arXiv subjects

Xu Han

Publications and source records attributed to Xu Han.

At least 343 records · Page 19Linked to original sources

arXiv2019

Evaluating Voice Skills by Design Guidelines Using an Automatic Voice Crawler

Currently, adaptive voice applications supported by voice assistants (VA) are very popular (i.e., Alexa skills and Google Home Actions). Under this circumstance, how to design and evaluate these voice interactions well is very important. In our study, we developed a voice crawler to collect responses from 100 most popular Alexa skills under 10 different categories and evaluated these responses to find out how they comply with 8 selected design guidelines published by Amazon. Our findings show that basic commands support are the most followed ones while those related to personalised interaction are relatively less. There also exists variation in design guidelines compliance across different skill categories. Based on our findings and real skill examples, we offer suggestions for new guidelines to complement the existing ones and propose agendas for future HCI research to improve voice applications' user experiences.

cs.HC↗

arXiv2019

VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation

Deep learning (DL) approaches are state-of-the-art for many medical image segmentation tasks. They offer a number of advantages: they can be trained for specific tasks, computations are fast at test time, and segmentation quality is typically high. In contrast, previously popular multi-atlas segmentation (MAS) methods are relatively slow (as they rely on costly registrations) and even though sophisticated label fusion strategies have been proposed, DL approaches generally outperform MAS. In this work, we propose a DL-based label fusion strategy (VoteNet) which locally selects a set of reliable atlases whose labels are then fused via plurality voting. Experiments on 3D brain MRI data show that by selecting a good initial atlas set MAS with VoteNet significantly outperforms a number of other label fusion strategies as well as a direct DL segmentation approach. We also provide an experimental analysis of the upper performance bound achievable by our method. While unlikely achievable in practice, this bound suggests room for further performance improvements. Lastly, to address the runtime disadvantage of standard MAS, all our results make use of a fast DL registration approach.

cs.CV↗

arXiv2019

Effects of Hexagonal Boron Nitride Encapsulation on the Electronic Structure of Few-layer MoS$_2$

The hexagonal boron nitride (hBN) encapsulation has been widely used in the electronics applications of 2D materials to improve device performance by protecting 2D materials against contamination and degradation. It is often assumed that hBN layers as a dielectric would not affect the electronic structure of encapsulated 2D materials. Here we studied few-layer MoS$_2$ encapsulated in hBN flakes by using a combination of theoretical and experimental Raman spectroscopy. We found that after the encapsulation the out-of-plane A$_{1g}$ mode is upshifted, while the in-plane E$_{2g}^1$ mode is downshifted. The measured downshift of the E$_{2g}^1$ mode does not decrease with increasing the thickness of MoS$_2$, which can be attributed to tensile strains in bilayer and trilayer MoS$_2$ caused by the typical experimental process of the hBN encapsulation. We estimated the strain magnitude and found that the induced strain may cause the K-Q crossover in the conduction band of few-layer MoS$_2$, so greatly modifies its electronic properties as an n-type semiconductor. Our study suggests that the hBN encapsulation should be used with caution, as it may affect the electronic properties of encapsulated few-layer 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

Estimation of False Discovery Proportion with Unknown Dependence

Large-scale multiple testing with highly correlated test statistics arises frequently in many scientific research. Incorporating correlation information in estimating false discovery proportion has attracted increasing attention in recent years. When the covariance matrix of test statistics is known, Fan, Han & Gu (2012) provided a consistent estimate of False Discovery Proportion (FDP) under arbitrary dependence structure. However, the covariance matrix is often unknown in many applications and such dependence information has to be estimated before estimating FDP (Efron, 2010). The estimation accuracy can greatly affect the convergence result of FDP or even violate its consistency. In the current paper, we provide methodological modification and theoretical investigations for estimation of FDP with unknown covariance. First we develop requirements for estimates of eigenvalues and eigenvectors such that we can obtain a consistent estimate of FDP. Secondly we give conditions on the dependence structures such that the estimate of FDP is consistent. Such dependence structures include sparse covariance matrices, which have been popularly considered in the contemporary random matrix theory. When data are sampled from an approximate factor model, which encompasses most practical situations, we provide a consistent estimate of FDP via exploiting this specific dependence structure. The results are further demonstrated by simulation studies and some real data applications.

stat.ME↗

arXiv2019

Networks for Joint Affine and Non-parametric Image Registration

We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of three stages. In the first stage, a multi-step affine network predicts affine transform parameters. In the second stage, we use a Unet-like network to generate a momentum, from which a velocity field can be computed via smoothing. Finally, in the third stage, we employ a self-iterable map-based vSVF component to provide a non-parametric refinement based on the current estimate of the transformation map. Once the model is trained, a registration is completed in one forward pass. To evaluate the performance, we conducted longitudinal and cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine and non-parametric registration.

cs.CV↗

arXiv2019

Heralded Generation and Detection of Entangled Microwave--Optical Photon Pairs

Quantum state transfer between microwave and optical frequencies is essential for connecting superconducting quantum circuits to coherent optical systems and extending microwave quantum networks over long distances. To build such a hybrid `quantum Internet,' an important experiment in the quantum regime is to entangle microwave and optical modes. Based on the model of a generic cavity electro-optomechanical system, we present a heralded scheme to generate entangled microwave--optical photon pairs, which can bypass the efficiency threshold for quantum channel capacity in direct transfer protocols. The parameter regime for entanglement verification is identified that is compatible with realistic experimental settings. Our scheme is feasible given the latest experimental progress on electro-optomechanics, and can be potentially generalized to various physical systems.

quant-ph↗

arXiv2018

Knowledge Representation Learning: A Quantitative Review

Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we introduce the reader to the motivations for KRL, and overview existing approaches for KRL. Afterwards, we extensively conduct and quantitative comparison and analysis of several typical KRL methods on three evaluation tasks of knowledge acquisition including knowledge graph completion, triple classification, and relation extraction. We also review the real-world applications of KRL, such as language modeling, question answering, information retrieval, and recommender systems. Finally, we discuss the remaining challenges and outlook the future directions for KRL. The codes and datasets used in the experiments can be found in https://github.com/thunlp/OpenKE.

cs.CL↗

arXiv2018

Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks

In this article, we propose a novel approach to achieve spectrum prediction, parameter fitting, inverse design and performance optimization for the plasmonic waveguide coupled with cavities structure (PWCCS) based on artificial neural networks (ANNs). The Fano resonance and plasmon induced transparency effect originated from the PWCCS have been selected as illustrations to verify the effectiveness of ANNs. We use the genetic algorithm to design the network architecture and select the hyper-parameters for ANNs. Once ANNs are trained by using a small sampling of the data generated by Monte Carlo method, the transmission spectrums predicted by the ANNs are quite approximate to the simulated results. The physical mechanisms behind the phenomena are discussed theoretically, and the uncertain parameters in the theoretical models are fitted by utilizing the trained ANNs. More importantly, our results demonstrate that this model-driven method not only realizes the inverse design of the PWCCS with high precision but also optimizes some critical performance metrics for transmission spectrum. Compared with previous works, we construct a novel model-driven analysis method for the PWCCS which are expected to have significant applications in the device design, performance optimization, variability analysis, defect detection, theoretical modeling, optical interconnects and so on.

physics.optics↗

arXiv2018

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-the-art few-shot learning methods for relation classification and conduct a thorough evaluation of these methods. Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. We also show that a range of different reasoning skills are needed to solve our task. These results indicate that few-shot relation classification remains an open problem and still requires further research. Our detailed analysis points multiple directions for future research. All details and resources about the dataset and baselines are released on http://zhuhao.me/fewrel.

cs.LG↗

arXiv2018

Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding

In this paper, we develop a low than character feature embedding called radical embedding, and apply it on LSTM model for sentence segmentation of pre modern Chinese texts. The datasets includes over 150 classical Chinese books from 3 different dynasties and contains different literary styles. LSTM CRF model is a state of art method for the sequence labeling problem. Our new model adds a component of radical embedding, which leads to improved performances. Experimental results based on the aforementioned Chinese books demonstrates a better accuracy than earlier methods on sentence segmentation, especial in Tang Epitaph texts.

cs.CL↗

arXiv2018

Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training

Existing neural relation extraction (NRE) models rely on distant supervision and suffer from wrong labeling problems. In this paper, we propose a novel adversarial training mechanism over instances for relation extraction to alleviate the noise issue. As compared with previous denoising methods, our proposed method can better discriminate those informative instances from noisy ones. Our method is also efficient and flexible to be applied to various NRE architectures. As shown in the experiments on a large-scale benchmark dataset in relation extraction, our denoising method can effectively filter out noisy instances and achieve significant improvements as compared with the state-of-the-art models.

cs.CL↗

arXiv2018

Superconducting cavity electro-optics: a platform for coherent photon conversion between superconducting and photonic circuits

Leveraging the quantum information processing ability of superconducting circuits and long-distance distribution ability of optical photons promises the realization of complex and large-scale quantum networks. In such a scheme, a coherent and efficient quantum transducer between superconducting and photonic circuits is critical. However, such quantum transducer is still challenging since the use of intermediate excitations in current schemes introduces extra noise and limits bandwidth. Here we realize direct and coherent transduction between superconducting and photonic circuits based on triple-resonance electro-optics principle, with integrated devices incorporating both superconducting and optical cavities on the same chip. Electromagnetically induced transparency is observed, indicating the coherent interaction between microwave and optical photons. Internal conversion efficiency of 25.9\pm0.3\% has been achieved, with 2.05\pm0.04\% total efficiency. Superconducting cavity electro-optics offers broad transduction bandwidth and high scalability, and represents a significant step towards the integrated hybrid quantum circuits and distributed quantum computation.

physics.optics↗

arXiv2018

Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach

Brain extraction from images is a common pre-processing step. Many approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images with strong pathologies, for example, the presence of a tumor or of a traumatic brain injury, is challenging. In such cases, tissue appearance may deviate from normal tissue and violates algorithmic assumptions for these approaches; hence, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis, (2) pathologies are captured via a total variation term, and (3) non-brain tissue is captured by a sparse term. Decomposition and image registration steps are alternated to allow statistical modeling in a fixed atlas space. As a beneficial side effect, the model allows for the identification of potential pathologies and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our method on four datasets: the IBSR and LPBA40 datasets which show normal images, the BRATS dataset containing images with brain tumors and a dataset containing clinical TBI images. We compare the performance with other popular models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing methods on all four datasets. Specifically, our model achieves the best median (97.11) and mean (96.88) Dice scores over all datasets. The two best performing competitors, ROBEX and MASS, achieve scores of 96.23/95.62 and 96.67/94.25 respectively. Hence, our approach is an effective method for high quality brain extraction on a wide variety of images.

cs.CV↗

arXiv2018

Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering

Locating the center of convex objects is important in both image processing and unsupervised machine learning/data clustering fields. The automated analysis of biological images uses both of these fields for locating cell nuclei and for discovering new biological effects or cell phenotypes. In this work, we develop a novel clustering method for locating the centers of overlapping convex objects by modeling particles that interact by a short-range attractive and long-range repulsive potential and are confined to a potential well created from the data. We apply this method to locating the centers of clumped nuclei in cultured cells, where we show that it results in a significant improvement over existing methods (8.2% in F$_1$ score); and we apply it to unsupervised learning on a difficult data set that has rare classes without local density maxima, and show it is able to well locate cluster centers when other clustering techniques fail.

cs.CV↗

arXiv2017

Phase sensitive imaging of 10 GHz vibrations in an AlN microdisk resonator

We demonstrate a high frequency phase-sensitive heterodyne vibrometer, operating up to 10 GHz. Using this heterodyne vibrometer, the amplitude and phase fields of the fundamental thickness mode, the radial fundamental and the 2nd-order modes of an AlN optomechanical microdisk resonator are mapped with a displacement sensitivity of around 0.36 picometer at 1 Hz resolution bandwidth. The simultaneous amplitude and phase measurement allows precise mode identification and characterization. The recorded modal frequencies and proles are consistent with numerical simulations. This vibrometer will be of great significance for the development of high frequency mechanical devices.

physics.ins-det↗

arXiv2017

Stationary state distribution and efficiency analysis of the Langevin equation via real or virtual dynamics

Langevin dynamics has become a popular tool to simulate the Boltzmann equilibrium distribution. When the repartition of the Langevin equation involves the exact realization of the Ornstein-Uhlenbeck noise, in addition to the conventional density evolution, there exists another type of discrete evolution that may not correspond to a continuous, real dynamical counterpart. This virtual dynamics case is also able to produce the desired stationary distribution. Different types of repartition lead to different numerical schemes, of which the accuracy and efficiency are investigated through studying the harmonic oscillator potential, an analytical solvable model. By analyzing the asymptotic distribution and characteristic correlation time that are derived by either directly solving the discrete equations of motion or using the related phase space propagators, it is shown that the optimal friction coefficient resulting in the minimal characteristic correlation time depends on the time interval chosen in the numerical implementation. When the recommended "middle" scheme is employed, both analytical and numerical results demonstrate that for a good numerical performance in efficiency as well as accuracy, one may choose a friction coefficient in a wide range from around the optimal value to the high friction limit.

physics.chem-ph↗

arXiv2017

Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach

Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue regions, which may degrade registration performance. This paper proposes an efficient alternative to LRS: (1) normal tissue appearance is captured by principal component analysis (PCA) and (2) blurring is avoided by an integrated model for pathology removal and image reconstruction. Results on synthetic and BRATS 2015 data demonstrate its utility.

cs.CV↗

arXiv2017

AstroCloud: A Distributed Cloud Computing and Application Platform for Astronomy

Virtual Observatory (VO) is a data-intensively online astronomical research and education environment, which takes advantages of advanced information technologies to achieve seamless and global access to astronomical information. AstroCloud is a cyber-infrastructure for astronomy research initiated by Chinese Virtual Observatory (China-VO) project, and also a kind of physical distributed platform which integrates lots of tasks such as telescope access proposal management, data archiving, data quality control, data release and open access, cloud based data processing and analysis. It consists of five application channels, i.e. observation, data, tools, cloud and public and is acting as a full lifecycle management system and gateway for astronomical data and telescopes. Physically, the platform is hosted in six cities currently, i.e. Beijing, Nanjing, Shanghai, Kunming, Lijiang and Urumqi, and serving more than 17 thousand users. Achievements from international Virtual Observatories and Cloud Computing are adopted heavily. In the paper, backgrounds of the project, architecture, Cloud Computing environment, key features of the system, current status and future plans are introduced.

astro-ph.IM↗