Tootfinder

Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering
Vidya Bhasker Shukla, Italo Atzeni
https://arxiv.org/abs/2508.08663 https://arxiv.o…

Sparse Near-Field Channel Estimation for XL-MIMO via Adaptive Filtering
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating at sub-THz carrier frequencies represent a promising solution to meet the demands of next-generation wireless applications. This work focuses on sparse channel estimation for XL-MIMO systems operating in the near-field (NF) regime. Assuming a practical subarray-based architecture, we develop a NF channel estimation framework based on adaptive filtering, referred to as \textit{polar-domain zero-attracting least mean…

Approximate Sparsity Class and Minimax Estimation
Lucas Z. Zhang
https://arxiv.org/abs/2508.09278 https://arxiv.org/pdf/2508.09278

Approximate Sparsity Class and Minimax Estimation
Motivated by the orthogonal series density estimation in $L^2([0,1],μ)$, in this project we consider a new class of functions that we call the approximate sparsity class. This new class is characterized by the rate of decay of the individual Fourier coefficients for a given orthonormal basis. We establish the $L^2([0,1],μ)$ metric entropy of such class, with which we show the minimax rate of convergence. For the density subset in this class, we propose an adaptive density estimator based on a…

Resonantly enhanced polariton-mediated superconductivity in a doped transition metal dichalcogenide monolayer
Kenneth Choo, Olivier Bleu, Meera M. Parish, Jesper Levinsen
https://arxiv.org/abs/2508.09619

Resonantly enhanced polariton-mediated superconductivity in a doped transition metal dichalcogenide monolayer
We present a proposal for achieving light-induced superconductivity using exciton polaritons - hybrid light-matter particles of excitons (bound electron-hole pairs) and microcavity photons. In contrast to previous theories of polariton-mediated superconductivity, which typically require multiple semiconductor layers, we show that superconductivity can be induced within a single semiconductor monolayer with inverted conduction bands, such as in the tungsten-based transition metal dichalcogenides…

Analytical solutions of CPT-odd Maxwell equations in Schwarzschild spacetime
Hao Wang, Zhi Xiao, Bing Sun
https://arxiv.org/abs/2506.10409 https://

Analytical solutions of CPT-odd Maxwell equations in Schwarzschild spacetime
In this work, we present the CPT-violating (CPTV) Maxwell equations in curved spacetime using the Newman-Penrose (NP) formalism. We obtain a semi-analytical solution to the Maxwell equations in Schwarzschild spacetime under the assumption that the CPT-odd $\left(k_{AF}\right)^μ$ term exhibits spherical symmetry in the Schwarzschild background. Retaining only terms up to linear order in the $\left(k_{AF}\right)^μ$ coefficient, we obtain perturbative solutions by treating the solutions of the L…

Extended JT supergravity and random matrix models: The power of the string equation
Clifford V. Johnson, Maciej Kolanowski
https://arxiv.org/abs/2507.07185

Extended JT supergravity and random matrix models: The power of the string equation
A number of supersymmetric Jackiw-Teitelboim (JT) gravity theories are known to be described (in the Euclidean path integral formulation) by double-scaled random matrix models. Such matrix models can be characterized using a certain ``string equation''. It was shown recently that in extended supergravity, when the number of BPS states scales as ${\rm e}^{S_0}$, where $S_0$ is the extremal entropy, a special ansatz for the leading order solution of the string equation yields the supergravity spe…

Retrospective Sparse Attention for Efficient Long-Context Generation
Seonghwan Choi, Beomseok Kang, Dongwon Jo, Jae-Joon Kim
https://arxiv.org/abs/2508.09001 https://

Retrospective Sparse Attention for Efficient Long-Context Generation
Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, inference over extended contexts is bottlenecked by the Key-Value (KV) cache, whose memory footprint grows linearly with sequence length and dominates latency at each decoding step. While recent KV cache compression methods identify and load important tokens, they focus predominantly on input contexts and fail to address the cumulative attention erro…

Harnack inequality for degenerate fully nonlinear parabolic equations
Vedansh Arya, Vesa Julin
https://arxiv.org/abs/2506.10608 https://

Harnack inequality for degenerate fully nonlinear parabolic equations
We consider degenerate fully nonlinear parabolic equations, which generalize the p-parabolic equation with $p>2$ to nondivergence form operators. We prove an intrinsic Harnack inequality for nonnegative solutions and a weak Harnack inequality for nonnegative supersolutions. These results can be seen as the nondivergence form counterparts of the results by DiBenedetto, Gianazza and Vespri (Acta Math. 2008) and Kuusi (Ann. Sc. Norm. Super. Pisa 2008).

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
Shurui Gui, Shuiwang Ji
https://arxiv.org/abs/2506.09433 https://

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
While large language models (LLMs) have demonstrated remarkable capabilities in language modeling, recent studies reveal that they often fail on out-of-distribution (OOD) samples due to spurious correlations acquired during pre-training. Here, we aim to mitigate such spurious correlations through causality-aware post-training (CAPT). By decomposing a biased prediction into two unbiased steps, known as \textit{event estimation} and \textit{event intervention}, we reduce LLMs' pre-training biases…

Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
Yohann De Castro, R\'emi Gribonval, Nicolas Jouvin
https://arxiv.org/abs/2507.08444

Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
This paper advances the general theory of continuous sparse regularisation on measures with the Beurling-LASSO (BLASSO). This TV-regularized convex program on the space of measures allows to recover a sparse measure using a noisy observation from an appropriate measurement operator. While previous works have uncovered the central role played by this operator and its associated kernel in order to get estimation error bounds, the latter requires a technical local positive curvature (LPC) assumpti…

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