Electrical Engineering and Computer Science

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

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Single photon emitters (SPEs) in hexagonal boron nitride (hBN) are elementary building blocks for room-temperature on-chip quantum photonic technologies. However, fundamental challenges, such as slow radiative decay and nondeterministic placement of the emitters, limit their full potential. Here, we demonstrate large-area arrays of plasmonic nanoresonators (PNRs) for Purcell-induced room-temperature SPEs by engineering emitter-cavity coupling and enhancing radiative emission. Gold-coated silicon pillars with an alumina spacer enable a 10-fold local-field enhancement in the emission band of native hBN defects. We observe bright SPEs with an average saturated emission rate surpassing 5 million counts per second, an average lifetime of <0.5 ns, and 29% yield. Density functional theory reveals the beneficial role of an alumina spacer between hBN and gold, mitigating the electronic broadening of emission from defects proximal to the metal. Our results offer arrays of bright, heterogeneously integrated single-photon sources, paving the way for robust and scalable quantum information systems.

Photo-sono therapy (PST) is an innovative anti-vascular approach based on cavitation-induced spallation. Currently, passive cavitation detection (PCD) is the prevalent technique for cavitation monitoring during treatment. However, the limitations of PCD are the lack of spatial information of bubbles and the difficulty of integration with the PST system. To address this, we proposed a new, to the best of our knowledge, cavitation mapping method that integrates Doppler optical coherence tomography (OCT) with PST to visualize bubble dynamics in real time. The feasibility of the proposed system has been confirmed through experiments on vascular-mimicking phantoms and in vivo rabbit ear vessels, and the results are compared to high-speed camera observations and PCD data. The findings demonstrate that Doppler OCT effectively maps cavitation in real time and holds promise for guiding PST treatments and other cavitation-related clinical applications.

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12-15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Nanophotonic structures have shown promising routes to controlling and enhancing nonlinear optical processes at the nanoscale. However, most nonlinear nanostructures require a handling substrate, reducing their application scope. Due to the underwhelming heat dissipation, it has been a challenge to evaluate the nonlinear optical properties of free-standing nanostructures. Here, we overcome this challenge by performing shot-controlled fifth harmonic generation (FHG) measurements on a SiC meta-membrane - a free-standing transmission metasurface with pronounced optical resonances in the mid-infrared (λ res ≈ 4,000 nm). Back focal plane imaging of the FHG diffraction orders and rigorous finite-difference time-domain simulations reveal at least two orders of magnitude enhancement of the FHG from the meta-membrane, compared to the unstructured SiC film of the same thickness. Single-shot measurements of the meta-membrane with varying resonance positions reveal an unusual spectral behavior that we explain with Kerr-driven intensity-dependent resonance dynamics. This work paves the way for novel substrate-less nanophotonic architectures.

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This paper presents a single-chip bits-to-antenna transmitter (TX) for >100 Gbps in 45nm CMOS SOI. The construction of the 64QAM constellation is achieved directly in the RF domain by utilizing three QPSK sub-TXs with weighted amplitude. This method significantly reduces the need to address power amplifier nonlinear effects in high-order modulation, thereby creating room for TX enhancements in both bandwidth and output power. To further improve TX performance, multi-step phase alignment strategies, and a local oscillator leakage suppression technique have been incorporated. With 40-GHz RF bandwidth, the RF-64QAM TX prototype is able to achieve a measured data rate of 120 Gbps with 15dBm effective isotropic radiated power (EIRP).

This dataset was collected from university students before, during, and after the COVID-19 lockdown in Southern California. Data collection happened continuously for the average of 7.8 months (SD=3.8, MIN=1.0, MAX=13.4) from a population of 21 students of which 12 have also completed an exit survey, and 7 started before the California COVID-19 lockdown order. This multimodal dataset included different means of data collection such as Samsung Galaxy Watch, Oura Ring, a Life-logger app named Personicle, a questionnaire mobile app named Personicle Questions, and periodical and personalised surveys. The dataset contains raw data from Photoplethysmogram (PPG), Inertial measurement unit (IMU), and pressure sensors in addition to processed data on heart rate, heart rate variability, sleep (bedtime, sleep stages, quality), and physical activity (step, active calories, type of activity). Ecological momentary assessments were collected from participants on daily and weekly bases containing their Positive and Negative Affect Schedule (PANAS) questionnaire and their emotional responses to COVID-19 and their health. Subjective data was also collected through monthly surveys containing standard mood and mental health surveys such as Beck Depression Inventory II (BDI-II), Brief Symptom Inventory (BSI), GAD-7, Inclusion of Other in the Self Scale (IOS-Partner), Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), Feasibility of Intervention Measure (FIM), Experiences in Close Relationships Scale Short Form (ECR-S), UCLA Three-Item Loneliness Scale (ULS), Multidimensional Scale of Perceived Social Support (MSPSS), Investment Model Scale (IMS), Conflict Management Scale (CMS), etc in addition to their response to important events and COVID-19. This dataset can be used to study emotions, mood, physical activity, and lifestyle of young adults through longitudinal subjective and objective measures. This dataset also contains valuable data regarding adjustment of lifestyle and emotions during the events of 2020 and 2021 including COVID-19 discovery and lockdown, Black Life Matter movement, 2020 US presidential elections, etc. On average, participants engaged in the EMA collection study at a rate of 86% (SD=10, MIN=65, MAX=99). Smartwatch usage saw an average participation rate of 51% (SD=20, MIN=16, MAX=88), while engagement with the Oura ring averaged at 85% (SD=12, MIN=60, MAX=99).