Tootfinder

Conditional existence of maximizers for the Tomas-Stein inequality for the sphere
Shuanglin Shao, Ming Wang
https://arxiv.org/abs/2509.10754 https://arxiv.…

Conditional existence of maximizers for the Tomas-Stein inequality for the sphere
The Tomas-Stein inequality for a compact subset $Γ$ of the sphere $S^d$ states that the mapping $f\mapsto \widehat{fσ}$ is bounded from $L^2(Γ,σ)$ to $L^{2+4/d}(\R^{d+1})$. Then conditional on a strict comparison between the best constants for the sphere and for the Strichartz inequality for the Schrödinger equations, we prove that there exist functions which extremize this inequality, and any extremising sequence has a subsequence which converges to an extremizer. The method is based on t…

Using the rejection sampling for finding tests
Markku Kuismin
https://arxiv.org/abs/2509.10325 https://arxiv.org/pdf/2509.10325

Using the rejection sampling for finding tests
A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three distinct empirical examples are presented: (1) examine the differences between group means of correlated (repeated) or independent samples, (2) examine if a mean vector equals to a specific fixed vector, and (3) investigate if samples come from a specific populati…

The high-order Hermite discrete correction function method for surface-driven electromagnetic problems
Yann-Meing Law
https://arxiv.org/abs/2509.09857 https://

The high-order Hermite discrete correction function method for surface-driven electromagnetic problems
The Hermite-Taylor method evolves all the variables and their derivatives through order $m$ in time to achieve a $2m+1$ order rate of convergence. The data required at each node of the staggered Cartesian meshes used by this method makes the enforcement of boundary and interface conditions challenging. In this work, we propose a novel correction function method, referred to as the discrete correction function method, which provides all the data required by the Hermite method near the surface wh…

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
Huizhen Shu, Xuying Li, Qirui Wang, Yuji Kosuga, Mengqiu Tian, Zhuo Li
https://arxiv.org/abs/2508.10404

Layer-Wise Perturbations via Sparse Autoencoders for Adversarial Text Generation
With the rapid proliferation of Natural Language Processing (NLP), especially Large Language Models (LLMs), generating adversarial examples to jailbreak LLMs remains a key challenge for understanding model vulnerabilities and improving robustness. In this context, we propose a new black-box attack method that leverages the interpretability of large models. We introduce the Sparse Feature Perturbation Framework (SFPF), a novel approach for adversarial text generation that utilizes sparse autoenc…

SAMPLE -- Stratospheric Altitude Microbiology Probe for Life Existence -- A Method of Collection of Stratospheric Samples Using Balloon-Borne Payload System
Margarita Safonova, Bharat Chandra P, Binukumar G. Nair, Akshay Datey, Dipshikha Chakravortty, Ajin Prakash, Mahesh Babu, Shubham Ghatul, Shubhangi Jain, Rekhesh Mohan, Jayant Murthy
https://

SAMPLE -- Stratospheric Altitude Microbiology Probe for Life Existence -- A Method of Collection of Stratospheric Samples Using Balloon-Borne Payload System
The Earth possesses many environmental extremes that mimic conditions on extraterrestrial worlds. The stratosphere at 30-40 km altitude closely resembles the surface of Mars in terms of pressure, temperature, and radiation levels (UV, proton, and Galactic cosmic rays). While microbial life in the troposphere is well documented, the true upper limit of Earth's biosphere remains unclear. The stratosphere offers a promising environment to explore microbial survival in such extreme conditions. Desp…

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Bowen Du, Qi Li, Mingwei Ge, Xintao Li, Yongqian Liu
https://arxiv.org/abs/2510.11392

A novel spatial distribution method for wind farm parameterizations based on the Gaussian function
Wind farm parameterizations are crucial for quantifying the wind-farm atmosphere interaction, where wind turbines are typically modeled as elevated momentum sinks and sources of turbulence kinetic energy (TKE). These quantities must be properly distributed to the mesoscale grid. Existing parameterizations use the single-column method. However, this method can easily lead to the errors of the spatial distribution of the sink and source, thereby impacting the accuracy of mesoscale flow simulation…

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
Ding Lin, Jianhui Wang, Tianqiao Zhao, Meng Yue
https://arxiv.org/abs/2507.11726

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching
Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While multi-objective transmission switching can balance cost savings with reliability improvements, feasible solutions become exceedingly difficult to obtain as system scale grows, due to the inherent nonlinearity and high computational demands involved. This paper proposes …

WAFER: A new method to retrieve sun-induced fluorescence based on spectral wavelet decompositions
Veronika Oehl, Alexander Damm
https://arxiv.org/abs/2509.11829 https://

WAFER: A new method to retrieve sun-induced fluorescence based on spectral wavelet decompositions
Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresp…

Near-Optimal Recovery Performance of PhaseLift for Phase Retrieval from Coded Diffraction Patterns
Meng Huang, Jinming Wen, Ran Zhang
https://arxiv.org/abs/2509.10300 https://…

Near-Optimal Recovery Performance of PhaseLift for Phase Retrieval from Coded Diffraction Patterns
The PhaseLift algorithm is an effective convex method for solving the phase retrieval problem from Fourier measurements with coded diffraction patterns (CDP). While exact reconstruction guarantees are well-established in the noiseless case, the stability of recovery under noise remains less well understood. In particular, when the measurements are corrupted by an additive noise vector $\mathbf{w} \in \mathbb{R}^m$, existing recovery bounds scale on the order of $\|\mathbf{w}\|_2$, whi…

Nonlinear filtering based on density approximation and deep BSDE prediction
Kasper B{\aa}gmark, Adam Andersson, Stig Larsson
https://arxiv.org/abs/2508.10630 https://

Nonlinear filtering based on density approximation and deep BSDE prediction
A novel approximate Bayesian filter based on backward stochastic differential equations is introduced. It uses a nonlinear Feynman--Kac representation of the filtering problem and the approximation of an unnormalized filtering density using the well-known deep BSDE method and neural networks. The method is trained offline, which means that it can be applied online with new observations. A mixed a priori-a posteriori error bound is proved under an elliptic condition. The theoretical convergence …