Learning to Diversify Web Search Results with a Document Repulsion Model

Search diversification (also called diversity search), is an important approach to tackling the query ambiguity problem in information retrieval. It aims to diversify the search results that are originally ranked according to their probabilities of relevance to a given query, by re-ranking them to cover as many as possible different aspects (or subtopics) of the query. Most existing diversity search models heuristically balance the relevance ranking and the diversity ranking, yet lacking an efficient learning mechanism to reach an optimized parameter setting. To address this problem, we propose a learning-to-diversify approach which can directly optimize the search diversification performance (in term of any effectiveness metric). We first extend the ranking function of a widely used learning-to-rank framework, i.e., LambdaMART, so that the extended ranking function can correlate relevance and diversity indicators. Furthermore, we develop an effective learning algorithm, namely Document Repulsion Model (DRM), to train the ranking function based on a Document Repulsion Theory (DRT). DRT assumes that two result documents covering similar query aspects (i.e., subtopics) should be mutually repulsive, for the purpose of search diversification. Accordingly, the proposed DRM exerts a repulsion force between each pair of similar documents in the learning process, and includes the diversity effectiveness metric to be optimized as part of the loss function. Although there have been existing learning based diversity search methods, they often involve an iterative sequential selection process in the ranking process, which is computationally complex and time consuming for training, while our proposed learning strategy can largely reduce the time cost. Extensive experiments are conducted on the TREC diversity track data (2009, 2010 and 2011). The results demonstrate that our model significantly outperforms a number of baselines in terms of effectiveness and robustness. Further, an efficiency analysis shows that the proposed DRM has a lower computational complexity than the state of the art learning-to-diversify methods

Extreme Learning Machine Based Non-Iterative and Iterative Nonlinearity Mitigation for LED Communications

This work concerns receiver design for light emitting diode (LED) communications where the LED nonlinearity can severely degrade the performance of communications. We propose extreme learning machine (ELM) based non-iterative receivers and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data is used as virtual training sequence in ELM training. It is shown that the ELM based receivers significantly outperform conventional polynomial based receivers; iterative receivers can achieve huge performance gain compared to non-iterative receivers; and the data-aided receiver can reduce training overhead considerably. This work can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers

Safety and Airworthiness Design of Ultra-Light and Very Light Amphibious Aircrafts

AbstractUltra-light and very light amphibious aircrafts are the special kinds of low-speed general aircrafts. They are low weighted and small sized but can takeoff and land either on land or water without changing the structure of any parts. These characteristics result in the distinctive configuration and structure design, and meanwhile bring about significant features of safety and airworthiness design. These problems are investigated by developing the ultra-light amphibious aircraft “Frigate bird” and analyzing the other aircrafts’ design. This paper mainly discusses the preliminary design about structure, aerodynamics, power effect, flying qualities, dynamics and statics on water. Some analysis methodologies and design parameters which are different from the conventional general aircrafts’ are also represented

Study on the Transit Network Evaluation Method Based on the Transit Ridership Model

AbstractTraditional four-step travel forecasting models are usually used to predict changes in car travel patterns and to evaluate the road transportation system. The application is unsatisfactory when they are used to evaluate the transit transportation system. Based on the transit origin-destination (OD) adjustment, this paper proposes a framework on future transit network evaluations, where the transit ridership model and OD difference method are simultaneously used. The proposed method formulates the relationship between transit ridership and zonal population, employment, transit service level, and so on. In addition, the difference between transit counts and estimates for base year are considered in the development of the transit OD for future year. It is expected to perform better than conventional models in terms of transit network evolutions. The validation of the proposed method is tested in Fuzhou City Transit Development Project

Towards High-Order Complementary Recommendation via Logical Reasoning Network

Complementary recommendation gains increasing attention in e-commerce since it expedites the process of finding frequently-bought-with products for users in their shopping journey. Therefore, learning the product representation that can reflect this complementary relationship plays a central role in modern recommender systems. In this work, we propose a logical reasoning network, LOGIREC, to effectively learn embeddings of products as well as various transformations (projection, intersection, negation) between them. LOGIREC is capable of capturing the asymmetric complementary relationship between products and seamlessly extending to high-order recommendations where more comprehensive and meaningful complementary relationship is learned for a query set of products. Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation. We demonstrate the effectiveness of our LOGIREC on multiple public real-world datasets in terms of various ranking-based metrics under both low-order and high-order recommendation scenarios.Comment: 6 pages, 3 figure

Exact conditions for antiUnruh effect in (1+1)-dimensional spacetime

Exact conditions for antiUnruh effect in (1+1)-dimensional spacetime are obtained. For detectors with Gaussian switching functions, the analytic results are similar to previous ones, indicating that antiUnruh effect occurs when the energy gap matches the characteristic time scale. However, this conclusion does not hold for detectors with square wave switching functions, in which case the condition turns out to depend on both the energy gap and the characteristic time scale in some nontrivial way. We also show analytically that there is no antiUnruh effect for detectors with Gaussian switching functions in (3+1)-dimensional spacetime.Comment: 16 page

Accelerating Unruh-DeWitt detectors coupled with a spinor field

The behavior of accelerating Unruh-DeWitt detectors coupled with a spinor field in (3+1)-dimensional spacetime is investigated. For a single point-like detector with Gaussian switching function, the transition probability increases with the acceleration and thus the antiUnruh effect effect cannot occur. Due to the spinor structure of the Dirac field, UV divergences are encountered in the calculation of the entanglement between the detectors. After introducing some UV cutoff $\Lambda$, the logarithmic negativity of detectors is shown to behave nonmonotonically with respect to the acceleration. Besides, the logarithmic negativity increases with the cutoff $\Lambda$ and decreases with the distance between the detectors. The mutual information between the two detectors is also discussed.Comment: 30 page