This Zenodo deposit contains a publicly available description of the Dataset: Title: "iSCORE-PD: Whole-genome sequencing and variant calling of isogenic hESC clones and controls". Description: Whole-genome sequencing (WGS) data for the iSCORE-PD isogenic hESC clone collection. 150bp paired-end WGS was performed on Illu...
Oriol Busquets, Hanqin Li, Khaja Mohieddin Syed et al.· Zenodo (CERN European Organi...· 0 citations
This Zenodo deposit contains a publicly available description of the Dataset: Title: "iSCORE-PD: NGS genotyping and zygosity testing of isogenic hESC clones". Description: Targeted DNA sequencing data for the iSCORE-PD isogenic hESC clone collection, encompassing three complementary analyses: (1) NGS genotyping of 53 m...
Oriol Busquets, Hanqin Li, Khaja Mohieddin Syed et al.· Zenodo (CERN European Organi...· 0 citations
This Zenodo deposit contains a publicly available description of the Dataset: Title: "iSCORE-PD: NGS genotyping and zygosity testing of isogenic hESC clones". Description: Targeted DNA sequencing data for the iSCORE-PD isogenic hESC clone collection, encompassing three complementary analyses: (1) NGS genotyping of 53 m...
Oriol Busquets, Hanqin Li, Khaja Mohieddin Syed et al.· Zenodo (CERN European Organi...· 0 citations
Artificial intelligence (AI) is transitioning from a supportive tool to a core component of clinical decision-making in dentistry. This review synthesizes advances in deep learning and machine learning for oral disease diagnosis, orthodontic treatment prediction, pediatric oral health management, adolescent psychologic...
Yuan-Qi Zhang, Tao Wen, Zheng-Rou Wang et al.· International Journal of Art...· 0 citations
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FluxLite is introduced, a lightweight, training-free proposal-control framework for discrete diffusion, identifying a tilted-path coverage factor that governs robustness to score error, together with finite-particle convergence for a fixed controlled Feynman-Kac recursion.
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff et al.· 1 citation
Recommendations are made for regulatory-science partnerships, human-centred design, interoperable digital pipelines, and investment in field-based validation and manufacturing to translate photonics innovations into resilient, equitable POC solutions across South Africa and the wider sub-Saharan context.
P. Mthunzi-Kufa, M. M. Mathipa, Tendai Makwikwi et al.· Journal of Biomedical Optics· 0 citations
An algorithm is introduced that uses the base model's next-token entropy as a proxy to identify key decision points and resample from those positions and it is proved that the method's mixing time scales with the number of decisions in a trace rather than with the number of tokens, which can be much larger.
Felix Zhou, Anay Mehrotra, Quanquan C. Liu· arXiv.org· 3 citations
We propose a novel amortized optimization method for predicting optimal transport (OT) plans across multiple pairs of measures by leveraging Kantorovich potentials derived from sliced OT. We introduce two amortization strategies: regression-based amortization (RA-OT) and objective-based amortization (OA-OT). In RA-OT,...
Diffusion models generate samples by iteratively querying learned score estimates. A rapidly growing literature focuses on accelerating sampling by minimizing the number of score evaluations, yet the information-theoretic limits of such acceleration remain unclear.
In this work, we establish the first score query low...
Conformal Prediction Active TTA is proposed, which first brings principled, conformal uncertainty with coverage-aware online calibration into ATTA, and consistently outperforms the state-of-the-art ATTA methods by around 5% in accuracy.
Ting-Yu Shi, Fan Lyu, Hai-Hua Zhu et al.· 1 citation
We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike coding into distinct regi...
Dominik Dold, Philipp Christian Petersen· 0 citations
RLTL;DR is introduced, a promising compacted training paradigm of the form"on this sort of task, keep this sort of thing in mind", which it is hoped will inspire future research on.
Michael Kirchhof, Eleonora Gualdoni, Andrew Szot et al.· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
