Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithm for Multi Subject Fmri Analysis Based on the SVD and Penalized Rank-1 Matrix Approximation

In recent years, data driven methods have been successfully used for analyzing multi-subject functional magnetic resonance imaging (fMRI) datasets. These methods attempt to learn shared spatial activation maps (SM) or voxel time courses (TC) from temporally or spatially concatenated fMRI datasets respectively. Most of the methods proposed so far do not distinguish whether a particular SM/TC is a group level component or only present in a certain subject dataset. In this paper we present a new two stage algorithm which aims to separate the joint and sub-specific information from the temporally concatenated multi-subject datasets. The proposed method is based on the singular value decomposition (SVD) and penalized rank-one matrix approximation. Simulation experiments are used to demonstrate this ability of the proposed algorithm followed by validation on real experimental task-fMRI datasets.
Clinical Isolation and Identification of Haematospirillum jordaniae

A clinical case study involving a man (35–49 years of age) with wounds to his lower right extremity. An isolate was sent to the Delaware Public Health Laboratory for confirmatory testing by genetic analysis of the 16S gene. Testing identified the isolate as a novel genus and species, Haematospirillum jordaniae.
An Analysis of the Performance of ML Blind OFDM Symbol Timing Estimation

Symbol timing offset (STO) estimation constitutes an important part in orthogonal frequency-division multiplexing (OFDM) signal synchronization, and many blind methods based on the maximum likelihood (ML) principle have been proposed. A theoretical analysis of the estimation performance, however, is very difficult, due in part to the discrete nature of the STO. As a result, most studies on OFDM STO estimation have relied only on computer simulation for performance evaluation. This paper presents a theoretical performance analysis for ML-based blind OFDM STO estimation. We start with a review of the formulation of ML blind STO estimation problem and its solution. We then conduct the said performance analysis. The analysis turns out to involve a study of the eigenvalues of some matrix functions of the autocorrelation of the received signal. As the general case leads to highly complicated mathematics, we treat the case of full-band OFDM in additive white Gaussian noise in more detail. In this case, we are able to obtain mathematically tractable bounds and approximation to the estimation error probability and the corresponding mean-square error. In high signal-to-noise ratio (SNR), these bounds and approximation show inverse power-law dependence on the SNR. We also compare the bounds with simulation results and with an existing approximation in the literature.
Linear Independent Component Analysis Over Finite Fields: Algorithms and Bounds

Independent component analysis (ICA) is a statistical tool that decomposes an observed random vector into components that are as statistically independent as possible. ICA over finite fields is a special case of ICA, in which both the observations and the decomposed components take values over a finite alphabet. This problem is also known as minimal redundancy representation or factorial coding. In this paper, we focus on linear methods for ICA over finite fields. We introduce a basic lower bound that provides a fundamental limit to the ability of any linear solution to solve this problem. Based on this bound, we present a greedy algorithm that outperforms all currently known methods. Importantly, we show that the overhead of our suggested algorithm (compared with the lower bound) typically decreases as the scale of the problem grows. In addition, we provide a sub-optimal variant of our suggested method that significantly reduces the computational complexity at a relatively small cost in performance. Finally, we discuss the universal abilities of linear transformations in decomposing random vectors, compared with existing non-linear solutions.
Axially and Radially Expandable Modular Helical Soft Actuator for Robotic Implantables

Soft robotics has advanced the field of biomedical engineering by creating safer technologies for interfacing with the human body. One of the challenges in this field is the realization of modular soft basic constituents and accessible assembly methods to increase the versatility of soft robots. We present a soft pneumatic actuator composed of two elastomeric strands that provide interdependent axial and radial expansion due to the modularity of the components and their helical arrangement. The actuator reaches 35% of elongation with respect to its initial height and both chambers achieve forces of 1N at about 19kPa. We describe the design, fabrication, modeling and benchtop testing of the soft actuator towards realizing 3D functional structures with potential medical applications. An example of application for soft medical robots is tissue regenerative for the long-gap esophageal atresia condition.
Learning-Based Acoustic Source-Microphone Distance Estimation Using the Coherent-to-Diffuse Power Ratio

We propose a method for estimating the distance between a sound source and a pair of recording microphones. The developed algorithm operates in the short-time Fourier transform domain and is based on estimates of the coherent-to-diffuse power ratio, which provides a measure for the amount of reverberation in each time-frequency bin. For a direct use of these estimates, precise knowledge on the room characteristics is necessary, which is in practice usually not available and hard to obtain. Therefore, we use a learning-based method, which adapts to the characteristics of the room in a training phase and estimates the source-microphone distance in a testing phase. The experiments comprise various setups with simulated and real data. It is shown that the proposed method generalizes well for different microphone positions and works robustly for different source signals, directions of arrival, reverberation times, and signal observation intervals. This leads to a high estimation accuracy at a low computational complexity with a small amount of training data.
Investigating an Adaptive Network Coding Control Protocol for Multihop-Multipath Networks for Network Reconfigurability

One of the challenges in current designs of wireless networks is that of guaranteeing end to end delivery of high priority data or packets in multi-path multi-hop networks for medical use especially for nodes with mobility. There are various solutions based on traffic engineering which are applied in wired networks to guarantee end to end delivery of packets. With mobile wireless networks, the environment is highly dynamic, therefore with different scenarios having multiple paths or routes in the network, the node has to adapt to the network for dependable delivery of priority packets. In so doing, a level of Qos can at least be guaranteed. This paper reviews some of the techniques to achieve adaptive network coding, and the objective is to implement adaptive network coding in multi-hop, multipath network for high reliability and dependability. As a preliminary study we investigate an Adaptive Network coding scheme in a Binary tree fashion with the root being the convolution coder for its feasibility. The objective is to design a control protocol to be used to adapt to the error changes that occur in the network thereby increasing performance.
Structural Similarity Index with Predictability of Image Blocks

Structural similarity index (SSIM) is a widely used full-reference metric for assessment of visual quality of images and remote sensing data. It is calculated in a block-wise manner and is based on multiplication of three components: similarity of means of image blocks, similarity of contrasts and a correlation factor. In this paper, two modifications of SSIM are proposed. First, a fourth multiplicative component is introduced to SSIM (thus obtaining SSIM4) that describes a similarity of predictability of image blocks. A predictability for a given block is calculated as a minimal value of mean square error between the considered block and the neighboring blocks. Second, a simple scheme for calculating the metrics SSIM and SSIM4 for color images is proposed and optimized. Effectiveness of the proposed modifications is confirmed for the specialized image databases TID2013, LIVE, and FLT. In particular, the Spearman rank order correlation coefficient (SROCC) for the recently introduced FLT Database, calculated between the proposed metric color SSIM4 and mean opinion scores (MOS), has reached the value 0.85 (the best result for all compared metrics) whilst for SSIM it is equal to 0.58.
Performance Prediction of Wireless Multi-Hop Networks Using Stored Data Sets for Persons during Exercises

Ahstract-To develop a wireless multi-hop networking protocol suited for real-time vital information monitoring from a group of exercisers, in this paper, we propose the use of computer simulation instead of real experiments. The computer simulation uses two stored data sets such as channel and mobility. The channel data set, which was generated by a channel measurement campaign, gives the distance-attenuation characteristic, obstruction characteristic and antenna radiation pattern between exercisers (wireless vital sensor nodes). In addition, the mobility data set, which was generated by video recording for a football game, gives the time-series of exercisers' locations. Computer simulation using the two stored data sets reveals that the types of antenna such as dipole and spiral and the types of body such as adult and child significantly affect the data collection rate.
Simplified Modelling of the F2F MMC-Based High Power DC-DC Converter Including the Effect of Circulating Current Dynamics

The Front-to-Front Modular Multilevel Converter (MMC) is one of the topologies being considered for highvoltage, high-power, dc-dc conversion. Hence, there is a need for the development of simplified models for such a converter in order to study how it behaves in a large power system. Direct interconnection of two MMC models is one option but it leads to high number of states. Moreover, symmetry of the converter offers further simplification opportunities. The use of this fact to develop a simplified models was reported in literature. However, the resulting models are only applicable when compensated modulation is used. Furthermore, these models neglect the effect of Circulating Current Suppression Controllers (CCSCs), which makes the models inaccurate in the presence of such controllers. This paper proposes a more general simplification approach that captures the effect of CCSC while minimizing the number of states. The proposed model is validated by using time domain simulations and modal (eigenvalue) analyses.
Degraded Synergistic Recruitment of sEMG Oscillations for Cerebral Palsy Infants Crawling

Background: Synergistic recruitment of muscular activities is a generally accepted mechanism for motor function control, and motor dysfunction, such as cerebral palsy (CP), destroyed the synergistic electromyography activities of muscle group for limb movement. However, very little is known how motor dysfunction of CP affects the organization of the myoelectric frequency components due to the abnormal motor unit recruiting patterns. Objectives: Exploring whether the myoelectric activity can be represented with synergistic recruitment of surface electromyography (sEMG) frequency components; evaluating the effect of CP motor dysfunction on the synergistic recruitment of sEMG oscillations. Methods: Twelve CP infants and 17 typically developed (TD) infants are recruited for self-paced crawling on hands and knees. sEMG signals have been recorded from bilateral biceps brachii (BB) and triceps brachii (TB) muscles. Multi-scale oscillations are extracted via multivariate empirical mode decomposition (MEMD), and non-negative matrix factorization (NMF) method is employed to obtain synergistic pattern of these sEMG oscillations. The coefficient curve of sEMG oscillation synergies are adopted to quantify the time-varying recruitment of BB and TB myoelectric activity during infants crawling. Results: Three patterns of sEMG oscillation synergies with specific frequency ranges are extracted in BB and TB of CP or TD infants. The contribution of low-frequency oscillation synergy of BB in CP group is significantly less than that in TD group (p < 0.05) during forward swing phase for slow contraction; however, this low-frequency oscillation synergy keep higher level during the backward swing phase crawling. For the myoelectric activities of TB, there is not enough high-frequency oscillation recruitment of sEMG for the fast contraction in propulsive phase of CP infants crawling. Conclusion: Our results reveal that, the myoelectric activities of a muscle can be manifested as sEMG oscillation synergies, and motor dysfunction of CP degrade the synergistic recruitment of sEMG oscillations due to the impaired CNS regulation and destroyed MU/muscle fiber. Our preliminary work suggests that time-varying coefficient curve of sEMG oscillation synergies is a potential index to evaluate the abnormal recruitment of electromyography activities affected by CP disorders.
Underwater Optical Sensor Networks Localization with Limited Connectivity

In this paper, a received signal strength (RSS) based localization technique is investigated for underwater optical wireless sensor networks (UOWSNs) where optical noise sources (e.g., sunlight, background, thermal, and dark current) and channel impairments of seawater (e.g., absorption, scattering, and turbulence) pose significant challenges. Hence, we propose a localization technique that works on the noisy ranging measurements embedded in a higher dimensional space and localize the sensor network in a low dimensional space. Once the neighborhood information is measured, a weighted network graph is constructed, which contains the one-hop neighbor distance estimations. A novel approach is developed to complete the missing distances in the kernel matrix. The output of the proposed technique is fused with Helmert transformation to refine the final location estimation with the help of anchors. The simulation results show that the root means square positioning error (RMSPE) of the proposed technique is more robust and accurate compared to baseline and manifold regularization.
Estimating Quality Ratings from Touch Interactions in Mobile Games

Obtaining reliable quality ratings for applications is a time-consuming and expensive process. Touch interaction data, however, can be recorded without additional effort when using mobile applications. It could be used to assess usability without having to conduct dedicated tests. This work investigates the possibility to estimate quality ratings for two mobile games solely from touch interactions without relying on performance or game logic measures. To capture pragmatic as well as hedonic quality aspects of usability as ground truth, the AttrakDiff Mini questionnaire was chosen. Two different simple mobile games were used to capture a variety of touch interactions (taps and swipes). To influence the user ratings, three versions of the games were presented: one in original quality, the two others with manipulated interaction quality. Touch interaction features were extracted from the recorded interaction data and used for training models using linear regression and multivariate adaptive regression splines (MARS). The models were built with three times 10-fold cross-validation on each game data set separately. The results indicate that the ratings for pragmatic quality are estimated significantly better than for hedonic quality and attractiveness. Furthermore, the transferability of the models from one game to the other was examined and the touch features that were most important for estimating quality ratings in this study were identified. Touch interactions seem to carry some information on usability, especially pragmatic quality, and it seems to be possible to estimate a broad direction of the user's usability perception. Just a few touch features carry the most information and can be used for simple but fast and powerful models. This could be applied as an automatic analytic tool for mobile applications as well as in adaptive applications that optimize themselves in terms of usability perceived by individual users.
Neuro-IoU: Learning a Surrogate Loss for Semantic Segmentation

Semantic segmentation is a popular task in computer vision today, and deep neural network models have emerged as the popular solution to this problem in recent times. The typical loss function used to train neural networks for this task is the cross-entropy loss. However, the success of the learned models is measured using Intersection-Over-Union ( IoU ), which is inherently non-differentiable. This gap between performance measure and loss function results in a fall in performance, which has also been studied by few recent efforts. In this work, we propose a novel method to automatically learn a surrogate loss function that approximates the IoU loss and is better suited for good IoU performance. To the best of our knowledge, this is the ﬁrst such work that attempts to learn a loss function for this purpose. The proposed loss can be directly applied over any network. We validated our method over different networks ( FCN , SegNet, UNet) on the PASCAL VOC and Cityscapes datasets. Our results on this work show consistent improvement over baseline methods.
Proof of General Switched-Capacitor DC-DC Converter Law using Voltage-Domain Analysis

This work presents a general law of switched-capacitor (SC) DC-DC converter topologies which is derived using a time-averaged voltage-domain analysis. Unlike previous formulations of constraints on a SC converter's basic properties, the presented law describes for the first time a fundamental relationship in equality between a topology's VCR and its capacitors' bias voltages, bottom-plate voltage swings and charge multiplier vector simultaneously. Using this law, a definition for topologically efficient topologies is identified. Furthermore, the proposed voltage-domain analysis can also be used for visual topology synthesis.
Jackknife approach to the estimation of mutual information

Significance As a fundamental concept in information theory, mutual information has been commonly applied to quantify the dependence between variables. However, existing estimations have unstable statistical performance since they involve a set of tuning parameters. We develop a jackknife approach that does not incur predetermined tuning parameters. The proposed approach enjoys several appealing theoretical properties and has stable numerical performance. Quantifying the dependence between two random variables is a fundamental issue in data analysis, and thus many measures have been proposed. Recent studies have focused on the renowned mutual information (MI) [Reshef DN, et al. (2011) Science 334:1518–1524]. However, “Unfortunately, reliably estimating mutual information from finite continuous data remains a significant and unresolved problem” [Kinney JB, Atwal GS (2014) Proc Natl Acad Sci USA 111:3354–3359]. In this paper, we examine the kernel estimation of MI and show that the bandwidths involved should be equalized. We consider a jackknife version of the kernel estimate with equalized bandwidth and allow the bandwidth to vary over an interval. We estimate the MI by the largest value among these kernel estimates and establish the associated theoretical underpinnings.
Isolation, Characterization, and Pharmaceutical Applications of an Exopolysaccharide from Aerococcus Uriaeequi

Many marine bacteria secrete exopolysaccharides (EPSs), which are made up of a substantial component of the macro-molecules surrounding cells. Recently, the wide demand for EPSs for food, cosmetics, pharmaceutical and other applications has led to great interest in them. In this study, an EPS produced by marine bacteria Aerococcus uriaeequi HZ strains (EPS-A) was isolated and purified to examine its structure and biological function. The molecular weight of EPS-A analyzed by high-performance liquid gel filtration chromatography (HPGFC) is found to have a number average of 2.22 × 105 and weight average of 2.84 × 105, respectively. High-performance liquid chromatography (HPLC) and Fourier-transform–infrared (FT–IR) analysis indicate that EPS-A was a polysaccharide composed of glucose and a little mannose. In addition, the flocculating rate of sewage of EPS-A was 79.90%. The hygroscopicity studies showed that hygroscopicity of EPS-A was higher than chitosan but lower than that of sodium hyaluronate. The moisture retention of EPS-A showed similar retention activity to both chitosan and sodium hyaluronate. EPS-A also can scavenge free radicals including both OH• free radical and O2•− free radical and the activity to O2•− free radical is similar to vitamin C. Safety assessment on mice indicated that the EPS-A is safe for external use and oral administration. EPS-A has great potential for applications in medicine due to its characteristics mentioned above.
Reduced Dimension Minimum BER PSK Precoding for Constrained Transmit Signals in Massive MIMO

Recently a number of nonlinear precoding algorithms have been developed for designing a downlink transmit signal that is constrained by some nonlinearity, such as one-bit quantization, power-amplifier saturation or constant modulus. These methods use iterative search algorithms to directly design the signal that is transmitted from each antenna. Since the dimension of the search space equals the number of antennas, the computational complexity of these approaches can be high for massive MIMO scenarios. Thus, in this paper we pose the problem in a smaller dimensional space by constraining the signal prior to the nonlinearity to be the output of a linear precoder. The search is then over the vector of predistorted symbols at the input to the linear precoder, which is typically much smaller than the number of antennas. We focus on algorithms that minimize the bit error rate at the receivers, and show that performance can be obtained that is similar to algorithms that operate directly in the antenna domain.
Image Alignment via Multi-Model Geometric Fitting and Hierarchical Homography Estimation

It is challenging to achieve accurate alignment for building images containing multiple planes. We propose a multi-model geometric fitting and hierarchical homography estimation method to improve the alignment performance for building images. We first extract scale-invariant feature transform (SIFT) features of the images, and then adopt the multi-homography fitting algorithm to classify the feature points into different deformation models. According to the deduced deformation models, we partition the source image into base and transition regions. For the base regions, we adopt the moving direct linear transformation (Moving DLT) to estimate homographies. For the transition regions, we propose a hierarchical homography estimation method to select appropriate homographies. Experimental results show that our method achieves more accurate alignment results compared with state-of-the-art alignment methods for building images.
Federation of Cross-Domain Edge Resources: A Brokering Architecture for Network Slicing

We present an architecture enabling a network operator to federate IT and network resources owned by private third-party actors located at the edge of the network. The cornerstone of this architecture is a brokering layer that relies on a graph-based resource database. The brokering layer contains all the necessary functions allowing such federation: exposure of cross-domain resources towards network operators and their qualification for deployment and provisioning. We have implemented a Proof-of-Concept of the proposed architecture which we illustrate through the federation of a Stadium infrastructure resources and then through the on-demand creation and deployment of end-to-end network slices over multiple domains for event services. Finally, we present and discuss performance evaluation results.