Tootfinder

In-situ tuning of cavity dissipation and a topological transition in an atom-nanotip-cavity system
Taegyu Ha, Kiyanoush Goudarzi, Dowon Lee, Donggeon Kim, Eunchul Jeong, Uijin Kim, Myunghun Kim, Jinuk Kim, Moonjoo Lee
https://arxiv.org/abs/2506.07800

In-situ tuning of cavity dissipation and a topological transition in an atom-nanotip-cavity system
We theoretically investigate a method for tuning a dissipation rate in an atom-cavity system. By inserting a nanotip into the cavity mode, we estimate that the cavity dissipation rate increases by a factor of approximately 20, due to the scattering loss at the tip. As applications of our in-situ technique, we demonstrate that an exceptional line can be obtained when a single atom or ion is coupled to the resonator. Moreover, the position of an exceptional point is tuned by adjusting the decay r…

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
Lijie Huang, Jingyi Yin, Jingke Zhang, U-Wai Lok, Ryan M. DeRuiter, Jieyang Jin, Kate M. Knoll, Kendra E. Petersen, James D. Krier, Xiang-yang Zhu, Gina K. Hesley, Kathryn A. Robinson, Andrew J. Bentall, Thomas D. Atwell, Andrew D. Rule, Lilach O. Lerman, Shigao Chen, Chengwu Huang

Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, which impairs subsequent vascular quantification and reliable disease diagnosis. To address this challenge, we propose Half-Angle-to-Half-Angle (HA2HA), a self-supervised denoising framework specifically designed for UMI. HA2HA constructs training pairs from complementary angular subsets of beamformed radio-frequency (RF) blood flow data, across whic…

Dark states of electrons in a quantum system with two pairs of sublattices
Yoonah Chung, Minsu Kim, Yeryn Kim, Seyeong Cha, Joon Woo Park, Jeehong Park, Yeonjin Yi, Dongjoon Song, Jung Hyun Ryu, Kimoon Lee, Timur K. Kim, Cephise Cacho, Jonathan Denlinger, Chris Jozwiak, Eli Rotenberg, Aaron Bostwick, Keun Su Kim
https://arxiv.or…

Dark states of electrons in a quantum system with two pairs of sublattices
A quantum state of matter that is forbidden to interact with photons and is therefore undetectable by spectroscopic means is called a dark state. This basic concept can be applied to condensed matter where it suggests that a whole band of quantum states could be undetectable across a full Brillouin zone. Here we report the discovery of such condensed matter dark states in palladium diselenide as a model system that has two pairs of sublattices in the primitive cell. By using angle-resolved phot…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Ailin Huang, Bingxin Li, Bruce Wang, Boyong Wu, Chao Yan, Chengli Feng, Heng Wang, Hongyu Zhou, Hongyuan Wang, Jingbei Li, Jianjian Sun, Joanna Wang, Mingrui Chen, Peng Liu, Ruihang Miao, Shilei Jiang, Tian Fei, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Ge, Zheng Gong, Zhewei Huang, Zixin Zhang, Bin Wang, Bo Li, Buyun Ma, Changxin Miao, Changyi Wan, Chen Xu, Dapeng Shi, Dingyuan Hu, Enle…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate natural speech responses directly, hindering seamless audio interactions. To address this, we introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction, a 130-billion-parameter…

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
Boris Bolliet, Aleksandra Kusiak, Fiona McCarthy, Alina Sabyr, Kristen Surrao, Jens Chluba, Carmen Embil Villagra, Simone Ferraro, Boryana Hadzhiyska, Dongwon Han, J. Colin Hill, Juan Francisco Mac\'ias-P\'erez, Mathew Madhavacheril, Abhishek Maniyar, Yogesh Mehta, Shivam Pandey, Emmanuel Schaan, Blake Sherwin, Alessio Spurio Mancini…

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
These notes are very much work-in-progress and simply intended to showcase, in various degrees of details (and rigour), some of the cosmology calculations that class_sz can do. We describe the class_sz code in C, Python and Jax. Based on the Boltzmann code class, it can compute a wide range of observables relevant to current and forthcoming CMB and Large Scale Structure surveys. This includes galaxy shear and clustering, CMB lensing, thermal and kinetic Sunyaev and Zeldovich observables, Cosmic…

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
Daniel H. Pak, Shubh Thaker, Kyle Baylous, Xiaoran Zhang, Danny Bluestein, James S. Duncan
https://arxiv.org/abs/2506.08280

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we …

Observation of superconductivity-induced leading-edge gap in Sr-doped $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films
Wenjie Sun, Zhicheng Jiang, Bo Hao, Shengjun Yan, Hongyi Zhang, Maosen Wang, Yang Yang, Haoying Sun, Zhengtai Liu, Dianxiang Ji, Zhengbin Gu, Jian Zhou, Dawei Shen, Donglai Feng, Yuefeng Nie
https://arx…

Observation of superconductivity-induced leading-edge gap in Sr-doped $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ thin films
The discovery of high-temperature superconductivity in pressurized bulk $\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}$ has ignited significant interest in nickelate superconductors. Unlike cuprates, where superconductivity predominantly originates from the $\mathrm{3}d_{x^2-y^2}$ orbital, nickelates exhibit additional complexities involving contributions from the $\mathrm{3}d_{z^2}$ orbital, prompting fundamental questions about their pairing mechanisms. Despite recent progress in stabilizing s…

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
Xuesong Li, Nassir Navab, Zhongliang Jiang
https://arxiv.org/abs/2507.06828 h…

Speckle2Self: Self-Supervised Ultrasound Speckle Reduction Without Clean Data
Image denoising is a fundamental task in computer vision, particularly in medical ultrasound (US) imaging, where speckle noise significantly degrades image quality. Although recent advancements in deep neural networks have led to substantial improvements in denoising for natural images, these methods cannot be directly applied to US speckle noise, as it is not purely random. Instead, US speckle arises from complex wave interference within the body microstructure, making it tissue-dependent. Thi…

Identifying vortex lattice in type-II superconductors via the dynamic magnetostrictive effect
Peipei Lu, Mengju Yuan, Jing Zhang, Qiang Gao, Shuang Liu, Yugang Zhang, Shipeng Shen, Long Zhang, Jun Lu, Xiaoyuan Zhou, Mingquan He, Aifeng Wang, Yang Li, Wenshan Hong, Shiliang Li, Huiqian Luo, Xingjiang Zhou, Xianhui Chen, Young Sun, Yisheng Chai
https:/…

Identifying vortex lattice in type-II superconductors via the dynamic magnetostrictive effect
In type-I superconductors, zero electrical resistivity and perfect diamagnetism define two fundamental criteria for superconducting behavior. In contrast, type-II superconductors exhibit more complex mixed-state physics, where magnetic flux penetrates the material above the lower critical field Hc1 in the form of quantized vortices, each carrying a single flux quantum. These vortices form a two-dimensional lattice which persists up to another irreversible field (Hirr) and then melts into a diss…