Tootfinder

Classical feature map surrogates and metrics for quantum control landscapes
Martino Calzavara, Tommaso Calarco, Felix Motzoi
https://arxiv.org/abs/2509.25930 https://

Classical feature map surrogates and metrics for quantum control landscapes
We derive and analyze three feature map representations of parametrized quantum dynamics, which generalize variational quantum circuits. These are (i) a Lie-Fourier partial sum, (ii) a Taylor expansion, and (iii) a finite-dimensional sinc kernel regression representation. The Lie-Fourier representation is shown to have a dense spectrum with discrete peaks, that reflects control Hamiltonian properties, but that is also compressible in typically found symmetric systems. We prove boundedness in th…

Dissecting the moat regime at low energies I: Renormalization and the phase structure
Fabian Rennecke, Shi Yin
https://arxiv.org/abs/2510.06712 https://arx…

Dissecting the moat regime at low energies I: Renormalization and the phase structure
Dense QCD matter can feature a moat regime, where the static energy of mesons is minimal at nonzero momentum. Valuable insights into this regime can be gained using low-energy models. This, however, requires a careful assessment of model artifacts. We therefore study the effects of renormalization and in-medium modifications of quark-meson interaction on the moat regime. To capture the main effects, we use a two-flavor quark-meson model at finite temperature and baryon density in the random pha…

PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation
Mahmoud Khater, Mona Strauss, Philipp von Olshausen, Alexander Reiterer
https://arxiv.org/abs/2509.20207 htt…

PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation
Point clouds produced by 3D sensors are often sparse and noisy, posing challenges for tasks requiring dense and high-fidelity 3D representations. Prior work has explored both implicit feature-based upsampling and distance-function learning to address this, but often at the expense of geometric interpretability or robustness to input sparsity. To overcome these limitations, we propose PU-Gaussian, a novel upsampling network that models the local neighborhood around each point using anisotropic 3…

DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping
Jose E. Maese, Luis Merino, Fernando Caballero
https://arxiv.org/abs/2509.20081

DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping
This paper presents a high-efficiency, CPU-only volumetric mapping framework based on a Truncated Signed Distance Field (TSDF). The system incrementally fuses raw LiDAR point-cloud data into a voxel grid using a directional bitmask-based integration scheme, producing dense and consistent TSDF representations suitable for real-time 3D reconstruction. A key feature of the approach is that the processing time per point-cloud remains constant, regardless of the voxel grid resolution, enabling high …

Moody Urbanity - ONE 1️⃣
情绪化城市 - 壹 1️⃣
📷 Nikon FE
🎞️ Fujifilm NEOPAN SS, expired 1993
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