Tootfinder

AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Zhenlong Yuan, Jing Tang, Jinguo Luo, Rui Chen, Chengxuan Qian, Lei Sun, Xiangxiang Chu, Yujun Cai, Dapeng Zhang, Shuo Li
https://arxiv.org/abs/2509.01944

AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimodal perception with decision-making capabilities. However, the interpretability and coherence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, we propose AutoDrive-R$^2$, a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems thr…

🇺🇦 Auf #radioeins läuft...
K. Flay:
🎵 Can't Sleep
#NowPlaying #KFlay
https://audiotree.bandcamp.com/track/cant-sleep-2
https://open.spotify.com/track/56lwHTCikHS3QwVGtfSelJ

Driving factors of auditory category learning success
Nan Wang, Gangyi Feng
https://arxiv.org/abs/2506.01508 https://arxiv.org/pdf/25…

Driving factors of auditory category learning success
Our brain learns to update its mental model of the environment by abstracting sensory experiences for adaptation and survival. Learning to categorize sounds is one essential abstracting process for high-level human cognition, such as speech perception, but it is also challenging due to the variable nature of auditory signals and their dynamic contexts. To overcome these learning challenges and enhance learner performance, it is essential to identify the impact of learning-related factors in dev…

EEG-Based Auditory BCI for Communication in a Completely Locked-In Patient Using Volitional Frequency Band Modulation
Deland Liu, Frigyes Samuel Racz, Zoe Lalji, Jose del R. Millan
https://arxiv.org/abs/2507.00305

EEG-Based Auditory BCI for Communication in a Completely Locked-In Patient Using Volitional Frequency Band Modulation
Patients with amyotrophic lateral sclerosis (ALS) in the completely locked-in state (CLIS) can lose all reliable motor control and are left without any means of communication. It remains unknown whether non-invasive electroencephalogram (EEG) based brain-computer interfaces (BCIs) can support volitional communication in CLIS. Here, we show that a CLIS patient was able to operate an EEG-based BCI across multiple online sessions to respond to both general knowledge and personally relevant assisti…

This is the third full build iteration of my miniature #QRP unun supporting both EFHW and "random wire" antennas. At this point, it is still fiddly to build, but I'd expect an experienced #HamRadio DIY / homebrew enthusiast to find the build not terribly difficult.
There's nothing…

AI4DiTraRe: Building the BFO-Compliant Chemotion Knowledge Graph
Ebrahim Norouzi, Nicole Jung, Anna M. Jacyszyn, J\"org Waitelonis, Harald Sack
https://arxiv.org/abs/2509.01536

AI4DiTraRe: Building the BFO-Compliant Chemotion Knowledge Graph
Chemistry is an example of a discipline where the advancements of technology have led to multi-level and often tangled and tricky processes ongoing in the lab. The repeatedly complex workflows are combined with information from chemical structures, which are essential to understand the scientific process. An important tool for many chemists is Chemotion, which consists of an electronic lab notebook and a repository. This paper introduces a semantic pipeline for constructing the BFO-compliant Ch…

Topographic alignment of auditory inputs to the visual cortex (in mice) https://www.biorxiv.org/content/10.1101/2025.07.29.667237v1 More information in the Bluesky thread

FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
Shunian Chen, Xinyuan Xie, Zheshu Chen, Liyan Zhao, Owen Lee, Zhan Su, Qilin Sun, Benyou Wang
https://arxiv.org/abs/2506.01111

FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion
High-quality, large-scale audio captioning is crucial for advancing audio understanding, yet current automated methods often generate captions that lack fine-grained detail and contextual accuracy, primarily due to their reliance on limited unimodal or superficial multimodal information. Drawing inspiration from human auditory perception, which adeptly integrates cross-modal cues and performs sophisticated auditory scene analysis, we introduce a novel two-stage automated pipeline. This pipeline…

Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Nicolas Heintz, Tom Francart, Alexander Bertrand
https://arxiv.org/abs/2506.24024

Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker environment. While state-of-the-art AAD algorithms can identify the attended speaker on short time windows, their predictions are often too inaccurate for practical use. In this work, we propose augmenting AAD with a hidden Markov model (HMM) that models the temporal structure of attention. More specifically, the HMM rel…

To Grok: If you compare a Neuralink Blindsight brain implant to The vOICe visual-to-auditory sensory substitution, which one really has the best long-term outlook? Don't speculate on implants going beyond normal vision because there is just zero evidence. Challenge your own bias. https://x.com/i/grok/share/XM32w4XapPi