Limiting Government Predation Through Anonymous Banking: A Theory with Evidence from China

China's economic performance of the past two decades presents a puzzle for the economics of transition and development: Enormous private business incentives were unleashed that have fueled rapid economic growth despite the fact that China has had very weak "conventional institutions" (such as the rule of law and separation of powers) to constrain the government from arbitrary intrusion into economic activities. We argue that one mechanism that has limited the government's ability for predation and harassment is commitment through information decentralization, where the key institutiton is "anonymous banking," that is, a combination of the use of cash for transactions and the use of anonymous savings deposits. The government's incentive for such a mechanism comes form the increased quasi-fiscal revenues collected from the state banking system through "financial repression," a combination of controls on international capital flows with restrictions on domestic interest rates. The major features of China's economy concerning its fiscal decline, financial deepening, and the sectoral dual-track can be better understood using this analytical framework.http://deepblue.lib.umich.edu/bitstream/2027.42/39659/3/wp275.pd

A Multi-Task Theory of the State Enterprise Reform

During transition, maintaining employment and providing a social safety net to the unemployed are important to social stability, which in turn is crucial for the productivity of the whole economy. Because independent institutions for social safety are lacking and firms with strong profit incentives have little incentives to promote social stability due to its public good nature, state-owned enterprises (SOEs) are needed to continue their role in providing social welfare. Charged with the multi-tasks of efficient production as well as social welfare provision, SOEs continue to be given low profit incentives and consequently, their financial performance continues to be poor.http://deepblue.lib.umich.edu/bitstream/2027.42/39751/3/wp367.pd

Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

Detection of presence of chemical precursors

Methods and systems for determining if one or more target molecules are present in a gas, by exposing a functionalized carbon nanostructure (CNS) to the gas and measuring an electrical parameter value EPV(n) associated with each of N CNS sub-arrays. In a first embodiment, a most-probable concentration value C(opt) is estimated, and an error value, depending upon differences between the measured values EPV(n) and corresponding values EPV(n;C(opt)) is computed. If the error value is less than a first error threshold value, the system interprets this as indicating that the target molecule is present in a concentration C.apprxeq.C(opt). A second embodiment uses extensive statistical and vector space analysis to estimate target molecule concentration

Limiting Government Predation Through Anonymous Banking: A Theory with Evidence from China

China's economic performance of the past two decades presents a puzzle for the economics of transition and development: Enormous private business incentives were unleashed that have fueled rapid economic growth despite the fact that China has had very weak "conventional institutions" (such as the rule of law and separation of powers) to constrain the government from arbitrary intrusion into economic activities. We argue that one mechanism that has limited the government's ability for predation and harassment is commitment through information decentralization, where the key institutiton is "anonymous banking," that is, a combination of the use of cash for transactions and the use of anonymous savings deposits. The government's incentive for such a mechanism comes form the increased quasi-fiscal revenues collected from the state banking system through "financial repression," a combination of controls on international capital flows with restrictions on domestic interest rates. The major features of China's economy concerning its fiscal decline, financial deepening, and the sectoral dual-track can be better understood using this analytical framework.

Multifractal and Network Analysis of Phase Transition

Many models and real complex systems possess critical thresholds at which the systems shift from one sate to another. The discovery of the early warnings of the systems in the vicinity of critical point are of great importance to estimate how far a system is from a critical threshold. Multifractal Detrended Fluctuation analysis (MF-DFA) and visibility graph method have been employed to investigate the fluctuation and geometrical structures of magnetization time series of two-dimensional Ising model around critical point. The Hurst exponent has been confirmed to be a good indicator of phase transition. Increase of the multifractality of the time series have been observed from generalized Hurst exponents and singularity spectrum. Both Long-term correlation and broad probability density function are identified to be the sources of multifractality of time series near critical regime. Heterogeneous nature of the networks constructed from magnetization time series have validated the fractal properties of magnetization time series from complex network perspective. Evolution of the topology quantities such as clustering coefficient, average degree, average shortest path length, density, assortativity and heterogeneity serve as early warnings of phase transition. Those methods and results can provide new insights about analysis of phase transition problems and can be used as early warnings for various complex systems.Comment: 23 pages, 11 figure

A decomposition framework for gas network design

Gas networks are used to transport natural gas, which is an important resource for both residential and industrial customers throughout the world. The gas network design problem is a challenging nonlinear and non-convex optimization problem. In this paper, we propose a decomposition framework to solve this problem. In particular, we utilize a two-stage procedure that involves a convex reformulation of the original problem. We conduct experiments on a benchmark network to validate and analyze the performance of our framework

More than Encoder: Introducing Transformer Decoder to Upsample

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such a schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model long-range dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic U-Net structure with lateral connections and achieve state-of-the-art performance on Synapse multi-organ segmentation, Medical Segmentation Decathlon (MSD) Brain, and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement.Comment: Accepted by BIBM202

Modelling the Age-Hardening Precipitation by a Revised Langer and Schwartz Approach with Log-Normal Size Distribution

A new numerical modelling approach integrating the Langer and Schwartz approach and log-normal particle size distribution has been developed to depict the precipitation kinetics of age-hardening precipitates in Al alloys. The modelling framework has been implemented to predict the precipitation behavior of the key secondary phases in 6xxx and 7xxx Al alloys subjected to artificial aging. The simulation results are in good agreement with the available experimental data in terms of precipitate number density, radius, and volume fraction. The initial shape parameter of the log-normal size distribution entering the modeling framework turns to play an important role in affecting the later-stage evolution of precipitation. It is revealed that the evolution of size distribution is not significant when a small shape parameter is adopted in the modelling, while an initial large shape parameter will cause substantial broadening of the particle size distribution during aging. Regardless of the magnitude of shape parameter, a broadening of the particle size distribution as predicted by the present model is in agreement with experimental observations. It is also shown that large shape parameter will accelerate the coarsening rate at later aging stage, which induces fast decreasing of number density and increased growth rate of mean/critical radius. A comparison to the Euler-like multi-class approach demonstrates that the integration of more realistic log-normal distribution and Langer and Schwartz model make the present modelling faster and equivalently accurate in precipitation prediction.publishedVersio