Miracle-dz/UniFAR-FTUs huggingface dataset:
The X-ray luminosity function of active galactic nuclei in the redshift interval z=3-5

We combine deep X-ray survey data from the Chandra observatory and the wide-area/shallow XMM-XXL field to estimate the AGN X-ray luminosity function in the redshift range z=3-5. The sample consists of nearly 340 sources with either photometric (212) or spectroscopic (128) redshift in the above range. The combination of deep and shallow survey fields provides a luminosity baseline of three orders of magnitude, Lx(2-10keV)~1e43-1e46erg/s at z>3. We follow a Bayesian approach to determine the binned AGN space density and explore their evolution in a model-independent way. Our methodology accounts for Poisson errors in the determination of X-ray fluxes and uncertainties in photometric redshift estimates. We demonstrate that the latter is essential for unbiased measurement of space densities. We find that the AGN X-ray luminosity function evolves strongly between the redshift intervals z=3-4 and z=4-5. There is also suggestive evidence that the amplitude of this evolution is luminosity dependent. The space density of AGN with Lx 1e45erg/s. The faint-end slope of UV/optical luminosity functions however, is steeper than for X-ray selected AGN. This implies that the type-I AGN fraction increases with decreasing luminosity at z>3, opposite to trends established at lower redshift. We also assess the significance of AGN in keeping the hydrogen ionised at high redshift. Our X-ray luminosity function yields ionising photon rate densities that are insufficient to keep the Universe ionised at redshift z>4. A source of uncertainty in this calculation is the escape fraction of UV photons for X-ray selected AGN.
Effect of drip irrigation and bioregulators on yield, economics and water use of fenugreek ( Trigonella foenum-graecum )

The field experiment was conducted during winter seasons of 2009–10 and 2010–11 to assess the effect of drip irrigation levels and bioregulators (thiourea and Thioglycollic acid) on fenugreek. Increasing levels of drip irrigation from 0.6 to 1.0 ETc enhanced yield attributes viz., branches plant-1, pods plant-1, pod length, seeds pod-1 and test weight. Increased yield attributes with 1.0 ETc thus, enhanced seed yield (15.53 q ha-1) and stover yield (31.54 q ha-1). However highest B:C ratio (2.11) was recorded with 0.8 ETc irrigation level and highest water use efficiency of 4.83 kg ha-1 mm with 0.6 ETc. Recommended surface irrigation treatment recorded 13.02 q ha-1 seed yield with using 540 mm of water. Whereas, fenugreek under drip irrigation at 0.6, 0.8 and 1.0 ETc used 278.16, 370.88 and 463.60 mm of water, respectively. Seed treatment of fenugreek before sowing with bio-regulator thiourea (500 ppm) for 4 h recorded higher yield attributes viz., pods plant-1, pod length and test weight, as well as, seed yield of 14.85 q ha-1 against 14.36 q ha-1 where no seed treatment was done. Thus, in turn fetched higher water use efficiency (4.00 kg ha-1 mm) and B:C ratio (2.08). Further, foliar spray with 200 ppm thioglycollic acid at vegetative and seed formation stages recorded highest seed yield of 15.01 q ha-1 over 100 ppm thioglycollic acid spray (14.59 q ha-1) or no spray treatment (14.21 q ha-1). Increased yield thus enhanced water use efficiency (3.93-4.05 kg ha-1 mm) and B:C ratio (2.06-2.11) in thioglycollic acid treated crop.
Siamese High-Resolution Network for Change Detection

Deep learning for change detection can provide effective guidance in many applications, such as agricultural development, urban planning, disaster avoidance, etc. In this study, a Siamese deep learning network based on High-Resolution Network (HRNet) is proposed to generate accurate results. HRNet can integrate multi-dimensional features and output high-resolution results which have attracted attention due to its reliable feature extraction ability. In this paper, we extract the feature pairs of several different dimensions, including the two features behind the down-sampling in the stem stage which is an important part of HRNet. Moreover, feature ex-traction and intensive up-sampling tasks are completed by using a variety of feature fusion sub-networks, which are used to enhance the learning ability. Experiments show the superiority of the proposed Siamese HRNet on a widely used change detection dataset.
Attention-Based Multi-Scale Graph Convolution for Point Cloud Semantic Segmentation

Geometric deep learning on non-Euclidean data, particularly point clouds, has in recent years been getting a lot of attention and success. Despite this success, the relationship between points in a delineated subgraph have still not been fully explored - like the under exploration of correlations between inter-class and/or intra-class point connections - leading to suboptimal point cloud segmentations. Thus, this study proposes a scale-invariant graph attention convolution network that has the capacity to dynamically adapt to sub-graph structures at varying scales, and reduce noisy local feature propagation due to mixed object class neighborhoods. The efficacy of the proposed framework is evaluated using the Toronto3D benchmark dataset and attained an mI-oU of 61.2%, outperforming all the existing methods it was compared to.
An Improved Deep Learning-based Approach to Dual-Polarization Radar Rainfall Estimation for Landfall Typhoon in South China

In recent years, the algorithms based on deep learning are proposed for quantitative precipitation estimation (QPE). Studies show that this approach is slightly better than the raindrop size distribution (DSD) fitting method. There is still room for improvement in these approaches, when estimating heavy rain. As we all know, precipitation is close related to geographical location, however these deep learning methods are location-independent. In this study, an innovative approach is proposed for dual-polarization radar (DPR) QPE, which is location-dependent. In particular, the entirely new radar-gauges dataset (RGD) is reconstructed for QPE using radar raw data, radar observational information, gauges measurements and digital elevation model (DEM) data of rain gauges during the landfall typhoons in South China. The deep neural network model is optimized and trained using the RGD. DPR data is fed into this model to estimate precipitation. Preliminary results show the promising performance of this novel method compared to previous QPE estimators of deep learning-based.
A Fuzzy Knowledge Map Framework for Knowledge Representation

Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.
A Hybrid Localization Approach for UAV based on AODV and RSSI in FANETs

Deployment of Unmanned Aerial Vehicle (UAV) and its localization is a big challenge in diverse applications due to cost, scalability, hardware dependencies, etc. To select and develop an appropriate routing protocol for UAV localization in a Flying Ad-hoc Network (FANET). This paper proposes a hybrid UAV localization approach in a master-slave architecture considering a diamond-shaped network (DSN), where only the master UAV needs to have a GPS. In our proposed method, the initial phase of UAV localization is implemented based on Ad Hoc On-Demand Distance Vector (AODV) routing. Then the Dynamic Triangulation (DTN) algorithm is used to estimate the position of a UAV based on Received Signal Strength (RSS). Our proposed approach is cost-effective; it can be used in indoor and outdoor environments for higher accuracy. On account of UAV's diversified and manifold applications, drones are also under the sea level, called underwater drones or Remotely Operated Vehicles (ROV). Eventually, this paper also elaborates on underwater drones exemplifying the Thorp model to determine the incurred attenuation and the network lifetime under channel conditions.
NEXT DAY REACTIVE POWER OPTIMIZATION: REAL-WORLD APPLICATION

E-REDES has been implementing methodologies to optimize reactive power flow by making use of the existing big data load and generation forecast platform (PREDIS) and leveraging on DPlan's powerful optimization tools for capacitor switch scheduling. Based on day-ahead MV load and generation forecast, times for switching HV/MV substation capacitors are optimized in order to comply with TSO/DSO interface contracts while minimizing HV energy losses. The benefits of the day-ahead optimization are illustrated, focusing on the importance of being able to adapt the schedules to the daily variability of load and generation. Results from the implementation of the methodology in a real-world environment are presented to validate the methodology and illustrate its advantages.
BiRank vs PageRank: Using SNA on Company Register Data for Fiscal Risk Prediction

Efficient financial administrations need to ensure compliant behavior of all tax subjects without excessive personnel costs or obstruction of compliant companies. To do so, accurate prediction of non-compliance or fraud is crucial. Social Network Analysis (SNA) provides powerful tools for fraud prediction as fraudulence is often clustered in certain areas of real world social networks. In this paper we present our results of comparing PageRank and the more recent BiRank to infer risk-ranks based on network structure and prior fraud information. Specifically, we model our social network from company register data. We find that in this case study BiRank outperforms PageRank in both quality of the resulting ranks for fraud prediction and run time. The results show that this class of algorithms is generally useful for fraud and risk prediction and more specifically also illustrate the potential of BiRank in comparison, as it opens up new modeling opportunities. Our results show that selecting companies for tax audits based on BiRank yields a precision of 16.38% for the top 20.000 subjects selecting 83.4% of all fraud cases (recall).
Maximal violation of the I3322 inequality using infinite dimensional quantum systems

The I3322 inequality is the simplest bipartite two-outcome Bell inequality beyond the ClauserHorne-Shimony-Holt (CHSH) inequality, consisting of three two-outcome measurements per party. In case of the CHSH inequality the maximal quantum violation can already be attained with local two-dimensional quantum systems, however, there is no such evidence for the I3322 inequality. In this paper a family of measurement operators and states is given which enables us to attain the largest possible quantum value in an infinite dimensional Hilbert space. Further, it is conjectured that our construction is optimal in the sense that measuring finite dimensional quantum systems is not enough to achieve the true quantum maximum. We also describe an efficient iterative algorithm for computing quantum maximum of an arbitrary two-outcome Bell inequality in any given Hilbert space dimension. This algorithm played a key role to obtain our results for the I3322 inequality, and we also applied it to improve on our previous results concerning the maximum quantum violation of several bipartite two-outcome Bell inequalities with up to five settings per party.
Electromagnetic momentum and the energy–momentum tensor in a linear medium with magnetic and dielectric properties

In a continuum setting, the energy–momentum tensor embodies the relations between conservation of energy, conservation of linear momentum, and conservation of angular momentum. The well-defined total energy and the well-defined total momentum in a thermodynamically closed system with complete equations of motion are used to construct the total energy–momentum tensor for a stationary simple linear material with both magnetic and dielectric properties illuminated by a quasimonochromatic pulse of light through a gradient-index antireflection coating. The perplexing issues surrounding the Abraham and Minkowski momentums are bypassed by working entirely with conservation principles, the total energy, and the total momentum. We derive electromagnetic continuity equations and equations of motion for the macroscopic fields based on the material four-divergence of the traceless, symmetric total energy–momentum tensor. We identify contradictions between the macroscopic Maxwell equations and the continuum form of th.
The specific frequencies of ultra-compact dwarf galaxies

Context. One formation channel discussed for ultra-compact dwarf galaxies (UCDs) is that of massive star clusters, and the other main scenario is that of tidally transformed dwarf galaxies. Aims. We aim at quantifying the specific frequency of UCDs in a range of environments and at relating this to the frequency of star clusters and potential progenitor dwarf galaxies. Are the frequencies of UCDs consistent with being the bright tail of the globular cluster luminosity function (GCLF)? Methods. We propose a definition for the specific frequency of UCDs, S N,UCD = NUCD10 0.4(MV,host −MV,0) cw. The parameter MV,0 is the zero point of the definition, chosen such that the specific frequency of UCDs is the same as those of globular clusters, S N,GC ,i f UCDs follow a simple extrapolation of the GCLF. Considering UCDs as compact stellar systems with MV < −10.25 mag (mass above ∼2 × 10 6 M� ), it is MV,0 = −20 mag. The parameter cw is a correction term to take the dependence of the GCLF width σ on the host galaxy luminosity into account. We apply our definition of S N,UCD to results of spectroscopic UCD searches in the Fornax, Hydra, and Centaurus galaxy clusters, two Hickson compact groups, and the Local Group. This includes a large database of 180 confirmed UCDs in Fornax. Results. We find that the specific frequencies derived for UCDs match those of GCs very well, to within 10‐50%. The ratio S is 1.00 ± 0.44 for the four environments Fornax, Hydra, Centaurus, and Local Group, which have S N,GC values. This good match also holds for individual giant galaxies in Fornax and in the Fornax intracluster-space. The error ranges of the derived UCD specific frequencies in the various environments then imply that not more than ∼50% of UCDs were formed from dwarf galaxies. We show that such a scenario would require 90% of primordial dwarfs in galaxy cluster centers (<100 kpc) to have been stripped of their stars. Conclusions. We conclude that the number counts of UCDs are fully consistent with them being the bright tail of the GC population. From a statistical point of view there is no need to invoke an additional formation channel.
Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers.

The Galaxy Zoo citizen science website invites anyone with an Internet connection to participate in research by classifying galaxies from the Sloan Digital Sky Survey. As of April 2009, more than 200,000 volunteers had made more than 100 million galaxy classifications. In this paper, we present results of a pilot study into the motivations and demographics of Galaxy Zoo volunteers, and define a technique to determine motivations from free responses that can be used in larger multiple-choice surveys with similar populations. Our categories form the basis for a future survey, with the goal of determining the prevalence of each motivation.
Processing and Detecting Artifacts in Multi-Input Multi-Output Phase-Sensitive ICE Penetrating Radar Data

Surface crevasses impact ice sheet mass loss by initiating hydrofracturing and calving at the margins and transporting supraglacial meltwater to the subglacial drainage sys-tem. This process subsequently modulates basal sliding and glacier motion. However, the development of robust models for calving and hydrofracture has been limited by a lack of field observations of crevasse formation and geometry. In this paper, we analyze a two-year Multi-Input Multi-Output Autonomous Phase-Sensitive Radio-Echo Sounder (MIMO ApRES) dataset collected at Store Glacier in West Greenland, which documents the formation of a crevasse that opened under the instrument. We present methods for processing the data as well as identifying and removing artifacts, including clipping, radio frequency interference (RFI), receiver failure events such as elevated thermal noise, and signal leakage between channels. Specifically, we perform a mean squared error (MSE) analysis, clipping detection and quantification, and calculations of total power over time in the frequency domain and the time domain. After characterizing and min-imizing these artifacts, we find that the bottom of a crevasse can be detected in the processed images. Our results suggest that, with appropriate data processing, the MIMO ApRES is a promising geophysical system for investigating future crevasse evolution.
Interstellar C2 in the Perseus molecular complex: excitation temperature and density of a molecular cloud with anomalous microwave emission

Interstellar absorption lines up to J" = 10 in the (2, 0) band and up to J" = 6 in the (3, 0) band of the C 2 A 1 Π u -X 1 ∑ + g system are detected toward star Cemis 52 (BD+31°640) in the Perseus molecular complex. The star lies in a reddened line of sight where various experiments have detected anomalous microwave emission spatially correlated with dust thermal emission. The inferred total C 2 column density of N(C 2 ) = (10.5 ± 0.2) x 10 13 cm -2 is well correlated with that of CH, as expected from theoretical models, and is among the highest reported for translucent clouds with similar extinction. The observed rotational C 2 lines constrain the gas kinetic temperature T and the density n = n(H) + n(H 2 ) of the intervening cloud to T = 40 ± 10 K and n = 250 ± 50 cm -3 , respectively. This is the first determination of the gas kinetic temperature and particle density of a cloud with known anomalous microwave emission.
Understanding and Predicting Tropical Cyclone Rapid Intensity Changes Using Passive Microwave Observations from GPM and TRMM

Recent advances in analyzing and predicting rapid intensity change in tropical cyclones suggest that the distribution and intensity of convective activity in the storm play an important role, particularly their occurrence with respect to the dynamically significant vortex structure. We developed a framework to detect and analyze these features from satellite observations of the condensed water, using it as a proxy for the distribution of the associated latent heating and, hence, the intensity of convective activity. Here we use passive microwave measurements of the condensed water from the GPM and TRMM constellations of conically-scanning radi-ometers and employ a low-wave-number decomposition of the 2D fields of columnar condensate to depict the radial distribution of the azimuthally-averaged fields (the magnitude of wave number 0 - WNO), as well as the radial distribution of the first order asymmetry that is captured by the magnitude and azimuthal orientation of the first harmonic in the Fourier decomposition (WN1). Our analyses of a number of hurricanes illustrate the potential predictive abilities of this satellite-based analysis framework, laying the ground for future investigations.
A Combination of Radiative Transfer Model Simulation and Neural Network Modeling for the Retrieval of Volcanic Ash Parameters by Means of Copernicus Sentinel-3/SLSTR Data

In this study we present a novel approach dedicated to the retrieval of volcanic ash parameters by means of data acquired from the Sea and Land Surface Temperature Radiometer on board of the Copernicus Sentinel-3. In this framework, we developed a procedure combining Radiative Transfer Model simulations and Neural Network for estimating three volcanic ash parameters such as aerosol optical depth, effective radius and ash mass. The Radiative Transfer Model simulations have been considered for producing synthetic training sets, which have been used in the training phase of the Neural Networks development. In particular, nine latitude's belts have been identified for training several Neural Networks ensuring the global coverage of the method. The approach has been tested by comparing the results of the trained NN with the ones obtained by applied the state-of-art Look Up Table and the Volcanic Plume Retrieval procedures. The results of the methodologies applied on Raikoke, 2019 eruption demonstrated the feasibility of the proposed approach by registering values of the correlation coefficient between all the three methods ranging between the 65% and the 94%.
High Resolution Remote Sensing Image Semantic Segmentation Based on Ultra-Lightweight Fully Convolution Neural Network

In recent years, fully convolutional neural networks (FCNs) have been widely used in the field of remote sensing image semantic segmentation. However, these networks have huge amount of parameters and cost much computational efficiency. In this paper, an ultra-lightweight network (ULN) is proposed to overcome this problem. The proposed ULN model uses the encoder-decoder architecture to acquire the pixelwise result. In ULN, the efficient spatial pyramid network (ESPNet) is used to extract deep semantic features with fewer parameters. Considering the dilated convolutions will lose some semantic information in the encoding process, the feature enhancement block (FEB) is proposed. The recurrent criss-cross attention module is added at the end of skip connection to acquire the global contextual information. The proposed ULN is tested on the ISPRS Vaihingen dataset, the results show that our network achieves competitive results with fewer parameters(1.5M).