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View article: Emulating CO Line Radiative Transfer with Deep Learning
Emulating CO Line Radiative Transfer with Deep Learning Open
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, three-dimensional stellar atmospheres. We present COEmuNet, a 3D convolutional neural network…
View article: Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations
Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations Open
Controlling crystal growth is a challenge across numerous industries, as the functional properties of crystalline materials are determined during formation and often depend on particle shape. Current approaches rely on expensive, time-cons…
View article: Emulating CO line radiative transfer with deep learning
Emulating CO line radiative transfer with deep learning Open
Modelling carbon monoxide (CO) line radiation is computationally expensive for traditional numerical solvers, especially when applied to complex, 3D stellar atmospheres. We present COEmuNet, a 3D convolutional neural network-based surrogat…
View article: Deep Learning Evidence for Global Optimality of Gerver’s Sofa
Deep Learning Evidence for Global Optimality of Gerver’s Sofa Open
The moving sofa problem, introduced by Leo Moser in 1966, seeks to determine the maximal area of a 2D shape that can navigate an L-shaped corridor of unit width. Joseph Gerver’s 1992 solution, providing a lower bound of approximately 2.219…
View article: Deep Learning Evidence for Global Optimality of Gerver's Sofa
Deep Learning Evidence for Global Optimality of Gerver's Sofa Open
The Moving Sofa Problem, formally proposed by Leo Moser in 1966, seeks to determine the largest area of a two-dimensional shape that can navigate through an $L$-shaped corridor with unit width. The current best lower bound is about 2.2195,…
View article: <i>EMinsight</i> : a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
<i>EMinsight</i> : a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition Open
The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedu…
View article: EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition
EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition Open
The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the Single Particle Analysis (SPA) workflow into a reasonably standardised procedu…
View article: Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data
Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data Open
We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct stor…
View article: Disentangling Autoencoders (DAE)
Disentangling Autoencoders (DAE) Open
Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of o…
View article: Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms
Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms Open
This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a mult…
View article: Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation
Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation Open
In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF$_6$ and N$_2$ mixture. Grand canonical Monte Carlo (GCMC) simula…
View article: Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation
Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation Open
In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF6 from SF 6 and N 2 mixture. Grand canonical Monte Carlo (GCMC) s…
View article: Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation
Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation Open
In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF$_6$ from SF$_6$ and N$_2$ mixture. Grand canonical Monte Carlo (…
View article: Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation
Integrated Material and Process Evaluation of Metal-Organic Frameworks Database for Energy-efficient SF6/N2 Separation Open
In this work, we proposed multi-scale screening, which employs both molecular and process-level models, to identify high-performing MOFs for energy-efficient separation of SF$_6$ and N$_2$ mixture. Grand canonical Monte Carlo (GCMC) simula…
View article: Hierarchical Auxiliary Learning
Hierarchical Auxiliary Learning Open
Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal (i.e. no hierarchy) and exclusive of one another (i.e. no overlap). CNN-…
View article: Artificial Neural Network Design Approaches to Multi-Channel Information Analysis
Artificial Neural Network Design Approaches to Multi-Channel Information Analysis Open
In recent years, a large amount of multi-channel data has been collected due to advances in technology such as with computers and the Internet. However, obtaining and labelling data are still laborious and time-consuming. Yet another issue…
View article: Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation
Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation Open
The importance of neural network (NN) modelling is evident from its performance benefits in a myriad of applications, where, unlike conventional techniques, NN modeling provides superior performance without relying on complex filtering and…
View article: Hierarchical Auxiliary Learning
Hierarchical Auxiliary Learning Open
Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i.e., no hierarchy) and exclusive of one another (i.e., no overlap). CNN…
View article: Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building
Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building Open
With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed…
View article: On the Transformation of Latent Space in Autoencoders
On the Transformation of Latent Space in Autoencoders Open
Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the tra…
View article: Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks
Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks Open
One of key technologies for future large-scale location-aware services in\naccess is a scalable indoor localization technique. In this paper, we report\npreliminary results from our investigation on the use of deep neural networks\n(DNNs) …
View article: Analysis of a Similarity Measure for Non-Overlapped Data
Analysis of a Similarity Measure for Non-Overlapped Data Open
A similarity measure is a measure evaluating the degree of similarity between two fuzzy data sets and has become an essential tool in many applications including data mining, pattern recognition, and clustering. In this paper, we propose a…