scholarly journals Strange Attractors in Writing Competence Development from the Perspective of Complexity Theory Based on Sensor Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chao Zhang
Keyword(s):  
Mathematical Modeling ◽  
Second Language ◽  
Complexity Theory ◽  
Writing Development ◽  
The State ◽  
Strange Attractors ◽  
Sensor Data ◽  
Probabilistic Prediction ◽  
Measurement Framework ◽  
Writing Competence

Writing competence is crucial for second language learners. Studying strange attractors in the development of writing competence is essential in understanding the laws of language development of foreign students. This study is aimed at investigating the state and laws of the development of Chinese as a second language (CSL) writing competence. Mathematical modeling and phase space construction methods in sensor research were used to investigate strange attractors in high-level Chinese learners studying in China in the development of CSL from the perspective of complexity theory based on the measurement framework of complexity, accuracy, and fluency. The results showed the following: (1) there are trends in the concentration and volatility of trigonometric function in different dimensions; (2) the group dynamic characteristics of writing development in CSL are simulated precisely by mathematical modeling; and (3) there are strange attractors with lexical density in CSL writing development. The development of CSL writing tends to maintain the state of strange attractors. The strange attractor reflects regularity in the dynamic, complex, and chaotic development of Chinese for international students, revealing the probabilistic prediction competence of different states in the development of CSL.

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

Exploiting native language interference for native language identification

2020 ◽  
pp. 1-31
Author(s):  
Ilia Markov ◽  
Vivi Nastase ◽  
Carlo Strapparava
Keyword(s):  
Second Language ◽  
State Of The Art ◽  
Native Language ◽  
Emotion Expression ◽  
The State ◽  
Language Identification ◽  
Independent Features ◽  
Insight Into

Abstract Native language identification (NLI)—the task of automatically identifying the native language (L1) of persons based on their writings in the second language (L2)—is based on the hypothesis that characteristics of L1 will surface and interfere in the production of texts in L2 to the extent that L1 is identifiable. We present an in-depth investigation of features that model a variety of linguistic phenomena potentially involved in native language interference in the context of the NLI task: the languages’ structuring of information through punctuation usage, emotion expression in language, and similarities of form with the L1 vocabulary through the use of anglicized words, cognates, and other misspellings. The results of experiments with different combinations of features in a variety of settings allow us to quantify the native language interference value of these linguistic phenomena and show how robust they are in cross-corpus experiments and with respect to proficiency in L2. These experiments provide a deeper insight into the NLI task, showing how native language interference explains the gap between baseline, corpus-independent features, and the state of the art that relies on features/representations that cover (indiscriminately) a variety of linguistic phenomena.

scholarly journals Every laboratory with a fluorescence microscope should consider counting molecules

2014 ◽  
Vol 25 (10) ◽  
pp. 1545-1548 ◽  
Author(s):  
Valerie C. Coffman ◽  
Jian-Qiu Wu
Keyword(s):  
Mathematical Modeling ◽  
Fluorescence Microscopy ◽  
Protein Complexes ◽  
The State ◽  
Fluorescence Microscope ◽  
Cellular Structures ◽  
Biological Processes ◽  
Protein Molecules ◽  
Parameter Values

Protein numbers in cells determine rates of biological processes, influence the architecture of cellular structures, reveal the stoichiometries of protein complexes, guide in vitro biochemical reconstitutions, and provide parameter values for mathematical modeling. The purpose of this essay is to increase awareness of methods for counting protein molecules using fluorescence microscopy and encourage more cell biologists to report these numbers. We address the state of the field in terms of utility and accuracy of the numbers reported and point readers to references for details of specific techniques and applications.

scholarly journals The second language acquisition: problems and challenges for Georgian Deaf community

2021 ◽  
Vol X (3) ◽  
pp. 95-100
Author(s):  
Tamar Makharoblidze ◽  
Keyword(s):  
Second Language ◽  
Second Language Acquisition ◽  
Language Acquisition ◽  
Hard Of Hearing ◽  
Native Language ◽  
The State ◽  
Short Paper ◽  
Deaf People ◽  
Key Issues ◽  
State Language

As stated in the title, the paper is devoted to the issue of second language acquisition by Deaf people in Georgia, describing the current situation and the challenges. There are about 2500 Deaf and hard of hearing residents in Georgia. Being the linguistic minority in the country, these people communicate with each-other in the Georgian Sign Language – GESL. The second native language for local Deaf and hard of hearing people is the Georgian spoken language – the State language. In many countries Deaf people are bilingual, while it is hard to consider the local Deaf and hard of hearing people bilingual, as the knowledge of spoken Georgian on the level of a native language among the Deaf residents is not observed. Unfortunately in Georgia there are no studies concerning the second language acquisition for Deaf and hard of hearing people. The main problems are the agrammatism in written communication on the state language and the ignorance of deferent hierarchical levels of spoken Georgian. This short paper offers the key issues for the plan of strategy of spoken Georgian acquisition for local Deaf and hard of hearing residents.

Language learning in a study-abroad context: Research agenda

2018 ◽  
Vol 51 (4) ◽  
pp. 553-566 ◽  
Author(s):  
Naoko Taguchi ◽  
Joseph Collentine
Keyword(s):  
Second Language ◽  
Study Abroad ◽  
Language Learning ◽  
Learning Outcomes ◽  
Research Agenda ◽  
State Of The Art ◽  
The State ◽  
Empirical Methods ◽  
Theoretical Frameworks ◽  
Half Century

Isabelli-García, Bown, Plew & Dewey (forthcoming) presented the ‘state of the art’ in research on language learning abroad. Beginning with Carroll's (1967) claim that ‘time spent abroad is one of the most potent variables’ predicting second language (L2) abilities (p. 137), the scope of study-abroad research has grown multifold in guiding theoretical frameworks, empirical methods, and objects of examination. A half-century of work surveyed in Isabelli-García et al.’s review reveals diverse goals of investigation, ranging from studies focusing on documenting learning outcomes, to studies aiming to unveil the process and nature of learning in a study-abroad context.

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