114 research outputs found
Dynamic Carrier and Power Amplifier Mapping for Energy Efficient Multi-Carrier Wireless Communications
The rapid increasing demand of wireless transmission has incurred mobile
broadband to continuously evolve through multiple frequency bands, massive
antennas and other multi-stream processing schemes. Together with the improved
data transmission rate, the power consumption for multi-carrier transmission
and processing is proportionally increasing, which contradicts with the energy
efficiency requirements of 5G wireless systems. To meet this challenge, multi
carrier power amplifier (MCPA) technology, e.g., to support multiple carriers
through a single power amplifier, is widely deployed in practical. With massive
carriers required for 5G communication and limited number of carriers supported
per MCPA, a natural question to ask is how to map those carriers into multiple
MCPAs and whether we shall dynamically adjust this mapping relation. In this
paper, we have theoretically formulated the dynamic carrier and MCPA mapping
problem to jointly optimize the traditional separated baseband and radio
frequency processing. On top of that, we have also proposed a low complexity
algorithm that can achieve most of the power saving with affordable
computational time, if compared with the optimal exhaustive search based
algorithm
A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
Deep neural networks (DNNs) have been widely employed in recommender systems
including incorporating attention mechanism for performance improvement.
However, most of existing attention-based models only apply item-level
attention on user side, restricting the further enhancement of recommendation
performance. In this paper, we propose a knowledge-enhanced recommendation
model ACAM, which incorporates item attributes distilled from knowledge graphs
(KGs) as side information, and is built with a co-attention mechanism on
attribute-level to achieve performance gains. Specifically, each user and item
in ACAM are represented by a set of attribute embeddings at first. Then, user
representations and item representations are augmented simultaneously through
capturing the correlations between different attributes by a co-attention
module. Our extensive experiments over two realistic datasets show that the
user representations and item representations augmented by attribute-level
co-attention gain ACAM's superiority over the state-of-the-art deep models
Biomarkers and C and S Isotopes of the Permian to Triassic Solid Bitumen and Its Potential Source Rocks in NE Sichuan Basin
LncRNAs: the bridge linking RNA and colorectal cancer.
Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application
Th17/Treg Cells Imbalance and GITRL Profile in Patients with Hashimoto’s Thyroiditis
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