48 research outputs found
Robust Representation Learning for Unified Online Top-K Recommendation
In large-scale industrial e-commerce, the efficiency of an online
recommendation system is crucial in delivering highly relevant item/content
advertising that caters to diverse business scenarios. However, most existing
studies focus solely on item advertising, neglecting the significance of
content advertising. This oversight results in inconsistencies within the
multi-entity structure and unfair retrieval. Furthermore, the challenge of
retrieving top-k advertisements from multi-entity advertisements across
different domains adds to the complexity. Recent research proves that
user-entity behaviors within different domains exhibit characteristics of
differentiation and homogeneity. Therefore, the multi-domain matching models
typically rely on the hybrid-experts framework with domain-invariant and
domain-specific representations. Unfortunately, most approaches primarily focus
on optimizing the combination mode of different experts, failing to address the
inherent difficulty in optimizing the expert modules themselves. The existence
of redundant information across different domains introduces interference and
competition among experts, while the distinct learning objectives of each
domain lead to varying optimization challenges among experts. To tackle these
issues, we propose robust representation learning for the unified online top-k
recommendation. Our approach constructs unified modeling in entity space to
ensure data fairness. The robust representation learning employs domain
adversarial learning and multi-view wasserstein distribution learning to learn
robust representations. Moreover, the proposed method balances conflicting
objectives through the homoscedastic uncertainty weights and orthogonality
constraints. Various experiments validate the effectiveness and rationality of
our proposed method, which has been successfully deployed online to serve real
business scenarios.Comment: 14 pages, 6 figures, submitted to ICD
Deficiency in pulmonary surfactant proteins in mice with fatty acid binding protein 4‐ Cre ‐mediated knockout of the tuberous sclerosis complex 1 gene
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96661/1/expphysiol.2012.069674.pd
Genome-wide association mapping identifies a new arsenate reductase enzyme critical for limiting arsenic accumulation in plants
Inorganic arsenic is a carcinogen, and its ingestion through foods such as rice presents a significant risk to human health. Plants chemically reduce arsenate to arsenite. Using genome-wide association (GWA) mapping of loci controlling natural variation in arsenic accumulation in Arabidopsis thaliana allowed us to identify the arsenate reductase required for this reduction, which we named High Arsenic Content 1 (HAC1). Complementation verified the identity of HAC1, and expression in Escherichia coli lacking a functional arsenate reductase confirmed the arsenate reductase activity of HAC1. The HAC1 protein accumulates in the epidermis, the outer cell layer of the root, and also in the pericycle cells surrounding the central vascular tissue. Plants lacking HAC1 lose their ability to efflux arsenite from roots, leading to both increased transport of arsenic into the central vascular tissue and on into the shoot. HAC1 therefore functions to reduce arsenate to arsenite in the outer cell layer of the root, facilitating efflux of arsenic as arsenite back into the soil to limit both its accumulation in the root and transport to the shoot. Arsenate reduction by HAC1 in the pericycle may play a role in limiting arsenic loading into the xylem. Loss of HAC1-encoded arsenic reduction leads to a significant increase in arsenic accumulation in shoots, causing an increased sensitivity to arsenate toxicity. We also confirmed the previous observation that the ACR2 arsenate reductase in A. thaliana plays no detectable role in arsenic metabolism. Furthermore, ACR2 does not interact epistatically with HAC1, since arsenic metabolism in the acr2 hac1 double mutant is disrupted in an identical manner to that described for the hac1 single mutant. Our identification of HAC1 and its associated natural variation provides an important new resource for the development of low arsenic-containing food such as rice
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data