On Ranking Consistency of Pre-ranking Stage

Industrial ranking systems, such as advertising systems, rank items by aggregating multiple objectives into one final objective to satisfy user demand and commercial intent. Cascade architecture, composed of retrieval, pre-ranking, and ranking stages, is usually adopted to reduce the computational cost. Each stage may employ various models for different objectives and calculate the final objective by aggregating these models' outputs. The multi-stage ranking strategy causes a new problem - the ranked lists of the ranking stage and previous stages may be inconsistent. For example, items that should be ranked at the top of the ranking stage may be ranked at the bottom of previous stages. In this paper, we focus on the \textbf{ranking consistency} between the pre-ranking and ranking stages. Specifically, we formally define the problem of ranking consistency and propose the Ranking Consistency Score (RCS) metric for evaluation. We demonstrate that ranking consistency has a direct impact on online performance. Compared with the traditional evaluation manner that mainly focuses on the individual ranking quality of every objective, RCS considers the ranking consistency of the fused final objective, which is more proper for evaluation. Finally, to improve the ranking consistency, we propose several methods from the perspective of sample selection and learning algorithms. Experimental results on one of the biggest industrial E-commerce platforms in China validate the efficacy of the proposed metrics and methods.Comment: 9 pagees, 5 figure

Cryptic t(15;17) acute promyelocytic leukemia with a karyotype of add(11)(p15) and t(13,20)- A case report with a literature review

Most acute promyelocytic leukemia (APL) are characterized by reciprocal translocations t(15;17)(q22;21), which results in the fusion of PML gene at 15q22 with RARα gene at 17q21. However, several complex variant translocations also have been reported. Here we report a 62-year-old man with typical morphology and clinical features of APL with a complex karyotype including add(11)(p15) and t(13,20)(q12;q11.2) without typical t(15;17) assayed by the G-banding analysis. FISH with a PML/RARα dual-color DNA probe showed an atypical fusion signal, RT-qPCR analysis showed PML/RARα fusion transcripts, and NGS detected FLT3, WT1, and KRAS mutations. The patient achieved complete remission after treatment with conventional chemotherapy combined ATRA and ATO. Although the mechanism of this kind of cryptic variant remains unknown, we conclude that the cryptic PML/RARα fusion with add(11)(p15), t(13,20)(q12;q11.2) seems not to alter the effectiveness of chemotherapy combined with ATRA and ATO

Temperature-Controlled Divergent Synthesis of Tetrasubstituted Alkenes and Pyrrolo[1,2-a]indole Derivatives via Iridium Catalysis

We have achieved an Ir(III)-catalyzed temperature-controlled divergent synthesis of tetrasubstituted alkenes and pyrrolo[1,2-a]indole derivatives through C−H alkenylation/DG migration and [3+2] annulation, respectively. This method has various advantageous features: a) excellent regio- and stereoselectivity and good functional group tolerance, b) broad substrate scope and moderate to excellent yields, c) mild redox-neutral reaction conditions and operational simplicity

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models

Acetaldehyde released by Lactobacillus plantarum enhances accumulation of pyranoanthocyanins in wine during malolactic fermentation

This study investigated the evolution of acetaldehyde and pyranoanthocyanins in wine during malolactic fermentation, and further evaluated the correlation between acetaldehyde and pyranoanthocyanins. Cabernet Gernischt wine after alcoholic fermentation was inoculated with four lactic acid bacteria strains. Malolactic fermentation kinetics and wine characteristics were compared. Results showed these strains exhibited different kinetics on wine malolactic fermentation. Wine with Lactobacillus plantarum had lower reducing sugar, total acid, and yellowness. Lactobacillus plantarum elevated the level of acetaldehyde in wine model medium and wine during malolactic fermentation. Malolactic fermentation using Lactobacillus plantarum significantly increased the concentration of pyranoanthocyanins, whereas O. oeni strain reduced the level of pyranoanthocyanins in wine. Polymerized anthocyanins percentage in wine was significantly enhanced after fermentation with Lactobacillus plantarum. Principal component analysis indicated that the characteristics of these strains inoculated wines after malolactic fermentation were segregated. The findings from this study could provide useful information on the wine color improvement through malolactic fermentation with suitable lactic acid bacteria strains

Residue management alters microbial diversity and activity without affecting their community composition in black soil, Northeast China

Residue management is an important agricultural practice for improving soil fertility. To reveal the impact of residue management on soil microbial community, we conducted a field experiment with three treatments: no straw returning (control, CK), straw returning (SR), and straw returning combined with cow manure (SM). Our results indicated that soil organic matter content was significantly higher in SR treatment than CK in both seedling and jointing stages. In seedling stage, the lowest total nitrogen content was observed in CK treatment, and significantly lower than that in SM and SR treatment. Furthermore, soil available phosphorus content was significantly higher in SM and SR treatment than CK in jointing stage. In the seedling stage, the soil microbial average wellcolor development (AWCD) value, microbial McIntosh index, and Shannon index of CK and SM treatments were significantly higher than those in SR treatment. The AWCD value and McIntosh index in the jointing stage showed similar patterns: SM > CK > SR. Permutational multivariate analysis of variance indicated that soil microbial community was significantly affected by growth stage, but unaffected by residue management. The partial Mantel test revealed that the available potassium and the C/N ratio had independent effects on soil microbial community. Overall, our results indicated that straw returning combined with cow manure had a beneficial effect on soil fertility, microbial activity and diversity