Tootfinder

Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
M A Al-Masud, Juan Miguel Lopez Alcaraz, Nils Strodthoff
https://arxiv.org/abs/2509.25095 https://

Benchmarking ECG Foundational Models: A Reality Check Across Clinical Tasks
The 12-lead electrocardiogram (ECG) is a long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. Foundation models promise broader adaptability, but their generalization across diverse ECG tasks is not well understood. We benchmarked eight ECG foundation models on 26 clinically relevant tasks using 12 public datasets comprising 1,650 regression and classification targets. Models were evaluated under fine-tuning an…

Carlos Alcaraz does not give a single fuck. Funniest thing I’ve watched all day
#carlosalacaraz https://www.threads.com/@okechukwu3286/post/DOy21IlDQcx

I always see this headline and think, "surely they stopped sending sinners to Alcatraz long ago". But what do I know?
Alcaraz vs Sinner – US Open final: Match time, head-to-head, stats, Trump | Tennis News | Al Jazeera
https://www.

Alcaraz vs Sinner – US Open final: Match time, head-to-head, stats, Trump
All to know as the world’s top two men’s tennis players meet in New York in their third straight Grand Slam final.

Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications
Dian Echevarr\'ia P\'erez, Onel L. Alcaraz L\'opez, Hirley Alves
https://arxiv.org/abs/2509.22547

Extreme Value Theory-enhanced Radio Maps for Handovers in Ultra-reliable Communications
Efficient handover (HO) strategies are essential for maintaining the stringent performance requirements of ultra-reliable communication (URC) systems. This work introduces a novel HO framework designed from a physical-layer perspective, where the decision-making process focuses on determining the optimal time and location for performing HOs. Leveraging extreme value theory (EVT) and statistical radio maps, the proposed method predicts signal behaviour and enables efficient resource allocation. …

Brisantes Gerücht - Datet Carlos Alcaraz die Ex von Jannik Sinner? #News #Nachrichten

Email: the US Tennis Association requested broadcasters to censor any possible protests or other reactions to Trump's presence at Sunday's US Open men's final (Ben Rothenberg/Bounces)
https://www.benrothenberg…

U.S. Open Orders Broadcasters to Censor Reactions to Trump
An email obtained by Bounces includes instructions from the U.S. Open on supporting Trump's planned stagecraft during the National Anthem.

U.S. Open Orders Broadcasters to Censor Reactions to Trump (Ben Rothenberg/Bounces)
https://www.benrothenberg.com/p/us-open-donald-trump-mens-final-attendance-visit-appearance-censorship-tv-booing-protest-jannik-sinner-carlos-alcaraz
http://www.memeorandum.com/250906/p39#a250906p39

The Bills are going to win the Super Bowl*, plus Alcaraz's reign https://www.nytimes.com/athletic/6609558/2025/09/08/bills-ravens-comeback-week-1-the-pulse/

Depression diagnosis from patient interviews using multimodal machine learning
Jana Weber, Marcel Weber, Juan Miguel Lopez Alcaraz
https://arxiv.org/abs/2508.19390 https://

Depression diagnosis from patient interviews using multimodal machine learning
Background: Depression is a major public health concern, affecting an estimated five percent of the global population. Early and accurate diagnosis is essential to initiate effective treatment, yet recognition remains challenging in many clinical contexts. Speech, language, and behavioral cues collected during patient interviews may provide objective markers that support clinical assessment. Methods: We developed a diagnostic approach that integrates features derived from patient interviews, …

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Julia Matejas, Olaf \.Zurawski, Nils Strodthoff, Juan Miguel Lopez Alcaraz
https://arxiv.org/abs/2509.17674

Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs), in contrast, are widely available, non-invasive, and often acquired earlier in clinical workflows. This study aims to assess whether ECG features and patient demographics can predict chest radiograph findings using an interpretable machine learning approach. Methods: Using the MIMIC-IV database, Extreme Gradient Boo…