Tootfinder

Coal miners with black lung fight Trump administration rollback of safety protections | Financial Post
https://financialpost.com/pmn/photo-essay-coal-miners-with-black-lung-fight-trump-administration-rollback-of-safety-protections

If you are using nextcloud as your main file and photo backup (for personal files, not work!), what are you using for organising your photos?
Photoprism? Memories? Something else?
photoprism
NextCloud photos
something else (add in comments)
NextCloud memories

Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs
Zacharia A. Rudge, Dominik Dold, Moritz Fieback, Dario Izzo, Said Hamdioui
https://arxiv.org/abs/2509.04506

Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs
Memristors are an emerging technology that enables artificial intelligence (AI) accelerators with high energy efficiency and radiation robustness -- properties that are vital for the deployment of AI on-board spacecraft. However, space applications require reliable and precise computations, while memristive devices suffer from non-idealities, such as device variability, conductance drifts, and device faults. Thus, porting neural networks (NNs) to memristive devices often faces the challenge of …

(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Memorization Assessment for LLMs
Jiashu Tao, Reza Shokri
https://arxiv.org/abs/2510.05582 https://

(Token-Level) \textbf{InfoRMIA}: Stronger Membership Inference and Memorization Assessment for LLMs
Machine learning models are known to leak sensitive information, as they inevitably memorize (parts of) their training data. More alarmingly, large language models (LLMs) are now trained on nearly all available data, which amplifies the magnitude of information leakage and raises serious privacy risks. Hence, it is more crucial than ever to quantify privacy risk before the release of LLMs. The standard method to quantify privacy is via membership inference attacks, where the state-of-the-art ap…

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Dayy\'an O'Brien, Barry Haddow, Emily Allaway, Pinzhen Chen
https://arxiv.org/abs/2510.05962

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Conducting contamination-free evaluation of mathematical capabilities can be difficult for two reasons: models may memorize a test set once it is made public, and current mathematical benchmarks are prone to overfitting due to having limited diversity of symbols and rules, coupled with closed-ended answers. This paper proposes a method to leverage these shortcomings as useful features to a construct dynamic, counterfactual benchmark, which can be used to both reveal overfitting and measure true…

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Boxiang Ma, Ru Li, Yuanlong Wang, Hongye Tan, Xiaoli Li
https://arxiv.org/abs/2509.04866

Memorization $\neq$ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?
Driven by vast and diverse textual data, large language models (LLMs) have demonstrated impressive performance across numerous natural language processing (NLP) tasks. Yet, a critical question persists: does their generalization arise from mere memorization of training data or from deep semantic understanding? To investigate this, we propose a bi-perspective evaluation framework to assess LLMs' scenario cognition - the ability to link semantic scenario elements with their arguments in context. …

Low-Rank-Based Approximate Computation with Memristors
Binyu Lu, Matthias Frey, Stark Draper, Jingge Zhu
https://arxiv.org/abs/2510.04402 https://arxiv.org…

Low-Rank-Based Approximate Computation with Memristors
Memristor crossbars enable vector-matrix multiplication (VMM), and are promising for low-power applications. However, it can be difficult to write the memristor conductance values exactly. To improve the accuracy of VMM, we propose a scheme based on low-rank matrix approximation. Specifically, singular value decomposition (SVD) is first applied to obtain a low-rank approximation of the target matrix, which is then factored into a pair of smaller matrices. Subsequently, a two-step serial VMM is …

Cowboys Tragedy Evokes Memories of 2012 Chiefs Incident https://heavy.com/sports/nfl/dallas-cowboys/marshawn-kneeland-tragedy-jovan-belcher/?adt_ei=[email]

Stability of Fractional-Order Discrete-Time Systems with Application to Rulkov Neural Networks and Asymmetric Memristor Synapses
Leila Eftekhari, Moein Khalighi, Saeid Abbasbandy
https://arxiv.org/abs/2510.03304

Stability of Fractional-Order Discrete-Time Systems with Application to Rulkov Neural Networks and Asymmetric Memristor Synapses
Memristors have emerged as ideal components for modeling synaptic connections in neural networks due to their ability to emulate synaptic plasticity and memory effects. Discrete models of memristor-coupled neurons are crucial for simplifying computations and efficiently analyzing large-scale neural networks. Furthermore, incorporating fractional-order calculus into discrete models enhances their capacity to capture the memory and hereditary properties inherent in biological neurons, thus reduci…

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
E. A. Dzhivelikian, A. I. Panov
https://arxiv.org/abs/2510.03286

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neura…