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CS-SHRED: Enhancing SHRED for Robust Recovery of Spatiotemporal Dynamics
Authors:
Romulo B. da Silva,
Diego Passos,
Cássio M. Oishi,
J. Nathan Kutz
Abstract:
We present CS-SHRED, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder (SHRED) to reconstruct spatiotemporal dynamics from incomplete, compressed, or corrupted data. Our approach introduces two key innovations. First, by incorporating CS techniques into the SHRED architecture, our method leverages a batch-based forward framework with…
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We present CS-SHRED, a novel deep learning architecture that integrates Compressed Sensing (CS) into a Shallow Recurrent Decoder (SHRED) to reconstruct spatiotemporal dynamics from incomplete, compressed, or corrupted data. Our approach introduces two key innovations. First, by incorporating CS techniques into the SHRED architecture, our method leverages a batch-based forward framework with $\ell_1$ regularization to robustly recover signals even in scenarios with sparse sensor placements, noisy measurements, and incomplete sensor acquisitions. Second, an adaptive loss function dynamically combines Mean Squared Error (MSE) and Mean Absolute Error (MAE) terms with a piecewise Signal-to-Noise Ratio (SNR) regularization, which suppresses noise and outliers in low-SNR regions while preserving fine-scale features in high-SNR regions.
We validate CS-SHRED on challenging problems including viscoelastic fluid flows, maximum specific humidity fields, sea surface temperature distributions, and rotating turbulent flows. Compared to the traditional SHRED approach, CS-SHRED achieves significantly higher reconstruction fidelity -- as demonstrated by improved SSIM and PSNR values, lower normalized errors, and enhanced LPIPS scores-thereby providing superior preservation of small-scale structures and increased robustness against noise and outliers.
Our results underscore the advantages of the jointly trained CS and SHRED design architecture which includes an LSTM sequence model for characterizing the temporal evolution with a shallow decoder network (SDN) for modeling the high-dimensional state space. The SNR-guided adaptive loss function for the spatiotemporal data recovery establishes CS-SHRED as a promising tool for a wide range of applications in environmental, climatic, and scientific data analyses.
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Submitted 31 July, 2025; v1 submitted 29 July, 2025;
originally announced July 2025.
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eXplainable Artificial Intelligence on Medical Images: A Survey
Authors:
Matteus Vargas Simão da Silva,
Rodrigo Reis Arrais,
Jhessica Victoria Santos da Silva,
Felipe Souza Tânios,
Mateus Antonio Chinelatto,
Natalia Backhaus Pereira,
Renata De Paris,
Lucas Cesar Ferreira Domingos,
Rodrigo Dória Villaça,
Vitor Lopes Fabris,
Nayara Rossi Brito da Silva,
Ana Claudia Akemi Matsuki de Faria,
Jose Victor Nogueira Alves da Silva,
Fabiana Cristina Queiroz de Oliveira Marucci,
Francisco Alves de Souza Neto,
Danilo Xavier Silva,
Vitor Yukio Kondo,
Claudio Filipi Gonçalves dos Santos
Abstract:
Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artificial intelligence, also known as XAI, which targets to explain the results of such…
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Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artificial intelligence, also known as XAI, which targets to explain the results of such black box models to permit the desired assessment. This survey analyses several recent studies in the XAI field applied to medical diagnosis research, allowing some explainability of the machine learning results in several different diseases, such as cancers and COVID-19.
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Submitted 12 May, 2023;
originally announced May 2023.
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Multilevel LDPC Lattices with Efficient Encoding and Decoding and a Generalization of Construction D'
Authors:
Paulo Ricardo Branco da Silva,
Danilo Silva
Abstract:
Lattice codes are elegant and powerful structures that not only can achieve the capacity of the AWGN channel but are also a key ingredient to many multiterminal schemes that exploit linearity properties. However, constructing lattice codes that can realize these benefits with low complexity is still a challenging problem. In this paper, efficient encoding and decoding algorithms are proposed for m…
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Lattice codes are elegant and powerful structures that not only can achieve the capacity of the AWGN channel but are also a key ingredient to many multiterminal schemes that exploit linearity properties. However, constructing lattice codes that can realize these benefits with low complexity is still a challenging problem. In this paper, efficient encoding and decoding algorithms are proposed for multilevel binary LDPC lattices constructed via Construction D' whose complexity is linear in the total number of coded bits. Moreover, a generalization of Construction D' is proposed that relaxes some of the nesting constraints on the parity-check matrices of the component codes, leading to a simpler and improved design. Based on this construction, low-complexity multilevel LDPC lattices are designed whose performance under multistage decoding is comparable to that of polar lattices and close to that of low-density lattice codes (LDLC) on the power-unconstrained AWGN channel.
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Submitted 22 November, 2018; v1 submitted 21 December, 2017;
originally announced December 2017.
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A topological formal treatment for scenario-based software specification of concurrent real-time systems
Authors:
Miriam C. B. Alves,
Christine C. Dantas,
Nanci N. Arai,
Rovedy B. da Silva
Abstract:
Real-time systems are computing systems in which the meeting of their requirements is vital for their correctness. Consequently, if the real-time requirements of these systems are poorly understood and verified, the results can be disastrous and lead to irremediable project failures at the early phases of development. The present work addresses the problem of detecting deadlock situations early…
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Real-time systems are computing systems in which the meeting of their requirements is vital for their correctness. Consequently, if the real-time requirements of these systems are poorly understood and verified, the results can be disastrous and lead to irremediable project failures at the early phases of development. The present work addresses the problem of detecting deadlock situations early in the requirements specification phase of a concurrent real time system, proposing a simple proof-of-concepts prototype that joins scenario-based requirements specifications and techniques based on topology. The efforts are concentrated in the integration of the formal representation of Message Sequence Chart scenarios into the deadlock detection algorithm of Fajstrup et al., based on geometric and algebraic topology.
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Submitted 1 February, 2008;
originally announced February 2008.