Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Method for Automatic Music Labeling

Automatic music labeling on large-scale bases is a premise to provide systems of music recommendation, importante subject in digital world, demanding countless research in music information retrieval field. Although there are large-scale musical bases, such as Million Song Dataset (MSD), that have lowlevel label signal audio, descendant from audio signal, they are weakly labelled, that is, songs labels may be incomplete at a high level regarding emotion, vocals and instrument. This work aims to present the Music Label Miner (MLM), a hybrid method based on grouping, genetic algorithm and statistical correlation, which generates mappings and ossible inferences of high-level labels based on audio signal, through the relationship of a Large-scale base, MSD, with a lower-dimensional Ground Truth reference base. By applying the proposed method, it will be possible to label songs automatically, which contain only low-level labels and, by the models generated from the method, reach high-level labels. The method is composed by: (i) selection and preprocessing of MSD high and low level data, (ii) reference data set called CAL500exp, (iii) MSD data grouping, (iv) CAL500exp vectorization, (v) relationship of vectorized and grouping datasets, (vi) statistical correlation, (vii) mapping, (viii) visualization of selected data characteristics, (ix) generation of models and (x) inference of high and low-level labels.
An iterative LMA method for parameter estimation in dynamic modeling of TGFβ pathway using ODE

Transforming Growth Factor (TGF) β signalling pathway is a key regulator of a variety of biological processes in physiological and pathological conditions. In spite of numerous investigations, the dynamics of this complex pathway is largely unknown. Hence, developing mathematical models can pave the way for discovering novel therapeutics. The pathway model has unknown parameters that could be estimated using experimental data. Nonlinear least square methods are commonly used to solve this problem. Because of the difficulties of measuring biological data and its high cost, most of the experiments on this pathway have few data samples. This makes parameter estimation harder and in some cases, with non-unique solutions. In this paper, first a model of TGFβ pathway and its parameters are chosen from the literature. After simulation, model outputs are sampled and used to estimate model parameters. A small number of samples are selected to emulate experimental data. After estimating model parameters multiple times with different initial points, estimation results are compared with the actual value of each parameter by analysing its probability distribution function. In addition, an iterative Levenberg-Marquardt algorithm (LMA) method is proposed in which parameters are divided into groups depending on the state variables they affect. Then, only one group of parameters is estimated in each iteration. Simulation results show the efficiency of the proposed method. By testing the method on the TGFβ model it is shown that it is able to find the optimum point of model residual and solves big network estimation problems with less computation cost.
An Economic Gap Between the Recommended Healthy Food Patterns and Existing Diets of Minority Groups in the US National Health and Nutrition Examination Survey 2013–14

The US Department of Agriculture (USDA) has identified three Healthy Food Patterns as ways to implement the 2015–2020 Dietary Guidelines for Americans. We estimated the daily cost of the Healthy Vegetarian, US-Style, and Mediterranean Food Patterns (at 2,000 kcal/d) using national food prices adjusted for inflation. We also estimated the cost of existing dietary intakes in $/2,000 kcal/d for persons ≥2 years in the National Health and Nutrition Examination Survey (NHANES 2013–2014) using the same national food prices. The Nutrient Rich Food index (NRF9.3) was used as a measure of diet quality. Compared to existing diets, the USDA Healthy Food Patterns were higher in protein, fiber, vitamins, and minerals; were lower in solid fats, sugars, and sodium, and had higher diet quality scores. However, they also cost more. The cost of existing diets in NHANES was $5.47/d for Hispanics, $5.48/d for African-Americans, $5.94/d for Whites and $6.57/d for Asians. By contrast, the recommended US-Style Pattern cost $8.27/d, the Vegetarian Pattern cost $5.90/d, and the Mediterranean Pattern cost $8.73/d. Further, the Healthy Food Patterns featured some of the recommended food groups in unrealistic amounts. To ensure that the US Dietary Guidelines are both feasible and relevant to minority health, economic modeling studies should accompany government-issued dietary advice.
Low Inductance Effects on Electric Drives using Slotless Permanent Magnet Motors: A Framework for Performance Analysis

The use of slotless Permanent Magnet machines is becoming the first choice among many other conventional solutions when improved features, such as smooth control and very high speed, are needed. However, it is not often mentioned that in order to take advantage of the main peculiarities of this kind of machines, the whole electric drive system should be conveniently designed to avoid an unexpected decline in performance during the operation. Whenever the behavior of a motor needs to be studied by means of Finite Element Analysis (FEA) as a verification of a design process, the under-load condition is typically simulated by assuming ideal current waveforms as coil sources i.e. sinusoidal for Synchronous machines and quasi-squared for Brush-less DC (BLDC) machines. However the characteristic low inductance of slotless machine can lead to high current ripple and hence, torque ripple, if the supply system is not consequently designed. This work aims to define a framework capable of giving some useful information regarding the electric drive performance. Such a framework is based on a model representing the electric drive system which gives, indirectly, the supply current to a Finite Element (FE) motor model, in order to carry out a harmonic losses analysis based on a temporal discretization technique. A slotless motor prototype has been considered as an example and different solutions for the current ripple reduction are also discussed.
Interpretable ECG Beat Embedding using Disentangled Variational Auto-Encoders

Electrocardiogram signals are often used in medicine. An important aspect of analyzing this data is identifying and classifying the type of beat. This classification is often done through an automated algorithm. Recent advancements in neural networks and deep learning have led to high classification accuracy. However, adoption of neural network models into clinical practice is limited due to the black-box nature of the classification method. In this work, the use of variational auto encoders to learn human-interpretable encodings for the beat types is analyzed. It is demonstrated that using this method, an interpretable and explainable representation of normal and paced beats can be achieved with neural networks.
Glacier Algae: A Dark Past and a Darker Future

“Glacier algae” grow on melting glacier and ice sheet surfaces across the cryosphere, causing the ice to absorb more solar energy and consequently melt faster, while also turning over carbon and nutrients. This makes glacier algal assemblages, which are typically dominated by just three main species, a potentially important yet under-researched component of the global biosphere, carbon, and water cycles. This review synthesizes current knowledge on glacier algae phylogenetics, physiology, and ecology. We discuss their significance for the evolution of early land plants and highlight their impacts on the physical and chemical supraglacial environment including their role as drivers of positive feedbacks to climate warming, thereby demonstrating their influence on Earth’s past and future. Four complementary research priorities are identified, which will facilitate broad advances in glacier algae research, including establishment of reliable culture collections, sequencing of glacier algae genomes, development of diagnostic biosignatures for remote sensing, and improved predictive modeling of glacier algae biological-albedo effects.
Mechanical Test Structures for the Determination of Normal Stress in Multilayer Freestanding MEMS-Membranes

Understanding the mechanical behavior of free standing membranes is a point of common interest for MEMS structures, where a certain distance or deflection of a cantilever or membrane refers directly to the detectors performance. A material system with different types of basic MEMS materials like doped silicon as well as a conducting and passivation layer was characterized. Mechanical stress was measured directly after deposition and at the freestanding membranes itself. It is found out, that the mechanical stress of the investigated material systems decreases up to two orders of magnitude due to relaxation between material deposition and releasing membranes from substrate.
Multiphysics Design and Optimization for a SPM Machine with MnBi Magnet and 6.5% Silicon Steel Materials for Traction Applications

This paper considers the feasibility of new Manganese Bismuth (MnBi) permanent magnet and 6.5% silicon steel materials in a traction motor application. The aim of this work is to reduce the reduce rare earth material dependency, lower cost, improve efficiency at high frequencies and assess advantages of the new material properties in a fractional slot surface permanent magnet (SPM) machine. A Finite Element (FE) based multi-physics optimization routine is used in the development of this SPM machine, which takes the electromagnetics, thermal management, and structural physics of the electric machine into consideration during design development and optimization. This paper presents improved performance along with excellent field weakening capability of the proposed design, which is both thermally and structurally viable. The results show that the new materials offer improved performance i.e., efficiency compared to conventional designs in traction applications.
EEG Source Imaging: A Practical Review of the Analysis Steps

The electroencephalogram (EEG) is one of the oldest technologies to measure neuronal activity of the human brain. It has its undisputed value in clinical diagnosis, particularly (but not exclusively) in the identification of epilepsy and sleep disorders and in the evaluation of dysfunctions in sensory transmission pathways. With the advancement of digital technologies, the analysis of EEG has moved from pure visual inspection of amplitude and frequency modulations over time to a comprehensive exploration of the temporal and spatial characteristics of the recorded signals. Today, EEG is accepted as a powerful tool to capture brain function with the unique advantage of measuring neuronal processes in the time frame in which these processes occur, namely in the sub-second range. However, it is generally stated that EEG suffers from a poor spatial resolution that makes it difficult to infer to the location of the brain areas generating the neuronal activity measured on the scalp. This statement has challenged a whole community of biomedical engineers to offer solutions to localize more precisely and more reliably the generators of the EEG activity. High-density EEG systems combined with precise information of the head anatomy and sophisticated source localization algorithms now exist that convert the EEG to a true neuroimaging modality. With these tools in hand and with the fact that EEG still remains versatile, inexpensive and portable, electrical neuroimaging has become a widely used technology to study the functions of the pathological and healthy human brain. However, several steps are needed to pass from the recording of the EEG to 3-dimensional images of neuronal activity. This review explains these different steps and illustrates them in a comprehensive analysis pipeline integrated in a stand-alone freely available academic software: Cartool. The information about how the different steps are performed in Cartool is only meant as a suggestion. Other EEG source imaging software may apply similar or different approaches to the different steps.
Design Guidelines for Synchronous Machine Topologies with High Torque and Wide Field Weakening Demands

In this paper, the characteristics of multiphase synchronous machines (PM Rare-earth, PM Ferrite, PM-free)are evaluated for a highly demanding application with limited installation space. Critical design criteria, both electrical and thermal, are recognized to assess various topologies and on that basis, four different rotor geometries are considered in detail. The first machine is an IPMSM with flat buried magnets. The second and third machines are IPMSM with an emphasis on reluctance structure with two flux barriers and rare-earth magnets. The fourth machine is an IPMSM with ferrite magnets and three flux barriers. The machines are compared based on their performance with respect to maximum phase current requirement, efficiencies at operating points, constant power range, and cost. The machines are also classified in the Synchronous-Machine (SM)design plane, which provides a guideline for designing machines with high torque and wide field weakening demands.
Roust Adaptive Tracking Control for Switched Reluctance Motor with Sensor Fault

In this paper, a novel fuzzy adaptive tracking control scheme is concerned for switched reluctance motor with sensor fault. The considered switched nonlinear system contains unknown nonlinearities and sensor fault. In the design process, firstly, fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear dynamics. Secondly, in order to solve the challenge of partial failure of the sensor, a novel fuzzy adaptive fault-tolerant controller is designed. Finally, the Lyapunov functions are constructed to prove the stability of switched reluctance motor and all of signals for closed-loop system is bounded.
A 130 nm CMOS Passive Mixer Utilizing Positive-Negative Feedback as the Input Transconductance

This paper describes the design of a novel passive mixer which utilizes an input Gm stage which enhances the noise performance and also provides a reasonable linearity for the mixer. The mixer is a double balanced passive mixer which utilizes an opamp as the output transimpedance amplifier for the output stage. A class A opamp is designed to ensure the functionality of the circuit and providing the requisite gain at the output stage with a reasonable phase margin. The mixer is simulated in a 130 nm CMOS process. It has a conversion gain of more than 9 dB for more than 4 GHz bandwidth from 1-5 GHz, input referred noise of 2.5 nV/√ Hz and dissipates 8.28 mW of power.
Lab-scale Combustion Control Setup for Test and Implementation of Control Algorithms

Boilers have many applications from residential and commercial heating to power generation and petrochemical plants. Better control of boilers reduces fossil fuel consumption and environmental pollution. In this paper, a manually operated lab-scale boiler is set up for automatic control and monitoring. Boiler variables are measured using machine vision techniques and added sensors, and then sent to a computer. An actuator delivers the control command to change air flow. A feedforward feedback control algorithm is then suggested to maintain the air/flow equivalence ratio, which is shown to be a suitable indicator for better efficiency and less pollution. Experimental results are presented to support the performance of the control mechanism.
Design and Simulation of New Soft-switched Three-Port Half Bridge Converter with Extended ZVS Range

Multi-port converters (MPC), used in multi-input/multi-output power electronic systems have low cost, low weight and low mass integrated control system. Besides these converters offer bidirectional and isolated ports, which are inevitable in some applications. Three-Port Half Bridge Converters (TPHBC) are the ones which have the lowest component count. But these converters suffer from a low Zero Voltage Switching (ZVS) range that is lost in medium to low load powers. Low ZVS range cause low efficiency, especially in low output powers. In this paper a modified Pulse Width Modulation (PWM) technique is introduced to increase the ZVS range of main switches which results higher efficiency and lifetime of the converter. Verification of theoretical results is done by using simulation in the MATLAB and PSIM softwares. All above-mentioned predictions are validated with simulations.
Auto-Conditioned LSTM Network for Extended Complex Human Motion Synthesis

We present a real-time method for synthesizing highly complex human motions using a novel LSTM network training regime we call the auto-conditioned LSTM (acLSTM). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acLSTM is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Furthermore, the structure of the acLSTM is modular and compatible with any other recurrent network architecture, and is usable for tasks other than motion. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r. t. different styles.
Assessing Software Development Teams' Efficiency using Process Mining

Context. Improving the efficiency and effectiveness of software development projects implies understanding their actual process. Given the same requirements specification, different software development teams may follow different strategies and that may lead to inappropriate use of tools or non-optimized allocation of effort on spurious activities, non-aligned with the desired goals. However, due to its intangibility, the actual process followed by each developer or team is often a black box. Objective. The overall goal of this study is to improve the knowledge on how to measure efficiency in development teams where a great deal of variability may exist due to the human-factor. The main focus is on the discovery of the underlying processes and compare them in terms of efficiency and effectiveness. By doing so, we expect to reveal potentially hidden costs and risks, so that corrective actions may take place on a timely manner during the software project life cycle. Method. Several independent teams of Java programmers, using the Eclipse IDE, were assigned the same software quality task, related to code smells detection for identifying refactoring opportunities and the quality of the outcomes were assessed by independent experts. The events corresponding to the activity of each team upon the IDE, while performing the given task, were captured. Then, we used process mining techniques to discover development process models, evaluate their quality and compare variants against a reference model used as "best practice". Results. Teams whose process model was less complex, had the best outcomes and vice-versa. Comparing less complex process variants with the ""best practice"" process, showed that they were also the ones with less differences in the control-flow perspective, based on activities frequencies. We have also determined which teams were most efficient through process analysis. Conclusions. We confirmed that, even for a well-defined software development task, there may be a great deal of process variability due to the human factor. We were able to identify when developers were more or less focused in the essential tasks they were required to perform. Less focused teams had the more complex process models, due to the spurious / non-essential actions that were carried out. In other words, they were less efficient. Experts' opinion confirmed that those teams also were less effective in their expected delivery. We therefore concluded that a self-awareness of the performed process rendered by our approach, may be used to identify corrective actions that will improve process efficiency (less wasted effort) and may yield to better deliverables, i.e. improved process effectiveness.
Evaluation of Fractional Slot Concentrated Winding Permanent Magnet Synchronous Machine for Electric Vehicle Application

This paper mainly designed and compared the fractional slot concentrated winding (FSCW) permanent magnet synchronous motors (PMSMs) with different rotor topologies. To obtain higher peak torque with the constrain for cogging torque and torque ripple, three kinds of motors with different rotor topologies, including surface-mounted rotor, rectangular interior rotor and V-type interior rotor, are optimized using Maxwell. The optimized motors are further analyzed and compared with their electromagnetic performance at no load and its efficiency map. It turns out that higher peak torque can be achieved with surface-mounted rotor topology while V-type interior rotor has better performance in terms of efficiency map.
Research on Web Service Selection Based on Parallel Skyline Algorithm

With the continuous development of the Internet, there are many web services with the same functional attributes but different functional attributes. It is urgent to find a web service that can satisfy itself quickly and efficiently from the massive web service data. This paper improves the traditional Skyline algorithm, divides the web service data set into regions, greatly reduces the data points without dominance, and saves memory usage. The improved Skyline algorithm can significantly improve the speed of Web service selection. However, the improved Skyline algorithm will still have insufficient computing resources when processing massive Web service data, resulting in a significant decrease in computing speed and even computer jam. In view of the above situation, this paper will parallelize the improved Skyline algorithm and parallelize the improved Skyline algorithm through the Spark platform. Experiments show that the parallelized Skyline algorithm can better handle massive Web service data. (Abstract)
GAN-Based Multi-level Mapping Network for Satellite Imagery Super-Resolution

Although many deep-learning-based image super-resolution (SR) methods have been proposed, most of them assume that all hierarchical features share the unified mapping equations. They ignore the differences between mapping equations at different feature levels, and create an average effect of mapping prediction, thus poorly building the mapping relations between low resolution (LR) and high resolution (HR) spaces. In this paper, we propose a multi-level mapping framework along with the adversarial learning strategy, namely MMGAN, for satellite imageries SR reconstruction. We also construct a feature extraction and tuning block (FETB) for fine feature expression. In particular, a novel two-dimension dense unit (DU) and a mapping attention unit (MAU) are constructed for building multi-level mappings in different stages. With our strategies, an HR image is reconstructed directly from the input image using multi-level mappings. Extensive experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images exhibit superior reconstruction performance when compared with the state-of-the-art SR approaches.