KEYWORDS: Reflection, Picosecond phenomena, Telecommunications, Spatial filtering, Numerical simulations, Signal processing, Matrices, Internet of things, Energy harvesting, Systems modeling
Simultaneous wireless information and power transfer (SWIPT) is an effective energy-saving technology, but its efficiency is hindered by environmental factors. The introduction of reconfigurable intelligent surfaces (RIS) has alleviated this issue, although it still faces significant constraints due to geographical limitations. This paper proposes a scheme that employs a simultaneously transmitting and reflecting (STAR)-RIS to assist SWIPT. It can overcome this limitation, achieve higher degrees of freedom (DoF), and provide better quality of service (QoS) for users on both sides of the ground. Meanwhile, we have considered a hybrid device based on a power splitting, which is capable of both energy harvesting and information decoding. We have proposed an efficient alternating optimization (AO) method to optimize the phase and amplitude vectors for reflection and transmission, beamforming and the optimal power splitting ratio, achieving an optimal balance between data rate and energy efficiency. Finally, simulation results demonstrate that the sum rate of the proposed model is superior to traditional RIS and other benchmark schemes.
KEYWORDS: Performance modeling, Data modeling, Ablation, Lab on a chip, Head, Education and training, Semantics, Classification systems, Proteins, Lithium
Named entity recognition (NER) involves two main types: nested NER and flat NER. The span-based approach classifies entity types by head-tail pair span representations and can handle nested and flat entities uniformly. However, the span-based approach uses a single feature and ignores the relative position information between head-tail pairs, which affects the precision of entity recognition. Therefore, we propose a nested entity recognition model that combines rotary position embedding and biaffine attention mechanism (RoPE-BAM) to improve the model performance by adding relative position features to the span representations. Concretely, we first obtain the head sequence and tail sequence representations through two feedforward networks. Then, to incorporate the relative position features, rotary position embedding is applied to both head and tail sequences. Finally, we use a biaffine attention mechanism to capture the span representations while generating the relative position information in the span. Extensive experiments were conducted on five widely-used benchmark datasets to demonstrate the effectiveness of our proposed RoPE-BAM model.
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