scholarly journals A Spectral Pitch Class Model of the Probe Tone Data and Scalic Tonality

2015 ◽  
Vol 32 (4) ◽  
pp. 364-393 ◽  
Author(s):  
Andrew J. Milne ◽  
Robin Laney ◽  
David B. Sharp
Keyword(s):  
Empirical Data ◽  
Cross Validation ◽  
Musical Culture ◽  
Class Model ◽  
Probe Tone ◽  
Pitch Class ◽  
Form Part ◽  
Validation Tests ◽  
Selection Of ◽  
Tonic Triad

In this paper, we introduce a small family of novel bottom-up (sensory) models of the Krumhansl and Kessler (1982) probe tone data. The models are based on the spectral pitch class similarities between all twelve pitch classes and the tonic degree and tonic triad. Cross-validation tests of a wide selection of models show ours to have amongst the highest fits to the data. We then extend one of our models to predict the tonics of a variety of different scales such as the harmonic minor, melodic minor, and harmonic major. The model produces sensible predictions for these scales. Furthermore, we also predict the tonics of a small selection of microtonal scales—scales that do not form part of any musical culture. These latter predictions may be tested when suitable empirical data have been collected.

scholarly journals Shape and Boundary Similarity Features for Accurate HCC Image Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Xiaoyu Duan ◽  
Huiyan Jiang ◽  
Siqi Li
Keyword(s):  
Cross Validation ◽  
Classification Model ◽  
Feature Points ◽  
Nucleus Shape ◽  
Comparison Results ◽  
Manual Selection ◽  
Validation Tests ◽  
Information Difference ◽  
Optimal Feature ◽  
Selection Of

Nucleus morphology is of great importance in conventional cancer pathological diagnosis, which could provide information difference between normal and abnormal nuclei visually. Therefore, this paper proposes two novel kinds of features for normal and hepatocellular carcinoma (HCC) nucleus recognition, including shape and boundary similarity. First, each individual nucleus patch with the fixed size is obtained using center-proliferation segmentation (CPS) method. Then, nucleus shape library is constructed based on manual selection by pathologists, which is utilized to measure nucleus shape similarity via Dice, Jaccard, precision, and recall coefficients. Meanwhile, boundary similarity is evaluated through triangles composed of some boundary feature points for each nucleus. Finally, the conventional random forest (RF) is used to train and test the classification model for HCC nucleus recognition. Extensive cross-validation tests could facilitate the selection of the optimal feature set and the experiment comparison results demonstrate that our proposed morphological features are more beneficial for classification compared with other traditional characteristics.

Intonazione e significato: l’esempio dell’italiano regionale di Bergamo

2018 ◽  
Vol 69 (1) ◽  
pp. 35-55
Author(s):  
Giorgio Antonioli ◽  
Manuela Caterina Moroni
Keyword(s):  
Conversation Analysis ◽  
Empirical Data ◽  
Speech Act ◽  
Research Project ◽  
Preliminary Results ◽  
Form Function ◽  
Distinctive Type ◽  
Selection Of

Abstract In this paper we present a selection of preliminary results of our research project “Intonation and Meaning”, in which we compare recurrent intonation contours in German and Italian regional varieties. We apply the method of German Interactional Prosody Research (Interaktionale Prosodieforschung), which in turn is based on Conversation Analysis, to a sample of selfcollected empirical data. Our aim is to show the value of intonation as a resource to contextualize speech activities and to point out form-function relationships between intonation patterns and speech act types. In this respect, we observe the usage of intonation contours with rising accent (L*H) and with falling accent (H*L) in the utterance of question activities, and provide evidence for the fact that the latter represent a distinctive type of questions with epistemic presupposition, whereas L*H correlates rather with default, modally unmarked questions.

scholarly journals Absolute Pitch in Oral Transmission of Folk Tunes as Constrained Random Walks

2015 ◽  
Vol 10 (3) ◽  
pp. 178
Author(s):  
Klaus Frieler
Keyword(s):  
Random Walks ◽  
Empirical Research ◽  
Empirical Data ◽  
Absolute Pitch ◽  
Folk Songs ◽  
Pitch Class ◽  
Oral Transmission

In this commentary, I would like to add a few of our own, still unpublished, empirical observations concerning the possible role of absolute pitch memory (APM) in the oral transmission of folksongs. This empirical data poses some questions on the likelihood of the observed inter-recording tonic pitch consistency of Olthof, Janssen & Honing (2015) and how these could come about. Based on simulations of absolute pitch class of tonics during oral transmission of folk songs, I argue that the interplay of melodic range and vocal range might actually be the main reason for the observed non-uniformity, in contrast to the conclusions presented in Olthof et al. (2015). However, this does not invalidate the therein presented evidence, but makes the case more puzzling, consequently calling for more empirical research on the interaction of melodic and vocal range and latent APM as well as for more detailed modeling of oral transmission of folk songs.

Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities

2002 ◽  
Vol 14 (10) ◽  
pp. 2439-2468 ◽  
Author(s):  
Aki Vehtari ◽  
Jouko Lampinen
Keyword(s):  
Expected Utility ◽  
Cross Validation ◽  
Monte Carlo Sampling ◽  
Point Estimate ◽  
Model Assessment ◽  
Predictive Densities ◽  
Fold Cross Validation ◽  
Selection Of ◽  
Natural Way ◽  
Real World Problems

In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate because it describes the uncertainty in the estimate. The distributions of the expected utility estimates can also be used to compare models, for example, by computing the probability of one model having a better expected utility than some other model. We propose an approach using cross-validation predictive densities to obtain expected utility estimates and Bayesian bootstrap to obtain samples from their distributions. We also discuss the probabilistic assumptions made and properties of two practical cross-validation methods, importance sampling and k-fold cross-validation. As illustrative examples, we use multilayer perceptron neural networks and gaussian processes with Markov chain Monte Carlo sampling in one toy problem and two challenging real-world problems.

scholarly journals A New Integrated Fuzzy Approach to Selecting the Best Solution for Business Balance of Passenger Rail Operator: Fuzzy PIPRECIA-Fuzzy EDAS Model

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 743 ◽  
Author(s):  
Slavko Vesković ◽  
Željko Stević ◽  
Darjan Karabašević ◽  
Snježana Rajilić ◽  
Sanjin Milinković ◽  
...  
Keyword(s):  
Fuzzy Model ◽  
Fuzzy Evaluation ◽  
Analytic Hierarchy ◽  
Ticket Sales ◽  
Passenger Traffic ◽  
Passenger Rail ◽  
The Republic ◽  
Criteria Importance ◽  
Validation Tests ◽  
Selection Of

The analysis of operations of the passenger traffic operator in the Republic of Srpska (RS) showed that the volume of passenger transport has, for the last fifteen years, been in constant decline. It is of particular importance that the operator has, year after year, recorded a negative balance of business. The way out of the current unfavorable situation in the sector of passenger traffic is based on the application of Public Service Obligation (PSO) based on the Regulation 1370/2007. In order to solve the problems, seven realistically possible variants have been identified. This paper defines the criteria for selecting the best variant, as well as a new integrated fuzzy model for the selection of the best variant that will enable the operator to make a profit. To define the weights of criteria in this paper, we have used the fuzzy PIvot Pairwise RElative Criteria Importance Assessment (F-PIPRECIA) method, while for ranking and selection of the best variant, we have used the Fuzzy Evaluation based on Distance from Average Solution (F-EDAS) method. Results show that the seventh variant: “Increase in revenue from ticket sales and PSO services and reduction in costs“ is the best solution in current conditions. Validation tests are performed with different scenarios and approaches and show that the model is stable. A validity test was created consisting of variations in the significance of model input parameters, testing of reverse rank, applying the fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (F-MARCOS), fuzzy Simple Additive Weighing (F-SAW) method, and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS). As a part of the validation tests, Spearman’s coefficient of correlation (SCC) in some scenarios is performed and weights of the criteria have been obtained using the Fuzzy Analytic Hierarchy Process (F-AHP) and Full Consistency Method (FUCOM).

scholarly journals Current Challenges and Future Trends in Analytical Fragility and Vulnerability Modeling

2019 ◽  
Vol 35 (4) ◽  
pp. 1927-1952 ◽  
Author(s):  
Vitor Silva ◽  
Sinan Akkar ◽  
Jack Baker ◽  
Paolo Bazzurro ◽  
José Miguel Castro ◽  
...  
Keyword(s):  
Ground Motion ◽  
Empirical Data ◽  
Current Practice ◽  
Earthquake Damage ◽  
Future Trends ◽  
Risk Estimates ◽  
Ground Motion Records ◽  
Vulnerability Functions ◽  
Selection Of

The lack of empirical data regarding earthquake damage or losses has propelled the development of dozens of analytical methodologies for the derivation of fragility and vulnerability functions. Each method will naturally have its strengths and weaknesses, which will consequently affect the associated risk estimates. With the purpose of sharing knowledge on vulnerability modeling, identifying shortcomings in the existing methods, and recommending improvements to the current practice, a group of vulnerability experts met in Pavia (Italy) in April 2017. Critical topics related to the selection of ground motion records, modeling of complex real structures through simplified approaches, propagation of aleatory and epistemic uncertainties, and validation of vulnerability results were discussed, and suggestions were proposed to improve the reliability and accuracy in vulnerability modeling.

DSRIG: Incorporating graphical structure in the regularized modeling of SNP data

2019 ◽  
Vol 17 (03) ◽  
pp. 1950017 ◽  
Author(s):  
Matthew Stephenson ◽  
Gerarda A. Darlington ◽  
Flavio S. Schenkel ◽  
E. James Squires ◽  
R. Ayesha Ali
Keyword(s):  
Cross Validation ◽  
Genetic Selection ◽  
Boar Taint ◽  
Farm Animals ◽  
Candidate Snps ◽  
Snp Data ◽  
Validation Procedure ◽  
Improvement Programs ◽  
Graphical Structure ◽  
Selection Of

Genetic selection of farm animals plays an important role in genetic improvement programs. Regularized regression methods on single nucleotide polymorphism (SNP) data from a set of candidate genes can help to identify genes that are associated with the trait of interest. This complex task must also consider the relative effect sizes on the desired trait and account for the relationships among the candidate SNPs so that selection of a SNP does not promote other undesirable traits through breeding. We present the Doubly Sparse Regression Incorporating Graphical structure (DSRIG), a novel regularized method for genetic selection that exploits the relationships among candidate SNPs to improve prediction. DSRIG was applied in the prediction of skatole and androstenone levels, two compounds known to be associated with boar taint. DSRIG was shown to provide a predictive benefit when compared to ordinary least squares (OLS) and the least absolute shrinkage and selection operator (LASSO) in a cross-validation procedure. The relative sizes of the coefficient estimates over the cross-validation procedure were compared to determine which SNPs may have the greatest impact on expression of the boar taint compounds and a consensus graph was used to infer the relationships among SNPs.

Smoothing Noisy Data Using Dynamic Programming and Generalized Cross-Validation

1988 ◽  
Vol 110 (1) ◽  
pp. 37-41 ◽  
Author(s):  
C. R. Dohrmann ◽  
H. R. Busby ◽  
D. M. Trujillo
Keyword(s):  
Dynamic Programming ◽  
Cross Validation ◽  
Present Model ◽  
Noisy Data ◽  
Smoothing Parameter ◽  
Spline Functions ◽  
General Context ◽  
Simple Extension ◽  
Generalized Cross Validation ◽  
Selection Of

Smoothing and differentiation of noisy data using spline functions requires the selection of an unknown smoothing parameter. The method of generalized cross-validation provides an excellent estimate of the smoothing parameter from the data itself even when the amount of noise associated with the data is unknown. In the present model only a single smoothing parameter must be obtained, but in a more general context the number may be larger. In an earlier work, smoothing of the data was accomplished by solving a minimization problem using the technique of dynamic programming. This paper shows how the computations required by generalized cross-validation can be performed as a simple extension of the dynamic programming formulas. The results of numerical experiments are also included.

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