AN INJECTION OF BASE MONEY AT ZERO INTEREST RATES: EMPIRICAL EVIDENCE FROM THE JAPANESE EXPERIENCE 2001-2006

Many macroeconomists and policymakers have debated the effectiveness of the quantitative monetary-easing policy (QMEP) that was introduced in Japan in 2001. This paper measures the effect of the QMEP on aggregate output and prices, and examines its transmission mechanism, based on the vector autoregressive (VAR) methodology. To ascertain the transmission mechanism, we include several financial market variables in the VAR system. The results show that the QMEP increased aggregate output through the stock price channel. This evidence suggests that further injection of base money is effective even when short-term nominal interest rates are at zero.Quantitative easing; Money injection; Portfolio rebalancing; Stock price channel; Vector autoregression

General Architecture for Hardware Implementation of Genetic Algorithm

FCCM 2006 : 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines , Apr 24-26, 2006 , Napa, CA, USAIn this paper, the authors propose a technique to flexibly implement genetic algorithms (GAs) for various problems on FPGAs. For the purpose, the authors propose a common architecture for GA. The proposed architecture allows designers to easily implement a GA as a hardware circuit consisting of parallel pipelines which execute GA operations. The proposed architecture is scalable to increase the number of parallel pipelines. The architecture is applicable to various problems and allows designers to estimate the size of resulting circuits. The authors give a model for predicting the size of resulting circuits from given parameters. Based on the proposed method, the authors have implemented a tool to facilitate GA circuit design and development. Through experiments using knapsack problem and traveling salesman problem (TSP), the authors show that the FPGA circuits synthesized based on the proposed method run much faster and consume much lower power than software implementation on a PC and the model can predict the size of the resulting circuit accurately enough

Flexible implementation of genetic algorithms on FPGAs

FPGA '06 : ACM/SIGDA 14th international symposium on Field programmable gate arrays , Feb 22-24, 2006 , Monterey, CA, USAGenetic algorithms (GAs) are useful since they can find near optimal solutions for combinatorial optimization problems quickly. Although there are many mobile/home applications of GAs such as navigation systems, QoS routing and video encoding systems, it was difficult to apply GAs to those applications due to low computational power of mobile/home appliances. In this paper, we propose a technique to flexibly implement genetic algorithms for various problems on FPGAs. For the purpose, we propose a basic architecture which consists of several modules for GA operations to compose a GA pipeline, and a parallel architecture consisting of multiple concurrent pipelines. The proposed architectures are simple enough to be implemented on FPGAs, applicable to various problems, and easy to estimate the size of the resulting circuit. We also propose a model for predicting the size of resulting circuit from given parameters consisting of the problem size, the number of concurrent pipelines and the number of candidate solutions for GA. Based on the proposed method, we have implemented a tool to facilitate GA circuit design and development. This tool allows designers to find appropriate parameter values so that the resulting circuit can be accommodated in the target FPGA device, and to automatically obtain RTL VHDL description. Through experiments using Knapsack Problem and TSP, we show that the FPGA circuits synthesized based on the proposed method run much faster and consume much lower power than software implementation on a PC and that our model can predict the size of the resulting circuit accurately enough

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

CEC2006 : IEEE International Conference on Evolutionary Computation , Jul 16-21, 2006 , Vancouver, BC, CanadaMulti-objective genetic algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method

Interleukin-6 is required for cell cycle arrest and activation of DNA repair enzymes after partial hepatectomy in mice

BACKGROUND: Interleukin-6 (IL-6) has been shown to be vital for liver regeneration, however the specific mechanisms and factors involved remain incompletely defined. The present study aimed to investigate whether IL-6 exerts its protective effects via arresting the cell cycle allowing base excision and repair of oxidized DNA after hepatectomy. RESULTS: Following seventy percent partial hepatectomy (PH) in wild type (WT) mice IL-6 serum levels increased reaching peak levels at 3 hours. This was associated with markers of cell cycle arrest as p21 expression was increased and cyclin D1 and proliferating cell nuclear antigen (PCNA) expression decreased. In the absence of IL-6, markers of cell cycle arrest were absent and the number of bromodeoxyuridine (BrdU) positive cells was significantly higher at 28, 32 and 36 hours after PH. The mRNAs for DNA repair enzymes, including Neil-1, 8-oxodGTPase, OGG1, Apex1, and UDG (DNA glycosylase) were increased 2 to 4 fold in WT mice at 6 and/or 12 hours after PH compared to IL-6 knockout (KO) mice. The protein levels of Neil1 and OGG1 were also significantly increased in WT mice compared to KO mice. Pathological changes were far greater and survival was less in IL-6 KO mice than in WT mice. Administration of IL-6 in KO mice restored p21 and DNA repair enzyme expression to wild-type levels and survival was improved. CONCLUSIONS: IL-6 caused cell cycle arrest and delayed proliferation during the first day after PH. This delay was associated with the activation of DNA repair enzymes resulting in accurate replication and restoration of hepatic mass