Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Advertiser reporting system and method in a networked database search system

Abstract: A system and method provide advertisement campaign information about an advertisement campaign to an advertiser. The system includes means for organizing advertisement campaign information into one or more ad groups and a web interface to receive advertiser inputs and provide a visual report including the advertisement campaign information to the advertiser. The system further includes a campaign data store configured to store advertisement campaign account data and a reporting system to produce the visual report about performance of the advertisement campaign. The visual report is based on the organization of the advertisement campaign information into the one or more ad groups.

Isolation and Identification of Cellulolytic Bacteria from the Gut of Holotrichia parallela Larvae (Coleoptera: Scarabaeidae)

Abstract: In this study, 207 strains of aerobic and facultatively anaerobic cellulolytic bacteria were isolated from the gut of Holotrichia parallela larvae. These bacterial isolates were assigned to 21 genotypes by amplified ribosomal DNA restriction analysis (ARDRA). A partial 16S rDNA sequence analysis and standard biochemical and physiological tests were used for the assignment of the 21 representative isolates. Our results show that the cellulolytic bacterial community is dominated by the Proteobacteria (70.05%), followed by the Actinobacteria (24.15%), the Firmicutes (4.35%), and the Bacteroidetes (1.45%). At the genus level, Gram-negative bacteria including Pseudomonas, Ochrobactrum, Rhizobium, Cellulosimicrobium, and Microbacterium were the predominant groups, but members of Bacillus, Dyadobacter, Siphonobacter, Paracoccus, Kaistia, Devosia, Labrys, Ensifer, Variovorax, Shinella, Citrobacter, and Stenotrophomonas were also found. Furthermore, our results suggest that a significant amount of bacterial diversity exists among the cellulolytic bacteria, and that Siphonobacter aquaeclarae, Cellulosimicrobium funkei, Paracoccus sulfuroxidans, Ochrobactrum cytisi, Ochrobactrum haematophilum, Kaistia adipata, Devosia riboflavina, Labrys neptuniae, Ensifer adhaerens, Shinella zoogloeoides, Citrobacter freundii, and Pseudomonas nitroreducens are reported to be cellulolytic for the first time in this study. Our results indicate that the scarab gut is an attractive source for the study of novel cellulolytic microorganisms and enzymes useful for cellulose degradation.

Adjunctive antidepressant use and symptomatic recovery among bipolar depressed patients with concomitant manic symptoms: findings from the STEP-BD.

Abstract: Objective: Practice guidelines have advised against treating patients with antidepressants during bipolar mixed states or dysphoric manias. However, few studies have examined the outcomes of patients with co-occurring manic and depressive symptoms who are treated with antidepressants plus mood stabilizing drugs. Method: The authors compared outcomes in patients with bipolar disorder who received a mood stabilizing agent with versus without an antidepressant for a bipolar depressive episode accompanied by ≥2 concurrent manic symptoms. The 335 participants were drawn from the first 2,000 enrollees in the National Institute of Mental Health (NIMH) Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Kaplan-Meier survival curves and Cox regression models were used to compare time to recovery. General linear models examined the relationship between antidepressant use or mania symptom load at the study entry and mania or depression symptom severity at the 3-month follow-up. Results: Adjuncti...

A sparse Johnson: Lindenstrauss transform

Abstract: Dimension reduction is a key algorithmic tool with many applications including nearest-neighbor search, compressed sensing and linear algebra in the streaming model. In this work we obtain a sparse version of the fundamental tool in dimension reduction -- the Johnson-Lindenstrauss transform. Using hashing and local densification, we construct a sparse projection matrix with just ~O(1/e) non-zero entries per column. We also show a matching lower bound on the sparsity for a large class of projection matrices. Our bounds are somewhat surprising, given the known lower bounds of Ω(1/e2) both on the number of rows of any projection matrix and on the sparsity of projection matrices generated by natural constructions. Using this, we achieve an ~O(1/e) update time per non-zero element for a (1 e)-approximate projection, thereby substantially outperforming the ~O(1/e2) update time required by prior approaches. A variant of our method offers the same guarantees for sparse vectors, yet its ~O(d) worst case running time matches the best approach of Ailon and Liberty.