97 research outputs found
Metal-Free Flat Lens Using Negative Refraction by Nonlinear Four-wave Mixing
A perfect lens with unlimited resolution has always posed a challenge to both
theoretical and experimental physicists. Recent developments in optical
meta-materials promise an attractive approach towards perfect lenses using
negative refraction to overcome the diffraction limit, improving resolution.
However, those artificially engineered meta-materials usually company by high
losses from metals and are extremely difficult to fabricate. An alternative
proposal using negative refraction by four-wave mixing has attracted much
interests recently, though most of existing experiments still require metals
and none of them has been implemented for an optical lens. Here we
experimentally demonstrate a metal-free flat lens for the first time using
negative refraction by degenerate four-wave mixing with a thin glass slide. We
realize optical lensing effect utilizing a nonlinear refraction law, which may
have potential applications in microscopy
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction
Large-scale commercial platforms usually involve numerous business domains
for diverse business strategies and expect their recommendation systems to
provide click-through rate (CTR) predictions for multiple domains
simultaneously. Existing promising and widely-used multi-domain models discover
domain relationships by explicitly constructing domain-specific networks, but
the computation and memory boost significantly with the increase of domains. To
reduce computational complexity, manually grouping domains with particular
business strategies is common in industrial applications. However, this
pre-defined data partitioning way heavily relies on prior knowledge, and it may
neglect the underlying data distribution of each domain, hence limiting the
model's representation capability. Regarding the above issues, we propose an
elegant and flexible multi-distribution modeling paradigm, named Adaptive
Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization
hierarchical structure consisting of a clustering process and classification
process. Specifically, we design a distribution adaptation module with a
customized dynamic routing mechanism. Instead of introducing prior knowledge
for pre-defined data allocation, this routing algorithm adaptively provides a
distribution coefficient for each sample to determine which cluster it belongs
to. Each cluster corresponds to a particular distribution so that the model can
sufficiently capture the commonalities and distinctions between these distinct
clusters. Extensive experiments on both public and large-scale Alibaba
industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our
model achieves impressive prediction accuracy and its time cost during the
training stage is more than 50% less than that of other models
- …