Tootfinder

Series D, Episode 07 - Assassin
AVON: [V.O.] Scorpio, come in Scorpio.
TARRANT: Avon, what's happening?
AVON: [V.O.] Never mind what's happening. Just get me up as quickly as you can. [Cut to Domo]
NEBROX: You promised! You promised! [Back to Scorpio]
https://blake.torpidity.net/m/407/181

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

This https://arxiv.org/abs/2502.17701 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMs
Evacuation decision prediction is critical for efficient and effective wildfire response by helping emergency management anticipate traffic congestion and bottlenecks, allocate resources, and minimize negative impacts. Traditional statistical methods for evacuation decision prediction fail to capture the complex and diverse behavioral logic of different individuals. In this work, for the first time, we introduce FLARE, short for facilitating LLM for advanced reasoning on wildfire evacuation dec…

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
Esteban Guti\'errez, Frederic Font, Xavier Serra, Lonce Wyse
https://arxiv.org/abs/2506.04073

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds characterized by stochastic structure and perceptual stationarity. Drawing inspiration from the statistical and perceptual framework of McDermott and Simoncelli, TexStat identifies similarities between signals belonging to the same texture category without relying on temporal structure. We also propose using TexStat as a validation metric alongside Frechet Audio Dista…

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Christian Tinauer, Maximilian Sackl, Stefan Ropele, Christian Langkammer
https://arxiv.org/abs/2506.03890

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering
Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, raising concerns about interpretability. Previous studies suggest biases in model decisions, necessitating further analysis. This study uses Layer-wise Relevance Propagation (LRP) and spectral clustering to explore classifier decision strategies across preprocessing and training configurations using R2* maps. We trained a 3D convolutional neural net…

This https://arxiv.org/abs/2506.01222 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning collective variables that preserve transition rates
Collective variables (CVs) play a crucial role in capturing rare events in high-dimensional systems, motivating the continual search for principled approaches to their design. In this work, we revisit the framework of quantitative coarse graining and identify the orthogonality condition from Legoll and Lelievre (2010) as a key criterion for constructing CVs that accurately preserve the statistical properties of the original process. We establish that satisfaction of the orthogonality condition …

Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
Jingyi Zhao, Claudia Archetti, Tuan Anh Pham, Thibaut Vidal
https://arxiv.org/abs/2506.03172

Large Neighborhood and Hybrid Genetic Search for Inventory Routing Problems
The inventory routing problem (IRP) focuses on jointly optimizing inventory and distribution operations from a supplier to retailers over multiple days. Compared to other problems from the vehicle routing family, the interrelations between inventory and routing decisions render IRP optimization more challenging and call for advanced solution techniques. A few studies have focused on developing large neighborhood search approaches for this class of problems, but this remains a research area with…

Looking for Attention: Randomized Attention Test Design for Validator Monitoring in Optimistic Rollups
Suhyeon Lee
https://arxiv.org/abs/2505.24393 https:/…

Looking for Attention: Randomized Attention Test Design for Validator Monitoring in Optimistic Rollups
Optimistic Rollups (ORUs) significantly enhance blockchain scalability but inherently suffer from the verifier's dilemma, particularly concerning validator attentiveness. Current systems lack mechanisms to proactively ensure validators are diligently monitoring L2 state transitions, creating a vulnerability where fraudulent states could be finalized. This paper introduces the Randomized Attention Test (RAT), a novel L1-based protocol designed to probabilistically challenge validators in ORUs, t…

This https://arxiv.org/abs/2306.11154 has been replaced.
link: https://scholar.google.com/scholar?q=a

An Isotonic Mechanism for Overlapping Ownership
Motivated by the problem of improving peer review at large scientific conferences, this paper studies how to elicit self-evaluations to improve review scores in a natural many-to-many owner-item (e.g., author-paper) situation with overlapping ownership. We design a simple, efficient and truthful mechanism to elicit self-evaluations from item owners that can be used to calibrate their noisy review scores in the existing evaluation process (e.g., papers' review scores from peers). Our approach …

Risk and Reward of Transitioning from a National to a Zonal Electricity Market in Great Britain
Lukas Franken, Andrew Lyden, Daniel Friedrich
https://arxiv.org/abs/2506.04107

Risk and Reward of Transitioning from a National to a Zonal Electricity Market in Great Britain
More spatially granular electricity wholesale markets promise more efficient operation and better asset siting in highly renewable power systems. Great Britain is considering moving from its current single-price national wholesale market to a zonal design. Existing studies reach varying and difficult-to-reconcile conclusions about the desirability of a zonal market in GB, partly because they rely on models that vary in their transparency and assumptions about future power systems. Using a novel…

Eye-tracking-Driven Shared Control for Robotic Arms:Wizard of Oz Studies to Assess Design Choices
Anke Fischer-Janzen, Thomas M. Wendt, Daniel G\"orlich, Kristof Van Laerhoven
https://arxiv.org/abs/2505.23147

Eye-tracking-Driven Shared Control for Robotic Arms:Wizard of Oz Studies to Assess Design Choices
Advances in eye-tracking control for assistive robotic arms provide intuitive interaction opportunities for people with physical disabilities. Shared control has gained interest in recent years by improving user satisfaction through partial automation of robot control. We present an eye-tracking-guided shared control design based on insights from state-of-the-art literature. A Wizard of Oz setup was used in which automation was simulated by an experimenter to evaluate the concept without requir…