Tootfinder

Tiny birds, and their tiny superfood, could decline due to ‘irreversible’ effects of Vancouver port expansion
The Roberts Bank Terminal 2 expansion at Canada’s busiest cargo port could be fast-tracked by the federal government. It’s a major stop for 3.5 million western sandpipers to eat and recharge while travelling the entire Pacific
From The Narwhal

Port of Vancouver expansion threatens superfood ecocsystem | The Narwhal
Roberts Bank Terminal 2 could lead to ‘irreversible’ change in the Fraser estuary, a critical feeding ground for western sandpiper flocks

Embracing Evolution: A Call for Body-Control Co-Design in Embodied Humanoid Robot
Guiliang Liu, Bo Yue, Yi Jin Kim, Kui Jia
https://arxiv.org/abs/2510.03081 https://

Embracing Evolution: A Call for Body-Control Co-Design in Embodied Humanoid Robot
Humanoid robots, as general-purpose physical agents, must integrate both intelligent control and adaptive morphology to operate effectively in diverse real-world environments. While recent research has focused primarily on optimizing control policies for fixed robot structures, this position paper argues for evolving both control strategies and humanoid robots' physical structure under a co-design mechanism. Inspired by biological evolution, this approach enables robots to iteratively adapt bot…

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
Manuel Hinz, Maximilian Mauel, Patrick Seifner, David Berghaus, Kostadin Cvejoski, Ramses J. Sanchez
https://arxiv.org/abs/2510.12618

Towards Fast Coarse-graining and Equation Discovery with Foundation Inference Models
High-dimensional recordings of dynamical processes are often characterized by a much smaller set of effective variables, evolving on low-dimensional manifolds. Identifying these latent dynamics requires solving two intertwined problems: discovering appropriate coarse-grained variables and simultaneously fitting the governing equations. Most machine learning approaches tackle these tasks jointly by training autoencoders together with models that enforce dynamical consistency. We propose to decou…

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Matthew R. Williams, F. Hunter McGuire, Terrance D. Savitsky
https://arxiv.org/abs/2510.09401

Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects regression models. We present a motivating example of Bayesian inference for a multi-level or mixed effects model in which both the local parameters (e.g. group level random effects) and the global parameters may need to be adjusted for the complex sampling design. We evaluate the limitations of the survey-weighted ps…

Dream to Recall: Imagination-Guided Experience Retrieval for Memory-Persistent Vision-and-Language Navigation
Yunzhe Xu, Yiyuan Pan, Zhe Liu
https://arxiv.org/abs/2510.08553 htt…

Dream to Recall: Imagination-Guided Experience Retrieval for Memory-Persistent Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) requires agents to follow natural language instructions through environments, with memory-persistent variants demanding progressive improvement through accumulated experience. Existing approaches for memory-persistent VLN face critical limitations: they lack effective memory access mechanisms, instead relying on entire memory incorporation or fixed-horizon lookup, and predominantly store only environmental observations while neglecting navigation behavioral …

Fast Visuomotor Policy for Robotic Manipulation
Jingkai Jia, Tong Yang, Xueyao Chen, Chenhuan Liu, Wenqiang Zhang
https://arxiv.org/abs/2510.12483 https://…

Fast Visuomotor Policy for Robotic Manipulation
We present a fast and effective policy framework for robotic manipulation, named Energy Policy, designed for high-frequency robotic tasks and resource-constrained systems. Unlike existing robotic policies, Energy Policy natively predicts multimodal actions in a single forward pass, enabling high-precision manipulation at high speed. The framework is built upon two core components. First, we adopt the energy score as the learning objective to facilitate multimodal action modeling. Second, we int…

The Cost of Simplicity: How Reducing EEG Electrodes Affects Source Localization and BCI Accuracy
Eva Guttmann-Flury, Yanyan Wei, Shan Zhao, Jian Zhao, Mohamad Sawan
https://arxiv.org/abs/2510.10770

The Cost of Simplicity: How Reducing EEG Electrodes Affects Source Localization and BCI Accuracy
Electrode density optimization in electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) requires balancing practical usability against signal fidelity, particularly for source localization. Reducing electrodes enhances portability but its effects on neural source reconstruction quality and source connectivity - treated as proxies to BCI performance - remain understudied. We address this gap through systematic evaluation of 62-, 32-, and 16-channel configurations using a fixed, ful…

dHPR: A Distributed Halpern Peaceman--Rachford Method for Non-smooth Distributed Optimization Problems
Zhangcheng Feng, Defeng Sun, Yancheng Yuan, Guojun Zhang
https://arxiv.org/abs/2511.10069 https://arxiv.org/pdf/2511.10069 https://arxiv.org/html/2511.10069
arXiv:2511.10069v1 Announce Type: new
Abstract: This paper introduces the distributed Halpern Peaceman--Rachford (dHPR) method, an efficient algorithm for solving distributed convex composite optimization problems with non-smooth objectives, which achieves a non-ergodic $O(1/k)$ iteration complexity regarding Karush--Kuhn--Tucker residual. By leveraging the symmetric Gauss--Seidel decomposition, the dHPR effectively decouples the linear operators in the objective functions and consensus constraints while maintaining parallelizability and avoiding additional large proximal terms, leading to a decentralized implementation with provably fast convergence. The superior performance of dHPR is demonstrated through comprehensive numerical experiments on distributed LASSO, group LASSO, and $L_1$-regularized logistic regression problems.
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