Tootfinder

Recommender systems, stigmergy, and the tyranny of popularity
Zackary Okun Dunivin, Paul E. Smaldino
https://arxiv.org/abs/2506.06162 https://

Recommender systems, stigmergy, and the tyranny of popularity
Scientific recommender systems, such as Google Scholar and Web of Science, are essential tools for discovery. Search algorithms that power work through stigmergy, a collective intelligence mechanism that surfaces useful paths through repeated engagement. While generally effective, this ``rich-get-richer'' dynamic results in a small number of high-profile papers that dominate visibility. This essay argues argue that these algorithm over-reliance on popularity fosters intellectual homogeneity and…

This https://arxiv.org/abs/2408.06208 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Event-triggered moving horizon estimation for nonlinear systems
This work proposes an event-triggered moving horizon estimation (ET-MHE) scheme for general nonlinear systems. The key components of the proposed scheme are a novel event-triggering mechanism (ETM) and the suitable design of the MHE cost function. The main characteristic of our method is that the MHE's nonlinear optimization problem is only solved when the ETM triggers the transmission of measured data to the remote state estimator. If no event occurs, then the current state estimate results fr…

This https://arxiv.org/abs/2503.01600 has been replaced.
initial toot: https://mastoxiv.page/@a…

Is Triboelectricity Confusing, Confused or Complex?
In this report, we look at the fundamental physics of triboelectricity, charge transfer due to contact and sliding. While much of the report focuses upon recent advances such as the incorporation of flexoelectric contributions, we also include older work, some from centuries ago, which only now can be understood in a general sense. Basic concepts and theories ranging from elements of tribology and contact mechanics through semiconductor built-in potentials, electromechanical terms, mechanochemi…

This https://arxiv.org/abs/2502.14753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders
Medical images are acquired at high resolutions with large fields of view in order to capture fine-grained features necessary for clinical decision-making. Consequently, training deep learning models on medical images can incur large computational costs. In this work, we address the challenge of downsizing medical images in order to improve downstream computational efficiency while preserving clinically-relevant features. We introduce MedVAE, a family of six large-scale 2D and 3D autoencoders c…

Fx-Encoder : Extracting Instrument-Wise Audio Effects Representations from Mixtures
Yen-Tung Yeh, Junghyun Koo, Marco A. Mart\'inez-Ram\'irez, Wei-Hsiang Liao, Yi-Hsuan Yang, Yuki Mitsufuji
https://arxiv.org/abs/2507.02273

Fx-Encoder++: Extracting Instrument-Wise Audio Effects Representations from Mixtures
General-purpose audio representations have proven effective across diverse music information retrieval applications, yet their utility in intelligent music production remains limited by insufficient understanding of audio effects (Fx). Although previous approaches have emphasized audio effects analysis at the mixture level, this focus falls short for tasks demanding instrument-wise audio effects understanding, such as automatic mixing. In this work, we present Fx-Encoder++, a novel model design…

This https://arxiv.org/abs/2502.06586 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Decay of correlation for edge colorings when $q>3Δ$
We examine various perspectives on the decay of correlation for the uniform distribution over proper $q$-edge colorings of graphs with maximum degree $Δ$. First, we establish the coupling independence property when $q\ge 3Δ$ for general graphs. Together with the work of Chen et al. (2024), this result implies a fully polynomial-time approximation scheme (FPTAS) for counting the number of proper $q$-edge colorings. Next, we prove the strong spatial mixing property on trees, provided that $…

Artifacts in Halo Shapes: Imprints of the Initial Condition
Yu Yu, Zhao Chen
https://arxiv.org/abs/2507.16745 https://arxiv.org/pdf/2…

Artifacts in Halo Shapes: Imprints of the Initial Condition
Grid type pre-initial conditions are commonly used to initialize particle positions in cosmological simulations. While these conditions are known to produce noticeable numerical artifacts in void regions, their impact on halo properties has generally been assumed to be negligible. In this work, we employ multiple simulations to demonstrate that grid initialization induces statistically significant artifacts in halo shapes, despite the modest absolute amplitude ($\sim 1\%$) making them unimporta…

This https://arxiv.org/abs/2505.11370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Understanding Nonlinear Implicit Bias via Region Counts in Input Space
One explanation for the strong generalization ability of neural networks is implicit bias. Yet, the definition and mechanism of implicit bias in non-linear contexts remains little understood. In this work, we propose to characterize implicit bias by the count of connected regions in the input space with the same predicted label. Compared with parameter-dependent metrics (e.g., norm or normalized margin), region count can be better adapted to nonlinear, overparameterized models, because it is de…

This https://arxiv.org/abs/2501.00296 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such models is the set of symbolic predicates that define properties of and relationships between objects. In this work, we leverage pretrained …