arXiv subjects

Xiang Wang

Publications and source records attributed to Xiang Wang.

At least 433 records · Page 24Linked to original sources

arXiv2018

Complementary-multiphase quantum search for all numbers of target items

Grover's algorithm achieves a quadratic speedup over classical algorithms, but it is considered necessary to know the value of $λ$ exactly [Phys. Rev. Lett. 95, 150501 (2005); Phys. Rev. Lett. 113, 210501 (2014)], where $λ$ is the fraction of target items in the database. In this paper, we find out that the Grover algorithm can actually apply to the case where one can identify the range that $λ$ belongs to from a given series of disjoint ranges. However, Grover's algorithm still cannot maintain high success probability when there exist multiple target items. For this problem, we proposed a complementary-multiphase quantum search algorithm, %with general iterations, in which multiple phases complement each other so that the overall high success probability can be maintained. Compared to the existing algorithms, in the case defined above, for the first time our algorithm achieves the following three goals simultaneously: (1) the success probability can be no less than any given value between 0 and 1, (2) the algorithm is applicable to the entire range of $λ$, and (3) the number of iterations is almost the same as that of Grover's algorithm. Especially compared to the optimal fixed-point algorithm [Phys. Rev. Lett. 113, 210501 (2014)], our algorithm uses fewer iterations to achieve success probability greater than 82.71\%, e.g., when the minimum success probability is required to be 99.25\%, the number of iterations can be reduced by 50\%.

quant-ph↗

arXiv2018

Explainable Reasoning over Knowledge Graphs for Recommendation

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.

cs.IR↗

arXiv2018

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user's profile with the items that the user has consumed, ICF recommends items that are similar to the user's profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user's profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.

cs.IR↗

arXiv2018

Outer Product-based Neural Collaborative Filtering

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: https://github.com/duxy-me/ConvNCF

cs.IR↗

arXiv2018

Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features

Weakly-supervised semantic segmentation under image tags supervision is a challenging task as it directly associates high-level semantic to low-level appearance. To bridge this gap, in this paper, we propose an iterative bottom-up and top-down framework which alternatively expands object regions and optimizes segmentation network. We start from initial localization produced by classification networks. While classification networks are only responsive to small and coarse discriminative object regions, we argue that, these regions contain significant common features about objects. So in the bottom-up step, we mine common object features from the initial localization and expand object regions with the mined features. To supplement non-discriminative regions, saliency maps are then considered under Bayesian framework to refine the object regions. Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks. These object masks provide more accurate localization and contain more regions of object. Further, we take these object masks as initial localization and mine common object features from them. These processes are conducted iteratively to progressively produce fine object masks and optimize segmentation networks. Experimental results on Pascal VOC 2012 dataset demonstrate that the proposed method outperforms previous state-of-the-art methods by a large margin.

cs.CV↗

arXiv2018

Investigation of the near-threshold cluster resonance in $^{14}\rm{C}$

An experiment for $p(^{14}\rm{C}$,$^{14}\rm{C}^{*}\rightarrow^{10}\rm{Be}+α)\mathit{p}$ inelastic excitation and decay was performed in inverse kinematics at a beam energy of 25.3 MeV/u. A series of $^{14}\rm{C}$ excited states, including a new one at 18.3(1) MeV, were observed which decay to various states of the final nucleus of $^{10}\rm{Be}$. A specially designed telescope-system, installed around the zero degree, played an essential role in detecting the resonant states near the $α$-separation threshold. A state at 14.1(1) MeV is clearly identified, being consistent with the predicted band-head of the molecular rotational band characterized by the $π$-bond linear-chain-configuration. Further clarification of the properties of this exotic state is suggested by using appropriate reaction tools.

nucl-ex↗

arXiv2018

t-multiple discrete logarithm problem and solving difficulty

Considering the difficult problem under classical computing model can be solved by the quantum algorithm in polynomial time, t-multiple discrete logarithm problems presented. The problem is non-degeneracy and unique solution. We talk about what the parameter effects the problem solving difficulty. Then we pointed out that the index-calculus algorithm is not suitable for the problem, and two sufficient conditions of resistance to the quantum algorithm for the hidden subgroup problem are given.

cs.CR↗

arXiv2018

Conditioning of Finite Volume Element Method for Diffusion Problems with General Simplicial Meshes

The conditioning of the linear finite volume element discretization for general diffusion equations is studied on arbitrary simplicial meshes. The condition number is defined as the ratio of the maximal singular value of the stiffness matrix to the minimal eigenvalue of its symmetric part. This definition is motivated by the fact that the convergence rate of the generalized minimal residual method for the corresponding linear systems is determined by the ratio. An upper bound for the ratio is established by developing an upper bound for the maximal singular value and a lower bound for the minimal eigenvalue of the symmetric part. It is shown that the bound depends on three factors, the number of the elements in the mesh, the mesh nonuniformity measured in the Euclidean metric, and the mesh nonuniformity measured in the metric specified by the inverse diffusion matrix. It is also shown that the diagonal scaling can effectively eliminates the effects from the mesh nonuniformity measured in the Euclidean metric. Numerical results for a selection of examples in one, two, and three dimensions are presented.

math.NA↗

arXiv2018

Research on phases of Grover-type algorithms

When applying Grover's algorithm to an unordered database, the probability of obtaining correct results usually decreases as the quantity of target increases. To amend the limitation, numbers of improved schemes are proposed. In this paper, we focus on four improved schemes from phases, and find that they are just differed by a global phase. Based on this conclusion, the extensive researches on one scheme can be easily generated to other three schemes, and some examples are presented to indicate the correctness.

quant-ph↗

arXiv2017

Ground state cooling in a hybrid optomechanical system with a three-level atomic ensemble

Cooling mechanical resonators is of great importance for both fundamental study and applied science. We investigate the hybrid optomechanical cooling with a three-level atomic ensemble fixed in a strong excited optical cavity. By using the quantum noise approach, we find the upper bound of the noise spectrum and further present three optimal parameter conditions, which can yield a small heating coefficient, a large cooling coefficient, and thus a small final phonon number. Moreover, through the covariance matrix approach, results of numerical simulation are obtained, which are consistent with the theoretical expectations. It is demonstrated that our scheme can achieve ground state cooling in the highly unresolved sideband regime, within the current experimental technologies. Compared with the previous cooling methods, in our scheme, there are fewer constraints on the drive strength of atomic ensemble and number of atoms in the ensemble. In addition, the tolerable ranges of parameters for ground state cooling are extended. As a result, our scheme is very suitable for experiments and can be a guideline for the research of hybrid optomechanical cooling.

quant-ph↗

arXiv2017

Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.

cs.CV↗

arXiv2017

Predicting the Popularity of Topics based on User Sentiment in Microblogging Websites

Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community.

cs.SI↗

arXiv2017

Homomorphic Encryption Experiments on IBM's Cloud Quantum Computing Platform

Quantum computing has undergone rapid development in recent years. Owing to limitations on scalability, personal quantum computers still seem slightly unrealistic in the near future. The first practical quantum computer for ordinary users is likely to be on the cloud. However, the adoption of cloud computing is possible only if security is ensured. Homomorphic encryption is a cryptographic protocol that allows computation to be performed on encrypted data without decrypting them, so it is well suited to cloud computing. Here, we first applied homomorphic encryption on IBM's cloud quantum computer platform. In our experiments, we successfully implemented a quantum algorithm for linear equations while protecting our privacy. This demonstration opens a feasible path to the next stage of development of cloud quantum information technology.

cs.CR↗

arXiv2017

Item Silk Road: Recommending Items from Information Domains to Social Users

Online platforms can be divided into information-oriented and social-oriented domains. The former refers to forums or E-commerce sites that emphasize user-item interactions, like Trip.com and Amazon; whereas the latter refers to social networking services (SNSs) that have rich user-user connections, such as Facebook and Twitter. Despite their heterogeneity, these two domains can be bridged by a few overlapping users, dubbed as bridge users. In this work, we address the problem of cross-domain social recommendation, i.e., recommending relevant items of information domains to potential users of social networks. To our knowledge, this is a new problem that has rarely been studied before. Existing cross-domain recommender systems are unsuitable for this task since they have either focused on homogeneous information domains or assumed that users are fully overlapped. Towards this end, we present a novel Neural Social Collaborative Ranking (NSCR) approach, which seamlessly sews up the user-item interactions in information domains and user-user connections in SNSs. In the information domain part, the attributes of users and items are leveraged to strengthen the embedding learning of users and items. In the SNS part, the embeddings of bridge users are propagated to learn the embeddings of other non-bridge users. Extensive experiments on two real-world datasets demonstrate the effectiveness and rationality of our NSCR method.

cs.IR↗

arXiv2017

Uncovering Group Level Insights with Accordant Clustering

Clustering is a widely-used data mining tool, which aims to discover partitions of similar items in data. We introduce a new clustering paradigm, \emph{accordant clustering}, which enables the discovery of (predefined) group level insights. Unlike previous clustering paradigms that aim to understand relationships amongst the individual members, the goal of accordant clustering is to uncover insights at the group level through the analysis of their members. Group level insight can often support a call to action that cannot be informed through previous clustering techniques. We propose the first accordant clustering algorithm, and prove that it finds near-optimal solutions when data possesses inherent cluster structure. The insights revealed by accordant clusterings enabled experts in the field of medicine to isolate successful treatments for a neurodegenerative disease, and those in finance to discover patterns of unnecessary spending.

cs.LG↗

arXiv2016

Top-k Spatial-keyword Publish/Subscribe Over Sliding Window

With the prevalence of social media and GPS-enabled devices, a massive amount of geo-textual data has been generated in a stream fashion, leading to a variety of applications such as location-based recommendation and information dissemination. In this paper, we investigate a novel real-time top-k monitoring problem over sliding window of streaming data; that is, we continuously maintain the top-k most relevant geo-textual messages (e.g., geo-tagged tweets) for a large number of spatial-keyword subscriptions (e.g., registered users interested in local events) simultaneously. To provide the most recent information under controllable memory cost, sliding window model is employed on the streaming geo-textual data. To the best of our knowledge, this is the first work to study top-k spatial-keyword publish/subscribe over sliding window. A novel centralized system, called Skype (Topk Spatial-keyword Publish/Subscribe), is proposed in this paper. In Skype, to continuously maintain top-k results for massive subscriptions, we devise a novel indexing structure upon subscriptions such that each incoming message can be immediately delivered on its arrival. To reduce the expensive top-k re-evaluation cost triggered by message expiration, we develop a novel cost-based k-skyband technique to reduce the number of re-evaluations in a cost-effective way. Extensive experiments verify the great efficiency and effectiveness of our proposed techniques. Furthermore, to support better scalability and higher throughput, we propose a distributed version of Skype, namely, DSkype, on top of Storm, which is a popular distributed stream processing system. With the help of fine-tuned subscription/message distribution mechanisms, DSkype can achieve orders of magnitude speed-up than its centralized version.

cs.DB↗

arXiv2016

A Multilevel Coordinate Search Algorithm for Well Placement, Control and Joint Optimization

Determining optimal well placements and controls are two important tasks in oil field development. These problems are computationally expensive, nonconvex, and contain multiple optima. The practical solution of these problems require efficient and robust algorithms. In this paper, the multilevel coordinate search (MCS) algorithm is applied for well placement and control optimization problems. MCS is a derivative-free algorithm that combines global and local search. Both synthetic and real oil fields are considered. The performance of MCS is compared to generalized pattern search (GPS), particle swarm optimization (PSO), and covariance matrix adaptive evolution strategy (CMA-ES) algorithms. Results show that the MCS algorithm is strongly competitive, and outperforms for the joint optimization problem and with a limited computational budget. The effect of parameter settings for MCS are compared for the test examples. For the joint optimization problem we compare the performance of the simultaneous and sequential procedures and show the utility of the latter.

math.OC↗

arXiv2016

Constructions of Snake-in-the-Box Codes under $\ell_{\infty}$-metric for Rank Modulation

In the rank modulation scheme, Gray codes are very useful in the realization of flash memories. For a Gray code in this scheme, two adjacent codewords are obtained by using one "push-to-the-top" operation. Moreover, snake-in-the-box codes under the $\ell_{\infty}$-metric are Gray codes, which can be capable of detecting one $\ell_{\infty}$-error. In this paper, we give two constructions of $\ell_{\infty}$-snakes. On the one hand, inspired by Yehezkeally and Schwartz's construction, we present a new construction of the $\ell_{\infty}$-snake. The length of this $\ell_{\infty}$-snake is longer than the length of the $\ell_{\infty}$-snake constructed by Yehezkeally and Schwartz. On the other hand, we also give another construction of $\ell_{\infty}$-snakes by using $\mathcal{K}$-snakes and obtain the longer $\ell_{\infty}$-snakes than the previously known ones.

cs.IT↗