Showing papers by "Southeast University published in 2020"

Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces

Abstract: Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS’s power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the non-convexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

Abstract: In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Abstract: Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a $K$ -client random scheduling strategy, where $K$ ( $1\leq K ) clients are randomly selected from the $N$ overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the $K$ -client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal $K$ that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.

Intubation and Ventilation amid the COVID-19 Outbreak: Wuhan's Experience.

Abstract: The COVID-19 outbreak has led to 80,409 diagnosed cases and 3,012 deaths in mainland China based on the data released on March 4, 2020. Approximately 3.2% of patients with COVID-19 required intubation and invasive ventilation at some point in the disease course. Providing best practices regarding intubation and ventilation for an overwhelming number of patients with COVID-19 amid an enhanced risk of cross-infection is a daunting undertaking. The authors presented the experience of caring for the critically ill patients with COVID-19 in Wuhan. It is extremely important to follow strict self-protection precautions. Timely, but not premature, intubation is crucial to counter a progressively enlarging oxygen debt despite high-flow oxygen therapy and bilevel positive airway pressure ventilation. Thorough preparation, satisfactory preoxygenation, modified rapid sequence induction, and rapid intubation using a video laryngoscope are widely used intubation strategies in Wuhan. Lung-protective ventilation, prone position ventilation, and adequate sedation and analgesia are essential components of ventilation management.

Critical care crisis and some recommendations during the COVID-19 epidemic in China

Abstract: Lack of critical care resource in face of COVID‐19 epidemics Based on data reported by the National Health Commission of China, there have been about 2000 new confirmed cases and > 4000 suspected cases daily over the past week in Wuhan [3]. About 15% of the patients have developed severe pneumonia, and about 6% need noninvasive or invasive ventilatory support. Currently, there are about 1000 patients who need ventilatory support and another 120 new patients daily who require noninvasive or invasive ventilation support in Wuhan city; however, there are only about 600 ICU beds [4]. To address this shortfall, 70 ICU beds were created from general beds and the government quickly transformed three general hospitals to critical care hospitals with a total of about 2500 beds that specialize in patients with severe SARS-CoV-2 pneumonia (equipped with monitors and high-flow nasal cannula, noninvasive ventilator or invasive ventilators). An equally great (or potentially greater) problem is the shortage of trained personnel to treat these critically ill patients. Until the crisis, there were about 300 ICU physicians and 1000 ICU nurses in Wuhan city. By the end of January, more than 600 additional ICU doctors and 1500 ICU nurses were transferred to Wuhan from the rest of China. As well, an additional 3000 staff including infectious disease, respiratory, internal medicine physicians and nurses were transferred to Wuhan by the government. There are logistical issues which make care of the patients difficult. These include donning of personal protective equipment (e.g., gloves, gowns, respiratory and eye protection), lack of instruments and disposables, and shortages of supplemental oxygen. Many severe hypoxemic patients only receive high-flow nasal oxygen (HFNO) or noninvasive mechanical ventilation rather than invasive mechanical ventilation because of intubation delay or lack of mechanical ventilators (especially at early phase). Our preliminary data show that only about 25% of patients who died were intubated and received mechanical ventilation.

Catechol-functionalized hydrogels: biomimetic design, adhesion mechanism, and biomedical applications.

Abstract: Hydrogels are a unique class of polymeric materials that possess an interconnected porous network across various length scales from nano- to macroscopic dimensions and exhibit remarkable structure-derived properties, including high surface area, an accommodating matrix, inherent flexibility, controllable mechanical strength, and excellent biocompatibility. Strong and robust adhesion between hydrogels and substrates is highly desirable for their integration into and subsequent performance in biomedical devices and systems. However, the adhesive behavior of hydrogels is severely weakened by the large amount of water that interacts with the adhesive groups reducing the interfacial interactions. The challenges of developing tough hydrogel-solid interfaces and robust bonding in wet conditions are analogous to the adhesion problems solved by marine organisms. Inspired by mussel adhesion, a variety of catechol-functionalized adhesive hydrogels have been developed, opening a door for the design of multi-functional platforms. This review is structured to give a comprehensive overview of adhesive hydrogels starting with the fundamental challenges of underwater adhesion, followed by synthetic approaches and fabrication techniques, as well as characterization methods, and finally their practical applications in tissue repair and regeneration, antifouling and antimicrobial applications, drug delivery, and cell encapsulation and delivery. Insights on these topics will provide rational guidelines for using nature's blueprints to develop hydrogel materials with advanced functionalities and uncompromised adhesive properties.

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

Abstract: Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity, leaving great room for improvement. In this work, we achieve improved mask prediction by effectively combining instance-level information with semantic information with lower-level fine-granularity. Our main contribution is a blender module which draws inspiration from both top-down and bottom-up instance segmentation approaches. The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference. BlendMask can be easily incorporate with the state-of-the-art one-stage detection frameworks and outperforms Mask R-CNN under the same training schedule while being faster. A light-weight version of BlendMask achieves 36.0 mAP at 27 FPS evaluated on a single 1080Ti. Because of its simplicity and efficacy, we hope that our BlendMask could serve as a simple yet strong baseline for a wide range of instance-wise prediction tasks.

A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications

Abstract: Generative adversarial networks (GANs) are a hot research topic recently. GANs have been widely studied since 2014, and a large number of algorithms have been proposed. However, there is few comprehensive study explaining the connections among different GANs variants, and how they have evolved. In this paper, we attempt to provide a review on various GANs methods from the perspectives of algorithms, theory, and applications. Firstly, the motivations, mathematical representations, and structure of most GANs algorithms are introduced in details. Furthermore, GANs have been combined with other machine learning algorithms for specific applications, such as semi-supervised learning, transfer learning, and reinforcement learning. This paper compares the commonalities and differences of these GANs methods. Secondly, theoretical issues related to GANs are investigated. Thirdly, typical applications of GANs in image processing and computer vision, natural language processing, music, speech and audio, medical field, and data science are illustrated. Finally, the future open research problems for GANs are pointed out.

Guidelines for the Diagnosis and Treatment of Hepatocellular Carcinoma (2019 Edition).

Abstract: Background: Primary liver cancer, around 90% are hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2017 Edition) in 2018, additional high-quality evidence has emerged with relevance to the diagnosis, staging, and treatment of liver cancer in and outside China that requires the guidelines to be updated. The new edition (2019 Edition) was written by more than 70 experts in the field of liver cancer in China. They reflect the real-world situation in China regarding diagnosing and treating liver cancer in recent years. Key Messages: Most importantly, the new guidelines were endorsed and promulgated by the Bureau of Medical Administration of the National Health Commission of the People’s Republic of China in December 2019.

The impact of COVID-19 on public space: an early review of the emerging questions – design, perceptions and inequities

Abstract: Restrictions on the use of public space and physical distancing have been key policy measures to reduce the transmission of COVID-19 and protect public health. At the time of writing, one half of t...

Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province

Abstract: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. A cluster of patients of novel coronavirus pneumonia (NCP) have been identified in Wuhan in December 2019 and soon this virus spread at a tremendous rate which swept through the whole China and more than 93 countries and regions around the world [1, 2]. This emerging, rapidly evolving situation has threatened the health of all mankind and WHO has raised COVID-19 risk to “very high” at global level. Up to now, 80,859 cases were confirmed, among which 10–15% patients were critically ill and 3100 (3.83%) died in China. The large number of transmission population between Jiangsu and Hubei provinces led to the infinite burden in controlling the COVID-19 epidemic in Jiangsu Province [3, 4]. By 24:00 on March 7, a total of 631 confirmed cases of NCP were reported with a portion of critically ill patients whose ages ranged from 9 months to 96 years old. A total of 610 cases have been discharged from hospital, and the cure rate of confirmed cases in our province has reached 96.67%, which is far exceeding that of national data [5–8]. Since the outcome of NCP patients in Jiangsu was much better than that in Hubei where the mortality of NCP patients was nearly 4.34%, we retrospectively summarized our therapeutic process and figured out that critical care-dominated treatment patterns might be the core in reducing mortality.

Engineered exosomes for targeted co-delivery of miR-21 inhibitor and chemotherapeutics to reverse drug resistance in colon cancer

Abstract: 5-Fluorouracil (5-FU) has been commonly prescribed for patients with colorectal cancer (CRC), but resistance to 5-FU is one of the main reasons for failure in CRC. Recently, microRNAs (miRNAs) have been established as a means of reversing the dilemma by regulating signaling pathways involved in initiation and progression of CRC. However, how to safely and effectively deliver miRNA to target cells becomes a main challenge. In this study, Engineered exosomes were exploited to simultaneously deliver an anticancer drug 5-FU and miR-21 inhibitor oligonucleotide (miR-21i) to Her2 expressing cancer cells. Purified engineered exosomes from the donor cells loaded with 5-FU and miR-21i via electroporation to introduce into 5-FU-resistant colorectal cancer cell line HCT-1165FR. Furthermore, systematic administration of 5-FU and miR-21i loaded exosomes in tumor bearing mice indicated a significantly anti-tumor effect. The results showed that the engineered exosome-based 5-FU and miR-21i co-delivery system could efficiently facilitate cellular uptake and significantly down-regulate miR-21 expression in 5-FU resistant HCT-1165FR cell lines. Consequently, the down-regulation of miR-21 induced cell cycle arrest, reduced tumor proliferation, increased apoptosis and rescued PTEN and hMSH2 expressions, regulatory targets of miR-21. Of particular importance was the significant reduction in tumor growth in a mouse model of colon cancer with systematic administration of the targeting miR-21i. More excitedly, the combinational delivery of miR-21i and 5-FU with the engineered exosomes effectively reverse drug resistance and significantly enhanced the cytotoxicity in 5-FU-resistant colon cancer cells, compared with the single treatment with either miR-21i or 5-FU. The strategy for co-delivering the functional small RNA and anticancer drug by exosomes foreshadows a potential approach to reverse the drug resistance in CRC and thus to enhance the efficacy of the cancer treatment.

Future Physics Programme of BESIII

Abstract: There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.

Timing of Initiation of Renal-Replacement Therapy in Acute Kidney Injury.

Abstract: Background Acute kidney injury is common in critically ill patients, many of whom receive renal-replacement therapy. However, the most effective timing for the initiation of such therapy remains uncertain. Methods We conducted a multinational, randomized, controlled trial involving critically ill patients with severe acute kidney injury. Patients were randomly assigned to receive an accelerated strategy of renal-replacement therapy (in which therapy was initiated within 12 hours after the patient had met eligibility criteria) or a standard strategy (in which renal-replacement therapy was discouraged unless conventional indications developed or acute kidney injury persisted for >72 hours). The primary outcome was death from any cause at 90 days. Results Of the 3019 patients who had undergone randomization, 2927 (97.0%) were included in the modified intention-to-treat analysis (1465 in the accelerated-strategy group and 1462 in the standard-strategy group). Of these patients, renal-replacement therapy was performed in 1418 (96.8%) in the accelerated-strategy group and in 903 (61.8%) in the standard-strategy group. At 90 days, death had occurred in 643 patients (43.9%) in the accelerated-strategy group and in 639 (43.7%) in the standard-strategy group (relative risk, 1.00; 95% confidence interval [CI], 0.93 to 1.09; P = 0.92). Among survivors at 90 days, continued dependence on renal-replacement therapy was confirmed in 85 of 814 patients (10.4%) in the accelerated-strategy group and in 49 of 815 patients (6.0%) in the standard-strategy group (relative risk, 1.74; 95% CI, 1.24 to 2.43). Adverse events occurred in 346 of 1503 patients (23.0%) in the accelerated-strategy group and in 245 of 1489 patients (16.5%) in the standard-strategy group (P Conclusions Among critically ill patients with acute kidney injury, an accelerated renal-replacement strategy was not associated with a lower risk of death at 90 days than a standard strategy. (Funded by the Canadian Institutes of Health Research and others; STARRT-AKI ClinicalTrials.gov number, NCT02568722.).

Clinical Characteristics of Patients Who Died of Coronavirus Disease 2019 in China.

Abstract: This case series describes the characteristics of a cohort of patients who died of coronavirus disease 2019 in China.

Model-Driven Deep Learning for MIMO Detection

Abstract: In this paper, we investigate the model-driven deep learning (DL) for MIMO detection. In particular, the MIMO detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO detector can be rapidly trained with a much smaller data set. The proposed MIMO detector can be extended to soft-input soft-output detection easily. Furthermore, we investigate joint MIMO channel estimation and signal detection (JCESD), where the detector takes channel estimation error and channel statistics into consideration while channel estimation is refined by detected data and considers the detection error. Based on numerical results, the model-driven DL based MIMO detector significantly improves the performance of corresponding traditional iterative detector, outperforms other DL-based MIMO detectors and exhibits superior robustness to various mismatches.

Hierarchical Carbon Fiber@MXene@MoS2 Core-sheath Synergistic Microstructure for Tunable and Efficient Microwave Absorption

Abstract: Microcosmic 3D hierarchical structural design has proved to be an effective strategy to obtain high‐performance microwave absorbers, although the treatments to low‐dimensional cells in monolithic framework are usually based on semiempirical rules. In this work, a hierarchical carbon fiber (CF)@MXene@MoS2 (CMM) core‐sheath synergistic structure with tunable and efficient microwave absorption (MA) properties is fabricated by introducing self‐assembled Ti3C2Tx MXene on the surface of CF and subsequent anchoring of MoS2. By the synergistic effects from the MXene sheath increasing the conductive loss and MoS2 at the outermost layer improving the impedance matching, the MA performance of CMM can be effectively regulated and optimized: the optimal reflection loss is −61.51 dB with a thickness of 3.5 mm and the maximum effective absorption bandwidth covers the whole Ku‐band with 7.6 GHz at 2.1 mm. Meanwhile, the whole X‐band absorption can also be achieved with specific MoS2 loading at an optimized thickness.

Indoor transmission of SARS-CoV-2

Abstract: Background: By early April 2020, the COVID-19 pandemic had infected nearly one million people and had spread to nearly all countries worldwide. It is essential to understand where and how SARS-CoV-2 is transmitted. Methods: Case reports were extracted from the local Municipal Health Commissions of 320 prefectural cities (municipalities) in China, not including Hubei province, between 4 January and 11 February 2020. We identified all outbreaks involving three or more cases and reviewed the major characteristics of the enclosed spaces in which the outbreaks were reported and associated indoor environmental issues. Results: Three hundred and eighteen outbreaks with three or more cases were identified, involving 1245 confirmed cases in 120 prefectural cities. We divided the venues in which the outbreaks occurred into six categories: homes, transport, food, entertainment, shopping, and miscellaneous. Among the identified outbreaks, 53.8% involved three cases, 26.4% involved four cases, and only 1.6% involved ten or more cases. Home outbreaks were the dominant category (254 of 318 outbreaks; 79.9%), followed by transport (108; 34.0%; note that many outbreaks involved more than one venue category). Most home outbreaks involved three to five cases. We identified only a single outbreak in an outdoor environment, which involved two cases. Conclusions: All identified outbreaks of three or more cases occurred in an indoor environment, which confirms that sharing indoor space is a major SARS-CoV-2 infection risk.

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

Abstract: This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)

Abstract: In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.