Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization

Reconfigurable intelligent surface (RIS) emerges as an efficient and promising technology for the next wireless generation networks and has attracted a lot of attention owing to the capability of extending wireless coverage by reflecting signals toward targeted receivers. In this paper, we consider a RIS-assisted high-speed train (HST) communication system to enhance wireless coverage and improve coverage probability. First, coverage performance of the downlink single-input-single-output system is investigated, and the closed-form expression of coverage probability is derived. Moreover, travel distance maximization problem is formulated to facilitate RIS discrete phase design and RIS placement optimization, which is subject to coverage probability constraint. Simulation results validate that better coverage performance and higher travel distance can be achieved with deployment of RIS. The impacts of some key system parameters including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST on system performance are investigated. In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications.Comment: 14 figures, accepted by IEEE Transactions on Vehicular Technolog

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite the growing popularity of MoE, little work investigated its potential to advance convolutional neural networks (CNNs), especially in the plane of adversarial robustness. Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model? Can we robustly train it like an ordinary CNN model? Our pilot study shows that the conventional adversarial training (AT) mechanism (developed for vanilla CNNs) no longer remains effective to robustify an MoE-CNN. To better understand this phenomenon, we dissect the robustness of an MoE-CNN into two dimensions: Robustness of routers (i.e., gating functions to select data-specific experts) and robustness of experts (i.e., the router-guided pathways defined by the subnetworks of the backbone CNN). Our analyses show that routers and experts are hard to adapt to each other in the vanilla AT. Thus, we propose a new router-expert alternating Adversarial training framework for MoE, termed AdvMoE. The effectiveness of our proposal is justified across 4 commonly-used CNN model architectures over 4 benchmark datasets. We find that AdvMoE achieves 1% ~ 4% adversarial robustness improvement over the original dense CNN, and enjoys the efficiency merit of sparsity-gated MoE, leading to more than 50% inference cost reduction. Codes are available at https://github.com/OPTML-Group/Robust-MoE-CNN.Comment: ICCV 202

Reconstructing Ocean-Plate Stratigraphy (OPS) to Understand Accretionary Style and Mélange Fabric:Insights From the Bangong-Nujiang Suture (Tibet, China)

Ocean-plate stratigraphy (OPS) refers to the lithostratigraphic column atop an ocean plate, which becomes scraped off during subduction and preserved in accretionary complex (AC). Herein, based on structural, stratigraphic, and geochronological studies of ACs from the Bangong-Nujiang suture, we demonstrate that OPS can facilitate interpreting structural and compositional heterogeneities in ACs. Carefully correlated OPSs reveal that, on the overall sediment-rich lower plate, different types of basement topography correspond to the accretion of distinct litho-structural assemblages. In particular, subduction of the major, high-relief Zhonggang seamount eroded the earlier margin and was subsequently accreted as coherent seamount slices. In contrast, subduction of the lower-relief, Gaize seamount halted frontal accretion of trailing sediments, which were dragged downward to the seismogenic depth and underplated as pervasive, shear-related broken formations. Such broken formations may fingerprint past lower-relief-seamount subduction in other fossil ACs

Digital Life Project: Autonomous 3D Characters with Social Intelligence

In this work, we present Digital Life Project, a framework utilizing language as the universal medium to build autonomous 3D characters, who are capable of engaging in social interactions and expressing with articulated body motions, thereby simulating life in a digital environment. Our framework comprises two primary components: 1) SocioMind: a meticulously crafted digital brain that models personalities with systematic few-shot exemplars, incorporates a reflection process based on psychology principles, and emulates autonomy by initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis paradigm for controlling the character's digital body. It integrates motion matching, a proven industry technique to ensure motion quality, with cutting-edge advancements in motion generation for diversity. Extensive experiments demonstrate that each module achieves state-of-the-art performance in its respective domain. Collectively, they enable virtual characters to initiate and sustain dialogues autonomously, while evolving their socio-psychological states. Concurrently, these characters can perform contextually relevant bodily movements. Additionally, a motion captioning module further allows the virtual character to recognize and appropriately respond to human players' actions. Homepage: https://digital-life-project.com/Comment: Homepage: https://digital-life-project.com

MEE-DBD Plasma Actuator Effect on Aerodynamics of a NACA0015 Aerofoil: Separation and 3D Wake

© 2020, Springer Nature Switzerland AG. Dielectric barrier discharge (DBD) plasma actuators have received considerable attention by many researchers for various flow control applications. Having no moving parts, being light-weight, easily manufacturable, and their ability to respond almost instantly are amongst the advantages which has made them a popular flow control device especially for application on aircraft wings. The new configuration of DBDs which uses multiple encapsulated electrodes (MEE) has been shown to produce a superior and more desirable performance over the standard actuator design. The objective of the current study is to examine the effect of this new actuator configuration on the aerodynamic performance of an aerofoil under leading edge separation and wake interaction conditions. The plasma actuator is placed at the leading edge of a symmetric NACA 0015 aerofoil which corresponds to the location of the leading edge slat. The aerofoil is operated in a chord Reynolds number of 0.2×106. Surface pressure measurements along with the mean velocity profile of the wake using pitot measurements are used to determine the lift and drag coefficients, respectively. Particle image velocimetry (PIV) is also utilised to visualise and quantify the induced flow field. The results show improvement in aerodynamic performances of aerofoil under leading edge separation and also facing the wake region

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

3D Based Visualization Tool to Analyze the Influential Topics via Hashtags on Instagram Platform

This paper intends to develop an interactive, comprehensive information visualization platform of Instagram hashtag analysis. Instagram hashtags has developed themselves into all different kinds of group or communities for users to share hobbies and find similar friends. In order to analyze topic influence and user interest trend from Instagram, which contains billions of end-users and has worldwide influence, hashtag analysis is necessary to gather such information and compare the proportion of people involving in each tags and rank them to visualize. The visualization is developed in 3D space and consists of time-varying data flow of tags, together with tag comparison analysis, as well as event researches. In the rest of the paper, we mainly discuss the design idea and the development process of the system. An example of the system design work will be shown in the discussion, which involves 4 popular hashtags discussed on Instagram and are shown on the system, displayed as an 3D histogram, together with another comparison histogram to compare different tags, as well as an event view in the back

Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment

As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification

Guest Editorial Artificial Intelligence in Radio Propagation for Communications

The objective of this Special Issue is to showcase a unified vision for the applications of AI in radio propagation for communications and other relevant aspects. More specifically, the initial announcement encouraged emphasis in, but not limited to, the following areas: novel AI or AI-enabled techniques for radio propagation characterization and analysis; AI-enabled data analysis and parameter extractions of wireless channels; clustering analysis for radio channel characterization and modeling; AI-enabled channel modeling and communication system simulation; AI algorithm design in radio propagation for the applications in communications