Tootfinder

Spatial Signal Focusing and Noise Suppression for Direction-of-Arrival Estimation in Large-Aperture 2D Arrays under Demanding Conditions
Xuyao Deng, Yong Dou, Kele Xu
https://arxiv.org/abs/2510.10923

Spatial Signal Focusing and Noise Suppression for Direction-of-Arrival Estimation in Large-Aperture 2D Arrays under Demanding Conditions
Direction-of-Arrival (DOA) estimation in sensor arrays faces limitations under demanding conditions, including low signal-to-noise ratio, single-snapshot scenarios, coherent sources, and unknown source counts. Conventional beamforming suffers from sidelobe interference, adaptive methods (e.g., MVDR) and subspace algorithms (e.g., MUSIC) degrade with limited snapshots or coherent signals, while sparse-recovery approaches (e.g., L1-SVD) incur high computational complexity for large arrays. In thi…

On the growth of Tate-Shafarevich groups of $p$-supersingular abelian varieties of ${\rm GL}_2$-type over $\mathbb{Z}_p$-extensions of number fields
Erman Isik, Antonio Lei
https://arxiv.org/abs/2510.11511

On the growth of Tate-Shafarevich groups of $p$-supersingular abelian varieties of ${\rm GL}_2$-type over $\mathbb{Z}_p$-extensions of number fields
We study the boundedness of the Mordell-Weil rank and the growth of the $v$-primary part of the Tate-Shafarevich group of $p$-supersingular abelian varieties of ${\rm GL}_2$-type with real multiplication over $\mathbb{Z}_p$-extensions of number fields, where $v$ is a prime lying above $p$. Building on the work of Iovita-Pollack in the case of elliptic curves, under precise ramification and splitting conditions on $p$, we construct explicit systems of local points using the theory of Lubin-Tate …

Sparse Polyak: an adaptive step size rule for high-dimensional M-estimation
Tianqi Qiao, Marie Maros
https://arxiv.org/abs/2509.09802 https://arxiv.org/pdf…

Sparse Polyak: an adaptive step size rule for high-dimensional M-estimation
We propose and study Sparse Polyak, a variant of Polyak's adaptive step size, designed to solve high-dimensional statistical estimation problems where the problem dimension is allowed to grow much faster than the sample size. In such settings, the standard Polyak step size performs poorly, requiring an increasing number of iterations to achieve optimal statistical precision-even when, the problem remains well conditioned and/or the achievable precision itself does not degrade with problem size.…

Superoscillations and the Klein-Gordon equation via the Fourier method
Kamal Diki, Simon Verbruggen
https://arxiv.org/abs/2510.10700 https://arxiv.org/pdf/…

Superoscillations and the Klein-Gordon equation via the Fourier method
We investigate the time-evolution problem associated with the Klein-Gordon equation, using superoscillations as initial data. Additionally, the Segal-Bargmann transform is used to derive integral representations of the resulting solutions.

Environmental Regulation of Dust and Star Formation Unveiled by Subaru Dual Narrow-band Imaging: Degree-scale Balmer Decrement Mapping across a z = 0.9 Supercluster
Zhaoran Liu, Tadayuki Kodama, Brian C. Lemaux, Mariko Kubo, Jose Manuel P\'erez-Mart\'inez, Yusei Koyama, Ichi Tanaka, Kazuki Daikuhara, Roy R. Gal, Denise Hung, Masahiro Konishi, Kosuke Kushibiki, Ronaldo Laishram, Lori M. Lubin, Kentaro Motohara, Hidenori Takahashi

Environmental Regulation of Dust and Star Formation Unveiled by Subaru Dual Narrow-band Imaging: Degree-scale Balmer Decrement Mapping across a z = 0.9 Supercluster
We present results from a dual narrow-band imaging survey targeting the CL1604 supercluster at z = 0.9 using the Subaru Telescope. By combining the NB921 filter on HSC and the NB1244 filter on SWIMS, we can detect redshifted H$α$ and H$β$ emission lines from the supercluster. This unique technique allows us to measure both star formation rates and dust extinction for a sample of 94 emission-line galaxies across the supercluster. We find that dust extinction, estimated from the Balmer decremen…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Kaiwen He, Petros Drineas, Rajiv Khanna
https://arxiv.org/abs/2510.12669 https://arxiv.or…

Structure-Aware Spectral Sparsification via Uniform Edge Sampling
Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges proportionally to their effective resistances, but require expensive preprocessing to estimate these resistances. We study whether uniform edge sampling-a simple, structure-agnostic strategy-can suffice for spectral clustering. Our main result shows that for graphs adm…

For #Caturday, a reminder that Widget remains available for adoption in the NYC metro area. She would prefer a calm house without kids (we are not a calm house). She's super affectionate! #CatsOfMastodon

Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
Amira Abbas, Nunzia Cerrato, Francisco Escudero Guti\'errez, Dmitry Grinko, Francesco Anna Mele, Pulkit Sinha
https://arxiv.org/abs/2509.09813

Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
We study the problem of learning Hamiltonians $H$ that are $s$-sparse in the Pauli basis, given access to their time evolution. Although Hamiltonian learning has been extensively investigated, two issues recur in much of the existing literature: the absence of matching lower bounds and the use of mathematically convenient but physically opaque error measures. We address both challenges by introducing two physically motivated distances between Hamiltonians and designing a nearly optimal algori…

Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas
R. Maydani, Y. Wang, J. Sarrazin, B. Ma
https://arxiv.org/abs/2510.10796

Spatially Filtered Sparse Bayesian Learning for Direction-of-Arrival Estimation with Leaky-Wave Antennas
Direction-of-arrival (DoA) estimation with leaky-wave antennas (LWAs) offers a compact and cost-effective alternative to conventional antenna arrays but remains challenging in the presence of coherent sources. To address this issue, we propose a spatially filtered sparse Bayesian learning (SF-SBL) framework. Firstly, the field of view (FoV) is divided into angular sectors according to the frequency beam-scanning property of LWAs, and Bayesian inverse problems are then solved within each sector …

Towards Foundation Inference Models that Learn ODEs In-Context
Maximilian Mauel, Manuel Hinz, Patrick Seifner, David Berghaus, Ramses J. Sanchez
https://arxiv.org/abs/2510.12650

Towards Foundation Inference Models that Learn ODEs In-Context
Ordinary differential equations (ODEs) describe dynamical systems evolving deterministically in continuous time. Accurate data-driven modeling of systems as ODEs, a central problem across the natural sciences, remains challenging, especially if the data is sparse or noisy. We introduce FIM-ODE (Foundation Inference Model for ODEs), a pretrained neural model designed to estimate ODEs zero-shot (i.e., in context) from sparse and noisy observations. Trained on synthetic data, the model utilizes a …