Contrasting Behavior of Carbon Nucleation in the Initial Stages of Graphene Epitaxial Growth on Stepped Metal Surfaces

Using first-principles calculations within density functional theory, we study the energetics and kinetics of carbon nucleation in the early stages of epitaxial graphene growth on three representative stepped metal surfaces: Ir(111), Ru(0001), and Cu(111). We find that on the flat surfaces of Ir(111) and Ru(0001), two carbon atoms repel each other, while they prefer to form a dimer on Cu(111). Moreover, the step edges on Ir and Ru surfaces cannot serve as effective trapping centers for single carbon adatoms, but can readily facilitate the formation of carbon dimers. These contrasting behaviors are attributed to the delicate competition between C-C bonding and C-metal bonding, and a simple generic principle is proposed to predict the nucleation sites of C adatoms on many other metal substrates with the C-metal bond strengths as the minimal inputs.Comment: 4 figures, submitted versio

The PARAFAC-MUSIC Algorithm for DOA Estimation with Doppler Frequency in a MIMO Radar System

The PARAFAC-MUSIC algorithm is proposed to estimate the direction-of-arrival (DOA) of the targets with Doppler frequency in a monostatic MIMO radar system in this paper. To estimate the Doppler frequency, the PARAFAC (parallel factor) algorithm is firstly utilized in the proposed algorithm, and after the compensation of Doppler frequency, MUSIC (multiple signal classification) algorithm is applied to estimate the DOA. By these two steps, the DOA of moving targets can be estimated successfully. Simulation results show that the proposed PARAFAC-MUSIC algorithm has a higher accuracy than the PARAFAC algorithm and the MUSIC algorithm in DOA estimation

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

Grasping for novel objects is important for robot manipulation in unstructured environments. Most of current works require a grasp sampling process to obtain grasp candidates, combined with local feature extractor using deep learning. This pipeline is time-costly, expecially when grasp points are sparse such as at the edge of a bowl. In this paper, we propose an end-to-end approach to directly predict the poses, categories and scores (qualities) of all the grasps. It takes the whole sparse point clouds as the input and requires no sampling or search process. Moreover, to generate training data of multi-object scene, we propose a fast multi-object grasp detection algorithm based on Ferrari Canny metrics. A single-object dataset (79 objects from YCB object set, 23.7k grasps) and a multi-object dataset (20k point clouds with annotations and masks) are generated. A PointNet++ based network combined with multi-mask loss is introduced to deal with different training points. The whole weight size of our network is only about 11.6M, which takes about 102ms for a whole prediction process using a GeForce 840M GPU. Our experiment shows our work get 71.43% success rate and 91.60% completion rate, which performs better than current state-of-art works.Comment: Accepted at the International Conference on Robotics and Automation (ICRA) 202

Half-Heusler Compounds as a New Class of Three-Dimensional Topological Insulators

Using first-principles calculations within density functional theory, we explore the feasibility of converting ternary half-Heusler compounds into a new class of three-dimensional topological insulators (3DTI). We demonstrate that the electronic structure of unstrained LaPtBi as a prototype system exhibits distinct band-inversion feature. The 3DTI phase is realized by applying a uniaxial strain along the [001] direction, which opens a bandgap while preserving the inverted band order. A definitive proof of the strained LaPtBi as a 3DTI is provided by directly calculating the topological Z2 invariants in systems without inversion symmetry. We discuss the implications of the present study to other half-Heusler compounds as 3DTI, which, together with the magnetic and superconducting properties of these materials, may provide a rich platform for novel quantum phenomena.Comment: 4 pages, 5 figures; Phys. Rev. Lett. (in press

The crosstalk between the gut microbiota and tumor immunity: Implications for cancer progression and treatment outcomes

The gastrointestinal tract is inhabited by trillions of commensal microorganisms that constitute the gut microbiota. As a main metabolic organ, the gut microbiota has co-evolved in a symbiotic relationship with its host, contributing to physiological homeostasis. Recent advances have provided mechanistic insights into the dual role of the gut microbiota in cancer pathogenesis. Particularly, compelling evidence indicates that the gut microbiota exerts regulatory effects on the host immune system to fight against cancer development. Some microbiota-derived metabolites have been suggested as potential activators of antitumor immunity. On the contrary, the disequilibrium of intestinal microbial communities, a condition termed dysbiosis, can induce cancer development. The altered gut microbiota reprograms the hostile tumor microenvironment (TME), thus allowing cancer cells to avoid immunosurvelliance. Furthermore, the gut microbiota has been associated with the effects and complications of cancer therapy given its prominent immunoregulatory properties. Therapeutic measures that aim to manipulate the interplay between the gut microbiota and tumor immunity may bring new breakthroughs in cancer treatment. Herein, we provide a comprehensive update on the evidence for the implication of the gut microbiota in immune-oncology and discuss the fundamental mechanisms underlying the influence of intestinal microbial communities on systemic cancer therapy, in order to provide important clues toward improving treatment outcomes in cancer patients