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The recent report of near-ambient superconductivity in nitrogen doped lutetium hydride has triggered a worldwide fanaticism and raised major questions about the latest claims. An intriguing phenomenon of color changes in pressurized samples from blue to pink to red was observed and correlated with the claimed superconducting transition, but the origin and underlying physics of these color changes have yet to be elucidated. Here we report synthesis and characterization of high-purity nitrogen doped lutetium hydride LuH$_{2\pm\text{x}}$N$_y$ with the same structure and composition as in the main phase of near-ambient superconductor1. We find a new purple phase of LuH$_{2\pm\text{x}}$N$_y$ between blue and pink phase, and reveal that the sample color changes likely stem from pressure-driven redistribution of nitrogen and its interaction with the LuH$_2$ framework. No superconducting transition is found in all blue, purple, pink and red phases at temperatures 1.8-300 K and pressures 0-30 GPa. Instead, we identify a notable temperature-induced resistance anomaly of structural and/or electronic origin in LuH$_{2\pm\text{x}}$N$_y$, which is most pronounced in the pink phase and may have been erroneously interpreted as a sign of superconducting transition. This work establishes key benchmarks for nitrogen doped lutetium hydrides, allowing an in-depth understanding of the novel pressure-induced phase changes., Comment: 17 pages and 5 figures in the main text. 9 pages and 12 figures in the Supplementary Material. Any valuable comments and suggestions are warmly welcomed

Superatomic crystal is a class of hierarchical materials composed of atomically precise clusters assembled via van der Waals or covalent-like interactions. AuTe$_2$Se$_{4/3}$, an all-inorganic superatomic superconductor exhibiting superatomic-charge-density-wave (S-CDW), provides a first platform to study the response of their collectively quantum phenomenon to the external pressure in superatomic crystals. We reveal a competition between S-CDW and superconductivity using cutting-edge measurements on thin flakes at low pressures. Prominently, the pressure modulation of S-CDW ordering is 1$\sim$2 order of magnitudes (0.1 GPa) lower than that of conventional atomic superconductors. As pressure increases to 2.5 GPa, the $T_{\mathrm{CDW}}$ is suppressed and the superconducting transition temperature ($T_{\mathrm{c}}$) is firstly enhanced, and reaches the maximum then quenches with increasing pressure. Above 7.3 GPa, a second superconducting phase emerges, and then a three-fold enhancement in the transition temperature ($T_{\mathrm{c}}$) happens. Analyses of the crystal structure and theoretical calculations suggest a pressure-mediated switch of the conduction channel from the $a$- to the $b$-axis occur, followed by a dimensional crossover of conductivity and the Fermi surface from 2D to 3D., Comment: 6 pages, 4 figures

Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training data poses a significant challenge to real-time training subsystems and ad-hoc sampling is the standard practice. Our key insight is that these deployed ML systems continuously perform forward passes on data instances during inference, but ad-hoc sampling does not take advantage of this substantial computational effort. Therefore, we propose to record a constant amount of information per instance from these forward passes. The extra information measurably improves the selection of which data instances should participate in forward and backward passes. A novel optimization framework is proposed to analyze this problem and we provide an efficient approximation algorithm under the framework of Mini-batch gradient descent as a practical solution. We also demonstrate the effectiveness of our framework and algorithm on several large-scale classification and regression tasks, when compared with competitive baselines widely used in industry., Comment: 13 pages

Two-dimensional (2D) materials with Dirac cones have been intrigued by many unique properties, i.e., the effective masses of carriers close to zero and Fermi velocity of ultrahigh, which yields a great possibility in high-performance electronic devices. In this work, using first-principles calculations, we have predicted a new Dirac cone material of silicon carbide with the new stoichiometries, named g-SiC6 monolayer, which is composed of sp2 hybridized with a graphene-like structure. The detailed calculations have revealed that g-SiC6 has outstanding dynamical, thermal, and mechanical stabilities, and the mechanical and electronic properties are still isotropic. Of great interest is that the Fermi velocity of g-SiC6 monolayer is the highest in silicon carbide Dirac materials until now. The Dirac cone of the g-SiC6 is controllable by an in-plane uniaxial strain and shear strain, which is promised to realize a direct application in electronics and optoelectronics. Moreover, we found that new stoichiometries AB6 (A, B = C, Si, and Ge) compounds with the similar SiC6 monolayer structure are both dynamics stable and possess Dirac cones, and their Fermi velocity was also calculated in this paper. Given the outstanding properties of those new types of silicon carbide monolayer, which is a promising 2D material for further exploring the potential applications.

Understanding individual mobility behavior is critical for modeling urban transportation. It provides deeper insights on the generative mechanisms of human movements. Emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used before to reveal individual mobility behavior. In this paper, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel information, we develop an algorithm to identify users' visited places and the category of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited places and their rank, the spatial distribution of residences and workplaces, and the distributions of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. It shows the potential of developing high-resolution individual-level mobility models that can predict the demand of emerging mobility services with high fidelity and accuracy.

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems., Comment: 9 pages, 6 figures