arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 1,513 records · Page 84Linked to original sources

arXiv2018

Two Classes of Singularities and Novel Topology in a Specially Designed Synthetic Photonic Crystals

Zak phase and topological protected edge state are usually studied in one-dimensional(1D) photonic systems with spatial inversion symmetry(SIS). Interestingly in this work, we find specific classes of 1D structure without SIS can be mapped to system with SIS and also exhibit novel topology, which manifest as phase cut lines(PCLs) in our specially designed synthetic photonic crystals(SPCs). Zak phase defined in SIS is extended to depict the topology of PCLs after redefinition and topological protected edge state is also achieved in our 1D structure without SIS. In our SPCs, the relationship between Chern numbers in two-dimension(2D) and the extended Zak phases of 1D PCLs is given, which are bound by the first type singularities. Higher Chern numbers and multi chiral edge states are achieved utilizing the concept of synthetic dimensions. The effective Hamiltonian is given, based on which we find that the band edges of each PCLs play a role analogous to the valley pseudospin and our SPC is actually a new type of valley photonic crystal which is usually studied in graphene-like honeycomb lattice. The chiral valley edge transport is also demonstrated. In higher dimension, the shift of the first type singularity in expanded parameter space will lead to the Weyl point topological transition which we proposed in our previous work. In this paper, we find a second type of singularities which manifests as a singular surface in our expanded parameter space. The shift of the singular surface will lead to the nodal line topological transition. Astonishingly, we find the states on the singular surface possess extremely high robust against certain randomness, based on which a topological wave filter is constructed.

physics.optics↗

arXiv2018

Casimir Thrust Force on a Rotating Chiral Particle

In the work, the thermal and vacuum fluctuation is predicted capable of generating a Casimir thrust force on a rotating chiral particle, which will push or pull the particle along the rotation axis. The Casimir thrust force comes from two origins: i) the rotation-induced symmetry-breaking in the vacuum and thermal fluctuation and ii) the chiral cross-coupling between electric and magnetic fields and dipoles, which can convert the vacuum spin angular momentum (SAM) to the vacuum force. Using the fluctuation dissipation theorem (FDT), we derive the analytical expressions for the vacuum thrust force in dipolar approximation and the dependences of the force on rotation frequency, temperature and material optical properties are investigated. The work reveals a new mechanism to generate a vacuum force, which opens a new way to exploit zero-point energy of vacuum.

quant-ph↗

arXiv2018

Optimal Rotational Load Shedding via Bilinear Integer Programming

This paper addresses the problem of managing rotational load shedding schedules for a power distribution network with multiple load zones. An integer optimization problem is formulated to find the optimal number and duration of planned power outages. Various types of damage costs are proposed to capture the heterogeneous load shedding preferences of different zones. The McCormick relaxation along with an effective procedure feasibility recovery is developed to solve the resulting bilinear integer program, which yields a high-quality suboptimal solution. Extensive simulation results corroborate the merit of the proposed approach, which has a substantial edge over existing load shedding schemes.

math.OC↗

arXiv2018

Spatial Confinement, Magnetic Localization and Their Interactions on Massless Dirac Fermions

It is of keen interest to researchers understanding different approaches to confine massless Dirac fermions in graphene, which is also a central problem in making electronic devices based on graphene. Here, we studied spatial confinement, magnetic localization and their interactions on massless Dirac fermions in an angled graphene wedge formed by two linear graphene p-n boundaries with an angle 34. Using scanning tunneling microscopy, we visualized quasibound states temporarily confined in the studied graphene wedge. Large perpendicular magnetic fields condensed the massless Dirac fermions in the graphene wedge into Landau levels (LLs). The spatial confinement of the wedge affects the Landau quantization, which enables us to experimentally measure the spatial extent of the wave functions of the LLs. The magnetic fields induce a sudden and large increase in energy of the quasibound states because of a pi Berry phase jump of the massless Dirac fermions in graphene. Such a behavior is the hallmark of the Klein tunneling in graphene. Our experiment demonstrated that the angled wedge is a unique system with the critical magnetic fields for the pi Berry phase jump depending on distance from summit of the wedge.

cond-mat.mes-hall↗

arXiv2018

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

Motivation: State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type. Results: We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.

cs.IR↗

arXiv2018

Underwater Anchor-AUV Localization Geometries with an Isogradient Sound Speed Profile: A CRLB-Based Optimality Analysis

Existing works have explored the anchor deployment for autonomous underwater vehicles (AUVs) localization under the assumption that the sound propagates straightly underwater at a constant speed. Considering that the underwater acoustic waves propagate along bent curves at varying speeds in practice, it becomes much more challenging to determine a proper anchor deployment configuration. In this paper, taking the practical variability of underwater sound speed into account, we investigate the anchor-AUV geometry problem in a 3-D time-of-flight (ToF) based underwater scenario from the perspective of localization accuracy. To address this problem, we first rigorously derive the Jacobian matrix of measurement errors to quantify the Cramer-Rao lower bound (CRLB) with a widely-adopted isogradient sound speed profile (SSP). We then formulate an optimization problem that minimizes the trace of the CRLB subject to the angle and range constraints to figure out the anchor-AUV geometry, which is multivariate and nonlinear and thus generally hard to handle. For mathematical tractability, by adopting tools from the estimation theory, we interestingly find that this problem can be equivalently transformed into a more explicit univariate optimization problem. By this, we obtain an easy-to-implement anchor-AUV geometry that yields satisfactory localization performance, referred to as the uniform sea-surface circumference (USC) deployment. Extensive simulation results validate our theoretical analysis and show that our proposed USC scheme outperforms both the cube and the random deployment schemes in terms of localization accuracy under the same parameter settings.

cs.IT↗

arXiv2018

A study of variable stars in the open cluster NGC 1582 and its surrounding field

This paper presents the Charge-Coupled Device time-series photometric observations of the open cluster NGC 1582 and its surrounding field with Johnson B, V and R filters by using Nanshan 1m telescope of Xinjiang Astronomical Observatory. 19 variable stars and 3 variable candidates were detected in a 45 {\times} 48.75 field around the cluster. 12 of the variable stars are newly-discovered variable objects. The physical properties, classifications, and membership of the 22 objects are studied through their light curves, their positions on the color-magnitude diagram, and with the archival data from the Naval Observatory Merged Astrometric Dataset. Among these objects, 5 are eclipsing binary systems, 6 are pulsating variable stars including one known Delta Scuti star and one newly-discovered RR Lyrae star. The distance of the RR Lyrae star is estimated to be 7.9 {\pm} 0.3 kpc, indicates that the star locates far behind the cluster. 4 variable stars are probable members of the cluster, and 13 of the 22 objects are confirmed to be field stars.

astro-ph.SR↗

arXiv2018

Human activity recognition based on time series analysis using U-Net

Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method. This method faces the multi-class window problem which mistakenly labels different classes of sampling points within a window as a class. In this paper, a HAR algorithm based on U-Net is proposed to perform activity labeling and prediction at each sampling point. The activity data of the triaxial accelerometer is mapped into an image with the single pixel column and multi-channel which is input into the U-Net network for training and recognition. Our proposal can complete the pixel-level gesture recognition function. The method does not need manual feature extraction and can effectively identify short-term behaviors in long-term activity sequences. We collected the Sanitation dataset and tested the proposed scheme with four open data sets. The experimental results show that compared with Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully Convolutional Networks (FCN) methods, our proposal has the highest accuracy and F1-socre in each dataset, and has stable performance and high robustness. At the same time, after the U-Net has finished training, our proposal can achieve fast enough recognition speed.

cs.LG↗

arXiv2018

Simple Recurrent Units for Highly Parallelizable Recurrence

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.

cs.CL↗

arXiv2018

Image Matters: Visually modeling user behaviors using Advanced Model Server

In Taobao, the largest e-commerce platform in China, billions of items are provided and typically displayed with their images. For better user experience and business effectiveness, Click Through Rate (CTR) prediction in online advertising system exploits abundant user historical behaviors to identify whether a user is interested in a candidate ad. Enhancing behavior representations with user behavior images will help understand user's visual preference and improve the accuracy of CTR prediction greatly. So we propose to model user preference jointly with user behavior ID features and behavior images. However, training with user behavior images brings tens to hundreds of images in one sample, giving rise to a great challenge in both communication and computation. To handle these challenges, we propose a novel and efficient distributed machine learning paradigm called Advanced Model Server (AMS). With the well known Parameter Server (PS) framework, each server node handles a separate part of parameters and updates them independently. AMS goes beyond this and is designed to be capable of learning a unified image descriptor model shared by all server nodes which embeds large images into low dimensional high level features before transmitting images to worker nodes. AMS thus dramatically reduces the communication load and enables the arduous joint training process. Based on AMS, the methods of effectively combining the images and ID features are carefully studied, and then we propose a Deep Image CTR Model. Our approach is shown to achieve significant improvements in both online and offline evaluations, and has been deployed in Taobao display advertising system serving the main traffic.

cs.CV↗

arXiv2018

Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis

Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality pairs for training, which are expensive to collect. In this paper, we propose a semi-supervised training framework to improve the data efficiency of Tacotron. The idea is to allow Tacotron to utilize textual and acoustic knowledge contained in large, publicly-available text and speech corpora. Importantly, these external data are unpaired and potentially noisy. Specifically, first we embed each word in the input text into word vectors and condition the Tacotron encoder on them. We then use an unpaired speech corpus to pre-train the Tacotron decoder in the acoustic domain. Finally, we fine-tune the model using available paired data. We demonstrate that the proposed framework enables Tacotron to generate intelligible speech using less than half an hour of paired training data.

cs.CL↗

arXiv2018

Optical Observations of the Young Type Ic Supernova SN 2014L in M99

We present optical spectroscopic and photometric observations of the nearby type Ic supernova (SN Ic) SN 2014L. This SN was discovered by the Tsinghua-NAOC Transient Survey (TNTS) in the nearby type-Sc spiral galaxy M99 (NGC 4254). Fitting to the early-time light curve indicates that SN 2014L was detected at only a few hours after the shock breakout, and it reached a peak brightness of $M_{\rm V} = -17.73 \pm 0.28$ mag ($L= [2.06\pm 0.50] \times 10^{42}$ erg s$^{-1}$) approximately 13 days later. SN 2014L shows a close resemblance to SN 2007gr in the photometric evolution, while it shows stronger absorption features of intermediate-mass elements (especially \CaII) in the early-time spectra. Based on simple modeling of the observed light curves, we derived the mass of synthesized $^{56}$Ni as $M_{\rm Ni} = 0.075 \pm 0.025 M_{\odot}$, and the mass and total energy of the ejecta as $M_{\rm ej} = 1.00 \pm 0.20 M_\odot$ and $E_{\rm ej}=1.45\pm0.25$ foe, respectively. Given these typical explosion parameters, the early detection, and the extensive observations, we suggest that SN 2014L could be a template sample for the investigation of SNe Ic.

astro-ph.HE↗

arXiv2018

Probing millicharge at BESIII

We propose to search for millicharged particles at the BESIII detector which is operated at the Beijing Electron Positron Collider. We compute the monophoton signal events at the BESIII detector due to millicharged particle production, as well as due to standard model irreducible/reducible backgrounds. By utilizing all the data accumulated at the BESIII detector since 2011, we derive new leading upper limits on millicharge, $\varepsilon \lesssim (0.86-2.5) \times10^{-3}$, for the mass range, $0.1~\text{GeV} \lesssim m \lesssim 1~\text{GeV}$. Furthermore, projections with more data to be collected at the BESIII detector are also made. Our analysis significantly reduces the parameter region of millicharge to account for the anomalous 21 cm absorption signal near redshift $z\simeq 17$ recently observed by the EDGES experiment

hep-ph↗

arXiv2018

Back-Translation-Style Data Augmentation for End-to-End ASR

In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-encoder model which predicts a sequence of hidden states extracted by a pre-trained E2E-ASR encoder from a sequence of characters. By using hidden states as a target instead of acoustic features, it is possible to achieve faster attention learning and reduce computational cost, thanks to sub-sampling in E2E-ASR encoder, also the use of the hidden states can avoid to model speaker dependencies unlike acoustic features. After training, the text-to-encoder model generates the hidden states from a large amount of unpaired text, then E2E-ASR decoder is retrained using the generated hidden states as additional training data. Experimental evaluation using LibriSpeech dataset demonstrates that our proposed method achieves improvement of ASR performance and reduces the number of unknown words without the need for paired data.

cs.CL↗

arXiv2018

An Efficient System for Subgraph Discovery

Subgraph discovery in a single data graph---finding subsets of vertices and edges satisfying a user-specified criteria---is an essential and general graph analytics operation with a wide spectrum of applications. Depending on the criteria, subgraphs of interest may correspond to cliques of friends in social networks, interconnected entities in RDF data, or frequent patterns in protein interaction networks to name a few. Existing systems usually examine a large number of subgraphs while employing many computers and often produce an enormous result set of subgraphs. How can we enable fast discovery of only the most relevant subgraphs while minimizing the computational requirements? We present Nuri, a general subgraph discovery system that allows users to succinctly specify subgraphs of interest and criteria for ranking them. Given such specifications, Nuri efficiently finds the k most relevant subgraphs using only a single computer. It prioritizes (i.e., expands earlier than others) subgraphs that are more likely to expand into the desired subgraphs (prioritized subgraph expansion) and proactively discards irrelevant subgraphs from which the desired subgraphs cannot be constructed (pruning). Nuri can also efficiently store and retrieve a large number of subgraphs on disk without being limited by the size of main memory. We demonstrate using both real and synthetic datasets that Nuri on a single core outperforms the closest alternative distributed system consuming 40 times more computational resources by more than 2 orders of magnitude for clique discovery and 1 order of magnitude for subgraph isomorphism and pattern mining.

cs.DB↗

arXiv2018

Deep Reinforcement Learning for Chinese Zero pronoun Resolution

Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sighted---they often make local decisions. They typically predict coreference chains between the zero pronoun and one single candidate antecedent one link at a time, while overlooking their long-term influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is critical when later predicting zero pronoun-candidate antecedent pairs. In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models. With the help of the reinforcement learning agent, our model learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 dataset show that our technique surpasses the state-of-the-art models.

cs.CL↗

arXiv2018

Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI

Canonical correlation analysis (CCA) is a state-of-the-art method for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. Various extended methods have been developed, and among such methods, a combination method of CCA and individual-template-based CCA (IT-CCA) has achieved excellent performance. However, CCA requires the canonical vectors to be orthogonal, which may not be a reasonable assumption for EEG analysis. In the current study, we propose using the correlated component analysis (CORRCA) rather than CCA to implement frequency recognition. CORRCA can relax the constraint of canonical vectors in CCA, and generate the same projection vector for two multichannel EEG signals. Furthermore, we propose a two-stage method based on the basic CORRCA method (termed TSCORRCA). Evaluated on a benchmark dataset of thirty-five subjects, the experimental results demonstrate that CORRCA significantly outperformed CCA, and TSCORRCA obtained the best performance among the compared methods. This study demonstrates that CORRCA-based methods have great potential for implementing high-performance SSVEP-based BCI systems.

q-bio.NC↗

arXiv2018

Future prospects of mass-degenerate Higgs bosons in the $CP$-conserving two-Higgs-doublet model

The scenario of two mass-degenerate Higgs bosons within the general two-Higgs-doublet model (2HDM) is revisited. We focus on the global picture when two $CP$-even Higgs bosons of $h$ and $H$ are nearly mass-degenerate. A global fit to the signal strength of the 125 GeV Higgs measured at the LHC is performed. Based on the best-fit result of the 2HDM mixing angles $(α,β)$, theoretical constraints, charged and $CP$-odd Higgs boson direct search constraints and the electroweak precision constraints are imposed to the 2HDM parameter space. We present the signal predictions of the $(4b\,, 2b\,2γ)$ channels for the benchmark models at the LHC 14 TeV runs. We also study the direct Higgs boson pair productions at the LHC, and the Z-associated Higgs boson pair production search at the ILC 500 GeV runs, as well as the indirect probes at the CEPC 250 GeV run. We find that the mass-degenerate Higgs boson scenario in the Type-II 2HDM can be fully probed by these future experimental searches.

hep-ph↗