Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.Comment: EMNLP'1

Successive Interference Cancellation and Fractional Frequency Reuse For LTE Uplink Communications

Cellular networks are increasingly densified to deal with fast growing wireless traffic. Interference mitigation plays a key role for the dense cellular networks. Successive interference cancellation (SIC) and fractional frequency reuse (FFR) are two representative inter-cell interference (ICI) mitigation techniques. In this paper we study the application of both SIC and FFR for LTE uplink networks, and develop an analytical model to investigate their interactions and impact on network performance. The performance gains with FFR and SIC are related to key system functionalities and variables, such as SIC parameters, FFR bandwidth partition, uplink power control and sector antennas. The ICIs from individual cell sectors are approximated by log-normal random variables, which enables low complexity computation of the aggregate ICI with FFR and SIC. Then network performance of site throughput and outage probability is computed. The model is fast and has small modelling deviation, which is validated by system level simulations. Numerical results show that both SIC and FFR can largely improve network performance, but SIC has an impact over FFR. In addition, most of the network performance gains with SIC could be obtained with a small number of SIC stages applied to a few sectors

Globally Optimal Beamforming Design for Integrated Sensing and Communication Systems

In this paper, we propose a multi-input multi-output (MIMO) beamforming transmit optimization model for joint radar sensing and multi-user communications, where the design of the beamformers is formulated as an optimization problem whose objective is a weighted combination of the sum rate and the Cram\'{e}r-Rao bound (CRB), subject to the transmit power budget constraint. The formulated problem is challenging to obtain a global solution, because the sum rate maximization (SRM) problem itself (even without considering the sensing metric) is known to be NP-hard. In this paper, we propose an efficient global branch-and-bound algorithm for solving the formulated problem based on the McCormick envelope relaxation and the semidefinite relaxation (SDR) technique. The proposed algorithm is guaranteed to find the global solution for the considered problem, and thus serves as an important benchmark for performance evaluation of the existing local or suboptimal algorithms for solving the same problem.Comment: 5 pages, 2 figures, submitted for possible publicatio

Regulation of AMPA receptors in spinal nociception

The functional properties of α-amino-3-hydroxy-5-methy-4-isoxazole propionate (AMPA) receptors in different brain regions, such as hippocampus and cerebellum, have been well studied in vitro and in vivo. The AMPA receptors present a unique characteristic in the mechanisms of subunit regulation during LTP (long-term potentiation) and LTD (long-term depression), which are involved in the trafficking, altered composition and phosphorylation of AMPA receptor subunits. Accumulated data have demonstrated that spinal AMPA receptors play a critical role in the mechanism of both acute and persistent pain. However, less is known about the biochemical regulation of AMPA receptor subunits in the spinal cord in response to painful stimuli. Recent studies have shown that some important regulatory processes, such as the trafficking of AMPA receptor subunit, subunit compositional changes, phosphorylation of AMPA receptor subunits, and their interaction with partner proteins may contribute to spinal nociceptive transmission. Of all these regulation processes, the phosphorylation of AMPA receptor subunits is the most important since it may trigger or affect other cellular processes. Therefore, these study results may suggest an effective strategy in developing novel analgesics targeting AMPA receptor subunit regulation that may be useful in treating persistent and chronic pain without unacceptable side effects in the clinics

Energy-Efficient Design of STAR-RIS Aided MIMO-NOMA Networks

Simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) can provide expanded coverage compared with the conventional reflection-only RIS. This paper exploits the energy efficient potential of STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal multiple access (NOMA) system. Specifically, we mainly focus on energy-efficient resource allocation with MIMO technology in the STAR-RIS assisted NOMA network. To maximize the system energy efficiency, we propose an algorithm to optimize the transmit beamforming and the phases of the low-cost passive elements on the STAR-RIS alternatively until the convergence. Specifically, we first decompose the formulated energy efficiency problem into beamforming and phase shift optimization problems. To efficiently address the non-convex beamforming optimization problem, we exploit signal alignment and zero-forcing precoding methods in each user pair to decompose MIMO-NOMA channels into single-antenna NOMA channels. Then, the Dinkelbach approach and dual decomposition are utilized to optimize the beamforming vectors. In order to solve non-convex phase shift optimization problem, we propose a successive convex approximation (SCA) based method to efficiently obtain the optimized phase shift of STAR-RIS. Simulation results demonstrate that the proposed algorithm with NOMA technology can yield superior energy efficiency performance over the orthogonal multiple access (OMA) scheme and the random phase shift scheme

Scholar2vec : vector representation of scholars for lifetime collaborator prediction

While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery