Miracle-dz/UniFAR-FTUs huggingface dataset:
DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features; a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset.
Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations

In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.
Analysis of the long and short-period terms due the nonsphericity of the central body: applications for Mercury

In this work, we present an approach taking into account the single-averaged equations and unaveraged equations to investigate the dynamics of artificial satellites on the effect due to the non-spherical shape of the planet Mercury. An analysis considering the long-period terms and another taking into account the short-period terms is presented. The numerical integrations of the equations developed are performed using the Maple software. We consider the numerical values of the most updated spherical harmonic coefficients in the literature. Emphasis is given to analyze the effect of the C22 term in the dynamics of the spacecraft. We show that the two techniques are in agreement (average or not average). We found orbits that librates around an equilibrium point with small variation of the orbital elements, in particular the eccentricity and argument of the pericenter. We also note that the C22 term contributes to reduce the growth of the orbital eccentricity.
Chromodomain Helicase DNA Binding Protein 1 (Chd1) is required for orofacial development in Xenopus

CHROMODOMAIN HELICASE DNA BINDING PROTEIN 1 (CHD1) IS REQUIRED FOR OROFACIAL DEVELOPMENT IN XENOPUS By Brent Hardin Wyatt, PhD. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University Virginia Commonwealth University, 2018 Major Director: Amanda Dickinson, PhD. Associate Professor, Department of Biology Abnormalities affecting orofacial development are some of the most common, expensive, and devastating birth defects. Children born with such defects may experience difficulties with eating, breathing, and speech and in addition, these defects often require multiple surgeries to correct them. Therefore, it is critical to understand how the orofacial region develops in order to better treat and prevent these types of birth defects. Xenopus laevis has emerged as a strong model in which to examine orofacial development and was utilized here to investigate the cellular and molecular mechanisms underlying the complex development of the orofacial region. Retinoic acid is one signal involved in orchestrating orofacial development and accomplishes this in part by regulating the nucleosome structure of target genes. The work presented here characterizes the role of an ATP-dependent chromatin remodeler, chromodomain helicase DNA binding protein 1 (Chd1), in orofacial development in X. laevis. The spatial expression of Chd1 supports its role in orofacial development and reduced expression of Chd1 resulted in abnormal facial development. Closer examination of Chd1 morphant embryos revealed that Chd1 is required for the expression of important neural crest and cartilage genes that are necessary for proper development of the face. In addition, there was an increase in apoptosis in regions consistent with migrating neural crest and neural crest derived structures. As a consequence, many of the facial cartilages do not form properly in morphant embryos resulting in a smaller face. Further, this work presents evidence that Chd1 may cooperate with retinoic acid to regulate orofacial development in X. laevis.
The critical factors of landfill fire impact on air quality

Simulations show that landfill fires are a serious regional concern. This study presents a three-parameter method for assessing the impact of landfill fires on the atmosphere based on landfill fire data in Poland from 2018. HYSPLIT simulations were performed to assess the spatial impact of the particulate matter (PM) <10 μm (PM10) emitted during each fire in regions with elevated concentrations of PM10. Additionally, for each fire, the population exposed to an increased PM10 concentration due to the landfill fire was determined. Three parameters, namely the area and range of the PM concentrations and the population affected, were calculated for each additional concentration level and analyzed using geodetic information software. The analysis of these three parameters shows that despite being correlated for concentrations of 10 and 1 μg m−3, there are no equations that can eliminate any of the three parameters, and all of them are required for the proper assessment of landfill fire impacts on air quality. Choosing the arbitrary reference concentration levels in the assessment shows that our method is adaptable for any law, health regulation, or recommendation.
Low molecular weight chitosan hydrolyzate inhibits the growth of some phytopathogenic Ascomycota fungi

The work studied the effect of low molecular weight chitosan hydrolyzate on the growth and development of phytopathogens Botrytis cinerea, Fusarium solani, Alternaria solani. Chitosan hydrolyzate, when added to potato-glucose agar, inhibits the growth of mycelium in vitro of Botrytis cinerea, Fusarium solani, Alternaria solani. Treatment with chitosan hydrolyzate suppresses the development of fusarium on tomato leaves and the development of gray rot on fruits. Further studies of chitosan hydrolyzate effect mechanisms on plants can expand the scope of its application.
A Channel Selection Algorithm of Power Line Communication Network Base on Double-layer Cascade Artificial Neural Network

A crucial issue in Power Line Communication (PLC) networks is how to improve the performance of power line channel in physical layer. In this paper, a multiple input multiple output (MIMO) PLC channel selection algorithm based on a double-layer cascaded artificial neural network model is proposed. After the model is trained, the best channel combination can be selected directly through the channel estimation information. The results show that compared with the communication system without channel selection, the model can improve the channel by 2-3dB, the probability of selecting the best channel is 95.02%, and the training has fast convergence speed and small amount of calculation.
Differential search algorithm for biojective feature selection

Feature selection (FS) is a complex optimization problem with important real-world applications. Generally, the main target of FS is to reduce the dimensionality of features and enhance the effectiveness of solving problems. Due to the population characteristics, various evolutionary algorithms (EAs) have been proposed to solve feature selection problems over the past decades. However, the majority of them only consider single-objective optimization, while many real-world problems have multiple objectives, therefore, it is need to design more suitable and effective EAs to deal with multi-objective FS. In this paper, a biobjective FS algorithm based on differential search algorithm (DSA) is designed to solve FS problems, which minimizes the number of selected features and maximizes the classification accuracy. The results of simulation experiments and statistical analysis on 15 classification datasets compared with other four state-of-the-art EAs show that the proposed DSA algorithm can not only obtain better optimization performance, but also achieve competitive convergence accuracy.
Chinese text multi-classification based on Sentences Order Prediction improved Bert model

For the strong noise interference brought by the NSP mechanism (Next Sentences Prediction) in Bert to the model, in order to improve the classification effect of the Bert model when it is used in text classification, an SOP (Sentences Order Prediction) mechanism is used to replace the Bert model of the NSP mechanism-Multi-classification of Chinese news texts. At first, use randomly sorted adjacent sentence pairs for segment embedding. Then use the Transformer structure of the Bert model to encode the Chinese text, and obtain the final CLS vector as the semantic vector of the text. Finally, connect the different semantic vectors to the multi-category Classification. After ablation experiments, the improved SOP-Bert model obtained the highest F1 value of 96.69. The results show that this model is more effective than the original Bert model on text multi-classification problems.
Justification of the model for assessing the quality of services in agrotourism

The article is devoted to the substantiation of the methodology for assessing the quality of services in agrotourism. The main goal is to develop a model that would allow us to take into account all the nuances of assessing the quality of services, taking into account agricultural specifics. In this case, it is difficult to justify ways to improve efficiency, operating solely on economic performance indicators, both absolute and relative: gross income, gross output, net profit, profitability, and others. The presence of both quantitative and qualitative components in this case causes certain difficulties in assessing the efficiency of production. The assessment should include separate components related to the quality side – the service. In this case, it is a component that affects the attraction of additional sources of financing for the main activities of agricultural enterprises. The work is primarily devoted to small businesses – farms as attractive objects from a tourist point of view. In addition, from our point of view, small businesses are able to extract the maximum possible cumulative effect from the combination of basic agricultural activities and the provision of related services. The article presents a methodology for assessing the quality of services based on the SERVQUAL model and adapted for use in the field of agriculture.
Low-compute facial expression recognition using fiducial feature-sets

Facial Expression Recognition is an exciting area of affective computing. As mobile and embedded devices become increasingly ubiquitous, exploration of low-compute approaches for facial expression recognition is essential. Facial landmark points are fiducial features that are used to localize and represent salient regions of the face, such as eyes, nose and lips. Any facial expression can be expressed as an activation of facial muscles in specific parts of the face, thereby affecting the locations of the facial landmark points in those parts. This relationship can be captured concretely by deriving appropriate feature-sets from these points. In this paper, an approach for deriving three types of feature-sets from a set of facial landmark points detected on a face is discussed. Such feature-sets are derived for three standard facial expression recognition datasets with labelled expression classes. The derived features-sets are used as inputs for training computationally light machine-learning classifiers, yielding encouraging classification accuracy. These results are presented and discussed with both quantitative and qualitative observations.
Highly Selective Adsorption of Antimonite by Novel Imprinted Polymer with Microdomain Confinement Effect

The design and synthesis of metalloid imprinted materials is a challenge due to lack of a feasible functional monomer. A novel cyclic functional monomer (CFM) was used to develop Sb(III)-ion imprinted polymer (CFM-IIP) for efficient and selective removal Sb(III). CFM possesses positively charged imidazolium moiety and specific cyclic size matching with Sb(III). CFM-IIP has a maximum Sb(III) adsorption capacity of 79.1 mg·g–1, while that of a noncyclic functional monomer imprinted polymer (NCFM-IIP) is only 30.9 mg Sb(III)·g–1. The relative selectivity coefficients of CFM-IIP compared to NCFM-IIP for Sb(III)/Cl–, Sb(III)/NO3–, Sb(III)/PO43–, Sb(III)/SO42–, and Sb(III)/Cr2O72– were 6.6, 78.4, 5.9, 3.0, and 3.2, respectively. Kinetic data fitted well with pseudo-second-order model. The adsorption between Sb(III) and CFM-IIP was identified to be feasible, spontaneous, and endothermic. ζ-Potential, X-ray photoelectron spectroscopic analysis, and density functional theory calculations revealed the mechanism of .
A Data Mining System for Potential Customers Based on One-Class Support Vector Machine

Commodity purchase data is usually severely skewed, which is reflected in the fact that there are far more negative data than positive data. This phenomenon makes it difficult for the binary classification model to obtain satisfactory results. Hence, we transform the binary classification problem into a one-class novelty detection problem. Specifically, this work proposes a potential customer mining system based on the One-class Support Vector Machine (OCSVM) and demonstrates its effectiveness for classification, prediction, and potential customer mining. This system allows merchants to focus on unpurchased customers with the strongest purchase intentions and to change their purchase decisions with minimal sale costs, which enables merchants to maximize their benefits.
Application of the decision tree method for predicting the yield of spring wheat

The results of the development of predictive models of the yield of spring wheat based on the use of the decision tree method are presented. When constructing the models, qualitative factors were taken into account (the level of intensification, the system of soil cultivation, the placement of the crop after steam) and agrometeorological resources (the sum of active air temperatures, precipitation). The minimum number of input parameters (public data) was used for the generality of the system and its versatility for different natural and agricultural conditions. The efficiency of using decision trees for forecasting wheat yield is shown. The accuracy of the constructed models was evaluated on the training and test samples and the following indicators were achieved (CART method): average absolute error - 3.455 (training sample) and 4.446 (test sample); determination coefficient - 0.895 (training sample) and 0.811 (test sample). A set of rules has been obtained that determine the level of crop yield depending on the complex of control actions and the prevailing conditions.
Improving Non-Autoregressive Machine Translation via Autoregressive Training

In recent years, non-autoregressive machine translation has attracted many researchers’ attentions. Non-autoregressive translation (NAT) achieves faster decoding speed at the cost of translation accuracy compared with autoregressive translation (AT). Since NAT and AT models have similar architecture, a natural idea is to use AT task assisting NAT task. Previous works use curriculum learning or distillation to improve the performance of NAT model. However, they are complex to follow and diffucult to be integrated into some new works. So in this paper, to make it easy, we introduce a multi-task framework to improve the performance of NAT task. Specially, we use a fully shared encoder-decoder network to train NAT task and AT task simultaneously. To evaluate the performance of our model, we conduct experiments on serval benchmask tasks, including WMT14 EN-DE, WMT16 EN-RO and IWSLT14 DE-EN. The experimental results demonstrate that our model achieves improvements but still keeps simple.
Anharmonic contributions to the Debye-Waller factor: Calculation and application to aluminum.

Presentation d'une methode de calcul exact des contributions quasiharmoniques, et des contributions des deux plus bas ordres cubiques, quartiques et anaharmoniques au facteur de Debye-Waller. Etude du deplacement carre moyen atomique d'un cristal anharmonique dans la limite de haute temperature. La methode est applicable a tout cristal cubique monoatomique, mais les resultats sont presentes pour un modele de force centrale de plus proche voisin d'un cristal fcc. Comparaison des resultats numeriques et experimentaux pour l'aluminium
Toward growth of wafer-scale single-crystal hexagonal boron nitride sheets

Hexagonal boron nitride (hBN) has a two-dimensional planar structure without dangling bonds and is considered an insulator material that can overcome the limitations of SiO2 and HfO2, which typically exhibit large densities of dangling bonds and charged impurities at the interface. However, most of the reported hBN films prepared by chemical vapor deposition (CVD) are polycrystalline with grain boundaries. The grain boundaries of a polycrystalline hBN cause current leakage and gas permeability. A recent notable study reports the growth of wafer-scale single-crystal hBN monolayer, which could mitigate the aforementioned problems caused by polycrystalline hBN films. In this perspective, we discuss the recent progress in the research on single-crystal hBN and the direction to be taken for single-crystal hBN in future. The progress is closely related to the development of a single-crystal substrate and large area of monolayer single-crystal was grown on Cu (111). In terms of the hBN growth, the next step would be to grow multilayer single-crystal hBN, which is expected to expand the scope of applications.
RF acoustic microsystems based on suspended lithium niobate thin films: advances and outlook

This paper presents a review of the radio frequency thin-film lithium niobate (LiNbO3) based acoustic microsystems. Thanks to their high electromechanical coupling (k 2), low loss, and great frequency scalability, thin-film LiNbO3 has outperformed state-of-the-art acoustic technologies in the past decade. This paper first reviews the acoustic wave transduction and propagation in thin-film LiNbO3 and then showcases the emerging acoustic applications. Outlook for further platform development and more functions is offered for future thin-film LiNbO3 based acoustic microsystems development.
Nitrogen substituted graphdiyne as electrode for high-performance lithium-ion batteries and capacitors

Herein, pyridinic nitrogen substituted graphdiyne (PyN-GDY) is directly prepared on the surface of copper foil via a facile Glaser-Hay reaction. PyN-GDY comprises GDY-like hybridized sp2 and sp-carbon structures, meanwhile one of the C atom in the benzene ring is replaced by a pyridinic-N atom. Hence, the lithium (Li) atoms intercalated into the PyN-GDY framework can be stabilized due to the interaction between the Li atoms and the pyridinic-N, which can be further confirmed through theoretical analysis on the binding energy of complex containing Li and PyN-GDY. Moreover, the different arrangement of monomers during the preparation process formed plenty of hierarchical pores in the 2D plane, which would shorten the diffusion path and facilitate the delivery of Li-ions. Based on the unique pyridine N doping structure, PyN-GDY exhibits outstanding electrochemical performance with superior cycling stability when applied as electrode in Li-ion half batteries and Li-ion capacitors.
Fabrication of Novel Nanoporous Films in Moisture-in-Oil Sensors via Chemical Dealloying of Cu-Cr using Combinatorial Search of Cu–Cr Alloy Compositions

An as-deposited film with a Cr compositional gradient (22–15 at.% Cr) was immersed in 22.5% HNO_3 for 15 hours. In the part of the film with initial Cr content in the range of 22–18 at. %, Cu dealloying resulted in sufficient Cu dealloying (final Cr content = 33–80 at.%) without film dissolution. Using the film with optimal initial composition Cu_82Cr_18, we successfully fabricated a nanoporous film with a pore size in the range of 20–40 nm. As a result of the formation of Cr_2O_3 during dealloying, this film was transparent and exhibited an insulation state. The novel nanoporous film is expected to be applied as a nanofilter in moisture-in-oil sensors.
Two new methods for facial expression recognition using Convolutional Neural Networks

In this research, we propose two novel methods for facial expression recognition to improve the accuracy of recognition. The first of our novel approach is to add the Batch Normalization (BN) layer to the CNN model, and the second of the novel approach is to preprocess the image before image training, such as rotating image, cropping the image and adding Gaussian noise in the picture, especially it is beneficial for unbalanced classifications. Our model consists of 3 CNN layers, 3 BN layers, three average-pooling layers, and three fully-connected layers; our model has a satisfying performance on the prediction category after adopting the two methods mentioned above. Our CNN model is trained and tested with Kaggle facial expression recognition challenge databases. The implemented system can automatically recognize seven expressions in real-time: anger, disgust, fear, happiness, neutral, sadness, and sur-prise. The experimental results demonstrate the effectiveness of our proposed approach.