Showing 1–50 of 72 results for author: Ortiz, A

Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model

Authors: Eloi Moliner, Christoph Hold, Juan Azcarreta Ortiz, Sebastian Prepelita, Ishwarya Ananthabhotla, Daniel Wong, Sanjeel Parekh, Sanha Lee

Abstract: We address the problem of encoding room impulse responses (RIRs) into high-order Ambisonics (HOA) representations from arbitrary and potentially insufficient or incomplete microphone array measurements. This task is fundamentally ill-posed for microphone arrays with limited spatial capture capabilities, such as irregular or sparse arrays, as classical linear methods fail to reconstruct high-order… ▽ More We address the problem of encoding room impulse responses (RIRs) into high-order Ambisonics (HOA) representations from arbitrary and potentially insufficient or incomplete microphone array measurements. This task is fundamentally ill-posed for microphone arrays with limited spatial capture capabilities, such as irregular or sparse arrays, as classical linear methods fail to reconstruct high-order spatial detail. We introduce a diffusion-based generative framework that models the statistical properties of HOA RIRs. This enables device-agnostic encoding from arbitrary microphone arrays, potentially unseen during data measurement. Our approach incorporates a posterior sampling procedure that enforces consistency between the estimated signals and the measurements while plausibly reconstructing spatial information that is unobservable from the limited measurements alone. Experiments on simulated data demonstrate that our method outperforms linear and neural baselines, achieving accurate HOA RIR estimation up to 12th order. A listening test with binaural renderings, including both simulated and measured RIRs, further confirms that the proposed method yields higher perceptual similarity to reference Ambisonics RIRs than all baselines. The flexibility and accuracy of the proposed framework opens new possibilities for scalable acoustics simulations. △ Less

Submitted 17 August, 2026; v1 submitted 14 August, 2026; originally announced August 2026.

Comments: IWAENC 2026

Threshold-Based Spiking Neural Networks for Event-Driven Status Update Systems

Authors: Marco Fries, Andrea Ortiz

Abstract: Event-driven sensing supports energy-efficient Internet-of-Things (IoT) devices by activating communication only when relevant events occur. In such systems, transmission decisions are governed by the monitored process rather than predefined schedules. Consequently, jointly optimising information freshness and energy consumption is challenging because transmission decisions are restricted to rando… ▽ More Event-driven sensing supports energy-efficient Internet-of-Things (IoT) devices by activating communication only when relevant events occur. In such systems, transmission decisions are governed by the monitored process rather than predefined schedules. Consequently, jointly optimising information freshness and energy consumption is challenging because transmission decisions are restricted to randomly occurring events. To address this challenge, we investigate an event-driven status update system in which wake-up events follow the dynamics of the monitored process. The problem of determining whether to transmit the sensing data or not is cast as a Markov Decision Process (MDP) that jointly minimises the Age of Information (AoI) and transmission energy. We prove the existence of an optimal threshold policy, thereby obtaining an interpretable characterisation of the optimal transmission strategy. Motivated by this result, we propose a lightweight Reinforcement Learning (RL) approach based on Spiking Neural Networks (SNNs) whose architecture explicitly represents threshold policies. The resulting policy representation has constant complexity with respect to the maximum AoI and enables a more energy-efficient implementation than a comparable Artificial Neural Network (ANN). Numerical results demonstrate that the proposed SNN reliably learns optimal thresholds across different operating regimes. △ Less

Submitted 11 August, 2026; originally announced August 2026.

Comments: Eight pages (including preprint notice). Four figures. Two tables

HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment

Authors: Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Cameron Birge, Meygha Machado, Marcelo Duarte, Joaquin Rivero Rodriguez, Anthony Cintron Roman, Kevin White, Inbal Becker-Reshef, Juan M. Lavista Ferres

Abstract: When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first day. We present HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web p… ▽ More When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first day. We present HASTE (High-speed Assessment and Satellite Tracking for Emergencies), a no-code web platform that lets analysts who are not machine learning engineers produce per-building damage maps from post-disaster satellite imagery. HASTE implements two methods that share one interface. The first requires the user to label polygons over the post-disaster scene, trains a small semantic segmentation model on that single scene, runs it over the whole image, and joins the per-pixel output to existing building footprints. The second embeds every footprint with a pretrained vision model, requires the user to label a handful of buildings, and fits a logistic regression in the browser that scores the rest of the scene in seconds. We describe the platform, both methods, and the engineering that supports them. We also report preliminary experiments on xBD showing that foundation-model embeddings pooled over footprints separate damaged from intact buildings using post-disaster imagery alone, matching a fully supervised ResNet-50 baseline with a twentieth of its labels. HASTE and its predecessors have supported more than thirty real-world disaster responses since 2023, spanning earthquakes, hurricanes, cyclones, floods, wildfires, and tornadoes, delivering results to humanitarian partners within hours to days of imagery becoming available. We close with the directions we think are most promising, including vision-language assessment, active learning, and damage models for roads and other infrastructure. HASTE is open source at https://github.com/microsoft/haste. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Resilience Analysis in Off-Grid LoRa Mesh Networks: Evaluation of Meshtastic Profiles in Long-Range Propagation Scenarios

Authors: Guillermo Antonio Hernandez Ortiz, Edgar Santiago Quiroz Puentes, José de Jesús Rugeles

Abstract: The integration of LoRa technologies with mesh topologies represents a robust alternative for off-grid communications in emergency scenarios within smart cities. Meshtastic firmware implements a decentralised mesh network over LoRa where each node acts simultaneously as end device and router, enabling communication via Bluetooth-connected mobile devices without reliance on conventional infrastruct… ▽ More The integration of LoRa technologies with mesh topologies represents a robust alternative for off-grid communications in emergency scenarios within smart cities. Meshtastic firmware implements a decentralised mesh network over LoRa where each node acts simultaneously as end device and router, enabling communication via Bluetooth-connected mobile devices without reliance on conventional infrastructure. Within the Colombian context (915 MHz ISM band), this work establishes design and planning criteria through a controlled guided-link methodology that isolates the LoRa physical layer from propagation effects, enabling deterministic characterisation of all eight Meshtastic modem presets at three transmission power levels (42 datasets). The results reveal a performance partitioning governed primarily by Spreading Factor (SF): "Short" presets (SF7-SF8) fail at 110-120 dB of path attenuation, "Medium" presets (SF9-SF10) sustain links up to 135-150 dB, and "Long" presets (SF11-SF12) maximise coverage, with "Long Slow" reaching 180 dB before failure - a 60-70 dB advantage over the fastest profiles. The SNR analysis confirms sub-noise-floor demodulation down to -18 dB for SF12, with abrupt link failure occurring within 2-4 dB of the theoretical limit. Based on these thresholds, three operational regimes are defined (high-density IoT, balanced urban mesh, and maximum-range emergency), providing network designers with quantitative criteria to select the appropriate configuration and node density for smart city deployments. △ Less

Submitted 16 May, 2026; originally announced May 2026.

Comments: 18 pages, 5 tables, 4 figures. To appear in the proceedings of CATAI 2026. Workshop website: https://www.catai.fr/catai2026.html

Dynamic Hypergame for Task Assignment in Multi-platform Mobile Crowdsensing Under Incomplete Information

Authors: Sumedh J. Dongare, Christo Kurisummoottil Thomas, Andrea Ortiz, Walid Saad, Anja Klein

Abstract: Mobile crowdsensing (MCS) is a promising distributed sensing paradigm for future wireless networks, where MCS platforms (MCSPs) recruit mobile units (MUs) through monetary incentives for sensing data collection. While most existing studies assume a single MCSP, practical deployments involve multiple competing MCSPs that simultaneously propose task offers to MUs, and MUs accept offers that maximize… ▽ More Mobile crowdsensing (MCS) is a promising distributed sensing paradigm for future wireless networks, where MCS platforms (MCSPs) recruit mobile units (MUs) through monetary incentives for sensing data collection. While most existing studies assume a single MCSP, practical deployments involve multiple competing MCSPs that simultaneously propose task offers to MUs, and MUs accept offers that maximize their revenue. This interaction gives rise to a two-sided matching game with contracts (MWC), decomposed into two components: (i) task proposal problem of the MCSPs and (ii) task acceptance problem of the MUs. To optimally solve (i), every MCSP requires information about other platforms' preferences and the qualities of the MUs in advance. Similarly, to solve (ii) optimally, the MUs require information about the task execution efforts of all tasks in advance. Such information is unavailable at the MCSPs and at the MUs. To address the challenge of unknown preferences of the other MCSPs, the MWC is posed as a dynamic hypergame, where every MCSP models the unknown preferences through perceptions and refines them over repeated interactions. To solve the dynamic hypergame under incomplete information, we propose PACMAB, a fully decentralized perception-aware two-sided learning framework where, (i) each MCSP learns an adaptive task proposal strategy under competition, and (ii) each MU learns task acceptance policy by estimating task execution efforts. Computational complexity of PACMAB shows that it scales favorably for the MCSPs as well as the MUs. Extensive simulations show that PACMAB consistently outperforms the benchmarks by completing at least 41% more tasks without assuming complete information. △ Less

Submitted 5 May, 2026; originally announced May 2026.

Comments: This work has been submitted to the IEEE for possible publication

Federated Reinforcement Learning for Efficient Mobile Crowdsensing under Incomplete Information

Authors: Sumedh J. Dongare, Patrick Weber, Andrea Ortiz, Walid Saad, Oliver Hinz, Anja Klein

Abstract: Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide whether to participate in exchange for money. The MCS system is dynamic: the task requirements, the MUs' availability, and their available resources change over time. The M… ▽ More Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide whether to participate in exchange for money. The MCS system is dynamic: the task requirements, the MUs' availability, and their available resources change over time. The MUs aim to find an efficient task participation strategy to maximize their income while the MCSP focuses on maximizing the number of completed tasks. As optimal strategies require perfect non-causal information about the MCS system, which is unavailable in realistic scenarios, the main challenge is to find an efficient task participation strategy for the MUs under incomplete information. To this end, a novel fully decentralized federated deep reinforcement learning algorithm, FDRL-PPO, is proposed. FDRL-PPO enables every MU to learn its own task participation strategy based on its experiences, available resources, and preferences, without relying on perfect non-causal information about the MCS system. To replenish their batteries, the MUs rely on energy harvesting. As a result, their available energy varies over time, leading to varying availability and fragmented learning experiences. To mitigate these challenges, the proposed approach leverages federated learning, enabling MUs to collaboratively improve their models without sharing private raw data like their own experiences. By exchanging only learned models, MUs collectively compensate for individual limitations, and find more scalable, robust, and efficient task participation strategies. Comprehensive evaluations on both synthetic and real-world datasets show that FDRL-PPO consistently outperforms benchmark algorithms in terms of task completion ratio, fairness in task completion, energy consumption, and number of conflicting proposals. △ Less

Submitted 4 May, 2026; originally announced May 2026.

Comments: This work has been submitted to the IEEE for possible publication

A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation

Authors: Yabin Zhang, Chong Wang, Yunhe Gao, Jiaming Liu, Maya Varma, Justin Xu, Sophie Ostmeier, Jin Long, Sergios Gatidis, Seena Dehkharghani, Arne Michalson, Eun Kyoung Hong, Christian Bluethgen, Haiwei Henry Guo, Alexander Victor Ortiz, Stephan Altmayer, Sandhya Bodapati, Joseph David Janizek, Ken Chang, Jean-Benoit Delbrouck, Akshay S. Chaudhari, Curtis P. Langlotz

Abstract: Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic errors. Although artificial intelligence (AI) systems have shown promise for CXR interpretation, most generate only final predictions, without making explicit how visual evidence is translated into radiographic findings and… ▽ More Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic errors. Although artificial intelligence (AI) systems have shown promise for CXR interpretation, most generate only final predictions, without making explicit how visual evidence is translated into radiographic findings and diagnostic predictions. We present CheXOne, a reasoning-enabled vision-language model for CXR interpretation. CheXOne jointly generates diagnostic predictions and explicit, clinically grounded reasoning traces that connect visual evidence, radiographic findings, and these predictions. The model is trained on 14.7 million instruction and reasoning samples curated from 30 public datasets spanning 36 CXR interpretation tasks, using a two-stage framework that combines instruction tuning with reinforcement learning to improve reasoning quality. We evaluate CheXOne in zero-shot settings across visual question answering, report generation, visual grounding and reasoning assessment, covering 17 evaluation settings. CheXOne outperforms existing medical and general-domain foundation models and achieves strong performance on independent public benchmarks. A clinical reader study demonstrates that CheXOne-drafted reports are comparable to or better than resident-written reports in 55% of cases, while effectively addressing clinical indications and enhancing both report writing and CXR interpretation efficiency. Further analyses involving radiologists reveal that the generated reasoning traces show high clinical factuality and provide causal support for the final predictions, offering a plausible explanation for the performance gains. These results suggest that explicit reasoning can improve model performance, interpretability and clinical utility in AI-assisted CXR interpretation. △ Less

Submitted 1 April, 2026; originally announced April 2026.

Comments: Codes: https://github.com/YBZh/CheXOne Models: https://huggingface.co/StanfordAIMI/CheXOne

DualFlexKAN: Dual-stage Kolmogorov-Arnold Networks with Independent Function Control

Authors: Andrés Ortiz, Nicolás J. Gallego-Molina, Carmen Jiménez-Mesa, Juan M. Górriz, Javier Ramírez

Abstract: Multi-Layer Perceptrons (MLPs) rely on pre-defined, fixed activation functions, imposing a static inductive bias that forces the network to approximate complex topologies solely through increased depth and width. Kolmogorov-Arnold Networks (KANs) address this limitation through edge-centric learnable functions, yet their formulation suffers from quadratic parameter scaling and architectural rigidi… ▽ More Multi-Layer Perceptrons (MLPs) rely on pre-defined, fixed activation functions, imposing a static inductive bias that forces the network to approximate complex topologies solely through increased depth and width. Kolmogorov-Arnold Networks (KANs) address this limitation through edge-centric learnable functions, yet their formulation suffers from quadratic parameter scaling and architectural rigidity that hinders the effective integration of standard regularization techniques. This paper introduces the DualFlexKAN (DFKAN), a flexible architecture featuring a dual-stage mechanism that independently controls pre-linear input transformations and post-linear output activations. This decoupling enables hybrid networks that optimize the trade-off between expressiveness and computational cost. Unlike standard formulations, DFKAN supports diverse basis function families, including orthogonal polynomials, B-splines, and radial basis functions, integrated with configurable regularization strategies that stabilize training dynamics. Comprehensive evaluations across regression benchmarks, physics-informed tasks, and function approximation demonstrate that DFKAN outperforms both MLPs and conventional KANs in accuracy, convergence speed, and gradient fidelity. The proposed hybrid configurations achieve superior performance with one to two orders of magnitude fewer parameters than standard KANs, effectively mitigating the parameter explosion problem while preserving KAN-style expressiveness. DFKAN provides a principled, scalable framework for incorporating adaptive non-linearities, proving particularly advantageous for data-efficient learning and interpretable function discovery in scientific applications. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Comments: 22 pages, 12 figures

Deep Sleep Scheduling for Satellite IoT via Simulation Based Optimization

Authors: Wanja de Sombre, Monika Tomová, Marek Galinski, Anja Klein, Andrea Ortiz

Abstract: The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep du… ▽ More The Satellite Internet of Things (S-IoT) enables global connectivity for remote sensing devices that must operate energy-efficiently over long time spans. We consider an S-IoT system consisting of a sender-receiver pair connected by a data channel and a feedback channel and capture its dynamics using a Markov Decision Process (MDP). To extend battery life, the sender has to decide on deep-sleep durations. Deep-sleep scheduling is the primary lever to reduce energy consumption, since sleeping devices consume only a fraction of their idle power. By choosing its deep-sleep duration online, the sender has to find a trade-off between energy consumption and data quality degradation at the receiver, captured by a weighted sum of costs. We quantify data quality degradation via the recently introduced Goal-Oriented Tensor (GoT) metric, which can take both age and content of delivered data into account. We assume a Markovian observed process and Markov channels with time-varying delay and erasure rates. The challenge is that content awareness of the GoT metric makes periodic transmissions inherently inefficient. Additionally, optimal sleep durations depends on the (unknown) future states of the observed process and the channels, both of which must be inferred online. We propose a novel algorithm using probabilistic simulation-based optimization (PSBO). With PSBO, the sensor forecasts future states based on estimated transition probabilities, and uses these forecasts to select the optimal deep-sleep duration. Extensive simulations and experiments with S-IoT hardware demonstrate superior performance of PSBO under diverse conditions. △ Less

Submitted 19 May, 2026; v1 submitted 27 February, 2026; originally announced February 2026.

Sound Event Detection with Boundary-Aware Optimization and Inference

Authors: Florian Schmid, Chi Ian Tang, Sanjeel Parekh, Vamsi Krishna Ithapu, Juan Azcarreta Ortiz, Giacomo Ferroni, Yijun Qian, Arnoldas Jasonas, Cosmin Frateanu, Camilla Clark, Gerhard Widmer, Çağdaş Bilen

Abstract: Temporal detection problems appear in many fields including time-series estimation, activity recognition and sound event detection (SED). In this work, we propose a new approach to temporal event modeling by explicitly modeling event onsets and offsets, and by introducing boundary-aware optimization and inference strategies that substantially enhance temporal event detection. The presented methodo… ▽ More Temporal detection problems appear in many fields including time-series estimation, activity recognition and sound event detection (SED). In this work, we propose a new approach to temporal event modeling by explicitly modeling event onsets and offsets, and by introducing boundary-aware optimization and inference strategies that substantially enhance temporal event detection. The presented methodology incorporates new temporal modeling layers - Recurrent Event Detection (RED) and Event Proposal Network (EPN) - which, together with tailored loss functions, enable more effective and precise temporal event detection. We evaluate the proposed method in the SED domain using a subset of the temporally-strongly annotated portion of AudioSet. Experimental results show that our approach not only outperforms traditional frame-wise SED models with state-of-the-art post-processing, but also removes the need for post-processing hyperparameter tuning, and scales to achieve new state-of-the-art performance across all AudioSet Strong classes. △ Less

Submitted 6 June, 2026; v1 submitted 7 January, 2026; originally announced January 2026.

Comments: Accepted for publication in IEEE Signal Processing Letters, 2026

SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation

Authors: Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Eric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar, Surya Parimi, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux

Abstract: Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-supervised speech models. To address this gap, this paper introduces SpidR-Adapt for rapid adaptation of speech units to new languages using minimal unlabeled data. We cast such low-resource speech representation learning as… ▽ More Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-supervised speech models. To address this gap, this paper introduces SpidR-Adapt for rapid adaptation of speech units to new languages using minimal unlabeled data. We cast such low-resource speech representation learning as a meta-learning problem and construct a multi-task adaptive pre-training (MAdaPT) protocol which formulates the adaptation process as a bi-level optimization framework. To enable scalable meta-training under this framework, we propose a novel heuristic solution, first-order bi-level optimization (FOBLO), avoiding heavy computation costs. Finally, we stabilize meta-training by using a robust initialization through interleaved supervision which alternates self-supervised and supervised objectives. Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability (ABX) and downstream spoken language modeling scores (sWUGGY, sBLIMP, tSC), surpassing in-domain toplines after training on less than 1h of target-language audio and delivering $100\times$ greater data efficiency than standard multi-task training. These findings highlight a practical, architecture-agnostic path toward biologically inspired, data-efficient representations. We open-source the training code and model checkpoints at https://github.com/facebookresearch/spidr-adapt. △ Less

Submitted 20 April, 2026; v1 submitted 24 December, 2025; originally announced December 2025.

Geopolitics, Geoeconomics, and Sovereign Risk: Different Shocks, Different Channels

Authors: Alvaro Ortiz, Tomasa Rodrigo, Pablo Saborido

Abstract: Geopolitical and geoeconomic shocks reprice sovereign credit risk through different transmission channels. Using a daily panel of 42 advanced and emerging economies over 2018--2025, we show that geopolitical shocks raise sovereign CDS spreads primarily through direct sovereign repricing, while the Global Financial Cycle (GFC) channel moves in the opposite direction and partly offsets that increase… ▽ More Geopolitical and geoeconomic shocks reprice sovereign credit risk through different transmission channels. Using a daily panel of 42 advanced and emerging economies over 2018--2025, we show that geopolitical shocks raise sovereign CDS spreads primarily through direct sovereign repricing, while the Global Financial Cycle (GFC) channel moves in the opposite direction and partly offsets that increase -- a ``scissors pattern.'' Geoeconomic shocks, by contrast, transmit mainly through financial conditions, policy uncertainty, and domestic amplification, with only a limited direct repricing component. A semistructural framework provides sign benchmarks for four transmission channels, and a Shapley--Taylor decomposition of nonlinear machine-learning predictions partitions each observation's spread into Direct, GFC, Uncertainty, and Local components. Narrative local projections around four dated crisis events recover the scissors pattern for Russia--Ukraine and support the broader channel taxonomy in the remaining episodes. Additional scorecard, placebo, and sign-restricted SVAR evidence corroborates the taxonomy beyond the baseline ML decomposition. Geopolitical direct effects decay with distance from the conflict zone in a gravity-style pattern (R2 = 0.35 for Russia--Ukraine), while policy-uncertainty shocks activate the Uncertainty channel more globally. The taxonomy implies that liquidity provision can mitigate GFC-driven spread widening, but not direct geopolitical sovereign repricing. △ Less

Submitted 24 March, 2026; v1 submitted 14 October, 2025; originally announced October 2025.

Graph-Radiomic Learning (GrRAiL) Descriptor to Characterize Imaging Heterogeneity in Confounding Tumor Pathologies

Authors: Dheerendranath Battalapalli, Apoorva Safai, Maria Jaramillo, Hyemin Um, Gustavo Adalfo Pineda Ortiz, Ulas Bagci, Manmeet Singh Ahluwalia, Marwa Ismail, Pallavi Tiwari

Abstract: A significant challenge in solid tumors is reliably distinguishing confounding pathologies from malignant neoplasms on routine imaging. While radiomics methods seek surrogate markers of lesion heterogeneity on CT/MRI, many aggregate features across the region of interest (ROI) and miss complex spatial relationships among varying intensity compositions. We present a new Graph-Radiomic Learning (GrR… ▽ More A significant challenge in solid tumors is reliably distinguishing confounding pathologies from malignant neoplasms on routine imaging. While radiomics methods seek surrogate markers of lesion heterogeneity on CT/MRI, many aggregate features across the region of interest (ROI) and miss complex spatial relationships among varying intensity compositions. We present a new Graph-Radiomic Learning (GrRAiL) descriptor for characterizing intralesional heterogeneity (ILH) on clinical MRI scans. GrRAiL (1) identifies clusters of sub-regions using per-voxel radiomic measurements, then (2) computes graph-theoretic metrics to quantify spatial associations among clusters. The resulting weighted graphs encode higher-order spatial relationships within the ROI, aiming to reliably capture ILH and disambiguate confounding pathologies from malignancy. To assess efficacy and clinical feasibility, GrRAiL was evaluated in n=947 subjects spanning three use cases: differentiating tumor recurrence from radiation effects in glioblastoma (GBM; n=106) and brain metastasis (n=233), and stratifying pancreatic intraductal papillary mucinous neoplasms (IPMNs) into no+low vs high risk (n=608). In a multi-institutional setting, GrRAiL consistently outperformed state-of-the-art baselines - Graph Neural Networks (GNNs), textural radiomics, and intensity-graph analysis. In GBM, cross-validation (CV) and test accuracies for recurrence vs pseudo-progression were 89% and 78% with >10% test-accuracy gains over comparators. In brain metastasis, CV and test accuracies for recurrence vs radiation necrosis were 84% and 74% (>13% improvement). For IPMN risk stratification, CV and test accuracies were 84% and 75%, showing >10% improvement. △ Less

Submitted 23 September, 2025; originally announced September 2025.

Comments: Under Review: npj Digital Medicine

SKYLINK: Scalable and Resilient Link Management in LEO Satellite Network

Authors: Wanja de Sombre, Arash Asadi, Debopam Bhattacherjee, Deepak Vasisht, Andrea Ortiz

Abstract: The rapid growth of space-based services has established LEO satellite networks as a promising option for global broadband connectivity. Next-generation LEO networks leverage inter-satellite links (ISLs) to provide faster and more reliable communications compared to traditional bent-pipe architectures, even in remote regions. However, the high mobility of satellites, dynamic traffic patterns, and… ▽ More The rapid growth of space-based services has established LEO satellite networks as a promising option for global broadband connectivity. Next-generation LEO networks leverage inter-satellite links (ISLs) to provide faster and more reliable communications compared to traditional bent-pipe architectures, even in remote regions. However, the high mobility of satellites, dynamic traffic patterns, and potential link failures pose significant challenges for efficient and resilient routing. To address these challenges, we model the LEO satellite network as a time-varying graph comprising a constellation of satellites and ground stations. Our objective is to minimize a weighted sum of average delay and packet drop rate. Each satellite independently decides how to distribute its incoming traffic to neighboring nodes in real time. Given the infeasibility of finding optimal solutions at scale, due to the exponential growth of routing options and uncertainties in link capacities, we propose SKYLINK, a novel fully distributed learning strategy for link management in LEO satellite networks. SKYLINK enables each satellite to adapt to the time-varying network conditions, ensuring real-time responsiveness, scalability to millions of users, and resilience to network failures, while maintaining low communication overhead and computational complexity. To support the evaluation of SKYLINK at global scale, we develop a new simulator for large-scale LEO satellite networks. For 25.4 million users, SKYLINK reduces the weighted sum of average delay and drop rate by 29% compared to the bent-pipe approach, and by 92% compared to Dijkstra. It lowers drop rates by 95% relative to k-shortest paths, 99% relative to Dijkstra, and 74% compared to the bent-pipe baseline, while achieving up to 46% higher throughput. At the same time, SKYLINK maintains constant computational complexity with respect to constellation size. △ Less

Submitted 19 May, 2026; v1 submitted 10 September, 2025; originally announced September 2025.

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data

Authors: Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse, Tammy Glazer, Gilles Hacheme, Anthony Ortiz, Rahul M Dodhia, Juan M Lavista Ferres

Abstract: Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot keep modern GPUs utilized when streaming GeoTIFF files directly from cloud storage. In this work, we benchmark GeoTIFF loading throughput from both cloud object storage and local SSD, systematically testing different load… ▽ More Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot keep modern GPUs utilized when streaming GeoTIFF files directly from cloud storage. In this work, we benchmark GeoTIFF loading throughput from both cloud object storage and local SSD, systematically testing different loader configurations and data parameters. We focus on tile-aligned reads and worker thread pools, using Bayesian optimization to find optimal settings for each storage type. Our optimized configurations increase remote data loading throughput by 20x and local throughput by 4x compared to default settings. On three public EO benchmarks, models trained with optimized remote loading achieve the same accuracy as local training within identical time budgets. We improve validation IoU by 6-15% and maintain 85-95% GPU utilization versus 0-30% with standard configurations. Code is publicly available at https://github.com/microsoft/pytorch-cloud-geotiff-optimization △ Less

Submitted 6 June, 2025; originally announced June 2025.

Core-Set Selection for Data-efficient Land Cover Segmentation

Authors: Keiller Nogueira, Akram Zaytar, Wanli Ma, Ribana Roscher, Ronny Hansch, Caleb Robinson, Anthony Ortiz, Simone Nsutezo, Rahul Dodhia, Juan M. Lavista Ferres, Oktay Karakus, Paul L. Rosin

Abstract: The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth Observation tasks. Traditionally, such models rely on large datasets. However, the common assumption that larger training datasets lead to better performance tends to overlook issues related to data redundancy, noise, and… ▽ More The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth Observation tasks. Traditionally, such models rely on large datasets. However, the common assumption that larger training datasets lead to better performance tends to overlook issues related to data redundancy, noise, and the computational cost of processing massive datasets. Effective solutions must therefore consider not only the quantity but also the quality of data. Towards this, in this paper, we introduce six basic core-set selection approaches -- that rely on imagery only, labels only, or a combination of both -- and investigate whether they can identify high-quality subsets of data capable of maintaining -- or even surpassing -- the performance achieved when using full datasets for remote sensing semantic segmentation. We benchmark such approaches against two traditional baselines on three widely used land-cover classification datasets (DFC2022, Vaihingen, and Potsdam) using two different architectures (SegFormer and U-Net), thus establishing a general baseline for future works. Our experiments show that all proposed methods consistently outperform the baselines across multiple subset sizes, with some approaches even selecting core sets that surpass training on all available data. Notably, on DFC2022, a selected subset comprising only 25% of the training data yields slightly higher SegFormer performance than training with the entire dataset. This result shows the importance and potential of data-centric learning for the remote sensing domain. The code is available at https://github.com/keillernogueira/data-centric-rs-classification/. △ Less

Submitted 18 December, 2025; v1 submitted 2 May, 2025; originally announced May 2025.

A Human Digital Twin Architecture for Knowledge-based Interactions and Context-Aware Conversations

Authors: Abdul Mannan Mohammed, Azhar Ali Mohammad, Jason A. Ortiz, Carsten Neumann, Grace Bochenek, Dirk Reiners, Carolina Cruz-Neira

Abstract: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) are creating new opportunities for Human-Autonomy Teaming (HAT) in tasks, missions, and continuous coordinated activities. A major challenge is enabling humans to maintain awareness and control over autonomous assets, while also building trust and supporting shared contextual understanding. To address this, we present a… ▽ More Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) are creating new opportunities for Human-Autonomy Teaming (HAT) in tasks, missions, and continuous coordinated activities. A major challenge is enabling humans to maintain awareness and control over autonomous assets, while also building trust and supporting shared contextual understanding. To address this, we present a real-time Human Digital Twin (HDT) architecture that integrates Large Language Models (LLMs) for knowledge reporting, answering, and recommendation, embodied in a visual interface. The system applies a metacognitive approach to enable personalized, context-aware responses aligned with the human teammate's expectations. The HDT acts as a visually and behaviorally realistic team member, integrated throughout the mission lifecycle, from training to deployment to after-action review. Our architecture includes speech recognition, context processing, AI-driven dialogue, emotion modeling, lip-syncing, and multimodal feedback. We describe the system design, performance metrics, and future development directions for more adaptive and realistic HAT systems. △ Less

Submitted 3 April, 2025; originally announced April 2025.

Comments: Presented at: 2024 Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Paper No. 24366, 10 pages, 5 figures

What Can 240,000 New Credit Transactions Tell Us About the Impact of NGEU Funds?

Authors: Alvaro Ortiz, Tomasa Rodrigo, David Sarasa, Sirenia Vazquez

Abstract: Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and v… ▽ More Using a panel data local projections model and controlling for firm characteristics, procurement bid attributes, and macroeconomic conditions, the study estimates the dynamic effects of procurement awards on new lending, a more precise measure than the change in the stock of credit. The analysis further examines heterogeneity in credit responses based on firm size, industry, credit maturity, and value chain position of the firms. The empirical evidence confirms that public procurement awards significantly increase new lending, with NGEU-funded contracts generating stronger credit expansion than traditional procurement during the recent period. The results show that the impact of NGEU procurement programs aligns closely with historical procurement impacts, with differences driven mainly by lower utilization rates. Moreover, integrating high-frequency financial data with procurement records highlights the potential of Big Data in refining public policy design. △ Less

Submitted 4 April, 2025; v1 submitted 16 March, 2025; originally announced April 2025.

Global Renewables Watch: A Temporal Dataset of Solar and Wind Energy Derived from Satellite Imagery

Authors: Caleb Robinson, Anthony Ortiz, Allen Kim, Rahul Dodhia, Andrew Zolli, Shivaprakash K Nagaraju, James Oakleaf, Joe Kiesecker, Juan M. Lavista Ferres

Abstract: We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, t… ▽ More We present a comprehensive global temporal dataset of commercial solar photovoltaic (PV) farms and onshore wind turbines, derived from high-resolution satellite imagery analyzed quarterly from the fourth quarter of 2017 to the second quarter of 2024. We create this dataset by training deep learning-based segmentation models to identify these renewable energy installations from satellite imagery, then deploy them on over 13 trillion pixels covering the world. For each detected feature, we estimate the construction date and the preceding land use type. This dataset offers crucial insights into progress toward sustainable development goals and serves as a valuable resource for policymakers, researchers, and stakeholders aiming to assess and promote effective strategies for renewable energy deployment. Our final spatial dataset includes 375,197 individual wind turbines and 86,410 solar PV installations. We aggregate our predictions to the country level -- estimating total power capacity based on construction date, solar PV area, and number of windmills -- and find an $r^2$ value of $0.96$ and $0.93$ for solar PV and onshore wind respectively compared to IRENA's most recent 2023 country-level capacity estimates. △ Less

Submitted 18 March, 2025; originally announced March 2025.

FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing

Authors: Isaac Corley, Simone Fobi Nsutezo, Anthony Ortiz, Caleb Robinson, Rahul Dodhia, Juan M. Lavista Ferres, Peyman Najafirad

Abstract: Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining performant encoders for downstream tasks. However, while contrastive image-text methods like CLIP enable vision-language alignment and zero-shot classification ability, vision-only downstream performance tends to degrade compar… ▽ More Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining performant encoders for downstream tasks. However, while contrastive image-text methods like CLIP enable vision-language alignment and zero-shot classification ability, vision-only downstream performance tends to degrade compared to image-only pretraining, such as MAE. In this paper, we propose FLAVARS, a pretraining method that combines the best of both contrastive learning and masked modeling, along with geospatial alignment via contrastive location encoding. We find that FLAVARS significantly outperforms a baseline of SkyCLIP for vision-only tasks such as KNN classification and semantic segmentation, +6\% mIOU on SpaceNet1, while retaining the ability to perform zero-shot classification, unlike MAE pretrained methods. △ Less

Submitted 14 January, 2025; originally announced January 2025.

PGRID: Power Grid Reconstruction in Informal Developments Using High-Resolution Aerial Imagery

Authors: Simone Fobi Nsutezo, Amrita Gupta, Duncan Kebut, Seema Iyer, Luana Marotti, Rahul Dodhia, Juan M. Lavista Ferres, Anthony Ortiz

Abstract: As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on trad… ▽ More As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate, with 8.7 million residing in refugee camps. A critical issue faced by these populations is the lack of access to electricity, with 80% of the 8.7 million refugees and displaced persons in camps globally relying on traditional biomass for cooking and lacking reliable power for essential tasks such as cooking and charging phones. Often, the burden of collecting firewood falls on women and children, who frequently travel up to 20 kilometers into dangerous areas, increasing their vulnerability.[7] Electricity access could significantly alleviate these challenges, but a major obstacle is the lack of accurate power grid infrastructure maps, particularly in resource-constrained environments like refugee camps, needed for energy access planning. Existing power grid maps are often outdated, incomplete, or dependent on costly, complex technologies, limiting their practicality. To address this issue, PGRID is a novel application-based approach, which utilizes high-resolution aerial imagery to detect electrical poles and segment electrical lines, creating precise power grid maps. PGRID was tested in the Turkana region of Kenya, specifically the Kakuma and Kalobeyei Camps, covering 84 km2 and housing over 200,000 residents. Our findings show that PGRID delivers high-fidelity power grid maps especially in unplanned settlements, with F1-scores of 0.71 and 0.82 for pole detection and line segmentation, respectively. This study highlights a practical application for leveraging open data and limited labels to improve power grid mapping in unplanned settlements, where the growing number of displaced persons urgently need sustainable energy infrastructure solutions. △ Less

Submitted 10 December, 2024; originally announced December 2024.

Comments: Accepted to WACV 2025 IEEE/CVF Winter Conference

Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants

Authors: Ibai Ramirez, Joel Pino, David Pardo, Mikel Sanz, Luis del Rio, Alvaro Ortiz, Kateryna Morozovska, Jose I. Aizpurua

Abstract: Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is… ▽ More Transformers are crucial for reliable and efficient power system operations, particularly in supporting the integration of renewable energy. Effective monitoring of transformer health is critical to maintain grid stability and performance. Thermal insulation ageing is a key transformer failure mode, which is generally tracked by monitoring the hotspot temperature (HST). However, HST measurement is complex, costly, and often estimated from indirect measurements. Existing HST models focus on space-agnostic thermal models, providing worst-case HST estimates. This article introduces a spatio-temporal model for transformer winding temperature and ageing estimation, which leverages physics-based partial differential equations (PDEs) with data-driven Neural Networks (NN) in a Physics Informed Neural Networks (PINNs) configuration to improve prediction accuracy and acquire spatio-temporal resolution. The computational accuracy of the PINN model is improved through the implementation of the Residual-Based Attention (PINN-RBA) scheme that accelerates the PINN model convergence. The PINN-RBA model is benchmarked against self-adaptive attention schemes and classical vanilla PINN configurations. For the first time, PINN based oil temperature predictions are used to estimate spatio-temporal transformer winding temperature values, validated through PDE numerical solution and fiber optic sensor measurements. Furthermore, the spatio-temporal transformer ageing model is inferred, which supports transformer health management decision-making. Results are validated with a distribution transformer operating on a floating photovoltaic power plant. △ Less

Submitted 3 October, 2024; v1 submitted 10 May, 2024; originally announced May 2024.

Comments: 23 pages, 18 figures

Journal ref: Engineering Applications of Artificial Intelligence 139, 109556 (2025)

ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing

Authors: Álvaro López-Chilet, Zhaoyi Liu, Jon Ander Gómez, Carlos Alvarez, Marivi Alonso Ortiz, Andres Orejuela Mesa, David Newton, Friedrich Wolf-Monheim, Sam Michiels, Danny Hughes

Abstract: This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configu… ▽ More This paper proposes a method for Acoustic Constrained Segmentation (ACS) in audio recordings of vehicles driven through a production test track, delimiting the boundaries of surface types in the track. ACS is a variant of classical acoustic segmentation where the sequence of labels is known, contiguous and invariable, which is especially useful in this work as the test track has a standard configuration of surface types. The proposed ConvDTW-ACS method utilizes a Convolutional Neural Network for classifying overlapping image chunks extracted from the full audio spectrogram. Then, our custom Dynamic Time Warping algorithm aligns the sequence of predicted probabilities to the sequence of surface types in the track, from which timestamps of the surface type boundaries can be extracted. The method was evaluated on a real-world dataset collected from the Ford Manufacturing Plant in Valencia (Spain), achieving a mean error of 166 milliseconds when delimiting, within the audio, the boundaries of the surfaces in the track. The results demonstrate the effectiveness of the proposed method in accurately segmenting different surface types, which could enable the development of more specialized AI systems to improve the quality inspection process. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Comments: 12 pages, 2 figures

A Change Detection Reality Check

Authors: Isaac Corley, Caleb Robinson, Anthony Ortiz

Abstract: In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, has the field truly made significant progress? In this paper we perform experiments which conclude a simple U-Net segmentation baseline without training… ▽ More In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, has the field truly made significant progress? In this paper we perform experiments which conclude a simple U-Net segmentation baseline without training tricks or complicated architectural changes is still a top performer for the task of change detection. △ Less

Submitted 12 April, 2024; v1 submitted 10 February, 2024; originally announced February 2024.

Is K-fold cross validation the best model selection method for Machine Learning?

Authors: Juan M Gorriz, R. Martin Clemente, F Segovia, J Ramirez, A Ortiz, J. Suckling

Abstract: As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance, and it frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine… ▽ More As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance, and it frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within machine learning pipelines, a permutation test or simple statistics from data partitions (i.e., folds) can be added to estimate confidence intervals. Unfortunately, neither parametric nor non-parametric tests solve the inherent problems of partitioning small sample-size datasets and learning from heterogeneous data sources. The fact that machine learning strongly depends on the learning parameters and the distribution of data across folds recapitulates familiar difficulties around excess false positives and replication. A novel statistical test based on K-fold CV and the Upper Bound of the actual risk (K-fold CUBV) is proposed, where uncertain predictions of machine learning with CV are bounded by the worst case through the evaluation of concentration inequalities. Probably Approximately Correct-Bayesian upper bounds for linear classifiers in combination with K-fold CV are derived and used to estimate the actual risk. The performance with simulated and neuroimaging datasets suggests that K-fold CUBV is a robust criterion for detecting effects and validating accuracy values obtained from machine learning and classical CV schemes, while avoiding excess false positives. △ Less

Submitted 8 November, 2024; v1 submitted 29 January, 2024; originally announced January 2024.

Comments: 40 pages, 24 figures

The Best Time for an Update: Risk-Sensitive Minimization of Age-Based Metrics

Authors: Wanja de Sombre, Andrea Ortiz, Frank Aurzada, Anja Klein

Abstract: Popular methods to quantify transmitted data quality are the Age of Information (AoI), the Query Age of Information (QAoI), and the Age of Incorrect Information (AoII). We consider these metrics in a point-to-point wireless communication system, where the transmitter monitors a process and sends status updates to a receiver. The challenge is to decide on the best time for an update, balancing the… ▽ More Popular methods to quantify transmitted data quality are the Age of Information (AoI), the Query Age of Information (QAoI), and the Age of Incorrect Information (AoII). We consider these metrics in a point-to-point wireless communication system, where the transmitter monitors a process and sends status updates to a receiver. The challenge is to decide on the best time for an update, balancing the transmission energy and the age-based metric at the receiver. Due to the inherent risk of high age-based metric values causing complications such as unstable system states, we introduce the new concept of risky states to denote states with high age-based metric. We use this new notion of risky states to quantify and minimize this risk of experiencing high age-based metrics by directly deriving the frequency of risky states as a novel risk-metric. Building on this foundation, we introduce two risk-sensitive strategies for AoI, QAoI and AoII. The first strategy uses system knowledge, i.e., channel quality and packet arrival probability, to find an optimal strategy that transmits when the age-based metric exceeds a tunable threshold. A lower threshold leads to higher risk-sensitivity. The second strategy uses an enhanced Q-learning approach and balances the age-based metric, the transmission energy and the frequency of risky states without requiring knowledge about the system. Numerical results affirm our risk-sensitive strategies' high effectiveness. △ Less

Submitted 17 March, 2025; v1 submitted 3 January, 2024; originally announced January 2024.

Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery

Authors: Caleb Robinson, Isaac Corley, Anthony Ortiz, Rahul Dodhia, Juan M. Lavista Ferres, Peyman Najafirad

Abstract: Fully understanding a complex high-resolution satellite or aerial imagery scene often requires spatial reasoning over a broad relevant context. The human object recognition system is able to understand object in a scene over a long-range relevant context. For example, if a human observes an aerial scene that shows sections of road broken up by tree canopy, then they will be unlikely to conclude th… ▽ More Fully understanding a complex high-resolution satellite or aerial imagery scene often requires spatial reasoning over a broad relevant context. The human object recognition system is able to understand object in a scene over a long-range relevant context. For example, if a human observes an aerial scene that shows sections of road broken up by tree canopy, then they will be unlikely to conclude that the road has actually been broken up into disjoint pieces by trees and instead think that the canopy of nearby trees is occluding the road. However, there is limited research being conducted to understand long-range context understanding of modern machine learning models. In this work we propose a road segmentation benchmark dataset, Chesapeake Roads Spatial Context (RSC), for evaluating the spatial long-range context understanding of geospatial machine learning models and show how commonly used semantic segmentation models can fail at this task. For example, we show that a U-Net trained to segment roads from background in aerial imagery achieves an 84% recall on unoccluded roads, but just 63.5% recall on roads covered by tree canopy despite being trained to model both the same way. We further analyze how the performance of models changes as the relevant context for a decision (unoccluded roads in our case) varies in distance. We release the code to reproduce our experiments and dataset of imagery and masks to encourage future research in this direction -- https://github.com/isaaccorley/ChesapeakeRSC. △ Less

Submitted 12 January, 2024; originally announced January 2024.

Comments: In submission to IGARSS 2024

EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia

Authors: Francisco Jesus Martinez-Murcia, Andrés Ortiz, Juan Manuel Górriz, Javier Ramírez, Pedro Javier Lopez-Perez, Miguel López-Zamora, Juan Luis Luque

Abstract: The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1 Hz), syllabic (4-8 Hz) or the phoneme (12… ▽ More The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1 Hz), syllabic (4-8 Hz) or the phoneme (12-40 Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ($p<0.005$) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG. △ Less

Submitted 23 November, 2023; originally announced November 2023.

Comments: 19 pages, 6 figures

Journal ref: INT J NEURAL SYST 30 (7), 2020, 2050037

Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?

Authors: Francisco J. Martinez-Murcia, Juan M. Górriz, Javier Ramírez, Andrés Ortiz

Abstract: Spatial and intensity normalization are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's Disease diagnosis, are especially dependent o… ▽ More Spatial and intensity normalization are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's Disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional Neural Networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analysing nuclear brain imaging? Are spatial and intensity normalization still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessing. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization -- and its type -- is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted. △ Less

Submitted 21 November, 2023; originally announced November 2023.

Comments: 19 pages, 7 figures

Journal ref: INT J NEURAL SYST 28 (10), 2018, 1850035

APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge

Authors: Santiago Gómez, Daniel Mantilla, Gustavo Garzón, Edgar Rangel, Andrés Ortiz, Franklin Sierra-Jerez, Fabio Martínez

Abstract: Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack se… ▽ More Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role regarding patient prognosis. The key to diagnosis consists in localizing and delineating brain lesions. Standard stroke examination protocols include the initial evaluation from a non-contrast CT scan to discriminate between hemorrhage and ischemia. However, non-contrast CTs may lack sensitivity in detecting subtle ischemic changes in the acute phase. As a result, complementary diffusion-weighted MRI studies are captured to provide valuable insights, allowing to recover and quantify stroke lesions. This work introduced APIS, the first paired public dataset with NCCT and ADC studies of acute ischemic stroke patients. APIS was presented as a challenge at the 20th IEEE International Symposium on Biomedical Imaging 2023, where researchers were invited to propose new computational strategies that leverage paired data and deal with lesion segmentation over CT sequences. Despite all the teams employing specialized deep learning tools, the results suggest that the ischemic stroke segmentation task from NCCT remains challenging. The annotated dataset remains accessible to the public upon registration, inviting the scientific community to deal with stroke characterization from NCCT but guided with paired DWI information. △ Less

Submitted 26 September, 2023; originally announced September 2023.

Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing

Authors: Bernd Simon, Andrea Ortiz, Walid Saad, Anja Klein

Abstract: The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must ad… ▽ More The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online. Our innovative "free-sensing" mechanism significantly improves the MU's learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%. △ Less

Submitted 19 September, 2023; originally announced September 2023.

SeBaSi system-level Integrated Access and Backhaul simulator for self-backhauling

Authors: Amir Ashtari Gargari, Matteo Pagin, Andrea Ortiz, Nairy Moghadas Gholian, Michele Polese, Michele Zorzi

Abstract: millimeter wave (mmWave) and sub-terahertz (THz) communications have the potential of increasing mobile network throughput drastically. However, the challenging propagation conditions experienced at mmWave and beyond frequencies can potentially limit the range of the wireless link down to a few meters, compared to up to kilometers for sub-6GHz links. Thus, increasing the density of base station de… ▽ More millimeter wave (mmWave) and sub-terahertz (THz) communications have the potential of increasing mobile network throughput drastically. However, the challenging propagation conditions experienced at mmWave and beyond frequencies can potentially limit the range of the wireless link down to a few meters, compared to up to kilometers for sub-6GHz links. Thus, increasing the density of base station deployments is required to achieve sufficient coverage in the Radio Access Network (RAN). To such end, 3rd Generation Partnership Project (3GPP) introduced wireless backhauled base stations with Integrated Access and Backhaul (IAB), a key technology to achieve dense networks while preventing the need for costly fiber deployments. In this paper, we introduce SeBaSi, a system-level simulator for IAB networks, and demonstrate its functionality by simulating IAB deployments in Manhattan, New York City and Padova. Finally, we show how SeBaSi can represent a useful tool for the performance evaluation of self-backhauled cellular networks, thanks to its high level of network abstraction, coupled with its open and customizable design, which allows users to extend it to support novel technologies such as Reconfigurable Intelligent Surfaces (RISs) △ Less

Submitted 11 September, 2023; originally announced September 2023.

Comments: Accepted for publication at 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). Copyright might transfer without notice

Poverty rate prediction using multi-modal survey and earth observation data

Authors: Simone Fobi, Manuel Cardona, Elliott Collins, Caleb Robinson, Anthony Ortiz, Tina Sederholm, Rahul Dodhia, Juan Lavista Ferres

Abstract: This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a single-step featurization method applied to freely available 10m/px Sentinel-2 surface reflectance satellite imagery. These visual features are combined wi… ▽ More This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region. Our approach utilizes visual features obtained from a single-step featurization method applied to freely available 10m/px Sentinel-2 surface reflectance satellite imagery. These visual features are combined with ten survey questions in a proxy means test (PMT) to estimate whether a household is below the poverty line. We show that the inclusion of visual features reduces the mean error in poverty rate estimates from 4.09% to 3.88% over a nationally representative out-of-sample test set. In addition to including satellite imagery features in proxy means tests, we propose an approach for selecting a subset of survey questions that are complementary to the visual features extracted from satellite imagery. Specifically, we design a survey variable selection approach guided by the full survey and image features and use the approach to determine the most relevant set of small survey questions to include in a PMT. We validate the choice of small survey questions in a downstream task of predicting the poverty rate using the small set of questions. This approach results in the best performance -- errors in poverty rate decrease from 4.09% to 3.71%. We show that extracted visual features encode geographic and urbanization differences between regions. △ Less

Submitted 21 July, 2023; originally announced July 2023.

Comments: In 2023 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS 23) Short Papers Track

Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event

Authors: Caleb Robinson, Simone Fobi Nsutezo, Anthony Ortiz, Tina Sederholm, Rahul Dodhia, Cameron Birge, Kasie Richards, Kris Pitcher, Paulo Duarte, Juan M. Lavista Ferres

Abstract: Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manner is non-trivial due to the challenges posed by variations in disaster-specific damage, diversity in satellite imagery, and the dearth of extensive, l… ▽ More Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. However, performing such building damage assessments in an automated manner is non-trivial due to the challenges posed by variations in disaster-specific damage, diversity in satellite imagery, and the dearth of extensive, labeled datasets. To circumvent these issues, this paper introduces a human-in-the-loop workflow for rapidly training building damage assessment models after a natural disaster. This article details a case study using this workflow, executed in partnership with the American Red Cross during a tornado event in Rolling Fork, Mississippi in March, 2023. The output from our human-in-the-loop modeling process achieved a precision of 0.86 and recall of 0.80 for damaged buildings when compared to ground truth data collected post-disaster. This workflow was implemented end-to-end in under 2 hours per satellite imagery scene, highlighting its potential for real-time deployment. △ Less

Submitted 24 August, 2023; v1 submitted 21 June, 2023; originally announced June 2023.

Comments: Accepted at the 2023 ICCV Humanitarian Assistance and Disaster Response workshop

Mask Conditional Synthetic Satellite Imagery

Authors: Van Anh Le, Varshini Reddy, Zixi Chen, Mengyuan Li, Xinran Tang, Anthony Ortiz, Simone Fobi Nsutezo, Caleb Robinson

Abstract: In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets. Given a dataset of real high-resolution images and accompanying land cover masks, we show that it is possible to train an upstream conditional synthetic imagery generator, use that generator to create synthetic imagery with the land cover masks, then train a downstream mo… ▽ More In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets. Given a dataset of real high-resolution images and accompanying land cover masks, we show that it is possible to train an upstream conditional synthetic imagery generator, use that generator to create synthetic imagery with the land cover masks, then train a downstream model on the synthetic imagery and land cover masks that achieves similar test performance to a model that was trained with the real imagery. Further, we find that incorporating a mixture of real and synthetic imagery acts as a data augmentation method, producing better models than using only real imagery (0.5834 vs. 0.5235 mIoU). Finally, we find that encouraging diversity of outputs in the upstream model is a necessary component for improved downstream task performance. We have released code for reproducing our work on GitHub, see https://github.com/ms-synthetic-satellite-image/synthetic-satellite-imagery . △ Less

Submitted 8 February, 2023; originally announced February 2023.

Safehaul: Risk-Averse Learning for Reliable mmWave Self-Backhauling in 6G Networks

Authors: Amir Ashtari Gargari, Andrea Ortiz, Matteo Pagin, Anja Klein, Matthias Hollick, Michele Zorzi, Arash Asadi

Abstract: Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station serve both acces… ▽ More Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks but also exhibits significantly more reliable performance (e.g., 71.4% less variance in achieved latency). △ Less

Submitted 12 January, 2023; v1 submitted 9 January, 2023; originally announced January 2023.

Comments: To appear in Proceedings of IEEE INFOCOM 2023

Neural source/sink phase connectivity in developmental dyslexia by means of interchannel causality

Authors: I. RodrÍguez-RodrÍguez, A. Ortiz, N. J. Gallego-Molina, M. A. Formoso, W. L. Woo

Abstract: While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners a… ▽ More While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively. △ Less

Submitted 2 January, 2023; originally announced January 2023.

Fast building segmentation from satellite imagery and few local labels

Authors: Caleb Robinson, Anthony Ortiz, Hogeun Park, Nancy Lozano Gracia, Jon Kher Kaw, Tina Sederholm, Rahul Dodhia, Juan M. Lavista Ferres

Abstract: Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrence when trying to replicate models that drive these analyses to new areas, particularly in the developing world. If a model is trained with imagery and labels from one l… ▽ More Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrence when trying to replicate models that drive these analyses to new areas, particularly in the developing world. If a model is trained with imagery and labels from one location, then it usually will not generalize well to new locations where the content of the imagery and data distributions are different. In this work, we consider the setting in which we have a single large satellite imagery scene over which we want to solve an applied problem -- building footprint segmentation. Here, we do not necessarily need to worry about creating a model that generalizes past the borders of our scene but can instead train a local model. We show that surprisingly few labels are needed to solve the building segmentation problem with very high-resolution (0.5m/px) satellite imagery with this setting in mind. Our best model trained with just 527 sparse polygon annotations (an equivalent of 1500 x 1500 densely labeled pixels) has a recall of 0.87 over held out footprints and a R2 of 0.93 on the task of counting the number of buildings in 200 x 200-meter windows. We apply our models over high-resolution imagery in Amman, Jordan in a case study on urban change detection. △ Less

Submitted 10 June, 2022; originally announced June 2022.

Comments: Accepted at EarthVision 2022

A hypothesis-driven method based on machine learning for neuroimaging data analysis

Authors: JM Gorriz, R. Martin-Clemente, C. G. Puntonet, A. Ortiz, J. Ramirez, J. Suckling

Abstract: There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is… ▽ More There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM. △ Less

Submitted 17 February, 2022; v1 submitted 9 February, 2022; originally announced February 2022.

Comments: 12 figures

Journal ref: Volume 510, 21 October 2022, Pages 159-171

An Artificial Intelligence Dataset for Solar Energy Locations in India

Authors: Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker, Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane Wang, Felipe Oviedo, Juan Lavista Ferres

Abstract: Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of so… ▽ More Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, or agricultural value. △ Less

Submitted 30 June, 2022; v1 submitted 31 January, 2022; originally announced February 2022.

Comments: Accepted for publication in Nature Scientific Data

TorchGeo: Deep Learning With Geospatial Data

Authors: Adam J. Stewart, Caleb Robinson, Isaac A. Corley, Anthony Ortiz, Juan M. Lavista Ferres, Arindam Banerjee

Abstract: Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery a… ▽ More Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that can have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub: https://github.com/microsoft/torchgeo. △ Less

Submitted 17 September, 2022; v1 submitted 16 November, 2021; originally announced November 2021.

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry

Authors: Joan P. Company-Corcoles, Emilio Garcia-Fidalgo, Alberto Ortiz

Abstract: Visual odometry algorithms tend to degrade when facing low-textured scenes -from e.g. human-made environments-, where it is often difficult to find a sufficient number of point features. Alternative geometrical visual cues, such as lines, which can often be found within these scenarios, can become particularly useful. Moreover, these scenarios typically present structural regularities, such as par… ▽ More Visual odometry algorithms tend to degrade when facing low-textured scenes -from e.g. human-made environments-, where it is often difficult to find a sufficient number of point features. Alternative geometrical visual cues, such as lines, which can often be found within these scenarios, can become particularly useful. Moreover, these scenarios typically present structural regularities, such as parallelism or orthogonality, and hold the Manhattan World assumption. Under these premises, in this work, we introduce MSC-VO, an RGB-D -based visual odometry approach that combines both point and line features and leverages, if exist, those structural regularities and the Manhattan axes of the scene. Within our approach, these structural constraints are initially used to estimate accurately the 3D position of the extracted lines. These constraints are also combined next with the estimated Manhattan axes and the reprojection errors of points and lines to refine the camera pose by means of local map optimization. Such a combination enables our approach to operate even in the absence of the aforementioned constraints, allowing the method to work for a wider variety of scenarios. Furthermore, we propose a novel multi-view Manhattan axes estimation procedure that mainly relies on line features. MSC-VO is assessed using several public datasets, outperforming other state-of-the-art solutions, and comparing favourably even with some SLAM methods. △ Less

Submitted 5 November, 2021; originally announced November 2021.

Comments: Submitted to RAL + ICRA 2022

LiODOM: Adaptive Local Mapping for Robust LiDAR-Only Odometry

Authors: Emilio Garcia-Fidalgo, Joan P. Company-Corcoles, Francisco Bonnin-Pascual, Alberto Ortiz

Abstract: In the last decades, Light Detection And Ranging (LiDAR) technology has been extensively explored as a robust alternative for self-localization and mapping. These approaches typically state ego-motion estimation as a non-linear optimization problem dependent on the correspondences established between the current point cloud and a map, whatever its scope, local or global. This paper proposes LiODOM… ▽ More In the last decades, Light Detection And Ranging (LiDAR) technology has been extensively explored as a robust alternative for self-localization and mapping. These approaches typically state ego-motion estimation as a non-linear optimization problem dependent on the correspondences established between the current point cloud and a map, whatever its scope, local or global. This paper proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose estimation and map-building, based on minimizing a loss function derived from a set of weighted point-to-line correspondences with a local map abstracted from the set of available point clouds. Furthermore, this work places a particular emphasis on map representation given its relevance for quick data association. To efficiently represent the environment, we propose a data structure that combined with a hashing scheme allows for fast access to any section of the map. LiODOM is validated by means of a set of experiments on public datasets, for which it compares favourably against other solutions. Its performance on-board an aerial platform is also reported. △ Less

Submitted 27 July, 2022; v1 submitted 5 November, 2021; originally announced November 2021.

Comments: In press

Journal ref: Robotics and Autonomous Systems, 2022

Survey Propagation: A Resource Allocation Solution for Large Wireless Networks

Authors: Andrea Ortiz, Daniel Barragan-Yani

Abstract: The ever-increasing number of nodes in current and future wireless communication networks brings unprecedented challenges for the allocation of the available communication resources. This is caused by the combinatorial nature of the resource allocation problems, which limits the performance of state-of-the-art techniques when the network size increases. In this paper, we take a new direction and i… ▽ More The ever-increasing number of nodes in current and future wireless communication networks brings unprecedented challenges for the allocation of the available communication resources. This is caused by the combinatorial nature of the resource allocation problems, which limits the performance of state-of-the-art techniques when the network size increases. In this paper, we take a new direction and investigate how methods from statistical physics can be used to address resource allocation problems in large networks. To this aim, we propose a novel model of the wireless network based on a type of disordered physical systems called spin glasses. We show that resource allocation problems have the same structure as the problem of finding specific configurations in spin glasses. Based on this parallel, we investigate the use of the Survey Propagation method from statistical physics in the solution of resource allocation problems in wireless networks. Through numerical simulations we show that the proposed statistical-physics-based resource allocation algorithm is a promising tool for the efficient allocation of communication resources in large wireless communications networks. Given a fixed number of resources, we are able to serve a larger number of nodes, compared to state-of-the-art reference schemes, without introducing more interference into the system △ Less

Submitted 6 January, 2022; v1 submitted 20 October, 2021; originally announced October 2021.

Big Data Information and Nowcasting: Consumption and Investment from Bank Transactions in Turkey

Authors: Ali B. Barlas, Seda Guler Mert, Berk Orkun Isa, Alvaro Ortiz, Tomasa Rodrigo, Baris Soybilgen, Ege Yazgan

Abstract: We use the aggregate information from individual-to-firm and firm-to-firm in Garanti BBVA Bank transactions to mimic domestic private demand. Particularly, we replicate the quarterly national accounts aggregate consumption and investment (gross fixed capital formation) and its bigger components (Machinery and Equipment and Construction) in real time for the case of Turkey. In order to validate the… ▽ More We use the aggregate information from individual-to-firm and firm-to-firm in Garanti BBVA Bank transactions to mimic domestic private demand. Particularly, we replicate the quarterly national accounts aggregate consumption and investment (gross fixed capital formation) and its bigger components (Machinery and Equipment and Construction) in real time for the case of Turkey. In order to validate the usefulness of the information derived from these indicators we test the nowcasting ability of both indicators to nowcast the Turkish GDP using different nowcasting models. The results are successful and confirm the usefulness of Consumption and Investment Banking transactions for nowcasting purposes. The value of the Big data information is more relevant at the beginning of the nowcasting process, when the traditional hard data information is scarce. This makes this information specially relevant for those countries where statistical release lags are longer like the Emerging Markets. △ Less

Submitted 5 July, 2021; originally announced July 2021.

Comments: 31 pages, 7 figures, 9 tables

Detecting Cattle and Elk in the Wild from Space

Authors: Caleb Robinson, Anthony Ortiz, Lacey Hughey, Jared A. Stabach, Juan M. Lavista Ferres

Abstract: Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Prior work has shown that this is feasible with deep learning based methods and sub-meter multi-spectral satellite imagery. We extend this line of work by proposing a baseline method, CowNet, that simultaneously estimates t… ▽ More Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Prior work has shown that this is feasible with deep learning based methods and sub-meter multi-spectral satellite imagery. We extend this line of work by proposing a baseline method, CowNet, that simultaneously estimates the number of animals in an image (counts), as well as predicts their location at a pixel level (localizes). We also propose an methodology for evaluating such models on counting and localization tasks across large scenes that takes the uncertainty of noisy labels and the information needed by stakeholders in ecological monitoring tasks into account. Finally, we benchmark our baseline method with state of the art vision methods for counting objects in scenes. We specifically test the temporal generalization of the resulting models over a large landscape in Point Reyes Seashore, CA. We find that the LC-FCN model performs the best and achieves an average precision between 0.56 and 0.61 and an average recall between 0.78 and 0.92 over three held out test scenes. △ Less

Submitted 29 June, 2021; originally announced June 2021.

Comments: Presented at the KDD 2021 Fragile Earth Workshop

Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images

Authors: Juan E. Arco, Andrés Ortiz, Javier Ramírez, Juan M Gorriz

Abstract: The ongoing crisis of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), 4 million people have died due to this disease, whereas there have been more than 180 million confirmed cases of COVID-19. The collapse of the health system in many countries has demonstrated the need of developing tools to automatize the diagnosis of the… ▽ More The ongoing crisis of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), 4 million people have died due to this disease, whereas there have been more than 180 million confirmed cases of COVID-19. The collapse of the health system in many countries has demonstrated the need of developing tools to automatize the diagnosis of the disease from medical imaging. Previous studies have used deep learning for this purpose. However, the performance of this alternative highly depends on the size of the dataset employed for training the algorithm. In this work, we propose a classification framework based on sparse coding in order to identify the pneumonia patterns associated with different pathologies. Specifically, each chest X-ray (CXR) image is partitioned into different tiles. The most relevant features extracted from PCA are then used to build the dictionary within the sparse coding procedure. Once images are transformed and reconstructed from the elements of the dictionary, classification is performed from the reconstruction errors of individual patches associated with each image. Performance is evaluated in a real scenario where simultaneously differentiation between four different pathologies: control vs bacterial pneumonia vs viral pneumonia vs COVID-19. The accuracy when identifying the presence of pneumonia is 93.85%, whereas 88.11% is obtained in the 4-class classification context. The excellent results and the pioneering use of sparse coding in this scenario evidence the applicability of this approach as an aid for clinicians in a real-world environment. △ Less

Submitted 28 June, 2021; originally announced June 2021.

Comments: 14 pages, 5 figures

Becoming Good at AI for Good

Authors: Meghana Kshirsagar, Caleb Robinson, Siyu Yang, Shahrzad Gholami, Ivan Klyuzhin, Sumit Mukherjee, Md Nasir, Anthony Ortiz, Felipe Oviedo, Darren Tanner, Anusua Trivedi, Yixi Xu, Ming Zhong, Bistra Dilkina, Rahul Dodhia, Juan M. Lavista Ferres

Abstract: AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Ba… ▽ More AI for good (AI4G) projects involve developing and applying artificial intelligence (AI) based solutions to further goals in areas such as sustainability, health, humanitarian aid, and social justice. Developing and deploying such solutions must be done in collaboration with partners who are experts in the domain in question and who already have experience in making progress towards such goals. Based on our experiences, we detail the different aspects of this type of collaboration broken down into four high-level categories: communication, data, modeling, and impact, and distill eleven takeaways to guide such projects in the future. We briefly describe two case studies to illustrate how some of these takeaways were applied in practice during our past collaborations. △ Less

Submitted 3 May, 2021; v1 submitted 23 April, 2021; originally announced April 2021.

Comments: Accepted to AIES-2021

Temporal EigenPAC for dyslexia diagnosis

Authors: Nicolás Gallego-Molina, Marco Formoso, Andrés Ortiz, Francisco J. Martínez-Murcia, Juan L. Luque

Abstract: Electroencephalography signals allow to explore the functional activity of the brain cortex in a non-invasive way. However, the analysis of these signals is not straightforward due to the presence of different artifacts and the very low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way to extract information from EEG, related to the synchronization among frequency bands.… ▽ More Electroencephalography signals allow to explore the functional activity of the brain cortex in a non-invasive way. However, the analysis of these signals is not straightforward due to the presence of different artifacts and the very low signal-to-noise ratio. Cross-Frequency Coupling (CFC) methods provide a way to extract information from EEG, related to the synchronization among frequency bands. However, CFC methods are usually applied in a local way, computing the interaction between phase and amplitude at the same electrode. In this work we show a method to compute PAC features among electrodes to study the functional connectivity. Moreover, this has been applied jointly with Principal Component Analysis to explore patterns related to Dyslexia in 7-years-old children. The developed methodology reveals the temporal evolution of PAC-based connectivity. Directions of greatest variance computed by PCA are called eigenPACs here, since they resemble the classical \textit{eigenfaces} representation. The projection of PAC data onto the eigenPACs provide a set of features that has demonstrates their discriminative capability, specifically in the Beta-Gamma bands. △ Less

Submitted 13 April, 2021; originally announced April 2021.

Modelling Brain Connectivity Networks by Graph Embedding for Dyslexia Diagnosis

Authors: Marco A. Formoso, Andrés Ortiz, Francisco J. Martínez-Murcia, Nicolás Gallego-Molina, Juan L. Luque

Abstract: Several methods have been developed to extract information from electroencephalograms (EEG). One of them is Phase-Amplitude Coupling (PAC) which is a type of Cross-Frequency Coupling (CFC) method, consisting in measure the synchronization of phase and amplitude for the different EEG bands and electrodes. This provides information regarding brain areas that are synchronously activated, and eventual… ▽ More Several methods have been developed to extract information from electroencephalograms (EEG). One of them is Phase-Amplitude Coupling (PAC) which is a type of Cross-Frequency Coupling (CFC) method, consisting in measure the synchronization of phase and amplitude for the different EEG bands and electrodes. This provides information regarding brain areas that are synchronously activated, and eventually, a marker of functional connectivity between these areas. In this work, intra and inter electrode PAC is computed obtaining the relationship among different electrodes used in EEG. The connectivity information is then treated as a graph in which the different nodes are the electrodes and the edges PAC values between them. These structures are embedded to create a feature vector that can be further used to classify multichannel EEG samples. The proposed method has been applied to classified EEG samples acquired using specific auditory stimuli in a task designed for dyslexia disorder diagnosis in seven years old children EEG's. The proposed method provides AUC values up to 0.73 and allows selecting the most discriminant electrodes and EEG bands. △ Less

Submitted 12 April, 2021; originally announced April 2021.