Tootfinder

Vi gästas av en av lärarna/tränare som bland annat hjälpt Ludvig Åberg, Maja Stark och Linn Grant nå världseliten.
Hans Larsson om talangutveckling inom golf | Podcast https://tyngre.se/podcast/tyngre-traningssnack/hans-larsson-om-talangu…

AMD unveils its new MI350X and MI355X GPUs for AI workloads, claiming up to 4x AI compute performance and 35x inference gains over the prior-gen MI300X (Paul Alcorn/Tom's Hardware)
https://www.to…

AMD announces MI350X and MI355X AI GPUs, claims up to 4X generational performance gain, 35X faster inference
More power consumption equals lower TCO.

Time-domain field correlation measurements enable tomography of highly multimode quantum states of light
Emanuel Hubenschmid, Guido Burkard
https://arxiv.org/abs/2506.10483

Time-domain field correlation measurements enable tomography of highly multimode quantum states of light
Recent progress in ultrafast optics facilitates the investigation of the dynamics of highly multimode quantum states of light, as demonstrated by the application of electro-optic sampling to quantum states of the electromagnetic field. Yet, the complete tomographic reconstruction of optical quantum states with prior unknown statistics and dynamics is still challenging, since state-of-the-art tomographic methods require the measurement of many orthogonal, distinguishable modes. Here, we propose …

Growing pains: The lives of Ukrainian teenagers at war: https://benborges.xyz/2025/07/14/growing-pains-the-lives-of.html

Alternating steepest descent methods for tensor completion with applications to spectromicroscopy
Oliver Townsend, Sergey Dolgov, Silvia Gazzola, Misha Kilmer
https://arxiv.org/abs/2506.10661

Alternating steepest descent methods for tensor completion with applications to spectromicroscopy
In this paper we develop two new Tensor Alternating Steepest Descent algorithms for tensor completion in the low-rank $\star_{M}$-product format, whereby we aim to reconstruct an entire low-rank tensor from a small number of measurements thereof. Both algorithms are rooted in the Alternating Steepest Descent (ASD) method for matrix completion, first proposed in [J. Tanner and K. Wei, Appl. Comput. Harmon. Anal., 40 (2016), pp. 417-429]. In deriving the new methods we target the X-ray spectromic…

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Wenjin Qin, Hailin Wang, Jingyao Hou, Jianjun Wang
https://arxiv.org/abs/2506.09594 https:/…

Accelerating Large-Scale Regularized High-Order Tensor Recovery
Currently, existing tensor recovery methods fail to recognize the impact of tensor scale variations on their structural characteristics. Furthermore, existing studies face prohibitive computational costs when dealing with large-scale high-order tensor data. To alleviate these issue, assisted by the Krylov subspace iteration, block Lanczos bidiagonalization process, and random projection strategies, this article first devises two fast and accurate randomized algorithms for low-rank tensor approx…

Hur människors energiförbrukning förändras i olika åldrar | Artikel https://tyngre.se/artiklar/forskning/hur-manniskors-energiforbrukning-forandras-i-olika-aldrar

Tensor perturbations in higher derivative gravity theories in $D$ dimensions
Ercan Kilicarslan
https://arxiv.org/abs/2506.10563 https://

Tensor perturbations in higher derivative gravity theories in $D$ dimensions
We study tensor perturbations of the ghost-free generic non-local gravity theory constructed infinite series of higher derivative terms in $D$ dimensions. The infinite series of higher derivative terms are encoded by form factors, the forms of which can be restricted by requiring that the action remains perturbatively free of ghosts and tachyons around maximally symmetric backgrounds for transverse traceless fluctuations. To demonstrate this, we obtain field equations for AdS plane wave metric …

Semi-Tensor-Product Based Convolutional Neural Networks
Daizhan Cheng
https://arxiv.org/abs/2506.10407 https://arxiv.org/pdf/2506.104…

Semi-Tensor-Product Based Convolutional Neural Networks
The semi-tensor product (STP) of vectors is a generalization of conventional inner product of vectors, which allows the factor vectors to of different dimensions. This paper proposes a domain-based convolutional product (CP). Combining domain-based CP with STP of vectors, a new CP is proposed. Since there is no zero or any other padding, it can avoid the junk information caused by padding. Using it, the STP-based convolutional neural network (CNN) is developed. Its application to image and thir…

Robotics company Tencore raises $3.7 million in one of the largest known investments in Ukraine defense tech: https://benborges.xyz/2025/07/11/robotics-company-tencore-raises-million.html