pinecone/core-2020-05-10-deduplication huggingface dataset:
Childlessness and intergenerational transfers in later life

First Online: 13 January 2017Childlessness in later life has been attracting increased attention from researchers and policy makers. Yet a number of misconceptions about childlessness among the elderly remain, such as the claim that elderly childless people are mainly on the receiving end of intergenerational exchanges, or that they are a homogeneous group. Contrary to these assumptions, we find that elderly childless people give as well as receive, and that parental status is a continuum, ranging from full childlessness across several intermediary conditions to full current natural parenthood. In a study of the elderly population across 11 European countries, we show that non-parents make significant contributions to their social networks of family and friends through financial and time transfers, and that the latter in particular differ little from those of natural parents. The same applies to their participation in charitable and voluntary work. Different parental statuses are significantly associated with the various dimensions of giving and receiving. Social parents (i.e., people who have no natural children, but who have adopted, foster, or stepchildren) are shown to be much more similar to natural parents than to non-parents. Family recomposition thus does not seem to inhibit intergenerational exchanges, as long as social parents have sufficient contact with their non-natural social children. On the other hand, parents who have lost contact with their children – natural or otherwise – are likely to require more formal care in later life
Who Will Be the Next President? A Guide to the U.S. Presidential Election System

Almost every American has either studied something about the Electoral
College in school or at least heard of it. Yet to many people used to electing
municipal, state, and federal officials by the democratic principle “the one who gets the most votes always wins,” the Electoral College looks quite mysterious and antiquated. The mystery concerns how such a system could have existed for so long, and why it has not been replaced by a system that is based on the above democratic principle. In contrast, people who are curious about the election system often try to grasp (a) how the Electoral College could have emerged in the first place, and (b) what could have been the Founding Fathers’ logic of designing the system for electing a President and a Vice President. This Chapter considers the Electoral College origins and analyzes a logical mistake made by the originators of
the Constitution, which still remains in its text, as well as the election problems that were overlooked by the Founding Fathers in the original design of the Constitutio
The Observational Status of Cosmic Inflation after Planck

The observational status of inflation after the Planck 2013 and 2015 results
and the BICEP2/Keck Array and Planck joint analysis is discussed. These
pedagogical lecture notes are intended to serve as a technical guide filling
the gap between the theoretical articles on inflation and the experimental
works on astrophysical and cosmological data. After a short discussion of the
central tenets at the basis of inflation (negative self-gravitating pressure)
and its experimental verifications, it reviews how the most recent Cosmic
Microwave Background (CMB) anisotropy measurements constrain cosmic inflation.
The fact that vanilla inflationary models are, so far, preferred by the
observations is discussed and the reason why plateau-like potential versions of
inflation are favored within this subclass of scenarios is explained. Finally,
how well the future measurements, in particular of $B$-Mode CMB polarization or
primordial gravity waves, will help to improve our knowledge about inflation is
also investigated.Comment: 108 pages, 22 figures. Lectures given at the II JPBCosmo school
  (Brazil
Scaling Properties of Human Brain Functional Networks

We investigate scaling properties of human brain functional networks in the
resting-state. Analyzing network degree distributions, we statistically test
whether their tails scale as power-law or not. Initial studies, based on
least-squares fitting, were shown to be inadequate for precise estimation of
power-law distributions. Subsequently, methods based on maximum-likelihood
estimators have been proposed and applied to address this question.
Nevertheless, no clear consensus has emerged, mainly because results have shown
substantial variability depending on the data-set used or its resolution. In
this study, we work with high-resolution data (10K nodes) from the Human
Connectome Project and take into account network weights. We test for the
power-law, exponential, log-normal and generalized Pareto distributions. Our
results show that the statistics generally do not support a power-law, but
instead these degree distributions tend towards the thin-tail limit of the
generalized Pareto model. This may have implications for the number of hubs in
human brain functional networks.Comment: International Conference on Artificial Neural Networks - ICANN 201
Combining spatial and parametric working memory in a dynamic neural field model

We present a novel dynamic neural field model consisting of two coupled fields of Amari-type which supports the existence of localized activity patterns or “bumps” with a continuum of amplitudes. Bump solutions have been used in the past to model spatial working memory. We apply the model to explain input-specific persistent activity that increases monotonically with the time integral of the input (parametric working memory). In numerical simulations of a multi-item memory task, we show that the model robustly memorizes the strength and/or duration of inputs. Moreover, and important for adaptive behavior in dynamic environments, the memory strength can be changed at any time by new behaviorally relevant information. A direct comparison of model behaviors shows that the 2-field model does not suffer the problems of the classical Amari model when the inputs are presented sequentially as opposed to simultaneously.EU-FP7 ITN project NETT: Neural Engineering Transformative Technologies (no. 289146)
DeepChess: End-to-End Deep Neural Network for Automatic Learning in
  Chess

We present an end-to-end learning method for chess, relying on deep neural
networks. Without any a priori knowledge, in particular without any knowledge
regarding the rules of chess, a deep neural network is trained using a
combination of unsupervised pretraining and supervised training. The
unsupervised training extracts high level features from a given position, and
the supervised training learns to compare two chess positions and select the
more favorable one. The training relies entirely on datasets of several million
chess games, and no further domain specific knowledge is incorporated.
  The experiments show that the resulting neural network (referred to as
DeepChess) is on a par with state-of-the-art chess playing programs, which have
been developed through many years of manual feature selection and tuning.
DeepChess is the first end-to-end machine learning-based method that results in
a grandmaster-level chess playing performance.Comment: Winner of Best Paper Award in ICANN 201
DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the
  Jigsaw Puzzle Problem

This paper introduces the first deep neural network-based estimation metric
for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network
predicts whether or not they should be adjacent in the correct assembly of the
puzzle, using nothing but the pixels of each piece. The proposed metric
exhibits an extremely high precision even though no manual feature extraction
is performed. When incorporated into an existing puzzle solver, the solution's
accuracy increases significantly, achieving thereby a new state-of-the-art
standard
DeepPainter: Painter Classification Using Deep Convolutional
  Autoencoders

In this paper we describe the problem of painter classification, and propose
a novel approach based on deep convolutional autoencoder neural networks. While
previous approaches relied on image processing and manual feature extraction
from paintings, our approach operates on the raw pixel level, without any
preprocessing or manual feature extraction. We first train a deep convolutional
autoencoder on a dataset of paintings, and subsequently use it to initialize a
supervised convolutional neural network for the classification phase.
  The proposed approach substantially outperforms previous methods, improving
the previous state-of-the-art for the 3-painter classification problem from
90.44% accuracy (previous state-of-the-art) to 96.52% accuracy, i.e., a 63%
reduction in error rate
Multivariate dynamic kernels for financial time series forecasting

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market.Peer ReviewedPostprint (author's final draft
SMS spam filtering using probabilistic topic modelling and Stacked Denoising Autoencoder.

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97 % accuracy which compares favourably to the best reported algorithms presented in the literature
Rotation-Invariant Restricted Boltzmann Machine Using Shared Gradient
  Filters

Finding suitable features has been an essential problem in computer vision.
We focus on Restricted Boltzmann Machines (RBMs), which, despite their
versatility, cannot accommodate transformations that may occur in the scene. As
a result, several approaches have been proposed that consider a set of
transformations, which are used to either augment the training set or transform
the actual learned filters. In this paper, we propose the Explicit
Rotation-Invariant Restricted Boltzmann Machine, which exploits prior
information coming from the dominant orientation of images. Our model extends
the standard RBM, by adding a suitable number of weight matrices, associated
with each dominant gradient. We show that our approach is able to learn
rotation-invariant features, comparing it with the classic formulation of RBM
on the MNIST benchmark dataset. Overall, requiring less hidden units, our
method learns compact features, which are robust to rotations.Comment: 8 pages, 3 figures, 1 tabl
Analysis of dropout learning regarded as ensemble learning

Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of
layers, huge number of units, and connections. Therefore, overfitting is a
serious problem. To avoid this problem, dropout learning is proposed. Dropout
learning neglects some inputs and hidden units in the learning process with a
probability, p, and then, the neglected inputs and hidden units are combined
with the learned network to express the final output. We find that the process
of combining the neglected hidden units with the learned network can be
regarded as ensemble learning, so we analyze dropout learning from this point
of view.Comment: 9 pages, 8 figures, submitted to Conferenc
Biomaterials as Tendon and Ligament Substitutes: Current Developments

Tendon and ligament have specialized dynamic microenvironment characterized by a complex hierarchical extracellular matrix essential for tissue functionality, and responsible to be an instructive niche for resident cells. Among musculoskeletal diseases, tendon/ligament injuries often result in pain, substantial tissue morbidity, and disability, affecting athletes, active working people and elder population. This represents not only a major healthcare problem but it implies considerable social and economic hurdles. Current treatments are based on the replacement and/or augmentation of the damaged tissue with severe associated limitations. Thus, it is evident the clinical challenge and emergent need to recreate native tissue featuresÂ and regenerate damaged tissues. In this context, the design and development of anisotropic bioengineered systems with potential to recapitulate the hierarchical architecture and organization of tendons and ligaments from nano to macro scale will be discussed in this chapter. Special attention will be given to the state-of-the-art fabrication techniques, namely spinning and electrochemical alignment techniques to address the demanding requirements for tendon substitutes, particularly concerning the importance of biomechanical and structural cues of these tissues. Moreover, the poor innate regeneration ability related to the low cellularity and vascularization of tendons and ligaments also anticipates the importance of cell based strategies, particularly on the stem cells role for the success of tissue engineered therapies. In summary, this chapter provides a general overview on tendon and ligaments physiology and current conventional treatments for injuries caused by trauma and/or disease. Moreover, this chapter presents tissue engineering approaches as an alternative to overcome the limitations of current therapies, focusing on the discussion about scaffolds design for tissue substitutes to meet the regenerative medicine challenges towards the functional restoration of damaged or degenerated tendon and ligament tissues.Portuguese Foundation for Science and Technology for the post-doctoral grant (SFRH/BPD/111729/2015) and for the projects Recognize (UTAP-ICDT/CTM-BIO/0023/2014) and POC
I-01-0145-FEDER-007
Private or Common Criteria in a Multi-criteria Group Decision Support System: An Experiment

Because collective decision processes are central to the management function of most organizations, it is important to understand them better and to improve them if possible. One common view of group decision processes is that they should offer participants the opportunity to confront and resolve the differences in their points of view. New cognitive and technical tools may help to facilitate the sharing of individuals’ reasoning and preferences, but only if they do not require participants to reveal information that they wish to keep private, perhaps for strategic or personal reasons. The aim of this study is to test experimentally one such approach, contained in the Group Decision Support System, GRUS, which allows decision makers to use a multi-criteria approach to problem structuring that can involve both public (shared) and private criteria
Applying the 3C Model to FLOSS communities

Publicado em "Collaboration and technology: 22nd International Conference, CRIWG 2016, Kanazawa, Japan, September 14-16, 2016, proceedings". ISBN 978-3-319-44798-8How learning occurs within Free/Libre Open Source (FLOSS)
communities and what is the dynamics such projects (e.g. the life cycle
of such projects) are very relevant questions when considering the use of
FLOSS projects in a formal education setting. This paper introduces an
approach based on the 3C collaboration model (communication, coordination
and cooperation) to represent the collaborative learning dynamics
within FLOSS communities. To explore the collaborative learning potential
of FLOSS communities a number of questionnaires and interviews
to selected FLOSS contributors were run. From this study a 3C collaborative
model applicable to FLOSS communities was designed and
discussed.Programa Operacional da Região Norte, NORTE2020, in the context of project NORTE-01-0145-FEDER-000037FCT under grant SFRH/BSAB/113890/201
Real-time Synthesis is Hard!

International audienceWe study the reactive synthesis problem (RS) for specifications given in Metric Interval Temporal Logic (MITL). RS is known to be undecidable in a very general setting, but on infinite words only; and only the very restrictive BResRS subcase is known to be decidable (see D'Souza et al. and Bouyer et al.). In this paper, we precise the decidabil-ity border of MITL synthesis. We show RS is undecidable on finite words too, and present a landscape of restrictions (both on the logic and on the possible controllers) that are still undecidable. On the positive side, we revisit BResRS and introduce an efficient on-the-fly algorithm to solve it
A Boyer-Moore Type Algorithm for Timed Pattern Matching

The timed pattern matching problem is formulated by Ulus et al. and has been
actively studied since, with its evident application in monitoring real-time
systems. The problem takes as input a timed word/signal and a timed pattern
(specified either by a timed regular expression or by a timed automaton); and
it returns the set of those intervals for which the given timed word, when
restricted to the interval, matches the given pattern. We contribute a
Boyer-Moore type optimization in timed pattern matching, relying on the classic
Boyer-Moore string matching algorithm and its extension to (untimed) pattern
matching by Watson and Watson. We assess its effect through experiments; for
some problem instances our Boyer-Moore type optimization achieves speed-up by
two times, indicating its potential in real-world monitoring tasks where data
sets tend to be massive
Impact of dwell time on vertical transportation through discrete simulation in SIMIO

This work has the objective of simulating an elevator system, using SIMIO software. Firstly, two different approaches, and its implementation, will be explained and compared: Vehicle vs. Entity. After selecting the Entity-approach, due to its more flexible processes and the limitations of the Vehicle-approach, it will be used to conduct the simulation experiments. The purpose is to evaluate the impact of dwell time - time in which the elevator remains stopped, allowing for clients to enter and exit - in the performance of the system. That will be achieved analysing the impact on the total time - spent by clients from placing a call until reaching its destination - number of clients inside the system and waiting for the elevator, waiting time, elevator occupation and number of elevator movements. The analysis of the results indicates that, for the properties defined, the best time for the elevator to stay with its doors open is around 10 seconds.This work has been supported by FCT – Fundação para a Ciência e Tecnologia in the scope of the project: PEst-OE/EEI/UI0319/2014 (ALGORITMI).info:eu-repo/semantics/publishedVersio
Improving production logistics through materials flow control and lot splitting

Competitive advantage of make-to-order manufacturing companies is highly dependent on their capability to offer short delivery times and on time delivery. This calls for effective production and materials flow control – a core part of production logistics. This paper applies discrete simulation to study the delivery performance of a make-to-order manufacturing system configured as a general flow shop, when operated under two card-based material flow control mechanisms: CONWIP and GKS. The influence of two lot splitting strategies on the performance of these mechanisms is also evaluated. Results show that GKS clearly outperforms CONWIP and that splitting strategies have a positive impact on the performance of both mechanisms. GKS also showed to be particularly robust to the variation of the number of production authorisation cards used. This, together with the fact that the card-based mechanisms require little data handling and simplify production control, makes GKS attractive for practical application in make-to-order companies.This study had the ﬁnancial support of 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
The Goals Approach: Enterprise Model-Driven Agile Human-Centered Software Engineering

Business Process Improvement (BPI) is a key issue in the development of the enterprise competitiveness. However, achieving a level of software development performance that matches enterprise BPI needs in terms of producing noticeable results in small amounts of time requires the existence of a comprehensive and also agile Software Development Process (SDP). Quite often, SDPs do not deliver software architectures that can be directly used for in-house development, as specifications are either too close to the user interface design or too close to business rules and application domain modeling, and produce architectures that do not cope with software development concerns. In this paper we present the Goals Approach, which structures business processes to extract requirements, and methodologically details them in order to specify the user interface, the business logic and the database structures for the architecture of a BPI. Our approach aims in-house software development in small and medium enterprises
Testing Prototypes and Final User Interfaces Through an Ontological Perspective for Behavior-Driven Development

In a user-centered development process, prototypes evolve in iterative cycles until they meet users’ requirements and then become the final product. Every cycle gives the opportunity to revise the design and to introduce new requirements which might affect the specification of artifacts that have been set in former development phases. Testing the consistency of multiple artifacts used to develop interactive systems every time that a new requirement is introduced is a cumbersome activity, especially if it is done manually. This paper proposes an approach based on Behavior-Driven Development (BDD) to support the automated assessment of artifacts along the development process of interactive systems. The paper uses an ontology for specifying tests that can run over multiple artifacts sharing similar concepts. A case study testing Prototypes and Final User Interfaces is presented to demonstrate the feasibility of this approach in early phases of the design process, providing a continuous quality assurance of requirements, and helping clients and development teams to identify potential problems and inconsistencies before commitments with software implementation