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Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has been observed. RaceTrack uses a novel hybrid detection(More)
Influence maximization is the problem of selecting top k seed nodes in a social network to maximize their influence coverage under certain influence diffusion models. In this paper, we propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the(More)
Spontaneous hemodynamic signals fluctuate coherently within many resting-brain functional networks not only in awake humans and lightly anesthetized primates but also in animals under deep anesthesia characterized by burst-suppression electroencephalogram (EEG) activity and unconsciousness. To understand the neural origin of spontaneous hemodynamic(More)
Influence maximization is a problem of finding a small set of highly influential users in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the(More)
The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal(More)
In many real-world situations, different and often opposite opinions, innovations, or products are competing with one another for their social influence in a networked society. In this paper, we study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model.(More)
We present a Kalman-filter method for the estimation of time-frequency-selective fading channels in OFDM systems. Based on the Jakes model, an autoregressive (AR) model of the channel dynamics is built. To reduce the complexity of the high-dimensional Kalman filer for joint estimation of the subchannels, we propose to use a low-dimensional Kalman filter for(More)
Recently, joint video-language modeling has been attracting more and more attention. However, most existing approaches focus on exploring the language model upon on a fixed visual model. In this paper, we propose a unified framework that jointly models video and the corresponding text sentences. The framework consists of three parts: a compositional(More)