Tootfinder

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Yuhao Wang, Lingjuan Miao, Zhiqiang Zhou, Yajun Qiao, Lei Zhang
https://arxiv.org/abs/2509.02424

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Infrared and visible image fusion plays a critical role in enhancing scene perception by combining complementary information from different modalities. Despite recent advances, achieving high-quality image fusion with lightweight models remains a significant challenge. To bridge this gap, we propose a novel collaborative distillation and self-learning framework for image fusion driven by reinforcement learning. Unlike conventional distillation, this approach not only enables the student model t…

Optimal distillation of photonic indistinguishability
Francesco Hoch, Anita Camillini, Giovanni Rodari, Eugenio Caruccio, Gonzalo Carvacho, Taira Giordani, Riccardo Albiero, Niki Di Giano, Giacomo Corrielli, Francesco Ceccarelli, Roberto Osellame, Marco Robbio, Leonardo Novo, Nicol\`o Spagnolo, Ernesto F. Galv\~ao, Fabio Sciarrino
https://…

Optimal distillation of photonic indistinguishability
Imperfect photons' indistinguishability limits the performance of photonic quantum communication and computation . Distillation protocols, inspired by entanglement purification, enhance photons' indistinguishability by leveraging quantum interference in linear optical circuits. In this work, we present a three-photon distillation protocol optimized to achieve the maximum visibility gain, which requires consideration of multi-photon effects such as collective photonic phases. We employ interfero…

Hybrid AI-Driven Intrusion Detection: Framework Leveraging Novel Feature Selection for Enhanced Network Security
Maryam Mahdi Alhusseini, Mohammad Reza Feizi Derakhshi
https://arxiv.org/abs/2509.00896 …

Hybrid AI-Driven Intrusion Detection: Framework Leveraging Novel Feature Selection for Enhanced Network Security
In today's rapidly evolving digital landscape, safeguarding network infrastructures against cyberattacks has become a critical priority. This research presents an innovative AI-driven real-time intrusion detection framework designed to enhance network security, particularly in Wireless Sensor Networks (WSNs) and Cloud Computing (CC) environments. The system employs classical machine learning models, Logistic Regression, Decision Tree, and K-Nearest Neighbors, optimized through the novel Energy …

Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network
Sara Strakosova, Petr Novak, Petr Kadera
https://arxiv.org/abs/2510.02167

Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network
In the context of the circular economy, products in their end-of-life phase should be either remanufactured or recycled. Both of these processes are crucial for sustainability and environmental conservation. However, manufacturers often do not support these processes enough by not sharing relevant data. This paper proposes use of a digital twin technology, which is capable to help optimizing the disassembly processes to reduce ecological impact and enhance sustainability. The proposed approach …

High-resolution single-pixel imaging in real time with iterative or deep learning-based reconstruction enhancement
Anna Pastuszczak, Rafa{\l} Stojek, Piotr Wr\'obel, Magdalena Cwojdzi\'nska, Kacper Sobczak, Rafa{\l} Koty\'nski
https://arxiv.org/abs/2509.01497

High-resolution single-pixel imaging in real time with iterative or deep learning-based reconstruction enhancement
We introduce a compressive single-pixel imaging (SPI) framework for high-resolution image capture in fractions of a second. This framework combines a dedicated sampling strategy with a tailored reconstruction method to enable high-quality imaging of spatially sparse scenes at the native 1024x768 resolution of a digital micromirror device (DMD). The reconstruction process consists of two phases: first, the measured data is processed using the generalized inverse of the measurement matrix for qui…

More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration
Xiaoyang Yuan, Yujuan Ding, Yi Bin, Wenqi Shao, Jinyu Cai, Jingkuan Song, Yang Yang, Hengtao Shen
https://arxiv.org/abs/2510.02227

More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration
Reinforcement Learning with Verifiable Rewards (RLVR) is a promising paradigm for enhancing the reasoning ability in Large Language Models (LLMs). However, prevailing methods primarily rely on self-exploration or a single off-policy teacher to elicit long chain-of-thought (LongCoT) reasoning, which may introduce intrinsic model biases and restrict exploration, ultimately limiting reasoning diversity and performance. Drawing inspiration from multi-teacher strategies in knowledge distillation, we…

A Stream Pipeline Framework for Digital Payment Programming based on Smart Contracts
Zijia Meng, Victor Feng
https://arxiv.org/abs/2508.21075 https://arxiv…

A Stream Pipeline Framework for Digital Payment Programming based on Smart Contracts
Digital payments play a pivotal role in the burgeoning digital economy. Moving forward, the enhancement of digital payment systems necessitates programmability, going beyond just efficiency and convenience, to meet the evolving needs and complexities. Smart contract platforms like Central Bank Digital Currency (CBDC) networks and blockchains support programmable digital payments. However, the prevailing paradigm of programming payment logics involves coding smart contracts with programming lang…

Manim for STEM Education: Visualizing Complex Problems Through Animation
Christina Zhang
https://arxiv.org/abs/2510.01187 https://arxiv.org/pdf/2510.01187

Manim for STEM Education: Visualizing Complex Problems Through Animation
Many STEM concepts pose significant learning challenges to students due to their inherent complexity and abstract nature. Visualizing complex problems through animations can significantly enhance learning outcomes. However, the creation of animations can be time-consuming and inconvenient. Hence, many educators illustrate complex concepts by hand on a board or a digital device. Although static graphics are helpful for understanding, they are less effective than animations. The free and open-sou…

The Application of Virtual Environments and Artificial Intelligence in Higher Education: Experimental Findings in Philosophy Teaching
Adel Vehrer, Zsolt Palfalusi
https://arxiv.org/abs/2509.00110

The Application of Virtual Environments and Artificial Intelligence in Higher Education: Experimental Findings in Philosophy Teaching
This study explores how virtual environments and artificial intelligence can enhance university students' learning experiences, with particular attention to the digital preferences of Generation Z. An experiment was conducted at the Faculty of Pedagogy, Humanities, and Social Sciences at University of Gyor, where Walter's Cube technology and a trained AI mediator were integrated into the instruction of ten philosophical topics. The curriculum was aligned with the official syllabus and enriched …

How to Make Museums More Interactive? Case Study of Artistic Chatbot
Filip J. Kucia, Bartosz Grabek, Szymon D. Trochimiak, Anna Wr\'oblewska
https://arxiv.org/abs/2509.00572

How to Make Museums More Interactive? Case Study of Artistic Chatbot
Conversational agents powered by Large Language Models (LLMs) are increasingly utilized in educational settings, in particular in individual closed digital environments, yet their potential adoption in the physical learning environments like cultural heritage sites, museums, and art galleries remains relatively unexplored. In this study, we present Artistic Chatbot, a voice-to-voice RAG-powered chat system to support informal learning and enhance visitor engagement during a live art exhibition …