Tootfinder

An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
Yuxi Cai, Lan Li, Yize Wang, Guodong Li
https://arxiv.org/abs/2506.01658

An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series
In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modeling but loses its efficiency rapidly with increasing tensor order. Conversely, canonical polyadic (CP) decomposition maintains efficiency but lacks a precise statistical interpretation. To attain both interpretability and powerful dimension reduction, this paper proposes a novel approach under the supervised factor mode…

Tor algebra of local rings with decomposable maximal ideal
Saeed Nasseh, Maiko Ono, Yuji Yoshino
https://arxiv.org/abs/2507.01784 https://

Tor algebra of local rings with decomposable maximal ideal
Let $(R,{\frak{m}}_R)$ be a commutative noetherian local ring. Assuming that ${\frak{m}}_R=$$I\oplus J$ is a direct sum decomposition, where $I$ and $J$ are non-zero ideals of $R$, we describe the structure of the Tor algebra of $R$ in terms of the Tor algebras of the rings $R/I$ and $R/J$.

A New State of Matter between the Hadronic Phase and the Quark-Gluon Plasma?
Yuki Fujimoto, Kenji Fukushima, Yoshimasa Hidaka, Larry McLerran
https://arxiv.org/abs/2506.00237

A New State of Matter between the Hadronic Phase and the Quark-Gluon Plasma?
Lattice-QCD simulations and theoretical arguments hint at the existence of an intermediate phase of strongly interacting matter between a confined hadron gas and a deconfined Quark-Gluon Plasma (QGP). We qualitatively and semi-quantitatively explore and differentiate the phase structures in the temperature window from the QCD pseudo-critical temperature $T_c\simeq 160\;\text{MeV}$ to the pure-gluonic deconfinement temperature $T_d\simeq 285\;\text{MeV}$. We propose a three-regime picture using …

Multilevel Decomposition of Generalized Entropy Measures Using Constrained Bayes Estimation: An Application to Japanese Regional Data
Yuki Kawakubo, Kazuhiko Kakamu
https://arxiv.org/abs/2506.21213

Multilevel Decomposition of Generalized Entropy Measures Using Constrained Bayes Estimation: An Application to Japanese Regional Data
We propose a method for multilevel decomposition of generalized entropy (GE) measures that explicitly accounts for nested population structures such as national, regional, and subregional levels. Standard approaches that estimate GE separately at each level do not guarantee compatibility with multilevel decomposition. Our method constrains lower-level GE estimates to match higher-level benchmarks while preserving hierarchical relationships across layers. We apply the method to Japanese income d…

How fake news can turn against its spreader
Dorje C. Brody, Tomooki Yuasa
https://arxiv.org/abs/2504.17820 https://arxiv.org/pdf/2504…

Circular Directional Flow Decomposition of Networks
Marc Homs-Dones, Robert S. MacKay, Bazil Sansom, Yijie Zhou
https://arxiv.org/abs/2506.12546 https://…

Circular Directional Flow Decomposition of Networks
We introduce the Circular Directional Flow Decomposition (CDFD), a new framework for analyzing circularity in weighted directed networks. CDFD separates flow into two components: a circular (divergence-free) component and an acyclic component that carries all nett directional flow. This yields a normalized circularity index between 0 (fully acyclic) and 1 (for networks formed solely by the superposition of cycles), with the complement measuring directionality. This index captures the proportion…

Bayesian decomposition using Besov priors
Andreas Horst, Babak Maboudi Afkham, Yiqiu Dong, Jakob Lemvig
https://arxiv.org/abs/2506.18846 https://

Bayesian decomposition using Besov priors
In many inverse problems, the unknown is composed of multiple components with different regularities, for example, in imaging problems, where the unknown can have both rough and smooth features. We investigate linear Bayesian inverse problems, where the unknown consists of two components: one smooth and one piecewise constant. We model the unknown as a sum of two components and assign individual priors on each component to impose the assumed behavior. We propose and compare two prior models: (i…

Orbital Pumping in Ferrimagnetic Insulators
Hanchen Wang, Min-Gu Kang, Davit Petrosyan, Shilei Ding, Richard Schlitz, Lauren J. Riddiford, William Legrand, Pietro Gambardella
https://arxiv.org/abs/2506.11878

Orbital Pumping in Ferrimagnetic Insulators
We report the detection of pure orbital currents generated by both coherent and thermal magnons in the magnetic insulator Bi-doped yttrium iron garnet (BiYIG). The pumping of orbital and spin currents is jointly investigated in nano-devices made of naturally oxidized Cu, pure Cu, Pt, and Cr. The absence of charge conduction in BiYIG and the negligible spin-to-charge conversion of oxidized Cu allows us to disambiguate the orbital current contribution. Comparative measurements on YIG and BiYIG sh…