Mean-field potential calculations of high-pressure equation of state for shock-compressed BeO

A systematic study of the Hugoniot equation of state, phase transition, and the other thermodynamic properties including the Hugoniot temperature, the electronic and ionic heat capacities, and the Gr\"{u}neisen parameter for shock-compressed BeO, is presented by calculating the total free energy. The method of calculations combines first-principles treatment for 0-K and finite-T electronic contribution and the mean-field-potential approach for the vibrational contribution of the lattice ion to the total energy. Our calculated Hugoniot shows good agreement with the experimental data.Comment: 9 figure

On Differentially Private Online Collaborative Recommendation Systems

In collaborative recommendation systems, privacy may be compromised, as users' opinions are used to generate recommendations for others. In this paper, we consider an online collaborative recommendation system, and we measure users' privacy in terms of the standard differential privacy. We give the first quantitative analysis of the trade-offs between recommendation quality and users' privacy in such a system by showing a lower bound on the best achievable privacy for any non-trivial algorithm, and proposing a near-optimal algorithm. From our results, we find that there is actually little trade-off between recommendation quality and privacy for any non-trivial algorithm. Our results also identify the key parameters that determine the best achievable privacy.Comment: 35 pages, 2 figure

The different roles of Pu-oxide overlayers in the hydrogenation of Pu-metal: An ab initio molecular dynamics study based on vdW-DFT+U

Based on the van der Waals density functional theory (vdW-DFT)+U scheme, we carry out the ab initio molecular dynamics (AIMD) study of the interaction dynamics for H$_{2}$ impingement against the stoichiometric PuO$_{2}$(111), the reduced PuO$_{2}$(111), and the stoichiometric $\alpha$-Pu$_{2}$O$_{3}$(111) surfaces. The hydrogen molecular physisorption states, which can not be captured by pure DFT+\textit{U} method, are obtained by employing the vdW-DFT+\textit{U} scheme. We show that except for the weak physisorption, PuO$_{2}$(111) surfaces are so difficult of access that almost all of the H$_{2}$ molecules will bounce back to the vacuum when their initial kinetic energies are not sufficient. Although the dissociative adsorption of H$_{2}$ on PuO$_{2}$(111) surfaces is found to be very exothermic, the collision-induced dissociation barriers of H$_{2}$ are calculated to be as high as $3.2$ eV and $2.0$ eV for stoichiometric and reduced PuO$_{2}$ surfaces, respectively. Unlike PuO$_{2}$, our AIMD study directly reveals that the hydrogen molecules can penetrate into $\alpha$-Pu$_{2}$O$_{3}$(111) surface and diffuse easily due to the $25$\ native O vacancies located along the $\langle$111$\rangle$ diagonals of $\alpha$-Pu$_{2}$O$_{3}$ matrix. By examining the temperature effect and the internal vibrational excitations of H$_{2}$, we provide a detailed insight into the interaction dynamics of H$_{2}$ in $\alpha$-Pu$_{2}$O$_{3}$. The optimum pathways for hydrogen penetration and diffusion, the corresponding energy barriers ($1.0$ eV and $0.53$ eV, respectively) and rate constants are systematically calculated. Overall, our study fairly reveals the different interaction mechanisms between H$_{2}$ and Pu-oxide surfaces, which have strong implications to the interpretation of experimental observations.Comment: 29 pages, 8 figure

Thermal-driven Flow inside Graphene Channels for Water Desalination

A novel concept of membrane process in thermal-driven system is proposed for water desalination. By means of molecular dynamics simulations, we show fast water transport through graphene galleries at a temperature gradient. Water molecules are driven to migrate through nanometer-wide graphene channels from cold reservoir to hot reservoir by the effect of thermal creep flow. Reducing the interlayer spacing to 6.5 {\AA}, an abrupt escalation occurs in water permeation between angstrom-distance graphene slabs. The change from disordered bulklike water to quasi-square structure have been found under this extremely confined condition. This leads to a transition to subcontinuum transport. Water molecules perform collective diffusion behaviors inside graphene channels. The special transport processes with structure change convert thermal energy into motion without dissipation, resulting in unexpected high water permeability. The thermal-driven system reaches maximum flowrate at temperature variance of 80 K, corresponding to the quantity at pressure difference up to 10^5 bar in commercial reverse osmosis processes and 230 bar in pressure-driven slip flow. Our results also reveal the movement of saline ions influenced by thermophoretic effect, which complement the geometry limitation at greater layer spacing, enhancing the blockage of ions. This finding aims to provide an innovational idea of developing a high-efficiency desalination technology able to utilize various forms of energy

Rediscovering the Galactic outer disk with LAMOST data

From the derived stellar density profile using LAMOST giant stars, we find that the Galactic disk does not show truncation or break, but smoothly transit to the halo from 19 kpc. The scale length of the outer disk is only $1.6\pm0.1$\,kpc, substantially smaller than previous results. This implies that the shapes of the inner and outer disk are different. Meanwhile, the disk flaring is not only found in older populations, but also in younger population. Moreover, the vertical oscillations of the disk are identified in a wide range or $R$ from 8 to 14 kpc. We also find that the velocity dispersion profile as a function of the Galactocentric radius is flat with scale length of $26.3\pm3.2$\,kpc. We confirm that the radial velocity profile in outer disk is significantly affected by asymmetric motion. The bar with either a slower or a faster pattern speed can induce the similar radial asymmetric motion.Comment: 7 pages, 7 figures, "Rediscovering our Galaxy" Proceedings IAU Symposium No. 334, 2017, C. Chiappini, I. Minchev, E. Starkenberg, M. Valentini, ed

Deep Rotation Equivariant Network

Recently, learning equivariant representations has attracted considerable research attention. Dieleman et al. introduce four operations which can be inserted into convolutional neural network to learn deep representations equivariant to rotation. However, feature maps should be copied and rotated four times in each layer in their approach, which causes much running time and memory overhead. In order to address this problem, we propose Deep Rotation Equivariant Network consisting of cycle layers, isotonic layers and decycle layers. Our proposed layers apply rotation transformation on filters rather than feature maps, achieving a speed up of more than 2 times with even less memory overhead. We evaluate DRENs on Rotated MNIST and CIFAR-10 datasets and demonstrate that it can improve the performance of state-of-the-art architectures

Exploiting Multi-typed Treebanks for Parsing with Deep Multi-task Learning

Various treebanks have been released for dependency parsing. Despite that treebanks may belong to different languages or have different annotation schemes, they contain syntactic knowledge that is potential to benefit each other. This paper presents an universal framework for exploiting these multi-typed treebanks to improve parsing with deep multi-task learning. We consider two kinds of treebanks as source: the multilingual universal treebanks and the monolingual heterogeneous treebanks. Multiple treebanks are trained jointly and interacted with multi-level parameter sharing. Experiments on several benchmark datasets in various languages demonstrate that our approach can make effective use of arbitrary source treebanks to improve target parsing models.Comment: 11 pages, 4 figure

Equation of state for shock-compressed porous molybdenum from first-principles mean-field potential calculations

The Hugoniot curves for shock-compressed molybdenum with initial porosities of 1.0, 1.26, 1.83, and 2.31 are theoretically investigated. The method of calculations combines the first-principles treatment for zero- and finite-temperature electronic contribution and the mean-field-potential approach for the ion-thermal contribution to the total free energy. Our calculated results reproduce the Hugoniot properties of porous molybdenum quite well. At low porosity, in particular, the calculations show a complete agreement with the experimental measurements over the full range of data. For the two large porosity values of 1.83 and 2.31, our results are well in accord with the experimental data points up to the particle velocity of 3.5 km/s, and tend to overestimate the shock-wave velocity and Hugoniot pressure when further increasing the particle velocity. In addition, the temperature along the principal Hugoniot is also extensively investigated for porous molybdenum.Comment: 4 pages, 5 figure

Towards Conversational Recommendation over Multi-Type Dialogs

We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.Comment: Accepted by ACL 202

Applicability of Kerker preconditioning scheme to the self-consistent density functional theory calculations of inhomogeneous systems

Kerker preconditioner, based on the dielectric function of homogeneous electron gas, is designed to accelerate the self-consistent field (SCF) iteration in the density functional theory (DFT) calculations. However, question still remains regarding its applicability to the inhomogeneous systems. In this paper, we develop a modified Kerker preconditioning scheme which captures the long-range screening behavior of inhomogeneous systems thus improve the SCF convergence. The effectiveness and efficiency is shown by the tests on long-z slabs of metals, insulators and metal-insulator contacts. For situations without a priori knowledge of the system, we design the a posteriori indicator to monitor if the preconditioner has suppressed charge sloshing during the iterations. Based on the a posteriori indicator, we demonstrate two schemes of the self-adaptive configuration for the SCF iteration