Tootfinder

This https://arxiv.org/abs/2410.02372 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCE_…

Revisiting the Canonicalization for Fast and Accurate Crystal Tensor Property Prediction
Predicting the tensor properties of crystalline materials is a fundamental task in materials science. Unlike single-value property prediction, which is inherently invariant, tensor property prediction requires maintaining O(3) group tensor equivariance. Such equivariance constraint often requires specialized architecture designs to achieve effective predictions, inevitably introducing tremendous computational costs. Canonicalization, a classical technique for geometry, has recently been explore…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
Moshi Wei, Sparks Li
https://arxiv.org/abs/2506.09335 https://…

Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds
The Intelligent System of Emergent Knowledge (ISEK) establishes a decentralized network where human and artificial intelligence agents collaborate as peers, forming a self-organizing cognitive ecosystem. Built on Web3 infrastructure, ISEK combines three fundamental principles: (1) a decentralized multi-agent architecture resistant to censorship, (2) symbiotic AI-human collaboration with equal participation rights, and (3) resilient self-adaptation through distributed consensus mechanisms. The…

iRonCub 3: The Jet-Powered Flying Humanoid Robot
Davide Gorbani, Hosameldin Awadalla Omer Mohamed, Giuseppe L'Erario, Gabriele Nava, Punith Reddy Vanteddu, Shabarish Purushothaman Pillai, Antonello Paolino, Fabio Bergonti, Saverio Taliani, Alessandro Croci, Nicholas James Tremaroli, Silvio Traversaro, Bruno Vittorio Trombetta, Daniele Pucci
https…

iRonCub 3: The Jet-Powered Flying Humanoid Robot
This article presents iRonCub 3, a jet-powered humanoid robot, and its first flight experiments. Unlike traditional aerial vehicles, iRonCub 3 aims to achieve flight using a full-body humanoid form, which poses unique challenges in control, estimation, and system integration. We highlight the robot's current mechanical and software architecture, including its propulsion system, control framework, and experimental infrastructure. The control and estimation framework is first validated in simulat…

Design, development and commissioning of a new towing tank at the University of Southampton: A decade of endeavour
B Malas (LHEEA)
https://arxiv.org/abs/2506.09057

Design, development and commissioning of a new towing tank at the University of Southampton: A decade of endeavour
The University of Southampton in the United Kingdom is worldly renowned in the naval architecture domain. For several decades, the researchers in hydrodynamics have been discussing the need of a middle-sized towing tank to accommodate the teaching, research and commercial experiments that previously had to be outsourced. In the early 2010s, the renovation of the Boldrewood campus was decided and opened the way for the construction of this 138 m long, 6 m wide and 3.5 m deep facility. Commission…

(1/3) Please join me today on Zoom at 12pm ET / 11am CT / 10am MT / 9am PT for "Beyond the Algorithmic Feed," a free MediaEd Club webinar for the Media Education Lab:
https://mediaeducationlab.com/index.php/events/beyond-algorithmic-feed
While som…

WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction
Jakaria Islam Emon, Kazi Tamanna Alam, Md. Abu Salek
https://arxiv.org/abs/2506.05899

WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction
Mean Opinion Score (MOS) prediction for text to music systems requires evaluating both overall musical quality and text prompt alignment. This paper introduces WhisQ, a multimodal architecture that addresses this dual-assessment challenge through sequence level co-attention and optimal transport regularization. WhisQ employs the Whisper Base pretrained model for temporal audio encoding and Qwen 3, a 0.6B Small Language Model (SLM), for text encoding, with both maintaining sequence structure for…

AI Accelerators for Large Language Model In-ference: Architecture Analysis and Scaling Strategies
Amit Sharma
https://arxiv.org/abs/2506.00008 https://

AI Accelerators for Large Language Model In-ference: Architecture Analysis and Scaling Strategies
The rapid growth of large-language models (LLMs) is driving a new wave of specialized hardware for inference. This paper presents the first workload-centric, cross-architectural performance study of commercial AI accelerators, spanning GPU-based chips, hybrid packages, and wafer-scale engines. We compare memory hierarchies, compute fabrics, and on-chip interconnects, and observe up to 3.7x performance variation across architectures as batch size and sequence length change. Four scaling techniqu…