Tootfinder

Positive Definiteness and Stability of Interval Tensors
Li Ye, Yisheng Song
https://arxiv.org/abs/2509.11540 https://arxiv.org/pdf/2509.11540

Positive Definiteness and Stability of Interval Tensors
In this paper, we focus on the positive definiteness and Hurwitz stability of interval tensors. First, we introduce auxiliary tensors $\mathcal{A}^z$ and establish equivalent conditions for the positive (semi-)definiteness of interval tensors. That is, an interval tensor is positive definite if and only if all $\mathcal{A}^z$ are positive (semi-)definite. For Hurwitz stability, it is revealed that the stability of the symmetric interval tensor $\mathcal{A}_s^I$ can deduce the stability of the i…

Styrketräning före konditionsträning för äldre och deras hälsa | Artikel https://tyngre.se/artiklar/forskning/styrketraning-fore-konditionstraning-for-aldre-och-deras-halsa

Styrketräning före konditionsträning för äldre och deras hälsa | Artikel
Det bästa är självklart om du utför båda typerna av träning, men om du bara ska utföra en av dem så är det styrketräning du bör göra.

Had a great time presenting our paper “Domain-specific tensor languages” today at #ICFP2025 in Singapore 🇸🇬
The images attached are screenshots from the paper as teasers — diagrams for derivative rules, the Riemann curvature 4-tensor in Einstein’s index notation, and the same in diagram form.
📄 Slides:

Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc
https://arxiv.org/abs/2509.11911 https://arxiv.org/pdf/2509.11911

Quantum Noise Tomography with Physics-Informed Neural Networks
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network's loss function, our approach simultaneously learns the quantum sta…

Symmetric tensor portals to dark matter I: creation of dark scalars
Brian Dignean, Alexander J. Magnus, Rainer Dick
https://arxiv.org/abs/2509.11010 https://

Symmetric tensor portals to dark matter I: creation of dark scalars
We examine the creation of scalar dark matter through a symmetric tensor portal. We find that both freeze-in and thermal freeze-out through a symmetric tensor mediator can create scalar dark matter. The required tensor masses for freeze-in are much larger than for freeze-out for the same dark-matter mass, in agreement with the nonthermalization assumption in freeze-in scenarios.

A novel imaging solution, called
"prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scanning",
can more effectively detect the recurrence of prostate cancer
compared to standard imaging methods,
and is associated with improved survival outcomes.

New imaging technique boosts survival in prostate cancer patients
A multicenter study led by London Health Sciences Centre Research Institute (LHSCRI), Lawson Research Institute of St. Joseph's Health Care London (Lawson), and University Health Network (UHN) has found a novel imaging solution, called prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scanning, can more effectively detect the recurrence of prostate cancer compared to standard imaging methods, and is associated with improved survival outcomes.

Tensor Logic: The Language of AI
Pedro Domingos
https://arxiv.org/abs/2510.12269 https://arxiv.org/pdf/2510.12269…

Tensor Logic: The Language of AI
Progress in AI is hindered by the lack of a programming language with all the requisite features. Libraries like PyTorch and TensorFlow provide automatic differentiation and efficient GPU implementation, but are additions to Python, which was never intended for AI. Their lack of support for automated reasoning and knowledge acquisition has led to a long and costly series of hacky attempts to tack them on. On the other hand, AI languages like LISP an Prolog lack scalability and support for learn…

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
Ning Bao, Jinwei Chu, Yikun Jiang, Jacob March
https://arxiv.org/abs/2509.10170 https://

Coarse-Grained BCFT Tensor Networks and Holographic Reflected Entropy in 3D Gravity
We use the framework of $\textit{BCFT tensor networks}$ to present a microscopic CFT derivation of the correspondence between reflected entropy (RE) and entanglement wedge cross section (EW) in AdS$_3$/CFT$_2$, for both bipartite and multipartite settings. These fixed-point tensor networks, obtained by triangulating Euclidean CFT path integrals, allow us to explicitly construct the canonical purification via cutting-and-gluing CFT path integrals. Employing modular flow in the large-$c$ limit, w…

LEGO: Spatial Accelerator Generation and Optimization for Tensor Applications
Yujun Lin, Zhekai Zhang, Song Han
https://arxiv.org/abs/2509.12053 https://ar…

LEGO: Spatial Accelerator Generation and Optimization for Tensor Applications
Modern tensor applications, especially foundation models and generative AI applications require multiple input modalities (both vision and language), which increases the demand for flexible accelerator architecture. Existing frameworks suffer from the trade-off between design flexibility and productivity of RTL generation: either limited to very few hand-written templates or cannot automatically generate the RTL. To address this challenge, we propose the LEGO framework, which targets tensor app…

An Inexact Tensor-Train Primal-Dual Interior-Point Method for Semidefinite Programs
Frederik Kelbel, Sergey Dolgov, Dante Kalise, Alessandra Russo
https://arxiv.org/abs/2509.11890

An Inexact Tensor-Train Primal-Dual Interior-Point Method for Semidefinite Programs
In this work, we introduce an interior-point method that employs tensor decompositions to efficiently represent and manipulate the variables and constraints of semidefinite programs, targeting problems where the solutions may not be low-rank but admit low-tensor-train rank approximations. Our method maintains approximate superlinear convergence despite inexact computations in the tensor format and leverages a primal-dual infeasible interior-point framework. In experiments on Maximum Cut, Maximu…