scholarly journals A Method for Projecting Features from Observed Sets of Phonological Classes

2020 ◽  
Vol 51 (4) ◽  
pp. 725-763 ◽  
Author(s):  
Connor Mayer ◽  
Robert Daland
Keyword(s):  
Computational Models ◽  
Feature Learning ◽  
Phonological Features ◽  
Feature System

Given a set of phonological features, we can enumerate a set of phonological classes. Here we consider the inverse of this problem: given a set of phonological classes, can we derive a feature system? We show that this is indeed possible, using a collection of algorithms that assign features to a set of input classes and differ in terms of what types of features are permissible. This work bears on theories of both language-specific and universal features, provides testable predictions of the featurizations available to learners, and serves as a useful component in computational models of feature learning.

Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs

2020 ◽  
Author(s):  
Qiu Xiao ◽  
Ning Zhang ◽  
Jiawei Luo ◽  
Jianhua Dai ◽  
Xiwei Tang
Keyword(s):  
Computational Models ◽  
Geometric Characteristic ◽  
Feature Learning ◽  
Cost Effective ◽  
Disease Diagnosis ◽  
Computational Method ◽  
Data Sources ◽  
Superior Performance ◽  
Graph Regularization ◽  
Latent Features

Abstract Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

Defying the stimulus: acquisition of complex onsets in Polish

Phonology ◽  
2017 ◽  
Vol 34 (2) ◽  
pp. 269-298 ◽  
Author(s):  
Gaja Jarosz
Keyword(s):  
Computational Models ◽  
Computational Analysis ◽  
The Other ◽  
Lexical Statistics ◽  
Phonological Features ◽  
Modelling Studies ◽  
Onset Clusters ◽  
Korean Speakers

Behavioural findings indicate that English, Mandarin and Korean speakers exhibit gradient sonority sequencing preferences among unattested initial clusters. While some have argued these results support an innate principle, recent modelling studies have questioned this conclusion, showing that computational models capable of inducing generalisations using abstract phonological features can detect these preferences from lexical statistics in the three languages. This paper presents a computational analysis of the development of initial clusters in Polish, which arguably presents a stronger test of these models. We show that (i) the statistics of Polish contradict the Sonority Sequencing Principle (SSP), favouring sonority plateaus, (ii) models that succeed in the other languages do not predict SSP preferences for Polish and (iii) children nonetheless exhibit sensitivity to the SSP, favouring onset clusters with larger sonority rises.

scholarly journals Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Han-Jing Jiang ◽  
Yu-An Huang ◽  
Zhu-Hong You
Keyword(s):  
Case Studies ◽  
Computational Models ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Feature Learning ◽  
Superior Performance ◽  
Reliable Model ◽  
Disease Associations ◽  
The Cost ◽  
Fold Cross Validation

Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug development. Prediction of drug-disease associations is promising for drug repositioning. Recent years have witnessed an increasing number of machine learning-based methods for calculating drug repositioning. In this paper, a novel feature learning method based on Gaussian interaction profile kernel and autoencoder (GIPAE) is proposed for drug-disease association. In order to further reduce the computation cost, both batch normalization layer and the full-connected layer are introduced to reduce training complexity. The experimental results of 10-fold cross validation indicate that the proposed method achieves superior performance on Fdataset and Cdataset with the AUCs of 93.30% and 96.03%, respectively, which were higher than many previous computational models. To further assess the accuracy of GIPAE, we conducted case studies on two complex human diseases. The top 20 drugs predicted, 14 obesity-related drugs, and 11 drugs related to Alzheimer's disease were validated in the CTD database. The results of cross validation and case studies indicated that GIPAE is a reliable model for predicting drug-disease associations.

Sequential Difficulty Effects During Strategy Execution

2012 ◽  
Vol 59 (5) ◽  
pp. 295-301 ◽  
Author(s):  
Kim Uittenhove ◽  
Patrick Lemaire
Keyword(s):  
Computational Models ◽  
Preceding Trial ◽  
Human Cognition ◽  
Strategy Execution ◽  
Strategy Performance ◽  
Empirical Implications

In two experiments, we tested the hypothesis that strategy performance on a given trial is influenced by the difficulty of the strategy executed on the immediately preceding trial, an effect that we call strategy sequential difficulty effect. Participants’ task was to provide approximate sums to two-digit addition problems by using cued rounding strategies. Results showed that performance was poorer after a difficult strategy than after an easy strategy. Our results have important theoretical and empirical implications for computational models of strategy choices and for furthering our understanding of strategic variations in arithmetic as well as in human cognition in general.

Does Viotin Activate Violin More Than Viocin?

2014 ◽  
Vol 61 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Manuel Perea ◽  
Victoria Panadero
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Developmental Dyslexia ◽  
Computational Models ◽  
Error Rates ◽  
Visual Word ◽  
Base Word ◽  
Similar Response ◽  
Skilled Readers ◽  
Young Readers

The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word’s overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children – this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word’s visual cues, presumably because of poor letter representations.

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