299 research outputs found
Advances in machine learning algorithms for financial risk management
In this thesis, three novel machine learning techniques are introduced to address distinct
yet interrelated challenges involved in financial risk management tasks. These approaches
collectively offer a comprehensive strategy, beginning with the precise classification of credit
risks, advancing through the nuanced forecasting of financial asset volatility, and ending
with the strategic optimisation of financial asset portfolios.
Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk
assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture
modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed
using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression
model is then applied to predict the probability of default using the heuristically balanced
datasets. The results underscore the effectiveness of our proposed technique, with superior
performance observed in comparison to other imbalanced preprocessing approaches. This
advancement in credit risk classification lays a solid foundation for understanding individual
financial behaviours, a crucial first step in the broader context of financial risk management.
Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a
Triple Discriminator Generative Adversarial Network with a continuous wavelet transform
is proposed. The proposed model has the ability to decompose volatility time series into
signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform
component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a
Generative Adversarial Network consisting of triple Discriminator and Generator networks.
The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised
loss and reconstruction loss as part of its framework. Data from nine financial assets are
employed to demonstrate the effectiveness of the proposed model. This approach not only
enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis.
Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio
optimisation using historical Low, High, and Close prices of assets as input with weights of
assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return
on investment based on deep reinforcement learning. To provide more learning stability in
an online training process, a Markov Differential Sharpe Ratio reward function has been
proposed as the reinforcement learning objective function. Additionally, a Multi-Memory
Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout
a specified trading period. The use of the insights gained from volatility forecasting into
this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving
superior results based on risk-adjusted reward performance measures.
In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the
accuracy of credit risk classification, through the improvement and understanding of market
volatility, to optimisation of investment strategies. These methodologies collectively show
the potential of the use of machine learning to improve financial risk management
Breaking Implicit Assumptions of Physical Delay-Feedback Reservoir Computing
The Reservoir Computing (RC) paradigm is a supervised machine learning scheme using the natural computational ability of dynamical systems. Such dynamical systems incorporate time delays showcasing intricate dynamics. This richness in dynamics, particularly the system's transient response to external stimuli makes them suitable for RC. A subset of RCs, Delay-Feedback Reservoir Computing (DFRC), is distinguished by its unique features: a system that consists of a single nonlinear node and a delay-line, with `virtual' nodes defined along the delay-line by time-multiplexing procedure of the input. These characteristics render DFRC particularly useful for hardware integration. In this thesis, the aim is to break the implicit assumptions made in the design of physical DFRC based on Mackey-Glass dynamical system.
The first assumption we address is the performance of DFRC is not affected by the attenuation in physcial delay-line as the nodes defined along it are 'virtual'. However, our experimental results contradict this. To mitigate the impact of losses along the delay line, we propose a methodology `Devirtualisation', which describes the procedure of directly tapping into the delay lines at the position of a `virtual' node, rather than at the delay line's end. It trade-offs the DFRC system's read-out frequency and the quantity of output lines. Masking plays a crucial role in DFRC, as it defines `virtual' nodes along the delay-line. The second assumption is that the mask used should randomly generated numbers uniformly distributed between [-u,u]. We experimentally compare Binary Weight Mask (BWM) vs. Random Weight Mask (RWM) under different scenarios; and investigate the randomness of BWM signal distribution's impact. The third implicit assumption is that, DFRC is designed to solve time series prediction tasks involving a single input and output with no external feedback. To break this assumption, we propose two approaches to mix multi-input signals into DFRC; to validate these approaches, a novel task for DFRC that inherently necessitates multiple inputs: the control of a forced Van der Pol oscillator system, is proposed
Spin Detection, Amplification, and Microwave Squeezing with Kinetic Inductance Parametric Amplifiers
Superconducting parametric amplifiers operating at microwave frequencies have become an essential component in circuit quantum electrodynamics experiments. They are used to amplify signals at the single-photon level, while adding only the minimum amount of noise required by quantum mechanics. To achieve gain, energy is transferred from a pump to the signal through a non-linear interaction. A common strategy to enhance this process is to place the non-linearity inside a high quality factor resonator, but so far, quantum limited amplifiers of this type have only been demonstrated from designs that utilize Josephson junctions. Here we demonstrate the Kinetic Inductance Parametric Amplifier (KIPA), a three-wave mixing resonant parametric amplifier that exploits the kinetic inductance intrinsic to thin films of disordered superconductors. We then utilize the KIPA for measurements of 209Bi spin ensembles in Si. First, we show that a KIPA can serve simultaneously as a high quality factor resonator for pulsed electron spin resonance measurements and as a low-noise parametric amplifier. Using this dual-functionality, we enhance the signal to noise ratio of our measurements by more than a factor of seven and ultimately achieve a measurement sensitivity of 2.4 x 10^3 spins. Then we show that pushed to the high-gain limit, KIPAs can serve as a `click'-detector for microwave wave packets by utilizing a hysteretic transition to a self-oscillating state. We calibrate the detector's sensitivity to be 3.7 zJ and then apply it to measurements of electron spin resonance. Finally, we demonstrate the suitability of the KIPA for generating squeezed vacuum states. Using a cryogenic noise source, we first confirm the KIPAs in our experiment to be quantum limited amplifiers. Then, using two KIPAs arranged in series, we make direct measurements of vacuum noise squeezing, where we generate itinerant squeezed states with minimum uncertainty more than 7 dB below the standard quantum limit.
High quality factor resonators have also recently been used to achieve strong coupling between the spins of single electrons in gate-defined quantum dots and microwave photons. We present our efforts to achieve the equivalent goal for the 31P flip-flop qubit. In doing so, we confirm previous predictions that the superconducting material MoRe would produce magnetic field-resilient resonators and demonstrate that it has kinetic inductance equivalent to the popular material NbTiN
Homodyne spin noise spectroscopy and noise spectroscopy of a single quantum dot
The steady-state fluctuations of a spin system are closely interlinked with its dynamics in linear response to external perturbations. Spin noise spectroscopy exploits this link to extract parameters characterizing the dynamics without needing an intricate spin polarization scheme. In samples with an accessible optical resonance, the spin fluctuations are imprinted onto a transmitted linearly polarized quasi-resonant probe laser beam according to the optical selection rules, making an all-optical observation of spin dynamics possible. The beam’s detuning and intensity determine whether the system is probed at thermal equilibrium or under optical driving. The technique is uniquely applicable for studying single quantum dots, where a charge carrier’s spin and occupancy dynamics can be observed simultaneously.
This thesis presents a step-by-step derivation of the shape and statistical properties of experimental spectra and highlights the experimental limitations faced by the technique at very low probe intensities through uncorrelated broadband technical noise contributions. Optical homodyne amplification is evaluated in a proof-of-principle experiment to determine whether this limitation can be overcome at low frequencies < 5 MHz. Unlike previous attempts, the presented proof-of-principle experiment demonstrates that shot-noise limited spin noise measurements are possible in low-frequency ranges down to ≳ 100 kHz. For even lower frequencies, the suppression of laser intensity noise by the limited common-mode rejection of conventional balanced detectors is found to be the limiting contribution.
In the second part of the thesis, optical spin noise spectroscopy is used to conduct a long-term study of spin and occupancy dynamics of an individual hole spin confined in an (In,Ga)As quantum dot with high radial symmetry in the high magnetic fields regime. For magnetic fields ≳ 250 mT, the splitting of the Zeeman branches with an effective g-factor of 2.159(2) exceeds the quantum dot’s trion resonance’s homogeneous line width of 6.3(2) μeV, revealing a rich spectral structure of spin and occupancy dynamics. This structure reveals a so far neglected contribution of an internal photoeffect to the charge dynamics between the quantum dot and its environment. Previously developed theoretical modeling is extended to incorporate the photoeffect and successfully achieves excellent qualitative correspondence with experimental spectra for almost all detuning ranges. The photoeffect shuffles the charge from and into the quantum dot with two distinct rates. Within the model, the previously required Auger process is unnecessary to describe the experimental data. The rates of discharging and recharging the quantum dot are determined to be on the order of 12(7) kHz·μm²·nW⁻¹ and 6(2) kHz·μm²·nW⁻¹, respectively.
For magnetic fields < 500 mT, very long T1 hole spin relaxation times ≫ 1 ms are observed, while above 500 mT, T1 falls to 5(2) μs at 2.5 T, qualitatively confirming the theoretical prediction of a single-phonon mediated relaxation process. Furthermore, the electron spin relaxation time T1 in the trion state shows no pronounced dependence on magnetic fields above 500 mT and stays at a constant value of 101(2) ns. The saturation intensity of the transition also does not depend on the magnetic field and stays at a constant value of 4.8(7) nW·μm⁻²
Processing the Lessons of War: Organizational Change and the U.S. Military
Failing to understand the lessons of war can cause militaries to repeat past failures, leading to increased costs in terms of resources and causalities in future conflicts. Modern Western militaries faced a range of difficulties on the battlefields of Iraq and Afghanistan that they struggled to address, and they need to learn and institutionalize the lessons of their experiences if they are to succeed in future conflicts. This dissertation addresses this by asking: to what extent has the battlefield experience of the U.S. military influenced post-war organizational change? The various service branches of the U.S. military have needed to adapt at the tactical, operational, and strategic levels of war. However, what remains to be understood is if, and more importantly how, such battlefield adaptations and the lessons of military operations were actually learned and thus influenced the overall organizational changes of the U.S. military. This dissertation examines whether battlefield adaptations of the U.S. Army, Air Force (then the Army Air Force), Navy and Marine Corps during the Second World War influenced the process of post-war organizational change within the military in the aftermath of that conflict. In particular, this dissertation explores the role of junior and midlevel officers in the change process, which is an area of focus that has been largely undervalued by much of the existing literature on military change. Building on archival research, this dissertation develops a framework to explain the process of how the lessons of combat become institutionalized in a post-war period
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th
Generation (6G) networks, providing ubiquitous, continuous, and scalable
services. Satellites emerge as the primary enabler for NTN, leveraging their
extensive coverage, stable orbits, scalability, and adherence to international
regulations. However, satellite-based NTN presents unique challenges, including
long propagation delay, high Doppler shift, frequent handovers, spectrum
sharing complexities, and intricate beam and resource allocation, among others.
The integration of NTNs into existing terrestrial networks in 6G introduces a
range of novel challenges, including task offloading, network routing, network
slicing, and many more. To tackle all these obstacles, this paper proposes
Artificial Intelligence (AI) as a promising solution, harnessing its ability to
capture intricate correlations among diverse network parameters. We begin by
providing a comprehensive background on NTN and AI, highlighting the potential
of AI techniques in addressing various NTN challenges. Next, we present an
overview of existing works, emphasizing AI as an enabling tool for
satellite-based NTN, and explore potential research directions. Furthermore, we
discuss ongoing research efforts that aim to enable AI in satellite-based NTN
through software-defined implementations, while also discussing the associated
challenges. Finally, we conclude by providing insights and recommendations for
enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve
Connectome-Constrained Artificial Neural Networks
In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron (MLP) and a reservoir computer (RC), in order to craft “fruit fly neural networks” (FFNNs). We study the impact on performance, variance, and prediction dynamics from using FFNNs compared to non-FFNN models on odour classification, chaotic time-series prediction, and multifunctionality tasks. From a series of four experimental studies, we observe that the fly olfactory brain is aligned towards recalling and making predictions from chaotic input data, with a capacity for executing two mutually exclusive tasks from distinct initial conditions, and with low sensitivity to hyperparameter fluctuations that can lead to chaotic behaviour. We also observe that the clustering coefficient of the fly network, and its particular non-zero weight positions, are important for reducing model variance. These findings suggest that BNNs have distinct advantages over arbitrarily-weighted ANNs; notably, from their structure alone. More work with connectomes drawn across species will be useful in finding shared topological features which can further enhance ANNs, and Machine Learning overall
A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
Reservoir computing (RC), first applied to temporal signal processing, is a
recurrent neural network in which neurons are randomly connected. Once
initialized, the connection strengths remain unchanged. Such a simple structure
turns RC into a non-linear dynamical system that maps low-dimensional inputs
into a high-dimensional space. The model's rich dynamics, linear separability,
and memory capacity then enable a simple linear readout to generate adequate
responses for various applications. RC spans areas far beyond machine learning,
since it has been shown that the complex dynamics can be realized in various
physical hardware implementations and biological devices. This yields greater
flexibility and shorter computation time. Moreover, the neuronal responses
triggered by the model's dynamics shed light on understanding brain mechanisms
that also exploit similar dynamical processes. While the literature on RC is
vast and fragmented, here we conduct a unified review of RC's recent
developments from machine learning to physics, biology, and neuroscience. We
first review the early RC models, and then survey the state-of-the-art models
and their applications. We further introduce studies on modeling the brain's
mechanisms by RC. Finally, we offer new perspectives on RC development,
including reservoir design, coding frameworks unification, physical RC
implementations, and interaction between RC, cognitive neuroscience and
evolution.Comment: 51 pages, 19 figures, IEEE Acces
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