Order Selection Criteria Research Articles

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization.

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps.

Parameter Selection Criteria for Tomo-SAR Focusing

The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direction-of-arrival estimation theory. The latter allows achieving super resolution, along with ambiguity levels reduction, thanks to the use of parametric focusing methods, as multiple signal classification (MUSIC) and statistical regularization techniques, like the maximum-likelihood-inspired adaptive robust iterative approach (MARIA). Nevertheless, in order to correctly suit the considered signal model, MUSIC and most regularization approaches require an appropriate setting of the involved parameters. In both cases, the accuracy of the retrieved solutions depends on the right selection of the assigned values. Thus, with the aim of dealing with such an issue, this article addresses several parameter selection strategies, adapted specifically to the TomoSAR scenario. Parametric techniques as MUSIC solve the TomoSAR problem in a different manner as the regularization methods do, hence, each approach demands different methodologies for the proper estimation of their parameters. Consequently, we refer to the Kullback-Leibler information criterion for the model order selection of parametric techniques as MUSIC, whereas we rather explore the Morozov's discrepancy principle, the L-Curve, the Stein's unbiased risk estimate, and the generalized cross-validation to choose the regularization parameters. After the incorporation of these criteria to MUSIC and MARIA, respectively, their capabilities are first analyzed through simulations, and later on, utilizing real data acquired from an urban area.

Upper airway stimulation for Obstructive Sleep Apnoea (OSA): A new paradigm of treatment

AbstractObstructive Sleep Apnoea (OSA) is a chronic condition, caused by anatomical factors associated with a progressive loss of muscular tone of the tongue and pharyngeal walls, producing upper airways collapse with oxygen desaturation during sleep. Hypoglossal nerve stimulation (HNS) is an innovative and physiological technique, with validated positive effects, to treat patients with OSA who have failed or refused continuous positive airway (cPAP) standard therapy. HNS is a mixed medical and surgical procedure consisting of an electrical stimulation of the medial branches of the hypoglossal nerve—activating the genioglossus muscle (GM) that is the primary protrusor muscle of the tongue—by an implanted cuff electrode connected to a pulse generator and to a respiratory‐sensing lead. The purpose of HNS is to increase the neuromuscular activity and the tone of the tongue during sleep in order to avoid upper airway collapse and improve airflow. Compared to traditional surgical procedures, HNS is more conservative and produces a titratable and potentially reversible activation of upper airway dilatators muscles, without definitive anatomical modifications. A strict multidisciplinar approach, from patient clinical evaluation to neurostimulator programming and follow‐up, is of crucial importance to achieve a successful implantation. Limitations of HNS are the uncertainty about the long‐term adherence and effectiveness and high costs of the device. Further clinical studies are needed to improve the technological features of the current devices and to optimize patients selection criteria in order to identify best responders and to achieve higher cure rates.

Prevalencia de depresión en soldados regulares de un batallón de una ciudad colombiana, 2017

Objetivo: Determinar la prevalencia de depresión y de sus dimensiones en soldados de un batallón colombiano.
 Métodos: Estudio transversal en 410 soldados a quienes se les aplicó un cuestionario de depresión. Se calcularon las prevalencias para cada dominio y para la depresión en general y la asociación con factores de interés.
 Resultados: La prevalencia de depresión fue del 8,8%; las prevalencias de los dominios fueron: 11,7% para la autoestima negativa, 24,6% para la ideación suicida, 56,8% para pobre imagen social, 26,3% para afecto negativo, 23,4% para desesperanza y 19,3% para evitación.
 Conclusión: Refinar los procedimientos de tamizaje en salud mental en el proceso de selección de los jóvenes soldados, para que sean desacuartelados aquellos aspirantes propensos a la depresión.

Function-Aware Network Coding for Dynamic Vision Sensor Networks

Research on new multimedia stream coding, and transmission mechanism and transceiver structure design has been motivated by the increasing demand for low-latency applications, as well as the development of new multimedia devices, such as dynamic vision sensors (DVS). We pose a concept of function awareness in the context of packet-level network coding in this letter, and a function-aware network coding approach with a parallel coding transceiver structure for DVS video streams from the perspective of real-time applications is proposed. The posed function awareness concept is used for determining a DVS data selection criterion in order to minimise the transmission and decoding latency of the selected data. The proposed parallel coding transceiver design for different DVS data streams achieves fast encoding and decoding, and simulation results illustrate that the proposed approach outperforms the conventional multilevel network coding schemes.

THE ORDERING OF SMOKERS’ TOOTHPASTE CHOICE CRITERIA WITH FUZZY MODEL

Aim: There are some measures that smokers can cease or prevent damage caused by smoking. They may also choose some product features that may be more helpful to them like toothpaste selection etc. The preferences of smokers vary because of the variety of toothpaste in the market and a high number of products. The aim of this study is to determine the importance of toothpaste selection criteria in order to determine the decision and toothpaste selection.

How Are Managers Making Tree Species Selection Decisions in the Pacific Northwest of the United States?

Trees provide an array of social, economic, and ecological benefits; furthermore, trees on public land are critical for providing those benefits to people who cannot afford their own trees. It is important to know how managers make trade-offs and prioritize different tree selection criteria in order to target educational campaigns at the state or regional level. Primary contacts for Tree City USA designated cities were surveyed across the Pacific Northwest. Of these municipalities, 79 out of 151 responded (52.3% response rate), with 6 municipalities providing responses from different departments for a total of 85 responses. Currently, there are primarily descriptive statistics in relation to tree species selection. This study provides a framework for future statistical analysis and greater exploration of how municipalities and managers are selecting tree species. Results were analyzed with a Mann-Whitney U test to compare International Society of Arboriculture (ISA) Certified Arborists® to those who are not certified across various tree species selection criteria. Another Mann-Whitney U test was used to compare small (≤ 50,000) and large (> 50,000) municipalities across the same criteria. ISA Certified Arborists® showed statistically significant differences from those who are not certified in a number of tree species selection criteria. ISA Certified Arborists® also differed in urban forest management on a city-wide scale, particularly in favoring greater tree species diversity. The differences in urban forest management between ISA Certified Arborists® and noncertified—and between municipality sizes—can help to influence future educational campaigns targeted toward increasing urban forest health and resiliency.

PopSyCLE: A New Population Synthesis Code for Compact Object Microlensing Events

Abstract We present a new Milky Way microlensing simulation code, dubbed PopSyCLE (Population Synthesis for Compact object Lensing Events). PopSyCLE is the first resolved microlensing simulation to include a compact object distribution derived from numerical supernova explosion models and both astrometric and photometric microlensing effects. We demonstrate the capabilities of PopSyCLE by investigating the optimal way to find black holes (BHs) with microlensing. Candidate BHs have typically been selected from wide-field photometric microlensing surveys, such as OGLE, by selecting events with long Einstein crossing times (t E > 120 days). These events can be selected at closest approach and monitored astrometrically in order to constrain the mass of each lens; PopSyCLE predicts a BH detection rate of ∼40% for such a program. We find that the detection rate can be enhanced to ∼85% by selecting events with both t E > 120 days and a microlensing parallax of π E < 0.08. Unfortunately, such a selection criterion cannot be applied during the event, as π E requires both pre- and post-peak photometry. However, historical microlensing events from photometric surveys can be revisited using this new selection criterion in order to statistically constrain the abundance of BHs in the Milky Way. The future Wide Field Infrared Survey Telescope (WFIRST) microlensing survey provides both precise photometry and astrometry and will yield individual masses of BHs, which is at least an order of magnitude more than is possible with individual candidate follow-up with current facilities. The resulting sample of BH masses from WFIRST will begin to constrain the shape of the BH present-day mass function, BH multiplicity, and BH kick velocity distributions.

Hotel selection using a modified TOPSIS-based decision support algorithm

While many studies pointed out that travellers often consider a basket of factors when they are selecting their accommodation, few online travel agency websites allow the travellers to actively express their preference about the selection criteria in order to receive tailor-made recommendations in return. The absence of this feature causes travellers to spend extra effort and time to compare the hotel options themselves. This paper presents a decision support algorithm, namely Vague Set TOPSIS, to help travellers to rank the hotel options. It was found in this research that the structure and assumptions of the conventional TOPSIS methods are not suitable for handling this decision problem. Therefore, this paper is novel in providing a foundational change to the conventional TOPSIS models such that it is more applicable to hotel selection. The validity of the proposed decision support algorithm was proved via three approaches, which are mathematical proofs, computer stochastic simulation experiments and a numerical case study using data obtained from Hotels.com.

Robust Beamforming and User Clustering for Guaranteed Fairness in Downlink NOMA With Partial Feedback

In this paper, a downlink multiuser non-orthogonal multiple access (NOMA) with full and partial channel state information (CSI) feedback is considered. We investigate beam design and user clustering from the throughput-fairness trade-off perspective. To enhance this trade-off, two proportional fairness (PF) based scheduling algorithms are proposed, each has two stages. The first algorithm is based on integrating the maximum product of effective channel gains and the maximum signal to interference ratio with the PF principle (PF-MPECG-SIR), to select the strong users in the first stage and the weak users in the second stage. This algorithm is designed to maximize the throughput with moderate fairness enhancement. Whereas, in the second algorithm, the MPECG and the maximum correlation are combined within the PF selection criterion (PF-MPECG-CORR) in order to maximize the fairness with a slight degradation in the total throughput. In addition, we present an optimal power allocation that can achieve a high data rate for the overall system without sacrificing the sum-rate of weak users under full and partial CSI. Simulation results show that the proposed PF-MPECG-CORR can significantly improve the fairness up to 50.82% and 44.90% with only 0.42% and 1.13% degradation in the total throughput, for full and partial CSI, respectively. All these performance gains are achieved without increasing the computational complexity.

Estimating the Number of Significant Canonical Coordinates

This study proposes a novel model selection criterion for dimensionality estimation in canonical correlation analysis (CCA), which can be used to estimate the number of correlated components between two sets of multivariate vectors, particularly in the context of canonical coordinates. The proposed method has a different form compared to existing model selection criteria, which typically use the Bartlett-Lawley measure as a goodness-of-fit term. Alternatively, we propose to use instead a goodness-of-fit term based on the sum of squares of the smallest canonical correlation coefficients. The asymptotic properties of the proposed goodness-of-fit term have been laid out and an appropriate penalty term with proof of consistency is derived based on these properties. Numerical examples are presented in the context of direction-of-arrival estimation using canonical coordinates for two channel array systems. It is shown that our proposed order selection criterion outperforms conventional criteria and is robust to various noise and interference scenarios.

Assessing the impact of EI Niño southern oscillation index and land surface temperature fluctuations on dengue fever outbreaks using ARIMAX(p)-PARX(p)-NBARX(p) models

The dengue infectious disease remnants a human health problem in tropical and subtropical countries. In an Auto Regressive model to assess the role of climatic parameter El Nino Southern Oscillation and land surface mean monthly temperture on dengue outbreaks of the Karachi region over the monthly time interval January 2001 to December 2016, subsequent to stabilization of variance, we are able to apply and predict an Auto Regressive Integrated Moving Average Exogenous-Transfer Function model by using the order selection criteria namely Final Prediction Error and Akaike’s information. The results confirmed that ARIMAX (2,1,2) has fitted model, although an Auto Regressive model predicts a smaller decline in dengue data series than the auto Poisson Regression model. Additionally, we developed an alternative model for the Poisson Autoregressive Exogenous model in order (p) and Negative Binomial Auto Regressive Exogenous model, deliver the best fit as compared to the Poisson Auto Regressive Exogenous model whereas indicated by the deviances. The Pearson test showed a strong positive association between temperature and dengue, while ENSO inverse indication. High dengue outbreaks are detected in the months of September, October, and November. This comparative study exposed a significant relationship among monthly dengue and climatic variation by Auto Regressive Integrated Moving Average Exogenous (ARIMAX), Poisson and Negative-Binomial Autoregressive Exogenous (PARX-NBARX) models, while smallest values of AIC (3.89), Negative Binomial Auto Regressive Exogenous, are preferred more accurate model for the next 12 months forecasting. This study has provided useful information for the development of dengue predictions and future warning systems.

Informational efficiency of sovereign bond markets of India and China: evidence from Toda and Yamamoto Granger causality (1995)

The study analyzes the causal relationship between macroeconomic indicators and sovereign bond indices of India and China. The main purpose of this research study is to answer the question how sovereign yields of bond indices in India and China reflects economic conditions and if sovereign bond market of India and China are informationally efficient. Toda and Yamamoto (J Econom 66:225–250, 1995) approach is used for testing Granger causality. Bivariate analysis is performed on monthly data from January 2000 to December 2016, and four macroeconomic indicators are examined such as interest rate, exchange rate, gross domestic product and fiscal deficit. Analysis applies unit root test to check maximal order of integration, VAR lag order selection criteria to check maximum lag order and Wald test for linear restrictions to examine Granger causality.

The Impact of Exchange Rate on Foreign Private Investment in Nigeria

The study examined the impact of exchange rate on foreign private investment using quarterly time series date from Nigeria for the period 2007 to 2017. Foreign private investment in the study was disaggregated into foreign direct investment and foreign portfolio investment in order to ascertain their separate reactions to changes in the exchange rate of the naira against the US dollars. The empirical analysis was based on the VAR estimation procedure using three lagged periods adopted on the basis of various lag order selection criteria. The empirical result revealed that devaluation/depreciation of the naira adversely affects foreign direct investment and foreign portfolio investment in Nigeria. Increased in the size of the domestic market and development of the financial sector were found to stimulate foreign private investment while high inflation rate in the domestic economy discourages foreign private investment in Nigeria. The study, therefore, recommended among others that the Central Bank of Nigeria should continue to initiate more proactive policy intervention policies to stabilize the exchange rate of the naira in order to stimulate more foreign private investment in Nigeria.

A Sequential Framework for Composite Hypothesis Testing

This paper addresses the problem of classification via composite sequential hypothesis testing. We focus on two possible schemes for the hypotheses: non-nested and nested. For the first case, we present the generalized sequential probability ratio test (GSPRT) and provide an analysis of its asymptotic optimality. Yet, for the nested case, this algorithm is shown to be inconsistent. Consequently, an alternative solution is derived based on Bayesian considerations, similar to the ones used for the Bayesian information criterion and asymptotic maximum a posteriori probability criterion for model order selection. The proposed test, named penalized GSPRT (PGSPRT), is based on restraining the exponential growth of the GSPRT with respect to the sequential probability ratio test. Furthermore, the commonly used performance measure for sequential tests, known as the average sample number, is evaluated for the PGSPRT under each of the hypotheses. Simulations are carried out to compare the performance measures of the proposed algorithms for two nested model order selection problems.

Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection

An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. This paper first proposes a variable selection algorithm based on principal component analysis with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, this paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, Supervisory control and data acquisition data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.

Autoregressive Order Selection Criteria and their Performances at Different Sizes of Time Series

Autoregressive Order Selection Criteria and their Performances at Different Sizes of Time Series

Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. We propose an automated model order selection (AMOS), a solution to the SGC model selection problem under a random interconnection model using a novel selection criterion that is based on an asymptotic phase transition analysis. AMOS can more generally be applied to discovering hidden block diagonal structure in symmetric non-negative matrices. Numerical experiments on simulated graphs validate the phase transition analysis, and real-world network data are used to validate the performance of the proposed model selection procedure.

Getting It Right Matters: Climate Spectra and Their Estimation

In many recent publications, climate spectra estimated with different methods from observed, GCM-simulated, and reconstructed time series contain many peaks at time scales from a few years to many decades and even centuries. However, respective spectral estimates obtained with the autoregressive (AR) and multitapering (MTM) methods showed that spectra of climate time series are smooth and contain no evidence of periodic or quasi-periodic behavior. Four order selection criteria for the autoregressive models were studied and proven sufficiently reliable for 25 time series of climate observations at individual locations or spatially averaged at local-to-global scales. As time series of climate observations are short, an alternative reliable nonparametric approach is Thomson’s MTM. These results agree with both the earlier climate spectral analyses and the Markovian stochastic model of climate.

Bridging AIC and BIC: A New Criterion for Autoregression

To address order selection for an autoregressive model fitted to time series data, we propose a new information criterion. It has the benefits of the two well-known model selection techniques: the Akaike information criterion and the Bayesian information criterion. When the data are generated from a finite-order autoregression, the Bayesian information criterion is known to be consistent, and so is the new criterion. When the true order is infinity or suitably high with respect to the sample size, the Akaike information criterion is known to be efficient in the sense that its predictive performance is asymptotically equivalent to the best offered by the candidate models; in this case, the new criterion behaves in a similar manner. Different from the two classical criteria, the proposed criterion adaptively achieves either consistency or efficiency depending on the underlying true model. In practice, where the observed time series is given without any prior information about the model specification, the proposed order selection criterion is more flexible and reliable compared with classical approaches. Numerical results are presented, demonstrating the adaptivity of the proposed technique when applied to various data sets.