Machine learning of phase transitions in nonlinear polariton lattices

AbstractPolaritonic lattices offer a unique testbed for studying nonlinear driven-dissipative physics. They show qualitative changes of their steady state as a function of system parameters, which resemble non-equilibrium phase transitions. Unlike their equilibrium counterparts, these transitions cannot be characterised by conventional statistical physics methods. Here, we study a lattice of square-arranged polariton condensates with nearest-neighbour coupling, and simulate the polarisation (pseudospin) dynamics of the polariton lattice, observing regions with distinct steady-state polarisation patterns. We classify these patterns using machine learning methods and determine the boundaries separating different regions. First, we use unsupervised data mining techniques to sketch the boundaries of phase transitions. We then apply learning by confusion, a neural network-based method for learning labels in a dataset, and extract the polaritonic phase diagram. Our work takes a step towards AI-enabled studies of polaritonic systems.

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A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Business Systems Research Journal ◽

10.2478/bsrj-2014-0021 ◽

2014 ◽

Vol 5 (3) ◽

pp. 82-96 ◽

Cited By ~ 3

Author(s):

Marijana Zekić-Sušac ◽

Sanja Pfeifer ◽

Nataša Šarlija

Keyword(s):

Neural Network ◽

Machine Learning ◽

Classification Accuracy ◽

Classification Problem ◽

High Dimensional ◽

Nearest Neighbour ◽

Learning Methods ◽

Machine Learning Methods ◽

Dimensional Classification ◽

Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

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Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/2041467 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

İlhan Umut ◽

Güven Çentik

Keyword(s):

Machine Learning ◽

Multilayer Perceptron ◽

Digital Signal ◽

Machine Learning Algorithms ◽

Classification Error ◽

Nearest Neighbour ◽

Learning Methods ◽

Classification Rate ◽

Periodic Leg Movements ◽

Machine Learning Methods

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron,K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that whileK-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

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TELESCOPIC LINKAGES AND A TOPOLOGICAL APPROACH TO PHASE TRANSITIONS

Journal of the Australian Mathematical Society ◽

10.1017/s144678871100125x ◽

2011 ◽

Vol 90 (2) ◽

pp. 183-195 ◽

Cited By ~ 2

Author(s):

MICHAEL FARBER ◽

VIKTOR FROMM

Keyword(s):

Magnetic Field ◽

Phase Transitions ◽

Configuration Space ◽

Betti Number ◽

Statistical Physics ◽

Betti Numbers ◽

Equilibrium Phase ◽

Mean Field ◽

Exponential Growth Rate ◽

Topological Approach

AbstractA topological approach to the theory of equilibrium phase transitions in statistical physics is based on the topological hypothesis, which claims that phase transitions are due to changes of the topology of suitable submanifolds in the configuration space. In this paper we examine in detail the antiferromagnetic mean-field XY model and study the topology of the subenergy manifolds. The latter can be interpreted mechanically as the configuration space of a linkage with one telescopic leg. We use methods of Morse theory to describe explicitly the Betti numbers of this configuration space. We apply these results to the antiferromagnetic mean-field XY model and compute the exponential growth rate of the total Betti number. Previous authors studied the Euler characteristic rather than the total Betti number. We show that in the presence of an external magnetic field the model undergoes a single ‘total Betti number phase transition’.

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Identification of Paddy Leaf Diseases using Evolutionary and Machine Learning Methods

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.1503 ◽

2021 ◽

Vol 12 (2) ◽

pp. 1672-1686

Author(s):

Nilam Sachin Patil

Keyword(s):

Machine Learning ◽

Early Stage ◽

Plant Diseases ◽

Automated System ◽

Machine Learning Techniques ◽

Nearest Neighbour ◽

Agricultural Plant ◽

Machine Learning Methods ◽

Cascaded Classifiers ◽

Learning Techniques

In the field of agriculture, especially paddy plants, there is a demand for research to classify the paddy diseases at early stages. This is feasible if there are automated systems that can assist the farmers to recognize the paddy diseases from the paddy leaf images of the plants. The recognition of agricultural plant diseases by utilizing the image-processing and machine learning techniques can certainly minimize the reliance on the farmers to protect the yield of paddy crops. In this paper, an attempt has been made to pre-process the images to prepare the feature-set for Classifiers and then feature extraction algorithms are used to extract the relevant features from the processed images. The feature-set is then supplied to the classifiers for identification of Paddy Leaf diseases. The usage of cascaded classifiers has been explored to detect the diseases of paddy leaves. An attempt has also been made to use genetic algorithm with nearest neighbour algorithm to identify the diseases of paddy leaves. The proposed automated system can be used on Android , Windows platform and Apple platform for quickly identifying the paddy leaf diseases as the entire implementation has been performed using MATLAB. The proposed automated system can certainly help the farmers to classify the diseased paddy leaves at early stage to protect the crops from further damage.

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Equilibrium Phase Transitions in Statistical Physics

Phase Transition Dynamics ◽

10.1007/978-3-030-29260-7_3 ◽

2019 ◽

pp. 129-278

Author(s):

Tian Ma ◽

Shouhong Wang

Keyword(s):

Phase Transitions ◽

Statistical Physics ◽

Equilibrium Phase

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Multiplex analysis of 40 cytokines do not allow separation between endometriosis patients and controls

Scientific Reports ◽

10.1038/s41598-019-52899-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Tamara Knific ◽

Dmytro Fishman ◽

Andrej Vogler ◽

Manuela Gstöttner ◽

René Wenzl ◽

...

Keyword(s):

Machine Learning ◽

Plasma Levels ◽

Principal Component ◽

Machine Learning Algorithms ◽

Nearest Neighbour ◽

Plasma Samples ◽

Diagnostic Biomarkers ◽

Clear Separation ◽

Non Invasive ◽

Machine Learning Methods

Abstract Endometriosis is a common gynaecological condition characterized by severe pelvic pain and/or infertility. The combination of nonspecific symptoms and invasive laparoscopic diagnostics have prompted researchers to evaluate potential biomarkers that would enable a non-invasive diagnosis of endometriosis. Endometriosis is an inflammatory disease thus different cytokines represent potential diagnostic biomarkers. As panels of biomarkers are expected to enable better separation between patients and controls we evaluated 40 different cytokines in plasma samples of 210 patients (116 patients with endometriosis; 94 controls) from two medical centres (Slovenian, Austrian). Results of the univariate statistical analysis showed no differences in concentrations of the measured cytokines between patients and controls, confirmed by principal component analysis showing no clear separation amongst these two groups. In order to validate the hypothesis of a more profound (non-linear) differentiating dependency between features, machine learning methods were used. We trained four common machine learning algorithms (decision tree, linear model, k-nearest neighbour, random forest) on data from plasma levels of proteins and patients’ clinical data. The constructed models, however, did not separate patients with endometriosis from the controls with sufficient sensitivity and specificity. This study thus indicates that plasma levels of the selected cytokines have limited potential for diagnosis of endometriosis.

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Investigation of the effectiveness of metric classification methods in identifying attacks in VANET

Journal of Physics Conference Series ◽

10.1088/1742-6596/2094/3/032066 ◽

2021 ◽

Vol 2094 (3) ◽

pp. 032066

Author(s):

D Parfenov ◽

I Bolodurina ◽

L Grishina ◽

A Zhigalov

Keyword(s):

Machine Learning ◽

Main Idea ◽

Nearest Neighbour ◽

Classification Methods ◽

Nonlinear Functions ◽

Machine Learning Methods ◽

Parzen Window ◽

Window Method ◽

Different Types ◽

Adhoc Networks

Abstract This paper discusses the problem of improving the efficiency of metric machine learning methods of identification attacks in vehicular adhoc networks (VANETs). The main idea of this research is to select the type of nonlinear functions for calculating the distances between the objects of the sample, describing the traffic of VANET using metric methods, such as the method of k-nearest neighbour with linearly decreasing weights and the Parzen window method. The analysis of the effectiveness of the methods considered was carried out on a synthetically generated sample with three different types of attacks on the network. Computational experiments have shown that the k-nearest neighbour method with decreasing weights based on an exponential function with base a < 1 is more efficient than the Parzen window method by about 0.3% and has an accuracy of 84.15%.

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Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways

Archives of Transport ◽

10.5604/01.3001.0010.4228 ◽

2017 ◽

Vol 43 (3) ◽

pp. 91-104 ◽

Cited By ~ 2

Author(s):

Ying Lee ◽

Chien-Hung Wei ◽

Kai-Chon Chao

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Drunk Driving ◽

Nearest Neighbour ◽

Learning Methods ◽

Machine Learning Methods ◽

Comparison Results ◽

Artificial Neural ◽

Duration Prediction

Traffic accidents usually cause congestion and increase travel-times. The cost of extra travel time and fuel consumption due to congestion is huge. Traffic operators and drivers expect an accurately forecasted accident duration to reduce uncertainty and to enable the implementation of appropriate strategies. This study demonstrates two non-parametric machine learning methods, namely the k-nearest neighbour method and artificial neural network method, to construct accident duration prediction models. The factors influencing the occurrence of accidents are numerous and complex. To capture this phenomenon and improve the performance of accident duration prediction, the models incorporated various data including accident characteristics, traffic data, illumination, weather conditions, and road geometry characteristics. All raw data are collected from two public agencies and were integrated and cross-checked. Before model development, a correlation analysis was performed to reduce the scale of interrelated features or variables. Based on the performance comparison results, an artificial neural network model can provide good and reasonable prediction for accident duration with mean absolute percentage error values less than 30%, which are better than the prediction results of a k-nearest neighbour model. Based on comparison results for circumstances, the Model which incorporated significant variables and employed the ANN method can provide a more accurate prediction of accident duration when the circumstances involved the day time or drunk driving than those that involved night time and did not involve drunk driving. Empirical evaluation results reveal that significant variables possess a major influence on accident duration prediction.

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