6月30日：理工楼321
Title:Finding rigid sub-structure patterns from 3D point-sets
Abstract: our model views each input point-set as a collection of k rigid substructures, and aims to extract similar rigid substructures from each input point-set to form k rigid clusters. The problem is motivated by an interesting biological application for determining the topological structure of chromosomes inside the cell nucleus. We propose a highly effective and practical solution based on a number of new insights to pattern reconstruction, clustering, and motion detection.
7月3日：理工楼321
Title: Big data transfer optimization based on offline knowledge discovery and adaptive sampling
Abstract: In this paper, we propose a novel dynamic throughput optimization model based on mathematical modeling with offline knowledge discovery/analysis and adaptive online decision making. As real-time investigation is expensive and provides partial knowledge about the current network status, our model uses historical knowledge about the network and data to reduce the real-time investigation overhead while ensuring near optimal throughput for each transfer.
7月4日：理工楼321
Title: Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing
Abstract: In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio.
7月6日：理工楼321
Title: Time–frequency image-based carrier tracking method for Global Navigation Satellite System signal with ultra-high dynamics
Abstract: In this study, the authors propose a novel tracking method for Global Navigation Satellite System (GNSS) signal carrier. Different from the single-domain tracking methods or Strapdown Inertial Navigation System, the presented method works in the time-frequency (TF) image generated by Wigner-Ville distribution.
7月9日：理工楼321
Title: Finding Global Optimum for Truth Discovery: Entropy Based Geometric Variance
Abstract: In this paper, we formulate the problem as a high dimensional geometric optimization problem, called Entropy based Geometric Variance. Relying on a number of novel geometric techniques (such as Log-Partition and Modified Simplex Lemma), we further discover new insights to this problem.
7月10日：理工楼321
Title: Joint Influence: From Theory to Applications
Abstract: Joint influence is a ubiquitously existing phenomenon in nature. In a nutshell, it refers to those scenarios where the behavior of an object is determined/affected jointly by a set of other objects. In this work, I will introduce the geometric model for joint influence and present several techniques for various types of joint influence computations, which includes: (1) Clustering Induced Voronoi Diagram (CIVD), a generalization of the classic Voronoi diagrams to capture joint influence, (2) Influence-Based Voronoi Diagram (IVD), a general technique for constructing IVD for given clusters, (3) Range Cover, a novel approach for joint influence related optimization problem in high dimensional space, by decomposing “hard” problem into parameterized easier sub-problems, (4) Geometric Sum Query Algorithm, an efficient way for computing joint influence in high dimensions. Furthermore, I will show how joint influence can be applied to solve problems in various fields of computer science, such as the Truth Discovery problem in big data and the pattern search problem in computer vision and biology. Finally, I will discuss several future works on extension of the joint influence model and applications of joint influence in 3D geological modeling and object positioning systems.
7月12日：理工楼321
Title: Approximating Global Optimum for Probabilistic Truth Discovery
Abstract. The problem of truth discovery arises in many areas such as database, data mining, data crowdsourcing and machine learning. It seeks trustworthy information from possibly conflicting data provided by multiple sources. Due to its practical importance, the problem has been studied extensively in recent years. Two competing models were pro- posed for truth discovery, weight-based model and probabilistic model. While (1+ε)-approximations have already been obtained for the weight- based model, no quality guaranteed solution has been discovered yet for the probabilistic model. In this paper, we focus on the probabilistic model and formulate it as a geometric optimization problem. Based on a sampling technique and a few other ideas, we achieve the first (1+ε)- approximation solution. The general technique we developed has the potential to be used to solve other geometric optimization problems.
7月14日：理工楼321
Title: Clustering-Based Collaborative Filtering for Link Prediction
Abstract: In this paper, we propose a novel collaborative filtering approach for predicting the unobserved links in a network (or graph) with both topological and node features. Our approach improves the well-known compressed sensing based matrix completion method by introducing a new multiple-independent-Bernoulli-distribution model as the data sampling mask.
7月17日：理工楼321
Title: Random Gradient Descent Tree: A Combinatorial Approach for SVM with Outliers
Abstract: In this paper, we present a new combinatorial approach, called Random Gradient Descent Tree (or RGD-tree), to explicitly deal with outliers; this results in a new algorithm called RGD-SVM. Our technique yields provably good solution and can be efficiently implemented for practical purpose.
7月20日：理工楼3218：30-11：30
Title: An Efficient Sum Query Algorithm for Distance-based Locally Dominating Function
Abstract: In this talk, we consider the following sum query problem: Given a point set P in Rd, and a distance-based function f(p,q) (i.e., a function of the distance between p and q) satisfying some general properties, the goal is to develop a data structure and a query algorithm for efficiently computing a (1+ε)-approximate solution to the sum p∈P f(p,q) for any query point q ∈ Rd and any small constant ε > 0. Existing techniques for this problem are mainly based on some core-set techniques which often have difficulties to deal with functions with local domination property. Based on several new insights to this problem, we develop in this paper a novel technique to overcome these encountered difficulties. Our algorithm is capable of answering queries with high success probability in time no more than Õε,d(n0.5+c), and the underlying data structure can be constructed in Õε,d(n1+c) time for any c > 0, where the hidden constant has only polynomial dependence on 1/ε and d. Our technique is simple and can be easily implemented for practical purpose.
7月24日：理工楼321
Title: A unified framework for clustering constrained data without locality property
Abstract: In this paper, we consider a class of constrained clustering problems of points in Rd space, where d could be rather high. To overcome the difficulty caused by the loss of locality, we present in this paper a unified framework, called Peeling-and-Enclosing, to iteratively solve two variants of the constrained clustering problems, constrained k-means clustering (k-CMeans) and constrained k-median clustering (k-CMedian).
7月26日：理工楼321
Title: One-pass online SVM with extremely small space complexity
Abstract: In this paper we consider the problem of training a Support Vector Machine (SVM)online using a stream of data in random order. We provide a fast online training algorithm for general SVM on very large datasets. Based on the geometric interpretation of SVM known as the polytope distance, our algorithm uses a gradient descent procedure to solve the problem.