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Computer-Assisted Design of Environmentally Friendly and Light-Stable Fluorescent Dyes for Textile Applications.
Five potentially environmentally friendly and light-stable hemicyanine dyes were designed based on integrated consideration of photo, environmental, and computational chemistry as well as textile applications. Two of them were synthesized and applied in dyeing polyacrylonitrile (PAN), cotton, and nylon fabrics, and demonstrated the desired properties speculated by the programs. The computer-assisted analytical processes includes estimation of the maximum absorption and emission wavelengths, aquatic environmental toxicity, affinity to fibers, and photo-stability. This procedure could effectively narrow down discovery of new potential dye structures, greatly reduce and prevent complex and expensive preparation processes, and significantly improve the development efficiency of novel environmentally friendly dyes
Semantics-Aligned Representation Learning for Person Re-identification
Person re-identification (reID) aims to match person images to retrieve the
ones with the same identity. This is a challenging task, as the images to be
matched are generally semantically misaligned due to the diversity of human
poses and capture viewpoints, incompleteness of the visible bodies (due to
occlusion), etc. In this paper, we propose a framework that drives the reID
network to learn semantics-aligned feature representation through delicate
supervision designs. Specifically, we build a Semantics Aligning Network (SAN)
which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder
(SA-Dec) for reconstructing/regressing the densely semantics aligned full
texture image. We jointly train the SAN under the supervisions of person
re-identification and aligned texture generation. Moreover, at the decoder,
besides the reconstruction loss, we add Triplet ReID constraints over the
feature maps as the perceptual losses. The decoder is discarded in the
inference and thus our scheme is computationally efficient. Ablation studies
demonstrate the effectiveness of our design. We achieve the state-of-the-art
performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the
partial person reID dataset Partial REID. Code for our proposed method is
available at:
https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20),
code has been release
On the Performance of Multi-tier Heterogeneous Cellular Networks with Idle Mode Capability
This paper studies the impact of the base station (BS) idle mode capability
(IMC) on the network performance of multi-tier and dense heterogeneous cellular
networks (HCNs). Different from most existing works that investigated network
scenarios with an infinite number of user equipments (UEs), we consider a more
practical setup with a finite number of UEs in our analysis. More specifically,
we derive the probability of which BS tier a typical UE should associate to and
the expression of the activated BS density in each tier. Based on such results,
analytical expressions for the coverage probability and the area spectral
efficiency (ASE) in each tier are also obtained. The impact of the IMC on the
performance of all BS tiers is shown to be significant. In particular, there
will be a surplus of BSs when the BS density in each tier exceeds the UE
density, and the overall coverage probability as well as the ASE continuously
increase when the BS IMC is applied. Such finding is distinctively different
from that in existing work. Thus, our result sheds new light on the design and
deployment of the future 5G HCNs.Comment: conference submissio
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