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Yu Zheng

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field...

Publication details
Date: 1 September 2015
Type: Article
Publisher: ACM – Association for Computing Machinery
Woodhouse S., Piterman N., Koksal A., and Fisher J.
Publication details
Date: 1 July 2015
Type: Proceedings
Publisher: Springer
Neeraj Kayal and Chandan Saha

Shpilka and Wigderson [SW99] had posed the problem of proving exponential lower bounds for (nonhomogeneous) depth three arithmetic circuits with bounded bottom fanin over a field F of characteristic zero. We resolve this problem by proving a NOmega(d/t) lower bound for (nonhomogeneous) depth three arithmetic circuits with bottom fanin at most t computing an explicit N-variate polynomial of degree d over F.

Publication details
Date: 1 June 2015
Type: Inproceeding
Publisher: LIPICS
Ying Yan, Jiaxing Zhang, Bojun Huang, Xuzhan Sun, Jiaqi Mu, Zheng Zhang, and Thomas Moscibroda

Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety of the data. To process a query on the global data, each node must transmit its local slice of data or an aggregated subset thereof to a global aggregator...

Publication details
Date: 1 April 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Ankush Desai, Shaz Qadeer, Sriram Rajamani, and Sanjit Seshia

Liveness specifications on finite-state concurrent programs are checked using algorithms to detect reachable cycles in the state-transition graph of the program. We present new algorithms for cycle detection based on the idea of prioritized search via a delaying explorer. We present thorough evaluation of our algorithms on a variety of reactive asynchronous programs, including device drivers, distributed protocols, and other benchmarks culled from the research literature.

Publication details
Date: 25 March 2015
Type: Technical report
Number: MSR-TR-2015-28
Neeraj Kayal and Chandan Saha

In a multi-k -ic depth three circuit every variable appears in at most k of the linear polynomials in every product gate of the circuit. This model is a natural generalization of multilinear depth three circuits that allows the formal degree of the circuit to exceed the number of underlying variables (as the formal degree of a multi-k-ic depth three circuit can be kn where n is the number of variables). The problem of proving lower bounds for depth three...

Publication details
Date: 1 March 2015
Type: Inproceeding
Publisher: LIPICS
Margus Veanes and Nikolaj Bjørner

We introduce symbolic tree automata as a generalization of finite tree automata with a parametric alphabet over any given background theory. We show that symbolic tree automata are closed under Boolean operations, and that the operations are effectively uniform in the given alphabet theory. This generalizes the corresponding classical properties known for finite tree automata.

Publication details
Date: 1 March 2015
Type: Article
Publisher: Elsevier
Number: 3
Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore

We present several quantum algorithms for performing nearest-neighbor learning. At the core of our algorithms are fast and coherent quantum methods for computing distance metrics such as the inner product and Euclidean distance. We prove upper bounds on the number of queries to the input data required to compute these metrics. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm. In certain cases, we show...

Publication details
Date: 1 March 2015
Type: Article
Publisher: Rinton Press
Number: 3&4
Ankush Desai, Shaz Qadeer, and Sanjit Seshia

We introduce the concept of a delaying explorer with the
goal of performing prioritized exploration of the behaviors
of an asynchronous reactive program. A delaying explorer
stratifies the search space using a custom strategy, and a de-
lay operation that allows deviation from that strategy. We
show that prioritized search with a delaying explorer per-
forms significantly better than existing prioritization tech-
niques. We also demonstrate empirically the need for...

Publication details
Date: 1 March 2015
Type: Technical report
Number: MSR-TR-2015-25
Alex Bocharov, Martin Roetteler, and Krysta M. Svore

Recently, it was shown that Repeat-Until-Success (RUS) circuits can achieve a 2.5 times reduction in expected T-count over ancilla-free techniques for single-qubit unitary decomposition. However, the previously best known algorithm to synthesize RUS circuits requires exponential classical runtime. In this paper we present an algorithm to synthesize an RUS circuit to approximate any given single-qubit unitary within precision ε in probabilistically polynomial classical runtime. Our synthesis approach...

Publication details
Date: 27 February 2015
Type: Article
Publisher: American Physical Society
Number: 080502
Chuang R., Hall B.A., Benque D., Cook B., Ishtiaq S., Piterman N., Taylor A., Vardi M., Koschmieder S., Gottgens B., and Fisher J.

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a...

Publication details
Date: 1 February 2015
Type: Article
Publisher: Nature Publishing Group
Moignard V., Woodhouse S., Haghverdi L., Lilly J., Tanaka Y., Wilkinson A., Buettner F., Nishikawa S.I., Piterman N., Kouskoff V., Theis F., Fisher J., and Gottgens B.

Reconstruction of the molecular pathways controlling organ development has been hampered by a lack of methods to resolve embryonic progenitor cells. Here we describe a strategy to address this problem that combines gene expression profiling of large numbers of single cells with data analysis based on diffusion maps for dimensionality reduction and network synthesis from state transition graphs. Applying the approach to hematopoietic development in the mouse embryo, we map the progression of mesoderm...

Publication details
Date: 1 February 2015
Type: Article
Publisher: Nature Publishing Group
Dan Alistarh, Rati Gelashvili, and Milan Vojnovic

Population protocols, roughly defined as systems consisting of large numbers of simple identical agents, interacting at random and updating their state following simple rules, are an important research topic at the intersection of distributed computing and biology. One of the fundamental tasks that a population protocol may solve is \emph{majority}: each node starts in one of two states; the goal is for all nodes to reach a correct consensus on which of the two states was initially the majority....

Publication details
Date: 1 February 2015
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2015-13
Dan Alistarh, Jennifer Iglesias, and Milan Vojnovic

In many applications, the structure of data can be represented by a hyper-graph, where the data items are vertices, and the associations among items are represented by hyper-edges. Equivalently, we are given as input a bipartite graph with two kinds of vertices: items, and associations (which we refer to as topics). We consider the problem of partitioning the set of items into a given number of partitions, such that the maximum number of topics covered by a partition is minimized.

This is a...

Publication details
Date: 1 February 2015
Type: Technical report
Publisher: Microsoft Research
Number: MSR-TR-2015-15
Nihar B. Shah and Dengyong Zhou

Human computation or crowdsourcing involves joint inference of the ground-truth-answers and the worker abilities by optimizing an objective function, for instance, by maximizing the data likelihood based on an assumed underlying model. A variety of methods have been proposed in the literature to address this inference problem. As far as we know, none of the objective functions in existing methods is convex. In machine learning and applied statistics, a convex function such as the objective function of...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Robert A Cochran, Loris D’Antoni, Benjamin Livshits, David Molnar, and Margus Veanes

In this paper, we investigate an approach to program synthesis that is based on crowd-sourcing. With the help of crowd-sourcing, we aim to capture the “wisdom of the crowds” to find good if not perfect solutions to inherently tricky programming tasks, which elude even expert developers and lack an easy-to-formalize specification.

We propose an approach we call program boosting, which involves crowd-sourcing imperfect solutions to a difficult programming problem from developers and then...

Publication details
Date: 1 January 2015
Type: Proceedings
Publisher: ACM – Association for Computing Machinery
Gao Huang, Jianwen Zhang, Shiji Song, and Zheng Chen

This paper proposes a new approach for discriminative clustering. The intuition is, for a good clustering, one should be able to learn a classifier from the clustering labels with high generalization accuracy. Thus we define a novel metric to evaluate the quality of a clustering labeling, named Minimum Separation Probability (MSP), which is a lower bound of the generalization accuracy of a classifier learnt from the clustering labeling. We take MSP as the objective to maximize and propose our...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Yuchen Zhang and Lin Xiao

We consider distributed convex optimization problems originated from sample average approximation of stochastic optimization, or empirical risk minimization in machine learning. We assume that each machine in the distributed computing system has access to a local empirical loss function, constructed with i.i.d. data sampled from a common distribution. We propose a communication-efficient distributed algorithm to minimize the overall empirical loss, which is the average of the local empirical losses. The...

Publication details
Date: 1 January 2015
Type: Technical report
Number: MSR-TR-2015-1
Margus Veanes, Todd Mytkowicz, David Molnar, and Benjamin Livshits

String-manipulating programs are an important class of programs with applications in malware detection, graphics, input sanitization for Web security, and large-scale HTML processing. This paper extends prior work on BEK, an expressive domain-specific language for writing string-manipulating programs, with algorithmic insights that make BEK both analyzable and data-parallel. By analyzable we mean that unlike most general purpose programming languages, many algebraic properties of a BEK...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Shipra Agrawal and Nikhil R. Devanur

We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. We present fast algorithms for these problems, which achieve near-optimal regret guarantees for both the i.i.d. and the...

Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: SIAM – Society for Industrial and Applied Mathematics
Publication details
Date: 1 January 2015
Type: Inproceeding
Publisher: ACM
Lin Xiao and Tong Zhang

We consider the problem of minimizing the sum of two convex functions: one is the average of a large number of smooth component functions, and the other is a general convex function that admits a simple proximal mapping. We assume the whole objective function is strongly convex. Such problems often arise in machine learning, known as regularized empirical risk minimization. We propose and analyze a new proximal stochastic gradient method, which uses a multistage scheme to progressively reduce the...

Publication details
Date: 1 December 2014
Type: Article
Publisher: SIAM – Society for Industrial and Applied Mathematics
Number: 4
Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: Springer
Fisher J., Piterman N., and Rastislav Bodik

Over the last decade, executable models of biological behaviors have repeatedly provided new scientific discoveries, uncovered novel insights, and directed new experimental avenues. These models are computer programs whose execution mechanistically simulates aspects of the cell’s behaviors. If the observed behavior of the program agrees with the observed biological behavior, then the program explains the phenomena. This approach has proven beneficial for gaining new biological insights and directing...

Publication details
Date: 1 December 2014
Type: Article
Publisher: Frontiers
Purushottam Kar, Harikrishna Narasimhan, and Prateek Jain

Modern applications in sensitive domains such as biometrics and medicine frequently require the use of non-decomposable loss functions such as precision@k, F-measure etc. Compared to point loss functions such as hinge-loss, these offer much more fine grained control over prediction, but at the same time present novel challenges in terms of algorithm design and analysis. In this work we initiate a study of online learning techniques for such non-decomposable loss functions with an aim to enable...

Publication details
Date: 1 December 2014
Type: Inproceeding
Publisher: Neural Information Processing Systems
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