Miracle-dz/UniFAR-FTUs huggingface dataset:
CellNet: An Improved Neural Architecture Search Method for Coal and Gangue Classification

This paper uses a neural architecture search (NAS) algorithm based on Bayesian optimization (BO), a version of neural architecture search adapted for network morphism, is proposed to automatically seek an optimized cell-based architecture approximation for CNN to classify the coal and gangue. Besides, we defined the cell-based search space, which has significantly reduced arithmetic operations and computing resources with fewer parameters. In hyperparameter optimization, BO has focused on Euclidean domains that only permit tuning scalar hyper-parameters. However, when BO applies to NAS, a suitable kernel function is needed to build GP, and a distance function is required to describe the differences between two neural network architectures. We define a kernel function suitable for the search space and describe the similarity of network architecture appropriately. We also apply the particle swarm optimization algorithm to optimize the acquisition function. Network morphism is combined with Bayesian optimization (BO), so the obtained architecture inherited the knowledge of the parent network, realizing a better trade-off between exploitation and exploration. The adaptability of the GP model is enhanced by self-optimization of network architecture, and dynamic variable model super parameters gradually fit the real data distribution. We can first obtain the cell architecture from a low-resolution and small dataset and then superimpose its number to expand to a more extensive neural network architecture suitable for the feature of coal from different coal washery. The network CellNet outperforms architectures manually designed that is validated on coal and gangue classification problem.
Analysis and Design of a Nonisolated Bidirectional DC–DC Converter Based on CLC Network

This article proposes a nonisolated bidirectional dc–dc converter based on the π type resonant network for renewable energy sources. The circuit consists of a front-stage boost inverter, a CLC resonant network and a poststage voltage doubled rectifier. The proposed converter has simple structure and smaller volume without conventional transformer, easier to achieve modularity and integration. Under bidirectional power flow, the switches can achieve zero voltage turn-on and the rectifier diodes can achieve zero current turn-off with the wide load range. In this article, the operation modes, output power characteristics, soft-switching conditions, and the optimization design of the circuit are analyzed. A 200 W prototype based on GaN device is implemented and tested to verify validity of the proposed converter. The prototype can achieve a peak efficiency 95.4% in boost mode and 94.1% in buck mode.
Person Re-IDentification Based on Mutual Learning With Embedded Noise Block

Some person re-identification(Re-ID) algorithms based on deep learning utilizes a baseline as basis to modify, and add some strategies to achieve better performance. Different from the conventional methods, this work combines distillation with mutual learning to construct a person Re-ID model of mutual learning. In training, in view of the characteristics of metric learning, we introduce a mutual loss $L_{M2}$ in the mutual learning network, so as to better promote the student networks to mine complementary information. In order to overcome the coupling problem in mutual learning, we designed a lightweight noise block and embedded it into mutual learning, which greatly improves the complementarity between networks. It should be added that the improvement achieved on the poor baseline can’t strictly prove the effectiveness of the research, so this paper constructs a person Re-ID baseline with relatively good performance, which is used as the student networks in mutual learning. Experiments demonstrate that the proposed person Re-ID algorithm based on mutual learning with embedded noise block achieves competitive performance on the Market1501, DukeMTMC-ReID, and CUHK-03 datasets.
Flower Detection Using Object Analysis: New Ways to Quantify Plant Phenology in a Warming Tundra Biome

Rising temperatures caused by global warming are affecting the distributions of many plant and animal species across the world. This can lead to structural changes in entire ecosystems, and serious, persistent environmental consequences. However, many of these changes occur in vast and poorly accessible biomes and involve myriad species. As a consequence, conventional methods of measurement and data analysis are resource-intensive, restricted in scope, and in some cases, intractable for measuring species changes in remote areas. In this article, we introduce a method for detecting flowers of tundra plant species in large data sets obtained by aerial drones, making it possible to understand ecological change at scale, in remote areas. We focus on the sedge species E. vaginatum that is dominant at the investigated tundra field site in the Canadian Arctic. Our system is a modified version of the Faster R-CNN architecture capable of real-world plant phenology analysis. Our model outperforms experienced human annotators in both detection and counting, recording much higher recall and comparable level of precision, regardless of the image quality caused by varying weather conditions during the data collection. (K. Stanski, GitHub - karoleks4/flower-detection: Flower detection using object analysis: New ways to quantify plant phenology in a warming tundra biome. GitHub. Accessed: Sep. 17, 2021. [Online]. Available: https://github.com/karoleks4/flower-detection.)
CataractNet: An Automated Cataract Detection System Using Deep Learning for Fundus Images

Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.
Preventing the Rollover Phenomenon of the Vehicle by Using the Hydraulic Stabilizer Bar Controlled by a Two-Input Fuzzy Controller

When the vehicle is traveling at high speed and suddenly steers, a rollover phenomenon may occur. The main cause of this phenomenon is the appearance of a centrifugal force, which is proportional to the mass and the square of the velocity. In order to limit this situation, the method of using the hydraulic stabilizer bar (active stabilizer bar) has been proposed. The performance of the hydraulic stabilizer bar is highly dependent on the control method, which has been designed to ensure the stabilizer bar’s operation. Previous research often only used simple dynamics models and conventional linear control methods. Therefore, the performance of the stabilizer bar is not guaranteed. At the same time, the factors affecting the movement of a vehicle are not mentioned. This will cause inaccuracies. This research used a spatial dynamics model combined with a non-linear double-track dynamics model, which fully describes the effects of vehicle oscillations. Besides, the two-input Fuzzy control method is also proposed. This is a completely novel model, and it is not like the previous models that have been used to study the stabilizer bar. The results of this research show that if the vehicle uses the hydraulic stabilizer bar controlled by a two-input Fuzzy controller, the values of the roll angle and roll index have been reduced. As a result, stability and safety have been significantly improved. The achievements of this research will be the basis for the development of other intelligent control methods in the future.
Matrix Element-Based Theory of Compressive Sensing and Its Application to Electromagnetic Imaging

We present a simple and general theory of compressive sensing (CS) that relies on elements of the sensing matrix rather than on the number of measurements. We prove the exact recovery using a dual certificate by showing that the sensing matrix satisfies an incoherence property and isotropy property if the sparsity level is kept lower than the reciprocal of the largest element of a matrix created from the sensing matrix. Unlike the CS literature, this unconventional approach does not require a linear relationship between the sparsity and the number of measurements and at the same time, can easily be evaluated. This adaptability captures anisotropic measurements appropriately as with anisotropic measurements, adding more measurements does not really imply that a signal with more nonzero elements will be recovered exactly. As an illustration, we demonstrate the theory’s ability to accurately handle the anisotropic (Green’s function-based sensing matrix) measurements and also its similarity to the existing CS literature for isotropic (Fourier) measurements. Further, we show the usefulness of the theory in comparing different sensing matrices and in generating dielectric images. The dielectric images are perfectly recovered even when there is only a single transmitter.
Influence of microstructure on symmetry determination of piezoceramics

The origin of the complex reflection splitting in potassium sodium niobate doped with lithium and manganese was investigated using temperature-dependent high-resolution X-ray and neutron diffraction as well as electron probe microanalysis and scanning electron microscopy. Two structural models were developed from the diffraction data. A single-phase monoclinic Pm model is known from the literature and is able to reproduce the diffraction patterns perfectly. However, a model with phase coexistence of two classical orthorhombic Amm2 phases can also reproduce the diffraction data with equal accuracy. Scanning electron microscopy in combination with electron probe microanalysis revealed segregation of the A-site substituents potassium and sodium. This favours the model with phase coexistence and confirms the need for comprehensive analyses with complementary methods to cover a broad range of length scales as well as to assess both average and local structure.
Frequency-Stable Full Maxwell in Electro-quasistatic Gauge

The electro-quasistatic approximation of Maxwell’s equations is commonly used to model coupled resistive/capacitive phenomena at low frequencies. It neglects induction and becomes unstable in the stationary limit. We introduce a stabilization that prevents this lowfrequency breakdown. It results in a system for the electric scalar potential that can be used for electro-quasistatics, electrostatics as well as DC-conduction. Our main finding is that the electro-quasistatic fields can be corrected for magnetic/inductive phenomena at any frequency in a second step. The combined field from both steps is a solution of the full Maxwell’s equations that consistently takes into account all electromagnetic effects. Electro-quasistatics serves as a gauge condition in this semi-decoupled procedure to calculate the electromagnetic potentials. We derive frequency-stable weak variational formulations for both steps that immediately lend themselves to finite-element Galerkin discretization.
The structural, electronic and magnetic properties of Co1−xFexS2

The structural, electronic and magnetic properties of Co1−xFexS2 (x = 0.00, 0.25, 0.50, 0.75 and 1.00) alloys have been investigated by the spin-polarized first-principles calculations. The lattice constants and volumes of the Co1−xFexS2 (x = 0.25, 0.50 0.75 and 1.00) are smaller than those of CoS2. The CoS2 is nearly half-metal (HM), while Co0.75Fe0.25S2 is HM and both Co0.50Fe0.50S2 and Co0.25Fe0.75S2 are metal. The Co1−xFexS2 (x = 0.00, 0.25, 0.50 and 0.75) is magnetic, while the FeS2 is a nonmagnetic semiconductor. The conduction band minimum in spin-down channel of Co1−xFexS2 (x = 0.00, 0.25, 0.50, 0.75 and 1.00) is contributed by antibonding S-3p pp σ * states. In HM Co0.75Fe0.25S2, the electrons are more localized around S atoms and there exists buildup of charge in Co/Fe-3d(t2g) states at the expense of charge in Co/Fe-3d(eg) states.
Non-negative Matrix Factorization: A Survey

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.
Changes of attitudes towards inclusion in the physical education after experiencing the educational programme Paralympic school day

The aim of the study is to find out to what extent do the cognitive attitudes of selected primary and lower secondary school pupils towards pupils with disabilities and attitudes to the inclusive physical education (PE) change after they experience the educational programme Paralympic school day (PSD). In total, 78 pupils (37 boys and 41 girls) aged from 9 to 14 (mean age 12.72 ± 1.22) participated in the research. In order to perform the presented research, the questionnaires CAIPECZ (attitudes towards the inclusive PE) and ACL (cognitive attitudes towards people with disabilities) were used. The change in the pupils’ attitudes was assessed in the span of three weeks (before and after they experienced PSD). From the results of the performed study, it may be concluded that when the pupils went through PSD, they statistically significantly improved p < 0,001 in the area of cognitive attitudes at the small effect size coefficient (r = 0,32). However, the attitudes towards a model pupil with disabilities did not prove a statistically significant change (p = 0.27) at the effect size coefficient r = 0.09.
Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+

Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs to be implemented using artificial intelligence. Convolutional neural network (CNN), a subset of artificial intelligence, is used to detect cracks on concrete surfaces through semantic image segmentation. The purpose of this research is to compare the effectiveness of cutting-edge encoder-decoder architectures in detecting cracks on concrete surfaces using U-Net and DeepLabV3+ architectures with potential in biomedical, and sparse multiscale image segmentations, respectively. Neural networks were trained using cloud computing with a high-performance Graphics Processing Unit NVIDIA Tesla V100 and 27.4 GB of RAM. This study used internal and external data. Internal data consisted of simple cracks and were used as the training and validation data. Meanwhile, external data consisted of more complex cracks, which were used for further testing. Both architectures were compared based on four evaluation metrics in terms of accuracy, F1, precision, and recall. U-Net achieved segmentation accuracy = 96.57%, F1 = 87.55%, precision = 88.15%, and recall = 88.94%, while DeepLabV3+ achieved segmentation accuracy = 96.47%, F1 = 85.29%, precision = 92.07%, and recall = 81.84%. Experiment results (internal and external data) indicated that both architectures were accurate and effective in segmenting cracks. Additionally, U-Net and DeepLabV3+ exceeded the performance of previously tested architecture, namely FCN.
Clustering of Healthy Behaviors and Related Factors among 19-64 Aged Korean Adults

Objectives: This study was performed to identify and find the clustering of healthy behaviors and the factors related to the clustering among Korean adults aged 19-64.Methods: The data of 9,519 Korean adults aged between 19 and 64 years who participated in the 6th Korea National Health and Nutrition Examination Survey were used as the analysis target. Following the recommendation of the Health Plan 2020 for health-promoting lifestyle, four variables, including nonsmoking, moderate drinking, sufficient physical activity, and healthy diet, were used in the study. The multinomial logistic regression analysis was performed to analyze the relationships among demographic characteristics, health-related characteristics, and clustering of healthy behaviors.Results: The significant elements of healthy behaviors clustering included sufficient physical activities for males, and non-smoking, moderate drinking, and healthy diet for females. Clustering healthy behaviors were higher for “females, the elderly, people with higher level of education, people with higher household income, people with diabetes, and people who perceive their subjective health status is good.Conclusions: In this study, the factors related to the clustering of healthy behaviors were identified. In order to spread the practice of healthy behaviors, multiple interventions are more effective than individual health behaviors.
Molecular Dynamics Simulation of Nanocrack Propagation in Single-Layer MoS2 Nanosheets

Single-layer MoS2 (SLMoS2) nanosheets promise potential applications in flexible electronic and optoelectronic nanodevices for which the mechanical stability is crucial. However, the measured fracture strength is extremely dispersive, which might be due to the random crack configuration. In this work, molecular dynamics (MD) simulations are conducted to investigate the propagation of nanocracks in SLMoS2 nanosheets and the fracture mechanism at atomic scale, and the modified Griffith criterion developed by Yin et al. is adopted to fit the dependence of fracture stress on the initial crack length. Moreover, the fracture stress is highly dependent on the initial crack configuration, crack length, and crack angle. The energy release rate (GS) decreases with increasing initial crack length, crack angle, and temperature but is not sensitive to strain rate. The average propagation velocity of cracks (V) is substantially reduced with increasing initial crack length and crack angle but is almost independent of t.
Influence of intraspecific competition for food on the bodyweight of the adult aphidophagous ladybird, Coccinella transversalis

Aggregation of conspecific predators sharing a common prey, influences their bodyweights. We investigated the influence of intraspecific competition of adult ladybirds of Coccinella transversalis Fabricius on their bodyweight feeding on rusty plum aphid, Hysteroneura setariae (Thomas). Adult males and females consumed a significantly greater number of aphids with increase in predator-density, however, the aphid-consumption per predator declined with this increase. The weight gain per predator also decreased linearly with increase in the density of both male and female predators. This indicates that the weight-gain of the predator is a function of the prey consumed. The searching efficiency decreased with increase in predator density due to mutual inference. The mutual interference constants for adult male and female ladybirds were −0.419 and −0.546, respectively. The females consumed a greater number of aphids than males. The killing power of the ladybird denoted by the k-value increased curvilinearly with increase in predator density. We conclude that prey consumption is a function of body size and that the offspring of those that aggregate at low densities in prey-rich habitats develop into large adults.
CMAR_A_240394 3163..3173

Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin, People’s Republic of China Purpose: We retrospectively evaluated the efficacy and safety of apatinib as a first-line treatment for advanced hepatocellular carcinoma (HCC) and explored whether drug-related hypertension (HTN) could predict its efficacy. Patients and Methods: This retrospective analysis included patients with advanced HCC who received oral treatment with apatinib. We evaluated the effectiveness by overall survival (OS), progression-free survival (PFS), time to progression (TTP), and disease control rate (DCR), and assessed the safety of the drug based on the occurrence of adverse events. In order to explore whether apatinib-related HTN can be used as a predictor of therapeutic effect, patients were divided into an HTN group and a non-HTN group and adjusted for propensity score-matched (PSM) to reduce mixed deviation. Subgroup analyses of negative prognostic factors for advanced HCC were also performed, including alpha-fetoprotein (AFP), Child–Pugh Score, macrovascular invasion, and extrahepatic metastasis. Results: A total of 208 patients were analyzed, of which 40.9% (n =85) developed drugrelated HTN. For all patients, the OS was 13.4 months (95% CI, 12.2–14.6), the PFS was 5.7 months (95% CI, 5.1–6.3), and the TTP was 6.9 months (95% CI, 6.0–7.8). The OS of the HTN group and the non-HTN group was 17.4 months (m) and 12.5m (p=0.001), and the PFS was 7.4m and 4.7m (p=0.000), respectively. After PSM, the OS (p=0.001) and PFS (p=0.003) of the HTN group were still significantly better than the non-HTN group. Subgroup analysis suggested that overall survival was significantly longer in patients with HTN when serum AFP ≤400 μg/L or extrahepatic metastases. Moreover, OS in the HTN group increased significantly with or without macrovascular invasion. In addition, through the analysis of two groups of patients with PFS>6m and PFS≤6m, we know that the patients with drug-related HTN may develop resistance later, so they have longer survival time. Conclusion: Apatinib demonstrates compelling anti-cancer activity and acceptable safety in advanced HCC. Apatinib-related HTN can potentially predict prolonged survival in patients with advanced HCC.