Tootfinder

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

This https://arxiv.org/abs/2506.04668 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Feature-Based Lie Group Transformer for Real-World Applications
The main goal of representation learning is to acquire meaningful representations from real-world sensory inputs without supervision. Representation learning explains some aspects of human development. Various neural network (NN) models have been proposed that acquire empirically good representations. However, the formulation of a good representation has not been established. We recently proposed a method for categorizing changes between a pair of sensory inputs. A unique feature of this approa…

A cavity array microscope for parallel single-atom interfacing
Adam L. Shaw, Anna Soper, Danial Shadmany, Aishwarya Kumar, Lukas Palm, Da-Yeon Koh, Vassilios Kaxiras, Lavanya Taneja, Matt Jaffe, David I. Schuster, Jonathan Simon
https://arxiv.org/abs/2506.10919

A cavity array microscope for parallel single-atom interfacing
Neutral atom arrays and optical cavity QED systems have developed in parallel as central pillars of modern experimental quantum science. While each platform has demonstrated exceptional capabilities-such as high-fidelity quantum logic in atom arrays, and strong light-matter coupling in cavities-their combination holds promise for realizing fast and non-destructive atom measurement, building large-scale quantum networks, and engineering hybrid atom-photon Hamiltonians. However, to date, experime…

A Review of Cloud Computing in Seismology
Yiyu Ni, Marine A. Denolle, Jannes Munchmeyer, Yinzhi Wang, Kuan-Fu Feng, Carlos Garcia Jurado Suarez, Amanda M. Thomas, Chad Trabant, Alex Hamilton, David Mencin
https://arxiv.org/abs/2506.11307

A Review of Cloud Computing in Seismology
Seismology has entered the petabyte era, driven by decades of continuous recordings of broadband networks, the increase in nodal seismic experiments, and the recent emergence of Distributed Acoustic Sensing (DAS). This review explains how commercial clouds - AWS, Google Cloud, and Azure - by providing object storage, elastic compute, and managed databases, enable researchers to "bring the code to the data," thereby overcoming traditional HPC solutions' bandwidth and capacity limitations. After …

Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
Xiangzhu Kong, Huang Hao, Zhijian Ou
https://arxiv.org/abs/2506.11630

Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform
This paper presents SHTNet, a lightweight spherical harmonic transform (SHT) based framework, which is designed to address cross-array generalization challenges in multi-channel automatic speech recognition (ASR) through three key innovations. First, SHT based spatial sound field decomposition converts microphone signals into geometry-invariant spherical harmonic coefficients, isolating signal processing from array geometry. Second, the Spatio-Spectral Attention Fusion Network (SSAFN) combines …

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Shinnosuke Hattori, Kohei Shimamura, Aiichiro Nakano, Rajiv K. Kalia, Priya Vashishta, Ken-ichi Nomura
https://arxiv.org/abs/2506.07579

Beyond Scaling: Chemical Intuition as Emergent Ability of Universal Machine Learning Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) have successfully demonstrated scaling behavior, i.e. the power-law improvement in training performance, however the emergence of novel capabilities at scale remains unexplored. We have developed Edge-wise Emergent Decomposition (E3D) framework to investigate how an MLIP develops the ability to derive physically meaningful local representations of chemical bonds without explicit supervision. Employing an E(3)-equivariant network (Allegro) trained …

This https://arxiv.org/abs/2505.20896 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

How Do Transformers Learn Variable Binding in Symbolic Programs?
Variable binding -- the ability to associate variables with values -- is fundamental to symbolic computation and cognition. Although classical architectures typically implement variable binding via addressable memory, it is not well understood how modern neural networks lacking built-in binding operations may acquire this capacity. We investigate this by training a Transformer to dereference queried variables in symbolic programs where variables are assigned either numerical constants or other …

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…