Tootfinder

Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Corey M. Abramson, Zhuofan Li, Tara Prendergast, Daniel Dohan
https://arxiv.org/abs/2509.12503

Qualitative Research in an Era of AI: A Pragmatic Approach to Data Analysis, Workflow, and Computation
Rapid computational developments - particularly the proliferation of artificial intelligence (AI) - increasingly shape social scientific research while raising new questions about in-depth qualitative methods such as ethnography and interviewing. Building on classic debates about using computers to analyze qualitative data, we revisit longstanding concerns and assess possibilities and dangers in an era of automation, AI chatbots, and 'big data.' We first historicize developments by revisiting c…

The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations
Yubo Zhu, Dongrui Liu, Zecheng Lin, Wei Tong, Sheng Zhong, Jing Shao
https://arxiv.org/abs/2509.12886

The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations
Estimating the difficulty of input questions as perceived by large language models (LLMs) is essential for accurate performance evaluation and adaptive inference. Existing methods typically rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself, which may incur substantial computational costs or compromise generality. In this paper, we propose a novel approach for difficulty estimation that leverages only the hidden representations produced by the target LL…

What Can I Do Right Now to Defend Democracy and Fight Fascism?
https://www.peoplefor.org/what-can-i-do-right-now-defend-democracy-and-fight-fascism

What Can I Do Right Now to Defend Democracy and Fight Fascism?
Learn about ways you can take action right now to combat the spread of fascism in the United States.

Efficient Image-to-Image Schr\"odinger Bridge for CT Field of View Extension
Zhenhao Li, Long Yang, Xiaojie Yin, Haijun Yu, Jiazhou Wang, Hongbin Han, Weigang Hu, Yixing Huang
https://arxiv.org/abs/2508.11211

Efficient Image-to-Image Schrödinger Bridge for CT Field of View Extension
Computed tomography (CT) is a cornerstone imaging modality for non-invasive, high-resolution visualization of internal anatomical structures. However, when the scanned object exceeds the scanner's field of view (FOV), projection data are truncated, resulting in incomplete reconstructions and pronounced artifacts near FOV boundaries. Conventional reconstruction algorithms struggle to recover accurate anatomy from such data, limiting clinical reliability. Deep learning approaches have been explor…

A meaningful optimal control problem in quantum and classical physics
Arnaud Lazarus (DALEMBERT), Emmanuel Tr\'elat (LJLL)
https://arxiv.org/abs/2507.13125

A meaningful optimal control problem in quantum and classical physics
In this paper we study and solve an optimal control problem motivated by applications in quantum and classical physics. Although apparently simple, this optimal control problem is not easy to solve and we resort to various elaborated methods of optimal control theory. We finally show its relationships to two problems in physics: the computation of the ground state for 1D Schr{ö}dinger operators with a finite potential well, and the optimal dynamical Kapitza stabilization problem.

🗷 Gerrymandering erodes confidence in democracy
#politics

Gerrymandering erodes confidence in democracy
When politicians redraw congressional district maps to favor their party, they may secure short-term victories. But those wins can come at a steep price—a loss of public faith in elections and, ultimately, in democracy itself.

Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Toufiq Musah, Chantelle Amoako-Atta, John Amankwaah Otu, Lukman E. Ismaila, Swallah Alhaji Suraka, Oladimeji Williams, Isaac Tigbee, Kato Hussein Wabbi, Samantha Katsande, Kanyiri Ahmed Yakubu, Adedayo Kehinde Lawal, Anita Nsiah Donkor, Naeem Mwinlanaah Adamu, Adebowale Akande, John Othieno, Prince Ebenezer Adjei, Zhang Dong, Confidence Raymond, Udunna C.…

Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Brain tumors are among the deadliest cancers worldwide, with particularly devastating impact in Sub-Saharan Africa (SSA) where limited access to medical imaging infrastructure and expertise often delays diagnosis and treatment planning. Accurate brain tumor segmentation is crucial for treatment planning, surgical guidance, and monitoring disease progression, yet manual segmentation is time-consuming and subject to inter-observer variability. Recent advances in deep learning, based on Convolutio…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schr\"odinger bridge
Davide La Vecchia, Hang Liu
https://arxiv.org/abs/2509.11532 https://…

E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge
We propose the Entropic-regularized Robust Optimal Transport (E-ROBOT) framework, a novel method that combines the robustness of ROBOT with the computational and statistical benefits of entropic regularization. We show that, rooted in the Schrödinger bridge problem theory, E-ROBOT defines the robust Sinkhorn divergence $\overline{W}_{\varepsilon,λ}$, where the parameter $λ$ controls robustness and $\varepsilon$ governs the regularization strength. Letting $n\in \mathbb{N}$ denote the sample …

The foundational value of quantum computing for classical fluids
Sauro Succi, Claudio Sanavio, Peter Love
https://arxiv.org/abs/2510.09178 https://arxiv.or…

The foundational value of quantum computing for classical fluids
Quantum algorithms for classical physics problems expose new patterns of quantum information flow as compared to the many-body Schrödinger equation. As a result, besides their potential practical applications, they also offer a valuable theoretical and computational framework to elucidate the foundations of quantum mechanics, particularly the validity of the many-body Schrödinger equation in the limit of large number of particles, on the order of the Avogadro number. This idea is illustrated …

Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
Mrigank Dhingra, Omer San
https://arxiv.org/abs/2509.12149

Superresolving Non-linear PDE Dynamics with Reduced-Order Autodifferentiable Ensemble Kalman Filtering For Turbulence Modeling and Flow Regulation
Accurately reconstructing and forecasting high-resolution (HR) states from computationally cheap low-resolution (LR) observations is central to estimation-and-control of spatio-temporal PDE systems. We develop a unified superresolution pipeline based on the reduced-order autodifferentiable Ensemble Kalman filter (ROAD-EnKF). The method learns a low-dimensional latent dynamics model and a nonlinear decoder from latent variables to HR fields; the learned pair is embedded in an EnKF, enabling simu…