Publications related to Songdo Traffic: High Accuracy Georeferenced Vehicle Trajectories from a Large-Scale Study in a Smart City

Related publications (16)

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

Alexandre Massoud Alahi, Saeed Saadatnejad, Yang Gao

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

IEEE Institute of Electrical and Electronics Engineers2025

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Fernando Perez Cruz

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

2025

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models

Francesco Croce

Adversarial robustness has been studied extensively in image classification, especially for the ℓ∞-threat model, but significantly less so for related tasks such as object detection and semantic segmentation, where attacks turn out to be a much harder opti ...

Springer Science and Business Media Deutschland GmbH2025

Fine-tuning protein language models to understand the functional impact of missense variants

Jacques Fellay

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we tra ...

2025

Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Zaffar Ahmed Shaikh

Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challengi ...

2025

A two-level resolution neural network with enhanced interpretability for freeway traffic forecasting

Nikolaos Geroliminis, Semin Kwak, Danya Li

© The Author(s) 2024.Deep learning models are widely used for traffic forecasting on freeways due to their ability to learn complex temporal and spatial relationships. In particular, graph neural networks, which integrate graph theory into deep learning, h ...

Nature Research2024

Unravelling the components of diffuse scattering using deep learning

Lucas Sebastian Powell-Rudden

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Int Union Crystallography2024

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Christian Leinenbach, Antonios Nikolaos Baganis

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Taylor & Francis Ltd2024

FVLLMONTI: The 3D Neural Network Compute Cube (N2C2) Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

This multi-partner-project contribution introduces the midway results of the Horizon 2020 FVLLMONTI project. In this project we develop a new and ultra-efficient class of ANN accelerators, the neural network compute cube (N2C2), which is specifically desig ...

Institute of Electrical and Electronics Engineers Inc.2024

Stripping the Swiss discount curve using kernel ridge regression

Nicolas Etienne Camenzind, Damir Filipovic

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Springer Heidelberg2024

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Filippo Bigi

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

2024

Point-process-based Representation Learning for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Irregular sampling of time series in electronic health records (EHRs) presents a challenge for the development of machine learning models. Additionally, the pattern of missing data in certain clinical variables is not random, but depends on the decisions o ...

IEEE2023

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Rahul Sharma

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

MDPI2023

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

Olga Fink, Gaëtan Michel Frusque

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

2023

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Martin Weigert

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

Elsevier2023

Ridges, Neural Networks, and the Radon Transform

Michaël Unser

A ridge is a function that is characterized by a one-dimensional profile (activation) and a multidimensional direction vector. Ridges appear in the theory of neural networks as functional descriptors of the effect of a neuron, with the direction vector bei ...

2023