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Deep convolutional neural networks have achieved significant improvements on face recognition task due to their ability to learn highly discriminative features from tremendous amounts of face images. Many large scale face datasets exhibit long-tail distribution where a small number of entities (persons) have large number of face images while a large number of persons only have very few face samples...
Predicting the gap between taxi demand and supply in taxi booking apps is completely new and important but challenging. However, manually mining gap rule for different conditions may become impractical because of massive and sparse taxi data. Existing works unilaterally consider demand or supply, used only few simple features and verified by little data, but not predict the gap value. Meanwhile, none...
The proliferation of location-based social networks, such as Foursquare and Facebook Places, offers a variety of ways to record human mobility, including user generated geo-tagged contents, check-in services, and mobile apps. Although trajectory data is of great value to many applications, it is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility,...
Manifold learning (ML) is a known non-linear technique for representing high dimensional data. Despite the potential power of ML techniques, they fail in representing an unseen test data accurately. To better model the geometric structure of manifolds, Manifold Alignment (MA) techniques have been proposed recently, where the majority of these algorithms rely on point correspondences between two manifolds...
Energy and cooling cost is becoming the main cost of data centers. Many studies focused on how to schedule job and migrate Virtual Machine between servers to save energy. An accurate energy consumption model is the basic of energy management. Most past studies show that energy consumption has linear relation with resource utilization. We found that different servers have different energy consumption...
Now the number of disks in Mass Storage System is on the increase, and Hard disk drive failure already is not small probability events. In mass storage system RAID technique commonly used to prevent a disk failure. RAID can save data and check data, when a disk becomes a failure disk, the lost data in the disk can be recovered through fine data and check data. For RAID5, when a disk is recovering,...
With the repaid development of multi-media and network, information are generated and spread quickly than before. New application such as search engine, video share depends on the scale and access speed of huge data set. Data-intensive computing became a hot point of computer science. In Data-intensive environments, data access ability is the bottleneck instead of computing ability. In this paper,...
This paper presents a reference framework, called BUD, to manage a large shared bank of unstructured data. This paper lists several important issues on managing or maintaining the unstructured data in BUD. BUD stores and manages the ever-growing unstructured data by introducing a novel technique called free-table, which is a conceptual view for end-users and a physical entity maintained by transactional...
In this study, we propose a new universal electron trapping/detrapping model of interface traps for the recoverable part of negative bias temperature instability (NBTI) using dispersive relaxation time. In particular, our model can separate different phases of NBTI process by different slopes of logarithmic time dependence for the first time. We have also successfully derived a normalized universal...
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