Modeling the contribution of phonotactic cues to the problem of word segmentation

2010 ◽  
Vol 37 (3) ◽  
pp. 487-511 ◽  
Author(s):  
DANIEL BLANCHARD ◽  
JEFFREY HEINZ ◽  
ROBERTA GOLINKOFF
Keyword(s):  
Language Acquisition ◽  
Computational Model ◽  
Experimental Research ◽  
Computational Models ◽  
Word Segmentation ◽  
Experimental Results ◽  
Speech Segmentation ◽  
Advantages And Disadvantages ◽  
Language Universal ◽  
Segmentation Models

ABSTRACTHow do infants find the words in the speech stream? Computational models help us understand this feat by revealing the advantages and disadvantages of different strategies that infants might use. Here, we outline a computational model of word segmentation that aims both to incorporate cues proposed by language acquisition researchers and to establish the contributions different cues can make to word segmentation. We present experimental results from modified versions of Venkataraman's (2001) segmentation model that examine the utility of: (1) language-universal phonotactic cues; (2) language-specific phonotactic cues which must be learned while segmenting utterances; and (3) their combination. We show that the language-specific cue improves segmentation performance overall, but the language-universal phonotactic cue does not, and that their combination results in the most improvement. Not only does this suggest that language-specific constraints can be learned simultaneously with speech segmentation, but it is also consistent with experimental research that shows that there are multiple phonotactic cues helpful to segmentation (e.g. Mattys, Jusczyk, Luce & Morgan, 1999; Mattys & Jusczyk, 2001). This result also compares favorably to other segmentation models (e.g. Brent, 1999; Fleck, 2008; Goldwater, 2007; Johnson & Goldwater, 2009; Venkataraman, 2001) and has implications for how infants learn to segment.

Segmenting words from natural speech: subsegmental variation in segmental cues

2010 ◽  
Vol 37 (3) ◽  
pp. 513-543 ◽  
Author(s):  
C. ANTON RYTTING ◽  
CHRIS BREW ◽  
ERIC FOSLER-LUSSIER
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Word Segmentation ◽  
Natural Speech ◽  
Speech Corpora ◽  
Acoustic Variation ◽  
Subsegmental Variation

ABSTRACTMost computational models of word segmentation are trained and tested on transcripts of speech, rather than the speech itself, and assume that speech is converted into a sequence of symbols prior to word segmentation. We present a way of representing speech corpora that avoids this assumption, and preserves acoustic variation present in speech. We use this new representation to re-evaluate a key computational model of word segmentation. One finding is that high levels of phonetic variability degrade the model's performance. While robustness to phonetic variability may be intrinsically valuable, this finding needs to be complemented by parallel studies of the actual abilities of children to segment phonetically variable speech.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals Numerical and Experimental Investigations on Vocal Fold Approximation in Healthy and Simulated Unilateral Vocal Fold Paralysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1817
Author(s):  
Zheng Li ◽  
Azure Wilson ◽  
Lea Sayce ◽  
Amit Avhad ◽  
Bernard Rousseau ◽  
...  
Keyword(s):  
Computational Model ◽  
Vocal Fold ◽  
High Speed ◽  
Computational Models ◽  
Ex Vivo ◽  
Vocal Fold Paralysis ◽  
Future Application ◽  
Experimental Investigations ◽  
Type I ◽  
Mri Scans

We have developed a novel surgical/computational model for the investigation of unilat-eral vocal fold paralysis (UVFP) which will be used to inform future in silico approaches to improve surgical outcomes in type I thyroplasty. Healthy phonation (HP) was achieved using cricothyroid suture approximation on both sides of the larynx to generate symmetrical vocal fold closure. Following high-speed videoendoscopy (HSV) capture, sutures on the right side of the larynx were removed, partially releasing tension unilaterally and generating asymmetric vocal fold closure characteristic of UVFP (sUVFP condition). HSV revealed symmetric vibration in HP, while in sUVFP the sutured side demonstrated a higher frequency (10–11%). For the computational model, ex vivo magnetic resonance imaging (MRI) scans were captured at three configurations: non-approximated (NA), HP, and sUVFP. A finite-element method (FEM) model was built, in which cartilage displacements from the MRI images were used to prescribe the adduction, and the vocal fold deformation was simulated before the eigenmode calculation. The results showed that the frequency comparison between the two sides was consistent with observations from HSV. This alignment between the surgical and computational models supports the future application of these methods for the investigation of treatment for UVFP.

Which came first: Infants learning language or motherese?

2004 ◽  
Vol 27 (4) ◽  
pp. 505-506 ◽  
Author(s):  
Heather Bortfeld
Keyword(s):  
Language Acquisition ◽  
Word Recognition ◽  
Language Learning ◽  
Learning Process ◽  
Building Blocks ◽  
Speech Segmentation ◽  
Learning Language

Although motherese may facilitate language acquisition, recent findings indicate that not all aspects of motherese are necessary for word recognition and speech segmentation, the building blocks of language learning. Rather, exposure to input that has prosodic, phonological, and statistical consistencies is sufficient to jump-start the learning process. In light of this, the infant-directedness of the input might be considered superfluous, at least insofar as language acquisition is concerned.

Experimental Research to Evaluate Thermal Behavior of a Brushless Driving Servomotor for Linear Motion NC Axis Experimental Stand

2015 ◽  
Vol 762 ◽  
pp. 55-60
Author(s):  
Georgia Cezara Avram ◽  
Florin Adrian Nicolescu ◽  
Radu Constantin Parpală ◽  
Constantin Dumitrascu
Keyword(s):  
Thermal Behavior ◽  
Mathematical Models ◽  
Experimental Research ◽  
Infrared Imaging ◽  
Experimental Results ◽  
Ball Screw ◽  
Linear Motion ◽  
Functional Optimization ◽  
Thermal Infrared Imaging ◽  
Experimental Stand

This paper presents the works carried out by the authors in the field of structural and functional optimization of industrial robot's numerically controlled (NC) axes. The study includes the results obtained in the research stage of the experimental measurements performed to evaluate the electrical servomotor's thermal behavior using a thermal (infrared) imaging camera. The analyzed servomotor is a brushless servomotor integrated in an experimental stand for linear motion NC axis experimental research, existing in the MMS department from EMTS faculty. Supplementary to the driving servomotor, the experimental stand includes a belt drive transmission, a ball screw - bearings assembly and a driven element guided by ball rail system. This experimental research phase is part of the doctoral thesis of first author and was conducted in order to validate the mathematical models developed in the PhD thesis. Thus, experimental results presented in the paper have been used to validate first mathematical models for electric motor's preliminary selection and checking, (performed by determining the total reflected inertia of the mechanical system on motor shaft level) as well as the mathematical models for final selection and checking (by evaluating the servomotor's thermal energy dissipation, and servomotor's internal and external maximum operating temperature). Second, the experimental results have been used to validate the assisted simulation for structural and functional optimization of industrial robot's NC axes based on both servomotor and drive's thermal behavior analysis, performed in the thesis by means of a dedicated commercial software package.

AFM-Based Nanoindentation Process: A Comparative Study

2012 ◽  
Author(s):  
Rapeepan Promyoo ◽  
Hazim El-Mounayri ◽  
Kody Varahramyan
Keyword(s):  
Md Simulation ◽  
Computational Models ◽  
Surface Deformation ◽  
Experimental Results ◽  
Computational Time ◽  
Machined Surface ◽  
Indentation Force ◽  
Subsurface Deformation ◽  
Different Types ◽  
Copper Aluminum

Atomic force microscopy (AFM) has been widely used for nanomachining and fabrication of micro/nanodevices. This paper describes the development and validation of computational models for AFM-based nanomachining. Molecular Dynamics (MD) technique is used to model and simulate mechanical indentation at the nanoscale for different types of materials, including gold, copper, aluminum, and silicon. The simulation allows for the prediction of indentation forces at the interface between an indenter and a substrate. The effects of tip materials on machined surface are investigated. The material deformation and indentation geometry are extracted based on the final locations of the atoms, which have been displaced by the rigid tool. In addition to the modeling, an AFM was used to conduct actual indentation at the nanoscale, and provide measurements to which the MD simulation predictions can be compared. The MD simulation results show that surface and subsurface deformation found in the case of gold, copper and aluminum have the same pattern. However, aluminum has more surface deformation than other materials. Two different types of indenter tips including diamond and silicon tips were used in the model. More surface and subsurface deformation can be observed for the case of nanoindentation with diamond tip. The indentation forces at various depths of indentation were obtained. It can be concluded that indentation force increases as depth of indentation increases. Due to limitations on computational time, the quantitative values of the indentation force obtained from MD simulation are not comparable to the experimental results. However, the increasing trends of indentation force are the same for both simulation and experimental results.

scholarly journals Integrated experimental-computational analysis of a liver-islet microphysiological system for human-centric diabetes research

2021 ◽  
Author(s):  
Belén Casas ◽  
Liisa Vilén ◽  
Sophie Bauer ◽  
Kajsa Kanebratt ◽  
Charlotte Wennberg Huldt ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Computational Model ◽  
In Silico ◽  
Experimental Results ◽  
Published Data ◽  
Diabetes Research ◽  
Glucose Response ◽  
High Blood Glucose

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behavior of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

scholarly journals The Exponential-Weight Mean-Variance Model: A novel computational model for the Balloon Analogue Risk Task

2019 ◽  
Author(s):  
Harhim Park ◽  
Jaeyeong Yang ◽  
Jasmin Vassileva ◽  
Woo-Young Ahn
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Model Comparison ◽  
Exponential Weight ◽  
Multiple Model ◽  
Variance Model ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Post Hoc ◽  
Mean Variance

The Balloon Analogue Risk Task (BART) is a popular task used to measure risk-taking behavior. To identify cognitive processes associated with choice behavior on the BART, a few computational models have been proposed. However, the extant models are either too simplistic or fail to show good parameter recovery performance. Here, we propose a novel computational model, the exponential-weight mean-variance (EWMV) model, which addresses the limitations of existing models. By using multiple model comparison methods, including post hoc model fits criterion and parameter recovery, we showed that the EWMV model outperforms the existing models. In addition, we applied the EWMV model to BART data from healthy controls and substance-using populations (patients with past opiate and stimulant dependence). The results suggest that (1) the EWMV model addresses the limitations of existing models and (2) heroin-dependent individuals show reduced risk preference than other groups in the BART.

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