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Inverse Reinforcement Learning via Matching of Optimality Profiles
(2020)arXivThe goal of inverse reinforcement learning (IRL) is to infer a reward function that explains the behavior of an agent performing a task. The assumption that most approaches make is that the demonstrated behavior is near-optimal. In many real-world scenarios, however, examples of truly optimal behavior are scarce, and it is desirable to effectively leverage sets of demonstrations of suboptimal or heterogeneous performance, which are easier ...Working Paper -
Possibility of Land Movement Prediction for Creep or before Earthquake Using Lidar Geodetic Data in a Machine Learning Scheme
(2020)arXivEarthquake prediction is one of the most pursued problems in geoscience. Different geological and seismological approaches exist for the prediction of the earthquake and its subsequent land change. However, in many cases, they fail in their mission. In this paper, we address the well-established earthquake prediction problem by a novel approach. We use a four-dimensional location-time machine learning scheme to estimate the time of ...Working Paper -
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction
(2020)arXivWe present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. This algorithm has four steps. First, the mean value of the time series is subtracted from it. Second, the trends in the time series are removed. Third, wavelets are used to separate the high and low frequencies. And fourth, a number of frequencies are derived and used for finding the weights between the ...Working Paper -
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm
(2020)arXivWe investigate the accuracy of conventional machine learning aided algorithms for the prediction of lateral land movement in an area using the precise position time series of permanent GNSS stations. The machine learning algorithms that are used are tantamount to the ones used in [1], except for the radial basis functions, i.e. multilayer perceptron, Bayesian neural network, Gaussian processes, k-nearest neighbor, generalized regression ...Working Paper -
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series
(2020)arXivThis paper is aimed at the problem of predicting the land subsidence or upheave in an area, using GNSS position time series. Since machine learning algorithms have presented themselves as strong prediction tools in different fields of science, we employ them to predict the next values of the GNSS position time series. For this reason, we present an algorithm that takes advantage of the machine learning algorithms for the prediction of ...Working Paper -
Comparison between compactly-supported spherical radial basis functions and interpolating moving least squares meshless interpolants for gravity data interpolation in geodesy and geophysics
(2020)arXivThe present paper is focused on the comparison of the efficiency of two important meshless interpolants for gravity acceleration interpolation. Compactly-supported spherical radial basis functions and interpolating moving least squares are used to interpolate actual gravity accelerations in southern Africa. Interpolated values are compared with actual values, gathered by observation. A thorough analysis is presented for the standard ...Working Paper -
Observation of Magnetic Proximity Effect Using Resonant Optical Spectroscopy of an Electrically Tunable MoSe2/CrBr3 Heterostructure
(2020)arXivVan der Waals heterostructures combining two-dimensional magnetic and semiconducting layers constitute a promising platform for interfacing magnetism, electronics, and optics. Here, we use resonant optical reflection spectroscopy to the observe magnetic proximity effect in a gate-tunable MoSe2/CrBr3 heterostructure. High quality of the interface leads to a giant zero-field splitting of the K and K' valley excitons in MoSe2, equivalent to ...Working Paper -
Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
(2019)arXivMany computer models such as cellular automata have been developed and successfully applied. However, in some cases these models might be restrictive on the possible solutions or their solution is difficult to interpret. To overcome this problem, we outline an approach, the so-called allagmatic method, that automatically creates and programs models with as little limitations as possible but still maintaining human interpretability. We ...Working Paper -
Cybernetical Concepts for Cellular Automaton and Artificial Neural Network Modelling and Implementation
(2019)arXivWorking Paper -
Parameters of walkability: A meta-analysis
(2018)Walkability can be understood as the walk-friendliness of a built environment. A wide range of walkability attributes are reported and measured in existing literature. However, a consensus on the relative importance of each parameter does not exist. General models for the interactions between pedestrians and their environment have only been suggested in a rudimentary form. To address these issues, an in-depth research of the relevant ...Working Paper