Filippo Simini
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View article: Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery
Aurora: Architecting Argonne's First Exascale Supercomputer for Accelerated Scientific Discovery Open
Aurora is Argonne National Laboratory's pioneering Exascale supercomputer, designed to accelerate scientific discovery with cutting-edge architectural innovations. Key new technologies include the Intel(TM) Xeon(TM) Data Center GPU Max Ser…
View article: A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation Open
Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for generative models s…
View article: Quality Measures for Dynamic Graph Generative Models
Quality Measures for Dynamic Graph Generative Models Open
Deep generative models have recently achieved significant success in modeling graph data, including dynamic graphs, where topology and features evolve over time. However, unlike in vision and natural language domains, evaluating generative…
View article: A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation Open
Recent advancements in graph representation learning have shifted attention towards dynamic graphs, which exhibit evolving topologies and features over time. The increased use of such graphs creates a paramount need for generative models s…
View article: In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD
In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD Open
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenec…
View article: A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems
A Multi-Level, Multi-Scale Visual Analytics Approach to Assessment of Multifidelity HPC Systems Open
The ability to monitor and interpret of hardware system events and behaviors are crucial to improving the robustness and reliability of these systems, especially in a supercomputing facility. The growing complexity and scale of these syste…
View article: Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks Open
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design. During the process of drug discovery specifically, protein-ligand docking, or chemical docki…
View article: Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications Open
As Graph Neural Networks (GNNs) increase in popularity for scientific machine learning, their training and inference efficiency is becoming increasingly critical. Additionally, the deep learning field as a whole is trending towards wider a…
View article: <b>scikit-mobility</b>: A <i>Python</i> Library for the Analysis, Generation, and Risk Assessment of Mobility Data
<b>scikit-mobility</b>: A <i>Python</i> Library for the Analysis, Generation, and Risk Assessment of Mobility Data Open
The last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific produc…
View article: A Deep Gravity model for mobility flows generation
A Deep Gravity model for mobility flows generation Open
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for …
View article: Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information Open
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for …
View article: Modeling Adversarial Behavior Against Mobility Data Privacy
Modeling Adversarial Behavior Against Mobility Data Privacy Open
Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without reali…
View article: Precise mapping, spatial structure and classification of all the human settlements on Earth
Precise mapping, spatial structure and classification of all the human settlements on Earth Open
Human settlements (HSs) on Earth have a direct impact on all natural and societal systems but detailed and quantitative measurements of the locations and spatial structures of all HSs on Earth are still under debate. We provide here the Wo…
View article: Social media usage reveals how regions recover after natural disaster
Social media usage reveals how regions recover after natural disaster Open
The challenge of nowcasting and forecasting the effect of natural disasters (e.g. earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme eve…
View article: Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data
Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data Open
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific prod…
View article: Testing Heaps’ law for cities using administrative and gridded population data sets
Testing Heaps’ law for cities using administrative and gridded population data sets Open
Since 2008 the number of individuals living in urban areas has surpassed that of rural areas and in the next decades urbanisation is expected to further increase, especially in developing countries. A country’s urbanisation depends both on…
View article: Distances real tiles - simulated tiles
Distances real tiles - simulated tiles Open
JSON files of all the distances between real and simulated tiles for the Energy distance, Kullback Leiber and Jenson Shannon measures
View article: Human Mobility from theory to practice:Data, Models and Applications
Human Mobility from theory to practice:Data, Models and Applications Open
The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mob…
View article: distances_JS.tgzaa
distances_JS.tgzaa Open
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View article: distances_energy.tgzac
distances_energy.tgzac Open
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View article: distances_energy.tgzab
distances_energy.tgzab Open
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View article: distances_energy.tgzac
distances_energy.tgzac Open
:unav
View article: distances_JS.tgzab
distances_JS.tgzab Open
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