arXiv subjects

Yue Zhang

Publications and source records attributed to Yue Zhang.

At least 721 records · Page 40Linked to original sources

arXiv2019

A Pilot Study for Chinese SQL Semantic Parsing

The task of semantic parsing is highly useful for dialogue and question answering systems. Many datasets have been proposed to map natural language text into SQL, among which the recent Spider dataset provides cross-domain samples with multiple tables and complex queries. We build a Spider dataset for Chinese, which is currently a low-resource language in this task area. Interesting research questions arise from the uniqueness of the language, which requires word segmentation, and also from the fact that SQL keywords and columns of DB tables are typically written in English. We compare character- and word-based encoders for a semantic parser, and different embedding schemes. Results show that word-based semantic parser is subject to segmentation errors and cross-lingual word embeddings are useful for text-to-SQL.

cs.CL↗

arXiv2019

Timing analysis of Swift J1658.2-4242's outburst in 2018 with Insight-HXMT, NICER and AstroSat

We present the observational results from a detailed timing analysis of the black hole candidate Swift J1658.2-4242 during its 2018 outburst with the observations of Hard X-ray Modulation Telescope (Insight-HXMT), Neutron Star Interior Composition Explorer (NICER) and AstroSat in 0.1-250keV. The evolution of intensity, hardness and integrated fractional root mean square (rms) observed by Insight-HXMT and NICER are presented in this paper. Type-C quasi-periodic oscillations (QPOs) observed by NICER (0.8-3.5Hz) and Insight-HXMT (1-1.6Hz) are also reported in this work. The features of the QPOs are analysed with an energy range of 0.5-50keV. The relations between QPO frequency and other characteristics such as intensity, hardness and QPO rms are carefully studied. The timing and spectral properties indicate that Swift J1658.2-4242 is a black hole binary system. Besides, the rms spectra of the source calculated from the simultaneous observation of Insight-HXMT, NICER and AstroSat support the Lense-Thirring origin of the QPOs. The relation between QPO phase lag and the centroid frequency of Swift J1658.2-4242 reveals a near zero constant when < 4Hz and a soft phase lag at 6.68 Hz. This independence follows the same trend as the high inclination galactic black hole binaries such as MAXI J1659-152.

astro-ph.HE↗

arXiv2019

Domain Representation for Knowledge Graph Embedding

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.

cs.AI↗

arXiv2019

On the (In)security of Bluetooth Low Energy One-Way Secure Connections Only Mode

To defeat security threats such as man-in-the-middle (MITM) attacks, Bluetooth Low Energy (BLE) 4.2 and 5.x introduce the Secure Connections Only mode, under which a BLE device accepts only secure paring protocols including Passkey Entry and Numeric Comparison from an initiator, e.g., an Android mobile. However, the BLE specification does not explicitly require the Secure Connection Only mode of the initiator. Taking the Android's BLE programming framework for example, we found that it cannot enforce secure pairing, invalidating the security protection provided by the Secure Connection Only mode. The same problem applies to Apple iOS too. Specifically, we examine the life cycle of a BLE pairing process in Android and identify four severe design flaws. These design flaws can be exploited by attackers to perform downgrading attacks, forcing the BLE pairing protocols to run in the insecure mode without the users' awareness. To validate our findings, we selected and tested 18 popular BLE commercial products and our experimental results proved that downgrading attacks and MITM attacks were all possible to these products. All 3501 BLE apps from Androzoo are also subject to these attacks. For defense, we have designed and implemented a prototype of the Secure Connection Only mode on Android 8 through the Android Open Source Project (AOSP). We have reported the identified BLE pairing vulnerabilities to Bluetooth Special Interest Group (SIG), Google, Apple, Texas Instruments (TI) and all of them are actively addressing this issue. Google rated the reported security flaw a High Severity.

cs.CR↗

arXiv2019

Joint Time and Energy-Optimal Control of Connected Automated Vehicles at Signal-Free Intersections with Speed-Dependent Safety Guarantees

We extend earlier work establishing a framework for optimally controlling Connected Automated Vehicles (CAVs) crossing a signal free intersection by jointly optimizing energy and travel time. We derive explicit optimal control solutions in a decentralized manner that guarantee both a speed-dependent rear-end safety constraint and a time-dependent lateral collision constraint, in addition to lower/upper bounds on speed and acceleration. Extensive simulation examples are included to illustrate this framework.

math.OC↗

arXiv2019

Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels

The research about deep learning application for physical layer has been received much attention in recent years. In this paper, we propose a Deep Learning (DL) based channel estimator under time varying Rayleigh fading channel. We build up, train and test the channel estimator using Neural Network (NN). The proposed DL-based estimator can dynamically track the channel status without any prior knowledge about the channel model and statistic characteristics. The simulation results show the proposed NN estimator has better Mean Square Error (MSE) performance compared with the traditional algorithms and some other DL-based architectures. Furthermore, the proposed DL-based estimator also shows its robustness with the different pilot densities.

eess.SP↗

arXiv2019

Coarse-to-fine Kidney Segmentation Framework Incorporating with Abnormal Detection and Correction

In this paper, we formulated the kidney segmentation task in a coarse-to-fine fashion, predicting a coarse label based on the entire CT image and a fine label based on the coarse segmentation and separated image patches. A key difference between the two stages lies in how input images were preprocessed; for the coarse segmentation, each 2D CT slice was normalized to be of the same image size (but possible different pixel size), and for the fine segmentation, each 2D CT slice was first resampled to be of the same pixel size and then cropped to be of the same image size. In other words, the image inputs to the coarse segmentation were 2D CT slices of the same image size whereas those to the fine segmentation were 2D MR patches of the same image size as well as the same pixel size. In addition, we design an abnormal detection method based on component analysis and use another 2D convolutional neural network to correct these abnormal regions between two stages. A total of 168 CT images were used to train the proposed framework and the evaluations were conducted qualitatively on other 42 testing images. The proposed method showed promising results and achieved 94.53 \% averaged DSC in testing data.

eess.IV↗

arXiv2019

SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects

Object detection has been a building block in computer vision. Though considerable progress has been made, there still exist challenges for objects with small size, arbitrary direction, and dense distribution. Apart from natural images, such issues are especially pronounced for aerial images of great importance. This paper presents a novel multi-category rotation detector for small, cluttered and rotated objects, namely SCRDet. Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects. Meanwhile, the supervised pixel attention network and the channel attention network are jointly explored for small and cluttered object detection by suppressing the noise and highlighting the objects feature. For more accurate rotation estimation, the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 as well as natural image datasets COCO, VOC2007 and scene text data ICDAR2015 show the state-of-the-art performance of our detector. The code and models will be available at https://github.com/DetectionTeamUCAS.

cs.CV↗

arXiv2019

Hausdorff-$(2n-2)$ dimensional measure zero set and compactness of the $\overline{\partial}$-Neumann operator on $(0,n-1)$ forms

By using a variant Property $(P_q)$ of Catlin, we discuss the relation of small set of weakly pseudoconvex points on the boundary of pseudoconvex domain and compactness of the $\overline{\partial}$-Neumann operator. In particular, we show that if the Hausdorff $(2n-2)$-dimensional measure of the weakly pseudoconvex points on the boundary of a smooth bounded pseudoconvex domain is zero, then the $\overline{\partial}$-Neumann operator $N_{n-1}$ is compact on $(0,n-1)$-level $L^2$-integrable forms.

math.CV↗

arXiv2019

Investigating Self-Attention Network for Chinese Word Segmentation

Neural network has become the dominant method for Chinese word segmentation. Most existing models cast the task as sequence labeling, using BiLSTM-CRF for representing the input and making output predictions. Recently, attention-based sequence models have emerged as a highly competitive alternative to LSTMs, which allow better running speed by parallelization of computation. We investigate self attention network for Chinese word segmentation, making comparisons between BiLSTM-CRF models. In addition, the influence of contextualized character embeddings is investigated using BERT, and a method is proposed for integrating word information into SAN segmentation. Results show that SAN gives highly competitive results compared with BiLSTMs, with BERT and word information further improving segmentation for in-domain and cross-domain segmentation. Our final models give the best results for 6 heterogenous domain benchmarks.

cs.CL↗

arXiv2019

Multi-Granular Text Encoding for Self-Explaining Categorization

Self-explaining text categorization requires a classifier to make a prediction along with supporting evidence. A popular type of evidence is sub-sequences extracted from the input text which are sufficient for the classifier to make the prediction. In this work, we define multi-granular ngrams as basic units for explanation, and organize all ngrams into a hierarchical structure, so that shorter ngrams can be reused while computing longer ngrams. We leverage a tree-structured LSTM to learn a context-independent representation for each unit via parameter sharing. Experiments on medical disease classification show that our model is more accurate, efficient and compact than BiLSTM and CNN baselines. More importantly, our model can extract intuitive multi-granular evidence to support its predictions.

cs.CL↗

arXiv2019

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.

astro-ph.HE↗

arXiv2019

Latent Variable Sentiment Grammar

Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.

cs.CL↗

arXiv2019

Voltage-controlled skyrmion-based artificial synapse in a synthetic antiferromagnet

Spintronics exhibits significant potential in neuromorphic computing system with high speed, high integration density, and low dissipation. In this letter, we propose an ultralow-dissipation spintronic memristor composed of a synthetic antiferromagnet (SAF) and a piezoelectric substrate. Skyrmions/skyrmion bubbles can be generated in the upper layer of SAF with weak anisotropy energy (Ea). With a weak electric field on the heterostructure, the interlayer antiferromagnetic coupling can be manipulated, giving rise to a continuous transition between a large skyrmion bubble and a small skyrmion. This thus induces the variation of the resistance of a magnetic tunneling junction. The synapse based on this principle may manipulate the weight in a wide range at a cost of a very low energy consumption of 0.3 fJ. These results pave a way to ultralow power neuromorphic computing applications.

physics.comp-ph↗

arXiv2019

Learning Word Embeddings with Domain Awareness

Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory performances when training data originate from heterogeneous domains. In this paper, we propose two novel mechanisms for domain-aware word embedding training, namely domain indicator and domain attention, which integrate domain-specific knowledge into the widely used SG and CBOW models, respectively. The two methods are based on a joint learning paradigm and ensure that words in a target domain are intensively focused when trained on a source domain corpus. Qualitative and quantitative evaluation confirm the validity and effectiveness of our models. Compared to baseline methods, our method is particularly effective in near-cold-start scenarios.

cs.CL↗

arXiv2019

Open Domain Event Extraction Using Neural Latent Variable Models

We consider open domain event extraction, the task of extracting unconstraint types of events from news clusters. A novel latent variable neural model is constructed, which is scalable to very large corpus. A dataset is collected and manually annotated, with task-specific evaluation metrics being designed. Results show that the proposed unsupervised model gives better performance compared to the state-of-the-art method for event schema induction.

cs.CL↗

arXiv2019

Voltage-induced high-speed DW motion in a synthetic antiferromagnet

Voltage-induced motion of a magnetic domain wall (DW) has potential in developing novel devices with ultralow dissipation. However, the speed for the voltage-induced DW motion (VIDWM) in a single ferromagnetic layer is usually very low. In this work, we proposed VIDWM with high speed in a synthetic antiferromaget (SAF). The velocity for the coupled DWs in the SAF is significantly higher than its counterpart in a single ferromagnetic layer. Strong interlayer antiferromagnetic exchange coupling plays a critical role for the high DW velocity since it inhibits the tilting of DW plane with strong Dzyaloshinskii-Moriya interaction. On the other hand, the Walker breakdown of DW motion is also inhibited due to the stabilization of moment orientation under a strong interlayer antiferromagnetic coupling. In theory, the voltage-induced gradient of magnetic anisotropy is proved to be equal to an effective magnetic field that drives DW.

physics.app-ph↗

arXiv2019

Code-Switching for Enhancing NMT with Pre-Specified Translation

Leveraging user-provided translation to constrain NMT has practical significance. Existing methods can be classified into two main categories, namely the use of placeholder tags for lexicon words and the use of hard constraints during decoding. Both methods can hurt translation fidelity for various reasons. We investigate a data augmentation method, making code-switched training data by replacing source phrases with their target translations. Our method does not change the MNT model or decoding algorithm, allowing the model to learn lexicon translations by copying source-side target words. Extensive experiments show that our method achieves consistent improvements over existing approaches, improving translation of constrained words without hurting unconstrained words.

cs.CL↗