Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis. (arXiv:1909.10660v3 [cs.LG] UPDATED)

Recently, there has been a surge of interest in the use of machine learning to help aid in the accurate predictions of financial markets. Despite the exciting advances in this cross-section of finance and AI, many of the current approaches are limited to using technical analysis to capture historical trends of each stock price and…

When a Period Is Not a Full Stop: Light Curve Structure Reveals Fundamental Parameters of Cepheid and RR Lyrae Stars. (arXiv:1911.11767v1 [astro-ph.SR])

The period of pulsation and the structure of the light curve for Cepheid and RR Lyrae variables depend on the fundamental parameters of the star: mass, radius, luminosity, and effective temperature. Here we train artificial neural networks on theoretical pulsation models to predict the fundamental parameters of these stars based on their period and light…

3D IC optimal layout design. A parallel and distributed topological approach. (arXiv:1911.11768v1 [cs.AR])

The task of 3D ICs layout design involves the assembly of millions of components taking into account many different requirements and constraints such as topological, wiring or manufacturability ones. It is a NP-hard problem that requires new non-deterministic and heuristic algorithms. Considering the time complexity, the commonly applied Fiduccia-Mattheyses partitioning algorithm is superior to any…

Blockchains: a Systematic Multivocal Literature Review. (arXiv:1911.11770v1 [cs.CR])

Blockchain technology has gained tremendous popularity both in practice and academia. The goal of this article is to develop a coherent overview of the state of the art in blockchain technology, using a systematic(i.e.,protocol-based, replicable), multivocal (i.e., featuring both white and grey literature alike) literature review, to (1) define blockchain technology (2) elaborate on its…

Sifted Randomized Singular Value Decomposition. (arXiv:1911.11772v1 [stat.ML])

We extend the randomized singular value decomposition (SVD) algorithm \citep{Halko2011finding} to estimate the SVD of a shifted data matrix without explicitly constructing the matrix in the memory. With no loss in the accuracy of the original algorithm, the extended algorithm provides for a more efficient way of matrix factorization. The algorithm facilitates the low-rank approximation…

Stable Matrix Completion using Properly Configured Kronecker Product Decomposition. (arXiv:1911.11774v1 [stat.ML])

Matrix completion problems are the problems of recovering missing entries in a partially observed high dimensional matrix with or without noise. Such a problem is encountered in a wide range of applications such as collaborative filtering, global positioning and remote sensing. Most of the existing matrix completion algorithms assume a low rank structure of the…

Improving Polyphonic Music Models with Feature-Rich Encoding. (arXiv:1911.11775v1 [cs.SD])

This paper explores sequential modeling of polyphonic music with deep neural networks. While recent breakthroughs have focussed on network architecture, we demonstrate that the representation of the sequence can make an equally significant contribution to the performance of the model as measured by validation set loss. By extracting salient features inherent to the dataset, the…

Noise Robust Generative Adversarial Networks. (arXiv:1911.11776v1 [cs.CV])

Generative adversarial networks (GANs) are neural networks that learn data distributions through adversarial training. In intensive studies, recent GANs have shown promising results for reproducing training data. However, in spite of noise, they reproduce data with fidelity. As an alternative, we propose a novel family of GANs called noise-robust GANs (NR-GANs), which can learn a…

Enabling real-time multi-messenger astrophysics discoveries with deep learning. (arXiv:1911.11779v1 [gr-qc])

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources…

Collaboration Drives Individual Productivity. (arXiv:1911.11787v1 [cs.HC])

How does the number of collaborators affect individual productivity? Results of prior research have been conflicting, with some studies reporting an increase in individual productivity as the number of collaborators grows, while other studies showing that the {free-rider effect} skews the effort invested by individuals, making larger groups less productive. The difference between these schools…