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Senaste avsnittet av Tyngre Träningssnack
Det senaste kring motstånd mot trötthet inom konditionsidrott | Podcast https://tyngre.se/podcast/tyngre-traningssnack/det-senaste-kring-motstand-mot-trotthet-inom-konditionsidro…

Oil tanker damaged by blast weeks after visiting Russian ports, Bloomberg reports: https://benborges.xyz/2025/06/30/oil-tanker-damaged-by-blast.html

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…

SVT har just nu en "dokumentär" skapad av en bokförfattare där han gör reklam för sakerna han påstod i sin bok 🤷‍♂️ Jag har precis börjat titta på den och halvvägs in följer den, som väntat, precis samma mönster som alla andra kostdokumentärer som påstår sig komma med något revolutionerande.
En dissektion av alla kostdokumentärer!

Why are Dutch kids the world’s happiest? A teenager weighs in.
https://www.cnn.com/2025/06/29/health/dutch-kids-happiest-essay-wellness

Why are Dutch kids the world’s happiest? A teenager weighs in
Freedom for kids to move through the world on their own may be the key to the Netherlands’ top spot in childhood happiness.

Latent Factorization of Tensors with Threshold Distance Weighted Loss for Traffic Data Estimation
Lei Yang
https://arxiv.org/abs/2506.22441 https://…

Latent Factorization of Tensors with Threshold Distance Weighted Loss for Traffic Data Estimation
Intelligent transportation systems (ITS) rely heavily on complete and high-quality spatiotemporal traffic data to achieve optimal performance. Nevertheless, in real-word traffic data collection processes, issues such as communication failures and sensor malfunctions often lead to incomplete or corrupted datasets, thereby posing significant challenges to the advancement of ITS. Among various methods for imputing missing spatiotemporal traffic data, the latent factorization of tensors (LFT) model…

Efficient Tensor Decomposition via Moment Matrix Extension
Bobby Shi, Julia Lindberg, Joe Kileel
https://arxiv.org/abs/2506.22564 https://

Efficient Tensor Decomposition via Moment Matrix Extension
Motivated by a flurry of recent work on efficient tensor decomposition algorithms, we show that the celebrated moment matrix extension algorithm of Brachat, Comon, Mourrain, and Tsigaridas for symmetric tensor canonical polyadic (CP) decomposition can be made efficient under the right conditions. We first show that the crucial property determining the complexity of the algorithm is the regularity of a target decomposition. This allows us to reduce the complexity of the vanilla algorithm, while …

Positivity of GCD tensors
Projesh Nath Choudhury, Krushnachandra Panigrahy
https://arxiv.org/abs/2506.24053 https://arxiv.org/pdf/250…

Positivity of GCD tensors
Let $S=\{s_{1},s_{2},\ldots,s_{n}\}$ be an ordered set of $n$ distinct positive integers. The $m$th-order $n$-dimensional tensor ${T}_{[S]}=(t_{i_{1}i_{2}\ldots i_{m}}),$ where $t_{i_{1}i_{2}\ldots i_{m}}=GCD(s_{i_{1}},s_{i_{2}},\ldots,s_{i_{m}}),$ the greatest common divisor (GCD) of $s_{i_{1}},s_{i_{2}},\ldots,$ and $s_{i_{m}}$ is called the GCD tensor on $S$. The earliest result on GCD tensors goes back to Smith [Proc. Lond. Math. Soc., 1976], who computed the determinant of GCD matrix on $S…

TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations
Stanislav Sedukhin (The University of Aizu, Japan), Yoichi Tomioka (The University of Aizu, Japan), Kazuya Matsumoto (The University of Aizu, Japan), Yuichi Okuyama (The University of Aizu, Japan)
ht…

TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations
Multilinear transformations are key in high-performance computing (HPC) and artificial intelligence (AI) workloads, where data is represented as tensors. However, their high computational and memory demands, which grow with dimensionality, often slow down critical tasks. Moreover, scaling computation by enlarging the number of parallel processing units substantially increases energy consumption, limiting widespread adoption, especially for sparse data, which is common in HPC and AI applications…

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication
Jinliang Shi, Shigang Li, Youxuan Xu, Xueying Wang, Rongtian Fu, Zhi Ma, Tong Wu
https://arxiv.org/abs/2506.22714

Libra: Synergizing CUDA and Tensor Cores for High-Performance Sparse Matrix Multiplication
Sparse matrix multiplication operators (i.e., SpMM and SDDMM) are widely used in deep learning and scientific computing. Modern accelerators are commonly equipped with Tensor cores and CUDA cores to accelerate sparse operators. The former brings superior computing power but only for structured matrix multiplication, while the latter has relatively lower performance but with higher programming flexibility. In this work, we discover that utilizing one resource alone leads to inferior performance …