Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmentation with GAN Synthesis for CT Liver Lesion Classification : Analysis from Qualitative Perspectives

Data augmentation is a common process for deep learning to improve learning efficiency and prevent the lack-of-training-data problems such as over-fitting. The geometric transformationbased technique is widely used for data augmentation, but this classic augmentation has limitations of repeating existing data patterns rather than diversifying them. Here, we apply a novel data augmentation technique based on generative adversarial network (GAN) which has been of great interest recently, for the task of classifying liver lesions in CT images. In addition to the quantitative performance evaluation, we further analyze the GAN-augmented data in two qualitative ways of observing feature distributions and nearest real data. Throughout this qualitative analysis, we provide in-depth discussions on advantages, limitations, and future directions of current GAN data augmentation.
Fake News Classification Web Service for Spanish News by using Artificial Neural Networks

—The use of digital media, such as social networks, has promoted the spreading of fake news on a large scale. Therefore, several Machine Learning techniques, such as artificial neural networks, have been used for fake news detection and classification. These techniques are widely used due to their learning capabilities. Besides, models based on artificial neural networks can be easily integrated into social media and websites to spot fake news early and avoid their propagation. Nevertheless, most fake news classification models are available only for English news, limiting the possibility of detecting fake news in other languages, such as Spanish. For this reason, this study proposes implementing a web service that integrates a deep learning model for the classification of fake news in Spanish. To determine the best model, the performance of several neural network architectures, including MLP, CNN, and LSTM, was evaluated using the F1 score. , and LSTM using the F1 score. The LSTM architecture was the best, with an F1 score of 0.746. Finally, the efficiency of web service was evaluated, applying temporal behavior as a metric, resulting in an average response time of 1.08 seconds.
Distributed subgradient method for multi‐agent optimization with quantized communication

This paper focuses on a distributed optimization problem associated with a time‐varying multi‐agent network with quantized communication, where each agent has local access to its convex objective function, and cooperatively minimizes a sum of convex objective functions of the agents over the network. Based on subgradient methods, we propose a distributed algorithm to solve this problem under the additional constraint that agents can only communicate quantized information through the network. We consider two kinds of quantizers and analyze the quantization effects on the convergence of the algorithm. Furthermore, we provide explicit error bounds on the convergence rates that highlight the dependence on the quantization levels. Finally, some simulation results on a l1‐regression problem are presented to demonstrate the performance of the algorithm. Copyright © 2016 John Wiley & Sons, Ltd.
Prediction of drug permeation through microneedled skin by machine learning

Abstract Stratum corneum is the outermost layer of the skin preventing external substances from entering human body. Microneedles (MNs) are sharp protrusions of a few hundred microns in length, which can penetrate the stratum corneum to facilitate drug permeation through skin. To determine the amount of drug delivered through skin, in vitro drug permeation testing is commonly used, but the testing is costly and time‐consuming. To address this issue, machine learning methods were employed to predict drug permeation through the skin, circumventing the need of conducting skin permeation experiments. By comparing the experimental data and simulated results, it was found extreme gradient boosting (XGBoost) was the best among the four simulation methods. It was also found that drug loading, permeation time, and MN surface area were critical parameters in the models. In conclusion, machine learning is useful to predict drug permeation profiles for MN‐facilitated transdermal drug delivery.
Physical and Mechanical Behaviors of Compacted Soils under Hydraulic Loading of Wetting–Drying Cycles

Exposed geo−infrastructures filled with compacted soils experience cyclic wetting–drying effects due to environment and underground water fluctuations. Soil physical and mechanical behaviors are prone to deterioration to a great extent, e.g., swelling, collapse, or even slope failure, resulting in huge losses to human life, safety, and engineering construction. In this paper, hydraulic loading tests of wetting–drying cycles were carried out on compacted fine soil via a one−dimensional pressure plate apparatus equipped with bender elements. The influences of wetting–drying paths on the soil characteristics of moisture content, void ratio and shear modulus were obtained and analyzed. Results showed that cyclic wetting–drying effects weakened the soil’s water retention capacity. It was observed that it was harder for pore water to approach saturation at a lower matric suction level and to be expelled at a higher matric suction level. Typical swelling and shrinkage deformations occurred during the hydraulic loading processes, and volume expansion was generated after the drying–wetting cycles at a given value of matric suction, which deteriorated the densely compacted soils to a relatively looser state. Then, a unified soil–water characteristic surface was proposed to describe the unique relationships of moisture content, void ratio, and matric suction. Moreover, the small−strain shear modulus of the soil, in terms of shear wave velocity, was reduced by 32.2–35.5% and 13.8–25.8% at the same degree of saturation during the first and second wetting paths, respectively. Therefore, the volume expansion and modulus degradation resulting from the wetting–drying cycles should attract particular attention to avoid further distresses in the practical engineering.
k-Mean Cluster Particle Swarm Optimization Algorithm Based on Self-adaptive Chaotic Mutation

A new k-mean cluster particle swarm algorithm (FCPSO) based on self-adaptive chaotic mutation is presented to overcome the default of the premature and low precision of the standard PSO algorithm. First,the particle swarm is divided into several sub-swarms by the k-mean cluster. Then,the current particles are dynamically updated by the personal best particle and global best particles in the sub-swarms. Second,by the self-adaptive chaotic mutation operator introduced to the algorithm,the information exchanged between different sub-swarms and the ability of standard PSO algorithm is break away from the local optimum are effectively improved. The computer simulations demonstrate the effectiveness of the proposed algorithm.
Functional characterization and expression analysis of c-type and
 g-like-type lysozymes in yellowtail clownfish (Amphiprion
 clarkii)

Lysozymes are well-known antibacterial enzymes that mainly target the peptidoglycan layer of the bacterial cell wall. Animal ly-sozymes are mainly categorized as g-type, c-type, and i-type based on protein sequence and structural differences. In this study, c-type (AcLysC) and g-like-type (AcLysG-like) lysozymes from Amphiprion clarkii were characterized in silico via expressional and functional approaches. According to in silico analysis, open reading frames of AcLysC and AcLysG-like were 429 bp and 570 bp, respectively, encoding the corresponding polypeptide chains with 142 and 189 amino acids. Elevated expression levels of AcLysC and AcLysG-like were observed in the liver and the heart tissues, respectively, as evidenced by quantitative real-time polymerase chain reaction assays. AcLysC and AcLysG-like transcript levels were upregulated in gills, head kidney, and blood cells following experimental immune stimulation. Recombinant AcLysC exhibited potent lytic activity against Vibrio anguillarum , whereas recombinant AcLysG-like showed remarkable antibacterial activity against Vibrio harveyi and Streptococcus parauberis , which was further evidenced by scanning electron microscopic imaging of destructed bacterial cell walls. The findings of this study collectively suggest the potential roles of AcLysC and AcLysG-like in host immune defense.
Gestalt formation promotes awareness of suppressed visual stimuli during binocular rivalry

ABSTRACT Continuous flash suppression leverages binocular rivalry to render observers unaware of a static image for several seconds. To achieve this effect, rapidly flashing noise masks are presented to the dominant eye while a static stimulus is presented to the non-dominant eye. Eventually “breakthrough” occurs, wherein awareness shifts to the static image shown to the non-dominant eye. We tested the hypothesis that Gestalt formation can promote breakthrough. In two experiments, we presented pacman-shaped objects that might or might not align to form illusory Kanizsa objects. To measure the inception of breakthrough, observers were instructed to press a key at the moment of partial breakthrough. After pressing the key, which stopped the trial, observers reported how many pacmen were seen and where they were located. Supporting the Gestalt hypothesis, breakthrough was faster when the pacmen were aligned and observers more often reported pairs of pacmen if they were aligned. To address whether these effects reflected illusory shape perception, a computational model was applied to the pacman report distributions and breakthrough times for an experiment with four pacmen. A full account of the data required an increased joint probability of reporting all four pacmen, suggesting an influence of a perceived illusory cross.
A novel optimized design of an energy harvester using magnetoelectric transducers

Energy harvesting mechanisms are becoming a trend in scientific projects which are related to small devices and wireless sensors. Among different energy harvesters, those which use vibration to harvest energy, have attracted attention recently. Magnetoelectric (ME) transducers are one of the newest mechanisms which can convert vibration energy to electrical energy. Frequency bandwidth and maximum output power are the two main features of energy harvesters, and almost based on these features, harvesters are designed. In this paper, a novel optimized design for ME harvester is proposed. First, the nonlinear finite element model of the proposed novel harvester is presented and validated. Then, the detailed design of the harvester is presented, and an optimization process based on a genetic algorithm is performed to improve the performance of the harvester. Finally, frequency bandwidth and output power of the optimized harvester are investigated, and also, the effect of variation in load resistance on its performance is studied. According to the obtained results, the proposed harvester will achieve a continuous frequency bandwidth of 14 Hz and maximum output power of 3.7 mW under the excitation amplitude of 0.1 mm.
1 8 D ec 2 01 5 Precise multi-emitter localization method for fast super-resolution imaging

We present a method that can simultaneously locate positions of overlapped multi-emitters at the theoreticallimit precision. We derive a set of simple equations whose solution gives the maximum likelihood estimator of multi-emitter positions. We compare the performance of our simultaneous localization analysis with the conventional single-molecule analysis for simulated images and show that our method can improve the time-resolution of superresolution microscopy an order of magnitude. In particular, we derive the information-theoretical bound on time resolution of localization-based superresolution microscopy and demonstrate that the bound can be closely attained by our analysis. c © 2018 Optical Society of America OCIS codes: (100.6640) Superresolution;(180.2520) Fluorescence microscopy; (110.4190) Multiple imaging.
Reliability of a set of Mandarin phonemic-balanced short-item monosyllable lists with equivalency in audibility

Objective To evaluate the reliability of Mandarin phonemic-balanced monosyllabic lists. Methods Thirty Mandarin phonemic-balanced monosyllabic lists (25 syllables in each list) were digitally recorded with monosyllabic items normalized. Two-way(intensity and list number) repeated measures was adapted as experimental design. Sixty normal-hearing young adults were recruited to repeat as many syllables which they heard as possible. Syllablc lists were counterbalanced across six intensities(5, 11, 17, 21, 27, 33 dB SPL) and across participants. Recognition scores in individual intensities for each list were calculated in order to analyze list equivalence via MANOVA. Results The scores of twenty-two equivalent lists was transformed into"Rationalized" arcsine unit (RAU), then the standard deviation of RAU were pooled across participants, intensities and lists as an index of grand reliability. The value of 7.6% represents the score variability due to a combination of differences in true list difficulty and random measurement error within a subject. The critical difference which must be exceeded for a difference between two scores based on single lists to be significant at the 95% confidence level is 21.2%. Conclusion The set of 22 Mandarin phonemic-balanced monosyllable lists can be accepted in view of short and reliable requirement in clinical practice.
FDTD algorithm for EMP response calculation on thin wire with resistance load

In this paper, a novel way that can be combined with FDTD methods to deal with the EMP response on thin wire with resistance load is developed, in which the thin wire is divided into three parts and described by different partial differential equations,respectively. This method can be used to calculate some coupling problems involving thin wires, which usually connected with circuit input or output ports. The dissipated energy and power on the equivalent resistance can be obtained. The results gotten from this new method are compared with the results from references,some typical results are given and analyzed as an example. Modified partly this algorithm can also be used to solve the non-resistance load cases.
Development and Testing of a High-Precision Position and Attitude Measuring System for a Space Mechanism

This paper describes a high-precision optical metrology system - a unique ground test equipment which was designed and implemented for simultaneous precise contactless measurements of 6 degrees-of-freedom (3 translational + 3 rotational) of a space mechanism end-effector [1] in a thermally controlled ISO 5 clean environment. The developed contactless method reconstructs both position and attitude of the specimen from three cross-sections measured by 2D distance sensors [2]. The cleanliness is preserved by the hermetic test chamber filled with high purity nitrogen. The specimen's temperature is controlled by the thermostat [7]. The developed method excludes errors caused by the thermal deformations and manufacturing inaccuracies of the test jig. Tests and simulations show that the measurement accuracy of an object absolute position is of 20 micron in in-plane measurement (XY) and about 50 micron out of plane (Z). The typical absolute attitude is determined with an accuracy better than 3 arcmin in rotation around X and Y and better than 10 arcmin in Z. The metrology system is able to determine relative position and movement with an accuracy one order of magnitude lower than the absolute accuracy. Typical relative displacement measurement accuracies are better than 1 micron in X and Y and about 2 micron in Z. Finally, the relative rotation can be measured with accuracy better than 20 arcsec in any direction.
Effect of zinc hydroxychloride supplementation combined with an anticoccidial drug on Eimeria tenella infection in broiler chickens

Background and Aim: Eimeria tenella is a causative agent of avian cecal coccidiosis resulting in bleeding, diarrhea, weight loss, high morbidity, and mortality in chickens. Zinc supplementation increases body weight gain, reduces mortality, and improves some immune response parameters of broilers infected with E. tenella. This study aimed to investigate the effects of zinc hydroxychloride (ZnOHCl) supplementation and ZnOHCl combined with an anticoccidial drug on E. tenella infection in broiler chickens. Materials and Methods: Forty one-day-old broilers were randomly divided into five groups, the study was replicated twice and had four chickens per replicate. Group 1 was an uninfected and unmedicated control group, and Group 2 was an infected but unmedicated control group. Group 3 was infected and treated with 120 mg/kg ZnOHCl, Group 4 was infected and medicated with 7 mg/kg toltrazuril (TOL), and Group 5 was infected and treated with 120 mg/kg ZnOHCl and 7 mg/kg TOL. Body weight gain, feed intake, and feed conversion ratio were monitored on days 15, 21, and 28. Oocyst shedding, hematological data, and lesion scores were analyzed on 7 days post-infection. Results: The average weight gain, feed intake, and packed cell volume of chickens treated with ZnOHCl and TOL were significantly higher than those of the infected and unmedicated controls (p ≤ 0.05). Lesion scores, oocyst output, and lymphocytes of the chickens treated with ZnOHCl and TOL were significantly lower than those of the infected and unmedicated controls (p ≤ 0.05). Conclusion: This study demonstrated that zinc supplementation alone reduced only oocyst output. However, growth performance, lesion scores, and oocyst output were affected by the combination of ZnOHCl and TOL supplementation. This suggests that ZnOHCl supplementation combined with an anticoccidial drug can improve growth performance and lessen the severity of E. tenella infection.
System model of neuromorphic sequence learning on a memristive crossbar array

Machine learning models for sequence learning and processing often suffer from high energy consumption and require large amounts of training data. The brain presents more efficient solutions to how these types of tasks can be solved. While this has inspired the conception of novel brain-inspired algorithms, their realizations remain constrained to conventional von-Neumann machines. Therefore, the potential power efficiency of the algorithm cannot be exploited due to the inherent memory bottleneck of the computing architecture. Therefore, we present in this paper a dedicated hardware implementation of a biologically plausible version of the Temporal Memory component of the Hierarchical Temporal Memory concept. Our implementation is built on a memristive crossbar array and is the result of a hardware-algorithm co-design process. Rather than using the memristive devices solely for data storage, our approach leverages their specific switching dynamics to propose a formulation of the peripheral circuitry, resulting in a more efficient design. By combining a brain-like algorithm with emerging non-volatile memristive device technology we strive for maximum energy efficiency. We present simulation results on the training of complex high-order sequences and discuss how the system is able to predict in a context-dependent manner. Finally, we investigate the energy consumption during the training and conclude with a discussion of scaling prospects.
Academic Achievement of Rural School Students: A Multi-Year Comparison with Their Peers in Suburban and Urban Schools.

This study examined the issue of whether any differences exist in school achievement among rural, suburban, and urban school students in four major areas of school learning: reading, mathematics, science, and social studies. Data from the National Education Longitudinal Study of 1988 (NELS: 88), which followed a nationally representative sample of 24,500 students from the 8th to the 12th grade, were used. Performance comparisons among rural, suburban, and urban students were made for the nationally representative samples of 8th, 10th, and 12th graders in four areas of school learning: reading, math, science, and social studies. Performance comparisons were made after adjusting for the potential influence of socioeconomic status. Performance comparison analyses were conducted separately for the four major ethnic groups (Whites, Blacks, Hispanics, and Asian Americans and Pacific Islanders) and separately for public and private school students. In addition, performance comparisons of rural, suburban, and urban students were made separately for the four geographic regions of the United States: the Northeast, Midwest, South, and West. Classifications of rural, suburban, and urban schools, and of geographic regions, were based on the 1980 Census classification of the schools from which the students were sampled. The results showed that students from rural schools performed as well as, if not better than, their peers in metropolitan schools in math, science, reading, and social studies. Contains 70 references and 7 data tables. (TD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************************
ReSE‐Net: Enhanced UNet architecture for lung segmentation in chest radiography images

Automatic lung segmentation in the chest x‐ray is important for computer aided diagnosis. It helps in the surgical planning and diagnosis of pulmonary diseases. Lung shape, size, overlapped area, and opacities make lung segmentation arduous. In this article, we have proposed a UNet‐based model for lung segmentation. We have evaluated the model on difficult datasets that have chest radiographs of patients affected by tuberculosis and other severe abnormalities. Three chest radiography datasets and a CT‐scan dataset are used to prove the model generalization. The proposed model efficiently uses the residual learning and attention mechanisms to improve the segmentation results against the original UNet for the dice coefficient index (DCI) and Jaccard index. We have also performed an ablation study to highlight the impact of the attention mechanism in the proposed model. The model obtained a 97.62% DCI, 95.43% Jaccard index, and a 4.00 Hausdorff distance on the Montgomery County dataset. While on the Shenzhen and NIH datasets, it achieved a 95.71% and 95.75% DCI, 91.90% and 91.95% Jaccard index, and a 5.23 and 5.20 Hausdorff distance, respectively. The proposed model has achieved better or comparable performance against other state‐of‐the‐art models.
Design and Control for WLR-3P: A Hydraulic Wheel-Legged Robot

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex terrain. The hybrid wheel-legged robot (WLR-3P, prototype of the third-generation hydraulic wheel-legged robot) has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough terrains. In this paper, 3 design requirements are proposed to improve the mobility and environmental adaptability of the robot. To meet these 3 requirements, 2 design principles for each requirement are put forward. First, for light weight and low inertia with high stiffness, 3-dimensional printing technology and lightweight material are adopted. Second, the integrated hydraulically driven unit is used for high power density and fast response actuation. Third, the micro-hydraulic power unit achieves power autonomy, adopting the hoseless design to strengthen the reliability of the hydraulic system. What is more, the control system including hierarchical distributed electrical system and control strategy is presented. The mobility and adaptability of WLR-3P are demonstrated with a series of experiments. Finally, the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.