Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Space Exploration of Convolution Algorithms to Accelerate CNNs on FPGA

Deep Neural Networks (DNN) are promising solutions for various artificial intelligence tasks. Convolutional Neural Network (CNN) is a variant of DNN, which is widely used in various computer vision tasks like image and face recognition, autonomous vehicles, games, video surveillance and various medical applications. CNNs are both compute and memory bound. Convolutional layers are the most computationally complex operation in CNN. Owing to the computation demanded by convolutions of CNNs, FPGAs are found to be suitable for accelerating CNNs. In this paper we have carried out a design space exploration of various algorithms for performing operations in different convolutional layers of CNNs. Analysis has been done to select an appropriate algorithm for various convolution layers of AlexNet CNN model based on the kernel size and input feature map. First convolution layer in AlexNet CNN model with three channels of $227 \times 227$ feature size and 96 channels of $11 \times 11$ kernel, has been implemented in Xilinx Virtex-7 FPGA.
Evaluation of the general sensitivity of the Wisconsin card sorting test among younger and older children

Although the Wisconsin Card Sorting Test (WCST) has been widely used to evaluate the cognitive effects of cerebral dysfunction, particularly of the frontal lobes, among adult populations, it has received little attention among children. To evaluate the general sensitivity of the WCST in this group, the present study compared the WCST performances of a heterogeneous sample (n = 77) of younger children (94 to 119 months of age) and older children (120 to 189 months of age) who were referred for neuropsychological evaluation on clinical grounds with the WCST performances of age‐appropriate controls (n = 80). Five measures derived from the standard WCST were examined, and the results revealed significant (p < .005) main effects of group and age for each of the WCST variables; no interactions were observed. These findings suggest that although WCST performance is age‐dependent, its sensitivity is not restricted by age; that is, it is still possible to identify differences in level of skill at older and younger.
Galaxy Evolution in the Virgo Cluster

Abstract In the last years we have gained new insights on how ram pressure acts in detail on Virgo spiral galaxies. This has been possible due to the combination of new deep HI observations, deep polarized radio continuum observations, and detailed dynamical modelling (sticky particles and MHD). As a major result a first complete ram pressure stripping time sequence could be established for the Virgo cluster.
Cep192 Controls the Balance of Centrosome and Non-Centrosomal Microtubules during Interphase

Cep192 is a centrosomal protein that contributes to the formation and function of the mitotic spindle in mammalian cells. Cep192’s mitotic activities stem largely from its role in the recruitment to the centrosome of numerous additional proteins such as gamma-tubulin and Pericentrin. Here, we examine Cep192’s function in interphase cells. Our data indicate that, as in mitosis, Cep192 stimulates the nucleation of centrosomal microtubules thereby regulating the morphology of interphase microtubule arrays. Interestingly, however, cells lacking Cep192 remain capable of generating normal levels of MTs as the loss of centrosomal microtubules is augmented by MT nucleation from other sites, most notably the Golgi apparatus. The depletion of Cep192 results in a significant decrease in the level of centrosome-associated gamma-tubulin, likely explaining its impact on centrosome microtubule nucleation. However, in stark contrast to mitosis, Cep192 appears to maintain an antagonistic relationship with Pericentrin at interphase centrosomes. Interphase cells depleted of Cep192 display significantly higher levels of centrosome-associated Pericentrin while overexpression of Cep192 reduces the levels of centrosomal Pericentrin. Conversely, depletion of Pericentrin results in elevated levels of centrosomal Cep192 and enhances microtubule nucleation at centrosomes, at least during interphase. Finally, we show that depletion of Cep192 negatively impacts cell motility and alters normal cell polarization. Our current working hypothesis is that the microtubule nucleating capacity of the interphase centrosome is determined by an antagonistic balance of Cep192, which promotes nucleation, and Pericentrin, which inhibits nucleation. This in turn determines the relative abundance of centrosomal and non-centrosomal microtubules that tune cell movement and shape.
Modeling and Simulation of EMG Signal and H-Reflex in Healthy Human Subject

Electromyography (EMG) is a signal representing the activity of skeletal muscles. Besides, H-reflex and M-wave are two responses to the electrical stimulation of the nerve, and they appear in EMG. In this paper, the purpose was to model the recorded EMG from the FCR muscle in response to both the electrical stimulation and the brain commands during motor imagery. This model could capture the qualitative behavior of the H-reflex and how physiological variables change its waveform. Two mathematical models of the EMG signal (i.e., baseline EMG and H-reflex) were developed. These models were designed to simulate the EMG signal with respect to the intensity of contraction and electrical stimulation. Also, the effect of the brain descending pathways was considered with a parameter. We used the Hodgkin-Huxley (HH) and Morris-Lecar (ML) equations for the action potentials (APs) of motor neurons and muscle fibers, respectively. After processing the sensory signals in the spinal cord, the response pulses are transmitted from the spinal cord to the muscle. When the AP is generated, the spike train passes to the muscle fiber. Each muscle contains several motor units (MUs), each of which consists of a motor neuron and its innervated muscle fibers. Moreover, summation of the total activity of the muscle fibers within each MU forms its activity. Likewise, total muscle activity (i.e., EMG) is the summation of the activity of all MUs. The H-reflex and M-wave models were developed by applying electrical stimulation in the EMG model. Moreover, mean square error (MSE) for the model was 0.00027%, and Spearman`s correlation coefficient was 0.722 (p-value < 0.05).
Performance Analysis of Proportional Fair Scheduling in OFDMA Wireless Systems

This paper analyzes the performance of Proportional Fair (PF) scheduling in Orthogonal Frequency Division Multiple Access (OFDMA) wireless systems. OFDMA represents a promising multiple access scheme for high-data-rate transmission over wireless channels as it combines the Orthogonal Frequency Division Multiplexing (OFDM) modulation and subcarrier allocation. On the other hand, the PF scheduling is an efficient resource allocation scheme with good fairness characteristics. Consequently, OFDMA with PF scheduling represents an attractive solution to deliver high data rate services to multiple users simultaneously with a high degree of fairness. We investigate a two dimensional (time slot and frequency subcarrier) PF scheduling algorithm for OFDMA systems, and evaluate its performance analytically and by simulation. We derive closed-form expressions for the average throughput and throughput fairness index. Computer simulations are used for verification. The analytical results agree well with the results from simulations, which verifies the correctness and accuracy of the analytical expressions.
Li-Ion Battery Charger Based on LDO Regulator for Portable Device Power Management

This paper presents an architecture of a charger based on an LDO regulator with high efficiency for a Li-Ion battery which controlled the three-mode: trickle current, fast constant current and constant voltage modes. The simulation results provide the trickle current of 250mA, maximum charging current of 1. 12A and charging voltage of 4. 2V at the power supply of 4.8-5V using TSMC 180nm CMOS technology.
Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for normal data

Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect. For the effect measure log-response-ratio (LRR, also known as the logarithm of the ratio of means, RoM), we review four point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird (DL), restricted maximum likelihood, and Mandel and Paule (MP), and the less-familiar method of Jackson), four interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, and Jackson), five point estimators of the overall effect (the four related to the point estimators of $\tau^2$ and an estimator whose weights use only study-level sample sizes), and seven interval estimators for the overall effect (four based on the point estimators for $\tau^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ that uses the MP estimator of $\tau^2$ instead of the DL estimator, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations of data from normal distributions. Simulations from lognormal distributions are in a separate report Bakbergenuly et al. 2019b.
A mixed integer linear programming formulation for the sparse recovery problem in compressed sensing

We propose a new mixed integer linear programming (MILP) formulation of the sparse signal recovery problem in compressed sensing (CS). This formulation is obtained by introduction of an auxiliary binary vector, where ones locate the recovered nonzero indices. Joint optimization for finding this auxiliary vector together with the underlying sparse vector leads to the proposed MILP formulation. By addition of a few appropriate constraints, this problem can be solved by existing MILP solvers. In contrast to other methods, this formulation is not an approximation of the sparse optimization problem, but is its equivalent. Hence, its solution is exactly equal to the optimal solution of the original sparse recovery problem, once it is feasible. We demonstrate this by recovery simulations involving different sparse signal types. The proposed scheme improves recovery over the mainstream CS recovery methods especially when the underlying sparse signals have constant amplitude nonzero elements.
Convergence of the proximal point method for metrically regular mappings

In this paper we consider the following general version of the proximal point algorithm for solving the inclusion T(x) 3 0, where T is a set-valued mapping acting from a Banach space X to a Banach space Y. First, choose any sequence of functions gn : X ! Y with gn(0) = 0 that are Lipschitz continuous in a neighborhood of the origin. Then pick an initial guess x0 and flnd a sequence xn by applying the iteration gn(xn+1ixn)+T(xn+1) 3 0 for n = 0;1;::: We prove that if the Lipschitz constants of gn are bounded by half the reciprocal of the modulus of regularity of T, then there exists a neighborhood O of " x ("x being a solution to T(x) 3 0) such that for each initial point x0 2 O one can flnd a sequence xn generated by the algorithm which is linearly convergent to ". Moreover, if the functions gn have their Lipschitz constants convergent to zero, then there exists a sequence starting from x0 2 O which is superlinearly convergent to ". Similar convergence results are obtained for the cases when the mapping T is strongly subregular and strongly regular. Resume. Nous considerons dans ces travaux une generalisation de l'algorithme du point proximal pour resoudre des inclusions du type T(x) 3 0, oµu T est une application multivoque agissant entre deux espaces de Banach X et Y. Tout d'abord, on se donne une suite de fonctions gn : X ! Y telle que chaque fonction gn soit lipschitzienne dans un voisinage de l'origine et verifle gn(0) = 0. Partant d'un point initial x0 on construit une suite xn en appliquant la procedure iterative gn(xn+1 i xn) + T(xn+1) 3 0 for n = 0;1;::: On montre que si les constantes de lipschitz des fonctions gn sont bornees superieurement par la moitie de l'inverse du module de regularite de T, alors il existe un voisinage O de " ("xune solution de T(x) 3 0) tel que pour tout point initial x0 2 O on peut trouver une suite xn engendree par l'algorithme et qui converge lineairement vers ". De plus, si la suite des constantes de lipschitz des fonctions gn converge vers 0, alors il existe une suite partant du point x0 2 O et qui converge super-lineairement vers ". Des resultats de convergence similaires sont obtenus quand l'application multivoque T est fortement sous-reguliµere et fortement reguliµere.
Big IoT data mining for real-time energy disaggregation in buildings

In the smart grid context, the identification and prediction of building energy flexibility is a challenging open question, thus paving the way for new optimized behaviors from the demand side. At the same time, the latest smart meters developments allow us to monitor in real-time the power consumption level of the home appliances, aiming at a very accurate energy disaggregation. However, due to practical constraints is infeasible in the near future to attach smart meter devices on all home appliances, which is the problem addressed herein. We propose a hybrid approach, which combines sparse smart meters with machine learning methods. Using a subset of buildings equipped with subset of smart meters we can create a database on which we train two deep learning models, i.e. Factored Four-Way Conditional Restricted Boltzmann Machines (FFW-CRBMs) and Disjunctive FFW-CRBM. We show how our method may be used to accurately predict and identify the energy flexibility of buildings unequipped with smart meters, starting from their aggregated energy values. The proposed approach was validated on a real database, namely the Reference Energy Disaggregation Dataset. The results show that for the flexibility prediction problem solved here, Disjunctive FFW-CRBM outperforms the FFW-CRBMs approach, where for classification task their capabilities are comparable.
Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly

The need for real time analysis of rapidly producing data streams (e.g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information from massive data "on the fly. " Such problems can often be reduced to maximizing a submodular set function subject to various constraints. While efficient streaming methods have been recently developed for monotone submodular maximization, in a wide range of applications, such as video summarization, the underlying utility function is non-monotone, and there are often various constraints imposed on the optimization problem to consider privacy or personalization. We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee α under a collection of independence systems I, provides a constant 1/(1+2/√α+1/α+2d(1+√α)) approximation guarantee for maximizing a non-monotone submodular function under the intersection of I and d knapsack constraints. Our experiments show that for video summarization, our method runs more than 1700 times faster than previous work, while maintaining practically the same performance.
Analysis of Tapered Slot Antenna With High Gain for 2D Indoor Wireless Positioning

In this paper, a tapered slot antenna (TSA) for indoor wireless positioning at 2D coordinates is proposed. Three square slots and a semi-circular structure are added to the radiation element and the tapered aperture edge to obtain a high gain. The typical performance of the proposed antenna is analyzed by the voltage standing wave ratio (VSWR), the radiation pattern, and the realized gain. In order to verify the practicality of the indoor wireless positioning system at 2D coordinates, the fabricated TSA was connected to an NVA-R661 module developed by Xethru Inc. The impedance bandwidth result of the fabricated TSA has a wide bandwidth of 7.5 GHz by satisfying a VSWR ≤2 in a band of 4.5–12.0 GHz. Therefore, the radiation pattern indicated good directional characteristics, the maximum gain obtained is 8.92 dBi in the 9-GHz band. Finally, the target detection results of the indoor positioning system showed a minimum error of 3.27 cm and a maximum error of 16.33 cm, and thereby confirming the possibility of its use.
Joint DOD and DOA Estimation for Bistatic MIMO Radar: A Covariance Trilinear Decomposition Perspective

The covariance methods exert an effect on spatially colored (correlated) noise elimination during direction finding in multiple-input multiple-output (MIMO) radar. However, most of the existing methods seem difficulty to achieve a good balance between accuracy and efficiency. This paper aims at formulating a covariance trilinear decomposition perspective for direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic MIMO radar. First, the array covariance matrix model is presented for de-noising. Furthermore, the noiseless covariance matrix is rearranged into a trilinear decomposition model. Finally, joint DOD and DOA estimation are linked to trilinear decomposition, which can be easily accomplished by exploiting the existing COMFAC technique. The proposed scheme can exploit the tensor structure of the covariance matrix, and it is attractive from the perspective of computational complexity. Moreover, it can be easily extended to the spatially colored noise scenario. The proposed algorithm is analyzed in terms of identifiability, flexibility, and complexity, and the stochastic Cramér-Rao bound on joint DOD and DOA estimation is derived. The computer simulations verify the effectiveness and improvement of the proposed method.
Direct-to-Consumer Antidepressant Advertising and Consumers’ Optimistic Bias about the Future Risk of Depression: The Moderating Role of Advertising Skepticism

Although exposure to direct-to-consumer prescription drug advertising (DTCA) is reported to influence the public’s beliefs about diseases, no research has investigated how DTCA may affect the extent of consumers’ optimistic bias about the future risk of diseases. Based on a survey with members of an online consumer panel (n = 699), the current study revealed that: (a) Consumers exhibited a tendency to believe they were at less risk of developing clinical depression in the future than their peers, demonstrating an optimistic bias. (b) Exposure to antidepressant DTCA acted to reduce the extent of such bias, especially when consumers were less skeptical of prescription drug advertising. When consumers were highly skeptical, DTCA exposure did not significantly relate to the extent of optimistic bias. (c) Once formed, the extent of optimistic bias negatively related to consumers’ intention to seek information about depression. Implications of the research for the theory and practice of DTCA were discussed.
Noise-Enhanced Associative Memories

Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms allow reliable learning and recall of exponential numbers of patterns. Though these designs correct external errors in recall, they assume neurons compute noiselessly, in contrast to highly variable neurons in hippocampus and olfactory cortex. Here we consider associative memories with noisy internal computations and analytically characterize performance. As long as internal noise is less than a specified threshold, error probability in the recall phase can be made exceedingly small. More surprisingly, we show internal noise actually improves performance of the recall phase. Computational experiments lend additional support to our theoretical analysis. This work suggests a functional benefit to noisy neurons in biological neuronal networks.
Identification and quantification of modular control during Roundhouse kick executed by elite Taekwondo players

The main goal of this study is to determine the neuromuscular coordination and synergies during roundhouse kick executed by elite taekwondo players. Muscles synergy (coordination) is a tactics of CNS (Central Nervous System) to consider a lower dimensional parameters of motor coordination commands from the central nervous system (CNS) of human body or animals. Fifteen elite players from Iran taekwondo national team participated in current study. Neuromuscular coordination (synergies) were extracted from six taekwondo specific muscles. Studies on athletics performance featured how factors such as the lowest number of motor coordination determination for most of the data variance correlate with impairments and motor task. Neuromuscular coordination (synergies) and their separate synergy activation curves were determined from six lower extremity muscles via a NNMF (Non Negative Matrix Factorization) theory. Three muscle synergies were capable to express 90% of the variance in electromyography signals across all players. The scalar product similarity of the neuromuscular synergies among the athletics is found to be 0.81±0.17.
An End to End Real Time Architecture for Analyzing and Clustering Time Series Data: Case of an Energy Management System

Big data is a field that fascinated many researchers from different areas to study intelligent and robust techniques to analyze extremely large data sets, reveal patterns about human behaviors and then make important decisions such as predicting the next human activity. Smart grid is a cyber physical system that aims at updating the current old-fashion electrical grid by incorporating the latest ICTs to improve the generation, the distribution and the consumption of electricity. The use of sensors in smart grids becomes crucial as it allows an energy management system to analyze massive generated sensory data and use machine learning algorithms to take advantage of the customer’s participation to reduce the cost of power. However, big data field revealed a very long list of tools to analyze data either in real time or batch modes so the decision of what tools to use for a particular case becomes a challenging one. The purpose of this paper is to present an end-to – end architecture for a real time test bed implemented at Al Akhawayn University in Ifrane Morocco to analyze and cluster time series sensor data using an IoT architecture composed of Kaa (a middleware), Kafka (a realtime data messaging queue), Spark (an in-memory data analytics platform) and k-means (a clustering algorithm).
The Effects of Teacher Qualification, Teacher Self-Efficacy, and Classroom Practices on Fifth Graders' Literacy Outcomes

This study examined the effects of teacher self-efficacy, education, and years of experience on observed classroom practices across 2 dimensions—teacher support for student learning and time in academics—as they related to fifth-grade students' (n = 1,043) literacy skills. To address these issues, the study used longitudinal data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development. Teacher self-efficacy is assessed on a survey that indicates the extent to which teachers believe they can make a difference in their students' achievement. Structural equation modeling results indicated that teachers with a higher sense of self-efficacy showed more support and provided a more positive classroom environment than did teachers with lower self-efficacy; in addition, their students had stronger literacy skills. Teachers with greater self-efficacy and more years of experience spent less time in academics.