pinecone/core-2020-05-10-deduplication huggingface dataset:
How to conduct studies with neonates combining near-infrared imaging and electroencephalography

The aims of this study were (1) to develop a set-up for co-registering functional near-infrared imaging (fNIRI) and electroencephalography in 15 healthy term neonates (mean gestational age 39.9 weeks, mean postnatal age 2.7 days) which underwent visual flash stimulation during sleep; (2) to optimize fNIRI sensitivity in regard to detectability of hemodynamic responses; and (3) to analyze whether oxy-hemoglobin concentration [O2Hb] rises or falls after stimulus onset. fNIRI sensitivity seems to depend mainly on luminance of the visual stimulation. The sensitivity of fNIRI is 63.6%. When hemodynamic response is detected, a rise of [O2Hb] after stimulus onset was shown in 75% and a decrease of [O2Hb] in 25%
Modelling and filtering of physiological oscillations in near-infrared spectroscopy by time-varying Fourier series

Raw near-infrared spectroscopy signals contain oscillatory components, namely low frequency oscillations (Mayer waves), breathing and the heartbeat. We propose an approach to model and estimate them from noisy measurements assuming that they are linearly superposed. Estimating them is important, since they pose disturbing effects, but they are also of scientific interest. These components are not strictly periodic; we characterize them as “almost periodic.” The model of an almost periodic signal is a Fourier series where the Fourier coefficients and the fundamental frequency are allowed to (slowly) change over time. This model can be represented by factor graphs which we use to derive message passing algorithms to estimate the time-dependent model parameters from a measured signal. An implementation of the proposed algorithm processes a 100 s long measurement in 2 s (on a modern PC) which is ∼10× faster than a comparable previous implementation. Thus, real-time applications, for example, online monitoring, could be realized using slower, inexpensive or power-saving hardware. The increase in speed was achieved by using a different parameterisation of the model which allows Gaussian message passing (with only two parameters: mean and variance), whereas previously some messages were digitized. In the previous implementation, the number of harmonics in the model is chosen manually (for each subject and data channel). In this chapter, we show an intuitive procedure to estimate this number from the measured signal. In conclusion, the proposed algorithm is able to separate the heartbeat and, in contrast to the previous implementation, the low frequency oscillation effectively and in real time
Colored light and brain and muscle oxygenation

Colored light is applied in medicine in the treatment of various diseases. The aim of this study was to investigate potential effects of exposure to blue and red light on brain and muscle blood volume ([tHb]) and tissue oxygenation (StO2) measured by noninvasive near-infrared spectrophotometry (NIRS). Ten healthy volunteers were included in a randomized crossover study. Blue light exposure leads to decreased oxygen consumption in the brain and the skeletal muscle. Blue and red light have significantly different effects on [tHb] and StO2
Model Adequacy Checks for Discrete Choice Dynamic Models

This paper proposes new parametric model adequacy tests for possibly
nonlinear and nonstationary time series models with noncontinuous data
distribution, which is often the case in applied work. In particular, we
consider the correct specification of parametric conditional distributions in
dynamic discrete choice models, not only of some particular conditional
characteristics such as moments or symmetry. Knowing the true distribution is
important in many circumstances, in particular to apply efficient maximum
likelihood methods, obtain consistent estimates of partial effects and
appropriate predictions of the probability of future events. We propose a
transformation of data which under the true conditional distribution leads to
continuous uniform iid series. The uniformity and serial independence of the
new series is then examined simultaneously. The transformation can be
considered as an extension of the integral transform tool for noncontinuous
data. We derive asymptotic properties of such tests taking into account the
parameter estimation effect. Since transformed series are iid we do not require
any mixing conditions and asymptotic results illustrate the double simultaneous
checking nature of our test. The test statistics converges under the null with
a parametric rate to the asymptotic distribution, which is case dependent,
hence we justify a parametric bootstrap approximation. The test has power
against local alternatives and is consistent. The performance of the new tests
is compared with classical specification checks for discrete choice models
Collaborative Work on 3D Educational Content

<p>The use of three-dimensional Collaborative Virtual Environments (3D CVEs) for educational purposes has been constantly increasing during the recent years. One of the reasons is the potential of such environments and the possibility they offer for supporting collaborative work with various types of content. Another important reason is an opportunity for participants to interact in a way that conveys a sense of presence lacking in other media. These opportunities result in a number of benefits for establishing and supporting learning communities, simulating various contexts and conducting educational activities. Nevertheless, this area is in the early stage of development and needs both theoretical concepts and empirical results.</p><p>The research work presented in the thesis has three main objectives. The first is to provide recommendations and guidelines for supporting collaborative work on 3D educational content. The second is to provide frameworks for designing tools and environments in 3D CVEs to benefit educational activities. The third is to provide frameworks for technological and instructional support of learning communities in 3D CVEs.</p><p>Within this research work, four empirical studies were conducted. The data were extracted from a number of sources, including direct observation, digital artefacts created by the participants and recorded interaction, reflection and feedback. Analysed data were applied to each next empirical study and, in addition, used for developing theoretical frameworks.</p><p>The research work presented in the thesis resulted in six main contributions. Two of them are related to the use of collaborative work on 3D content for learning: C1 – Typology of 3D Content and Visualization Means; and C2 – a methodology for learning with educational visualizations in 3D CVEs. Other two contributions are related to the design of tools and environments for supporting educational activities in 3D CVEs: C3 – a framework for designing tools in 3D CVEs called Creative Virtual Workshop; and C4 – guidelines for designing environments based on a virtual campus and virtual city metaphors. The two final contributions are related to the support of learning communities in 3D CVEs: C5 – a framework called Virtual Research Arena for creating awareness about educational and research activities, promoting cross-fertilization between different environments, and engaging the general public; and C6 – a framework called ‘Universcity’ for integrating the cultural, social, educational, and entertainment aspects of a city community life in a single 3D CVE.</p><p>The findings presented in the thesis can be applied by developers for creating educational 3D CVEs and by educators for conducting educational activities in 3D CVEs. All the findings can also be used for further research.</p>PhD i informasjonsteknologiPhD in Information Technolog
On the calculation of price sensitivities with jump-diffusion structure

We provide a new theoretical framework for estimating the price sensitivities of a trading position with regard to five underlying factors in jump-diffusion models using jump times Poisson noise. The proposition that results in a general solution is mathematically proved. The general solution that this paper offers can be applied to compute each price sensitivity. The suggested modeling approach deals with the shortcomings of the Black-Scholes formula such as the jumps that can occur at any time in the stock's price. Via the Malliavin calculus we show that differentiation can be transformed into integration, which makes the price sensitivities operational and more efficient. Thus, the solution that is provided in this paper is expected to make decision making under uncertainty more efficient.
Reliability of dynamically measured flexibility matrix on damage detection of frame structures

The use of dynamically measured flexibility matrix to indicate and locate damage is reported in many studies. However their focus is mostly on structures like beams or slabs that each node has one degree of freedom and all DOFs are in the same direction. For a structure with degrees of freedoms in three directions e.g. frame structures, both modal testing and damage detection become more complicated. It is because each DOF in the stiffness matrix is influenced by all the members connected to it. When it comes to flexibility matrix, another problem arises. Since flexibility matrix is more global, damage does not only affect the related DOFs, but others too. This article aims to address these problems in order to develop a reliable damage detection method for frame structures. The modal parameters of a four story frame structure are calculated for intact and damage cases. Changes in the flexibility matrix were analyzed for different damage scenarios when the damage member is known. It was suggested that there are some patterns that relate DOFs and members which can be used to locate the damage member. Â© The Society for Experimental Mechanics, Inc. 2012
Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)

The adaptive neuro-fuzzy inference system (ANFIS) is a process for mapping from a given input to a single output using the fuzzy logic and neuro-adaptive learning algorithms. Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function parameters are adjusted using combination of back propagation algorithm with a least square type of method. The feasibility of ANFIS as strong tool for predicting the severity of damage in a model steel girder bridge is examined in this research. Reduction in the structural stiffness produces changes in the dynamics properties, such as the natural frequencies and mode shapes. In this study, natural frequencies of a structure are applied as effective input parameters to train the ANFIS and the required data are obtained from experimental modal analysis. The performance of ANFIS model was assessed using Mean Square Error (MSE) and coefficient of determination (R 2). The ANFIS model could predict the severity of damage with MSE of 0.0049 and correlation coefficient (R 2) of 0.9976 for traing data sets. The results show the ability of an adaptive neuro-fuzzy inference system to predict the damage severity of the structure with high accuracy. Â© The Society for Experimental Mechanics, Inc. 2012
Extension of Subspace Identification to LPTV Systems: Application to Helicopters

In this paper, we focus on extending the subspace identification to the class of linear periodically time-varying (LPTV) systems. The Lyapunov-Floquet transformation is first applied to the system’s state-space model in order to get the monodromy matrix (MM) and, thus, a necessary and sufficient condition for system stability. Then, given two successive covariance-driven Hankel matrices, the MM matrix is extracted by some calculus of a simultaneous singular value decomposition (SVD) and a least square optimization. The method is illustrated by a simulation application to the model of a hinged-blades helicopter
Enhancing learning with visualization techniques

vThe use of visualization techniques in learning is not new. They have been used in maps and drawings for thousands of years. This chapter analyzes how more novel visualization techniques can be used to enhance various activities during the learning process: finding and understanding educational resources, collaboration with learners and teachers, (self-) reflecting about learners’ progress, and designing learning experiences. We illustrate our analysis with example tools and visualizations. Results of our analysis indicate that visualization techniques are beginning to be more widely used for learning but further research is needed to assess the added value of these visual approaches in terms of effectiveness, efficiency or other criteria that pertain to learning.status: publishe
A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering

This chapter describes a design-based research project that developed an early warning system for an undergraduate engineering mentoring program. Using near real-time data from a university’s learning management system, we provided academic advisors with timely and targeted data on students’ academic progress. We discuss the development of the early warning system and detail how academic advisors used it. Our findings point to the value of providing academic advisors with performance data that can be used to direct students to appropriate sources of support
A new approach to the optimization of the extraction of astrometric and
  photometric information from multi-wavelength images in cosmological fields

This paper describes a new approach to the optimization of information
extraction in multi-wavelength image cubes of cosmological fields. The
objective is to create a framework for the automatic identification and tagging
of sources according to various criteria (isolated source, partially
overlapped, fully overlapped, cross-matched, etc) and to set the basis for the
automatic production of the SEDs (spectral energy distributions) for all
objects detected in the many multi-wavelength images in cosmological fields.In
order to do so, a processing pipeline is designed that combines Voronoi
tessellation, Bayesian cross-matching, and active contours to create a
graph-based representation of the cross-match probabilities. This pipeline
produces a set of SEDs with quality tags suitable for the application of
already-proven data mining methods. The pipeline briefly described here is also
applicable to other astrophysical scenarios such as star forming regions.Comment: GREAT Workshop. This paper will be published in Springer as part of
  the proceedings for the GREAT Worksho
Screening for Bioactive Compounds from Algae

At present, functional foods are seen as a good alternative to maintain or even improve human health, mainly for the well-known correlation between diet and health. This fact has brought about a great interest for seeking new bioactive products of natural origin to be used as functional ingredients, being, nowadays, one of the main areas of research in Food Science and Technology. Among the different sources that can be used to extract bioactives, algae have become one of the most promising. Algae have an enormous biodiversity and can be seen as natural factories for producing bioactive compounds since either by growing techniques or by genetic engineering approaches, they can improve their natural content of certain valuable compounds. In this book chapter, a revision about the different types of bioactives that have been described in algae is presented including compounds, such as lipids, carotenoids, proteins, phenolics, vitamins, polysaccharides, etc. Also, the modern green techniques used to achieve the selective extraction of such bioactives are presented and the methods for fast screening of bioactivity described.Peer Reviewe
Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of
Bayesian methods in astrophysics; (2) current misconceptions about both
frequentist and Bayesian statistical inference that hinder wider adoption of
Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian
modeling as a major future direction for research in Bayesian astrostatistics,
exemplified in part by presentations at the first ISI invited session on
astrostatistics, commemorated in this volume. It closes with an intentionally
provocative recommendation for astronomical survey data reporting, motivated by
the multilevel Bayesian perspective on modeling cosmic populations: that
astronomers cease producing catalogs of estimated fluxes and other source
properties from surveys. Instead, summaries of likelihood functions (or
marginal likelihood functions) for source properties should be reported (not
posterior probability density functions), including nontrivial summaries (not
simply upper limits) for candidate objects that do not pass traditional
detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in
  "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed.,
  Springer, New York, forthcoming in 2012), the inaugural volume for the
  Springer Series in Astrostatistics. Version 2 has minor clarifications and an
  additional referenc
Opportunistic routing in wireless mesh networks

Opportunistic Routing (OR) has been proposed as a way to increase the performance of wireless networks by exploiting its broadcast nature. In OR, instead of pre-selecting a single specific node to be the next-hop as a forwarder for a packet, multiple nodes can potentially be selected as the next-hop forwarder. Thus the source can use multiple potential paths to deliver the packets to the destination. More specially, when the current node transmits a packet, all the candidates that receive the packet successfully will coordinate with each other to determine which one would actually forward the packet according to some criteria, while the other nodes will simply discard the packet. In this chapter, we survey the state of the art in OR, then focus on the candidates selection algorithms and carry out a comparative performance evaluation of the most relevant proposals appeared in the literature.Peer ReviewedPostprint (author’s final draft
Social-aware Opportunistic Routing: The New Trend

Since users move around based on social relationships and interests, the
resulting movement patterns can represent how nodes are socially connected
(i.e., nodes with strong social ties, nodes that meet occasionally by sharing
the same working environment). This means that social interactions reflect
personal relationships (e.g., family, friends, co-workers, passers-by) that may
be translated into statistical contact opportunities within and between social
groups over time. Such contact opportunities may be exploited to ensure good
data dissemination and retrieval, even in the presence of intermittent
connectivity. Thus, in the last years, a new trend based on social similarity
emerged where social relationships, interests, popularity and among others, are
used to improve opportunistic routing. In this chapter, the reader will learn
about the different approaches related to opportunistic routing focusing on the
social-aware approaches and how such approaches make use of social information
derived from opportunistic contacts to improve data forwarding. Additionally, a
brief overview on the existing taxonomies for opportunistic routing as well as
an updated one are provided along with a set of experiments in scenarios based
on synthetic mobility models and human traces in order to show the potential of
social-aware solutions.Comment: 42 pages, 10 figures, 1 tabl
On the future of astrostatistics: statistical foundations and
  statistical practice

This paper summarizes a presentation for a panel discussion on "The Future of
Astrostatistics" held at the Statistical Challenges in Modern Astronomy V
conference at Pennsylvania State University in June 2011. I argue that the
emerging needs of astrostatistics may both motivate and benefit from
fundamental developments in statistics. I highlight some recent work within
statistics on fundamental topics relevant to astrostatistical practice,
including the Bayesian/frequentist debate (and ideas for a synthesis),
multilevel models, and multiple testing. As an important direction for future
work in statistics, I emphasize that astronomers need a statistical framework
that explicitly supports unfolding chains of discovery, with acquisition,
cataloging, and modeling of data not seen as isolated tasks, but rather as
parts of an ongoing, integrated sequence of analyses, with information and
uncertainty propagating forward and backward through the chain. A prototypical
example is surveying of astronomical populations, where source detection,
demographic modeling, and the design of survey instruments and strategies all
interact.Comment: 8 pp, 2 figures. To appear in "Statistical Challenges in Modern
  Astronomy V," (Lecture Notes in Statistics, Vol. 209), ed. Eric D. Feigelson
  and G. Jogesh Babu; publication planned for Sep 2012; see
  http://www.springer.com/statistics/book/978-1-4614-3519-
Region-based caption text extraction

This chapter presents a method for caption text detection. The proposed\ud
method will be included in a generic indexing system dealing with other semantic\ud
concepts which are to be automatically detected as well. To have a coherent detection\ud
system, the various object detection algorithms use a common image description,\ud
a hierarchical region-based image model. The proposed method takes advantage\ud
of texture and geometric features to detect the caption text. Texture features are\ud
estimated using wavelet analysis and mainly applied for text candidate spotting. In\ud
turn, text characteristics verification relies on geometric features,which are estimated\ud
exploiting the region-based image model. Analysis of the region hierarchy provides\ud
the final caption text objects. The final step of consistency analysis for output is\ud
performed by a binarization algorithm that robustly estimates the thresholds on the\ud
caption text area of support.Peer ReviewedPostprint (published version
Anatomical Distribution of Nucleoside System in the Human Brain and Implications for Therapy

Nucleosides have a wide range of physiological and pathophysiological roles in the human brain as modulators of a variety of neural functions. For example, adenosine, inosine, guanosine, and uridine participate in the mechanisms underlying memory, cognition, sleep, pain, depression, schizophrenia, epilepsy, Alzheimer’s disease, Huntington’s disease, and Parkinson’s disease. Consequently, increasing attention is now being given to the speci fi c role of nucleosides in physiological and pathological processes in the human brain. Different elements of nucleoside system, including nucleoside concentrations, metabolic enzyme activity, and expression of nucleoside transporters and receptors, may be changed under normal and pathological conditions. The alterations suggest that interlinked elements of the nucleoside system are functioning in a tightly concerted manner.\ud
Nucleoside levels, activity of nucleoside metabolic enzymes, and expression of nucleoside transporters and receptors are unevenly distributed in the brain, suggesting that nucleosides have different roles in functionally distinct human brain areas. The aim of this chapter is to summarize our present knowledge of the anatomical distribution of nucleoside system in the human brain, placing emphasis on potential therapeutic pharmacological strategies