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Machine learning techniques and data for stock market forecasting : A literature review
(2022-07-01) Kumbure, Mahinda Mailagaha; Lohrmann, Christoph; Luukka, Pasi; Porras, Jari; Department of Computer Science
In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles.
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The contact-less method of chip-to-chip high speed data transmission monitoring
(2014-03) Zmuda, M.; Szczepanski, S.; Koziel, S.; Graczyk, S.; Department of Engineering
This paper presents a technique of decoupling differential signals transmitted in a pair of microstrip lines on a printed circuit board (PCB), using dedicated coupler for high speed data transmission monitoring in chip-to-chip interconnections. The coupler used for signal probing is overlayed on the pair of microstrip lines under test, and provides a signal to the next blocks of the measurement system without disturbing transmission in the lines. Starting from the basic configuration obtained in the previous work, we describe a procedure of re-designing the coupler for enhanced directivity so that it is suitable for high-speed data transmission monitoring. The new coupler structure has improved topology based on stepped-impedance resonators with geometry parameters adjusted using numerical optimization techniques. The correctness of the design has been confirmed by the time-domain measurements performed for real chip-to-chip communication signals, using a dedicated measurement system based on Xilinx Virtex-6 FPGA Development Kit. The presented technology can be used for reducing the cost of the product diagnostics by eliminating dedicated measurement connectors, run-time system debugging or reverse engineering.
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Updating the national average figures for likelihood and severity of harms for fire and explosions in English dwellings
(2025-11) Dawson, Henry; O’Donoghue, Peter; Khandokar, Fahmida; Grinnell, Sophie
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Shiba states coupled to a resonant cavity
(American Institute of Physics Inc., 2017-01-04) Chirla, Razvan; Horea, Cristian Dorin; Costea, Traian Octavian; Dragomir, Radu; Manolescu, Andrei; Moca, Ctlin Paşcu; Popescu, Alexandra; Vizman, Daniel; Department of Engineering
When a quantum dot is embedded into a superconducting environment, it leads to the formations of localized Shiba states inside the gap. If the Coulomb interaction is sufficiently small, the Shiba states consist of a pair of singlet states and a doublet, that compete for the ground state and induce a quantum phase transition. In the presence of an external microwave field, the Shiba energy spectrum is significantly modified. Moreover, the transmission of the cavity inherits features that can pinpoint the exact location of the quantum critical point. In terms of methods used, our analytical calculations are supplemented by state of the art numerical renormalization group calculations.
Verk
Fast low-fidelity wing aerodynamics model for surrogate-based shape optimization
(2014) Leifsson, Leifur; Koziel, Slawomir; Bekasiewicz, Adrian; Department of Engineering
Variable-fidelity optimization (VFO) can be efficient in terms of the computational cost when compared with traditional approaches, such as gradient-based methods with adjoint sensitivity information. In variable-fidelity methods, the direct optimization of the expensive high-fidelity model is replaced by iterative re-optimization of a physics-based surrogate model, which is constructed from a corrected low-fidelity model. The success of VFO is dependent on the reliability and accuracy of the low-fidelity model. In this paper, we present a way to develop a fast and reliable low-fidelity model suitable for aerodynamic shape of transonic wings. The low-fidelity model is component based and accounts for the zero-lift drag, induced drag, and wave drag. The induced drag can be calculated by a proper method, such lifting line theory or a panel method. The zero-lift drag and the wave drag can be calculated by two-dimensional flow model and strip theory. Sweep effects are accounted for by simple sweep theory. The approach is illustrated by a numerical example where the induced drag is calculated by a vortex lattice method, and the zero-lift drag and wave drag are calculated by MSES (a viscous-inviscid method). The low-fidelity model is roughly 320 times faster than a high-fidelity computational fluid dynamics models which solves the Reynolds-averaged Navier-Stokes equations and the Spalart-Allmaras turbulence model. The responses of the high- And low-fidelity models compare favorably and, most importantly, show the same trends with respect to changes in the operational conditions (Mach number, angle of attack) and the geometry (the airfoil shapes).

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