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Recent Submissions

Offline versus real-time grasp prediction employing a wearable high-density lightmyography armband: on the control of prosthetic hands
(IEEE, 2025-04-01) Guan, B; Godoy, RV; Shahmohammadi, M; Dwivedi, Anany; Liarokapis, M
Several studies over the last decades have investigated the use of myoelectric upper-arm prostheses to restore lost functionality and perform complex activities of daily life. Muscle-machine interfacing methods employing electromyography, sonomyography, forcemyography, and mechanomyography, have been developed offering users intuitive control of prostheses. However, these methods suffer their own drawbacks and the practical, robust, real-time control of prostheses in the execution of complex everyday life tasks remains difficult to accomplish. In this study, a wearable high-density lightmyography armband is proposed, and the offline and real-time grasp prediction schemes are compared in an attempt to deepen our understanding in real-time decoding employing lightmyography signals. Thus bringing lightmyography closer to advanced real-time prosthetic control scenarios. Offline experiments were conducted where models decoding 10 classes were trained and tested, achieving accuracies exceeding 91% with a mean of 94.11% when a Random Forest model was employed and a mean of 95.87% when a Convolutional Neural Networks model was employed. However, as models were deployed in real-time, a decline in performance was notable, reducing the accuracies to 60-70%. Other limitations were observed in this transition. Although average accuracies were high, the precision and recall for a small fraction of grasps were often low, hindering overall real-time performance. Moreover, prolonged usage led to decreased decoding accuracy, with accuracies dropping to below 35%, indicating that fatigue, sweat, or sensor shift may have affected the signals over time. Thus for the real-time application of lightmyography, it is recommended to discriminate between a smaller number of classes for which accuracies are higher (around 80% for 6 classes without prolonged use).
Direct power spectral density estimation from structure functions without Fourier transforms
(AIP, 2026-02-02) Bishop, MA; Oughton, Sean; Parashar, TN; Perrott, YC
Second-order structure functions and power spectral densities are popular tools in the study of statistical properties across scales, particularly for the analysis of turbulent flows. Although intimately related, analyses primarily use one or the other. We introduce a framework for estimating the power spectrum using the second-order structure function without applying Fourier transforms—enabling one to take advantage of the real-space structure function calculations. We validate and showcase this method, comparing it to classical Fourier power spectrum estimates determined from analytical calculations, fractional Brownian motion, turbulence simulations, and space-physics and astrophysical observations of turbulence. We show that this method is able to robustly obtain the expected power law behavior where we use turbulence ranges as test-cases.
Understanding medical information and emotional support needs in mental health questions with large language models
(Emerald, 2025) Liu, Chen; Wang, William Yu Chung; Khan, Gohar
Purpose – This study seeks to bridge the gap between users’ multidimensional needs and the single-task capabilities of existing Mental Health Question Answering (MHQA) systems by tackling the underexplored challenge of jointly understanding medical informational needs and emotional support needs within complex consumer mental health inquiries. Design/methodology/approach – Grounded in Rhetorical Structure Theory (RST), the proposed Multi-Needs and Context Recognition (MNCR) framework decomposes mental health question understanding task into four interrelated subtasks: Medical Needs Recognition (MNR), Medical Needs-related Context Extraction (MNCE), Emotional Needs Recognition (ENR) and Emotional Needs-related Context Extraction (ENCE). A new benchmark dataset, MHQ-MedEmo, was constructed through multi-layered semantic annotation of 703 clinical queries sourced from real-world online health consultation platforms. The performances of six base LLMs and two fine-tuned LLMs were evaluated across precision, recall, F1 score and latency metrics. Findings – Dense, fine-tuned models strike the optimal balance between accuracy and latency for end-to-end MNCR tasks; subtask sensitivity varies markedly across different model architectures; fine-tuning consistently enhances overall performance; the joint-prompt strategy consistently improves both effectiveness and efficiency over the separate-prompt strategy and model architecture and scale significantly influence performance on MNCR subtasks. Originality/value – This study introduces MNCR and MHQ-MedEmo, the first framework and benchmark for simultaneously understanding medical informational needs and emotional support needs in mental health questions. Comparative evaluation of eight LLMs reveals distinct model-specific strengths, guiding future architectures that balance accuracy and latency and offering concrete guidance for healthcare organizations seeking to deploy LLM-based MHQA solutions in practice. Keywords Mental health, Rhetorical structure theory, Large language models Paper type Research article
Effective viscosity, resistivity, and Reynolds number in weakly collisional plasma turbulence
(Oxford University Press, 2024) Yang, Yan; Matthaeus, William H.; Oughton, Sean; Bandyopadhyay, Riddhi; Pecora, Francesco; Parashar, Tulasi N.; Roytershteyn, Vadim; Chasapis, Alexandros; Shay, Michael A.
We examine dissipation and energy conversion in weakly collisional plasma turbulence, employing in situ observations from the Magnetospheric Multiscale mission and kinetic particle-in-cell simulations of proton–electron plasma. A previous result indicated the presence of viscous-like and resistive-like scaling of average energy conversion rates – analogous to scalings characteristic of collisional systems. This allows for extraction of collisional-like coefficients of effective viscosity and resistivity, and thus also determination of effective Reynolds numbers based on these coefficients. The effective Reynolds number, as a measure of the available bandwidth for turbulence to populate various scales, links turbulence macroscale properties with kinetic plasma properties in a novel way.
Machine learning (in) security: A stream of problems
(Association for Computing Machinery, 2024) Ceschin, Fabrício; Botacin, Marcus; Bifet, Albert; Pfahringer, Bernhard; Oliveira, Luiz S.; Gomes, Heitor Murilo; Grégio, André
Machine Learning (ML) has been widely applied to cybersecurity and is considered state-of-the-art for solving many of the open issues in that field. However, it is very difficult to evaluate how good the produced solutions are, since the challenges faced in security may not appear in other areas. One of these challenges is the concept drift, which increases the existing arms race between attackers and defenders: malicious actors can always create novel threats to overcome the defense solutions, which may not consider them in some approaches. Due to this, it is essential to know how to properly build and evaluate an ML-based security solution. In this article, we identify, detail, and discuss the main challenges in the correct application of ML techniques to cybersecurity data. We evaluate how concept drift, evolution, delayed labels, and adversarial ML impact the existing solutions. Moreover, we address how issues related to data collection affect the quality of the results presented in the security literature, showing that new strategies are needed to improve current solutions. Finally, we present how existing solutions may fail under certain circumstances and propose mitigations to them, presenting a novel checklist to help the development of future ML solutions for cybersecurity.