arXiv subjects

Jun Hu

Publications and source records attributed to Jun Hu.

At least 199 records · Page 11Linked to original sources

arXiv2020

Unexpectedly strong diamagnetism and superparamagnetism of aromatic peptides due to self-assembling and cations

There is a considerable amount of work that shows the biomagnetism of organic components without ferromagnetic components at the molecular level, but it is of great challenge to cover the giant gap of biomagnetism between their experimental and theoretical results. Here, we show that the diamagnetism of an aromatic peptide, the AYFFF, is greatly enhanced for about 11 times by self-assembling, reaching two orders of magnitude higher than the mass susceptibility of pure water. Moreover, the AYFFF self-assemblies further mixed with ZnCl2 solution of sufficiently high concentrations display superparamagnetism, with the mass susceptibility reaching more than two orders of magnitude higher than the absolute value of pure water, which may approach the mass susceptibility of ferromagnetism. The aromatic rings in the peptide molecules and the cations are the keys to such a strong diamagnetism and superparamagnetism of aromatic peptides.

physics.bio-ph↗

arXiv2020

Finite Element Methods For Interface Problems On Local Anisotropic Fitting Mixed Meshes

A simple and efficient interface-fitted mesh generation algorithm is developed in this paper. This algorithm can produce a local anisotropic fitting mixed mesh which consists of both triangles and quadrilaterals near the interface. A new finite element method is proposed for second order elliptic interface problems based on the resulting mesh. Optimal approximation capabilities on anisotropic elements are proved in both the $H^1$ and $L^2$ norms. The discrete system is usually ill-conditioned due to anisotropic and small elements near the interface. Thereupon, a multigrid method is presented to handle this issue. The convergence rate of the multigrid method is shown to be optimal with respect to both the coefficient jump ratio and mesh size. Numerical experiments are presented to demonstrate the theoretical results.

math.NA↗

arXiv2020

Asymptotic expansions of eigenvalues by both the Crouzeix-Raviart and enriched Crouzeix-Raviart elements

Asymptotic expansions are derived for eigenvalues produced by both the Crouzeix-Raviart element and the enriched Crouzeix--Raviart element. The expansions are optimal in the sense that extrapolation eigenvalues based on them admit a fourth order convergence provided that exact eigenfunctions are smooth enough. The major challenge in establishing the expansions comes from the fact that the canonical interpolation of both nonconforming elements lacks a crucial superclose property, and the nonconformity of both elements. The main idea is to employ the relation between the lowest-order mixed Raviart--Thomas element and the two nonconforming elements, and consequently make use of the superclose property of the canonical interpolation of the lowest-order mixed Raviart--Thomas element. To overcome the difficulty caused by the nonconformity, the commuting property of the canonical interpolation operators of both nonconforming elements is further used, which turns the consistency error problem into an interpolation error problem. Then, a series of new results are obtained to show the final expansions.

math.NA↗

arXiv2020

Preconditioned Legendre spectral Galerkin methods for the non-separable elliptic equation

The Legendre spectral Galerkin method of self-adjoint second order elliptic equations usually results in a linear system with a dense and ill-conditioned coefficient matrix. In this paper, the linear system is solved by a preconditioned conjugate gradient (PCG) method where the preconditioner $M$ is constructed by approximating the variable coefficients with a ($T$+1)-term Legendre series in each direction to a desired accuracy. A feature of the proposed PCG method is that the iteration step increases slightly with the size of the resulting matrix when reaching a certain approximation accuracy. The efficiency of the method lies in that the system with the preconditioner $M$ is approximately solved by a one-step iterative method based on the ILU(0) factorization. The ILU(0) factorization of $M\in \mathbb{R}^{(N-1)^d\times(N-1)^d}$ can be computed using $\mathcal{O}(T^{2d} N^d)$ operations, and the number of nonzeros in the factorization factors is of $\mathcal{O}(T^{d} N^d)$, $d=1,2,3$. To further speed up the PCG method, an algorithm is developed for fast matrix-vector multiplications by the resulting matrix of Legendre-Galerkin spectral discretization, without the need to explicitly form it. The complexity of the fast matrix-vector multiplications is of $\mathcal{O}(N^d (\log N)^2)$. As a result, the PCG method has a $\mathcal{O}(N^d (\log N)^2)$ total complexity for a $d$ dimensional domain with $(N-1)^d$ unknows, $d=1,2,3$. Numerical examples are given to demonstrate the efficiency of proposed preconditioners and the algorithm for fast matrix-vector multiplications.

math.NA↗

arXiv2020

Jantzen coefficients and radical filtrations for generalized Verma modules

In this paper we give a sum formula for the radical filtration of generalized Verma modules in any (possibly singular) blocks of parabolic BGG category which can be viewed as a generalization of Jantzen sum formula for Verma modules in the usual BGG category $\mathcal{O}$. Combined with Jantzen coefficients, we determine the radical filtrations for all basic generalized Verma modules. The proof makes use of the graded version of parabolic BGG category. Explicit formulae for the graded decomposition numbers and inverse graded decomposition numbers of generalized Verma modules in any (possibly singular) integral blocks of the parabolic BGG category are also given.

math.RT↗

arXiv2020

VGPN: Voice-Guided Pointing Robot Navigation for Humans

Pointing gestures are widely used in robot navigationapproaches nowadays. However, most approaches only use point-ing gestures, and these have two major limitations. Firstly, they need to recognize pointing gestures all the time, which leads to long processing time and significant system overheads. Secondly,the user's pointing direction may not be very accurate, so the robot may go to an undesired place. To relieve these limitations,we propose a voice-guided pointing robot navigation approach named VGPN, and implement its prototype on a wheeled robot,TurtleBot 2. VGPN recognizes a pointing gesture only if voice information is insufficient for navigation. VGPN also uses voice information as a supplementary channel to help determine the target position of the user's pointing gesture. In the evaluation,we compare VGPN to the pointing-only navigation approach. The results show that VGPN effectively reduces the processing timecost when pointing gesture is unnecessary, and improves the usersatisfaction with navigation accuracy.

cs.RO↗

arXiv2020

Bayes' Theorem under Conditional Independence

In this article we provide a substantial discussion on the statistical concept of conditional independence, which is not routinely mentioned in most elementary statistics and mathematical statistics textbooks. Under the assumption of conditional independence, an extended version of Bayes' Theorem is then proposed with illustrations from both hypothetical and real-world examples of disease diagnosis.

stat.OT↗

arXiv2020

Hyperfine Structure and Coherent Dynamics of Rare Earth Spins Explored with Electron-Nuclear Double Resonance at Sub-Kelvin Temperatures

An experimental platform of ultralow-temperature pulsed ENDOR (electron-nuclear double resonance) spectroscopy is constructed for the bulk materials. Coherent property of the coupled electron and nuclear spins of the rare-earth (RE) dopants in a crystal (143Nd3+:Y2SiO5) is investigated from 100 mK to 6 K. At the lowest working temperatures, two-pulse-echo coherence time exceeding 2 ms and 40 ms are achieved for the electron and nuclear spins, while the electronic Zeeman and hyperfine population lifetimes are more than 15 s and 10 min. With the aid of the near-unity electron spin polarization at 100 mK, the complete hyperfine level structure with 16 energy levels is measured using ENDOR technique without the assistance of the reconstructed spin Hamiltonian. These results demonstrate the suitability of the deeply cooled paramagnetic RE-doped solids for memory components aimed for quantum communication and quantum computation. The developed experimental platform is expected to be a powerful tool for paramagnetic materials from various research fields.

quant-ph↗

arXiv2020

PEL-BERT: A Joint Model for Protocol Entity Linking

Pre-trained models such as BERT are widely used in NLP tasks and are fine-tuned to improve the performance of various NLP tasks consistently. Nevertheless, the fine-tuned BERT model trained on our protocol corpus still has a weak performance on the Entity Linking (EL) task. In this paper, we propose a model that joints a fine-tuned language model with an RFC Domain Model. Firstly, we design a Protocol Knowledge Base as the guideline for protocol EL. Secondly, we propose a novel model, PEL-BERT, to link named entities in protocols to categories in Protocol Knowledge Base. Finally, we conduct a comprehensive study on the performance of pre-trained language models on descriptive texts and abstract concepts. Experimental results demonstrate that our model achieves state-of-the-art performance in EL on our annotated dataset, outperforming all the baselines.

cs.CL↗

arXiv2020

Topological transition in monolayer blue phosphorene with transiton-metal adatom under stain

We carried out first-principles calculations to investigate the electronic properties of the monolayer blue phosphorene (BlueP) decorated by the group-IVB transition-metal adatoms (Cr, Mo and W), and found that the Cr-decorated BlueP is a magnetic half metal, while the Mo- and W-decorated BlueP are semiconductors with band gaps smaller than 0.2 eV. Compressive strains make the band gaps close and reopen and band inversions occur during this process, which induces topological transitions in the Mo-decorated BlueP (with strain of -5.75%) and W-decorated BlueP (with strain of -4.25%) from normal insulators to topological insulators (TIs). The TI gap is 94 meV for the Mo-decorated BlueP and 150~220 meV for the W-decorated BlueP. Our findings pave the way to engineer topological phases in the monolayer BlueP with transition-metal adatoms.

cond-mat.mtrl-sci↗

arXiv2019

Asymptotically exact a posteriori error estimates of eigenvalues by the Crouzeix-Raviart element and enriched Crouzeix-Raviart element

Two asymptotically exact a posteriori error estimates are proposed for eigenvalues by the nonconforming Crouzeix--Raviart and enriched Crouzeix-- Raviart elements. The main challenge in the design of such error estimators comes from the nonconformity of the finite element spaces used. Such nonconformity causes two difficulties, the first one is the construction of high accuracy gradient recovery algorithms, the second one is a computable high accuracy approximation of a consistency error term. The first difficulty was solved for both nonconforming elements in a previous paper. Two methods are proposed to solve the second difficulty in the present paper. In particular, this allows the use of high accuracy gradient recovery techniques. Further, a post-processing algorithm is designed by utilizing asymptotically exact a posteriori error estimators to construct the weights of a combination of two approximate eigenvalues. This algorithm requires to solve only one eigenvalue problem and admits high accuracy eigenvalue approximations both theoretically and numerically.

math.NA↗

arXiv2019

Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account. The effectiveness of the GCN model is corroborated by the IEEE 123 bus benchmark system. Simulation results show that the GCN model significantly outperforms other widely-used machine learning schemes with very high fault location accuracy. In addition, the proposed approach is robust to measurement noise and data loss errors. Data visualization results of two competing neural networks are presented to explore the mechanism of GCN's superior performance. A data augmentation procedure is proposed to increase the robustness of the model under various levels of noise and data loss errors. Further experiments show that the model can adapt to topology changes of distribution networks and perform well with a limited number of measured buses.

cs.LG↗

arXiv2019

Scale- and Context-Aware Convolutional Non-intrusive Load Monitoring

Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective non-intrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.

eess.SP↗

arXiv2019

Synthesizing the Quantum Spin Hall Phase for Ultracold Atoms in Bichromatic Chiral Optical Ladders

Realizing the topological bands of helical states poses a challenge in studying ultracold atomic gases. Motivated by the recent experimental success in realizing chiral optical ladders, here we present a scheme for synthesizing topological quantum matter, especially the quantum spin Hall phase, in the chiral optical ladders. More precisely, we first establish the synthetic pseudo-spin-orbit coupling and Zeeman splitting in the chiral ladders. We analyze the band structure of the ladders exposed to the bichromatic optical potentials and report the existence of quantum spin Hall phase. We further identify a rich phase diagram of the bichromatic chiral ladders, illustrating that our proposal features a large space of system parameters exhibiting a variety of quantum phase transitions. Our scheme can be readily implemented in the existing experimental systems and hence provides a new method to engineer the sophisticated topological bands for cold atomic gases.

cond-mat.quant-gas↗

arXiv2019

Strict experimental test of macroscopic realism in a light-matter-interfaced system

Macroscopic realism is a classical worldview that a macroscopic system is always determinately in one of the two or more macroscopically distinguishable states available to it, and so is never in a superposition of these states. The question of whether there is a fundamental limitation on the possibility to observe quantum phenomena at the macroscopic scale remains unclear. Here we implement a strict and simple protocol to test macroscopic realism in a light-matter interfaced system. We create a micro-macro entanglement with two macroscopically distinguishable solid-state components and rule out those theories which would deny coherent superpositions of up to 76 atomic excitations shared by 10^10 ions in two separated solids. These results provide a general method to enhance the size of superposition states of atoms by utilizing quantum memory techniques and to push the envelope of macroscopicity at higher levels.

quant-ph↗

arXiv2019

Optimal Superconvergence Analysis for the Crouzeix-Raviart and the Morley elements

In this paper, an improved superconvergence analysis is presented for both the Crouzeix-Raviart element and the Morley element. The main idea of the analysis is to employ a discrete Helmholtz decomposition of the difference between the canonical interpolation and the finite element solution for the first order mixed Raviart--Thomas element and the mixed Hellan--Herrmann--Johnson element, respectively. This, in particular, allows for proving a full one order superconvergence result for these two mixed finite elements. Finally, a full one order superconvergence result of both the Crouzeix-Raviart element and the Morley element follows from their special relations with the first order mixed Raviart--Thomas element and the mixed Hellan--Herrmann--Johnson element respectively. Those superconvergence results are also extended to mildly-structured meshes.

math.NA↗

arXiv2019

3D $H^2$-nonconforming tetrahedral finite elements for the biharmonic equation

In this article, a family of $H^2$-nonconforming finite elements on tetrahedral grids is constructed for solving the biharmonic equation in 3D. In the family, the $P_\ell$ polynomial space is enriched by some high order polynomials for all $\ell\ge 3$ and the corresponding finite element solution converges at the optimal order $\ell-1$ in $H^2$ norm. Moreover, the result is improved for two low order cases by using $P_6$ and $P_7$ polynomials to enrich $P_4$ and $P_5$ polynomial spaces, respectively. The optimal order error estimate is proved. The numerical results are provided to confirm the theoretical findings.

math.NA↗

arXiv2019

Partial relaxation of C^0 vertex continuity of stresses of conforming mixed finite elements for the elasticity problem

A conforming triangular mixed element recently proposed by Hu and Zhang for linear elasticity is extended by rearranging the global degrees of freedom. More precisely, adaptive meshes $\mathcal{T}_1$, $\cdots$, $\mathcal{T}_N$ which are successively refined from an initial mesh $\mathcal{T}_0$ through a newest vertex bisection strategy, admit a crucial hierarchical structure, namely, a newly added vertex $\boldsymbol{x}$ of the mesh $\mathcal{T}_\ell$ is the midpoint of an edge $e$ of the coarse mesh $\mathcal{T}_{\ell-1}$. Such a hierarchical structure is explored to partially relax the $C^0$ vertex continuity of symmetric matrix-valued functions in the discrete stress space of the original element on $\mathcal{T}_\ell$ and results in an extended discrete stress space. A feature of this extended discrete stress space is its nestedness in the sense that a space on a coarse mesh $\mathcal{T}$ is a subspace of a space on any refinement $\hat{\mathcal{T}}$ of $\mathcal{T}$, which allows a proof of convergence of a standard adaptive algorithm. The idea is extended to impose a general traction boundary condition on the discrete level. Numerical experiments are provided to illustrate performance on both uniform and adaptive meshes.

math.NA↗