Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction

There are many destructive diseases in the world which cause rapid death by taking time to affect such as cancer and diabetes. They take a lot of time to spread, thus they are curable or somewhat scalable to a great extent if they are diagnosed soon after introduced into the human body. Research shows that almost all type of cancer can be cured if they are detected in the early stage. It is also true for diabetes as they can be controlled if they are detected at the right time. So, a prediction technique that takes help from the computer and processes data from affected user to detect possible contamination can be a great tool for assisting both the doctors and patients with these diseases. A challenge in the process is that the detection accuracy has to be acceptable in order to make the system a reliable one. In this study, we have analyzed medical data using several classification algorithms in order to optimize classifier performance for cancer and diabetes prediction.
Event-Triggered Control for Strict-Feedback Nonlinear Systems With External Disturbances

In this paper, the event-trigger adaptive control problem is investigated for a class of nonlinear systems with unknown external disturbance. An estimator is designed to estimate the unknown upper bound of the disturbance-like term, which presents the uncertainties caused by external disturbance, a measurement error of input, and an unknown gain parameter. Unlike the existing results, the bound of tracking error is not only independent of the size of unknown system parameters but also can converge to zero. Moreover, new proof of Zeno-free under the discontinuous input is presented. It is shown that the designed adaptive controller ensures the stability of the closed-loop system and achieves desired tracking performance.
On Tail Dependence and Multifractality

We study whether, and if yes then how, a varying auto-correlation structure in different parts of distributions is reflected in the multifractal properties of a dynamic process. Utilizing the quantile autoregressive process with Gaussian copula using three popular estimators of the generalized Hurst exponent, our Monte Carlo simulation study shows that such dynamics translate into multifractal dynamics of the generated series. The tail-dependence of the auto-correlations forms strong enough non-linear dependencies to be reflected in the estimated multifractal spectra and separated from the case of the standard auto-regressive process. With a quick empirical example from financial markets, we argue that the interaction is more important for the asymmetric tail dependence. In addition, we discuss and explain the often reported paradox of higher multifractality of shuffled series compared to the original financial series. In short, the quantile-dependent auto-correlation structures qualify as sources of multifractality and they are worth further theoretical examination.
Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS. Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels. Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only. Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance. In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain. In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus. The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness. The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.
Frozen Leg Operation of a Three-Phase Dual Active Bridge Converter

Three-phase dual active bridge (DAB) topology is a potential alternative for high-power applications when a compact and efficient converter with a bidirectional power transfer capability is desired. In a constructed prototype, high-power SiC modules with dedicated drivers are utilized to achieve high-efficiency and compact size. Each module has two interconnected switches with anti-parallel diodes resembling a converter leg. It is observed that the driver halts the module operation as a result of protective actions such as overcurrent, gate undervoltage, or gate overvoltage. In this frozen leg mode, the module operates as a leg with two diodes until an external hardware signal resets the driver. The converter continues operation but with a reduced performance. Analysis, simulation, and verification of a three-phase DAB converter under a frozen leg operation are considered in this paper. The converter with a frozen leg has two different behaviors at light loads and heavy loads. Consequently, two different analysis methods are developed to solve converter operation in different load conditions. Results show that the power transfer capability is reduced, but this fault mode is nondestructive.
Joint Relay and Eavesdropper Selection Strategy Against Multiple Eavesdroppers over Nakagami- $m$  Fading Channels in Cooperative Decode-and-Forward Relay Networks

Physical layer security (PLS) in the presence of multiple eavesdroppers over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channels for cooperative decode-and-forward (DF) relay network that consists of one source, one destination, and multiple relays is investigated. Different from the recent PLS system model that only considers one eavesdropper during eavesdropping attack, this paper extends the one-eavesdropper case to a multiple-eavesdropper scenario and investigates joint relay and eavesdropper selection (JRES) strategy against eavesdropping attack over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading channels. In the proposed strategy, the best relay is selected via the maximum relay forward channel capacity. In addition, since eavesdroppers are non-cooperative, the worst case is considered. Namely, the wiretap channel between the best relay and the chosen eavesdropper has the maximum capacity. Traditional direct transmission and opportunistic relay selection (ORS) scheme in the presence of one eavesdropper over Rayleigh channel are regarded as benchmarks. Moreover, a security–reliability tradeoff (SRT) performance is analyzed, where the reliability performance is expressed by outage probability (OP), while the security performance is measured by intercept probability (IP). Closed-form expressions of OP and IP are derived. The numerical results show that the proposed JRES scheme outperforms the traditional direct transmission and the ORS scheme in the presence of one eavesdropper over Rayleigh channel. The SRT performance is enhanced obviously with the increasing of relay numbers and Nakagami channel fading factor <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> for a given number of eavesdroppers, which extends the PLS and SRT performance analysis to a more general case in a cooperative DF relay network.
Decoding Two-Class Motor Imagery EEG with Capsule Networks

Recently, deep learning approaches such as convolutional neural networks (CNN) have been widely applied to improve the classification performance of motor imagery-based brain-computer interfaces (BCI). However, CNN is known to have a limitation that its classification performance is degraded when the target data are distorted. Particularly in case of electroencephalography (EEG), the signals measured from the same user are not consistent. To address this issue, we propose to apply Capsule networks (CapsNet) which implicitly learn various features, thereby achieving more robust and reliable performance than traditional CNN approaches. In this paper, a novel method based on CapsNet to classify two-class motor imagery signals is presented. The motor imagery EEG signals are transformed into time-frequency images using Short-Time Fourier Transform (STFT) and then supplied for training and testing capsule networks. The experimental results on BCI competition IV 2b dataset show that the proposed CapsNet based architecture outperforms previous CNN-based approaches.
Overcritical Current Resistivity of YBCO-Coated Conductors Through Combination of PCM and Finite-Element Analysis

Understanding the electro-thermal behavior of high-temperature superconductor (HTS) materials is a critical aspect for designing efficient and reliable applications. In order to optimize cost and performance, one needs to understand the role played by the various layers of an HTS tape during a quench. On one hand, the electrical and thermal properties of the materials used in the manufacturing of those tapes (e.g., alloy substrate, silver, and copper) are well known. Knowledge of the functional dependence of the superconductor's resistivity <inline-formula><tex-math notation="LaTeX">$\rho (J,T)$</tex-math></inline-formula> above the critical current in 2G HTS CCs is very limited. In the flux creep or normal state regime, the resistivity can be approximated by empirical laws. In the flux-flow regime, it is difficult to extract <inline-formula><tex-math notation="LaTeX">$\rho (J,T)$</tex-math></inline-formula> due to the presence of Joule heating. In this contribution, using finite-element analysis, we present a method to retrieve the overcritical current resistivity, by estimating the amount of current, temperature, and heat present in the various layers.
Modeling and Analysis of Differential CQI Feedback in 4G/5G OFDM Cellular Systems

Reduced feedback schemes are crucial in achieving the high data rates expected of orthogonal frequency-division multiplexing-based 4G and 5G cellular systems. They ensure that the feedback overhead required for acquiring the channel-state information to enable downlink scheduling and adaptive modulation and coding at the base station (BS) does not overwhelm the uplink. We present a novel modeling and analysis of the single-user and multi-user throughputs of the differential feedback scheme that is used in both 4G and 5G standards. In this feedback scheme, a user feeds back a 4-bit wideband channel quality indicator (CQI), which indicates the rate that the user can decode if the BS was to transmit to it over the entire system bandwidth, and a 2-bit differential CQI for each subband relative to it. Our analysis incorporates co-channel interference, different single-stream multi-antenna modes, and different schedulers, which cover a wide range of the tradeoff between the throughput and user fairness. It brings out several insights such as the increase in the throughput as the correlation between subbands increases and how differential feedback reduces the overhead significantly while only marginally reducing the throughput.
Impact of Dividends Announcements on Stock Returns: Evidence from Casablanca Stock Market

According to Efficient Market Hypothesis (EMH), stock prices reflect all available information, therefore, in an efficient market, no abnormal returns can be earned from this information since current prices already incorporate all existing information. Hence, the purpose of this study is to investigate the semi-strong form of market efficiency by examining the reaction of stock prices to dividend announcements in the Casablanca Stock Market. Thus, the basic question of this study is: Do dividend announcements affect stock prices? We used the Event Study approach to measure the impact of dividends announcement on stock prices by computing the average abnormal return (AAR) and cumulated average abnormal return (CAAR) , so as to evaluate their impact on the stock performance around the announcement day (for a period of 20 days prior and post-announcement). The empirical results indicate that the (AARs) and (CAARs) are statistically insignificant for the whole event window, implying that dividend announcements do not convey any information content, therefore, the results do not confirm the information content of the dividend hypothesis. Furthermore, the empirical results support the dividend irrelevance theory introduced by Miller & Modigliani (1961) and contradict the cash flow signaling theory, which argues that dividends are informative. Overall, results confirm the semi-strong form of market efficiency in the Casablanca Stock Market.
Abnormal Relationship Between Hot Carrier Stress Degradation and Body Current in High-k Metal Gate in the 14-nm Node

This letter discusses the hot carrier stress (HCS) degradation mechanisms for different gate voltages (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {G}}$ </tex-math></inline-formula>) in FinFET devices, and finds the abnormal relationship between HCS degradation and body current under high <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {G}}$ </tex-math></inline-formula>. According to the distributions of lifetime (<inline-formula> <tex-math notation="LaTeX">${\tau }$ </tex-math></inline-formula>)-<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\textsf {B}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${\tau }$ </tex-math></inline-formula>-drain current (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\textsf {D}}$ </tex-math></inline-formula>), it is found that the <inline-formula> <tex-math notation="LaTeX">${\tau }$ </tex-math></inline-formula> under the high <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {G}}$ </tex-math></inline-formula> is related to the <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\textsf {D}}$ </tex-math></inline-formula> instead of the <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\textsf {B}}$ </tex-math></inline-formula>. In addition, as the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {G}}$ </tex-math></inline-formula> increases from low to middle to high values, the HCS degradation mechanism exhibits single vibrational excitation, electron-electron scattering, and multiple vibrational excitation (MVE) models, respectively. Therefore, the HCS degradation mechanism at higher <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textsf {G}}$ </tex-math></inline-formula> is not dominated by the traditional impact ionization but by the carrier concentration of the channel. Finally, according to the results of fitting MVE model, the reason for breaking the Si-H bonds to form dangling bonds is the bending vibrational mode.
What Drives the Willingness to Pay for Insurance Contracts with Nonperformance Risk? Experimental Evidence

An insurance contract may be nonperforming—resulting in a situation in which the insured might be worse off than without insurance since the insured also loses the premium. This study analyzes different drivers influencing the willingness to pay for insurance contracts with inherent contract nonperformance risk. In an incentive-compatible laboratory experiment, subjects state their maximum willingness to pay for three different insurance contracts, which only differ in their risk of insurer nonperformance. While the median willingness to pay for no-default contracts is above the actuarially fair premium, both the mere existence of default risk (only 0.1 percent) and an increase in default risk sharply decrease participants’ median willingness to pay below the actuarially fair premia. Individuals, thus, are willing to pay considerably more for default-free insurance policies. Among others, the willingness to pay is influenced by framing, age, self-assessed risk attitude, and gender.
Spatial Clustering of Receptors and Signaling Molecules Regulates NK Cell Response to Peptide Repertoire Changes

Natural Killer (NK) cell activation requires integration of inhibitory and activating signaling. Inhibitory signals are determined by members of the killer cell immunoglobulin-like receptor (KIR) family, which have major histocompatibility complex (MHC) class I ligands. Loss of this inhibitory signal leads to NK cell activation. Thus, down-regulation of MHC I during viral infection or cancer induces NK cell activation. However, NK cell activation in the presence of MHC-I has been demonstrated for HLA-C*0102 through changes in its peptide content: “peptide antagonism. ” Here we identify an antagonist peptide for HLA-C*0304 suggesting that peptide antagonism is a generalizable phenomenon and, using a combination of mathematical modeling, confocal imaging, and immune-assays, we quantitatively determine mechanisms that underlie peptide antagonism in inhibitory KIR2DL2/3 signaling. These data provide a mechanism for NK cell activation based on a reduction of inhibitory signaling in the presence of preserved levels of MHC class I.
Analysis on Novel Metamaterial Resonator: A Critical Study to Enhance the Performance by Tuning Parameters

In this paper, a new approach on the characterization of dumbbell annular ring (DAR) resonators have been presented. Moreover, the electromagnetic nature of these resonators on different dimensions as well as their coupling strength to the host 50-Ω transmission line are investigated. Furthermore, an equivalent circuit model has been developed to understand their overall performance. In fact, from this model, the passband and stopband characteristics of DAR to the loaded transmission line are analyzed. Nevertheless, this study reveals that DAR provides an inductance which is less than 16% with respect to conventional defected ground structure (DGS), as well as the aspect ratio, can be tuned for certain analysis. This proposed resonator can be of great interest in terms of designing compact RF and microwave devices based on electromagnetic bandgap (EBG) concept.
Combining Bottom-Up and Top-Down Visual Mechanisms for Color Constancy Under Varying Illumination

Multi-illuminant-based color constancy (MCC) is quite a challenging task. In this paper, we proposed a novel model motivated by the bottom-up and top-down mechanisms of human visual system (HVS) to estimate the spatially varying illumination in a scene. The motivation for bottom-up based estimation is from our finding that the bright and dark parts in a scene play different roles in encoding illuminants. However, handling the color shift of large colorful objects is difficult using pure bottom-up processing. Thus, we further introduce a top-down constraint inspired by the findings in visual psychophysics, in which high-level information (e.g., the prior of light source colors) plays a key role in visual color constancy. In order to implement the top-down hypothesis, we simply learn a color mapping between the illuminant distribution estimated by bottom-up processing and the ground truth maps provided by the dataset. We evaluated our model on four datasets and the results show that our method obtains very competitive performance compared with the state-of-the-art MCC algorithms. Moreover, the robustness of our model is more tangible considering that our results were obtained using the same parameters for all the datasets or the parameters of our model were learned from the inputs, that is, mimicking how HVS operates. We also show the color correction results on some real-world images taken from the web.
Reliability Analysis of On-Chip Wireless Links for Many Core WNoCs

On-chip wireless interconnects in Wireless Network-on-Chip (WNoC) show considerable promise for significantly improving communication performance and energy in many core processors. Wireless links offer high bandwidth, low latency and low per bit energy consumption, along with broadcast capability. While, they offer several advantages, wireless links are inherently unreliable, unlike wired links. Wireless transmission in on-chip network faces challenges from complex channel characteristics, channel sharing and allocation, choice of modulation, noise in analog circuits and channel, etc. Unlike traditional wireless communication that generally uses error correction and data retransmission techniques to improve reliability, on-chip wireless network suffers from high performance and power overheads in using such techniques. This paper explores the challenges for reliable wireless transmission in WNoCs, especially the impact of wireless channel and choice of modulation schemes. The complex nature of on-chip wireless channel adds adverse effects like high path loss, dispersion, etc. along with noise in channel. This results in packet loss/errors or performance and energy degradation. While modulation schemes like OOK are susceptible to these channel effects, techniques like OFDM, CDMA are robust against such effects but add power/area overheads. Using these analyses, we propose techniques to improve wireless link reliability, while not adding high performance and energy overheads. The proposed improvements are evaluated for reliability using MATLAB modeling of the wireless communication and for network performance and energy.
Adeno-Associated Virus as an Effective Malaria Booster Vaccine Following Adenovirus Priming

An ideal malaria vaccine platform should potently induce protective immune responses and block parasite transmission from mosquito to human, and it should maintain these effects for an extended period. Here, we have focused on vaccine development based on adeno-associated virus serotype 1 (AAV1), a viral vector widely studied in the field of clinical gene therapy that is able to induce long-term transgene expression without causing toxicity in vivo. Our results show the potential utility of AAV1 vectors as an extremely potent booster vaccine to induce durable immunity when combined with an adenovirus-priming vaccine in a rodent malaria model. We generated a series of recombinant AAV1s and human adenovirus type 5 (AdHu5) expressing either Plasmodium falciparum circumsporozoite protein (PfCSP) or P25 (Pfs25) protein. Heterologous two-dose immunization with an AdHu5-prime and AAV1-boost (AdHu5-AAV1) elicited robust and durable PfCSP- or Pfs25-specific functional antibodies over 280 days. Regarding protective efficacy, AdHu5-AAV1 PfCSP achieved high sterile protection (up to 80% protection rate) against challenge with transgenic Plasmodium berghei sporozoites expressing PfCSP. When examining transmission-blocking (TB) efficacy, we found that immunization with AdHu5-AAV1 Pfs25 maintained TB activity in vivo against transgenic P. berghei expressing Pfs25 for 287 days (99% reduction in oocyst intensity, 85% reduction in oocyst prevalence). Our data indicate that AAV1-based malaria vaccines can confer potent and durable protection as well as TB efficacy when administered following an AdHu5 priming vaccine, supporting the further evaluation of this regimen in clinical trials as a next-generation malaria vaccine platform.