24 papers · ranked by Valyu relevance
Dongwon Han, Neelima Sehgal, Francisco Villaescusa-Navarro
We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being…
Kyuri Park, Vítor V. Vasconcelos, Mike Lees
Understanding how network structure influences system dynamics is essential for advancing psychological modeling. This tutorial introduces the causalnet R package, which enables researchers to systematically enumerate candidate directed networks by orienting a user-specified undirected or partially directed adjacency…
Pablo Vera-Soto, Javier Villegas, Sergio Fortes, José Pulido + 3 more
Aircraft are composed of many electronic systems: sensors, displays, navigation equipment and communication elements. These elements require a reliable interconnection, which is a major challenge for communication networks as high reliability and predictability requirements must be verified for safe operation. In…
Khandaker Akramul Haque, Shining Sun, Xiang Huo, Ana E. Goulart + 1 more
'Katherine R. Davis'] Abstract—Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment…
Julia Elina Stocker, Erfan Nozari, Marieke van Vugt, Andreas Jansen + 1 more
Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different…
Miquel Farreras, Jordi Paillissé, Lluís Fàbrega, Pere Vilà
This paper presents a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and data-based performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three…
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillissé, Albert López-Brescó + 2 more
'Albert López-Brescó' 'Pere Barlet‐Ros' 'Albert Cabellos‐Aparicio'] Abstract—Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost…
Daniel Barter, Evan Walter Clark Spotte-Smith, Nikita S. Redkar, Shyam Dwaraknath + 2 more
Chemical reaction networks (CRNs) are powerful tools for obtaining mechanistic insight into complex reactive processes. However, they are limited in their applicability where reaction mechanisms are unintuitive, and products are unknown. Here we report new methods of CRN generation and analysis that overcome these…
Erik Kusch, Anna C. Vinton
Understanding how the differential magnitude and sign of ecological interactions vary across space is vital to assessing ecosystem resilience to biodiversity loss and predict community assemblies. This necessity for ecological network knowledge and their labour-intensive sampling requirements has spurred the creation…
Yun Wang, Hazer Inaltekin
This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources.…
Khandaker Akramul Haque, Leen Al Homoud, Xin Zhuang, Mariam Elnour + 2 more
Topology-Informed Assessment of Power Grid Cyber Risk Authors: ['Khandaker Akramul Haque' 'Leen Al Homoud' 'Xin Zhuang' 'Mariam Elnour' 'Ana Goulart' 'Katherine Davis'] Abstract—The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and…
Azra Seyyedi, Mahdi Bohlouli, SeyedEhsan Nedaaee Oskoee
High connectivity and robustness are essential in distributed networks, ensuring resilience, efficient communication, and adaptability. Optimizing energy consumption is also crucial for sustaining energy-constrained networks and extending their operational lifespan. In this study, we introduce an Artificial…
Alexander P. Clark, Mukti Chowkwale, Alexander Paap, Stephen Dang + 1 more
Molecular signaling networks drive a diverse range of cellular decisions, including whether to proliferate, how and when to die, and many processes in between. Such networks often connect hundreds of proteins, genes, and processes. Understanding these complex networks is aided by computational modeling, but these tools…
Maahiya Shaik, Sung Won Kim, Naveen Chilamkurti
With the essential increase in the use of wireless sensor networks, security is a major concern in every field. Intrusions have become frequent and present a significant challenge in today’s world. It is valuable to explore the feasibility of designing and rigorously assessing intrusion detection systems within network…
Zhen Cao, Huw A. Ogilvie, Luay Nakhleh
The development of statistical methods to infer species phylogenies with reticulation (species networks) has led to many discoveries of gene flow between distinct species. However, because the dimensionality of species networks is not fixed, these methods may compensate for kinds of model misspecification, such as…
Authors not listed
Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological systems. This…
Carlos Güemes-Palau, Miquel Ferriol-Galmés, Albert Cabellos‐Aparicio, Pere Barlet‐Ros
In recent years, network modeling has risen in prominence as one of the most active research fields related to computer networks. Correctly designed network models can be used to simulate network configurations without risk, as it does not involve using the actual network, and try out scenarios that may be too rare or…
Andrew Stokely, Lane Votapka, Marcus Hock, Abigail Teitgen + 3 more
We present the Netsci program - an open-source scientific software package that leverages GPU acceleration and a k-nearest-neighbor algorithm in order to estimate the mutual information (MI) between data in a set. The GPU acceleration presented here, as an improvement upon existing estimators, enables calculation…
Matthew Bailey, Mark Wilson
One of the critical tools of persistent homology is the persistence diagram. We demonstrate the applicability of a persistence diagram showing the existence of topological features (here rings in a 2D network) generated over time instead of space as a tool to analyse trajectories of biological networks. We show how the…
Benjamin Ries, Richard J Gowers, James RB Eastwood, Irfan Alibay + 4 more
Alchemical free energy campaigns can be planned using graph theory by building up networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy…
Raelynn Wonnacott, David S. Ching, John Chilleri, Cosmin Safta + 4 more
'Lee Rashkin' 'Thomas A. Reichardt' 'Fang Yang' 'Sicong Liu'] A multiple input multiple output (MIMO) power line communication (PLC) model for industrial facilities was developed that uses the physics of a bottom-up model but can be calibrated like top-down models. The PLC model considers 4-conductor cables…
Jakub Kubečka, Daniel Ayoubi, Zeyuan Tang, Yosef Knattrup + 3 more
The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN)…
Christos Mitsanis, Nicole Fortuna, Christine Beveridge, David Kainer
Mechanistic networks that encode causal regulatory logic can predict the effects of genetic and environmental perturbations but constructing them is a bottleneck in systems biology because the relevant knowledge lies scattered across thousands of resources, untapped for both building and validating such networks. Here…
Authors not listed
Obtaining quantitative information about residence time behavior (i.e., the residence time distribution function) in realistic experimental systems is oftentimes experimentally challenging and numerically complex. The conventional way is to conduct very simple pulse or step tracer experiments or construct elaborate…