Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Compressed Sensing of Microseismic Signals Through First Break Time Extraction and Signal Alignment

Microseismic monitoring is widely applied in dams, mines, and various fields of underground engineering. The number of sensors in microseismic monitoring systems is usually very large, which will result in a huge amount of data being produced if the Nyquist sampling theorem is used to acquire microseismic signals. To reduce the data storage costs and accelerate the transmission speed, we propose a distributed compressed sensing (CS) scheme for microseismic monitoring signals in this paper. The distributed compressed sensing scheme begins when it detects the first break time in the microseismic signal. The data recoding of the first break time is coded and transmitted together with the measured values of CS. Depending on the correlations between the microseismic signals, the first break time of the signals are aligned to that of the reference signal. Furthermore, we make use of the distributed CS to reduce the amount of data to be transmitted and to increase the reconstruction accuracy. Simulation results show that, compared with the sampling scheme based on the Nyquist sampling theorem, the independent CS scheme or the traditional distributed CS scheme, our proposed scheme improves the accuracy in the first break time detection and the reconstruction accuracy, and the scheme reduces the energy consumption at the same time.
UvA-DARE ( Digital Academic Repository ) Variance in animal longevity : contributions of heterogeneity and stochasticity

Variance in longevity among individuals may arise as an effect of heterogeneity (differences in mortality rates experienced at the same age or stage) or as an effect of individual stochasticity (the outcome of random demographic events during the life cycle). Decomposing the variance into components due to heterogeneity and stochasticity is crucial for evolutionary analyses.In this study, we analyze longevity from ten studies of invertebrates in the laboratory, and use the results to partition the variance in longevity into its components. To do so, we fit finite mixtures of Weibull survival functions to each data set by maximum likelihood, using the EM algorithm. We used the Bayesian Information Criterion to select the most well supported model. The results of the mixture analysis were used to construct an age × stage-classified matrix model, with heterogeneity groups as stages, from which we calculated the variance in longevity and its components. Almost all data sets revealed evidence of some degree of heterogeneity. The median contribution of unobserved heterogeneity to the total variance was 35%, with the remaining 65% due to stochasticity. The differences among groups in mean longevity were typically on the order of 30% of the overall life expectancy. There was considerable variation among data sets in both the magnitude of heterogeneity and the proportion of variance due to heterogeneity, but no clear patterns were apparent in relation to sex, taxon, or environmental conditions.
Bridging the Gap between Polymer Melts and Solutions in Extensional Rheology

Since its inception, the tube model of polymer dynamics has undergone several modifications to account for observed experimental trends. One trend that has yet to be captured by a modified version of the tube model is the observed experimental difference between concentrated polymer solutions and polymer melts. We compare the nonlinear extensional rheology of a series of polystyrene solutions with wide concentration range between 10% and 100% (melt) in order to determine the key missing physics that can account for dilution effects. All the solutions studied have the same number of entanglements per chain and are diluted in the same solvent (oligomeric styrene). We show that the difference in nonlinear rheological behavior between polystyrene melts reported by Bach et al.1 and polystyrene solutions reported by Bhattacharjee et al.2 and Sridhar et al.3 can be bridged by changing the polystyrene concentration. The results presented represent a unique benchmark for all future modifications to the tube model.
Advocacy for children with epilepsy: Leveraging the WHA resolution. Advocacy Task Force, Commission of Pediatrics, International League Against Epilepsy

In May 2015 the World Health Assembly (WHA) approved the Resolution on the Global Burden of Epilepsy. This report addresses how the Resolution can be leveraged to improve the care of children with epilepsy worldwide. Children with epilepsy have unique needs and face unique challenges from stigma at all levels of society. Children lack a voice to lobby for their own needs, including their right to have access to education. Effective leadership and governance should be enhanced through the support of stakeholders empowered to counsel, advise, and lobby for appropriate care. National health care plans should integrate primary and specialist care, and they need to be adapted to local specificities. Antiepileptic medicines should be widely accessible in appropriate, sustained, and affordable ways. Public awareness initiatives are needed to improve the inclusion of affected children in society and to reduce stigma. Cost‐effective interventions are also needed to address preventable causes of epilepsy. Without greater investment in research, evidence‐based interventions cannot be implemented. Through all of this, civil society must be engaged to ensure that the multivariate dimensions from the clinic to the community are addressed to fulfil the needs of children with epilepsy.
Effect of healthy aging on cerebral blood flow, CO2 reactivity, and neurovascular coupling during exercise.

We sought to make the first comparisons of duplex Doppler ultrasonography-derived measures of cerebral blood flow during exercise in young and older individuals and to assess whether healthy aging influences the effect of exercise on neurovascular coupling (NVC) and cerebral vascular reactivity to changes in carbon dioxide (CVRco2). In 10 healthy young (23 ± 2 yr; mean ± SD) and 9 healthy older (66 ± 3 yr) individuals, internal carotid artery (ICA) and vertebral artery (VA) blood flows were concurrently measured, along with middle and posterior cerebral artery mean blood velocity (MCAvmean and PCAvmean). Measures were made at rest and during leg cycling (75 W and 35% maximum aerobic workload). ICA and VA blood flow during dynamic exercise, undertaken at matched absolute (ICA: young 336 ± 95, older 352 ± 155; VA: young 95 ± 43, older 100 ± 30 ml/min) and relative (ICA: young 355 ± 125, older 323 ± 153; VA: young 115 ± 48, older 110 ± 32 ml/min) intensities, were not different between groups ( P > 0.670). The PCAvmean responses to visual stimulation (NVC) were blunted in older versus younger group at rest (16 ± 6% vs. 23 ± 7%, P < 0.026) and exercise; however, these responses were not changed from rest to exercise in either group. The ICA and VA CVRco2 were comparable in both groups and unaltered during exercise. Collectively, our findings suggest that 1) ICA and VA blood flow responses to dynamic exercise are similar in healthy young and older individuals, 2) NVC is blunted in healthy older individuals at rest and exercise but is not different between rest to exercise in either group, and 3) CVRco2 is similar during exercise in healthy young and older groups. NEW & NOTEWORTHY Internal carotid artery and vertebral artery blood flow responses to dynamic exercise are similar in healthy young and older individuals. Neurovascular coupling and cerebrovascular carbon dioxide reactivity, two key mechanisms mediating the cerebral blood flow responses to exercise, are generally unaffected by exercise in both healthy young and older individuals.
Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.
Automatic mapping of gaze position coordinates of eye-tracking glasses video on a common static reference image

This paper describes a method for automatic semantic gaze mapping from video obtained by eye-tracking glasses to a common reference image. Image feature detection and description algorithms are utilized to find the position of subsequent video frames and map corresponding gaze coordinates on a common reference image. This process allows aggregate experiment results for further experiment analysis and provides an alternative for manual semantic gaze mapping methods.
Neural correlates of improved recognition of happy faces after erythropoietin treatment in bipolar disorder

Bipolar disorder is associated with impairments in social cognition including the recognition of happy faces. This is accompanied by imbalanced cortico‐limbic response to emotional faces. We found that EPO improved the recognition of happy faces in patients with bipolar disorder. This randomized, controlled, longitudinal fMRI study explores the neuronal underpinnings of this effect.
Predicting observer's task from eye movement patterns during motion image analysis

Predicting an observer's tasks from eye movements during several viewing tasks has been investigated by several authors. This contribution adds task prediction from eye movements tasks occurring during motion image analysis: Explore, Observe, Search, and Track. For this purpose, gaze data was recorded from 30 human observers viewing a motion image sequence once under each task. For task decoding, the classification methods Random Forest, LDA, and QDA were used; features were fixation- or saccade-related measures. Best accuracy for prediction of the three tasks Observe, Search, Track from the 4-minute gaze data samples was 83.7% (chance level 33%) using Random Forest. Best accuracy for prediction of all four tasks from the gaze data samples containing the first 30 seconds of viewing was 59.3% (chance level 25%) using LDA. Accuracy decreased significantly for task prediction on small gaze data chunks of 5 and 3 seconds, being 45.3% and 38.0% (chance 25%) for the four tasks, and 52.3% and 47.7% (chance 33%) for the three tasks.
Location Privacy amidst Local Eavesdroppers

Mobile social networks and location-aware applications are becoming more and more widespread in current wireless networks. Thanks to Wifi or Bluetoothenabled devices, everyday mobile users can enjoy new services. However, the deployment of these mobile technologies leads to many concerns for privacy, especially location privacy. In wireless networks, external malicious parties can monitor pseudonyms used for identification to learn mobile users’ location and track their movements. A common technique for protecting location privacy consists in changing pseudonyms in regions called mix zones. In this report, we present a game-theoretic approach to evaluate the interaction and behaviors of an attacker aiming to jeopardize mobile nodes’ location privacy and nodes willing to thwart adversary’s spiteful plans. Assuming that such an attacker is armed with local eavesdropping devices, he must deploy them in an efficient way to track as many nodes as possible. On the other hand, mobile users have to define where are the best places to locate their mix zones. In order to evaluate their potential benefit, mobile nodes need to know the mixing effectiveness of possible mix zone locations. We propose a simplified metric based on the mobility profiles to determine the location privacy achieved with mix zones. We also build a payoff model to deal with costs, as well as benefits, led by this extended hide-and-seek game. By means of analytical and numerical results, we show the existence of Nash equilibria for all values of players’ costs and mobility parameters. We prove that the adversary’s best behavior evolves regarding the benefit he gets from traffic sniffing and the cost led by deployment of eavesdropping stations. On the other hand, we show that, when the mobility profile is homogeneous, the mobile nodes’ best response is independent of the adversary’s deployment of sniffing stations. Furthermore, the nodes’ best response is only dependent on the number of mix zones they deploy, not on their particular locations.
An Image Focusing Method for Sparsity-Driven Radar Imaging of Rotating Targets

This paper presents a new image focusing algorithm for sparsity-driven radar imaging of rotating targets. In the general formulation of off-grid scatterers, the sparse reconstruction algorithms may result in blurred and low-contrast images due to dictionary mismatch. Motivated by the natural clustering of atoms in the sparsity-based reconstructed images, the proposed algorithm first partitions the atoms into separate clusters, and then the true off-grid scatterers associated with each cluster are estimated. Being a post-processing technique, the proposed algorithm is computationally simple, while at the same time being capable of producing a sharp and correct-contrast image, and attaining a scatterer parameter estimation performance close to the Cramér–Rao lower bound. Numerical simulations are presented to corroborate the effectiveness of the proposed algorithm.
Genetic Fuzzy Tree Based Node Moving Strategy of Target Tracking in Multimodal Wireless Sensor Network

Multimodal wireless sensor networks (WSNs) consist of variable types of sensor nodes can do many important applications, such as environment monitoring, health care, and target tracking. In this paper, we utilize the multimodal WSN to keep track of targets in 3-D space. All the nodes in the network are different. They can be pyroelectric infrared nodes, radio frequency nodes, nodes with cameras, and so on. Based on this, we assume each of them varies in battery, mobility, and target recognition performance. To achieve a better performance of target tracking in the multimodal WSN, we propose a node moving strategy. Genetic fuzzy tree is employed in this paper. It is a two-layer fuzzy tree system (FTS) optimized by the genetic algorithm. The first layer gives a score to each node and selects the moving nodes. The second layer then controls the moving distances. The Pittsburgh genetic algorithm is used to optimize the whole rule base and data base of the FTS. Simulation results prove that the tracking error can be reduced applying our approach.
Spectral Network Embedding: A Fast and Scalable Method via Sparsity

Network embedding aims to learn low-dimensional representations of nodes in a network, while the network structure and inherent properties are preserved. It has attracted tremendous attention recently due to significant progress in downstream network learning tasks, such as node classification, link prediction, and visualization. However, most existing network embedding methods suffer from the expensive computations due to the large volume of networks. In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis. In Progle, we first construct a \textit{sparse} proximity matrix and train the network embedding efficiently via sparse matrix decomposition. Then we introduce a network propagation pattern via spectral analysis to incorporate local and global structure information into the embedding. Besides, this model can be generalized to integrate network information into other insufficiently trained embeddings at speed. Benefiting from sparse spectral network embedding, our experiment on four different datasets shows that Progle outperforms or is comparable to state-of-the-art unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and HOPE, regarding accuracy, while is $10\times$ faster than the fastest word2vec-based method. Finally, we validate the scalability of Progle both in real large-scale networks and multiple scales of synthetic networks.
Binocular model-based gaze estimation with a camera and a single infrared light source

We propose a binocular model-based method that only uses a single camera and an infrared light source. Most gaze estimation approaches are based on single eye models and with binocular models they are addressed by averaging the results from each eye. In this work, we propose a geometric model of both eyes for gaze estimation. The proposed model is implemented and evaluated in a simulated environment and is compared to a binocular model-based method and polynomial regression-based method with one camera and two infrared lights that average the results from both eyes. The method performs on par with methods using multiple light sources while maintaining robustness to head movements. The study shows that when using both eyes in gaze estimation models it is possible to reduce the hardware requirements while maintaining robustness.
Simulation of drastic lag phenomena in GaAs-based FETs for large voltage swing

In GaAs MESFETs and HEMTs, slow current transients are often observed experimentally even if the drain voltage or the gate voltage is changed abruptly. They are called drain-lag or gate-lag and could be fatal when the high-speed and high-frequency operation is considered. As for factors of these slow transients, effects of deep traps in the semi-insulating substrate and surface states on the active layer are suggested, but the detailed mechanisms are not well clarified. In this work, we have made transient simulations of GaAs MESFETs and AlGaAs/GaAs HEMTs when the gate voltage or the drain voltage is swung significantly, and showed that the lag phenomena become very pronounced for larger changes of the voltages. This indicates that I-V characteristics should be quite different between DC and RF conditions and the lag phenomena should be considered in the modeling of GaAs-based power devices.
A Novel Diversity Receiver Design for Cooperative Transmission System

In this paper, we consider the performance of an amplify-and-forward relaying-based multiple-input multiple-output space–time-block-coded cooperative communication system. We propose an efficient two-stage receiver design for a relay-assisted transmission scenario over frequency-selective channels. It is also demonstrated that the receiver is able to achieve the full diversity order of the system by incorporating linear processing techniques. With this method, the potential spatial diversity and multipath diversity can be obtained by using the diversity-combining technique, which frequency-selective fading channels can be equivalently transformed. Simulation results show that the proposed scheme significantly outperforms the minimum mean square error and ZF receivers, while maintaining nearly linear complexity.
Biportal Endoscopic Spine Surgery for Various Foraminal Lesions at the Lumbosacral Lesion

The stenosing foramen of L5–S1 by several degenerative diseases is one of the challenging areas on surgical approaching because of the deeper depth and steep slope in the lumbosacral junction. The floating view using unilateral biportal endoscopic spine surgery rather than docking into the Kambin’s zone can make the foraminal structures seen panoramically and permit dynamic handling of various instruments without destroying the facet joint and causing iatrogenic instability. Fine discrimination of structural margins in helps of the higher magnification and gentle manipulation of neural structures just as in open spine surgery could be guaranteed using floating technique from the target structures. Selective decompression with preserving innocent structures including facet joints could relieve foraminal lesions at the L5–S1 and decrease the necessity of fusion surgery caused by wider decompression and iatrogenic instability.
Temperature compensation model of MEMS inertial sensors based on neural network

Micro-electromechanical Systems (MEMS) inertial sensors are lightweight, small size and low-cost sensors that consume less power energy compared to their high-precision bulky counterparts. However, this miniaturization is a double-edged sword and MEMS-based inertial sensors suffer from various error sources, noises and instabilities. Indeed, inertial sensor errors vary with time, temperature and from turn on to turn on. In order to exploit the full potential of a MEMS-based inertial navigation system (INS), and to enhance its accuracy, it is indispensable to develop a temperature-dependent model that compensates these errors. Traditional temperature compensation methods rely on polynomial regression method, which fails to take into account the nonlinearities inherent in the sensor errors. This paper proposes a new temperature compensation model for a full inertial measurement unit (IMU), based on a radial basis function neural network (RBFNN) that compensates the significant deterministic errors of both accelerometer and gyroscope triads in a wide temperature range. A high precision rate table and a thermal chamber are used for accurate testing. The effectiveness of the method is investigated with various static and dynamics tests in the laboratory and with a car, and results are compared with the traditional polynomial fitting method.
Incidence and risk factors for back pain in young floorball and basketball players: A Prospective study

The aim of this study was to investigate the incidence of back pain in young basketball and floorball players under 21 years of age. The secondary aim was to examine risk factors especially for low back pain (LBP). Nine basketball and nine floorball teams (n = 396) participated in this prospective follow‐up study (2011‐2014). Young athletes (mean age 15.8 ± 1.9) performed physical tests and completed a questionnaire at baseline. The follow‐up lasted 1‐3 years per player. During the follow‐up, back pain reported by the players was registered on a weekly basis and verified by a study physician. The exposure time (AE) on team practices and games was recorded by the coach. Altogether back pain was reported 61 times by 51 players. The incidence of back pain was 87 per 1000 athlete‐years and 0.4 per 1000 hours of AE. Hamstrings, quadriceps and iliopsoas extensibility and general joint hypermobility were not associated with LBP. Furthermore, no association between LBP and leg extension strength or isometric hip abduction strength asymmetry was found in these young basketball and floorball players. In conclusion, back pain can lead to a considerable time‐loss from training and competition among young basketball and floorball players and the pain tends to reoccur. Lower extremity muscle extensibility, general joint hypermobility or investigated lower extremity strength measures were not associated with the risk of LBP.