3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses

3D-aware face generators are typically trained on 2D real-life face image datasets that primarily consist of near-frontal face data, and as such, they are unable to construct one-quarter headshot 3D portraits with complete head, neck, and shoulder geometry. Two reasons account for this issue: First, existing facial recognition methods struggle with extracting facial data captured from large camera angles or back views. Second, it is challenging to learn a distribution of 3D portraits covering the one-quarter headshot region from single-view data due to significant geometric deformation caused by diverse body poses. To this end, we first create the dataset 360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality single-view real portraits annotated with a variety of camera parameters (the yaw angles span the entire 360{\deg} range) and body poses. We then propose 3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with body pose self-learning. Our model can generate view-consistent portrait images from all camera angles with a canonical one-quarter headshot 3D representation. Our experiments show that the proposed framework can accurately predict portrait body poses and generate view-consistent, realistic portrait images with complete geometry from all camera angles

Capacity Constrained Influence Maximization in Social Networks

Influence maximization (IM) aims to identify a small number of influential individuals to maximize the information spread and finds applications in various fields. It was first introduced in the context of viral marketing, where a company pays a few influencers to promote the product. However, apart from the cost factor, the capacity of individuals to consume content poses challenges for implementing IM in real-world scenarios. For example, players on online gaming platforms can only interact with a limited number of friends. In addition, we observe that in these scenarios, (i) the initial adopters of promotion are likely to be the friends of influencers rather than the influencers themselves, and (ii) existing IM solutions produce sub-par results with high computational demands. Motivated by these observations, we propose a new IM variant called capacity constrained influence maximization (CIM), which aims to select a limited number of influential friends for each initial adopter such that the promotion can reach more users. To solve CIM effectively, we design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the $1/2$-approximation ratio. To improve the efficiency, we devise the scalable implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and near-linear running time. We extensively evaluate the performance of 9 approaches on 6 real-world networks, and our solutions outperform all competitors in terms of result quality and running time. Additionally, we deploy RR-OPIM+ to online game scenarios, which improves the baseline considerably.Comment: The technical report of the paper entitled 'Capacity Constrained Influence Maximization in Social Networks' in SIGKDD'2

Understand Group Interaction and Cognitive State in Online Collaborative Problem Solving: Leveraging Brain-to-Brain Synchrony Data

The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed

Some novel results for DNNs via relaxed Lyapunov functionals

The focus of this paper was to explore the stability issues associated with delayed neural networks (DNNs). We introduced a novel approach that departs from the existing methods of using quadratic functions to determine the negative definite of the Lyapunov-Krasovskii functional's (LKFs) derivative $\dot{V}(t)$. Instead, we proposed a new method that utilizes the conditions of positive definite quadratic function to establish the positive definiteness of LKFs. Based on this approach, we constructed a novel the relaxed LKF that contains delay information. In addition, some combinations of inequalities were extended and used to reduce the conservatism of the results obtained. The criteria for achieving delay-dependent asymptotic stability were subsequently presented in the framework of linear matrix inequalities (LMIs). Finally, a numerical example confirmed the effectiveness of the theoretical result

Pregnancy Loss and Risk of All-Cause Mortality in Chinese Women: Findings From the China Kadoorie Biobank

Objectives: Pregnancy loss is a common obstetric complication that may be associated with maternal mortality. However, evidence is sparse and inconsistent. This study aims to investigate the association between pregnancy loss with the risk of all-cause mortality among Chinese women.Methods: Data on 299,582 women aged 30–79 years old from the China Kadoorie Biobank were used. Cox proportional hazard regression was conducted to investigate the association between the occurrence of pregnancy loss and all-cause mortality.Results: Two or more pregnancy losses was associated with long-term all-cause mortality (adjusted hazard ratio (aHR) of 1.10, 95% CI: 1.03–1.18). Specifically, more than one spontaneous abortion or stillbirth was associated with long-term all-cause mortality (aHR 1.10, 95% CI: 1.01–1.21 and 1.14, 95% CI: 1.04–1.25, respectively). When stratified by the presence of cardiovascular disease or diabetes, as well as age at baseline, two or more pregnancy losses in women aged ≥50 diagnosed with cardiovascular disease (aHR 1.32, 95% CI: 1.18–1.48) or diabetes (aHR 1.30, 95% CI: 1.06–1.60) was associated with all-cause mortality.Conclusion: Recurrent pregnancy loss, in particular two or more spontaneous abortions and stillbirths were associated with increased risk of all-cause mortality. The associations between recurrent pregnancy losses and all-cause mortality were more pronounced in women aged ≥50 with cardiovascular disease or diabetes at baseline

Further Improvement on Stability Results for Delayed Load Frequency Control System via Novel LKFs and Proportional-Integral Control Strategy

In this work, novel stability results for load frequency control (LFC) system considering time-varying delays, nonlinearly perturbed load, and time-varying disturbance of system parameters are proposed by using proportional-integral control strategy. Considering the nonlinearly exogenous load disturbance and system parameters disturbance, an improved stability criterion in the form of linear matrix inequalities (LMIs) is derived by novel simple Lyapunov–Krasovskii functionals (LKFs). The delay-dependent matrix in quadratic term, cross terms of variables, and quadratic terms multiplied by 1st, 2nd, and 3rd degrees of scalar functions are included in the new simple LKF. Taking the single-area and two-area LFC system installed with proportional-integral (PI) controller as example, our results surpass the previous maximum allowable size of time delay. Meanwhile, the relationship between time delay varying rate, load disturbance degree, gains of PI controller, and delay margin of the LFC system is researched separately. The results can provide guidance to tune the PI controller for achieving maximum delay margin, in which the LFC system can withstand without losing stability. At last, the simulation results verify the effectiveness and superiority of the proposed stability criterion

A New Slack Lyapunov Functional for Dynamical System with Time Delay

The traditional method of constructing a Lyapunov functional for dynamical systems with time delay is usually dependent on positive definite matrices in the quadratic form. In this paper, a new Lyapunov functional is proposed and the corresponding proof is given. It do not require that all matrices in the quadratic form of Lyapunov functionals are positive definite, while the quadratic form is still positive definite, which makes the estimate more relaxed due to special construction of matrices. It is a general form and can be used in the performance analysis of a variety of dynamical systems. Moreover, a lemma concerning the quadratic function is applied to deal with the quadratic term of time-varying delay. Lastly, in the case of classical dynamical systems with time delay, the verification results are given to illustrate the usefulness of the new slack Lyapunov functional

Bridge Employment and Longevity: Evidence from a 10-year follow-up cohort study in 0.16 million Chinese

Objectives: Bridge employment has been encouraged by many countries worldwide as the societies age rapidly. However, the health impact on bridge employment is not consistent in previous studies. This study aims to explore the association between bridge employment and the long-term health outcome among the Chinese population.Method: In this prospective cohort study, we used a subset of the China Kadoorie Biobank study, in which 163,619 participants who reached the statutory age of retirement at baseline (2004-2008) were included in this study. Mortality statistics were obtained from death registries in the Death Surveillance Points system annually. We used a Cox proportional hazard model to analyze the association between bridge employment and all-cause mortality.Results: Overall, we found that compared to retired/non-employed men and women, hazards of all-cause mortality were lower in older people with bridge employment (Men: 0.82, 95% CI: 0.77-0.88; Women: 0.79, 95% CI: 0.74-0.94) in healthy populations. The protective effect of bridge employment was stronger among older adults living in rural areas and among those from a relatively low socioeconomic status.Conclusion: The lower risk of all-cause mortality associated with bridge employment was consistently observed among older men and women. Our findings may provide important insights from the health dimension on the retirement policy-making in China as a hyper-ageing society

Ilizarov Bone Transfer for Treatment of Large Tibial Bone Defects: Clinical Results and Management of Complications

Backgrounds: The purpose of this study is to present our clinical experience using the Ilizarov bone transfer technique and free-flap technique in the reconstruction of large tibial bone and soft tissue defects, including an evaluation of both the management of postoperative complications and long-term outcomes. Methods: From January 2010 to May 2020, 72 patients with tibia bone and soft tissue defects were retrospectively evaluated. Either an anterolateral thigh perforator flaps (ALTP) or latissimus dorsi musculocutaneous flaps (LD), solely or in combination, were used to cover soft tissue defects. Once the flap was stabilized, an Ilizarov external fixator was applied to the limb. Follow-up was postoperatively performed at 1, 3, 6, 9, and 12 months. Results: Postoperatively, there were two cases of total and five of partial flap necrosis, and two cases of subcutaneous ulcers, which were caused by vascular crisis, infection, and hematoma, respectively. All the patients underwent Ilizarov external fixator surgery after flap recovery. A total of 16 complications occurred, including 3 cases of simple needle tract infection (antibiotic treatment) and 13 cases of complications requiring reoperation. A correlation factor analysis revealed that the main factors affecting the healing time were the defect length and operative complications. All patients with complications treated with the vascularized iliac flap eventually healed completely. Conclusions: The Ilizarov method used together with an ALTP, LD, or a combination thereof yields good clinical results for repairing large bone and soft tissue defects of the tibia, thus reducing the incidence of amputations. However, longer treatment times may be involved, and postoperative complications can occur. The vascularized iliac flap may be a suitable choice for the treatment of postoperative complications of this type of Ilizarov bone transport

The Implementation of A Crop Diseases APP Based on Deep Transfer Learning

Classifying the severity of crop diseases is the staple-basic element of the plant pathology for making disease prevent and control strategies. The diagnosis of disease needs timeliness and accuracy. Thanks to the development and popularity of smart phones and mobile networks, this makes possibly to develop mobile applications that can be widely accepted by users in the agricultural community. This paper provides a system that can detect the severity of crop diseases automatically and intelligently through taking photos. The development of this mobile app is based on deep transfer learning that we proposed an improved method with nearly 92% accuracy based on ResNet 50. The significantly high success rate makes the model a very useful advisory or warning tool. This project provides a new idea and solution for the detection of crop diseases in agriculture. 2020 IEEE.This research grateful for ‘AI Challenger 2018’ providing the dataset of crop diseases. It is supported by the open fund (MSSB-2019-02) of Key Laboratory of Pattern Recognition and Intelligent Information Processing, Chengdu University, China and Erasmus+ SHYFTE project (598649-EPP-1-2018-1-FR-EPPKA2-CBHE-JP) which funded with support from the European Commission.Scopu