Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Application based on Spectrogram and Convolutional Neural Network for ECG Classification

Electrocardiogram (ECG) is a biomedical signal which represents the electrical activity of the human heart. Various cardiac diseases have been detected using the outputs of ECG devices. Recently, advances in signal processing techniques bring out a new horizon for processing the ECG signals. In this scope, a novel application based on the spectrogram, which is a graphical representation of time-frequency information of the signal, and the convolutional neural network (CNN) is proposed so as to distinguish ECG signals. To this aim, a publicly available data set in Physionet was utilized. Firstly, the spectrograms of each signal were obtained. Then, these colorful spectrogram images were applied as the input to CNNs that are AlexNet, VGG-16, and ResNet-18. The transfer learning and fine-tuning approach were used for training and validation of the models. As a result, the most efficient results were provided by AlexNet with an accuracy of 83.82%. The experimental results of this study show that the proposed model ensures promising results for the ECG signal classification.
A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques

With the continuing increase in the number of the deadly diseases that threaten both human health and life, medical Decision Support Systems (DSS) continue to prove their effectiveness in providing physicians and other healthcare professionals with support in clinical decision making. Among these dangerous diseases, diabetes continues to be one of the leading one that has caused several deaths in the world. It is characterized by an increase in blood sugar levels which can have severe effects on other human organs. According to the International Diabetes Federation (IDA), 382 million people are living with diabetes and by 2035, these statistics will double to reach 592 million. In this paper, we propose a DSS for diabetes prediction based on Machine Learning (ML) techniques. We compared conventional machine learning with deep learning approaches. For conventional machine learning method, we considered the most commonly used classifiers: Support Vector Machine (SVM) and the Random Forest(RF). On the other hand, for Deep Learning (DL) we employed a fully Convolutional Neural Network (CNN) to predict and detect the diabetes patients. The proposed system is evaluated on publicly available Pima Indians Diabetes database which consisted of total 768 samples each with 8 features. 500 samples were labeled as non-diabetic while 268 were diabetic patients. The overall accuracy obtained using DL, SVM and RF was 76.81%, 65.38% and 83.67% respectively. The experimental results show that RF was more effective for diabetes prediction compared to deep learning and SVM methods.
Symmetrical/Asymmetrical Winding Reconfiguration in Multiphase Machines

This paper investigates multiphase drives in which winding configuration (symmetrical or asymmetrical) can be easily obtained by only rearranging voltage source inverter (VSI) power supply cables at the machine’s terminal box. The type of the machines where this is possible is identified and the examples of reconfiguration are given and explained. Following from the examples (for nine- and fifteen-phase cases), a general reconfiguration algorithm is introduced. As shown, changing asymmetrical to symmetrical winding configuration (and vice versa) means that just another mimic diagram needs to be placed over the existing one on the machine’s terminal box. Possible reconfigurations of a six-phase machine, which do not follow the same pattern, are also addressed. Differences caused by different winding configuration are identified and experimentally confirmed using a nine-phase surface mounted permanent magnet synchronous machine (PMSM) and a nine-phase induction machine (IM).
Study on Consensus of the Forth-Order Discrete-Time Multiagent System in Directed Networks

In this paper, we are concerned with the consensus of the forth-order discrete-time multiagent system in directed networks. First, by defining a group of new variables, the multiagent system is converted to the corresponding reduced order system. Based on the stability of the reduced order system, we discuss the consensus of the original multiagent system. Then, two necessary and sufficient conditions for the consensus of the forth-order discrete-time multiagent system are established by using the related knowledge of algebraic graph theory and matrix theory. Compared with the existing results, the obtained conditions of this paper are more extensive. In order to reduce the number of information exchange between agents, we use event-triggered mechanism to deal with the consensus of the multiagent system. Furthermore, Zeno behavior of multiagent system will not make an appearance due to the reasonable design of the triggering function. A numerical example is finally given to demonstrate the effectiveness of theoretical results.
A Coarse-Fine VCO-ADC for MEMS Microphones With Sampling Synchronization by Data Scrambling

This letter presents a mostly digital analog-to-digital converter implemented with voltage-controlled oscillators that can directly interface a capacitive MEMS microphone. The ADC is based on two ring oscillators and a coarse-fine counting circuitry. Coarse and fine counters are synchronized using a novel data scrambling technique to mitigate metastability and timing errors. This method enables a very low power consumption in the digital post-processing circuit. The proposed ADC, prototyped in 130-nm CMOS, achieves 73.8 dB-A of signal-to-noise and distortion ratio (SNDR) peak and 97 dB of dynamic range (DR) in a 20-kHz BW, while consuming 240 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> from the 1.5-V/1.2-V power supplies. In a reduced power mode (8 kHz BW) with relaxed oscillation parameters, it reaches 66.4 dB-A of SNDR peak and 93 dB of DR with a power consumption of only 77 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>.
Enhanced-Performance Circularly Polarized MIMO Antenna With Polarization/Pattern Diversity

Design of a compact wideband circularly polarized (CP) multiple-input multiple-output (MIMO) antenna with polarization diversity is proposed and characterized for off-body communication. The antenna is based on a simple coplanar waveguide (CPW)-fed monopole extension of the microstrip line. The orthogonal field components required by CP are induced using a simply modified right/left side ground plane. In particular, a stub extending from the ground plane along the length of the microstrip line generates the vertical component, whereas the current along the width of the ground plane contributes to the horizontal components. To obtain a unidirectional radiation pattern in the off-body direction and to reduce the sensitivity to the human body loading effects, a flat reflector printed on a high permittivity flexible substrate is applied. The simple topology of the antenna can be described by a few adjustable parameters, which facilitates its EM design closure. Prior to the experimental validation in the free space and on the body, the antenna is optimized at the full-wave level of description for all major performance figures. The overall footprint of the antenna radiator is only <inline-formula> <tex-math notation="LaTeX">$L_{s}\,\,\times $ </tex-math></inline-formula> <inline-formula> <tex-math notation="LaTeX">$W_{s} =0.24\lambda _{0} \times 0.64\,\,\lambda _{0} = 0.15\,\,\lambda _{0}^{2}$ </tex-math></inline-formula>. The proposed MIMO antenna features <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert \le -10$ </tex-math></inline-formula> dB, average isolation <inline-formula> <tex-math notation="LaTeX">$\vert S_{21}\vert \le -22$ </tex-math></inline-formula> dB, and axial ratio (AR) ≤3 dB from 5.2 GHz to 6.3 GHz with 100% bandwidth overlap between the impedance and axial ratio bandwidths. The envelope correlation coefficient (ECC) is less than 0.004 with the maximum diversity gain (DG) of approximately 9.99 dB. Moreover, the antenna maintains a high efficiency of up to 90% when loaded on the body, and a low specific absorption rate (SAR).
An Evaluation of e-Money Products Using UTAUT 2 Model (The Case of Bank Mandiri)

In recent years new financial services have emerged using technology as an intermediary media, one of which is e-money. Every year transactions using electronic money continue to increase. One of them is Bank Mandiri E-Money. In 2017, Bank Mandiri's E-Money became electronic money with the most ownership. As electronic money with the most ownership, Bank Mandiri E-Money is not immune from various problems. So, research is conducted regarding the evaluation of Bank Mandiri's E-Money usage, especially in Ciputat District. The purpose of this study was to evaluate the use of Bank Mandiri E-Money in Ciputat District and examine the variables of the UTAUT 2 model that influenced the use of Bank Mandiri E-Money in Ciputat District using a quantitative method with the PLS-SEM analysis technique approach. Determination of sampling in this study using a purposive sampling method with slovin formula calculation technique. Furthermore, recommendations can be made to Bank Mandiri for further development. For this reason, this research was conducted with a survey using a questionnaire on 127 Bank Mandiri E-Money users in the Ciputat District area. The results of this study show that of the 10 hypotheses tested, most of the hypotheses were accepted as many as 7 hypotheses and the ones rejected were 3 hypotheses. The Habit variable (HT) is the factor with the greatest influence and Effort Expectancy (EE) becomes the factor with the smallest influence. The benefits of this study are that it provides recommendations that related to model UTAUT 2 which can be used as material for consideration in the future development of Bank Mandiri E-Money.
CMOS Low-Voltage Indirect Current Feedback Instrumentation Amplifiers With Improved Performance

Indirect current feedback (ICF) is a very common operation principle used in the design of CMOS instrumentation amplifiers (IA). The input and output transconductors required in an ICF IA usually consist on resistive-degenerated simple differential pairs. In this contribution conventional source-follower (SF) structures used in IA transconductors are replaced by flipped-voltage-follower (FVF) and super-source-follower (SSF) cells, respectively. As a result, the overall performance and efficiency of the IA is enhanced with a minimum cost. The principle of operation governing CMRR is also analyzed and its behavior is confirmed by means of extensive simulations. A single-stage IA with a fixed voltage gain equal to 10 V/V has been designed in $0.35-\boldsymbol{\mu} \mathbf{m}$ standard CMOS technology to operate with 1.8 V supply. Simulation results show improved metrics in terms of voltage gain accuracy, while the overall performance of the IA is comparable to other contributions in the literature.
A Review of Gamification for Learning Programming Fundamental

The games, nowadays, are extensively used in different fields for learning purposes. They are getting popularity progressively because of their efficacy when used in non-game context, such as in educational and business domain. Game based learning helps the instructors to teach difficult concepts of programming fundamentals to novice students, in order to keep their interest and motivation. Therefore, researchers these days seems quite interested in the field of game based learning i.e. gamification. Hence, this paper presents a review of the existing literature of serious programming games to analyze the researcher's effort in the field and intends to find the possible gaps that exist in the current gamification approaches. Initially, we considered the concepts and knowledge that most of the games have addressed so far pertaining to programming fundamentals. Then we presented important components required for the development of an interactive game by mapping it on (Mechanics, Dynamics, Aesthetics) MDA Framework. Moreover, we presented all the methods that are used for evaluating the effectiveness of games. Based on our review, we concluded our study by identifying the loopholes in the existing literature on game based learning. Furthermore, we identified several open problems in this area and their possible suggestions that will be a contributing factor for future researchers.
Equation of State and Progenitor Dependence of Stellar-mass Black Hole Formation

The core collapse of a massive star results in the formation of a proto-neutron star (PNS). If enough material is accreted onto a PNS, it will become gravitationally unstable and further collapse into a black hole (BH). We perform a systematic study of failing core-collapse supernovae in spherical symmetry for a wide range of pre-supernova progenitor stars and equations of state (EOSs) of nuclear matter. We analyze how variations in progenitor structure and the EOS of dense matter above nuclear saturation density affect the PNS evolution and subsequent BH formation. Comparisons of core collapse for a given progenitor star and different EOSs show that the path traced by the PNS in mass-specific entropy phase space is well correlated with the progenitor compactness and is almost EOS independent, apart from the final end point. Furthermore, BH formation occurs, to a very good approximation, soon after the PNS overcomes the maximum gravitational mass supported by a hot NS with constant specific entropy equal to . These results show a path to constraining the temperature dependence of the EOS through the detection of neutrinos from a failed galactic supernova.
Realization of a Density-Dependent Peierls Phase in a Synthetic, Spin-Orbit Coupled Rydberg System

We experimentally realize a Peierls phase in the hopping amplitude of excitations carried by Rydberg atoms, and observe the resulting characteristic chiral motion in a minimal setup of three sites. Our demonstration relies on the intrinsic spin-orbit coupling of the dipolar exchange interaction combined with time-reversal symmetry breaking by a homogeneous external magnetic field. Remarkably, the phase of the hopping amplitude between two sites strongly depends on the occupancy of the third site, thus leading to a correlated hopping associated to a density-dependent Peierls phase. We experimentally observe this density-dependent hopping and show that the excitations behave as anyonic particles with a non-trivial phase under exchange. Finally, we confirm the dependence of the Peierls phase on the geometrical arrangement of the Rydberg atoms.
Can graph properties have exponential quantum speedup?

Quantum computers can sometimes exponentially outperform classical ones, but only for problems with sufficient structure. While it is well known that query problems with full permutation symmetry can have at most polynomial quantum speedup -- even for partial functions -- it is unclear how far this condition must be relaxed to enable exponential speedup. In particular, it is natural to ask whether exponential speedup is possible for (partial) graph properties, in which the input describes a graph and the output can only depend on its isomorphism class. We show that the answer to this question depends strongly on the input model. In the adjacency matrix model, we prove that the bounded-error randomized query complexity $R$ of any graph property $\mathcal{P}$ has $R(\mathcal{P}) = O(Q(\mathcal{P})^{6})$, where $Q$ is the bounded-error quantum query complexity. This negatively resolves an open question of Montanaro and de Wolf in the adjacency matrix model. More generally, we prove $R(\mathcal{P}) = O(Q(\mathcal{P})^{3l})$ for any $l$-uniform hypergraph property $\mathcal{P}$ in the adjacency matrix model. In direct contrast, in the adjacency list model for bounded-degree graphs, we exhibit a promise problem that shows an exponential separation between the randomized and quantum query complexities.
Robust edge modes in dislocated systems of subwavelength resonators

Robustly manipulating waves on subwavelength scales can be achieved by, first, designing a structure with a subwavelength band gap and, second, introducing a defect so that eigenfrequencies fall within the band gap. Such frequencies are well known to correspond to localized modes. We study a one‐dimensional array of subwavelength resonators, prove that there is a subwavelength band gap, and show that by introducing a dislocation we can place localized modes at any point within the band gap. We complement this analysis by studying the stability properties of the corresponding finite array of resonators, demonstrating the value of being able to customize the position of eigenvalues within the band gap.
Multiscale simulations of electron and ion dynamics in self-irradiated silicon

The interaction of energetic ions with the electronic and ionic system of target materials is an interesting but challenging multi-scale problem and understanding of the early stages after impact of heavy, initially charged ions is particularly poor. At the same time, energy deposition during these early stages determines later formation of damage cascades. We address the multi-scale character by combining real-time time-dependent density functional theory for electron dynamics with molecular dynamics simulations of damage cascades. Our first-principles simulations prove that core electrons affect electronic stopping and have an unexpected influence on the charge state of the projectile. We show that this effect is absent for light projectiles, but dominates the stopping physics for heavy projectiles. By parameterizing an inelastic energy loss friction term in the molecular dynamics simulations using our first-principles results, we also show a qualitative influence of electronic stopping physics on radiation-damage cascades.
In-beam 
γ
-ray and electron spectroscopy of 
Md249,251

The odd-$Z$ $^{251}$Md nucleus was studied using combined $\gamma$-ray and conversion-electron in-beam spectroscopy. Besides the previously observed rotational band based on the $[521]1/2^-$ configuration, another rotational structure has been identified using $\gamma$-$\gamma$ coincidences. The use of electron spectroscopy allowed the rotational bands to be observed over a larger rotational frequency range. Using the transition intensities that depend on the gyromagnetic factor, a $[514]7/2^-$ single-particle configuration has been inferred for this band, i.e., the ground-state band. A physical background that dominates the electron spectrum with an intensity of $\simeq$ 60% was well reproduced by simulating a set of unresolved excited bands. Moreover, a detailed analysis of the intensity profile as a function of the angular momentum provided a method for deriving the orbital gyromagnetic factor, namely $g_K = 0.69^{+0.19}_{-0.16}$ for the ground-state band. The odd-$Z$ $^{249}$Md was studied using $\gamma$-ray in-beam spectroscopy. Evidence for octupole correlations resulting from the mixing of the $\Delta l = \Delta j = 3$ $[521]3/2^-$ and $[633]7/2^+$ Nilsson orbitals were found in both $^{249,251}$Md. A surprising similarity of the $^{251}$Md ground-state band transition energies with those of the excited band of $^{255}$Lr has been discussed in terms of identical bands. Skyrme-Hartree-Fock-Bogoliubov calculations were performed to investigate the origin of the similarities between these bands.
Stochastic homogenization on randomly perforated domains

We study the existence of uniformly bounded extension and trace operators for $W^{1,p}$-functions on randomly perforated domains, where the geometry is assumed to be stationary ergodic. Such extension and trace operators are important for compactness in stochastic homogenization. In contrast to former approaches and results, we use very weak assumptions on the geometry which we call local $(\delta,M)$-regularity, isotropic cone mixing and bounded average connectivity. The first concept measures local Lipschitz regularity of the domain while the second measures the mesoscopic distribution of void space. The third is the most tricky part and measures the "mesoscopic" connectivity of the geometry. In contrast to former approaches we do not require a minimal distance between the inclusions and we allow for globally unbounded Lipschitz constants and percolating holes. We will illustrate our method by applying it to the Boolean model based on a Poisson point process and to a Delaunay pipe process. We finally introduce suitable Sobolev spaces on $\mathbb{R}^d$ and $\Omega$ in order to construct a stochastic two-scale convergence method and apply the resulting theory to the homogenization of a $p$-Laplace problem on a randomly perforated domain.
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Graph Convolutional Networks~(GCNs) are state-of-the-art graph based representation learning models by iteratively stacking multiple layers of convolution aggregation operations and non-linear activation operations. Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals with GCNs. These GCN based recommender models show superior performance compared to traditional works. However, these models suffer from training difficulty with non-linear activations for large user-item graphs. Besides, most GCN based models could not model deeper layers due to the over smoothing effect with the graph convolution operation. In this paper, we revisit GCN based CF models from two aspects. First, we empirically show that removing non-linearities would enhance recommendation performance, which is consistent with the theories in simple graph convolutional networks. Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data. The proposed model is a linear model and it is easy to train, scale to large datasets, and yield better efficiency and effectiveness on two real datasets. We publish the source code at https://github.com/newlei/LR-GCCF.
Benchmark Design and Prior-independent Optimization

This paper compares two leading approaches for robust optimization in the models of online algorithms and mechanism design. Competitive analysis compares the performance of an online algorithm to an offline benchmark in worst-case over inputs, and prior-independent mechanism design compares the expected performance of a mechanism on an unknown distribution (of inputs, i.e., agent values) to the optimal mechanism for the distribution in worst case over distributions. For competitive analysis, a critical concern is the choice of benchmark. This paper gives a method for selecting a good benchmark. We show that optimal algorithm/mechanism for the optimal benchmark is equal to the prior-independent optimal algorithm/mechanism. We solve a central open question in prior-independent mechanism design, namely we identify the prior-independent revenue-optimal mechanism for selling a single item to two agents with i.i. d. and regularly distributed values. We use this solution to solve the corresponding benchmark design problem. Via this solution and the above equivalence of prior-independent mechanism design and competitive analysis (a.k.a. prior-free mechanism design) we show that the standard method for lower bounds of prior-free mechanisms is not generally tight for the benchmark design program.11For the full version of this work, see https://arxiv.org/abs/2001.10157.