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Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
Long-Vu Hoang, Tuan Nguyen, Tran Huy Dat
https://arxiv.org/abs/2506.14148

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment
This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark compr…

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh
https://arxiv.org/abs/2506.14318

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-g…

👉 Ihr könnt euch noch für den zweiteiligen Online-Workshop „Iconclass - Content Classification and Technical Features“ am Montag, 30.06. und Dienstag, 1.07. (jeweils 10 - 13 Uhr) anmelden!
💡 In Kooperation mit dem @… werden ausgewählte Use Cases präsentiert und die Möglichkeit zum Austausch angeboten, unter anderem in Breakout-Sessio…

At this year's Berlin Buzzwords, Viola Rädle & Raphael Franke will present gamma_flow, an open-source Python package for real-time spectral data analysis.
Learn more: https://2025.berlinbuzzwords.de/session/gamma_flow-deno…

A ringed-space-like structure on coalgebras for noncommutative algebraic geometry
So Nakamura
https://arxiv.org/abs/2506.14689 https://

A ringed-space-like structure on coalgebras for noncommutative algebraic geometry
Inspired by the perspective of Reyes' noncomutative spectral theory, we attempt to develop noncommutative algebraic geometry by introducing ringed coalgebras, which can be thought of as a noncommutative generalization of schemes over a field $k$. These objects arise from fully residually finite-dimensional(RFD) algebras introduced by Reyes and from schemes locally of finite type over $k$. The construction uses the Heyneman-Sweedler finite dual coalgebra and the Takeuchi underlying coalgebra. Wh…

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
https://arxiv.org/abs/2506.12363 …

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature e…

Vogel's universality and the classification problem for Jacobi identities
A. Morozov, A. Sleptsov
https://arxiv.org/abs/2506.15280 https://

Vogel's universality and the classification problem for Jacobi identities
This paper is a summary of discussions at the recent ITEP-JINR-YerPhI workshop on Vogel theory in Dubna. We consider relation between Vogel divisor(s) and the old Dynkin classification of simple Lie algebras. We consider application to knot theory and the hidden role of Jacobi identities in the definition/invariance of Kontsevich integral, which is the knot polynomial with the values in diagrams, capable of revealing all Vassiliev invariants -- including the ones, not visible in other approache…

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Chungang Lin, Yilong Jiang, Weiyao Zhang, Xuying Meng, Tianyu Zuo, Yujun Zhang
https://arxiv.org/abs/2506.14151

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Traffic classification has a significant impact on maintaining the Quality of Service (QoS) of the network. Since traditional methods heavily rely on feature extraction and large scale labeled data, some recent pre-trained models manage to reduce the dependency by utilizing different pre-training tasks to train generic representations for network packets. However, existing pre-trained models typically adopt pre-training tasks developed for image or text data, which are not tailored to traffic d…

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Yusdivia Molina-Rom\'an, David G\'omez-Ortiz, Ernestina Menasalvas-Ruiz, Jos\'e Gerardo Tamez-Pe\~na, Alejandro Santos-D\'iaz
https://arxiv.org/abs/2506.13964

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography
Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This study compares multimodal and CNN-based methods for automated classification using the BI-RADS system, evaluating BioMedCLIP and ConvNeXt across three learning scenarios: zero-shot classification, linear probing with textual descriptions, and fine-tuning with numerical labels. Results show that zero-shot classification a…

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
Matt Poyser, Toby P. Breckon
https://arxiv.org/abs/2506.14667

DDS-NAS: Dynamic Data Selection within Neural Architecture Search via On-line Hard Example Mining applied to Image Classification
In order to address the scalability challenge within Neural Architecture Search (NAS), we speed up NAS training via dynamic hard example mining within a curriculum learning framework. By utilizing an autoencoder that enforces an image similarity embedding in latent space, we construct an efficient kd-tree structure to order images by furthest neighbour dissimilarity in a low-dimensional embedding. From a given query image from our subsample dataset, we can identify the most dissimilar image wit…