Tootfinder

📣 In Kooperation mit @… laden wir Euch herzlich zum zweiteiligen Online-Workshop „Iconclass - Content Classification and Technical Features“ am Montag, 30.06. und Dienstag, 1.07. (jeweils 10 - 13 Uhr) ein. In beiden Workshops werden ausgewählte Use Cases präsentiert. Allen Teilnehmenden bietet sich, die Möglichkeit zum Austausch, unter ande…

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
The Undertones:
🎵 Here Comes Summer
#TheUndertones
https://coolcovercomps.bandcamp.com/track/here-comes-the-summer-the-undertones
https://open.spotify.com/track/3sutvdPZJYrmLAOhTa33q8

dbpedia_genre: DBpedia work-genre network
A bipartite network of the affiliations between artists and their works on one side and genre classifications on the other, as extracted from Wikipedia by the DBpedia project. The date of this snapshot is uncertain.
This network has 266717 nodes and 463497 edges.
Tags: Informational, Relatedness, Unweighted

Time-dependent density estimation using binary classifiers
Agnimitra Dasgupta, Javier Murgoitio-Esandi, Ali Fardisi, Assad A Oberai
https://arxiv.org/abs/2506.15505

Time-dependent density estimation using binary classifiers
We propose a data-driven method to learn the time-dependent probability density of a multivariate stochastic process from sample paths, assuming that the initial probability density is known and can be evaluated. Our method uses a novel time-dependent binary classifier trained using a contrastive estimation-based objective that trains the classifier to discriminate between realizations of the stochastic process at two nearby time instants. Significantly, the proposed method explicitly models th…

Every newborn baby in England will have their DNA mapped to assess their risk of hundreds of diseases, under NHS plans for the next 10 years.
The scheme, first reported by the Daily Telegraph, is part of a government drive towards predicting and preventing illness, which will also see £650m invested in DNA research for all patients by 2030.
Health Secretary Wes Streeting said gene technology would enable the health service to "leapfrog disease, so we're in front of it ra…

NHS plans to DNA test all babies in England to assess disease risk
Scheme is part of the government's 10-year plan for the NHS in England aimed at easing pressure on services.

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm, Peter A. Pinto, Ronald M. Summers
https://arxiv.org/abs/2506.15182

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep lea…

📣 In Kooperation mit @… laden wir Euch herzlich zum zweiteiligen Online-Workshop „Iconclass - Content Classification and Technical Features“ am Montag, 30.06. und Dienstag, 1.07. (jeweils 10 - 13 Uhr) ein. In beiden Workshops werden ausgewählte Use Cases präsentiert. Allen Teilnehmenden bietet sich, die Möglichkeit zum Austausch, unter ande…

The Independent just made the clickbait over all clickbaits...

A novel model class for bowtie biological networks with universal classification properties
Charles A. Johnson (Daniel), Keon Ho (Daniel), Park, Enoch Yeung
https://arxiv.org/abs/2505.13703

A novel model class for bowtie biological networks with universal classification properties
Cell sensory transcription networks are the intracellular computation structure that regulates and drives cellular activity. Activity in these networks determines the the cell's ability to adapt to changes in its environment. Resilient cells successfully identify (classify) and appropriately respond to environmental shifts. We present a model for identification and response to environmental changes in resilient bacteria. This model combines two known motifs in transcription networks: dense over…

Optimal Convergence Rates of Deep Neural Network Classifiers
Zihan Zhang, Lei Shi, Ding-Xuan Zhou
https://arxiv.org/abs/2506.14899 https://

Optimal Convergence Rates of Deep Neural Network Classifiers
In this paper, we study the binary classification problem on $[0,1]^d$ under the Tsybakov noise condition (with exponent $s \in [0,\infty]$) and the compositional assumption. This assumption requires the conditional class probability function of the data distribution to be the composition of $q+1$ vector-valued multivariate functions, where each component function is either a maximum value function or a Hölder-$β$ smooth function that depends only on $d_*$ of its input variables. Notably, $d_…