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Perfect Quantum State Revivals: Designing Arbitrary Potentials with Specified Energy Levels
Aaron Danner, Tom\'a\v{s} Tyc
https://arxiv.org/abs/2510.00874 https://

Perfect Quantum State Revivals: Designing Arbitrary Potentials with Specified Energy Levels
It is known that there exist a limited number of analytic potentials with the unusual property that any bound quantum state therein will be periodic in time. This is known as a perfect quantum state revival. Examples of such potentials are the infinite well, quantum harmonic oscillator and the Pöschl-Teller potentials; here, we present a general method of designing such potentials. A key requirement is that their energy eigenvalues have integer spacings (up to a prefactor). We first analyze th…

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Vishal Pandey, Debasmita Biswas
https://arxiv.org/abs/2508.21505

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Reinforcement learning agents based on Transformer architectures have achieved impressive performance on sequential decision-making tasks, but their reliance on dense matrix operations makes them ill-suited for energy-constrained, edge-oriented platforms. Spiking neural networks promise ultra-low-power, event-driven inference, yet no prior work has seamlessly merged spiking dynamics with return-conditioned sequence modeling. We present the Spiking Decision Transformer (SNN-DT), which embeds Lea…

Crosslisted article(s) found for stat.OT. https://arxiv.org/list/stat.OT/new
[1/1]:
- Higher-order spacings in the superposed spectra of random matrices with comparison to spacing rat...
Sashmita Rout, Udaysinh T. Bhosale

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
Giacomo Rizzieri, Federico Lanteri, Liberato Ferrara, Massimiliano Cremonesi
https://arxiv.org/abs/2510.02009 …

ShapeGen3DCP: A Deep Learning Framework for Layer Shape Prediction in 3D Concrete Printing
This work introduces ShapeGen3DCP, a deep learning framework for fast and accurate prediction of filament cross-sectional geometry in 3D Concrete Printing (3DCP). The method is based on a neural network architecture that takes as input both material properties in the fluid state (density, yield stress, plastic viscosity) and process parameters (nozzle diameter, nozzle height, printing and flow velocities) to directly predict extruded layer shapes. To enhance generalization, some inputs are refo…

Uncertainty Quantification of Drag Reduction over Superhydrophobic Surfaces by Unified Parameterizing Structure Spacing
Byeong-Cheon Kim, Kyoungsik Chang, Sang-Wook Lee, Hoai-Thanh Nguyen, Eun Seok Oh, Jaiyoung Ryu, Minjae Kim, Jaemoon Yoon
https://arxiv.org/abs/2509.01239

Uncertainty Quantification of Drag Reduction over Superhydrophobic Surfaces by Unified Parameterizing Structure Spacing
Superhydrophobic surfaces (SHS) have demonstrated significant potential in reducing turbulent drag by introducing slip conditions through micro-structured geometries. While previous studies have examined individual SHS configurations such as post-type, ridge-type, and transverse ridge-type surfaces, a unified analysis that connects these patterns through geometric parameterization remains limited. In this study, we propose a systematic framework to explore the drag reduction characteristics by …

On the role of the design phase in a linear regression
Junho Choi
https://arxiv.org/abs/2509.01861 https://arxiv.org/pdf/2509.01861

On the role of the design phase in a linear regression
The "design phase" refers to a stage in observational studies, during which a researcher constructs a subsample that achieves a better balance in covariate distributions between the treated and untreated units. In this paper, we study the role of this preliminary phase in the context of linear regression, offering a justification for its utility. To that end, we first formalize the design phase as a process of estimand adjustment via selecting a subsample. Then, we show that covariate balance o…

Free-energy variations for determinantal 2D plasmas with holes
Nicolas Rougerie (UMPA-ENSL)
https://arxiv.org/abs/2510.01745 https://arxiv.org/pdf/2510.017…

Free-energy variations for determinantal 2D plasmas with holes
We study the Gibbs equilibrium of a classical 2D Coulomb gas in the determinantal case = 2. The external potential is the sum of a quadratic term and the potential generated by individual charges pinned in several extended groups. This leads to an equilibrium measure (droplet) with flat density and macroscopic holes. We consider ''correlation energy'' (free energy minus its mean-field approximation) expansions, for large particle number . Under the assumptions that the holes are sufficiently sm…

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Swadhin Biswas, Imran, Tuhin Sheikh
https://arxiv.org/abs/2509.00988 https://

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Automatic Speech Recognition (ASR) for Bengali, the world's fifth most spoken language, remains a significant challenge, critically hindering technological accessibility for its over 270 million speakers. This challenge is compounded by two persistent and intertwined factors: the language's vast dialectal diversity and the prevalence of acoustic noise in real-world environments. While state-of-the-art self-supervised learning (SSL) models have advanced ASR for low-resource languages, they often…

A simple method for seniority-zero quantum state preparation
Michal Krompiec, Josh J. M. Kirsopp, Antonio M\'arquez Romero, Vicente Perez Soloviev
https://arxiv.org/abs/2508.21679

A simple method for seniority-zero quantum state preparation
Quantum Phase Estimation (QPE), the quantum algorithm for estimating eigenvalues of a given Hermitian matrix and preparing its eigenvectors, is considered the most promising approach to finding the ground states and their energies of electronic systems using a quantum computer. It requires, however, to be warm--started from an initial state with sufficiently high overlap with the ground state. For strongly-correlated states, where QPE is expected to have advantage over classical methods, prepar…