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Quantum secret sharing in a triangular superconducting quantum network
Haoxiong Yan, Allen Zang, Joel Grebel, Xuntao Wu, Ming-Han Chou, Gustav Andersson, Christopher R. Conner, Yash J. Joshi, Shiheng Li, Jacob M. Miller, Rhys G. Povey, Hong Qiao, Eric Chitambar, Andrew N. Cleland
https://arxiv.org/abs/2506.10878

Quantum secret sharing in a triangular superconducting quantum network
We present a three-node quantum communication testbed with a triangular topology, each side of the triangle formed by a 1.3-meter-long transmission line. We demonstrate state transfer and entanglement generation between any two nodes, generate genuine multipartite entangled GHZ states, and implement quantum secret sharing (QSS) of classical information. Our experiments show that the QSS protocol can effectively detect eavesdropping, ensuring the secure sharing of secrets. This device forms a te…

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Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Kaijie Shi, Wanglong Lu, Hanli Zhao, Vinicius Prado da Fonseca, Ting Zou, Xianta Jiang
https://arxiv.org/abs/2506.08795

Towards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning
Limb loss affects millions globally, impairing physical function and reducing quality of life. Most traditional surface electromyographic (sEMG) and semi-autonomous methods require users to generate myoelectric signals for each control, imposing physically and mentally taxing demands. This study aims to develop a fully autonomous control system that enables a prosthetic hand to automatically grasp and release objects of various shapes using only a camera attached to the wrist. By placing the ha…

XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Ioan-Paul Ciobanu, Andrei-Iulian Hiji, Nicolae-Catalin Ristea, Paul Irofti, Cristian Rusu, Radu Tudor Ionescu
https://arxiv.org/abs/2506.00462

XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
Recent advances in audio generation led to an increasing number of deepfakes, making the general public more vulnerable to financial scams, identity theft, and misinformation. Audio deepfake detectors promise to alleviate this issue, with many recent studies reporting accuracy rates close to 99%. However, these methods are typically tested in an in-domain setup, where the deepfake samples from the training and test sets are produced by the same generative models. To this end, we introduce XMAD-…

CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Yawen Luo, Jianhong Bai, Xiaoyu Shi, Menghan Xia, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, Tianfan Xue
https://arxiv.org/abs/2506.03140

CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbersome for users to construct, particularly for intricate camera movements. To provide a more intuitive camera control method, we propose CamCloneMaster, a framework that enables users to replicate camera movements from reference videos without requiring camera parameters or test-time fine-tuning. CamCloneMaster seamle…

Kilobyte-Scale, Selector-Free, Temperature-Hard AlScN Ferroelectric Diode Crossbar Arrays
Zirun Han, Chao-Chuan Chen, Dhiren K. Pradhan, David C. Moore, Ravali Gudavalli, Xindi Yang, Kwan-Ho Kim, Hyunmin Cho, Zachary Anderson, Spencer Ware, Harsh Yellai, W. Joshua Kennedy, Nicholas R. Glavin, Roy H. Olsson III, Deep Jariwala
https://

Kilobyte-Scale, Selector-Free, Temperature-Hard AlScN Ferroelectric Diode Crossbar Arrays
We report the fabrication and characterization of kilobyte-scale, selector-free, ferroelectric (FE) diode crossbar memory arrays based on aluminum scandium nitride (AlScN). Utilizing a fully CMOS back-end-of-line (BEOL) compatible process, we fabricated 2-kilobyte (128 $\times$ 128) arrays with device diameters down to 5 $μ$m, achieving memory densities up to 2500 bits/mm$^2$. Large-scale electrical characterization across 1000 randomly selected devices reveals a yield rate of 95.2%, a tight s…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
Marcos V. Conde, Radu Timofte, Zihao Lu, Xiangyu Kongand Xiaoxia Xingand Fan Wangand Suejin Hanand MinKyu Parkand Tianyu Zhangand Xin Luoand Yeda Chenand Dong Liuand Li Pangand Yuhang Yangand Hongzhong Wangand Xiangyong Caoand Ruixuan Jiangand Senyan Xuand Siyuan Jiangand Xueyang Fuand Zheng-Jun Zhaand Tianyu Haoand Yuhong Heand Ruoqi Liand Yueqi Yangand Xiang Yuand Guanlan Hongand Minmin Yiand Yuanjia Chenand Liwen Zha…

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution
This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the…

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Jiwen Yu, Jianhong Bai, Yiran Qin, Quande Liu, Xintao Wang, Pengfei Wan, Di Zhang, Xihui Liu
https://arxiv.org/abs/2506.03141

Context as Memory: Scene-Consistent Interactive Long Video Generation with Memory Retrieval
Recent advances in interactive video generation have shown promising results, yet existing approaches struggle with scene-consistent memory capabilities in long video generation due to limited use of historical context. In this work, we propose Context-as-Memory, which utilizes historical context as memory for video generation. It includes two simple yet effective designs: (1) storing context in frame format without additional post-processing; (2) conditioning by concatenating context and frame…

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Xiantao Fan, Xinyang Liu, Meng Wang, Jian-Xun Wang
https://arxiv.org/abs/2505.23940

Diff-FlowFSI: A GPU-Optimized Differentiable CFD Platform for High-Fidelity Turbulence and FSI Simulations
Turbulent flows and fluid-structure interactions (FSI) are ubiquitous in scientific and engineering applications, but their accurate and efficient simulation remains a major challenge due to strong nonlinearities, multiscale interactions, and high computational demands. Traditional CFD solvers, though effective, struggle with scalability and adaptability for tasks such as inverse modeling, optimization, and data assimilation. Recent advances in machine learning (ML) have inspired hybrid modelin…

Electronic structures and magnetism in van der Waals flat-band material Ni$_{3}$GeTe$_{2}$
Yuanji Xu, Xintao Jin, Haoyuan Tang, Fuyang Tian
https://arxiv.org/abs/2506.02498

Electronic structures and magnetism in van der Waals flat-band material Ni$_{3}$GeTe$_{2}$
The study of magnetism in two-dimensional materials has garnered significant interest, driven by fundamental investigations into low-dimensional magnetic phenomena and their potential for applications in spintronic devices. Through dynamical mean-field theory calculations, we demonstrate that Ni$_{3}$GeTe$_{2}$ exhibits flat-band characteristics resulting from the geometric frustration of its layered triangular lattice. These flat bands are further renormalized due to moderate electronic correl…