arXiv subjects

Bin Xu

Publications and source records attributed to Bin Xu.

At least 181 records · Page 10Linked to original sources

arXiv2021

Ultrahigh energy storage density in epitaxial AlN/ScN superlattices

Dielectric and antiferroelectric materials are particularly promising for high-power energy-storage applications. However, relatively low energy density greatly hinders their usage in storage technologies. Here, we report first-principles-based calculations predicting that epitaxial and initially non-polar AlN/ScN superlattices can achieve an ultrahigh energy density of up to 200 J/cm$^{\textrm{3}}$, accompanied by an ideal efficiency of 100%. We also show that high energy density requires the system being neither too close nor too far from a ferroelectric phase transition under zero electric field. A phenomenological model is further proposed to rationalize such striking features.

cond-mat.mtrl-sci↗

arXiv2021

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions. Prototypical network shows superior performance on few-shot NER. However, existing prototypical methods fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different undefined classes from the other class to improve few-shot NER. With these extra-labeled undefined classes, our method will improve the discriminative ability of NER classifier and enhance the understanding of predefined classes with stand-by semantic knowledge. Experimental results demonstrate that our model outperforms five state-of-the-art models in both 1-shot and 5-shots settings on four NER benchmarks. We will release the code upon acceptance. The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.

cs.CL↗

arXiv2021

Two-dimensional multiferroic metal with voltage-tunable magnetization and metallicity

We design a multiferroic metal that combines seemingly incompatible ferromagnetism, ferroelectricity, and metallicity by hole doping a two-dimensional (2D) ferroelectric with high density of states near the Fermi level. The strong magnetoelectric effect is demonstrated in hole-doped and arsenic-doped monolayer α-In2Se3 using first-principles calculations. Taking advantage of the oppositely charged surfaces created by an out-of-plane polarization, the 2D magnetization and metallicity can be electrically switched on and off in an asymmetrically doped monolayer. The substitutional arsenic defect pair exhibits an intriguing electric field-tunable charge disproportionation process accompanied with an on-off switch of local magnetic moments. The charge ordering process can be controlled by tuning the relative strength of on-site Coulomb repulsion and defect dipole-polarization coupling via strain engineering. Our design principle relying on no transition metal broadens the materials design space for 2D multiferroic metals.

cond-mat.mtrl-sci↗

arXiv2021

Spectral properties of reducible conical metrics

We show that the monodromy of a spherical conical metric is reducible if and only if it has a real-valued eigenfunction with eigenvalue 2 in the holomorphic extension of the associated Laplace--Beltrami operator. Such an eigenfunction produces a meromorphic vector field, which is then related to the developing maps of the conical metric. We also give a lower bound of the first nonzero eigenvalue, and a complete classification of the eigenspace dimension depending on the monodromy. This paper can be seen as a new connection between the complex analysis method and the PDE approach in the study of spherical conical metrics.

math.DG↗

arXiv2021

A Multilingual Modeling Method for Span-Extraction Reading Comprehension

Span-extraction reading comprehension models have made tremendous advances enabled by the availability of large-scale, high-quality training datasets. Despite such rapid progress and widespread application, extractive reading comprehension datasets in languages other than English remain scarce, and creating such a sufficient amount of training data for each language is costly and even impossible. An alternative to creating large-scale high-quality monolingual span-extraction training datasets is to develop multilingual modeling approaches and systems which can transfer to the target language without requiring training data in that language. In this paper, in order to solve the scarce availability of extractive reading comprehension training data in the target language, we propose a multilingual extractive reading comprehension approach called XLRC by simultaneously modeling the existing extractive reading comprehension training data in a multilingual environment using self-adaptive attention and multilingual attention. Specifically, we firstly construct multilingual parallel corpora by translating the existing extractive reading comprehension datasets (i.e., CMRC 2018) from the target language (i.e., Chinese) into different language families (i.e., English). Secondly, to enhance the final target representation, we adopt self-adaptive attention (SAA) to combine self-attention and inter-attention to extract the semantic relations from each pair of the target and source languages. Furthermore, we propose multilingual attention (MLA) to learn the rich knowledge from various language families. Experimental results show that our model outperforms the state-of-the-art baseline (i.e., RoBERTa_Large) on the CMRC 2018 task, which demonstrate the effectiveness of our proposed multi-lingual modeling approach and show the potentials in multilingual NLP tasks.

cs.CL↗

arXiv2021

Effect of biaxial strain and hydrostatic pressure on the magnetic properties of bilayer CrI3

Two-dimensional van der Waals magnetic materials are intriguing for applications in the future spintronics devices, so it is crucial to explore strategy to control the magnetic properties. Here, we carried out first-principles calculations and Monte Carlo simulations to investigate the effect of biaxial strain and hydrostatic pressure on the magnetic properties of the bilayer CrI3. We found that the magnetic anisotropy, intralayer and interlayer exchange interactions, and Curie temperature can be tuned by biaxial strain and hydrostatic pressure. Large compressive biaxial strain may induce a ferromagnetic-to-antiferromagnetic transition of both CrI3 layers. The hydrostatic pressure could enhance the intralayer exchange interaction significantly and hence largely boost the Curie temperature. The effect of the biaxial strain and hydrostatic pressure revealed in the bilayer CrI3 may be generalized to other two-dimensional magnetic materials.

cond-mat.mtrl-sci↗

arXiv2021

Neutrino Decoherence in Simple Open Quantum Systems

Neutrinos lose coherence as they propagate, which leads to the fading away of oscillations. In this work, we model neutrino decoherence induced in open quantum systems from their interaction with the environment. We first present two different models in the quantum mechanical framework, in which the environment is modeled as forced harmonic oscillators with white noise interactions, or two-level systems with stochastic phase kicks. We then look at the decoherence process in the quantum field theoretic framework induced by elastic scatterings with environmental particles. The exponential decay is obtained as a common feature for all models, which shows the universality of the decoherence processes. We discuss connections to the GKSL master equation approach and give a clear physical meaning of the Lindblad operators. We demonstrate that the universality of exponential decay of coherence is based on the Born-Markov approximation. The models in this work are suitable to be extended to describe real physical processes that could be non-Markovian.

hep-ph↗

arXiv2021

The characteristic cycles and semi-canonical bases on type $A$ quiver variety

In this article we study a conjecture of Geiss-Leclerc-Schr{ö}er, which is an analogue of a classical conjecture of Lusztig in the Weyl group case. It concerns the relation between canonical basis and semi-canonical basis through the characteristic cycles. We formulate an approach to this conjecture and prove it for type $A_2$ quiver. In general type A case, we reduce the conjecture to show that certain nearby cycles have vanishing Euler characteristic.

math.RT↗

arXiv2021

Bilateral Grid Learning for Stereo Matching Networks

Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance real-time performance and accuracy. In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid. The slicing layer is parameter-free, which allows us to obtain a high quality cost volume of high resolution from a low-resolution cost volume under the guide of the learned guidance map efficiently. The proposed cost volume upsampling module can be seamlessly embedded into many existing stereo matching networks, such as GCNet, PSMNet, and GANet. The resulting networks are accelerated several times while maintaining comparable accuracy. Furthermore, we design a real-time network (named BGNet) based on this module, which outperforms existing published real-time deep stereo matching networks, as well as some complex networks on the KITTI stereo datasets. The code is available at https://github.com/YuhuaXu/BGNet.

cs.CV↗

arXiv2021

Electrocaloric effects in multiferroics

An atomistic effective Hamiltonian is used to compute electrocaloric (EC) effects in rare-earth substituted BiFeO$_{3}$ multiferroics. A phenomenological model is then developed to interpret these computations, with this model indicating that the EC coefficient is the sum of two terms, that involve electric quantities (polarization, dielectric response), the antiferromagnetic order parameter, and the coupling between polarization and antiferromagnetic order. The first one depends on the polarization and dielectric susceptibility, has the analytical form previously demonstrated for ferroelectrics, and is thus enhanced at the ferroelectric Curie temperature. The second one explicitly involves the dielectric response, the magnetic order parameter and a specific magnetoelectric coupling, and generates a peak of the EC response at the Néel temperature. These atomistic results and phenomenological model may be put in use to optimize EC coefficients.

cond-mat.mtrl-sci↗

arXiv2021

Arthur packets for $p$-adic groups by way of microlocal vanishing cycles of perverse sheaves, with examples

In this article we propose a geometric description of Arthur packets for $p$-adic groups using vanishing cycles of perverse sheaves. Our approach is inspired by the 1992 book by Adams, Barbasch and Vogan on the Langlands classification of admissible representations of real groups and follows the direction indicated by Vogan in his 1993 paper on the Langlands correspondence. Using vanishing cycles, we introduce and study a functor from the category of equivariant perverse sheaves on the moduli space of certain Langlands parameters to local systems on the regular part of the conormal bundle for this variety. In this article we establish the main properties of this functor and show that it plays the role of microlocalization in the work of Adams, Barbasch and Vogan. We use this to define ABV-packets for pure rational forms of $p$-adic groups and propose a geometric description of the transfer coefficients that appear in Arthur's main local result in the endoscopic classification of representations. This article includes conjectures modelled on Vogan's work, especially the prediction that Arthur packets are ABV-packets for $p$-adic groups. We gather evidence for these conjectures by verifying them in numerous examples.

math.RT↗

arXiv2021

Atomic nonaffinity as a predictor of plasticity in amorphous solids

Structural heterogeneity of amorphous solids present difficult challenges that stymie the prediction of plastic events, which are intimately connected to their mechanical behavior. Based on a perturbation analysis of the potential energy landscape, we derive the atomic nonaffinity as an indicator with intrinsic orientation, which quantifies the contribution of an individual atom to the total nonaffine modulus of the system. We find that the atomic nonaffinity can efficiently characterize the locations of the shear transformation zones, with a predicative capacity comparable to the best indicators. More importantly, the atomic nonaffinity, combining the sign of third order derivative of energy with respect to coordinates, reveals an intrinsic softest shear orientation. By analyzing the angle between this orientation and the shear loading direction, it is possible to predict the protocol-dependent response of plastic events. Employing the new method, the distribution of orientations of shear transformation zones in a model two-dimensional amorphous solids can be measured. The resulting plastic events can be understood from a simple model of independent plastic events occurring at variously oriented shear transformation zones. These results shed light on the characterization and prediction of the mechanical response of amorphous solids.

cond-mat.mtrl-sci↗

arXiv2021

Super-R BiFeO$_3$: Epitaxial stabilization of a low-symmetry phase with giant electromechanical response

Piezoelectrics interconvert mechanical energy and electric charge and are widely used in actuators and sensors. The best performing materials are ferroelectrics at a morphotropic phase boundary (MPB), where several phases can intimately coexist. Switching between these phases by electric field produces a large electromechanical response. In the ferroelectric BiFeO$_3$, strain can be used to create an MPB-like phase mixture and thus to generate large electric field dependent strains. However, this enhanced response occurs at localized, randomly positioned regions of the film, which potentially complicates nanodevice design. Here, we use epitaxial strain and orientation engineering in tandem - anisotropic epitaxy - to craft a hitherto unavailable low-symmetry phase of BiFeO$_3$ which acts as a structural bridge between the rhombohedral-like and tetragonal-like polymorphs. Interferometric displacement sensor measurements and first-principle calculations reveal that under external electric bias, this phase undergoes a transition to the tetragonal-like polymorph, generating a piezoelectric response enhanced by over 200%, and associated giant field-induced reversible strain. These results offer a new route to engineer giant electromechanical properties in thin films, with broader perspectives for other functional oxide systems.

cond-mat.mtrl-sci↗

arXiv2021

Scalable monolayer-functionalized nanointerface for thermal conductivity enhancement in copper/diamond composite

Aiming at developing high thermal conductivity copper/diamond composite, an unconventional approach applying self-assembled monolayer (SAM) prior to the high-temperature sintering of copper/diamond composite was utilized to enhance the thermal boundary conductance (TBC) between copper and diamond. The enhancement was first systematically confirmed on a model interface system by detailed SAM morphology characterization and TBC measurements. TBC significantly depends on the SAM coverage and ordering, and the formation of high-quality SAM promoted the TBC to 73 MW/m^2-K from 27 MW/m^2-K, the value without SAM. With the help of molecular dynamics simulations, the TBC enhancement was identified to be determined by the number of SAM bridges and the overlap of vibrational density of states. The diamond particles of 210 {\micro\metre} in size were simultaneously functionalized by SAM with the condition giving the highest TBC in the model system and sintered together with the copper to fabricate isotropic copper/diamond composite of 50% volume fraction. The measured thermal conductivity marked 711 W/m-K at room temperature, the highest value among the ones with similar diamond-particles volume fraction and size. This work demonstrates a novel strategy to enhance the thermal conductivity of composite materials by SAM functionalization.

cond-mat.mtrl-sci↗

arXiv2020

Above-room-temperature giant thermal conductivity switching in spintronic multilayer

Thermal switching provides an effective way for active heat flow control, which has recently attracted increasing attention in terms of nanoscale thermal management technologies. In magnetic and spintronic materials, the thermal conductivity depends on the magnetization configuration: this is the magneto-thermal resistance effect. Here we show that an epitaxial Cu/Co$_{50}$Fe$_{50}$ multilayer film exhibits giant magnetic-field-induced modulation of the cross-plane thermal conductivity. The magneto-thermal resistance ratio for the Cu/Co$_{50}$Fe$_{50}$ multilayer reaches 150% at room temperature, which is much larger than the previous record high. Although the ratio decreases with increasing the temperature, the giant magneto-thermal resistance effect of ~100% still appears up to 400 K. The magnetic field dependence of the thermal conductivity of the Cu/Co$_{50}$Fe$_{50}$ multilayer was observed to be about twice greater than that of the cross-plane electrical conductivity. The observation of the giant magneto-thermal resistance effect clarifies a potential of spintronic multilayers as thermal switching devices.

cond-mat.mtrl-sci↗

arXiv2020

Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle

Propulsion system electrification revolution has been undergoing in the automotive industry. The electrified propulsion system improves energy efficiency and reduces the dependence on fossil fuel. However, the batteries of electric vehicles experience degradation process during vehicle operation. Research considering both battery degradation and energy consumption in battery/ supercapacitor electric vehicles is still lacking. This study proposes a Q-learning-based strategy to minimize battery degradation and energy consumption. Besides Q-learning, two heuristic energy management methods are also proposed and optimized using Particle Swarm Optimization algorithm. A vehicle propulsion system model is first presented, where the severity factor battery degradation model is considered and experimentally validated with the help of Genetic Algorithm. In the results analysis, Q-learning is first explained with the optimal policy map after learning. Then, the result from a vehicle without ultracapacitor is used as the baseline, which is compared with the results from the vehicle with ultracapacitor using Q-learning, and two heuristic methods as the energy management strategies. At the learning and validation driving cycles, the results indicate that the Q-learning strategy slows down the battery degradation by 13-20% and increases the vehicle range by 1.5-2% compared with the baseline vehicle without ultracapacitor.

eess.SY↗

arXiv2020

Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications

Reinforcement Learning (RL) is widely utilized in the field of robotics, and as such, it is gradually being implemented in the Hybrid Electric Vehicle (HEV) supervisory control. Even though RL exhibits excellent performance in terms of fuel consumption minimization in simulation, the large learning iteration number needs a long learning time, making it hardly applicable in real-world vehicles. In addition, the fuel consumption of initial learning phases is much worse than baseline controls. This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods. Different from previous studies, which initiated Q-learning with zero or random Q values, this study initiates the Q-learning with different supervisory controls (i.e., Equivalent Consumption Minimization Strategy control and heuristic control), and detailed analysis is given. The results show that the proposed warm start Q-learning requires 68.8% fewer iterations than cold start Q-learning. The trained Q-learning is validated in two different driving cycles, and the results show 10-16% MPG improvement when compared to Equivalent Consumption Minimization Strategy control. Furthermore, real-time feasibility is analyzed, and the guidance of vehicle implementation is provided. The results of this study can be used to facilitate the deployment of RL in vehicle supervisory control applications.

cs.RO↗

arXiv2020

A gradient model for the spatial patterns of cities

The dynamics of city's spatial structures are determined by the coupling of functional components (such as restaurants and shops) and human beings within the city. Yet, there still lacks mechanism models to quantify the spatial distribution of functional components. Here, we establish a gradient model to simulate the density curves of multiple types of components based on the equilibria of gravitational and repulsive forces along the urban-rural gradient. The forces from city center to components are determined by both the city's attributes (land rent, population and people's environmental preferences) and the components attributes (supply capacity, product transportability and environmental impacts). The simulation for the distribution curves of 22 types of components on the urban-rural gradient are a good fit for the real-world data in cities. Based on the 4 typical types of components, the model reveals a bottom-up self-organizing mechanism that is, the patterns in city development are determined by the economic, ecological, and social attributes of both cities and components. Based on the mechanism, we predict the distribution curves of many types of components along with the development of cities. The model provides a general tool for analyzing the distribution of objects on the gradients.

physics.soc-ph↗