Tootfinder

I expect I'm late to the party as usual and everyone's already seen this - but yeah, for once the description of this video is not hyperbole.
The most INSANE Bohemian Rhapsody Flashmob you will ever see!
Not sure about insane but it's very, very impressive. Worth a few minutes of your time and it'll cheer up your day.
#queen #BohemianRhapsody #flashmob

In Zeiten von Fake News und Klimawandel-Leugnern kann man nur froh sein, dass es noch aufrechte Wissenschaftler gibt, die sich nicht mundtot machen lassen.
https://youtube.com/watch?v=7-h0zKgJkBk&si=rbXw3ad8twO7jUQO

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Yang Zhang, Zhanxiang Cao, Buqing Nie, Haoyang Li, Yue Gao
https://arxiv.org/abs/2507.08303

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Humanoid robots show significant potential in daily tasks. However, reinforcement learning-based motion policies often suffer from robustness degradation due to the sim-to-real dynamics gap, thereby affecting the agility of real robots. In this work, we propose a novel robust adversarial training paradigm designed to enhance the robustness of humanoid motion policies in real worlds. The paradigm introduces a learnable adversarial attack network that precisely identifies vulnerabilities in motio…

MIRRAMS: Towards Training Models Robust to Missingness Distribution Shifts
Jihye Lee, Minseo Kang, Dongha Kim
https://arxiv.org/abs/2507.08280 https://

MIRRAMS: Towards Training Models Robust to Missingness Distribution Shifts
In real-world data analysis, missingness distributional shifts between training and test input datasets frequently occur, posing a significant challenge to achieving robust prediction performance. In this study, we propose a novel deep learning framework designed to address such shifts in missingness distributions. We begin by introducing a set of mutual information-based conditions, called MI robustness conditions, which guide a prediction model to extract label-relevant information while rema…

Revisiting Data Attribution for Influence Functions
Hongbo Zhu, Angelo Cangelosi
https://arxiv.org/abs/2508.07297 https://arxiv.org/pdf/2508.07297

Revisiting Data Attribution for Influence Functions
The goal of data attribution is to trace the model's predictions through the learning algorithm and back to its training data. thereby identifying the most influential training samples and understanding how the model's behavior leads to particular predictions. Understanding how individual training examples influence a model's predictions is fundamental for machine learning interpretability, data debugging, and model accountability. Influence functions, originating from robust statistics, offer …

A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
Zihang Qiu, Ali Ajdari, Mislav Bobi\'c, Thomas Bortfeld, Dick den Hertog, Jannis Kurtz, Hoyeon Lee
https://arxiv.org/abs/2508.07368

A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust …

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Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling
Hua-Liang Liu, Hao Su, Si-Qiu Gong, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Qian Wei, Yukun Song, Dongzhou Wang, Mingyang Zheng, Faxi Chen, Libo Li, Siyu Ren, Xuezhi Zhu, Meihong Wang, Yaojian Chen, Yanfei Liu, Longsheng Song, Pengyu Yang, Junshi Chen, Hong An, Lei Zhang, Lin Gan, Guangwen Yang, Jia-Min Xu, Yu-Ming He, Hui Wang, Han-Sen Zhong, Ming-Cheng Chen, Xiao Jiang, Li Li, Nai-Le Liu, Yu…

Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling
The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum computational advantage (QCA) in the pres- ence of unavoidable noise and the dynamic competition with classical algorithm improvements. To overcome the biggest challenge of photon-based QCA experiments, photon loss, we report new Gaussian boson sampling (GBS) experiments with 1024 high-efficiency squeezed states injected i…

If only reposting this Blahaj shark could actually make transphobia disappear, I wish it were that simple! 😔
#Blahaj #TransRightsAreHumanRights #TransRights

Universally Robust Control of Open Quantum Systems
Lixiang Ding, Jingtao Fan, Xingze Qiu
https://arxiv.org/abs/2508.07379 https://arxiv.org/pdf/2508.07379

Universally Robust Control of Open Quantum Systems
Mitigating noise-induced decoherence is the central challenge in controlling open quantum systems. While existing robust protocols often require precise noise models, we introduce a universal framework for noise-agnostic quantum control that achieves high-fidelity operations without prior environmental noise characterization. This framework capitalizes on the dynamical modification of the system-environment coupling through control drives, an effect rigorously encoded in the dynamical equation.…