arXiv subjects

Jun Zhao

Publications and source records attributed to Jun Zhao.

At least 487 records · Page 27Linked to original sources

arXiv2019

Analyzing resilience of interest-based social networks against node and link failures

In typical online social networks, users are linked by symmetric friend relations and can define circles of friends based on shared interests. In this paper, we look at social networks where users form links subject to both friendships and shared interests. Our goal is to understand resilience of these networks in terms of connectivity when both nodes and links are allowed to fail. We derive a zero-one law as well as the asymptotically exact probability result for connectivity under both node and link failures. The results answer the question of how to set the network parameters such that reliable message dissemination can be achieved. We formally prove the results and confirm the results via experiments as well.

cs.SI↗

arXiv2019

Adaptive Statistical Learning with Bayesian Differential Privacy

In statistical learning, a dataset is often partitioned into two parts: the training set and the holdout (i.e., testing) set. For instance, the training set is used to learn a predictor, and then the holdout set is used for estimating the accuracy of the predictor on the true distribution. However, often in practice, the holdout dataset is reused and the estimates tested on the holdout dataset are chosen adaptively based on the results of prior estimates, leading to that the predictor may become dependent of the holdout set. Hence, overfitting may occur, and the learned models may not generalize well to the unseen datasets. Prior studies have established connections between the stability of a learning algorithm and its ability to generalize, but the traditional generalization is not robust to adaptive composition. Recently, Dwork et al. in NIPS, STOC, and Science 2015 show that the holdout dataset from i.i.d. data samples can be reused in adaptive statistical learning, if the estimates are perturbed and coordinated using techniques developed for differential privacy, which is a widely used notion to quantify privacy. Yet, the results of Dwork et al. are applicable to only the case of i.i.d. samples. In contrast, correlations between data samples exist because of various behavioral, social, and genetic relationships between users. Our results in adaptive statistical learning generalize the results of Dwork et al. for i.i.d. data samples to arbitrarily correlated data. Specifically, we show that the holdout dataset from correlated samples can be reused in adaptive statistical learning, if the estimates are perturbed and coordinated using techniques developed for Bayesian differential privacy, which is a privacy notion recently introduced by Yang et al. in SIGMOD 2015 to broaden the application scenarios of differential privacy when data records are correlated.

cs.LG↗

arXiv2019

Incorporating Interlocutor-Aware Context into Response Generation on Multi-Party Chatbots

Conventional chatbots focus on two-party response generation, which simplifies the real dialogue scene. In this paper, we strive toward a novel task of Response Generation on Multi-Party Chatbot (RGMPC), where the generated responses heavily rely on the interlocutors' roles (e.g., speaker and addressee) and their utterances. Unfortunately, complex interactions among the interlocutors' roles make it challenging to precisely capture conversational contexts and interlocutors' information. Facing this challenge, we present a response generation model which incorporates Interlocutor-aware Contexts into Recurrent Encoder-Decoder frameworks (ICRED) for RGMPC. Specifically, we employ interactive representations to capture dialogue contexts for different interlocutors. Moreover, we leverage an addressee memory to enhance contextual interlocutor information for the target addressee. Finally, we construct a corpus for RGMPC based on an existing open-access dataset. Automatic and manual evaluations demonstrate that the ICRED remarkably outperforms strong baselines.

cs.CL↗

arXiv2019

Generating Questions for Knowledge Bases via Incorporating Diversified Contexts and Answer-Aware Loss

We tackle the task of question generation over knowledge bases. Conventional methods for this task neglect two crucial research issues: 1) the given predicate needs to be expressed; 2) the answer to the generated question needs to be definitive. In this paper, we strive toward the above two issues via incorporating diversified contexts and answer-aware loss. Specifically, we propose a neural encoder-decoder model with multi-level copy mechanisms to generate such questions. Furthermore, the answer aware loss is introduced to make generated questions corresponding to more definitive answers. Experiments demonstrate that our model achieves state-of-the-art performance. Meanwhile, such generated question can express the given predicate and correspond to a definitive answer.

cs.CL↗

arXiv2019

Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach

Federated learning allows mobile devices, i.e., workers, to use their local data to collaboratively train a global model required by the model owner. Federated learning thus addresses the privacy issues of traditional machine learning. However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy. To address the energy constraint, a power beacon can be used that recharges energy to the workers. However, the model owner may need to pay an energy cost to the power beacon for the energy recharge. To address the high network resource cost, the model owner can use a WiFi channel, called default channel, for the global model transmissions. However, communication interruptions may occur due to the instability of the default channel quality. For this, special channels such as LTE channels can be used, but this incurs channel cost. As such, the problem of the model owner is to decide amounts of energy recharged to the workers and to choose channels used to transmit its global model to the workers to maximize the number of global model transmissions while minimizing the energy and channel costs. This is challenging for the model owner under the uncertainty of the channel, energy and mobility states of the workers. In this paper, we thus propose to employ the Deep Q-Network (DQN) that enables the model owner to find the optimal decisions on the energy and the channels without any a priori network knowledge. Simulation results show that the proposed DQN always achieves better performance compared to the conventional algorithms.

cs.NI↗

arXiv2019

Intertwined dipolar and multipolar order in the triangular-lattice magnet TmMgGaO$_4$

A phase transition is often accompanied by the appearance of an order parameter and symmetry breaking. Certain magnetic materials exhibit exotic hidden-order phases, in which the order parameters are not directly accessible to conventional magnetic measurements. Thus, experimental identification and theoretical understanding of a hidden order are difficult. Here we combine neutron scattering and thermodynamic probes to study the newly discovered rare-earth triangular-lattice magnet TmMgGaO$_4$. Clear magnetic Bragg peaks at K points are observed in the elastic neutron diffraction measurements. More interesting, however, is the observation of sharp and highly dispersive spin excitations that cannot be explained by a magnetic dipolar order, but instead is the direct consequence of the underlying multipolar order that is "hidden" in the neutron diffraction experiments. We demonstrate that the observed unusual spin correlations and thermodynamics can be accurately described by a transverse field Ising model on the triangular lattice with an intertwined dipolar and ferro-multipolar order.

cond-mat.str-el↗

arXiv2019

Robust Estimation and Shrinkage in Ultrahigh Dimensional Expectile Regression with Heavy Tails and Variance Heterogeneity

High-dimensional data subject to heavy-tailed phenomena and heterogeneity are commonly encountered in various scientific fields and bring new challenges to the classical statistical methods. In this paper, we combine the asymmetric square loss and huber-type robust technique to develop the robust expectile regression for ultrahigh dimensional heavy-tailed heterogeneous data. Different from the classical huber method, we introduce two different tuning parameters on both sides to account for possibly asymmetry and allow them to diverge to reduce bias induced by the robust approximation. In the regularized framework, we adopt the generally folded concave penalty function like the SCAD or MCP penalty for the seek of bias reduction. We investigate the finite sample property of the corresponding estimator and figure out how our method plays its role to trades off the estimation accuracy against the heavy-tailed distribution. Also, noting that the robust asymmetric loss function is everywhere differentiable, based on our theoretical study, we propose an efficient first-order optimization algorithm after locally linear approximation of the non-convex problem. Simulation studies under various distributions demonstrates the satisfactory performances of our method in coefficient estimation, model selection and heterogeneity detection.

math.ST↗

arXiv2019

Joint optimization of train blocking and shipment path:An integrated model and a sequential algorithm

The INFORMS RAS 2019 Problem Solving Competition is focused on the integrated train blocking and shipment path (TBSP) optimization for tonnage-based operating railways. In nature, the TBSP problem could be viewed as a multi-commodity network design problem with a double-layer network structure. By introducing a directed physical railway network and a directed train services (blocks) network, we formulate completely the TBSP problem as a mixed integer linear programming (MILP) model that incorporates all decisions, objectives and constraints especially the merge flow (called intree rule here) in an integrated manner. The scale of the MILP model can be reduced efficiently if we only enumerate the arc selection and block sequence variables for each shipment on the legal paths from its origin to its destination satisfying the given detour ratio. We further develop a sequential algorithm that decomposes the TBSP problem into the shipment path subproblem and train blocking subproblem which are solved sequentially. Computational tests on the three given data sets show that the reduced MILP model can solve DataSet\_1 to optimality in 8.48 seconds and DataSet\_2 with a gap of 0.16\% in 6 hours on a GPU workstation. The reduced model also can provide strong lower bounds for DataSet\_2 and DataSet\_3. The sequential algorithm can find a high quality solution with 0.04\% gap within 0.26 seconds for DataSet\_1, 0.42\% gap within 4.53 seconds for DataSet\_2 and 1.54\% gap within 0.58 hours for DataSet\_3 respectively on a Thinkpad laptop.

math.OC↗

arXiv2019

Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas

With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course resulting in hurting pedestrians or swerving leading to hurting passengers. To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision. Although a useful methodology for building ethical AI systems, such an approach can potentially violate the privacy of voters since moral preferences are sensitive information and their disclosure can be exploited by malicious parties. In this paper, we report a first-of-its-kind privacy-preserving crowd-guided AI decision-making approach in ethical dilemmas. We adopt the notion of differential privacy to quantify privacy and consider four granularities of privacy protection by taking voter-/record-level privacy protection and centralized/distributed perturbation into account, resulting in four approaches VLCP, RLCP, VLDP, and RLDP. Moreover, we propose different algorithms to achieve these privacy protection granularities, while retaining the accuracy of the learned moral preference model. Specifically, VLCP and RLCP are implemented with the data aggregator setting a universal privacy parameter and perturbing the averaged moral preference to protect the privacy of voters' data. VLDP and RLDP are implemented in such a way that each voter perturbs her/his local moral preference with a personalized privacy parameter. Extensive experiments on both synthetic and real data demonstrate that the proposed approach can achieve high accuracy of preference aggregation while protecting individual voter's privacy.

cs.CR↗

arXiv2019

Fractionalized excitations in the partially magnetized spin liquid candidate YbMgGaO4

Quantum spin liquids (QSLs) are exotic states of matter characterized by emergent gauge structures and fractionalized elementary excitations. The recently discovered triangular lattice antiferromagnet YbMgGaO$_4$ is a promising QSL candidate, and the nature of its ground state is still under debate. Here, we use neutron scattering to study the spin excitations in YbMgGaO$_4$ under various magnetic fields. Our data reveal a dispersive spin excitation continuum with clear upper and lower excitation edges under a weak magnetic field ($H=2.5$ T). Moreover, a spectral crossing emerges at the $Γ$ point at the Zeeman-split energy. The corresponding redistribution of the spectral weight and its field-dependent evolution are consistent with the theoretical prediction based on the inter-band and intra-band spinon particle-hole excitations associated with the Zeeman-split spinon bands, implying the presence of fractionalized excitations and spinon Fermi surfaces in the partially magnetized YbMgGaO$_4$.

cond-mat.str-el↗

arXiv2019

Copy-Enhanced Heterogeneous Information Learning for Dialogue State Tracking

Dialogue state tracking (DST) is an essential component in task-oriented dialogue systems, which estimates user goals at every dialogue turn. However, most previous approaches usually suffer from the following problems. Many discriminative models, especially end-to-end (E2E) models, are difficult to extract unknown values that are not in the candidate ontology; previous generative models, which can extract unknown values from utterances, degrade the performance due to ignoring the semantic information of pre-defined ontology. Besides, previous generative models usually need a hand-crafted list to normalize the generated values. How to integrate the semantic information of pre-defined ontology and dialogue text (heterogeneous texts) to generate unknown values and improve performance becomes a severe challenge. In this paper, we propose a Copy-Enhanced Heterogeneous Information Learning model with multiple encoder-decoder for DST (CEDST), which can effectively generate all possible values including unknown values by copying values from heterogeneous texts. Meanwhile, CEDST can effectively decompose the large state space into several small state spaces through multi-encoder, and employ multi-decoder to make full use of the reduced spaces to generate values. Multi-encoder-decoder architecture can significantly improve performance. Experiments show that CEDST can achieve state-of-the-art results on two datasets and our constructed datasets with many unknown values.

cs.CL↗

arXiv2019

Semiparametric Expectile Regression for High-dimensional Heavy-tailed and Heterogeneous Data

Recently, high-dimensional heterogeneous data have attracted a lot of attention and discussion. Under heterogeneity, semiparametric regression is a popular choice to model data in statistics. In this paper, we take advantages of expectile regression in computation and analysis of heterogeneity, and propose the regularized partially linear additive expectile regression with nonconvex penalty, for example, SCAD or MCP for such high-dimensional heterogeneous data. We focus on a more realistic scenario: the regression error is heavy-tailed distributed and only has finite moments, which is violated with the classical sub-gaussian distribution assumption and more common in practise. Under some regular conditions, we show that with probability tending to one, the oracle estimator is one of the local minima of our optimization problem. The theoretical study indicates that the dimension cardinality of linear covariates our procedure can handle with is essentially restricted by the moment condition of the regression error. For computation, since the corresponding optimization problem is nonconvex and nonsmooth, we derive a two-step algorithm to solve this problem. Finally, we demonstrate that the proposed method enjoys good performances in estimation accuracy and model selection through Monto Carlo simulation studies and a real data example. What's more, by taking different expectile weights $α$, we are able to detect heterogeneity and explore the entire conditional distribution of the response variable, which indicates the usefulness of our proposed method for analyzing high dimensional heterogeneous data.

math.ST↗

arXiv2019

Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

Intelligent reflecting surfaces (IRSs) have received much attention recently and are envisioned to promote 6G communication networks. In this paper, for wireless communications aided by IRS units, we formulate optimization problems for power control under quality of service (QoS) and max-min fair QoS under three kinds of traffic patterns from a base station (BS) to mobile users (MUs): unicast, broadcast, and multicast. The optimizations are achieved by jointly designing the transmit beamforming of the BS and the phase shift matrix of the IRS. For power control under QoS, existing IRS studies in the literature address only the unicast setting, whereas no IRS work has considered max-min fair QoS. Furthermore, we extend our above optimization studies to the novel settings of multi-antenna mobile users or/and multiple intelligent reflecting surfaces. For all the above optimizations, we provide detailed analyses to propose efficient algorithms. To summarize, our paper presents a comprehensive study of optimization problems involving power control, QoS, and fairness in wireless networks enhanced by IRSs.

cs.IT↗

arXiv2019

The study of intrinsic defect state of FeSe with scanning tunneling microscopy

We apply high resolution scanning tunneling microscopy to study intrinsic defect states of bulk FeSe. Four types of intrinsic defects including the type I dumbbell, type II dumbbell, top-layer Se vacancy and inner-layer Se-site defect are extensively analyzed by scanning tunneling spectroscopy. From characterized depression and enhancement of density of states measured in a large energy range, the type I dumbbell and type II dumbbell are determined to be the Fe vacancy and Se$_\mathrm{Fe}$ defect, respectively. The top-layer Se vacancy and possible inner-layer Se-site vacancy are also determined by spectroscopy analysis. The determination of defects are compared and largely confirmed in the annular dark-field scanning transmission electron microscopy measurement of the exfoliated FeSe. The detailed mapping of defect states in our experiment lays the foundation for a comparison with complex theoretical calculations in the future.

cond-mat.supr-con↗

arXiv2019

Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.

cs.IR↗

arXiv2019

IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation

The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences) on websites remains a big challenge for predicting users' behaviors. Although research efforts have been made in utilizing some auxiliary information (e.g., social relations between users) to solve the problem, the existing rich heterogeneous auxiliary relationships are still not fully exploited. Moreover, previous works relied on linearly combined regularizers and suffered parameter tuning. In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks. In addition to the capability of modeling heterogeneity, IntentGC can learn the importance of different relationships automatically by the neural model in a nonlinear sense. To apply IntentGC to web-scale applications, we design a faster graph convolutional model named IntentNet by avoiding unnecessary feature interactions. Empirical experiments on two large-scale real-world datasets and online A/B tests in Alibaba demonstrate the superiority of our method over state-of-the-art algorithms.

cs.IR↗

arXiv2019

Quantitative characterization of short-range orthorhombic fluctuations in FeSe through pair distribution function analysis

Neutron and x-ray total scattering measurements have been performed on powder samples of the iron chalcogenide superconductor FeSe. Using pair distribution function (PDF) analysis of the total scattering data to investigate short-range atomic correlations, we establish the existence of an instantaneous, local orthorhombic structural distortion attributable to nematic fluctuations that persists well into the high-temperature tetragonal phase, at least up to 300 K and likely to significantly higher temperatures. This short-range orthorhombic distortion is correlated over a length scale of about 1 nm at 300 K and grows to several nm as the temperature is lowered toward the long-range structural transition temperature. In the low-temperature nematic state, the local instantaneous structure exhibits an enhanced orthorhombic distortion relative to the average structure with a typical relaxation length of 3 nm. The quantitative characterization of these orthorhombic fluctuations sheds light on nematicity in this canonical iron-based superconductor.

cond-mat.supr-con↗

arXiv2019

Task Allocation and Mobile Base Station Deployment in Wireless Powered Spatial Crowdsourcing

Wireless power transfer (WPT) is a promising technology to prolong the lifetime of sensor and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing (SC) framework which consists of two mutual dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the SC platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its own utility, which manipulates the SC platform. To address this issue, we present a strategyproof deployment mechanism for the SC platform to deploy its mobile base station. We apply the Moulin's generalized median mechanism and analyze the worst-case performance in maximizing the SC platform's utility. Finally, numerical experiments reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

cs.GT↗