Tootfinder

Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation
Carolina Higuera, Akash Sharma, Taosha Fan, Chaithanya Krishna Bodduluri, Byron Boots, Michael Kaess, Mike Lambeta, Tingfan Wu, Zixi Liu, Francois Robert Hogan, Mustafa Mukadam
https://arxiv.org/abs/2506.14754…

Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation
We present Sparsh-X, the first multisensory touch representations across four tactile modalities: image, audio, motion, and pressure. Trained on ~1M contact-rich interactions collected with the Digit 360 sensor, Sparsh-X captures complementary touch signals at diverse temporal and spatial scales. By leveraging self-supervised learning, Sparsh-X fuses these modalities into a unified representation that captures physical properties useful for robot manipulation tasks. We study how to effectively …

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
Asbj{\o}rn Munk, Stefano Cerri, Jakob Ambsdorf, Julia Machnio, Sebastian N{\o}rgaard Llambias, Vardan Nersesjan, Christian Hedeager Krag, Peirong Liu, Pablo Rocamora Garc\'ia, Mostafa Mehdipour Ghazi, Mikael Boesen, Michael Eriksen Benros, Juan Eugenio Iglesias, Mads Nielsen

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning
We present FOMO60K, a large-scale, heterogeneous dataset of 60,529 brain Magnetic Resonance Imaging (MRI) scans from 13,900 sessions and 11,187 subjects, aggregated from 16 publicly available sources. The dataset includes both clinical- and research-grade images, multiple MRI sequences, and a wide range of anatomical and pathological variability, including scans with large brain anomalies. Minimal preprocessing was applied to preserve the original image characteristics while reducing barriers t…

A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook
Ahmed Y. Radwan, Mustafa Yildirim, Navid Hasanzadeh, Hina Tabassum, Shahrokh Valaee
https://arxiv.org/abs/2506.12052

A Tutorial-cum-Survey on Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook
Wi-Fi technology has evolved from simple communication routers to sensing devices. Wi-Fi sensing leverages conventional Wi-Fi transmissions to extract and analyze channel state information (CSI) for applications like proximity detection, occupancy detection, activity recognition, and health monitoring. By leveraging existing infrastructure, Wi-Fi sensing offers a privacy-preserving, non-intrusive, and cost-effective solution which, unlike cameras, is not sensitive to lighting conditions. Beginn…

Russia is targeting what's left of its capacity for innovation in space toward pestering the US military.
US intelligence officials last year said they believed Russia was pursuing a project to place a nuclear weapon in space.
The detonation of a nuclear bomb in orbit could muck up the space environment for years,
indiscriminately disabling countless satellites,
whether they're military or civilian.
Russia denied that it planned to launch a satellite w…

It’s hunting season in orbit as Russia’s killer satellites mystify skywatchers
“Once more, we play our dangerous game—a game of chess—against our old adversary.”…

Towards Neural Audio Codec Source Parsing
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Arun Balaji Buduru, Rajesh Sharma
https://arxiv.org/abs/2506.12627

Towards Neural Audio Codec Source Parsing
A new class of audio deepfakes-codecfakes (CFs)-has recently caught attention, synthesized by Audio Language Models that leverage neural audio codecs (NACs) in the backend. In response, the community has introduced dedicated benchmarks and tailored detection strategies. As the field advances, efforts have moved beyond binary detection toward source attribution, including open-set attribution, which aims to identify the NAC responsible for generation and flag novel, unseen ones during inference.…

Now listening to: https://www.nts.live/infinite-mixtapes/4-to-the-floor

4 To The Floor - Infinite Mixtapes | Listen on NTS
House and techno from past to present.

There are any number of ways to keep Altadena affordable.
Ban single-family detached dwellings.
Ban off-street parking.
Build 6-story affordable housing on scattered sites.
The aim here is to maintain the area's high social status via exclusion. Based on uncool-ness rather than income or race.
[reply to: https://

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mahmoud Assran
https://arxiv.org/abs/2506.09987

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Existing benchmarks for assessing the spatio-temporal understanding and reasoning abilities of video language models are susceptible to score inflation due to the presence of shortcut solutions based on superficial visual or textual cues. This paper mitigates the challenges in accurately assessing model performance by introducing the Minimal Video Pairs (MVP) benchmark, a simple shortcut-aware video QA benchmark for assessing the physical understanding of video language models. The benchmark is…

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Mustafa Melih Toslak, Stefano Carpin
https://arxiv.org/abs/2506.10106

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogen…

AI-Augmented Visible Light Communication: A Framework for Noise Mitigation and Secure Data Transmission
A. A. Nutfaji, Moustafa Hassan Elmallah
https://arxiv.org/abs/2507.08145

AI-Augmented Visible Light Communication: A Framework for Noise Mitigation and Secure Data Transmission
This paper presents a proposed AI Deep Learning model that addresses common challenges encountered in Visible Light Communication (VLC) systems. In this work, we run a Python simulation that models a basic VLC system primarily affected by Additive White Gaussian Noise (AWGN). A Deep Neural Network (DNN) is then trained to equalize the noisy signal received and improve signal integrity. The system evaluates and compares the Bit Error Rate (BER) before and after equalization to demonstrate the ef…