Research on Zipf\u27s Law of Hot Events in Search Engines

This paper focuses on the amount of searching and browsing of hot events in China and finds that the searching index sequences of daily hot events and weekly hot events are in line with Zipf\u27s law. Through continuous collection of large data samples of multiple dates , We find that the Zipf index of the searching index series for daily hot events fluctuates in a very small range.Through Zipf analysis, we find that only a few events maintain long-term heat. A few events will be the focus of most people, while a few will focus on some directional events. So Zipf distribution describes the balance of economic propensity of sender and receiver during the transmission of information. This research is of some reference to commercial activities that make use of hot events for e-commerce

Efficiently Disassemble-and-Pack for Mechanism

In this paper, we present a disassemble-and-pack approach for a mechanism to seek a box which contains total mechanical parts with high space utilization. Its key feature is that mechanism contains not only geometric shapes but also internal motion structures which can be calculated to adjust geometric shapes of the mechanical parts. Our system consists of two steps: disassemble mechanical object into a group set and pack them within a box efficiently. The first step is to create a hierarchy of possible group set of parts which is generated by disconnecting the selected joints and adjust motion structures of parts in groups. The aim of this step is seeking total minimum volume of each group. The second step is to exploit the hierarchy based on breadth-first-search to obtain a group set. Every group in the set is inserted into specified box from maximum volume to minimum based on our packing strategy. Until an approximated result with satisfied efficiency is accepted, our approach finish exploiting the hierarchy.Comment: 2 pages, 2 figure

Person Re-identification with Correspondence Structure Learning

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach