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MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Caixu Xu, Junming Wei, Huizhen Chen, Pengchen Liang, Bocheng Liang, Ying Tan, Xintong Wei
https://arxiv.org/abs/2506.12441

MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Recently, Mamba-based methods have become popular in medical image segmentation due to their lightweight design and long-range dependency modeling capabilities. However, current segmentation methods frequently encounter challenges in fetal ultrasound images, such as enclosed anatomical structures, blurred boundaries, and small anatomical structures. To address the need for balancing local feature extraction and global context modeling, we propose MS-UMamba, a novel hybrid convolutional-mamba mo…

This is not fair…that food would make anyone love the editor too 🤣😇🤪 https://indieweb.social/@xenodium/114671523638724577

Edit360: 2D Image Edits to 3D Assets from Any Angle
Junchao Huang, Xinting Hu, Zhuotao Tian, Shaoshuai Shi, Li Jiang
https://arxiv.org/abs/2506.10507 https…

Edit360: 2D Image Edits to 3D Assets from Any Angle
Recent advances in diffusion models have significantly improved image generation and editing, but extending these capabilities to 3D assets remains challenging, especially for fine-grained edits that require multi-view consistency. Existing methods typically restrict editing to predetermined viewing angles, severely limiting their flexibility and practical applications. We introduce Edit360, a tuning-free framework that extends 2D modifications to multi-view consistent 3D editing. Built upon vi…

Tug-of-war between idiom's figurative and literal meanings in LLMs
Soyoung Oh, Xinting Huang, Mathis Pink, Michael Hahn, Vera Demberg
https://arxiv.org/abs/2506.01723

Tug-of-war between idiom's figurative and literal meanings in LLMs
Idioms present a unique challenge for language models due to their non-compositional figurative meanings, which often strongly diverge from the idiom's literal interpretation. This duality requires a model to learn representing and deciding between the two meanings to interpret an idiom in a figurative sense, or literally. In this paper, we employ tools from mechanistic interpretability to trace how a large pretrained causal transformer (LLama3.2-1B-base) deals with this ambiguity. We localize …

State Similarity in Modular Superconducting Quantum Processors with Classical Communications
Bujiao Wu, Changrong Xie, Peng Mi, Zhiyi Wu, Zechen Guo, Peisheng Huang, Wenhui Huang, Xuandong Sun, Jiawei Zhang, Libo Zhang, Jiawei Qiu, Xiayu Linpeng, Ziyu Tao, Ji Chu, Ji Jiang, Song Liu, Jingjing Niu, Yuxuan Zhou, Yuxuan Du, Wenhui Ren, Youpeng Zhong, Tongliang Liu, Dapeng Yu

State Similarity in Modular Superconducting Quantum Processors with Classical Communications
As quantum devices continue to scale, distributed quantum computing emerges as a promising strategy for executing large-scale tasks across modular quantum processors. A central challenge in this paradigm is verifying the correctness of computational outcomes when subcircuits are executed independently following circuit cutting. Here we propose a cross-platform fidelity estimation algorithm tailored for modular architectures. Our method achieves substantial reductions in sample complexity compar…

Boundedness of complements for fibered Fano threefolds in positive characteristic
Xintong Jiang
https://arxiv.org/abs/2506.00553 https://

Boundedness of complements for fibered Fano threefolds in positive characteristic
In this paper, we prove Shokurov's conjecture on boundedness of complements for Fano type threefold pairs $(X,B)$ with fibration structures in large characteristics. In particular, we prove the conjecture when $-(K_X+B)\not\equiv 0$ is nef and not big in large characteristics.

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang, Huiyu Yang, Jianchun Wang
https://arxiv.org/abs/2506.04898

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the …