scholarly journals Montague Grammar Induction

2021 ◽  
Vol 30 ◽  
pp. 227
Author(s):  
Gene Louis Kim ◽  
Aaron Steven White
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
Behavioral Data ◽  
Grammar Induction ◽  
Montague Grammar ◽  
Categorial Grammars ◽  
Direct Interpretation ◽  
Semantic Types ◽  
Clausal Complements ◽  
Acceptability Judgment

We propose a computational model for inducing full-fledged combinatory categorial grammars from behavioral data. This model contrasts with prior computational models of selection in representing syntactic and semantic types as structured (rather than atomic) objects, enabling direct interpretation of the modeling results relative to standard formal frameworks. We investigate the grammar our model induces when fit to a lexicon-scale acceptability judgment dataset – Mega Acceptability – focusing in particular on the types our model assigns to clausal complements and the predicates that select them.

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.

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.

A Mechanistic Motor-Clutch Model That Explains Cell Shape Dynamics to Cyclic Stretch

2022 ◽  
Author(s):  
Benjamin W. Scandling ◽  
Jia Gou ◽  
Jessica Thomas ◽  
Jacqueline Xuan ◽  
Chuan Xue ◽  
...  
Keyword(s):  
Computational Model ◽  
Computational Models ◽  
The Body ◽  
Cyclic Stretch ◽  
Substrate Stiffness ◽  
Cell Alignment ◽  
Cellular Mechanisms ◽  
Actin Bundles ◽  
The Impact

Many cells in the body experience cyclic mechanical loading, which can impact cellular processes and morphology. In vitro studies often report that cells reorient in response to cyclic stretch of their substrate. To explore cellular mechanisms involved in this reorientation, a computational model was developed by utilizing the previous computational models of the actin-myosin-integrin motor-clutch system developed by others. The computational model predicts that under most conditions, actin bundles align perpendicular to the direction of applied cyclic stretch, but under specific conditions, such as low substrate stiffness, actin bundles align parallel to the direction of stretch. The model also predicts that stretch frequency impacts the rate of reorientation, and that proper myosin function is critical in the reorientation response. These computational predictions are consistent with reports from the literature and new experimental results presented here. The model suggests that the impact of different stretching conditions (stretch type, amplitude, frequency, substrate stiffness, etc.) on the direction of cell alignment can largely be understood by considering their impact on cell-substrate detachment events, specifically whether detachment occurs during stretching or relaxing of the substrate.

The Implicit Learning of Noise: Behavioral Data and Computational Models

2010 ◽  
pp. 571-579
Author(s):  
Trevor R. Agus ◽  
Marion Beauvais ◽  
Simon J. Thorpe ◽  
Daniel Pressnitzer
Keyword(s):  
Implicit Learning ◽  
Computational Models ◽  
Behavioral Data

scholarly journals Computational simulation of fracture behaviour in auxetic cellular structure by multiaxial loading

2019 ◽  
Vol 300 ◽  
pp. 03001
Author(s):  
Branko Nečemer ◽  
Janez Kramberger ◽  
Nejc Novak ◽  
Srečko Glodež
Keyword(s):  
Computational Model ◽  
Cellular Structure ◽  
Computational Models ◽  
Fracture Behaviour ◽  
Computational Simulation ◽  
Tangential Direction ◽  
Multiaxial Loading ◽  
Loading Conditions ◽  
Normal Contact ◽  
Auxetic Structure

A computational simulation of fracture behaviour in auxetic cellular structure, subjected to multiaxial loading is presented in this paper. A fracture behaviour of the 3D (three-dimensional) chiral auxetic structure under multiaxial loading conditions was studied. The computational models were used to study the geometry effect of the unit cell on the Poisson’s ratio and fracture behaviour of the analysed chiral auxetic structure. A 3D computational model was built using FEM-code LS DYNA. The discrete computational model of chiral auxetic structure was built using beam finite elements. The lattice model of the analysed auxetic structure was positioned between rigid plates and assembled in a way to simulate a hydro-compression loading conditions. Between the contacting surfaces interactions in normal (contact) and tangential direction (friction) with the node-to-surface approach were simulated. A developed computational model offers insight in the fracture behaviour of considered auxetic cellular structure and helps to better understanding their crushing behaviour under impact multiaxial loading.

scholarly journals A Computational Model for Cardiomyocytes Mechano-Electric Stimulation to Enhance Cardiac Tissue Regeneration

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1875
Author(s):  
Pau Urdeitx ◽  
Mohamed H. Doweidar
Keyword(s):  
Extracellular Matrix ◽  
Computational Model ◽  
Electric Fields ◽  
Electric Stimulation ◽  
Computational Models ◽  
Cell Maturation ◽  
Cardiac Cell ◽  
Cell Orientation ◽  
Aspect Ratios

Electrical and mechanical stimulations play a key role in cell biological processes, being essential in processes such as cardiac cell maturation, proliferation, migration, alignment, attachment, and organization of the contractile machinery. However, the mechanisms that trigger these processes are still elusive. The coupling of mechanical and electrical stimuli makes it difficult to abstract conclusions. In this sense, computational models can establish parametric assays with a low economic and time cost to determine the optimal conditions of in-vitro experiments. Here, a computational model has been developed, using the finite element method, to study cardiac cell maturation, proliferation, migration, alignment, and organization in 3D matrices, under mechano-electric stimulation. Different types of electric fields (continuous, pulsating, and alternating) in an intensity range of 50–350 Vm−1, and extracellular matrix with stiffnesses in the range of 10–40 kPa, are studied. In these experiments, the group’s morphology and cell orientation are compared to define the best conditions for cell culture. The obtained results are qualitatively consistent with the bibliography. The electric field orientates the cells and stimulates the formation of elongated groups. Group lengthening is observed when applying higher electric fields in lower stiffness extracellular matrix. Groups with higher aspect ratios can be obtained by electrical stimulation, with better results for alternating electric fields.

Analytical Computational Models for Relationship between Ultimate Bending Moment of Concrete Segments and Axial Force

2020 ◽  
Vol 853 ◽  
pp. 177-181
Author(s):  
Zhi Yun Wang ◽  
Shou Ju Li
Keyword(s):  
Numerical Simulation ◽  
Computational Model ◽  
Axial Force ◽  
Computational Models ◽  
Plane Deformation ◽  
Bending Moment ◽  
Analytical Models ◽  
Bending Moments ◽  
Axial Forces ◽  
Practical Engineering

Concrete segments are widely used to support soil and water loadings in shield-excavated tunnels. Concrete segments burden simultaneously to the loadings of bending moments and axial forces. Based on plane deformation assumption of material mechanics, in which plane section before bending remains plane after bending, ultimate bending moment model is proposed to compute ultimate bearing capacity of concrete segments. Ultimate bending moments of concrete segments computed by analytical models agree well with numerical simulation results by FEM. The accuracy of proposed analytical computational model for ultimate bending moment of concrete segments is numerically verified. The analytical computational model and numerical simulation for a practical engineering case indicate that the ultimate bending moment of concrete segments increases with increase of axial force on concrete segment in the case of large eccentricity compressive state.

scholarly journals Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia

2006 ◽  
Vol 18 (2) ◽  
pp. 283-328 ◽  
Author(s):  
Randall C. O'Reilly ◽  
Michael J. Frank
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Basal Ganglia ◽  
Computational Model ◽  
Computational Models ◽  
Memory Task ◽  
Learning Mechanisms ◽  
Subcortical Structures ◽  
Credit Assignment ◽  
Gating Mechanism

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.

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