Recombining dependent data: an Order Statistics

This article discusses the problem of forming groups from previously split data. Algorithms for Cluster Analysis like SAR proposed by Peña, Rodriguez and Tiao (2004), divide the sample into small very homogeneous groups and then recombine them to form the definitive data configuration. This kind of splitting leads to dependent data in the sense that the groups are disjoint, so no traditional homogeneity of means or variances tests can be used. We propose an alternative by using Order Statistics. Studying the distribution and some moments of linear combination of Order Statistics it is possible to recombine disjoint data groups when they merge into a sample from the same population.SAR, Cluster Analysis, Order Statistics, L-statistics, Bootstrapping

On the von Neumann and Frank-Wolfe Algorithms with Away Steps

The von Neumann algorithm is a simple coordinate-descent algorithm to determine whether the origin belongs to a polytope generated by a finite set of points. When the origin is in the of the polytope, the algorithm generates a sequence of points in the polytope that converges linearly to zero. The algorithm's rate of convergence depends on the radius of the largest ball around the origin contained in the polytope. We show that under the weaker condition that the origin is in the polytope, possibly on its boundary, a variant of the von Neumann algorithm that includes generates a sequence of points in the polytope that converges linearly to zero. The new algorithm's rate of convergence depends on a certain geometric parameter of the polytope that extends the above radius but is always positive. Our linear convergence result and geometric insights also extend to a variant of the Frank-Wolfe algorithm with away steps for minimizing a strongly convex function over a polytope

The isovector dipole strength in nuclei with extreme neutron excess

The E1 strength is systematically analyzed in very neutron-rich Sn nuclei, beyond $^{132}$Sn until $^{166}$Sn, within the Relativistic Quasiparticle Random Phase Approximation. The great neutron excess favors the appearance of a deformed ground state for $^{142-162}$Sn. The evolution of the low-lying strength in deformed nuclei is determined by the interplay of two factors, isospin asymmetry and deformation: while greater neutron excess increases the total low-lying strength, deformation hinders and spreads it. Very neutron rich deformed nuclei may not be as good candidates as stable spherical nuclei like $^{132}$Sn for the experimental study of low-lying E1 strength

A robust partial least squares method with applications

Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper we show that if the sample covariance matrix is properly robustified further robustification of the linear regression steps of the PLS algorithm becomes unnecessary. The robust estimate of the covariance matrix is computed by searching for outliers in univariate projections of the data on a combination of random directions (Stahel-Donoho) and specific directions obtained by maximizing and minimizing the kurtosis coefficient of the projected data, as proposed by Peña and Prieto (2006). It is shown that this procedure is fast to apply and provides better results than other procedures proposed in the literature. Its performance is illustrated by Monte Carlo and by an example, where the algorithm is able to show features of the data which were undetected by previous methods.

Robust estimation in linear regression models with fixed effects

In this work we extend the procedure proposed by Peña and Yohai (1999) for computing robust regression estimates in linear models with fixed effects. We propose to calculate the principal sensitivity components associated to each cluster and delete the set of possible outliers based on an appropriate robust scale of the residuals. Some advantage of our robust procedure are: (a) it is computationally low demanding, (b) it is able to avoid the swamping effect often present in similar methods, (c) it is appropriate for contamination in the error term (vertical outliers) and possibly masked high leverage points (horizontal outliers). The performance of the robust procedure is investigated through several simulation studies.Fixed effects models, Outlier detection, Principal sensitivity vector

Time series segmentation by Cusum, AutoSLEX and AutoPARM methods

Time series segmentation has many applications in several disciplines as neurology, cardiology, speech, geology and others. Many time series in this fields do not behave as stationary and the usual transformations to linearity cannot be used. This paper describes and evaluates different methods for segmenting non-stationary time series. We propose a modification of the algorithm in Lee et al. (2003) which is designed to searching for a unique change in the parameters of a time series, in order to find more than one change using an iterative procedure. We evaluate the performance of three approaches for segmenting time series: AutoSLEX (Ombao et al., 2002), AutoPARM (Davis et al., 2006) and the iterative cusum method mentioned above and referred as ICM. The evaluation of each methodology consists of two steps. First, we compute how many times each procedure fails in segmenting stationary processes properly. Second, we analyze the effect of different change patterns by counting how many times the corresponding methodology correctly segments a piecewise stationary process. ICM method has a better performance than AutoSLEX for piecewise stationary processes. AutoPARM presents a very satisfactory behaviour. The performance of the three methods is illustrated with time series datasets of neurology and speechTime series segmentation, AutoSLEX, AutoPARM, Cusum Methods

Lost in Translation

At the conclusion of a patient’s hospitalization the Registered Nurse (RN) will review all discharge instructions in the patients room in their preferred language. Plan time for the discharge and facilitate a calm quiet environment, such as the patient’s room with the door closed, if possible. Get the discharge instructions printed out in their preferred language and a set for yourself in English would be best so you are able to review the instructions line by line with them. When the patients medical team, such as but not limited to, medical doctors, pharmacists, physical therapists, and social work have completed their parts of the discharge planning then the RN can review the instructions for their own understanding. The RN can have the patient’s medications ready or know where the patient can get them when leaving the facility. When ready to present the instructions to the patient inform your charge nurse or break nurse that you are going to be discharging a patient with translation so they may tend to your other patients needs. Conduct the discharge by selected option via phone or in person translator and be attentive to the patient and to remember basic communication skills and be aware of your non-verbal communication as well. Always conclude with them asking questions and be sure to ask them questions of understanding in open ended format to ensure their understanding of the teaching

MIDI-based music score editor

[Abstract] Music has always played a fundamental role in society. It is a basic human function and, as a consequence people have always found music significant whether it is for enjoyment in listening, its emotional response, performing or creating. Music is represented using music notation. Music notation is the transcription of music into music scores. Music scores have been a way of teaching and sharing music with others. In addition, it was a key contributor to evolving music language and allowing the works of composers to be preserved over time. The continuous advancements in technology have influenced both the creation and recording of music. One of the milestones for music creation and recording was Musical Instrument Digital Interface (MIDI). MIDI is a protocol that provides a standardized and efficient means of conveying musical performance information as electronic data. Then, it is possible to obtain musical notation information by just processing the electronic data inside a MIDI file. The objective of this project is to develop and implement a MIDI based music score creator. Users will be able to load MIDI files, process them and generate the correspondent music score. To achieve this, a combination of musical knowledge and information obtained by the MIDI protocol will be used to create the accurate scores.[Resumo] A música sempre xogou un papel fundamental na sociedade. A música é unha función básica nos humanos e polo tanto, as música sempre foi importante para as persoas, xa fora para disfrutar escoitándoa, pola resposta emocional, para interpretala ou para creala. A música é representada pola notación musical. A notación musical consiste na transcipción de música en partituras. As partituras músicales sempre foron unha forma de enseñar e compartir música entre as persoas. Ademais, as partituras foron un contribuinte clave na evolución da linguaxe musical e permitiron preservar as obras creadas por diferentes compositores a través do tempo. Os avances continuos en tecnoloxía influenciaron tanto a creación como a grabación da música. Un dos hitos máis importantes para a creación e grabación de musica foi a creación de MIDI. O protocolo MIDI proporciona un medio estandarizado e eficiente para transmitir información musical como datos electrónicos. Polo tanto, é posible obtener información sobre notación musical simplemente procesando os datos electrónicos dentro dun archivo MIDI. O obxectivo deste proxecto e desarrollar e implementar un creador de partituras musicales baseado en MIDI. Os usuarios poderán cargar archivos MIDI, procesales e xerar a partitura correspondiente. Para logralo, utilizarase unha combination de conocimientos musicales e de información obtida polo protocolo MIDI para crear partituras precisas.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2019/202