Authors: Andrii Babii, Ryan T. Ball, Eric Ghysels, Jonas Striaukas
arXiv
The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings f...
Authors: Roozbeh Farhoodi, Phil Wilkes, Anirudh M. Natarajan, Samantha Ing-Esteves, Julie L. Lefebvre, Mathias Disney, Konrad P. Kording
arXiv
Since they became observable, neuron morphologies have been informally compared with biological trees but they are studied by distinct communities, neuroscientists, and ecologists. The apparent struct...
Authors: Paul Stoewer, Achim Schilling, Andreas Maier, Patrick Krauss
arXiv
The human brain possesses the extraordinary capability to contextualize the information it receives from our environment. The entorhinal-hippocampal plays a critical role in this function, as it is de...
Authors: M. Bonamente
arXiv
This paper presents the application of a new semi-analytical method of linear regression for Poisson count data to COVID-19 events. The regression is based on the Bonamente and Spence (2022) maximum-l...
Authors: Renee Obringer, Roshanak Nateghi, Jessica Knee, Kaveh Madani, Rohini Kumar
arXiv
Despite the coupled nature of water and electricity demand, the two utilities are often managed by different entities with minimal interaction. Neglecting the water-energy demand nexus leads to to sub...
Authors: Banoth Veeranna
arXiv
In this present work, we discuss the Bayesian inference for the bivariate pseudo-exponential distribution. Initially, we assume independent gamma priors and then pseudo-gamma priors for the pseudo-exp...
Authors: Marcin Wątorek, Maria Skupień, Jarosław Kwapień, Stanisław Drożdż
arXiv
This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity mea...
Authors: M. Z. Naser
arXiv
Causal diagrams are logic and graphical tools that depict assumptions about presumed causal relations. Such diagrams have proven effective in tackling a variety of problems in social sciences and epid...
Authors: Johanna Sommer, Leon Hetzel, David Lüdke, Fabian Theis, Stephan Günnemann
arXiv
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic mo...
Authors: Vito Dichio, Fabrizio De Vico Fallani
arXiv
The stochastic exploration of the configuration space and the exploitation of functional states underlie many biological processes. The evolutionary dynamics stands out as a remarkable example. Here,...
Authors: Fatou K. Ndow, Zahra Aminzare
arXiv
Synchronization of coupled dynamical systems is a widespread phenomenon in both biological and engineered networks, and understanding this behavior is crucial for controlling such systems. Considerabl...
Authors: Johannes Diehl, Jakob Knollmüller, Oliver Schulz
arXiv
We present a method for obtaining unbiased signal estimates in the presence of a significant unknown background, eliminating the need for a parametric model for the background itself. Our approach is...
Authors: Thoa Thieu, Roderick Melnik
arXiv
In this chapter, we consider probabilistic drift-diffusion models and Bayesian inference frameworks to address this issue, assisting better social human decision-making. We provide details of the mode...
Authors: Aytekin Demirci, Dominik Steinberger, Markus Stricker, Nina Merkert, Daniel Weygand, Stefan Sandfeld
arXiv
Over the past decades, discrete dislocation dynamics simulations have been shown to reliably predict the evolution of dislocation microstructures for micrometer-sized metallic samples. Such simulation...
Authors: Jonatan A. González, Paula Moraga
arXiv
The tumour microenvironment plays a fundamental role in understanding the development and progression of cancer. This paper proposes a novel spatial point process model that accounts for inhomogeneity...