Petar M. Djurić

Correction: Dynamic soaring decouples dynamic body acceleration and energetics in albatrosses

Melinda G. Conners, Jonathan A. Green, Richard A. Phillips, Rachael A. Orben et autres

There were several errors in J. Exp. Biol. (2024) 227, jeb247431 (doi:10.1242/jeb.247431).The units for V̇O2 were incorrectly given as ml min−1 kg−1 throughout; the correct units are ml min−1. This affects parts of Materials and Methods (‘Estimation of V̇O2 from heart rate’, …

Bio-integrated systems for silent speech recognition: from advanced bioplatforms to machine learning-assisted biosignal decoding

Penghao Dong, Yuanqing Song, Yizong Li, Petar M. Djurić et autres

Silent speech interfaces decode intended speech from physiological signals without the need for vocalized sound. These systems provide an alternative modality to voice-based spoken communication, addressing limitations posed by physiological constraints and environmental interferences. This review presents a comprehensive overview of bio-integrated …

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Sequential Inference for Gaussian Processes: A Signal Processing Perspective

Daniel Waxman, Fernando Llorente, Petar M. Djurić

The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive …

Sequential Inference for Gaussian Processes: A Signal Processing Perspective

Daniel Waxman, Fernando Llorente, Petar M. Djurić

The proliferation of capable and efficient machine learning (ML) models marks one of the strongest methodological shifts in signal processing (SP) in its nearly 100-year history. ML models support the development of SP systems that represent complex, nonlinear relationships with high predictive …

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Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing

Kurt Butler, Guanchao Feng, Petar M. Djurić

Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but it becomes less clear when the predictive model involves interactions, such …

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Continual Time Series Forecasting with Diffusion Models Under Functional Regularization

Anand Ravishankar, Petar M. Djurić

Diffusion models have recently achieved state-of-the-art performance on time series inference tasks such as forecasting and imputation. However, these models are usually trained unconditionally in a dataset-specific manner, which limits their ability to be applied cross-domain. Sequentially training the model on new …

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State Space Clustering for Interpretable Fetal Heart Rate Characterization

T A Chen, Guanchao Feng, Kurt Butler, Cassandra J. Heiselman et autres

Computerized cardiotocography (CTG) often relies on fetal heart rate (FHR) features with weak correlations to umbilical cord blood pH, the gold standard for neonatal acidosis, whereas deep learning features lack interpretability. In this work, we propose a novel family of interpretable FHR …

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A model of hydrocephalus due to windkessel impairment caused by subarachnoid space obstruction

Michael R. Egnor, Nahid Shirdel Abdolmaleki, Anand Ravishankar, Racheed M. Mani et autres

OBJECTIVE: The traditional view that hydrocephalus due to obstruction in the subarachnoid space (SAS) is caused by malabsorption of CSF does not account for many experimental and clinical aspects of the disorder. Flow MRI reveals that nearly all CSF motion is pulsatile, …

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Multi-branch convolutional neural network and intracranial EEG high-frequency oscillations predict post-surgical seizure outcomes

Madhumathi Devaraj, Yihe Chen, Shuang Wang, Michael R. Sperling et autres

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Designing an Optimal Sensor Network via Minimizing Information Loss

Daniel Waxman, Fernando Llorente, Katia Lamer, Petar M. Djurić

Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We …

Multi-branch convolutional neural network using intracranial EEG high frequency oscillation features for predicting post-surgical seizure outcomes

Yihe Chen, Shuang Wang, Michael R. Sperling, Noa Herz et autres

Abstract Pathological high-frequency oscillations (HFOs 80-600 Hz) in intracranial EEG distinguish epileptogenic cortex. However, it is uncertain whether utilizing HFO measures for surgical planning improve epilepsy surgery seizure outcomes and minimize morbidity. The clinical gold standard for planning an epilepsy surgery involves …

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Measuring Strength of Joint Causal Effects

Kurt Butler, Guanchao Feng, Petar M. Djurić

In the study of causality, we often seek not only to detect the presence of cause-effect relationships, but also to characterize how multiple causes combine to produce an effect. When the response to a change in one of the causes depends on …

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