Tootfinder

Uncertain Mode Surfaces in 3D Symmetric Second-Order Tensor Field Ensembles
Tim Gerrits
https://arxiv.org/abs/2506.23406 https://arxi…

Uncertain Mode Surfaces in 3D Symmetric Second-Order Tensor Field Ensembles
The analysis of 3D symmetric second-order tensor fields often relies on topological features such as degenerate tensor lines, neutral surfaces, and their generalization to mode surfaces, which reveal important structural insights into the data. However, uncertainty in such fields is typically visualized using derived scalar attributes or tensor glyph representations, which often fail to capture the global behavior. Recent advances have introduced uncertain topological features for tensor field …

I disse skimmeltider går tingene ind i mellem stærkt - vi har ikke adgang til vores gamle kontorer og dermed heller ikke computerudstyr mv. Så vi har gået og ventet på udstyr, og jeg har medbragt mit eget eksterne tastatur og mus i rygsækken hver dag.
Nu ser der så sådan her ud 👀

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
Sanaa Khobizy
https://arxiv.org/abs/2506.22292 ht…

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
In this paper, we extend the analysis of random Kronecker graphs to multi-dimensional networks represented as tensors, enabling a more detailed and nuanced understanding of complex network structures. We decompose the adjacency tensor of such networks into two components: a low-rank signal tensor that captures the essential network structure and a zero-mean noise tensor that accounts for random variations. Building on recent advancements in tensor decomposition and random tensor theory, we intr…

Resolving structural dynamics in situ through cryogenic electron tomography
Jackson Carrion, Joseph H. Davis
https://arxiv.org/abs/2506.22719 https://

Resolving structural dynamics in situ through cryogenic electron tomography
Cryo-electron tomography (cryo-ET) has emerged as a powerful tool for studying the structural heterogeneity of proteins and their complexes, offering insights into macromolecular dynamics directly within cells. Driven by recent computational advances, including powerful machine learning frameworks, researchers can now resolve both discrete structural states and continuous conformational changes from 3D subtomograms and stacks of 2D particle-images acquired across tilt-series. In this review, we…

Tensor-based reduction of linear parameter-varying state-space models
Bogoljub Terzin, E. Javier Olucha, Amritam Das, Siep Weiland, Roland T\'oth
https://arxiv.org/abs/2507.23591

Tensor-based reduction of linear parameter-varying state-space models
The Linear Parameter-Varying (LPV) framework is a powerful tool for controlling nonlinear and complex systems, but the conversion of nonlinear models into LPV forms often results in high-dimensional and overly conservative LPV models. To be able to apply control strategies, there is often a need for model reduction in order to reduce computational needs. This paper presents the first systematic approach for the joint reduction of state order and scheduling signal dimension of LPV state space mo…

Att "The Anti-Autocracy Handbook - A Scholars’ Guide to Navigating Democratic Backsliding" ens existerar är i sig självt olycksbådande, och läsningen fångar mig. Nedmonteringen av USA:s demokratiska institutioner går fort och långsamt på en och samma gång.
Jag reflekterar på bloggen över konsekvenserna av den svåra och problematiska utveckling som syns i USA, inte minst för forskare och akademin, och ger några tankar om vad vi behöver göra.

Seeding neural network quantum states with tensor network states
Ryui Kaneko, Shimpei Goto
https://arxiv.org/abs/2506.23550 https://a…

Seeding neural network quantum states with tensor network states
We find an efficient approach to approximately convert matrix product states (MPSs) into restricted Boltzmann machine wave functions consisting of a multinomial hidden unit through a canonical polyadic (CP) decomposition of the MPSs. This method allows us to generate well-behaved initial neural network quantum states for quantum many-body ground-state calculations in polynomial time of the number of variational parameters and systematically shorten the distance between the initial states and th…

Ofte kommer disse to bots lige efter hinanden i mit feed. Det giver på en måde ekstra mening (og gŸr de lidt anstrengende selfcare-tips mere til at holde ud):

Deep learning-derived arterial input function
Junyu Chen, Zirui Jiang, Jennifer M. Coughlin, Martin G. Pomper, Yong Du
https://arxiv.org/abs/2505.24166 htt…

Deep learning-derived arterial input function
Dynamic positron emission tomography (PET) imaging combined with radiotracer kinetic modeling is a powerful technique for visualizing biological processes in the brain, offering valuable insights into brain functions and neurological disorders such as Alzheimer's and Parkinson's diseases. Accurate kinetic modeling relies heavily on the use of a metabolite-corrected arterial input function (AIF), which typically requires invasive and labor-intensive arterial blood sampling. While alternative non…

TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
Zimu Liao, Jifeng Ding, Rong Fu, Siwei Cui, Ruixuan Gong, Li Wang, Boni Hu, Yi Wang, Hengjie Li, XIngcheng Zhang, Hui Wang
https://arxiv.org/abs/2505.24796

TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores
3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the time cost in the rendering pipeline. This paper proposes TC-GS, an algorithm-independent universal module that expands Tensor Core (TCU) applicability for 3DGS, leading to substantial speedups and seamless integration into existing 3DGS optimization frameworks. The key innovation lies in mapping alpha computation to matrix multiplication, fully utilizing otherwise idle …