Tootfinder

Newsmax refiles lawsuit v Fox News in Wisconsin - National Zero
https://nationalzero.com/2025/09/12/newsmax-refiles-lawsuit-v-fox-news-in-wisconsin/

An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities
Shahid Shafi Dar, Bharat Kaurav, Arnav Jain, Chandravardhan Singh Raghaw, Mohammad Zia Ur Rehman, Nagendra Kumar
https://arxiv.org/abs/2509.08003

An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities
In an era of escalating climate change, urban flooding has emerged as a critical challenge for sustainable cities, threatening lives, infrastructure, and ecosystems. Traditional flood detection methods are constrained by their reliance on unimodal data and static rule-based systems, which fail to capture the dynamic, non-linear relationships inherent in flood events. Furthermore, existing attention mechanisms and ensemble learning approaches exhibit limitations in hierarchical refinement, cross…

US-based Refinery29 closes its UK office, lays off staff, and shuts down the UK operation of its Black women-focused sub-brand Unbothered (Alice Brooker/Press Gazette)
https://pressgazette.co.uk/news/lifestyle-brand-refinery29-closes-uk-o…

Lifestyle brand Refinery29 closes UK office and lays off journalists
Lifestyle, fashion and wellness site Refinery29 has made an unknown number of cuts in the shutting of its UK office.

Generative Data Refinement: Just Ask for Better Data
Minqi Jiang, Jo\~ao G. M. Ara\'ujo, Will Ellsworth, Sian Gooding, Edward Grefenstette
https://arxiv.org/abs/2509.08653 h…

Generative Data Refinement: Just Ask for Better Data
For a fixed parameter size, the capabilities of large models are primarily determined by the quality and quantity of its training data. Consequently, training datasets now grow faster than the rate at which new data is indexed on the web, leading to projected data exhaustion over the next decade. Much more data exists as user-generated content that is not publicly indexed, but incorporating such data comes with considerable risks, such as leaking private information and other undesirable conten…

TGPR: Tree-Guided Policy Refinement for Robust Self-Debugging of LLMs
Daria Ozerova, Ekaterina Trofimova
https://arxiv.org/abs/2510.06878 https://arxiv.org…

TGPR: Tree-Guided Policy Refinement for Robust Self-Debugging of LLMs
Iterative refinement has been a promising paradigm to enable large language models (LLMs) to resolve difficult reasoning and problem-solving tasks. One of the key challenges, however, is how to effectively search through the enormous search space of possible refinements. Existing methods typically fall back on predefined heuristics, which are troubled by the exploration-exploitation dilemma and cannot adapt based on past refinement outcomes. We introduce Tree-Guided Policy Refinement (TGPR), a …

Even Realities' G2 glasses hands-on: lighter than G1, subtle notifications on lens that cross eye line, designed to be a "passive wearable" akin to Apple Watch (Fernando Silva/9to5Mac)
https://9to5mac.com/2025/11/12/hands-o…

Smart glasses that finally look & feel normal - Even Realities G2 - 9to5Mac
The Even G2 smart glasses refine everything that made the original great—sleeker design, smarter AI, better comfort, and all-day wearability.

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
Jakob Nyberg, Pontus Johnson
https://arxiv.org/abs/2509.09219 https://…

Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinement
We present and evaluate Vejde; a framework which combines data abstraction, graph neural networks and reinforcement learning to produce inductive policy functions for decision problems with richly structured states, such as object classes and relations. MDP states are represented as data bases of facts about entities, and Vejde converts each state to a bipartite graph, which is mapped to latent states through neural message passing. The factored representation of both states and actions allows …

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Yuelin Guo, Haoyu He, Zhiyuan Chen, Zitong Huang, Renhao Lu, Lu Shi, Zejun Wang, Weizhe Zhang
https://arxiv.org/abs/2509.08289

Dual-Thresholding Heatmaps to Cluster Proposals for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) has attracted significant attention in recent years, as it does not require box-level annotations. State-of-the-art methods generally adopt a multi-module network, which employs WSDDN as the multiple instance detection network module and multiple instance refinement modules to refine performance. However, these approaches suffer from three key limitations. First, existing methods tend to generate pseudo GT boxes that either focus only on discriminative …