Exploring travellers’ risk preferences with regard to travel time reliability on the basis of GPS trip records

Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance systems. In general, existing navigation systems provide the shortest path on the basis of distance or travel time, even though many travellers do not intend to choose the shortest path. Several studies have shown that the probability of delay or travel time reliability is an important factor in a traveller’s route choice decision. Learning a traveller’s risk preference with regard to travel time reliability is important for designing a preferable route. Traditionally, route choice data for individual preference analysis are collected by conducting stated preference surveys. However, this approach is difficult to avoid its inherent limitation, namely a lack of honest, accurate, and bias-free reporting. To overcome these problems, the present study proposes a new data collection methodology that facilitates estimation of a traveller’s risk preference on the basis of large-scale GPS trip records. The lower and upper bounds of individual risk preference can be estimated by exhausting a series of reliable paths with different on-time arrival probabilities and using the theory of stochastic dominance. Then, a regression model based on a logistic function is established to explore how socio-demographic and trip characteristics influence the lower and upper bounds. Thus, individual properties, such as age, and pre-trip information, such origindestination (OD) distance, departure time, and day of week, are found to have a significant influence on the degree of risk preference

Linear Precoding for Relay Networks with Finite-Alphabet Constraints

In this paper, we investigate the optimal precoding scheme for relay networks with finite-alphabet constraints. We show that the previous work utilizing various design criteria to maximize either the diversity order or the transmission rate with the Gaussian-input assumption may lead to significant loss for a practical system with finite constellation set constraint. A linear precoding scheme is proposed to maximize the mutual information for relay networks. We exploit the structure of the optimal precoding matrix and develop a unified two-step iterative algorithm utilizing the theory of convex optimization and optimization on the complex Stiefel manifold. Numerical examples show that this novel iterative algorithm achieves significant gains compared to its conventional counterpart.Comment: Accepted by IEEE Int. Conf. Commun. (ICC), Kyoto, Japan, 201

Neural Topological Ordering for Computation Graphs

Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance. In this paper, we consider the problem of finding an optimal topological order on a directed acyclic graph with focus on the memory minimization problem which arises in compilers. We propose an end-to-end machine learning based approach for topological ordering using an encoder-decoder framework. Our encoder is a novel attention based graph neural network architecture called \emph{Topoformer} which uses different topological transforms of a DAG for message passing. The node embeddings produced by the encoder are converted into node priorities which are used by the decoder to generate a probability distribution over topological orders. We train our model on a dataset of synthetically generated graphs called layered graphs. We show that our model outperforms, or is on-par, with several topological ordering baselines while being significantly faster on synthetic graphs with up to 2k nodes. We also train and test our model on a set of real-world computation graphs, showing performance improvements.Comment: To appear in NeurIPS 202

Identification of the Signature Associated With m6A RNA Methylation Regulators and m6A-Related Genes and Construction of the Risk Score for Prognostication in Early-Stage Lung Adenocarcinoma

BackgroundN6-methyladenosine (m6A) RNA modification is vital for cancers because methylation can alter gene expression and even affect some functional modification. Our study aimed to analyze m6A RNA methylation regulators and m6A-related genes to understand the prognosis of early lung adenocarcinoma.MethodsThe relevant datasets were utilized to analyze 21 m6A RNA methylation regulators and 5,486 m6A-related genes in m6Avar. Univariate Cox regression analysis, random survival forest analysis, Kaplan–Meier analysis, Chi-square analysis, and multivariate cox analysis were carried out on the datasets, and a risk prognostic model based on three feature genes was constructed.ResultsRespectively, we treated GSE31210 (n = 226) as the training set, GSE50081 (n = 128) and TCGA data (n = 400) as the test set. By performing univariable cox regression analysis and random survival forest algorithm in the training group, 218 genes were significant and three prognosis-related genes (ZCRB1, ADH1C, and YTHDC2) were screened out, which could divide LUAD patients into low and high-risk group (P < 0.0001). The predictive efficacy of the model was confirmed in the test group GSE50081 (P = 0.0018) and the TCGA datasets (P = 0.014). Multivariable cox manifested that the three-gene signature was an independent risk factor in LUAD. Furthermore, genes in the signature were also externally validated using the online database. Moreover, YTHDC2 was the important gene in the risk score model and played a vital role in readers of m6A methylation.ConclusionThe findings of this study suggested that associated with m6A RNA methylation regulators and m6A-related genes, the three-gene signature was a reliable prognostic indicator for LUAD patients, indicating a clinical application prospect to serve as a potential therapeutic target

Frequency Spectrum Analysis of High Frequency Cycle Square Wave Signal Based on Discrete Fourier Transform

Controlling single pulse energy of pulse power, is one of the key factors to improve the processing ability of micro electrical machining, because of high frequency and small energy of single pulse, high-frequency periodic square wave power is the ideal power for micro electrical machining. High-frequency periodic square wave is composed of multiple frequency signals, vulnerable to be jammed by equipment noise and environmental noise, making the waveform deformation seriously, weakening the micro machining capability. In this paper, the detailed spectral analysis of the high frequency cycle square wave was made by the discrete Fourier transform, with the help of MATLAB, to extract the effective frequency components, determining the corresponding amplitude and the frequency equation. It provides a theoretical basis to suppress the interference signal, so as to achieve the ideal square wave signal, to provide protection for the precision machining and micro machining

Multiantenna Secure Cognitive Radio Networks with Finite-Alphabet Inputs: A Global Optimization Approach for Precoder Design

This paper considers the precoder design for multiantenna secure cognitive radio networks. We use finite-alphabet inputs as the signaling and exploit statistical channel state information (CSI) at the transmitter. We maximize the secrecy rate of the secondary user and control the transmit power and the power leakage to the primary receivers that share the same frequency spectrum. The secrecy rate maximization is important for practical systems, but challenging to solve, mainly due to two reasons. First, the secrecy rate with statistical CSI is computationally prohibitive to evaluate. Second, the optimization over the precoder is a nondeterministic polynomial-time hard (NP-hard) problem. We utilize an accurate approximation of the secrecy rate to reduce the computational effort and then propose a global optimization approach based on branch-and-bound method. The idea is to define a simplex and transform the secrecy rate into a concave function. The derived concave function converges to the secrecy rate when the defined simplex shrinks down. Using this feature, we solve a sequence of concave maximization problems over iteratively shrinking simplices and eventually attain the globally optimal solution that maximizes the approximation of the secrecy rate. When the complexity is concerned, a low-complexity variant with limited number of iterations can be used in practice. We demonstrate the performance gains when compared with others through numerical examples