Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Activation and Gait Alteration During Cognitive and Motor Dual Task Walking in Stroke—A Functional Near-Infrared Spectroscopy Study

This paper investigated the effects of cognitive and motor dual tasks on gait performance and brain activities in stroke; 23 stroke subjects performed single walking (SW), walking while performing cognitive task (WCT), and walking while performing motor task (WMT) at self-selected speed. The gait performance was recorded, including speed, cadence, stride time, stride length, and dual task cost (DTC). Brain activities in prefrontal cortex, premotor cortex (PMC), and supplementary motor areas (SMAs) were measured by functional near-infrared spectroscopy during walking. Results showed significant decrease in speed, cadence, and stride length, and increase in stride time was noted in both WCT and WMT compared with SW condition. There was no significant difference in DTC between WCT and WMT. The non-lesioned SMA and most channels of bilateral PMCs exhibited significant increases in the index of hemoglobin differential during WCT and WMT compared with SW. Moreover, gait performance was negatively correlated with bilateral PMCs and lesioned SMA during different walking tasks. In conclusion, deteriorated gait performance was noted in stroke attempting dual tasks. There is no significant difference between the two dual tasks on gait performance. Nevertheless, SMA and especially PMC were crucial in cognitive and motor dual task walking after stroke.
A Unified Optic Nerve Head and Optic Cup Segmentation Using Unsupervised Neural Networks for Glaucoma Screening

Segmentation of retinal anatomical features such as optic nerve head (ONH) and optic cup, the brightest area in the center of ONH which is devoid of neural elements, is a prerequisite for computer-aided diagnosis and follow-up of glaucoma. The ONH segmentation methods, which imposed shape and intensity constraints, are unable to identify ONH and optic cup boundaries at the same time. On the other hand, recent efficient supervised learning-based methods, which provide a unified system, require combination of many informative features as their inputs, as well as ground truth for the training phase. This paper uses a saliency map including color, intensity and orientation contrasts as the input of a winner-take-all neural network, and color visual features as the input of a self-organizing map neural network to segment ONH and optic cup, simultaneously. Our method is evaluated on a database of 205 ocular fundus images provided by local eye hospitals and publicly available image databases RIMONE and DIARETDB0 comprising 60 non-glaucomtous and 145 glaucomatous images. The ground truth is provided by two expert ophthalmologists. The method attained an average overlapping error of 9.6% and 25.1% for ONH and cup segmentation, respectively. Cup-to-disc area ratio (CDR) is computed for glaucoma assessment. The mean and standard deviation of the CDR differences between our method and the ground truth in all images are 0.11 and 0.09, respectively.
Design and Evaluation of Optimal Orthogonal Wavelet With the Least Length of Wavelet Filters Using Spectral Matching

This paper presents a method to find a matched wavelet with the least length of wavelet filters using spectral matching. The existing problem in algorithm of designing in spectral fashion is described, and the corresponding solution is provided. The problem encountered in the common algorithm is the heavy computations in spectral phase matching that often produces errors in machine running, and consequently, the phase matching algorithm becomes incomplete. In this paper, the computational costs and errors arising from such phase matching are improved. Then, a method is proposed, thereby enabling the construction of orthogonal wavelet transform filters with minimum length. The proposed approach is examined with a signal from the real world. The compression of neural signals in implantable microsystems is provided, and its performance is compared with the superior wavelets. It yields higher compression rate than other well-reported wavelets, such as sym4, db4, and sym7, as well as the designed wavelet is able to perform more efficient than general wavelets in noisy conditions. These appropriate results are obtained because of designing matched wavelet with the least length of filters.
Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory

Heart disease classification based on electrocardiogram(ECG) signal has become a priority topic in the diagnosis of heart diseases because it can be obtained with a simple diagnostic tool of low cost. Since early detection of heart disease can enable us to ease the treatment as well as save people's lives, accurate detection of heart disease using ECG is very important. In this paper, we propose a classification method of heart diseases based on ECG by adopting a machine learning method, called Long Short-Term Memory (LSTM), which is a state-of-the-art technique analyzing time series sequences in deep learning. As suitable data preprocessing, we also utilize symbolic aggregate approximation (SAX) to improve the accuracy. Our experiment results show that our approach not only achieves significantly better accuracy but also classifies heart diseases correctly in smaller response time than baseline techniques.
Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography

Several studies have been proposed to estimate blood pressure (BP) with cuffless devices using only a Photoplethysmograph (PPG) sensor on the basis of the physiological knowledge that the PPG changes depend on the state of the cardiovascular system. In these studies, machine learning algorithms were used to extract various features from the wave height and the elapsed time from the rising point of the pulse wave to feature points have been used to estimate the BP. However, the accuracy is still not adequate to be used as medical equipment because their features cannot express fully information of the pulse waveform which changes according to the BP. And, no other effective knowledge about the pulse waveform for estimating BP has been found yet. Therefore, in this study, we focus on the autoencoder which can extract complex features and can add new features of the pulse waveform for estimating the BP. By using autoencoder, we extracted 100 features from the coupling signal of the pulse wave and from its first-order differentiation and second-order differentiation. The result of examination with 1363 test subjects show that the correlation coefficients and the standard deviation of the difference between the measured BP and the estimated BP got improved from R = 0.67, SD = 13.97 without autoencoder to R = 0.78, SD = 11.86 with autoencoder.
The 2β Splice Variation Alters the Structure and Function of the Stromal Interaction Molecule Coiled-Coil Domains

Stromal interaction molecule (STIM)-1 and -2 regulate agonist-induced and basal cytosolic calcium (Ca2+) levels after oligomerization and translocation to endoplasmic reticulum (ER)-plasma membrane (PM) junctions. At these junctions, the STIM cytosolic coiled-coil (CC) domains couple to PM Orai1 proteins and gate these Ca2+ release-activated Ca2+ (CRAC) channels, which facilitate store-operated Ca2+ entry (SOCE). Unlike STIM1 and STIM2, which are SOCE activators, the STIM2β splice variant contains an 8-residue insert located within the conserved CCs which inhibits SOCE. It remains unclear if the 2β insert further depotentiates weak STIM2 coupling to Orai1 or independently causes structural perturbations which prevent SOCE. Here, we use far-UV circular dichroism, light scattering, exposed hydrophobicity analysis, solution small angle X-ray scattering, and a chimeric STIM1/STIM2β functional assessment to provide insights into the molecular mechanism by which the 2β insert precludes SOCE activation. We find that the 2β insert reduces the overall α-helicity and enhances the exposed hydrophobicity of the STIM2 CC domains in the absence of a global conformational change. Remarkably, incorporation of the 2β insert into the STIM1 context not only affects the secondary structure and hydrophobicity as observed for STIM2, but also eliminates the more robust SOCE response mediated by STIM1. Collectively, our data show that the 2β insert directly precludes Orai1 channel activation by inducing structural perturbations in the STIM CC region.
Reconfigurable Network-on-Chip for 3D Neural Network Accelerators

Parallel hardware accelerators for large-scale neural networks typically consist of several processing nodes, arranged as a multi- or many-core system-on-chip, connected by a network-on-chip (NoC). Recent proposals also benefit from the emerging 3D memory-on-logic architectures to provide sufficient bandwidth for neural networks. Handling the heavy traffic between neurons and memory and also the multicast-based inter-neuron traffic, which often varies over time, is the most challenging design consideration for the networks-on-chip in such accelerators. To address these issues, a reconfigurable network-on-chip architecture for 3D memory-on-logic neural network accelerators is presented in this paper. The reconfigurable NoC can adapt its topology to the on-chip traffic patterns. It can be also configured as a tree-like structure to support multicast-based neuron-to-neuron and memory-to-neuron traffic of neural networks. The evaluation results show that the proposed architecture can better manage the multicast-based traffic of neural networks than some state-of-the-art topologies and considerably increase throughput and power efficiency.
Average-Consensus Filter of First-Order Multi-Agent Systems With Disturbances

Average-consensus filter problem of assigned constant inputs is investigated for the first-order multi-agent systems with heterogeneous disturbances, and a proportional-integral consensus filter algorithm accompanied with disturbances’ observers is designed. By using matrix theory and frequency-domain analysis, sufficient and necessary convergence conditions are obtained for our proposed algorithm without and with identical communication delay, respectively. Numerical simulations illustrate the correctness of theoretical results.
Comparing Physical Human-Robot Interaction with Spring-and Elastomer-Based Series Elastic Actuators

Physical human-robot interaction (pHRI) is an important consideration in the design of rehabilitation exoskeletons. Series Elastic Actuators (SEAs) are seen as a promising method of introducing compliance and comfort to robotic exoskeletons. Recently, authors have proposed the use of elastomers in SEAs, rather than springs, but there is a dearth of literature comparing how the two compliant elements are perceived by humans. In this paper, we quantitatively and qualitatively compare human interaction with an elastomer and a spring in terms of comfort and motion smoothness, with the aim of informing future SEA design. Two mechanisms were designed to compare human interaction with a spring and elastomer, and a cross-over experimental design was used to help eliminate any learning effects. Overall, the elastomer appeared to be slightly more comfortable and allow slightly smoother motion than the spring, holding promise for the use of elastomers as a means of improving pHRI in exoskeletons actuated by SEAs.
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data

Word embeddings are powerful tools that facilitate better analysis of natural language. However, their quality highly depends on the resource used for training. There are various approaches relying on n-gram corpora, such as the Google n-gram corpus. However, n-gram corpora only offer a small window into the full text – 5 words for the Google corpus at best. This gives way to the concern whether the extracted word semantics are of high quality. In this paper, we address this concern with two contributions. First, we provide a resource containing 120 word-embedding models – one of the largest collection of embedding models. Furthermore, the resource contains the n-gramed versions of all used corpora, as well as our scripts used for corpus generation, model generation and evaluation. Second, we define a set of meaningful experiments allowing to evaluate the aforementioned quality differences. We conduct these experiments using our resource to show its usage and significance. The evaluation results confirm that one generally can expect high quality for n-grams with n > 3.
Phase Self-Calibration for Millimeter Wave MIMO Imaging

A simple approach of phase self-calibration for millimeter wave imaging is proposed. By exploiting features of direct coupling between pairs of transmitting and receiving antennas, the channel group delay caused by the hardware can be calculated. Then the data after pulse compressionis phase-shifted before imagingto remove the phase error. This phase calibration procedure requires no additional reference target. Experimental results verify the validity and effectiveness of the proposed self-calibrationapproach, which can serve as a benchmark or basis for more sophisticated phase calibration approaches.
Bringing Order to Neural Word Embeddings with Embeddings Augmented by Random Permutations (EARP)

Word order is clearly a vital part of human language, but it has been used comparatively lightly in distributional vector models. This paper presents a new method for incorporating word order information into word vector embedding models by combining the benefits of permutation-based order encoding with the more recent method of skip-gram with negative sampling. The new method introduced here is called Embeddings Augmented by Random Permutations (EARP). It operates by applying permutations to the coordinates of context vector representations during the process of training. Results show an 8% improvement in accuracy on the challenging Bigger Analogy Test Set, and smaller but consistent improvements on other analogy reference sets. These findings demonstrate the importance of order-based information in analogical retrieval tasks, and the utility of random permutations as a means to augment neural embeddings.
Short-Term Model-Based Multiscale Complexity Analysis of Cardiac Control Provides Complementary Information to Single-Scale Approaches

The study compares a recently proposed shortterm model-based linear multiscale complexity approach to a single-scale application of the same method and to a model-free nonlinear one based on the computation of conditional entropy with the aim at assessing the complementary information. Comparison was carried out over 24 hours Holter recordings of heart period variability during daytime and nighttime in 12 healthy men (age: 34-55 years). Single-scale methods were able to detect the increased complexity of the cardiac control during nighttime. Multiscale complexity analysis showed that this increase was due to an increase of complexity in the low frequency band (from 0.04 to 0.15 Hz), while complexity in the range of frequencies typical of the respiratory rate was unmodified. Regardless of the method (i.e. linear or nonlinear) single-scale complexity indexes were uncorrelated to the multiscale ones. We conclude that short-term model-based linear multiscale complexity approach provides complementary information to single-scale methods in an application devoted to the analysis of cardiac control from 24 hours Holter recordings.
Materialism, Body Surveillance, Body Shame, and Body Dissatisfaction: Testing a Mediational Model

The present study aimed to examine the mechanisms underlying the links between materialism and body dissatisfaction. A sample of 583 Chinese undergraduate women completed a questionnaire measuring materialism, body surveillance, body shame, and body dissatisfaction. Correlational analysis showed that materialism, body surveillance, and body shame were significantly positively correlated with body dissatisfaction. The results of path analyses revealed that higher materialism predicted more body dissatisfaction, albeit indirectly, via higher body surveillance and body shame.
Wireless Channel Modeling for Leadless Cardiac Pacemaker: Effects ofVentricular Blood Volume

In this paper, the effects of ventricular blood volume change on the cardiac wireless channel modeling at 2.4 GHz are presented for future multi-node leadless capsule pacemakers. Numerical electromagnetic simulations are implemented in the anatomical model of human body and experiments are done in a homogeneous liquid phantom. The results show that the coupling between the capsule antennas decreases with increasing blood volume, as the blood is a highly attenuating medium. The received power varies by 3.1 dB for the implant-to-implant link inside the heart and varies by 1.3 dB for the heart implant to sub-cutaneous implant, during the diastole and systole cycle. The findings are useful to develop an intra-body communication between leadless pacemakers providing timing information for synchronized pacing in a multi-chamber leadless pacemaker system.
Harmonic Error Cancellation for Accurate Square-wave-based Bio-Impedance Measurements

Bio-impedance spectroscopy, which measures impedance of the tissue over a frequency range, has been widely used to provide crucial information for monitoring human health. Its conventional methods using sine wave current stimulation of the tissue and sine wave demodulation of the resultant voltage provide an accurate impedance measurement, but involve bulky components and power inefficiency due to sinusoidal waveform generation and analog signal multiplication. Instead, the method using square wave clocks can be much more area and power efficient, but inherently has substantial errors in the measured result due to the presence of harmonics in square waves. In this paper we propose a technique to cancel the errors caused by harmonics of the square wave stimulation and demodulation. The technique, based on the fact that the magnitude ratio of all the harmonics of a square wave are known, cancels out harmonic errors by subtracting or adding the square-wave-based measured results at higher harmonics to the fundamental output as a simple post-processing calculation. Simulation results using a generic electrode and tissue model show that this technique can provide a precise measurement of the bio-impedance with <0.5% magnitude error and < 0.2 phase error considering just five frequency multiples. Because this method does not involve sinusoidal signal generation and analog mixing, it is adequate to be integrated in a wearable health monitoring device at low area and power overhead.
Decision Boundary Formation of Deep Convolution Networks with ReLU

Although deep convolution networks have been successfully applied to many applications, their working mechanisms have been barely understood. In this paper, we investigate the decision boundary formation of convolution neural networks that use the ReLU function. Due to the non-linearity of ReLU, the convolution neural networks can be viewed as a non-linear transformation. However, the decision boundary in the original input space will be always locally linear, which may produce undesirable decision boundaries. We also present some examples that show interesting properties of decision boundary of convolution neural networks with ReLU.
Revisiting human T-cell lymphotropic virus types 1 and 2 infections among rural population in Gabon, central Africa thirty years after the first analysis

HTLV-1 infection is considered as highly endemic in central Africa. Thirty years ago, a first epidemiological study was performed in Gabon, central Africa, and revealed that the prevalence varied from 5.0 to 10.5%. To evaluate current distribution of HTLVs in Gabon, 4.381 samples were collected from rural population living in 220 villages distributed within the 9 provinces of country. HTLVs prevalence was determined using two ELISA tests and positive results were confirmed by Western Blot. The overall HTLV-1 seroprevalence was of 7.3% among the rural Gabonese population; with 5.4% for men and 9.0% for women. Prevalence of HTLV-1 differed by province, ranging from 2.3% to 12.5% into the rain forest. Being a woman older than 51 years represented a high risk for HTLV-1 acquisition. Hospitalization, operation/surgery, transfusion and medical abortion or fever, arthritis and abdominal pain are also significant risk factors. In addition, 0.1% of samples were found as HTLV-2 positive, while 12.0% had an indeterminate HTLV serological pattern. HTLV-3 and HTLV-4 were not found. Phylogenetic analysis was performed on 87 samples and demonstrated that HTLV-1 present in Gabon belongs mostly to subtype B, however the rare subtype D was also found. Altogether, our results demonstrate that almost thirty years after the first epidemiological study prevention of HTLVs infection is still an issue in Gabon.
Hermetic packaging for implantable microsystems: Effectiveness of sequentially electroplated AuSn alloy

With modern microtechnology, there is an aggressive miniaturization of smart devices, despite an increasing level of integration and overall complexity. It is therefore becoming increasingly important to be achieve reliable, compact packaging. For implantable medical devices (IMDs), the package must additionally provide a high quality hermetic environment to protect the device from the human body. For chip-scale devices, AuSn eutectic bonding offers the possibility of forming compact seals that achieve ultra-low permeability. A key feature is this can be achieved at process temperatures of below 350315°C, therefore allowing for the integration of sensors and microsystems with CMOS electronics within a single package. Issues however such as solder wetting, void formation and controlling composition make formation of high-quality repeatable seals highly challenging. Towards this aim, this paper presents our experimental work characterizing the eutectic stack deposition. We detail our designmethods and process flow, share our experiences in controlling electrochemical deposition of AuSn alloy and finally discuss usability of sequential electroplating process for the formation of hermetic eutectic bonds.
Human Object Detection in Forest with Deep Learning based on Drone’s Vision

In the past decade, various new and impressive applications have been developed and implemented on drones, for instance search and rescue, surveillance, traffic monitoring, weather monitoring and so on. The current advances in drone technology provoked significant changes in enabling drones to perform a wide range of missions with increasing level of complexity. Missions such as search and rescue or forest surveillance require a large camera coverage and thus making drone a suitable tool to perform advanced tasks. Meanwhile, the increasing trend of deep learning applications in computer vision provides a remarkable insight into the initiative of this project. This paper presents a technique which allows detecting the existence of human in forestry environment with human object detection algorithm using deep learning framework. The purpose of detecting human existence in forestry area is to reduce illegal forestry activities such as illegal entry into prohibited area and illegal logging activities. Also, the outcome of this project is expected to aggrandize the usage of drone for forest surveillance purpose to save time and cost.