arXiv subjects

Gang Liu

Publications and source records attributed to Gang Liu.

At least 163 records · Page 9Linked to original sources

arXiv2022

Can phantom transition at $z\sim 1$ restore the Cosmic concordance?

The tension among inferences of Hubble constant ($H_0$) is found in a large array of datasets combinations. Modification to the late expansion history is the most direct solution to this discrepancy. In this work, we examine the viability of restoring the cosmological concordance with a novel version of transitional dark energy (TDE). The main anchors for the cosmic distance scale: cosmic microwave background (CMB) radiation, baryon acoustic oscillation (BAO), and Type Ia supernova (SNe Ia) calibrated by Cepheids form a "impossible trinity", i.e., it's plausible to reconcile with any two of the three but unlikely to accommodate them all. Particularly, the tension between BAO and the calibrated SNe Ia can not be reconciled within the scenarios of late dark energy. Nevertheless, our analysis suggests that the TDE model can reconcile with CMB and SNe Ia calibrated by its absolute magnitude ($M_{\rm{B}}$) when the equation of state (EoS) of DE transits around $z\sim1$. Meanwhile, we see a positive sign that the EoS transits with the inclusion of a local prior on $M_{\rm{B}}$, whereas the opposite is true without the $M_{\rm{B}}$ prior.

astro-ph.CO↗

arXiv2021

Dendrite Net: A White-Box Module for Classification, Regression, and System Identification

The simulation of biological dendrite computations is vital for the development of artificial intelligence (AI). This paper presents a basic machine learning algorithm, named Dendrite Net or DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's main concept is that the algorithm can recognize this class after learning, if the output's logical expression contains the corresponding class's logical relationship among inputs (and$\backslash$or$\backslash$not). Experiments and main results: DD, a white-box machine learning algorithm, showed excellent system identification performance for the black-box system. Secondly, it was verified by nine real-world applications that DD brought better generalization capability relative to MLP architecture that imitated neurons' cell body (Cell body Net) for regression. Thirdly, by MNIST and FASHION-MNIST datasets, it was verified that DD showed higher testing accuracy under greater training loss than Cell body Net for classification. The number of modules can effectively adjust DD's logical expression capacity, which avoids over-fitting and makes it easy to get a model with outstanding generalization capability. Finally, repeated experiments in MATLAB and PyTorch (Python) demonstrated that DD was faster than Cell body Net both in epoch and forward-propagation. The main contribution of this paper is the basic machine learning algorithm (DD) with a white-box attribute, controllable precision for better generalization capability, and lower computational complexity. Not only can DD be used for generalized engineering, but DD has vast development potential as a module for deep learning. DD code is available at GitHub: https://github.com/liugang1234567/Gang-neuron .

cs.LG↗

arXiv2021

Gaussian Mixture Model Based Distributionally Robust Optimal Power Flow With CVaR Constraints

This paper proposes a distributionally robust optimal power flow (OPF) model for transmission grids with wind power generation. The model uses the conditional value-at-risk (CVaR) constraints to control the reserve and branch flow limit violations caused by wind power forecast errors. Meanwhile, the Gaussian mixture model (GMM) is integrated into the CVaR constraints to guard against the non-Gaussian forecast error distributions. Unlike the previous studies considering the GMM with fixed parameters, this paper allows the GMM parameters to be variable within some credible regions and develops a data-driven GMM-based ambiguity set to achieve the distributional robustness. Also, rather than using the traditional sample-based approximation of CVaR with high computational burden, this paper designs a scalable cutting-plane algorithm to handle the distributionally robust CVaR constraints. Case studies on the IEEE 2736-bus system show the effectiveness and scalability of the proposed OPF model.

eess.SY↗

arXiv2021

Large Contribution of Quasi-Acoustic Shear Phonon Modes to Thermal Conductivity in Novel Monolayer Ga2O3

Bulk gallium oxide (Ga2O3) has been widely used in lasers, dielectric coatings for solar cells, deep-ultraviolet transistor applications due to the large band gap over 4.5 eV. With the miniaturization of electronic devices, atomically thin Ga2O3 monolayer has been unveiled recently, which features an asymmetric configuration with a quintuple-layer atomic structure. The superior stability, the strain-tunable electronic properties, high carrier mobility and optical absorption indicate the promising applications in the electronic and photoelectronic devices. However, the strict investigation of lattice thermal conductivity (kappa_L) of 2D Ga2O3 is still lacking, which has impeded the widespread use in practical applications. Here, we report the computational discovery of low kappa_L with a value of 10.28 W m-1 K-1 at 300 K in atomically thin Ga2O3. Unexpectedly, two quasi-acoustic shear phonon modes contribute as high as 27% to the kappa_L at 300 K, leading to 37% contribution of optical phonon modes, much larger than many other 2D materials. We also find that the quasi-acoustic shear mode can emerge in the system without van der Waals interactions. This work provides new insight into the nature of thermal transport in non-van der Waals monolayer materials and predicts a new low kappa_L material of potential interest for thermal insulation in transistor applications.

cond-mat.mtrl-sci↗

arXiv2021

Restriction of irreducible unitary representations of Spin(N,1) to parabolic subgroups

In this paper, we obtain explicit branching laws for all irreducible unitary representations of $\Spin(N,1)$ restricted to a parabolic subgroup $P$. The restriction turns out to be a finite direct sum of irreducible unitary representations of $P$. We also verify Duflo's conjecture for the branching law of tempered representations of $\Spin(N,1)$ with respect to a parabolic subgroup $P$. That is to show: in the framework of the orbit method, the branching law of a tempered representation is determined by the behavior of the moment map from the corresponding coadjoint orbit. A few key tools used in this work include: Fourier transform, Knapp-Stein intertwining operator, Casselman-Wallach globalization, Zuckerman translation principle, du Cloux's results for smooth representations of semi-algebraic groups.

math.RT↗

arXiv2021

A proof of Casselman's comparison theorem for standard minimal parabolic subalgebra

Let $G$ be a real linear reductive group and $K$ be a maximal compact subgroup. Let $P$ be a minimal parabolic subgroup of $G$ with complexified Lie algebra $\mathfrak{p}$, and $\mathfrak{n}$ be its nilradical. In this paper we show that: for any admissible finitely generated moderate growth smooth Fréchet representation $V$ of $G$, the inclusion $V_{K}\subset V$ induces isomorphisms $H_{i}(\mathfrak{n},V_{K})\cong H_{i}(\mathfrak{n},V)$ ($i\geq 0$), where $V_{K}$ denotes the $(\mathfrak{g},K)$ module of $K$ finite vectors in $V$. This is called Casselman's comparison theorem. As a consequence, we show that: for any $k\geq 1$, $\mathfrak{n}^{k}V$ is a closed subspace of $V$ and the inclusion $V_{K}\subset V$ induces an isomorphism $V_{K}/\mathfrak{n}^{k}V_{K}= V/\mathfrak{n}^{k}V$. This strengthens Casselman's automatic continuity theorem.

math.RT↗

arXiv2021

Fluctuations in crystalline plasticity

Recently acoustic signature of dislocation avalanches in HCP materials was found to be long tailed in size and energy, suggesting critical dynamics. Even more recently, the intermittent plastic response was found to be generic for micro- and nano-sized systems independently of their crystallographic symmetry. These rather remarkable discoveries are reviewed in this paper in the perspective of the recent studies performed in our group. We discuss the physical origin and the scaling properties of plastic fluctuations and address the nature of their dependence on crystalline symmetry, system size, and disorder content. A particular emphasis is placed on the associated emergent behaviors, including the formation of dislocation structures, and on our ability to temper plastic fluctuations by alloying. We also discuss the "smaller is wilder" size effect that culminates in a paradoxical crack-free brittle behavior of very small, initially dislocation free crystals. We show that the implied transition between different rheological behaviors is regulated by the ratio of length scales $R=L/l$, where $L$ is the system size and $l$ is the internal length. We link this new size effect with other related phenomena like size dependence of strength ("smaller is stronger") and the size induced switch between different hardening mechanisms. One of the technological challenges in nanoscience is to tame the intermittency of plastic flow. We show that this task can be accomplished by generating tailored quenched disorder which allows one to control micro- and nano-scale forming and opens new perspectives in micro-metallurgy and structural engineering of ultra-small load-carrying elements. These results could not be achieved by conventional methods that do not explicitly consider the stochastic nature of collective dislocation dynamics.

cond-mat.mes-hall↗

arXiv2021

Improved Bohr inequality for harmonic mappings

Based on improving the classical Bohr inequality, we get in this paper some refined versions for a quasi-subordination family of functions, one of which is key to build our results. By means of these investigations, for a family of harmonic mappings defined in the unit disk $\D$, we establish an improved Bohr inequality with refined Bohr radius under particular conditions. Along the line of extremal problems concerning the refined Bohr radius, we derive a series of results. % in a logical way. Here the family of harmonic mappings have the form $f=h+\overline{g}$, where $g(0)=0$, the analytic part $h$ is bounded by 1 and that $|g'(z)|\leq k|h'(z)|$ in $\D$ and for some $k\in[0,1]$.

math.CV↗

arXiv2020

Microsoft Speaker Diarization System for the VoxCeleb Speaker Recognition Challenge 2020

This paper describes the Microsoft speaker diarization system for monaural multi-talker recordings in the wild, evaluated at the diarization track of the VoxCeleb Speaker Recognition Challenge(VoxSRC) 2020. We will first explain our system design to address issues in handling real multi-talker recordings. We then present the details of the components, which include Res2Net-based speaker embedding extractor, conformer-based continuous speech separation with leakage filtering, and a modified DOVER (short for Diarization Output Voting Error Reduction) method for system fusion. We evaluate the systems with the data set provided by VoxSRCchallenge 2020, which contains real-life multi-talker audio collected from YouTube. Our best system achieves 3.71% and 6.23% of the diarization error rate (DER) on development set and evaluation set, respectively, being ranked the 1st at the diarization track of the challenge.

eess.AS↗

arXiv2020

A Relation Spectrum Inheriting Taylor Series: Muscle Synergy and Coupling for Hand

There are two famous function decomposition methods in math: Taylor Series and Fourier Series. Fourier series developed into Fourier spectrum, which was applied to signal decomposition\analysis. However, because the Taylor series whose function without a definite functional expression cannot be solved, Taylor Series has rarely been used in engineering. Here, we developed Taylor series by our Dendrite Net, constructed a relation spectrum, and applied it to model or system decomposition\analysis. Specific engineering: the knowledge of the intuitive link between muscle activity and the finger movement is vital for the design of commercial prosthetic hands that do not need user pre-training. However, this link has yet to be understood due to the complexity of human hand. In this study, the relation spectrum was applied to analyze the muscle-finger system. One single muscle actuates multiple fingers, or multiple muscles actuate one single finger simultaneously. Thus, the research was in muscle synergy and muscle coupling for hand. This paper has two main contributions. (1) The findings of hand contribute to designing prosthetic hands. (2) The relation spectrum makes the online model human-readable, which unifies online performance and offline results. Code (novel tool for most fields) is available at https://github.com/liugang1234567/Gang-neuron.

eess.SP↗

arXiv2020

Refined Bohr inequality for bounded analytic functions

In this article, by combining appropriate refined Bohr's inequalities with some techniques concerning bounded analytic functions defined in the unit disk, we generalize and improve several Bohr type inequalities for such functions.

math.CV↗

arXiv2020

Gromov-Hausdorff limits of Kähler manifolds with Ricci curvature bounded below

We show that non-collapsed Gromov-Hausdorff limits of polarized Kahler manifolds, with Ricci curvature bounded below, are normal projective varieties, and the metric singularities of the limit space are precisely given by a countable union of analytic subvarieties. This extends a fundamental result of Donaldson-Sun, in which 2-sided Ricci curvature bounds were assumed. As a basic ingredient we show that, under lower Ricci curvature bounds, almost Euclidean balls in Kahler manifolds admit good holomorphic coordinates. Further applications are integral bounds for the scalar curvature on balls, and a rigidity theorem for Kahler manifolds with almost Euclidean volume growth.

math.DG↗

arXiv2020

A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.

cs.CV↗

arXiv2020

Abnormally low thermal conductivity of 2D selenene: An ab initio study

The lattice thermal conductivity and thermal transport properties of 2D $α$-selenene are investigated based on the first-principles calculations. The isotropic in-plane thermal conductivity is as low as 3.04 W m$^{-1}$ K$^{-1}$ at room temperature, even abnormally lower than $α$-tellurene which processes analogous configuration and lower Debye temperature. We find this abnormal phenomenon reasonably stems from the larger anharmonicity of the acoustic phonon branch. Moreover, the phonon spectra, elastic properties, and related thermal properties are also exhibited. Acoustic phonons contribute mainly to the total thermal conductivity. Furthermore, size effect, boundary effect, the total phase space for three-phonon processes, phonon group velocity and relaxation time are further investigated, and the last one is unveiled to be the key ingredient of thermal transport in 2D selenene.

physics.comp-ph↗

arXiv2019

A Heuristic Algorithm Based on Tour Rebuilding Operator for the Traveling Salesman Problem

TSP (Traveling Salesman Problem), a classic NP-complete problem in combinatorial optimization, is of great significance in multiple fields. Exact algorithms for TSP are not practical due to their exponential time cost. Thus, approximate algorithms become the research focus and can be further divided into two types, tour construction algorithms and tour improvement algorithms. Researches show that the latter type of algorithms can obtain better solutions than the former one. However, traditional tour improvement algorithms have shortcomings. They converge very slowly and tend to be trapped in local optima. In practice, tour construction algorithms are often used in initialization of tour improvement algorithms to speed up convergence. Nevertheless, such a combination leads to no improvement on quality of solutions. In this paper, a heuristic algorithm based on the new tour rebuilding operator is proposed. The algorithm features rapid convergence and powerful global search. In the experiments based on 40 instances in TSPLIB, the best known solutions of 22 instances are refreshed by the proposed method. Meanwhile, the best known solutions of the other 18 instances are obtained.

cs.DS↗

arXiv2019

Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams

Nearest neighbor (NN) problem is an important scientific problem. The NN query, to find the closest one to a given query point among a set of points, is widely used in applications such as density estimation, pattern classification, information retrieval and spatial analysis. A direct generalization of the NN query is the k nearest neighbors (kNN) query, where the k closest point are required to be found. Since NN and kNN problems were raised, many algorithms have been proposed to solve them. It has been indicated in literature that the only method to solve these problems exactly with sublinear time complexity, is to filter out the unnecessary spatial computation by using the pre-processing structure, commonly referred to as the spatial index. The recently proposed spatial indices available for NN search, are almost constructed through spatial partition. These indices are tree-like, and the tree-like hierarchical structure can usually significantly improve the efficiency of NN search. However, when the data are distributed extremely unevenly, it is difficult to satisfy both the balance of the tree and the non-overlap of the subspace corresponding to the nodes. Thus the acceleration performance of the tree-like indices is severely jeopardized. In this paper, we propose a non-tree spatial index which consists of multiple layers of Voronoi diagrams (MVD). This index can entirely avoid the dilemma tree-like structures face, and solve the NN problems stably with logarithmic time complexity. Furthermore, it is convenient to achieve kNN search by extending NN search on MVD. In the experiments, we evaluate the efficiency of this indexing for both NN search and kNN search by comparing with VoR-tree, R-tree and kd-tree. The experiments indicate that compared to NN search and kNN search with the other three indices, these two search methods have significantly higher efficiency with MVD.

cs.DB↗

arXiv2019

Ultralow lattice thermal conductivity of monolayer penta-silicene and penta-germanene

We study the lattice thermal conductivity of two-dimensional (2D) pentagonal systems, such as penta-silicene and penta-germanene. Penta-silicene has been recently reported, while the stable penta-germanene, belonging to the same group IV element, is revealed firstly by our ab initio calculations. We find that both penta-silicene and penta-germanene at room temperature have ultralow lattice thermal conductivities $κ$ of 1.29 W/mK and 0.30 W/mK respectively. To the best of our knowledge, penta-germanene may have the lowest $κ$ in 2D crystal materials. We attribute ultralow $κ$ to the weak phonon harmonic interaction and strong anharmonic scattering. A small phonon group velocity, a small Debye frequency, a large Gruneisen parameter, and a large number of modes available for phonon-phonon interplay together lead to the ultralow $κ$ of penta-silicene and penta-germanene. These discoveries provide new insight into the manipulation of ultralow $κ$ in 2D materials and highlight the potential applications of designing silicon and germanium based high thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2019

Plate-like precipitate effects on plasticity of Al-Cu micro-pillar: {100}-interfacial slip

In this paper, we study the effects of $θ^\prime$-Al$_2$Cu plate-like precipitates on the plasticity of Al-Cu micro-pillars, with a sample size allowing the precipitates to cross the entire micro-pillar. {100}-slip traces are identified for the first time in Al and Al alloys at room temperature. We investigate the underlying mechanisms of this unusual {100}-slip, and show that it operates along the coherent $θ^\prime$-Al$_2$Cu precipitate/$α$-Al matrix interface. A combination of molecular dynamics simulations and stress analysis indicates that screw dislocations can cross-slip from the {111} plane onto the {100} $θ^\prime$-Al$_2$Cu/$α$-Al interface, then move on it through a kink-pair mechanism, providing a reasonable explanation to the observed {100}-slips. The roles of the $θ^\prime$-Al$_2$Cu/$α$-Al matrix interface on the properties of interfacial dislocations are studied within the Peierls-Nabarro framework, showing that the interface can stabilize the {100} screw dislocations from the spreading of the core, and increases the Peierls stress. These results improve our understanding of the mechanical behavior of Al-Cu micro-pillars at room temperature, and imply an enhanced role of interfacial slip in Al-Cu based alloys at elevated temperature in consideration of the underlying kink-pair mechanism.

cond-mat.mtrl-sci↗