Tootfinder

Scalable Adaptive Traffic Light Control Over a Traffic Network Including Turns, Transit Delays, and Blocking
Yingqing Chen, Christos G. Cassandras
https://arxiv.org/abs/2404.17479

Scalable Adaptive Traffic Light Control Over a Traffic Network Including Turns, Transit Delays, and Blocking
We develop adaptive data-driven traffic light controllers for a grid-like traffic network considering straight, left-turn, and right-turn traffic flows. The analysis incorporates transit delays and blocking effects on vehicle movements between neighboring intersections. Using a stochastic hybrid system model with parametric traffic light controllers, we use Infinitesimal Perturbation Analysis (IPA) to derive a data-driven cost gradient estimator with respect to controllable parameters. We then …

This https://arxiv.org/abs/2210.00802 has been replaced.
link: https://scholar.google.com/scholar?q=a

DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm
Drug synergy arises when the combined impact of two drugs exceeds the sum of their individual effects. While single-drug effects on cell lines are well-documented, the scarcity of data on drug synergy, considering the vast array of potential drug combinations, prompts a growing interest in computational approaches for predicting synergies in untested drug pairs. We introduce a Graph Neural Network (\textit{GNN}) based model for drug synergy prediction, which utilizes drug chemical structures an…

This https://arxiv.org/abs/2306.14142 has been replaced.
link: https://scholar.google.com/scholar?q=a

Estimating Policy Effects in a Social Network with Independent Set Sampling
Evaluating the impact of policy interventions on respondents who are embedded in a social network is often challenging due to the presence of network interference within the treatment groups, as well as between treatment and non-treatment groups throughout the network. In this paper, we propose a modeling strategy that combines existing work on stochastic actor-oriented models (SAOM) with a novel network sampling method based on the identification of independent sets. By assigning respondents f…

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors
Francisco E Enriquez-Mier-y-Teran, Luping Zhou, Steven R Meikle, Andre Z Kyme
https://arxiv.org/abs/2403.18240

A deep neural network for positioning and inter-crystal scatter identification in multiplexed PET detectors
Objective: Conventional event positioning algorithms in light-sharing PET detectors are often limited by edge effects and the impact of inter-crystal scattering (ICS). This study explores the feasibility of deep neural network (DNN) techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing. Approach: A DNN was designed for crystal localisation, and trained/tested with light distributions of photoelectric (P) and Compton/photoelectr…

Cross-checking the geometric effects in heavy-ion collisions at 1 GeV/nucleon
Zu-Xing Yang, Xiao-Hua Fan, Zhi-Pan Li, Shunji Nishimura
https://arxiv.org/abs/2403.19208

Cross-checking the geometric effects in heavy-ion collisions at 1 GeV/nucleon
Employing the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model, the 1 GeV/nucleon deformed uranium-uranium ultra-central collisions are simulated. Based on sensitive observables, mean square collective flow and pion meson multiplicity, the impacts of high-momentum tails caused by short-range correlations and the symmetry energy in high-density regions on geometric effects are discussed under different reaction orientations. Finally, the neural network model for identifying reaction…

This https://arxiv.org/abs/2402.17039 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method
The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system -- a system where electricity supply covers demand -- is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. …

This https://arxiv.org/abs/2311.16548 has been replaced.
initial toot: https://mastoxiv.page/@a…

Graph Theoretic Analysis of Three-Terminal Quantum Dot Thermocouples: Onsager Relations and Spin-Thermoelectric Effects
We introduce a simplified model for a three-terminal quantum thermocouple consisting of two strongly-coupled quantum dots. To elucidate spin-dependent Seebeck and Peltier effects, we employ a microscopic Hamiltonian and map the Lindblad master equation onto a quantum transition network, capturing the key working principles for both reciprocal effects. Our analysis reveals quantum thermodynamic networks encompassing both Coulomb interaction and spin-flipping processes, lead to the emergence of s…

Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
David Gamba, Yulin Yu, Yuan Yuan, Grant Schoenebeck, Daniel M. Romero
https://arxiv.org/abs/2402.15683

Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
Amidst growing uncertainty and frequent restructurings, the impacts of employee exits are becoming one of the central concerns for organizations. Using rich communication data from a large holding company, we examine the effects of employee departures on socialization networks among the remaining coworkers. Specifically, we investigate how network metrics change among people who historically interacted with departing employees. We find evidence of ``breakdown" in communication among the remaini…

This https://arxiv.org/abs/2403.02331 has been replaced.
link: https://scholar.google.com/scholar?q=a

Toward Neuromic Computing: Neurons as Autoencoders
This short paper presents the idea that neural backpropagation is using dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted to the standard layered approach to autoencoding. It is shown that such individualised processing is not detrimental and can im…

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Diego A. de Aguiar, Hugo L. Fran\c{c}a, Cassio M. Oishi
https://arxiv.org/abs/2403.16144

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static outputs for fluid flows under surface tension effects. Specifically, we explore two distinct droplet dynamic scenarios: droplets with divers…

A/B testing under Interference with Partial Network Information
Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau
https://arxiv.org/abs/2404.10547

A/B testing under Interference with Partial Network Information
A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control the spread of an epidemic. In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to group A can potentially also affect the control group B. When the underlying social network is known exactly, prior works have demonstrated …

Sandwich GAN: Image Reconstruction from Phase Mask based Anti-dazzle Imaging
Xiaopeng Peng, Erin F. Fleet, Abbie T. Watnik, Grover A. Swartzlander
https://arxiv.org/abs/2402.15919

Sandwich GAN: Image Reconstruction from Phase Mask based Anti-dazzle Imaging
Conventional camera systems are susceptible to the adverse effects of laser dazzle, which may over-saturate an image or cause permanent damage to pixels. To address this problem, we developed an approach combining point spread function engineering whereby a wavefront-coded mask in the pupil plane blurs both the laser and scene, together with a deep neural sandwich network. In addition to protecting the sensor, our approach jointly removes the laser from the scene and reconstructs a satisfactory…

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method
In\`es Harang, Fabian Heymann, Laurens P. Stoop
https://arxiv.org/abs/2402.17039

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method
The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system -- a system where electricity supply covers demand -- is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. …

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
Shaomin Wu
https://arxiv.org/abs/2404.12581 http…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first st…

Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health
Kevin L. Chen, Falco J. Bargagli-Stoffi, Raphael C. Kim, Lucas R. F. Henneman, Rachel C. Nethery
https://arxiv.org/abs/2404.13442…

Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health
Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of emissions control technologies on power plants. However, assessing the health impacts of these measures, particularly over longer periods of time, is complicated by several factors. First, the units that potentially receive the intervention (power pl…

Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Network
Junhyun Park, Seonghyeok Jang, Hyojae Park, Seongjun Bae, Minho Hwang
https://arxiv.org/abs/2402.11319

Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Network
Flexible continuum manipulators are valued for minimally invasive surgery, offering access to confined spaces through nonlinear paths. However, cable-driven manipulators face control difficulties due to hysteresis from cabling effects such as friction, elongation, and coupling. These effects are difficult to model due to nonlinearity and the difficulties become even more evident when dealing with long and multi-segmented manipulator. This paper proposes a data-driven approach based on recurrent…

Analysis of effects to scientific impact indicators based on the coevolution of coauthorship and citation networks
Haobai Xue
https://arxiv.org/abs/2404.12765

Analysis of effects to scientific impact indicators based on the coevolution of coauthorship and citation networks
While computer modeling and simulation are crucial for understanding scientometrics, their practical use in literature remains somewhat limited. In this study, we establish a joint coauthorship and citation network using preferential attachment. As papers get published, we update the coauthorship network based on each paper's author list, representing the collaborative team behind it. This team is formed considering the number of collaborations each author has, and we introduce new authors at a…

Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network
Chukwuemeka Muonagor, Mounir Bensalem, Admela Jukan
https://arxiv.org/abs/2404.15091

Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network
Intent-Based Networking (IBN) is a known concept for enabling the autonomous configuration and self-adaptation of networks. One of the major issues in IBN is maintaining the applied intent due the effects of drifts over time, which is the gradual degradation in the fulfillment of the intents, before they fail. Despite its critical role to intent assurance and maintenance, intent drift detection was largely overlooked in the literature. To fill this gap, we propose an intent drift detection algo…

Presentation from Torben Christensen on new work just published in @… #ClimateChange on the effects of the Russian invasion of #Ukraine on what we know about the

Analysis of Ethanol Blending Effects on Auto-Ignition and Heat Release in n-Heptane/Ethanol Non-Premixed Flames
Liang Ji, Kalyanasundaram Seshadri
https://arxiv.org/abs/2404.16762

Analysis of Ethanol Blending Effects on Auto-Ignition and Heat Release in n-Heptane/Ethanol Non-Premixed Flames
This study delves into the auto-ignition temperature of n-heptane and ethanol mixtures within a counterflow flame configuration under low strain rate, with a particular focus on the impact of ethanol blending on heat release rates. Employing the sensitivity analysis method inspired by Zurada's sensitivity approach for neural network, this study identifies the group of critical species influencing the heat release rate. Further analysis concentration change reveals the intricate interactions amo…

Attention-Based Neural Network Emulators for Multi-Probe Data Vectors Part II: Assessing Tension Metrics
Evan Saraivanov, Kunhao Zhong, Vivian Miranda, Supranta S. Boruah, Tim Eifler, Elisabeth Krause
https://arxiv.org/abs/2403.12337

Attention-Based Neural Network Emulators for Multi-Probe Data Vectors Part II: Assessing Tension Metrics
The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyzing theoretical models of cosmology. Additionally, new mitigation techniques of baryonic effects, intrinsic alignment, and other systematic effects will inevitably introduce more parameters, slowing down the convergence of Bayesian analyses. In this…

This https://arxiv.org/abs/2310.07760 has been replaced.
initial toot: https://mastoxiv.page/@a…

Extended critical phase in quasiperiodic quantum Hall systems
We consider the effects of quasiperiodic spatial modulation on the quantum Hall plateau transition, by analyzing the Chalker-Coddington network model for the integer quantum Hall transition with quasiperiodically modulated link phases. In the conventional case (uncorrelated random phases), there is a critical point separating topologically distinct integer quantum Hall insulators. Surprisingly, the quasiperiodic version of the model supports an extended critical phase for some angles of modulat…

This https://arxiv.org/abs/2305.01256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Impact of astrophysical effects on the dark matter mass constraint with 21cm intensity mapping
We present an innovative approach to constraining the non-cold dark matter model using a convolutional neural network (CNN). We perform a suite of hydrodynamic simulations with varying dark matter particle masses and generate mock 21cm radio intensity maps to trace the dark matter distribution. Our proposed method complements the traditional power spectrum analysis. We compare our CNN classification results with those from the power spectrum of the differential brightness temperature map of 21c…

Field calibration and analysis of a municipal low-cost air quality sensor network
Robert Blaga
https://arxiv.org/abs/2402.12942 https://

Field calibration and analysis of a municipal low-cost air quality sensor network
Low-cost particulate matter sensors (LCS) are an important source of air quality data, improving the spatial and temporal resolution of data gathered by sparsely placed official monitoring stations. Their readings, however, are subject to bias due to unaccounted for effects that pertain to both the physical properties of the aerosol particles and design limitations of the devices. A calibration model is paramount in order to valorize the output of LCS devices. In this paper, a calibration mo…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
Shaomin Wu
https://arxiv.org/abs/2404.12581 http…

Two-step Estimation of Network Formation Models with Unobserved Heterogeneities and Strategic Interactions
In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through individual fixed effects. Using data from a single large network, I propose a two-step estimator for the model primitives. In the first st…

Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health
Kevin L. Chen, Falco J. Bargagli-Stoffi, Raphael C. Kim, Lucas R. F. Henneman, Rachel C. Nethery
https://arxiv.org/abs/2404.13442…

Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health
Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of emissions control technologies on power plants. However, assessing the health impacts of these measures, particularly over longer periods of time, is complicated by several factors. First, the units that potentially receive the intervention (power pl…

This https://arxiv.org/abs/2312.07958 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Neural network based time-resolved state tomography of superconducting qubits
Superconducting qubits have emerged as a premier platform for large-scale quantum computation, yet the fidelity of state readout is often hindered by random noise and crosstalk, especially in multi-qubit systems. While neural networks trained on labeled data have shown promise in reducing crosstalk effects during readout, their current capabilities are limited to binary discrimination of joint-qubit states due to architectural constraints. Here we introduce a time-resolved modulated neural netw…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
Maxime HerbrichIPHC, Eythan CousinIPHC, Ivan Puga-GonzalezIPHC, Barbara TiddiIPHC, Claudia FichtelIPHC, Meg CrofootIPHC, Andrew Jj MacintoshIPHC, Erica van de WaalIPHC, C\'edric SueurIPHC
https://arxiv.org/abs/2402.13658…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
This study investigates the prevalence and implications of nestedness within primate social networks, examining its relationship with cognitive and structural factors. We analysed data from 51 primate groups across 21 species, employing network analysis to evaluate nestedness and its correlation with modularity, neocortex ratio, and group size. We used Bayesian mixed effects modelling to investigate nestedness in primate social networks, controlling for phylogenetic dependencies and exploring v…

Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network
Chukwuemeka Muonagor, Mounir Bensalem, Admela Jukan
https://arxiv.org/abs/2404.15091

Predictive Intent Maintenance with Intent Drift Detection in Next Generation Network
Intent-Based Networking (IBN) is a known concept for enabling the autonomous configuration and self-adaptation of networks. One of the major issues in IBN is maintaining the applied intent due the effects of drifts over time, which is the gradual degradation in the fulfillment of the intents, before they fail. Despite its critical role to intent assurance and maintenance, intent drift detection was largely overlooked in the literature. To fill this gap, we propose an intent drift detection algo…

Policymakers, planners and site operators need a strong grasp of the costs and likely market effects of megawatt charging along key 🇪🇺 highways to build a network for e-trucks at a reasonable cost and offer competitive charging services. My colleagues @…, @…

The power of moving loads: Cost analysis of megawatt charging in Europe 
Electrifying long-haul freight across Europe can dramatically reduce emissions from transport to help meet climate goals. To advance the use of electric RAP analysis of the cost to charge e-trucks at selected EU border crossings, reasons for stark differences in prices and their impact.

Default Resilience and Worst-Case Effects in Financial Networks
Giuseppe Calafiore, Giulia Fracastoro, Anton Proskurnikov
https://arxiv.org/abs/2403.10631 …

Default Resilience and Worst-Case Effects in Financial Networks
In this paper we analyze the resilience of a network of banks to joint price fluctuations of the external assets in which they have shared exposures, and evaluate the worst-case effects of the possible default contagion. Indeed, when the prices of certain external assets either decrease or increase, all banks exposed to them experience varying degrees of simultaneous shocks to their balance sheets. These coordinated and structured shocks have the potential to exacerbate the likelihood of defaul…

This https://arxiv.org/abs/2310.10917 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Modeling and Analysis of Near-Field ISAC
As the technical trends for the next-generation wireless network significantly extend the near-field region, a performance reevaluation of integrated sensing and communications (ISAC) with an appropriate channel model to account for the effects introduced by the near field becomes essential. In this paper, a near-field ISAC framework is proposed for both downlink and uplink scenarios based on an accurate channel model. A uniform planar array is equipped at a base station, where the impacts of t…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
Maxime HerbrichIPHC, Eythan CousinIPHC, Ivan Puga-GonzalezIPHC, Barbara TiddiIPHC, Claudia FichtelIPHC, Meg CrofootIPHC, Andrew Jj MacintoshIPHC, Erica van de WaalIPHC, C\'edric SueurIPHC
https://arxiv.org/abs/2402.13658…

Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
This study investigates the prevalence and implications of nestedness within primate social networks, examining its relationship with cognitive and structural factors. We analysed data from 51 primate groups across 21 species, employing network analysis to evaluate nestedness and its correlation with modularity, neocortex ratio, and group size. We used Bayesian mixed effects modelling to investigate nestedness in primate social networks, controlling for phylogenetic dependencies and exploring v…

Field calibration and analysis of a municipal low-cost air quality sensor network
Robert Blaga
https://arxiv.org/abs/2402.12942 https://

Field calibration and analysis of a municipal low-cost air quality sensor network
Low-cost particulate matter sensors (LCS) are an important source of air quality data, improving the spatial and temporal resolution of data gathered by sparsely placed official monitoring stations. Their readings, however, are subject to bias due to unaccounted for effects that pertain to both the physical properties of the aerosol particles and design limitations of the devices. A calibration model is paramount in order to valorize the output of LCS devices. In this paper, a calibration mo…

Effects of spin-orbit coupling in a valley chiral kagom\'e network
P. Wittig, F. Dominguez, P. Recher
https://arxiv.org/abs/2403.10181 https://<…

Effects of spin-orbit coupling in a valley chiral kagomé network
Valley chiral kagomé networks can arise in various situations, like for example, in double-aligned graphene-hexagonal boron nitride and periodically strained graphene. Here, we construct a phenomenological scattering model based on the symmetries of the network to investigate the energy spectrum and magnetotransport in this system. Additionally, we consider the effects of a finite Rashba spin-orbit coupling on the transport properties of the kagomé network. We identify conditions where the in…

"Network Rail to spend £2.8bn to cope with effects of climate crisis"
#UK #UnitedKingdom #Trains #NetworkRail

This https://arxiv.org/abs/2404.12902 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Urban topology and dynamics can assess green areas importance
Green areas are a crucial element in evolution of a city, contributing to improve citizens' life, to reduce effects of climate change, and to make possible the survival of other species in urban areas. Unfortunately, the above effects are difficult to assess quantitatively for regulators, stakeholders and experts, making troublesome the planning of city development. Here we present a method to estimate the impact of these areas in the city life based on the network topology of the city itself a…

Meta-plasticity and memory in multi-level recurrent feed-forward networks
Gianmarco Zanardi, Paolo Bettotti, Jules Morand, Lorenzo Pavesi, Luca Tubiana
https://arxiv.org/abs/2403.13967

Meta-plasticity and memory in multi-level recurrent feed-forward networks
Network systems can exhibit memory effects in which the interactions between different pairs of nodes adapt in time, leading to the emergence of preferred connections, patterns, and sub-networks. To a first approximation, this memory can be modelled through a ``plastic'' Hebbian or homophily mechanism, in which edges get reinforced proportionally to the amount of information flowing through them. However, recent studies on glia-neuron networks have highlighted how memory can evolve due to more …

This https://arxiv.org/abs/2211.07823 has been replaced.
link: https://scholar.google.com/scholar?q=a

Graph Neural Networks for Causal Inference Under Network Confounding
This paper studies causal inference with observational network data. A challenging aspect of this setting is the possibility of interference in both potential outcomes and selection into treatment, for example due to peer effects in either stage. We therefore consider a nonparametric setup in which both stages are reduced forms of simultaneous-equations models. This results in high-dimensional network confounding, where the network and covariates of all units constitute sources of selection bia…

When Crypto Economics Meet Graph Analytics and Learning
Bingqiao Luo
https://arxiv.org/abs/2403.06454 https://arxiv.org/pdf/2403.0645…

When Crypto Economics Meet Graph Analytics and Learning
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope…

This https://arxiv.org/abs/2402.11319 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Network
Flexible continuum manipulators are valued for minimally invasive surgery, offering access to confined spaces through nonlinear paths. However, cable-driven manipulators face control difficulties due to hysteresis from cabling effects such as friction, elongation, and coupling. These effects are difficult to model due to nonlinearity and the difficulties become even more evident when dealing with long and coupled, multi-segmented manipulator. This paper proposes a data-driven approach based on …

This https://arxiv.org/abs/2311.17175 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Kicking it Off(-shell) with Direct Diffusion
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We show how a generative diffusion network learns off-shell kinematics given the much simpler on-shell process. It generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.

How Temporal Unrolling Supports Neural Physics Simulators
Bjoern List, Li-Wei Chen, Kartik Bali, Nils Thuerey
https://arxiv.org/abs/2402.12971 https://

How Temporal Unrolling Supports Neural Physics Simulators
Unrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze these effects by studying three variants of training neural networks on discrete ground truth trajectories. In addition to commonly used one-step setups and fully differentiable unrolling, we include a third, less widely used variant: unrolling without temporal gradients. Comparing networks trained with these three modalities makes it possible to disent…

Joint mixed-effects models for causal inference in clustered network-based observational studies
Vanessa McNealis, Erica E. M. Moodie, Nema Dean
https://arxiv.org/abs/2404.07411 <…

Joint mixed-effects models for causal inference in clustered network-based observational studies
Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption frequently does not hold. Existing methods developed in the context of network interference rely upon the assumption of no unmeasured confounding. However, when faced with multilevel network data, there may be a latent factor influencing both the exposure and the outcome at the cluster level. We propose a Bayesian inference approach that combines a joint mixed-eff…

Effects of diffusion and advection on predator prey dynamics in an advective patchy environment
Qi Wang
https://arxiv.org/abs/2403.07520 https://

Effects of diffusion and advection on predator prey dynamics in an advective patchy environment
In this paper, we consider a specialist predator-prey patchy model over the closed stream network. We study the dynamics and the asymptotic profiles of positive steady states according to the mortality rate of the specialist predators, advection and diffusion rates. We verify that the specialist predators can successfully invade as long as the mortality rate is sufficiently small. On the other hand, the impacts of diffusion and advection on the asymptotic profiles of positive steady states and …

This https://arxiv.org/abs/2311.16548 has been replaced.
initial toot: https://mastoxiv.page/@a…

Graph theoretic analysis of three-terminal quantum dot thermocouples: Onsager relations and spin-thermoelectric effects
We introduce a simplified model for a three-terminal quantum thermocouple consisting of two strongly-coupled quantum dots. To elucidate spin-dependent Seebeck and Peltier effects, we employ a microscopic Hamiltonian and map the Lindblad master equation onto a quantum transition network, capturing the key working principles for both reciprocal effects. Our analysis reveals quantum thermodynamic networks encompassing both Coulomb interaction and spin-flipping processes, lead to the emergence of s…

This https://arxiv.org/abs/2402.03229 has been replaced.
link: https://scholar.google.com/scholar?q=a

Disentangling high order effects in the transfer entropy
Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and target, as in its fully conditio…

This https://arxiv.org/abs/2301.00270 has been replaced.
link: https://scholar.google.com/scholar?q=a

NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks? The knowledge of GNE is valuable for various tasks like node classification, and targeted advertising. However, identifying GNE such as homophily, heterophily or their combination is challenging in real-world graphs due to limi…

This https://arxiv.org/abs/2403.02331 has been replaced.
link: https://scholar.google.com/scholar?q=a

Toward Neuromic Computing: Neurons as Autoencoders
The computational capabilities of dendrites have become increasingly clear. This letter presents the idea that neural backpropagation is using dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted to the standard layered approach to autoencoding. It is …

Joint mixed-effects models for causal inference in clustered network-based observational studies
Vanessa McNealis, Erica E. M. Moodie, Nema Dean
https://arxiv.org/abs/2404.07411 <…

Joint mixed-effects models for causal inference in clustered network-based observational studies
Causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption frequently does not hold. Existing methods developed in the context of network interference rely upon the assumption of no unmeasured confounding. However, when faced with multilevel network data, there may be a latent factor influencing both the exposure and the outcome at the cluster level. We propose a Bayesian inference approach that combines a joint mixed-eff…

A Comprehensive Evaluation of the Impact of ATM QoS Mechanisms on Network Performance for Multimedia and Data Applications
Mahdi Manavi
https://arxiv.org/abs/2403.19914

A Comprehensive Evaluation of the Impact of ATM QoS Mechanisms on Network Performance for Multimedia and Data Applications
The Asynchronous Transfer Mode (ATM) network is crucial due to its ability to efficiently transmit data, provide reliable connections, and support various service classes with specific Quality of Service (QoS) requirements. In this paper, we utilize the OPNET network simulation software to model an ATM network and analyze the impact of QoS classification on network performance. We investigate the effects of Constant Bit Rate (CBR), Variable Bit Rate (VBR), Available Bit Rate (ABR) and Unspecifi…

Gaussian Mixture Models for Affordance Learning using Bayesian Networks
Pedro Os\'orio, Alexandre Bernardino, Ruben Martinez-Cantin, Jos\'e Santos-Victor
https://arxiv.org/abs/2402.06078

Gaussian Mixture Models for Affordance Learning using Bayesian Networks
Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Altho…

High temperature transport in the one dimensional mass-imbalanced Fermi-Hubbard model
Thomas G. Kiely, Erich J. Mueller
https://arxiv.org/abs/2404.08076 ht…

High temperature transport in the one dimensional mass-imbalanced Fermi-Hubbard model
We study transport in the one-dimensional mass-imbalanced Fermi-Hubbard model at infinite temperature, focusing on the case of strong interactions. Prior theoretical and experimental investigations have revealed unconventionally long transport timescales, with complications due to strong finite size effects. We compute the dynamical current-current correlation function directly in the thermodynamic limit using infinite tensor network techniques. We show that transport in the strong-imbalance li…

This https://arxiv.org/abs/2201.06694 has been replaced.
link: https://scholar.google.com/scholar?q=a

Homophily in preferences or meetings? Identifying and estimating an iterative network formation model
Is homophily in social and economic networks driven by a taste for homogeneity (preferences) or by a higher probability of meeting individuals with similar attributes (opportunity)? This paper studies identification and estimation of an iterative network game that distinguishes between these two mechanisms. Our approach enables us to assess the counterfactual effects of changing the meeting protocol between agents. As an application, we study the role of preferences and meetings in shaping clas…

This https://arxiv.org/abs/2301.00270 has been replaced.
link: https://scholar.google.com/scholar?q=a

NetEffect: Discovery and Exploitation of Generalized Network Effects
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks? The knowledge of GNE is valuable for various tasks like node classification, and targeted advertising. However, identifying GNE such as homophily, heterophily or their combination is challenging in real-world graphs due to limi…

Machine learning and economic forecasting: the role of international trade networks
Thiago C. Silva, Paulo V. B. Wilhelm, Diego R. Amancio
https://arxiv.org/abs/2404.08712

Machine learning and economic forecasting: the role of international trade networks
This study examines the effects of de-globalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from nearly 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. Our analysis highlights key global players through centrality rankings, with the United States, China, and Germany maintaining consistent dominance. Using a horse race of supe…

Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
Bastian Wittmann, Lukas Glandorf, Johannes C. Paetzold, Tamaz Amiranashvili, Thomas W\"alchli, Daniel Razansky, Bjoern Menze
https://arxiv.org/abs/2403.07116

Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images
Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzheimer's, on the vascular network. However, to accurately segment blood vessels with state-of-the-art deep learning methods, a vast amount of voxel-level annotations is required. Since cerebral 3D OCTA images are typically plagued by artifacts and generally have a low signal-to-noise ratio, acquiring manual annotations …

When Crypto Economics Meet Graph Analytics and Learning
Bingqiao Luo
https://arxiv.org/abs/2403.06454 https://arxiv.org/pdf/2403.0645…

When Crypto Economics Meet Graph Analytics and Learning
Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope…

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems
Meghdad Saeedian, Chengyi Tu, Fabio Menegazzo, Paolo D'Odorico, Sandro Azaele, Samir Suweis
https://arxiv.org/abs/2403.10938

Modelling co-evolution of resource feedback and social network dynamics in human-environmental systems
Games with environmental feedback have become a crucial area of study across various scientific domains, modelling the dynamic interplay between human decisions and environmental changes, and highlighting the consequences of our choices on natural resources and biodiversity. In this work, we propose a co-evolutionary model for human-environment systems that incorporates the effects of knowledge feedback and social interaction on the sustainability of common pool resources. The model represents …

This https://arxiv.org/abs/2311.17175 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Kicking it Off(-shell) with Direct Diffusion
Off-shell effects in large LHC backgrounds are crucial for precision predictions and, at the same time, challenging to simulate. We show how a generative diffusion network learns off-shell kinematics given the much simpler on-shell process. It generates off-shell configurations fast and precisely, while reproducing even challenging on-shell features.

Effects of clustering heterogeneity on the spectral density of sparse networks
Tuan Minh Pham, Thomas Peron, Fernando L. Metz
https://arxiv.org/abs/2404.08152

Effects of clustering heterogeneity on the spectral density of sparse networks
We derive exact equations for the spectral density of sparse networks with an arbitrary distribution of the number of single edges and triangles per node. These equations enable a systematic investigation of the effect of clustering on the spectral properties of the network adjacency matrix. In the case of heterogeneous networks, we demonstrate that the spectral density becomes more symmetric as the fluctuations in the triangle-degree sequence increase. This phenomenon is explained by the small…

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
https://arxiv.org/abs/2404.04241

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry giv…

Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus
Xuenan Peng, Chengqing Li, Yicheng Zeng, Chun-Lai Li
https://arxiv.org/abs/2402.18584

Adjusting Dynamics of Hopfield Neural Network via Time-variant Stimulus
As a paradigmatic model for nonlinear dynamics studies, the Hopfield Neural Network (HNN) demonstrates a high susceptibility to external disturbances owing to its intricate structure. This paper delves into the challenge of modulating HNN dynamics through time-variant stimuli. The effects of adjustments using two distinct types of time-variant stimuli, namely the Weight Matrix Stimulus (WMS) and the State Variable Stimulus (SVS), along with a Constant Stimulus (CS) are reported. The findings re…

This https://arxiv.org/abs/2402.03229 has been replaced.
link: https://scholar.google.com/scholar?q=a

Disentangling high order effects in the transfer entropy
Transfer Entropy (TE), the primary method for determining directed information flow within a network system, can exhibit bias - either in deficiency or excess - during both pairwise and conditioned calculations, owing to high-order dependencies among the dynamic processes under consideration and the remaining processes in the system used for conditioning. Here, we propose a novel approach. Instead of conditioning TE on all network processes except the driver and target, as in its fully conditio…

Epidemic modelling requires knowledge of the social network
Samuel Johnson
https://arxiv.org/abs/2403.07881 https://arxiv.org/pdf/240…

Epidemic modelling requires knowledge of the social network
Compartmental models of epidemics are widely used to forecast the effects of communicable diseases such as COVID-19 and to guide policy. Although it has long been known that such processes take place on social networks, the assumption of random mixing is usually made, which ignores network structure. However, super-spreading events have been found to be power-law distributed, suggesting that the underlying networks may be scale free or at least highly heterogeneous. The random-mixing assumption…

This https://arxiv.org/abs/2402.12942 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Field calibration and analysis of a low-cost sensor network based on the existing air quality infrastructure of a city
Low-cost particulate matter sensors (LCS) are an important source of air quality data, improving the spatial and temporal resolution of data gathered by sparsely placed official monitoring stations. Their readings, however, are subject to bias due to unaccounted for effects pertaining to both the physical properties of the aerosol particles and design limitations of the devices. A calibration model is paramount in order to valorize the output of LCS devices. In this paper, a calibration model…

This https://arxiv.org/abs/2303.10117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Estimation of Grouped Time-Varying Network Vector Autoregression Models
This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estim…

This https://arxiv.org/abs/2306.09806 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Testing for Peer Effects without Specifying the Network Structure
This paper proposes an Anderson-Rubin (AR) test for the presence of peer effects in panel data without the need to specify the network structure. The unrestricted model of our test is a linear panel data model of social interactions with dyad-specific peer effects. The proposed AR test evaluates if the peer effect coefficients are all zero. As the number of peer effect coefficients increases with the sample size, so does the number of instrumental variables (IVs) employed to test the restrictio…

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Jordan Thompson, Brian Y. Cho, Daniel S. Brown, Alan Kuntz
https://arxiv.org/abs/2404.04241

Modeling Kinematic Uncertainty of Tendon-Driven Continuum Robots via Mixture Density Networks
Tendon-driven continuum robot kinematic models are frequently computationally expensive, inaccurate due to unmodeled effects, or both. In particular, unmodeled effects produce uncertainties that arise during the robot's operation that lead to variability in the resulting geometry. We propose a novel solution to these issues through the development of a Gaussian mixture kinematic model. We train a mixture density network to output a Gaussian mixture model representation of the robot geometry giv…

This https://arxiv.org/abs/2403.02331 has been replaced.
link: https://scholar.google.com/scholar?q=a

Toward Neuromic Computing: Neurons as Autoencoders
The computational capabilities of dendrites have become increasingly clear. This letter presents the idea that neural backpropagation is using dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted to the standard layered approach to autoencoding. It is …

This https://arxiv.org/abs/2302.11322 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Causal inference with misspecified network interference structure
Under interference, the potential outcomes of a unit depend on treatments assigned to other units. A network interference structure is typically assumed to be given and accurate. In this paper, we study the problems resulting from misspecifying these networks. First, we derive bounds on the bias arising from estimating causal effects under a misspecified network. We show that the maximal possible bias depends on the divergence between the assumed network and the true one with respect to the ind…

Linking Through Time: Memory-Enhanced Community Discovery in Temporal Networks
Giulio Virginio Clemente, Diego Garlaschelli
https://arxiv.org/abs/2402.10141

Linking Through Time: Memory-Enhanced Community Discovery in Temporal Networks
Temporal Networks, and more specifically, Markovian Temporal Networks, present a unique challenge regarding the community discovery task. The inherent dynamism of these systems requires an intricate understanding of memory effects and structural heterogeneity, which are often key drivers of network evolution. In this study, we address these aspects by introducing an innovative approach to community detection, centered around a novel modularity function. We focus on demonstrating the improvement…

This https://arxiv.org/abs/2302.00230 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Revisiting the Effects of Maternal Education on Adolescents' Academic Performance: Doubly Robust Estimation in a Network-Based Observational Study
In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their own mother's educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct …

This https://arxiv.org/abs/1904.00136 has been replaced.
link: https://scholar.google.com/scholar?q=a

Estimating spillovers using imprecisely measured networks
In many experimental contexts, whether and how network interactions impact the outcome of interest for both treated and untreated individuals are key concerns. Networks data is often assumed to perfectly represent these possible interactions. This paper considers the problem of estimating treatment effects when measured connections are, instead, a noisy representation of the true spillover pathways. We show that existing methods, using the potential outcomes framework, yield biased estimators i…