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Abstract Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a subset of TALKIN-Family. It indicates that humans have higher accuracy when they have access to both faces and voices. The machine-learning methods could effectively and efficiently outperform the human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.
Abstract The goal of Facial Kinship Verification (FKV) is to automatically determine whether two individuals have a kin relationship or not from their given facial images or videos. It is an emerging and challenging problem that has attracted increasing attention due to its practical applications. Over the past decade, significant progress has been achieved in this new field. Handcrafted features and deep learning techniques have been widely studied in FKV. The goal of this paper is to conduct a comprehensive review of the problem of FKV. We cover different aspects of the research, including problem definition, challenges, applications, benchmark datasets, a taxonomy of existing methods, and state-of-the-art performance. In retrospect of what has been achieved so far, we identify gaps in current research and discuss potential future research directions.
Abstract Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
Abstract Integrins are heterodimeric adhesion receptors that maintain cell–extracellular matrix (ECM) interactions in diverse tissue microenvironments. They mediate cell adhesion and signaling through the assembly of large cytoplasmic multiprotein complexes that focally connect with the cytoskeleton. Integrin adhesion complexes (IAC) are specialized by the type of integrin-ECM contact and are sensitive to mechanical forces. Thus, they encrypt context-dependent information about the microenvironment in their composition. Signals mediated through IACs modulate many aspects of cell behavior, which allows cells to adapt to their surroundings. To gain insights into their function, IACs have been isolated from cultured cells and explored by proteomics. IACs are insoluble by nature and held together by transient/weak interactions, which makes it challenging to isolate intact IACs. Usually all IACs coupled to a specified ECM, which may employ different integrins, are isolated. Here we describe an alternative method based on proximity-dependent biotin identification (BioID), where specific integrin interaction partners are labeled in live cells and isolated without the need to isolate intact IACs.
Abstract In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is proposed for large-scale model training while preserving data privacy. However, the imbalanced data distribution has a significant impact on the convergence rate and learning accuracy. In addition, a large learning latency is incurred due to the traffic load imbalance among base stations (BSs) and limited wireless resources. To cope with these challenges, we first provide an analysis of the model error and learning latency in wireless HFL. Then, joint user association and wireless resource allocation algorithms are investigated under independent identically distributed (IID) and non-IID training data, respectively. For the IID case, a learning latency aware strategy is designed to minimize the learning latency by optimizing user association and wireless resource allocation, where a mobile device selects the BS with the maximal uplink channel signal-to-noise ratio (SNR). For the non-IID case, the total data distribution distance and learning latency are jointly minimized to achieve the optimal user association and resource allocation. The results show that both data distribution and uplink channel SNR should be taken into consideration for user association in the non-IID case. Finally, the effectiveness of the proposed algorithms are demonstrated by the simulations.
Abstract Multiple kernel learning (MKL) has been intensively studied during the past decade. It optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels of the samples are missing, which is not uncommon in practical applications. This paper proposes three absent MKL (AMKL) algorithms to address this issue. Different from existing approaches where missing channels are firstly imputed and then a standard MKL algorithm is deployed on the imputed data, our algorithms directly classify each sample based on its observed channels, without performing imputation. Specifically, we define a margin for each sample in its own relevant space, a space corresponding to the observed channels of that sample. The proposed AMKL algorithms then maximize the minimum of all sample-based margins, and this leads to a difficult optimization problem. We first provide two two-step iterative algorithms to approximately solve this problem. After that, we show that this problem can be reformulated as a convex one by applying the representer theorem. This makes it readily be solved via existing convex optimization packages. In addition, we provide a generalization error bound to justify the proposed AMKL algorithms from a theoretical perspective. Extensive experiments are conducted on nine UCI and six MKL benchmark datasets to compare the proposed algorithms with existing imputation-based methods. As demonstrated, our algorithms achieve superior performance and the improvement is more significant with the increase of missing ratio.
Abstract Class-Incremental Learning (CIL) aims at incrementally learning novel classes without forgetting old ones. This capability becomes more challenging when novel tasks contain one or a few labeled training samples, which leads to a more practical learning scenario, i.e., Few-Shot Class-Incremental Learning (FSCIL). The dilemma on FSCIL lies in serious overfitting and exacerbated catastrophic forgetting caused by the limited training data from novel classes. In this paper, excited by the easy accessibility of unlabeled data, we conduct a pioneering work and focus on a Semi-Supervised Few-Shot Class-Incremental Learning (Semi-FSCIL) problem, which requires the model incrementally to learn new classes from extremely limited labeled samples and a large number of unlabeled samples. To address this problem, a simple but efficient framework is first constructed based on the knowledge distillation technique to alleviate catastrophic forgetting. To efficiently mitigate the overfitting problem on novel categories with unlabeled data, uncertainty-guided semi-supervised learning is incorporated into this framework to select unlabeled samples into incremental learning sessions considering the model uncertainty. This process provides extra reliable supervision for the distillation process and contributes to better formulating the class means. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.
Abstract The recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) optimally integrates a group of pre-specified incomplete kernel matrices to improve clustering performance. Though it demonstrates promising performance in various applications, we observe that it does not \emph{sufficiently consider the local structure among data and indiscriminately forces all pairwise sample similarity to equally align with their ideal similarity values}. This could make the incomplete kernels less effectively imputed, and in turn adversely affect the clustering performance. In this paper, we propose a novel localized incomplete multiple kernel k-means (LI-MKKM) algorithm to address this issue. Different from existing MKKM-IK, LI-MKKM only requires the similarity of a sample to its k-nearest neighbors to align with their ideal similarity values. This helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. We carefully design a three-step iterative algorithm to solve the resultant optimization problem and theoretically prove its convergence. Comprehensive experiments on eight benchmark datasets demonstrate that our algorithm significantly outperforms the state-of-the-art comparable algorithms proposed in the recent literature, verifying the advantage of considering local structure.
Abstract In non-resonant Auger electron spectroscopies, multi core-ionized states lead to numerous energetically close-lying electronic transitions in Auger spectra, this hampering the assignment and interpretation of the experimental results. Here we reveal a new method to overcome this intrinsic limitation of non-resonant inner-shell spectroscopies. In a proof-of-principle experiment performed for the O2 molecule, most of the Auger final states are dissociative, and we measure in coincidence the kinetic energy of the photoelectron and the kinetic energy release of the (O+, O+) ion pairs produced after the Auger decay of the O 1s−1 core-ionized states. The Auger final states are assigned using energy conservation. We fully separate the contributions from the 4Σ− and 2Σ− intermediate ionic states and conclusively demonstrate that the Auger decay probability can dramatically depend on the different O2 1s−1 intermediate multiplet states. In addition, a metastable Auger final state also exists, with lifetime longer than 3.8 μs, and clear changes are observed in both branching ratio and spectral profile of the O 1s photoelectron spectrum when they are recorded in coincidence with either O2++ or with other ionic species. These changes are attributed to the population of the metastable B′3Σ−u(ν′′=0) Auger final state via different intermediate states.
Abstract As a close relative to graphene, silicene is advanced in high lithium capacity, yet attracting various manipulation strategies to promote its role in energy storage. Following grain boundary (GB) engineering route as widely used in graphene studies, in this work, first‐principles calculations were performed to investigate adsorption and diffusion behaviors of lithium on silicene with GBs of 4|8 or 5|5|8 defects. In both GB forms, donation of the Li 2s electron to the GBs significantly increases the Li adsorption energy, whereas small energy barriers facilitate the Li migration on the silicene surface. Furthermore, the large hole of GB(4‐8) also permits easy penetration of the Li ions through the defective silicene sieve. These important features demonstrate GBs are beneficial for enhancing capacity and charge speed of the Li batteries. Thus, superior anodes made of silicene with GBs are expected to serve a key solution for future energy storages.
Abstract Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k -means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance. In this paper, we first propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. Moreover, we further improve this algorithm by incorporating prior knowledge to regularize the learned consensus clustering matrix. Two three-step iterative algorithms are carefully developed to solve the resultant optimization problems with linear computational complexity, and their convergence is theoretically proven. After that, we theoretically study the generalization bound of the proposed algorithms. Furthermore, we conduct comprehensive experiments to study the proposed algorithms in terms of clustering accuracy, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithms deliver their effectiveness by significantly and consistently outperforming some state-of-the-art ones.
Abstract The goal of this work is to recognize words, phrases, and sentences being spoken by a talking face without given the audio. Current deep learning approaches for lip reading focus on exploring the appearance and optical flow information of videos. However, these methods do not fully exploit the characteristics of lip motion. In addition to appearance and optical flow, the mouth contour deformation usually conveys significant information that is complementary to others. However, the modeling of dynamic mouth contour has received little attention than that of appearance and optical flow. In this work, we propose a novel model of dynamic mouth contours called Adaptive Semantic-Spatio-Temporal Graph Convolution Network (ASST-GCN), to go beyond previous methods by automatically learning both the spatial and temporal information from videos. To combine the complementary information from appearance and mouth contour, a two-stream visual front-end network is proposed. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art lip reading methods on several large-scale lip reading benchmarks.
Abstract Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.
Abstract Multiple kernel clustering (MKC) has been intensively studied during the last few decades. Even though they demonstrate promising clustering performance in various applications, existing MKC algorithms do not sufficiently consider the intrinsic neighborhood structure among base kernels, which could adversely affect the clustering performance. In this paper, we propose a simple yet effective neighbor-kernel-based MKC algorithm to address this issue. Specifically, we first define a neighbor kernel, which can be utilized to preserve the block diagonal structure and strengthen the robustness against noise and outliers among base kernels. After that, we linearly combine these base neighbor kernels to extract a consensus affinity matrix through an exact-rank-constrained subspace segmentation. The naturally possessed block diagonal structure of neighbor kernels better serves the subsequent subspace segmentation, and in turn, the extracted shared structure is further refined through subspace segmentation based on the combined neighbor kernels. In this manner, the above two learning processes can be seamlessly coupled and negotiate with each other to achieve better clustering. Furthermore, we carefully design an efficient iterative optimization algorithm with proven convergence to address the resultant optimization problem. As a by-product, we reveal an interesting insight into the exact-rank constraint in ridge regression by careful theoretical analysis: it back-projects the solution of the unconstrained counterpart to its principal components. Comprehensive experiments have been conducted on several benchmark data sets, and the results demonstrate the effectiveness of the proposed algorithm.
Abstract Surface-enhanced Raman spectroscopy (SERS) finds wide applications in the field of organic molecule detection. However, reliable SERS detection of organic molecules and in situ monitoring of organic reactions under natural conditions by metal colloids are still challenging due to the formation of unstable nanoparticle clusters in solution and the low solubility of the organic molecules. Here, we approach the problems by introducing calcium ions to aggregate silver nanoparticles to form stable hot spots and acetone to promote uniform distribution of organic molecules on the nanoparticle surface. Significantly, our method exhibits stable SERS detection of up to 6 types of organic molecules in liquid. With acetone signals as an internal standard, we are able to determine molecule concentrations as well as monitor 3 kinds of organic reactions in situ. Our method shows potential for biomedical analysis, environmental analysis, and organic catalysis research.
Abstract This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.
Abstract Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions of data collected from embedded sensors. This paper proposes a graph neural network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both temporal and inner logic relations of data, in which vertices denote sensor data and edges denote relationships between vertices. The GNNM-IoT model’s relationships between sensors with neural networks to produce nonlinear complex relationships. We have evaluated the GNNM-IoT using air-conditioner data from a world leading IoT company, which demonstrates that it is effective and outperforms ARIMA and LSTM methods.
The interfacial energetics are known to play a crucial role in organic diodes, transistors, and sensors. Designing the metal-organic interface has been a tool to optimize the performance of organic (opto)electronic devices, but this is not reported for organic thermoelectrics. In this work, it is demonstrated that the electrical power of organic thermoelectric generators (OTEGs) is also strongly dependent on the metal-organic interfacial energetics. Without changing the thermoelectric figure of merit (ZT) of polythiophene-based conducting polymers, the generated power of an OTEG can vary by three orders of magnitude simply by tuning the work function of the metal contact to reach above 1000 µW cm−2. The effective Seebeck coefficient (Seff) of a metal/polymer/metal single leg OTEG includes an interfacial contribution (Vinter/ΔT) in addition to the intrinsic bulk Seebeck coefficient of the polythiophenes, such that Seff = S + Vinter/ΔT varies from 22.7 µV K−1 [9.4 µV K−1] with Al to 50.5 µV K−1 [26.3 µV K−1] with Pt for poly(3,4-ethylenedioxythiophene):p-toluenesulfonate [poly(3,4-ethylenedioxythiophene):poly(4-styrenesulfonate)]. Spectroscopic techniques are used to reveal a redox interfacial reaction affecting locally the doping level of the polymer at the vicinity of the metal-organic interface and conclude that the energetics at the metal-polymer interface provides a new strategy to enhance the performance of OTEGs.
Abstract This paper investigates the problem of age of information (AoI)-aware resource awareness in an unmanned aerial vehicle (UAV)-assisted mobile-edge computing (MEC) system, which is deployed by an infrastructure provider (InP). A service provider leases resources from the InP to serve the mobile users (MUs) with sporadic computation requests. Due to the limited number of channels and the finite shared I/O resource of the UAV, the MUs compete to schedule local and remote task computations in accordance with the observations of system dynamics. The aim of each MU is to selfishly maximize the expected long-term computation performance. We formulate the non-cooperative interactions among the MUs as a stochastic game. To approach the Nash equilibrium solutions, we propose a novel online deep reinforcement learning (DRL) scheme, which enables each MU to behave using its local conjectures only. The DRL scheme employs two separate deep Q- networks to approximate the Q-factor and the post-decision Q-factor for each MU. Numerical experiments show the potentials of the online DRL scheme in balancing the tradeoff between AoI and energy consumption.
Abstract Research in texture recognition often concentrates on recognizing textures with intraclass variations, such as illumination, rotation, viewpoint, and small-scale changes. In contrast, in real-world applications, a change in scale can have a dramatic impact on texture appearance to the point of changing completely from one texture category to another. As a result, texture variations due to changes in scale are among the hardest to handle. In this paper, we conduct the first study of classifying textures with extreme variations in scale. To address this issue, we first propose and then reduce scale proposals on the basis of dominant texture patterns. Motivated by the challenges posed by this problem, we propose a new GANet network where we use a genetic algorithm to change the filters in the hidden layers during network training in order to promote the learning of more informative semantic texture patterns. Finally, we adopt a Fisher vector pooling of a convolutional neural network filter bank feature encoder for global texture representation. Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding, we are developing a new dataset, the extreme scale variation textures (ESVaT), to test the performance of our framework. It is demonstrated that the proposed framework significantly outperforms the gold-standard texture features by more than 10% on ESVaT. We also test the performance of our proposed approach on the KTHTIPS2b and OS datasets and a further dataset synthetically derived from Forrest, showing the superior performance compared with the state-of-the-art.
This work aims to quantify the effect of a diamond-like carbon coating (DLC) treatment of aramid fibres and to reveal the conversion of a fibre-level performance leap on the macroscale mechanical behaviour. The DLC-based coating is applied directly to the reinforcement and laminates are infused with an epoxy matrix. After characterisation of the coated surfaces, the performance of the composite is analysed via interlaminar shear testing, fatigue testing and damage tolerance testing, microbond tests, and 3D finite element simulation using a cohesive zone model of the interface. The results show that the coating treatment improves the fatigue life and the S-N curve slope for the laminates, while the residual strength after impact damage and environmental conditioning (water immersion at 60 °C) remains high. The scaling factor to convert the performance on macroscale was determined to be 0.17–0.39 for the DLC-based fibre treatment.
Abstract Although Convolution Neural Networks(CNNs) are unprecedentedly powerful to learn effective representations, they are still parameter expensive and limited by the lack of ability to handle with the orientation transformation of the input data. To alleviate this problem, we propose a deep architecture named Rotation Invariant Local Binary Convolution Neural Network(RI-LBCNN). RI-LBCNN is a deep convolution neural network consisting of Local Binary orientation Module(LBoM). A LBoM is composed of two parts, i.e., three layers steerable module (two layers for the first and one for the second part), which is a combination of Local Binary Convolution (LBC)[19] and Active Rotating Filters (ARFs)[38]. Through replacing the basic convolution layer in DCNN with LBoMs, RI-LBCNN can be easily implemented and LBoM can be naturally inserted to other popular models without any extra modification to the optimisation process. Meanwhile, the proposed RI-LBCNN thus can be easily trained end to end. Extensive experiments show that the updating with the proposed LBoMs leads to significant reduction of learnable parameters and the reasonable performance improvement on three benchmarks.
Abstract Binary neural networks (BNNs) constrain weights and activations to +1 or -1 with limited storage and computational cost, which is hardware-friendly for portable devices. Recently, BNNs have achieved remarkable progress and been adopted into various fields. However, the performance of BNNs is sensitive to activation distribution. The existing BNNs utilized the Sign function with predefined or learned static thresholds to binarize activations. This process limits representation capacity of BNNs since different samples may adapt to unequal thresholds. To address this problem, we propose a dynamic BNN (DyBNN) incorporating dynamic learnable channel-wise thresholds of Sign function and shift parameters of PReLU. The method aggregates the global information into the hyper function and effectively increases the feature expression ability. The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs. The DyBNN based on two backbones of ReActNet (MobileNetV1 and ResNet18) achieve 71.2% and 67.4% top1-accuracy on ImageNet dataset, outperforming baselines by a large margin (i.e., 1.8% and 1.5% respectively).
Abstract Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.
Abstract The Bi-based hierarchical multi-component photocatalytic system is an important player in energy and environmental catalysis due to its full-spectrum light absorption and stable separation of photo-generated charges. In this study, a novel family of iodine doped Bi2O2CO3/Bi2WO6 heterojunctions was successfully developed by a facile ionic liquid assisted solvothermal method. A visible light photocatalytic H2 production rate of 664.5 μmol g−1 h−1 (0.3I-Bi2O2CO3/Bi2WO6 with 3% Pt) has been durably obtained, much higher than those of pristine and undoped counterparts. The photocatalysts are also capable for persistent pollutant and dye degradation. Through electrochemical and spectroscopic tests and density functional theory calculation, the enhanced photocatalytic activity is attributed to formation of the Z-scheme photocatalyst. Therein, the lowered and indirect bandgap and heterojunctional band alignment spontaneously benefit visible light harvesting, electron-hole pair separation, and proton reduction. Besides a facile synthesis for effective photocatalysis, the Z-scheme mechanism is hoped to facilitate the design and realization of similar systems for photocatalytic H2 evolution and organic hazards decontamination.
Abstract Edge computing is an emerging concept based on distributed computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth-generation (5G) wireless systems and beyond. While the current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This paper will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultrareliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so on. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.
This work aims to quantify the effect of a diamond-like carbon coating (DLC) treatment of aramid fibres and to reveal the conversion of a fibre-level performance leap on the macroscale mechanical behaviour. The DLC-based coating is applied directly to the reinforcement and laminates are infused with an epoxy matrix. After characterisation of the coated surfaces, the performance of the composite is analysed via interlaminar shear testing, fatigue testing and damage tolerance testing, microbond tests, and 3D finite element simulation using a cohesive zone model of the interface. The results show that the coating treatment improves the fatigue life and the S-N curve slope for the laminates, while the residual strength after impact damage and environmental conditioning (water immersion at 60 °C) remains high. The scaling factor to convert the performance on macroscale was determined to be 0.17–0.39 for the DLC-based fibre treatment.
Abstract Integrin-mediated laminin adhesions mediate epithelial cell anchorage to basement membranes and are critical regulators of epithelial cell polarity. Integrins assemble large multiprotein complexes that link to the cytoskeleton and convey signals into the cells. Comprehensive proteomic analyses of actin network-linked focal adhesions (FA) have been performed, but the molecular composition of intermediate filament-linked hemidesmosomes (HD) remains incompletely characterized. Here we have used proximity-dependent biotin identification (BioID) technology to label and characterize the interactome of epithelia-specific β4-integrin that, as α6β4-heterodimer, forms the core of HDs. The analysis identified ∼150 proteins that were specifically labeled by BirA-tagged integrin-β4. In addition to known HDs proteins, the interactome revealed proteins that may indirectly link integrin-β4 to actin-connected protein complexes, such as FAs and dystrophin/dystroglycan complexes. The specificity of the screening approach was validated by confirming the HD localization of two candidate β4-interacting proteins, utrophin (UTRN) and ELKS/Rab6-interacting/CAST family member 1 (ERC1). Interestingly, although establishment of functional HDs depends on the formation of α6β4-heterodimers, the assembly of β4-interactome was not strictly dependent on α6-integrin expression. Our survey to the HD interactome sets a precedent for future studies and provides novel insight into the mechanisms of HD assembly and function of the β4-integrin.
Abstract In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network. With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUE-pairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs. Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.
Abstract With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.
We thoroughly investigate the electromagnetic response of intelligent functional metasurfaces. We study two distinct designs operating at different frequency regimes, namely, a switch-fabric-based design for GHz frequencies and a graphene-based approach for THz band, and discuss the respective practical design considerations. The performance for tunable perfect absorption applications is assessed in both cases.
Abstract In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.
A thermal ion driven bursting instability with rapid frequency chirping, considered as an Alfvénic ion temperature gradient mode, has been observed in plasmas having reactor-relevant temperature in the DIII-D tokamak. The modes are excited over a wide spatial range from macroscopic device size to microturbulence size and the perturbation energy propagates across multiple spatial scales. The radial mode structure is able to expand from local to global in ∼0.1 ms and it causes magnetic topology changes in the plasma edge, which can lead to a minor disruption event. Since the mode is typically observed in the high ion temperature ≳10 keV and high-β plasma regime, the manifestation of the mode in future reactors should be studied with development of mitigation strategies, if needed. This is the first observation of destabilization of the Alfvén continuum caused by the compressibility of ions with reactor-relevant ion temperature.
We perform correlation and periodicity search analyses on long-term multiband light curves of the flat-spectrum radio quasar PKS 1510−089 observed by the space-based Fermi-Large Area Telescope in γ-rays, the SMARTS and Steward Observatory telescopes in optical and near-infrared (NIR), and the 13.7 m radio telescope in Metsähovi Radio Observatory between 2008 and 2018. The z-transform discrete correlation function method is applied to study the correlation and possible time lags among these multiband light curves. Among all pairs of wavelengths, the γ-ray versus optical/NIR and optical versus NIR correlations show zero time lags; however, both the γ-ray and optical/NIR emissions precede the radio radiation. The generalized Lomb–Scargle periodogram, weighted wavelet z-transform, and REDFIT techniques are employed to investigate the unresolved core emission–dominated 37 GHz light curve and yield evidence for a quasi period around 1540 days, although given the length of the whole data set it cannot be claimed to be significant. We also investigate the optical/NIR color variability and find that this source shows a simple redder-when-brighter behavior over time, even in the low-flux state.
Abstract Aim: The interspecific relationships between species occupancy and abundance have been broadly studied in terrestrial systems; however, the causes underlying this relationship in freshwater fishes remain poorly addressed. The main aims of this study were (a) to examine the occupancy–abundance relationship in 62 fish species in a monsoon climate river; (b) to determine the relative importance of species niche, functional traits and phylogenetic relatedness in shaping the occupancy–abundance relationship. Location: The Chishui River, the Yunnan-Guizhou Plateau and the Szechwan Basin, China. Taxon: Freshwater fishes. Methods: A linear model was used to test the relationship between distribution and mean abundance across fish species. The outlying mean index analysis was performed to estimate niche breadth and niche position parameters for each fish species. In addition, using principal coordinates analysis, we created four trait vectors describing the similarity of species’ traits. Fish phylogeny was obtained from a global molecular phylogenetic tree of actinopterygian fishes. We used a combination of linear models, commonality analyses and boosted regression trees to evaluate the relative effects of the niche metrics, traits and phylogenetic relatedness on occupancy and abundance. Results: Fish occupancy was strongly (R2 = 0.749) and positively correlated with mean abundance in the Chishui River. Niche parameters, especially niche position, were the main determinants of variation in occupancy (e.g. total contribution of niche position and breadth using commonality analyses: 0.587 and 0.318) and abundance (0.323 and 0.151) across species. Furthermore, traits and phylogeny had little to no effect on fish occupancy (0.034 and 0.037) and abundance (0.021 and 0.003). Main conclusions: Freshwater fishes in the Chishui River fit the common macroecological pattern of positive relationship between occupancy and abundance. Niche parameters, but not traits and phylogeny, are the primary correlates of riverine fish distribution and abundance.
Abstract Transcription factors (TFs) interact with several other proteins in the process of transcriptional regulation. Here, we identify 6703 and 1536 protein–protein interactions for 109 different human TFs through proximity-dependent biotinylation (BioID) and affinity purification mass spectrometry (AP-MS), respectively. The BioID analysis identifies more high-confidence interactions, highlighting the transient and dynamic nature of many of the TF interactions. By performing clustering and correlation analyses, we identify subgroups of TFs associated with specific biological functions, such as RNA splicing or chromatin remodeling. We also observe 202 TF-TF interactions, of which 118 are interactions with nuclear factor 1 (NFI) family members, indicating uncharacterized cross-talk between NFI signaling and other TF signaling pathways. Moreover, TF interactions with basal transcription machinery are mainly observed through TFIID and SAGA complexes. This study provides a rich resource of human TF interactions and also act as a starting point for future studies aimed at understanding TF-mediated transcription.
Abstract Light-weight, high-strength metal matrix composites (MMCs) have been gaining prominence in various industrial applications in which the materials are exposed to static and dynamic loading conditions. Unfortunately, micron-sized MMCs frequently encounter challenges such as particle breakage and debonding at the reinforcement-matrix interface, resulting in premature failure due to the decline in their mechanical properties, making them impractical to be utilized in some crucial applications. On the other hand, metal matrix nanocomposites (MMNCs) have been proven to improve strength, ductility, and fracture toughness characteristics, which are greatly beneficial in various industrial applications such as automotive, aerospace structures, and biomaterials. This review provides a comprehensive insight into the effect of nanoparticle addition on the fatigue performance of metals and alloys. Firstly, special attention has been given to the factors influencing the fatigue life of MMNCs. Secondly, the effect of nanoparticle incorporation on the fatigue performance of common metal matrixes, including aluminum, magnesium, titanium, and steel alloys, is reviewed in detail. Finally, a summary of this review and the future aspects related to the behavior of metals with nanoparticles at cyclic loading is provided.
Abstract The regolith transport near the surface of an asteroid is inherently sensitive to the local topography. In this paper, conditions of surface mass shedding and the subsequent evolution of the shedding material are studied for the primary of 65803 Didymos, serving as a representative for a large group of top-shaped asteroids that rotate near their critical spin limits. We considered the influences of an asymmetric shape and a non-spherical gravity, and demonstrate that these asymmetries play a significant role in the shedding process as well as in the subsequent orbital motion. The mass shedding conditions are given as a function of the geological coordinates, and show a clear-cut dependency on the local topographic features. We find that at different stages of the Yarkovsky–O’Keefe–Radzievskii–Paddack spin-up, the bulged areas exhibit a uniform superior advantage of enabling mass shedding over the depressed areas. "Dead zones" free from mass shedding are found around the polar sites. Numerical simulations show that the orbital motion of the shedding material experiences a drastic change as the spin rate is approaching the critical limit. The "mass leaking" effect is reinforced as the spin rate increases; the lower spin rates correspond to a higher capability of trapping the lofted particles in the vicinity of the asteroid, which statistically improves the probability of collisional growth in orbit. We also find that the topological transition of the equilibrium point can in practice lead to rapid clearance of the shedding material and transport of their orbits to larger distances from the surface.
Abstract Cold sintering is a promising technology for preparing electronic materials, enabling densification at low temperature, but rarely employed for thermoelectrics. Herein, high-quality Ca2.7Bi0.3Co3.92O9+δ ceramics were synthesised by a combination of cold sintering and annealing processes. Stoichiometric mixtures of raw materials were calcined once or twice at 1203 K for 12 h in air, and then cold sintered at 673 K for 60 min under a pressure of 85 MPa, followed by annealing at 1203 K for 12 h or 24 h in air. The effects of the calcination processes and annealing conditions on the thermoelectric performance of cold sintered samples were investigated. By optimising heat-treatment, the formation of secondary phases, texture development and porosity were controlled, leading to enhanced electrical conductivity and reduced thermal conductivity. Consequently, at 800 K there was 85% increase in power factor and 35% increase in ZT (value of 0.15) compared to previous studies.
HyperSurfaces (HSFs) are devices whose electromagnetic (EM) behavior is software-driven, i.e., it can be defined programmatically. The key components of this emerging technology are the metasurfaces, artificial layered materials whose EM properties depend on their internal subwavelength structuring. HSFs merge metasurfaces with a network of miniaturized custom electronic controllers, the nanonetwork, in an integrated scalable hardware platform. The nanonetwork receives external programmatic commands expressing the desired end-functionality and appropriately alters the metasurface configuration thus yielding the respective EM behavior for the HSF. In this work, we will present all the components of the HSF paradigm, as well as highlight the underlying challenges and future prospects.