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The cubic perovsike strontium titanate SrTiO$_3$ (STO) is one of the most studied, polarizable transition metal oxides. When excess charge is introduced to this material, e.g., through doping or atomic defects, STO tends to host polarons: Quasi-particles formed by excess charge carriers coupling with the crystal phonon field. Their presence alters the materials properties, and is a key for many applications. Considering that polarons form preferentially on or near surfaces, we study small polaron formation at the TiO$_2$ termination of the STO(001) surface via density functional theory calculations. We model several supercell slabs of Nb-doped and undoped STO(001) surfaces with increasing size, also considering the recently observed as-cleaved TiO$_2$ terminated surface hosting Sr-adatoms. Our findings suggest that small polarons become less stable at low concentrations of Nb-doping, in analogy with polarons localized in the bulk. Further, we inspect the stability of different polaron configurations with respect to Nb- and Sr-impurities, and discuss their spectroscopic properties., Comment: 8 pages, 7 figures, 1 table

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public-private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

A new approach to seismic interpretation is proposed to leverage visual perception and human visual system modeling. Specifically, a saliency detection algorithm based on a novel attention model is proposed for identifying subsurface structures within seismic data volumes. The algorithm employs 3D-FFT and a multi-dimensional spectral projection, which decomposes local spectra into three distinct components, each depicting variations along different dimensions of the data. Subsequently, a novel directional center-surround attention model is proposed to incorporate directional comparisons around each voxel for saliency detection within each projected dimension. Next, the resulting saliency maps along each dimension are combined adaptively to yield a consolidated saliency map, which highlights various structures characterized by subtle variations and relative motion with respect to their neighboring sections. A priori information about the seismic data can be either embedded into the proposed attention model in the directional comparisons, or incorporated into the algorithm by specifying a template when combining saliency maps adaptively. Experimental results on two real seismic datasets from the North Sea, Netherlands and Great South Basin, New Zealand demonstrate the effectiveness of the proposed algorithm for detecting salient seismic structures of different natures and appearances in one shot, which differs significantly from traditional seismic interpretation algorithms. The results further demonstrate that the proposed method outperforms comparable state-of-the-art saliency detection algorithms for natural images and videos, which are inadequate for seismic imaging data., Comment: Published in Applied Computing and Intelligence, Nov. 2021

In chemical graph theory, a topological index is a numerical representation of a chemical network while a topological descriptor correlates certain physico-chemical characteristics of underlying chemical compounds besides its chemical representation. Graph plays an vital role in modeling and designing any chemical network. F. Simonraj et al. derived new third type of Hex derived networks [27]. In our work, we discuss the third type of hex derived networks HDN3(r), THDN3(r) and RHDN3(r) and computed exact results for topological indices which are based on degrees of end vertices.

In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map. To accommodate the directional nature of seismic data, in this work, we modify the center-surround model, proven to be biologically plausible for visual attention, to incorporate directional comparisons around each voxel in a 3D volume. Experimental results on real seismic dataset from the F3 block in Netherlands offshore in the North Sea prove that the proposed algorithm is effective, efficient, and scalable. Furthermore, a subjective comparison of the results shows that it outperforms the state-of-the-art methods for saliency detection.

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human vision system (HVS). In this work, we aim to highlight the parts of the seismic volume that receive highest attention from the human interpreter, and based on the salient features of a seismic image, we detect the salt domes. Experimental results show the effectiveness of SalSi on the real seismic dataset acquired from the North Sea, F3 block. Subjectively, we have used the ground truth and the output of different salt dome delineation algorithms to validate the results of SalSi. For the objective evaluation of results, we have used the receiver operating characteristics (ROC) curves and area under the curves (AUC) to demonstrate SalSi is a promising and an effective attribute for seismic interpretation., Comment: Proceedings of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, Mar. 2016. arXiv admin note: text overlap with arXiv:1812.11960

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation.

It is well known that the form of grain size distribution strongly influences the linear dielectric response of a dusty plasma. In previous results [IEEE Trans. Plasma Sci. 29, 182 (2001)], it was shown that for a class of size distributions, there is an equivalence to a Lorentzian distribution of mono-sized particles. The electrostatic response to a slowly moving test charge, using a second order approximation can then be found [Phys. Lett. A 305, 79 (2002)]. It is also well known [Phys. Plasmas 10, 3484 (2003)] that the dynamical charging of grains in a dusty plasma enhances the shielding of a test charge. It seems natural at this stage to seek the combined effects of grain size distribution and grain charging dynamics to a test charge moving through the dusty plasma. Here we consider the effects of both grain size distribution and dynamical grain charging to a test charge moving slowly in a dusty plasma by expressing the plasma dielectric response as a function of both grain size distribution and grain charging dynamics. Both analytical as well as the numerical results are presented. It is interesting to note that the previous results can be retrieved by choosing appropriate values for different parameters. This kind of study is relevant for both laboratory and space plasmas.

We present RPWS Langmuir probe data from the third Enceladus flyby (E3) showing (he presence of dusty plasma near Enceladus' South Pole. There is a sharp rise in both the electron and ion number densities when the spacecraft traverses through Enceladus plume. The ion density near Enceladus is found to increase abruptly from about 10(exp 2) cm (exp -3) before the closest approach to 10(exp 5) cm (exp -3) just 30 s after the closest approach, an amount two orders of magnitude higher than the electron density. Assuming that the inconsistency between the electron and ion number densities is due to the presence of dust particles that are collecting the missing electron charges, we present dusty plasma characteristics down to sub-micron particle sizes. By assuming a differential dust number density for a range in dust sizes and by making use of Langmuir probe data, the dust densities for certain lower limits in dust size distribution were estimated. In order to achieve the dust densities of micrometer and larger sized grains comparable to the ones reported in the literature. we show that the power law size distribution must hold down to at least 0.03 micron such that the total differential number density is dominated by the smallest sub-micron sized grains. The total dust number density in Enceladus' plume is of the order of l0(exp 2) cm(exp -3) reducing to 1 cm(exp -3) in the E- ring. The dust density for micrometer and larger sized grains is estimated to be about 10(exp -4) cm(exp -3) in the plume while it is about 10(exp -6) - 10(exp -7) cm(exp -3) in the E-ring. Dust charge for micron sized grains is estimated to be about eight thousand electron charges reducing to below one hundred electron charges for 0.03 micron sized grains. The effective dusty plasma Debye length is estimated and compared with intergrain distance as well as the electron Debye length. The maximum dust charging time of 1.4 h is found for 0.03 11mmicron sized grains just 1 min before the closest approach. The charging time decreases substantially in the plume where it is only a fraction of a second for 1 micron sized grains, 1 s for 0.l micron sized grains and about 10 s for 0.03 micron sized grains.