Tutorial: Neuromorphic spiking neural networks for temporal learning

Spiking neural networks (SNN) as time-dependent hypotheses consisting of spiking nodes (neurons) and directed edges (synapses) are believed to offer unique solutions to reward prediction tasks and the related feedback that are classified as reinforcement learning. Generally, temporal difference (TD) learning renders it possible to optimize a model network to predict the delayed reward in an ad hoc manner. Neuromorphic SNNs--networks built using dedicated hardware--particularly leverage such TD learning for not only reward prediction but also temporal sequence prediction in a physical time domain. In this tutorial, such learning in a physical time domain is referred to as temporal learning to distinguish it from conventional TD learning-based methods that generally involve algorithmic (rather than physical) time. This tutorial addresses neuromorphic SNNs for temporal learning from the scratch. It first concerns general characteristics of SNNs including spiking neurons and information coding schemes and then moves on to temporal learning including its general concept, feasible algorithms, and their association with neurophysiological learning rules that have intensively been enriched for the last few decades.Comment: 40 pages, 10 figure

ECONOMIC IMPACTS OF THE FINANCIAL CRISIS ON THE KOREAN FARM AND NON-FARM SECTORS

The objective of this study is to construct a macroeconomic model emphasizing agriculture and analyze the economic impacts of the financial crisis on the Korean farm and non-farm sectors. The simulation results show that financial shocks have great impacts on general economy and change the resource allocation within and between farm and non-farm sectors.Financial Crisis, Macroeconomic Model, Agricultural Finance, Research Methods/ Statistical Methods,

The Impact of Capital Inflows on Emerging East Asian Economies: Is Too Much Money Chasing Too Little Good?

In recent years, emerging East Asian economies have experienced large capital inflows-especially a surge in portfolio inflows-and an appreciation of asset prices such as equities, land, and both nominal and real exchange rates. The paper reviews why a surge in capital inflows can increase asset prices, and then empirically investigates the effects by employing a panel vector autoregression (VAR) model. The empirical results suggest that capital inflows have indeed contributed to the asset price appreciation in this region, although capital inflow shocks explain a relatively small part of asset price fluctuations. How to manage these capital inflows is also discussed.Capital inflows; portfolio inflows; asset prices

Empirical Evaluation of Mutation-based Test Prioritization Techniques

We propose a new test case prioritization technique that combines both mutation-based and diversity-based approaches. Our diversity-aware mutation-based technique relies on the notion of mutant distinguishment, which aims to distinguish one mutant's behavior from another, rather than from the original program. We empirically investigate the relative cost and effectiveness of the mutation-based prioritization techniques (i.e., using both the traditional mutant kill and the proposed mutant distinguishment) with 352 real faults and 553,477 developer-written test cases. The empirical evaluation considers both the traditional and the diversity-aware mutation criteria in various settings: single-objective greedy, hybrid, and multi-objective optimization. The results show that there is no single dominant technique across all the studied faults. To this end, \rev{we we show when and the reason why each one of the mutation-based prioritization criteria performs poorly, using a graphical model called Mutant Distinguishment Graph (MDG) that demonstrates the distribution of the fault detecting test cases with respect to mutant kills and distinguishment

Investigation of refractory dielectric for integrated circuits Second quarterly report, Dec. 1968

Process development for chemical deposition of aluminum oxide films as refractory dielectrics for integrated circuit