Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network

The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors

Effective melanoma recognition using deep convolutional neural network with covariance discriminant loss.

Melanoma recognition is challenging due to data imbalance and high intra-class variations and large inter-class similarity. Aiming at the issues, we propose a melanoma recognition method using deep convolutional neural network with covariance discriminant loss in dermoscopy images. Deep convolutional neural network is trained under the joint supervision of cross entropy loss and covariance discriminant loss, rectifying the model outputs and the extracted features simultaneously. Specifically, we design an embedding loss, namely covariance discriminant loss, which takes the first and second distance into account simultaneously for providing more constraints. By constraining the distance between hard samples and minority class center, the deep features of melanoma and non-melanoma can be separated effectively. To mine the hard samples, we also design the corresponding algorithm. Further, we analyze the relationship between the proposed loss and other losses. On the International Symposium on Biomedical Imaging (ISBI) 2018 Skin Lesion Analysis dataset, the two schemes in the proposed method can yield a sensitivity of 0.942 and 0.917, respectively. The comprehensive results have demonstrated the efficacy of the designed embedding loss and the proposed methodology

Exemplar-supported representation for effective class-incremental learning

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new exemplar-supported representation for incremental learning (ESRIL) approach that consists of three components. First, we use memory aware synapses (MAS) pre-trained on the ImageNet to retain the ability of robust representation learning and classification for old classes from the perspective of the model. Second, exemplar-based subspace clustering (ESC) is utilized to construct the exemplar set, which can keep the performance from various views of the data. Third, the nearest class multiple centroids (NCMC) is used as the classifier to save the training cost of the fully connected layer of MAS when the criterion is met. Intensive experiments and analyses are presented to show the influence of various backbone structures and the effectiveness of different components in our model. Experiments on several general-purpose and fine-grained image recognition datasets have fully demonstrated the efficacy of the proposed methodology

Two-stage time-domain pilot contamination elimination in large-scale multiple-antenna aided and TDD based OFDM systems

Pilot contamination (PC) is a major impediment of large-scale multi-cell multiple-input multiple-output (MIMO) systems. Hence we propose an optimal pilot design for timedomain channel estimation, which is capable of completely eliminating PC. More specifically, a sophisticated combination of downlink training and ‘scheduled’ uplink training is designed with the aid of the optimal pilot set. Given the optimal pilot set, every user acquires its unique downlink time-domain channel state information (CSI) through downlink training. The estimated downlink CSIs are then embedded in the uplink training. As a result, PC can be completely eliminated, at the cost of a slight increase in training computational complexity. Our simulation results demonstrate the power of the proposed scheme. Most significantly, our scheme imposes a modest training overhead of (L + 3), training-phase durations corresponding to the number of OFDM symbols, where L is the number of cells, which is substantially lower than that imposed by some of the existing PC elimination schemes. Therefore, it imposes a less stringent requirement on the channel’s coherence time. Finally, our scheme does not need any information exchange between base stations

Urban PM2.5 concentration prediction via attention-based CNN–LSTM.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance

Improving Medication-taking Behavior and Blood Pressure in Hypertensive Patients Using the Stages of Change Model-based Health Education

BackgroundHypertension in Chinese adults has been featured by high prevalence and low control rate recently. And medication-taking behavior is one of the important factors greatly affecting hypertension control in this group.ObjectiveTo assess whether stages of change model (SCM) based-health education could improve medication-taking behavior in hypertensive patients.MethodsThis cluster randomized controlled trial was conducted in six community health centers with comparable service population size and healthcare conditions selected from Shunyi District, Beijing during September 2016 to June 2018. By use of coin tossing, three of the community health centers were randomly assigned to an intervention group, and another three to a control group, from which, a total of 400 hypertensive patients were recruited, 206 from the intervention group and 194 from the control group, receiving three times of SCM-based health education, and usual management, respectively. Questionnaire surveys and blood pressure measuring were performed in all participants at baseline, 3, 6 and 12 months after intervention for understanding of their sociodemographic characteristics, medication-taking behavior, blood pressure level and barriers to medication adherence.ResultsThe patients who completed the 3-month, 6-month and 12-month follow-up numbered 375 (intervention: 202, control: 173) , 290 (intervention: 147, control: 143) and 263 (intervention: 134, control: 129) , respectively. After the end of the 12-month intervention, the percentage of patients in action and maintenance stages increased from 27.7% (57/206) to 60.5% (81/134) in the intervention group, while that in the control group decreased from 50.0% (97/194) to 38.9% (49/129) . Generalized estimating equations on action stage showed statistically significant inter-group differences in the interaction terms at group enrollment time and follow-up periods after controlling for other confounding variables (P<0.05) , that was intervention group×3-month follow-up〔OR (95%CI) =3.928 (2.628, 5.870) 〕, intervention group×6-month follow-up〔OR (95%CI) =3.651 (2.333, 5.712) 〕, and intervention group×12-month follow-up〔OR (95%CI) =4.133 (2.315, 7.377) 〕. Compared with the control group at baseline, the systolic blood pressure of the intervention group was continuously, significantly improved with the prolongation of intervention time at 3-month intervention〔b (95%CI) =-4.616 (-8.558, -0.675) 〕, at 6-month intervention〔b (95%CI) =-4.348 (-8.569, -0.127) 〕, and 12-month intervention〔b (95%CI) =-6.462 (-11.208, -1.716) , P<0.05〕, but the diastolic blood pressure of the intervention group achieved significant improvement only at the 3-month intervention〔b (95%CI) =-3.549 (-6.271, -0.827) , P<0.05〕.ConclusionThis research indicated that SCM-based healtheducation could effectively contribute to hypertension control via improvingthe medication-taking behavior of hypertension patients, and increasing the percentage of these patients entering the stages of action and maintenance

Association of Lifestyle Factors with Multimorbidity Risk in China: A National Representative Study

Multimorbidity significantly impacts health, well-being, and the economy; therefore, exploring notable factors associated with multimorbidity across all age groups is critical. For this investigation, we focused on the relationship between four lifestyle factors and multimorbidity risk. We recruited 11,031 Chinese citizens aged ≥ 12 years from 31 provinces between July 2021 and September 2021 using a quota sampling strategy to ensure that the socioeconomic characteristics (sex, age, rural–urban distribution) of those participating in this research were representative of national demographics. In the first stage, multivariable logistic regression models were utilized as a means of investigating the relationship between lifestyle factors and multimorbidity. Then, a multinomial logistic regression model was used with the aim of examining the Healthy Lifestyle Profile (HLP) related to the number of chronic diseases. Multivariable logistic regression models assessed the interaction effects and joint association among the four lifestyle factors. Overall, 18% of the participants had at least one disease, and 5.9% had multimorbidity. Approximately two-thirds of the participants were physically inactive, 40% had consumed alcohol, 39% were underweight or overweight, and 20% were or had been smokers. Participants who maintained one HLP showed a 34% lower multimorbidity risk (adjusted OR, 0.66; 95% CI, 0.48 to 0.92), while participants who maintained 4 HLP showed a 73% lower multimorbidity risk (adjusted OR, 0.27; 95% CI, 0.17 to 0.43), as compared to those who had 0 HLP. The joint association analysis revealed that participants with all four healthy lifestyle factors had 0.92 times lower odds of multimorbidity (95% CI: 0.90, 0.94) in comparison with the all-unhealthy reference cluster. Notably, individuals with a combination of healthy smoking status and healthy body weight had the highest minimized odds of multimorbidity (OR: [0.92], 95% CI: 0.91, 0.94). Common lifestyle habits, alone or in combination, are associated with multimorbidity risk. This study provides insights for public health programs to promote a healthy lifestyle at a younger age and to alleviate multimorbidity risk in older people

The impact of mass gatherings on the local transmission of COVID-19 and the implications for social distancing policies: Evidence from Hong Kong

Mass gatherings provide conditions for the transmission of infectious diseases and pose complex challenges to public health. Faced with the COVID-19 pandemic, governments and health experts called for suspension of gatherings in order to reduce social contact via which virus is transmitted. However, few studies have investigated the contribution of mass gatherings to COVID-19 transmission in local communities. In Hong Kong, the coincidence of the relaxation of group gathering restrictions with demonstrations against the National Security Law in mid-2020 raised concerns about the safety of mass gatherings under the pandemic. Therefore, this study examines the impacts of mass gatherings on the local transmission of COVID-19 and evaluates the importance of social distancing policies. With an aggregated dataset of epidemiological, city-level meteorological and socioeconomic data, a Synthetic Control Method (SCM) is used for constructing a ‘synthetic Hong Kong’ from over 200 Chinese cities. This counterfactual control unit is used to simulate COVID-19 infection patterns (i.e., the number of total cases and daily new cases) in the absence of mass gatherings. Comparing the hypothetical trends and the actual ones, our results indicate that the infection rate observed in Hong Kong is substantially higher than that in the counterfactual control unit (2.63% vs. 0.07%). As estimated, mass gatherings increased the number of new infections by 62 cases (or 87.58% of total new cases) over the 10–day period and by 737 cases (or 97.23%) over the 30-day period. These findings suggest the necessity of tightening social distancing policies, especially the prohibition on group gathering regulation (POGGR), to prevent and control COVID-19 outbreaks