Word-Entity Duet Representations for Document Ranking

This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc retrieval. In this work, the query and documents are modeled by word-based representations and entity-based representations. Ranking features are generated by the interactions between the two representations, incorporating information from the word space, the entity space, and the cross-space connections through the knowledge graph. To handle the uncertainties from the automatically constructed entity representations, an attention-based ranking model AttR-Duet is developed. With back-propagation from ranking labels, the model learns simultaneously how to demote noisy entities and how to rank documents with the word-entity duet. Evaluation results on TREC Web Track ad-hoc task demonstrate that all of the four-way interactions in the duet are useful, the attention mechanism successfully steers the model away from noisy entities, and together they significantly outperform both word-based and entity-based learning to rank systems

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score. The whole model is trained end-to-end. The ranking layer learns desired feature patterns from the pairwise ranking loss. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix. The word embeddings are tuned accordingly so that they can produce the desired soft matches. Experiments on a commercial search engine's query log demonstrate the improvements of K-NRM over prior feature-based and neural-based states-of-the-art, and explain the source of K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric tailored for matching query words to document words, and provides effective multi-level soft matches

An Architecture to Support Learning-based Adaptation of Persistent Queries in Mobile Environments

Queries are frequently used by applications in dynamically formed mobile networks to discover and acquire information and services available in the surrounding environment. A number of inquiry strategies exist, each of which embodies an approach to disseminating a query and collecting results. The choice of inquiry strategy has different tradeoffs under different operating conditions. Therefore, it is beneficial to allow a query-based application to dynamically adapt its inquiry strategy to the changing environmental conditions. To promote development by non-expert domain programmers, we can automate the decision-making process associated with adapting the inquiry strategy. In this paper, we propose an architecture to support automated adaptative query processing for dynamic mobile environments. The decision-support module of our architecture relies on an instance-based learning approach to support context-aware adaptation of the inquiry strategy

Consistency and Variation in Kernel Neural Ranking Model

This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry. We find that K-NRM has low variance on relevance-based metrics across experimental trials. In spite of this low variance in overall performance, different trials produce different document rankings for individual queries. The main source of variance in our experiments was found to be different latent matching patterns captured by K-NRM. In the IR-customized word embeddings learned by K-NRM, the query-document word pairs follow two different matching patterns that are equally effective, but align word pairs differently in the embedding space. The different latent matching patterns enable a simple yet effective approach to construct ensemble rankers, which improve K-NRM's effectiveness and generalization abilities.Comment: 4 pages, 4 figures, 2 table

The Role of Notch1 and Notch3 in hADSC Adipogenesis

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TFAP2C regulates transcription in human naive pluripotency by opening enhancers.

Naive and primed pluripotent human embryonic stem cells bear transcriptional similarity to pre- and post-implantation epiblast and thus constitute a developmental model for understanding the pluripotent stages in human embryo development. To identify new transcription factors that differentially regulate the unique pluripotent stages, we mapped open chromatin using ATAC-seq and found enrichment of the activator protein-2 (AP2) transcription factor binding motif at naive-specific open chromatin. We determined that the AP2 family member TFAP2C is upregulated during primed to naive reversion and becomes widespread at naive-specific enhancers. TFAP2C functions to maintain pluripotency and repress neuroectodermal differentiation during the transition from primed to naive by facilitating the opening of enhancers proximal to pluripotency factors. Additionally, we identify a previously undiscovered naive-specific POU5F1 (OCT4) enhancer enriched for TFAP2C binding. Taken together, TFAP2C establishes and maintains naive human pluripotency and regulates OCT4 expression by mechanisms that are distinct from mouse

The Role of Notch3 in Self-Renewal of Adipose Derived Stem Cells

Hannah Logan is an undergraduate student in Biology at Louisiana Tech University. Avery Bryan is an undergraduate student in Biology at Louisiana Tech University. Mengcheng Liu is a graduate student in Biology at Louisiana Tech University. Jamie Newman is an Assistant Professor in Biological Sciences at Louisiana Tech University