arXiv subjects

Wei Chu

Publications and source records attributed to Wei Chu.

129 records · Page 8Linked to original sources

arXiv2011

High-brightness switchable multi-wavelength remote laser in air

Remote laser in air based on amplified spontaneous emission (ASE) has produced rather well-collimated coherent beams in both backward and forward propagation directions, opening up possibilities for new remote sensing approaches. The remote ASE-based lasers were shown to enable operation either at ~391 and 337 nm using molecular nitrogen or at ~845 nm using molecular oxygen as gain medium, depending on the employed pump lasers. To date, a multi-wavelength laser in air that allows for dynamically switching the operating wavelength has not yet been achieved, although this type of laser is certainly of high importance for detecting multiple hazard gases. In this Letter, we demonstrate, for the first time to our knowledge, a harmonic-seeded switchable multi-wavelength laser in air driven by intense mid-infrared femtosecond laser pulses. Furthermore, population inversion in the multi-wavelength remote laser occurs at an ultrafast time-scale (i.e., less than ~200 fs) owing to direct formation of excited molecular nitrogen ions by strong-field ionization of inner-valence electrons, which is fundamentally different from the previously reported pumping mechanisms based either on electron recombination of ionized molecular nitrogen or on resonant two-photon excitation of atomic oxygen fragments resulting from resonant two-photon dissociation of molecular oxygen. The bright multi-wavelength laser in air opens the perspective for remote detection of multiple pollutants based on nonlinear spectroscopy.

physics.atom-ph↗

arXiv2011

Refining Recency Search Results with User Click Feedback

Traditional machine-learned ranking systems for web search are often trained to capture stationary relevance of documents to queries, which has limited ability to track non-stationary user intention in a timely manner. In recency search, for instance, the relevance of documents to a query on breaking news often changes significantly over time, requiring effective adaptation to user intention. In this paper, we focus on recency search and study a number of algorithms to improve ranking results by leveraging user click feedback. Our contributions are three-fold. First, we use real search sessions collected in a random exploration bucket for \emph{reliable} offline evaluation of these algorithms, which provides an unbiased comparison across algorithms without online bucket tests. Second, we propose a re-ranking approach to improve search results for recency queries using user clicks. Third, our empirical comparison of a dozen algorithms on real-life search data suggests importance of a few algorithmic choices in these applications, including generalization across different query-document pairs, specialization to popular queries, and real-time adaptation of user clicks.

cs.IR↗

arXiv2010

Learning to Blend by Relevance

Emergence of various vertical search engines highlights the fact that a single ranking technology cannot deal with the complexity and scale of search problems. For example, technology behind video and image search is very different from general web search. Their ranking functions share few features. Question answering websites (e.g., Yahoo! Answer) can make use of text matching and click features developed for general web, but they have unique page structures and rich user feedback, e.g., thumbs up and thumbs down ratings in Yahoo! answer, which greatly benefit their own ranking. Even for those features shared by answer and general web, the correlation between features and relevance could be very different. Therefore, dedicated functions are needed in order to better rank documents within individual domains. These dedicated functions are defined on distinct feature spaces. However, having one search box for each domain, is neither efficient nor scalable. Rather than typing the same query two times into both Yahoo! Search and Yahoo! Answer and retrieving two ranking lists, we would prefer putting it only once but receiving a comprehensive list of documents from both domains on the subject. This situation calls for new technology that blends documents from different sources into a single ranking list. Despite the content richness of the blended list, it has to be sorted by relevance none the less. We call such technology blending, which is the main subject of this paper.

cs.IR↗