Tootfinder

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Correctness of Flow Migration Across Network Function Instances
Ranjan Patowary, Gautam Barua, Radhika Sukapuram
https://arxiv.org/abs/2404.07701 https://<…

Correctness of Flow Migration Across Network Function Instances
Network Functions (NFs) improve the safety and efficiency of networks. Flows traversing NFs may need to be migrated to balance load, conserve energy, etc. When NFs are stateful, the information stored on the NF per flow must be migrated before the flows are migrated, to avoid problems of consistency. We examine what it means to correctly migrate flows from a stateful NF instance. We define the property of Weak-O, where only the state information required for packets to be correctly forwarded is…

Data-driven modeling of unsteady flow based on deep operator network
Heming Bai, Zhicheng Wang, Xuesen Chu, Jian Deng, Xin Bian
https://arxiv.org/abs/2404.06791

Data-driven modeling of unsteady flow based on deep operator network
Time-dependent flow fields are typically generated by a computational fluid dynamics (CFD) method, which is an extremely time-consuming process. However, the latent relationship between the flow fields is governed by the Navier-Stokes equations and can be described by an operator. We therefore train a deep operator network, or simply DeepONet, to learn the temporal evolution between flow snapshots. Once properly trained, given a few consecutive snapshots as input, the network has a great potent…

Improved Algebraic Inverter Modelling for Four-Wire Power Flow Optimization
Rahmat Heidari, Frederik Geth
https://arxiv.org/abs/2403.07285 https://<…

Improved Algebraic Inverter Modelling for Four-Wire Power Flow Optimization
This paper discusses the modeling of inverters used in distributed energy resources in steady state. Modeling the interaction between distribution grids and inverter-based resources is crucial to understand the consequences for the network's operational and planning processes. This work highlights the limitations of existing models and emphasizes the need for better representations of inverters and their control laws in decision-making contexts. Improved steady-state grid-following and grid-for…

This https://arxiv.org/abs/2310.09489 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Study of residual artificial neural network for particle identification in the CEPC high-granularity calorimeter prototype
Particle Identification (PID) plays a central role in associating the energy depositions in calorimeter cells with the type of primary particle in a particle flow oriented detector system. In this paper, we propose novel PID methods based on the Residual Network (ResNet) architecture which enable the training of very deep networks, bypass the need to reconstruct feature variables, and ensure the generalization ability among various geometries of detectors, to classify electromagnetic showers an…

Cooperative Route Guidance and Flow Control for Mixed Road Networks Comprising Expressway and Arterial Network
Yunran Di, Haotian Shi, Weihua Zhang, Heng Ding, Xiaoyan Zheng, Bin Ran
https://arxiv.org/abs/2405.06125

Improved Algebraic Inverter Modelling for Four-Wire Power Flow Optimization
Rahmat Heidari, Frederik Geth
https://arxiv.org/abs/2403.07285 https://<…

Improved Algebraic Inverter Modelling for Four-Wire Power Flow Optimization
This paper discusses the modeling of inverters used in distributed energy resources in steady state. Modeling the interaction between distribution grids and inverter-based resources is crucial to understand the consequences for the network's operational and planning processes. This work highlights the limitations of existing models and emphasizes the need for better representations of inverters and their control laws in decision-making contexts. Improved steady-state grid-following and grid-for…

Profile of Vulnerability Remediations in Dependencies Using Graph Analysis
Fernando Vera, Palina Pauliuchenka, Ethan Oh, Bai Chien Kao, Louis DiValentin, David A. Bader
https://arxiv.org/abs/2403.04989

Profile of Vulnerability Remediations in Dependencies Using Graph Analysis
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) to the critical challenge of open source package vulnerability remediation by analyzing control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Our approach uniquely applies node centrality metrics -- degree, norm, and closeness centrality -- to the GAT model, enabling a detailed examination of package…

Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
Michel Nohra, Steven Dufour
https://arxiv.org/abs/2405.05371

Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
Multi-fluid flows are found in various industrial processes, including metal injection molding and 3D printing. The accuracy of multi-fluid flow modeling is determined by how well interfaces and capillary forces are represented. In this paper, the multi-fluid flow problem is discretized using a combination of a Physics-Informed Neural Network (PINN) with a finite element discretization. To determine the best PINN formulation, a comparative study is conducted using a manufactured solution. We co…

Understanding the shear modulus of dense microgel suspensions
Maxime Bergman, Yixuan Xu, Zhang Chi, Thomas G. Mason, Frank Scheffold
https://arxiv.org/abs/2403.07388

Understanding the shear modulus of dense microgel suspensions
Polymer microgels exhibit intriguing macroscopic flow properties arising from their unique microscopic structure. Microgel colloids comprise a crosslinked polymer network with a radially decaying density profile, resulting in a dense core surrounded by a fuzzy corona. Notably, microgels synthesized from poly(N-isopropylacrylamide) (PNIPAM) are thermoresponsive, capable of adjusting their size and density profile based on temperature. Above the lower critical solution temperature ($T_\text{LCST}…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

I followed that #UFO on the mysterious "The Bahlsen Family" logo into the web and found several #TIL​​​​s.

On Approximating the Dynamic and Discrete Network Flow Problem
Bubai Manna, Bodhayan Roy, Vorapong Suppakitpaisarn
https://arxiv.org/abs/2404.16329 https:/…

On Approximating the Dynamic and Discrete Network Flow Problem
We examine the dynamic network flow problem under the assumption that the flow consists of discrete units. The dynamic network flow problem is commonly addressed in the context of developing evacuation plans, where the flow is typically treated as a continuous quantity. However, real-world scenarios often involve moving groups, such as families, as single units. We demonstrate that solving the dynamic flow problem with this consideration is APX-hard. Conversely, we present a PTAS for instances …

Homelab NAS upgrade done (well, phase 1). Moved the NAS into the HL15 case from 45Drives and I absolutely love it. Granted, it *could* use a few more drives, but I'll deal with that issue later. The NAS pulls double duty: serves files via SMB/NFS which are all on the spinners living on the 10GbE network, and iSCSI duties on an NVMe array on the 40GbE network. #homelab

Convergence Analysis of Flow Matching in Latent Space with Transformers
Yuling Jiao, Yanming Lai, Yang Wang, Bokai Yan
https://arxiv.org/abs/2404.02538 htt…

Convergence Analysis of Flow Matching in Latent Space with Transformers
We present theoretical convergence guarantees for ODE-based generative models, specifically flow matching. We use a pre-trained autoencoder network to map high-dimensional original inputs to a low-dimensional latent space, where a transformer network is trained to predict the velocity field of the transformation from a standard normal distribution to the target latent distribution. Our error analysis demonstrates the effectiveness of this approach, showing that the distribution of samples gener…

VAEMax: Open-Set Intrusion Detection based on OpenMax and Variational Autoencoder
Zhiyin Qiu, Ding Zhou, Yahui Zhai, Bo Liu, Lei He, Jiuxin Cao
https://arxiv.org/abs/2403.04193

VAEMax: Open-Set Intrusion Detection based on OpenMax and Variational Autoencoder
Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on system or equipment. This paper aims to develop an open-set intrusion detection model to classify known attacks as well as inferring unknown ones. To achieve this, we employ OpenMax and variational autoencoder to propose a dual detection model, VAEMax. First, we extract flow payload feature based on one-dimensional convolutional neural network. Then, the OpenMax is used to classify flows, dur…

This https://arxiv.org/abs/2211.07125 has been replaced.
link: https://scholar.google.com/scholar?q=a

Global Performance Guarantees for Neural Network Models of AC Power Flow
Machine learning, which can generate extremely fast and highly accurate black-box surrogate models, is increasingly being applied to a variety of AC power flow problems. Rigorously verifying the accuracy of the resulting black-box models, however, is computationally challenging. This paper develops a tractable neural network verification procedure which incorporates the ground truth of the non-linear AC power flow equations to determine worst-case neural network prediction error. Our approach, …

This https://arxiv.org/abs/2309.06782 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based…

Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
Michel Nohra, Steven Dufour
https://arxiv.org/abs/2405.05371

Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem
Multi-fluid flows are found in various industrial processes, including metal injection molding and 3D printing. The accuracy of multi-fluid flow modeling is determined by how well interfaces and capillary forces are represented. In this paper, the multi-fluid flow problem is discretized using a combination of a Physics-Informed Neural Network (PINN) with a finite element discretization. To determine the best PINN formulation, a comparative study is conducted using a manufactured solution. We co…

Structural and flow approaches to complex network systems lesions scale analysis
Olexandr Polishchuk
https://arxiv.org/abs/2404.01662 https://

Structural and flow approaches to complex network systems lesions scale analysis
A comparative analysis of structural and flow approaches to analysis of vulnerability of complex network systems (NS) from targeted attacks and non-target lesions of various types was carried out. Typical structural and functional scenarios of successive targeted attacks on the most important by certain characteristics system elements were considered, and scenarios of simultaneous group attacks on the most significant NS's components were proposed. The problem of system lesions scale from heter…

Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Hao Miao, Senzhang Wang, Meiyue Zhang, Diansheng Guo, Funing Sun, Fan Yang
https://arxiv.org/abs/2404.15034

Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this problem include both the dynamic mobility patterns of the people and the complex spatial-temporal correlations of the urban traffic data. Meanwhile, most existing models ignore the diverse impacts of the various traffic observations (e.g. vehicle speed and road occupancy) on the traffic flow prediction, and different traffi…

Complex network approach to the turbulent velocity gradient dynamics: High- and low-probability Lagrangian paths
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2404.05453

Complex network approach to the turbulent velocity gradient dynamics: High- and low-probability Lagrangian paths
Understanding the dynamics of the turbulent velocity gradient tensor (VGT) is essential to gain insights into the Navier-Stokes equations and improve small-scale turbulence modeling. However, characterizing the VGT dynamics conditional on all its relevant invariants in a continuous fashion is extremely difficult. In this paper, we represent the VGT Lagrangian dynamics using a network where each node represents a unique flow state. This approach enables us to discern how the VGT transitions from…

Traffic Divergence Theory: An Analysis Formalism for Dynamic Networks
Matin Macktoobian, Zhan Shu, Qing Zhao
https://arxiv.org/abs/2404.03066 https://

Traffic Divergence Theory: An Analysis Formalism for Dynamic Networks
Traffic dynamics is universally crucial in analyzing and designing almost any network. This article introduces a novel theoretical approach to analyzing network traffic dynamics. This theory's machinery is based on the notion of traffic divergence, which captures the flow (im)balance of network nodes and links. It features various analytical probes to investigate both spatial and temporal traffic dynamics. In particular, the maximal traffic distribution in a network can be characterized by spat…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

On Approximating the Dynamic and Discrete Network Flow Problem
Bubai Manna, Bodhayan Roy, Vorapong Suppakitpaisarn
https://arxiv.org/abs/2404.16329 https:/…

On Approximating the Dynamic and Discrete Network Flow Problem
We examine the dynamic network flow problem under the assumption that the flow consists of discrete units. The dynamic network flow problem is commonly addressed in the context of developing evacuation plans, where the flow is typically treated as a continuous quantity. However, real-world scenarios often involve moving groups, such as families, as single units. We demonstrate that solving the dynamic flow problem with this consideration is APX-hard. Conversely, we present a PTAS for instances …

This https://arxiv.org/abs/2310.14463 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Tightening QC Relaxations of AC Optimal Power Flow through Improved Linear Convex Envelopes
AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for global optima, recent research has developed a variety of convex relaxations that bound the optimal objective values of AC OPF problems. The well-known QC relaxation convexifies the AC OPF problem by enclosing the non-convex terms (trigonometric functions and products) within convex…

This https://arxiv.org/abs/2309.11948 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A structure-preserving finite element method for the multi-phase Mullins-Sekerka problem with triple junctions
We consider a sharp interface formulation for the multi-phase Mullins-Sekerka flow. The flow is characterized by a network of curves evolving such that the total surface energy of the curves is reduced, while the areas of the enclosed phases are conserved. Making use of a variational formulation, we introduce a fully discrete finite element method. Our discretization features a parametric approximation of the moving interfaces that is independent of the discretization used for the equations in …

LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow
Ziyu Yue, Jiaxin Gao, Sihan Xie, Yang Liu, Zhixun Su
https://arxiv.org/abs/2402.18871

LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow
The visibility of real-world images is often limited by both low-light and low-resolution, however, these issues are only addressed in the literature through Low-Light Enhancement (LLE) and Super- Resolution (SR) methods. Admittedly, a simple cascade of these approaches cannot work harmoniously to cope well with the highly ill-posed problem for simultaneously enhancing visibility and resolution. In this paper, we propose a normalizing flow network, dubbed LoLiSRFLow, specifically designed to co…

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2405.03589

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Complex network analysis methods have been widely applied to nonlinear systems, but applications within fluid mechanics are relatively few. In this paper, we use a network for the Lagrangian dynamics of the velocity gradient tensor (VGT), where each node is a flow state, and the probability of transitioning between states follows from a direct numerical simulation of statistically steady and isotropic turbulence. The network representation of the VGT dynamics is much more compact than the conti…

Two-Stage Super-Resolution Simulation Method for Three-Dimensional Flow Fields Around Buildings for Real-Time Prediction of Urban Micrometeorology
Yuki Yasuda, Ryo Onishi
https://arxiv.org/abs/2404.02631

Two-Stage Super-Resolution Simulation Method for Three-Dimensional Flow Fields Around Buildings for Real-Time Prediction of Urban Micrometeorology
A two-stage super-resolution simulation method is proposed for building-resolving micrometeorology simulations, which considerably reduces the computation time while maintaining accuracy. The first stage employs a convolutional neural network (CNN) to correct large-scale flows above buildings in the input of low-resolution (LR) simulation results. The second stage uses another CNN to reconstruct small-scale flows between buildings from the output of the first stage, resulting in high-resolution…

This https://arxiv.org/abs/2402.01066 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the Hardness of Short and Sign-Compatible Circuit Walks
The circuits of a polyhedron are a superset of its edge directions. Circuit walks, a sequence of steps along circuits, generalize edge walks and are "short" if they have few steps or small total length. Both interpretations of short are relevant to the theory and application of linear programming. We study the hardness of several problems relating to the construction of short circuit walks. We establish that for a pair of vertices of a $0/1$-network-flow polytope, it is NP-complete to determi…

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Christopher J. Keylock, Maurizio Carbone
https://arxiv.org/abs/2405.03589

Communities for the Lagrangian Dynamics of the Turbulent Velocity Gradient Tensor: A Network Participation Approach
Complex network analysis methods have been widely applied to nonlinear systems, but applications within fluid mechanics are relatively few. In this paper, we use a network for the Lagrangian dynamics of the velocity gradient tensor (VGT), where each node is a flow state, and the probability of transitioning between states follows from a direct numerical simulation of statistically steady and isotropic turbulence. The network representation of the VGT dynamics is much more compact than the conti…

SF-MMCN: A Low Power Re-configurable Server Flow Convolution Neural Network Accelerator
Huan-Ke Hsu, I-Chyn Wey, T. Hui Teo
https://arxiv.org/abs/2403.10542

SF-MMCN: A Low Power Re-configurable Server Flow Convolution Neural Network Accelerator
Convolution Neural Network (CNN) accelerators have been developed rapidly in recent studies. There are lots of CNN accelerators equipped with a variety of function and algorithm which results in low power and high-speed performances. However, the scale of a PE array in traditional CNN accelerators is too big, which costs the most energy consumption while conducting multiply and accumulation (MAC) computations. The other issue is that due to the advance of CNN models, there are enormous models c…

MetroGNN: Metro Network Expansion with Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
https://arxiv.org/abs/2403.09197 htt…

MetroGNN: Metro Network Expansion with Reinforcement Learning
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-…

This https://arxiv.org/abs/2404.02538 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Convergence Analysis of Flow Matching in Latent Space with Transformers
We present theoretical convergence guarantees for ODE-based generative models, specifically flow matching. We use a pre-trained autoencoder network to map high-dimensional original inputs to a low-dimensional latent space, where a transformer network is trained to predict the velocity field of the transformation from a standard normal distribution to the target latent distribution. Our error analysis demonstrates the effectiveness of this approach, showing that the distribution of samples gener…

Australian Energy Market Operator National Electricity Market Network Optimal Power Flow Modelling
Brandon Curtis Colelough
https://arxiv.org/abs/2403.18851

Australian Energy Market Operator National Electricity Market Network Optimal Power Flow Modelling
The HELM algorithm was used in this project to solve the optimal power flow problem introduced by a radial PandaPower network formulated from the data given by AEMO on the NEM network. Large losses were observed in the transmission infrastructure surrounding base-load power plants. These losses were not observed in areas that had a higher percentage of renewable power generation. Furthermore, the voltage levels present at the Hydro and Wind farms across Tasmania and South Australia were found t…

Mobile Edge Computing
Sohaib Ahmed, Hassan Khalid, Muhammad Hamza, Danyal Farhat
https://arxiv.org/abs/2404.17944 https://arxiv.org/p…

Mobile Edge Computing
Mobile Edge Computing (MEC) has emerged as a solution to the high latency and suboptimal Quality of Experience (QoE) associated with Mobile Cloud Computing (MCC). By processing data near the source, MEC reduces the need to send information to distant data centers, resulting in faster response times and lower latency. This paper explores the differences between MEC and traditional cloud computing, emphasizing architecture, data flow, and resource allocation. Key technologies like Network Functio…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

This https://arxiv.org/abs/2305.13446 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The Power Domination Toolbox
Phasor Measurement Units (PMUs) are placed at strategic vertices in an electrical power network to monitor the flow of power. Determining the minimum number and optimal placement of PMUs is modeled by the graph theoretic process called Power Domination. This paper describes the Power Domination Toolbox (PDT), which efficiently identifies a minimum number of PMU locations that monitor the entire network. The PDT leverages graph theoretic literature to reduce the complexity of determining optimal…

This https://arxiv.org/abs/2404.08412 has been replaced.
initial toot: https://mastoxiv.page/@ar…

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations. Yet, a notable challenge with existing convolutional neural network (CNN)-based methods for data fidelity enhancement is their reliance on specific low-fidelity data patterns and distributions during the training phase. In addition, the CNN-based method essentially treats the flow reconstruction task as a computer vision t…

This https://arxiv.org/abs/2307.10583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Deep fused flow and topology features for botnet detection basing on pretrained GCN
Nowadays, botnets have become one of the major threats to cyber security. The characteristics of botnets are mainly reflected in bots network behavior and their intercommunication relationships. Existing botnet detection methods use flow features or topology features individually, which overlook the other type of feature. This affects model performance. In this paper, we propose a botnet detection model which uses graph convolutional network (GCN) to deeply fuse flow features and topology featu…

A multicore parallel algorithm for multiscale modelling of an entire human blood circulation network
Jiawei Liu, Hiroshi Suito
https://arxiv.org/abs/2404.01680

A multicore parallel algorithm for multiscale modelling of an entire human blood circulation network
The presented multi-scale, closed-loop blood circulation model includes arterial, venous, and portal venous systems, heart-pulmonary circulation, and micro-circulation in capillaries. One-dimensional models simulate large blood vessel flow, whereas zerodimensional models are used for simulating blood flow in vascular subsystems corresponding to peripheral arteries and organs. Transmission conditions at bifurcation and confluence are solved using Riemann invariants. Blood circulation simulation …

This https://arxiv.org/abs/2210.17264 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation
This paper presents a method for end-to-end cross-lingual text-to-speech (TTS) which aims to preserve the target language's pronunciation regardless of the original speaker's language. The model used is based on a non-attentive Tacotron architecture, where the decoder has been replaced with a normalizing flow network conditioned on the speaker identity, allowing both TTS and voice conversion (VC) to be performed by the same model due to the inherent linguistic content and speaker identity disen…

This https://arxiv.org/abs/2401.15538 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Ambitions for theory in the physics of life
Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we engage with more realistic descriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each c…

Emergent dynamics of the inertial Kuramoto model with frustration on a locally coupled graph
Tingting Zhu, Xiongtao Zhang
https://arxiv.org/abs/2404.18488 …

Emergent dynamics of the inertial Kuramoto model with frustration on a locally coupled graph
We study the synchronized behavior of the inertial Kuramoto oscillators with frustration effect under a symmetric and connected network. Due to the lack of second-order gradient flow structure and singularity of second-order derivative of diameter, we shift to construct convex combinations of oscillators and related new energy functions that can control the phase and frequency diameters. Under sufficient frameworks on initial data and system parameters, we derive first-order dissipative differe…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

This https://arxiv.org/abs/2212.07078 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Predictive Operation Controller for an Electro-Thermal Microgrid Utilizing Variable Flow Temperatures
We propose an optimal operation control strategy for an electro-thermal microgrid. Compared to existing work, our approach increases flexibility by operating the thermal network with variable flow temperatures and in that way explicitly exploits its inherent storage capacities. To this end, the microgrid is represented by a multi-layer network composed of an electrical and a thermal layer. We show that the system behavior can be represented by a discrete-time state model derived from DC power f…

Routing entanglement through quantum networks
Karl Pelka, Matteo Aquilina, Andr\'e Xuereb
https://arxiv.org/abs/2402.08102 https://

Routing entanglement through quantum networks
Entanglement, one of the clearest manifestations of non-classical physics, holds significant promise for technological applications such as more secure communications and faster computations. In this paper we explore the use of non-reciprocal transport in a network of continuous-variable systems to route entanglement in one direction through the network. We develop the theory and discuss a potential realization of controllable flow of entanglement in quantum systems; our method employs only Gau…

This https://arxiv.org/abs/2303.05412 has been replaced.
link: https://scholar.google.com/scholar?q=a

Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations
Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that …

Extending Network Calculus To Deal With Partially Negative And Decreasing Service Curves
Anja Hamscher, Vlad-Cristian Constantin, Jens B. Schmitt
https://arxiv.org/abs/2403.18042 …

Extending Network Calculus To Deal With Partially Negative And Decreasing Service Curves
Network Calculus (NC) is a versatile analytical methodology to efficiently compute performance bounds in networked systems. The arrival and service curve abstractions allow to model diverse and heterogeneous distributed systems. The operations to compute residual service curves and to concatenate sequences of systems enable an efficient and accurate calculation of per-flow timing guarantees. Yet, in some scenarios involving multiple concurrent flows at a system, the central notion of so-called …

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
Shaghayegh Z. Ashtiani, Mohammad Sarabian, Kaveh Laksari, Hessam Babaee
https://arxiv.org/abs/2403.09758

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, Transcranial Doppler ultrasound (TCD) is a noninvasive clinical tool that is commonly used in the clinical settings to measure blood velocity waveform at several locations on brain's vasculature. This amount of data is grossly insufficient for training machine learning surrogate models, such as deep neural networks or …

Characterising Payload Entropy in Packet Flows
Anthony Kenyon, Lipika Deka, David Elizondo
https://arxiv.org/abs/2404.19121 https://arxiv.org/pdf/2404.19121
arXiv:2404.19121v1 Announce Type: new
Abstract: Accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity - such as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge there are no published baselines for payload entropy across common network services. We offer two contributions: 1) We analyse several large packet datasets to establish baseline payload information entropy values for common network services, 2) We describe an efficient method for engineering entropy metrics when performing flow recovery from live or offline packet data, which can be expressed within feature subsets for subsequent analysis and machine learning applications.

This https://arxiv.org/abs/2212.14424 has been replaced.
link: https://scholar.google.com/scholar?q=a

Normalizing flow neural networks by JKO scheme
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions. The flow is often implemented using a sequence of invertible residual blocks. Existing works adopt special network architectures and regularization of flow trajectories. In this paper, we develop a neural ODE flow network called JKO-iFlow, inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which unfolds the discrete-…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

This https://arxiv.org/abs/2207.14624 has been replaced.
link: https://scholar.google.com/scholar?q=a

Post-processing of coronary and myocardial spatial data
Numerical simulations of real-world phenomenon are implemented with at least two parts: the computational scheme and the computational domain. In the context of hemodynamics, the computational domain of a simulation represents the blood vessel network through which blood flows. Such blood vessel networks can contain millions of individual vessels that are joined together to form a in series and parallel to form the network. It is computationally unfeasible to explicitly simulate blood flow in a…

MetroGNN: Metro Network Expansion with Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
https://arxiv.org/abs/2403.09197 htt…

MetroGNN: Metro Network Expansion with Reinforcement Learning
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-…

Hub Network Design Problem with Capacity, Congestion and Heterogeneous Economies of Scale
Xiaotong Liu
https://arxiv.org/abs/2404.03521 https://

Hub Network Design Problem with Capacity, Congestion and Heterogeneous Economies of Scale
We propose a joint model that links the strategic level location and capacity decisions with the operational level routing and hub assignment decisions to solve hub network design problem with congestion and heterogeneous economics of scale. We also develop a novel flow-based mixed-integer second-order cone programming (MISOCP) formulation. We perform numerical experiments on a real-world data set to validate the efficiency of solving the MISOCP reformulation. The numerical studies yield observ…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Determination of output composition in reaction-advection-diffusion systems on network reactors
Renato Feres, Eric Pasewark, Gregory Yablonsky
https://arxiv.org/abs/2403.18239

Determination of output composition in reaction-advection-diffusion systems on network reactors
We consider reaction-transport processes in open reactors in which systems of first order reactions involving a number of gas species and solid catalysts can occur at localized active regions. Reaction products flow out of the reactor into vacuum conditions and are collected at an exit boundary. The output composition problem (OCP) is to determine the composition (molar fractions) of the collected gas after the reactor is fully emptied. We provide a solution to this problem in the form of a bou…

This https://arxiv.org/abs/2212.14424 has been replaced.
link: https://scholar.google.com/scholar?q=a

Normalizing flow neural networks by JKO scheme
Normalizing flow is a class of deep generative models for efficient sampling and likelihood estimation, which achieves attractive performance, particularly in high dimensions. The flow is often implemented using a sequence of invertible residual blocks. Existing works adopt special network architectures and regularization of flow trajectories. In this paper, we develop a neural ODE flow network called JKO-iFlow, inspired by the Jordan-Kinderleherer-Otto (JKO) scheme, which unfolds the discrete-…

FaiRTT: An Empirical Approach for Enhanced RTT Fairness and Bottleneck Throughput in BBR
Akshita Abrol, Purnima Murali Mohan, Tram Truong-Huu
https://arxiv.org/abs/2403.19973

FaiRTT: An Empirical Approach for Enhanced RTT Fairness and Bottleneck Throughput in BBR
In next-generation networks, achieving Round-trip Time (RTT) fairness is essential for ensuring fair bandwidth distribution among diverse flow types, enhancing overall network utilization. The TCP congestion control algorithm -- BBR, was proposed by Google to dynamically adjust sending rates in response to changing network conditions. While BBRv2 was implemented to overcome the unfairness limitation of BBRv1, it still faces intra-protocol fairness challenges in balancing the demands of high-ban…

Fast Computation of Robust Dynamic Operating Envelopes Based on Non-convex OPF for Unbalanced Distribution Networks
Bin Liu, Julio H. Braslavsky
https://arxiv.org/abs/2404.03355 <…

Fast Computation of Robust Dynamic Operating Envelopes Based on Non-convex OPF for Unbalanced Distribution Networks
Robust dynamic operating envelopes (RDOEs) solve the problem of secure allocation of latent network capacity to flexible distributed energy resources (DER) in unbalanced distribution networks. As the computational complexity of RDOEs is much higher than that of DOEs, which disregard uncertainties in network parameters and DER capacity utilisation, existing approaches to computing RDOEs have relied on linearised unbalanced three-phase optimal power flow (UTOPF) models to numerate the network fea…

Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks
Arivazhagan G. Balasubramanian, Ricardo Vinuesa, Outi Tammisola
https://arxiv.org/abs/2404.14121

Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks
Neural-network models have been employed to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. The numerical simulation data at the wall is utilized to predict the instantaneous velocity fluctuations and polymeric-stress fluctuations at three different wall-normal positions. Apart from predicting the velocity fluctuations well in a hibernating flow, the neural-network models are also shown to predict the polymeric shear stress and the trace of the polymer…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

This https://arxiv.org/abs/2301.00693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classificat…

This https://arxiv.org/abs/2403.13462 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Note on Using Singular Value Decomposition to Solve Diluted Pore Networks
Diluted pore networks or networks that are obtained directly from image analysis (e.g., CT scans of rock samples) are commonly used to evaluate flow through multiphase and complex porous media. These networks are often subjected to singular points and regions, that is, isolated sub-networks that are not connecting the inlet and outlet. These regions impair the invertibility of the Laplacian matrix and affect the accuracy and robustness of the pore network models. Searching and eliminating these…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Applications of Lifted Nonlinear Cuts to Convex Relaxations of the AC Power Flow Equations
Sergio I. Bugosen, Robert B. Parker, Carleton Coffrin
https://arxiv.org/abs/2404.17541 <…

Applications of Lifted Nonlinear Cuts to Convex Relaxations of the AC Power Flow Equations
We demonstrate that valid inequalities, or lifted nonlinear cuts (LNC), can be projected to tighten the Second Order Cone (SOC), Convex DistFlow (CDF), and Network Flow (NF) relaxations of the AC Optimal Power Flow (AC-OPF) problem. We conduct experiments on 36 cases from the PGLib-OPF library for two objective functions, (1) power generation maximization and (2) generation cost minimization. Significant optimality gap improvements are shown for the maximization problem, where the LNC strengthe…

Deep learning-based predictive modelling of transonic flow over an aerofoil
Li-Wei Chen, Nils Thuerey
https://arxiv.org/abs/2403.17131 https://

Deep learning-based predictive modelling of transonic flow over an aerofoil
Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network repr…

Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observability
Marco Fabris, Riccardo Ceccato, Andrea Zanella
https://arxiv.org/abs/2404.08588

Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observability
The emergence of 6G-enabled Internet of Vehicles (IoV) promises to revolutionize mobility and connectivity, integrating vehicles into a mobile Internet-of-Things (IoT)-oriented wireless sensor network (WSN). 5G technologies and mobile edge computing further support this vision by facilitating real-time connectivity and empowering massive access to the Internet. In this context, IoT-oriented WSNs play a crucial role in intelligent transportation systems, offering affordable alternatives for traf…

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

Deep learning-based predictive modelling of transonic flow over an aerofoil
Li-Wei Chen, Nils Thuerey
https://arxiv.org/abs/2403.17131 https://

Deep learning-based predictive modelling of transonic flow over an aerofoil
Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network repr…

AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
Md Rubel Ahmed, Toshiaki Koike-Akino, Kieran Parsons, Ye Wang
https://arxiv.org/abs/2403.10686

AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs
High-level synthesis (HLS) is a design flow that leverages modern language features and flexibility, such as complex data structures, inheritance, templates, etc., to prototype hardware designs rapidly. However, exploring various design space parameters can take much time and effort for hardware engineers to meet specific design specifications. This paper proposes a novel framework called AutoHLS, which integrates a deep neural network (DNN) with Bayesian optimization (BO) to accelerate HLS har…

This https://arxiv.org/abs/2301.00693 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classificat…

Network Flow Models for Robust Binary Optimization with Selective Adaptability
Merve Bodur, Timothy C. Y. Chan, Ian Yihang Zhu
https://arxiv.org/abs/2403.19471

Network Flow Models for Robust Binary Optimization with Selective Adaptability
Adaptive robust optimization problems have received significant attention in recent years, but remain notoriously difficult to solve when recourse decisions are discrete in nature. In this paper, we propose new reformulation techniques for adaptive robust binary optimization (ARBO) problems with objective uncertainty. Without loss of generality, we focus on ARBO problems with "selective adaptability", a term we coin to describe a common class of linking constraints between first-stage and secon…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Pore network models to determine flow statistics and structural controls in variably saturated porous media
Ilan Ben-Noah, Juan J. Hidalgo, Marco Dentz
https://arxiv.org/abs/2403.13519

Pore network models to determine flow statistics and structural controls in variably saturated porous media
Conceptualizing a porous media as a network of conductors sets a compromise between the oversimplifying conceptualization of the media as a bundle of capillary tubes and the computationally expensive and unobtainable detailed description of the media's geometry needed for direct numerical simulations. These models are abundantly being used to evaluate single and multiphase flow characteristics. The different flow characteristics are valuable in evaluating phenomena that may or may not be releva…

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu, Jianping Wu
https://arxiv.org/abs/2403.11090

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We observe a fundamental limitation in tree-based INDP approaches: although it is possible to represent even larger tree/forest tables on the data plane, the flow features that are computable on the data plane are fundamentally limited by hardware constrai…

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

This https://arxiv.org/abs/2310.13843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Long Solution Times or Low Solution Quality: On Trade-Offs in Choosing a Power Flow Formulation for the Optimal Power Shutoff Problem
The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in th…

This https://arxiv.org/abs/2307.06553 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Coordinated Path Following of UAVs over Time-Varying Digraphs Connected in an Integral Sense
This paper presents a new connectivity condition on the information flow between UAVs to achieve coordinated path following. The information flow is directional, so that the underlying communication network topology is represented by a time-varying digraph. We assume that this digraph is connected in an integral sense. This is a much more general assumption than the one currently used in the literature. Under this assumption, it is shown that a decentralized coordination controller ensures expo…

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Min Zhang, Runze Mao, Han Li, Zhenhua An, Zhi X. Chen
https://arxiv.org/abs/2402.18858

Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands as the primary demand on computational resources. Meanwhile, as the number of transport equations that need to be solved increases, the computational cost grows more substantially, particularly for those combustion models involving direct coupling of chemistry and flow such as the transported probability density funct…

This https://arxiv.org/abs/2310.13843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Long Solution Times or Low Solution Quality: On Trade-Offs in Choosing a Power Flow Formulation for the Optimal Power Shutoff Problem
The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flow when making decisions on the network topology is known to cause challenges with AC feasibility of the resulting network, as studied in th…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
Jinzhu Yan, Haotian Xu, Zhuotao Liu, Qi Li, Ke Xu, Mingwei Xu, Jianping Wu
https://arxiv.org/abs/2403.11090

Brain-on-Switch: Towards Advanced Intelligent Network Data Plane via NN-Driven Traffic Analysis at Line-Speed
The emerging programmable networks sparked significant research on Intelligent Network Data Plane (INDP), which achieves learning-based traffic analysis at line-speed. Prior art in INDP focus on deploying tree/forest models on the data plane. We observe a fundamental limitation in tree-based INDP approaches: although it is possible to represent even larger tree/forest tables on the data plane, the flow features that are computable on the data plane are fundamentally limited by hardware constrai…

This https://arxiv.org/abs/2402.02498 has been replaced.
link: https://scholar.google.com/scholar?q=a

Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion
Image-based rigid 2D/3D registration is a critical technique for fluoroscopic guided surgical interventions. In recent years, some learning-based fully differentiable methods have produced beneficial outcomes while the process of feature extraction and gradient flow transmission still lack controllability and interpretability. To alleviate these problems, in this work, we propose a novel fully differentiable correlation-driven network using a dual-branch CNN-transformer encoder which enables th…

Parametric and inverse analysis of flow inside an obstructed channel under the influence of magnetic field using physics informed neural networks
Ehsan Ghaderi, MohammadAli Bijarchi, Siamak Kazemzadeh Hannani, Ali Nouri-Borujerdi
https://arxiv.org/abs/2404.17261

Parametric and inverse analysis of flow inside an obstructed channel under the influence of magnetic field using physics informed neural networks
In this study, fluid flow inside of an obstructed channel under the influence of magnetic field has been analyzed using physics informed neural networks(PINNs). Governing equations have been utilized in low-order form and the solution has been obtained in dimensionless form. Geometric and physics-related dimensionless parameters have been used as input variables of the neural network in the learning process. The radius and longitudinal position of the obstruction have also been involved in the …

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Zifan Zhang, Minghong Fang, Jiayuan Huang, Yuchen Liu
https://arxiv.org/abs/2404.14389

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial auto…

This https://arxiv.org/abs/2403.13519 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Pore network models to determine flow statistics and structural controls in variably saturated porous media
Conceptualizing a porous media as a network of conductors sets a compromise between the oversimplifying conceptualization of the media as a bundle of capillary tubes and the computationally expensive and unobtainable detailed description of the media's geometry needed for direct numerical simulations. These models are abundantly being used to evaluate single and multiphase flow characteristics. The different flow characteristics are valuable in evaluating phenomena that may or may not be releva…

On well-posedness of the leak localization problem in parallel pipe networks
Victor Moln\"o, Henrik Sandberg
https://arxiv.org/abs/2403.10396 https://…

On well-posedness of the leak localization problem in parallel pipe networks
With the advent of integrated sensor technology (smart flow meters and pressure sensors), various new numerical algorithms for leak localization (a core element of water distribution system operation) have been developed. However, there is a lack of theory regarding the limitations of leak localization. In this work, we contribute to the development of such a theory by introducing an example water network structure with parallel pipes that is tractable for analytical treatment. We define the le…

Percolation without trapping: how Ostwald ripening during two-phase displacement in porous media alters capillary pressure and relative permeability
Ademola Isaac Adebimpe (Department of Earth Science and Engineering, Imperial College London, London, UK, Department of Chemical Engineering, Obafemi Awolowo University, Nigeria), Sajjad Foroughi (Department of Earth Science and Engineering, Imperial College London, London, UK), Branko Bijeljic (Department of Earth Science and Engineering,…

Percolation without trapping: how Ostwald ripening during two-phase displacement in porous media alters capillary pressure and relative permeability
Conventional measurements of gas-liquid flow in subsurface gas storage and recovery do not allow for Ostwald ripening, significantly over-estimating the amount of trapping and hysteresis. When there is transport of dissolved species, local capillary equilibrium is achieved, and two-phase displacement in porous media needs to be modelled as percolation without trapping. A pore network model is used to demonstrate how to scale trapped saturation, capillary pressure and relative permeability to ac…

This https://arxiv.org/abs/2309.10050 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Finite Volume Graph Network(FVGN): Predicting unsteady incompressible fluid dynamics with finite volume informed neural network
The rapid development of deep learning has significant implications for the advancement of Computational Fluid Dynamics (CFD). Currently, most pixel-grid-based deep learning methods for flow field prediction exhibit significantly reduced accuracy in predicting boundary layer flows and poor adaptability to geometric shapes. Although Graph Neural Network (GNN) models for unstructured grids based unsteady flow prediction have better geometric adaptability, these models suffer from error accumulati…

Innovation Diffusion in EV Charging Location Decisions: Integrating Demand & Supply through Market Dynamics
Xiangyong LuoSimon, Michael J. KubySimon, Yudai HonmaSimon, Mouna Kchaou-BoujelbenSimon, XuesongSimon, Zhou
https://arxiv.org/abs/2402.14263

Innovation Diffusion in EV Charging Location Decisions: Integrating Demand & Supply through Market Dynamics
This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics via continuous-time fluid queue models and discrete flow refueling location modeling, all in the context of innovation diffusion principles. We employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook i…

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
Siming Shan, Pengkai Wang, Song Chen, Jiaxu Liu, Chao Xu, Shengze Cai
https://arxiv.org/abs/2404.08412

PiRD: Physics-informed Residual Diffusion for Flow Field Reconstruction
The use of machine learning in fluid dynamics is becoming more common to expedite the computation when solving forward and inverse problems of partial differential equations. Yet, a notable challenge with existing convolutional neural network (CNN)-based methods for data fidelity enhancement is their reliance on specific low-fidelity data patterns and distributions during the training phase. In addition, the CNN-based method essentially treats the flow reconstruction task as a computer vision t…

The Inverse Carson's Equations Problem: Definition, Implementation and Experiments
Ching Hong Tam, Frederik Geth, Nadarajah Mithulananthan
https://arxiv.org/abs/2404.08210

The Inverse Carson's Equations Problem: Definition, Implementation and Experiments
In recent years, with the increase in renewable energy and storage penetration, power flow studies in low-voltage networks have become of interest in both industry and academia. Many studies use impedance represented by sequence components due to the lack of datasets with fully parameterized impedance matrices. This assumes that the network impedance is balanced, which is typically not the case in the low voltage network and therefore risks the accuracy of the study. This paper proposes a metho…

The Inverse Carson's Equations Problem: Definition, Implementation and Experiments
Ching Hong Tam, Frederik Geth, Nadarajah Mithulananthan
https://arxiv.org/abs/2404.08210

The Inverse Carson's Equations Problem: Definition, Implementation and Experiments
In recent years, with the increase in renewable energy and storage penetration, power flow studies in low-voltage networks have become of interest in both industry and academia. Many studies use impedance represented by sequence components due to the lack of datasets with fully parameterized impedance matrices. This assumes that the network impedance is balanced, which is typically not the case in the low voltage network and therefore risks the accuracy of the study. This paper proposes a metho…

This https://arxiv.org/abs/2206.01876 has been replaced.
link: https://scholar.google.com/scholar?q=a

A modularized algorithmic framework for interface related optimization problems using characteristic functions
In this paper, we consider the algorithms and convergence for a general optimization problem, which has a wide range of applications in image segmentation, topology optimization, flow network formulation, and surface reconstruction. In particular, the problem focuses on interface related optimization problems where the interface is implicitly described by characteristic functions of the corresponding domains. Under such representation and discretization, the problem is then formulated into a di…