Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing Deep Local and Global Features for Remote Sensing Image Scene Classification

High-resolution remote sensing image scene classification problem has attracted lots of attentions due to its crucial role in a wide range of applications. Recently, many convolutional neural network (CNN) based methods have significantly boosted the performance of image scene classification. However, most of these algorithms only make use of global features learned in the fully connected layers of a CNN, neglecting local features learned in the convolutional layers that is of crucial importance for some remote sensing scenes. Therefore, a sparse representation framework is proposed to make use of both local and global features learned in a CNN for classification of remote sensing scenes by balancing sparse representation based classifiers using these two kinds of features. Specially, in order to reduce redundancy of local features learned in a convolutional layer, the most effective local feature is generated from each convolutional layer using global average pooling and these selected local features of different convolutional layers are cascaded to form local feature representation of the scene. Finally, experimental results on UC-Merced and WHU-RS19 datasets demonstrate that fusing global and local features in a CNN using the proposed sparse representation framework can certainly improve the performance of classification using single kind of features. Moreover, the proposed global average pooling strategy is very effective to fuse local features select most representative features from convolutional layers of a CNN.
Ten Years of Patch-Based Approaches for Sar Imaging: A Review

Speckle reduction is a major issue for many SAR imaging applications using amplitude, interferometric, polarimetric or tomographic data. This subject has been widely investigated using various approaches. Since a decade, breakthrough methods based on patches have brought unprecedented results to improve the estimation of radar properties. In this paper, we give a review of the different adaptations which have been proposed in the past years for different SAR modalities (mono-channel data like intensity images, multi-channel data like interferometric, tomographic or polarimetric data, or multimodalities combining optic and SAR images), and discuss the new trends on this subject.
Contrast Enhancement and Pseudo Coloring Techniques for Infrared Thermal Images

Infrared thermography is a non-invasive and noncontact type radiometric approach that is widely used in medical diagnosis and many other fields. The thermal camera provides the temperature variation information in the form of 2D false color image (or thermogram). These thermograms are either grey scale image or pseudo color image. Grey scale images have poor contrast and difficult for human interpretation. So, there is need of some algorithms to process the image to improve the geometric information of the Region Of Interest (ROI). Various pseudo coloring and contrast enhancement algorithms are developed to improve the visual appearance of the thermogram. In this paper, the overview of some contrast enhancement and pseudo-coloring algorithms with future directions are discussed. In addition, the basic introduction of infrared radiations, thermal imaging, contrast enhancement and pseudo coloring are included.
Functional Pre- and Postsynaptic Changes between the Retinohypothalamic Tract and Suprachiasmatic Nucleus during Rat Postnatal Development

The suprachiasmatic nucleus (SCN) is the main brain clock in mammals. The SCN synchronizes to the light-dark cycle through the retinohypothalamic tract (RHT). RHT axons release glutamate to activate AMPA-kainate and N-methyl-D-aspartate (NMDA) postsynaptic receptors in ventral SCN neurons. Stimulation of SCN NMDA receptors is necessary for the activation of the signaling cascades that govern the advances and delays of phase. To our knowledge, no research has been performed to analyze the functional synaptic modifications occurring during postnatal development that prepare the circadian system for a proper synchronization to light at adult ages. Here, we studied the pre- and postsynaptic developmental changes between the unmyelinated RHT-SCN connections. Spontaneous NMDA excitatory postsynaptic currents (EPSCs) were greater in amplitude and frequency at postnatal day 34 (P34) than at P8. Similarly, both quantal EPSCs (miniature NMDA and evoked quantal AMPA-kainate) showed a development-dependent increase at analyzed stages, P3-5, P7-9, and P13-18. Moreover, the electrically evoked NMDA and AMPA-kainate components were augmented with age, although the increment was larger for the latter, and the membrane resting potential was more depolarized at early postnatal ages. Finally, the short-term synaptic plasticity was significantly modified during postnatal development as was the estimated number of quanta released and the initial release probability. All of these synaptic modifications in the unmyelinated RHT-SCN synapses suggest that synchronization to light at adult ages requires developmental changes similar to those that occur in myelinated fast communication systems.
Comparative study and Investigation of the Broadband Powerline Channel Model

This paper presents the characterization and modeling of several correlative powerline channel models, namely, the Phillips model and Zimmermann and Dostert model. In this work we describe some important features and obtain the analytical expressions of the power line channel, explore the validity of the channel transfer function, reports the important applications to contribute to the future consensus of the broadband powerline communication. The proposed modeling approach is based on the radial structure of the low voltage electrical networks and this is an extension of the Zimmermann model.
B-UAVC: Buffered Uncertainty-Aware Voronoi Cells for Probabilistic Multi-Robot Collision Avoidance

This paper presents B-UAVC, a distributed collision avoidance method for multi-robot systems that accounts for uncertainties in robot localization. In particular, Buffered Uncertainty-Aware Voronoi Cells (B-UAVC) are employed to compute regions where the robots can safely navigate. By computing a set of chance constraints, which guarantee that the robot remains within its B-UAVC, the method can be applied to non-holonomic robots. A local trajectory for each robot is then computed by introducing these chance constraints in a receding horizon model predictive controller. The method guarantees, under the assumption of normally distributed position uncertainty, that the collision probability between the robots remains below a specified threshold. We evaluate the proposed method with a team of quadrotors in simulations and in real experiments.
Urban Functional Regions Discovering Based on Deep Learning

In recent years, the big data industry chain has become more mature. Analyzing and managing cities by utilizing various big data in cities has become a hot research topic. Urban functional regions discovering is one of the important applications. The mainstream in urban functional regions discovering are probabilistic topic models, such as latent Dirichlet allocation (LDA) based topic model, which seeing the regions as documents and their functions are their topics. These methods require feature engineering by hand, which will construct features of limited expressiveness. To overcome these methods’ shortcomings, we introduced a deep learning topic model called document neural autoregressive distribution estimation (DocNADE) into urban functional regions mining. And we did an experiment to test its effect. The experimental result shows that this DocNADE framework has achieved a considerable result in urban function inference compared with Dirichlet Multinomial Regression (DMR) based topic model which is a state of the art of urban functional regions discovering.
Highly Sensitive Temperature Sensing Performance of a Microfiber Fabry-Perot Interferometer with Sealed Micro-Spherical Reflector

A temperature probe has been proposed by inserting a microfiber taper into a silica hollow core fiber with a microsphere end. The sealed air cavity in the microsphere and the inserted microfiber acted as the two reflectors of a Fabry-Perot interferometer, respectively. The contribution of both microfiber diameter and cavity length on the interference spectra was analyzed and discussed in detail. The temperature change was experimentally determined by monitoring the wavelength location of the special resonance dip. By filling the air cavity with poly-dimethylsiloxane (PDMS), a high temperature sensitivity of 3.90 nm/°C was experimentally demonstrated. This temperature probe with the diameter of 150 μm and length of 10 mm will be a promising candidate for exploring the miniature or implantable sensors.
UV Radiation Estimation in the United States Using Modis Data

UVB radiation refers to ultraviolet (UV) with wavelength ranging from 280nm to 320nm and plays a major role in vitamin D synthesis, plant growth, and human health. In this article, erythemal weighted UVB irradiance (UVER) is modeled on the Surface Radiation Budget Network (SURFRAD) stations based on the relationship with solar zenith angel (SZA), clearness index (Kt), and ozone (O3). Two models are established, one of which can be used when the O3 information is missing (Model I) and the other one (Model II) takes O3 into consideration. Verification indicates both Model I and Model II show good performance on Fort Peck, Montana with tiny mean bias error (MBE), within ±0.2%, while Model II performs more stable when verified on all SURFRAD stations, with smaller MBE (-1.61%) and root mean square error (RMSE). By using MODIS downward shortwave radiation (DSR) as model input, a UVER product with a resolution of 5km×5km can be obtained. The MBE of this product on SURFRAD stations is 0.82% and 2.85% for the instantaneous and 3-hour estimation, respectively. And similar result can be obtained on stations of UVB monitoring and research program (UVMRP) maintained by U.S. department of agriculture. Erythemal daily dose (EDD) is further calculated from the hourly UVER product, and the result corresponds to that from measurement within ±10% bias in 33 out of total 35 stations and within ±5% bias in 18 stations. In addition, comparison with OMI product OMUVBd shows that our result corresponds the ground measurements better.
Deep Reinforcement Learning Based Dynamic Resource Allocation in 5G Ultra-Dense Networks

The rapid development of Internet of things (IoT) technology has promoted the densification of network infrastructure. Ultra-dense networks (UDN) will become a key technology in 5G networks. We investigated the dynamic resource allocation problem over 5G UDN. We considered the energy efficiency (EE) and spectral efficiency (SE) of the network. Therefore, the resource allocation problem at different moments was expressed as a joint optimization problem. Considering the dynamic nature of the environment, the EE and SE were dynamically weighted. In order to guarantee the long-term performance of UDN system, the joint optimization problem was described as a markov decision process (MDP). In view of the fact that the densification of the network makes the space explosion of MDP and makes it difficult to solve by traditional methods, the dueling deep Q network (Dueling DQN) method was proposed. Simulation results showed that compared with traditional Q-learning and DQN, this algorithm has obvious performance improvement.
Indigo Naturalis Ameliorates Dextran Sulfate Sodium-Induced Colitis in Mice by Modulating the Intestinal Microbiota Community

Indigo naturalis (IN) is a traditional Chinese medicine, named Qing-Dai, which is extracted from indigo plants and has been used to treat patients with inflammatory bowel disease (IBD) in China and Japan. Though there are notable effects of IN on colitis, the mechanisms remain elusive. Regarding the significance of alterations of intestinal flora related to IBD and the poor water solubility of the blue IN powder, we predicted that the protective action of IN on colitis may occur through modifying gut microbiota. To investigate the relationships of IN, colitis, and gut microbiomes, a dextran sulfate sodium (DSS)-induced mice colitis model was tested to explore the protective effects of IN on macroscopic colitis symptoms, the histopathological structure, inflammation cytokines, and gut microbiota, and their potential functions. Sulfasalazine (SASP) was used as the positive control. Firstly, because it was a mixture, the main chemical compositions of indigo and indirubin in IN were detected by ultra-performance liquid chromatography (UPLC). The clinical activity score (CAS), hematoxylin and eosin (H&E) staining results, and enzyme-linked immunosorbent assay (ELISA) results in this study showed that IN greatly improved the health conditions of the tested colitis mice, ameliorated the histopathological structure of the colon tissue, down-regulated pro-inflammatory cytokines, and up-regulated anti-inflammatory cytokines. The results of 16S rDNA sequences analysis with the Illumina MiSeq platform showed that IN could modulate the balance of gut microbiota, especially by down-regulating the relative quantity of Turicibacter and up-regulating the relative quantity of Peptococcus. The therapeutic effect of IN may be closely related to the anaerobic gram-positive bacteria of Turicibacter and Peptococcus. The inferred metagenomes from 16S data using PICRUSt demonstrated that decreased metabolic genes, such as through biosynthesis of siderophore group nonribosomal peptides, non-homologous end-joining, and glycosphingolipid biosynthesis of lacto and neolacto series, may maintain microbiota homeostasis during inflammation from IN treatment in DSS-induced colitis.
Combined Use of the Ab105-2φΔCI Lytic Mutant Phage and Different Antibiotics in Clinical Isolates of Multi-Resistant Acinetobacter baumannii

Phage therapy is an abandoned antimicrobial therapy that has been resumed in recent years. In this study, we mutated a lysogenic phage from Acinetobacter baumannii into a lytic phage (Ab105-2phiΔCI) that displayed antimicrobial activity against A. baumannii clinical strain Ab177_GEIH-2000 (isolated in the GEIH-REIPI Spanish Multicenter A. baumannii Study II 2000/2010, Umbrella Genbank Bioproject PRJNA422585, and for which meropenem and imipenem MICs of respectively, 32 µg/mL, and 16 µg/mL were obtained). We observed an in vitro synergistic antimicrobial effect (reduction of 4 log–7 log CFU/mL) between meropenem and the lytic phage in all combinations analyzed (Ab105-2phiΔCI mutant at 0.1, 1 and 10 MOI and meropenem at 1/4 and 1/8 MIC). Moreover, bacterial growth was reduced by 8 log CFU/mL for the combination of imipenem at 1/4 MIC plus lytic phage (Ab105-2phiΔCI mutant) and by 4 log CFU/mL for the combination of imipenem at 1/8 MIC plus lytic phage (Ab105-2phiΔCI mutant) at both MOI 1 and 10. These results were confirmed in an in vivo model (G. mellonella), and the combination of imipenem and mutant Ab105-2phiΔCI was most effective (p < 0.05). This approach could help to reduce the emergence of phage resistant bacteria and restore sensitivity to antibiotics used to combat multi-resistant strains of Acinetobacter baumannii.
Data augmentation for self-paced motor imagery classification with C-LSTM

Objective. Brain–computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events. Approach. In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored. Main results. The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian MDM classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively. Significance. This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.
Mergenet: Feature-Merged Network for Multi-Scale Object Detection in Remote Sensing Images

Object detection has been playing a significant role in the field of remote sensing for a long period while it is still full of challenges. The biggest one is how to detect multi-scale objects with high accuracy and fast speed in remote sensing images. One-stage object detectors have been achieving relatively high accuracy and efficiency with small memory footprint. However, they have a not very well performance on small objects. In this paper, we discuss the importance of the context information between feature maps in different scales which is helpful for detecting small objects. Especially, we propose a Feature-merged detection networks (MergeNet), which can be inserted into the one-stage detectors easily, to unify the multi-scale feature and context information effectively. Experiments on DOTA dataset demonstrate that our model can significantly improve the performance of the one-stage method.
Object-Oriented Automatic and Accurate Shadow Detection for Very High Spatial Resolution Satellite Images

Several existing shadow detection methods cannot keep the balance between accuracy and automaticity well. To overcome the weakness, we present a novel method to detect shadow in very high spatial resolution satellite images. First, a new shadow detection index is developed to obtain the shadow ratio map. The initial shadow mask map is then obtained by utilizing the Gaussian mixture mode and the Otsu’s method automatically. Finally, the initial shadow mask map is refined by jointly using the object spectral characteristics and the spatial-correlation relationship between objects. The experimental results performed on different images show that the accuracy and automation of the proposed method are over several state-of-the-art methods.
In vitro effects of standard antioxidants on lactoperoxidase enzyme–A molecular docking approach

Lactoperoxidase (LPO), an antioxidant enzyme, is a natural antimicrobial system that eliminates the harmful effects of microorganisms in milk. It has a wide range of applications and is also preferred in cosmetic and clinical applications, as well as used in foods. The use of antioxidants is well recognized in the food and feed industries to improve the shelf life of products. This study aimed to determine the in vitro inhibition effects of Trolox, α‐tocopherol, butylated hydroxyanisole, butylated hydroxytoluene, and propyl gallate, which are commonly used as antioxidants in food and pharmaceutical products. For this purpose, LPO was first purified in a single step using sepharose‐4B‐l‐tyrosine‐sulfanilamide affinity gel chromatography. Also, some inhibition parameters, including half‐maximal inhibitory concentration (IC50), Ki values, and inhibition types, were calculated for each antioxidant molecule. The IC50 values of these molecules, which exhibited competitive inhibition, varied between 377.7 and 3397.8 nM. Molecular docking studies were also performed for all compounds. According to the binding scores, α‐tocopherol was shown to exhibit the most effective inhibitor property (IC50: 377.7 nM and Ki: 635.8 ± 16.8 nM) among the standard antioxidants used in this study. Inhibiting the LPO activity by standard antioxidants results in the weakening of the immune system during lactation, which is important for metabolism.
Changes of Scattering Mechanisms in Boreal Forests Under Freezing Conditions by Means of SAR Polarimetry

The paper aims at polarimetric analysis of PALSAR and PALSAR-2 quad-polarimetric data over Canadian boreal forests. Low air temperature in winter in the area leads to decreasing the radar cross-section, changes of scattering matrix properties, and results in confusing classification maps, which are variant in different seasons. The study demonstrates that air temperature is a simple weather parameter correlating with some polarimetric characteristics of a target. The mean daily temperature in the day before observations is found to be more suitable parameter than current temperature. The increasing interval of temperature averaging (2-7 days) does not improve the resulting correlation. Under freezing conditions the dominating scattering mechanism is a surface scattering even for dense forests. Time series of the polarimetric classification parameters alpha angle and entropy gives a possibility to distinguish real surface scattering from temporal one, that is caused by frost.
Rapid and repeatable host plant shifts drive reproductive isolation following a recent human‐mediated introduction of the apple maggot fly, Rhagoletis pomonella

Ecological speciation via host‐shifting is often invoked as a mechanism for insect diversification, but the relative importance of this process is poorly understood. The shift of Rhagoletis pomonella in the 1850s from the native downy hawthorn, Crataegus mollis, to introduced apple, Malus pumila, is a classic example of sympatric host race formation, a hypothesized early stage of ecological speciation. The accidental human‐mediated introduction of R. pomonella into the Pacific Northwest (PNW) in the late 1970s allows us to investigate how novel ecological opportunities may trigger divergent adaptation and host race formation on a rapid timescale. Since the introduction, the fly has spread in the PNW, where in addition to apple, it now infests native black hawthorn, Crataegus douglasii, and introduced ornamental hawthorn, Crataegus monogyna. We use this “natural experiment” to test for genetic differentiation among apple, black, and ornamental hawthorn flies co‐occurring at three sympatric sites. We report evidence that populations of all three host‐associations are genetically differentiated at the local level, indicating that partial reproductive isolation has evolved in this novel habitat. Our results suggest that conditions suitable for initiating host‐associated divergence may be common in nature, allowing for the rapid evolution of new host races when ecological opportunity arises.
Terrain Mapping of a Tropical Rainforest with Dual-Polarimetric P-Band InSAR Backscatter-Phase Histograms

We employ dual-polarimetric P-band interferometric synthetic aperture radar (InSAR) backscatter-phase histograms of the Amazon rainforest of Urucu to estimate the elevation of the forest terrain. High-resolution single-look phase and phase-height histograms weighted by backscatter intensity are projected in the elevation direction and are combined in the range and azimuth directions to form a three-dimensional backscatter-phase histogram. Profiles of the resulting histograms resemble forest reflectivity. Higher ground backscatter is observed in the P-HH compared to the P-HV profile, in agreement with scattering models. Results suggest that the peak of the difference between the normalized P-HH and P-HV profiles can be used as an estimator for the terrain elevation from a model-free approach. We qualitatively compare the results to traditional P-band interferometry and polarimet-ric InSAR (PolInSAR) random-volume over ground (RVoG) three-stage approach over five different baselines 20m, 65m, 80m, 100m, and 145m.