pinecone/core-2020-05-10-deduplication huggingface dataset:
Asteroseismic Data Analysis with DIAMONDS

Since the advent of the space-based photometric missions such as CoRoT and
NASA's Kepler, asteroseismology has acquired a central role in our
understanding about stellar physics. The Kepler spacecraft, especially, is
still releasing excellent photometric observations that contain a large amount
of information not yet investigated. For exploiting the full potential of these
data, sophisticated and robust analysis tools are now essential, so that
further constraining of stellar structure and evolutionary models can be
obtained. In addition, extracting detailed asteroseismic properties for many
stars can yield new insights on their correlations to fundamental stellar
properties and dynamics. After a brief introduction to the Bayesian notion of
probability, I describe the code DIAMONDS for Bayesian parameter estimation and
model comparison by means of the nested sampling Monte Carlo (NSMC) algorithm.
NSMC constitutes an efficient and powerful method, in replacement to standard
Markov chain Monte Carlo, very suitable for high-dimensional and multimodal
problems that are typical of detailed asteroseismic analyses, such as the
fitting and mode identification of individual oscillation modes in stars (known
as peak-bagging). DIAMONDS is able to provide robust results for statistical
inferences involving tens of individual oscillation modes, while at the same
time preserving a considerable computational efficiency for identifying the
solution. In the tutorial, I will present the fitting of the stellar background
signal and the peak-bagging analysis of the oscillation modes in a red-giant
star, providing an example to use Bayesian evidence for assessing the peak
significance of the fitted oscillation peaks.Comment: Lecture presented at the IVth Azores International Advanced School in
  Space Sciences on "Asteroseismology and Exoplanets: Listening to the Stars
  and Searching for New Worlds" (arXiv:1709.00645), which took place in Horta,
  Azores Islands, Portugal in July 201
Asteroseismic Stellar Modelling with AIMS

The goal of AIMS (Asteroseismic Inference on a Massive Scale) is to estimate
stellar parameters and credible intervals/error bars in a Bayesian manner from
a set of asteroseismic frequency data and so-called classical constraints. To
achieve reliable parameter estimates and computational efficiency, it searches
through a grid of pre-computed models using an MCMC algorithm -- interpolation
within the grid of models is performed by first tessellating the grid using a
Delaunay triangulation and then doing a linear barycentric interpolation on
matching simplexes. Inputs for the modelling consist of individual frequencies
from peak-bagging, which can be complemented with classical spectroscopic
constraints. AIMS is mostly written in Python with a modular structure to
facilitate contributions from the community. Only a few computationally
intensive parts have been rewritten in Fortran in order to speed up
calculations.Comment: 11 pages, 4 figures. Tutorial presented at the IVth Azores
  International Advanced School in Space Sciences on "Asteroseismology and
  Exoplanets: Listening to the Stars and Searching for New Worlds"
  (arXiv:1709.00645), which took place in Horta, Azores Islands, Portugal in
  July 201
Semi-automated Model-Based Generation of Enterprise Architecture Deliverables

As part of Enterprise Architecture projects, models are built using different languages and tools to document and analyze the state of business and IT. However, models are just intermediate assets: deliverables are the actual outputs, but they are typically hand built using information from the models, and following the structures specified in EA methods. This requires manual effort, is error-prone, and results in artifacts that might be out-of-date very quickly. This paper addresses this by making a proposal to support the semi-automated generation of EA deliverables using a scripting Domain Specific Language for the creation of deliverable templates
Rockfall occurrence and fragmentation

Rockfalls are very rapid and damaging slope instability processes that affect mountainous regions, coastal cliffs and slope cuts. This contribution focuses on fragmental rockfalls in which the moving particles, particularly the largest ones, propagate following independent paths with little interaction among them. The prediction of the occurrence and frequency of the rockfalls has benefited by the rapid development of the techniques for the detection and the remote acquisition of the rock mass surface features such as the 3D laser scanner and the digital photogrammetry. These techniques are also used to monitor the deformation experienced by the rock mass before failure. The quantitative analysis of the fragmental rockfalls is a useful approach to assess risk and for the design of both stabilization and protection measures. The analysis of rockfalls must consider not only the frequency and magnitude of the potential events but also the fragmentation of the detached rock mass. The latter is a crucial issue as it affects the number, size and the velocity of the individual rock blocks. Several case studies of the application of the remote acquisition techniques for determining the size and frequency of rockfall events and their fragmentation are presented. The extrapolation of the magnitude-frequency relationships is discussed as well as the role of the geological factors for constraining the size of the largest detachable mass from a cliff. Finally, the performance of a fractal fragmentation model for rockfalls is also discussed.Peer ReviewedPostprint (published version
Assessing the life cycle of existing maritime structures – application to a harbour bridge in Porto port

Since the construction industry is the one that bears most expenses, both in financial and environmental terms, it is of the upmost importance that these expenses originate a product with a long term exploitation, so as to mitigate them. Having this problem in consideration, in this paper the bridge located in the north of Portugal, near Porto, in the oil tanker terminal at the Leixões port was studied. This structure is located in one of the most aggressive environments for concrete structures, a maritime zone. The most accepted durability models in the country, related to deterioration induced by sea chlorides penetration, were then implemented. Thereby, it was possible to identify which model better reflects reality, since the structure in analysis is now at the end of its lifetime, after 50 years of service and shows advanced degradation due to chloride attack.

In the context of this work, the structure and the test methods relevant to the theme being studied were described by inspection reports and in situ test results, made available by the Douro and Viana do Castelo Port Authority. From this data it was possible to study the structure deterioration by introducing them into durability models. These models range from prescriptive to performance based approaches, being possible to identify, from the later ones, a deterministic model, based on the Model Code 2010, a semi-probabilistic based on the E465 specification from the Portuguese National Laboratory for Civil Engineering (LNEC) and two probabilistic models, based on the same standards, for which a computer code was developed during this work. Through these deterioration prediction tools, different project scenarios were established, originating a list of minimal concrete covers to ensure 100 years of lifetime to a structure built in the studied exposure zone.(undefined)info:eu-repo/semantics/publishedVersio
Life cycle cost, as a tool for decision making on concrete infrastructures

The use of Life Cycle Cost (LCC) tools in civil engineering is increasing, due to the need of infrastructure owners and operators to guarantee their assets maximum performance with an optimized budget. By considering these tools it will be possible to manage assets along their lifetime in a more sustainable and efficient way. Due to this reason, it was recently constituted a Task Group on fib to deal with existing LCC tools for concrete infrastructures. This paper gives an introduction to these tools, with a special emphasis to the added-value of LCC, and to the main contents of the fib TG 8.4 state-of-art technical report. This covers a description of existing LCC standards and guidelines, their applicability, the definition of different cost elements, the incorporation of risk in the analysis, etc.(undefined)info:eu-repo/semantics/publishedVersio
Assessment of the shear strength of steel fibre-reinforced concrete

Steel fibres when added to concrete, form what has been termed steel fiber-reinforced concrete (SFRC). The merger of these two materials boasts an improved post-cracking strength maintaining a high durability. Such features enable the use of SFRC in structural applications thanks to the appearance in recent times of standards and recommendations that assess its structural capacity based on flexural tests. However, the shear behaviour of a structural material is of key importance and up to now there are few studies dealing with this issue. In order to widen the knowledge in this area this contribution presents the experimental results of push-off shear tests performed in SFRC. The samples employed were double-notched ones and they were subjected to compressive forces in the tip of both notches creating pure shear stresses in the ligament. The influence of the fibre dosage was analysed by manufacturing two self-compacting concretes with 26 and 70 kg/m3 of fibre. Moreover, the influence of the fibre length was studied by using 35 and 50 mm-long fibres. Furthermore, the shear behaviour was also connected with the fracture behaviour of the same specimens shown in previous studies of the same authors
Predicting Car Park Occupancy Rates in Smart Cities

In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.DOI: 10.1007/978-3-319-59513-9_11Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports
Discovering causal factors explaining business process performance

Business process performance may be affected by a range of factors, such as the volume and characteristics of ongoing cases or the performance and availability of individual resources. Event logs collected by modern information systems provide a wealth of data about the execution of business processes. However, extracting root causes for performance issues from these event logs is a major challenge. Processes may change continuously due to internal and external factors. Moreover, there may be many resources and case attributes influencing performance. This paper introduces a novel approach based on time series analysis to detect cause-effect relations between a range of business process characteristics and process performance indicators. The scalability and practical relevance of the approach has been validated by a case study involving a real-life insurance claims handling process
Aligning textual and graphical descriptions of processes through ILP techniques

With the aim of having individuals from different backgrounds and expertise levels examine the operations in an organization, different representations of business processes are maintained. To have these different representations aligned is not only a desired feature, but also a real challenge due to the contrasting nature of each process representation. In this paper we present an efficient technique for aligning a textual description and a graphical model of a process. The technique is grounded on using natural language processing techniques to extract linguistic features of each representation, and encode the search as a mathematical optimization encoded using Integer Linear Programming (ILP) whose resolution ensures an optimal alignment between both descriptions. The technique has been implemented and the experiments witness the significance of the approach with respect to the state-of-the-art technique for the same task.Peer ReviewedPostprint (author's final draft
Table identification and reconstruction in spreadsheets

Spreadsheets are one of the most successful content generation tools, used in almost every enterprise to perform data transformation, visualization, and analysis. The high degree of freedom provided by these tools results in very complex sheets, intermingling the actual data with formatting, formulas, layout artifacts, and textual metadata. To unlock the wealth of data contained in spreadsheets, a human analyst will often have to understand and transform the data manually. To overcome this cumbersome process, we propose a framework that is able to automatically infer the structure and extract the data from these documents in a canonical form. In this paper, we describe our heuristics-based method for discovering tables in spreadsheets, given that each cell is classified as either header, attribute, metadata, data, or derived. Experimental results on a real-world dataset of 439 worksheets (858 tables) show that our approach is feasible and effectively identifies tables within partially structured spreadsheets.Peer ReviewedPostprint (author's final draft
Linking data and BPMN processes to achieve executable models

We describe a formally well founded approach to link data and processes conceptually, based on adopting UML class diagrams to represent data, and BPMN to represent the process. The UML class diagram together with a set of additional process variables, called Artifact, form the information model of the process. All activities of the BPMN process refer to such an information model by means of OCL operation contracts. We show that the resulting semantics while abstract is fully executable. We also provide an implementation of the executor.Peer ReviewedPostprint (author's final draft
Quality of Word Embeddings on Sentiment Analysis Tasks

Word embeddings or distributed representations of words are being used in various applications like machine translation, sentiment analysis, topic identification etc. Quality of word embeddings and performance of their applications depends on several factors like training method, corpus size and relevance etc. In this study we compare performance of a dozen of pretrained word embedding models on lyrics sentiment analysis and movie review polarity tasks. According to our results, Twitter Tweets is the best on lyrics sentiment analysis, whereas Google News and Common Crawl are the top performers on movie polarity analysis. Glove trained models slightly outrun those trained with Skipgram. Also, factors like topic relevance and size of corpus significantly impact the quality of the models. When medium or large-sized text sets are available, obtaining word embeddings from same training dataset is usually the best choice
“What does my classifier learn?” : A visual approach to understanding natural language text classifiers

Neural Networks have been utilized to solve various tasks such as image recognition, text classification, and machine translation and have achieved exceptional results in many of these tasks. However, understanding the inner workings of neural networks and explaining why a certain output is produced are no trivial tasks. Especially when dealing with text classification problems, an approach to explain network decisions may greatly increase the acceptance of neural network supported tools. In this paper, we present an approach to visualize reasons why a classification outcome is produced by convolutional neural networks by tracing back decisions made by the network. The approach is applied to various text classification problems, including our own requirements engineering related classification problem. We argue that by providing these explanations in neural network supported tools, users will use such tools with more confidence and also may allow the tool to do certain tasks automatically
Distributed Random Process for a Large-Scale Peer-to-Peer Lottery

Most online lotteries today fail to ensure the verifiability of the random
process and rely on a trusted third party. This issue has received little
attention since the emergence of distributed protocols like Bitcoin that
demonstrated the potential of protocols with no trusted third party. We argue
that the security requirements of online lotteries are similar to those of
online voting, and propose a novel distributed online lottery protocol that
applies techniques developed for voting applications to an existing lottery
protocol. As a result, the protocol is scalable, provides efficient
verification of the random process and does not rely on a trusted third party
nor on assumptions of bounded computational resources. An early prototype
confirms the feasibility of our approach
Private Data System Enabling Self-Sovereign Storage Managed by
  Executable Choreographies

With the increased use of Internet, governments and large companies store and
share massive amounts of personal data in such a way that leaves no space for
transparency. When a user needs to achieve a simple task like applying for
college or a driving license, he needs to visit a lot of institutions and
organizations, thus leaving a lot of private data in many places. The same
happens when using the Internet. These privacy issues raised by the centralized
architectures along with the recent developments in the area of serverless
applications demand a decentralized private data layer under user control. We
introduce the Private Data System (PDS), a distributed approach which enables
self-sovereign storage and sharing of private data. The system is composed of
nodes spread across the entire Internet managing local key-value databases. The
communication between nodes is achieved through executable choreographies,
which are capable of preventing information leakage when executing across
different organizations with different regulations in place. The user has full
control over his private data and is able to share and revoke access to
organizations at any time. Even more, the updates are propagated instantly to
all the parties which have access to the data thanks to the system design.
Specifically, the processing organizations may retrieve and process the shared
information, but are not allowed under any circumstances to store it on long
term. PDS offers an alternative to systems that aim to ensure self-sovereignty
of specific types of data through blockchain inspired techniques but face
various problems, such as low performance. Both approaches propose a
distributed database, but with different characteristics. While the
blockchain-based systems are built to solve consensus problems, PDS's purpose
is to solve the self-sovereignty aspects raised by the privacy laws, rules and
principles.Comment: DAIS 201
Honey health benefits and uses in medicine

The generation of reactive oxygen species (ROS) and other free radicals during
metabolism is an essential and normal process that ideally is compensated through
the antioxidant system. However, due to many environmental, lifestyle, and pathological
situations, free radicals and oxidants can be produced in excess, resulting in
oxidative damage of biomolecules (e.g., lipids, proteins, and DNA). This plays a
major role in the development of chronic and degenerative illness such as cancer,
autoimmune disorders, aging, cataract, rheumatoid arthritis, cardiovascular, and
neurodegenerative diseases (Pham-Huy et al. 2008; Willcox et al. 2004). The human
body has several mechanisms to counteract oxidative stress by producing antioxidants, which are either naturally synthetized in situ, or externally supplied
through foods, and/or supplements (Pham-Huy et al. 2008).info:eu-repo/semantics/publishedVersio
Learning Concept-Driven Document Embeddings for Medical Information Search

Many  medical  tasks  such  as  self-diagnosis,  health-care  assessment, and clinical trial patient recruitment involve the usage of information  access  tools.  A  key  underlying  step  to  achieve  such  tasks  is the  document-to-document  matching  which  mostly  fails  to  bridge  the gap identified between raw level representations of information in documents  and  high-level  human  interpretation.  In  this  paper,  we  study how to optimize the document representation by leveraging neural-based approaches to capture latent representations built upon both validated medical  concepts  specified  in  an  external  resource  as  well  as  the  used words. We experimentally show the effectiveness of our proposed model used as a support of two different medical search tasks, namely health search and clinical search for cohorts
Bayesian Unbiasing of the Gaia Space Mission Time Series Database

21 st century astrophysicists are confronted with the herculean task of
distilling the maximum scientific return from extremely expensive and complex
space- or ground-based instrumental projects. This paper concentrates in the
mining of the time series catalog produced by the European Space Agency Gaia
mission, launched in December 2013. We tackle in particular the problem of
inferring the true distribution of the variability properties of Cepheid stars
in the Milky Way satellite galaxy known as the Large Magellanic Cloud (LMC).
Classical Cepheid stars are the first step in the so-called distance ladder: a
series of techniques to measure cosmological distances and decipher the
structure and evolution of our Universe. In this work we attempt to unbias the
catalog by modelling the aliasing phenomenon that distorts the true
distribution of periods. We have represented the problem by a 2-level
generative Bayesian graphical model and used a Markov chain Monte Carlo (MCMC)
algorithm for inference (classification and regression). Our results with
synthetic data show that the system successfully removes systematic biases and
is able to infer the true hyperparameters of the frequency and magnitude
distributions
Experimental validation of volume-based comparison for double-McCormick
  relaxations

Volume is a natural geometric measure for comparing polyhedral relaxations of
non-convex sets. Speakman and Lee gave volume formulae for comparing
relaxations of trilinear monomials, quantifying the strength of various natural
relaxations. Their work was motivated by the spatial branch-and-bound algorithm
for factorable mathematical-programming formulations. They mathematically
analyzed an important choice that needs to be made whenever three or more terms
are multiplied in a formulation. We experimentally substantiate the relevance
of their main results to the practice of global optimization, by applying it to
difficult box cubic problems (boxcup). In doing so, we find that, using their
volume formulae, we can accurately predict the quality of a relaxation for
boxcups based on the (box) parameters defining the feasible region