Exchange Is No Change

With the continuing and seemingly unabated increase in the prices of oil and oil products, the clamor for government to do something to alleviate the situation has escalated. Amidst this clamor, the proposal to create a National Oil Exchange has sprung forward. The question is: will this oil exchange reduce oil prices? Dr. Peter Lee U unequivocally says "no" and states his reasons in this Policy Notes.oil price, oil deregulation, oil exchange

Global climate evolution during the last deglaciation

Deciphering the evolution of global climate from the end of the Last Glacial Maximum approximately 19 ka to the early Holocene 11 ka presents an outstanding opportunity for understanding the transient response of Earth’s climate system to external and internal forcings. During this interval of global warming, the decay of ice sheets caused global mean sea level to rise by approximately 80 m; terrestrial and marine ecosystems experienced large disturbances and range shifts; perturbations to the carbon cycle resulted in a net release of the greenhouse gases CO_2 and CH_4 to the atmosphere; and changes in atmosphere and ocean circulation affected the global distribution and fluxes of water and heat. Here we summarize a major effort by the paleoclimate research community to characterize these changes through the development of well-dated, high-resolution records of the deep and intermediate ocean as well as surface climate. Our synthesis indicates that the superposition of two modes explains much of the variability in regional and global climate during the last deglaciation, with a strong association between the first mode and variations in greenhouse gases, and between the second mode and variations in the Atlantic meridional overturning circulation

Cosmological Constant and Axions in String Theory

String theory axions appear to be promising candidates for explaining cosmological constant via quintessence. In this paper, we study conditions on the string compactifications under which axion quintessence can happen. For sufficiently large number of axions, cosmological constant can be accounted for as the potential energy of axions that have not yet relaxed to their minima. In compactifications that incorporate unified models of particle physics, the height of the axion potential can naturally fall close to the observed value of cosmological constant.Comment: 22 page

The macroeconomics of public sector deficits : the case of Pakistan

For almost twenty years, Pakistan's fiscal deficit, at about 7 percent of GNP, averaged nearly twice the level for Asian countries as a whole. This paper examines the causes of Pakistan's fiscal deficits. The authors examine why, despite these deficits, the country's macroeconomic performance has been surprisingly good. The equilibrium deficit is estimated to have been quite high in recent years (about 5.5 percent of GNP), despite a low inflation rate, because of a very high underlying rate of growth of real output (about 6 percent a year). This allowed a fairly rapid expansion of debt without recourse to inflationary finance. To gain additional insight into the role of fiscal deficit in Pakistan, the authors analyze how alternative fiscal policies would have affected the country's economic performance during the 1980s. They find that : i) reducing the deficit by cutting public expenditure could have had a favorable effect on the trade balance, but at a cost to economic growth and with few price payoffs; ii) increasing tax revenues could achieve a similar external adjustment while reducing the output cost; and iii) altering the composition of deficit financing would have predictable results - shifting to more money financing would mean higher prices, lower interest rates, and higher growth.Environmental Economics&Policies,Public Sector Economics&Finance,Economic Theory&Research,Economic Stabilization,Banks&Banking Reform

Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware

In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While neuromorphic adaptations of representation learning algorithms are now emerging, efficient processing of temporal sequences or variable length-inputs remain difficult. Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons, while being compatible with the capabilities of current and near-future neuromorphic systems. This "train-and-constrain" method consists of first training RNNs using backpropagation through time, then discretizing the weights and finally converting them to spiking RNNs by matching the responses of artificial neurons with those of the spiking neurons. We demonstrate our approach by mapping a natural language processing task (question classification), where we demonstrate the entire mapping process of the recurrent layer of the network on IBM's Neurosynaptic System "TrueNorth", a spike-based digital neuromorphic hardware architecture. TrueNorth imposes specific constraints on connectivity, neural and synaptic parameters. To satisfy these constraints, it was necessary to discretize the synaptic weights and neural activities to 16 levels, and to limit fan-in to 64 inputs. We find that short synaptic delays are sufficient to implement the dynamical (temporal) aspect of the RNN in the question classification task. The hardware-constrained model achieved 74% accuracy in question classification while using less than 0.025% of the cores on one TrueNorth chip, resulting in an estimated power consumption of ~17 uW