arXiv subjects

Hao Yuan

Publications and source records attributed to Hao Yuan.

At least 73 records · Page 4Linked to original sources

arXiv2021

Semi-discrete and fully discrete mixed finite element methods for Maxwell viscoelastic model of wave propagation

Semi-discrete and fully discrete mixed finite element methods are considered for Maxwell-model-based problems of wave propagation in linear viscoelastic solid. This mixed finite element framework allows the use of a large class of existing mixed conforming finite elements for elasticity in the spatial discretization. In the fully discrete scheme, a Crank-Nicolson scheme is adopted for the approximation of the temporal derivatives of stress and velocity variables. Error estimates of the semi-discrete and fully discrete schemes, as well as an unconditional stability result for the fully discrete scheme, are derived. Numerical experiments are provided to verify the theoretical results.

math.NA↗

arXiv2021

On Explainability of Graph Neural Networks via Subgraph Explorations

We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore the substructures of graphs, which are more intuitive and human-intelligible. In this work, we propose a novel method, known as SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained GNN model and an input graph, our SubgraphX explains its predictions by efficiently exploring different subgraphs with Monte Carlo tree search. To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs. To expedite computations, we propose efficient approximation schemes to compute Shapley values for graph data. Our work represents the first attempt to explain GNNs via identifying subgraphs explicitly and directly. Experimental results show that our SubgraphX achieves significantly improved explanations, while keeping computations at a reasonable level.

cs.LG↗

arXiv2021

Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents

Turn-level user satisfaction is one of the most important performance metrics for conversational agents. It can be used to monitor the agent's performance and provide insights about defective user experiences. Moreover, a powerful satisfaction model can be used as an objective function that a conversational agent continuously optimizes for. While end-to-end deep learning has shown promising results, having access to a large number of reliable annotated samples required by these methods remains challenging. In a large-scale conversational system, there is a growing number of newly developed skills, making the traditional data collection, annotation, and modeling process impractical due to the required annotation costs as well as the turnaround times. In this paper, we suggest a self-supervised contrastive learning approach that leverages the pool of unlabeled data to learn user-agent interactions. We show that the pre-trained models using the self-supervised objective are transferable to the user satisfaction prediction. In addition, we propose a novel few-shot transfer learning approach that ensures better transferability for very small sample sizes. The suggested few-shot method does not require any inner loop optimization process and is scalable to very large datasets and complex models. Based on our experiments using real-world data from a large-scale commercial system, the suggested approach is able to significantly reduce the required number of annotations, while improving the generalization on unseen out-of-domain skills.

cs.LG↗

arXiv2021

Node2Seq: Towards Trainable Convolutions in Graph Neural Networks

Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features. While great performance has been achieved, the weights learning for different neighboring nodes is still less explored. In this work, we propose a novel graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores. Experimental results demonstrate the effectiveness of our proposed Node2Seq layer and show that the proposed adaptively non-local information learning can improve the performance of feature learning.

cs.LG↗

arXiv2020

Nonlinear steering criteria for arbitrary two-qubit quantum systems

By employing Pauli measurements, we present some nonlinear steering criteria applicable for arbitrary two-qubit quantum systems and optimized ones for symmetric quantum states. These criteria provide sufficient conditions to witness steering, which can recover the previous elegant results for some well-known states. Compared with the existing linear steering criterion and entropic criterion, ours can certify more steerable states without selecting measurement settings or correlation weights, which can also be used to verify entanglement as all steerable quantum states are entangled.

quant-ph↗

arXiv2020

Unidirectional transmission of single photons under non-ideal chiral photon-atom interactions

Single-photon transport in non-ideal chiral photon-atom interaction structures generally contains information backflow and thus limits the capabilities to transfer information between distant emitters in cascaded quantum networks. Here, in the non-ideal chiral case, a V -type atom coupled to a waveguide is proposed to realize completely unidirectional transmission of the single photons in a superposition state of different frequencies. A microwave field is introduced to drive the two excited states of the atom and results in photon conversion between two transitions. By adjusting the Rabi frequency and the phase of the external driving field, the transport behaviors of incident photons with specific frequencies can be optimized to complete transmission or reflection. Based on the constructive interferences between photons from different traveling paths, the transmission probabilities of the specific-frequency photons could be enhanced. Due to photon conversions with compensating the dissipations and ensuring the complete destructive interference, the ideal unidirectional transmission contrast can be maintained to 1, even when the atom has dissipations into the non-waveguide modes.

physics.optics↗

arXiv2020

Experimental demonstration of complementarity relations between quantum steering criteria

The ability that one system immediately affects another one by using local measurements is regarded as quantum steering, which can be detected by various steering criteria. Recently, Mondal et al. [Phys. Rev. A 98, 052330 (2018)] derived the complementarity relations of coherence steering criteria, and revealed that the quantum steering of system can be observed through the average coherence of subsystem. Here, we experimentally verify the complementarity relations between quantum steering criteria by employing two-photon Bell-like states and three Pauli operators. The results demonstrate that if prepared quantum states can violate two setting coherence steering criteria and turn out to be steerable states, then it cannot violate the complementary settings criteria. Three measurement settings inequality, which establish a complementarity relation between these two coherence steering criteria, always holds in experiment. Besides, we experimentally certify that the strengths of coherence steering criteria dependent on the choice of coherence measure. In comparison with two setting coherence steering criteria based on l1 norm of coherence and relative entropy of coherence, our experimental results show that the steering criterion based on skew information of coherence is more stronger in detecting the steerability of quantum states. Thus, our experimental demonstrations can deepen the understanding of the relation between the quantum steering and quantum coherence.

quant-ph↗

arXiv2020

Deep Learning of High-Order Interactions for Protein Interface Prediction

Protein interactions are important in a broad range of biological processes. Traditionally, computational methods have been developed to automatically predict protein interface from hand-crafted features. Recent approaches employ deep neural networks and predict the interaction of each amino acid pair independently. However, these methods do not incorporate the important sequential information from amino acid chains and the high-order pairwise interactions. Intuitively, the prediction of an amino acid pair should depend on both their features and the information of other amino acid pairs. In this work, we propose to formulate the protein interface prediction as a 2D dense prediction problem. In addition, we propose a novel deep model to incorporate the sequential information and high-order pairwise interactions to perform interface predictions. We represent proteins as graphs and employ graph neural networks to learn node features. Then we propose the sequential modeling method to incorporate the sequential information and reorder the feature matrix. Next, we incorporate high-order pairwise interactions to generate a 3D tensor containing different pairwise interactions. Finally, we employ convolutional neural networks to perform 2D dense predictions. Experimental results on multiple benchmarks demonstrate that our proposed method can consistently improve the protein interface prediction performance.

cs.LG↗

arXiv2020

XGNN: Towards Model-Level Explanations of Graph Neural Networks

Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information, which have achieved promising performance on many graph tasks. However, GNNs are mostly treated as black-boxes and lack human intelligible explanations. Thus, they cannot be fully trusted and used in certain application domains if GNN models cannot be explained. In this work, we propose a novel approach, known as XGNN, to interpret GNNs at the model-level. Our approach can provide high-level insights and generic understanding of how GNNs work. In particular, we propose to explain GNNs by training a graph generator so that the generated graph patterns maximize a certain prediction of the model.We formulate the graph generation as a reinforcement learning task, where for each step, the graph generator predicts how to add an edge into the current graph. The graph generator is trained via a policy gradient method based on information from the trained GNNs. In addition, we incorporate several graph rules to encourage the generated graphs to be valid. Experimental results on both synthetic and real-world datasets show that our proposed methods help understand and verify the trained GNNs. Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.

cs.LG↗

arXiv2020

Large-scale Hybrid Approach for Predicting User Satisfaction with Conversational Agents

Measuring user satisfaction level is a challenging task, and a critical component in developing large-scale conversational agent systems serving the needs of real users. An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling. Human annotation based approaches are easier to control, but hard to scale. A novel alternative approach is to collect user's direct feedback via a feedback elicitation system embedded to the conversational agent system, and use the collected user feedback to train a machine-learned model for generalization. User feedback is the best proxy for user satisfaction, but is not available for some ineligible intents and certain situations. Thus, these two types of approaches are complementary to each other. In this work, we tackle the user satisfaction assessment problem with a hybrid approach that fuses explicit user feedback, user satisfaction predictions inferred by two machine-learned models, one trained on user feedback data and the other human annotation data. The hybrid approach is based on a waterfall policy, and the experimental results with Amazon Alexa's large-scale datasets show significant improvements in inferring user satisfaction. A detailed hybrid architecture, an in-depth analysis on user feedback data, and an algorithm that generates data sets to properly simulate the live traffic are presented in this paper.

cs.HC↗

arXiv2019

Experimental observation the Einstein-Podolsky-Rosen Steering based on the detection of entanglement

The Einstein-Podolsky-Rosen (EPR) steering is an intermediate quantum nonlocality between entanglement and Bell nonlocality, which plays an important role in quantum information processing tasks. In the past few years, the investigations concerning EPR steering have been demonstrated in a series of experiments. However, these studies rely on the relevant steering inequalities and the choices of measurement settings. Here, we experimentally verify the EPR steering via entanglement detection without using any steering inequality and measurement setting. By constructing two new states from a two-qubit target state, we observe the EPR steering by detecting the entanglement of these new states. The results show that the entanglement of the newly constructed states can be regarded as a new kind of steering witness for target states. Compared to the results of Xiao et al. [Phys. Rev. Lett. 118, 140404 (2017)], we find that the ability of detecting EPR steering in our scenario is stronger than two-setting projective measurements, which can observe more steerable states. Hence, our demonstrations can deepen the understanding of the connection between the EPR steering and entanglement.

quant-ph↗

arXiv2019

Experimental investigation of entropic uncertainty relations and coherence uncertainty relations

Uncertainty relation usually is one of the most important features in quantum mechanics, and is the backbone of quantum theory, which distinguishes from the rule in classical counterpart. Specifically, entropy-based uncertainty relations are of fundamental importance in the region of quantum information theory, offering one nontrivial bound of key rate towards quantum key distribution. In this work, we experimentally demonstrate the entropic uncertainty relations and coherence-based uncertainty relations in an all-optics platform. By means of preparing two kinds of bipartite initial states with high fidelity, i.e., Bell-like states and Bell-like diagonal states, we carry on local projective measurements over a complete set of mutually unbiased bases on the measured subsystem. In terms of quantum tomography, the density matrices of the initial states and the post-measurement states are reconstructed. It shows that our experimental results coincide with the theoretical predictions very well. Additionally, we also verify that the lower bounds of both the entropy-based and coherence-based uncertainty can be tightened by imposing the Holevo quantity and mutual information, and the entropic uncertainty is inversely correlated with the coherence. Our demonstrations might offer an insight into their uncertainty relations and their connection to quantum coherence in quantum information science, which might be applicable to the security analysis of quantum key distributions.

quant-ph↗

arXiv2019

Global Pixel Transformers for Virtual Staining of Microscopy Images

Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures directly due to complex cellular environments. Fluorescence staining is a popular technique to label different structures but has several drawbacks. In particular, label staining is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled microscopy images and labeled fluorescence images, and to infer fluorescence labels of other microscopy images excluding the physical staining process. We propose to develop a novel deep model for virtual staining of unlabeled microscopy images. We first propose a novel network layer, known as the global pixel transformer layer, that fuses global information from inputs effectively. The proposed global pixel transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global pixel transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various fluorescence image prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.

eess.IV↗

arXiv2019

Experimental certification of steering criterion based on general entropic uncertainty relation

Quantum steering describes the phenomenon that one system can be immediately influenced by another with local measurements. It can be detected by the violation of a powerful and useful steering criterion from general entropic uncertainty relation. This criterion, in principle, can be evaluated straightforwardly and achieved by only probability distributions from a finite set of measurement settings. Herein, we experimentally verify the steering criterion by means of the two-photon Werner-like states and three Pauli measurements. The results indicate that quantum steering can be verified by the criterion in a convenient way. In particular, it is no need to perform the usual quantum state tomography in experiment, which reduces the required experimental resources greatly. Moreover, we demonstrate that the criterion is stronger than the linear one for the detecting quantum steering of the Werner-like states.

quant-ph↗

arXiv2019

XFake: Explainable Fake News Detector with Visualizations

In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility. To effectively detect and interpret the fakeness of news items, we jointly consider both attributes (e.g., speaker) and statements. Specifically, MIMIC, ATTN and PERT frameworks are designed, where MIMIC is built for attribute analysis, ATTN is for statement semantic analysis and PERT is for statement linguistic analysis. Beyond the explanations extracted from the designed frameworks, relevant supporting examples as well as visualization are further provided to facilitate the interpretation. Our implemented system is demonstrated on a real-world dataset crawled from PolitiFact, where thousands of verified political news have been collected.

cs.CY↗

arXiv2019

Experimental investigation of nonlocal advantage of quantum coherence

Nonlocal advantage of quantum coherence (NAQC) based on coherence complementarity relations is generally viewed as a stronger nonclassical correlation than Bell nonlocality. An arbitrary two-qubit state with NAQC must be an entangled state, which demonstrates that the criterion of NAQC can also be regarded as an entanglement witness. In this paper, we experimentally investigate the NAQC for Bell-diagonal states with high fidelity in an optics-based platform. We perform local measurements on a subsystem in three mutually unbiased bases and reconstruct the density matrices of the measured states by quantum state tomography process. By analyzing characteristic of the $l_1$ norm, relative entropy and skew information of coherence with parameters of quantum states, NAQC for the quantum states is accurately captured, and it shows that our experimental results are well compatible with the theoretical predictions. It is worth mentioning that quantum states with NAQC would have higher entanglement, and thus NAQC could be expected to be a kind of useful physical resource for quantum information processing.

quant-ph↗

arXiv2019

Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions

The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent space. However, the latent variables in VAEs are vectors, which can be interpreted as multiple feature maps of size 1x1. Such representations can only convey spatial information implicitly when coupled with powerful decoders. In this work, we propose spatial VAEs that use feature maps of larger size as latent variables to explicitly capture spatial information. This is achieved by allowing the latent variables to be sampled from matrix-variate normal (MVN) distributions whose parameters are computed from the encoder network. To increase dependencies among locations on latent feature maps and reduce the number of parameters, we further propose spatial VAEs via low-rank MVN distributions. Experimental results show that the proposed spatial VAEs outperform original VAEs in capturing rich structural and spatial information.

cs.LG↗

arXiv2018

Interfacial properties of black phosphorus/transition metal carbide van der Waals heterostructures

Owing to its outstanding electronic properties, black phosphorus (BP) is considered as a promising material for next-generation optoelectronic devices. In this work, devices based on BP/MXene (Zrn+1CnT2, T = O, F, OH, n = 1, 2) van der Waals (vdW) heterostructures are designed via first-principles calculations. Zrn+1CnT2 compositions with appropriate work functions lead to the formation of Ohmic contact with BP in the vertical direction. Low Schottky barriers are found along the lateral direction in BP/Zr2CF2, BP/Zr2CO2H2, BP/Zr3C2F2, and BP/Zr3C2O2H2 bilayers, and BP/Zr3C2O2 even exhibits Ohmic contact behavior. BP/Zr2CO2 is a semiconducting heterostructure with type-II band alignment, which facilitates the separation of electron-hole pairs. The band structure of BP/Zr2CO2 can be effectively tuned via a perpendicular electric field, and BP is predicted to undergo a transition from donor to acceptor at a 0.4 V/Å electric field. The versatile electronic properties of the BP/MXene heterostructures examined in this work highlight their promising potential for applications in electronics.

cond-mat.mtrl-sci↗