Tootfinder

First article of mine published in TanahAir.net, and the first one in Indonesian in several months!
Pengaruh pertumbuhan LNG Cina pada rencana energi Asia Tenggara
https://tanahair.net/id/pengaruh-pertumbuhan-lng-cina-pada-rencana-energi-asia-te…

I built GeoOpen as an open, historical IP geolocation database. I didn’t expect it to attract so many different users so fast.
Tenzir’s latest blog shows how they use it: "Time-Travel for Investigators: Live & Retro GeoIP/ASN Enrichment".
🔗 Tenzir blog post https://tenzir.com/blog/live-and-retro

Time-Travel for Investigators: Live & Retro GeoIP/ASN Enrichment - Tenzir
The data pipeline engine for security teams. Collect, shape, optimize, enrich, store, replay, and route your security data. As a data pipeline management (DPM) solution, Tenzir automates security data across its entire lifecycle.

Senaste avsnittet av Hälsoveckan by Tyngre
En injektion som dödar fettceller lokalt | Podcast https://tyngre.se/podcast/halsoveckan-by-tyngre/en-injektion-som-dodar-fettceller-lokalt

En injektion som dödar fettceller lokalt | Podcast
Jacob och Erik diskuterar åldrande utan inflammation och en ny injektionsbehandling som påstås kunna döda fettceller lokalt – men hur mycket är egentligen hype?

Calculating the Projective Norm of higher-order tensors using a gradient descent algorithm
Aaditya Rudra, Maria Anastasia Jivulescu
https://arxiv.org/abs/2508.07933 https://

Calculating the Projective Norm of higher-order tensors using a gradient descent algorithm
Projective Norms are a class of tensor norms that map on the input and output spaces. These norms are useful for providing a measure of entanglement. Calculating the projective norms is an NP-hard problem, which creates challenges in computing due to the complexity of the exponentially growing parameter space for higher-order tensors. We develop a novel gradient descent algorithm to estimate the projective norm of higher-order tensors. The algorithm guarantees convergence to a minimum nuclear r…

Minimality of Tree Tensor Network Ranks
Jana Jovcheva, Tim Seynnaeve, Nick Vannieuwenhoven
https://arxiv.org/abs/2509.09463 https://arxiv.org/pdf/2509.0946…

Minimality of Tree Tensor Network Ranks
For a given tree tensor network $G$, we call a tuple of bond dimensions minimal if there exists a tensor $T$ that can be represented by this network but not on the same tree topology with strictly smaller bond dimensions. We establish necessary and sufficient conditions on the bond dimensions of a tree tensor network to be minimal, generalizing a characterization of Carlini and Kleppe about existence of tensors with a given multilinear rank. We also show that in a minimal tree tensor network, t…

Oscillations and parity violation in gravitational wave background from extra tensor modes
Jaume Garriga, Mohammad Ali Gorji, Fazlollah Hajkarim, Misao Sasaki
https://arxiv.org/abs/2508.08481

Oscillations and parity violation in gravitational wave background from extra tensor modes
Spectator fields which provide additional tensor degrees of freedom, on top of the standard metric tensor perturbations, can produce significant amounts of gravitational waves (GWs). Employing the effective field theory approach for spin-2 fields, we find a universal prediction that linear mixing between the metric and extra tensor modes inevitably induces oscillatory features in the GW spectrum, reminiscent of the so-called neutrino oscillation. Moreover, parity-violating operators in the spin…

Fast adaptive tubal rank-revealing algorithm for t-product based tensor approximation
Qiaohua Liu, Jiehui Gu
https://arxiv.org/abs/2508.08557 https://arxiv…

Fast adaptive tubal rank-revealing algorithm for t-product based tensor approximation
Color images and video sequences can be modeled as three-way tensors, which admit low tubal-rank approximations via convex surrogate minimization. This optimization problem is efficiently addressed by tensor singular value thresholding (t-SVT). To mitigate the computational burden of tensor singular value decomposition (t-SVD) in each iteration, this paper introduces an adaptive randomized algorithm for tubal rank revelation in data tensors \(\mathcal{A}\). Our method selectively captures the p…

Tomography-assisted noisy quantum circuit simulator using matrix product density operators
Wei-guo Ma, Yun-Hao Shi, Kai Xu, Heng Fan
https://arxiv.org/abs/2508.07610 https://

Tomography-assisted noisy quantum circuit simulator using matrix product density operators
In recent years, efficient quantum circuit simulations incorporating ideal noise assumptions have relied on tensor network simulators, particularly leveraging the matrix product density operator (MPDO) framework. However, experiments on real noisy intermediate-scale quantum (NISQ) devices often involve complex noise profiles, encompassing uncontrollable elements and instrument-specific effects such as crosstalk. To address these challenges, we employ quantum process tomography (QPT) techniques …

A Low-Rank tensor framework for THB-Splines
Tom-Christian Riemer, Martin Stoll
https://arxiv.org/abs/2509.09434 https://arxiv.org/pdf/2509.09434

A Low-Rank tensor framework for THB-Splines
We introduce a low-rank framework for adaptive isogeometric analysis with truncated hierarchical B-splines (THB-splines) that targets the main bottleneck of local refinement: memory- and time-intensive matrix assembly once the global tensor-product structure is lost. The method interpolates geometry-induced weight and source terms in separable spline spaces and computes their tensor-train (TT) representations via the alternating minimal energy (AMEn) solver, enabling level-wise assembly of syst…

Quantum Circuit Complexity of Matrix-Product Unitaries
Georgios Styliaris, Rahul Trivedi, J. Ignacio Cirac
https://arxiv.org/abs/2508.08160 https://arxiv.o…

Quantum Circuit Complexity of Matrix-Product Unitaries
Matrix-product unitaries (MPUs) are many-body unitary operators that, as a consequence of their tensor-network structure, preserve the entanglement area law in 1D systems. However, it is unknown how to implement an MPU as a quantum circuit since the individual tensors describing the MPU are not unitary. In this paper, we show that a large class of MPUs can be implemented with a polynomial-depth quantum circuit. For an $N$-site MPU built from a repeated bulk tensor with open boundary, we explici…