arXiv subjects

Qing Li

Publications and source records attributed to Qing Li.

At least 541 records · Page 30Linked to original sources

arXiv2019

Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.

cs.CV↗

arXiv2019

Graph Neural Networks for Social Recommendation

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec. Our code is available at \url{https://github.com/wenqifan03/GraphRec-WWW19}

cs.IR↗

arXiv2019

Absence of superconductivity in bulk Nd$_{1-x}$Sr$_x$NiO$_2$

Recently superconductivity at 9 - 15 K was discovered in an infinite-layer nickelate (Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ films), which has received enormous attention. Since the $Ni^{1+}$ ionic state in NdNiO$_2$ may have the $3d^9$ outer-shell electronic orbit which resembles that of the cuprates, it is very curious to know whether superconductivity discovered here has similar mechanism as that in the cuprates. By using a three-step method, we successfully synthesize the bulk samples of Nd$_{1-x}$Sr$_x$NiO$_2$ (x=0, 0.2, 0.4). The X-ray diffractions reveal that all the samples contain mainly the infinite layer phase of 112 with some amount of segregated Ni. This has also been well proved by the SEM image and the EDS composition analysis. The resistive measurements on the Sr doped samples show insulating behavior without the presence of superconductivity. Temperature dependence of the magnetic moment under high magnetic fields exhibits a Curie-Weiss law feature with the paramagnetic moment of about 2$μ_B$/f.u.. By applying pressure on Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ up to about 50.2 GPa, we find that the strong insulating behavior at ambient pressure is significantly suppressed, but superconductivity has not been observed either. Since the lattice constants derived from our XRD data are very close to those of the reported superconducting films, we argue that the superconductivity in the reported film may not originate from the expected Nd$_{0.8}$Sr$_{0.2}$NiO$_2$, but arise from the interface or the stress effect.

cond-mat.supr-con↗

arXiv2019

Decoupling of itinerant and localized $d$-orbital electrons in the compound Sc$_{0.5}$Zr$_{0.5}$Co

By using the arc-melting method, we successfully synthesized the compound Sc$_{0.5}$Zr$_{0.5}$Co with the space group of $Pm$-$3m$. Both the resistivity and magnetic susceptibility measurements reveal a phase transition at about 86 K. This transition might be attributed to the establishment of an antiferromagnetic order. The magnetization hysteresis loop measurements in wide temperature region show a weak ferromagnetic feature, which suggests a possible canted arrangement of the magnetic moments. Bounded by the phase transition temperature, the resistivity at ambient pressure shows a change from Fermi liquid behavior to a super-linear behavior as temperature increases. By applying pressures up to 32.1 GPa, the transition temperature does not show a clear change and no superconductivity is observed above 2 K. The density functional theory (DFT) calculations confirm the existence of the antiferromagnetic order and reveal a gap between the spin-up and spin-down $d$-orbital electrons. This kind of behavior may suggest that the antiferromagnetic order in this compound originates from the localized $d$-electrons which do not contribute to the conductance. Thus the itinerant and localized $d$-orbital electrons in the compound are decoupled.

cond-mat.mtrl-sci↗

arXiv2019

Learning Category Correlations for Multi-label Image Recognition with Graph Networks

Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies. Specifically, we introduce a plug-and-play Label Graph (LG) module to learn label correlations with word embeddings, and then utilize traditional GCN to map this graph into label-dependent object classifiers which are further applied to image features. The basic LG module incorporates two 1x1 convolutional layers and uses the dot product to generate label graphs. In addition, we propose a sparse correlation constraint to enhance the LG module and also explore different LG architectures. We validate our method on two diverse multi-label datasets: MS-COCO and Fashion550K. Experimental results show that our A-GCN significantly improves baseline methods and achieves performance superior or comparable to the state of the art.

cs.CV↗

arXiv2019

Kerr Microresonator Soliton Frequency Combs at Cryogenic Temperatures

We present measurements of silicon nitride nonlinear microresonators and frequency comb generation at cryogenic temperatures as low as 7~K. The resulting two orders of magnitude reduction in the thermo-refractive coefficient relative to room-temperature enables direct access to single bright Kerr soliton states through adiabatic frequency tuning of the pump laser while remaining in thermal equilibrium. Our experimental results, supported by theoretical modeling, show that single solitons are easily accessible at temperatures below 60~K for the microresonator device under study. We further demonstrate that the cryogenic temperature primarily impacts the thermo-refractive coefficient. Other parameters critical to the generation of solitons, such as quality factor, dispersion, and effective nonlinearity, are unaltered. Finally, we discuss the potential improvement in thermo-refractive noise resulting from cryogenic operation. The results of this study open up new directions in advancing chip-scale frequency comb optical clocks and metrology at cryogenic temperatures.

physics.optics↗

arXiv2019

Scalable Traffic Engineering for Higher Throughput in Heavily-loaded Software Defined Networks

Existing traffic engineering (TE) solutions performs well for software defined network (SDN) in average cases. However, during peak hours, bursty traffic spikes are challenging to handle, because it is difficult to react in time and guarantee high performance even after failures with limited flow entries. Instead of leaving some capacity empty to guarantee no congestion happens due to traffic rerouting after failures or path updating after demand or topology changes, we decide to make full use of the network capacity to satisfy the demands for heavily-loaded peak hours. The TE system also needs to react to failures quickly and utilize the priority queue to guarantee the transmission of loss and delay sensitive traffic. We propose TED, a scalable TE system that can guarantee high throughput in peak hours. TED can quickly compute a group of maximum number of edge-disjoint paths for each ingress-egress switch pair. We design two methods to select paths under the flow entry limit. We then input the selected paths to our TE to minimize the maximum link utilization. In case of large traffic matrix making the maximum link utilization larger than 1, we input the utilization and the traffic matrix to the optimization of maximizing overall throughput under a new constrain. Thus we obtain a realistic traffic matrix, which has the maximum overall throughput and guarantees no traffic starvation for each switch pair. Experiments show that TED has much better performance for heavily-loaded SDN and has 10% higher probability to satisfy all (> 99.99%) the traffic after a single link failure for G-Scale topology than Smore under the same flow entry limit.

cs.NI↗

arXiv2019

Broadband Resonator-Waveguide Coupling for Efficient Extraction of Octave Spanning Microcombs

Frequency combs spanning over an octave have been successfully demonstrated in Kerr nonlinear microresonators on-chip. These micro-combs rely on both engineered dispersion, to enable generation of frequency components across the octave, and on engineered coupling, to efficiently extract the generated light into an access waveguide while maintaining a close to critically-coupled pump. The latter is challenging as the spatial overlap between the access waveguide and the ring modes decays with frequency. This leads to strong coupling variation across the octave, with poor extraction at short wavelengths. Here, we investigate how a waveguide wrapped around a portion of the resonator, in a pulley scheme, can improve extraction of octave-spanning microcombs, in particular at short wavelengths. We use coupled mode theory to predict the performance of the pulley couplers, and demonstrate good agreement with experimental measurements. Using an optimal pulley coupling design, we demonstrate a 20~dB improvement in extraction at short wavelengths compared to straight waveguide coupling.

physics.optics↗

arXiv2019

Milliwatt-threshold visible-telecom optical parametric oscillation using silicon nanophotonics

The on-chip creation of coherent light at visible wavelengths is crucial to field-level deployment of spectroscopy and metrology systems. Although on-chip lasers have been implemented in specific cases, a general solution that is not restricted by limitations of specific gain media has not been reported. Here, we propose creating visible light from an infrared pump by widely-separated optical parametric oscillation (OPO) using silicon nanophotonics. The OPO creates signal and idler light in the 700 nm and 1300 nm bands, respectively, with a 900 nm pump. It operates at a threshold power of (0.9 +/- 0.1) mW, over 50x smaller than other widely-separated microcavity OPO works, which have only been reported in the infrared. This low threshold enables direct pumping without need of an intermediate optical amplifier. We further show how the device design can be modified to generate 780 nm and 1500 nm light with a similar power efficiency. Our nanophotonic OPO shows distinct advantages in power efficiency, operation stability, and device scalability, and is a major advance towards flexible on-chip generation of coherent visible light.

physics.optics↗

arXiv2019

Virtual Mixup Training for Unsupervised Domain Adaptation

We study the problem of unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain. Recently, the cluster assumption has been applied to unsupervised domain adaptation and achieved strong performance. One critical factor in successful training of the cluster assumption is to impose the locally-Lipschitz constraint to the model. Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data. In this paper, we address this issue by encouraging the model to behave linearly in-between training points. We propose a new regularization method called Virtual Mixup Training (VMT), which is able to incorporate the locally-Lipschitz constraint to the areas in-between training data. Unlike the traditional mixup model, our method constructs the combination samples without using the label information, allowing it to apply to unsupervised domain adaptation. The proposed method is generic and can be combined with most existing models such as the recent state-of-the-art model called VADA. Extensive experiments demonstrate that VMT significantly improves the performance of VADA on six domain adaptation benchmark datasets. For the challenging task of adapting MNIST to SVHN, VMT can improve the accuracy of VADA by over 30\%. Code is available at \url{https://github.com/xudonmao/VMT}.

cs.CV↗

arXiv2019

Why Does a Visual Question Have Different Answers?

Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ~45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.

cs.CV↗

arXiv2019

Product Image Recognition with Guidance Learning and Noisy Supervision

This paper considers recognizing products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category diversities, noisy labels, etc. We address this problem by two contributions. First, we introduce a novel large-scale product image dataset, termed as Product-90. Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers. Labels are assigned automatically by the categories of e-commerce websites. Totally the Product-90 consists of more than 140K images with 90 categories. Due to the fact that consumers may upload unrelated images, it is inevitable that our Product-90 introduces noisy labels. As the second contribution, we develop a simple yet efficient \textit{guidance learning} (GL) method for training convolutional neural networks (CNNs) with noisy supervision. The GL method first trains an initial teacher network with the full noisy dataset, and then trains a target/student network with both large-scale noisy set and small manually-verified clean set in a multi-task manner. Specifically, in the stage of student network training, the large-scale noisy data is supervised by its guidance knowledge which is the combination of its given noisy label and the soften label from the teacher network. We conduct extensive experiments on our Products-90 and public datasets, namely Food101, Food-101N, and Clothing1M. Our guidance learning method achieves performance superior to state-of-the-art methods on these datasets.

cs.CV↗

arXiv2019

Deep Social Collaborative Filtering

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users' preference as suggested by the social theories such as homophily and influence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations. However, for a specific recommendation case, the information relevant to the specific item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while different opinions could affect the user differently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the effectiveness of the proposed framework.

cs.IR↗

arXiv2019

Deep Adversarial Social Recommendation

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.

cs.IR↗

arXiv2019

pyLLE: a Fast and User Friendly Lugiato-Lefever Equation Solver

We present the development of pyLLE, a freely accessible and cross-platform Lugiato-Lefever equation solver programmed in Python and Julia and optimized for the simulation of microresonator frequency combs. Examples illustrating its operation, the simplicity of use, and performance against other programming language are presented. The documentation of the software can be found at https://gregmoille.github.io/pyLLE/

cs.MS↗

arXiv2019

Learning Shared Semantic Space with Correlation Alignment for Cross-modal Event Retrieval

In this paper, we propose to learn shared semantic space with correlation alignment (${S}^{3}CA$) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a neural network with convolutional layers and fully-connected layers to extract features for images, including images on Flickr-like social media. Simultaneously, we exploit a fully-connected neural network to extract semantic features for texts, including news articles from news media. In particular, nonlinear correlations of layer activations in the two neural networks are aligned with correlation alignment during the joint training of the networks. Furthermore, we project the multimodal data into a shared semantic space for cross-modal (event) retrieval, where the distances between heterogeneous data samples can be measured directly. In addition, we contribute a Wiki-Flickr Event dataset, where the multimodal data samples are not describing each other in pairs like the existing paired datasets, but all of them are describing semantic events. Extensive experiments conducted on both paired and unpaired datasets manifest the effectiveness of ${S}^{3}CA$, outperforming the state-of-the-art methods.

cs.IR↗

arXiv2019

Tunable quantum beat of single photons enabled by nonlinear nanophotonics

We demonstrate the tunable quantum beat of single photons through the co-development of core nonlinear nanophotonic technologies for frequency-domain manipulation of quantum states in a common physical platform. Spontaneous four-wave mixing in a nonlinear resonator is used to produce non-degenerate, quantum-correlated photon pairs. One photon from each pair is then frequency shifted, without degradation of photon statistics, using four-wave mixing Bragg scattering in a second nonlinear resonator. Fine tuning of the applied frequency shift enables tunable quantum interference of the two photons as they are impinged on a beamsplitter, with an oscillating signature that depends on their frequency difference. Our work showcases the potential of nonlinear nanophotonic devices as a valuable resource for photonic quantum information science.

quant-ph↗

arXiv2019

Quantum Frequency Conversion of a Quantum Dot Single-Photon Source on a Nanophotonic Chip

Single self-assembled InAs/GaAs quantum dots are promising bright sources of indistinguishable photons for quantum information science. However, their distribution in emission wavelength, due to inhomogeneous broadening inherent to their growth, has limited the ability to create multiple identical sources. Quantum frequency conversion can overcome this issue, particularly if implemented using scalable chip-integrated technologies. Here, we report the first demonstration of quantum frequency conversion of a quantum dot single-photon source on a silicon nanophotonic chip. Single photons from a quantum dot in a micropillar cavity are shifted in wavelength with an on-chip conversion efficiency of approximately 12 %, limited by the linewidth of the quantum dot photons. The intensity autocorrelation function g(2)(tau) for the frequency-converted light is antibunched with g(2)(0) = 0.290 +/- 0.030, compared to the before-conversion value g(2)(0) = 0.080 +/- 0.003. We demonstrate the suitability of our frequency conversion interface as a resource for quantum dot sources by characterizing its effectiveness across a wide span of input wavelengths (840 nm to 980 nm), and its ability to achieve tunable wavelength shifts difficult to obtain by other approaches.

quant-ph↗