143,426 research outputs found
Generative Adversarial Networks for Bitcoin Data Augmentation
In Bitcoin entity classification, results are strongly conditioned by the
ground-truth dataset, especially when applying supervised machine learning
approaches. However, these ground-truth datasets are frequently affected by
significant class imbalance as generally they contain much more information
regarding legal services (Exchange, Gambling), than regarding services that may
be related to illicit activities (Mixer, Service). Class imbalance increases
the complexity of applying machine learning techniques and reduces the quality
of classification results, especially for underrepresented, but critical
classes.
In this paper, we propose to address this problem by using Generative
Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have
shown promising results in the domain of image classification. However, there
is no "one-fits-all" GAN solution that works for every scenario. In fact,
setting GAN training parameters is non-trivial and heavily affects the quality
of the generated synthetic data. We therefore evaluate how GAN parameters such
as the optimization function, the size of the dataset and the chosen batch size
affect GAN implementation for one underrepresented entity class (Mining Pool)
and demonstrate how a "good" GAN configuration can be obtained that achieves
high similarity between synthetically generated and real Bitcoin address data.
To the best of our knowledge, this is the first study presenting GANs as a
valid tool for generating synthetic address data for data augmentation in
Bitcoin entity classification.Comment: 8 pages, 5 figures, 4 table
Adiabatic evolution on a spatial-photonic Ising machine
Combinatorial optimization problems are crucial for widespread applications
but remain difficult to solve on a large scale with conventional hardware.
Novel optical platforms, known as coherent or photonic Ising machines, are
attracting considerable attention as accelerators on optimization tasks
formulable as Ising models. Annealing is a well-known technique based on
adiabatic evolution for finding optimal solutions in classical and quantum
systems made by atoms, electrons, or photons. Although various Ising machines
employ annealing in some form, adiabatic computing on optical settings has been
only partially investigated. Here, we realize the adiabatic evolution of
frustrated Ising models with 100 spins programmed by spatial light modulation.
We use holographic and optical control to change the spin couplings
adiabatically, and exploit experimental noise to explore the energy landscape.
Annealing enhances the convergence to the Ising ground state and allows to find
the problem solution with probability close to unity. Our results demonstrate a
photonic scheme for combinatorial optimization in analogy with adiabatic
quantum algorithms and enforced by optical vector-matrix multiplications and
scalable photonic technology.Comment: 9 pages, 4 figure
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