Coded Computing for Half-Duplex Wireless Distributed Computing Systems via Interference Alignment

Distributed computing frameworks such as MapReduce and Spark are often used to process large-scale data computing jobs. In wireless scenarios, exchanging data among distributed nodes would seriously suffer from the communication bottleneck due to limited communication resources such as bandwidth and power. To address this problem, we propose a coded parallel computing (CPC) scheme for distributed computing systems where distributed nodes exchange information over a half-duplex wireless interference network. The CPC scheme achieves the multicast gain by utilizing coded computing to multicast coded symbols {intended to} multiple receiver nodes and the cooperative transmission gain by allowing multiple {transmitter} nodes to jointly deliver messages via interference alignment. To measure communication performance, we apply the widely used latency-oriented metric: \emph{normalized delivery time (NDT)}. It is shown that CPC can significantly reduce the NDT by jointly exploiting the parallel transmission and coded multicasting opportunities. Surprisingly, when $K$ tends to infinity and the computation load is fixed, CPC approaches zero NDT while all state-of-the-art schemes achieve positive values of NDT. Finally, we establish an information-theoretic lower bound for the NDT-computation load trade-off over \emph{half-duplex} network, and prove our scheme achieves the minimum NDT within a multiplicative gap of $3$, i.e., our scheme is order optimal.Comment: 17 pages, 6 figure

Robust Power Allocation for Integrated Visible Light Positioning and Communication Networks

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.Comment: 13 pages, 15 figures, accepted by IEEE Transactions on Communication

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System with a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver's location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.Comment: 13 pages, 14 figures, accepted by IEEE Transactions on Communication

Serial negative response after standard and third (Booster) dose of COVID-19 inactivated vaccine is associated with low vitamin D levels in patients with solid cancers

IntroductionThe response is poorly understood to the third dose in patients with cancer who failed the standard dose of inactivated SARS-CoV-2 vaccines (CoronaVac). We aim to assess the immune response to the third dose and identify whether vitamin D deficiency is associated with serial serologic failure in patients with cancer.MethodsSolid cancer patients (SCP-N) and healthy controls (HCs) who were seronegative after the standard-dose vaccines in our previous study were prospectively recruited, from October 2021 to February 2022, to receive the third dose vaccines and anti-SARS-CoV-2S antibodies were measured. SCP-N who failed the third dose (serial seronegative group, SSG) were matched by propensity scores with the historical standard-dose positive cancer patient group (robust response group, RRG). An exploratory analysis was carried out to validate the role of vitamin D on the serology response.ResultsThe multi-center study recruited 97 SCP-N with 279 positive controls as RRG and 82 negative controls as HC group. The seroconversion rate after third-dose vaccination was higher in SCP-N than in HC (70.6% vs. 29.4%, p < 0.01). The matched comparison showed that patients in SSG had a significantly lower level of vitamin D and consumption rate than RRG or RRG-B (RRG with third-dose positive) (all p < 0.01). None had serious (over grade II) adverse events after the third dose.ConclusionSolid cancer patients with second-dose vaccine failure may have a relatively poor humoral response to the third dose of COVID-19 vaccines as compared with the seronegative HC group. The consecutively poor humoral response could be associated with poor vitamin D levels and intake. Vitamin D status and cancer-related immune compromise may jointly affect the humoral response following booster vaccination

Thyroid function and associated mood changes after COVID-19 vaccines in patients with Hashimoto thyroiditis

ContextSevere acute respiratory syndrome-coronavirus 2 (COVID-19) vaccines may incur changes in thyroid functions followed by mood changes, and patients with Hashimoto thyroiditis (HT) were suggested to bear a higher risk.ObjectivesWe primarily aim to find whether COVID-19 vaccination could induce potential subsequent thyroid function and mood changes. The secondary aim was to find inflammatory biomarkers associated with risk.MethodsThe retrospective, multi-center study recruited patients with HT receiving COVID-19–inactivated vaccines. C-reactive proteins (CRPs), thyroid-stimulating hormones (TSHs), and mood changes were studied before and after vaccination during a follow-up of a 6-month period. Independent association was investigated between incidence of mood state, thyroid functions, and inflammatory markers. Propensity score–matched comparisons between the vaccine and control groups were carried out to investigate the difference.ResultsFinal analysis included 2,765 patients with HT in the vaccine group and 1,288 patients in the control group. In the matched analysis, TSH increase and mood change incidence were both significantly higher in the vaccine group (11.9% versus 6.1% for TSH increase and 12.7% versus 8.4% for mood change incidence). An increase in CRP was associated with mood change (p< 0.01 by the Kaplan–Meier method) and severity (r = 0.75) after vaccination. Baseline CRP, TSH, and antibodies of thyroid peroxidase (anti-TPO) were found to predict incidence of mood changes.ConclusionCOVID-19 vaccination seemed to induce increased levels and incidence of TSH surge followed by mood changes in patients with HT. Higher levels of pre-vaccine serum TSH, CRP, and anti-TPO values were associated with higher incidence in the early post-vaccine phase

Human resources for health policies: a critical component in health policies

In the last few years, increasing attention has been paid to the development of health policies. But side by side with the presumed benefits of policy, many analysts share the opinion that a major drawback of health policies is their failure to make room for issues of human resources. Current approaches in human resources suggest a number of weaknesses: a reactive, ad hoc attitude towards problems of human resources; dispersal of accountability within human resources management (HRM); a limited notion of personnel administration that fails to encompass all aspects of HRM; and finally the short-term perspective of HRM. There are three broad arguments for modernizing the ways in which human resources for health are managed: • the central role of the workforce in the health sector; • the various challenges thrown up by health system reforms; • the need to anticipate the effect on the health workforce (and consequently on service provision) arising from various macroscopic social trends impinging on health systems. The absence of appropriate human resources policies is responsible, in many countries, for a chronic imbalance with multifaceted effects on the health workforce: quantitative mismatch, qualitative disparity, unequal distribution and a lack of coordination between HRM actions and health policy needs. Four proposals have been put forward to modernize how the policy process is conducted in the development of human resources for health (HRH): • to move beyond the traditional approach of personnel administration to a more global concept of HRM; • to give more weight to the integrated, interdependent and systemic nature of the different components of HRM when preparing and implementing policy; • to foster a more proactive attitude among human resources (HR) policy-makers and managers; • to promote the full commitment of all professionals and sectors in all phases of the process. The development of explicit human resources policies is a crucial link in health policies and is needed both to address the imbalances of the health workforce and to foster implementation of the health services reforms

Enhanced Photocatalytic Hydrogen Generation by Optimized Plasmonic Hot Electron Injection in Structure-Adjustable Au-ZnO Hybrids

Plasmonic Au-ZnO hybrids with adjustable structures (including Au-decorated ZnO and core–shell Au@ZnO with dense and porous ZnO shells) and the optimized hot electron-driven photocatalytic activity were successfully prepared. It was found that the Au@ZnO core–shell hybrids with porous morphology had the highest plasmon-enhanced photocatalytic hydrogen generation activity under visible light irradiation. The wavelength-dependent photocatalytic tests verified that Au@ZnO with porous ZnO shells had the highest apparent quantum efficiency upon resonance excitation. The ultrafast transient absorption measurements revealed that Au@ZnO with porous ZnO shells had the fastest plasmon-induced hot electron injection, which was thought to be the reason for the improved photocatalytic activity. This work might provide a promising route to designing photocatalytic and photoelectric materials

A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysis performance. To solve this problem, an author name disambiguation task is designed to divide documents related to an author name reference into several parts and each part is associated with a real-life person. Existing methods usually use either attributes of documents or relationships between documents and co-authors. However, methods of feature extraction using attributes cause inflexibility of models while solutions based on relationship graph network ignore the information contained in the features. In this paper, we propose a novel name disambiguation model based on representation learning which incorporates attributes and relationships. Experiments on a public real dataset demonstrate the effectiveness of our model and experimental results demonstrate that our solution is superior to several state-of-the-art graph-based methods. We also increase the interpretability of our method through information theory and show that the analysis could be helpful for model selection and training progress