1,736 research outputs found
A brief history of plastic surgery
Historically, plastic surgery have been practiced for thousands of years, going back to more primitive methods that were seen in India since around 800 B.C. At that time, plastic surgery procedures consisted of skin grafts that were performed on those that suffered from skin damaging injures. Ancient doctors developed methods to help suture the skin to the body, to help prevent scarring. They performed reconstructive operations on ears and noses that were lost in war or through punishment for a crime. The Romans were also practicing plastic surgery by the first century B.C. Their culture greatly admired the beauty of naked body thus promoting them to improve or eliminate the appearance of any bodily defect or deformity. Their procedures included breast reduction and scar removal.
When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2601
Many-body correlations in one-dimensional optical lattices with alkaline-earth(-like) atoms
We explore the rich nature of correlations in the ground state of ultracold
atoms trapped in state-dependent optical lattices. In particular, we consider
interacting fermionic ytterbium or strontium atoms, realizing a two-orbital
Hubbard model with two spin components. We analyze the model in one-dimensional
setting with the experimentally relevant hierarchy of tunneling and interaction
amplitudes by means of exact diagonalization and matrix product states
approaches, and study the correlation functions in density, spin, and orbital
sectors as functions of variable densities of atoms in the ground and
metastable excited states. We show that in certain ranges of densities these
atomic systems demonstrate strong density-wave, ferro- and antiferromagnetic,
as well as antiferroorbital correlations.Comment: 8 pages, 5 figure
C++ Design Patterns for Low-latency Applications Including High-frequency Trading
This work aims to bridge the existing knowledge gap in the optimisation of
latency-critical code, specifically focusing on high-frequency trading (HFT)
systems. The research culminates in three main contributions: the creation of a
Low-Latency Programming Repository, the optimisation of a market-neutral
statistical arbitrage pairs trading strategy, and the implementation of the
Disruptor pattern in C++. The repository serves as a practical guide and is
enriched with rigorous statistical benchmarking, while the trading strategy
optimisation led to substantial improvements in speed and profitability. The
Disruptor pattern showcased significant performance enhancement over
traditional queuing methods. Evaluation metrics include speed, cache
utilisation, and statistical significance, among others. Techniques like Cache
Warming and Constexpr showed the most significant gains in latency reduction.
Future directions involve expanding the repository, testing the optimised
trading algorithm in a live trading environment, and integrating the Disruptor
pattern with the trading algorithm for comprehensive system benchmarking. The
work is oriented towards academics and industry practitioners seeking to
improve performance in latency-sensitive applications
From Deep Filtering to Deep Econometrics
Calculating true volatility is an essential task for option pricing and risk
management. However, it is made difficult by market microstructure noise.
Particle filtering has been proposed to solve this problem as it favorable
statistical properties, but relies on assumptions about underlying market
dynamics. Machine learning methods have also been proposed but lack
interpretability, and often lag in performance. In this paper we implement the
SV-PF-RNN: a hybrid neural network and particle filter architecture. Our
SV-PF-RNN is designed specifically with stochastic volatility estimation in
mind. We then show that it can improve on the performance of a basic particle
filter
Transformers versus LSTMs for electronic trading
With the rapid development of artificial intelligence, long short term memory
(LSTM), one kind of recurrent neural network (RNN), has been widely applied in
time series prediction.
Like RNN, Transformer is designed to handle the sequential data. As
Transformer achieved great success in Natural Language Processing (NLP),
researchers got interested in Transformer's performance on time series
prediction, and plenty of Transformer-based solutions on long time series
forecasting have come out recently. However, when it comes to financial time
series prediction, LSTM is still a dominant architecture. Therefore, the
question this study wants to answer is: whether the Transformer-based model can
be applied in financial time series prediction and beat LSTM.
To answer this question, various LSTM-based and Transformer-based models are
compared on multiple financial prediction tasks based on high-frequency limit
order book data. A new LSTM-based model called DLSTM is built and new
architecture for the Transformer-based model is designed to adapt for financial
prediction. The experiment result reflects that the Transformer-based model
only has the limited advantage in absolute price sequence prediction. The
LSTM-based models show better and more robust performance on difference
sequence prediction, such as price difference and price movement
Applying Deep Learning to Calibrate Stochastic Volatility Models
Stochastic volatility models, where the volatility is a stochastic process,
can capture most of the essential stylized facts of implied volatility surfaces
and give more realistic dynamics of the volatility smile/skew. However, they
come with the significant issue that they take too long to calibrate.
Alternative calibration methods based on Deep Learning (DL) techniques have
been recently used to build fast and accurate solutions to the calibration
problem. Huge and Savine developed a Differential Machine Learning (DML)
approach, where Machine Learning models are trained on samples of not only
features and labels but also differentials of labels to features. The present
work aims to apply the DML technique to price vanilla European options (i.e.
the calibration instruments), more specifically, puts when the underlying asset
follows a Heston model and then calibrate the model on the trained network. DML
allows for fast training and accurate pricing. The trained neural network
dramatically reduces Heston calibration's computation time.
In this work, we also introduce different regularisation techniques, and we
apply them notably in the case of the DML. We compare their performance in
reducing overfitting and improving the generalisation error. The DML
performance is also compared to the classical DL (without differentiation) one
in the case of Feed-Forward Neural Networks. We show that the DML outperforms
the DL.
The complete code for our experiments is provided in the GitHub repository:
https://github.com/asridi/DML-Calibration-Heston-Mode
Automation of the process of forming of an IT-project team based on competency model (using logistics network project development as an example)
Derivatives Sensitivities Computation under Heston Model on GPU
This report investigates the computation of option Greeks for European and
Asian options under the Heston stochastic volatility model on GPU. We first
implemented the exact simulation method proposed by Broadie and Kaya and used
it as a baseline for precision and speed. We then proposed a novel method for
computing Greeks using the Milstein discretisation method on GPU. Our results
show that the proposed method provides a speed-up up to 200x compared to the
exact simulation implementation and that it can be used for both European and
Asian options. However, the accuracy of the GPU method for estimating Rho is
inferior to the CPU method. Overall, our study demonstrates the potential of
GPU for computing derivatives sensitivies with numerical methods
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