Showing papers by "University of Electronic Science and Technology of China published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Abstract: The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Transition metal nitrides for electrochemical energy applications.

Abstract: Transition metal nitrides (TMNs), by virtue of their unique electronic structure, high electrical conductivity, superior chemical stability, and excellent mechanical robustness, have triggered tremendous research interest over the past decade, and showed great potential for electrochemical energy conversion and storage. However, bulk TMNs usually suffer from limited numbers of active sites and sluggish ionic kinetics, and eventually ordinary electrochemical performance. Designing nanostructured TMNs with tailored morphology and good dispersity has proved an effective strategy to address these issues, which provides a larger specific surface area, more abundant active sites, and shorter ion and mass transport distances over the bulk counterparts. Herein, the most up-to-date progress on TMN-based nanomaterials is comprehensively reviewed, focusing on geometric-structure design, electronic-structure engineering, and applications in electrochemical energy conversion and storage, including electrocatalysis, supercapacitors, and rechargeable batteries. Finally, we outline the future challenges of TMN-based nanomaterials and their possible research directions beyond electrochemical energy applications.

Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

Abstract: The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes’ Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC’17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris’ hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm

Small molecules in targeted cancer therapy: advances, challenges, and future perspectives.

Abstract: Due to the advantages in efficacy and safety compared with traditional chemotherapy drugs, targeted therapeutic drugs have become mainstream cancer treatments. Since the first tyrosine kinase inhibitor imatinib was approved to enter the market by the US Food and Drug Administration (FDA) in 2001, an increasing number of small-molecule targeted drugs have been developed for the treatment of malignancies. By December 2020, 89 small-molecule targeted antitumor drugs have been approved by the US FDA and the National Medical Products Administration (NMPA) of China. Despite great progress, small-molecule targeted anti-cancer drugs still face many challenges, such as a low response rate and drug resistance. To better promote the development of targeted anti-cancer drugs, we conducted a comprehensive review of small-molecule targeted anti-cancer drugs according to the target classification. We present all the approved drugs as well as important drug candidates in clinical trials for each target, discuss the current challenges, and provide insights and perspectives for the research and development of anti-cancer drugs.

11 TOPS photonic convolutional accelerator for optical neural networks

Abstract: Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1–7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels—sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition. An optical vector convolutional accelerator operating at more than ten trillion operations per second is used to create an optical convolutional neural network that can successfully recognize handwritten digit images with 88 per cent accuracy.

Silicon electro-optic micro-modulator fabricated in standard CMOS technology as components for all silicon monolithic integrated optoelectronic systems *

Abstract: In this paper, optoelectronic characteristics and related switching behavior of one monolithically integrated silicon light-emitting device (LED) with an interesting wavelength range of 400–900 nm are studied. Through the comparison of two types of geometry, Si avalanche-based LED and Si field-effect LED (Si FE LED), in the same device, we establish the dimensional dependence of the switching speed of the LED. Almost-linear modulation curve implies lower distortion is shown for the Si FE LED with light emission enhancement, and technology computer aided design (TCAD) simulations are in line with the experimental results. Our findings indicate that ON–OFF keying up to GHz frequencies should be feasible with such diodes. Potential applications should include Si FE LED integrated into the micro-photonic systems.

An artificial hybrid interphase for an ultrahigh-rate and practical lithium metal anode

Abstract: The solid electrolyte interphase (SEI) layer is pivotal for stable lithium (Li) metal batteries especially under a high rate. However, the mechanism of Li+ transport through the SEI has not been clearly elucidated to build robust Li anodes for practical Li metal batteries. Herein, an artificial hybrid SEI layer consisting of lithium-antimony (Li3Sb) alloy and lithium fluoride (LiF) is constructed to explore the ion diffusion behaviors within the SEI. As evidenced theoretically and experimentally, Li3Sb is identified as a superionic conductor for Li+ transport and as an interfacial stabilizer for the SEI layer while the LiF component with superior electron-blocking capability reduces the electron tunneling from the Li anode into the SEI, resulting in uniform dendrite-free Li deposition at the SEI/Li interface and stable Li plating/stripping behaviors at an ultrahigh rate of 20 mA cm−2. A practical 325.28 W h kg−1 pouch cell is well demonstrated under a high sulfur loading of 6 mg cm−2 and a low electrolyte/sulfur ratio of 3 μl mg−1. This work uncovers the internal mechanism of Li+ transport within the SEI component, and provides an avenue to stabilize the Li anode under practical high-rate conditions.

General synthesis of single-atom catalysts with high metal loading using graphene quantum dots.

Abstract: Transition-metal single-atom catalysts present extraordinary activity per metal atomic site, but suffer from low metal-atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Here we report a general method for the synthesis of single-atom catalysts with high transition-metal-atom loadings of up to 40 wt% or 3.8 at.%, representing several-fold improvements compared to benchmarks in the literature. Graphene quantum dots, later interweaved into a carbon matrix, were used as a support, providing numerous anchoring sites and thus facilitating the generation of high densities of transition-metal atoms with sufficient spacing between the metal atoms to avoid aggregation. A significant increase in activity in electrochemical CO2 reduction (used as a representative reaction) was demonstrated on a Ni single-atom catalyst with increased Ni loading. Transition-metal single-atom catalysts display excellent activity per metal atom site, but suffer from low metal atom densities (typically less than 5 wt% or 1 at.%), which limits their overall catalytic performance. Now, the use of a graphene-quantum-dot primary support, later interweaved into a carbon matrix, has enabled the synthesis of single-atom catalysts with high transition-metal atom loadings of up to 40 wt% or 3.84 at.%.

Deep Fuzzy Hashing Network for Efficient Image Retrieval

Abstract: Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The traditional hashing methods usually represent image content by hand-crafted features. Deep hashing methods based on deep neural network (DNN) architectures can generate more effective image features and obtain better retrieval performance. However, the underlying data structure is hardly captured by existing DNN models. Moreover, the similarity (either visually or semantically) between pairwise images is ambiguous, even uncertain, to be measured in the existing deep hashing methods. In this article, we propose a novel hashing method termed deep fuzzy hashing network (DFHN) to overcome the shortcomings of existing deep hashing approaches. Our DFHN method combines the fuzzy logic technique and the DNN to learn more effective binary codes, which can leverage fuzzy rules to model the uncertainties underlying the data. Derived from fuzzy logic theory, the generalized hamming distance is devised in the convolutional layers and fully connected layers in our DFHN to model their outputs, which come from an efficient xor operation on given inputs and weights. Extensive experiments show that our DFHN method obtains competitive retrieval accuracy with highly efficient training speed compared with several state-of-the-art deep hashing approaches on two large-scale image datasets: CIFAR-10 and NUS-WIDE.

Digital Twin Networks: A Survey

Abstract: Digital twin network (DTN) is an emerging network that utilizes digital twin (DT) technology to create the virtual twins of physical objects. DTN realizes co-evolution between physical and virtual spaces through DT modeling, communication, computing, data processing technologies. In this article, we present a comprehensive survey of DTN to explore the potentiality of DT. First, we elaborate key features and definitions of DTN. Next, the key technologies and the technical challenges in DTN are discussed. Furthermore, we depict the typical application scenarios, such as manufacturing, aviation, healthcare, 6G networks, intelligent transportation systems, and urban intelligence in smart cities. Finally, the new trends and open research issues related to DTN are pointed out.

Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks

Abstract: Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods.

Robust superhydrophobicity: mechanisms and strategies

Abstract: Superhydrophobic surfaces hold great prospects for extremely diverse applications owing to their water repellence property The essential feature of superhydrophobicity is micro-/nano-scopic roughness to reserve a large portion of air under a liquid drop However, the vulnerability of the delicate surface textures significantly impedes the practical applications of superhydrophobic surfaces Robust superhydrophobicity is a must to meet the rigorous industrial requirements and standards for commercial products In recent years, major advancements have been made in elucidating the mechanisms of wetting transitions, design strategies and fabrication techniques of superhydrophobicity This review will first introduce the mechanisms of wetting transitions, including the thermodynamic stability of the Cassie state and its breakdown conditions Then we highlight the development, current status and future prospects of robust superhydrophobicity, including characterization, design strategies and fabrication techniques In particular, design strategies, which are classified into passive resistance and active regeneration for the first time, are proposed and discussed extensively

Active Reconfigurable Intelligent Surface-Aided Wireless Communications

Abstract: Reconfigurable Intelligent Surface (RIS) is a promising solution to reconfigure the wireless environment in a controllable way. To compensate for the double-fading attenuation in the RIS-aided link, a large number of passive reflecting elements (REs) are conventionally deployed at the RIS, resulting in large surface size and considerable circuit power consumption. In this paper, we propose a new type of RIS, called active RIS, where each RE is assisted by active loads (negative resistance), that reflect and amplify the incident signal instead of only reflecting it with the adjustable phase shift as in the case of a passive RIS. Therefore, for a given power budget at the RIS, a strengthened RIS-aided link can be achieved by increasing the number of active REs as well as amplifying the incident signal. We consider the use of an active RIS to a single input multiple output (SIMO) system. However, it would unintentionally amplify the RIS-correlated noise, and thus the proposed system has to balance the conflict between the received signal power maximization and the RIS-correlated noise minimization at the receiver. To achieve this goal, it has to optimize the reflecting coefficient matrix at the RIS and the receive beamforming at the receiver. An alternating optimization algorithm is proposed to solve the problem. Specifically, the receive beamforming is obtained with a closed-form solution based on linear minimum-mean-square-error (MMSE) criterion, while the reflecting coefficient matrix is obtained by solving a series of sequential convex approximation (SCA) problems. Simulation results show that the proposed active RIS-aided system could achieve better performance over the conventional passive RIS-aided system with the same power budget.

Optical trapping with structured light: a review

Abstract: Optical trapping describes the interaction between light and matter to manipulate micro-objects through momentum transfer. In the case of 3D trapping with a single beam, this is termed optical tweezers. Optical tweezers are a powerful and noninvasive tool for manipulating small objects, and have become indispensable in many fields, including physics, biology, soft condensed matter, among others. In the early days, optical trapping was typically accomplished with a single Gaussian beam. In recent years, we have witnessed rapid progress in the use of structured light beams with customized phase, amplitude, and polarization in optical trapping. Unusual beam properties, such as phase singularities on-axis and propagation invariant nature, have opened up novel capabilities to the study of micromanipulation in liquid, air, and vacuum. We summarize the recent advances in the field of optical trapping using structured light beams.

Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design

Abstract: Wireless communication in the TeraHertz band (0.1–10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation

Abstract: Accurate medical image segmentation is essential for diagnosis and treatment planning of diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they are still challenged by complicated conditions where the segmentation target has large variations of position, shape and scale, and existing CNNs have a poor explainability that limits their application to clinical decisions. In this work, we make extensive use of multiple attentions in a CNN architecture and propose a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time. In particular, we first propose a joint spatial attention module to make the network focus more on the foreground region. Then, a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels. Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object. Extensive experiments on skin lesion segmentation from ISIC 2018 and multi-class segmentation of fetal MRI found that our proposed CA-Net significantly improved the average segmentation Dice score from 87.77% to 92.08% for skin lesion, 84.79% to 87.08% for the placenta and 93.20% to 95.88% for the fetal brain respectively compared with U-Net. It reduced the model size to around 15 times smaller with close or even better accuracy compared with state-of-the-art DeepLabv3+. In addition, it has a much higher explainability than existing networks by visualizing the attention weight maps. Our code is available at https://github.com/HiLab-git/CA-Net .

How to overcome taxonomical problems in the study of Internet use disorders and what to do with "smartphone addiction"?

Abstract: AimsThe present theoretical paper introduces the smartphone technology as a challenge for diagnostics in the study of Internet use disorders and reflects on the term “smartphone addiction.”MethodsS...

Sulfur-anchoring synthesis of platinum intermetallic nanoparticle catalysts for fuel cells.

Abstract: Atomically ordered intermetallic nanoparticles are promising for catalytic applications but are difficult to produce because the high-temperature annealing required for atom ordering inevitably acc...

Intelligent Reflecting Surface-Assisted Cognitive Radio System

Abstract: Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users (PUs). Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple PU receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

Blockchain-Based Public Integrity Verification for Cloud Storage against Procrastinating Auditors

Abstract: The deployment of cloud storage services has significant benefits in managing data for users. However, it also causes many security concerns, and one of them is data integrity. Public verification techniques can enable a user to employ a third-party auditor to verify the data integrity on behalf of her/him, whereas existing public verification schemes are vulnerable to procrastinating auditors who may not perform verifications on time. Furthermore, most of public verification schemes are constructed on the public key infrastructure (PKI), and thereby suffer from certificate management problem. In this paper, we propose a c ertificateless p ublic v erification scheme against p rocrastinating a uditors (CPVPA) by using blockchain technology . The key idea is to require auditors to record each verification result into a transaction on a blockchain. Because transactions on the blockchain are time-sensitive, the verification can be time-stamped after the transaction is recorded into the blockchain, which enables users to check whether auditors perform the verifications at the prescribed time. Moreover, CPVPA is built on certificateless cryptography, and is free from the certificate management problem. We present rigorous security proofs to demonstrate the security of CPVPA, and conduct a comprehensive performance evaluation to show that CPVPA is efficient.

A NiCo LDH nanosheet array on graphite felt: an efficient 3D electrocatalyst for the oxygen evolution reaction in alkaline media

Abstract: The development of efficient electrocatalysts from Earth-abundant elements for the oxygen evolution reaction (OER) is highly desired. Here, we report the electrodeposition of a NiCo layered double hydroxide nanosheet array on graphite felt (NiCo LDHs/GF) as a 3D OER electrocatalyst. Such NiCo LDHs/GF exhibits superior electrocatalytic activity with the need for an overpotential of 249 mV to drive a current density of 20 mA cm−2 in 1.0 M KOH. It also shows strong long-term electrochemical durability with its activity being maintained for at least 24 h.

User-Oriented Virtual Mobile Network Resource Management for Vehicle Communications

Abstract: Currently, advanced communications and networks greatly enhance user experiences and have a major impact on all aspects of people’s lifestyles in terms of work, society, and the economy. However improving competitiveness and sustainable vehicle network services, such as higher user experience, considerable resource utilization and effective personalized services, is a great challenge. Addressing these issues, this paper proposes a virtual network resource management based on user behavior to further optimize the existing vehicle communications. In particular, ensemble learning is implemented in the proposed scheme to predict the user’s voice call duration and traffic usage for supporting user-centric mobile services optimization. Sufficient experiments show that the proposed scheme can significantly improve the quality of services and experiences and that it provides a novel idea for optimizing vehicle networks.