Tootfinder

Statistical Inference for Subgraph Frequencies of Exchangeable Hyperedge Models
Ayoushman Bhattacharya, Nilanjan Chakraborty, Robert Lunde
https://arxiv.org/abs/2508.13258 https…

Statistical Inference for Subgraph Frequencies of Exchangeable Hyperedge Models
In statistical network analysis, models for binary adjacency matrices satisfying vertex exchangeability are commonly used. However, such models may fail to capture key features of the data-generating process when interactions, rather than nodes, are fundamental units. We study statistical inference for subgraph counts under an exchangeable hyperedge model. We introduce several classes of subgraph statistics for hypergraphs and develop inferential tools for subgraph frequencies that account for …

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…

Distributed Coherent Beamforming at 60 GHz Enabled by Optically-Established Coherence
Drake Silbernagel, Yu Rong, Isabella Lenz, Prithvi Hemanth, Carl Morgenstern, Owen Ma, Nolan Matthews, Nader Zaki, Kyle W. Martin, John D. Elgin, Jacob Holtom, Daniel W. Bliss, Kimberly Frey
https://arxiv.org/abs/2509.13984

Distributed Coherent Beamforming at 60 GHz Enabled by Optically-Established Coherence
We implement and experimentally demonstrate a 60 GHz distributed system leveraging an optical time synchronization system that provides precise time and frequency alignment between independent elements of the distributed mesh. Utilizing such accurate coherence, we perform receive beamforming with interference rejection and transmit nulling. In these configurations, the system achieves a coherent gain over an incoherent network of N nodes, significantly improving the relevant signal power ratios…

Superintegrable bosonic star networks
Angela Foerster, Jon Links
https://arxiv.org/abs/2509.05590 https://arxiv.org/pdf/2509.05590

Superintegrable bosonic star networks
We introduce a class of bosonic star networks involving a central site interacting with the surrounding environment sites. These networks are shown to be superintegrable. We present two forms of Bethe Ansatz solution providing expressions for the energy eigenvalues. A brief discussion is included on the potential applications.

Assumption-lean Inference for Network-linked Data
Wei Li, Nilanjan Chakraborty, Robert Lunde
https://arxiv.org/abs/2510.00287 https://arxiv.org/pdf/2510.00…

Assumption-lean Inference for Network-linked Data
We consider statistical inference for network-linked regression problems, where covariates may include network summary statistics computed for each node. In settings involving network data, it is often natural to posit that latent variables govern connection probabilities in the graph. Since the presence of these latent features makes classical regression assumptions even less tenable, we propose an assumption-lean framework for linear regression with jointly exchangeable regression arrays. We …

A Deep Neural Network Codebook Approach for Near-Field Nulling Control Beam Focusing
Mohammadhossein Karimi, Yuanzhe Gong, Tho Le-Ngoc
https://arxiv.org/abs/2509.22204 https://

A Deep Neural Network Codebook Approach for Near-Field Nulling Control Beam Focusing
This paper proposes a deep neural network (DNN) codebook approach for multi-user interference (MUI) mitigation in extremely large multiple-input multiple-output (XL-MIMO) systems operating in the near-field region. Unlike existing DNN-based nulling control beamforming (NCBF) methods that face scalability and complexity challenges, the proposed framework partitions the Fresnel region using correlation-based sampling and assigns a lightweight fully connected DNN model to each subsection. Each mod…

On the model attractor in a high-dimensional neural network dynamics of reservoir computing
Miki U. Kobayashi, Kengo Nakai, Yoshitaka Saiki, Natsuki Tsutsumi
https://arxiv.org/abs/2509.12733

On the model attractor in a high-dimensional neural network dynamics of reservoir computing
The theory of embedding and generalized synchronization in reservoir computing has recently been developed. Under ideal conditions, reservoir computing exhibits generalized synchronization during the learning process. These insights form a rigorous basis for understanding reservoir computing ability to reconstruct and predict complex dynamics. In this study, we clarified the dynamical system structures of generalized synchronization and embedding by comparing the Lyapunov exponents of a high di…

Replaced article(s) found for nlin.CD. https://arxiv.org/list/nlin.CD/new
[1/1]:
- Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic D...
Kuei-Jan Chu, Nozomi Akashi, Akihiro Yamamoto

DNN-Based Nulling Control Beam Focusing for Near-Field Multi-User Interference Mitigation
Mohammadhossein Karimi, Yuanzhe Gong, Tho Le-Ngoc
https://arxiv.org/abs/2509.19594 http…

DNN-Based Nulling Control Beam Focusing for Near-Field Multi-User Interference Mitigation
This paper proposes a deep learning-based framework for near-field nulling control beam focusing (NCBF) in extra-large MIMO (XL-MIMO) systems to mitigate multi-user interference (MUI). A dual-estimator architecture comprising two fully connected deep neural networks (FCDNNs) is developed to separately predict the phase and magnitude components of NCBF weights, using locations of both desired and interfering users. The models are trained on a large dataset generated via a Linearly Constrained Mi…

Enhancing cluster synchronization in phase-lagged multilayer networks
Abhijit Mondal, Pitambar Khanra, Subrata Ghosh, Prosenjit Kundu, Chittaranjan Hens, Pinaki Pal
https://arxiv.org/abs/2508.06502

Enhancing cluster synchronization in phase-lagged multilayer networks
Cluster synchronization in multilayer networks of phase oscillators with phase-lag poses significant challenges due to the destabilizing effects of delayed interactions. Leveraging the Sakaguchi-Kuramoto model, this study addresses these challenges by systematically exploring the role of natural frequency distributions in sustaining cluster synchronization under high phase-lag conditions. We focus on four distributions: uniform (uni-uni), partially degree-correlated (deg-uni, uni-deg), and full…