3D Printed Discrete Dielectric Lens With Improved Matching Layers

This paper presents a non-zoned discrete dielectric lens comprising two or three matching layers to reduce the 50-110 GHz frequency range reflections. Based on Chebyshev and binomial multi-section transformers, the designed models use matching layers at the top and bottom. In addition, the presented designs use pins instead of the conventional slots for the matching layers, thus easing the manufacturing process. The results show that the broadband realized gain obtained using the proposed design is higher for both the two- and three-layer design than the commonly used quarter-wave transformer. A Binomial lens with two matchings layers using 38 unit cells is fabricated and illuminated by an open-ended waveguide to validate the simulation results obtained using CST Microwave Studio. The fabrication process uses stereolithography additive manufacturing.

Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions

This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engineering methods for time-series to US market price data. The predictive power of models are tested against Numerai-Signals targets.

Understanding Model Complexity for temporal tabular and multi-variate time series, case study with Numerai data science tournament

In this paper, we explore the use of different feature engineering and dimensionality reduction methods in multi-variate time-series modelling. Using a feature-target cross correlation time series dataset created from Numerai tournament, we demonstrate under over-parameterised regime, both the performance and predictions from different feature engineering methods converge to the same equilibrium, which can be characterised by the reproducing kernel Hilbert space. We suggest a new Ensemble method, which combines different random non-linear transforms followed by ridge regression for modelling high dimensional time-series. Compared to some commonly used deep learning models for sequence modelling, such as LSTM and transformers, our method is more robust (lower model variance over different random seeds and less sensitive to the choice of architecture) and more efficient. An additional advantage of our method is model simplicity as there is no need to use sophisticated deep learning frameworks such as PyTorch. The learned feature rankings are then applied to the temporal tabular prediction problem in the Numerai tournament, and the predictive power of feature rankings obtained from our method is better than the baseline prediction model based on moving averages

Robust machine learning pipelines for trading market-neutral stock portfolios

The application of deep learning algorithms to financial data is difficult due to heavy non-stationarities which can lead to over-fitted models that underperform under regime changes. Using the Numerai tournament data set as a motivating example, we propose a machine learning pipeline for trading market-neutral stock portfolios based on tabular data which is robust under changes in market conditions. We evaluate various machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering, as the building blocks for the pipeline. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in volatile market conditions. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results.