Tootfinder

Maryland-based quantum computing company IonQ agrees to acquire Oxford Ionics, which spun out of Oxford University, in a $1.07B all-stock deal, closing in 2025 (Oliver Barnes/Financial Times)
https://www.ft.com/content/dde7bac2-cacb-4deb-9223-5bcfe285db15

US quantum computing company IonQ to buy Oxford university start-up
Acquisition of Oxford Ionics for $1.1bn shows advances in processing are kick-starting dealmaking

🇺🇦 #NowPlaying on BBCRadio3's #EssentialClassics
Antonín Dvořšk, François Leleux, Barbara Stegemann, Sharon Kam, Diemut Schneider, Stefan Schweigert, Dag Jensen, Marie Luise Neunecker, Sibylle Mahni, Ozan Çakar, Tanja Tetzlaff & Hiroaki Aoe:
🎵 Wind Serenade Op.44 (3rd mvt)

Here’s a person, Baldur Bjarnason, @…, who thinks I’m really wrong about LLMs and coding. I mostly don’t agree but the argument is well-presented: https://www.baldurbjarna…

Trusting your own judgement on 'AI' is a huge risk
Web dev at the end of the world, from Hveragerði, Iceland

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
https://arxiv.org/abs/2506.09234

Transaction Categorization with Relational Deep Learning in QuickBooks
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in this domain stem from the unique formatting of transaction descriptions, the wide variety of transaction categories, and the vast scale of the data involved. Furthermore, organizing transaction data in a relational database creates difficulties in developing a unified model that covers the entire database. In this work, …

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Hiroshi Matsuda, Chunpeng Ma, Masayuki Asahara
https://arxiv.org/abs/2506.09983

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Recent advances in large language models (LLMs) have enabled impressive performance in various tasks. However, standard prompting often struggles to produce structurally valid and accurate outputs, especially in dependency parsing. We propose a novel step-by-step instruction strategy, where universal part-of-speech tagging precedes the prediction of syntactic heads and dependency labels, and a simplified CoNLL-U like output format, our method achieves state-of-the-art accuracy on Universal Depe…

This https://arxiv.org/abs/2407.19353 has been replaced.
initial toot: https://mastoxiv.page/@arX…

A spring-block theory of feature learning in deep neural networks
Feature-learning deep nets progressively collapse data to a regular low-dimensional geometry. How this emerges from the collective action of nonlinearity, noise, learning rate, and other factors, has eluded first-principles theories built from microscopic neuronal dynamics. We exhibit a noise-nonlinearity phase diagram that identifies regimes where shallow or deep layers learn more effectively and propose a macroscopic mechanical theory that reproduces the diagram and links feature learning acr…

@… I'm not sure I agree 😄 But it's certainly a good one.

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

Fechando isso aqui agora pois hoje o dia vai render! rs

This https://arxiv.org/abs/2405.10723 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
Modern diffusion MRI sequences commonly acquire a large number of volumes with diffusion sensitization gradients of differing strengths or directions. Such sequences rely on echo-planar imaging (EPI) to achieve reasonable scan duration. However, EPI is vulnerable to off-resonance effects, leading to tissue susceptibility and eddy-current induced distortions. The latter is particularly problematic because it causes misalignment between volumes, disrupting downstream modelling and analysis. The e…