Tootfinder

Generalizable Geometric Image Caption Synthesis
Yue Xin, Wenyuan Wang, Rui Pan, Ruida Wang, Howard Meng, Renjie Pi, Shizhe Diao, Tong Zhang
https://arxiv.org/abs/2509.15217 http…

Generalizable Geometric Image Caption Synthesis
Multimodal large language models have various practical applications that demand strong reasoning abilities. Despite recent advancements, these models still struggle to solve complex geometric problems. A key challenge stems from the lack of high-quality image-text pair datasets for understanding geometric images. Furthermore, most template-based data synthesis pipelines typically fail to generalize to questions beyond their predefined templates. In this paper, we bridge this gap by introducing…

\(0^{th}\) announcement for the 2025 Midwestern Workshop on Asymptotic Analysis - October 10 - 12 at #PurdueFortWayne 🐘 .
The web site has a preliminary list of 2025 speakers including our keynote speaker Alexandre Sukhov 🇫🇷 . Online registration and the request form for travel support will be available soon on the web site:

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Niklas Bubeck, Suprosanna Shit, Chen Chen, Can Zhao, Pengfei Guo, Dong Yang, Georg Zitzlsberger, Daguang Xu, Bernhard Kainz, Daniel Rueckert, Jiazhen Pan
https://arxiv.org/abs/2508.13826

Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction
Cardiac Magnetic Resonance (CMR) imaging is a critical tool for diagnosing and managing cardiovascular disease, yet its utility is often limited by the sparse acquisition of 2D short-axis slices, resulting in incomplete volumetric information. Accurate 3D reconstruction from these sparse slices is essential for comprehensive cardiac assessment, but existing methods face challenges, including reliance on predefined interpolation schemes (e.g., linear or spherical), computational inefficiency, an…

Measuring the Two-Dimensional Thermal Structures of Protoplanetary Disks
Anna J. Fehr, Sean M. Andrews
https://arxiv.org/abs/2509.15196 https://arxiv.org/p…

Measuring the Two-Dimensional Thermal Structures of Protoplanetary Disks
We present a flexible, annulus-by-annulus method to constrain the 2-D thermal structure of a protoplanetary disk from optically thick spectral line emission. Using synthetic disk models with a known temperature and density structure, we extracted the vertical emission surfaces and brightness temperatures in radial annuli for multiple CO isotopologue transitions and used them to infer the vertical temperature profiles. This approach reliably recovers the injected temperature structure despite no…

Granular focused jet
Kazuya U. Kobayashi, Pradipto, Yoshiyuki Tagawa
https://arxiv.org/abs/2508.11905 https://arxiv.org/pdf/2508.11905

Granular focused jet
We investigated the formation of granular focused jets defined as narrow upward ejections of grains triggered by impulsive forces and shaped by kinematic focusing on a concave free surface. The jets were generated from nonfluidized granular beds, in contrast to existing granular Worthington jets that originate from fluidized layers. To elucidate the jet dynamics, we performed experiments in which a test tube partially filled with dry glass beads was dropped onto a flat rigid floor, systematical…

Next-generation soliton frequency combs in photonic-crystal and nanocomposite microresonators
Haixin Liu, Alexa Carollo, Jizhao Zang, Scott B. Papp
https://arxiv.org/abs/2508.13393

Next-generation soliton frequency combs in photonic-crystal and nanocomposite microresonators
Microresonator frequency combs offer tremendous opportunity to advance applications in fundamental research and technology by linking the optical and microwave frequency domains. Kerr-nonlinear microresonators further enable the generation of portable, integrated optical frequency combs, which are called microcombs. However, the dispersion engineering usually suffers from the small, geometric parameter space, and achieving soliton microcombs is challenging and usually requires complicated exper…

Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
Shaofeng Kang, Zeying Tian
https://arxiv.org/abs/2508.11856

Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
We propose a two-level, learning-based portfolio method (RL-BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid-2025, we design the model using 2012 to 2019 data, and evaluate it out-of-sample from 2020 to 2025 against a sector index built from exchange-traded funds and a static risk-balanced portfolio. Over the test window, the adaptive portfolio compounds wealth by approximately 120 percent, compared with 1…

YouTube is like heroin because it pulls you in, hooks you, and slowly drains you. It is designed to be addictive and destructive, and that is exactly why it feels so hard to step away from it.
And yes, I know the excuse, maybe your favorite dealer has not set up shop on PeerTube. But that does not change the truth of the comparison.
PeerTube is like weed, it grows more naturally, it is gentler on you, and it still gives you a high without tearing apart your health and your lif…

From fair price to fair volatility: Towards an Efficiency-Consistent Definition of Financial Risk
Sergio Bianchi, Daniele Angelini, Massimiliano Frezza, Augusto Pianese
https://arxiv.org/abs/2508.11649

From fair price to fair volatility: Towards an Efficiency-Consistent Definition of Financial Risk
Volatility, as a primary indicator of financial risk, forms the foundation of classical frameworks such as Markowitz's Portfolio Theory and the Efficient Market Hypothesis (EMH). However, its conventional use rests on assumptions-most notably, the Markovian nature of price dynamics-that often fail to reflect key empirical characteristics of financial markets. Fractional stochastic volatility models expose these limitations by demonstrating that volatility alone is insufficient to capture the fu…

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Mohammadreza Narimani, Alireza Pourreza, Ali Moghimi, Parastoo Farajpoor, Hamid Jafarbiglu, Mohsen B. Mesgaran
https://arxiv.org/abs/2509.12074

Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning
Branched broomrape (Phelipanche ramosa) is a chlorophyll-deficient parasitic weed that threatens tomato production by extracting nutrients from the host. We investigate early detection using leaf-level spectral reflectance (400-2500 nm) and ensemble machine learning. In a field experiment in Woodland, California, we tracked 300 tomato plants across growth stages defined by growing degree days (GDD). Leaf reflectance was acquired with a portable spectrometer and preprocessed (band denoising, 1 n…