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Kai Wang

Publications and source records attributed to Kai Wang.

At least 739 records · Page 41Linked to original sources
arXiv2019

Experimental parity-time symmetry quantum walks on a directed graph

Quantum walks (QW) are of crucial importance in the development of quantum information processing algorithms. Recently, several quantum algorithms have been proposed to implement network analysis, in particular to rank the centrality of nodes in networks represented by graphs. Employing QW in centrality ranking is advantageous comparing to certain widely used classical algorithms (e.g. PageRank) because QW approach can lift the vertex rank degeneracy in certain graphs. However, it is challenging to implement a directed graph via QW, since it corresponds to a non-Hermitian Hamiltonian and thus cannot be accomplished by conventional QW. Here we report the realizations of centrality rankings of both a three-vertex and four-vertex directed graphs with parity-time (PT) symmetric quantum walks. To achieve this, we use high-dimensional photonic quantum states, optical circuitries consisting of multiple concatenated interferometers and dimension dependent loss. Importantly, we demonstrate the advantage of QW approach experimentally by breaking the vertex rank degeneracy in a four-vertex graph. Our work shows that PT-symmetric quantum walks may be useful for realizing advanced algorithm in a quantum network.

quant-ph↗
arXiv2019

Dynamics of a mean-reverting stochastic volatility model with regime switching

In this paper, we consider a mean-reverting stochastic volatility equation with regime switching, and present some sufficient conditions for the existence of global positive solution, asymptotic boundedness in pth moment, positive recurrence and existence of stationary distribution of this equation. Some results obtained in this paper extend the ones in literature. Example is given to verify the results by simulation.

math.PR↗
arXiv2019

On-demand generation of entangled photon pairs in the telecom C-band for fiber-based quantum networks

On-demand sources of entangled photons for the transmission of quantum information in the telecom C-band are required to realize fiber-based quantum networks. So far, non-deterministic sources of quantum states of light were used for long distance entanglement distribution in this lowest loss wavelength range. However, they are fundamentally limited in either efficiency or security due to their Poissonian emission statistics. Here, we show on-demand generation of entangled photon pairs in the telecom C-band by an InAs/GaAs semiconductor quantum dot. Using a robust phonon-assisted excitation scheme we measure a concurrence of $91.4\,\%$ and a fidelity of $95.2\,\%$ to $Φ^+$. On-demand generation of polarization entangled photons will enable secure quantum communication in fiber-based networks. Furthermore, applying this excitation scheme to several remote quantum dots tuned into resonance will enable first on-demand entanglement distribution over large distances for scalable real-life quantum applications.

cond-mat.mes-hall↗
arXiv2019

On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.

cs.AI↗
arXiv2019

Trade-off relation among genuine three-qubit nonlocalities in four-qubit systems

We study the trade-off relations satisfied by the genuine tripartite nonlocality in multipartite quantum systems. From the reduced three-qubit density matrices of the four-qubit generalized Greenberger-Horne-Zeilinger (GHZ) states and W states (4-qubit entangled state), we find that there exists a trade-off relation among the mean values of the Svetlichny operators associated with these reduced states. Namely, the genuine three-qubit nonlocalities are not independent. For four-qubit generalized GHZ states and W states, the summation of all their three-qubit maximal (squared) mean values of the Svetlichny operator has an upper bound. This bound is better than the one derived from the upper bounds of individual three-qubit mean values of the Svetlichny operator. Detailed examples are presented to illustrate the trade-off relation among the three-qubit nonlocalities.

quant-ph↗
arXiv2019

Towards More Realistic Human-Robot Conversation: A Seq2Seq-based Body Gesture Interaction System

This paper presents a novel system that enables intelligent robots to exhibit realistic body gestures while communicating with humans. The proposed system consists of a listening model and a speaking model used in corresponding conversational phases. Both models are adapted from the sequence-to-sequence (seq2seq) architecture to synthesize body gestures represented by the movements of twelve upper-body keypoints. All the extracted 2D keypoints are firstly 3D-transformed, then rotated and normalized to discard irrelevant information. Substantial videos of human conversations from Youtube are collected and preprocessed to train the listening and speaking models separately, after which the two models are evaluated using metrics of mean squared error (MSE) and cosine similarity on the test dataset. The tuned system is implemented to drive a virtual avatar as well as Pepper, a physical humanoid robot, to demonstrate the improvement on conversational interaction abilities of our method in practice.

cs.CV↗
arXiv2019

CBOWRA: A Representation Learning Approach for Medication Anomaly Detection

Electronic health record is an important source for clinical researches and applications, and errors inevitably occur in the data, which could lead to severe damages to both patients and hospital services. One of such error is the mismatches between diagnoses and prescriptions, which we address as 'medication anomaly' in the paper, and clinicians used to manually identify and correct them. With the development of machine learning techniques, researchers are able to train specific model for the task, but the process still requires expert knowledge to construct proper features, and few semantic relations are considered. In this paper, we propose a simple, yet effective detection method that tackles the problem by detecting the semantic inconsistency between diagnoses and prescriptions. Unlike traditional outlier or anomaly detection, the scheme uses continuous bag of words to construct the semantic connection between specific central words and their surrounding context. The detection of medication anomaly is transformed into identifying the least possible central word based on given context. To help distinguish the anomaly from normal context, we also incorporate a ranking accumulation strategy. The experiments were conducted on two real hospital electronic medical records, and the topN accuracy of the proposed method increased by 3.91 to 10.91% and 0.68 to 2.13% on the datasets, respectively, which is highly competitive to other traditional machine learning-based approaches.

cs.CL↗
arXiv2019

A Possible ~20 yr Periodicity in Long-term Optical Photometric and Spectral Variations of the Nearby Radio-Quiet Active Galactic Nucleus Ark 120

We study the long-term variability in the optical monitoring database of Ark~120, a nearby radio-quiet active galactic nucleus (AGN) at a distance of 143 Mpc (z=0.03271). We compiled the historical archival photometric and spectroscopic data since 1974 and conducted a new two-year monitoring campaign in 2015-2017, resulting in a total temporal baseline over four decades. The long-term variations in the optical continuum exhibit a wave-like pattern and the Hbeta integrated flux series varies with a similar behavior. The broad Hbeta profiles have asymmetric double peaks, which change strongly with time and tend to merge into a single peak during some epochs. The period in the optical continuum determined from various period-search methods is about 20 yr and the estimated false alarm probability with null hypothesis simulations is about 1*10^-3. The overall variations of the broad Hbeta profiles also follow the same period. However, the present database only covers two cycles of the suggested period, which strongly encourages continued monitoring to track more cycles and confirm the periodicity. Nevertheless, in light of the possible periodicity and the complicated Hbeta profile, Ark~120 is one candidate of the nearest radio-quiet AGNs with possible periodic variability, and it is thereby a potential candidate host for a sub-parsec supermassive black hole binary.

astro-ph.HE↗
arXiv2019

Probe Charm Yukawa at the future $e^{-}p$ and $e^{+}e^{-}$ colliders

Large Hadron Collider (LHC) has provided direct evidence of Yukawa couplings between the third generation charged fermions and the 125 GeV Higgs boson. Whether the first two generation charged fermions arise from exactly the same mechanism becomes the next interesting question. Therefore, direct measurements of charm or muon Yukawa couplings will be crucial to answering this puzzle. The charm Yukawa measurement at the LHC suffers from severe QCD background and it is extremely difficult to reach the sensitivity. In this paper, we compare the potential of probing charm Yukawa coupling at the two proposed future "Higgs Factory" experiments, the Large Hadron electron Collider (LHeC) and Circular electron positron collider (CEPC). At the LHeC, Higgs bosons will be produced via weak boson fusion and the energetic forward jet may suppress the background significantly. However, due to huge $γ-g$ scattering background, the potential of LHeC search is still limited. With $-80\%$ polarized electron beam of 60 GeV, the signal significance can only reach $2σ$ for $ κ_{c}\simeq 1$ with a 3 ab$^{-1}$ integrated luminosity. In comparison, measurement at the CEPC can then reach $8.0σ$ for $κ_{c}\simeq 1$ with a 2 ab$^{-1}$ of data.

hep-ph↗
arXiv2019

Parallel Explicit Tube Model Predictive Control

This paper is about a parallel algorithm for tube-based model predictive control. The proposed control algorithm solves robust model predictive control problems suboptimally, while exploiting their structure. This is achieved by implementing a real-time algorithm that iterates between the evaluation of piecewise affine functions, corresponding to the parametric solution of small-scale robust MPC problems, and the online solution of structured equality constrained QPs. The performance of the associated real-time robust MPC controllers is illustrated by a numerical case study.

math.OC↗
arXiv2019

BTS-DSN: Deeply Supervised Neural Network with Short Connections for Retinal Vessel Segmentation

Background and Objective: The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results. Methods:In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images. Results: The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments. Conclusions: BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at https://github.com/guomugong/BTS-DSN.

cs.CV↗
arXiv2019

Frame attention networks for facial expression recognition in videos

The video-based facial expression recognition aims to classify a given video into several basic emotions. How to integrate facial features of individual frames is crucial for this task. In this paper, we propose the Frame Attention Networks (FAN), to automatically highlight some discriminative frames in an end-to-end framework. The network takes a video with a variable number of face images as its input and produces a fixed-dimension representation. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors. The frame attention module learns multiple attention weights which are used to adaptively aggregate the feature vectors to form a single discriminative video representation. We conduct extensive experiments on CK+ and AFEW8.0 datasets. Our proposed FAN shows superior performance compared to other CNN based methods and achieves state-of-the-art performance on CK+.

cs.CV↗
arXiv2019

Video synthesis of human upper body with realistic face

This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.

cs.CV↗
arXiv2019

Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition

Occlusion and pose variations, which can change facial appearance significantly, are two major obstacles for automatic Facial Expression Recognition (FER). Though automatic FER has made substantial progresses in the past few decades, occlusion-robust and pose-invariant issues of FER have received relatively less attention, especially in real-world scenarios. This paper addresses the real-world pose and occlusion robust FER problem with three-fold contributions. First, to stimulate the research of FER under real-world occlusions and variant poses, we build several in-the-wild facial expression datasets with manual annotations for the community. Second, we propose a novel Region Attention Network (RAN), to adaptively capture the importance of facial regions for occlusion and pose variant FER. The RAN aggregates and embeds varied number of region features produced by a backbone convolutional neural network into a compact fixed-length representation. Last, inspired by the fact that facial expressions are mainly defined by facial action units, we propose a region biased loss to encourage high attention weights for the most important regions. We validate our RAN and region biased loss on both our built test datasets and four popular datasets: FERPlus, AffectNet, RAF-DB, and SFEW. Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose. Our method also achieves state-of-the-art results on FERPlus, AffectNet, RAF-DB, and SFEW. Code and the collected test data will be publicly available.

cs.CV↗
arXiv2019

Exact Hochschild extensions and deformed Calabi-Yau completions

We introduce the Hochschild extensions of dg algebras, which are $A_\infty$-algebras. We show that all exact Hochschild extensions are symmetric Hochschild extensions, more precisely, every exact Hochschild extension of a finite dimensional complete typical dg algebra is a symmetric $A_\infty$-algebra. Moreover, we prove that the Koszul dual of trivial extension is Calabi-Yau completion and the Koszul dual of exact Hochschild extension is deformed Calabi-Yau completion, more precisely, the Koszul dual of the trivial extension of a finite dimensional complete dg algebra is the Calabi-Yau completion of its Koszul dual, and the Koszul dual of an exact Hochschild extension of a finite dimensional complete typical dg algebra is the deformed Calabi-Yau completion of its Koszul dual.

math.RA↗
arXiv2019

A two-zone model for blazar emission: implications for TXS 0506+056 and the neutrino event IceCube-170922A

A high-energy muon neutrino event, IceCube-170922A, was recently discovered in both spatial and temporal coincidence with a gamma-ray flare of the blazar TXS 0506+056. It has been shown, with standard one-zone models, that neutrinos can be produced in the blazar jet via hadronic interactions, but with a flux which is mostly limited by the X-ray data. In this work, we explore the neutrino production from TXS 0506+056 by invoking two physically distinct emission zones in the jet, separated by the broad line region (BLR). Using the Doppler-boosted radiation of the BLR as the target photon field, the inner zone accounts for the neutrino and gamma-ray emission via $pγ$ interactions and inverse Compton scattering respectively, while the outer zone produces the optical and X-ray emission via synchrotron and synchrotron self-Compton processes. The different conditions of the two zones allow us to suppress the X-ray emission from the electromagnetic cascade, and set a much higher upper limit on the muon neutrino flux (i.e., $\sim 10^{-11}\rm erg~cm^{-2}s^{-1}$) than in one-zone models. We compare, in detail, our scenario with one-zone models discussed in the literature, and argue that differentiating between such scenarios will become possible with next generation neutrino telescopes, such as IceCube-Gen2.

astro-ph.HE↗
arXiv2019

A Realistic Face-to-Face Conversation System based on Deep Neural Networks

To improve the experiences of face-to-face conversation with avatar, this paper presents a novel conversation system. It is composed of two sequence-to-sequence models respectively for listening and speaking and a Generative Adversarial Network (GAN) based realistic avatar synthesizer. The models exploit the facial action and head pose to learn natural human reactions. Based on the models' output, the synthesizer uses the Pixel2Pixel model to generate realistic facial images. To show the improvement of our system, we use a 3D model based avatar driving scheme as a reference. We train and evaluate our neural networks with the data from ESPN shows. Experimental results show that our conversation system can generate natural facial reactions and realistic facial images.

cs.CV↗