Mix 'n Match: Integrating Text Matching and Product Substitutability within Product Search

Two products are substitutes if both can satisfy the same consumer need. Intrinsic incorporation of product substitutability - where substitutability is integrated within latent vector space models - is in contrast to the extrinsic re-ranking of result lists. The fusion of text matching and product substitutability objectives allows latent vector space models to mix and match regularities contained within text descriptions and substitution relations. We introduce a method for intrinsically incorporating product substitutability within latent vector space models for product search that are estimated using gradient descent; it integrates flawlessly with state-of-the-art vector space models. We compare our method to existing methods for incorporating structural entity relations, where product substitutability is incorporated extrinsically by re-ranking. Our method outperforms the best extrinsic method on four benchmarks. We investigate the effect of different levels of text matching and product similarity objectives, and provide an analysis of the effect of incorporating product substitutability on product search ranking diversity. Incorporating product substitutability information improves search relevance at the cost of diversity

Semantic Entities

Entity retrieval has seen a lot of interest from the research community over the past decade. Ten years ago, the expertise retrieval task gained popularity in the research community during the TREC Enterprise Track [10]. It has remained relevant ever since, while broadening to social media, to tracking the dynamics of expertise [1-5, 8, 11], and, more generally, to a range of entity retrieval tasks. In the talk, which will be given by the second author, we will point out that existing methods to entity or expert retrieval fail to address key challenges: (1) Queries and expert documents use different representations to describe the same concepts [6, 7]. Term mismatches between entities and experts [7] occur due to the inability of widely used maximum-likelihood language models to make use of semantic similarities between words [9]. (2) As the amount of available data increases, the need for more powerful approaches with greater learning capabilities than smoothed maximum-likelihood language models is obvious [13]. (3) Supervised methods for entity or expertise retrieval [5, 8] were introduced at the turn of the last decade. However, the acceleration of data availability has the major disadvantage that, in the case of supervised methods, manual annotation efforts need to sustain a similar order of growth. This calls for the further development of unsupervised methods. (4) According to some entity or expertise retrieval methods, a language model is constructed for every document in the collection. These methods lack efficient query capabilities for large document collections, as each query term needs to be matched against every document [2]. In the talk we will discuss a recently proposed solution [12] that has a strong emphasis on unsupervised model construction, efficient query capabilities and, most importantly, semantic matching between query terms and candidate entities. We show that the proposed approach improves retrieval performance compared to generative language models mainly due to its ability to perform semantic matching [7]. The proposed method does not require any annotations or supervised relevance judgments and is able to learn from raw textual evidence and document-candidate associations alone. The purpose of the proposal is to provide insight in how we avoid explicit annotations and feature engineering and still obtain semantically meaningful retrieval results. In the talk we will provide a comparative error analysis between the proposed semantic entity retrieval model and traditional generative language models that perform exact matching, which yields important insights in the relative strengths of semantic matching and exact matching for the expert retrieval task in particular and entity retrieval in general. We will also discuss extensions of the proposed model that are meant to deal with scalability and dynamic aspects of entity and expert retrieval

Circum-Antarctic abundance and properties of CCN and INPs

Aerosol particles acting as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs) play a major role in the formation and glaciation of clouds. Thereby they exert a strong impact on the radiation budget of the Earth. Data on abundance and properties of both types of particles are sparse, especially for remote areas of the world, such as the Southern Ocean (SO). In this work, we present unique results from ship-borne aerosol-particle-related in situ measurements and filter sampling in the SO region, carried out during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016–2017. An overview of CCN and INP concentrations over the Southern Ocean is provided and, using additional quantities, insights regarding possible CCN and INP sources and origins are presented. CCN number concentrations spanned 2 orders of magnitude, e.g. for a supersaturation of 0.3 % values ranged roughly from 3 to 590 cm−3. CCN showed variable contributions of organic and inorganic material (inter-quartile range of hygroscopicity parameter κ from 0.2 to 0.9). No distinct size dependence of κ was apparent, indicating homogeneous composition across sizes (critical dry diameter on average between 30 and 110 nm). The contribution of sea spray aerosol (SSA) to the CCN number concentration was on average small. Ambient INP number concentrations were measured in the temperature range from −5 to −27 ∘C using an immersion freezing method. Concentrations spanned up to 3 orders of magnitude, e.g. at −16 ∘C from 0.2 to 100 m−3. Elevated values (above 10 m−3 at −16 ∘C) were measured when the research vessel was in the vicinity of land (excluding Antarctica), with lower and more constant concentrations when at sea. This, along with results of backward-trajectory analyses, hints towards terrestrial and/or coastal INP sources being dominant close to ice-free (non-Antarctic) land. In pristine marine areas INPs may originate from both oceanic sources and/or long-range transport. Sampled aerosol particles (PM10) were analysed for sodium and methanesulfonic acid (MSA). Resulting mass concentrations were used as tracers for primary marine and secondary aerosol particles, respectively. Sodium, with an average mass concentration around 2.8 µg m−3, was found to dominate the sampled, identified particle mass. MSA was highly variable over the SO, with mass concentrations up to 0.5 µg m−3 near the sea ice edge. A correlation analysis yielded strong correlations between sodium mass concentration and particle number concentration in the coarse mode, unsurprisingly indicating a significant contribution of SSA to that mode. CCN number concentration was highly correlated with the number concentration of Aitken and accumulation mode particles. This, together with a lack of correlation between sodium mass and Aitken and accumulation mode number concentrations, underlines the important contribution of non-SSA, probably secondarily formed particles, to the CCN population. INP number concentrations did not significantly correlate with any other measured aerosol physico-chemical parameter.</p

Antimicrobial protein and Peptide concentrations and activity in human breast milk consumed by preterm infants at risk of late-onset neonatal sepsis

Objective: We investigated the levels and antimicrobial activity of antimicrobial proteins and peptides (AMPs) in breast milk consumed by preterm infants, and whether deficiencies of these factors were associated with late-onset neonatal sepsis (LOS), a bacterial infection that frequently occurs in preterm infants in the neonatal period. Study design: Breast milk from mothers of preterm infants (≤32 weeks gestation) was collected on days 7 (n = 88) and 21 (n = 77) postpartum. Concentrations of lactoferrin, LL-37, beta-defensins 1 and 2, and alpha-defensin 5 were measured by enzyme-linked immunosorbent assay. The antimicrobial activity of breast milk samples against Staphylococcus epidermidis, Staphylococcus aureus, Escherichia coli, and Streptococcus agalactiae was compared to the activity of infant formula, alone or supplemented with physiological levels of AMPs. Samples of breast milk fed to infants with and without subsequent LOS were compared for levels of AMPs and inhibition of bacterial growth. Results: Levels of most AMPs and antibacterial activity in preterm breast milk were higher at day 7 than at day 21. Lactoferrin was the only AMP that limited pathogen growth >50% when added to formula at a concentration equivalent to that present in breast milk. Levels of AMPs were similar in the breast milk fed to infants with and without LOS, however, infants who developed LOS consumed significantly less breast milk and lower doses of milk AMPs than those who were free from LOS. Conclusions: The concentrations of lactoferrin and defensins in preterm breast milk have antimicrobial activity against common neonatal pathogens