover, the seminal paper of Tarki shows up unclear foundations and we argue that mereogeometry as it is introduced by Tarski, can be more suited to extend the whole theory of Lesniewski. For that purpose, we investigate a type-theoretical representation of space more closely related with the original ideas of Lesniewski and expressed with the Coq language. We show that (i) it can be given a more clear foundation, (ii) it can be based on three axioms instead of four and (iii) it can serve as a basis for spatial reasoning with full compliance with Lesniewski's systems
Where snow is a landmark: route direction elements in Alpine contexts

Route directions research has mostly focused on urban space so far, highlighting human concepts of street networks based on a range of recurring elements such as route segments, decision points, landmarks and actions. We explored the way route directions reflect the features of space and activity in the context of mountaineering. Alpine route directions are only rarely segmented through decision points related to reorientation; instead, segmentation is based on changing topography. Segments are described with various degrees of detail, depending on difficulty. For landmark description, direction givers refer to prop‐ erties such as type of surface, dimension, colour of landscape features; terrain properties (such as snow) can also serve as landmarks. Action descriptions reflect the geometrical conceptualization of landscape features and dimensionality of space. Further, they are very rich in the semantics of manner of motion
Efficient stochastic simulation of systems with multiple time scales via statistical abstraction

Stiffness in chemical reaction systems is a frequently encountered computational problem, arising when different reactions in the system take place at different time-scales. Computational savings can be obtained under time-scale separation. Assuming that the system can be partitioned into slow- and fast- equilibrating subsystems, it is then possible to efficiently simulate the slow subsystem only, provided that the corresponding kinetic laws have been modified so that they reflect their dependency on the fast system. We show that the rate expectation with respect to the fast subsystem’s steady-state is a continuous function of the state of the slow system. We exploit this result to construct an analytic representation of the modified rate functions via statistical modelling, which can be used to simulate the slow system in isolation. The computational savings of our approach are demonstrated in a number of non-trivial examples of stiff systems
Model counting for complex data structures

We extend recent approaches for calculating the probability of program behaviors, to allow model counting for complex data structures with numeric fields. We use symbolic execution with lazy initialization to compute the input structures leading to the occurrence of a target event, while keeping a symbolic representation of the constraints on the numeric data. Off-the-shelf model counting tools are used to count the solutions for numerical constraints and field bounds encoding data structure invariants are used to reduce the search space. The technique is implemented in the Symbolic PathFinder tool and evaluated on several complex data structures. Results show that the technique is much faster than an enumeration-based method that uses the Korat tool and also highlight the benefits of using the field bounds to speed up the analysis
Comparative Analysis of Leakage Tools on Scalable Case Studies

International audienceQuantitative security techniques have been proven effective to measure the security of systems against various types of attackers. However, such techniques are often tested against small-scale academic examples. In this paper we use analyze two scalable, real life privacy case studies: the privacy of the energy consumption data of the users of a smart grid network and the secrecy of the voters' voting preferences with different types of voting protocols. We analyze both case studies with three state-of-the-art information leakage computation tools: LeakWatch, Moped-QLeak, and our tool QUAIL equipped with a new trace analysis algorithm. We highlight the relative advantages and drawbacks of the tools and compare their usability and effectiveness in analyzing the case studies
Electro-manipulation of droplets for microfluidic applications

This paper discusses electro-manipulation of droplets for microfluidic applications. There is a growing technology-driven interest in using external influences to move or shape small quantities of liquids, referred to as microfluidic actuation. The use of electrical, rather than mechanical, forces to achieve this actuation is convenient because the resultant devices contain no moving parts. Existing non-mechanical microfluidic actuation techniques that are driven by the application of a voltage include electrowetting and liquid dielectrophoresis, with many applications including lab-on-a-chip, polymer surface patterning, as well as optimisation of optical properties for polymer microlenses, and droplet driven displays
A Reduced Nonlinear Model for the Simulation of Two Phase Flow in a Horizontal Pipe.

In the last 10 years many 3D numerical schemes have been developed for the study the flow of a mixture of liquid and gas in a pipeline (Frank, Numerical simulation of slug flow regime for an air-water two-phase flow in horizontal pipes. In: The 11th international topical meeting on nuclear reactor thermal-hydraulics (NURETH-11), Avignon, 2005; Vallée et al., Nucl Eng Des 238(3):637–646, 2008; Höhne, Experiments and numerical simulations of horizontal two-phase flow regimes. In: Proceeding of the seventh international conference on CFD in the minerals and process industries, Melbourne, 2009; Bartosiewicz et al., Nucl Eng Des 240(9):2375–2381, 2010) but although they offer a very good accuracy, they are rarely fit for modelling a long pipe, due to the high computational costs. Then one is usually led to consider 1D models, see e.g. the works of Issa and his group (Issa and Kempf, Int J Multiphase Flow 29(1):69–95, 2003). Such models offer much faster simulations than 3D schemes, on the other hand they almost completely miss the dynamics in the transversal direction. Here we present a model able of representing the full 3D dynamics, but with the computational cost typical of 1D simulation. The main feature of our model consists in describing the dynamical variables in the direction transversal to the pipe by means of a family of functions depending on a set of parameters. The model is then solved by a standard finite volume scheme
A Centralized Mechanism to Make Predictions Based on Data From Multiple
  WSNs

In this work, we present a method that exploits a scenario with
inter-Wireless Sensor Networks (WSNs) information exchange by making
predictions and adapting the workload of a WSN according to their outcomes. We
show the feasibility of an approach that intelligently utilizes information
produced by other WSNs that may or not belong to the same administrative
domain. To illustrate how the predictions using data from external WSNs can be
utilized, a specific use-case is considered, where the operation of a WSN
measuring relative humidity is optimized using the data obtained from a WSN
measuring temperature. Based on a dedicated performance score, the simulation
results show that this new approach can find the optimal operating point
associated to the trade-off between energy consumption and quality of
measurements. Moreover, we outline the additional challenges that need to be
overcome, and draw conclusions to guide the future work in this field.Comment: 10 pages, simulation results and figures. Published i
A program for branching problems in the representation theory of real
  reductive groups

We wish to understand how irreducible representations of a group G behave
when restricted to a subgroup G' (the branching problem). Our primary concern
is with representations of reductive Lie groups, which involve both algebraic
and analytic approaches. We divide branching problems into three stages:
  (A) abstract features of the restriction;
  (B) branching laws (irreducible decompositions of the restriction); and
  (C) construction of symmetry breaking operators on geometric models.
  We could expect a simple and detailed study of branching problems in Stages B
and C in the settings that are {\it{a priori}} known to be "nice" in Stage A,
and conversely, new results and methods in Stage C that might open another
fruitful direction of branching problems including Stage A.
  The aim of this article is to give new perspectives on the subjects, to
explain the methods based on some recent progress, and to raise some
conjectures and open questions.Comment: to appear in Progress in Mathematics 312, Representations of
  Reductive Groups, In Honor of the 60th Birthday of David A. Vogan, J
Comparing and characterizing some constructions of canonical bases from
  Coxeter systems

The Iwahori-Hecke algebra $\mathcal{H}$ of a Coxeter system $(W,S)$ has a
"standard basis" indexed by the elements of $W$ and a "bar involution" given by
a certain antilinear map. Together, these form an example of what Webster calls
a pre-canonical structure, relative to which the well-known Kazhdan-Lusztig
basis of $\mathcal{H}$ is a canonical basis. Lusztig and Vogan have defined a
representation of a modified Iwahori-Hecke algebra on the free
$\mathbb{Z}[v,v^{-1}]$-module generated by the set of twisted involutions in
$W$, and shown that this module has a unique pre-canonical structure satisfying
a certain compatibility condition, which admits its own canonical basis which
can be viewed as a generalization of the Kazhdan-Lusztig basis. One can modify
the parameters defining Lusztig and Vogan's module to obtain other
pre-canonical structures, each of which admits a unique canonical basis indexed
by twisted involutions. We classify all of the pre-canonical structures which
arise in this fashion, and explain the relationships between their resulting
canonical bases. While some of these canonical bases are related in a trivial
fashion to Lusztig and Vogan's construction, others appear to have no simple
relation to what has been previously studied. Along the way, we also clarify
the differences between Webster's notion of a canonical basis and the related
concepts of an IC basis and a $P$-kernel.Comment: 32 pages; v2: additional discussion of relationship between canonical
  bases, IC bases, and P-kernels; v3: minor revisions; v4: a few corrections
  and updated references, final versio
Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing

This article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances\ud
are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile.Postprint (published version
Country-scale Exploratory Analysis of Call Detail Records through the Lens of Data Grid Models

International audienceCall Detail Records (CDRs) are data recorded by telecommunications companies, consisting of basic informations related to several dimensions of the calls made through the network: the source, destination , date and time of calls. CDRs data analysis has received much attention in the recent years since it might reveal valuable information about human behavior. It has shown high added value in many application domains like e.g., communities analysis or network planning. In this paper, we suggest a generic methodology based on data grid models for summarizing information contained in CDRs data. The method is based on a parameter-free estimation of the joint distribution of the variables that describe the calls. We also suggest several well-founded criteria that allows one to browse the summary at various granularities and to explore the summary by means of insightful visualizations. The method handles network graph data, temporal sequence data as well as user mobility data stemming from original CDRs data. We show the relevance of our methodology on real-world CDRs data from Ivory Coast for various case studies, like network planning strategy and yield management pricing strategy
Interactively exploring supply and demand in the UK independent music scene

We present an exploratory data mining tool useful for finding patterns in the geographic distribution of independent UK-based music artists. Our system is interactive, highly intuitive, and entirely browser-based, meaning it can be used without any additional software installations from any device. The target audiences are artists, other music professionals, and the general public. Potential uses of our software include highlighting discrepancies in supply and demand of specific music genres in different parts of the country, and identifying at a glance which areas have the highest densities of independent music artists
ProbLog2: Probabilistic logic programming

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for querying such models as well as for learning their parameters from data. It is available as an online tool on the web and for download. The offline version offers both command line access to inference and learning and a Python library for building statistical relational learning applications from the system’s components.status: publishe
Statistical training of pre-service teachers with application in school practice

This chapter discusses the way that actions developed under a programme from the Brazilian government can contribute to the learning of teaching, by considering four pre-service mathematics teachers (undergraduate students), from the university in São Paulo, who participated in a training activity using Didactical Sequences (DS) of Statistics, and then applied these to students in their sixth to ninth years of schooling. It is considered that programme has provided training times to pre-service teachers, who learn the necessary concepts for teaching, as well as how to prepare and execute activities in the classroom with the guidance and under the supervision of professors and teachers. Therefore, those activities provide security for pre-service teachers in their teacher training.CAPES (PIBID ICMC-USP)CNPq (472295/2010-1
Defining agents’ behaviour for negotiation contexts

Agents who represent participants in the group decision-making context
require a certain number of individual traits in order to be successful. By
using argumentation models, agents are capable to defend the interests of those
who they represent, and also justify and support their ideas and actions. However,
regardless of how much knowledge they might hold, it is essential to define
their behaviour. In this paper (1) is presented a study about the most important
models to infer different types of behaviours that can be adapted and
used in this context, (2) are proposed rules that must be followed to affect positively
the system when defining behaviours and (3) is proposed the adaptation
of a conflict management model to the context of Group Decision Support Systems.
We propose one approach that (a) intends to reflect a natural way of human
behaviour in the agents, (b) provides an easier way to reach an agreement
between all parties involved and (c) does not have high configuration costs to
the participants. Our approach will offer a simple yet perceptible configuration
tool that can be used by the participants and contribute to more intelligent
communications between agents and makes possible for the participants to have
a better understanding of the types of interactions experienced by the agents belonging
to the system.This work is part-funded by ERDF - European Regional Development Fund through
the COMPETE Programme (operational programme for competitiveness) within project
FCOMP-01-0124-FEDER-028980 (PTDC/EEISII/1386/2012) and by National
Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation
for Science and Technology) with the João Carneiro PhD grant with the reference
SFRH/BD/89697/2012.info:eu-repo/semantics/publishedVersio
Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the
birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births
in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting
physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively
Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved
by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma
A critical evaluation of methods for the reconstruction of tissue-specific models

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared.

This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.Acknowledgments. S.C. thanks the FCT for the Ph.D. Grant SFRH/BD/ 80925/2011. The authors thank the FCT Strategic Project of UID/BIO/04469/2013 unit, the project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and the project “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE-07-0124-FEDER-000028 Co-funded by the Programa Operacional Regional do Norte (ON.2 - O Novo Norte), QREN, FEDER
A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013. The authors would like to thank Pedro Sernadela for his contributions in previous wor
Ramex-forum: sequential patterns of prices in the petroleum production chain

We present a sensibility analysis and new visualizations using an improved version of the Ramex-Forum algorithm applied to the study of the petroleum production chain. Di erent combinations of parameters
and new ways to visualize data will be used. Results will highlight the importance of Ramex-Forum and its proper parameterizations for analyzing relevant relations among price variations in petroleum and other
similar markets.This research is supported by the GoBusiness Research project (http://www.
gobusinessfinance.ch/en/research). The authors would like to thank GoB-
usiness Finance for providing the data,  nancial knowledge and funds for the
present work