Miracle-dz/UniFAR-FTUs huggingface dataset:
A scalable soft richardson method for detection in a massive MIMO system

The exponential growth in traffic data transmission rates is outpacing current technologies. To overcome these barriers, we propose to use the modified Richardson method, which offers a lower number of iterations and an optimal scalability condition for parallel architecture. Research indicates that the convergence provided by the channel hardening effect offers a good performance in Richardson detection. We then show a simulation of the proposed detector that allows the iteration methods to be set in systems with a large number of antennas. Streszczenie. Metoda Richardsona został użyta do rozwiązania problemu wzrostu wielkości przesyłu danych. Metoda ta umożliwia użycie mniejszej liczby iteracji i optymalizację skalowalności w architekturze równoległej. Przedstawiono symulacje wykazującą że zaproponowana detekcja umożliwia iterację z duzą liczbą anten. (Skalowalna metoda Richardsona do detekcji w dużych systemach MIMO)
ASSESSMENT OF THE IMPACT OF EMPLOYEE TRAINING ON ORGANIZATIONAL PERFORMANCE OF VITAFOAM NIGERIA PLC

BEING A RESEARCH PROJECT SUBMITTED TO THE POSTGRADUATE SCHOOL, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS IN BUSINESS ADMINISTRATION (MBA) DEPARTMENT OF BUSINESS ADMINISTRATION, FACULTY OF ADMINISTRATION, AHMADU BELLO UNIVERSITY, ZARIA. KADUNA STATE NIGERIA January , 2013-
Semiconductor pattern overlay

Advanced semiconductor manufacturing processes require tight overlay registration tolerances. These strict overlay performance specifications dictate the wafer level overlay metrology performance required. Achieving a high level of performance from overlay metrology equipment requires attention to all aspects of the measurement process. A typical measurement system configuration is reviewed and elements of optical overlay measurement, as they relate to measurement uncertainty, are discussed in detail. Data analysis techniques, used to quantify tool induced measurement uncertainty, are demonstrated with supporting examples. Process and measurement target induced uncertainties are reviewed. Current developments in both target design and measurement algorithms are proposed to address these uncertainties. Measurement optimization and its role in process control applications and future developments in overlay processing are also discussed.
HARNESSING THE POWER OF SERVICE QUALITY TO ATTRACT AND RETAINSTUDENTS: AN OUM EXPERIENCE

The increase in the number of open and distance learning (ODL) providers in Malaysia has provided potential students with bigger opportunities to access tertiary education. However, the challenge faced by the providers is that they have to compete with each other to capture the largest number of students. Getting a good intake is a necessity, retaining the existing students is equally critical as these two factors determine the financial sustainability of the institution. As in any service industry, service quality is key to the success of any higher education including ODL institution. Past studies have proposed that satisfaction is one of the key competitive advantages for a HEI as it will lead to profitability and customer loyalty. In this light, this study examines the relationship between service quality and satisfaction of Open University Malaysia (OUM) students. A performance-based instrument called SERVPERF developed by Cronin and Taylor (1992) was used as a guide for this study, some modifications were made to suit the context of OUM. The instrument consists of two sections, one of which contains 15 demographic questions and the other 65 questions measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Out of 22,000 active students population, 4062 (18.5%) responded and 3290 completed questionnaires were used. Data were analysed using descriptive statistics, correlation and multiple regression. The results indicated that ‘programme’, ‘teaching & learning’, ‘assurance’, ‘responsiveness’, ‘empathy’ and ‘reliability’ explained 77.9% of the variation in student satisfaction. ‘Empathy’ and ‘responsiveness’ have the greatest impact on satisfaction in the students’ perception of service quality rendered by OUM. The overall satisfaction level measured falls at 77.0% with a mean score of 3.85 on a 5 point Likert scale.
QRS Detection in Electrocardiogram Signal of Exercise Physical Activity

Accurate estimation of heart beats from electrocardiogram (ECG) signals during exercise activity is a very challenging problem. Unlike standard ECG, the signals recorded during exercise activity can affect the accuracy of QRS detection due to the noises and artifacts arise from body movements activity. However, most of the studies on QRS detection often used clean and standard data for the evaluations and assumed to reflect the overall performance of detectors. In addition, there are not many methods that evaluated using real ECG signal in their studies especially during the exercise activity. Therefore, this study was undertaken to access and evaluated the performance of QRS detection algorithms on the real ECG signal data. Three well-known QRS detection algorithms were re-implemented in this study. The ECG signal recorded under realistic movement conditions and can serving as a realistic data to assess the performance of QRS detection is used. The performance of the algorithm in real ECG signal data in sitting, walking, and jogging was then presented. The results show the algorithms capable to detect the QRS in ECG signal of exercise activity.
Cross-field and parallel dynamics of SOL filaments in TCV

Using recently installed scrape-off layer diagnostics on the tokamak à configuration variable, we characterise the poloidal and parallel properties of turbulent filaments. We access both attached and detached divertor conditions across a wide range of core densities (f G ∈ [0.09, 0.66]) in diverted L-mode plasma configurations. With a gas puff imaging (GPI) diagnostic at the outer midplane we observed filaments with a monotonic increase in radial velocity (from 390 m s−1 to 800 m s−1) and cross-field radii (from 8.5 mm to 13.4 mm) with increasing core density. Interpreting the filament behaviour in the context of the two-region model by Myra et al (2006 Phys. Plasmas 13 112502), we find that they populate the ideal-interchange regime (C i) in discharges at very low densities, and the resistive X (RX)-point regime for all other discharges. The scaling of filament velocity versus size shows good agreement with this interpretation. These results are discussed and compared with previous probe-based measurements for similar conditions, which mostly placed filaments in TCV in the resistive ballooning (RB) regime (Tsui et al 2018 Phys. Plasmas 25 072506). In addition, for the first time in TCV, the parallel filament extension is studied by magnetically aligning the GPI measurements at the outboard midplane with a reciprocating probe in the divertor. In agreement with the filaments being in the ideal-interchange and the RX-point regimes, they are found to extend beyond the X-point into the outer divertor leg.
Mechanical Characteristics Of Concrete With The Addition Of Integral Waterproof Using Local Aggregate East Kalimantan

Nowadays, there are various ways to deal with leaks in concrete. One way that is often used to deal with leaks is to provide a waterproofing or waterproofing layer on the surface of the floor or wall. Along with technological developments, research on the quality of concrete waterproofing continues to be improved. One of them is the use of Concrete Integral waterproofing, which is a mixture of concrete that is added with integral waterproofing to minimize water penetration into the concrete, thereby increasing the concrete’s impermeability and protecting the concrete against leakage. The purpose of this study was to determine the effect of adding Integral waterproofing to the compressive and tensile strength of concrete and to determine the optimum level of addition of Integral waterproofing. The aggregates used are Palu coarse aggregate and Kalimantan local fine aggregate to utilize the natural resources in East Kalimantan. Addition of Integral Waterproofing into the concrete mixture with a percentage of 0%, 0.5%, 1% and 2% of the cement weight. Concrete testing was carried out at the age of 28 days by testing its mechanical properties, namely compressive strength and tensile strength. Based on research results, for concrete that uses Mahakam fine aggregate, the highest compressive strength value at 2% addition is 34.73 MPa, and the highest tensile strength value at 2% addition is 2.38 MPa. And for concrete that uses Sambera fine aggregate, the highest compressive strength value at 2% addition is 30.91 MPa, and the highest tensile strength value at 1.5% addition is 2.34 Mpa.
Majorana fermions in superconducting wires: Effects of long-range hopping, broken time-reversal symmetry, and potential landscapes

We present a comprehensive study of two of the most experimentally relevant extensions of Kitaev's spinless model of a one-dimensional p-wave superconductor: those involving (i) longer-range hopping and superconductivity and (ii) inhomogeneous potentials. We commence with a pedagogical review of the spinless model and, as a means of characterizing topological phases exhibited by the systems studied here, we introduce bulk topological invariants as well as those derived from an explicit consideration of boundary modes. In time-reversal symmetric systems, we find that the longer range hopping leads to topological phases characterized by multiple Majorana modes. In particular, we investigate a spin model that respects a duality and maps to a fermionic model with multiple Majorana modes; we highlight the connection between these topological phases and the broken symmetry phases in the original spin model. In the presence of time-reversal symmetry breaking terms, we show that the topological phase diagram is characterized by an extended gapless regime. For the case of inhomogeneous potentials, we explore phase diagrams of periodic, quasiperiodic, and disordered systems. We present a detailed mapping between normal state localization properties of such systems and the topological phases of the corresponding superconducting systems. This powerful tool allows us to leverage the analyses of Hofstadter's butterfly and the vast literature on Anderson localization to the question of Majorana modes in superconducting quasiperiodic and disordered systems, respectively. We briefly touch upon the synergistic effects that can be expected in cases where long-range hopping and disorder are both present.
Explainable Reinforcement Learning on Financial Stock Trading using SHAP

Explainable Artificial Intelligence (XAI) research gained prominence in recent years in response to the demand for greater transparency and trust in AI from the user communities. This is especially critical because AI is adopted in sensitive fields such as finance, medicine etc. where implications for society, ethics, and safety are immense. Following thorough systematic evaluations, work in XAI has primarily focused on Machine Learning (ML) for categorization, decision, or action. To the best of our knowledge, no work is reported that offers an Explainable Reinforcement Learning (XRL) method for trading financial stocks. In this paper, we proposed to employ SHapley Additive exPlanation (SHAP) on a popular deep reinforcement learning architecture viz., deep Q network (DQN) to explain an agent’s action at a given instance in financial stock trading. To demonstrate the effectiveness of our method, we tested it on two popular datasets namely, SENSEX and DJIA and reported the results. predictions are made using a sliding window with actions from the past 30 days. The opening, low, high, and closing prices of each stock are among the inputs that are sent to DQN for both and (or testing). SHAP provides an explanation of the prediction of the maximum cumulative The user is shown these explanations via waterfall plots.
An Adaptively Resized Parametric Bootstrap for Inference in High-dimensional Generalized Linear Models

Accurate statistical inference in logistic regression models remains a critical challenge when the ratio between the number of parameters and sample size is not negligible. This is because approximations based on either classical asymptotic theory or bootstrap calculations are grossly off the mark. This paper introduces a resized bootstrap method to infer model parameters in arbitrary dimensions. As in the parametric bootstrap, we resample observations from a distribution, which depends on an estimated regression coefficient sequence. The novelty is that this estimate is actually far from the maximum likelihood estimate (MLE). This estimate is informed by recent theory studying properties of the MLE in high dimensions, and is obtained by appropriately shrinking the MLE towards the origin. We demonstrate that the resized bootstrap method yields valid confidence intervals in both simulated and real data examples. Our methods extend to other high-dimensional generalized linear models.
"Task-relevant autoencoding" enhances machine learning for human neuroscience

In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects' behavior. However, state-of-the-art models typically require large datasets to train, so are prone to overfitting on human neuroimaging data that often possess few samples but many input dimensions. Here, we capitalized on the fact that the features we seek in human neuroscience are precisely those relevant to subjects' behavior. We thus developed a Task-Relevant Autoencoder via Classifier Enhancement (TRACE), and tested its ability to extract behaviorally-relevant, separable representations compared to a standard autoencoder, a variational autoencoder, and principal component analysis for two severely truncated machine learning datasets. We then evaluated all models on fMRI data from 59 subjects who observed animals and objects. TRACE outperformed all models nearly unilaterally, showing up to 12% increased classification accuracy and up to 56% improvement in discovering"cleaner", task-relevant representations. These results showcase TRACE's potential for a wide variety of data related to human behavior.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on randomly expose items, i.e., the missing-at-random data. A few works have asked certain users to rate or select randomly recommended items, e.g., Yahoo! , Coat, and OpenBandit. However, these datasets are either too small in size or lack key information, such as unique user ID or the features of users/items. In this work, we present KuaiRand, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou. Different from existing datasets, KuaiRand records 12 kinds of user feedback signals (e.g., click, like, and view time) on randomly exposed videos inserted in the recommendation feeds in two weeks. To facilitate model learning, we further collect rich features of users and items as well as users' behavior history. By releasing this dataset, we enable the research of advanced debiasing large-scale recommendation scenarios for the first time. Also, with its distinctive features, KuaiRand can support various other research directions such as interactive recommendation, long sequential behavior modeling, and multi-task learning. The dataset is available at https://kuairand.com.
On the Integral and Derivative Identities of Bivariate Fox H-Function: Application in Wireless System Performance Analysis

The present work proposes analytical solutions for the integral of bivariate Fox H-function in combination with algebraic, exponential, and complementary error functions. In addition, the work also presents the derivative identities with respect to function arguments. Further, the suitability of the proposed mathematical solutions is verified with reference to wireless communication environment, where a fading behaviour of the channel acquired the bivariate Fox H-function structure. Further more, asymptotic results for the outage probability and average symbol error probability are presented utilizing the origin probability density function based approach. The obtained results are free from complex analytical functions. At last, the analytical findings of the paper are compared with the numerical results and also with the Monte-Carlo simulation results to confirm their accuracy.
Adversarial Learning Based Structural Brain-network Generative Model for Analyzing Mild Cognitive Impairment

Mild cognitive impairment(MCI) is a precursor of Alzheimer’s disease(AD), and the detection of MCI is of great clinical significance. Analyzing the structural brain networks of patients is vital for the recognition of MCI. However, the current studies on structural brain networks are totally dependent on specific toolboxes, which is time-consuming and subjective. Few tools can obtain the structural brain networks from brain diffusion tensor images. In this work, an adversarial learning-based structural brain-network generative model(SBGM) is proposed to directly learn the structural connections from brain diffusion tensor images. By analyzing the differences in structural brain networks across subjects, we found that the structural brain networks of subjects showed a consistent trend from elderly normal controls(NC) to early mild cognitive impairment(EMCI) to late mild cognitive impairment(LMCI): structural connectivity progressed in a progressively weaker direction as the condition worsened. In addition, our proposed model tri-classifies EMCI, LMCI, and NC subjects, achieving a classification accuracy of 83.33% on the Alzheimer’s Disease Neuroimaging Initiative(ADNI) database.
Target Selection and Validation of DESI Luminous Red Galaxies

The Dark Energy Spectroscopic Instrument (DESI) is carrying out a five-year survey that aims to measure the redshifts of tens of millions of galaxies and quasars, including 8 million luminous red galaxies (LRGs) in the redshift range 0.4 < z ≲ 1.0. Here we present the selection of the DESI LRG sample and assess its spectroscopic performance using data from Survey Validation (SV) and the first two months of the Main Survey. The DESI LRG sample, selected using g, r, z, and W1 photometry from the DESI Legacy Imaging Surveys, is highly robust against imaging systematics. The sample has a target density of 605 deg−2 and a comoving number density of 5 × 10−4 h 3 Mpc−3 in 0.4 < z < 0.8; this is a significantly higher density than previous LRG surveys (such as SDSS, BOSS, and eBOSS) while also extending to z ∼ 1. After applying a bright star veto mask developed for the sample, 98.9% of the observed LRG targets yield confident redshifts (with a catastrophic failure rate of 0.2% in the confident redshifts), and only 0.5% of the LRG targets are stellar contamination. The LRG redshift efficiency varies with source brightness and effective exposure time, and we present a simple model that accurately characterizes this dependence. In the appendices, we describe the extended LRG samples observed during SV.
Thermal conductivity and heat diffusion in the two-dimensional Hubbard model

We study the electronic thermal conductivity $\kappa_\textrm{el}$ and the thermal diffusion constant $D_\textrm{Q,el}$ in the square lattice Hubbard model using the finite-temperature Lanczos method. We exploit the Nernst-Einstein relation for thermal transport and interpret the strong non-monotonous temperature dependence of $\kappa_\textrm{el}$ in terms of that of $D_\textrm{Q,el}$ and the electronic specific heat $c_\textrm{el}$. We present also the results for the Heisenberg model on a square lattice and ladder geometries. We study the effects of doping and consider the doped case also with the dynamical mean-field theory. We show that $\kappa_\textrm{el}$ is below the corresponding Mott-Ioffe-Regel value in almost all calculated regimes, while the mean free path is typically above or close to lattice spacing. We discuss the opposite effect of quasi-particle renormalization on charge and heat diffusion constants. We calculate the Lorenz ratio and show that it differs from the Sommerfeld value. We discuss our results in relation to experiments on cuprates. Additionally, we calculate the thermal conductivity of overdoped cuprates within the anisotropic marginal Fermi liquid phenomenological approach.
Optimal Energy Signal Design for Multiuser MISO WPCNs With Non-Linear Energy Harvesting Circuits

The optimal energy signal design for wireless powered communication networks (WPCNs) enabling energy-sustainable communication for a large number of low-power devices is still an open problem in practical systems. In this work, we study a multi-user WPCN, where a multi-antenna base station (BS) sends an energy signal to multiple single-antenna users, which, in turn, harvest energy from the received signal and utilize it for information transmission in the uplink. In contrast to the existing works on multiple-input single-output (MISO) WPCN design, in this paper, we jointly optimize the energy signal waveform and downlink beamforming at the BS for energy harvesting (EH) devices described by non-linear circuit-based models. To this end, we assume that the BS broadcasts a pulse-modulated signal employing multiple energy signal vectors and we formulate an optimization problem for the joint design of the downlink transmit energy signal vectors, their number, the durations of the transmit pulses, and the time allocation policy for minimization of the average transmit power at the BS. We show that for single-user WPCNs, a single energy signal vector, which is collinear with the maximum ratio transmission (MRT) vector and drives the EH circuit at the user device into saturation, is optimal. Next, for the general multi-user case, we show that the optimal signal design requires a maximum number of energy signal vectors that exceeds the number of users by one and propose an algorithm to obtain the optimal energy signal vectors. Since the complexity of the optimal design is high, we also propose two suboptimal schemes for WPCN design. First, for asymptotic massive WPCNs, where the ratio of the number of users to the number of BS antennas, i.e., the system load, tends to zero, we show that the optimal downlink transmit signal can be obtained in closed-form and comprises a sequence of weighted sums of MRT vectors. Next, based on this result, for general WPCNs with finite system loads, we propose a suboptimal closed-form MRT-based design and a suboptimal semidefinite relaxation (SDR)-based scheme. Our simulation results reveal that the proposed optimal scheme and suboptimal SDR-based design achieve nearly identical performance and outperform two baseline schemes, which are based on linear and sigmoidal EH models. Furthermore, we show that, if the system load of the WPCN is low, the performance gap between the proposed suboptimal solutions is small and becomes negligible as the number of BS antennas tends to infinity.