Tootfinder

»Clickbait führt bei KI-Modellen zu kognitivem Rückbau«
Dies ist nicht neu und schon gar nicht unvorhersehbar. Dass die KI sich selber betrügt (aus der Sicht der Menschen) ist nun mal seit jeher. Ich möchte hier nicht permanent KI-Kritik eusern aber zu viele Menschen folgen dem Marketing leichtgläubig so wie blind.
P.S. Der KI Kognition zu zuordnen ist schon Propaganda der Tech-Macht.
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Clickbait führt bei KI-Modellen zu kognitivem Rückbau
Eine Studie beleuchtet die Auswirkungen von Clickbait auf KI-Modelle. Hohe Datenqualität ist entscheidend für die Leistung der Modelle.

Mosquitoes found in Iceland for first time after record heat https://www.bbc.com/news/articles/clyz3vv62pgo?at_medium=RSS/Mastodon
Mosquitoes found in Iceland for first time as climate crisis warms country

Mosquitoes found in Iceland for first time after record heat
Previously the country was one of the world's only mosquito-free zones.

Anatomically Constrained Transformers for Cardiac Amyloidosis Classification
Alexander Thorley, Agis Chartsias, Jordan Strom, Roberto Lang, Jeremy Slivnick, Jamie O'Driscoll, Rajan Sharma, Dipak Kotecha, Jinming Duan, Alberto Gomez
https://arxiv.org/abs/2509.19691

Anatomically Constrained Transformers for Cardiac Amyloidosis Classification
Cardiac amyloidosis (CA) is a rare cardiomyopathy, with typical abnormalities in clinical measurements from echocardiograms such as reduced global longitudinal strain of the myocardium. An alternative approach for detecting CA is via neural networks, using video classification models such as convolutional neural networks. These models process entire video clips, but provide no assurance that classification is based on clinically relevant features known to be associated with CA. An alternative p…

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo
https://arxiv.org/abs/2509.20240 https://

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classificat…

Low-Cost Sensor Fusion Framework for Organic Substance Classification and Quality Control Using Classification Methods
Borhan Uddin Chowdhury, Damian Valles, Md Raf E Ul Shougat
https://arxiv.org/abs/2509.19367

Low-Cost Sensor Fusion Framework for Organic Substance Classification and Quality Control Using Classification Methods
We present a sensor-fusion framework for rapid, non-destructive classification and quality control of organic substances, built on a standard Arduino Mega 2560 microcontroller platform equipped with three commercial environmental and gas sensors. All data used in this study were generated in-house: sensor outputs for ten distinct classes - including fresh and expired samples of apple juice, onion, garlic, and ginger, as well as cinnamon and cardamom - were systematically collected and labeled u…

Deep Clustering for Blood Cell Classification and Quantification
Mihaela Macarie-Ancau, Adrian Groza
https://arxiv.org/abs/2509.19399 https://arxiv.org/pdf…

Deep Clustering for Blood Cell Classification and Quantification
Accurate classification of blood cells plays a key role in improving automated blood analysis for both medical and veterinary applications. This work presents a two-stage deep clustering method for classifying blood cells from high-dimensional signal data. In the first stage, red blood cells (RBCs) and platelets (PLTs) are separated using a combination of an improved autoencoder and the IDEC algorithm. The second stage further classifies RBC subtypes, pure RBCs, reticulocytes, and clumped RBCs,…

Efficient Online Large-Margin Classification via Dual Certificates
Nam Ho-Nguyen, Fatma K{\i}l{\i}n\c{c}-Karzan, Ellie Nguyen, Lingqing Shen
https://arxiv.org/abs/2509.19670 htt…

Efficient Online Large-Margin Classification via Dual Certificates
Online classification is a central problem in optimization, statistical learning and data science. Classical algorithms such as the perceptron offer efficient updates and finite mistake guarantees on linearly separable data, but they do not exploit the underlying geometric structure of the classification problem. We study the offline maximum margin problem through its dual formulation and use the resulting geometric insights to design a principled and efficient algorithm for the online setting.…

Thinking While Listening: Simple Test Time Scaling For Audio Classification
Prateek Verma, Mert Pilanci
https://arxiv.org/abs/2509.19676 https://arxiv.org/…

Thinking While Listening: Simple Test Time Scaling For Audio Classification
We propose a framework that enables neural models to "think while listening" to everyday sounds, thereby enhancing audio classification performance. Motivated by recent advances in the reasoning capabilities of large language models, we address two central questions: (i) how can thinking be incorporated into existing audio classification pipelines to enable reasoning in the category space and improve performance, and (ii) can a new architecture be designed from the ground up to support both thi…

MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series
Dohyun Ku, Catherine D. Chong, Visar Berisha, Todd J. Schwedt, Jing Li
https://arxiv.org/abs/2509.19577

MAGIC: Multi-task Gaussian process for joint imputation and classification in healthcare time series
Time series analysis has emerged as an important tool for improving patient diagnosis and management in healthcare applications. However, these applications commonly face two critical challenges: time misalignment and data sparsity. Traditional approaches address these issues through a two-step process of imputation followed by prediction. We propose MAGIC (Multi-tAsk Gaussian Process for Imputation and Classification), a novel unified framework that simultaneously performs class-informed missi…

Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
Woo-Jin Cho Kim, Jorge Oliveira, Arian Beqiri, Alex Thorley, Jordan Strom, Jamie O'Driscoll, Rajan Sharma, Jeremy Slivnick, Roberto Lang, Alberto Gomez, Agisilaos Chartsias
https://arxiv.org/abs/2509.19694

Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification
Guidelines for transthoracic echocardiographic examination recommend the acquisition of multiple video clips from different views of the heart, resulting in a large number of clips. Typically, automated methods, for instance disease classifiers, either use one clip or average predictions from all clips. Relying on one clip ignores complementary information available from other clips, while using all clips is computationally expensive and may be prohibitive for clinical adoption. To select the…