Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Resilience in a Network of Caches With Perturbations

Caching in a network of caches has been widely investigated for improving information/content delivery efficiency (e.g., for reducing content delivery latency, server load and bandwidth utilization). In this work, we look into another dimension of network of caches – enhancing resilience in information dissemination rather than improving delivery efficiency. The underlying premise is that when information is cached at more locations, its availability is increased and thus, in turn, improve information delivery resiliency. This is especially important for networks with perturbations (e.g., node failures). Considering a general network of caches, we present a collaborative caching framework for maximizing the availability of the information. Specifically, we formulate an optimization problem for maximizing the joint utility of caching nodes in serving content requests in perturbed networks. We first solve the centralized version of the problem and then propose a distributed caching algorithm that approximates the centralized solution. We compare our proposal against different caching schemes under a range of parameters, using both real-world and synthetic network topologies. The results show that our algorithm can significantly improve the joint utility of caching nodes. With our distributed caching algorithm, the achieved caching utility is up to five times higher than greedy caching scheme. Furthermore, our scheme is found to be robust against increasing node failure rate, even for networks with a high number of vulnerable nodes.
Electric Equipment Panel Detection and Segmentation based on Mask R-CNN

This paper proposed a method for detecting the panel of equipment in the power room based on deep learning technology. For a real-time image, we can achieve the purpose of equipment panel detection and segmentation, which facilitate the task of equipment monitoring. We use the Mask R-CNN algorithm to detect how many target devices there are in the picture and to find the category corresponding to each device. In each target device, the foreground and background are distinguished at the pixel level. The method is described as follows. First of all, we collected a total of 4,500 pictures in 60 categories of power room equipment for our experiments. Next, we labeled all the pictures using Labelimg, which is an open source software for data annotation, the marked pictures were preprocessed as the data set to train and test the proposed model. The well performance on the test set shows that our proposed method has a certain implementability and effectiveness.
Energy-Efficient Mapping for a Network of DNN Models at the Edge

This paper describes a novel framework for executing a network of trained deep neural network (DNN) models on commercial-off-the-shelf devices that are deployed in an IoT environment. The scenario consists of two devices connected by a wireless network: a user-end device (U), which is a low-end, energy and performance-limited processor, and a cloudlet (C), which is a substantially higher performance and energy-unconstrained processor. The goal is to distribute the computation of the DNN models between U and C to minimize the energy consumption of U while taking into account the variability in the wireless channel delay and the performance overhead of executing models in parallel. The proposed framework was implemented using an NVIDIA Jetson Nano for U and a Dell workstation with Titan Xp GPU as C. Experiments demonstrate significant improvements both in terms of energy consumption of U and processing delay.
A Unified Analysis on ARQ-Based Broadcast: A Road to the Optimum

This paper presents a unified framework for Automatic Repeat reQuest (ARQ)-based retransmission schemes in multi-user broadcast environments. Two perspectives, namely, coded packet design and the enhancement of the ARQ protocol, are used to establish the unified framework. Consequently, the combined use of index coding and ARQ incorporated with memory is demonstrated to be beneficial toward performance enhancement in terms of transmission efficiency and memory overhead. Specifically, the asymptotic and finite transmission efficiencies of the index-coded ARQ incorporated with memory are analyzed to be close to the optimum over a wide range of packet error probabilities and the number of users, which is validated numerical evaluations. Furthermore, the use of index coding is analyzed to be also favourable in terms of memory overhead in comparison with the memory ARQ. Thus, index-coded ARQ with appropriate use of memory is shown to be a reasonable option in a practical sense, which sheds light on the possibility that multi-user coded ARQ can play a crucial role for a reliable broadcast beyond unicast.
Power and Rate Adaptive Pushing Over Fading Channels

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.
Estimating the Net Ecosystem Exchange at Global FLUXNET Sites Using a Random Forest Model

Despite considerable progress in scaling carbon fluxes from eddy covariance sites to globe, significant uncertainties still exist when estimating the global net ecosystem exchange (NEE). In this study, the site-level NEE was estimated from FLUXNET, a global network of eddy covariance towers, using a random forest (RF) model based on remote sensing products and precipitation data. The plant function type (PFT) had the highest relative explanatory power in predicting the global site-level NEE. However, within PFTs, water-related variables (i.e., the total precipitation, remotely sensed evapotranspiration, land surface water index, and the difference between daytime and nighttime land surface temperature) and soil respiration (<italic>R</italic><sub>s</sub>) were strong predictors of NEE variability. Cross-validation analyses revealed the good performance of RF in predicting the spatiotemporal variability of monthly NEE at 168 global FLUXNET sites, with <italic>R</italic><sup>2</sup> of 0.72 and RMSE of 0.96 g·C·m<sup>−2</sup>·day<sup>−1</sup>. The performance was also good when predicting across-site (<italic>R</italic><sup>2</sup> = 0.75) and seasonal patterns (<italic>R</italic><sup>2</sup> = 0.92) over the 58 sites with available data being longer than two years and the 12-month value being present for each year. The RF-estimated NEE showed better relationships with the tower-measured NEE than a global NEE product from FLUXCOM across all PFTs. The difference between the RF-estimated NEE and FLUXCOM NEE was likely linked to the different predictor sets, such as those with more water-related variables and <italic>R</italic><sub>s</sub>. This study indicates the importance of considering the influence of water-related variables and <italic>R</italic><sub>s</sub> in the estimation of NEE at the global scale.
Graph-Theoretical Dynamic User Pairing for Downlink NOMA Systems

We propose a novel graph-theoretical dynamic user pairing strategy based on the user rate requirements in cellular networks employing non-orthogonal multiple access (NOMA). The proposed approach relies on first constructing a conflict graph corresponding to all possible user pairings and then reformulating the problem of finding the best user pairs as that of finding the maximum weighted independent set (MWIS) on the conflict graph. This formulation turns the originally NP-hard problem into one that can be solvable in polynomial time thanks to the claw-freeness property of the conflict graph. The proposed user pairing method satisfies the maximum number of user demands with optimal network sum-rate as shown theoretically and as validated by the simulation results.
Ultrasound-assisted synthesis of ordered mesoporous silica FDU-12 with a hollow structure

An ordered three-dimensional cubic mesoporous structure with uniform particle sizes (3.5–5 μm) and a thick shell was achieved by a sonochemical method. The reaction time of TEOS during the coassembly process could be shortened from 24 h to 8 h compared with the conventional synthesis route. The mechanism for the formation of the hollow silica was proposed.
Applied Research on the Orderly Charging of Large-scale Electric Vehicles in Residential Areas

As a new type and large capacity load, electric vehicle will bring new challenges to the safety and economic operation of power system when it is connected to power grid in large quantities. According to the development trend of electric vehicle construction in residential area, this paper expounds the concept of orderly charging and demonstrates the necessity of its large-scale promotion. And from the point of view of suitable for rapid promotion and application, a scheme suitable for the orderly charging of large-scale electric vehicles in residential areas is put forward, and its architecture, business process, control strategy, reward and punishment mechanism are described in detail. The research results of this paper will provide useful reference for the future large-scale promotion of intelligent orderly charging in residential areas.
Detecting the Deformation Anomalies Induced by Underground Construction Using Multiplatform MT-InSAR: A Case Study in To Kwa Wan Station, Hong Kong

Underground constructions are implemented in cities to relieve the pressure of urbanization. Ground deformation often occurs with improper construction activities. Such deformation can be measured with techniques such as multitemporal interferometric synthetic aperture radar (MT-InSAR). However, the deformation anomalies, such as the building horizontal tilt can cause the MT-InSAR line-of-sight deformation to tend to zero, the deformed area is likely to be ignored when the individual SAR dataset is applied. In this article, we explore deformation induced by the underground construction of To Kwa Wan (TKW) station in Hong Kong with an improved MT-InSAR method. Multiplatform SAR datasets from TerraSAR-X, COSMO-SkyMed, and Sentinel-1A are adopted to retrieve comprehensive deformation of ground buildings for stability investigation. The results are validated through cross-validation between SAR datasets. Both spatial and temporal variations of ground deformation are revealed. Ground buildings suffered significant deformation in vertical and horizontal directions, the maximum deformation is around 30 and 40 mm, respectively. Due to the anomalous deformation, the ground buildings around the station are dumped towards the street center. In view of temporal evolution, the deformation is strongly related to the construction stages. The deformation turned to be stable after the completion of the main framework of the TKW station in December 2016. This article suggests that the deformation obtained from individual SAR datasets may sometimes is not noticeable or reliable in the area with complex underground construction. Such complex phenomena can be revealed by using the multiplatform SAR datasets with MT-InSAR.
Optimal Task Assignment for Integrated Cloud and Edge Networks With Tree Topology

The rapid growth of mobile applications and the Internet of Things generates a huge amount of data that can hardly be processed by end devices. Recently, Integrated Cloud and Edge (ICE) networks have been proposed to support smart applications and services by leveraging both remote clouds and edge clusters. However, task assignment over ICE networks has been proved NP-hard, which makes it challenging in practice. In this work, we discover that a polynomial-time optimal solution does exist for the task assignment problem when the ICE network is organized with tree topology. Specifically, the studied problem for the ICE network with tree topology is transformed into an equivalent graph optimization problem. Theoretical analysis is then provided to find the polynomial-time optimal solution. Evaluation results demonstrate the correctness and efficiency of the discovered solution.
Manipulating the Redox Kinetics of Li–S Chemistry by Tellurium Doping for Improved Li–S Batteries

Fundamentally altering the essential properties of a material itself is always of great interest but challenging as well. Herein, we demonstrate a simple tellurium doping method to intrinsically reshape the electronic properties of the sulfur and manipulate the kinetics of Li–S chemistry for improving the performance of Li–S batteries. DFT calculation indicates that Te doping can effectively facilitate the lithiation/delithiation reactions and lower the lithium ion diffusion energy barrier in Li2S. Additionally, electrochemical studies prove that the reaction kinetics of Li–S chemistry and cycling performance of Li–S batteries have been significantly improved with Te dopants. An exceptional specific capacity of ∼656 mA h g–1 and a high Coulombic efficiency of ∼99% have been achieved at 5 A g–1 even after 1000 cycles. More importantly, the capability to manipulate the intrinsic properties of materials and explore the synergistic effects between conventional strategies and element doping provides new avenue.
Aspect-Based Community Detection of Cultural Heritage Streaming Data

Twitter is considered a major and very popular social network providing an abundance of data generated by users’ interactions through tweets. After an appropriate analysis of this information, sets consisting of users who share similar attributes, and preferences can be identified. Massive cultural content management is important because reviews can be analyzed for extracting significant representations. In this study, an aspect mining method of a cultural heritage approach by incorporating big data methods, is proposed. We propose the combination of a community detection algorithm, i.e., the Parallel Structural Clustering Algorithm for Networks (PSCAN), with topic modelling methods, i.e., the Latent Dirichlet Allocation (LDA), for performing large-scale data analysis in Twitter.
Five-Factor Musical Preference Prediction for Solving New User Cold-Start Problem in Content-Based Music Recommender System

Recent years witness a boom in music recommender systems due to the success of online streaming services. Even though such systems have brought relatively high-quality recommendations to the users, they are still facing the cold-start problem, especially for new user case. This problem happens when the system does not have information about the new user’s preferences to provide recommendations. Therefore, effectively predicting musical preferences for the new user becomes vital. In this paper, we leverage a five-factor MUSIC model which is characterized by Mellow, Unpretentious, Sophisticated, Intense, and Contemporary to represent the user’s preference. Then, towards solving the new user cold-start problems in the content-based music recommender system, we propose a method to predict the five-factor preference profile of the novel user. We consider an early-stage scenario when there are no and few rating data of the user available in the system. Accordingly, we first use the information of age and brain type extracted from questionnaires to build regression models. These models are used to predict the first five-factor musical preference profile for initial recommendations. We then estimate the second five-factor profile based on the user’s rating data and linearly combine it with the first profile for improving recommendations. The results demonstrated the effectiveness of the proposed method in predicting the musical preference of the new user in the assumed scenario.
A 16-kb 9T Ultralow-Voltage SRAM With Column-Based Split Cell-VSS, Data-Aware Write-Assist, and Enhanced Read Sensing Margin in 28-nm FDSOI

This work proposes an static random access memory (SRAM) with column-based split cell-VSS (CS-CVSS), data-aware write-assist (DAWA), and enhanced read sensing margin in 28-nm FDSOI technology. The proposed CS-CVSS and DAWA techniques improve both half-selected (HS) static noise margin (SNM) and write margin. They also improve HS dynamic noise margin (HS-DNM) by leveraging write through virtual ground with reduced load in the proposed write port. The proposed 3T read port enhances sensing margin by minimizing read bitline leakage through negative gate-to-source voltage regardless of cell data. A 16-kb 9T SRAM test chip demonstrated the minimum operating voltage for write and read operations as 0.47 and 0.25 V, respectively. The minimum energy of 6.72 pJ is achieved at 0.5 V.
Modular Four-channel 50 kW WPT System with Decoupled Coil Design for Fast EV Charging

High-power wireless power transfer (WPT) is an attractive option for fast charging of electric vehicles (EVs), but the power capability of the WPT system is limited by power devices and resonant elements. This paper presents a scalable modular four-channel WPT system for fast EV charging. Owing to the modular design, the four-channel system achieves 50 kW output power with low-power Silicon devices, and the reliability and robustness of the system are significantly improved. Through the coil structure and placement design, the four-channel magnetic coupler achieves inter-channel decoupling, eliminating interference between channels, simplifying control logic, and enabling scalable modular design. The four-channel magnetic coupler also has rotational symmetry, making the system have a balanced tolerance to misalignment in both lateral and vertical directions. The proposed magnetic coupler is verified by 3-D finite element method (FEM) simulation. A modular four-channel WPT prototype is built and tested. Experiments show that the prototype can deliver 50.8 kW to load with 97% dc-to-dc efficiency over a 200-mm airgap in aligned case, and has a balanced tolerance to misalignment in both the lateral and vertical directions.
An Enhanced Belief Propagation Decoder for Polar Codes

Belief propagation (BP) has attracted increasing attention due to its high parallelism. In this letter, a correction method is proposed to improve the block error rate (BLER) performance of the BP decoder for polar codes so that it is comparable to that of the successive cancellation list (SCL) decoder. Compared with BP flipping (BPF) decoding, the proposed decoder focuses on prior knowledge of the code bits in the factor graph. Two strategies have been proposed to construct the correction set. The simulation results show that the BPC decoder exhibits a BLER similar to that of BPF decoder while reducing the average latency and complexity by half. In addition, the BLER of the BPC decoder can approach that of the cyclic redundancy check (CRC)-aided SCL (CA-SCL) decoder in regions with low and medium signal-to-noise ratios (SNRs).
Design of an Adaptive Fuzzy Observer-Based Fault Tolerant Controller for Pneumatic Active Suspension With Displacement Constraint

This paper proposes an adaptive fuzzy observer based fault tolerant controller for a pneumatic active suspension system considering unknown parameters, actuator failures, and displacement constraints. A pneumatic spring is used for a quarter car model to enhance the vibration attenuation performance. Since the pneumatic system contains uncertain nonlinearities, fuzzy logic systems are utilized to approximate unknown nonlinear functions of unmodeled dynamics and various masses of passengers. Besides, a nonlinear disturbance observer is proposed to estimate the effects of the actuator failures, approximation errors, and external disturbances. By utilizing the disturbance estimation and fuzzy approximation techniques, an adaptive fault tolerant control (FTC) is designed to enhance the output performance of the vehicle suspension. Meanwhile, the command filtered scheme is introduced to solve the explosion of complexity problem in the traditional backstepping approach by avoiding virtual controller derivatives. In contrast to previous results, the proposed control can handle the fault tolerant problem and ensure the tracking error of vertical displacement converges into a small-predefined boundary by introducing the prescribed performance function. Moreover, the stability of the closed-loop system is analyzed according to the Lyapunov theory. Finally, comparative simulation examples and experimental studies are performed on the active pneumatic suspension test bench to verify the feasibility of the proposed scheme.
ARIS: A Real Time Edge Computed Accident Risk Inference System

To deploy an intelligent transport system in urban environment, an effective and real-time accident risk prediction method is required that can help maintain road safety, provide adequate level of medical assistance and transport in case of an emergency. Reducing traffic accidents is an important problem for increasing public safety, so accident analysis and prediction have been a subject of extensive research in recent time. Even if a traffic hazard occurs, a readily deployable structure with an accurate prediction of accident can contribute to better management of rescue resources. But the significant shortcomings of current studies are the use of small-scale datasets with minimal scope, being based on extensive data sets, and not being applicable for real-time purposes. To overcome these challenges, we propose ARIS: a system for real-time traffic accident prediction built on a traffic accident dataset named ‘US-Accidents’ which covers 49 states of United States, collected from February 2016 to June 2020. Our approach is based on a deep neural network model that utilizes a variety of data characteristics, such as time-sensitive weather data, textual information, and discerning factors. We have tested ARIS against multiple baselines through a comprehensive series of experiments across several major cities of USA, and we have noticed significant improvement during inference especially in detecting accident classes. Additionally, to make our model edge-implementable we have compressed our model using a joint technique of magnitude-based weight pruning and model quantization. We have also demonstrated the inference results along with power consumption profiling after deploying the model on a resource constrained environment that consists of Intel Neural Compute Stick 2 (NCS2) with Raspberry Pi 4B (RPi4). Our investigation and observations indicate major improvements to predict unusual traffic accident event even after model compression and deployment. We have managed to reduce the model size and inference time by ≈ 6x, and ≈ 70 % respectively with insignificant drop in performance. Furthermore, to better understand the importance of each individual type of variables used in our analysis, we have showcased a comprehensive ablation study.