Tootfinder

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
Nick Theisen, Kenny Schlegel, Dietrich Paulus, Peer Neubert
https://arxiv.org/abs/2509.13809

Data-Efficient Spectral Classification of Hyperspectral Data Using MiniROCKET and HDC-MiniROCKET
The classification of pixel spectra of hyperspectral images, i.e. spectral classification, is used in many fields ranging from agricultural, over medical to remote sensing applications and is currently also expanding to areas such as autonomous driving. Even though for full hyperspectral images the best-performing methods exploit spatial-spectral information, performing classification solely on spectral information has its own advantages, e.g. smaller model size and thus less data required for …

Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training
Marc Brinner, Sina Zarrie{\ss}
https://arxiv.org/abs/2508.11393 https://a…

Rationalizing Transformer Predictions via End-To-End Differentiable Self-Training
We propose an end-to-end differentiable training paradigm for stable training of a rationalized transformer classifier. Our approach results in a single model that simultaneously classifies a sample and scores input tokens based on their relevance to the classification. To this end, we build on the widely-used three-player-game for training rationalized models, which typically relies on training a rationale selector, a classifier and a complement classifier. We simplify this approach by making …

Classification Filtering
Ilker Bayram
https://arxiv.org/abs/2509.13975 https://arxiv.org/pdf/2509.13975

Classification Filtering
We consider a streaming signal in which each sample is linked to a latent class. We assume that multiple classifiers are available, each providing class probabilities with varying degrees of accuracy. These classifiers are employed following a straightforward and fixed policy. In this setting, we consider the problem of fusing the output of the classifiers while incorporating the temporal aspect to improve classification accuracy. We propose a state-space model and develop a filter tailored for…

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Manal Binkhonain, Reem Alfayaz
https://arxiv.org/abs/2509.13868

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Requirements classification assigns natural language requirements to predefined classes, such as functional and non functional. Accurate classification reduces risk and improves software quality. Most existing models rely on supervised learning, which needs large labeled data that are costly, slow to create, and domain dependent; they also generalize poorly and often require retraining for each task. This study tests whether prompt based large language models can reduce data needs. We benchmark…

Classified Navy Files Were Left On The Curb. A Dumpster Diver Found Them
https://www.forbes.com/sites/the-wiretap/2025/09/16/classified-navy-files-were…

Classified Navy Files Were Left On The Curb. A Dumpster Diver Found Them.
Hundreds of pages in classified NCIS files were left out for the trash collectors, a search warrant reveals.

Contractor Used Classified CIA Systems as ‘His Own Personal Google’ https://www.404media.co/contractor-used-classified-cia-systems-as-his-own-personal-google/

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka, Elisa Donati, Sara Fabrikant
https://arxiv.org/abs/2509.14056

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify cognitive load during map-based wayfinding and whether classification accuracy depends more on task complexity or on individual traits. EEG recordings from forty-six participants navigating routes with 3, 5, or 7 map landmarks were analyzed with a nested cross…

Defining a classification system for augmentation technology in socio-technical terms
Isabel Pedersen, Ann Hill Duin
https://arxiv.org/abs/2509.13340 https://

Defining a classification system for augmentation technology in socio-technical terms
This short paper provides a means to classify augmentation technologies to reconceptualize them as sociotechnical, discursive and rhetorical phenomena, rather than only through technological classifications. It identifies a set of value systems that constitute augmentation technologies within discourses, namely, the intent to enhance, automate, and build efficiency. This short paper makes a contribution to digital literacy surrounding augmentation technology emergence, as well as the more speci…

Deep Learning-Driven Peptide Classification in Biological Nanopores
Samuel Tovey, Julian Ho{\ss}bach, Sandro Kuppel, Tobias Ensslen, Jan C. Behrends, Christian Holm
https://arxiv.org/abs/2509.14029

Deep Learning-Driven Peptide Classification in Biological Nanopores
A device capable of performing real time classification of proteins in a clinical setting would allow for inexpensive and rapid disease diagnosis. One such candidate for this technology are nanopore devices. These devices work by measuring a current signal that arises when a protein or peptide enters a nanometer-length-scale pore. Should this current be uniquely related to the structure of the peptide and its interactions with the pore, the signals can be used to perform identification. While s…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Jingsong Xia, Yue Yin, Xiuhan Li
https://arxiv.org/abs/2508.11532 htt…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while redu…