In this article, a heterogeneous opinion dynamics model is proposed by multiple interdependent topics in a time-varying interactive network with competing relationships. A reducible topic matrix is used to describe logical interdependent relationships within different topics. Based on a topic partition method for the multitopics, consensus and convergence are analyzed for the heterogeneous opinion dynamics model with the presence of multiple stubborn individuals. Simulation results demonstrate effects of the number of stubborn individuals on the opinion evolution processes with the reducible topic matrix.

Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity of data. Many works focus on adjusting the distribution constraint under the Information Bottleneck (IB) principle to enhance representation learning. However, these proposed regularization terms only consider the constraint of each latent variable, omitting the structural information between latent variables. In this paper, we propose a novel structural entropy-guided probabilistic coding model, named SEPC. Specifically, we incorporate the relationship between latent variables into the optimization by proposing a structural entropy regularization loss. Besides, as traditional structural information theory is not well-suited for regression tasks, we propose a probabilistic encoding tree, transferring regression tasks to classification tasks while diminishing the influence of the transformation. Experimental results across 12 natural language understanding tasks, including both classification and regression tasks, demonstrate the superior performance of SEPC compared to other state-of-the-art models in terms of effectiveness, generalization capability, and robustness to label noise.

Designing nanoscale electronic devices such as the currently manufactured nanoribbon field-effect transistors (NRFETs) requires advanced modeling tools capturing all relevant quantum mechanical effects. State-of-the-art approaches combine the non-equilibrium Green’s function (NEGF) formalism and density functional theory (DFT). However, as device dimensions do not exceed a few nanometers anymore, electrons are confined in ultra-small volumes, giving rise to strong electron-electron interactions. To account for these critical effects, DFT+NEGF solvers should be extended with the GW approximation, which massively increases their computational intensity. Here, we present the first implementation of the NEGF+GW scheme capable of handling NRFET geometries with dimensions comparable to experiments. This package, called QuaTrEx, makes use of a novel spatial domain decomposition scheme, can treat devices made of up to 84,480 atoms, scales very well on the Alps and Frontier supercomputers (> 80% weak scaling efficiency), and sustains an exascale FP64 performance on 42,240 atoms (1.15 Eflop/s).

Experimental development of gate-all-around silicon nanowire field-effect transistors (NWFETs), a viable replacement for FinFETs, can be complemented by technology computer-aided design. This requires the availability of advanced device simulators relying on a quantum transport (QT) approach without any empirical parameters as inputs. Concretely, all material properties should be described from first-principles, and the whole physics at play should be accurately modeled, particularly the strong electron-electron interactions occurring in highly confined structures such as NWFETs. To shed light on these many-body effects, we implement them within the self-consistent GW approximation into an ab initio QT solver called QuaTrEx, based on density functional theory and the Non-equilibrium Green’s Function formalism. We then simulate transistors made of up to 10,560 atoms on the LUMI supercomputer’s GPU partition, reaching a parallel efficiency of $\mathbf{7 4 \%}(\mathbf{6 0 \%}$) in weak (strong) scaling and an overall computational performance of 69.3 Pflop/s in double precision on 1,800 nodes.

With the evolutional development of maritime networks, the explosive growth of maritime data has put forward elevated demands for the computing capabilities of maritime devices (MDs). Unmanned aerial vehicle (UAV) is able to alleviate the computing pressure of MDs by forwarding the computing tasks to the edge server on the coast. However, UAV relaying introduces a significant security challenge due to the vulnerability of line-of-sight (LoS) communication channels, which can be exploited for eavesdropping on computing tasks. In this paper, an efficient secure communication scheme is proposed for UAV-relay-assisted maritime mobile edge computing (MEC) with a flying eavesdropper. The secure computing capacity of MDs is maximized by jointly optimizing the transmit power, time slot allocation factor, computation optimization and UAV trajectory. Due to multi-variable coupling, the formulated optimization problem (OP) is non-convex. We first transform OP by introducing auxiliary variables. Then, the transformed OP is decomposed and solved in an iterative manner by applying block coordinate descent (BCD) and successive convex approximation (SCA). Numerical results show that the secure computing capability of the UAV-relay-assisted maritime MEC system of proposed secure communication scheme can be effectively improved compared with benchmarks.

The vigorous development of maritime networks and the explosive growth of maritime sampling data put forward more and more high demands on the computing and communication capability of maritime equipment. Unmanned aerial vehicle (UAV), as the mobile relay device guarantees the capability by transferring part of the computing tasks of maritime equipment to carrying mobile edge computing (MEC) servers on land. However, the transmitting data of the UAV's communication channel can be easily intercepted due to the line of sight (LoS) feature, which brings the secure data transmission issue. To solve this issue, we propose a secure data transmission scheme in the UAV-relay assisted maritime MEC system. Specifically, a malicious UAV attempts to intercept the transmission data while another UAV helps forward the offloading computational data to the maritime surface users. A ground jammer transmits jamming signals with the object to block data intercepting. To maximize the users' minimum secure calculation capacity, we jointly optimize the transmit power of users and the relay UAV, the time slot allocation factor, and the UAV flight trajectory with block coordinate descent (BCD) and successive convex approximation (SCA) techniques. Numerical findings demonstrate that the proposed scheme can effectively improve the secure calculation capability of the system compared with four benchmark schemes.

Abstract Deep learning (DL) has been applied to digital signal modulation identification (DSMI) due to its powerful feature learning ability. However, most of the existing DL‐based DSMI methods are limited to specific experimental scene relating to the additive white Gaussian noise (AWGN) channel or static multipath channel. The result is that the trained network has deteriorative identification accuracy when the channel conditions change unless retrained. To solve the problem, this paper proposes a DSMI method suitable for orthogonal frequency division multiplexing (OFDM) under different multipath channels, including the variation of delay, path number and channel coefficient. This method can accurately detect the modulation feature rather than the channel's to identify the modulation type, thus reducing the network training amount. The method is divided into two parts. Firstly, traditional signal processing methods are combined, including various channel estimators and equalisers to compensate for the channel. Then a robust DL network, RSN‐MI, is designed as a classifier. Unlike other DL‐based DSMI methods, the influence of signal processing algorithms on DSMI performance are focused on rather than model parameters. Besides, the proposed classifier is compared with the DSMI classifier in other contributions. The results show that the classifier works better in different multipath channels.

Requirement Engineering (RE) has become an attractive field in both industry and academic. Many RE approaches have been presented in the past years to support eliciting, modeling, analyzing and specifying requirements of system to be built. However, requirement characteristics of kinds of systems like large scale software intensive systems pose several issues to requirements analysis and therefore challenge the extant RE approaches. This paper investigates a number of important metaphors in RE and proposes a novel RE approach that adopts capability as requirement metaphor. We discuss the requirements challenges coming from the changes of system-to-be and argue the necessity to introduce new abstraction and technology into RE to deal with the problems. The notions of capability and the reason to adopt capability as requirement metaphor are analyzed. The meta-model and framework of capability-based requirement engineering is proposed. A case is also studied in order to illustrate our approach. Capability as new abstraction in RE provides a new way to represent, analyze and tradeoff requirements.