Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Thermodynamic Properties of Ionic Liquids

Basic physicochemical properties were discussed at different temperatures for 18 hydrophobic ionic liquids (ILs) which containing imidazolium and pyridinium as cations, separately. The ILs include 1-ethyl-3-methylimizazolium tris(pentafluoroethyl)trifluorophosphate ([C2mim][PF3(CF2CF3)3]), 1-acetonitrile-3-ethylimimdazolium bis(trifluoromethylsulfonyl)imide ([MCNMIM][NTf2]), 1-(cyanopropyl)-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide [PCNMIM][NTf2], 1-ethanol-3-ethylimimdazolium bis(trifluoromethylsulfonyl)imide ([EOHMIM][NTf2]), 1-butylamide-3-ethylimimdazolium bis(trifluoromethylsulfonyl)-imide ([CH2CONHBuEIM][NTf2]), N-alkylpyridinium bis(trifluoromethylsulfonyl)imide {[Cnpy][NTf2] (n = 2, 3, 4, 5, 6)}, N-alkyl-3-methylpyridinium bis(trifluoromethyl-sulfonyl)imide {[Cn3Mpy][NTf2] (n = 3, 4, 6)}, and N-alkyl-4-methylpyridinium bis(trifluoromethylsulfonyl)imide {[Cn4Mpy][NTf2] (n = 2, 4, 6)}. The molar volume, standard molar entropy, and lattice energy were estimated by the empirical and semiempirical equations. The dependences of density, dynamic viscosity, and electrical conductivity on temperature are discussed in the measured temperature range. It is found that with the increasing temperature, the density and dynamic viscosity decreased, while the electrical conductivity increases. The influences of microstructures of ILs, such as the introduction of the methylene, methyl, and functional groups on cations, on their basic physicochemical properties are discussed

Narrow-line-width UV bursts in the transition region above Sunspots observed by IRIS

Various small-scale structures abound in the solar atmosphere above active regions, playing an important role in the dynamics and evolution therein. We report on a new class of small-scale transition region structures in active regions, characterized by strong emissions but extremely narrow Si IV line profiles as found in observations taken with the Interface Region Imaging Spectrograph (IRIS). Tentatively named as Narrow-line-width UV bursts (NUBs), these structures are located above sunspots and comprise of one or multiple compact bright cores at sub-arcsecond scales. We found six NUBs in two datasets (a raster and a sit-and-stare dataset). Among these, four events are short-living with a duration of $\sim$10 mins while two last for more than 36 mins. All NUBs have Doppler shifts of 15--18 km/s, while the NUB found in sit-and-stare data possesses an additional component at $\sim$50 km/s found only in the C II and Mg II lines. Given that these events are found to play a role in the local dynamics, it is important to further investigate the physical mechanisms that generate these phenomena and their role in the mass transport in sunspots.Comment: 8 pages, 4 figures and 1 table, accepted for publication in ApJ

Plasma parameters and geometry of cool and warm active region loops

How the solar corona is heated to high temperatures remains an unsolved mystery in solar physics. In the present study we analyse observations of 50 whole active-region loops taken with the Extreme-ultraviolet Imaging Spectrometer (EIS) on board the Hinode satellite. Eleven loops were classified as cool (<1 MK) and 39 as warm (1-2 MK) loops. We study their plasma parameters such as densities, temperatures, filling factors, non-thermal velocities and Doppler velocities. We combine spectroscopic analysis with linear force-free magnetic-field extrapolation to derive the three-dimensional structure and positioning of the loops, their lengths and heights as well as the magnetic field strength along the loops. We use density-sensitive line pairs from Fe XII, Fe XIII, Si X and Mg VII ions to obtain electron densities by taking special care of intensity background-subtraction. The emission-measure loci method is used to obtain the loop temperatures. We find that the loops are nearly isothermal along the line-of-sight. Their filling factors are between 8% and 89%. We also compare the observed parameters with the theoretical RTV scaling law. We find that most of the loops are in an overpressure state relative to the RTV predictions. In a followup study, we will report a heating model of a parallel-cascade-based mechanism and will compare the model parameters with the loop plasma and structural parameters derived here.Comment: ApJ, accepted for publicatio

Anomaly Detection in Aerial Videos with Transformers

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this paper, we create a new dataset, named DroneAnomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from 7 different realistic scenes with various anomalous events. There are 87,488 color video frames (51,635 for training and 35,853 for testing) with the size of $640 \times 640$ at 30 frames per second. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, ANomaly Detection with Transformers (ANDT), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY. We make our dataset and code publicly available

Toll-like receptor 2 -196 to -174 del polymorphism influences the susceptibility of Han Chinese people to Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 2 (<it>TLR2</it>) represents a reasonable functional and positional candidate gene for Alzheimer's disease (AD) as it is located under the linkage region of AD on chromosome 4q, and functionally is involved in the microglia-mediated inflammatory response and amyloid-β clearance. The -196 to -174 del polymorphism affects the <it>TLR2 </it>gene and alters its promoter activity.</p> <p>Methods</p> <p>We recruited 800 unrelated Northern Han Chinese individuals comprising 400 late-onset AD (LOAD) patients and 400 healthy controls matched for gender and age. The -196 to -174 del polymorphism in the <it>TLR2 </it>gene was genotyped using the polymerase chain reaction (PCR) method.</p> <p>Results</p> <p>There were significant differences in genotype (P = 0.026) and allele (P = 0.009) frequencies of the -196 to -174 del polymorphism between LOAD patients and controls. The del allele was associated with an increased risk of LOAD (OR = 1.31, 95% CI = 1.07-1.60, Power = 84.9%). When these data were stratified by apolipoprotein E (<it>ApoE</it>) ε4 status, the observed association was confined to <it>ApoE </it>ε4 non-carriers. Logistic regression analysis suggested an association of LOAD with the polymorphism in a recessive model (OR = 1.64, 95% CI = 1.13-2.39, Bonferroni corrected P = 0.03).</p> <p>Conclusions</p> <p>Our data suggest that the -196 to -174 del/del genotype of <it>TLR2 </it>may increase risk of LOAD in a Northern Han Chinese population.</p

Coronal Sources and In Situ Properties of the Solar Winds Sampled by ACE During 1999 - 2008

We identify the coronal sources of the solar winds sampled by the ACE spacecraft during 1999-2008, and examine the in situ solar wind properties as a function of wind sources. The standard two-step mapping technique is adopted to establish the photospheric footpoints of the magnetic flux tubes along which the ACE winds flow. The footpoints are then placed in the context of EIT 284~\AA\ images and photospheric magnetograms, allowing us to categorize the sources into four groups: coronal holes (CHs), active regions (ARs), the quiet Sun (QS), and "Undefined". This practice also enables us to establish the response to solar activity of the fractions occupied by each kind of solar winds, and of their speeds and O$^{7+}$/O$^{6+}$ ratios measured in situ. We find that during the maximum phase, the majority of ACE winds originate from ARs. During the declining phase, CHs and ARs are equally important contributors to the ACE solar winds. The QS contribution increases with decreasing solar activity, and maximizes in the minimum phase when QS appear to be the primary supplier of the ACE winds. With decreasing activity, the winds from all sources tend to become cooler, as represented by the increasingly low O$^{7+}$/O$^{6+}$ ratios. On the other hand, during each activity phase, the AR winds tend to be the slowest and associated with the highest O$^{7+}$/O$^{6+}$ ratios, and the CH winds correspond to the other extreme, with the QS winds lying in between. Applying the same analysis method to the slow winds only, here defined as the winds with speeds lower than 500 km s$^{-1}$, we find basically the same overall behavior, as far as the contributions of individual groups of sources are concerned. This statistical study indicates that QS regions are an important source of the solar wind during the minimum phase.Comment: 24 pages, 7 figures, accepted for publication in Solar Physic