Artem Lensky

Wenbo Ge, Pooia Lalbakhsh, Leigh Isai et al.

This study aims to compare multiple deep learning-based forecasters for the task of predicting volatility using multivariate data. The paper evaluates a range of models, starting from simpler and shallower ones and progressing to deeper and more complex architectures. Additionally, the performance of these models is compared against naive predictions and variations of classical GARCH models. The prediction of volatility for five assets, namely S&P500, NASDAQ100, gold, silver, and oil, is specifically addressed using GARCH models, Multi-Layer Perceptrons, Recurrent Neural Networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer. In the majority of cases, the Temporal Fusion Transformer, followed by variants of the Temporal Convolutional Network, outperformed classical approaches and shallow networks. These experiments were repeated, and the differences observed between the competing models were found to be statistically significant, thus providing strong encouragement for their practical application.

Muhammad S. Battikh, Artem Lensky

Batch effects in high-dimensional Cytometry by Time-of-Flight (CyTOF) data pose a challenge for comparative analysis across different experimental conditions or time points. Traditional batch normalization methods may fail to preserve the complex topological structures inherent in cellular populations. In this paper, we present a residual neural network-based method for point set registration specifically tailored to address batch normalization in CyTOF data while preserving the topological structure of cellular populations. By viewing the alignment problem as the movement of cells sampled from a target distribution along a regularized displacement vector field, similar to coherent point drift (CPD), our approach introduces a Jacobian-based cost function and geometry-aware statistical distances to ensure local topology preservation. We provide justification for the k-Nearest Neighbour (kNN) graph preservation of the target data when the Jacobian cost is applied, which is crucial for maintaining biological relationships between cells. Furthermore, we introduce a stochastic approximation for high-dimensional registration, making alignment feasible for the high-dimensional space of CyTOF data. Our method is demonstrated on high-dimensional CyTOF dataset, effectively aligning distributions of cells while preserving the kNN-graph structure. This enables accurate batch normalization, facilitating reliable comparative analysis in biomedical research.

Andrea Papaluca, Daniel Krefl, Sergio Mendez Rodriguez et al.

In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs.