arXiv subjects

Chao Jiang

Publications and source records attributed to Chao Jiang.

At least 55 records · Page 3Linked to original sources

arXiv2022

Capturing the ground state of uranium dioxide from first principles: crystal distortion, magnetic structure, and phonons

Uranium dioxide (UO$_2$) remains a formidable challenge for first-principles approaches, due to the complex interplay among spin-orbit coupling, Mott physics, magnetic ordering, and crystal distortions. Here we use DFT+$U$ to explore UO$_2$ at zero temperature, incorporating all the aforementioned phenomena. The technical challenge is to navigate the many metastable electronic states produced by DFT+$U$, which is acomplished using $f$-orbital occupation matrix control to search for the ground state. We restrict our search to the high-symmetry ferromagnetic phase, including spin-orbit coupling, which produces a previously unreported occupation matrix. This newfound occupation matrix is then used as an initialization to explore the broken symmetry phases. We find the oxygen cage distortion of the 3k antiferromagnetic state to be in excellent agreement with experiments, and both the spin-orbit coupling and the Hubbard $U$ are critical ingredients. We demonstrate that only select phonon modes have a strong dependence on the Hubbard $U$, whereas magnetic ordering has only a small influence overall. We perform measurements of the phonon dispersion curves using inelastic neutron scattering, and our calculations show good agreement when using reasonable values of $U$. The quantitative success of DFT+$U$ warrants exploration of thermal transport and other observables within this level of theory.

cond-mat.mtrl-sci↗

arXiv2022

Application of simultaneous and continuous measurement of noncommutative observables: Preparation of the pure ideal quadrature-squeezed state by feedback control

As an application of the simultaneous and continuous measurement of noncommutative observables formulated in our previous paper [C. Jiang and G. Watanabe, Phys. Rev. A 102, 062216 (2020)], we propose a scheme to generate the pure ideal quadrature-squeezed state in a one-dimensional harmonic oscillator system by the feedback control based on such type of measurement of noncommutative quadrature observables. We find that, by appropriately setting the strengths of the measurement and the feedback control, the pure ideal quadrature-squeezed state with arbitrary squeezedness can be produced. This is in contrast to the scheme based on the single-observable measurement and the feedback control, where only nonideal squeezed states with squeezing of the measured quadrature are produced.

quant-ph↗

arXiv2021

A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking

Kalman filters are widely used for object tracking, where process and measurement noise are usually considered accurately known and constant. However, the exact known and constant assumptions do not always hold in practice. For example, when lidar is used to track noncooperative targets, the measurement noise is different under different distances and weather conditions. In addition, the process noise changes with the object's motion state, especially when the tracking object is a pedestrian, and the process noise changes more frequently. This paper proposes a new estimation-calibration-correction closed-loop estimation method to estimate the Kalman filter process and measurement noise covariance matrices online. First, we decompose the noise covariance matrix into an element distribution matrix and noise intensity and improve the Sage filter to estimate the element distribution matrix. Second, we propose a calibration method to accurately diagnose the noise intensity deviation. We then propose a correct method to adaptively correct the noise intensity online. Third, under the assumption that the system is detectable, the unbiased and convergence of the proposed method is mathematically proven. Simulation results prove the effectiveness and reliability of the proposed method. Finally, we apply the proposed method to multiobject tracking of lidar and evaluate it on the official KITTI server. The proposed method on the KITTI pedestrian multiobject tracking leaderboard (http://www.cvlibs.net/datasets /kitti/eval_tracking.php) surpasses all existing methods using lidar, proving the feasibility of the method in practical applications. This work provides a new way to improve the performance of the Kalman filter and multiobject tracking.

cs.CV↗

arXiv2021

Neural CRF Model for Sentence Alignment in Text Simplification

The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia. We propose a novel neural CRF alignment model which not only leverages the sequential nature of sentences in parallel documents but also utilizes a neural sentence pair model to capture semantic similarity. Experiments demonstrate that our proposed approach outperforms all the previous work on monolingual sentence alignment task by more than 5 points in F1. We apply our CRF aligner to construct two new text simplification datasets, Newsela-Auto and Wiki-Auto, which are much larger and of better quality compared to the existing datasets. A Transformer-based seq2seq model trained on our datasets establishes a new state-of-the-art for text simplification in both automatic and human evaluation.

cs.CL↗

arXiv2021

Densely connected neural networks for nonlinear regression

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimension of proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation (0.91) with observations, which indicates that our model could advance environmental data analysis.

cs.LG↗

arXiv2021

Neural semi-Markov CRF for Monolingual Word Alignment

Monolingual word alignment is important for studying fine-grained editing operations (i.e., deletion, addition, and substitution) in text-to-text generation tasks, such as paraphrase generation, text simplification, neutralizing biased language, etc. In this paper, we present a novel neural semi-Markov CRF alignment model, which unifies word and phrase alignments through variable-length spans. We also create a new benchmark with human annotations that cover four different text genres to evaluate monolingual word alignment models in more realistic settings. Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment as well as a competitive QA-based baseline, which was previously only applied to bilingual data. Our model demonstrates good generalizability to three out-of-domain datasets and shows great utility in two downstream applications: automatic text simplification and sentence pair classification tasks.

cs.CL↗

arXiv2021

Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy

Despite a cost-effective option in practical engineering, Reynolds-averaged Navier-Stokes simulations are facing the ever-growing demand for more accurate turbulence models. Recently, emerging machine learning techniques are making promising impact in turbulence modeling, but in their infancy for widespread industrial adoption. Towards this end, this work proposes a universal, inherently interpretable machine learning framework of turbulence modeling, which mainly consists of two parallel machine-learning-based modules to respectively infer the integrity basis and closure coefficients. At every phase of the model development, both data representing the evolution dynamics of turbulence and domain-knowledge representing prior physical considerations are properly fed and reasonably converted into modeling knowledge. Thus, the developed model is both data- and knowledge-driven. Specifically, a version with pre-constrained integrity basis is provided to demonstrate detailedly how to integrate domain-knowledge, how to design a fair and robust training strategy, and how to evaluate the data-driven model. Plain neural network and residual neural network as the building blocks in each module are compared. Emphases are made on three-fold: (i) a compact input feature parameterizing the newly-proposed turbulent timescale is introduced to release nonunique mappings between conventional input arguments and output Reynolds stress; (ii) the realizability limiter is developed to overcome under-constraint of modeled stress; and (iii) constraints of fairness and noisy-sensitivity are first included in the training procedure. In such endeavors, an invariant, realizable, unbiased and robust data-driven turbulence model is achieved, and does gain good generalization across channel flows at different Reynolds numbers and duct flows with various aspect ratios.

physics.flu-dyn↗

arXiv2021

Quantum dynamics under simultaneous and continuous measurement of noncommutative observables

We consider simultaneous and continuous measurement of two noncommutative observables of the system whose commutator is not necessarily a $c$-number. We revisit the Arthurs-Kelly model and generalize it to describe the simultaneous measurement of two observables of the system. Using this generalized model, we continuously measure the system by following the scheme proposed by Scott and Milburn [Scott and Milburn, Phys. Rev. A 63, 042101 (2001)]. We find that the unconditioned master equation reduces to the Lindblad form in the continuous limit. In addition, we find that the master equation does not contain a cross term of these two measurements. Finally, we propose a scheme to prepare the state of a two-level system in an external field by feedback control based on the simultaneous, continuous measurement of the two observables.

quant-ph↗

arXiv2020

Discourse Level Factors for Sentence Deletion in Text Simplification

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion -- a prevalent but understudied phenomenon in document simplification -- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.

cs.CL↗

arXiv2018

Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks

Learning distributed sentence representations is one of the key challenges in natural language processing. Previous work demonstrated that a recurrent neural network (RNNs) based sentence encoder trained on a large collection of annotated natural language inference data, is efficient in the transfer learning to facilitate other related tasks. In this paper, we show that joint learning of multiple tasks results in better generalizable sentence representations by conducting extensive experiments and analysis comparing the multi-task and single-task learned sentence encoders. The quantitative analysis using auxiliary tasks show that multi-task learning helps to embed better semantic information in the sentence representations compared to single-task learning. In addition, we compare multi-task sentence encoders with contextualized word representations and show that combining both of them can further boost the performance of transfer learning.

cs.CL↗

arXiv2018

LearningWord Embeddings for Low-resource Languages by PU Learning

Word embedding is a key component in many downstream applications in processing natural languages. Existing approaches often assume the existence of a large collection of text for learning effective word embedding. However, such a corpus may not be available for some low-resource languages. In this paper, we study how to effectively learn a word embedding model on a corpus with only a few million tokens. In such a situation, the co-occurrence matrix is sparse as the co-occurrences of many word pairs are unobserved. In contrast to existing approaches often only sample a few unobserved word pairs as negative samples, we argue that the zero entries in the co-occurrence matrix also provide valuable information. We then design a Positive-Unlabeled Learning (PU-Learning) approach to factorize the co-occurrence matrix and validate the proposed approaches in four different languages.

cs.CL↗

arXiv2018

Pedestrian-Robot Interaction Experiments in an Exit Corridor

The study of human-robot interaction (HRI) has received increasing research attention for robot navigation in pedestrian crowds. In this paper, we present empirical study of pedestrian-robot interaction in an uni-directional exit corridor. We deploy a mobile robot moving in a direction perpendicular to that of the pedestrian flow, and install a pedestrian motion tracking system to record the collective motion. We analyze both individual and collective motion of pedestrians, and measure the effect of the robot motion on the overall pedestrian flow. The experimental results show the effect of passive HRI, where the pedestrians' overall speed is slowed down in the presence of the robot, and the faster the robot moves, the lower the average pedestrian velocity becomes. Experiment results show qualitative consistency of the collective HRI effect with simulation results that was previously reported. The study can be used to guide future design of robot-assisted pedestrian evacuation algorithms.

cs.RO↗

arXiv2016

Accelerated atomistic simulation study on the stability and mobility of carbon tri-interstitial cluster in cubic SiC

Using a combination of kinetic Activation Relaxation Technique with empirical potential and ab initio based climbing image nudged elastic band method, we perform an extensive search of the migration and rotation paths of the most stable carbon tri-interstitial cluster in cubic SiC. Our research reveals paths with the lowest energy barriers to migration, rotation, and dissociation of the most stable cluster. The kinetic properties of the most stable cluster, including its mobility, rotation behavior at different temperatures and stability against high temperature annealing, are discussed based on the calculated transition barriers. In addition to fundamental insights, our study provides a methodology for investigation of other extended defects in a technologically important material.

cond-mat.mtrl-sci↗

arXiv2016

Using machine learning to identify factors that govern amorphization of irradiated pyrochlores

Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that, when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction but when the machine learning model is restricted to only the $B$=Ti pyrochlores, the energetics of disordering and amorphization are optimal. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.

cond-mat.mtrl-sci↗

arXiv2016

Probabilistic Human Mobility Model in Indoor Environment

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.

cs.HC↗

arXiv2015

Band-gap and Band-edge Engineering of Multicomponent Garnet Scintillators: A First-principles Study

Complex doping schemes in RE$_3$Al$_5$O$_{12}$ (RE=rare earth element) garnet compounds have recently led to pronounced improvements in scintillator performance. Specifically, by admixing lutetium and yttrium aluminate garnets with gallium and gadolinium, the band-gap was altered in a manner that facilitated the removal of deleterious electron trapping associated with cation antisite defects. Here, we expand upon this initial work to systematically investigate the effect of substitutional admixing on the energy levels of band edges. Density functional theory was used to survey potential admixing candidates that modify either the conduction band minimum (CBM) or valence band maximum (VBM). We considered two sets of compositions based on Lu$_3$B$_5$O$_{12}$ where B = Al, Ga, In, As, and Sb; and RE$_3$Al$_5$O$_{12}$, where RE = Lu, Gd, Dy, and Er. We found that admixing with various RE cations does not appreciably effect the band gap or band edges. In contrast, substituting Al with cations of dissimilar ionic radii has a profound impact on the band structure. We further show that certain dopants can be used to selectively modify only the CBM or the VBM. Specifically, Ga and In decrease the band gap by lowering the CBM, while As and Sb decrease the band gap by raising the VBM. These results demonstrate a powerful approach to quickly screen the impact of dopants on the electronic structure of scintillator compounds, identifying those dopants which alter the band edges in very specific ways to eliminate both electron and hole traps responsible for performance limitations. This approach should be broadly applicable for the optimization of electronic and optical performance for a wide range of compounds by tuning the VBM and CBM.

cond-mat.mtrl-sci↗

arXiv2012

First-principles based modeling of hydrogen permeation through Pd-Cu alloys

The solubility and diffusivity of hydrogen in disordered Pd1-xCux alloys are investigated using a combination of first-principles calculations, a composition-dependent local cluster expansion (CDLCE) technique, and kinetic Monte Carlo simulations. We demonstrate that a linear CDCLE model can already accurately describe interstitial H in Pd1-xCux alloys over the entire composition range (0\leqx\leq1) with accuracy comparable to that of direct first-principles calculations. Our predicted H solubility and permeability results are in reasonable agreement with experimental measurements. The proposed model is quite general and can be employed to rapidly and accurately screen a large number of alloy compositions for potential membrane applications. Extension to ternary or higher-order alloy systems should be straightforward. Our study also highlights the significant effect of local lattice relaxations on H energetics in size-mismatched disordered alloys, which has been largely overlooked in the literature.

cond-mat.mtrl-sci↗

arXiv2008

Site preference of transition-metal elements in L10-TiAl: A first-principles study

The site preference of 3d (Ti-Cu), 4d (Zr-Ag) and 5d (Hf-Au) transition-metal elements in L10 TiAl is studied using a combination of first-principles supercell calculations and the statistical mechanical Wagner-Schottky model. Our key finding is that both temperature and alloy stoichiometry can strongly affect the site occupancy behavior of ternary alloying elements in L10 TiAl. We further predict that the tendency of transition metals to occupy the Al sites in TiAl increases with increasing d-electron number along a series.

cond-mat.mtrl-sci↗