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Accompanied by the development of digital media, the threat of information cocoon has become a significant issue. However, little is known about the measure of information cocoon as a cultural space and its relationship with social class. This study addresses this problem by constructing the cultural space with word embedding models and random shuffling methods among three large-scale digital media use datasets. In the light of field theory of cultural production, we investigate the information cocoon effect on different social classes among 979 computer users, 100,000 smartphone users, and 159,373 mobile reading application users. Our analysis reveals that information cocoons widely exist in the daily use of digital media. Moreover, people of lower social class have a higher probability of getting stuck in the information cocoon filled with the entertainment content. In contrast, the people of higher social class have more capability to stride over the constraints of the information cocoon. The results suggest that the disadvantages for vulnerable groups in acquiring knowledge may further widen social inequality.

It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high computational cost. This situation has changed drastically in recent years due to the advances in machine learning-based interatomic potentials. Here we implement the Deep Potential Generator scheme to \textit{automatically} generate interatomic potentials for LiGePS-type solid-state electrolyte materials. This increases our ability to simulate such materials by several orders of magnitude without sacrificing {\it ab initio} accuracy. Important technical aspects like the statistical error and size effects are carefully investigated. We further establish a reliable protocol for accurate computation of Li-ion diffusion processes at experimental conditions, by investigating important technical aspects like the statistical error and size effects. Such a protocol and the automated workflow allow us to screen materials for their relevant properties with much-improved efficiency. By using the protocol and automated workflow developed here, we obtain the diffusivity data and activation energies of Li-ion diffusion that agree well with the experiment. Our work paves the way for future investigation of Li-ion diffusion mechanisms and optimization of Li-ion conductivity of solid-state electrolyte materials.

Nowadays, vision-based computing tasks play an important role in various real-world applications. However, many vision computing tasks, e.g. semantic segmentation, are usually computationally expensive, posing a challenge to the computing systems that are resource-constrained but require fast response speed. Therefore, it is valuable to develop accurate and real-time vision processing models that only require limited computational resources. To this end, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet) for achieving real-time semantic segmentation. In SGCPNet, we propose the strategy of spatial-detail guided context propagation. It uses the spatial details of shallow layers to guide the propagation of the low-resolution global contexts, in which the lost spatial information can be effectively reconstructed. In this way, the need for maintaining high-resolution features along the network is freed, therefore largely improving the model efficiency. On the other hand, due to the effective reconstruction of spatial details, the segmentation accuracy can be still preserved. In the experiments, we validate the effectiveness and efficiency of the proposed SGCPNet model. On the Citysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation accuracy, while its speed reaches 178.5 FPS on 768x1536 images on a GeForce GTX 1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains 0.61 M parameters., Comment: The paper has been accepted in the IEEE Transactions on Neural Networks and Learning Systems

Resistive random-access memory (ReRAM) is a promising candidate for the next generation non-volatile memory technology due to its simple read/write operations and high storage density. However, its crossbar array structure causes a severe interference effect known as the "sneak path." In this paper, we propose channel coding techniques that can mitigate both the sneak-path interference and the channel noise. The main challenge is that the sneak-path interference is data-dependent, and also correlated within a memory array, and hence the conventional error correction coding scheme will be inadequate. In this work, we propose an across-array coding strategy that assigns a codeword to multiple independent memory arrays, and exploit a real-time channel estimation scheme to estimate the instantaneous status of the ReRAM channel. Since the coded bits from different arrays experience independent channels, a "diversity" gain can be obtained during decoding, and when the codeword is adequately distributed over different memory arrays, the code actually performs as that over an uncorrelated channel. By performing decoding based on the scheme of treating-interference-as-noise (TIN), the ReRAM channel over different memory arrays is equivalent to a block varying channel we defined, for which we propose both the capacity bounds and a coding scheme. The proposed coding scheme consists of a serial concatenation of an optimized error correction code with a data shaper, which enables the ReRAM system to achieve a near capacity limit storage efficiency.

We investigate systematically the quark-hadron mixed phase in dense stellar matter, and its influence on compact star structures. The properties of quark matter and hadronic matter are fixed based on various model predictions. Beside adopting constant values, the surface tension $\Sigma$ for the quark-hadron interface is estimated with the multiple reflection expansion method and equivparticle model. To fix the structures of quark-hadron pasta phases, a continuous dimensionality of the structure is adopted as proposed by Ravenhall, Pethick, and Wilson. The corresponding properties of hybrid stars are then obtained and confronted with pulsar observations. It is found that the correlation between radius and tidal deformability in traditional neutron stars preserves in hybrid stars. For those permitted by pulsar observations, in almost all cases the quark phase persists inside the most massive compact stars. The quark-hadron interface plays an important role on hybrid star structures once quark matter emerges. The surface tension $\Sigma$ estimated with various methods increases with density, which predicts stiffer EOSs for the quark-hadron mixed phase and increases the maximum mass of hybrid stars. The EOSs of hybrid star matter are well constrained at densities $n\lesssim 0.8$ fm${}^{-3}$, while larger uncertainty is expected at higher densities.

Deformation field estimation is an important and challenging issue in many medical image registration applications. In recent years, deep learning technique has become a promising approach for simplifying registration problems, and has been gradually applied to medical image registration. However, most existing deep learning registrations do not consider the problem that when the receptive field cannot cover the corresponding features in the moving image and the fixed image, it cannot output accurate displacement values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution. Multi-resolution and multi-convolution techniques can improve but fail to avoid this problem. In this study, we constructed pyramidal feature sets on moving and fixed images and used the warped moving and fixed features to estimate their "residual" deformation field at each scale, called the Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The "total" deformation field at each scale was computed by upsampling and weighted summing all the "residual" deformation fields at all its previous scales, which can effectively and accurately transfer the deformation fields from low resolution to high resolution and is used for warping the moving features at each scale. Simulation and real brain data results show that our method improves the accuracy of the registration and the rationality of the deformation field.

Photocatalysis induced by sunlight is one of the most promising approach to environmental protection, solar energy conversion and sustainable production of fuels. The computational modeling of photocatalysis is a rapidly expending field which requires to adapt and further develop the available theoretical tools. The coupled transfer of proton and electron is an important reaction during photocatalysis. In this work, we present the first step of our methodology development in which we apply existing kinetic theory of such coupled transfer to a model system, namely, methanol photo-dissociation on rutile TiO$_2$(110) surface, with the help of high-level first-principles calculations. Moreover, we adapt the Stuchebrukhov-Hammes-Schiffer kinetic theory, where we use the Georgievskii-Stuchebrukhova vibronic coupling, to calculate the rate constant of the proton coupled electron transfer reaction for a particular pathway. In particular, we propose a modified expression to calculate the rate constant which enforces the near-resonance condition for the vibrational wavefunction during proton tunneling.

The symmetry energy obtained with the effective Skyrme energy density functional is related to the values of isoscalar effective mass and isovector effective mass, which is also indirectly related to the incompressibility of symmetric nuclear matter. In this work, we analyze the values of symmetry energy and its related nuclear matter parameters in five-dimensional parameter space by describing the heavy ion collision data, such as isospin diffusion data at 35 MeV/u and 50 MeV/u, neutron skin of $^{208}$Pb, and tidal deformability and maximum mass of neutron star. We obtain the parameter sets which can describe the isospin diffusion, neutron skin, tidal deformability and maximum mass of neutron star, and give the incompressibility $K_0$=250.23$\pm$20.16 MeV, symmetry energy coefficient $S_0$=31.35$\pm$2.08 MeV, the slope of symmetry energy $L$=59.57$\pm$10.06 MeV, isoscalar effective mass $m_s^*/m$=0.75$\pm$0.05 and quantity related to effective mass splitting $f_I$=0.005$\pm$0.170. At two times normal density, the symmetry energy we obtained is in 35-55 MeV. To reduce the large uncertainties of $f_I$, more critical works in heavy ion collisions at different beam energies are needed., Comment: 10 pages, 11 figures, 2 tables, accepted by Phys.Rev.C. This article draws heavily from arXiv:1911.05380. In the v1 version, we modified two typos in Table II

We investigate the stability and $e^+e^-$ pair creation of supercritically charged superheavy nuclei, $ud$QM nuggets, strangelets, and strangeon nuggets based on Thomas-Fermi approximation. The model parameters are fixed by reproducing their masses and charge properties reported in earlier publications. It is found that $ud$QM nuggets, strangelets, and strangeon nuggets may be more stable than ${}^{56}$Fe at $A\gtrsim 315$, $5\times10^4$, and $1.2\times10^8$, respectively. For those stable against neutron emission, the most massive superheavy element has a baryon number $\sim$965, while $ud$QM nuggets, strangelets, and strangeon nuggets need to have baryon numbers larger than $39$, 433, and $2.7\times10^5$. The $e^+e^-$ pair creation will inevitably start for superheavy nuclei with charge numbers $Z\geq177$, $ud$QM nuggets with $Z\geq163$, strangelets with $Z\geq 192$, and strangeon nuggets with $Z\geq 212$. A universal relation $Q/R_e = \left(m_e - \bar{\mu}_e\right)/\alpha$ is obtained at a given electron chemical potential $\bar{\mu}_e$, where $Q$ is the total charge and $R_e$ the radius of electron cloud. This predicts the maximum charge number by taking $\bar{\mu}_e=-m_e$. For supercritically charged objects with $\bar{\mu}_e<-m_e$, the decay rate for $e^+e^-$ pair production is estimated based on the JWKB approximation. It is found that most positrons are emitted at $t\lesssim 10^{-15}$ s, while a long lasting positron emission is observed for large objects with $R\gtrsim 1000$ fm. The emission and annihilation of positrons from supercritically charged objects may be partially responsible for the short $\gamma$-ray burst during the merger of binary compact stars, the 511 keV continuum emission, as well as the narrow faint emission lines in X-ray spectra from galaxies and galaxy clusters.

A superstructure can elicit versatile new properties of materials by breaking their original geometrical symmetries. It is an important topic in the layered graphene-like two-dimensional transition-metal dichalcogenides (TMDs), but its origin remains unclear. Using diamond-anvil cell techniques, synchrotron x-ray diffraction, x-ray absorption, and the first-principles calculations, we show that the evolution from the weak Van der Waals bonding to the Heisenberg covalent bonding between layers induces an isostructural transition in quasi-two-dimensional 1$\textit{T}$-type VSe$_2$ at high pressure. Furthermore, our results show that high-pressure induce a novel superstructure at 15.5 GPa, rather than suppress as it would normally, which is unexpected. It is driven by the Fermi surface nesting, enhanced by the pressure-induced distortion. The results suggest that the superstructure not only appears in the two-dimensional structure but also can emerge in the pressure-tuned three-dimensional structure with new symmetry and develop superconductivity., Comment: 17 pages, 5 figures

Choroid is the vascular layer of the eye, which is directly related to the incidence and severity of many ocular diseases. Optical Coherence Tomography (OCT) is capable of imaging both the cross-sectional view of retina and choroid, but the segmentation of the choroid region is challenging because of the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker infused global-to-local network (BioNet) for choroid segmentation, which segments the choroid with higher credibility and robustness. Firstly, our method trains a biomarker prediction network to learn the features of the biomarker. Then a global multi-layers segmentation module is applied to segment the OCT image into 12 layers. Finally, the global multi-layered result and the original OCT image are fed into a local choroid segmentation module to segment the choroid region with the biomarker infused as regularizer. We conducted comparison experiments with the state-of-the-art methods on a dataset (named AROD). The experimental results demonstrate the superiority of our method with $90.77\%$ Dice-index and 6.23 pixels Average-unsigned-surface-detection-error, etc., Comment: This paper has been cast for ISBI 2020