Liu W.,Columbia University |
Ma S.,Columbia University |
Tao D.,Nanyang Technological University |
Liu J.,Chinese University of Hong Kong |
Liu P.,Barclays Capital
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2010
In plenty of scenarios, data can be represented as vectors and then mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining applications need proximity measures over data, a simple and universal distance metric is desirable, and metric learning methods have been explored to produce sensible distance measures consistent with data relationship. However, most existing methods suffer from limited labeled data and expensive training. In this paper, we address these two issues through employing abundant unlabeled data and pursuing sparsity of metrics, resulting in a novel metric learning approach called semi-supervised sparse metric learning. Two important contributions of our approach are: 1) it propagates scarce prior affinities between data to the global scope and incorporates the full affinities into the metric learning; and 2) it uses an efficient alternating linearization method to directly optimize the sparse metric. Compared with conventional methods, ours can effectively take advantage of semi-supervision and automatically discover the sparse metric structure underlying input data patterns. We demonstrate the efficacy of the proposed approach with extensive experiments carried out on six datasets, obtaining clear performance gains over the state-of-the-arts. © 2010 ACM.
Hazra A.,Indian Institute of Technology Kharagpur |
Goyal S.,Barclays Capital |
Dasgupta P.,Indian Institute of Technology Kharagpur |
Pal A.,Indian Institute of Technology Kharagpur
IEEE Transactions on Very Large Scale Integration (VLSI) Systems | Year: 2013
This paper presents a verification framework that attempts to bridge the disconnect between high-level properties capturing the architectural power management strategy and the implementation of the power management control logic using low-level per-domain control signals. The novelty of the proposed framework is in demonstrating that the architectural power intent properties developed using high-level artifacts can be automatically translated into properties over low-level control sequences gleaned from UPF specifications of power domains, and that the resulting properties can be used to formally verify the global on-chip power management logic. The proposed translation uses a considerable amount of domain knowledge and is also not purely syntactic, because it requires formal extraction of timing information for the low-level control sequences. We present a tool, called POWER-TRUCTOR which enables the proposed framework, and several test cases of significant complexity to demonstrate the feasibility of the proposed framework. © 2012 IEEE.
Liang C.,New York University |
Fu Z.,Barclays Capital |
Fu Z.,IBM |
Liu Y.,New York University |
IEEE Transactions on Parallel and Distributed Systems | Year: 2010
As an efficient distribution mechanism, Peer-to-Peer (P2P) technology has become a tremendously attractive solution to offload servers in large-scale video streaming applications. However, in providing on-demand asynchronous streaming services, P2P streaming design faces two major challenges: how to schedule efficient video sharing between peers with asynchronous playback progresses? how to provide incentives for peers to contribute their resources to achieve a high level of system-wide Quality-of-Experience (QoE)? In this paper, we present iPASS, a novel mesh-based P2P VoD system, to address these challenges. Specifically, iPASS adopts a dynamic buffering-progress-based peering strategy to achieve high peer bandwidth utilization with low system maintenance cost. To provide incentives for peer uploading, iPASS employs a differentiated prefetching design that enables peers with higher contribution prefetch content at higher speed. A distributed adaptive taxation algorithm is developed to balance the system-wide QoE and service differentiations among heterogeneous peers. To assess the performance of iPASS, we built a detailed packet-level P2P VoD simulator and conducted extensive simulations. It was demonstrated that iPASS can completely offload server when the average peer upload bandwidth is more than 1.2 times the streaming rate. Furthermore, we showed that the distributed incentive algorithm motivates peers to contribute and collaboratively achieve a high level of system wide QoE. © 2010 IEEE.
Zhang X.,Vertex Pharmaceuticals |
Hu X.,Drexel University |
Hu X.,Central China Normal University |
He T.,Central China Normal University |
And 2 more authors.
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2012
With the fast growing number of works utilizing link information in enhancing unsupervised document clustering, it is becoming necessary to make a comparative evaluation of the impacts of different link types on document clustering. Various types of links between text documents, including explicit links such as citation links and hyperlinks, implicit links such as coauthorship and cocitation links, and similarity links such as content similarity links, convey topic similarity or topic transferring patterns, which is very useful for document clustering. In this paper, we adopt a clustering algorithm based on Markov random field and relaxation labeling, which employs both content and linkage information, to evaluate the effectiveness of the aforementioned types of links for document clustering on ten data sets. The experimental results show that linkage information is quite effective in improving content-based document clustering. Furthermore, a series of important findings regarding the impacts of different link types on document clustering is discovered through our experiments. © 1996-2012 IEEE.
Bartz D.,TU Berlin |
Hatrick K.,Barclays Capital |
Hesse C.W.,Global Markets Equity |
Muller K.-R.,TU Berlin |
And 2 more authors.
PLoS ONE | Year: 2013
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. © 2013 Bartz et al.