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The Mallee Daily
Great Australian Pods Podcast Directory: #GreatAusPods

Wiecie, czego naprawdę nie znoszę w długie weekendy?
Nawet nie zmian rozkładu, ale tego, że dla mniejszych przewoźników, po prostu nie idzie znaleźć informacji, czy w piątek autobusy kursują według rozkładu dla dni roboczych czy weekendowego. I oczywiście jeden z drugim z reguły różnią się tak bardzo, że nawet nie można wybrać jednego kursu, który na pewno pojedzie.
Tak, pomyślałem, żeby jutro rano pojechać do Kórnika. Pewnie nikt tu przypadkiem nie orientuje się, jak jutro jeżdż…

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Min Li, Chen Chen, Zhenghao Li, Yin Liu, Shanshan Shan, Peng Wu, Pengfei Rong, Feng Liu, G. Bruce Pike, Alan H. Wilman, Hongfu Sun, Yang Gao
https://arxiv.org/abs/2506.13293

SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation
Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sources (i.e., paramagnetic and diamagnetic), may coexist in a single voxel, and cancel each other out in net QSM images. Susceptibility source separation techniques enable the extraction of sub-voxel information from QSM maps. This study proposes a novel SUSEP-Net for susceptibility source separation by training a dual-br…

AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multi-modal Information Between Reality and Videos
Zheng Ning, Leyang Li, Daniel Killough, JooYoung Seo, Patrick Carrington, Yapeng Tian, Yuhang Zhao, Franklin Mingzhe Li, Toby Jia-Jun Li
https://arxiv.org/abs/2507.10963

AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multi-modal Information Between Reality and Videos
Videos offer rich audiovisual information that can support people in performing activities of daily living (ADLs), but they remain largely inaccessible to blind or low-vision (BLV) individuals. In cooking, BLV people often rely on non-visual cues, such as touch, taste, and smell, to navigate their environment, making it difficult to follow the predominantly audiovisual instructions found in video recipes. To address this problem, we introduce AROMA, an AI system that provides timely responses t…

RationalVLA: A Rational Vision-Language-Action Model with Dual System
Wenxuan Song, Jiayi Chen, Wenxue Li, Xu He, Han Zhao, Pengxiang Ding Shiyan Su, Feilong Tang, Xuelian Cheng, Donglin Wang, Zongyuan Ge, Xinhu Zheng, Zhe Liu, Hesheng Wang, Yunhui Liu, Haoang Li
https://arxiv.org/abs/2506.10826

RationalVLA: A Rational Vision-Language-Action Model with Dual System
A fundamental requirement for real-world robotic deployment is the ability to understand and respond to natural language instructions. Existing language-conditioned manipulation tasks typically assume that instructions are perfectly aligned with the environment. This assumption limits robustness and generalization in realistic scenarios where instructions may be ambiguous, irrelevant, or infeasible. To address this problem, we introduce RAtional MAnipulation (RAMA), a new benchmark that challen…

Controlling quantum entanglement with classical non-separable light
R. F. Barros, A. L. S. Santos Junior, A. Z. Khoury, R. Fickler
https://arxiv.org/abs/2507.06462

Controlling quantum entanglement with classical non-separable light
Here we investigate the quantum frequency conversion of entangled photons driven by a classically non-separable laser beam. We show that the frequency conversion dynamics is described by a quantum channel that stems from the classical drive field through the channel-state duality - the quantum channel is dual to the classical coherence matrix of the drive field. This implies that the evolution of entanglement in the conversion process is bound by the classical non-separability of the drive fiel…

Semantic Communication-Enabled Cloud-Edge-End-collaborative Metaverse Services Architecure
Yuxuan Li, Sheng Jinag, Bizhu Wang
https://arxiv.org/abs/2506.10001

Semantic Communication-Enabled Cloud-Edge-End-collaborative Metaverse Services Architecure
With technology advancing and the pursuit of new audiovisual experiences strengthening, the metaverse has gained surging enthusiasm. However, it faces practical hurdles as substantial data like high-resolution virtual scenes must be transmitted between cloud platforms and VR devices. Specifically, the VR device's wireless transmission hampered by insufficient bandwidth, causes speed and delay problems. Meanwhile, poor channel quality leads to data errors and worsens user experience. To solve th…

SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals
Jun Wu, Yuye Shi, Weijie Yuan, Qingqing Cheng, Buyi Li, Xinyuan Wei
https://arxiv.org/abs/2507.01427

SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals
This paper investigates the system design and experimental validation of integrated sensing and communication (ISAC) for environmental sensing, which is expected to be a critical enabler for next-generation wireless networks. We advocate exploiting orthogonal time frequency space (OTFS) modulation for its inherent sparsity and stability in delay-Doppler (DD) domain channels, facilitating a low-overhead environment sensing design. Moreover, a comprehensive environmental sensing framework is deve…