Tree Sampling for Detection of Information Source in Densely Connected Networks

We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O(n2logn, where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks

Propagation Control of Octahedral Tilt in SrRuO(3)via Artificial Heterostructuring

Bonding geometry engineering of metal-oxygen octahedra is a facile way of tailoring various functional properties of transition metal oxides. Several approaches, including epitaxial strain, thickness, and stoichiometry control, have been proposed to efficiently tune the rotation and tilt of the octahedra, but these approaches are inevitably accompanied by unnecessary structural modifications such as changes in thin-film lattice parameters. In this study, a method to selectively engineer the octahedral bonding geometries is proposed, while maintaining other parameters that might implicitly influence the functional properties. A concept of octahedral tilt propagation engineering is developed using atomically designed SrRuO3/SrTiO3(SRO/STO) superlattices. In particular, the propagation of RuO(6)octahedral tilt within the SRO layers having identical thicknesses is systematically controlled by varying the thickness of adjacent STO layers. This leads to a substantial modification in the electromagnetic properties of the SRO layer, significantly enhancing the magnetic moment of Ru. This approach provides a method to selectively manipulate the bonding geometry of strongly correlated oxides, thereby enabling a better understanding and greater controllability of their functional properties

Synergistic interaction of high blood pressure and cerebral beta-amyloid on tau pathology

Background Hypertension has been associated with Alzheimer’s disease (AD) dementia as well as vascular dementia. However, the underlying neuropathological changes that link hypertension to AD remain poorly understood. In our study, we examined the relationships of a history of hypertension and high current blood pressure (BP) with in vivo AD pathologies including β-amyloid (Aβ) and tau and also investigated whether a history of hypertension and current BP respectively affect the association between Aβ and tau deposition. Methods This cross-sectional study was conducted as part of the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease, a prospective cohort study. Cognitively normal older adults who underwent both Aβ and tau positron emission tomography (PET) (i.e., [11C]-Pittsburgh compound B and [18F] AV-1451 PET) were selected. History of hypertension and current BP were evaluated and cerebral Aβ and tau deposition measured by PET were used as main outcomes. Generalized linear regression models were used to estimate associations. Results A total of 68 cognitively normal older adults (mean [SD] age, 71.5 [7.4] years; 40 women [59%]) were included in the study. Neither a history of hypertension nor the current BP exhibited a direct association with Aβ or tau deposition. However, the synergistic interaction effects of high current systolic (β, 0.359; SE, 0.141; p = 0.014) and diastolic (β, 0.696; SE, 0.158; p < 0.001) BP state with Aβ deposition on tau deposition were significant, whereas there was no such effect for a history of hypertension (β, 0.186; SE, 0.152; p = 0.224). Conclusions The findings suggest that high current BP, but not a history of hypertension, synergistically modulate the relationship between cerebral Aβ and tau deposition in late-life. In terms of AD prevention, the results support the importance of strict BP control in cognitively normal older adults with hypertension.This study was supported by a grant from the Ministry of Science and ICT, Republic of Korea (grant No: NRF‑2014M3C7A1046042), a grant from the Ministry of Health & Welfare, Republic of Korea (HI18C0630 & HI19C0149), a grant from the Seoul National University Hospital, Republic of Korea (No. 3020200030), and a grant from the National Institute on Aging, USA (U01AG072177). The funding sources played no role in the study design, data collection, data analysis, data interpretation, writing of the manuscript, or decision to submit it for publication

Cooperative evolution of polar distortion and nonpolar rotation of oxygen octahedra in oxide heterostructures

Polarity discontinuity across LaAlO3/SrTiO3 (LAO/STO) heterostructures induces electronic reconstruction involving the formation of two-dimensional electron gas (2DEG) and structural distortions characterized by antiferrodistortive (AFD) rotation and ferroelectric (FE) distortion. We show that AFD and FE modes are cooperatively coupled in LAO/STO (111) heterostructures; they coexist below the critical thickness (t(c)) and disappear simultaneously above tc with the formation of 2DEG. Electron energy-loss spectroscopy and density functional theory (DFT) calculations provide direct evidence of oxygen vacancy (VO) formation at the LAO (111) surface, which acts as the source of 2DEG. Tracing the AFD rotation and FE distortion of LAO reveals that their evolution is strongly correlated with VO distribution. The present study demonstrates that AFD and FE modes in oxide heterostructures emerge as a consequence of interplay between misfit strain and polar field, and further that their combination can be tuned to competitive or cooperative coupling by changing the interface orientation

The Method in Madness: A Processual Account of a South Korean Conspiracy Theory “Community”

Why did the South Korean conspiracy theory on the 2020 general election fail to garner political influence like QAnon and anti-vaccine conspiracy theories? This paper argues that answering this question requires a processual approach to social phenomena. Appreciating a conspiracy theory “community” as a gathering of multiple entities constantly recreated in relation to others is crucial for explaining the failure of the Wuhan Gallery, a South Korean “community” that gave birth to the conspiracy theory. Even though the claims of election fraud gained traction after the landslide loss, other South Korean online “communities” criticized the conspiracy theory and engaged with the Wuhan Gallery with mockery involving rhetoric against the elderly. Wuhan Gallery responded by banning users that seemingly came from other “communities” as trolls, which hindered different readings of the conspiracy theory and its relationship with politicians. Critics of conservative YouTubers and politicians were stigmatized as trolls, making the conspiracy theory on the election fraud no different from failed conspiracy theories of the past. The result of the research implies that the conspiracy theory “community,” as well as discourse on conspiracy theory, was constantly shifting over time and that social factors play a crucial role in the rise or fall of a conspiracy theory. Furthermore, the trajectory of the conspiracy theory “community” demonstrates how users in South Korean cyberspace constantly misrecognize what is taking place by neglecting the processual character of the internet and social entities and underlines the importance of a processual approach in social sciences

Tree Sampling for Detection of Information Source in Densely Connected Networks

We investigate the problem of source detection in information spreading throughout a densely-connected network. Previous works have been developed mostly for tree networks or applied the tree-network results to non-tree networks assuming that the infection occurs in the breadth first manner. However, these approaches result in low detection performance in densely-connected networks, since there is a substantial number of nodes that are infected through the non-shortest path. In this work, we take a two-step approach to the source detection problem in densely-connected networks. By introducing the concept of detour nodes, we first sample trees that the infection process likely follows and effectively compare the probability of the sampled trees. Our solution has low complexity of O ( n 2 log n ) , where n denotes the number of infected nodes, and thus can be applied to large-scale networks. Through extensive simulations including practical networks of the Internet autonomous system and power grid, we evaluate our solution in comparison with two well-known previous schemes and show that it achieves the best performance in densely-connected networks

자기조립 단분자막 도입을 통한 PbS – IGZO 광 센서 반응 속도의 개선

2

Defect Generation in Data-Storage Layer by Strong Ion Bombardment for Multi-level Non-volatile Memory Applications

11Nsciescopu

Growth Analysis of Plant Factory-Grown Lettuce by Deep Neural Networks Based on Automated Feature Extraction

The mechanisms of lettuce growth in plant factories under artificial light (PFALs) are well known, whereby the crop is generally used as a model in horticultural science. Deep learning has also been tested several times using PFALs. Despite their numerous advantages, the performance of deep learning models is commonly evaluated based only on their accuracy. Therefore, the objective of this study was to train deep neural networks and analyze the deeper abstraction of the trained models. In total, 443 images of three lettuce cultivars were used for model training, and several deep learning algorithms were compared using multivariate linear regression. Except for linear regression, all models showed adequate accuracies for the given task, and the convolutional neural network (ConvNet) model showed the highest accuracy. Based on color mapping and the distribution of the two-dimensional t-distributed stochastic neighbor embedding (t-SNE) results, ConvNet effectively perceived the differences among the lettuce cultivars under analysis. The extension of the target domain knowledge with complex models and sufficient data, similar to ConvNet with multitask learning, is possible. Therefore, deep learning algorithms should be investigated from the perspective of feature extraction