Ground Communication Research Articles

Convex and Nonconvex Risk-Based Linear Regression at Scale

The value at risk (VaR) and the conditional value at risk (CVaR) are two popular risk measures to hedge against the uncertainty of data. In this paper, we provide a computational toolbox for solving high-dimensional sparse linear regression problems under either VaR or CVaR measures, the former being nonconvex and the latter convex. Unlike the empirical risk (neutral) minimization models in which the overall losses are decomposable across data, the aforementioned risk-sensitive models have nonseparable objective functions so that typical first order algorithms are not easy to scale. We address this scaling issue by adopting a semismooth Newton-based proximal augmented Lagrangian method of the convex CVaR linear regression problem. The matrix structures of the Newton systems are carefully explored to reduce the computational cost per iteration. The method is further embedded in a majorization–minimization algorithm as a subroutine to tackle the nonconvex VaR-based regression problem. We also discuss an adaptive sieving strategy to iteratively guess and adjust the effective problem dimension, which is particularly useful when a solution path associated with a sequence of tuning parameters is needed. Extensive numerical experiments on both synthetic and real data demonstrate the effectiveness of our proposed methods. In particular, they are about 53 times faster than the commercial package Gurobi for the CVaR-based sparse linear regression with 4,265,669 features and 16,087 observations. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported in part by the NSF, the Division of Computing and Communication Foundations [Grant 2153352], the National Natural Science Foundation of China [Grant 12271187], and the Hong Kong Research Grant Council [Grant 15304019]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1282 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0012 ) at ( http://dx.doi.org/10.5281/zenodo.7483279 ).

The specificity of the representation of speech in family communication of the characters in A.P. Chekhov's novels

This article presents an analysis of the ways of representing inner speech in the stories of Anton Chekhov, depicting the family communication of the characters. The aim of this study is to investigate the introduction of verbs with a common meaning of speech activity, their semantics and functions in depicting speech as a component of family communication in the stories of Chekhov's mature period. The research is based on the principles and main provisions of the structural-semantic and functional approach; theories of conceptualization and language representation; linguistic and cultural theory; theory of artistic text and discourse, as well as theoretical foundations of family communication. During the study, it was found that the main technique used by Chekhov to convey inner speech is improperly direct speech, which serves to unite and interfere with the author's and character's modus in the hero's inner speech. In those Chekhov's texts where the narration is in the first person, in most cases verbs are used that convey external speech in dialogic family communication. Inner speech in these stories is not typical and is rare.

Embedding animals within a definition of sustainability

Current definitions of sustainability and sustainable development are problematic. They reinforce the dominant capitalist paradigm of economic growth as a goal—a system which has contributed in no uncertain terms to the current state of the planet—and they exclude animals as moral entities. We propose that sustainability is primarily an ethical issue which connects people, the planet on which we live, and the animals and other organisms which inhabit it. Our definition of sustainability states that in all decisions and actions on any scale, from the individual to the institutional, we should minimise our immediate and future negative impact on humans, other animals, and the planet, while simultaneously maximising our positive impacts on these domains. We take an interdisciplinary approach in discussing trade-offs between these three broad interests, providing a rationale for adopting this more inclusive definition at every level of society. Our definition calls for a normative shift in discussions around sustainability, one of which is more inclusive of the animals and other organisms with which we share the planet. We believe that the paper forms a strong and coherent foundation for policy and communication about sustainability going forward.

Machine Impostors Can Avoid Human Detection and Interrupt the Formation of Stable Conventions by Imitating Past Interactions: A Minimal Turing Test.

Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans' ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants' own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily.

Integrating Terrestrial and Non-Terrestrial Networks: 3D Opportunities and Challenges

Integrating terrestrial and non-terrestrial networks has the potential of connecting the unconnected and enhancing the user experience for the already-connected, with technological and societal implications of the greatest long-term significance. A convergence of ground, air, and space wireless communications also represents a formidable endeavor for the mobile and satellite communications industries alike, as it entails defining and intelligently orchestrating a new 3D wireless network architecture. In this article, we present the key opportunities and challenges arising from this revolution by presenting some of its disruptive use cases and key building blocks, reviewing the relevant standardization activities, and pointing to open research problems. By considering two multi-operator paradigms, we also showcase how terrestrial networks could be efficiently re-engineered to cater for aerial services, or opportunistically complemented by non-terrestrial infrastructure to augment their current capabilities.

English

Navigation of a lander craft to accurately soft land in a predetermined spot on an extra-terrestrial body is a challenging task. Due to the absence of GPS and real-time ground communication links, the lander craft has to navigate autonomously to the targeted landing site solely with the help of its onboard sensors. One of the major challenges in position estimation of the lander craft is the measurement inaccuracies associated with the inertial navigation systems. Optical landmark detection-based techniques are employed to aid the lander navigation and guidance system. In this technique, craters and their relative positions are used as landmarks for position estimation. Typical crater detection algorithms employ handcrafted feature extractors for crater identification. Recently deep learning-based approaches have been employed for crater detection, mainly for geomorphological studies and cataloguing. This paper proposes using deep learning-based object detection techniques applied to autonomous landing missions and their implementation on a Xilinx MPSoC FPGA device. Prototype hardware has been developed, and the YOLOv4-tiny algorithm has been implemented on it with an inference time of 471 ms per image. The results are presented, and their performance is evaluated.

Secure Networking with Software-Defined Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RIS) are considered of paramount importance to improve air-ground and THz communications performance for 6G systems. Recently, RISs were proposed in Physical Layer Security (PLS), as they can (i) improve the secrecy capacity due to the controlled directional reflections' capability of RIS elements and (ii) avoid potential eavesdroppers, redirecting data streams towards the intended users. This paper proposes the integration of a multi-RISs system within a Software Defined Networking (SDN) architecture to provide a specific control layer for secure data flows forwarding. The optimisation problem is properly characterised in terms of an objective function and an equivalent graph theory model is considered to address the optimal solution. Moreover, different heuristics are proposed, trading off complexity and PLS performance, to evaluate the more suitable multi-beam routing strategy. Numerical results are also provided, focusing on a worst case scenario which points out the improvement of the secrecy rate from the increase in the number of eavesdroppers. Furthermore, the security performance is investigated for a specific user mobility pattern in a pedestrian scenario.

Green Edge Intelligence for Smart Management of a FANET in Disaster-Recovery Scenarios

Disruption of the ground communication infrastructure in emergency scenarios makes post-disaster rescue operations very complicate. This paper proposes to use edge intelligence to support rescue operators in managing these emergency scenarios. A set of Unmanned Aerial Vehicles (UAV), organized as Flying Ad-Hoc network (FANET), autonomously takeoff and land to provide emergency operators with edge computing services. A charging station for batteries is supplied by a renewable-energy generator. The FANET Controller applies model-based Reinforcement Learning to decide how many UAVs have to take off, according to the current edge-computing service requests and the power availability, and a forecast of them. The optimal management policy has to provide the necessary level of edge-computing avoiding wide use of satellite channels in a short-time horizon during low green-energy generation and high service request periods. Results highlight that the optimal policy is an efficient modification of the greedy one, i.e., the policy enabling the takeoff of all the necessary UAVs without being care of challenging events in the future. A deep analysis has revealed that the level of modification depends on the combination of the edge-computing service request and the green power availability.

Ready for Action! Destination Climate Change Communication: An Archetypal Branding Approach.

At the destination level, destination branding may coexist with climate change communication. These two communication streams often overlap because they are both designed for large audiences. This poses a risk to the effectiveness of climate change communication and its ability to prompt a desired climate action. The viewpoint paper advocates the use of archetypal branding approach to ground and center climate change communication at a destination level while concurrently maintaining the uniqueness of destination branding. Three archetypes of destinations are distinguished: villains, victims, and heroes. Destinations should refrain from actions that would make them appear to be climate change villains. A balanced approach is further warranted when portraying destinations as victims. Lastly, destinations should aim at assuming the heroic archetypes by excelling in climate change mitigation. The basic mechanisms of the archetypal approach to destination branding are discussed alongside a framework that suggests areas for further practical investigation of climate change communication at a destination level.

IScotland: building a unified model of activism in multi-platform communication environments from traditional PR theory

PurposeIn recent years, there has been a growth in research aimed at understanding the foundations of modern activist communication in media-rich and multi-platform environments. For example, Chon and Park's analysis of the American Black Lives Matter (BLM) movement built on Kim and Grunig's STOPS model. Yet, social and political injustice can exist for extended periods of time without successful movements emerging, so what leads people to demand social and political change through activism? This paper posits that crisis is trigger that motivates people to activism and evaluates that within the context of the Scottish independence movement.Design/methodology/approachThe study is based on in-depth interviews with 26 advocates for Scottish independence, which yielded more than 32 h of data. Data were analysed using Strauss and Corbin's (1990) constant comparative method approach using open coding, axial coding and selective coding finding thematic saturation after only 10 interviews.FindingsThe study provides a clear extension of Chon and Park's model of activism by finding that crises are critical triggers for activism. Moreover, these findings also provide insights into not only the Scottish independence movement but more broadly the extension of traditional public relations and communication theory in multi-platform and multi-actor environments.Originality/valueThere are several contributions this piece makes. First, this paper extends activist, crisis and strategic communication research to more systematically consider the role that crisis plays in social and political advocacy. Second, this paper affords the opportunity to consider the challenges of communication, democracy and activism in the social media age. Finally, this paper supports an international view that discrimination and affective injustice experiences cut across many different kinds of identities and experiences instead of the traditionally considered ethnic, religious and gender-based experiences traditionally addressed.

Distributed Reconfigurable Intelligent Surfaces for Energy-Efficient Indoor Terahertz Wireless Communications

With the fifth-generation (5G) networks widely commercialized and fast deployed, the sixth-generation (6G) wireless communication is envisioned to provide competitive quality of service (QoS) in multiple aspects to global users. The critical and underlying research of the 6G is, firstly, highly dependent on the precise modeling and characterization of the wireless propagation when the spectrum is believed to expand to the terahertz (THz) domain. Moreover, future networks' power consumption and energy efficiency are critical factors to consider. In this research, based on a review of the fundamental mechanisms of reconfigurable intelligent surface (RIS) assisted wireless communications, we utilize the 3D ray-tracing method to analyze a realistic indoor THz propagation environment with the existence of human blockers. Furthermore, we propose a distributed RISs framework (DRF) to assist the indoor THz wireless communication to achieve overall energy efficiency. The numerical analysis of simulation results based on more than 2,900 indoor THz wireless communication sub-scenarios has demonstrated the significant efficacy of applying distributed RISs to overcome the mobile human blockage issue, improve the THz signal coverage, increase signal-to-noise ratios (SNRs), and QoS. With practical hardware design constraints investigated, we eventually envision how to utilize the existing integrated sensing and communication techniques to deploy and operate such a system in reality. Such a distributed RISs framework can also lay the foundation of efficient THz communications for Internet-of-Things (IoT) networks.

Energy Efficiency of Unmanned Aerial Vehicle with Reconfigurable Intelligent Surfaces: A Comparative Study

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) are the promising emerging technologies proposed for the 6th-Generation (6G) network to improve the capacity, reliability, and coverage of wireless communications. By integrating the UAV with RIS (RIS-UAV), the three-dimensional (3D) mobility of the UAV can be leveraged to establish strong line-of-sight links with the ground nodes, while the RIS intelligently reflect the signals toward the desired directions. However, the existing literature on RIS-UAV systems mainly assumes the use of passive elements, which suffers from the double path-loss problem. The use of active elements in RIS, which could improve the reflected link performance at the cost of increased energy consumption, has not been considered for the RIS-UAV system. Further, the energy efficiency of a RIS-UAV with active elements remains as an open direction because there is a need to investigate the feasibility of either an active or hybrid RIS-UAV implementation. This paper proposes active and hybrid RIS-UAVs and investigates the energy efficiencies of active and hybrid RIS-UAVs in comparison with existing passive RIS-UAVs and conventional UAV relays. The numerical results reveal that the proposed hybrid and active RIS-UAV relaying schemes can provide up to 14 times and 26 times improvement as compared to the passive RIS-UAV, respectively. As opposed to the active RIS-UAV that requires a larger power budget, half-duplex UAV relays that have a lower spectral efficiency, and full-duplex UAV relays that suffer from self-interference, the hybrid RIS-UAV emerges as a promising option to assist the ground communication system.

A location‐aware physical unclonable function and Chebyshev map‐based mutual authentication mechanism for internet of surveillance drones

SummaryThe primary concern for securing the Internet of Drones (IoD) is authentication. Drone location is a vital information that must be verified before a secure communication link with the ground station can be established. In this paper, a location aware mutual authentication technique for secure inter‐drone and drone to ground communication using physical unclonable functions and Chebyshev chaotic maps is proposed. This is the first work that addresses the location validation of drones along with server‐less inter‐drone communication with a fail‐safe mechanism designed using a fuzzy inference system to tackle drone failures. The security of the system is analyzed using Burrows–Abadi–Needham logic, real‐or‐random oracle model and ProVerif. The processing, communication, and storage costs of the proposed scheme are determined and compared to other drone mutual authentication protocols. The proposed mechanism outperforms the existing mutual authentication protocols with superior security features and performance attributes and is well suited for surveillance drone networks.

Scheduling optimization for UAV communication coverage using virtual force-based PSO model

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%, and 2.0–56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

Scaling of ant colony interaction networks

In social insect colonies, individuals are physically independent but functionally integrated by interaction networks which provide a foundation for communication and drive the emergence of collective behaviors, including nest architecture, division of labor, and potentially also the social regulation of metabolic rates. To investigate the relationship between interactions, metabolism, and colony size, we varied group size for harvester ant colonies (Pogonomyrmex californicus) and assessed their communication networks based on direct antennal contacts and compared these results with proximity networks and a random movement simulation. We found support for the hypothesis of social regulation; individuals did not interact with each other randomly but exhibited restraint. Connectivity scaled hypometrically with colony size, per-capita interaction rate was scale-invariant, and smaller colonies exhibited higher measures of closeness centrality and edge density, correlating with higher per-capita metabolic rates. Although the immediate energetic cost for two ants to interact is insignificant, the downstream effects of receiving and integrating social information can have metabolic consequences. Our results indicate that individuals in larger colonies are relatively more insulated from each other, a factor that may reduce or filter noisy stimuli and contribute to the hypometric scaling of their metabolic rates, and perhaps more generally, the evolution of larger colony sizes.

Biased Decoding and the Foundations of Communication

Biased Decoding and the Foundations of Communication

Biased Decoding and the Foundations of Communication

Biased Decoding and the Foundations of Communication

Distributed Estimation With Adaptive Cluster Learning Over Asynchronous Data Fusion

In the electronic information era, wireless sensor network (WSN) has always been an essential foundation for information collection, processing and communication. In WSN with multi-task estimation, distributed cooperation estimation with cluster learning has always been an attractive topic. When the unknown estimation parameters become complex, some cluster learning algorithms may not work, and their estimation performance could degrade. In addition, the problems of time delay, caused by synchronous data fusion, and different sampling rates between different types of sensors are usually neglected in practical applications. To solve these problems, an unsupervised distributed multi-task estimation algorithm with adaptive cluster learning over asynchronous data is proposed to obtain a more accurate estimation. In the proposed algorithm, the time delay and different sampling rates are fully considered and investigated. The mean stability, mean-square convergence, and behavior of adaptive cluster learning are analyzed for the proposed algorithm with asynchronous data. Finally, simulations are provided to demonstrate the robustness and effectiveness of the proposed algorithm.

Research progress of nonlinear optical properties of integrated two-dimensional materials

Photonic platforms with excellent nonlinear optical characteristics are very important to improve the devices' performance parameters such as integration, modulation speeds and working bandwidths for all-optical signal processing. The traditional processing technology of photonic platforms based on silicon, silicon nitride and silicon oxide is mature, but the nonlinear function of these optical platforms is limited due to the characteristics of materials; Although two-dimensional (2D) materials possess excellent nonlinear optical properties, their nonlinear potentials cannot be fully utilized because of their atomic layer thickness. Integrating 2D materials with mature photonic platforms can significantly improve the interaction between light and matter, give full play to the potentials of 2D materials in the field of nonlinear optics, and improve the nonlinear optical performances of the integrated platforms on the basis of fully utilizing the mature processing technology of the photonic platforms. Based on the above ideas, starting from the basic principle of nonlinear optics (Section 2), this review combs the research progress of various nonlinear photonic platforms (resonators, metasurfaces, optical fibers, on-chip waveguides, etc.) heterogeneously integrated with 2D materials, realized by traditional transfer methods (Section 3) and emerging direct-growth methods (Section 4) in recent years, and the introduction is divided into second-order and third-order nonlinearity. Comparing with the transfer methods, the advantages of using direct-growth methods to realize the heterogeneous integration of 2D materials and photonic platforms for the study of nonlinear optics are expounded, and the technical difficulties to be overcome in preparing the actual devices are also pointed. In the future, we can try to grow 2D materials directly onto the surfaces of various cavities to study the enhancement of second-order nonlinearity; we can also try to grow 2D materials directly onto the on-chip waveguides or microrings to study the enhancement of third-order nonlinearity. Generally speaking, the research on integrated nonlinearity by directly growing 2D materials onto various photonic structures has aroused great interest of researchers in this field. As time goes on, breakthrough progress will be made in this field, and technical problems such as continuous growth of high-quality 2D materials onto photonic structures and wafer-level large-scale preparation will be broken through, further improving the performance parameters of chips and laying a good foundation for optical communication, signal processing, optical sensing, all-optical computing, quantum technology and so on.

Socially evaluative contexts facilitate mentalizing.

Our ability to understand others' minds stands at the foundation of human learning, communication, cooperation, and social life more broadly. Although humans' ability to mentalize has been well-studied throughout the cognitive sciences, little attention has been paid to whether and how mentalizing differs across contexts. Classic developmental studies have examined mentalizing within minimally social contexts, in which a single agent seeks a neutral inanimate object. Such object-directed acts may be common, but they are typically consequential only to the object-seeking agent themselves. Here, we review a host of indirect evidence suggesting that contexts providing the opportunity to evaluate prospective social partners may facilitate mentalizing across development. Our article calls on cognitive scientists to study mentalizing in contexts where it counts.