Tootfinder

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Keyu Chen, Yuheng Lei, Hao Cheng, Haoran Wu, Wenchao Sun, Sifa Zheng
https://arxiv.org/abs/2406.02983

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-driven approaches. However, without an appropriate upper bound for adversariality, the scenarios might exhibit excessive adversariality, potentially leading to unavoidable collisions…

DistR: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency
Haoran Ma, Yifan Qiao, Shi Liu, Shan Yu, Yuanjiang Ni, Qingda Lu, Jiesheng Wu, Yiying Zhang, Miryung Kim, Harry Xu
https://arxiv.org/abs/2406.02803

DistR: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency
Despite being a powerful concept, distributed shared memory (DSM) has not been made practical due to the extensive synchronization needed between servers to implement memory coherence. This paper shows a practical DSM implementation based on the insight that the ownership model embedded in programming languages such as Rust automatically constrains the order of read and write, providing opportunities for significantly simplifying the coherence implementation if the ownership semantics can be ex…

No, me.
You don't try to open a 700k JSON file in Godot editor. Especially when you have just written it.
You're stupid, me.

I quite like the idea of forging a faux display context and lying to whatever WindowsHook handler is driving the Recall collection and then filling it with garbage nonsense, surreal poetry and invented scenes from Waiting for Godot and Krapps Last Tape.
#Recall #performancearts

Don’t forget to pack your running shoes for the #tncmorningrun 🏃 On Tuesday 11 June 07:00 - 08:00 you are invited to participate in a gentle 5km run through the streets and squares of Rennes, following the river Vilaine. It will be guided by a Rennes sports coach, and the link for the route will be shared soon.
Register now to join 👉

TNC24
TNC24 will be hosted by RENATER, the French Research and Education Network, and will take place in Rennes, France, from June 10-14, 2024. About TNC The largest and most prestigious research and education networking conference, TNC attracts a truly diverse audience of over 800 participants from more than 70 countries, representing national and regional research and education networks, schools and universities, technology providers, and many of the world’s most exciting scientific projects. B…

Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI
Jonghun Kim, Hyunjin Park
https://arxiv.org/abs/2406.02936

Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI
Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical imaging diagnosis, particularly when utilizing multiple imaging modalities to enhance accuracy. This study presents a model that predicts pCR in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion…

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Keyu Chen, Yuheng Lei, Hao Cheng, Haoran Wu, Wenchao Sun, Sifa Zheng
https://arxiv.org/abs/2406.02983

FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-driven approaches. However, without an appropriate upper bound for adversariality, the scenarios might exhibit excessive adversariality, potentially leading to unavoidable collisions…

Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Juanhua Zhang, Ruodan Yan, Alessandro Perelli, Xi Chen, Chao Li
https://arxiv.org/abs/2406.03002

Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs. Deep learning approaches have been used to enhance dMRI and predict diffusion biomarkers through undersampled dMRI. To generate more comprehensive raw dMRI, generative adversarial network based methods are proposed to include b-values and b-vectors as conditions, but they are limited by unstable training and less desirable diversity. The emerging diffusion model (DM) promises to improve generative performan…

Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning
Kaveh Malek, Claus Danielson, Fernando Moreu
https://arxiv.org/abs/2406.02799

Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning
Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundan…

Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning
Kaveh Malek, Claus Danielson, Fernando Moreu
https://arxiv.org/abs/2406.02799

Immersive Robot Programming Interface for Human-Guided Automation and Randomized Path Planning
Researchers are exploring Augmented Reality (AR) interfaces for online robot programming to streamline automation and user interaction in variable manufacturing environments. This study introduces an AR interface for online programming and data visualization that integrates the human in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items which correspond to physical elements to interact with a redundan…