Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.
Size and body condition of sympatric killer whale ecotypes around the Antarctic Peninsula

Killer whales Orcinus orca are apex predators, and their health can indicate trophic dynamics in ecosystems that support them. We used aerial photogrammetry to estimate body lengths, to better understand size differences and food requirements, and widths, to infer current nutritional condition, of killer whales in the rapidly warming waters around the Antarctic Peninsula. A remotely controlled hexacopter drone was used to collect aerial images of 242 killer whales of 3 sympatric ecotypes (Type A, n = 34; Type B1, n = 19; and Type B2, n = 189) in the austral summers between 2015/2016 and 2018/2019. Total length (TL) varied between ecotypes, with B2s being diminutive in size, indicating large differences in energy requirements. The mean length for adult females ranged from 5.82 m (B2s) to 6.93 m (B1s), and the mean for adult males ranged from 6.44 m (B2s) to 7.80 m (As). We also found significant differences in head width (HW, proxy for body condition), with B2s being significantly leaner. Although this variation may reflect natural shape differences, we also estimated divergent regression lines of HW~TL indicating that this difference was greater at larger body sizes, with some anomalously thin adult female B2s. We suggest that these dissimilarities may indicate a density-dependent response, with leaner body condition in adults with higher energetic requirements, as the abundance of B2s is almost an order of magnitude greater than that of B1s and As. We hypothesize that food limitation resulted from a decline in carrying capacity during recent reductions in sea ice and warmer ocean temperatures.
The Role of Children’s Age and Gender, and Friends’ Gender in the Children’s Interaction towards Social Problem Solving Strategies in Preschool Children

Social problem-solving strategy accepted by society is the essential accomplishments in the development of pre-school children. However, until recent studies, social problem-solving strategies among pre-school children in Indonesia is still rarely conducted. This research examines the role of age, gender in children, and friends’ gender towards social problem-solving strategies. This research utilised a purposive sampling that voluntarily involved 162 children 4-6 years old. Those children were selected from an intact family consisting of a father, mother and children who lived together. A hypothetical social situation dilemma was utilised to gather social problem-solving strategies data from the subjects. The data were analysed with the use of cross-tabulation and chi-square test. Concerning the data analysis, the results reveal no significant difference in social problem-solving strategies when viewed in terms of age and gender of the children and friends’ gender. This suggests that children ages 4 to 6 years for boys and girls still have the same patterns of social behaviour dealing with similar gender and different ones.
Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is a promising solution to speed up the simulation while providing the necessary physics performance. In this paper we propose the Self-Attention Generative Adversarial Network as a possible improvement of the network architecture. The application is demonstrated on the performance of generating responses of the LHCb type of the electromagnetic calorimeter.
On the LoRa Chirp Spread Spectrum Modulation: Signal Properties and Their Impact on Transmitter and Receiver Architectures

The LoRa modulation scheme is arousing a growing interest in the Internet of Things community as it is adopted by the emerging LoRaWAN technology. In this paper, we firstly analyse the baseband processing for the generation of LoRa signals at the transmitter side, providing a simple algorithm that leverages digital signal processing techniques to reduce the modulator complexity. Secondly, we analytically investigate the signal demodulation technique. Quite surprisingly, we found that its effectiveness depends on the particular choice of the sampling frequency at the receiver side, which purposely does not meet the sampling theorem requirement. Finally, we consider the actual architecture of digital receivers investigating the trade-off between the selectivity of receive digital filters, which impacts on the required computational effort and power consumption, and the receiver performance.
GPU-Oriented Environmental Cognition of Power Transmission Lines Through LiDAR-Equipped UAVs

This article proposes an environmental cognition method for execution on a graphics processing unit (GPU) oriented single-board computer (SBC). We develop a lightweight environmental cognition system for real-time mapping by an unmanned aerial vehicle (UAV) by considering the weight of a cognition system as a tradeoff between the flight time and mapping efficiency and loss of environmental cognition capabilities. Heavy systems consume battery power rapidly while ensuring high computational performance and impacting flight envelope. The proposed method enhances real-time mapping speed by using GPU parallelism and minimizing the data transfer between the embedded central processing unit (CPU) and GPU. Specifically, the point cloud data (PCD) from the light detection and ranging are transformed into global coordinates and voxelized. The occupancies of the voxels are updated in a probabilistic manner to eliminate dynamic noise. The analysis of field tests confirms that the proposed method generated and updated the voxel map in real time without losses. In contrast, Octomap executed on a CPU- or GPU-oriented SBC generated and updated the voxel map in a limited manner, resulting in the significant loss of PCD due to the computational burden or heavy data transfer traffic from GPU to CPU. The proposed method contributes to develop a smart environmental cognition system for the autonomous flight of UAVs.
AGRONOMIC AND PHYSIOLOGICAL STUDIES ON SOME EXOTIC AND LOCAL BREAD WHEAT GENOTYPES UNDER SALINE SOIL CONDITIONS IN NORTH DELTA REGION

Soil salinity is the major global limitation to wheat production. Thus, ten bread wheat exotic lines and local cultivars were studied under normal and moderate saline soil conditions during 2017/18 and 2018/19 growing seasons. the objective was to understand the effects of salinity stress on some agronomic and physiological characters and to estimate some selection indices for salt tolerance in wheat. The studied characters were plant height and grain yield and its components in addition to relative water content, contents of chlorophyll a and b, proline and malondialdehyde and catalase activity in the flag leaves. The two seasons and saline conditions behaved differently. Besides, sufficient genetic variability among the studied genotypes was detected. Moreover, the variance due to saline conditions was the most important comparing to the other sources. Most of studied characters were higher in their mean values in the second season than the first one. All mean values of the studied characters decreased under the saline conditions, except for proline and malondialdehyde contents and catalase activity. Genotypic main effect plus genotype by environment interaction (GGE) Biplot analysis revealed that Line 2, Sakha 95, Misr 3 and Sids 14 had high yielding ability and relative tolerance under salinity conditions. Based on correlation coefficients, the high values of relative water content, chlorophyll, proline contents and catalase activity in addition to the low value of malondialdehyde content may be used as physiological selection criteria for salt tolerance screening of wheat genotypes
Multiscale CNN Based on Component Analysis for SAR ATR

This article proposes a multiscale convolutional neural network (CNN) based on component analysis (CA-MCNN) for synthetic aperture radar (SAR) automatic target recognition (ATR). The component information of a target is robust to the local variations of the target, which is not made the best of by traditional CNN-based methods. For learning the component information, we use the attributed scattering centers (ASCs) extracted from the target echoes as the components of the target for SAR ATR, which divides the SAR target according to the geometric scattering types of ASCs and can not only make the division results more robust but also accurately characterize the electromagnetic scattering characteristics of the target. Since the global information provided by the whole image is also important for SAR ATR, CA-MCNN combines the global information with component information to learn a more efficient and robust target feature representation. In addition, considering that the feature maps of the shallower layer in CNN focus on local and fine-grained information while the feature maps in the deeper layer focus on global and coarse-grained information, we fuse the multiscale feature maps obtained from different layers to enhance the feature description ability. Extensive experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) data set prove the superior performance of CA-MCNN.
Seamless Transition Strategy for Micro-grid Inverter based on Improved Droop Control Strategy

Microgrid is a new way to solve current energy problems, such as environmental pollution and resource shortages, for using renewable energy effectively to improve power supply reliability and flexibility. Microgrid inverter has two working modes, on-grid mode and off-grid mode, and seamless transition between the two modes is the key to ensure continuous uninterrupted power supply. The focus of this paper is mainly to achieve the smooth transition from island mode to grid connected mode, to limit the abrupt and transient changes. To achieve this objective, an improved droop control method has been suggested. Voltage-control method based on droop control is used during both on-grid mode and off-grid mode to avoid the forced transition in traditional control strategies. Simulation results are presented using PLECS to validate the seamless transition between the two modes
An EPSO_CNN Approach to Predict the Soil Texture Properties in Smart Farming

Prediction of soil is one the important factors in smart farming. The use of near infrared spectroscopy that has exclusive benefit in the forecast of soil moisture content is an appropriate and operative method. Convolutional Neural Network (CNN) is a kind of deep learning model with extraordinary performance. Effective attributes can be mined from the complex spectral data and the internal organisation of attributes can be learned with the help of CNN. When a comparison has been made with conventional models, CNN has influential modelling capability since it has objective of attaining better outcomes in soil prediction for hyperspectral data. CNN is proficient to comprehend the arrangement of hyperspectral data by spatial interpolation. The concept of CNN was utilized to forecast the soil humidity content by near infrared spectroscopy. LUCAS dataset have been adopted in this which have nearly 84 attributes. In order to enhance the results of CNN, EPSO-CNN is proposed. The projected PSO variant is used to optimize the learning hyper-parameters of CNNs to overcome performance barriers. The empirical results have showed that the proposed EPSO CNN achieves better results in terms of precision, recall and accuracy and that too in minimum time.
НОВI ПIДХОДИ ПУБЛIЧНОГО AДМIНIСТРУВAННЯ СФЕРИ РОЗШИРЕНИХ СIМЕЙНИХ ВIДНОСИН: РЕГIОНAЛЬНИЙ AСПЕКТ

Проaнaлiзовано специфiку держaвної полiтики регiонaльного розвитку зон впливу регiонaльних систем. Визнaчено основнi пpоблеми, що склaлися в регiонaх у сферi реaлiзaцiї держaвної сiмейної полiтики. Виявлено, що сучaснi глобaльнi виклики i тенденцiї рaзом iз зaзнaченими проблемaми регiонaльного розвитку Укрaїни зумовлюють необхiднiсть розроблення нової держaвної регiонaльної полiтики, покликaної подолaти зaгрози, що iснують, і створити новi можливостi для розвитку регiонiв, якiсної реaлiзaцiї сiмейної полiтики.
Analisis Wacana Free West Papua Campaign pada Operasi Militer Indonesia dalam Konflik Nduga: Sebuah Tinjauan Kritis

Mainstream narratives on Indonesia's military operation in West Papua describe it to help the conflicting region achieve peace and prosperity. This hegemonic and widely held public opinion is more often used to justify the behavior of the Indonesian Government in West Papua. This research aims to offer an alternative perspective on the matter by looking at the narrative promoted and written by Free West Papua Campaign (FWPC) on their news reports about the Nduga Conflict in 2018-2019 from their website: freewestpapua.org. Norman Fairclough's critical discourse analysis examines the discourse created from the news coverage within the three-dimensional analysis, namely the textual, the discourse practices, and sociocultural dimensions. Results show that FWPC depicts Indonesia's military as exploiters of the indigenous Papuans. At the same time, FWPC also tries to build a sense of belonging to gain support from the international society and online activists.
Photoluminescence for Defect Detection on Full-Sized Photovoltaic Modules

Cost-effective, fast, and nondestructive on-site characterization of photovoltaic plants is required to determine countermeasures against power loss, defects, or safety problems. Methods with small impact on the operation and a high throughput, such as infrared thermography (IR), or methods with high resolution for detailed defect information, such as electroluminescence (EL) imaging, are expedient. To combine high resolution and high throughput, we propose to use photoluminescence (PL) as an outdoor characterization method for full-sized module imaging. With PL imaging as with IR imaging, no electrical contact is necessary, yet image resolution is on par with EL images. Our outdoor PL setup features an excitation source with 18 broadband, white, high power, chip on board LEDs coupled with low-cost short pass filters. This setup is suitable for indium gallium arsenide and silicon detectors. Here, we compare the visibility of common defects, including short-circuited bypass diodes, cracks, potential induced degradation, snail trails, ethylene-vinyl acetate degradation, and interconnection failures, in PL images to that in state-of-the-art imaging techniques IR, EL, and ultraviolet fluorescence. We find that out of these seven defects, five can be detected well, cell cracks under certain conditions, and interconnection failures not at all. We discuss how different techniques are complementary to enable better defect detection.
Copyright Throughout a Creative AI Pipeline

Consider the following fact pattern: Alex paints some original works on canvas and posts photos of them online. Becca downloads those images and uses them to train an AI (training configures the AI’s model parameters to useful values). Becca posts the resulting trained parameter values on her website under a license that reserves to Becca the right to use the parameters commercially. Cory uses those parameter values in a program that is designed to produce artwork. Cory clicks create and the program produces a work. This work is new to Cory, but it looks a lot like one of Alex’s original canvas images. Cory sells the work. Advise Cory about their potential copyright liability to Alex (for the substantially similar work that the program produced and that Cory subsequently sold) and to Becca (for taking Becca’s parameters and using them commercially, contrary to the license). Cory clicks create again. The program produces another work, this time quite different from any of Alex’s original paintings. Cory shares new work on Instagram. Danny copies this image from Cory’s Instagram feed and sells a bunch of postcards that feature that image. Advise Danny about their copyright liability to Cory. These scenarios are not as contrived as they might initially seem. Copyrighted works are frequently required to train an AI (more precisely: to train an AI’s parameters). The resulting trained parameters are being shared under licenses that assume that copyright applies. People do use these parameters in programs that can produce novel content. The resulting work can be quite surprising to the end-user and there are generally no checks in place to ensure that the new works don’t take too directly from the original training data. However, many of the new works will be quite different from any content already in the world. And the end-users of the creative program often claim copyright ownership over the resulting novel work. I will first present the training and use of a creative program based on a neural network, a popular model that forms the basis of state-of-the-art creative AIs. Then, I will examine each of the issues just raised: 1. Does the person managing the automatic training of a neural network’s parameters obtain a copyright in the resulting trained parameters? 2. Does a person using a program that produces artistic output obtain a copyright in that output? 3. The automatic training of a neural network requires large amounts of example data (a training set). Can images from around the internet be copied for the purpose of training a neural network? 4. What if a person uses an AI to produce a work that looks substantially similar to one of the training examples? Is that an infringement? And who is infringing?
Comparison of sulphonamides decomposition efficiency in ozonation and enzymatic oxidation processes

: Sulphonamides (SAs) are one of the most frequently detected anthropogenic micropollutants in the aquatic environment and their presence in it may pose a threat to living organisms. The aim of the study was to determine susceptibility of selected sulphonamides, i.e. sulfadiazine (SDZ) and sulfamethazine (SMZ), to degradation in the ozonation process and in enzymatic oxidation by unspecific peroxygenase extracted from Agrocybe aegerita mushroom ( Aae UPO). Moreover, the acute toxicity of the aqueous solution of the selected sulphonamides (SMZ and SDZ) before and after mentioned treatment processes were studied on the freshwater crustacean Daphnia magna. Initial concentrations were equal to 2×10 -5 M for sulfadiazine and 1.8×10 -5 M for sulfamethazine. The percentage of transformation for the O 3 process was at the level 95% for both SDZ and SMZ (after 10 s of the process), whilst enzymatic oxidation of SDZ and SMZ by Aae UPO caused transformation efficiencies at the levels of 97% and 94% (after 1 minute of the process), respectively. The second order rate constants of selected sulfonamides with molecular ozone and fungal peroxidase were also determined in the research. EC 50 (median effective concentration) values from toxicity test on D. magna were found in the range from 14.6% to 37.2%, depending on the type of the process. The conducted oxidation processes were efficient in degradation of selected sulphonamides. The toxicity of the mixtures before and after treatment was comparable and did not change significantly. The research have shown that biological processes are not always safer for living organisms compared to the chemical processes.
Antibacterial Properties of Silver Nanoparticle (AgNPs) on Stainless Steel 316L

Objective(s) The probability of contracting an infection when implanting an Stainless Steel 316L (SS316L) implant has been increasing. Infection due to implant placement is called osteomyelitis which is bone inflammation caused by biofilms formed by pyogenic bacteria. Biofilms can be prevented by giving antibacterial agents. This study aims to examine the potential of silver nanoparticles (AgNPs) as an antibacterial agent in SS316L implants. Methods AgNPs are made through a chemical synthesis process using the Gallic acid reduction method. AgNPs solution with 5 variations of precursor concentration, namely 0.1 mM, 1 mM, 10 mM, 100 mM each added with gelatin was sprayed on SS316L by the airbrush spray coating method with a distance between nozzle and substrate of 20 cm at a pressure of 40 psi. Result AgNPs solutions produced from various concentrations of AgNO3 precursors have a range of λmax = 401.5 nm- 424.5 nm and a particle size distribution of 0.97 - 4.88 nm. The AgNPs layer on SS316L was characterized by its crystalline phase, crystal size, and anti-bacterial activity. It has a cubic structure with a phase fraction of 6.5-19%. Based on the antibacterial activity test, all AgNPs layer samples had inhibitory zone diameters in the range of 12-16 mm. AgNPs (10mM) + Gelatin layer showed the best antibacterial ability with an inhibitory zone diameter of 16.63 mm.Conclusion The variation in the concentration of the 10 mM AgNPs precursor-Gelatin can be developed into a coating on the surface of the SS316L implant material.
Gene flow and population structure in Allochrusa (caryophylloideae, caryophyllaceae) with the use of molecular markers

Allochrusa Bunge is a genus of subfamily Caryophylloideae Rabeler & Bittrich contains about 8 species distributed in Turkey, Central Asia, Afghanistan, Caucasus, Transcaucasus and Iran. Three species of Allochrusa versicolor, A. bungei and A. persica occur in Iran show some degree of morphological overlaps that make the species delimitation difficult. Till present time, there has been no detailed information available on morphological and genetic structure of these species in the country. The aims of the present study are: 1) to find the diagnostic value of SCoT markers in delimitation of Allochrusa species, 2) to find the genetic structure of these taxa in Iran, and 3) to investigate the species inter-relationship. For this study, 97 randomly collected plants from 7 geographical populations in three Allochrusa species were used. We encountered extensive within species genetic and morphological diversity. SCoT molecular markers could delimit the studied species. AMOVA and STRUCTURE analysis revealed that the species of Allochrusa are genetically differentiated. The Mantel test showed correlation between genetic distance and geographical distance of the populations studied. Genetic affinity of the studied species has been discussed.