Tootfinder

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

A polynomial-based Monte Carlo approach for estimating long-term collision probabilities
Andrea Zollo, Cristina Parigini, Roberto Armellin, Juan F\'elix San Juan D\'iaz, Annarita Trombetta, Ralph Kahle
https://arxiv.org/abs/2509.07607

A polynomial-based Monte Carlo approach for estimating long-term collision probabilities
This paper introduces a versatile approach for computing the risk of collision specifically tailored for scenarios featuring low relative encounter velocities, but with potential applicability across a wide range of situations. The technique employs Differential Algebra (DA) to express the non-linear dynamical flow of the initial distribution in the primary-secondary objects relative motion through high-order Taylor polynomials. The entire initial uncertainty set is subdivided into subsets thro…

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation
Hiroto Sakimura, Takayuki Nagaya, Tomoki Nishi, Tetsuo Kurahashi, Katsunori Kohda, Nobuhiko Muramoto
https://arxiv.org/abs/2508.18782

Long-Term Variability in Physiological-Arousal Relationships for Robust Emotion Estimation
Estimating emotional states from physiological signals is a central topic in affective computing and psychophysiology. While many emotion estimation systems implicitly assume a stable relationship between physiological features and subjective affect, this assumption has rarely been tested over long timeframes. This study investigates whether such relationships remain consistent across several months within individuals. We developed a custom measurement system and constructed a longitudinal data…

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
Qiong Wu, Yu Xie, Pingyi Fan, Dong Qin, Kezhi Wang, Nan Cheng, Khaled B. Letaief
https://arxiv.org/abs/2507.19050

Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context lear…

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
Li-Hsiang Shen, Jyun-Jhe Huang
https://arxiv.org/abs/2507.16204

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
A space-air-ground integrated network (SAGIN) architecture is proposed, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in shadowed regions, while explicitly accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term en…

Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability
Yufei Ye, Shijian Gao, Xinhu Zheng, Liuqing Yang
https://arxiv.org/abs/2508.14601 http…

Multi-Tier UAV Edge Computing for Low Altitude Networks Towards Long-Term Energy Stability
This paper presents a novel multi-tier UAV-assisted edge computing system designed for low-altitude networks. The system comprises vehicle users, lightweight Low-Tier UAVs (L-UAVs), and High-Tier UAV (H-UAV). L-UAVs function as small-scale edge servers positioned closer to vehicle users, while the H-UAV, equipped with more powerful server and larger-capacity battery, serves as mobile backup server to address the limitations in endurance and computing resources of L-UAVs. The primary objective i…

Crosslisted article(s) found for cs.NE. https://arxiv.org/list/cs.NE/new
[1/1]:
- Quantum Long Short-term Memory with Differentiable Architecture Search
Samuel Yen-Chi Chen, Prayag Tiwari

Can Classical Initialization Help Variational Quantum Circuits Escape the Barren Plateau?
Yifeng Peng, Xinyi Li, Zhemin Zhang, Samuel Yen-Chi Chen, Zhiding Liang, Ying Wang
https://arxiv.org/abs/2508.18497

Can Classical Initialization Help Variational Quantum Circuits Escape the Barren Plateau?
Variational quantum algorithms (VQAs) have emerged as a leading paradigm in near-term quantum computing, yet their performance can be hindered by the so-called barren plateau problem, where gradients vanish exponentially with system size or circuit depth. While most existing VQA research employs simple Gaussian or zero-initialization schemes, classical deep learning has long benefited from sophisticated weight initialization strategies such as Xavier, He, and orthogonal initialization to improv…

Synaptic plasticity in Co/Nb:STO memristive devices: The role of oxygen vacancies
Walter Qui\~nonez, Anouk Goossens, Diego Rubi, Tamalika Banerjee, Mar\'ia Jos\'e S\'anchez
https://arxiv.org/abs/2506.11965

Synaptic plasticity in Co/Nb:STO memristive devices: The role of oxygen vacancies
Neuromorphic computing aims to develop energy-efficient devices that mimic biological synapses. One promising approach involves memristive devices that can dynamically adjust their electrical resistance in response to stimuli, similar to synaptic weight changes in the brain. However, a key challenge is understanding and controlling the coexistence of different types of synaptic plasticity, such as short-term and long-term plasticity. In this work, we show that plasticity behaviors in Co/Nb:STO …

Artificial Intelligence for Atmospheric Sciences: A Research Roadmap
Martha Arbayani Zaidan, Naser Hossein Motlagh, Petteri Nurmi, Tareq Hussein, Markku Kulmala, Tuukka Pet\"aj\"a, Sasu Tarkoma
https://arxiv.org/abs/2506.16281

Artificial Intelligence for Atmospheric Sciences: A Research Roadmap
Atmospheric sciences are crucial for understanding environmental phenomena ranging from air quality to extreme weather events, and climate change. Recent breakthroughs in sensing, communication, computing, and Artificial Intelligence (AI) have significantly advanced atmospheric sciences, enabling the generation of vast amounts of data through long-term Earth observations and providing powerful tools for analyzing atmospheric phenomena and predicting natural disasters. This paper contributes a c…