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

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

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…

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Akira SaiToh, Arezoo Modiri, Amit Sawant, Robabeh Rahimi
https://arxiv.org/abs/2506.04328

Quantum-Inspired Genetic Optimization for Patient Scheduling in Radiation Oncology
Among the genetic algorithms generally used for optimization problems in the recent decades, quantum-inspired variants are known for fast and high-fitness convergence and small resource requirement. Here the application to the patient scheduling problem in proton therapy is reported. Quantum chromosomes are tailored to possess the superposed data of patient IDs and gantry statuses. Selection and repair strategies are also elaborated for reliable convergence to a clinically feasible schedule alt…

TRACE: Contrastive learning for multi-trial time-series data in neuroscience
Lisa Schmors, Dominic Gonschorek, Jan Niklas B\"ohm, Yongrong Qiu, Na Zhou, Dmitry Kobak, Andreas Tolias, Fabian Sinz, Jacob Reimer, Katrin Franke, Sebastian Damrich, Philipp Berens
https://arxiv.org/abs/2506.04906

TRACE: Contrastive learning for multi-trial time-series data in neuroscience
Modern neural recording techniques such as two-photon imaging allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing applications for neural time series rely on generic data augmentations and do not exploit the multi-trial data structure inherent in many neural datasets. Here we present TRACE, a new contrastive learning framework that av…

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

System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm
Kosei Tsuji, Ichiro Maruta, Kenji Fujimoto, Tomoyuki Maeda, Yoshihisa Tamase, Tsukasa Shinohara
https://arxiv.org/abs/2505.23138

System Identification for Virtual Sensor-Based Model Predictive Control: Application to a 2-DoF Direct-Drive Robotic Arm
Nonlinear Model Predictive Control (NMPC) offers a powerful approach for controlling complex nonlinear systems, yet faces two key challenges. First, accurately modeling nonlinear dynamics remains difficult. Second, variables directly related to control objectives often cannot be directly measured during operation. Although high-cost sensors can acquire these variables during model development, their use in practical deployment is typically infeasible. To overcome these limitations, we propose a…

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Zhennan Lin, Kaixun Huang, Wei Ren, Linju Yang, Lei Xie
https://arxiv.org/abs/2505.23077

Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation
Deep biasing improves automatic speech recognition (ASR) performance by incorporating contextual phrases. However, most existing methods enhance subwords in a contextual phrase as independent units, potentially compromising contextual phrase integrity, leading to accuracy reduction. In this paper, we propose an encoder-based phrase-level contextualized ASR method that leverages dynamic vocabulary prediction and activation. We introduce architectural optimizations and integrate a bias loss to ex…

Diversity of Cold Worlds: A Near Complete Spectral Energy Distribution for 2MASS J04151954-0935066 using JWST
Sherelyn Alejandro Merchan, Jacqueline K. Faherty, Genaro Su\'arez, Kelle L. Cruz, Adam J. Burgasser, Jonathan Gagn\'e, Callie E. Hood, Eileen C. Gonzales, Daniella C. Bardalez Gagliuffi, Jolie L'Heureux, Johanna M. Vos, Adam C. Schneider, Aaron M. Meisner, Caroline Morley, J. Davy Kirkpatrick, Federico Marocco, Rocio Kiman, Charles A. Beichman, Ben Burningham, Dan …

Diversity of Cold Worlds: A Near Complete Spectral Energy Distribution for 2MASS J04151954-0935066 using JWST
We present the a near complete spectral energy distribution (SED) for an extrasolar world: the T8 brown dwarf 2MASS~J04151954$-$0935066. Spanning from optical to mid-infrared (0.7--20.4 micron) wavelengths, the SED for this substellar atmosphere is constructed from new JWST NIRSpec G395H ($R\sim$2700) and Magellan FIRE echelle ($R\sim$8000) near-infrared spectra, along with MIRI mid-infrared photometry complemented by spectra from Keck I, IRTF, Magellan, AKARI, Spitzer and photometry from vario…