@article{jiang_fang_an_lavery_2019, title={A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems}, volume={487}, ISSN={0020-0255}, url={http://dx.doi.org/10.1016/j.ins.2019.03.011}, DOI={10.1016/j.ins.2019.03.011}, abstractNote={Collaborative filtering (CF) is one of the most successful approaches for an online store to make personalized recommendations through its recommender systems. A neighborhood-based CF method makes recommendations to a target customer based on the similar preference of the target customer and those in the database. Similarity measuring between users directly contributes to an effective recommendation. In this paper, we propose a sub-one quasi-norm-based similarity measure for collaborative filtering in a recommender system. The proposed similarity measure shows its advantages over those commonly used similarity measures in the literature by making better use of rating values and deemphasizing the dissimilarity between users. Computational experiments using various real-life datasets clearly indicate the superiority of the proposed similarity measure, no matter in fully co-rated, sparsely co-rated or cold-start scenarios.}, journal={Information Sciences}, publisher={Elsevier BV}, author={Jiang, Shan and Fang, Shu-Cherng and An, Qi and Lavery, John E.}, year={2019}, month={Jun}, pages={142–155} }
 @article{nie_fang_deng_lavery_2016, title={On linear conic relaxation of discrete quadratic programs}, volume={31}, ISSN={1055-6788 1029-4937}, url={http://dx.doi.org/10.1080/10556788.2015.1134528}, DOI={10.1080/10556788.2015.1134528}, abstractNote={A special reformulation-linearization technique based linear conic relaxation is proposed for discrete quadratic programming (DQP). We show that the proposed relaxation is tighter than the traditional positive semidefinite programming relaxation. More importantly, when the proposed relaxation problem has an optimal solution with rank one or two, optimal solutions to the original DQP problem can be explicitly generated. This rank-two property is further extended to binary quadratic optimization problems and linearly constrained DQP problems. Numerical results indicate that the proposed relaxation is capable of providing high quality and robust lower bounds for DQP.}, number={4}, journal={Optimization Methods and Software}, publisher={Informa UK Limited}, author={Nie, Tiantian and Fang, Shu-Cherng and Deng, Zhibin and Lavery, John E.}, year={2016}, month={Jan}, pages={737–754} }
 @inproceedings{luo_deng_bulatov_lavery_fang_2013, series={Proceedings of SPIE}, title={Comparison of an ℓ1-regression-based and a RANSAC-based planar segmentation procedure for urban terrain data with many outliers}, volume={8892}, ISSN={["1996-756X"]}, url={http://dx.doi.org/10.1117/12.2028627}, DOI={10.1117/12.2028627}, abstractNote={For urban terrain data with many outliers, we compare an ℓ1-regression-based and a RANSAC-based planar segmentation procedure. The procedure consists of 1) calculating the normal at each of the points using ℓ1 regression or RANSAC, 2) clustering the normals thus generated using DBSCAN or fuzzy c-means, 3) within each cluster, identifying segments (roofs, walls, ground) by DBSCAN-based-subclustering of the 3D points that correspond to each cluster of normals and 4) fitting the subclusters by the same method as that used in Step 1 (ℓ1 regression or RANSAC). Domain decomposition is used to handle data sets that are too large for processing as a whole. Computational results for a point cloud of a building complex in Bonnland, Germany obtained from a depth map of seven UAV-images are presented. The ℓ1-regression-based procedure is slightly over 25% faster than the RANSAC-based procedure and produces better dominant roof segments. However, the roof polygonalizations and cutlines based on these dominant segments are roughly equal in accuracy for the two procedures. For a set of artificial data, ℓ1 regression is much more accurate and much faster than RANSAC. We outline the complete building reconstruction procedure into which the ℓ1-regression-based and RANSAC-based segmentation procedures will be integrated in the future.}, booktitle={Image and Signal Processing for Remote Sensing XIX}, publisher={SPIE}, author={Luo, Jian and Deng, Zhibin and Bulatov, Dimitri and Lavery, John E. and Fang, Shu-Cherng}, editor={Bruzzone, LorenzoEditor}, year={2013}, month={Oct}, pages={889209}, collection={Proceedings of SPIE} }
 @article{jin_yu_lavery_fang_2012, title={Univariate cubic L-1 interpolating splines based on the first derivative and on 5-point windows: analysis, algorithm and shape-preserving properties}, volume={51}, ISSN={["1573-2894"]}, DOI={10.1007/s10589-011-9426-y}, number={2}, journal={COMPUTATIONAL OPTIMIZATION AND APPLICATIONS}, author={Jin, Qingwei and Yu, Lu and Lavery, John E. and Fang, Shu-Cherng}, year={2012}, month={Mar}, pages={575–600} }