Probabilistic Feature Attention as an Alternative to Variables in Phonotactic Learning

2021 ◽  
pp. 1-56
Author(s):  
Brandon Prickett

Abstract Since Halle (1962), explicit algebraic variables (often called alpha notation) have been commonplace in phonological theory. However, Hayes and Wilson (2008) proposed a variable-free model of phonotactic learning, sparking a debate about whether such algebraic representations are necessary to capture human phonological acquisition. While past experimental work has found evidence that suggested a need for variables in models of phonology (Berent et al. 2012, Moreton 2012, Gallagher 2013), this paper presents a novel mechanism, Probabilistic Feature Attention (PFA), that allows a variable-free model of phonotactics to predict a number of these phenomena. Additionally, experimental results involving phonological generalization that cannot be explained by variables are captured by this novel approach. These results cast doubt on whether variables are necessary to capture human-like phonotactic learning and provide a useful alternative to such representations.

1971 ◽  
Vol 49 (2) ◽  
pp. 399-414 ◽  
Author(s):  
Warren C. Strahle

Upon review of past experimental results and theoretical efforts it is apparent that the mechanism by which combustion noise is generated is not well understood. A theory of combustion noise is developed in this paper which follows rigorously from the principles of fluid mechanics. Lighthill's approach, used in his studies of aerodynamic noise, is closely followed in the present work. The sound radiated from open, turbulent flames is found to depend strongly upon the structure of such flames; at present their structure is not well known. However, meaningful bounds and scaling rules for the sound power output and spectral content are derived based upon the present limited knowledge. A framework is developed which explains past experimental work and the origin of combustion noise.


2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.


2012 ◽  
Vol 626 ◽  
pp. 85-89 ◽  
Author(s):  
Kay Dora Abdul Ghani ◽  
Nor Hayati Hamid

The experimental work on two full-scale precast concrete beam-column corner joints with corbels was carried out and their seismic performance was examined. The first specimen was constructed without steel fiber, while second specimen was constructed by mixed up steel fiber with concrete and placed it at the corbels area. The specimen were tested under reversible lateral cyclic loading up to ±1.5% drift. The experimental results showed that for the first specimen, the cracks start to occur at +0.5% drifts with spalling of concrete and major cracks were observed at corbel while for the second specimen, the initial cracks were observed at +0.75% with no damage at corbel. In this study, it can be concluded that precast beam-column joint without steel fiber has better ductility and stiffness than precast beam-column joint with steel fiber. However, precast beam-column joint with steel fiber has better energy dissipation and fewer cracks at corbel as compared to precast beam-column joint without steel fiber.


2016 ◽  
Vol 09 (03) ◽  
pp. 1650043 ◽  
Author(s):  
Haolin Wu ◽  
Jie Yang ◽  
Haibiao Chen ◽  
Feng Pan

Preferentially etching either carbon or silica from silicon oxycarbide (SiOC) created a porous network as an inverse image of the removed phase. The porous structure was analyzed by gas adsorption, and the experimental results verified the nanodomain structure of SiOC. This work demonstrated a novel approach for analyzing materials containing nanocomposite structures.


1966 ◽  
Vol 1 (4) ◽  
pp. 331-338 ◽  
Author(s):  
T C Hsu

Three different definitions of the yield point have been used in experimental work on the yield locus: proportional limit, proof strain and the ‘yield point’ by backward extrapolation. The theoretical implications of the ‘yield point’ by backward extrapolation are examined in an analysis of the loading and re-loading stress paths. It is shown, in connection with experimental results by Miastkowski and Szczepinski, that the proportional limit found by inspection is in fact a point located by backward extrapolation based on a small section of the stress-strain curve, near the elastic portion of the curve. The effect of different definitions of the yield point on the shape of the yield locus and some considerations for the choice between them are discussed.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chun-Hui Wu ◽  
Chia-Wei Chen ◽  
Long-Sheng Kuo ◽  
Ping-Hei Chen

A novel approach was proposed to measure the hydraulic capacitance of a microfluidic membrane pump. Membrane deflection equations were modified from various studies to propose six theoretical equations to estimate the hydraulic capacitance of a microfluidic membrane pump. Thus, measuring the center deflection of the membrane allows the corresponding pressure and hydraulic capacitance of the pump to be determined. This study also investigated how membrane thickness affected the Young’s modulus of a polydimethylsiloxane (PDMS) membrane. Based on the experimental results, a linear correlation was proposed to estimate the hydraulic capacitance. The measured hydraulic capacitance data and the proposed equations in the linear and nonlinear regions qualitatively exhibited good agreement.


2013 ◽  
Vol 11 (06) ◽  
pp. 1343003 ◽  
Author(s):  
JING-DOO WANG

In this paper, three genomic materials — DNA sequences, protein sequences, and regions (domains) are used to compare methods of virus classification. Virus classes (categories) are divided by various taxonomic level of virus into three datasets for 6 order, 42 family, and 33 genera. To increase the robustness and comparability of experimental results of virus classification, the classes are selected that contain at least 10 instances, and meanwhile each instance contains at least one region name. Experimental results show that the approach using region names achieved the best accuracies — reaching 99.9%, 97.3%, and 99.0% for 6 orders, 42 families, and 33 genera, respectively. This paper not only involves exhaustive experiments that compare virus classifications using different genomic materials, but also proposes a novel approach to biological classification based on molecular biology instead of traditional morphology.


Author(s):  
Judy C.R. Tseng ◽  
Wen-Ling Tsai ◽  
Gwo-Jen Hwang ◽  
Po-Han Wu

In developing traditional learning materials, quality is the key issue to be considered. However, for high technical e-training courses, not only the quality of the learning materials but also the efficiency of developing the courses needs to be taken into consideration. It is a challenging issue for experienced engineers to develop up-to-date e-training courses for inexperienced engineers before further new technologies are proposed. To cope with these problems, a concept relationship-oriented approach is proposed in this paper. A system for developing e-training courses has been implemented based on the novel approach. Experimental results showed that the novel approach can significantly shorten the time needed for developing e-training courses, such that engineers can receive up-to-date technologies in time.


Author(s):  
John Harney ◽  
Prashant Doshi

Web Service compositions (WSC) often operate in volatile environments where the parameters of the component services change during execution. To remain optimal, the WSC could adapt to these changes by querying the participating providers for their revised parameters. Previously, the value of changed information (VOC) has been utilized in simple WSCs to selectively query only those services whose revised parameters are expected to bring about significant changes in the composition. In many cases, however, in order to promote scalability, a WSC is formulated as a more complex, nested structure – a higher-level WSC may be composed of WSs and lower-level WSCs – inducing a natural hierarchy over the composition. This chapter presents a novel approach that extends the capabilities of VOC-driven querying to address the problem of adapting hierarchical WSCs. It shows how to compose and adapt hierarchical WSCs by first deriving a model of volatility for lower-level WSCs and then by descending down the levels of nesting and computing the VOC for WSCs at each level. Experimental results demonstrate that this approach provides an effective and efficient solution for complex, hierarchical WSCs.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haitao He ◽  
Chun Shan ◽  
Xiangmin Tian ◽  
Yalei Wei ◽  
Guoyan Huang

Identifying influential nodes is important for software in terms of understanding the design patterns and controlling the development and the maintenance process. However, there are no efficient methods to discover them so far. Based on the invoking dependency relationships between the nodes, this paper proposes a novel approach to define the node importance for mining the influential software nodes. First, according to the multiple execution information, we construct a weighted software network (WSN) to denote the software execution dependency structure. Second, considering the invoking times and outdegree about software nodes, we improve the method PageRank and put forward the targeted algorithm FunctionRank to evaluate the node importance (NI) in weighted software network. It has higher influence when the node has lager value of NI. Finally, comparing the NI of nodes, we can obtain the most influential nodes in the software network. In addition, the experimental results show that the proposed approach has good performance in identifying the influential nodes.


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