Investigation of the alignment mechanism and loss of collectivity in Pm135

2021 ◽  
Vol 103 (1) ◽  
Author(s):  
F. S. Babra ◽  
S. Jehangir ◽  
R. Palit ◽  
S. Biswas ◽  
B. Das ◽  
...  
Keyword(s):  
Alignment Mechanism

Precise Collimation and Alignment Mechanism for the Spinco Model E Ultracentrifuge

1961 ◽  
Vol 32 (1) ◽  
pp. 94-95 ◽  
Author(s):  
R. J. Bolen ◽  
E. A. Pavelka ◽  
J. R. Lindley ◽  
C. W. Dwiggins
Keyword(s):  
Alignment Mechanism

Alignment mechanism of carbon nanofibers produced by plasma-enhanced chemical-vapor deposition

2001 ◽  
Vol 79 (18) ◽  
pp. 2970-2972 ◽  
Author(s):  
Vladimir I. Merkulov ◽  
Anatoli V. Melechko ◽  
Michael A. Guillorn ◽  
Douglas H. Lowndes ◽  
Michael L. Simpson
Keyword(s):  
Chemical Vapor Deposition ◽  
Carbon Nanofibers ◽  
Vapor Deposition ◽  
Chemical Vapor ◽  
Alignment Mechanism

MOOC opinion mining based on attention alignment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuanxin Ouyang ◽  
Hongbo Zhang ◽  
Wenge Rong ◽  
Xiang Li ◽  
Zhang Xiong
Keyword(s):  
Opinion Mining ◽  
Language Models ◽  
Online Course ◽  
Data Sets ◽  
Alignment Method ◽  
Dependency Analysis ◽  
Content Type ◽  
Massive Open Online Course ◽  
Frequent Sets ◽  
Alignment Mechanism

Purpose The purpose of this paper is to propose an attention alignment method for opinion mining of massive open online course (MOOC) comments. Opinion mining is essential for MOOC applications. In this study, the authors analyze some of bidirectional encoder representations from transformers (BERT’s) attention heads and explore how to use these attention heads to extract opinions from MOOC comments. Design/methodology/approach The approach proposed is based on an attention alignment mechanism with the following three stages: first, extracting original opinions from MOOC comments with dependency parsing. Second, constructing frequent sets and using the frequent sets to prune the opinions. Third, pruning the opinions and discovering new opinions with the attention alignment mechanism. Findings The experiments on the MOOC comments data sets suggest that the opinion mining approach based on an attention alignment mechanism can obtain a better F1 score. Moreover, the attention alignment mechanism can discover some of the opinions filtered incorrectly by the frequent sets, which means the attention alignment mechanism can overcome the shortcomings of dependency analysis and frequent sets. Originality/value To take full advantage of pretrained language models, the authors propose an attention alignment method for opinion mining and combine this method with dependency analysis and frequent sets to improve the effectiveness. Furthermore, the authors conduct extensive experiments on different combinations of methods. The results show that the attention alignment method can effectively overcome the shortcomings of dependency analysis and frequent sets.

scholarly journals Supervector Extraction for Encoding Speaker and Phrase Information with Neural Networks for Text-Dependent Speaker Verification

2019 ◽  
Vol 9 (16) ◽  
pp. 3295 ◽  
Author(s):  
Victoria Mingote ◽  
Antonio Miguel ◽  
Alfonso Ortega ◽  
Eduardo Lleida
Keyword(s):  
Neural Network ◽  
Speaker Verification ◽  
Temporal Structure ◽  
Temporal Dimension ◽  
Global Average ◽  
Alignment Process ◽  
Alignment Mechanism ◽  
Verification Process ◽  
Phrase Alignment ◽  
Text Dependent Speaker Verification

In this paper, we propose a new differentiable neural network with an alignment mechanism for text-dependent speaker verification. Unlike previous works, we do not extract the embedding of an utterance from the global average pooling of the temporal dimension. Our system replaces this reduction mechanism by a phonetic phrase alignment model to keep the temporal structure of each phrase since the phonetic information is relevant in the verification task. Moreover, we can apply a convolutional neural network as front-end, and, thanks to the alignment process being differentiable, we can train the network to produce a supervector for each utterance that will be discriminative to the speaker and the phrase simultaneously. This choice has the advantage that the supervector encodes the phrase and speaker information providing good performance in text-dependent speaker verification tasks. The verification process is performed using a basic similarity metric. The new model using alignment to produce supervectors was evaluated on the RSR2015-Part I database, providing competitive results compared to similar size networks that make use of the global average pooling to extract embeddings. Furthermore, we also evaluated this proposal on the RSR2015-Part II. To our knowledge, this system achieves the best published results obtained on this second part.

Fiber Reinforced Multiphase Polymer Composites by In situ Fiber Alignment

2007 ◽  
Vol 1054 ◽  
Author(s):  
Wantinee Viratyaporn ◽  
Nancy Twu ◽  
Richard Lehman
Keyword(s):  
Polymer Blends ◽  
Glass Fibers ◽  
Model System ◽  
Immiscible Polymer Blends ◽  
Immiscible Polymer ◽  
Novel Approach ◽  
Micron Size ◽  
The Matrix ◽  
Alignment Mechanism

ABSTRACTA novel approach has been explored for the efficient dispersion and uniaxial alignment of fibers in dual phase polymer matrices based on the streaming flow that occurs when two immiscible polymer blends are melt processed under high shear conditions. Such conditions improve the alignment and distribution of fibers in the matrix, a feature of particular importance when fine nanofibers are used. This self-alignment mechanism seeks to produce optimum properties from relatively small volume fractions of fiber. Recent efforts have focused on a model system containing micron-size glass fibers in immiscible polymer blends. This paper presents selected mechanical properties measured for the model system and the flow/orientation paradigm that produces the observed morphologies.

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