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"From chalkboards to e-learning, solar boosts schooling in an Indian floodplain"
#India #SolarPower #Energy

From chalkboards to e-learning, solar boosts schooling in an Indian floodplain
On remote river islands in eastern India’s Assam state, solar power is helping children access education more reliably, reports Mongabay India’s Shailesh Shrivastava. Schools in Assam’s Brahmaputra River Valley recently introduced digital classroom infrastructure, bringing audio-visual learning to children. For some students, this shift from chalkboard-only teaching has been a game changer. “Until last year, […]

AutoIntent: AutoML for Text Classification
Ilya Alekseev, Roman Solomatin, Darina Rustamova, Denis Kuznetsov
https://arxiv.org/abs/2509.21138 https://arxiv…

AutoIntent: AutoML for Text Classification
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and …

Hybrid Quantum-Classical Learning for Multiclass Image Classification
Shuchismita Anwar, Sowmitra Das, Muhammad Iqbal Hossain, Jishnu Mahmud
https://arxiv.org/abs/2508.18161 htt…

Hybrid Quantum-Classical Learning for Multiclass Image Classification
This study explores the challenge of improving multiclass image classification through quantum machine-learning techniques. It explores how the discarded qubit states of Noisy Intermediate-Scale Quantum (NISQ) quantum convolutional neural networks (QCNNs) can be leveraged alongside a classical classifier to improve classification performance. Current QCNNs discard qubit states after pooling; yet, unlike classical pooling, these qubits often remain entangled with the retained ones, meaning valua…

Sparse Representations Improve Adversarial Robustness of Neural Network Classifiers
Killian Steunou, Sigurd Saue, Th\'eo Druilhe
https://arxiv.org/abs/2509.21130 https://

Sparse Representations Improve Adversarial Robustness of Neural Network Classifiers
Deep neural networks perform remarkably well on image classification tasks but remain vulnerable to carefully crafted adversarial perturbations. This work revisits linear dimensionality reduction as a simple, data-adapted defense. We empirically compare standard Principal Component Analysis (PCA) with its sparse variant (SPCA) as front-end feature extractors for downstream classifiers, and we complement these experiments with a theoretical analysis. On the theory side, we derive exact robustnes…

EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Riccardo La Grassa, Ignazio Gallo, Nicola Landro
https://arxiv.org/abs/2509.21061

EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Fine-grained classification models are designed to focus on the relevant details necessary to distinguish highly similar classes, particularly when intra-class variance is high and inter-class variance is low. Most existing models rely on part annotations such as bounding boxes, part locations, or textual attributes to enhance classification performance, while others employ sophisticated techniques to automatically extract attention maps. We posit that part-based approaches, including automatic…

A Coalgebraic Model of Quantum Bisimulation
Lorenzo Ceragioli (IMT School for Advanced Studies, Lucca, Italy), Elena Di Lavore (University of Pisa, Italy), Giuseppe Lomurno (University of Pisa, Italy), Gabriele Tedeschi (University of Pisa, Italy)
https://arxiv.org/abs/2509.20933

A Coalgebraic Model of Quantum Bisimulation
Recent works have shown that defining a behavioural equivalence that matches the observational properties of a quantum-capable, concurrent, non-deterministic system is a surprisingly difficult task. We explore coalgebras over distributions taking weights from a generic effect algebra, which subsumes probabilities and quantum effects, a physical formalism that represents the probabilistic behaviour of an open quantum system. To abide by the properties of quantum theory, we introduce monads grade…

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
ZiqiZhang, Jianfei Ma, Emmanuele Chersoni, Jieshun You, Zhaoxin Feng
https://arxiv.org/abs/2508.18253

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature. To address such a question, we employ various masking strategies to evaluate the LLMs' intrinsic ability, the contribution of different sent…

Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering
Chun Liu, Chen Zhang, Zhuo Li, Zheng Li, Wei Yang
https://arxiv.org/abs/2508.18075

Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering
Open-set few-shot hyperspectral image (HSI) classification aims to classify image pixels by using few labeled pixels per class, where the pixels to be classified may be not all from the classes that have been seen. To address the open-set HSI classification challenge, current methods focus mainly on distinguishing the unknown class samples from the known class samples and rejecting them to increase the accuracy of identifying known class samples. They fails to further identify or discovery the …

Automated Classification of Normal and Atypical Mitotic Figures Using ConvNeXt V2: MIDOG 2025 Track 2
Yosuke Yamagishi, Shouhei Hanaoka
https://arxiv.org/abs/2508.18831 https://…

Automated Classification of Normal and Atypical Mitotic Figures Using ConvNeXt V2: MIDOG 2025 Track 2
This paper presents our solution for the MIDOG 2025 Challenge Track 2, which focuses on binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs) in histopathological images. Our approach leverages a ConvNeXt V2 base model with center cropping preprocessing and 5-fold cross-validation ensemble strategy. The method addresses key challenges including severe class imbalance, high morphological variability, and domain heterogeneity across different tumor types, s…

BirdRecorder's AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines
Nico Klar, Nizam Gifary, Felix P. G. Ziegler, Frank Sehnke, Anton Kaifel, Eric Price, Aamir Ahmad
https://arxiv.org/abs/2508.18136

BirdRecorder's AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines
The urgent need for renewable energy expansion, particularly wind power, is hindered by conflicts with wildlife conservation. To address this, we developed BirdRecorder, an advanced AI-based anti-collision system to protect endangered birds, especially the red kite (Milvus milvus). Integrating robotics, telemetry, and high-performance AI algorithms, BirdRecorder aims to detect, track, and classify avian species within a range of 800 m to minimize bird-turbine collisions. BirdRecorder integrat…