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…

Slot Attention with Re-Initialization and Self-Distillation
Rongzhen Zhao, Yi Zhao, Juho Kannala, Joni Pajarinen
https://arxiv.org/abs/2507.23755 https://a…

Slot Attention with Re-Initialization and Self-Distillation
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with inf…

Searching for missing interstellar oxygen in the far-infrared
Takashi Onaka, Itsuki Sakon, Takashi Shimonishi, Mitsuhiko Honda
https://arxiv.org/abs/2509.01846 https://

Searching for missing interstellar oxygen in the far-infrared
Study of interstellar elemental depletion poses an important problem in the interstellar matter that at least a quarter of the total oxygen ($\sim 160$ ppm relative to hydrogen) is not accounted for in any known form of oxygen in the translucent or dense interstellar medium (ISM). Detailed analysis of the absorption feature of water ice at 3 $μ$m suggests that one fifth of the missing oxygen may reside in 3 $μ$m-sized water ice grains. However, the 3 $μ$m feature becomes complex and weak for…

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Jikai Wang, Yunqi Cheng, Zonghai Chen
https://arxiv.org/abs/2507.12800

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Though visual and repeat navigation is a convenient solution for mobile robot self-navigation, achieving balance between efficiency and robustness in task environment still remains challenges. In this paper, we propose a novel visual and repeat robotic autonomous navigation method that requires no accurate localization and dense reconstruction modules, which makes our system featured by lightweight and robustness. Firstly, feature flow is introduced and we develop a qualitative mapping between …

C-DOG: Training-Free Multi-View Multi-Object Association in Dense Scenes Without Visual Feature via Connected {\delta}-Overlap Graphs
Yung-Hong Sun, Ting-Hung Lin, Jiangang Chen, Hongrui Jiang, Yu Hen Hu
https://arxiv.org/abs/2507.14095

C-DOG: Training-Free Multi-View Multi-Object Association in Dense Scenes Without Visual Feature via Connected δ-Overlap Graphs
Multi-view multi-object association is a fundamental step in 3D reconstruction pipelines, enabling consistent grouping of object instances across multiple camera views. Existing methods often rely on appearance features or geometric constraints such as epipolar consistency. However, these approaches can fail when objects are visually indistinguishable or observations are corrupted by noise. We propose C-DOG, a training-free framework that serves as an intermediate module bridging object detecti…

Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination
David Nunez, Francesc Wilhelmi, Maksymilian Wojnar, Katarzyna Kosek-Szott, Szymon Szott, Boris Bellalta
https://arxiv.org/abs/2507.19377

Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination
Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn, with a potential impact on future Wi-Fi networks. MAPC enables joint scheduling decisions across multiple access points (APs) to improve throughput, latency, and reliability in dense Wi-Fi deployments. However, implementing efficient scheduling policies under diverse traffic and interference conditions in overlapping basic service sets (OBSSs) remains a complex task. This paper presents a method to minimize the network-wi…

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 …

Renormalization group analysis of color superconductivity revisited
Yuki Fujimoto
https://arxiv.org/abs/2508.19728 https://arxiv.org/pdf/2508.19728

Renormalization group analysis of color superconductivity revisited
Color superconductivity in cold, dense quark matter is a key feature of the QCD phase diagram, whose present theoretical understanding relies predominantly on weak-coupling calculations. In this work, we revisit the evaluation of the color-superconducting gap using a renormalization group (RG) framework formulated in effective theory near the Fermi surface. By incorporating quark self-energy corrections into the RG equation, we reproduce the known weak-coupling results from the gap equation at …

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur, Mathias Zinnen, Tri-Thien Nguyen, Awais Mansoor, Karim Khalifa Elbarbary, Siming Bayer, Florin-Cristian Ghesu, Sasa Grbic, Andreas Maier
https://arxiv.org/abs/2507.19621

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abnormalities. This is especially evident in mammography, where dense breast tissue can obscure lesions, complicating radiological interpretation. Despite leveraging anatomical and semantic context, existing detection methods struggle to learn effective class-specific features, limiting their applicability across differe…

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…

Mapping feedback signatures in 3C 297: A quasar-host merger at Cosmic Noon
Chetna Duggal, Christopher P. O'Dea, Stefi A. Baum, John Jiwa, Grant Tremblay, Marco Chiaberge, George Miley, Carlo Stanghellini, William Sparks
https://arxiv.org/abs/2508.11926

Mapping feedback signatures in 3C 297: A quasar-host merger at Cosmic Noon
We present a study of quasar host galaxy 3C 297 which is home to a powerful bent-jet radio source suggesting vigorous interaction with a dense ISM and/or jet precession. Archival HST imaging showed interestingly perturbed morphology of the host with a bright ~30 kpc arc feature, extended filamentary structure of line-emitting gas and clumpy blue excess emission co-spatial with the radio hotspots. Our VLT/SINFONI integral-field observations reveal complex, spatially-resolved Hα+[NII] emission i…

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Jianfei Jiang, Qiankun Liu, Haochen Yu, Hongyuan Liu, Liyong Wang, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2507.11333

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Learning-based Multi-View Stereo (MVS) methods aim to predict depth maps for a sequence of calibrated images to recover dense point clouds. However, existing MVS methods often struggle with challenging regions, such as textureless regions and reflective surfaces, where feature matching fails. In contrast, monocular depth estimation inherently does not require feature matching, allowing it to achieve robust relative depth estimation in these regions. To bridge this gap, we propose MonoMVSNet, a …

SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
Melih Yazgan, Qiyuan Wu, Iramm Hamdard, Shiqi Li, J. Marius Zoellner
https://arxiv.org/abs/2508.13007

SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
Collaborative perception allows connected autonomous vehicles (CAVs) to overcome occlusion and limited sensor range by sharing intermediate features. Yet transmitting dense Bird's-Eye-View (BEV) feature maps can overwhelm the bandwidth available for inter-vehicle communication. We present SlimComm, a communication-efficient framework that integrates 4D radar Doppler with a query-driven sparse scheme. SlimComm builds a motion-centric dynamic map to distinguish moving from static objects and gene…