Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Strategy for Sliding ECG Analysis during Cardiopulmonary Resuscitation: Influence of the Hands-Off Time on Accuracy

This study aims to present a novel deep learning algorithm for a sliding shock advisory decision during cardiopulmonary resuscitation (CPR) and its performance evaluation as a function of the cumulative hands-off time. We retrospectively used 13,570 CPR episodes from out-of-hospital cardiac arrest (OHCA) interventions reviewed in a period of interest from 30 s before to 10 s after regular analysis of automated external defibrillators (AEDs). Three convolutional neural networks (CNNs) with raw ECG input (duration of 5, 10, and 15 s) were applied for the shock advisory decision during CPR in 26 sequential analyses shifted by 1 s. The start and stop of chest compressions (CC) can occur at arbitrary times in sequential slides; therefore, the sliding hands-off time (sHOT) quantifies the cumulative CC-free portion of the analyzed ECG. An independent test with CPR episodes in 393 ventricular fibrillations (VF), 177 normal sinus rhythms (NSR), 1848 other non-shockable rhythms (ONR), and 3979 asystoles (ASYS) showed a substantial improvement of VF sensitivity when increasing the analysis duration from 5 s to 10 s. Specificity was not dependent on the ECG analysis duration. The 10 s CNN model presented the best performance: 92–94.4% (VF), 92.2–94% (ASYS), 96–97% (ONR), and 98.2–99.5% (NSR) for sliding decision times during CPR; 98–99% (VF), 98.2–99.8% (ASYS), 98.8–99.1 (ONR), and 100% (NSR) for sliding decision times after end of CPR. We identified the importance of sHOT as a reliable predictor of performance, accounting for the minimal sHOT interval of 2–3 s that provides a reliable rhythm detection satisfying the American Heart Association (AHA) standards for AED rhythm analysis. The presented technology for sliding shock advisory decision during CPR achieved substantial performance improvement in short hands-off periods (>2 s), such as insufflations or pre-shock pauses. The performance was competitive despite 1–2.8% point lower ASYS detection during CPR than the standard requirement (95%) for non-noisy ECG signals. The presented deep learning strategy is a basis for improved CPR practices involving both continuous CC and CC with insufflations, associated with minimal CC interruptions for reconfirmation of non-shockable rhythms (minimum hands-off time) and early treatment of VF (minimal pre-shock pauses).
Loss Function Design for DNN-Based Sound Event Localization and Detection on Low-Resource Realistic Data

This study focuses on the design of a loss function for a deep neural network (DNN)-based model with two branches, which is used to solve sound event localization and detection (SELD) on low-resource realistic data. To this end, we employ a secondary network for audio classification, which provides global event information to the main network, enabling it to make robust SELD predictions. Furthermore, we suggest utilizing a momentum strategy for direction-of-arrival (DOA) estimation, taking advantage of the strong temporal consistency of sound events, thereby effectively reducing localization error. Lastly, we incorporate a regularization term into the loss function to alleviate the overfitting problem on the small dataset. We evaluate our proposed methods on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3 dataset, and the results demonstrate consistent improvements in SELD performance. In comparison to the baseline system, the proposed loss function yields significantly improved results for both localization and detection metrics on realistic data. Moreover, the proposed loss function demonstrates its ability to generalize across different network architectures, as evidenced by the consistent improvements achieved.
Application and Progress of Artificial Intelligence in Fetal Ultrasound

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician’s workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.
An ellipsoid‐based framework for fault estimation and remaining useful life prognosis

In this paper, a novel ellipsoid‐based framework for fault estimation and remaining useful life prognosis is presented. The fault is considered to be a slow‐changing fault caused by actuator performance degradation. The proposed framework is divided into three steps: First, the actuator fault interval estimation is determined by combining observer design with ellipsoidal analysis. The fault estimation results allow to evaluate the system's current health condition and predict the remaining useful life based on the performance degeneration model. Since the model parameters are constantly changing, an ellipsoid‐based extended Kalman filter is presented to estimate the parameters via the fault estimation results. Specially, a trace optimization method is proposed to design the filter gain matrix, which offers high computational efficiency. Finally, the future degradation states are predicted via the latest parameters until the failure thresholds are reached, which determines the remaining useful life interval prediction results. Numerical simulations are conducted to verify the viability and validity of the presented approach.
A Novel Image Super-resolution Method Combined with Edge Focusing

There are some problems existing in traditional image interpolation algorithms such as blockiness, blurring, etc. To overcome these problems, a novel image super-resolution algorithm based on edge detection and different filters was proposed. The algorithm firstly calculates an initial super-resolution image by Bicubic interpolation method. Then edge focusing is used to determine whether the interpolated point is on the edges. Finally, the intensity value of super-resolution image is achieved by calculating the weighting values of filters for the neighboring region. Experimental results demonstrate that the proposed algorithm makes full use of the edge information and local features to obtain better performance.
Deep Learning Denoising Improves and Homogenizes Patient [18F]FDG PET Image Quality in Digital PET/CT

Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CVliv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CVliv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CVliv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CVliv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUVmax and SUVpeak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of −7.7% and −2.8%, respectively. DL-based PET denoising by Subtle PETTM allowed [18F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities.
Synchronization between two Chaotic Systems with Different Order by Using Active Control

This paper addresses the synchronization of two chaotic systems with different order. By applying the techniques of active control, this investigation demonstrates that the time evolution of the slave system can be synchronized by the master system, even though these two systems are strictly different. Two examples are given to demonstrate the techniques. In the first example, Duffing oscillator (second order) is selected as the slave system and Rössler (third order) system as the master system. In the second example, Lorenz system (third order) is synchronized by two-level laser system (fifth order). Furthermore, the mean squared error (MSE) of two synchronized systems is discussed, and the method is analyzed further.
Flexible InSnZnO Thin Film Phototransistors for Deep Ultraviolet Detection

Three-terminal phototransistors, with a regular photoconductor and the flexibility to control channel carriers, have been considered to be an alternative solution for the improvement in photodetector (PD) performance. This work first demonstrates ZnO-based deep ultraviolet (DUV) phototransistors prepared by co-sputtering. The response of the InSnZnO (ITZO) phototransistor to near-ultraviolet (NUV) light is effectively suppressed at low levels by the passivation layer that is modified with diethylamino hydroxybenzoyl hexyl benzoate. Moreover, the fabricated DUV phototransistors exhibited excellent electrical characteristics and competitive DUV photodetection performance, such as a low threshold voltage of 0.8 V, a high responsivity of 416 AW<inline-formula> <tex-math notation="LaTeX">$^{-{1}}$ </tex-math></inline-formula>, and a considerable rejection ratio of <inline-formula> <tex-math notation="LaTeX">${3.26}\times {10}^{{3}}$ </tex-math></inline-formula> at <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {gs }}= {0}$ </tex-math></inline-formula> V. Unlike a-Ga<sub>2</sub>O<sub>3</sub> phototransistors, the ITZO phototransistors exhibit a more considerable carrier mobility (30.7 cm<inline-formula> <tex-math notation="LaTeX">$^{{2}}\,\,\text{V}^{-{1}}\text{s}^{-{{1}}}$ </tex-math></inline-formula>), which means this sensor could work as the sensing part and compose the readout circuit, thereby simplifying the process of the sensor system.
Natural Compounds, Optimal Combination of Brusatol and Polydatin Promote Anti-Tumor Effect in Breast Cancer by Targeting Nrf2 Signaling Pathway

Triple-negative breast cancer (TNBC) has been clearly recognized as a heterogeneous tumor with the worst prognosis among the subtypes of breast cancer (BC). The advent and application of current small-molecule drugs for treating TNBC, as well as other novel inhibitors, among others, have made treatment options for TNBC more selective. However, there are still problems, such as poor patient tolerance, large administration doses, high dosing frequency, and toxic side effects, necessitating the development of more efficient and less toxic treatment strategies. High expression of Nrf2, a vital antioxidant transcription factor, often promotes tumor progression, and it is also one of the most effective targets in BC therapy. We found that in MDA-MB-231 cells and SUM159 cells, brusatol (BRU) combined with polydatin (PD) could significantly inhibit cell proliferation in vitro, significantly downregulate the expression of Nrf2 protein as well as the expression of downstream related target genes Heme Oxygenase-1 (HO-1) and NAD(P)H dehydrogenase, quinone 1 (NQO1), and promote reactive oxygen species (ROS) levels to further strengthen the anti-tumor effect. Furthermore, we discovered in our in vivo experiments that by reducing the drug dosage three times, we could significantly reduce tumor cell growth while avoiding toxic side effects, providing a treatment method with greater clinical application value for TNBC treatment.
Model-Free Online Learning for Waveform Optimization In Integrated Sensing And Communications

This paper considers waveform optimization problems for managing and mitigating interference in integrated sensing and communications (ISAC) systems. In particular, we consider dynamic shared spectrum scenarios where channel and interference statistics are nonstationary. The focus is on allocating frequency and power resources in multicarrier ISAC systems. A data-efficient Model-Free Online Learning (MFOL) algorithm is proposed as an alternative to the previously proposed Model-Based Online Learning (MBOL) approach. Empirical results show that the MFOL method learns slower than the MBOL method but can perform better when a large number of training samples are available.
Glycemic Abnormalities in Pancreatic Cystic Lesions—A Single-Center Retrospective Analysis

Background and Objectives: Glucose metabolism alterations are very common in solid pancreatic lesions, particularly in pancreatic cancer. Similarly, diabetes and especially new-onset diabetes (NOD) have been associated with the malignant transformation of pancreatic cysts. We aimed to assess the prevalence and relevant associations of glycemic abnormalities in pancreatic cystic lesions (PCLs) in a retrospective analysis. Materials and Methods: We retrospectively recruited all patients who underwent endoscopic ultrasound for a PCL over a period of 36 months (January 2018 to December 2021). Final diagnosis was set by means of tissue acquisition, surgery, follow-up, or board decision. Demographic and clinical data, laboratory workup, and imaging features were extracted from the patients’ charts according to a predefined protocol. We considered fasting blood glucose (FBG) and HbA1c values and stratified the patients as nondiabetic (FBG ≤ 99 mg/dL, HbA1c ≤ 5.6%, no history of glycemic abnormalities), prediabetic (FBG 100–125 mg/dL, HbA1c 5.7–6.4%), or diabetic (long-lasting diabetes or NOD). Results: Altogether, 81 patients were included, with a median age of 66 years, and 54.3% of them were male. The overall prevalence of fasting hyperglycemia was 54.3%, comprising 34.6% prediabetes and 22.2% diabetes, of which 16.7% had NOD. The mean FBG and HbA1c levels were higher in malignant and premalignant PCLs (intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), cystadenocarcinoma, and cystic neuroendocrine tumor) compared to the benign lesions (pseudocysts, walled-off necrosis, and serous cystadenoma): 117.0 mg/dL vs. 108.3 mg/dL and 6.1% vs. 5.5%, respectively. Conclusions: Hyperglycemia and diabetes are common in PCLs, with a high prevalence in premalignant and malignant cysts. Screening and follow-up for glycemic abnormalities should be routinely conducted for PCLs, as they can contribute to a tailored risk assessment of cysts.
A Frequency-Domain Recursive Least-Squares Adaptive Filtering Algorithm Based On A Kronecker Product Decomposition

This paper proposes a frequency-domain recursive least-squares (RLS) adaptive filtering algorithm for identifying time-varying acoustic systems in noisy environments. The Kronecker product (KP) is employed to decompose the model filter of the acoustic channel impulse response into two sets of short sub-filters, based on which a generalized frequency-domain signal model and the associated cost function are established. A KP based RLS algorithm is subsequently deduced. In comparison with the conventional frequency-domain RLS adaptive filter, the presented algorithm is not only computationally more efficient, but also has a faster convergence rate for the identification of acoustic systems regardless of whether the excitation is a white sequence or a speech signal.
DÉVELOPPEMENT D'OUTILS PRONOSTIQUES DYNAMIQUES DANS LE CANCER DE LA PROSTATE LOCALISÉ TRAITÉ PAR RADIOTHÉRAPIE

Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the timeto-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.
Hierarchical Distribution-Based Tightly-Coupled LiDAR Inertial Odometry

LiDAR inertial odometry (LIO) has attracted much attention due to the complementarity of LiDAR and IMU measurements. In the distribution-based LIO, the components related to distribution covariance in the residual and residual uncertainty from the LiDAR measurement noise is neutralized. And the resultant point cloud constraint degeneration problem severely affects the accuracy of pose estimation. In this article, a hierarchical tightly-coupled LIO based on distribution is proposed. By excluding the eigenvalue elements in the distribution covariance component with the designed loss function, the uncertainty of corresponding residual is rectified. As a result, the degeneration problem is solved. With anti-degeneration point-to-distribution constraints, a LiDAR inertial odometry based on iterated extended Kalman filter and a factor graph optimization are designed and organized in a hierarchical way to achieve coarse-to-fine pose estimation, where LiDAR and IMU measurements are tightly coupled in both layers. In this way, the respective advantages of high efficiency and high accuracy from filtering and optimization are combined, which offers high-fidelity estimation results in real time. The effectiveness of the proposed method is verified through experiments on the public NC and ENC datasets.
Nord: Non-Matching Reference Based Relative Depth Estimation from Binaural Speech

We propose NORD: a novel framework for estimating the relative depth between two binaural speech recordings. In contrast to existing depth estimation techniques, ours only requires audio signals as input. We trained the framework to solve depth preference (i.e. which input perceptually sounds closer to the listener’s head), and quantification tasks (i.e. quantifying the depth difference between the inputs). In addition, training leverages recent advances in metric and multi-task learning, which allows the framework to be invariant to both signal content (i.e. non-matched reference) and directional cues (i.e. azimuth and elevation). Our framework has additional useful qualities that make it suitable for use as an objective metric to benchmark binaural audio systems, particularly depth perception and sound externalization, which we demonstrate through experiments. We also show that NORD generalizes well under different reverberation and environments. The results from preference and quantification tasks correlate well with measured results.
Low Precision Representations for High Dimensional Models

The large memory footprint of high dimensional models require quantization to a lower precision for deployment on resource constrained edge devices. With this motivation, we consider the problems of learning a (i) linear regressor, and a (ii) linear classifier from a given training dataset, and quantizing the learned model parameters subject to a pre-specified bit-budget. The error metric is the prediction risk of the quantized model, and our proposed randomized embedding-based quantization methods attain near-optimal error while being computationally efficient. We provide fundamental bounds on the bit-budget constrained minimax risk that, together with our proposed algorithms, characterize the minimum threshold budget required to achieve a risk comparable to the unquantized setting. We also show the efficacy of our strategy by quantizing a two-layer ReLU neural network for non-linear regression. Numerical simulations show the improved performance of our proposed scheme as well as its closeness to the lower bound.
Accounting for fuel in fire danger forecasts: the fire occurrence probability index (FOPI)

A new fire danger index is proposed to overcome one of the most important limitations of current fire danger metrics. The fire occurrence probability index (FOPI) combines the Canadian fire weather index (FWI) with remote observations of vegetation characteristics to better predict landscape flammability. The FOPI is designed to improve fire danger predictions in all fuel-limited environments where fire is driven by the short-term drying of intermittently-available fuel. The FOPI considerably outperforms the FWI in arid biomes while remaining comparable to the FWI where fuel is abundant.
Influence of long-scale length radial electric field components on zonal flow-like structures in the TJ-II stellarator

The influence of long-scale length radial electric fields on zonal flows-like structures has been studied in the TJ-II stellarator. This relation has been investigated in the edge plasma using two electrical rake probes. The results presented here show an empirical correlation between the properties of long-range correlations (LRCs) with zonal flow-like structures and the magnitude of radial (neoclassical, NC) electric fields in TJ-II neutral beam heated plasmas. These experimental findings show that the enhancement of the NC radial electric field Er increases the magnitude of LRCs, considered as a proxy of zonal flows, while the radial correlation length of the plasma potential fluctuations was found to decrease by about 40%. A strong relation between the magnitude of electric field structures with long and short radial scales was found. The calculated Er×B shearing rate corresponding to the short scale length structures of the radial electric field may be sufficient to regulate turbulence.
Targeting Sarcopenia as an Objective Clinical Outcome in the Care of Children with Spinal Cord-Related Paralysis: A Clinician’s View

Muscle loss is consistently associated with immobility and paralysis and triggers significant metabolic and functional changes. The negative effects of sarcopenia are amplified in children who are in the process of building their muscle mass as part of development. Because muscle mass loss is consistently associated with increased morbidity and mortality throughout life, optimizing the size and health of muscles following a neurologic injury is an objective target for therapeutic interventions. This review hypothesizes that muscle mass correlates with functional outcomes in children with paralysis related to spinal cord-related neurologic deficits. We propose that the measurement of muscle mass in this population can be used as an objective outcome for clinical long-term care. Finally, some practical clinical approaches to improving muscle mass are presented.