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Found 25 papers, 2 papers with code
PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning
Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints.
One-Shot Initial Orbit Determination in Low-Earth Orbit
Due to the importance of satellites for society and the exponential increase in the number of objects in orbit, it is important to accurately determine the state (e. g., position and velocity) of these Resident Space Objects (RSOs) at any time and in a timely manner.
Predicting the Probability of Collision of a Satellite with Space Debris: A Bayesian Machine Learning Approach
Such a dense space environment increases the risk of collisions between space objects endangering the whole space population.
Finding Real-World Orbital Motion Laws from Data
The method offers the advantage of delivering interpretable, accurate, and complex models of orbital motion that can be employed for propagation or as inputs to predictive models for other variables of interest, such as atmospheric drag or the probability of collision in an encounter with a spacecraft or space objects.
Probability of Collision of satellites and space debris for short-term encounters: Rederivation and fast-to-compute upper and lower bounds
It is estimated that, in orbit, there are millions of fragments a few millimeters in size and thousands of inoperative satellites and discarded rocket stages.
Taxonomy for Resident Space Objects in LEO: A Deep Learning Approach
Our proposed taxonomy and model offer a significant contribution to the ongoing efforts to mitigate the overall risks posed by the increasing number of RSOs in orbit.
Statistical Learning of Conjunction Data Messages Through a Bayesian Non-Homogeneous Poisson Process
In fact, the rate at which the CDMs are issued depends on the behaviour of the objects as well as on the screening process performed by third parties.
Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models
As a result, spacecraft collision avoidance procedures have become an essential part of satellite operations.
Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach
Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting.
A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning
Communication efficiency is a major challenge in federated learning (FL).
Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata
Our research focuses on medical referrals and aims to predict recommendations in different specialties of physicians for both new patients and those with a consultation history.
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification
In this paper, we propose SCENE-Net, a low-resource white-box model for 3D point cloud semantic segmentation.
EAMDrift: An interpretable self retrain model for time series
The use of machine learning for time series prediction has become increasingly popular across various industries thanks to the availability of time series data and advancements in machine learning algorithms.
Conjunction Data Messages for Space Collision Behave as a Poisson Process
(2) When exactly and with what uncertainty will the next message arrive?
Distributed Solution of the Inverse Rig Problem in Blendshape Facial Animation
The method applies to an arbitrary clustering of the face.
High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model
We propose a method to fit arbitrarily accurate blendshape rig models by solving the inverse rig problem in realistic human face animation.
Machine Learning in Orbit Estimation: a Survey
Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased.
A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding
Emergency Departments (EDs) are a fundamental element of the Portuguese National Health Service, serving as an entry point for users with diverse and very serious medical problems.
Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data
We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data.
Ranking with Confidence for Large Scale Comparison Data
In this work, we leverage a generative data model considering comparison noise to develop a fast, precise, and informative ranking algorithm from pairwise comparisons that produces a measure of confidence on each comparison.
Decentralized EM to Learn Gaussian Mixtures from Datasets Distributed by Features
Expectation Maximization (EM) is the standard method to learn Gaussian mixtures.
A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems
Good knowledge of users' transition patterns is a decisive contribution to the quality and operability of the service.
Clustering of the Blendshape Facial Model
We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time.
From noisy point clouds to complete ear shapes: unsupervised pipeline
Ears are a particularly difficult region of the human face to model, not only due to the non-rigid deformations existing between shapes but also to the challenges in processing the retrieved data.
LocDyn: Robust Distributed Localization for Mobile Underwater Networks
LocDyn is robust: it rejects outlier noise, while the comparing methods succumb in terms of positioning error.
