Tootfinder

Estimating the heterogeneity of pressure profiles within a complete sample of 55 galaxy clusters: a Bayesian Hierarchical Model
Fabio Castagna, Stefano Andreon, Marco Landoni, Alberto Trombetta
https://arxiv.org/abs/2509.07095

Estimating the heterogeneity of pressure profiles within a complete sample of 55 galaxy clusters: a Bayesian Hierarchical Model
Galaxy clusters exhibit heterogeneity in their pressure profiles, even after rescaling, highlighting the need for adequately sized samples to accurately capture variations across the cluster population. We present a Bayesian hierarchical model that simultaneously fits individual cluster parameters and the underlying population distribution, providing estimates of the population-averaged pressure profile and the intrinsic scatter, as well as accurate pressure estimates for individual objects. We…

Beyond Hierarchical Mergers: Accretion-Driven Origins of Massive, Highly Spinning Black Holes in Dense Star Clusters
Fulya K{\i}ro\u{g}lu, Kyle Kremer, Frederic A. Rasio
https://arxiv.org/abs/2509.05415

Beyond Hierarchical Mergers: Accretion-Driven Origins of Massive, Highly Spinning Black Holes in Dense Star Clusters
GW231123, the most massive binary black hole (BBH) merger detected by LIGO/Virgo/KAGRA, highlights the need to understand the origins of massive, high-spin stellar black holes (BHs). Dense star clusters provide natural environments for forming such systems, beyond the limits of standard massive star evolution to core collapse. While repeated BBH mergers can grow BHs through dynamical interactions (the so-called "hierarchical merger" channel), most star clusters with masses $\lesssim 10^6\,M_\od…

Bayesian Nonparametric Dynamical Clustering of Time Series
Adri\'an P\'erez-Herrero, Paulo F\'elix, Jes\'us Presedo, Carl Henrik Ek
https://arxiv.org/abs/2510.06919

Bayesian Nonparametric Dynamical Clustering of Time Series
We present a method that models the evolution of an unbounded number of time series clusters by switching among an unknown number of regimes with linear dynamics. We develop a Bayesian non-parametric approach using a hierarchical Dirichlet process as a prior on the parameters of a Switching Linear Dynamical System and a Gaussian process prior to model the statistical variations in amplitude and temporal alignment within each cluster. By modeling the evolution of time series patterns, the method…

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
Wenyang Jia, Jingjing Wang, Ziwei Yan, Xiangli Peng, Guohui Yuan
https://arxiv.org/abs/2510.08139

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
The scalability of blockchain systems is constrained by inefficient P2P broadcasting, as most existing optimizations focus only on the logical layer without considering physical network conditions. To address this, we propose BlockSDN, the first SDN-based integrated architecture for blockchain. BlockSDN employs a distributed control plane for a global network view, a graph engine for hierarchical clustering, and a hybrid macro-micro neighbor selection with hierarchical broadcasting. A dedicated…

I hold that genuine freedom can only be realized through the direct self-management of labor and the collective organization of social life by those engaged in it. No political vanguard, governmental apparatus, or capitalist hierarchy can provide emancipation, it must emerge from the autonomous initiative of workers acting in solidarity, constructing federations of production and community based on voluntary association and mutual responsibility.
For me, anarcho-syndicalism represents …

Approximation in Lattice Field Theories
Thomas Banks, Anish Suresh
https://arxiv.org/abs/2509.08501 https://arxiv.org/pdf/2509.08501

Approximation in Lattice Field Theories
We investigate an approximation to the Schwinger-Dyson (SD) equations of the collective Coulomb field of the large $N$ homogeneous electron fluid. The large $N$ approximation transforms the infinite SD hierarchy is into a set of closed, equations for 1 and 2-pt correlators. In this paper, the dynamics of a toy model - a small square Euclidean lattice with periodic boundary conditions - are considered. The Markov Chain Monte Carlo numerical method evaluated the 1 and 2-pt correlation functions o…

Hierarchical community detection via maximum entropy partitions and the renormalization group
Jorge Martinez Armas
https://arxiv.org/abs/2508.04034 https://

Hierarchical community detection via maximum entropy partitions and the renormalization group
Identifying meaningful structure across multiple scales remains a central challenge in network science. We introduce Hierarchical Clustering Entropy (HCE), a general and model-agnostic framework for detecting informative levels in hierarchical community structures. Unlike existing approaches, HCE operates directly on dendrograms without relying on edge-level statistics. It selects resolution levels that maximize a principled trade-off between the entropy of the community size distribution and t…

Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich
https://arxiv.org/abs/2510.04774 https://

Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the …

A Dynamic Allocation Scheme for Adaptive Shared-Memory Mapping on Kilo-core RV Clusters for Attention-Based Model Deployment
Bowen Wang, Marco Bertuletti, Yichao Zhang, Victor J. B. Jung, Luca Benini
https://arxiv.org/abs/2508.01180

A Dynamic Allocation Scheme for Adaptive Shared-Memory Mapping on Kilo-core RV Clusters for Attention-Based Model Deployment
Attention-based models demand flexible hardware to manage diverse kernels with varying arithmetic intensities and memory access patterns. Large clusters with shared L1 memory, a common architectural pattern, struggle to fully utilize their processing elements (PEs) when scaled up due to reduced throughput in the hierarchical PE-to-L1 intra-cluster interconnect. This paper presents Dynamic Allocation Scheme (DAS), a runtime programmable address remapping hardware unit coupled with a unified memo…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Ryan DeWolfe
https://arxiv.org/abs/2509.02334 https://arxiv.org/p…

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers
Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based Spatial Clustering for Applications with Noise (HDBSCAN) has emerged as a leading clustering algorithm that allows for outlier points that do not belong to any cluster. The first step in HDBSCAN is to redefine the distance between vectors in such a way that single-…