arXiv subjects

Meng He

Publications and source records attributed to Meng He.

At least 37 records · Page 2Linked to original sources

arXiv2020

Strain-mediated high conductivity in ultrathin antiferromagnetic metallic nitrides

Strain engineering provides the ability to control the ground states and associated phase transition in the epitaxial films. However, the systematic study of intrinsic characters and their strain dependency in transition-metal nitrides remains challenging due to the difficulty in fabricating the stoichiometric and high-quality films. Here we report the observation of electronic state transition in highly crystalline antiferromagnetic CrN films with strain and reduced dimensionality. Shrinking the film thickness to a critical value of ~ 30 unit cells, a profound conductivity reduction accompanied by unexpected volume expansion is observed in CrN films. The electrical conductivity is observed surprisingly when the CrN layer as thin as single unit cell thick, which is far below the critical thickness of most metallic films. We found that the metallicity of an ultrathin CrN film recovers from an insulating behavior upon the removal of as-grown strain by fabrication of first-ever freestanding nitride films. Both first-principles calculations and linear dichroism measurements reveal that the strain-mediated orbital splitting effectively customizes the relatively small bandgap at the Fermi level, leading to exotic phase transition in CrN. The ability to achieve highly conductive nitride ultrathin films by harness strain-controlling over competing phases can be used for utilizing their exceptional characteristics.

cond-mat.mtrl-sci↗

arXiv2020

Distance Oracles for Interval Graphs via Breadth-First Rank/Select in Succinct Trees

We present the first succinct distance oracles for (unweighted) interval graphs and related classes of graphs, using a novel succinct data structure for ordinal trees that supports the mapping between preorder (i.e., depth-first) ranks and level-order (breadth-first) ranks of nodes in constant time. Our distance oracles for interval graphs also support navigation queries -- testing adjacency, computing node degrees, neighborhoods, and shortest paths -- all in optimal time. Our technique also yields optimal distance oracles for proper interval graphs (unit-interval graphs) and circular-arc graphs. Our tree data structure supports all operations provided by different approaches in previous work, as well as mapping to and from level-order ranks and retrieving the last (first) internal node before (after) a given node in a level-order traversal, all in constant time.

cs.DS↗

arXiv2020

Fast Preprocessing for Optimal Orthogonal Range Reporting and Range Successor with Applications to Text Indexing

Under the word RAM model, we design three data structures that can be constructed in $O(n\sqrt{\lg n})$ time over $n$ points in an $n \times n$ grid. The first data structure is an $O(n\lg^ε n)$-word structure supporting orthogonal range reporting in $O(\lg\lg n+k)$ time, where $k$ denotes output size and $ε$ is an arbitrarily small constant. The second is an $O(n\lg\lg n)$-word structure supporting orthogonal range successor in $O(\lg\lg n)$ time, while the third is an $O(n\lg^ε n)$-word structure supporting sorted range reporting in $O(\lg\lg n+k)$ time. The query times of these data structures are optimal when the space costs must be within $O(n\ polylog\ n)$ words. Their exact space bounds match those of the best known results achieving the same query times, and the $O(n\sqrt{\lg n})$ construction time beats the previous bounds on preprocessing. Previously, among 2d range search structures, only the orthogonal range counting structure of Chan and Pǎtraşcu (SODA 2010) and the linear space, $O(\lg^ε n)$ query time structure for orthogonal range successor by Belazzougui and Puglisi (SODA 2016) can be built in the same $O(n\sqrt{\lg n})$ time. Hence our work is the first that achieve the same preprocessing time for optimal orthogonal range reporting and range successor. We also apply our results to improve the construction time of text indexes.

cs.DS↗

arXiv2020

Path Query Data Structures in Practice

We perform experimental studies on data structures that answer path median, path counting, and path reporting queries in weighted trees. These query problems generalize the well-known range median query problem in arrays, as well as the $2d$ orthogonal range counting and reporting problems in planar point sets, to tree structured data. We propose practical realizations of the latest theoretical results on path queries. Our data structures, which use tree extraction, heavy-path decomposition and wavelet trees, are implemented in both succinct and pointer-based form. Our succinct data structures are further specialized to be plain or entropy-compressed. Through experiments on large sets, we show that succinct data structures for path queries may present a viable alternative to standard pointer-based realizations, in practical scenarios. Compared to na{ï}ve approaches that compute the answer by explicit traversal of the query path, our succinct data structures are several times faster in path median queries and perform comparably in path counting and path reporting queries, while being several times more space-efficient. Plain pointer-based realizations of our data structures, requiring a few times more space than the na{ï}ve ones, yield up to $100$-times speed-up over them.

cs.DS↗

arXiv2019

Path and Ancestor Queries on Trees with Multidimensional Weight Vectors

We consider an ordinal tree $T$ on $n$ nodes, with each node assigned a $d$-dimensional weight vector $\pnt{w} \in \{1,2,\ldots,n\}^d,$ where $d \in \mathbb{N}$ is a constant. We study path queries as generalizations of well-known {\textit{orthogonal range queries}}, with one of the dimensions being tree topology rather than a linear order. Since in our definitions $d$ only represents the number of dimensions of the weight vector without taking the tree topology into account, a path query in a tree with $d$-dimensional weight vectors generalize the corresponding $(d+1)$-dimensional orthogonal range query. We solve {\textit{ancestor dominance reporting}} problem as a direct generalization of dominance reporting problem, %in time $Ø((\lg^{d-1} n)/(\lg\lg n)^{d-2}+k)$ in time $Ø(\lg^{d-1}{n}+k)$ %and space of $Ø(n(\lg n)^{d-1}/(\lg \lg n)^{d-2})$ words, and space of $Ø(n\lg^{d-2}n)$ words, where $k$ is the size of the output, for $d \geq 2.$ We also achieve a tradeoff of $Ø(n\lg^{d-2+\eps}{n})$ words of space, with query time of $Ø((\lg^{d-1} n)/(\lg\lg n)^{d-2}+k),$ for the same problem, when $d \geq 3.$ We solve {\textit{path successor problem}} in $Ø(n\lg^{d-1}{n})$ words of space and time $Ø(\lg^{d-1+\eps}{n})$ for $d \geq 1$ and an arbitrary constant $\eps > 0.$ We propose a solution to {\textit{path counting problem}}, with $Ø(n(\lg{n}/\lg\lg{n})^{d-1})$ words of space and $Ø((\lg{n}/\lg\lg{n})^{d})$ query time, for $d \geq 1.$ Finally, we solve {\textit{path reporting problem}} in $Ø(n\lg^{d-1+\eps}{n})$ words of space and $Ø((\lg^{d-1}{n})/(\lg\lg{n})^{d-2}+k)$ query time, for $d \geq 2.$ These results match or nearly match the best tradeoffs of the respective range queries. We are also the first to solve path successor even for $d = 1$.

cs.DS↗

arXiv2019

On Approximate Range Mode and Range Selection

For any $ε\in (0,1)$, a $(1+ε)$-approximate range mode query asks for the position of an element whose frequency in the query range is at most a factor $(1+ε)$ smaller than the true mode. For this problem, we design an $O(n/ε)$ bit data structure supporting queries in $O(\lg(1/ε))$ time. This is an encoding data structure which does not require access to the input sequence; we prove the space cost is asymptotically optimal for constant $ε$. Our solution improves the previous best result of Greve et al. (Cell Probe Lower Bounds and Approximations for Range Mode, ICALP'10) by reducing the space cost by a factor of $\lg n$ while achieving the same query time. We also design an $O(n)$-word dynamic data structure that answers queries in $O(\lg n /\lg\lg n)$ time and supports insertions and deletions in $O(\lg n)$ time, for any constant $ε\in (0,1)$. This is the first result on dynamic approximate range mode; it can also be used to obtain the first static data structure for approximate 3-sided range mode queries in two dimensions. We also consider approximate range selection. For any $α\in (0,1/2)$, an $α$-approximate range selection query asks for the position of an element whose rank in the query range is in $[k - αs, k + αs]$, where $k$ is a rank given by the query and $s$ is the size of the query range. When $α$ is a constant, we design an $O(n)$-bit encoding data structure that can answer queries in constant time and prove this space cost is asymptotically optimal. The previous best result by Krizanc et al. (Range Mode and Range Median Queries on Lists and Trees, Nordic Journal of Computing, 2005) uses $O(n\lg n)$ bits, or $O(n)$ words, to achieve constant approximation for range median only. Thus we not only improve the space cost, but also provide support for any arbitrary $k$ given at query time.

cs.DS↗

arXiv2018

Tree Path Majority Data Structures

We present the first solution to $τ$-majorities on tree paths. Given a tree of $n$ nodes, each with a label from $[1..σ]$, and a fixed threshold $0<τ<1$, such a query gives two nodes $u$ and $v$ and asks for all the labels that appear more than $τ\cdot |P_{uv}|$ times in the path $P_{uv}$ from $u$ to $v$, where $|P_{uv}|$ denotes the number of nodes in $P_{uv}$. Note that the answer to any query is of size up to $1/τ$. On a $w$-bit RAM, we obtain a linear-space data structure with $O((1/τ)\log^* n \log\log_w σ)$ query time. For any $κ> 1$, we can also build a structure that uses $O(n\log^{[κ]} n)$ space, where $\log^{[κ]} n$ denotes the function that applies logarithm $κ$ times to $n$, and answers queries in time $O((1/τ)\log\log_w σ)$. The construction time of both structures is $O(n\log n)$. We also describe two succinct-space solutions with the same query time of the linear-space structure. One uses $2nH + 4n + o(n)(H+1)$ bits, where $H \le \lgσ$ is the entropy of the label distribution, and can be built in $O(n\log n)$ time. The other uses $nH + O(n) + o(nH)$ bits and is built in $O(n\log n)$ time w.h.p.

cs.DS↗

arXiv2018

Improved Time and Space Bounds for Dynamic Range Mode

Given an array A of $n$ elements, we wish to support queries for the most frequent and least frequent element in a subrange $[l, r]$ of $A$. We also wish to support updates that change a particular element at index $i$ or insert/ delete an element at index $i$. For the range mode problem, our data structure supports all operations in $O(n^{2/3})$ deterministic time using only $O(n)$ space. This improves two results by Chan et al. \cite{C14}: a linear space data structure supporting update and query operations in $\tilde{O}(n^{3/4})$ time and an $O(n^{4/3})$ space data structure supporting update and query operations in $\tilde{O}(n^{2/3})$ time. For the range least frequent problem, we address two variations. In the first, we are allowed to answer with an element of $A$ that may not appear in the query range, and in the second, the returned element must be present in the query range. For the first variation, we develop a data structure that supports queries in $\tilde{O}(n^{2/3})$ time, updates in $O(n^{2/3})$ time, and occupies $O(n)$ space. For the second variation, we develop a Monte Carlo data structure that supports queries in $O(n^{2/3})$ time, updates in $\tilde{O}(n^{2/3})$ time, and occupies $\tilde{O}(n)$ space, but requires that updates are made independently of the results of previous queries. The Monte Carlo data structure is also capable of answering $k$-frequency queries; that is, the problem of finding an element of given frequency in the specified query range. Previously, no dynamic data structures were known for least frequent element or $k$-frequency queries.

cs.DS↗

arXiv2018

Compressed Dynamic Range Majority and Minority Data Structures

In the range $α$-majority query problem, we are given a sequence $S[1..n]$ and a fixed threshold $α\in (0, 1)$, and are asked to preprocess $S$ such that, given a query range $[i..j]$, we can efficiently report the symbols that occur more than $α(j-i+1)$ times in $S[i..j]$, which are called the range $α$-majorities. In this article we first describe a dynamic data structure that represents $S$ in compressed space --- $nH_k+ o(n\lg σ)$ bits for any $k = o(\log_σ n)$, where $σ$ is the alphabet size and $H_k \le H_0 \le \lgσ$ is the $k$-th order empirical entropy of $S$ --- and answers queries in $O \left(\frac{\log n}{α\log \log n} \right)$ time while supporting insertions and deletions in $S$ in $O \left( \frac{\lg n}α \right)$ amortized time. We then show how to modify our data structure to receive some $β\ge α$ at query time and report the range $β$-majorities in $O \left( \frac{\log n}{β\log \log n} \right)$ time, without increasing the asymptotic space or update-time bounds. The best previous dynamic solution has the same query and update times as ours, but it occupies $O(n)$ words and cannot take advantage of being given a larger threshold $β$ at query time. [ABSTRACT CLIPPED DUE TO LENGTH.]

cs.DS↗

arXiv2018

Fast and Compact Planar Embeddings

There are many representations of planar graphs, but few are as elegant as Turán's (1984): it is simple and practical, uses only 4 bits per edge, can handle self-loops and multi-edges, and can store any specified embedding. Its main disadvantage has been that "it does not allow efficient searching" (Jacobson, 1989). In this paper we show how to add a sublinear number of bits to Turán's representation such that it supports fast navigation while retaining simplicity. As a consequence of the inherited simplicity, we offer the first efficient parallel construction of a compact encoding of a planar graph embedding. Our experimental results show that the resulting representation uses about 6 bits per edge in practice, supports basic navigation operations within a few microseconds, and can be built sequentially at a rate below 1 microsecond per edge, featuring a linear speedup with a parallel efficiency around 50\% for large datasets.

cs.DS↗

arXiv2017

Evaluating the electron density model by applying an imaginary modification

A function has been proposed to evaluate the electron density model constructed by inverse Fourier transform using the observed structure amplitudes and trial phase set. The strategy of this function is applying an imaginary electron density modification to the model, and then measuring how well the calculated structure amplitudes of the modified model matches the expected structure amplitudes for the modified correct model. Since the correct model is not available in advance, a method has been developed to estimate the structure amplitudes of the modified correct model. With the estimated structure amplitudes of the modified correct model, the evaluation function can be calculated approximately. Limited tests on simulated diffraction data indicate that this evaluation function may be valid at the data resolution better than 2.5 Å.

cond-mat.mtrl-sci↗

arXiv2017

Parallel Construction of Compact Planar Embeddings

The sheer sizes of modern datasets are forcing data-structure designers to consider seriously both parallel construction and compactness. To achieve those goals we need to design a parallel algorithm with good scalability and with low memory consumption. An algorithm with good scalability improves its performance when the number of available cores increases, and an algorithm with low memory consumption uses memory proportional to the space used by the dataset in uncompact form. In this work, we discuss the engineering of a parallel algorithm with linear work and logarithmic span for the construction of the compact representation of planar embeddings. We also provide an experimental study of our implementation and prove experimentally that it has good scalability and low memory consumption. Additionally, we describe and test experimentally queries supported by the compact representation.

cs.DS↗

arXiv2017

The Tian Pseudo-Atom Method

In this work, the authors give a new method for phase determination, the Tian pseudo atom method (TPAM) or pseudo atom method (PAM) for short. In this new method, the figure of merit function, Rtian, replaces Rcf in the charge flipping algorithm. The key difference between Rcf and Rtian is the oberved structure factor was replaced by the pseudo structure factor. The test results show that Rtian is more powerful and robust than Rcf to estimate the correct structure especially with low resolution data. Therefore, the pseudo atom method could overcome the charge flipping method's defeat to some extent. In theory, the pseudo atom method could deal with quite low resolution data but it needs a further test.

cond-mat.mtrl-sci↗

arXiv2016

Insulating phase at low temperature in ultrathin La0.8Sr0.2MnO3 films

Metal-insulator transition is observed in the La0.8Sr0.2MnO3 thin films with thickness larger than 5 unit cells. Insulating phase at lower temperature appeared in the ultrathin films with thickness ranging from 6 unit cells to 10 unit cells and it is found that the Mott variable range hopping conduction dominates in this insulating phase at low temperature with a decrease of localization length in thinner films. A deficiency of oxygen content and a resulted decrease of the Mn valence have been observed in the ultrathin films with thickness smaller than or equal to 10 unit cells by studying the aberration-corrected scanning transmission electron microscopy and electron energy loss spectroscopy of the films. These results suggest that the existence of the oxygen vacancies in thinner films suppresses the double-exchange mechanism and contributes to the enhancement of disorder, leading to a decrease of the Curie temperature and the low temperature insulating phase in the ultrathin films. In addition, the suppression of the magnetic properties in thinner films indicates stronger disorder of magnetic moments, which is considered to be the reason for this decrease of the localization length.

cond-mat.mtrl-sci↗

arXiv2015

Image definition evaluation functions for X-ray crystallography: a new perspective on the phase problem

The core theme of X-ray crystallography is reconstructing the electron density distribution of crystals under the constraints of observed diffraction data. Nevertheless, the reconstruction of electron density distribution by straightforward Fourier synthesis is usually hindered due to the well-known phase problem and finite resolution of diffraction data. In analogy with optical imaging system, the reconstructed electron density map may be regarded as the image of the real electron density distribution in crystals. Inspired by image definition evaluation functions applied in auto-focusing process, we proposed two evaluation functions for the reconstructed electron density images. One of them is based on atomicity of electron density distribution and properties of Fourier synthesis. Tests were performed on synthetic data of known structures, and it was found that this evaluation function can distinguish the correctly reconstructed electron density image from wrong ones when diffraction data of atomic resolution is available. An algorithm was established based on this evaluation function and applied in reconstructing the electron density image from synthetic data of known structures. The other evaluation function, which is based on the positivity of electron density and constrained entropy maximization, was designed for cases where only diffraction data of rather limited resolution is available. Tests on synthetic data indicate that this evaluation function may identify the correct phase set even for a dataset at the resolution as low as 3.5 Å. Though no algorithm of structure solution has been figured out based on the latter function, the results presented here provide a new perspective on the phase problem.

cond-mat.mtrl-sci↗

arXiv2013

Dynamic Range Selection in Linear Space

Given a set $S$ of $n$ points in the plane, we consider the problem of answering range selection queries on $S$: that is, given an arbitrary $x$-range $Q$ and an integer $k > 0$, return the $k$-th smallest $y$-coordinate from the set of points that have $x$-coordinates in $Q$. We present a linear space data structure that maintains a dynamic set of $n$ points in the plane with real coordinates, and supports range selection queries in $O((\lg n / \lg \lg n)^2)$ time, as well as insertions and deletions in $O((\lg n / \lg \lg n)^2)$ amortized time. The space usage of this data structure is an $Θ(\lg n / \lg \lg n)$ factor improvement over the previous best result, while maintaining asymptotically matching query and update times. We also present a succinct data structure that supports range selection queries on a dynamic array of $n$ values drawn from a bounded universe.

cs.CG↗

arXiv2012

Dynamic Range Majority Data Structures

Given a set $P$ of coloured points on the real line, we study the problem of answering range $α$-majority (or "heavy hitter") queries on $P$. More specifically, for a query range $Q$, we want to return each colour that is assigned to more than an $α$-fraction of the points contained in $Q$. We present a new data structure for answering range $α$-majority queries on a dynamic set of points, where $α\in (0,1)$. Our data structure uses O(n) space, supports queries in $O((\lg n) / α)$ time, and updates in $O((\lg n) / α)$ amortized time. If the coordinates of the points are integers, then the query time can be improved to $O(\lg n / (α\lg \lg n) + (\lg(1/α))/α))$. For constant values of $α$, this improved query time matches an existing lower bound, for any data structure with polylogarithmic update time. We also generalize our data structure to handle sets of points in d-dimensions, for $d \ge 2$, as well as dynamic arrays, in which each entry is a colour.

cs.DS↗

arXiv2010

Superconductivity in the iron selenide KxFe2Se2 (0 <= x <= 1)

We report the superconductivity at above 30 K in a new FeSe-layer compound K0.8Fe2Se2 (nominal composition) achieved by metal K intercalating in between FeSe layers. It is isostructural to BaFe2As2 and possesses the highest Tc for FeSe-layer materials so far under ambient pressure. Hall effect indicates the carriers are dominated by electron in this superconductor. We confirm that the observed superconductivity at above 30 K is due to this new FeSe-based 122 phase. Our results demonstrate that FeSe-layer materials are really remarkable superconductors via structure and carrier modulation.

cond-mat.supr-con↗