Short-reach nanophotonic interconnects are promising to solve the communication bottleneck in data centers and chip-level scenarios. However, the nanophotonic interconnects are sensitive to process and thermal variations, especially for the microring structures, resulting in significant variation of an optical link's bit error rate (BER). In this paper, we propose a power-efficient adaptive tuning approach for nanophotonic interconnects to address the variation issues. During the adaptive tuning process, each nanophotonic interconnect is adaptively allocated just enough power to meet the BER requirement. The proposed adaptive tuning approach could reduce the photonic receiver power by 8% - 34% than the worst-case based fixed design while achieving the same BER. Our evaluation results show that the adaptive tuning approach scales well with the process variation, the thermal variation and the number of communication nodes, and can accommodate different types of NoC architectures and lasers.

There has been significant recent interest in using the aggregate information from social media sites (e.g., Twitter) to identify influencers. To investigate this issue, one dynamic diversity-dependent algorithm is proposed for detecting the influencers by evaluating the influence of users throughout social networks. Comparative analyses with the existing methods on either synthetic social networks or real Twitter data show that our strategy performs best. It implies that the pattern of the influence propagation should be updated dynamically to reflect the flow of influence spread to better capture the rapidly changing dynamics of microblogs. Our proposed scheme is therefore practical and feasible to be deployed in the real world.

Plotting lots of geographical data points usually clutters up a map. In this paper, we propose an approach to provide a summary view of geographical data by efficiently clustering. We present a novel data structure, called Geo-tree, which is extended from quad tree, and then develop two algorithms, which use Geo-tree to cluster geographic data and visualize the clusters with a heat map-like representation. The experimental results show that our approach is very efficient in a large scale, compared to K-means and HAC, and the clustering results are comparable to theirs.

Query translation is an important task in cross-language information retrieval (CLIR) aiming to translate queries into languages used in documents. The purpose of this paper is to investigate the necessity of translating query terms, which might differ from one term to another. Some untranslated terms cause irreparable performance drop while others do not. We propose an approach to estimate the translation probability of a query term, which helps decide if it should be translated or not. The approach learns regression and classification models based on a rich set of linguistic and statistical properties of the term. Experiments on NTCIR-4 and NTCIR-5 English-Chinese CLIR tasks demonstrate that the proposed approach can significantly improve CLIR performance. An in-depth analysis is also provided for discussing the impact of untranslated out-of-vocabulary (OOV) query terms and translation quality of non-OOV query terms on CLIR performance.