We report measurements of anisotropic triple-$q$ charge density wave (CDW) fluctuations in the transition metal dichalcogenide 1$T$-TiSe$_2$ over a large volume of reciprocal space with X-ray diffuse scattering. Above the transition temperature, $T_{\text{CDW}}$, the out-of-plane diffuse scattering is characterized by rod-like structures which indicate that the CDW fluctuations in neighboring layers are largely decoupled. In addition, the in-plane diffuse scattering is marked by ellipses which reveal that the in-plane fluctuations are anisotropic. Our analysis of the diffuse scattering line shapes and orientations suggests that the three charge density wave components contain independent phase fluctuations. At $T_{\text{CDW}}$, long range coherence is established in both the in-plane and out-of-plane directions, consistent with the large observed value of the CDW gap compared to $T_{\text{CDW}}$, and the predicted presence of a hierarchy of energy scales.
Despite significant progress of deep neural networks in image classification, it has been reported that CNNs trained on ImageNet have heavily focused on local texture information, rather than capturing complex visual concepts of the objects. To delve into this phenomenon, recent studies proposed to generate images with modified texture information for training the model. However, these methods largely sacrifice the classification accuracy on the in-domain dataset while achieving improved performance on the out-of-distribution dataset. Motivated by the fact that human tends to focus on shape information, we aim to resolve this issue by proposing a shape-focused augmentation where the texture in the object's foreground and background are separately changed. Key idea is that by applying different modifications to the inside and outside of an object, not only the bias toward texture is reduced but also the model is induced to focus on shape. Experiments show that the proposed method successfully reduces texture bias and also improves the classification performance on the original dataset.
Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labels are strongly correlated with undesirable features. To prevent a network from learning such features, recent methods augment training data such that examples displaying spurious correlations (i.e., bias-aligned examples) become a minority, whereas the other, bias-conflicting examples become prevalent. However, these approaches are sometimes difficult to train and scale to real-world data because they rely on generative models or disentangled representations. We propose an alternative based on mixup, a popular augmentation that creates convex combinations of training examples. Our method, coined SelecMix, applies mixup to contradicting pairs of examples, defined as showing either (i) the same label but dissimilar biased features, or (ii) different labels but similar biased features. Identifying such pairs requires comparing examples with respect to unknown biased features. For this, we utilize an auxiliary contrastive model with the popular heuristic that biased features are learned preferentially during training. Experiments on standard benchmarks demonstrate the effectiveness of the method, in particular when label noise complicates the identification of bias-conflicting examples.
SUMMARY Jeju Island offshore of the southern Korean Peninsula is an isolated intraplate volcano formed by multiple basaltic eruptions from the Pleistocene (∼1.8 Ma) to the Holocene (∼3.7 ka). Due to the lack of available seismic data, magma structures at upper crustal depths of the island have not been clearly revealed. In this study, we imaged upper crustal isotropic and radial anisotropic structures beneath the island using ambient noise data from a temporary seismic network. A series of transdimensional hierarchical Bayesian inversions were performed to construct upper crustal (1–10 km) isotropic and anisotropic structures. Surface wave (Rayleigh and Love wave) group and phase velocity dispersion data were jointly inverted for 2–15 s. The results show that layers of negative anisotropy (VSH < VSV) are predominant at shallower (<2 km) and deeper (>5 km) depths, which was interpreted as reflecting dyke swarms responsible for the more than 400 cinder cones at the surface and the vertical plumbing systems supplying magma from deeper sources, respectively. Additionally, a layer with significantly positive radial anisotropy (VSH > VSV, up to 5 per cent) was found at middle depths (2–5 km), and was interpreted as horizontally aligned magma plumbing systems (e.g. sills) through comparisons with several other volcanoes worldwide. In comparison with the isotropic structure, the positive anisotropic layer was separated into upper and lower layers with locally neutral to slightly fast and slower shear wave velocities, respectively, beneath the largest central crater (Mt Halla). Such a structure indicates that the cooled upper part of the magma plumbing systems formed within the horizontally developed sill complex, and is underlain by still-warm sill structures, potentially with a small fraction of melting. With dykes predominant above and below, the island-wide sill layer and locally high-temperature body at the centre explain the evolution of the Jeju Island volcanoes by island-forming surface lava flows and central volcanic eruptions before and after the eruptions of cinder cones.
Entanglement-assisted quantum error correcting codes have the nice feature that they can be constructed from classical additive codes that do not need to be self-orthogonal - an advantage over stabilizer codes. In the literature, these codes were investigated for finite fields, mostly binary fields. A generalization of the Pauli basis to nice error bases indexed by rings allows one to consider alphabet sizes that are not restricted to powers of a prime. The main goal of this paper is to show how entanglement-assisted quantum error correcting codes over nice rings can be constructed. We develop the rudiments of symplectic geometry over rings and prove that an R-module with antisymmetric bicharacter can be decomposed as an orthogonal direct sum of hyperbolic pairs.
<p>Surface wave dispersion is one of the essential means to study the structure of Earth&#8217;s crust and upper mantle. However, complicated structures of the Earth&#8217;s surface cause multipathing effects that make accurate phase measurement difficult. In this study, teleseismic Rayleigh-wave tomography was performed to estimate the crustal velocity structure of the southern part of the Korean Peninsula using phase differences between station pairs in a dense array instead of absolute phase arrival times and amplitudes. In order to estimate the phase change across the seismic array, the phase delay time between the nearby stations is inverted using the Eikonal equation. The structural velocity that is more consistent with the actual substructure is derived from adding the amplitude term to the inversion process by the Helmholtz equation. The earthquake data of 219 teleseismic events recorded at 146 broadband stations in the Korean Peninsula and Tsushima Island from 2018 to 2020 were used to build up Rayleigh wave phase velocity maps between the periods of 10 to 100 seconds. The teleseismic events were chosen with a magnitude of 6.0 or larger, a hypocenter depth shallower than 50 km, and an epicentral distance between 5&#730; and 115&#730;. The final phase velocity maps for each period were obtained by stacking the results of the tomographic inversions of each event. Bayesian 1-D joint inversion using the phase velocity data of this study, those from ambient noise tomography and global velocity models is performed to obtain 3-D shear wave velocity models of the study region. The derived shear wave velocity maps show the clear boundary of velocity anomalies at short periods (&#8804; 20 s), which is parallel with the major geological structures, such as the Gyeongsang Basin and the Okcheon fold belt. At longer periods, we observe a significant velocity contrast between the southwestern and the northeastern region of the Korean Peninsula.</p>
The effects of porous wind fence on the pressure characteristics around a 2-dimensional prism model of triangular cross-section were investigated experimentally. The fence and prism model were embedded in a neutral atmospheric surface boundary layer over the city suburb. In this study, various fences of different porosity, back fence, inclination angle of prism and location of additional back prisms were tested to investigate their effects on the pressure and wall shear stress of the prism surface. The fence and prism had the same height of 40 mm and Reynolds number based on the model height was Re=3.9*10. The porous fence with porosity 40% was found to be the best wind fence for decreasing the mean and pressure fluctuations on the prism surface. By installing the fence of porosity 40%, the wall shear stress on the windward surface of prism was largely decreased up to 1/3 of that without the fence. This indicates that the porous fence is most effective to abate the wind erosion. Pressure fluctuations on the model surface were decreased more than half when a back fence was located behind the prism in addition to the front fence. With locating several back prisms and decreasing the inclination angle of triangular prism, the pressure fluctuations on the model surface were increased on the contrary.
Various services are developed from advancement of satellite imagery methodologies and internet infrastructure expansions. However, most of these services still rely upon low-resolution satellite images combined with DEM models. In this paper, we have implemented the raw data processing modules and other modules that transfer and render high-spatial resolution satellite images for efficient streaming services in web environments. By utilizing the Bukhan-mountain data as a pilot study, the paper has proposed the efficient approach to solve graphical problems in real time processing the large geographical area.
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