Experimental approaches to low x at HERA

Data are presented on the production of jets and pi^0 mesons at low Bjorken x in a kinematic region where standard DGLAP evolution in Q^2 gives little phase space for high p_t particle and jet production. The data are compared with various QCD models based on different treatments of parton emissions at small x.Comment: 4 pages, talk presented at ISMD2002, Alushta, Crimea, September 200

Selected Results on the Transition from Short to Long Distance Physics at HERA

Recent results from the HERA ep collider are discussed with emphasis on the transition from short to long distance phenomena in QCD. The results cover inclusive ep scattering, inclusive diffractive scattering, vector meson production, and deeply virtual Compton scattering (DVCS).Comment: 10 pages, 8 figures, talk at MAD'01, Antananarivo, October 200

Comment: Classifier Technology and the Illusion of Progress--Credit Scoring

Comment on Classifier Technology and the Illusion of Progress--Credit Scoring [math.ST/0606441]Comment: Published at http://dx.doi.org/10.1214/088342306000000051 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Noise-tolerant approximate blocking for dynamic real-time entity resolution

Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach