Tootfinder

CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Debopom Sutradhar, Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Reem E. Mohamed, Sheikh Izzal Azid, Sami Azam
https://arxiv.org/abs/2509.11952

CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation
Accurate land cover classification from satellite imagery is crucial in environmental monitoring and sustainable resource management. However, it remains challenging due to the complexity of natural landscapes, the visual similarity between classes, and the significant class imbalance in the available datasets. To address these issues, we propose a dual encoder architecture that independently extracts modality-specific features from optical and Synthetic Aperture Radar (SAR) imagery, which are …

Mechanizing Synthetic Tait Computability in Istari
Runming Li, Yue Yao, Robert Harper
https://arxiv.org/abs/2509.11418 https://arxiv.org/pdf/2509.11418

Mechanizing Synthetic Tait Computability in Istari
Categorical gluing is a powerful technique for proving meta-theorems of type theories such as canonicity and normalization. Synthetic Tait Computability (STC) provides an abstract treatment of the complex gluing models by internalizing the gluing category into a modal dependent type theory with a phase distinction. This work presents a mechanization of STC in the Istari proof assistant. Istari is a Martin-Löf-style extensional type theory with equality reflection. Equality reflection eliminate…

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records
Abdulrahman Allam, Seif Ahmed, Ali Hamdi, Khaled Shaban
https://arxiv.org/abs/2509.10108

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records
The development of medical chatbots in Arabic is significantly constrained by the scarcity of large-scale, high-quality annotated datasets. While prior efforts compiled a dataset of 20,000 Arabic patient-doctor interactions from social media to fine-tune large language models (LLMs), model scalability and generalization remained limited. In this study, we propose a scalable synthetic data augmentation strategy to expand the training corpus to 100,000 records. Using advanced generative AI system…

heise | Wie Sie gratis KI-Stimmen klonen – für mehrsprachige Podcasts und Hörbücher
Mit dem KI-Tool Chatterbox werden synthetische Stimmen lebendig. Wir zeigen, wie Sie gratis und lokal mehrsprachige Podcasts oder Hörbücher generieren.

I used a speech to text program today to type “Shirley” and it entered “Surely”
#airplane

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Shanmuka Sadhu, Arca Baran, Preeti Pandey, Ayush Kumar
https://arxiv.org/abs/2509.11547 https://

Task Decoding based on Eye Movements using Synthetic Data Augmentation
Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding …

Großen Dank nochmal an die @… und den @… für das phänomenale Event. Ich hatte meinen Talk weitaus chaotischer in Erinnerung, vielleicht folgt noch eine…

SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar
Omkar Shailendra Vengurlekar, Adithya Pediredla, Suren Jayasuriya
https://arxiv.org/abs/2509.11087

SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar
Synthetic aperture sonar (SAS) reconstruction requires recovering both the spatial distribution of acoustic scatterers and their direction-dependent response. Time-domain backprojection is the most common 3D SAS reconstruction algorithm, but it does not model directionality and can suffer from sampling limitations, aliasing, and occlusion. Prior neural volumetric methods applied to synthetic aperture sonar treat each voxel as an isotropic scattering density, not modeling anisotropic returns. We…

A Synthetic Data Pipeline for Supporting Manufacturing SMEs in Visual Assembly Control
Jonas Werheid, Shengjie He, Aymen Gannouni, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2509.13089

A Synthetic Data Pipeline for Supporting Manufacturing SMEs in Visual Assembly Control
Quality control of assembly processes is essential in manufacturing to ensure not only the quality of individual components but also their proper integration into the final product. To assist in this matter, automated assembly control using computer vision methods has been widely implemented. However, the costs associated with image acquisition, annotation, and training of computer vision algorithms pose challenges for integration, especially for small- and medium-sized enterprises (SMEs), whic…

Multi-Model Synthetic Training for Mission-Critical Small Language Models
Nolan Platt, Pragyansmita Nayak
https://arxiv.org/abs/2509.13047 https://arxiv.or…

Multi-Model Synthetic Training for Mission-Critical Small Language Models
Large Language Models (LLMs) have demonstrated remarkable capabilities across many domains, yet their appli- cation to specialized fields remains constrained by the scarcity and complexity of domain-specific training data. We present a novel approach that achieves a 261x cost reduction for maritime intelligence by using LLMs as one-time teachers rather than using them directly for inference. Our method transforms 3.2 billion Automatic Identification System (AIS) vessel tracking records into 21,…