8 research outputs found
Enhancing Item-level Bundle Representation for Bundle Recommendation
Bundle recommendation approaches offer users a set of related items on a
particular topic. The current state-of-the-art (SOTA) method utilizes
contrastive learning to learn representations at both the bundle and item
levels. However, due to the inherent difference between the bundle-level and
item-level preferences, the item-level representations may not receive
sufficient information from the bundle affiliations to make accurate
predictions. In this paper, we propose a novel approach EBRec, short of
Enhanced Bundle Recommendation, which incorporates two enhanced modules to
explore inherent item-level bundle representations. First, we propose to
incorporate the bundle-user-item (B-U-I) high-order correlations to explore
more collaborative information, thus to enhance the previous bundle
representation that solely relies on the bundle-item affiliation information.
Second, we further enhance the B-U-I correlations by augmenting the observed
user-item interactions with interactions generated from pre-trained models,
thus improving the item-level bundle representations. We conduct extensive
experiments on three public datasets, and the results justify the effectiveness
of our approach as well as the two core modules. Codes and datasets are
available at https://github.com/answermycode/EBRec
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
Cascaded Cross-Layer Fusion Network for Pedestrian Detection
The detection method based on anchor-free not only reduces the training cost of object detection, but also avoids the imbalance problem caused by an excessive number of anchors. However, these methods only pay attention to the impact of the detection head on the detection performance, thus ignoring the impact of feature fusion on the detection performance. In this article, we take pedestrian detection as an example and propose a one-stage network Cascaded Cross-layer Fusion Network (CCFNet) based on anchor-free. It consists of Cascaded Cross-layer Fusion module (CCF) and novel detection head. Among them, CCF fully considers the distribution of high-level information and low-level information of feature maps under different stages in the network. First, the deep network is used to remove a large amount of noise in the shallow features, and finally, the high-level features are reused to obtain a more complete feature representation. Secondly, for the pedestrian detection task, a novel detection head is designed, which uses the global smooth map (GSMap) to provide global information for the center map to obtain a more accurate center map. Finally, we verified the feasibility of CCFNet on the Caltech and CityPersons datasets