Chatterjee, D Marx, E Benoit, W Kumar, R Desai, M Govorkova, E Gunny, A Moreno, E Omer, R Raikman, R
...
Published in
Machine Learning: Science and Technology
We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done re...
Langer, Marcel F Pozdnyakov, Sergey N Ceriotti, Michele
Published in
Machine Learning: Science and Technology
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almos...
Christiansen, Mads-Peter Verner Rønne, Nikolaj Hammer, Bjørk
Published in
Machine Learning: Science and Technology
Reliable uncertainty measures are required when using data-based machine learning interatomic potentials (MLIPs) for atomistic simulations. In this work, we propose for sparse Gaussian process regression (GPR) type MLIPs a stochastic uncertainty measure akin to the query-by-committee approach often used in conjunction with neural network based MLIP...
Lu, Junjian Liu, Siwei Kobylianskii, Dmitrii Dreyer, Etienne Gross, Eilam Liang, Shangsong
Published in
Machine Learning: Science and Technology
In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better ...
Yao, Juan
Published in
Machine Learning: Science and Technology
In this work, we report a novel quantum state reconstruction process based on the disentanglement algorithm. We propose a sequential disentanglement scheme, which can transform an unknown quantum state into a product of computational zero states. The inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequ...
Pham, Thang M Do, Nam Bui, Hanh T Hoang, Manh V
Published in
Machine Learning: Science and Technology
This study presents a hybrid architecture tailored for semantic segmentation challenges, mainly targeting the water area extraction for flood detection and monitoring. The model integrates an efficient transformer-based encoder, utilizing an efficient multi-head self-attention module for capturing hierarchical feature maps through a ‘downsample-ups...
Wang, Mingyu Li, Jianping
Published in
Machine Learning: Science and Technology
Accurately predicting chaotic dynamical systems is a crucial task in various fields, and recent advancements have leveraged deep learning for this purpose. However, in the era of big data, the inevitable challenge of data contamination caused by invalid information from other interfering systems becomes increasingly prominent and complicates accura...
Ozelbas, Enes Sevimoglu, Tuba Kahveci, Tamer
Published in
Machine Learning: Science and Technology
Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called...
Tripathy, Susmita Das, Surajit Jindal, Shweta Ramakrishnan, Raghunathan
Published in
Machine Learning: Science and Technology
We present machine learning models based on kernel-ridge regression for predicting x-ray photoelectron spectra of organic molecules originating from the K-shell ionization energies of carbon (C), nitrogen (N), oxygen (O), and fluorine (F) atoms. We constructed the training dataset through high-throughput calculations of K-shell core-electron bindin...
Jung, Hanbeen Yeo, Chaebeom Jang, Eunsil Chang, Yeonhee Song, Cheol
Published in
Machine Learning: Science and Technology
Diabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both acc...