Tootfinder

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
Vu Duc Anh Nguyen, Ziyue Li
https://arxiv.org/abs/2510.09350 https://…

Deep Learning to Identify the Spatio-Temporal Cascading Effects of Train Delays in a High-Density Network
The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railways, a significant gap remains in developing frameworks that provide multi-step autoregressive forecasts at a network-wide scale, while sim…

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Yuhua Zhang, Jukka-Pekka Onnela, Shuo Sun, Ruoyu Wang
https://arxiv.org/abs/2510.10508 …

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network
Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most existing methods for estimating interference effects assume that the interference networks are known. In many practical settings, this assumption is unrealistic as such networks are typically latent. To address this challenge, we propose a novel framework for identifying and estimating heterogeneous group-level interferen…

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
Donghan Hu, Rameen Mahmood, Annabelle David, Danny Yuxing Huang
https://arxiv.org/abs/2510.09763

Network Traffic as a Scalable Ethnographic Lens for Understanding University Students' AI Tool Practices
AI-driven applications have become woven into students' academic and creative workflows, influencing how they learn, write, and produce ideas. Gaining a nuanced understanding of these usage patterns is essential, yet conventional survey and interview methods remain limited by recall bias, self-presentation effects, and the underreporting of habitual behaviors. While ethnographic methods offer richer contextual insights, they often face challenges of scale and reproducibility. To bridge this gap…

Efficient and accurate tensor network algorithm for Anderson impurity problems
Zhijie Sun, Zhenyu Li, Chu Guo
https://arxiv.org/abs/2510.11459 https://arxi…

Efficient and accurate tensor network algorithm for Anderson impurity problems
The Anderson impurity model (AIM) is of fundamental importance in condensed matter physics to study strongly correlated effects. However, accurately solving its long-time dynamics still remains a great numerical challenge. An emergent and rapidly developing numerical strategy to solve the AIM is to represent the Feynman-Vernon influence functional (IF), which encodes all the bath effects on the impurity dynamics, as a matrix product state (MPS) in the temporal domain. The computational cost of …

Interplay of choice and topology in percolation on mediation-driven attachment networks
Nilomber Roy, M. M. B. Sheraj, M. K. Hassan
https://arxiv.org/abs/2510.10076 https://

Interplay of choice and topology in percolation on mediation-driven attachment networks
We investigate bond percolation on mediation-driven attachment (MDA) networks under the generalized Achlioptas process, where $M>1$ candidate bonds are sampled and the one that minimizes the resulting cluster size is selected the best-of-$M$ rule. This framework offers a systematic approach to investigate how network topology and choice mechanisms jointly shape percolation behavior. We analyze the effects of the degree exponent $ω$ and the choice parameter $M$ on the critical point $t_c$ and t…

Uncovering All Highly Credible Binary Treatment Hierarchy Questions in Network Meta-Analysis
Caitlin H. Daly, Chloe Tan, Audrey B\'eliveau
https://arxiv.org/abs/2510.08853 h…

Uncovering All Highly Credible Binary Treatment Hierarchy Questions in Network Meta-Analysis
In recent years, there has been growing research interest in addressing treatment hierarchy questions within network meta-analysis (NMA). In NMAs involving many treatments, the number of possible hierarchy questions becomes prohibitively large. To manage this complexity, previous work has recommended pre-selecting specific hierarchy questions of interest (e.g., ``among options A, B, C, D, E, do treatments A and B have the two best effects in terms of improving outcome X?") and calculating the e…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
Yu Liu, Wenwen Li, Yifan Dou, Guangnan Ye
https://arxiv.org/abs/2510.06903 htt…

When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI
As artificial intelligence (AI) enters the agentic era, large language models (LLMs) are increasingly deployed as autonomous agents that interact with one another rather than operate in isolation. This shift raises a fundamental question: how do machine agents behave in interdependent environments where outcomes depend not only on their own choices but also on the coordinated expectations of peers? To address this question, we study LLM agents in a canonical network-effect game, where economic …

Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
Achraf Zinihi
https://arxiv.org/abs/2509.22760 https://

Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
We develop a physics-informed neural network (PINN) framework for parameter estimation in fractional-order SEIRD epidemic models. By embedding the Caputo fractional derivative into the network residuals via the L1 discretization scheme, our method simultaneously reconstructs epidemic trajectories and infers both epidemiological parameters and the fractional memory order $α$. The fractional formulation extends classical integer-order models by capturing long-range memory effects in disease prog…

The impact of observation losses on IVS-R1/R4 VLBI sessions
Tiege McCarthy, Lucia McCallum
https://arxiv.org/abs/2510.11436 https://arxiv.org/pdf/2510.1143…

The impact of observation losses on IVS-R1/R4 VLBI sessions
Global VLBI observations, to measure Earth orientation and station positions, are organised into 24-hour sessions. Each session has a bespoke schedule created, optimised for the particular time period and the station network that is available during it. Due to various factors, whether it be station outages, sensitivity issues or source effects, not all scheduled observations are available, or of sufficient quality, to be included in the final geodetic analysis. In this paper we derive statistic…

MEGATRON: the impact of non-equilibrium effects and local radiation fields on the circumgalactic medium at cosmic noon
Corentin Cadiou, Harley Katz, Martin P. Rey, Oscar Agertz, Jeremy Blaizot, Alex J. Cameron, Nicholas Choustikov, Julien Devriendt, Uliana Hauk, Gareth C. Jones, Taysun Kimm, Isaac Laseter, Sergio Mart\'in \'Alvarez, Kosei Matsumoto, Camilla T. Nyhagen, Autumn Pearce, Francisco Rodr\'iguez Montero, Joki Rosdahl, V\'ictor Rufo Pastor, Mahsa Sanati, Aayush…

MEGATRON: the impact of non-equilibrium effects and local radiation fields on the circumgalactic medium at cosmic noon
We present three cosmological radiation-hydrodynamic zoom simulations of the progenitor of a Milky Way-mass galaxy from the MEGATRON suite. The simulations combine on-the-fly radiative transfer with a detailed non-equilibrium thermochemical network (81 ions and molecules), resolving the cold and warm gas in the circumgalactic medium (CGM) on spatial scales down to 20 pc and on average 200 pc at cosmic noon. Comparing our full non-equilibrium calculation with local radiation to traditional post-…

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Cristian F. Jim\'enez-Var\'on, Marina I. Knight
https://arxiv.org/abs/2510.06157

A GNAR-Based Framework for Spectral Estimation of Network Time Series: Application to Global Bank Network Connectedness
Patterns of dependence in financial networks, such as global bank connectedness, evolve over time and across frequencies. Analysing these systems requires statistical tools that jointly capture temporal dynamics and the underlying network topology. This work develops a novel spectral analysis framework for Generalized Network Autoregressive (GNAR) processes, modeling dependencies beyond direct neighbours by incorporating r-stage neighbourhood effects, unlike existing methods that at best rely s…

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Muhammad Kabeer, Rosdiadee Nordin, Mehran Behjati, Lau Sian Lun
https://arxiv.org/abs/2510.02958

Sequence-Based Deep Learning for Handover Optimization in Dense Urban Cellular Network
Efficient handover management remains a critical challenge in dense urban cellular networks, where high cell density, user mobility, and diverse service demands increase the likelihood of unnecessary handovers and ping-pong effects. This paper leverages a real-world, multi-operator drive-test dataset of 30,925 labelled records collected within a 2 km area around Sunway City to investigate sequence-based deep learning approaches for handover detection and avoidance. We formulate handover predict…

The effects and interactions of epiretinal electrical stimulation parameters on direct and network-mediated activation of retinal ganglion cells #NeuroTech

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee
https://arxiv.org/abs/2509.22758

Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symmetric XY Hamiltonian; the ancilla interacts with a noisy environment and is the only qubit we measure. A feedforward neural network, trained on short sliding windows of supplementary data from the past, forecasts the observable system $\langle Z_{(S)…

Triadic Network Formation
Chris Muris, Cavit Pakel
https://arxiv.org/abs/2509.26420 https://arxiv.org/pdf/2509.26420

Triadic Network Formation
We study estimation and inference for triadic link formation with dyad-level fixed effects in a nonlinear binary choice logit framework. Dyad-level effects provide a richer and more realistic representation of heterogeneity across pairs of dimensions (e.g. importer--exporter, importer--product, exporter--product), yet their sheer number creates a severe incidental parameter problem. We propose a novel ``hexad logit'' estimator and establish its consistency and asymptotic normality. Identificati…

About 20 years ago, I was part of the Neighborhood Network - people concerned about good planning and good governance. The Mayor's office was pushing to allow more nmarket rate housing. When Jim Meko and I met with the then housing czar, he explained supply and demand. When I argued commodity vs luxury item and profit motive, he admitted that unrestricted building would reduce prices by maybe 3%. Very close to the current conclusion.

S.F. upzoning would lead to $75 to $125 decrease in monthly rent, city economist says
Citywide zoning changes would have modest effects on housing construction and affordability, but big boost to GDP

About 20 years ago, I was part of the Neighborhood Network - people concerned about good planning and good governance. The Mayor's office was pushing to allow more nmarket rate housing. When Jim Meko and I met with the then housing czar, he explained supply and demand. When I argued commodity vs luxury item and profit motive, he admitted that unrestricted building would reduce prices by maybe 3%. Very close to the current conclusion.

S.F. upzoning would lead to $75 to $125 decrease in monthly rent, city economist says
Citywide zoning changes would have modest effects on housing construction and affordability, but big boost to GDP

Pretty fascinating - can a chat app with a #Tamil name really be a threat to #Whatsapp's dominance at the #chatapp in #India

Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects
Supantha Dey, Ratul Chowdhury
https://arxiv.org/abs/2509.22950

Twin Peaks: Dual-Head Architecture for Structure-Free Prediction of Protein-Protein Binding Affinity and Mutation Effects
We present a novel dual-head deep learning architecture for protein-protein interaction modeling that enables simultaneous prediction of binding affinity ($ΔG$) and mutation-induced affinity changes ($ΔΔG$) using only protein sequence information. Our approach offers a significant advancement over existing methods by employing specialized prediction heads that operate on a shared representation network, allowing direct and optimized prediction of both values. To ensure robust generalization,…

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Shengjiao Ji, Yujin Zhang, Zihan Zou, Bin Jiang, Jun Jiang, Yi Luo, Wei Hu
https://arxiv.org/abs/2510.04227

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction
Accurate and efficient simulation of infrared (IR) and Raman spectra is essential for molecular identification and structural analysis. Traditional quantum chemistry methods based on the harmonic approximation neglect anharmonicity and nuclear quantum effects, while ab initio molecular dynamics (AIMD) remains computationally expensive. Here, we integrate our deep equivariant tensor attention network (DetaNet) with a velocity-Verlet integrator to enable fast and accurate machine learning molecul…

Difference-in-Differences Under Network Interference
Kuan Sun, Zhiguo Xiao
https://arxiv.org/abs/2509.24259 https://arxiv.org/pdf/2509.24259

Difference-in-Differences Under Network Interference
This paper develops doubly robust estimators for direct (DATT) and spillover (SATT) average treatment effects on the treated in network-based difference-in-differences (DiD) designs. Unlike standard DiD methods, the proposed approach explicitly accommodates treatment spillovers and high-dimensional network confounding arising from complex inter-unit dependencies. Identification relies on a conditional parallel-trends assumption that holds after adjusting for high-dimensional network confounders…

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
Walker Gosrich, Saurav Agarwal, Kashish Garg, Siddharth Mayya, Matthew Malencia, Mark Yim, Vijay Kumar
https://arxiv.org/abs/2509.15052

Online Multi-Robot Coordination and Cooperation with Task Precedence Relationships
We propose a new formulation for the multi-robot task allocation problem that incorporates (a) complex precedence relationships between tasks, (b) efficient intra-task coordination, and (c) cooperation through the formation of robot coalitions. A task graph specifies the tasks and their relationships, and a set of reward functions models the effects of coalition size and preceding task performance. Maximizing task rewards is NP-hard; hence, we propose network flow-based algorithms to approximat…

Replaced article(s) found for physics.optics. https://arxiv.org/list/physics.optics/new
[1/1]:
- LLM4Laser: Large Language Models Automate the Design of Lasers
Renjie Li, Ceyao Zhang, Sixuan Mao, Xiyuan Zhou, Feng Yin, Sergios Theodoridis, Zhaoyu Zhang
https://arxiv.org/abs/2104.12145
- Room-temperature valley-selective emission in Si-MoSe2 heterostructures enabled by high-quality-f...
Feng Pan, et al.
https://arxiv.org/abs/2409.09806 https://mastoxiv.page/@arXiv_physicsoptics_bot/113152185040115763
- 1T'-MoTe$_2$ as an integrated saturable absorber for photonic machine learning
Maria Carolina Volpato, Henrique G. Rosa, Tom Reep, Pierre-Louis de Assis, Newton Cesario Frateschi
https://arxiv.org/abs/2507.16140 https://mastoxiv.page/@arXiv_physicsoptics_bot/114901571498004090
- NeOTF: Guidestar-free neural representation for broadband dynamic imaging through scattering
Yunong Sun, Fei Xia
https://arxiv.org/abs/2507.22328 https://mastoxiv.page/@arXiv_physicsoptics_bot/114947052118796753
- Structured Random Models for Phase Retrieval with Optical Diffusers
Zhiyuan Hu, Fakhriyya Mammadova, Juli\'an Tachella, Michael Unser, Jonathan Dong
https://arxiv.org/abs/2510.14490 https://mastoxiv.page/@arXiv_physicsoptics_bot/115388901264416806
- Memory Effects in Time-Modulated Radiative Heat Transfer
Riccardo Messina, Philippe Ben-Abdallah
https://arxiv.org/abs/2510.19378 https://mastoxiv.page/@arXiv_physicsoptics_bot/115422659227231796
- Mie-tronics supermodes and symmetry breaking in nonlocal metasurfaces
Thanh Xuan Hoang, Ayan Nussupbekov, Jie Ji, Daniel Leykam, Jaime Gomez Rivas, Yuri Kivshar
https://arxiv.org/abs/2511.03560 https://mastoxiv.page/@arXiv_physicsoptics_bot/115502066008543828
- Integrated soliton microcombs beyond the turnkey limit
Wang, Xu, Wang, Zhu, Luo, Luo, Wang, Ni, Yang, Gong, Xiao, Li, Yang
https://arxiv.org/abs/2511.06909 https://mastoxiv.page/@arXiv_physicsoptics_bot/115530791701071777
- Ising accelerator with a reconfigurable interferometric photonic processor
Rausell-Campo, Al Kayed, P\'erez-L\'ppez, Aadhi, Shastri, Francoy
https://arxiv.org/abs/2511.13284 https://mastoxiv.page/@arXiv_physicsoptics_bot/115570439939074488
- Superradiance in dense atomic samples
I. M. de Ara\'ujo, H. Sanchez, L. F. Alves da Silva, M. H. Y. Moussa
https://arxiv.org/abs/2504.20242 https://mastoxiv.page/@arXiv_quantph_bot/114425762810828336
- Fluctuation-induced Hall-like lateral forces in a chiral-gain environment
Daigo Oue, M\'ario G. Silveirinha
https://arxiv.org/abs/2507.14754 https://mastoxiv.page/@arXiv_condmatmeshall_bot/114896308178114535
- Tensor-network approach to quantum optical state evolution beyond the Fock basis
Nikolay Kapridov, Egor Tiunov, Dmitry Chermoshentsev
https://arxiv.org/abs/2511.15295 https://mastoxiv.page/@arXiv_quantph_bot/115581390666689204
- OmniLens : Blind Lens Aberration Correction via Large LensLib Pre-Training and Latent PSF Repres...
Jiang, Qian, Gao, Sun, Yang, Yi, Li, Yang, Van Gool, Wang
https://arxiv.org/abs/2511.17126 https://mastoxiv.page/@arXiv_eessIV_bot/115603729319581340
toXiv_bot_toot

Modeling Bilateral Lymphatic Head and Neck Tumour Progression for Personalized Elective Target Volume Definition
Kristoffer Moos, Anne Ivalu Sander Holm, Yoel Perez Haas, Roman Ludwig, Jesper Grau Eriksen, Stine Sofia Korreman
https://arxiv.org/abs/2509.24911

Modeling Bilateral Lymphatic Head and Neck Tumour Progression for Personalized Elective Target Volume Definition
Objective: Large irradiated volumes are a major contributor to severe side-effects in patients with head and neck cancer undergoing curatively intended radiotherapy. We propose a data-driven approach for defining the elective clinical target volume (CTV-E) on a patient-specific basis, with the potential to reduce irradiated volumes compared to standard guidelines. Approach: We introduce a bilateral Bayesian Network (BN), trained on a large cohort, to estimate the patient-specific risk of undete…

Dynamic effects of electric field in hybrid coupling thermosensitive neuronal network
Ediline L. F. Nguessap (Department of Physics, FFCLRP, Sao Paulo, Brazil), Antonio C. Roque (Department of Physics, FFCLRP, Sao Paulo, Brazil), Fernando F. Ferreira (Department of Physics, FFCLRP, Sao Paulo, Brazil)
https://arxiv.org/abs/2509.14910…

Dynamic effects of electric field in hybrid coupling thermosensitive neuronal network
The dynamic of thermosensitive neuronal networks under the influence of external electric fields is explored, focusing on hybrid coupling models that incorporate both electrical and chemical synapses. Numerical simulations reveal a variety of complex behaviors, including coherent, incoherent, and chimera states, which are influenced by the frequency of the external field and the cell size. Specificaly chemical coupling is shown to enhances the appearance of a typical chimera states called trave…