Crystal and Electronic Structure of GaTa4_4Se8_8 From First-Principle Calculations

GaTa$_4$Se$_8$ belongs to the lacunar spinel family. Its crystal structures is still a puzzle though there have been intensive studies on its novel properties, such as the Mott insulator phase and superconductivity under pressure. In this work, we investigate its phonon spectra through first-principle calculations and proposed it most probably has crystal structure phase transition, which is consistent with several experimental observations. For the prototype lacunar spinel with cubic symmetry of space group $F\bar{4}3m$, its phonon spectra have three soft modes in the whole Brillouin zone, indicating the strong dynamical instability of such crystal structure. In order to find the dynamically stable crystal structure, further calculations indicate two new structures of GaTa$_4$Se$_8$, corresponding to $R3m$ and $P\bar{4}2_{1}m$, verifying that at the ambient pressure, there does exist structure phase transition of GaTa$_4$Se$_8$ from $F\bar{4}3m$ to other structures when the temperature is lowered. We also performed electronic structure calculation for $R3m$ and $P\bar{4}2_{1}m$ structure, showing that $P\bar{4}2_{1}m$ structure GaTa$_4$Se$_8$ is band insulator, and obtained Mott insulator state for $R3m$ structure by DMFT calculation under single-band Hubbard model picture when interaction parameter U is larger than 0.40 eV vs. band width of 0.25 eV. It is reasonable to assume that while lowering the temperature, $F\bar{4}3m$ structure GaTa$_4$Se$_8$ becomes $R3m$ structure GaTa$_4$Se$_8$ first, then $P\bar{4}2_{1}m$ structure GaTa$_4$Se$_8$, because of the symmetry of $P\bar{4}2_{1}m$ is lower than $R3m$ after Jahn-Teller distortion. The structure transition may explain the magnetic susceptibility anomalous at low temperature

Knowledge mapping of the research on lexical inferencing: A bibliometric analysis

Lexical inferencing functions as one of the most important and effective skills used in language comprehension pertaining to psychological, cognitive and neurological aspects. Given its complex nature and crucial role in language comprehension, lexical inferencing has received considerable attention. The present study visualized the knowledge domain of the research on lexical inferencing based on a total of 472 articles collected from Web of Science (WoS) Core Collection of Thomson Reuters from 2001 to 2021. The bibliographic data were analyzed through co-cited articles, co-citation clusters of references, and co-occurring keywords to identify holistic intellectual landscape of lexical inferencing with special focus on its intellectual structure and base, and hot research topics. The main intellectual base includes probability of activating lexical inferencing in working memory and encoding in long-term memory, the role of lexical inferencing in reading comprehension, in connected speech, in children’s derivation under pragmatic context, and in psychological and neurocognitive processes underlying language processing mechanism. Hot topics are comprised the impacts of lexical inferencing on language acquisition and comprehension (written and spoken language comprehension), the factors (context variables, vocabulary knowledge, and morphological awareness) affecting the presence and efficacy of lexical inferencing, and the time course of lexical inferencing during reading. Critically, the results of this study demonstrated that the contribution of lexical inferencing to language comprehension was strongly correlated with learner-related and discourse-related variables. The study shed valuable light on the understanding of the intellectual background and the dynamic patterns of lexical inferencing over the past two decades, thereby future work in lexical inferencing is suggested as well

Prolonged mixed phase induced by high pressure in MnRuP

Hexagonally structured MnRuP was studied under high pressure up to 35 GPa from 5 to 300 K using synchrotron X-ray diffraction. We observed that a partial phase transition from hexagonal to orthorhombic symmetry started at 11 GPa. The new and denser orthorhombic phase coexisted with its parent phase for an unusually long pressure range, {\Delta}P ~ 50 GPa. We attribute this structural transformation to a magnetic origin, where a decisive criterion for the boundary of the mixed phase lays in the different distances between the Mn-Mn atoms. In addition, our theoretical study shows that the orthorhombic phase of MnRuP remains steady even at very high pressures up to ~ 250 GPa, when it should transform to a new tetragonal phase.Comment: 15 pages, 5 figures, supplementary materia

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure