arXiv subjects

Qin Zhang

Publications and source records attributed to Qin Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2017

Memory-efficient and Ultra-fast Network Lookup and Forwarding using Othello Hashing

Network algorithms always prefer low memory cost and fast packet processing speed. Forwarding information base (FIB), as a typical network processing component, requires a scalable and memory-efficient algorithm to support fast lookups. In this paper, we present a new network algorithm, Othello Hashing, and its application of a FIB design called Concise, which uses very little memory to support ultra-fast lookups of network names. Othello Hashing and Concise make use of minimal perfect hashing and relies on the programmable network framework to support dynamic updates. Our conceptual contribution of Concise is to optimize the memory efficiency and query speed in the data plane and move the relatively complex construction and update components to the resource-rich control plane. We implemented Concise on three platforms. Experimental results show that Concise uses significantly smaller memory to achieve much faster query speed compared to existing solutions of network name lookups.

cs.NI↗

arXiv2017

EmbedJoin: Efficient Edit Similarity Joins via Embeddings

We study the problem of edit similarity joins, where given a set of strings and a threshold value $K$, we want to output all pairs of strings whose edit distances are at most $K$. Edit similarity join is a fundamental problem in data cleaning/integration, bioinformatics, collaborative filtering and natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we have observed that all the existing algorithms fall short on long strings and large distance thresholds. In this paper we propose an algorithm named EmbedJoin which scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that EmbedJoin significantly outperforms the previous best algorithms on long strings and large distance thresholds.

cs.DB↗

arXiv2017

Distributed Partial Clustering

Recent years have witnessed an increasing popularity of algorithm design for distributed data, largely due to the fact that massive datasets are often collected and stored in different locations. In the distributed setting communication typically dominates the query processing time. Thus it becomes crucial to design communication efficient algorithms for queries on distributed data. Simultaneously, it has been widely recognized that partial optimizations, where we are allowed to disregard a small part of the data, provide us significantly better solutions. The motivation for disregarded points often arise from noise and other phenomena that are pervasive in large data scenarios. In this paper we focus on partial clustering problems, $k$-center, $k$-median and $k$-means, in the distributed model, and provide algorithms with communication sublinear of the input size. As a consequence we develop the first algorithms for the partial $k$-median and means objectives that run in subquadratic running time. We also initiate the study of distributed algorithms for clustering uncertain data, where each data point can possibly fall into multiple locations under certain probability distribution.

cs.DS↗

arXiv2017

Adaptive Multiple-Arm Identification

We study the problem of selecting $K$ arms with the highest expected rewards in a stochastic $n$-armed bandit game. This problem has a wide range of applications, e.g., A/B testing, crowdsourcing, simulation optimization. Our goal is to develop a PAC algorithm, which, with probability at least $1-δ$, identifies a set of $K$ arms with the aggregate regret at most $ε$. The notion of aggregate regret for multiple-arm identification was first introduced in \cite{Zhou:14} , which is defined as the difference of the averaged expected rewards between the selected set of arms and the best $K$ arms. In contrast to \cite{Zhou:14} that only provides instance-independent sample complexity, we introduce a new hardness parameter for characterizing the difficulty of any given instance. We further develop two algorithms and establish the corresponding sample complexity in terms of this hardness parameter. The derived sample complexity can be significantly smaller than state-of-the-art results for a large class of instances and matches the instance-independent lower bound upto a $\log(ε^{-1})$ factor in the worst case. We also prove a lower bound result showing that the extra $\log(ε^{-1})$ is necessary for instance-dependent algorithms using the introduced hardness parameter.

cs.LG↗

arXiv2017

Prediction of Sea Surface Temperature using Long Short-Term Memory

This letter adopts long short-term memory(LSTM) to predict sea surface temperature(SST), which is the first attempt, to our knowledge, to use recurrent neural network to solve the problem of SST prediction, and to make one week and one month daily prediction. We formulate the SST prediction problem as a time series regression problem. LSTM is a special kind of recurrent neural network, which introduces gate mechanism into vanilla RNN to prevent the vanished or exploding gradient problem. It has strong ability to model the temporal relationship of time series data and can handle the long-term dependency problem well. The proposed network architecture is composed of two kinds of layers: LSTM layer and full-connected dense layer. LSTM layer is utilized to model the time series relationship. Full-connected layer is utilized to map the output of LSTM layer to a final prediction. We explore the optimal setting of this architecture by experiments and report the accuracy of coastal seas of China to confirm the effectiveness of the proposed method. In addition, we also show its online updated characteristics.

cs.CV↗

arXiv2017

Communication complexity of approximate maximum matching in the message-passing model

We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model. The maximum matching problem is one of the most fundamental graph combinatorial problems, with a variety of applications. The input to the problem is a graph $G$ that has $n$ vertices and the set of edges partitioned over $k$ sites, and an approximation ratio parameter $α$. The output is required to be a matching in $G$ that has to be reported by one of the sites, whose size is at least factor $α$ of the size of a maximum matching in $G$. We show that the communication complexity of this problem is $Ω(α^2 k n)$ information bits. This bound is shown to be tight up to a $\log n$ factor, by constructing an algorithm, establishing its correctness, and an upper bound on the communication cost. The lower bound also applies to other graph combinatorial problems in the message-passing communication model, including max-flow and graph sparsification.

cs.DS↗

arXiv2017

Bias-Aware Sketches

Linear sketching algorithms have been widely used for processing large-scale distributed and streaming datasets. Their popularity is largely due to the fact that linear sketches can be naturally composed in the distributed model and be efficiently updated in the streaming model. The errors of linear sketches are typically expressed in terms of the sum of coordinates of the input vector excluding those largest ones, or, the mass on the tail of the vector. Thus, the precondition for these algorithms to perform well is that the mass on the tail is small, which is, however, not always the case -- in many real-world datasets the coordinates of the input vector have a {\em bias}, which will generate a large mass on the tail. In this paper we propose linear sketches that are {\em bias-aware}. We rigorously prove that they achieve strictly better error guarantees than the corresponding existing sketches, and demonstrate their practicality and superiority via an extensive experimental evaluation on both real and synthetic datasets.

cs.DS↗

arXiv2017

On the Communication Complexity of Distributed Clustering

In this paper we give a first set of communication lower bounds for distributed clustering problems, in particular, for k-center, k-median and k-means. When the input is distributed across a large number of machines and the number of clusters k is small, our lower bounds match the current best upper bounds up to a logarithmic factor. We have designed a new composition framework in our proofs for multiparty number-in-hand communication complexity which may be of independent interest.

cs.CC↗

arXiv2017

Communication-Optimal Distributed Clustering

Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site. In this work, we study both graph and geometric clustering problems in two distributed models: (1) a point-to-point model, and (2) a model with a broadcast channel. We give protocols in both models which we show are nearly optimal by proving almost matching communication lower bounds. Our work highlights the surprising power of a broadcast channel for clustering problems; roughly speaking, to spectrally cluster $n$ points or $n$ vertices in a graph distributed across $s$ servers, for a worst-case partitioning the communication complexity in a point-to-point model is $n \cdot s$, while in the broadcast model it is $n + s$. A similar phenomenon holds for the geometric setting as well. We implement our algorithms and demonstrate this phenomenon on real life datasets, showing that our algorithms are also very efficient in practice.

cs.DS↗

arXiv2016

Submodular Maximization over Sliding Windows

In this paper we study the extraction of representative elements in the data stream model in the form of submodular maximization. Different from the previous work on streaming submodular maximization, we are interested only in the recent data, and study the maximization problem over sliding windows. We provide a general reduction from the sliding window model to the standard streaming model, and thus our approach works for general constraints as long as there is a corresponding streaming algorithm in the standard streaming model. As a consequence, we obtain the first algorithms in the sliding window model for maximizing a monotone/non-monotone submodular function under cardinality and matroid constraints. We also propose several heuristics and show their efficiency in real-world datasets.

cs.DS↗

arXiv2016

Computing Skylines on Distributed Data

In this paper we study skyline queries in the distributed computational model, where we have $s$ remote sites and a central coordinator (the query node); each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the $s$ datasets. The computation is in terms of rounds, and the goal is to minimize both the total communication cost and the round cost. Viewing data objects as points in the Euclidean space, we consider both the horizontal data partition case where each site holds a subset of points, and the vertical data partition case where each site holds one coordinate of all the points. We give a set of algorithms that have provable theoretical guarantees, and complement them with information theoretical lower bounds. We also demonstrate the superiority of our algorithms over existing heuristics by an extensive set of experiments on both synthetic and real world datasets.

cs.DB↗

arXiv2016

Improved Algorithms for Distributed Entropy Monitoring

Modern data management systems often need to deal with massive, dynamic and inherently distributed data sources. We collect the data using a distributed network, and at the same time try to maintain a global view of the data at a central coordinator using a minimal amount of communication. Such applications have been captured by the distributed monitoring model which has attracted a lot of attention in recent years. In this paper we investigate the monitoring of the entropy functions, which are very useful in network monitoring applications such as detecting distributed denial-of-service attacks. Our results improve the previous best results by Arackaparambil et al. [2]. Our technical contribution also includes implementing the celebrated AMS sampling method (by Alon et al. [1]) in the distributed monitoring model, which could be of independent interest.

cs.DS↗

arXiv2016

Edit Distance: Sketching, Streaming and Document Exchange

We show that in the document exchange problem, where Alice holds $x \in \{0,1\}^n$ and Bob holds $y \in \{0,1\}^n$, Alice can send Bob a message of size $O(K(\log^2 K+\log n))$ bits such that Bob can recover $x$ using the message and his input $y$ if the edit distance between $x$ and $y$ is no more than $K$, and output "error" otherwise. Both the encoding and decoding can be done in time $\tilde{O}(n+\mathsf{poly}(K))$. This result significantly improves the previous communication bounds under polynomial encoding/decoding time. We also show that in the referee model, where Alice and Bob hold $x$ and $y$ respectively, they can compute sketches of $x$ and $y$ of sizes $\mathsf{poly}(K \log n)$ bits (the encoding), and send to the referee, who can then compute the edit distance between $x$ and $y$ together with all the edit operations if the edit distance is no more than $K$, and output "error" otherwise (the decoding). To the best of our knowledge, this is the first result for sketching edit distance using $\mathsf{poly}(K \log n)$ bits. Moreover, the encoding phase of our sketching algorithm can be performed by scanning the input string in one pass. Thus our sketching algorithm also implies the first streaming algorithm for computing edit distance and all the edits exactly using $\mathsf{poly}(K \log n)$ bits of space.

cs.DS↗

arXiv2015

On Sketching Quadratic Forms

We undertake a systematic study of sketching a quadratic form: given an $n \times n$ matrix $A$, create a succinct sketch $\textbf{sk}(A)$ which can produce (without further access to $A$) a multiplicative $(1+ε)$-approximation to $x^T A x$ for any desired query $x \in \mathbb{R}^n$. While a general matrix does not admit non-trivial sketches, positive semi-definite (PSD) matrices admit sketches of size $Θ(ε^{-2} n)$, via the Johnson-Lindenstrauss lemma, achieving the "for each" guarantee, namely, for each query $x$, with a constant probability the sketch succeeds. (For the stronger "for all" guarantee, where the sketch succeeds for all $x$'s simultaneously, again there are no non-trivial sketches.) We design significantly better sketches for the important subclass of graph Laplacian matrices, which we also extend to symmetric diagonally dominant matrices. A sequence of work culminating in that of Batson, Spielman, and Srivastava (SIAM Review, 2014), shows that by choosing and reweighting $O(ε^{-2} n)$ edges in a graph, one achieves the "for all" guarantee. Our main results advance this front. $\bullet$ For the "for all" guarantee, we prove that Batson et al.'s bound is optimal even when we restrict to "cut queries" $x\in \{0,1\}^n$. In contrast, previous lower bounds showed the bound only for {\em spectral-sparsifiers}. $\bullet$ For the "for each" guarantee, we design a sketch of size $\tilde O(ε^{-1} n)$ bits for "cut queries" $x\in \{0,1\}^n$. We prove a nearly-matching lower bound of $Ω(ε^{-1} n)$ bits. For general queries $x \in \mathbb{R}^n$, we construct sketches of size $\tilde{O}(ε^{-1.6} n)$ bits.

cs.DS↗

arXiv2015

Lower Bounds for Number-in-Hand Multiparty Communication Complexity, Made Easy

In this paper we prove lower bounds on randomized multiparty communication complexity, both in the \emph{blackboard model} (where each message is written on a blackboard for all players to see) and (mainly) in the \emph{message-passing model}, where messages are sent player-to-player. We introduce a new technique for proving such bounds, called \emph{symmetrization}, which is natural, intuitive, and often easy to use. For example, for the problem where each of $k$ players gets a bit-vector of length $n$, and the goal is to compute the coordinate-wise XOR of these vectors, we prove a tight lower bounds of $Ω(nk)$ in the blackboard model. For the same problem with AND instead of XOR, we prove a lower bounds of roughly $Ω(nk)$ in the message-passing model (assuming $k \le n/3200$) and $Ω(n \log k)$ in the blackboard model. We also prove lower bounds for bit-wise majority, for a graph-connectivity problem, and for other problems; the technique seems applicable to a wide range of other problems as well. All of our lower bounds allow randomized communication protocols with two-sided error. We also use the symmetrization technique to prove several direct-sum-like results for multiparty communication.

cs.CC↗

arXiv2014

A Sketching Algorithm for Spectral Graph Sparsification

We study the problem of compressing a weighted graph $G$ on $n$ vertices, building a "sketch" $H$ of $G$, so that given any vector $x \in \mathbb{R}^n$, the value $x^T L_G x$ can be approximated up to a multiplicative $1+ε$ factor from only $H$ and $x$, where $L_G$ denotes the Laplacian of $G$. One solution to this problem is to build a spectral sparsifier $H$ of $G$, which, using the result of Batson, Spielman, and Srivastava, consists of $O(n ε^{-2})$ reweighted edges of $G$ and has the property that simultaneously for all $x \in \mathbb{R}^n$, $x^T L_H x = (1 \pm ε) x^T L_G x$. The $O(n ε^{-2})$ bound is optimal for spectral sparsifiers. We show that if one is interested in only preserving the value of $x^T L_G x$ for a {\it fixed} $x \in \mathbb{R}^n$ (specified at query time) with high probability, then there is a sketch $H$ using only $\tilde{O}(n ε^{-1.6})$ bits of space. This is the first data structure achieving a sub-quadratic dependence on $ε$. Our work builds upon recent work of Andoni, Krauthgamer, and Woodruff who showed that $\tilde{O}(n ε^{-1})$ bits of space is possible for preserving a fixed {\it cut query} (i.e., $x\in \{0,1\}^n$) with high probability; here we show that even for a general query vector $x \in \mathbb{R}^n$, a sub-quadratic dependence on $ε$ is possible. Our result for Laplacians is in sharp contrast to sketches for general $n \times n$ positive semidefinite matrices $A$ with $O(\log n)$ bit entries, for which even to preserve the value of $x^T A x$ for a fixed $x \in \mathbb{R}^n$ (specified at query time) up to a $1+ε$ factor with constant probability, we show an $Ω(n ε^{-2})$ lower bound.

cs.DS↗

arXiv2014

Electron and Hole Photoemission Detection for Band Offset Determination of Tunnel Field-Effect Transistor Heterojunctions

The electrical performance of a tunnel field-effect transistor depends critically on the band offset at their semiconductor heterojunction interface. Historically, it has been difficult to experimentally determine how the electronic bands align at the heterojunction interface. We report here on experimental methods to ascertain a complete energy band alignment of a broken-gap tunnel field-effect transistor based on an InAs/GaSb hetero-junction. By using graphene as an optically transparent electrode in a traditional internal photoemission measurement, both the electron and hole barrier heights at the InAs/GaSb interface can be quantified. For a Al2O3/InAs/GaSb layer structure, the barrier height from the top of InAs and GaSb valence band to the bottom of Al2O3 conduction band is inferred from electron emission whereas hole emissions reveal the barrier height from the top of Al2O3 valence band to the bottom of InAs and GaSb conduction band. Subsequently, the offset parameter at the broken gap InAs/GaSb interface is extracted and thus can be used to facilitate the development of predicted model of electron quantum tunneling efficiency and transistor performance.

cond-mat.mes-hall↗

arXiv2014

Subspace Embeddings and $\ell_p$-Regression Using Exponential Random Variables

Oblivious low-distortion subspace embeddings are a crucial building block for numerical linear algebra problems. We show for any real $p, 1 \leq p < \infty$, given a matrix $M \in \mathbb{R}^{n \times d}$ with $n \gg d$, with constant probability we can choose a matrix $Π$ with $\max(1, n^{1-2/p}) \poly(d)$ rows and $n$ columns so that simultaneously for all $x \in \mathbb{R}^d$, $\|Mx\|_p \leq \|ΠMx\|_{\infty} \leq \poly(d) \|Mx\|_p.$ Importantly, $ΠM$ can be computed in the optimal $O(\nnz(M))$ time, where $\nnz(M)$ is the number of non-zero entries of $M$. This generalizes all previous oblivious subspace embeddings which required $p \in [1,2]$ due to their use of $p$-stable random variables. Using our matrices $Π$, we also improve the best known distortion of oblivious subspace embeddings of $\ell_1$ into $\ell_1$ with $\tilde{O}(d)$ target dimension in $O(\nnz(M))$ time from $\tilde{O}(d^3)$ to $\tilde{O}(d^2)$, which can further be improved to $\tilde{O}(d^{3/2}) \log^{1/2} n$ if $d = Ω(\log n)$, answering a question of Meng and Mahoney (STOC, 2013). We apply our results to $\ell_p$-regression, obtaining a $(1+\eps)$-approximation in $O(\nnz(M)\log n) + \poly(d/\eps)$ time, improving the best known $\poly(d/\eps)$ factors for every $p \in [1, \infty) \setminus \{2\}$. If one is just interested in a $\poly(d)$ rather than a $(1+\eps)$-approximation to $\ell_p$-regression, a corollary of our results is that for all $p \in [1, \infty)$ we can solve the $\ell_p$-regression problem without using general convex programming, that is, since our subspace embeds into $\ell_{\infty}$ it suffices to solve a linear programming problem. Finally, we give the first protocols for the distributed $\ell_p$-regression problem for every $p \geq 1$ which are nearly optimal in communication and computation.

cs.DS↗