Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid battery-supercapacitor mathematical modeling for PV application using Matlab/Simulink

Energy storage plays an important role in the renewable energy sources integration. Additionally, hybrid energy storage can be integrated into various systems to achieve different applications. Specifically, the combination of high energy and power rating, increased life cycle, duration of discharge period and other features may not be satisfied by the single storage technology. There are several storage technologies that may be used in a photovoltaic (PV) system. This paper focuses on the mathematical modeling of the hybrid battery-supercapacitor storage system. The hybrid storage combines the advantages of both battery and supercapacitor storage. Also, supercapacitors can reduce stresses on battery storage and thus extend their battery life. The proposed mathematical model is implemented using Matlab/Simulink. Regarding the supercapacitor equivalent circuit, the two branches model is examined. For the lithium-ion battery storage model, a dual polarization model with two parallel RC networks is studied. The next step is to integrate the hybrid battery-supercapacitor storage into a grid-connected PV system.
TLR3 agonism re‐establishes CNS immune competence during α4‐integrin deficiency

Natalizumab blocks α4‐integrin‐mediated leukocyte migration into the central nervous system (CNS). It diminishes disease activity in multiple sclerosis (MS), but carries a high risk of progressive multifocal encephalopathy (PML), an opportunistic infection with JV virus that may be prompted by diminished CNS immune surveillance. The initial host response to viral infections entails the synthesis of type I interferons (IFN) upon engagement of TLR3 receptors. We hypothesized that TLR3 agonism reestablishes CNS immune competence in the setting of α4‐integrin deficiency.
TSV-Defect Modeling, Detection and Diagnosis Based on 3-D Full Wave Simulation and Parametric Measurement

In this paper, we present a TSV low-bandwidth equivalent lumped circuit model of common TSV defects as analyzed using a high-frequency structure simulator 3-D full wave simulation. By using the proposed model, the physical parameters of TSV are related to its electrical parameters, providing the possibility for defect diagnosis. The simulation results show that only some of the common defects tend to vary the electrical parameters of TSVs. Based on these conclusions, we present a novel TSV-defect detection and diagnosis method using switched-capacitor circuits. The proposed method can effectively detect significant void defects, pinhole defects, and missing micro-bump defects using parametric measurement and has been verified through HSPICE simulation.
Social decision-making driven by artistic explore–exploit tension

ABSTRACT We studied social decision-making in the rule-based improvisational dance There Might Be Others, where dancers make in-the-moment compositional choices. Rehearsals provided a natural test-bed with communication restricted to non-verbal cues. We observed a key artistic explore–exploit tension in which the dancers switched between exploitation of existing artistic opportunities and riskier exploration of new ones. We investigated how the rules influenced the dynamics using rehearsals together with a model generalized from evolutionary dynamics. We tuned the rules to heighten the tension and modelled nonlinear fitness and feedback dynamics for mutation rate to capture the observed temporal phasing of the dancers' exploration-versus-exploitation. Using bifurcation analysis, we identified key controls of the tension and showed how they could shape the decision-making dynamics of the model much like turning a ‘dial’ in the instructions to the dancers could shape the dance. The investigation became an integral part of the development of the dance.
Intravoxel incoherent motion and diffusion kurtosis imaging for discriminating soft tissue sarcoma from vascular anomalies

Abstract To investigate the feasibility of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in discriminating soft tissue sarcoma from vascular anomalies. Twenty-two patients with lower extremity soft tissue sarcoma and 15 patients with lower extremity vascular anomalies underwent IVIM-DWI and DKI. IVIM model generated true diffusion (D), perfusion fraction (f), and pseudo-diffusion coefficient (D*). DKI model generated mean kurtosis (MK) and mean diffusion (MD). These parameters were measured by 2 radiologists separately through drawing region of interest. Intraclass correlation coefficient (ICC) was calculated to evaluate the inter-reader viability in measurement. The Mann–Whitney test was used to compare the parameters between vascular anomalies and soft tissue sarcoma. Receiver operating characteristic curves were constructed for assessing diagnostic accuracies. ICC was more than 0.8 for apparent diffusion coefficient (ADC), D, D*, f, MK, and MD. Mean ADC, D, and MD were significantly lower in soft tissue sarcoma versus vascular anomalies (P < .05). Mean D* and f were not significantly different (P > .05). Soft tissue sarcoma had significantly higher MK than vascular anomalies (P < .05). Areas under curve for ADC, D, MK, and MD were 0.876, 0.885, 0.894, and 0.812, respectively. IVIM and DKI are feasible in discriminating soft tissue sarcoma from vascular anomalies.
Extraction of Earth Surface Texture Features from Multispectral Remote Sensing Data

Earth surface texture features referring to as visual features of homogeneity in remote sensing images are very important to understand the relationship between surface information and surrounding environment. Remote sensing data contain rich information of earth surface texture features (image gray reflecting the spatial distribution information of texture features, for instance). Here, we propose an efficient and accurate approach to extract earth surface texture features from remote sensing data, called gray level difference frequency spatial (GLDFS). The gray level difference frequency spatial approach is designed to extract multiband remote sensing data, utilizing principle component analysis conversion to compress the multispectral information, and it establishes the gray level difference frequency spatial of principle components. In the end, the texture features are extracted using the gray level difference frequency spatial. To verify the effectiveness of this approach, several experiments are conducted and indicate that it could retain the coordination relationship among multispectral remote sensing data, and compared with the traditional single-band texture analysis method that is based on gray level co-occurrence matrix, the proposed approach has higher classification precision and efficiency.
Comparison of Kernels in Online SVM Classification of fNIRS Data

The purpose of this study is to reduce the online data processing time while maintaining good classification accuracy. For this purpose, kernel techniques are important means to enable a support vector machine (SVM) classifier to distinguish the non-linearly separable data set. This study investigated the performance of Gaussian kernel-based SVM and polynomial kernel-based SVM for processing functional near-infrared spectroscopy (fNIRS) data. Eight subjects participated in the experiment and performed 5 trials of mental arithmetic tasks. The brain signals were acquired using an fNIRS system from the prefrontal cortex. Five distinctive features (the slope, mean, variance, maximum and minimum of signal) were extracted and 3,000 samples of signals were used to train the SVM classifier. $16 \times 8$ classifiers were tested with a Gaussian kernel using 16 values of box constraint $( C)$ and 8 values of standard deviation $( \sigma )$, while $16 \times 9$ classifiers were tested with a polynomial kernel using 16 values of $C$ and 9 values of the kernel order $( p)$. The performance of the classifiers with different kernels and various configurations showed that, the Gaussian kernel (74.63% on average) outperformed the polynomial kernel (73.71% on average), but the latter was found to be more stable. The computation time analysis of both kernels shows great variation and it can be concluded that the polynomial kernel-based SVM is more robust for online systems due to the absence of singularity problem in numeric approximation.
Neural Network Specialists for Inverse Spiral Inductor Design

Integrated spiral inductors are a fundamental part of Radio-Frequency (RF) circuits. In certain scenarios, a solution to the inverse spiral inductor design problem is required; given the desired properties of an inductor, locate the most suitable geometric characteristics. This problem does not have a unique solution and current approaches approximate it through a number of differential equations and the subsequent application of optimization techniques that narrow down the set of feasible solutions. In this work, the Neural Network Specialists model is outlined; a preliminary approach to solving the aforementioned problem using fully connected neural network models. The obtained results on a first round of experiments are encouraging, especially in terms of the reduction in time complexity.
Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA

Purpose Quantification of optical coherence tomography angiography (OCTA) is confounded by the prevalence of bulk motion. We have previously developed a regression-based bulk motion subtraction (rb-BMS) algorithm that estimates bulk motion velocity and corrects for its effect on flow signal. Here, we aim to investigate its ability to improve the reliability of capillary density (CD) quantification. Methods Two spectral-domain systems (70-kHz Avanti/AngioVue and 68-kHz Cirrus/AngioPlex) acquired 6 × 6-mm OCTA scans. The rb-BMS algorithm was applied on each OCTA volume. Regression analysis of angiographic versus reflectance signal of avascular A-lines in B-frames was used to set an optimized reflectance-adjusted threshold for discriminating vascular versus nonvascular voxels. The CD was calculated from en face maximum projections of the superficial vascular complex in macular scans and the nerve fiber layer plexus in disc scans, excluding large vessels. The retinal signal strength (RSS) was calculated by averaging the logarithmic-scale OCT reflectance signal, and its correlation with CD was investigated. Results Eight healthy eyes were scanned with each instrument on 2 separate days. The rb-BMS algorithm improved within-visit repeatability and between-visit reproducibility of CD compared with a global-threshold measurement algorithm. Using the rb-BMS algorithm, the CD results were less affected by RSS and the population variation was reduced. Motion-induced line artifacts were also reduced. Conclusions The rb-BMS algorithm improved the reliability of perfusion quantification in OCTA on both Food and Drug Administration–cleared spectral-domain OCTA systems. Translational Relevance The rb-BMS method helped reduce the inter-scan variability by generating accurate vessel maps, improving the reliability of retinal perfusion quantification.
Scale Factor Calibration for a Rotating Accelerometer Gravity Gradiometer

Rotating Accelerometer Gravity Gradiometers (RAGGs) play a significant role in applications such as resource exploration and gravity aided navigation. Scale factor calibration is an essential procedure for RAGG instruments before being used. In this paper, we propose a calibration system for a gravity gradiometer to obtain the scale factor effectively, even when there are mass disturbance surroundings. In this system, four metal spring-based accelerometers with a good consistency are orthogonally assembled onto a rotary table to measure the spatial variation of the gravity gradient. By changing the approaching pattern of the reference gravity gradient excitation object, the calibration results are generated. Experimental results show that the proposed method can efficiently and repetitively detect a gravity gradient excitation mass weighing 260 kg within a range of 1.6 m and the scale factor of RAGG can be obtained as (5.4 ± 0.2) E/μV, which is consistent with the theoretical simulation. Error analyses reveal that the performance of the proposed calibration scheme is mainly limited by positioning error of the excitation and can be improved by applying higher accuracy position rails. Furthermore, the RAGG is expected to perform more efficiently and reliably in field tests in the future.
Lars von Trier : interviews

With six entries at the Cannes Film Festival thus far, Lars von Trier has been a Cannes award winner four times. Without question, he is the most intriguing film director to emerge in Denmark since the days of his great mentor in spirit Carl Theodor Dreyer. A relentless visionary, von Trier (b. 1956) has succeeded not only in realizing his projects but also in managing to gather substantial audiences to his films. Breaking the Waves (1996) made him a well-known figure to American audiences, as did Dancer in the Dark (2000), winner of the Palme d'Or at Cannes. His work on the groundbreaking TV series The Kingdom (1994-97) made him a household name in Denmark. He has continued to stir controversy for his polarizing views on the characters and subject matter of his films, as well as for his film technique. Media attention reached its peak when von Trier created Dogme 95, a movement dedicated to the "Vow of Chastity," which strips cinema of its artifice, flash, and polish. Rather than being strident or shrill, however, these collected interviews reveal the Danish filmmaker to be impish, forthright, witty, sometimes infuriating, and deeply committed to the possibilities of cinema. The conversations in this collection trace his development from the structured, image-obsessed formalist of The Element of Crime (1984) and Europa (U.S. title Zentropa, 1991) to the control-shunning game master of the 1990s. Most of these interviews, two previously unpublished, are translated into English for the first time. They begin in 1968, when von Trier was the lead actor in a children's TV series, and end in 2001. Von Trier speaks of his visions, ideals, dislikes, and technical experimentations, of his conception of actors, his childhood, his phobias, and of his views on religion and his ill-fated female protagonists. His style in conversation is relaxed and honest, his mood affirmative and passionate.
Modeling of Dielectric Resonator Filter for Multiplexer Design

In this paper, a hybrid model of a dielectric resonator (DR) Ku-band filter is presented. The model is developed to accurately simulate the DR filter’s response over a wide frequency range for applications in multiplexer design. The hybrid model contains a neural network (NN) model in combination with a circuit model. To ensure the accuracy and improve the efficiency, the NN model is based on a simplified structure of the output iris of a DR filter. The circuit model represents the rest of the DR filter. A good agreement is observed between the hybrid model and EM simulation results.
ECGNET: Learning Where to Attend for Detection of Atrial Fibrillation with Deep Visual Attention

The complexity of the patterns associated with atrial fibrillation (AF) and the high level of noise affecting these patterns have significantly limited the application of current signal processing and shallow machine learning approaches to accurately detect this condition. Deep neural networks have shown to be very powerful to learn the non-linear patterns in various problems such as computer vision tasks. While deep learning approaches have been utilized to learn complex patterns related to the presence of AF in electrocardiogram (ECG) signals, they can considerably benefit from knowing which parts of the signal is more important to focus on during learning. In this paper, we introduce a two-channel deep neural network to more accurately detect the presence of AF in the ECG signals. The first channel takes in an ECG signal and automatically learns where to attend for detection of AF. The second channel simultaneously takes in the same ECG signal to consider all features of the entire signal. Besides improving detection accuracy, this model can guide the physicians via visualization that what parts of the given ECG signal are important to attend while trying to detect atrial fibrillation. The experimental results confirm that the proposed model significantly improves the performance of AF detection on well-known MIT-BIH AF database with 5-s ECG segments (achieved a sensitivity of 99.53%, specificity of 99.26% and accuracy of 99.40%).
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel commands to explicitly manage the complexities that arise when targeting these systems. The framework is designed for the areas of image processing, stencils, linear algebra and deep learning. Tiramisu has two main features: it relies on a flexible representation based on the polyhedral model and it has a rich scheduling language allowing fine-grained control of optimizations. Tiramisu uses a four-level intermediate representation that allows full separation between the algorithms, loop transformations, data layouts, and communication. This separation simplifies targeting multiple hardware architectures with the same algorithm. We evaluate Tiramisu by writing a set of image processing, deep learning, and linear algebra benchmarks and compare them with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu matches or outperforms existing compilers and libraries on different hardware architectures, including multicore CPUs, GPUs, and distributed machines.
Team Tactics Estimation in Soccer Videos via Deep Extreme Learning Machine Based on Players Formation

A method of team tactics estimation in soccer videos is presented in this paper. Our method enables estimation of basic tactics in each team on the basis of the Deep-Extreme Learning Machine (DELM) by using features of players formation. In the soccer games, team tactics relate to each other team. Therefore, the proposed method obtains final estimation results by utilizing two DELMs of each team and their relationship. Since the proposed method takes into consideration the relevance of the estimated tactics in each team, we realize accurate tactics estibimation. Experimental results using actual soccer videos showed the effectiveness of our method.
Intensity-Specific and Modified Effects of Physical Activity on Serum Adiponectin in a Middle-Aged Population

Abstract Context The effects of intensity-specific physical activity (PA) and its interaction with other lifestyle factors on serum adiponectin are currently unclear. Objective To investigate the effects of replacing sedentary time with either light-intensity PA (LPA) or moderate- to vigorous-intensity PA (MVPA) on total and high-molecular-weight (HMW) adiponectin and to examine interactions with smoking, alcohol drinking, coffee consumption, and menopausal status in a general population. Design/Setting Cross-sectional study of 4013 men and 6050 women (40 to 69 years of age). Main Outcome Measures The associations of replacing sedentary time with LPA or MVPA on total and HMW adiponectin were analyzed using an isotemporal substitution model. Results In men, reallocating 60 minutes of sedentary time to 60 minutes of LPA was associated with 9% and 13% higher total and HMW adiponectin levels even after adjusting for confounders, although such associations were not observed for MVPA. A similar pattern of results was also seen in women. The effect of replacing sedentary time with LPA on adiponectin was clearer in middle/high coffee consumers than in low coffee consumers among women. Although increasing the effect of replacing sedentary time with MVPA on adiponectin was clearer in former/current smokers than in never smokers among men, the replacement effect for MVPA on total adiponectin was clearer in premenopausal women than in postmenopausal women. Conclusions Replacing sedentary time with LPA resulted in increased levels of total and HMW adiponectin. The replacement effects for LPA or MVPA were found to be multiply modified by smoking, coffee consumption, and menopausal status.
The Middle-Income Trap and the Coping Strategies From Network-Based Perspectives

When a developing country reaches a relatively average income level, it often stops growing further and its income does not improve. This is known as the middle-income trap. How to overcome this trap is a longstanding problem for developing countries, and has been studied in various research fields. In this work, we use the Fitness-Complexity method (FCM) to analyze the common characteristics of the countries that successfully get through the middle-income trap, and show the origin of the middle-income trap based on the international trade network. In the analysis, a novel method is proposed to characterize the interdependency between products. The results show that some middle-complexity products depend much on each other, which indicates that developing countries should focus on them simultaneously, implying high difficulty to escape the middle-income trap. To tackle the middle-income trap, developing countries should learn experiences from developed countries that share similar development history. we then design an effective method to evaluate the similarity between countries and recommend developed countries to a certain developing country. The effectiveness of our method is validated in the international trade network.
Integrating Word Embeddings into IBM Word Alignment Models

Word alignment models are used to generate word-aligned parallel text which is used in statistical machine translation systems. Currently, the most popular word alignment models are IBM models which have been widely applied in a large number of translation systems. The parameters of IBM models are estimated by using Maximum Likelihood principle, i.e. by counting the co-occurrence of words in the parallel text. This way of parameter estimation leads to the “ambiguity” problem when some words stand together in many sentence pairs but each of them is not translation of any other. Additionally, this method requires large amount of training data to achieve good results. However, parallel text which is used to train the IBM models is usually limited for low-resource languages. In this work, we try to solve these two problems by adding semantic information to the models. Our semantic information is derived from word embeddings which only need monolingual data to train. We deploy evaluation on a language pair that has great differences in grammar structure, English-Vietnamese. Even with this challenged task, our proposed models gain significant improvements in word alignment result and help increasing translation quality.
Controlling the Position of the Carriage in Real-Time Using the RBF Neural Network Based PID Controller

This paper proposes an algorithm for estimating parameters of a proportional integral derivative (PID) controller in real-time using the radial basis function (RBF) neural network. The proposed algorithm is then applied to precisely control the position of a carriage. An Arduino control Board is utilized to connect between the proposed approach using Matlab Simulink and the actual position control model (i.e., carriage model). The control signal of the PID controller is sent to the Arduino control board to control the position of the carriage through a serial protocol. The actual position of the carriage is read by a displacement infrared sensor and sent to the developed algorithm using the same serial transceiver protocol. The experimental results show that the RBF based PID controller gives better performance than the conventional PID by reducing the overshoot. It is noteworthy that the proposed approach could precisely control the position of the carriage with its uncertain mathematical model.