arXiv subjects

Qing Li

Publications and source records attributed to Qing Li.

At least 505 records · Page 28Linked to original sources

arXiv2021

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off.

cs.IR↗

arXiv2021

Charge-stripe Fluctuations in Nd$_{4}$Ni$_{3}$O$_{8}$ as Evidenced by Optical Spectroscopy

We present an investigation into the optical properties of Nd$_{4}$Ni$_{3}$O$_{8}$ at different temperatures from 300 down to 5~K over a broad frequency range. The optical conductivity at 5~K is decomposed into IR-active phonons, a far-infrared band $α$, a mid-infrared band $β$, and a high-energy absorption edge. By comparing the measured optical conductivity to first-principles calculations and the optical response of other nickelates, we find that Nd$_{4}$Ni$_{3}$O$_{8}$ features evident charge-stripe fluctuations. The $β$ band is attributed to electronic transitions between the gapped Ni-$d_{x^2-y^2}$ bands due to fluctuating charge stripes, while the high-frequency absorption edge corresponds to the onset of transitions involving other high-energy bands. Furthermore, an analysis of the temperature-dependent optical spectral weight reveals a $T^{2}$ law, which is likely to originate from strong correlation effects.

cond-mat.supr-con↗

arXiv2021

Twofold symmetry of $c$-axis resistivity in topological kagome superconductor CsV$_3$Sb$_5$ with in-plane rotating magnetic field

In transition metal compounds, due to the interplay of charge, spin, lattice and orbital degrees of freedom, many intertwined orders exist with close energies. One of the commonly observed states is the so-called nematic electron state, which breaks the in-plane rotational symmetry. This nematic state appears in cuprates, iron-based superconductor, etc. Nematicity may coexist, affect, cooperate or compete with other orders. Here we show the anisotropic in-plane electronic state and superconductivity in a recently discovered kagome metal CsV$_3$Sb$_5$ by measuring $c$-axis resistivity with the in-plane rotation of magnetic field. We observe a twofold symmetry of superconductivity in the superconducting state and a unique in-plane nematic electronic state in normal state when rotating the in-plane magnetic field. Interestingly these two orders are orthogonal to each other in terms of the field direction of the minimum resistivity. Our results shed new light in understanding non-trivial physical properties of CsV$_3$Sb$_5$.

cond-mat.supr-con↗

arXiv2021

Multiparameter quantum metrology using strongly interacting spin systems

Interacting quantum systems are attracting increasing interest for developing precise metrology. In particular, the realisation that quantum-correlated states and the dynamics of interacting systems can lead to entirely new and unexpected phenomena have initiated an intense research effort to explore interaction-based metrology both theoretically and experimentally. However, the current framework of interaction-based metrology mainly focuses on single-parameter estimations, a demonstration of multiparameter metrology using interactions as a resource was heretofore lacking. Here we demonstrate an interaction-based multiparameter metrology with strongly interacting nuclear spins. We show that the interacting spins become intrinsically sensitive to all components of a multidimensional field when their interactions are significantly larger than their Larmor frequencies. Using liquid-state molecules containing strongly interacting nuclear spins, we demonstrate the proof-of-principle estimation of all three components of an unknown magnetic field and inertial rotation. In contrast to existing approaches, the present interaction-based multiparameter sensing does not require external reference fields and opens a path to develop an entirely new class of multiparameter quantum sensors.

quant-ph↗

arXiv2021

VLGrammar: Grounded Grammar Induction of Vision and Language

Cognitive grammar suggests that the acquisition of language grammar is grounded within visual structures. While grammar is an essential representation of natural language, it also exists ubiquitously in vision to represent the hierarchical part-whole structure. In this work, we study grounded grammar induction of vision and language in a joint learning framework. Specifically, we present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously. We propose a novel contrastive learning framework to guide the joint learning of both modules. To provide a benchmark for the grounded grammar induction task, we collect a large-scale dataset, \textsc{PartIt}, which contains human-written sentences that describe part-level semantics for 3D objects. Experiments on the \textsc{PartIt} dataset show that VLGrammar outperforms all baselines in image grammar induction and language grammar induction. The learned VLGrammar naturally benefits related downstream tasks. Specifically, it improves the image unsupervised clustering accuracy by 30\%, and performs well in image retrieval and text retrieval. Notably, the induced grammar shows superior generalizability by easily generalizing to unseen categories.

cs.CV↗

arXiv2021

Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering

Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this paper, we address the unsupervised person Re-ID with a conceptually novel yet simple framework, termed as Multi-label Learning guided self-paced Clustering (MLC). MLC mainly learns discriminative features with three crucial modules, namely a multi-scale network, a multi-label learning module, and a self-paced clustering module. Specifically, the multi-scale network generates multi-granularity person features in both global and local views. The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank. After multi-label training for several epochs, the self-paced clustering joins in training and assigns a pseudo label for each image. The benefits of our MLC come from three aspects: i) the multi-scale person features for better similarity measurement, ii) the multi-label assignment based on the whole dataset ensures that every image can be trained, and iii) the self-paced clustering removes some noisy samples for better feature learning. Extensive experiments on three popular large-scale Re-ID benchmarks demonstrate that our MLC outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID.

cs.CV↗

arXiv2021

Solving Cold Start Problem in Recommendation with Attribute Graph Neural Networks

Matrix completion is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness, existing methods focus on modeling the user-item interaction graph. The inherent drawback of such methods is that their performance is bound to the density of the interactions, which is however usually of high sparsity. More importantly, for a cold start user/item that does not have any interactions, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. This leads to the capability of learning embeddings for cold start users/items. Our AGNN can produce the preference embedding for a cold user/item by learning on the distribution of attributes with an extended variational auto-encoder structure. Moreover, we propose a new graph neural network variant, i.e., gated-GNN, to effectively aggregate various attributes of different modalities in a neighborhood. Empirical results on two real-world datasets demonstrate that our model yields significant improvements for cold start recommendations and outperforms or matches state-of-the-arts performance in the warm start scenario.

cs.IR↗

arXiv2021

Synthesis and physical properties of perovskite Sm$_{1-x}$Sr$_x$NiO$_3$ (x = 0, 0.2) and infinite-layer Sm$_{0.8}$Sr$_{0.2}$NiO$_2$ nickelates

Recently, superconductivity at about 9-15K was discovered in Nd$_{1-x}$Sr$_x$NiO$_2$ infinite-layer thin films, which has stimulated enormous interests in related rare-earth nickelates. Usually, the first step to synthesize this 112 phase is to fabricate the RNiO$_3$ phase, however, it was reported that the 113 phase is very difficult to be synthesized successfully due to the formation of unusual Ni$^{3+}$ oxidation state. And the difficulty of preparation is enhanced as the ionic radius of rare-earth element decreases. In this work, we report the synthesis and investigation on multiple physical properties of polycrystalline perovskites Sm$_{1-x}$Sr$_x$NiO$_3$ (x = 0, 0.2) in which the ionic radius of Sm$^{3+}$ is smaller than that of Pr$^{3+}$ and Nd$^{3+}$ in related superconducting thin films. The structural and compositional analyses conducted by X-ray diffraction and energy dispersive X-ray spectrum reveal that the samples mainly contain the perovskite phase of Sm$_{1-x}$Sr$_x$NiO$_3$ with small amount of NiO impurities. Magnetization and resistivity measurements indicate that the parent phase SmNiO$_3$ undergoes a paramagnetic-antiferromagnetic transition at about 224K on a global insulating background. In contrast, the Sr-doped sample Sm$_{0.8}$Sr$_{0.2}$NiO$_3$ shows a metallic behavior from 300K down to about 12K, while below 12K the resistivity exhibits a slight logarithmic increase. Meanwhile, from the magnetization curves, we can see that a possible spin-glass state occurs below 12K in Sm$_{0.8}$Sr$_{0.2}$NiO$_3$. Using a soft chemical reduction method, we also obtain the infinite-layer phase Sm$_{0.8}$Sr$_{0.2}$NiO$_2$ with square NiO$_2$ planes. The compound shows an insulating behavior which can be described by the three-dimensional variable-range-hopping model. And superconductivity is still absent in the polycrystalline Sm$_{0.8}$Sr$_{0.2}$NiO$_2$.

cond-mat.supr-con↗

arXiv2021

Physical properties revealed by transport measurements on superconducting Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin films

The newly found superconductivity in infinite-layer nickelate superconducting films has attracted much attention, because their crystalline and electronic structures are similar to high-$T_c$ cuprate superconductors. The upper critical field can provide much information on superconductivity, but detailed experimental data are still lacking in these films. Here we present temperature and angle dependence of resistivity measured under different magnetic fields ($H$) in Nd$_{0.8}$Sr$_{0.2}$NiO$_{2}$ thin films. The onset superconducting transition occurs at about 16.2 K at 0 T. Temperature dependent upper critical fields determined by using a criterion very close to the onset transition show a clear negative curvature near the critical transition temperature, which is explained as the consequence of the paramagnetically limited effect on superconductivity. The temperature dependent anisotropy of the upper critical field is obtained from resistivity data, which yields a value decreasing from 3 to 1.2 with lowering temperature. This can be explained by a variable contribution from the orbital limit effect on upper critical field. The angle dependent resistivity at a fixed temperature and different magnetic fields cannot be scaled to one curve, which deviates from the prediction of the anisotropic Ginzburg-Landau theory. However, at low temperatures, the increased resistivity by magnetic field can be scaled by the parameter $H^β|\cosθ|$ ($1<β<6$) with $θ$ the angle enclosed between $c$-axis and the applied magnetic field. As the first detailed study on the upper critical field of the nickelate thin films, our results clearly indicate a small anisotropy and paramagnetically limited effect of superconductivity in nickelate superconductors.

cond-mat.supr-con↗

arXiv2020

CauchyCP: a powerful test under non-proportional hazards using Cauchy combination of change-point Cox regressions

Non-proportional hazards data are routinely encountered in randomized clinical trials. In such cases, classic Cox proportional hazards model can suffer from severe power loss, with difficulty in interpretation of the estimated hazard ratio since the treatment effect varies over time. We propose CauchyCP, an omnibus test of change-point Cox regression models, to overcome both challenges while detecting signals of non-proportional hazards patterns. Extensive simulation studies demonstrate that, compared to existing treatment comparison tests under non-proportional hazards, the proposed CauchyCP test 1) controls the type I error better at small $α$ levels ($< 0.01$); 2) increases the power of detecting time-varying effects; and 3) is more computationally efficient. The superior performance of CauchyCP is further illustrated using retrospective analyses of two randomized clinical trial datasets and a pharmacogenetic biomarker study dataset. The R package $\textit{CauchyCP}$ is publicly available on CRAN.

stat.ME↗

arXiv2020

Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc. This challenge mainly pays attention to the webly-supervised fine-grained recognition problem. In the literature, existing deep learning methods highly rely on large-scale and high-quality labeled training data, which poses a limitation to their practicability and scalability in real world applications. In particular, for fine-grained recognition, a visual task that requires professional knowledge for labeling, the cost of acquiring labeled training data is quite high. It causes extreme difficulties to obtain a large amount of high-quality training data. Therefore, utilizing free web data to train fine-grained recognition models has attracted increasing attentions from researchers in the fine-grained community. This challenge expects participants to develop webly-supervised fine-grained recognition methods, which leverages web images in training fine-grained recognition models to ease the extreme dependence of deep learning methods on large-scale manually labeled datasets and to enhance their practicability and scalability. In this technical report, we have pulled together the top WebFG 2020 solutions of total 54 competing teams, and discuss what methods worked best across the set of winning teams, and what surprisingly did not help.

cs.CV↗

arXiv2020

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability.

cs.CL↗

arXiv2020

Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition

Graph convolutional networks (GCNs) can effectively capture the features of related nodes and improve the performance of the model. More attention is paid to employing GCN in Skeleton-Based action recognition. But existing methods based on GCNs have two problems. First, the consistency of temporal and spatial features is ignored for extracting features node by node and frame by frame. To obtain spatiotemporal features simultaneously, we design a generic representation of skeleton sequences for action recognition and propose a novel model called Temporal Graph Networks (TGN). Secondly, the adjacency matrix of the graph describing the relation of joints is mostly dependent on the physical connection between joints. To appropriately describe the relations between joints in the skeleton graph, we propose a multi-scale graph strategy, adopting a full-scale graph, part-scale graph, and core-scale graph to capture the local features of each joint and the contour features of important joints. Experiments were carried out on two large datasets and results show that TGN with our graph strategy outperforms state-of-the-art methods.

cs.CV↗

arXiv2020

Zero- to ultralow-field nuclear magnetic resonance and its applications

As a complementary analysis tool to conventional high-field NMR, zero- to ultralow-field (ZULF) NMR detects nuclear magnetization signals in the sub-microtesla regime. Spin-exchange relaxation-free (SERF) atomic magnetometers provide a new generation of sensitive detector for ZULF NMR. Due to the features such as low-cost, high-resolution and potability, ZULF NMR has recently attracted considerable attention in chemistry, biology, medicine, and tests of fundamental physics. This review describes the basic principles, methodology and recent experimental and theoretical development of ZULF NMR, as well as its applications in spectroscopy, quantum control, imaging, NMR-based quantum devices, and tests of fundamental physics. The future prospects of ZULF NMR are also discussed.

quant-ph↗

arXiv2020

Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications

Estimating depth from RGB images can facilitate many computer vision tasks, such as indoor localization, height estimation, and simultaneous localization and mapping (SLAM). Recently, monocular depth estimation has obtained great progress owing to the rapid development of deep learning techniques. They surpass traditional machine learning-based methods by a large margin in terms of accuracy and speed. Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions. In this survey, we first introduce the datasets for depth estimation, and then give a comprehensive introduction of the methods from three perspectives: supervised learning-based methods, unsupervised learning-based methods, and sparse samples guidance-based methods. In addition, downstream applications that benefit from the progress have also been illustrated. Finally, we point out the future directions and conclude the paper.

cs.CV↗

arXiv2020

Suppressing Mislabeled Data via Grouping and Self-Attention

Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM), which allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups. Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples. The AFM has several appealing benefits for noise-robust deep learning. (i) It does not rely on any assumptions and extra clean subset. (ii) With massive interpolations, the ratio of useless samples is reduced dramatically compared to the original noisy ratio. (iii) \pxj{It jointly optimizes the interpolation weights with classifiers, suppressing the influence of mislabeled data via low attention weights. (iv) It partially inherits the vicinal risk minimization of mixup to alleviate over-fitting while improves it by sampling fewer feature-target vectors around mislabeled data from the mixup vicinal distribution.} Extensive experiments demonstrate that AFM yields state-of-the-art results on two challenging real-world noisy datasets: Food101N and Clothing1M. The code will be available at https://github.com/kaiwang960112/AFM.

cs.CV↗

arXiv2020

When Machine Learning Meets Congestion Control: A Survey and Comparison

Machine learning (ML) has seen a significant surge and uptake across many diverse applications. The high flexibility, adaptability and computing capabilities it provides extends traditional approaches used in multiple fields including network operation and management. Numerous surveys have explored ML in the context of networking, such as traffic engineering, performance optimization and network security. Many ML approaches focus on clustering, classification, regression and reinforcement learning (RL). The innovation of this research and contribution of this paper lies in the detailed summary and comparison of learning-based congestion control (CC) approaches. Compared with traditional CC algorithms which are typically rule-based, capabilities to learn from historical experience are highly desirable. From the literature, it is observed that RL is a crucial trend among learning-based CC algorithms. In this paper, we explore the performance of RL-based CC algorithms and present current problems with RL-based CC algorithms. We outline challenges and trends related to learning-based CC algorithms.

cs.NI↗

arXiv2020

Interference in Atomic Magnetometry

Atomic magnetometers are highly sensitive detectors of magnetic fields that monitor the evolution of the macroscopic magnetic moment of atomic vapors, and opening new applications in biological, physical, and chemical science. However, the performance of atomic magnetometers is often limited by hidden systematic effects that may cause misdiagnosis for a variety of applications, e.g., in NMR and in biomagnetism. In this work, we uncover a hitherto unexplained interference effect in atomic magnetometers, which causes an important systematic effect to greatly deteriorate the accuracy of measuring magnetic fields. We present a standard approach to detecting and characterizing the interference effect in, but not limited to, atomic magnetometers. As applications of our work, we consider the effect of the interference in NMR structural determination and locating the brain electrophysiological symptom, and show that it will help to improve the measurement accuracy by taking interference effects into account. Through our experiments, we indeed find good agreement between our prediction and the asymmetric amplitudes of resonant lines in ultralow-field NMR spectra -- an effect that has not been understood so far. We anticipate that our work will stimulate interesting new researches for magnetic interference phenomena in a wide range of magnetometers and their applications.

quant-ph↗