ECONOMIC PERFORMANCE THROUGH TIME: A GENERAL EQUILIBRIUM MODEL

This paper presents a simple general equilibrium model of economic performance through time. The model incorporates 4 main determinants of economic performance: technology, capital investment, the division of labor and institutions. It demonstrates that growth is not automatic even with technological progress. In order to maintain economic growth, it is important to continuously implement new technologies through capital investment. It also shows that institutional improvement promotes the social division of labour, which is an independent source of economic growth.economic growth, savings and investment, transaction costs, division of labor, financial and production institutions

Receiver-channel based adaptive blind equalization approach for GPS dynamic multipath mitigation

AbstractAiming at mitigating multipath effect in dynamic global positioning system (GPS) satellite navigation applications, an approach based on channel blind equalization and real-time recursive least square (RLS) algorithm is proposed, which is an application of the wireless communication channel equalization theory to GPS receiver tracking loops. The blind equalization mechanism builds upon the detection of the correlation distortion due to multipath channels; therefore an increase in the number of correlator channels is required compared with conventional GPS receivers. An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response. Then, the code and carrier phase receiver tracking errors are compensated by removing the estimated multipath components from the correlators’ outputs. To demonstrate the capabilities of the proposed approach, this technique is integrated into a GPS software receiver connected to a navigation satellite signal simulator, thus simulations under controlled dynamic multipath scenarios can be carried out. Simulation results show that in a dynamic and fairly severe multipath environment, the proposed approach achieves simultaneously instantaneous accurate multipath channel estimation and significant multipath tracking errors reduction in both code delay and carrier phase

Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance

Fine-grained multi-label classification models have broad applications in Amazon production features, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfactory performance for those classification tasks in real world is the wild visual background signal that contains irrelevant pixels which confuses model to focus onto the region of interest and make prediction upon the specific region. In this paper, we introduce a generic semantic-embedding deep neural network to apply the spatial awareness semantic feature incorporating a channel-wise attention based model to leverage the localization guidance to boost model performance for multi-label prediction. We observed an Avg.relative improvement of 15.27% in terms of AUC score across all labels compared to the baseline approach. Core experiment and ablation studies involve multi-label fashion attribute classification performed on Instagram fashion apparels' image. We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features. Results show favorable performance for our approach

The impact of dissection and re-entry versus wire escalation techniques on long-term clinical outcomes in patients with chronic total occlusion lesions following percutaneous coronary intervention: An updated meta-analysis

Background: The meta-analysis was performed to evaluate the effect of dissection and re-entry (DR) vs. wire escalation (WE) techniques on long-term clinical outcomes in patients with chronic total occlusion (CTO) lesions undergoing percutaneous coronary intervention. Methods: Studies were searched in electronic databases from inception to September, 2019. Results were pooled using random effects model and fixed effects model and are presented as risk ratios (RR) with 95% confidence intervals (CI). Results: Pooled analyses revealed that patients with DR techniques had overall higher complexity CTO lesions than patients with WE techniques and required a greater number of stents and a greater mean stent length. The “extensive” DR techniques may have a higher incidence of target vessel revascularization (TVR) (RR = 2.30, 95% CI: 1.77–2.98), in-stent restenosis (RR = 1.71, 95% CI: 1.30–2.23), in-stent reocclusion (RR = 1.86, 95% CI: 1.03–3.3) and death/myocardial infarction/TVR (RR = 2.10, 95% CI: 1.71–2.58), when compared with WE techniques, during the long-term follow-up. However, “limited” DR techniques result in more promising outcomes, and are comparable to conventional WE techniques. Conclusions: Dissection and re-entry techniques were associated with increased risk of long-term negative clinical events, especially “extensive” DR techniques. However, “limited” DR techniques resulted in good long-term outcomes, comparable to WE techniques

Asymptotic behavior of 3-D evolutionary model of Magnetoelasticity for small data

In this article, we consider the evolutionary model for magnetoelasticity with vanishing viscosity/damping, which is a nonlinear dispersive system. The global regularity and scattering of the evolutionary model for magnetoelasticity under small size of initial data is proved. Our proof relies on the idea of vector-field method due to the quasilinearity and the presence of convective term. A key observation is that we construct a suitable energy functional including the mass quantity, which enable us to provide a good decay estimates for Schr\"odinger flow. In particular, we establish the asymptotic behavior in both mass and energy spaces for Schr\"odinger map, not only for gauged equation.Comment: 32 page

A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics

This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field programmable gate array (FPGA) and application specific integrated circuit (ASIC) working in heterogeneous parallelization. To be specific, a multiplication-less neural network (NN) is deployed on the non-von Neumann (NvN)-based ASIC (SilTerra 180 nm process) to evaluate atomic forces, which is the most computationally expensive part of MD. All other calculations of MD are done using FPGA (Xilinx XC7Z100). It is shown that, to achieve similar-level accuracy, the proposed NvN-based system based on low-end fabrication technologies (180 nm) is 1.6x faster and 10^2-10^3x more energy efficiency than state-of-the-art vN based MLMD using graphics processing units (GPUs) based on much more advanced technologies (12 nm), indicating superiority of the proposed NvN-based heterogeneous parallel architecture

Differences in the impact of land transfer on poverty vulnerability among households with different livelihood structures

IntroductionEradicating poverty is the primary objective of the United Nations 2030 Agenda for Sustainable Development. While China has achieved great success in achieving poverty reduction targets, reducing the poverty vulnerability of rural households is crucial for ensuring the sustainability of poverty reduction gains. The purpose of land transfer is to ensure the continuous increase of farmers’ income through efficient land use; it has become an important initiative for poverty alleviation in rural areas. Existing studies have confirmed the positive effect of land transfer on poverty alleviation, but few have explored the difference in the impact of land transfer on poverty vulnerability of households with different income structures.MethodsUsing data from the China Family Panel Survey (CFPS) from 2010 to 2020, this paper empirically examines the impact of land transfer on poverty vulnerability.Results and discussionThe results show that land transfer has a significant positive impact on poverty vulnerability alleviation among rural households. Further comparing households with different livelihood structures, we find that land transfer is more effective in reducing poverty for non-farm employment-oriented household. Therefore, we suggest that the government should improve the land transfer system, increase agricultural subsidies, and consider the occupational differentiation among farmers to improve the poverty reduction effect of land transfer. These suggestions also provide a reference for promoting sustainable agricultural development and consolidating the achievements of poverty alleviation