Elliott Wave Pattern Recognition for Forecasting GBP/USD Foreign Exchange Market

This research presents an approach to the Elliott wave pattern implicates a forecast of future movements in foreign exchange (forex) rates of the previous movement inductive analysis. Elliott wave is defined that each individual wave has its own characteristic or pattern, which as expected reflects the psychology of the moment. The forex market is one of the utmost intricate markets through the characteristics of high volatility, nonlinearity and irregularity. Meantime, these characteristics also make it very difficult to forecast forex. The problem is contained pattern recognition, classification, and forecasting. The research objectives are to recognize the pattern using the Elliott wave pattern, to validate accuracy patterns classification using Linear Discriminant Analysis (LDA) and to forecast short-term forex market using Elliott wave method. LDA is employed to obtain in term of classification’s accuracy between 2 classes of selected data. The result shows the accuracy selected data is equal to 99.43%. Among of three levels of Fibonacci retracement which are 38.2%, 50.0%, and 61.8% results, the 38.2% shows the best forecasting for GBP/USD currency by using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (r) as the statistical measurements

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.Comment: 93 pages, 18 figures, under revie

Scope and limitations of ad hoc neural network reconstructions of solar wind parameters

Solar wind properties are determined by the conditions of their solar source region and transport history. Solar wind parameters, such as proton speed, proton density, proton temperature, magnetic field strength, and the charge state composition of oxygen, are used as proxies to investigate the solar source region of the solar wind. The transport and conditions in the solar source region affect several solar wind parameters simultaneously. The observed redundancy could be caused by a set of hidden variables. We test this assumption by determining how well a function of four of the selected solar wind parameters can model the fifth solar wind parameter. If such a function provided a perfect model, then this solar wind parameter would be uniquely determined from hidden variables of the other four parameters. We used a neural network as a function approximator to model unknown relations between the considered solar wind parameters. This approach is applied to solar wind data from the Advanced Composition Explorer (ACE). The neural network reconstructions are evaluated in comparison to observations. Within the limits defined by the measurement uncertainties, the proton density and proton temperature can be reconstructed well. We also found that the reconstruction is most difficult for solar wind streams preceding and following stream interfaces. For all considered solar wind parameters, but in particular the proton density, temperature, and the oxygen charge-state ratio, parameter reconstruction is hindered by measurement uncertainties. The reconstruction accuracy of sector reversal plasma is noticeably lower than that of streamer belt or coronal hole plasma. The fact that the oxygen charge-state ratio, a non-transport-affected property, is difficult to reconstruct may imply that recovering source-specific information from the transport-affected proton plasma properties is challenging

OCM 2023 - Optical Characterization of Materials : Conference Proceedings

The state of the art in the optical characterization of materials is advancing rapidly. New insights have been gained into the theoretical foundations of this research and exciting developments have been made in practice, driven by new applications and innovative sensor technologies that are constantly evolving. The great success of past conferences proves the necessity of a platform for presentation, discussion and evaluation of the latest research results in this interdisciplinary field

Feasibility of improving risk stratification in the inherited cardiac conditions

Fatal ventricular arrhythmias can occur in patients with Hypertrophic Cardiomyopathy, Brugada Syndrome and rarely in patients with normal cardiac investigations. Despite very different pathogeneses, we hypothesised that a common electrophysiological substrate precipitates these arrhythmias and could be used as a marker for risk stratification. In Chapter 3 of this thesis, we found that fewer than half the cardiac arrest survivors with Brugada Syndrome would have been offered prophylactic defibrillators based on current risk scoring, highlighting the need for better risk stratification. Our group previously used a commercially available 252-electrode vest which constructs ventricular electrograms onto a CT image of the heart to show exercise related differences in high-risk patients. In Chapter 4, we applied this method to Brugada patients, but could not reproduce prior results. Further investigation revealed periodic changes in activation patterns after exercise that could explain this discrepancy. An alternative matrix approach was developed to overcome this problem. Exercise induced conduction heterogeneity differentiated Brugada patients from unaffected controls, but not those surviving cardiac arrest. However, if considered alongside spontaneous type 1 ECG and syncope, inducible conduction heterogeneity markedly improved identification of Brugada cardiac arrest survivors. In Chapter 5 the method was shown to differentiate idiopathic ventricular fibrillation patients from those fully recovered from acute ischaemic cardiac arrest, implying a permanent electrophysiological abnormality. In Chapter 8, we showed prolonged mean local activation times and activation-recovery intervals in hypertrophic cardiomyopathy cardiac arrest survivors compared to those without previous ventricular arrhythmia. These metrics were combined into both logistic regression and support vector machine models to strongly differentiate the groups. We concluded that electrophysiological changes could identify cardiac arrest survivors in various cardiac conditions, but a single factor common pathway was not established. Prospective studies are required to determine if using these parameters could enhance current risk stratification for sudden death.Open Acces

Essays on Wealth, Liquidity and Household Finance

In recent years, the analysis of household wealth has become a important field of study for both academics and policymakers. Consequently, in this thesis, household wealth, its determinants, and its effect on house- hold consumption, poverty, and understanding the determinants of wealth are studied. Firstly, changes in household wealth are identified through expected and unexpected changes in house prices and disposable income. Changes in house prices and income are studied to determine if there is a relationship between consumption, investment and spending decisions. The results suggest no significant positive correlation between unexpected house price changes and household consumption in the United States. Secondly, income and asset poverty rates in the United States are studied. We define asset poverty and review how demographic and household events affect as- set poverty rates. The findings suggest one in every four households did not have the financial assets to cover three months of their basic consumption needs in 2011. Black, single female headed households, and renters are more likely to be asset poor, and remain asset poor over multiple years. Finally, this thesis examines the application of modern machine learning techniques to estimate a households net wealth, and net wealth minus housing equity. The findings reported across the 1999-2017 period, suggest variables such as profit on stock, house value, and profit on business are the most important features in predicting household wealth. Secondly, the results identify alter- native variables such as dividends, years left on mortgage, and interest in- come are also important factors in determining a households’ wealth. Thus, this thesis provides new findings that further the current understanding of how household wealth affects non-durable consumption; provides evidence that current poverty rates are underestimated in the United States; and provides new variables that can be used to estimate household wealth

States and sequences of paired subspace ideals and their relationship to patterned brain function

It is found here that the state of a network of coupled ordinary differential equations is partially localizable through a pair of contractive ideal subspaces, chosen from dual complete lattices related to the synchrony and synchronization of cells within the network. The first lattice is comprised of polydiagonal subspaces, corresponding to synchronous activity patterns that arise from functional equivalences of cell receptive fields. This lattice is dual to a transdiagonal subspace lattice ordering subspaces transverse to these network-compatible synchronies. Combinatorial consideration of contracting polydiagonal and transdiagonal subspace pairs yields a rich array of dynamical possibilities for structured networks. After proving that contraction commutes with the lattice ordering, it is shown that subpopulations of cells are left at fixed potentials when pairs of contracting subspaces span the cells' local coordinates - a phenomenon named glyph formation here. Treatment of mappings between paired states then leads to a theory of network-compatible sequence generation. The theory's utility is illustrated with examples ranging from the construction of a minimal circuit for encoding a simple phoneme to a model of the primary visual cortex including high-dimensional environmental inputs, laminar speficicity, spiking discontinuities, and time delays. In this model, glyph formation and dissolution provide one account for an unexplained anomaly in electroencephalographic recordings under periodic flicker, where stimulus frequencies differing by as little as 1 Hz generate responses varying by an order of magnitude in alpha-band spectral power. Further links between coupled-cell systems and neural dynamics are drawn through a review of synchronization in the brain and its relationship to aggregate observables, focusing again on electroencephalography. Given previous theoretical work relating the geometry of visual hallucinations to symmetries in visual cortex, periodic perturbation of the visual system along a putative symmetry axis is hypothesized to lead to a greater concentration of harmonic spectral energy than asymmetric perturbations; preliminary experimental evidence affirms this hypothesis. To conclude, connections drawn between dynamics, sensation, and behavior are distilled to seven hypotheses, and the potential medical uses of the theory are illustrated with a lattice depiction of ketamine xylazine anaesthesia and a reinterpretation of hemifield neglect