Publications

Satellite Safe Margin: Fast Solutions for Conjunction Analysis

Published in Journal of Guidance, Control, and Dynamics, 2026

The amount of debris in orbit has increased significantly over the years. With the recent growth of interest in space exploration, conjunction assessment has become a central issue. One important metric to evaluate conjunction risk is the miss distance. However, this metric does not intrinsically take into account uncertainty distributions. Some work has been developed to consider the uncertainty associated with the position of the… Read full abstract

Recommended citation: Ricardo N. Ferreira, Marta Guimarães, Cláudia Soares, "Satellite Safe Margin: Fast Solutions for Conjunction Analysis." Journal of Guidance, Control, and Dynamics, 2026. https://doi.org/10.2514/1.G008922

ASTRA: ADMM-Accelerated Topology Reconfiguration for Dynamic Satellite Constellations

Published in NeurIPS 2026, 2026

Dynamic topology reconfiguration is central to the reliability and efficiency of large satellite constellations, yet many existing approaches rely on idealized assumptions such as full constellation deployment or uniform orbital spacing. We present Adaptive Satellite Topology via Regret-Aware learning (ASTRA), a theoretically-grounded framework for dynamic satellite topology reconfiguration that builds on an online learning formulation and makes it computationally practical. ASTRA combines an ADMM-based offline… Read full abstract

Recommended citation: João Norberto, Ricardo Ferreira, Cláudia Soares, "ASTRA: ADMM-Accelerated Topology Reconfiguration for Dynamic Satellite Constellations." NeurIPS 2026, 2026.

Dissecting medical referrals in health services: role of physician professional networks

Published in BMC Health Services Research, 2026

Background Medical referrals between primary care (PC) and specialist care (SC) physicians are essential to integrated healthcare delivery but often encounter inefficiencies arising from communication gaps, coordination issues, and organizational structure. While prior studies have explored patient-sharing networks, limited quantitative evidence exists on how physicians’ professional relationships, such as co-training, co-affiliation, or network embeddedness, relate to referral behavior. This study develops a data-driven framework combining… Read full abstract

Recommended citation: Regina de Brito Duarte, Qiwei Han, Claudia Soares, "Dissecting medical referrals in health services: role of physician professional networks." BMC Health Services Research, 2026. http://dx.doi.org/10.1186/s12913-026-14099-9

CLASP: Online learning algorithms for Convex Losses And Squared Penalties

Published in Proceedings of the 43rd International Conference on Machine Learning, 2026

Addressing Constrained Online Convex Optimization (COCO), we introduce CLASP (Convex Losses And Squared Penalties), a framework that minimizes cumulative loss together with squared constraint violations. We propose two variants of CLASP, CLASP-I and CLASP-F, allowing for a joint or separate handling of the static decision set and the time-varying constraints, a decoupling flexibility that affords simpler implementations when projections onto the static decision set are… Read full abstract

Recommended citation: Ricardo N. Ferreira, Joao Xavier, Cláudia Soares, "CLASP: Online learning algorithms for Convex Losses And Squared Penalties." Proceedings of the 43rd International Conference on Machine Learning, 2026. https://proceedings.mlr.press/v306/ferreira26a.html

A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations

Published in AI4Space, 2026

The rapid expansion of space activities has led to an unprecedented accumulation of technical documentation, operational guidelines, and scientific literature, creating challenges for timely decision-making in space operations. Effective management in space operations requires tools capable of efficiently processing vast and heterogeneous information sources. This paper systematically evaluates the performance of Retrieval-Augmented Generation (RAG) pipelines, combining Large Language Models (LLMs) with information retrieval techniques for… Read full abstract

Recommended citation: Ruben Catarino Belo, Marta Guimaraes, Claudia Soares, "A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations." AI4Space, 2026. https://openreview.net/forum?id=hLv173CNNk

A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning

Published in IEEE Transactions on Signal Processing, 2026

Most federated learning (FL) methods use a client-server scheme, where clients communicate only with a central server. However, this scheme is prone to bandwidth bottlenecks at the server and has a single point of failure. In contrast, in a (fully) decentralized approach, clients communicate directly with each other, dispensing with the server and mitigating these issues. Yet, as the client network grows larger and sparser… Read full abstract

Recommended citation: Pedro Valdeira, Yuejie Chi, Cláudia Soares, João Xavier, "A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning." IEEE Transactions on Signal Processing, 2026. http://dx.doi.org/10.1109/tsp.2026.3652487

SCENE-Net: Geometric induction for interpretable and low-resource 3D pole detection with Group-Equivariant Non-Expansive Operators

Published in Computer Vision and Image Understanding, 2025

This paper introduces SCENE-Net, a novel low-resource, white-box model that serves as a compelling proof-of-concept for 3D point cloud segmentation. At its core, SCENE-Net employs Group Equivariant Non-Expansive Operators (GENEOs), a mechanism that leverages geometric priors for enhanced object identification. Our contribution extends the theoretical landscape of geometric learning, highlighting the utility of geometric observers as intrinsic biases in analyzing 3D environments. Through empirical testing… Read full abstract

Recommended citation: Diogo Lavado, Alessandra Micheletti, Giovanni Bocchi, Patrizio Frosini, Cláudia Soares, "SCENE-Net: Geometric induction for interpretable and low-resource 3D pole detection with Group-Equivariant Non-Expansive Operators." Computer Vision and Image Understanding, 2025. http://dx.doi.org/10.1016/j.cviu.2025.104531

Generalizing Trilateration: Approximate Maximum Likelihood Estimator for Initial Orbit Determination in Low Earth Orbit

Published in IEEE Transactions on Aerospace and Electronic Systems, 2025

With the increase in the number of active satellites and space debris in orbit, the problem of initial orbit determination (IOD) becomes increasingly important, demanding a high accuracy. Over the years, different approaches have been presented such as filtering methods (for example, Extended Kalman Filter), differential algebra or solving Lambert's problem. In this work, we consider a setting of three monostatic radars, where all available… Read full abstract

Recommended citation: Ricardo Ferreira, Filipa Valdeira, Marta Guimarães, Cláudia Soares, "Generalizing Trilateration: Approximate Maximum Likelihood Estimator for Initial Orbit Determination in Low Earth Orbit." IEEE Transactions on Aerospace and Electronic Systems, 2025. http://dx.doi.org/10.1109/taes.2025.3566085

C-PATH: Conversational Patient Assistance and Triage in Healthcare System

Published in 2025 IEEE International Conference on Digital Health (ICDH), 2025

Navigating healthcare systems can be complex and overwhelming, creating barriers for patients seeking timely and appropriate medical attention. In this paper, we introduce C-PATH (Conversational Patient Assistance and Triage in Healthcare), a novel conversational AI system powered by large language models (LLMs) designed to assist patients in recognizing symptoms and recommending appropriate medical departments through natural, multi-turn dialogues. C-PATH is fine-tuned on medical knowledge, dialogue… Read full abstract

Recommended citation: Qi Shi, Qiwei Han, Cláudia Soares, "C-PATH: Conversational Patient Assistance and Triage in Healthcare System." 2025 IEEE International Conference on Digital Health (ICDH), 2025. http://dx.doi.org/10.1109/icdh67620.2025.00016

Learning Under Noisy Labels, Spurious Points, and Diverse Structures: TS40K, a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems

Published in 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

Research in 3D scene understanding, particularly in autonomous driving and indoor segmentation, has made significant strides. However, most available datasets focus on urban settings. We introduce TS40K, a 3D point cloud dataset spanning 40,000 km of electrical transmission systems in rural terrain, addressing power-grid inspections to prevent outages, damages, and fires. TS40K offers high point density and no occlusion, presenting challenges like noisy labels, diverse… Read full abstract

Recommended citation: Diogo Lavado, Ricardo Santos, André Coelho, João Santos, Alessandra Micheletti, Claudia Soares, "Learning Under Noisy Labels, Spurious Points, and Diverse Structures: TS40K, a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission Systems." 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. http://dx.doi.org/10.1109/wacv61041.2025.00712

Ranking with Confidence for Large Scale Comparison Data

Published in Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 2025

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. The problem of ranking a large number of items from noisy and sparse pairwise comparison data arises in diverse applications, like ranking players in online games, document retrieval or ranking human perceptions… Read full abstract

Recommended citation: Filipa Valdeira, Cláudia Soares, "Ranking with Confidence for Large Scale Comparison Data." Proceedings of the 2025 SIAM International Conference on Data Mining (SDM), 2025. http://dx.doi.org/10.1137/1.9781611978520.21

OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning

Published in NeurIPS 2025 poster, 2025

The increasing number of satellites and orbital debris has made space congestion a critical issue, threatening satellite safety and sustainability. Challenges such as collision avoidance, station-keeping, and orbital maneuvering require advanced techniques to handle dynamic uncertainties and multi-agent interactions. Reinforcement learning (RL) has shown promise in this domain, enabling adaptive, autonomous policies for space operations; however, many existing RL frameworks rely on custom-built environments developed… Read full abstract

Recommended citation: Alexandre Oliveira, Katarina Dyreby, Francisco Miguel Caldas, Claudia Soares, "OrbitZoo: Real Orbital Systems Challenges for Reinforcement Learning." NeurIPS 2025 poster, 2025. https://openreview.net/forum?id=oElWLpkOux

Language Modeling and Retrieval-Augmented Generation for Integration in Space Operation Decision Support Tools

Published in 58th IAA Symposium on Safety, Quality and Knowledge Management in Space Activities, 2025

The rapid expansion of the space industry has led to an increasing volume of technical documents, operational guidelines, and scientific publications. As space activities become more complex, effective decision-making and operations management require efficient processing of vast and ever-changing information sources to support new developments in the sector. Space operators must navigate regulations, guidelines, safety protocols, and mission-critical data, which makes knowledge retrieval a significant… Read full abstract

Recommended citation: Ruben Belo, Claudia Soares, Marta Guimaraes, "Language Modeling and Retrieval-Augmented Generation for Integration in Space Operation Decision Support Tools." 58th IAA Symposium on Safety, Quality and Knowledge Management in Space Activities, 2025. http://dx.doi.org/10.52202/083095-0016

Communication-Efficient Vertical Federated Learning via Compressed Error Feedback

Published in IEEE Transactions on Signal Processing, 2025

Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizontal FL, where each client holds a subset of the samples, such communication-compressed training methods have recently seen significant progress. However, in their vertical FL counterparts, where each client holds a subset of the features… Read full abstract

Recommended citation: Pedro Valdeira, João Xavier, Cláudia Soares, Yuejie Chi, "Communication-Efficient Vertical Federated Learning via Compressed Error Feedback." IEEE Transactions on Signal Processing, 2025. http://dx.doi.org/10.1109/tsp.2025.3540655

Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape Animation

Published in SIGGRAPH Asia 2024 Conference Papers, 2024

In this paper, we present an advanced approach to solving the inverse rig problem in blendshape animation, using high-quality corrective blendshapes. Our algorithm focuses on three key areas: ensuring high data fidelity in reconstructed meshes, achieving greater sparsity in weight distributions, and facilitating smoother frame-to-frame transitions. While the incorporation of corrective terms is a known practice, our method differentiates itself by employing a unique combination… Read full abstract

Recommended citation: Stevo Racković, Dušan Jakovetić, Cláudia Soares, "Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape Animation." SIGGRAPH Asia 2024 Conference Papers, 2024. http://dx.doi.org/10.1145/3680528.3687670

Maximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurements

Published in Signal Processing, 2024

Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like indoor or underwater environments, and, for large networks, the instrumentation cost is prohibitive. We develop a localization algorithm from ranges and bearings, suitable for generic mobile networks. Our algorithm is built on a tight… Read full abstract

Recommended citation: Filipa Valdeira, Cláudia Soares, João Gomes, "Maximum likelihood localization of a network of moving agents from ranges, bearings and velocity measurements." Signal Processing, 2024. http://dx.doi.org/10.1016/j.sigpro.2024.109471

Machine learning in orbit estimation: A survey

Published in Acta Astronautica, 2024

Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased. It is estimated that around one million objects larger than one cm are currently orbiting the Earth, with only thirty thousand larger than ten cm being tracked. To avert a chain reaction of collisions, known as Kessler Syndrome, it is essential to accurately track and… Read full abstract

Recommended citation: Francisco Caldas, Cláudia Soares, "Machine learning in orbit estimation: A survey." Acta Astronautica, 2024. http://dx.doi.org/10.1016/j.actaastro.2024.03.072

Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables

Published in 2024 IEEE Congress on Evolutionary Computation (CEC), 2024

The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is crucial to anticipate the position and velocity of space objects, for collision avoidance and space debris mitigation. When performing Orbit Prediction (OP), it is necessary to consider the impact of non-conservative forces, such as atmospheric drag and gravitational perturbations, that… Read full abstract

Recommended citation: Francisco Caldas, Cláudia Soares, "Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables." 2024 IEEE Congress on Evolutionary Computation (CEC), 2024. http://dx.doi.org/10.1109/cec60901.2024.10611996

One-Shot Initial Orbit Determination in Low-Earth Orbit

Published in Accepted at the IEEE Aerospace Conference, 2024

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. State-of-the-art methodologies for initial orbit determination consist of Kalman-type filters that process sequential data over time and return the state… Read full abstract

Recommended citation: Ricardo Ferreira, Marta Guimarães, Filipa Valdeira, Cláudia Soares, "One-Shot Initial Orbit Determination in Low-Earth Orbit." Accepted at the IEEE Aerospace Conference, 2024. http://dx.doi.org/10.1109/aero58975.2024.10521177

SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors

Published in ICML 2024 Workshop GRaM, 2024

In this paper, we present SCENE-Net V2, a new resource-efficient, gray-box model for multiclass 3D scene understanding. SCENE-Net V2 leverages Group Equivariant Non-Expansive Operators (GENEOs) to incorporate fundamental geometric priors as inductive biases, offering a more transparent alternative to the prevalent black-box models in the domain. This model addresses the limitations of its white-box predecessor, SCENE-Net, by expanding its applicability from pole-like structures to a… Read full abstract

Recommended citation: Diogo Mateus Lavado, Claudia Soares, Alessandra Micheletti, "SCENE-Net V2: Interpretable Multiclass 3D Scene Understanding with Geometric Priors." ICML 2024 Workshop GRaM, 2024. https://openreview.net/forum?id=6pKRn6tttu

PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning

Published in Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2024

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. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative… Read full abstract

Recommended citation: Frederico Metelo, Cláudia Soares, Stevo Racković, Pedro Ákos Costa, "PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning." Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2024. http://dx.doi.org/10.1007/978-3-031-70378-2_3

Causal Inference Explanations for Graph Neural Networks

Published in Causal@UAI2024 Poster, 2024

Explainable Artificial Intelligence has emerged, aiming to enhance the trustworthiness of black box models by devising explanation methods that clarify their inner workings. However, prevalent explanation techniques predominantly leverage correlation and association rather than employing causality, a significant aspect of human comprehension. We propose a novel explanation method grounded in causal inference tailored specifically for Graph Neural Networks. Our approach seeks to illuminate the decision-making… Read full abstract

Recommended citation: Sahil Satish Kumar, Claudia Soares, "Causal Inference Explanations for Graph Neural Networks." Causal@UAI2024 Poster, 2024. https://openreview.net/forum?id=xB99i5yHtm

AI for space traffic management

Published in Journal of Space Safety Engineering, 2023

Morgan Stanley forecasts the space industry to top 1 trillion dollars by 2040. Of these 1 trillion dollars, 1.5 billion dollars are expected to be the contribution of the space situational market alone. Satellite operators are already paying the price of space debris. Current approaches for collision avoidance and space traffic management face serious challenges, mainly: (1) Insufficient data and endangered autonomy of action in… Read full abstract

Recommended citation: Chiara Manfletti, Marta Guimarães, Claudia Soares, "AI for space traffic management." Journal of Space Safety Engineering, 2023. http://dx.doi.org/10.1016/j.jsse.2023.08.007

Distributed Solution of the Blendshape Rig Inversion Problem

Published in SIGGRAPH Asia 2023 Technical Communications, 2023

The problem of rig inversion is central in facial animation, but with the increasing complexity of modern blendshape models, execution times increase beyond practically feasible solutions. A possible approach towards a faster solution is clustering, which exploits the spacial nature of the face, leading to a distributed method. In this paper, we go a step further, involving cluster coupling to get more confident estimates of… Read full abstract

Recommended citation: Stevo Racković, Cláudia Soares, Dušan Jakovetić, "Distributed Solution of the Blendshape Rig Inversion Problem." SIGGRAPH Asia 2023 Technical Communications, 2023. http://dx.doi.org/10.1145/3610543.3626166

Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data

Published in SIAM Journal on Mathematics of Data Science, 2023

We propose a shape fitting/registration method based on a Gaussian processes formulation, suitable for shapes with extensive regions of missing data. Gaussian processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed… Read full abstract

Recommended citation: Filipa M. Valdeira, Ricardo Ferreira, Alessandra Micheletti, Cláudia Soares, "Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data." SIAM Journal on Mathematics of Data Science, 2023. http://dx.doi.org/10.1137/22m1495494

A majorization-minimization-based method for nonconvex inverse rig problems in facial animation: algorithm derivation

Published in Optimization Letters, 2023

Abstract Automated methods for facial animation are a necessary tool in the modern industry since the standard blendshape head models consist of hundreds of controllers, and a manual approach is painfully slow. Different solutions have been proposed that produce output in real-time or generalize well for different face topologies. However, all these prior works consider a linear approximation of the blendshape function and hence do… Read full abstract

Recommended citation: Stevo Racković, Cláudia Soares, Dušan Jakovetić, Zoranka Desnica, "A majorization-minimization-based method for nonconvex inverse rig problems in facial animation: algorithm derivation." Optimization Letters, 2023. http://dx.doi.org/10.1007/s11590-023-02012-w

Taxonomy for Resident Space Objects in LEO

Published in Accepted in the International Astronautical Congress 2023, 2023

The increasing number of RSOs has raised concerns about the risk of collisions and catastrophic incidents for all direct and indirect users of space. To mitigate this issue, it is essential to have a good understanding of the various RSOs in orbit and their behaviour. A well-established taxonomy defining several classes of RSOs is a critical step in achieving this understanding. This taxonomy helps assign… Read full abstract

Recommended citation: Marta Guimaraes, Claudia Soares, Chiara Manfletti, "Taxonomy for Resident Space Objects in LEO." Accepted in the International Astronautical Congress 2023, 2023.

Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models

Published in Accepted in the International Astronautical Congress 2023, 2023

The risk of collision between resident space objects has significantly increased in recent years. As a result, spacecraft collision avoidance procedures have become an essential part of satellite operations. To ensure safe and effective space activities, satellite owners and operators rely on constantly updated estimates of encounters. These estimates include the uncertainty associated with the position of each object at the expected TCA. These estimates… Read full abstract

Recommended citation: Marta Guimaraes, Claudia Soares, Chiara Manfletti, "Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models." Accepted in the International Astronautical Congress 2023, 2023.

A Temporal Fusion Transformer for Long-Term Explainable Prediction of Emergency Department Overcrowding

Published in Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023

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. Due to the inherent characteristics of the ED, forecasting the number of patients using the services is particularly challenging. And a mismatch between the affluence and the number of medical professionals can lead to a decrease in the… Read full abstract

Recommended citation: Francisco M. Caldas, Cláudia Soares, "A Temporal Fusion Transformer for Long-Term Explainable Prediction of Emergency Department Overcrowding." Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023. http://dx.doi.org/10.1007/978-3-031-23618-1_5

Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data

Published in IEEE Transactions on Intelligent Transportation Systems, 2022

Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management, namely for strategic, pre-tactical, tactical and post-operations. We predict missed flight connections in an airline’s hub airport using historical data on flights and passengers, and analyse the factors that contribute additively to the… Read full abstract

Recommended citation: Marta Guimaraes, Claudia Soares, Rodrigo Ventura, "Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data." IEEE Transactions on Intelligent Transportation Systems, 2022. http://dx.doi.org/10.1109/tits.2022.3147155

Robust Localization With Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations

Published in IEEE Transactions on Signal Processing, 2022

Locating a target is key in many applications, namely in high-stakes real-world scenarios, like detecting humans or obstacles in vehicular networks. In scenarios where precise statistics of the measurement noise are unavailable, applications require localization methods that assume minimal knowledge on the noise distribution. We present a scalable algorithm delimiting a tight superset of all possible target locations, assuming range measurements to known landmarks, contaminated… Read full abstract

Recommended citation: Joao Domingos, Claudia Soares, Joao Xavier, "Robust Localization With Bounded Noise: Creating a Superset of the Possible Target Positions via Linear-Fractional Representations." IEEE Transactions on Signal Processing, 2022. http://dx.doi.org/10.1109/tsp.2022.3185899

STRONG: Synchronous and asynchronous robust network localization, under non-Gaussian noise

Published in Signal Processing, 2021

Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data — data classified as outliers. Our work addresses these concerns in the scope of the sensor network localization problem where, despite the abundance of technical literature, prior research seldom considered outlier data. We propose robust, fast, and distributed network localization algorithms, resilient to high-power noise, but also precise under regular… Read full abstract

Recommended citation: Cláudia Soares, João Gomes, "STRONG: Synchronous and asynchronous robust network localization, under non-Gaussian noise." Signal Processing, 2021. http://dx.doi.org/10.1016/j.sigpro.2021.108066

Clustering of the Blendshape Facial Model

Published in 2021 29th European Signal Processing Conference (EUSIPCO), 2021

Digital human animation relies on high-quality 3D models of the human face-rigs. A face rig must be accurate and, at the same time, fast to compute. One of the most common rigging models is the blendshape model. We present a novel approach for learning the inverse rig parameters at increased accuracy and decreased computational cost at the same time. It is based on a two… Read full abstract

Recommended citation: Stevo Rackovic, Claudia Soares, Dusan Jakovetic, Zoranka Desnica, Relja Ljubobratovic, "Clustering of the Blendshape Facial Model." 2021 29th European Signal Processing Conference (EUSIPCO), 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9616061

Referral prediction in Healthcare using Graph Neural Networks

Published in GReS – Workshop on Graph Neural Networks for Recommendation and Search. Co-located with the ACM RecSys ’21 conference, 2021

Medical referrals between primary care doctors and specialists affect many aspects of patient care, such as quality of care, patient satisfaction, and health care costs. In this work, we hypothesize that the social network of medical doctors can influence the referral process. We analyse primary-specialty referrals through transaction data over medical appointments gathered between 2012 and 2017 in all hospital centers of an European private… Read full abstract

Recommended citation: REGINA DUARTE, QIWEI HAN, CLAUDIA SOARES, "Referral prediction in Healthcare using Graph Neural Networks." GReS – Workshop on Graph Neural Networks for Recommendation and Search. Co-located with the ACM RecSys ’21 conference, 2021. https://europe.naverlabs.com/wp-content/uploads/2021/09/DuarteEtAl2021.pdf

ISEE.U: Distributed online estimation and control for improved target localization accuracy

Published in Publication, 2021

This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not… Read full abstract

Recommended citation: Claudia Soares, Miguel Vasques, Joao Gomes, "ISEE.U: Distributed online estimation and control for improved target localization accuracy." Publication, 2021.

From Noisy Point Clouds to Complete Ear Shapes: Unsupervised Pipeline

Published in IEEE Access, 2021

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. The first step towards obtaining a good model is to have complete scans in correspondence, but these usually present a higher amount of occlusions, noise and outliers when compared to most face regions… Read full abstract

Recommended citation: Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti, Claudia Soares, "From Noisy Point Clouds to Complete Ear Shapes: Unsupervised Pipeline." IEEE Access, 2021. http://dx.doi.org/10.1109/access.2021.3111811

Conjunction Data Messages behave as a Poisson Process

Published in Workshop on AI for Spacecraft Longevity, 2021

Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction data messages. An important question for satellite operators is to estimate when fresh information will arrive so that they can react timely but sparingly with satellite maneuvers. We propose a statistical learning model of the message arrival process… Read full abstract

Recommended citation: Francisco Caldas, Claudia Soares, Cláudia Nunes, Marta Guimarães, Mariana Filipe, Rodrigo Ventura, "Conjunction Data Messages behave as a Poisson Process." Workshop on AI for Spacecraft Longevity, 2021. https://ai4spacecraft.github.io/papers/AI4Spacecraft_paper_2.pdf

Range and Bearing Data Fusion for Precise Convex Network Localization

Published in IEEE Signal Processing Letters, 2020

Hybrid localization in GNSS-challenged environments using measured ranges and angles is becoming increasingly popular, in particular with the advent of multimodal communication systems. Here, we address the hybrid network localization problem using ranges and bearings to jointly determine the positions of a number of agents through a single maximum-likelihood (ML) optimization problem that seamlessly fuses all the available pairwise range and angle measurements. We propose… Read full abstract

Recommended citation: Claudia Soares, Filipa Valdeira, Joao Gomes, "Range and Bearing Data Fusion for Precise Convex Network Localization." IEEE Signal Processing Letters, 2020. http://dx.doi.org/10.1109/lsp.2020.2988178

Dissecting medical referral mechanisms in health services using graph neural networks

Published in The International Conference on Complex Networks and Their Applications, 2020

The medical referrals between primary care doctor and specialist affect many aspects of patient care, such as quality of care, patient satisfaction and health care costs, etc. In this work, we hypothesize that the social network of medical doctors can influence the referral process. We analyse primary-specialty referrals through transaction data over medical appointments gathered between 2012 and 2017 in all hospital centers of a… Read full abstract

Recommended citation: Regina Duarte, Qiwei Han, Claudia Soares, "Dissecting medical referral mechanisms in health services using graph neural networks." The International Conference on Complex Networks and Their Applications, 2020. https://easychair.org/smart-program/COMPLEXNETWORKS2020/2020-12-03.html#talk:162210

Finding Common Image Semantics for Urban Perceived Safety Based on Pairwise Comparisons

Published in 2019 27th European Signal Processing Conference (EUSIPCO), 2019

What influences people’s perception of safety in an urban environment? Does everyone perceive safety the same way or do different people look for different contents in an image, safety-wise? We present a user analysis on a crowd-sourced dataset that contains pairwise comparisons regarding the perceived safety of street imagery from different municipalities in the greater Lisbon area, Portugal. We use state-of-the-art semantic segmentation to extract… Read full abstract

Recommended citation: Gabriel Costa, Claudia Soares, Manuel Marques, "Finding Common Image Semantics for Urban Perceived Safety Based on Pairwise Comparisons." 2019 27th European Signal Processing Conference (EUSIPCO), 2019. http://dx.doi.org/10.23919/eusipco.2019.8903115

Locating a target from uncertain data: convex supersets based on linear-fractional representations

Published in IEEE EUROCON 2019 -18th International Conference on Smart Technologies, 2019

We address the problem of locating a target by using measurements from a set of sensors called anchors. Each anchor has a known location and measures its distance to the target, the measurement being contaminated with additive noise. The noise vector, which contains the noise realizations across the anchors, is naturally unknown, but we assume it to be drawn from a known bounded uncertainty set.Because… Read full abstract

Recommended citation: Claudia Soares, Joao Xavie, "Locating a target from uncertain data: convex supersets based on linear-fractional representations." IEEE EUROCON 2019 -18th International Conference on Smart Technologies, 2019. http://dx.doi.org/10.1109/eurocon.2019.8861977

Fast distributed MAP inference for large-scale graphical models

Published in IEEE EUROCON 2019 -18th International Conference on Smart Technologies, 2019

In every domain of life and society, real-world data gains pull for both a more informed decision making and citizenship. Social and human phenomena carry intricate and unknown dependencies unreachable by traditional machine learning approaches, like regression or classification. How to extract value from large amounts of complex and noisy data? Assuming we know the generative model of our data, inference itself is a combinatorial… Read full abstract

Recommended citation: Claudia Soares, Joao Gomes, "Fast distributed MAP inference for large-scale graphical models." IEEE EUROCON 2019 -18th International Conference on Smart Technologies, 2019. http://dx.doi.org/10.1109/eurocon.2019.8861615

Understanding the Primary-Specialty Referral Mechanism using Network Science

Published in The International Conference on Complex Networks and Their Applications, 2019

The medical referral between Primary Care Physicians (PCP) and specialists represents the formal mechanism in the health system to address the need of patients for specialty care [5]. Typically, for a given patient with clinical needs, PCP can make a choice of several specialists to whom they may refer and their choice would have important downstream effects. As such, primary-specialty referral may affect many aspects… Read full abstract

Recommended citation: Joao Casal da Veiga, Qiwei Han, Claudia Soares, "Understanding the Primary-Specialty Referral Mechanism using Network Science." The International Conference on Complex Networks and Their Applications, 2019. https://shirshendu.ccny.cuny.edu/PDF%20Files/CPD_BookOfAbs.pdf#page=57

FLORIS and CLORIS: Hybrid source and network localization based on ranges and video

Published in Signal Processing, 2018

We propose hybrid methods for localization in wireless sensor networks fusing noisy range measurements with angular information (extracted from video). Compared with conventional methods that rely on a single sensed variable, this may pave the way for improved localization accuracy and robustness. We address both the single-source and network (i.e., cooperative multiple-source) localization paradigms, solving them via optimization of a convex surrogate. The formulations for… Read full abstract

Recommended citation: Beatriz Quintino Ferreira, João Gomes, Cláudia Soares, João P. Costeira, "FLORIS and CLORIS: Hybrid source and network localization based on ranges and video." Signal Processing, 2018. http://dx.doi.org/10.1016/j.sigpro.2018.08.003

Parameter-free network localization for mobile vehicles by learning from data

Published in 2018 Fourth Underwater Communications and Networking Conference (UComms), 2018

For any operation involving autonomous vehicles, localization of mobile agents is a critical module. Unfortunately, localization methods are still dependant on tweaked parameters, under assumptions of poor trajectories, and that can fail disgracefully under real-world conditions. As our systems evolve to deployment-ready state, we see the looming need for more adaptive responses, where sudden changes of conditions can be accommodated by the localization algorithm. This… Read full abstract

Recommended citation: Claudia Soares, Joao Gomes, "Parameter-free network localization for mobile vehicles by learning from data." 2018 Fourth Underwater Communications and Networking Conference (UComms), 2018. http://dx.doi.org/10.1109/ucomms.2018.8493177

LocDyn: Robust Distributed Localization for Mobile Underwater Networks

Published in IEEE Journal of Oceanic Engineering, 2017

How do we self-localize large teams of underwater nodes using only noisy range measurements? How do we do it in a distributed way, and incorporating dynamics into the problem? How do we reject outliers and produce trustworthy position estimates? And what if some of the vehicles can measure angular information? The stringent acoustic communication constraints and accuracy needs of our geophysical survey application demand fast… Read full abstract

Recommended citation: Claudia Soares, Joao Gomes, Beatriz Quintino Ferreira, Joao Paulo Costeira, "LocDyn: Robust Distributed Localization for Mobile Underwater Networks." IEEE Journal of Oceanic Engineering, 2017. http://dx.doi.org/10.1109/joe.2017.2736951

DIeSEL: Distributed self-localization of a network of underwater vehicles

Published in OCEANS 2017 - Anchorage, 2017

How can teams of artificial agents localize and position themselves in GPS-denied environments? How can each agent determine its position from pairwise ranges, own velocity, and limited interaction with neighbors? This paper addresses this problem from an optimization point of view: we directly optimize the nonconvex maximum-likelihood estimator in the presence of range measurements contaminated with Gaussian noise, and we obtain a provably convergent, accurate… Read full abstract

Recommended citation: Cláudia Soares, Pusheng Ji, João Gomes, António Pascoal, "DIeSEL: Distributed self-localization of a network of underwater vehicles." OCEANS 2017 - Anchorage, 2017.

Collaborative localization of vehicle formations based on ranges and bearings

Published in 2016 IEEE Third Underwater Communications and Networking Conference (UComms), 2016

We examine the problem of jointly determining the positions of multiple underwater vehicles based on a set of pairwise range and bearing measurements taken over time. This extends prior work on the so-called (static) collaborative localization paradigm where a hybrid approach was proposed for seamless instantaneous fusion (i.e., no time dependence) of range and bearing measurements. To incorporate time we add to the original convexified… Read full abstract

Recommended citation: Beatriz Quintino Ferreira, Joao Gomes, Claudia Soares, Joao P. Costeira, "Collaborative localization of vehicle formations based on ranges and bearings." 2016 IEEE Third Underwater Communications and Networking Conference (UComms), 2016. http://dx.doi.org/10.1109/ucomms.2016.7583426

Distributed and robust network localization algorithms

Published in Instituto Superior Técnico Universidade de Lisboa, 2016

Recommended citation: Claudia Soares, "Distributed and robust network localization algorithms." Instituto Superior Técnico Universidade de Lisboa, 2016. https://isr.tecnico.ulisboa.pt/publications/distributed-and-robust-network-localization-algorithms/

Simple and Fast Convex Relaxation Method for Cooperative Localization in Sensor Networks Using Range Measurements

Published in IEEE Transactions on Signal Processing, 2015

We address the sensor network localization problem given noisy range measurements between pairs of nodes. We approach the nonconvex maximum-likelihood formulation via a known simple convex relaxation. We exploit its favorable optimization properties to the full to obtain an approach that is completely distributed, has a simple implementation at each node, and capitalizes on an optimal gradient method to attain fast convergence. We offer a… Read full abstract

Recommended citation: Claudia Soares, Joao Xavier, Joao Gomes, "Simple and Fast Convex Relaxation Method for Cooperative Localization in Sensor Networks Using Range Measurements." IEEE Transactions on Signal Processing, 2015. http://dx.doi.org/10.1109/tsp.2015.2454853

Distributed, simple and stable network localization

Published in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014

We propose a simple, stable and distributed algorithm which directly optimizes the nonconvex maximum likelihood criterion for sensor network localization, with no need to tune any free parameter. We reformulate the problem to obtain a gradient Lipschitz cost; by shifting to this cost function we enable a Majorization-Minimization (MM) approach based on quadratic upper bounds that decouple across nodes; the resulting algorithm happens to be… Read full abstract

Recommended citation: Claudia Soares, Joao Xavier, Joao Gomes, "Distributed, simple and stable network localization." 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014. http://dx.doi.org/10.1109/globalsip.2014.7032222