Miracle-dz/UniFAR-FTUs huggingface dataset:
Business Μodels of Βanks for the Financial Markets in the EU*

Purpose: The purpose of the article is to identify models of banks’ activity in the securities and derivatives markets, as well as to analyse changes in these models on the example of the largest banks in the EU. Design/Methodology/Approach: The proposed method uses cluster analysis of the main indicators for banking investment based on the agglomerative hierarchical clustering algorithm of Ward and the Tau Index as the criterion for evaluating the optimal number of clusters. The research covers 29 largest EU banks, spanning the period 2007–2018. Findings: Before the global financial crisis it was possible to clearly identify two clusters, one of which reflected the high engagement of a number of banks in the securities and derivatives market, then after the Great Recession, can be distinguished four main models: (1) a highly active investment strategy in the securities and derivatives markets; (2) an active investment strategy in the securities market; (3) active mix-investment strategy in the securities and derivatives markets; (4) moderate investment strategy in the securities and derivatives markets. Practical implications: The data analysis shows that significant modifications in the investment strategies of banks confirm to some extent the effectiveness and the further need of certain additional regulations to ensure the financial soundness of banking institutions at the EU level, as well as the effectiveness and the further need of the changes in the national legislation of individual EU countries concerning the banking proprietary trading in the securities and derivatives markets. Originality/Value: The paper has enabled to develop an understanding of the modification of the business models of largest EU banks in the financial instruments market.
Growth Response of Five Cultivars Stevia (Stevia rebaudiana Bertoni) Using Commercial Fertilizers as Basic Media In Vitro

Background: Conventional propagation of seedling stevia is limited by parent material. Propagation by seeds that are difficult due to low of germination rate. Propagation through tissue culture can be applied as an alternative for propagation stevia and can produce last of seedling and in short-time in suitable media. Generaly, culture media is quite expensive, so it is necessary to find an alternative types of culture media. This study aimed to evaluate the growth response of five stevia cultivars on culture media containing commercial fertilizers. Materials and Methods: A Randomized Block Design (RBD) was used in this experiment with two factors and replicated three times. The first factor consisted of five culture media DKW, MS, AB Mix, Grandasil, and Growmore. The second factor consisted of Stevia cultivars i.e. Kanada, Bogor, Garut, Tawangmangu, and Pengalengan. Results and Conclusion: The result showed that the best responses to the character number of shoots (28.9 shoots), shoot height (10.47 cm), and number of leaves (136.6 leaves) were obtained on DKW medium compared to MS, AB Mix, Grandasil D, and Growmore medium. The commercial fertilizer (AB Mix, Grandasil D, and Growmore) can be used as basic medium in vitro. All cultivar have same responses in number of shoots, shoot height, and number of leaves on all medium. Key Word: Stevia, Cultivars, Basic Medium, Commercial Fertilizers, In Vitro. --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 05-06-2021 Date of Acceptance: 19-06-2021 --------------------------------------------------------------------------------------------------------------------------------------
Hydrothermal synthesis of tetragonal barium titanate nanopowders under moderate conditions

Monodispersed tetragonal barium titanate (BaTiO3) nanopowders were synthesized by a convenient hydrothermal route at a low temperature of 200 °C in only 24 h. The key point of this method is to promote the generation of ultrafine titanium hydroxide precipitation precursors with the help of absolute ethyl alcohol and ammonia solution during the hydrolysis of Ti(OC4H9)4. The results of X-ray diffraction and Raman spectra show that the as-prepared BaTiO3 nanopowders possess tetragonal-dominant structure. The synthesized tetragonal BaTiO3 nanopowders exhibit relatively uniform size and good dispersity, with the average particle size of 96.1 nm and a tetragonality of 1.0073, which enable broad application prospects in the field of multilayer ceramic capacitors.
Energy Consumption and Packet Loss Analysis of LEACH Routing Protocol on WSN over DTN

: Wireless sensor network is a group of nodes that are connected to each other by wireless connection. These type of network work on the dynamic topology of the network because positions of nodes in the wireless network are changing continuously. The nodes in WSN are basically made up of small electronics device which are used for sensing, computing and transmitting the data. The nodes are run on the battery power during communication process. The battery consumption in WSN is very high due to high computation operations on it. In the recent years WSN grows at very high at the research area is also increased in this field to provide effective computation. By considering the network structure routing is categorized into two parts that are flat and hierarchical routing. In this proposed work cluster are made by WCA-GA (Grey Wolf optimization) on the basis of distance and energy parameters. The cluster head is also selected on the basis of WCA-GA and EXISTING-DTN in three different metrics. At the end the performance evaluation of the proposed work is compared with the existing approach Leach on the parameters of Throughput, Dead node, Alive nodes, energy.
Operation Allied Force: Unintended Consequences of the NATO Bombing on Children's Outcomes

This is the first paper that estimates the causal effect of the NATO’s Operation Allied Force in Serbia in 1999, on children who were in the womb during the bombing. We investigate the in utero effect in terms of short-term outcomes, such as birthweight, as well as medium-term outcomes measured by grades of 15-year-old pupils at the end of primary school. Using the birth records of the Serbian Statistical Office, we estimate difference-in-differences models, combined with propensity score matching. We compare the birthweight of children born in the same year (1999) and in the months just before and after the bombing, and children born in the same months of the previous year (1998). We then exploit the data on educational achievement at the end of primary school, provided by the Ministry of Education, to estimate matching models of the effect of the bombing on individual grades. Our findings suggest that children in utero during the bombing were 2pp more likely to be born with a lower than average birthweight. In the medium-term, we find a statistically significant and negative effect (around − 1%) of the bombing on maths grades and Serbian language at primary school, and a 1% increase in the probability to enrol on vocational secondary schools. Overall, our results confirm the importance of the negative effects on children in the aftermath of large-scale disasters, and the necessity of policy interventions to mitigate them.
A comparison of shear bond strength of brackets bonded to zirconia

Abstract Purpose: The objective of this study was to compare the shear bond strength (SBS) of ceramic orthodontic brackets bonded to highly translucent zirconia surfaces following the use of two different primers. Materials and methods:<Three types of highly translucent zirconia crowns, Cercon xt ML, e.max ZirCAD, and STML-ML Katana Zirconia, were milled, sandblasted, and primed using two zirconia primers, Monobond Etch and Prime and Z-Prime Plus. A ceramic bracket (Radiance) was bonded onto the facial surface of each crown. Shear bond strength was evaluated using a universal testing machine. Results: There was no significant overall difference across the six experimental groups regarding shear bond strength. The use of the two tested zirconia primers resulted in comparable and clinically acceptable shear bond strengths. Conclusions: Both tested zirconia primers were associated with adequate bond strength when bonding a ceramic orthodontic bracket to the three highly translucent zirconia substrates.
THE EFFECTS OF SOCIAL SUPPORT AND DISTRIBUTIVE JUSTICE TOWARDS WITHDRAWAL COGNITIONS THROUGH EMOTIONAL EXHAUSTION: EMPIRICAL STUDY OF FEMALE GARMENT WORKERS IN INDONESIA

High competition in the garment industry has resulted in increasing production demand for developing countries such as Indonesia, which can provide workers with low wages and flexible regulations, to reduce production costs. Indonesian garment workers, dominated by women workers, often face injustice in terms of promotion and task assignment, and lack of social support. These problems can be associated to emotional exhaustion and organizational withdrawal cognitions. This study aims to determine the effect of social support and distributive justice on withdrawal cognitions with emotional exhaustion as a mediation, by using Structural Equation Modeling (SEM) method. The research data was obtained through interviewing 491 female workers in Indonesian garment factories. The results showed that social support and distributive justice have an indirect effect on withdrawal cognitions through emotional exhaustion, but social support doesn’t have a direct effect on withdrawal cognitions. These results are discussed further in this study.
Detection and Discrimination of Natural and Synthetic Polysaccharides in a Solid-State Nanopore

In 2007-08 over 100 people died as a result of a contaminated batch of the polysaccharide heparin, an otherwise life-saving anticoagulant drug. After the contaminant was discovered, the development of assays that detect the contaminant, a structurally similar molecule, oversulfated chondroitin sulfate, became a necessity. Solid-state nanopores, which can, with appropriate experimental design, readily detect single molecules of analyte, may be able to help distinguish the two with greater ease than conventional assays, and with greater throughput even at concentrations well below that of USP assays. Polysaccharides, especially naturally occurring polysaccharides, have a vast range of structures characterized by widely varying molecular weights and charge distributions, and variability in linkage type. These polymers are challenging to analyze, and so studies using synthetic glycopolymers with known sizes and charge distributions, should be able to help one establish conditions to probe differences in molecular structure more easily. Under the right experimental conditions, solid-state nanopores were readily able to detect and distinguish between oversulfated chondroitin sulfate and heparin, and also synthetic glycopolymers of varying charge and length. This work may provide the necessary context to use nanopores for drug purity assays, to aid in understanding glycopolymer interactions, and also as a tool for characterizing polysaccharide structure and properties.
Developing a Novel Continuous Metabolic Syndrome Score: A Data Mining Based Model

Today, Metabolic Syndrome in the age group of children and adolescents has become a global concern. In this paper, a data mining model is used to determine a continuous Metabolic Syndrome (cMetS) score using Linear Discriminate Analysis (cMetS-LDA). The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, multilayer perceptron neural network (NN) and Support Vector Machine (SVM) models were used and statistical significance of the results was tested with Wilcoxon signed-rank test. According to the results of this test, the proposed CART is significantly better than the NN and SVM models. The ranking results in this study showed that the most important risk factors in making cMetS-LDA were WC, SBP, HDL and TG for males and WC, TG, HDL and SBP for females. Our research results show that high TG and central obesity have the greatest impact on MetS and FBS has no effect on the final prognosis. The results also indicate that in the preliminary stages of MetS, WC, HDL and SBP are the most important influencing factors that play an important role in forecasting.
Canny Edge Detection Algorithm Using DWT for PCB Laminates

: Edge is one of the most fundamental algorithms in digital image processing. The canny edge detector is the most implemented edge detection algorithm because of its ability to detect edges even in images that are intensely contaminated by noise. However, there is a time consuming algorithm and therefore its implementations are difficult to reach real time response speeds. Especially nowadays where the demand for high resolution image processing is constantly increasing, the need for fast and efficient edge detector implementation is ever so present. This paper proposes new methods to detect the edges based on one-dimensional and two-dimensional discrete wavelet transformations (DWTs). Recent literature shows that edge detection based on DWT requires less memory space compared to others. Within the contexts of all statistical parameters in detection of edges, DWT shows better results than that of others.
Music Generation using Recurrent Neural Network

: With the development of deep learning, neural networks are increasingly used in various art fields such as music, literature and pictures, and even comparable to humans. This paper proposes a music generation model based on bidirectional recurrent neural network, which can effectively explore the complex relationship between notes and obtain the conditional probability from time and pitch dimensions. The existing system usually ignored the information in the negative time direction, however which is non-trivial in the music prediction task, so we propose a bidirectional LSTM model to generate the note sequence. Experiments with classical piano datasets have demonstrated that we achieve high performance in music generation tasks compared to the existing unidirectional biaxial LSTM method
Anisotropic curvature measures and uniqueness of convex bodies

We prove that an arbitrary convex body C ⊆ R n +1 , whose k -th anisotropic curvature measure (for k = 0 , . . . , n − 1) is a multiple constant of the anisotropic perimeter of C , must be a rescaled and translated Wulﬀ shape. This result provides a generalization of a theorem of Schneider (1979) and resolves a conjecture of Andrews and Wei (2017).
High-order topological quantum optics in ultracold atomic metasurfaces

Ultracold atom arrays in optical lattices emerge as an excellent playground for the integration of topological photonics and quantum optics. Here, we study high-order topological quantum optics in an ultracold atom metasurface intended to mimic the two-dimensional Su-Schrieffer-Heeger model. We find the existence of long-range interactions beyond nearest-neighbor ones leads to isolated corner states in the band gap, and show a corner atom can be addressed by a laser drive far away from it via these nontrivial states. We demonstrate the Purcell factor can be used as a powerful tool to examine the existence of topological edge and corner states. We predict topological edge states can mediate strong coherent interactions between two remote impurity quantum emitters while suppressing dissipative losses thanks to the higher-order topology, generating robust and long-lived quantum entanglement, without the need for additional photonic structures.
(k, H)-Kernels in Nearly Tournaments

Abstract Let H be a digraph, possibly with loops, D a digraph without loops, and ρ : A(D) → V (H) a coloring of A(D) (D is said to be an H-colored digraph). If W = (x0, . . ., xn) is a walk in D, and i 2 {0, . , n − 1}, then we say that there is an obstruction on xi whenever (ρ(xi−1, xi), ρ(xi, xi+1)) ∉A(H) (when x0 = xn the indices are taken modulo n). We denote by OH(W ) the set {i ∈ {0, . , n − 1}: there is an obstruction on xi}. The H-length of W, denoted by lH(W), is defined by |OH(W)| if W is closed or |OH(W )| + 1 in the other case. A (k, H)-kernel of an H-colored digraph D (k ≥ 2) is a subset of vertices of D, say S, such that, for every pair of different vertices in S, every path between them has H-length at least k, and for every vertex x ∈ V (D) \ S there exists an xS-path with H-length at most k −1. This concept widely generalize previous nice concepts such as kernel, k-kernel, kernel by monochromatic paths, kernel by properly colored paths, and H-kernel. In this paper, we introduce the concept of (k, H)-kernel and we will study the existence of (k, H)-kernels in interesting classes of digraphs, called nearly tournaments, which have been large and widely studied due to its applications and theoretical results. We will show several conditions that guarantee the existence of a (k, H)-kernel in tournaments, r-transitive digraphs, r-quasi-transitive digraphs, multipartite tournaments, and local tournaments. As a consequence, previous results for k-kernels and kernels by alternating paths will be generalized, and some conditions for the existence of kernels by monochromatic paths and H-kernels in nearly tournaments will be shown.
On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM

We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context acquired from synthetic LiDAR intensity images. This approach allows a single multi-beam LiDAR to produce rich and highly descriptive location signatures. The method is tested on two public datasets, demonstrating an improved descriptiveness of the new descriptors, and more reliable loop closure detection in SLAM. Attention analysis of the network is used to show the importance of focusing on the broader context rather than only on the 3-D segment.
Variational Actor-Critic Algorithms

We introduce a class of variational actor-critic algorithms based on a variational formulation over both the value function and the policy. The objective function of the variational formulation consists of two parts: one for maximizing the value function and the other for minimizing the Bellman residual. Besides the vanilla gradient descent with both the value function and the policy updates, we propose two variants, the clipping method and the flipping method, in order to speed up the convergence. We also prove that, when the prefactor of the Bellman residual is sufficiently large, the fixed point of the algorithm is close to the optimal policy.
Product Lifecycle Management with the Asset Administration Shell

Product lifecycle management (PLM) as a holistic process encompasses the idea generation for a product, its conception, and its production, as well as its operating phase. Numerous tools and data models are used throughout this process. In recent years, industry and academia have developed integration concepts to realize efficient PLM across all domains and phases. However, the solutions available in practice need specific interfaces and tend to be vendor dependent. The Asset Administration Shell (AAS) aims to be a standardized digital representation of an asset (e.g., a product). In accordance with its objective, it has the potential to integrate all data generated during the PLM process into one data model and to provide a universally valid interface for all PLM phases. However, to date, there is no holistic concept that demonstrates this potential. The goal of this research work is to develop and validate such an AAS-based concept. This article demonstrates the application of the AAS in an order-controlled production process, including the semi-automatic generation of PLM-related AAS data. Furthermore, it discusses the potential of the AAS as a standard interface providing a smooth data integration throughout the PLM process.
Higman-Thompson groups from self-similar groupoid actions

Given a self-similar groupoid action pG,Eq on a finite directed graph, we prove some properties of the corresponding ample groupoid of germs GpG,Eq. We study the analogue of the Higman-Thompson group associated to pG,Eq using G-tables and relate it to the topological full group of GpG,Eq, which is isomorphic to a subgroup of unitaries in the algebra CpG,Eq. After recalling some concepts in groupoid homology, we discuss the Matui’s AH-conjecture for GpG,Eq in some particular cases.
An Empirical Study on the Usage of Transformer Models for Code Completion

Code completion aims at speeding up code writing by predicting the next code token(s) the developer is likely to write. Works in this field focused on improving the accuracy of the generated predictions, with substantial leaps forward made possible by deep learning (DL) models. However, code completion techniques are mostly evaluated in the scenario of predicting the next token to type, with few exceptions pushing the boundaries to the prediction of an entire code statement. Thus, little is known about the performance of state-of-the-art code completion approaches in more challenging scenarios in which, for example, an entire code block must be generated. We present a large-scale study exploring the capabilities of state-of-the-art Transformer-based models in supporting code completion at different granularity levels, including single tokens, one or multiple entire statements, up to entire code blocks (e.g., the iterated block of a <italic>for</italic> loop). We experimented with several variants of two recently proposed Transformer-based models, namely RoBERTa and the Text-To-Text Transfer Transformer (T5), for the task of code completion. The achieved results show that Transformer-based models, and in particular the T5, represent a viable solution for code completion, with perfect predictions ranging from <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="bavota-ieq1-3128234.gif"/></alternatives></inline-formula>29%, obtained when asking the model to guess entire blocks, up to <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="bavota-ieq2-3128234.gif"/></alternatives></inline-formula>69%, reached in the simpler scenario of few tokens masked from the same code statement.
Does a long-lived remnant neutron star exist after short gamma-ray burst GRB 160821B?

Mergers of double neutron stars (DNSs) could lead to the formation of a long-lived massive remnant NS, which has been previously suggested to explain the AT 2017gfo kilonova emission in the famous GW170817 event. For an NS-affected kilonova, it is expected that a non-thermal emission component can be contributed by a pulsar wind nebula (PWN), which results from the interaction of the wind from the remnant NS with the preceding merger ejecta. Then, the discovery of such a non-thermal PWN emission can provide an evidence for the existence of the remnant NS. Similar to GRB 170817A, GRB 160821B is also one of the nearest short gamma-ray bursts (SGRBs). A candidate kilonova is widely believed to appear in the ultraviolet-optical-infrared afterglows of GRB 160821B. Here, by modeling the afterglow light curves and spectra of GRB 160821B, we find that the invoking of a non-thermal PWN emission can indeed be well consistent with the observational data. This may indicate that the formation of a stable massive NS could be not rare in the DNS merger events and, thus, the equation of state of the post-merger NSs should be stiff enough.