Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel-Based Reconstruction of C-11-Hydroxyephedrine Cardiac PET Images of the Sympathetic Nervous System

Image reconstruction for positron emission tomography (PET) can be challenging and the resulting image typically has high noise. The kernel-based reconstruction method [1], incorporates prior anatomic information in the reconstruction algorithm to reduce noise while preserving resolution. Prior information is incorporated in the reconstruction algorithm by means of spatial kernels originally used in machine learning. In this paper, the kernel-based method is used to reconstruct PET images of sympathetic innervation in the heart. The resulting images are compared with standard Ordered Subset Expectation Maximization (OSEM) reconstructed images qualitatively and quantitatively using data from 6 human subjects. The kernel-based method demonstrated superior SNR with preserved contrast and accuracy compared to OSEM.
Cuff-Less Blood Pressure Monitoring with a 3-Axis Accelerometer

Objective: The aim was to propose a cuff-less, cost-efficient, and ultra-convenient blood pressure monitoring technique with a 3-axis accelerometer. Methods: The efficacy of the proposed approach was examined in 8 young healthy volunteers undergoing different activities with a 3-axis accelerometer leveled on their upper chest. The 3-dimensional accelerations were exploited to select features for the calculation of systolic pressure (SP) and diastolic pressure (DP); the whole process involved signal processing, feature extraction, linear multivariate regression, and leave-one-out cross validations (LOOCV). Results: DP and SP could be approximated with the linear combination of the extracted features: the L2 norm of lateral acceleration for both DP and SP, state variation (defined in the proposed algorithm) of vertical acceleration for SP, and I-J interval (defined in ballistocardiogram) of vertical acceleration for DP. The correlation coefficient (r) of the estimated and the measured DP was 0.97, and for SP, r = 0.96. In LOOCV, our best validated results in difference errors were −0.02±3.82 mmHg for DP and −0.59 ± 7.46 mmHg for SP. Conclusion: Compared to AAMI criteria, the proposed acceleration-based technique fulfilled the requirement. The accelerometer-based technique showed the potential to monitor blood pressure cuff-lessly, cost-efficiently, ultra-conveniently, and to be embedded in a long-term wearable device for clinical usage.
Automated Identification and Localization of Premature Ventricle Contractions in Standard 12-Lead ECGs

It can take up to twelve hours to identify and precisely localize the origin of a premature ventricle contraction (PVC). This work is investigating a neural network (NN) as an automated alternative to a human expert for detecting and locating the arrhythmogenic zone—with the goal of accelerating the PVC detection process. The proposed shallow neural network contains one hidden layer with multiple hidden units. Three data sets consisting of a total of 328 samples of 12 lead resting ECGs were used to train as well as to evaluate the NN. After performing several iteration tests with different training sets, the most promising configuration was established. The first cohort consisted of a ratio of 1:1, the second cohort of a ratio of 25:4 (NO PVC:PVC). The study has resulted in high sensitivity and specificity values in NN’s performance given uniformly distributed training data. The proposed NN was shown to perform at a level comparable to that of a human expert.
Recurrent neural networks for reconstructing complex directed brain connectivity

While Granger Causality(GC)-based approaches have been widely employed in a vast number of problems in network science, the vast majority of GC applications are based on linear multivariate autoregressive (MVAR) models. However, it is well known that real-life system (and biological networks in particular) exhibit notable nonlinear behavior, hence undermining that validity of MVAR-based approaches to estimating GC (MVAR-GC). In this paper, we define a novel approach to estimating nonlinear, directed within-network interactions based on a specific class of recurrent neural networks (RNN) termed echo-state networks (ESN). We reformulate the classical GC framework in terms of ESN-based models for multivariate signals generated by arbitrarily complex networks, and characterize the ability of our ESN-based Granger Causality (ES-GC) to capture nonlinear causal relations by simulating multivariate coupling in a network of nonlinearly interacting, noisy Duffing oscillators operating in a chaotic regime. Synthetic validation shows a net advantage of ES-GC over all other estimators in detecting nonlinear, causal links. We then explore the structure of EC-GC networks in the human brain in functional MRI data from 1003 healthy subjects scanned at rest at 3T, discovering previously unknown between-network interactions. In summary, ES-GC performs significantly better than commonly used and recently developed GC detection tools, making it a superior tool for the analysis of e.g. multivariate biological networks.
Analysis of Time Delay between Bioimpedance and Respiratory Volume Signals under Inspiratory Loaded Breathing

Bioimpedance is known for its linear relation with volume during normal breathing. For that reason, bioimpedance can be used as a noninvasive and comfortable technique for measuring respiration. The goal of this study is to analyze the temporal behavior of bioimpedance measured in four different electrode configurations during inspiratory loaded breathing. We measured four bioimpedance channels and airflow simultaneously in 10 healthy subjects while incremental inspiratory loads were imposed. Inspiratory loading threshold protocols are associated with breathing pattern changes and were used in respiratory mechanics studies. Consequently, this respiratory protocol allowed us to induce breathing pattern changes and evaluate the temporal relationship of bioimpedance with volume. We estimated the temporal delay between bioimpedance and volume respiratory cycles to evaluate the differences in their temporal behavior. The delays were computed as the lag which maximize the cross-correlation of the signals cycle by cycle. Six of the ten subjects showed delays in at least two different inspiratory loads. The delays were dependent on electrode configuration, hence the appearance of the delays between bioimpedance and volume were conditioned to the location and geometry of the electrode configuration. In conclusion, the delays between these signals could provide information about breathing pattern when breathing conditions change.
Saliency Map Extraction in Human Crowd RGB Data

Saliency map in human crowded scene is a prediction of regions which attracts human visual attention. Humans have an ability to analyze the context of visual scene and focus their attention to salient regions in the crowd scene. In this work, we propose a novel convolutional neural network based method for saliency prediction. Unlike classical works on crowd scene using hand-crafted face features, our model extracts deep features using convolutional layers from image classification model and learns the global context using large receptive convolutional layers. Self-attention mechanism is applied to detect the dependency between elements of feature maps. This model overperformed state-of-the-art methods on the saliency in human crowd Eyecrowd dataset.
Towards a fully spatially coded brain-computer interface: simultaneous decoding of visual eccentricity and direction

By encoding visual targets with different locations relative to a stimulus, spatially coded brain-computer interface (BCI) has regained interest nowadays. Recent spatially coded BCI studies have demonstrated the feasibility of single-stimulus, multi-target BCIs, suggesting their potentials for simple and efficient applications. However, these studies have only decoded the visual direction information from the neural responses. To fully utilize the visual spatial information, it is necessary to include the visual eccentricity information as well. In the present study, the decodability of visual eccentricity information for BCI application was investigated for the first time. Sixteen targets were encoded simultaneously with eight directions and two eccentricities relative to a visual motion stimulus. Distinct neural spatial patterns and response strengths of motion-onset visual evoked potentials were elicited in the 16 attention conditions. The offline analysis reached an average classification accuracy of 63.1±11.5%, and the best-performing participant achieved an accuracy of 81.9%, well above the chance level (i.e., 6.25%) for 16-target classification. The results suggested the feasibility of simultaneous decoding of visual eccentricity and direction information towards a fully spatially coded BCI.
Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels*

Intrinsic connectivity networks (ICNs) have been widely studied using functional magnetic resonance imaging (fMRI) data and electrophysiological data (e.g., electroencephalography (EEG) or magnetoencephalography (MEG)). Two major methods, i.e., seed-based correlation analysis (SBCA) and independent component analysis (ICA), are widely used to extract ICNs. Among them, ICA usually involves a dual regression analysis in order to obtain final spatial definitions of ICNs. Recently, we proposed a framework that includes cortical source imaging, source-level ICA, and statistical correlation analysis, to extract cortical ICNs from resting-state EEG data. In the present study, we proposed an alternative framework that uses sensor-level ICA and regression analysis instead of source-level ICA and correlation analysis, considering the well-studied characteristics of sensor-level ICs in differentiating neural activities from artifacts and the benefit of regression in accommodating multivariate analysis over correlation. In the present study, we mainly investigated the performance of the proposed procedure in extracting cortical ICNs. Meanwhile, we also investigated different variants of the regressors sampled at different frequencies to formulate the regression model. The results demonstrated that cortical ICNs corresponding to major ICNs identified in literature could be obtained by the proposed framework. In general, spatial patterns of cortical ICNs obtained via both correlation and regression analyses show statistically significant similarity. However, the cortical ICNs reconstructed using the regression analysis exhibit more focal and more superficial spatial patterns, in general, that the cortical ICNs from the correlation analysis. The different variants of regressors at the same sampling frequency do not produce obvious impacts on spatial patterns of cortical ICNs, while the different sampling frequencies show large effects on extracted spatial patterns of cortical ICNs. In summary, it is suggested that the proposed framework with the regression analysis is promising in reconstructing cortical ICNs from EEG, while the sampling frequency used in the formulation process of regressors may have large impacts on reconstructed cortical ICN patterns.
Analysis of Educational Data Mining using Classification

Higher education institutions are often very curious to know about the success rate of the students throughout their study. For this reason, they need to use several methods like physical examination, Statistical methods and currently prevailing data mining techniques for the prediction of student’s performance. An upcoming area of research which uses techniques of data mining is known as Educational Data Mining. It involves machine learning algorithms and statistical techniques to help the user for interpretation of student’s learning habits, their academic performance and further improvement if required. In this paper we will discuss various techniques of data mining which are useful for predicting performance level of students. For this we used dataset of kalboard 360and applied it on weka to analyze the data mining techniques
A Magnetic Actuation System for the Active Microrheology in Soft Biomaterials

Microrheology is a key technique to characterize soft materials at small scales. The microprobe is wirelessly actuated and therefore typically only low forces or torques can be applied, which limits the range of the applied strain. Here, we report a new magnetic actuation system for microrheology consisting of an array of rotating permanent magnets, which achieves a rotating magnetic field with a spatially homogeneous high field strength of ~100 mT in a working volume of~20 × 20× 20 mm3, Compared to a traditional electromagnetic coil system, the permanent magnet assembly is portable and does not require cooling, and it exerts a large magnetic torque on the microprobe that is an order of magnitude higher than previous setups. Experimental results demonstrate that the measurement range of the soft gels' elasticity covers at least five orders of magnitude. With the large actuation torque, it is also possible to study the fracture mechanics of soft biomaterials at small scales.
B-SEFI: A Binary Level Soft Error Fault Injection Tool

Soft errors are becoming more prominent in modern computing systems due to the increasing integration of chips. These faults pose a major challenge for memories and logic circuits in high performance microprocessors. In this paper, a tool based on PIN was designed to simulate soft error, which can implement hardware fault injection at the machine code level. Our tool is based on binary instrumentation that supports an accurate and low-cost fault injection. We apply the tool to five classic machine learning programs and analyze the programs' weakness to soft errors by simulating bit flips. We investigate the soft fault effect on performance and accuracy according to experimental results. Our study reveals that these programs are highly sensitive to soft errors, therefore, the tool provides a new effective approach for future resiliency research.
Evaluating the Accuracy of Consensus Nanosequencer Squiggles Generated by Dynamic Time Warp Barycentre Averaging (DBA)*

Picoamperage signals are generated as each nucleotide of a DNA or RNA molecule is ratcheted through a nanosequencer’s nanopores by motor proteins. These are segmented into step-current level signals, "squiggles", representing the nucleotide sequence. It has been suggested that applying dynamic time warp Barycentre Averaging (DBA) to multiple noisy squiggles can generate a lower noise, less-distorted, consensus signal that retains the key squiggle characteristics that would be distorted by other averaging approaches. We discuss experimental results obtained when developing DBA consensus signals from squiggles produced by an Oxford MinION nanosequencer squiggle convertor during an Enolase study. Metrics are proposed to identify differences between the known gold standard and consensus signals, and the level of self-consistency between consensus signals developed from noisy squiggles with different length distortions. A number of location-specific differences between the gold and consensus squiggles were identified.
Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation

Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset.
A Graph Signal Processing Approach to Study High Density EEG Signals in Patients with Disorders of Consciousness

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.
INSA Wound Mapping System: II. Preliminary Porcine Results*

In the first paper, a versatile wound monitoring system, capable of mapping the extent of a wound and characterizing the tissues involved without removing the dressing, was presented. This was achieved through electrical impedance measurements, made via an electrode matrix embedded within the wound dressing.In this second paper, we present early mapping results obtained on porcine models. It was found that there was a good correlation between wound areas derived using digital image planimetry and those using a 49-electrode impedimetric electrode array.
Electrical breakdown of sandwiched polymers — The effect of dielectric properties and trapped electrons

Nanocomposite (NC) polymers are commonly employed because of their high breakdown strength, but the underlying mechanism of this phenomenon is not well understood. In the present study, specimens of polyethylene terephthalate (PET) films coated with epoxy resin (EP) and filled with nano-SiO2 and Si3N4 at concentrations of 0, 0.5, 1, 2, 5 and 10 wt % were subjected to DC and impulse voltages to breakdown. The effects of the types of nano-fillers and concentrations on the dielectric properties, crystallinity, and space charge trap characteristics were investigated on the breakdown strength. The experimental results show that improved DC breakdown strength of a sandwich structure of PET/nano-EP film is obtained even at 10 wt%, which is attributed to the effect of increased trap density for deep traps. In addition, to experimentally establish the dominant factors during impulse breakdown, a systematic theoretical model was used to account for the variations of impulse breakdown performance. It is concluded that the frequency-dependent dielectric characteristics plays an important role in determining the absorption of activation energy for trapped electrons, which affects the instantaneous insulating performance of NC polymers.
Fair Down to the Device: A GC-Aware Fair Scheduler for SSD

Solid-state drives (SSD) are the mainstream solutions for massive data storage today. For modern computer systems, fair resource assignment is a critical design consideration and has drawn great interests in recent years. Although there are several I/O fairness schedulers proposed on the host side for SSDs, process fairness could still be dramatically degraded if garbage collection (GC) is triggered in the device side. A GC operation could block I/O requests, which causes unpredictable read/write latency variation and further impacts fairness between processes. This paper proposes Fair-GC, a novel coordinated host and device I/O scheduling strategy to achieve true fairness considering GC interferences. The key idea is to orchestrate GC operations inside SSDs carefully such that performance of a process is penalized by GC in the same degree (or comparable) as when it runs alone. In this way, the I/O fairness maintained by the host-side scheduler can be maintained in the presence of GC. Furthermore, our scheduler ensures that the timeslice of a process maintained at the host-side scheduler is updated in a timely manner to avoid unnecessary slowdown for maintaining fairness. Experimental results with a wide range of workloads verify that the proposed technique can achieve fairness as well as improve the throughput significantly. Compared to conventional fairness-based I/O scheduler, Fair-GC can reduce the slowdown of real applications by up to 99%, and improve the throughput by as much as 225%, respectively.
Facial Expression Recognition using Convolutional Neural Network with Data Augmentation

Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.
Assessment of In-Meal Eating Behaviour using Fuzzy SVM

Certain patterns of eating behaviour during meal have been identified as risk factors for long-term abnormal eating development in healthy individuals and, eventually, can affect the body weight. To detect early signs of problematic eating behaviour, this paper proposes a novel method for building behaviour assessment models. The goal of the models is to predict whether the in-meal eating behaviour resembles patterns associated with obesity, eating disorders, or low-risk behaviours. The models are trained using meals recorded with a plate scale from a reference population and labels annotated by a domain expert. In addition, the domain expert assigned scores that characterise the degree of any exhibited abnormal patterns. To improve model effectiveness, we use the domain expert’s scores to create training error regularisation weights that alter the importance of each training instance for its class during model training. The behaviour assessment models are based on the SVM algorithm and the fuzzy SVM algorithm for their instance-weighted variation. Experiments conducted on meals recorded from 120 individuals show that: (a) the proposed approach can produce effective models for eating behaviour classification (for individuals), or for ranking (for populations); and (b) the instance-weighted fuzzy SVM models achieve significant performance improvements, compared to the non-weighted, standard SVM models.