Tootfinder

Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect
Atomsa Gemechu Abdisa, Yingchun Zhou, Yuqi Qiu
https://arxiv.org/abs/2507.12818

Self Balancing Neural Network: A Novel Method to Estimate Average Treatment Effect
In observational studies, confounding variables affect both treatment and outcome. Moreover, instrumental variables also influence the treatment assignment mechanism. This situation sets the study apart from a standard randomized controlled trial, where the treatment assignment is random. Due to this situation, the estimated average treatment effect becomes biased. To address this issue, a standard approach is to incorporate the estimated propensity score when estimating the average treatment e…

Guaranteeing and Explaining Stability across Heterogeneous Load Balancing using Calculus Network Dynamics
Mengbang Zou, Yun Tang, Adolfo Perrusqu\'ia, Weisi Guo
https://arxiv.org/abs/2507.12892

Guaranteeing and Explaining Stability across Heterogeneous Load Balancing using Calculus Network Dynamics
Load balancing between base stations (BSs) allows BS capacity to be efficiently utilised and avoid outages. Currently, data-driven mechanisms strive to balance inter-BS load and reduce unnecessary handovers. The challenge is that over a large number of BSs, networks observe an oscillatory effect of load evolution that causes high inter-BS messaging. Without a calculus function that integrates network topology to describe the evolution of load states, current data-driven algorithms cannot explai…

Binary choice logit models with general fixed effects for panel and network data
Kevin Dano, Bo E. Honor\'e, Martin Weidner
https://arxiv.org/abs/2508.11556 https://

Binary choice logit models with general fixed effects for panel and network data
This paper systematically analyzes and reviews identification strategies for binary choice logit models with fixed effects in panel and network data settings. We examine both static and dynamic models with general fixed-effect structures, including individual effects, time trends, and two-way or dyadic effects. A key challenge is the incidental parameter problem, which arises from the increasing number of fixed effects as the sample size grows. We explore two main strategies for eliminating nui…

Effect of Phase Shift Errors on the Security of UAV-assisted STAR-RIS IoT Networks
Mustafa Gusaibat, Mohammed Hnaish, Abdelhamid Salem, Khaled Rabie, Zubair Md Fadlullah, Wali Ullah Khan, Mohamad A. Alawad, Yazeed Alkhrijah
https://arxiv.org/abs/2508.12114

Effect of Phase Shift Errors on the Security of UAV-assisted STAR-RIS IoT Networks
Unmanned aerial vehicles (UAV)-mounted simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems can provide full-dimensional coverage and flexible deployment opportunities in future 6G-enabled IoT networks. However, practical imperfections such as jittering and airflow of UAV could affect the phase shift of STAR-RIS, and consequently degrade network security. In this respect, this paper investigates the impact of phase shift errors on the secrecy performanc…

Topology-Enhanced Superconducting Qubit Networks for In-Sensor Quantum Information Processing
J. Settino, G. G. Luciano, A. Di Bartolomeo, P. Silvestrini, M. Lisitskiy, B. Ruggiero, F. Romeo
https://arxiv.org/abs/2507.13228

Topology-Enhanced Superconducting Qubit Networks for In-Sensor Quantum Information Processing
We investigate the influence of topology on the magnetic response of inductively coupled superconducting flux-qubit networks. Using exact diagonalization methods and linear response theory, we compare the magnetic response of linear and cross-shaped array geometries, used as paradigmatic examples. We find that the peculiar coupling matrix in cross-shaped arrays yields a significant enhancement of the magnetic flux response compared to linear arrays, this network-topology effect arising from coo…

Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines
David M. Phillippo (University of Bristol, Bristol, UK), Ayman Sadek (University of Bristol, Bristol, UK), Hugo Pedder (University of Bristol, Bristol, UK), Nicky J. Welton (University of Bristol, Bristol, UK)
https://arxiv.org/abs/2509.10383

Network Meta-Analysis of survival outcomes with non-proportional hazards using flexible M-splines
Network meta-analysis (NMA) is widely used in healthcare decision-making, where estimates of the effect of multiple treatments on outcomes are required. For time-to-event outcomes such as survival or disease progression the most common approach is to model log hazard ratios; however, this relies on the proportional hazards assumption. Novel treatments such as immunotherapies are expected to display complex hazard functions that cannot be captured by standard parametric models, which results in …

Cluster breaking and reconfiguration effects in 12 Lambda B hypernucleus
Jiaqi Tian, Mengjiao Lyu, Akinobu Dote, Zheng Cheng, Takayuki Myo, Masahiro Isaka, Hisashi Horiuchi, Hiroki Takemoto, Hiroshi Toki, Niu Wan, Qing Zhao
https://arxiv.org/abs/2508.11937

Cluster breaking and reconfiguration effects in 12 Lambda B hypernucleus
We investigate the cluster-breaking effect and spatial distribution of negative-parity states in the 12 Lambda B hypernucleus using the Hyper-Brink model with cluster-breaking(CB-Hyper-Brink) optimized via Control Neural Network (Ctrl.NN). The results demonstrate that the inclusion of cluster-breaking is essential for accurately reproducing the observed low-lying energy levels and for making reliable predictions of the Hoyle-analog state 1-4 in 12 Lambda B. Cluster-breaking manifests as strong …

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Koen T. W. Teuwen, Sam Baggen, Emmanuele Zambon, Luca Allodi
https://arxiv.org/abs/2507.01571

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE
Automation in Security Operations Centers (SOCs) plays a prominent role in alert classification and incident escalation. However, automated methods must be robust in the presence of imbalanced input data, which can negatively affect performance. Additionally, automated methods should make explainable decisions. In this work, we evaluate the effect of label imbalance on the classification of network intrusion alerts. As our use-case we employ DeepCASE, the state-of-the-art method for automated a…

How Complex is a Complex Network? Insights from Linear Systems Theory
Giacomo Baggio, Marco Fabris
https://arxiv.org/abs/2507.06389 https://

How Complex is a Complex Network? Insights from Linear Systems Theory
This paper leverages linear systems theory to propose a principled measure of complexity for network systems. We focus on a network of first-order scalar linear systems interconnected through a directed graph. By locally filtering out the effect of nodal dynamics in the interconnected system, we propose a new quantitative index of network complexity rooted in the notion of McMillan degree of a linear system. First, we show that network systems with the same interconnection structure share the s…

The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Yigit Ege Bayiz, Arash Amini, Radu Marculescu, Ufuk Topcu
https://arxiv.org/abs/2506.22293

The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Online social networks exert a powerful influence on public opinion. Adversaries weaponize these networks to manipulate discourse, underscoring the need for more resilient social networks. To this end, we investigate the impact of network connectivity on Stackelberg equilibria in a two-player game to shape public opinion. We model opinion evolution as a repeated competitive influence-propagation process. Players iteratively inject \textit{messages} that diffuse until reaching a steady state, mo…

Analyzing the Effect of an Extreme Weather Event on Telecommunications and Information Technology: Insights from 30 Days of Flooding
Leandro M\'arcio Bertholdo, Renan Barreto Paredes, Gabriela de Lima Marin, Cesar A. H. Loureiro, Milton Kaoru Kashiwakura Pedro de Botelho Marcos
https://arxiv.org/abs/2509.04219

Analyzing the Effect of an Extreme Weather Event on Telecommunications and Information Technology: Insights from 30 Days of Flooding
In May 2024, weeks of severe rainfall in Rio Grande do Sul, Brazil caused widespread damage to infrastructure, impacting over 400 cities and 2.3 million people. This study presents the construction of comprehensive telecommunications datasets during this climatic event, encompassing Internet measurements, fiber cut reports, and Internet Exchange routing data. By correlating network disruptions with hydrological and operational factors, the dataset offers insights into the resilience of fiber ne…

On Optimality of Private Information in Bayesian Routing Games
Alexia Ambrogio, Leonardo Cianfanelli, Giacomo Como
https://arxiv.org/abs/2509.03357 https://

On Optimality of Private Information in Bayesian Routing Games
We study an information design problem in transportation networks, in the presence of a random state that affects the travel times on the links. An omniscient system planner -- aiming at reducing congestion -- observes the network state realization and sends private messages to the users -- who share a common prior on the network state but do not observe it directly -- in order to nudge them towards a socially desirable behavior. The desired effect of these private signals is to correlate the u…

Biological detail and graph structure in network neuroscience
David Papo, Javier M. Buld\'u
https://arxiv.org/abs/2507.15789 https://

Biological detail and graph structure in network neuroscience
Endowing brain anatomy, dynamics, and function with a network structure is becoming standard in neuroscience. In its simplest form, a network is a collection of units and relationships between them. The pattern of relations among the units encodes numerous properties which have been shown to have a profound effect on networked systems' dynamics and function. In an effort to strike a balance between idealization and detail, network neuroscience studies typically involve simplifying assumptions a…

Structure Maintained Representation Learning Neural Network for Causal Inference
Yang Sun, Wenbin Lu, Yi-Hui Zhou
https://arxiv.org/abs/2508.01865 https://…

Structure Maintained Representation Learning Neural Network for Causal Inference
Recent developments in causal inference have greatly shifted the interest from estimating the average treatment effect to the individual treatment effect. In this article, we improve the predictive accuracy of representation learning and adversarial networks in estimating individual treatment effects by introducing a structure keeper which maintains the correlation between the baseline covariates and their corresponding representations in the high dimensional space. We train a discriminator at …

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
Cameron T. Pratt, Zhijie Qu, Joel N. Bregman
https://arxiv.org/abs/2507.13400

Supervised Extraction of the Thermal Sunyaev$-$Zel'dovich Effect with a Three-Dimensional Convolutional Neural Network
The thermal Sunyaev$-$Zel'dovich (SZ) effect offers a unique probe of the hot and diffuse universe that could help close the missing baryon problem. Traditional extractions of the SZ effect, however, exhibit systematic noise that may lead to unreliable results. In this work, we provide an alternative solution using a three-dimensional Attention Nested U-Net trained end-to-end with supervised learning. Our labeled data consists of simulated SZ signals injected into $\textit{Planck}$ frequency ma…

Emergent Rashba spin-orbit coupling in bulk gold with buried network of nanoscale interfaces
Shreya Kumbhakar, Banashree Debnath, Tuhin Kumar Maji, Binita Tongbram, Shinjan Mandal, T. Phanindra Sai, T. V. Ramakrishnan, Manish Jain, H. R. Krishnamurthy, Anshu Pandey, Arindam Ghosh
https://arxiv.org/abs/2509.03620

Emergent Rashba spin-orbit coupling in bulk gold with buried network of nanoscale interfaces
The Rashba effect, which plays a crucial role in fundamental materials physics and potential spintronics applications, has been engineered in diverse systems, including semiconductor quantum wells, oxide heterostructures, metallic surfaces, topological insulators, ferroelectrics, etc. However, generating it in systems that preserve bulk inversion symmetry (BIS), for example, in bulk metals, has not been possible so far. We demonstrate a unique strategy to introduce and tune Rashba spin-orbit in…

Spin Seebeck Effect of Triangular-lattice Spin Supersolid
Yuan Gao, Yixuan Huang, Sadamichi Maekawa, Wei Li
https://arxiv.org/abs/2506.22414 https://

Spin Seebeck Effect of Triangular-lattice Spin Supersolid
Using our developed thermal tensor-network approach, we investigate the spin Seebeck effect (SSE) of the triangular-lattice quantum antiferromagnet hosting spin supersolid phase. We focus on the low-temperature scaling of normalized spin current $\tilde{I}_S$ through the interface, and benchmark our approach on 1D Heisenberg chain, with the negative spinon spin current reproduced. In addition, we find an algebraic scaling $\tilde{I}_S \sim T^α$ with varying exponent $α$ in the Tomonaga-Luttin…

Poroelasticity of bottlebrush and linear polymer networks
Nolan A. Miller, Alfred J. Crosby
https://arxiv.org/abs/2509.00022 https://arxiv.org/pdf/2509.000…

Poroelasticity of bottlebrush and linear polymer networks
The transport of solvent molecules through soft, swollen networks is critical in both natural and engineered systems. While this poroelastic flow has traditionally been explored in networks where the mesh size is comparable to the solvent molecule size, the effect of network architecture on permeability remains underexplored. Here we investigate solvent transport in linear polymer networks (LPN) and highly elastic bottlebrush elastomer networks (BBN), where the presence of densely grafted sidec…

Practical insights on the effect of different encodings, ans\"atze and measurements in quantum and hybrid convolutional neural networks
Jes\'us Lozano-Cruz, Albert Nieto-Morales, Oriol Ball\'o-Gimbernat, Adan Garriga, Ant\'on Rodr\'iguez-Otero, Alejandro Borrallo-Rentero
https://arxiv.org/abs/2506.20355

Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset. We systematically evaluate the performance implications of data encoding techniques, variational ansätze, and measurement in approx. 500 distinct model configurations. Our analysis reveals a clear hierarchy of influence on model performance. For h…

Multifractality and statistical localization in the sparse Barrat-M\'ezard trap model
Diego Tapias, Peter Sollich
https://arxiv.org/abs/2507.19030 https://

Multifractality and statistical localization in the sparse Barrat-Mézard trap model
We study within a paradigmatic model for glassy dynamics, the Barrat-Mézard trap model, the effect of a nontrivial network structure in the connectivity among traps. Sparseness of this network has recently been shown to lead to divergences in the bulk of the spectrum of the associated master operator [1, 2]. We analyse here specifically the properties of the relaxation modes that contribute to these spectral divergences. We characterize the statistics of the corresponding wavefunctions and dem…

Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators
Somdeep Dey, Abhisek Saha, Soma Sanyal
https://arxiv.org/abs/2507.19135

Classification of flavor dependence of Chiral Magnetic Effect with Deep Neural Network using multiple correlators
We study the flavor dependence of the Chiral Magnetic Effect (CME) by analyzing two key charge-separation correlators used to characterize the charge separation effect: the conventional $Δγ$ and the recently proposed $R_{ψ_2}$. Using the AMPT (A Multiphase Transport) model with an initial-state centrality-dependent charge separation, we evaluate the sensitivity of these correlators to 2-flavor ($u,d$) and 3-flavor ($u,d,s$) quark scenarios. While both correlators exhibit modest flavor depend…

Simulation-Prior Independent Neural Unfolding Procedure
Anja Butter, Theo Heimel, Nathan Huetsch, Michael Kagan, Tilman Plehn
https://arxiv.org/abs/2507.15084

Simulation-Prior Independent Neural Unfolding Procedure
Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding the forward mapping, making it independent of the prior from the simulated training data. It is made efficient through neural importance sampling, and ensembling can be used to estimate the effect of information loss in the forward process. We showcase SPINUP for unfolding detector effects on jet substructure observable…

Joint Fiber and Free Space Optical Infrastructure Planning for Hybrid Integrated Access and Backhaul Networks
Charitha Madapatha, Piotr Lechowicz, Carlos Natalino, Paolo Monti, Tommy Svensson
https://arxiv.org/abs/2507.20367

Joint Fiber and Free Space Optical Infrastructure Planning for Hybrid Integrated Access and Backhaul Networks
Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellular services in a multi-hop architecture. Due to the sensitivity of the backhaul links with high rate/reliability demands, proper network planning is needed to ensure the IAB network performs with the desired performance levels. In this paper, we study the effect of infrastructure planning and optimization on the cover…

And why the Social Web is going to give back choice and the network effect to people! #socialweb #opensocialweb #fediverse #fedicon

Revisiting the Effect of Grid-Following Converter on Frequency Dynamics -- Part II: Spatial Variation
Jiahao Liu, Cheng Wang, Tianshu Bi
https://arxiv.org/abs/2507.15362

Revisiting the Effect of Grid-Following Converter on Frequency Dynamics -- Part II: Spatial Variation
Besides the center of inertia (COI) frequency dynamics addressed in Part I, the spatial frequency variation in power systems with grid-following (GFL) converters is also crucial. Part II revisits the effect of GFLs on frequency spatial variation. Leveraging the interfacing state variables and equivalent frequency defined in Part I, an extended frequency divider (FD) formula is proposed. The linearized mapping relationship between network node frequency and synchronous generator (SG) rotor frequ…

PBPU Elastomer Network Architecture Determination via Corresponding States Analysis of Mechanical Behavior
Sushanta Das (Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India Defence Research,Development Organization, New Delhi, India), Hari Ramakrishna Sudhakar (Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India), Hemant Nanavati (Department of Chemical Engineering, Indian Institute of T…

PBPU Elastomer Network Architecture Determination via Corresponding States Analysis of Mechanical Behavior
In this work we examine the effect of R=[NCO]/[OH] in the R=<1 regime, on the resultant structural topology of polybutadiene polyurethane (PBPU) elastomer networks based on hydroxy-terminated polybutadiene (HTPB). We employ stress-elongation behavior and its modeling, as a tool. We examine this property via a combination of our model for the finite chain phantom networks incorporating the HTPB structural information, with the slip-tube model from the literature, suitably modified phenomenologic…

Role of bubble positioning in force induced melting of DNA
Bidisha Mukherjee, Amit Raj Singh, Garima Mishra
https://arxiv.org/abs/2506.18821 https://

Role of bubble positioning in force induced melting of DNA
We investigate the role of bubble positioning in the force-induced melting of double-stranded DNA using two distinct approaches: Brownian Dynamics simulations and the Gaussian Network Model. We isolate the effect of bubble positioning by using DNA molecules with 50% AT - 50% GC base-pair composition which ensures constant enthalpy. Our results reveal that it is not just the sequence itself, but its specific arrangement that influences DNA stability. We examine two types of DNA sequences contain…