output
20072026
most citedTrAISformer -- A Transformer Network with Sparse Augmented Data Representation and Cross Entropy Loss for AIS-based Vessel Trajectory Prediction

99 citations

104 papers

cs.HC2026

The Nocturnity Scale: Measuring the Sense of Being at Night in Virtual Urban Environments

Anthony Le Gourri{é}rec, Etienne Peillard, Nicolas Houel +1

Nighttime environments are increasingly used in virtual urban studies, yet darkness alone does not fully recreate the subjective sense of being at night. Prior work suggests that t…

quant-ph2026

Free-Space CV-QKD with Single-Mode Fiber Reception: Effective Coupling Statistics and Protocol-Dependent Reference Noise

Hesham S. Ibrahim, Arnaud Coatanhay

We study free-space continuous-variable quantum key distribution (CV-QKD) with single-mode fiber (SMF) reception under atmospheric turbulence. The optical channel is modeled by spl…

cs.AR2026

Bit-Width-Aware Design Environment for Few-Shot Learning on Edge AI Hardware

R. Kanda, H. L. Blevec, N. Onizawa +3

In this study, we propose an implementation methodology of real-time few-shot learning on tiny FPGA SoCs such as the PYNQ-Z1 board with arbitrary fixed-point bit-widths. Tensil-bas…

cs.AR2026★ 3 cited

Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning

R. Kanda, N. Onizawa, M. Leonardon +2

This study aims to ensure consistency in accuracy throughout the entire design flow in the implementation of edge AI hardware for few-shot learning, by implementing fixed-point dat…

cs.DB2025

Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances

José-Luis Vilchis Medina

This article proposes a paraconsistent framework for evaluating similarity in knowledge bases. Unlike classical approaches, this framework explicitly integrates contradictions, ena…

cs.LG2025

RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory

Yi-Chun Liao, Chieh-Lin Tsai, Yuan-Hao Chang +3

Although deep learning has demonstrated remarkable capability in learning from unstructured data, modern tree-based ensemble models remain superior in extracting relevant informati…