Tootfinder

Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Eric C. Joyce, Qianwen Zhao, Nathaniel Burgdorfer, Long Wang, Philippos Mordohai
https://arxiv.org/abs/2506.20045

Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
Deep object pose estimators are notoriously overconfident. A grasping agent that both estimates the 6-DoF pose of a target object and predicts the uncertainty of its own estimate could avoid task failure by choosing not to act under high uncertainty. Even though object pose estimation improves and uncertainty quantification research continues to make strides, few studies have connected them to the downstream task of robotic grasping. We propose a method for training lightweight, deep networks t…

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang, Weizhi Chen, Sheng Zhou, Hao Chen, Yong Li, Jiajun Bu, Shirui Pan, Yizhou Yu, Irwin King, Fakhri Karray, Philip S. Yu
https://arxiv.org/abs/2506.18019

Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further advancing towards a synergistic fusion of RL and LLM capabilities. This progression has endowed AI agents with increasingly strong abilities. Despite these advances, to accomplish complex real-world tasks, agents are required to plan and execute effectively, maintain reliable memory, and coordinate smoothly with other age…

Rare-earth atoms on Nb(110) as a platform to engineer topological superconductivity
David Antognini Silva, Yu Wang, Nicolae Atodiresei, Felix Friedrich, Stefan Bl\"ugel, Matthias Bode, Philipp R\"u{\ss}mann, Artem Odobesko
https://arxiv.org/abs/2506.19514

Rare-earth atoms on Nb(110) as a platform to engineer topological superconductivity
Helical spin textures in one-dimensional magnetic chains on superconductors can enable topological superconductivity and host Majorana zero modes, independent of the presence of intrinsic spin-orbit coupling. Here, we show that gadolinium (Gd) adatoms, possessing large 4f magnetic moments when placed on a Nb(110) surface, establish indirect exchange interactions mediated by valence electrons, manifesting as Yu-Shiba-Rusinov states. By combining scanning tunneling microscopy and spectroscopy wit…

DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
Anindita Ghosh, Bing Zhou, Rishabh Dabral, Jian Wang, Vladislav Golyanik, Christian Theobalt, Philipp Slusallek, Chuan Guo
https://arxiv.org/abs/2506.18680

DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
We present DuetGen, a novel framework for generating interactive two-person dances from music. The key challenge of this task lies in the inherent complexities of two-person dance interactions, where the partners need to synchronize both with each other and with the music. Inspired by the recent advances in motion synthesis, we propose a two-stage solution: encoding two-person motions into discrete tokens and then generating these tokens from music. To effectively capture intricate interactions…

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Muhammad Daud, Philippe Charton, Cedric Damour, Jingbo Wang, Frederic Cadet
https://arxiv.org/abs/2506.16921

EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation
Understanding the relationship between protein sequences and their functions is fundamental to protein engineering, but this task is hindered by the combinatorially vast sequence space and the experimental noise inherent in fitness measurements. In this study, we present a novel framework that models the sequence landscape as a hypercube $H(k,2)$ and integrates wavelet-based signal denoising with a graph convolutional neural network (GCN) to predict protein fitness across rugged fitness landsca…

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Shuyuan Wang, Philip D. Loewen, Michael Forbes, Bhushan Gopaluni, Wei Pan
https://arxiv.org/abs/2506.17473

DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
While differentiable control has emerged as a powerful paradigm combining model-free flexibility with model-based efficiency, the iterative Linear Quadratic Regulator (iLQR) remains underexplored as a differentiable component. The scalability of differentiating through extended iterations and horizons poses significant challenges, hindering iLQR from being an effective differentiable controller. This paper introduces DiLQR, a framework that facilitates differentiation through iLQR, allowing it …

Wrongly deported Kilmar Abrego Garcia returning to U.S. (Philip Wang/Axios)
https://www.axios.com/2025/06/06/kiilmar-abrego-garcia-returns-trafficking-charges
http://www.memeorandum.com/250606/p122#a250606p122

High-fidelity collisional quantum gates with fermionic atoms
Petar Bojovi\'c, Timon Hilker, Si Wang, Johannes Obermeyer, Marnix Barendregt, Dorothee Tell, Thomas Chalopin, Philipp M. Preiss, Immanuel Bloch, Titus Franz
https://arxiv.org/abs/2506.14711

High-fidelity collisional quantum gates with fermionic atoms
Quantum simulations of electronic structure and strongly correlated quantum phases are widely regarded as among the most promising applications of quantum computing. These simulations require the accurate implementation of motion and entanglement of fermionic particles. Instead of the commonly applied costly mapping to qubits, fermionic quantum computers offer the prospect of directly implementing electronic structure problems. Ultracold neutral atoms have emerged as a powerful platform for spi…

A Tale of Two Shocks
Robert F. Wimmer-Schweingruber, Domenico Trotta, Rungployphan Kieokaew, Liu Yang, Alexander Kollhoff, Lars Berger, Patrick K\"uhl, Stephan I. B\"ottcher, Bernd Heber, Philippe Louarn, Andrey Fedorov, Javier Rodriguez-Pacheco, Ra\'ul G\'omez-Herrero, Francisco Espinosa Lara, Ignacio Cernuda, Yulia Kartavykh, Linghua Wang, George C. Ho, Robert C. Allen, Glenn M. Mason, Zheyi Ding, Andrea Larosa, G. Sindhuja, Sandra Eldrum, Sebastian Fleth, David Lar…

A Tale of Two Shocks
It was the best of times, it was the worst of times, . . . - for the thermal/suprathermal particle populations in the vicinity of two traveling interplanetary shocks observed by Solar Orbiter on 2023-11-29 07:51:17 UTC and 2023-11-30 10:47:26 UTC at $\sim 0.83$ astronomical units from the Sun. We investigate these two very dissimilar shocks and elucidate their non-equilibrium features. We do not provide explanations of all observed features, our aim is to report them here for future reference…

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models
Philip Liu, Sparsh Bansal, Jimmy Dinh, Aditya Pawar, Ramani Satishkumar, Shail Desai, Neeraj Gupta, Xin Wang, Shu Hu
https://arxiv.org/abs/2506.07400

MedChat: A Multi-Agent Framework for Multimodal Diagnosis with Large Language Models
The integration of deep learning-based glaucoma detection with large language models (LLMs) presents an automated strategy to mitigate ophthalmologist shortages and improve clinical reporting efficiency. However, applying general LLMs to medical imaging remains challenging due to hallucinations, limited interpretability, and insufficient domain-specific medical knowledge, which can potentially reduce clinical accuracy. Although recent approaches combining imaging models with LLM reasoning have …