Miracle-dz/UniFAR-FTUs huggingface dataset:
CNN-Based Heart Sound Classification with an Imbalance-Compensating Weighted Loss Function

Heart sound auscultation is an effective method for early-stage diagnosis of heart disease. The application of deep neural networks is gaining increasing attention in automated heart sound classification. This paper proposes deep Convolutional Neural Networks (CNNs) to classify normal/abnormal heart sounds, which takes two-dimensional Mel-scale features as input, including Mel frequency cepstral coefficients (MFCCs) and the Log Mel spectrum. We employ two weighted loss functions during the training to mitigate the class imbalance issue. The model was developed on the public PhysioNet/Computing in Cardiology Challenge (CinC) 2016 heart sound database. On the considered test set, the proposed model with Log Mel spectrum as features achieves an Unweighted Average Recall (UAR) of 89.6%, with sensitivity and specificity being 89.5% and 89.7% respectively. This work proposes a CNN-based model to enable automated heart sound classification, which can provide auxiliary assistance for heart auscultation and has the potential to screen for heart pathologies in clinical applications at a relatively low cost.
Rethinking ImageNet Pre-training for Computational Histopathology

Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on a Histopathology dataset may provide better initialization. To prove this hypothesis, we train two commonly used Deep Learning model architectures - ResNet and DenseNet on a complex Histopathology classification dataset, and compare transfer learning performance with ImageNet pretrained weights. Based on the fine-tuning on three histopathology datasets including two different stains (H&E and IHC), we show that the domain specific pretrained weights are better suited for transfer learning. This is reflected by higher performance, lower training time as well as better feature reuse. Clinical Relevance — The paper establishes merit of using Histopathology domain specific pretrained weights rather than ImageNet pretrained weights.
Efficient and Private ECG Classification on the Edge Using a Modified Split Learning Mechanism

Cardiovascular diseases are the number one cause of death, comparable only to cancer in high-income countries. Electrocardiography (ECG) is an important non-invasive diagnostic technique for evaluating a patient's cardiac clinical status. Because of the large number of ECGs that are routinely taken, advanced decision support systems based on automatic ECG interpretation algorithms promise significant assistance to medical staff. This paper proposes a modified distributed machine learning system that builds on top of a technique called split learning. The modified system aims at reducing the communications and computation overhead of split learning achieving a private and secure ECG classification that is possible to implement on edge hardware. The modified SL system is tested on the PTB-XL data set using hardware representing edge devices. The results showed a significant reduction in the computation and communication overhead with minimal loss of performance.
Fake News Detection: An Investigation based on Machine Learning

The increasing level of fake news and its impact on social media networks has become a topic of huge concern. These kinds of news can create a negative and disastrous impact on individuals and the society. The reason behind the growth of such news is that it can be created in low cost and can easily be accessible and misinterpreted to harm anyone's social and religious emotions. Therefore, it is important to stop such news in spreading among the society. It is evident from the literature that different machine learning models are capable in handling such fake data. In this research, we conducted a preliminary investigation on the performance of five machine learning models on LIAR dataset. Also, we analyzed the efficiency of two feature extraction techniques such as Count Vectorizer and Term Frequency-Inverse Document Frequency (TF-IDF) Vectorizer and our results showed that TF-IDF performed slightly better than the Count Vectorizer.
Lightning YOLOv4 for a Surface Defect Detection System for Sawn Lumber

Lumber is a primary material for the production of various types of wood products. However, many industries still carry out the lumber quality inspection process manually, relying on human sight and instinct to compare many similar objects. To streamline the inspection process, this study developed a deep learning-based surface defect detection system with a proposed “lightning YOLOv4” model. Specifically, to improve the model's performance speed, we simplify CSPDarknet53 and path aggregation network (PANet) for the feature extraction stage of YOLOv4 by reducing the convolution layers. Moreover, we introduce the simplification technique to reduce the number of channels in CSPDarknet53 by multiplying it with the scaling coefficient. In addition, we add spatial attention module (SAM) to the structures, which can improve whole system performance on two types of lumber datasets (pine and rubber lumber). According to the experimental results, the proposed detection system improves the average precision of defect localization with the highest gap of 1.3%, as well as improves the frames per second (FPS) by 10.8 points over the baseline.
HIPAA Controlled Patient Information Exchange and Traceability in Clinical Processes

The digital transformation of healthcare processes is deeply changing the quality of healthcare services offered to the patient. Although it is often seen as highly beneficial, the move to digital connected health systems exposes both health providers and individuals to many risks ranging from privacy violations to medical identity usurpation Throughout this process of digitisation, it is essential that privacy protection and systemic compliance to personal data regulations such as HIPAA and GDPR are ensured by all healthcare stakeholders. This is essential to setup the boundaries and limitation imposed by the legislative framework with relation to the legitimate processing of sensitive data. In this paper, we are aiming to represent privacy and security controls as tags/labels to data elements highlighted in clinical processes and their automation as privacy protection filters to the data at user interface layers of the healthcare information system. As a data model we rely on the HL7-CDA standard for medical documents architecture thanks to its seamless and extensible data integration capabilities while exchanging EHR data.
Primordial black hole formation and hybrid inflation

We calculate the contribution to the curvature perturbation, that is generated while the waterfall field grows exponentially (which can occur only if the waterfall field mass is $m\gg H$). We consider the upper bound on its spectrum coming from constraints on the abundance of primordial black holes, taking account of possible non-gaussianity. The constraint is satisfied, but extrapolation of our result to the regime $m\sim H$ suggests that the constraint might not be satisfied there.
Improving Respiratory Timing Estimation Using Quality Indexing and Electrocardiogram-Derived Respiration

Numerous applications require accurate estimation of respiratory timings. Respiratory effort (RSP) measurement is a popular approach to accomplish this, especially when the tightness of the sensing belt around the chest can be ensured. In less controlled settings, however, belt looseness and artifacts from movement of the belt on the chest can corrupt the signal. This paper demonstrates that respiration quality indexing and outlier removal can help mitigate these issues, improving estimates of respiration rate (RR), inspiration time (Ti), and expiration time $(\mathrm{T}_{\mathrm{e}})$. , In a sample of 15 healthy human participants undergoing a protocol of five controlled breathing exercises in four postures each, electrocardiogram (ECG) and RSP signals were collected. RSP signals were processed to extract breath-by-breath estimates of RR, $\mathrm{T}_{\mathrm{i}}$, and $\mathrm{T}_{\mathrm{e}}$. These estimates were compared against ground truth spirometry-based estimates using Bland-Altman analysis. We find that incorporating quality indexing and outlier removal prior to feature extraction improves the 95% limits of agreement by 10-40%. We also find that by using ECG-derived respiration (EDR) during periods of RSP artifact, the data removal necessary for accurate respiratory timing estimation is significantly reduced ($\mathrm{P} < 0.05$ for all postures). These findings encourage the use of quality assessment and EDR to enhance the robustness of RR, $\mathrm{T}_{\mathrm{i}}$, and $\mathrm{T}_{\mathrm{e}}$ estimation from RSP signals. Clinical Relevance— Detecting stimulus-induced or pathological changes in respiratory function can enhance our understanding and monitoring of respiratory health. Quality assessment and the use of EDR help accomplish this by enabling more accurate measurement of respiratory timings.
Effect of the Current Intensity and Inter-Electrode Distance in Surface Electrical Stimulation: A FEM Simulation Study

In functional electric stimulation, variables such as electrode size, shape, and inter-electrodes distance can produce different neural and functional responses. In this work, a computational model combining FEM and MRG axon models is implemented to replicate two experimental studies that compare the effect of changing inter-electrode distance when applying FES to induce knee flexion. One work affirms that the stronger torque happens for greater distances, while the other obtain its maximum at lower distances. Using a simplified computational model gave another study perspective to understand why these two stimulation methodologies obtain different results. According to our results, an anodic stimulation occurs with greater current intensities and inter-electrode distances. This anodic effect can activate other nerve or motor points in the vicinity of the anode, explaining that more muscle fibers are recruited and generate an increased torque. Clinical Relevance - This work gives another view to understanding how the distance between electrodes affects neural activation, which has implications for optimizing clinical and exercise protocols using electrical stimulation techniques.
Patient Portal Adoption, Use, and Satisfaction among U.S. Adults in Late-stage COVID-19 Pandemic

Patient portals have been around for decades, but only became widely available to patients in the last 10 years as cultural shifts (i.e., patient-centered care) and policies (e.g., HITECH Act) encouraged healthcare organizations to offer patients electronic access to their medical records, as well as digital health services such as secure messaging with their healthcare team. Unfortunately, despite high interest among patients, research has documented generally slow uptake and low levels of use. However, the COVID-19 pandemic has brought new incentives to adopt and use portals (e.g., to access video visits, COVID-19 test results). While evidence from some healthcare organizations suggests that this has translated to higher patient engagement with portals, there have not been national-level studies to investigate this. Our cross-sectional survey addresses that gap by assessing a sample of U.S. adults' use, perceptions of, and satisfaction with patient portals at the end of 2021. We found high levels of portal awareness, adoption, and use, as well as satisfaction among survey participants that, in many cases, exceeds what has been found in past surveys of U.S. adults. In addition, while we did find some evidence of some disparities in portal adoption and use (e.g., lower adoption among those with lower levels of education completed), many of the factors that have been found in previous literature such as race were not associated with portal engagement in this study. Our results provide early evidence that there may finally be some progress in portal adoption and use, but more research is needed to understand what is driving this and whether and how this progress can be maintained post-pandemic. Addressing common patient concerns and suggestions may be one path forward.
Machine-Learning-Based Soft Sensors for Energy Efficient Operation of Crude Distillation Units

The oil refining industry is considered one of the largest energy-consuming industrial sectors worldwide and the third-largest global source of greenhouse gas emissions. In addition, increasingly restrictive environmental quality specifications for oil products worldwide require increased use of energy during the distillation process, further increasing the emissions. Therefore, energy usage reduction could help in two ways: to reduce the amount of greenhouse gas emissions and maximize the profits of the oil refining plants. The development and use of soft sensors to monitor the crude refining process in real-time enables timely insights and decision-making to ensure the products meet the required quality. Furthermore, the same approach can be used to further optimize the energy consumption over the multiple stages of the refining process. In this paper, we address the problem of predicting the energy consumption in a crude oil refinery. We do so on a real-world use case, with data obtained from a Tüpras refinery. We found the best performance was achieved with a CatBoost regressor when performing a tenfold cross-validation and assessing their significance against other models when comparing confidence intervals at a 95% level of significance.
Users Maintain Task Accuracy and Gait Characteristics During Missed Exoskeleton Actuations Through Adaptations In Joint Kinematics

In operational settings, lower-limb active exoskeletons may experience errors, where an actuation that should be present is missed. These missed actuations may impact users' trust in the system and the adapted human-exoskeleton coordination strategies. In this study, we introduced pseudorandom catch trials, in which an assistive exoskeleton torque was not applied, to understand the immediate responses to missed actuations and how users' internal models to an exoskeleton adapt upon repeated exposure to missed actuations. Participants (N = 15) were instructed to complete a stepping task while wearing a bilateral powered ankle exoskeleton. Human-exoskeleton coordination and trust were inferred from task performance (step accuracy), step characteristics (step length and width), and joint kinematics at selected peak locations of the lower limb. Step characteristics and task accuracy were not impacted by the loss of exoskeleton torque as hip flexion was modulated to support completing the stepping task during catch trials, which supports an impacted human-exoskeleton coordination. Reductions in ankle plantarflexion during catch trials suggest user adaptation to the exoskeleton. Trust was not impacted by catch trials, as there were no significant differences in task performance or gait characteristics between earlier and later strides. Understanding the interactions between human-exoskeleton coordination, task accuracy, and step characteristics will support development of exoskeleton controllers for non-ideal operational settings.
A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System

PPG (Photoplethysmography) and ECG (Electro-cardiogram) physiological signals have been known to have certain indicators for establishing blood pressure (BP) levels. Continuous monitoring of blood pressure (BP) is highly valuable for cardiovascular patients; however the existing non-invasive cuff-based blood pressure monitoring system is discreet and applies artificial pressure on patients' arms that is uncomfortable. The other invasive method is highly interventional in nature and is highly disturbing when the patient is recuperating in the hospital wards or elsewhere. A non-invasive cuff-less, non-disturbing, and continuous BP measurement system targeted toward surgical, clinical, and domestic usage are proposed in this work. A convolutional neural network (CNN) followed by a long short-term memory layer (LSTM) was designed and applied to ECG and PPG signals to present accurate systolic blood pressure (SBP), and diastolic blood pressure (DBP). For developing the CNN-LSTM layers, a novel and open-source dataset was compiled that consisted of PPG and ECG signals from 30 healthy participants and is made publicly available for further usage to the research community. The novel CNN-LSTM based cuff-less blood pressure evaluation system presented a mean absolute error (MAE) of 2.57 mmHg and 3.44 mmHg for SBP and DBP respectively with similar standard-deviation (SD) metrics. The characterized error metrics of the proposed system are the lowest to date when compared to other prior work. Clinical Relevance- A cuff-less BP estimation system allows patients to have easy access to blood pressure evaluation as well as aid in determining unsafe health ailments like hypertension. Ready access to such system will not only allow practitioners to continuously monitor BP in hospitals but also help patients to regularly monitor BP more frequently at their convenience.
Stable GSTC formulation for Maxwell’s equations

We revisit the classical zero-thickness Generalized Sheet Transition Conditions (GSTCs) which are a key tool for efficiently designing metafilms able to control the flow of light in a desired way. It is shown that it is more convenient to use an enlarged formulation of the GSTC in which the original metafilm is replaced by GSTCs that exclude the layer from the physical or computational domain. These new "layer" transition conditions have the same form as their "sheet" analogues hence they do not necessitate additional complications in their use; their advantage is that they provide a well-posed problem hence guaranty the stability of numerical schemes in the time-domain. These assessments are demonstrated for an all-dielectric structure; the effective susceptibility tensors are derived thanks to asymptotic analysis combined with homogenization technique and bounds for the susceptibilities entering the balance of energy are provided. While negative constant susceptibilities appear in the classical zero-thickness GSTCs, their values in the enlarged formulation are always positive which ensure the stability of the effective problem. Validation of the effective model is provided by means of comparison with direct numerics in two and three dimensions.
Implementation of an Intelligent Caravan Monitoring System Using the Controller Area Network

Nowadays, safety systems are an important feature for modern vehicles. Many accidents would have been occurred without them. In comparison with older vehicles, modern vehicles have a much better crumple zone, more airbags, a better braking system, as well as a much better and safer driving behaviour. Although, the vehicles safety systems are working well in these days, there is still space for improvement and for adding new security features. This paper describes the implementation of an Intelligent Caravan Monitoring System (ICMS) using the Controller Area Network (CAN), for the communication between the vehicle’s electronic system and the trailer’s electronic system. Furthermore, a comparison between the communication technology of this paper and a previous published paper will be made. The new system is faster, more flexible and more energy efficient.
Synthetic Generation of 3D Microscopy Images using Generative Adversarial Networks

Fluorescence microscopy images of cell organelles enable the study of various complex biological processes. Recently, deep learning (DL) models are being used for the accurate automatic analysis of these images. DL models present state-of-the-art performance in many image analysis tasks such as object classification, segmentation and detection. However, to train a DL model a large manually annotated dataset is required. Manual annotation of 3D microscopy images is a time-consuming task and must be performed by specialists in the area. Thus, only a few images with annotations are typically available. Recent advances in generative adversarial networks (GANs) have allowed the translation of images with some conditions into realistic looking synthetic images. Therefore, in this work we explore approaches based on GANs to create synthetic 3D microscopy images. We compare four approaches that differ in the conditions of the input image. The quality of the generated images was assessed visually and using a quantitative objective GAN evaluation metric. The results showed that the GAN is able to generate synthetic images similar to the real ones. Hence, we have presented a method based on GANs to overcome the issue of small annotated datasets in the biomedical imaging field.
Blood Pressure Estimation using a Single Channel Bio-Impedance Ring Sensor

The demand for non-obtrusive, accurate, and continuous blood pressure (BP) monitoring systems is becoming more prevalent with the realization of its significance in preventable cardiovascular disease (CVD) globally. Current cuff-based standards are bulky, uncomfortable, and are limited to discrete recording periods. Wearable sensor technologies such as those using optical photoplethysmography (PPG) have been used to develop blood pressure estimation models through a variety of methods. However, this technology falls short as optical based systems have bias favoring lighter skin tones and lower body fat compositions. Bioimpedance (Bio-Z) is a capable modality of sensing arterial blood flow without implicit inadvertent bias towards individuals. In this paper we propose a ring-based bioimpedance system to capture arterial blood flow from the digital artery of the finger. The ring design provides a more compact wearable device utilizing only a single Bio-Z channel, making it a familiar fit to individuals. Post-processing the acquired Bio-Z signals, we extracted 9 frequency domain features from windowed beat cycles to train subject specific regression models. Results indicate the average mean absolute errors for systolic/diastolic BP to be 4.38/3.63mmHg, consistent with AAMI standards.
Magnetic Field Instabilities in Neutron Stars

Magnetic fields represent a crucial aspect of the physics and astrophysics of neutron stars. Despite its great relevance, the internal magnetic field configuration of neutron stars is very poorly constrained by the observations, and understanding its properties is a long-standing theoretical challenge. The investigation on the subject is focused on the search for those magnetic field geometries which are stable on several Alfv\`en timescales, thus constituting a viable description of neutron star interiors. Assesing the stability of a given magnetic field geometry is therefore an important part of this research. So far only simple configurations, such as the purely poloidal or purely toroidal ones, have been studied in detail in perturbation theory and, most recently, by means of nonlinear magnetohydrodynamic simulations. Here we review the basic results of the state-of-the-art general relativistic nonlinear studies, discussing the present status of the field and its future directions.