Sonnets Polonaise II

Includes sonnet by Albert Solomon: Polonaise II

Revision of the Japanese Mining Law Under the Occupation

In line with the policy of the Supreme Commander for the Allied Powers to democratize the mining industry of Japan, a study of existing mining law was undertaken in 1946 by a National Mining Law Revision Committee appointed by the Minister of Commerce and Industry (now Minister of International Trade and Industry). After four years of investigation, drafting, and redrafting of a revised Mining Bill, with technical assistance from a visiting mineral law expert from the United States and from lawyers and agricultural and forest economists employed in the Japanese government and in the Headquarters of SCAP, as well as public hearings throughout Japan, the Japanese government on December 20, 1950, promulgated the finished product, a revised Mining Law (Law No. 289, 1950) effective January 31, 1951. [Reprinted by permission from Weekly Summary No. 286, Natural Resources Section, General Headquarters, Supreme Commander for the Allied Powers.

Monetary policy and economic activity: a postwar review

Monetary policy ; Financial markets ; Economic conditions

Innovation flow through social networks: Productivity distribution

A detailed empirical analysis of the productivity of non financial firms across several countries and years shows that productivity follows a non-Gaussian distribution with power law tails. We demonstrate that these empirical findings can be interpreted as consequence of a mechanism of exchanges in a social network where firms improve their productivity by direct innovation or/and by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we obtain that the expectation values of the productivity level are proportional to the connectivity of the network of links between firms. The comparison with the empirical distributions reveals that such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.Comment: 14 pages, 4 figures, submitted to Phys. Rev.

Adjustment and social choice

We discuss the influence of information contagion on the dynamics of choices in social networks of heterogeneous buyers. Starting from an inhomogeneous cellular automata model of buyers dynamics, we show that when agents try to adjust their reservation price, the tatonement process does not converge to equilibrium at some intermediate market share and that large amplitude fluctuations are actually observed. When the tatonnement dynamics is slow with respect to the contagion dynamics, large periodic oscillations reminiscent of business cycles appear.Comment: 13 pages, 6 figure

Squamous carcinoma of the paranasal sinuses in the Bantu

Thirty-three cases of squamous carcinoma of the paranasal sinuses in South African Bantu are presented. The incidence, pathology, clinical features and treatment of this disease are discussed. Radical telecobalt therapy, followed by extended maxillectomy after an interval of 4 weeks, is recommended as the most effective form of therapy in selected cases

Elucidation of Directionality for Co-Expressed Genes: Predicting Intra-Operon Termination Sites

We present a novel framework for inferring regulatory and sequence-level information from gene co-expression networks. The key idea of our methodology is the systematic integration of network inference and network topological analysis approaches for uncovering biological insights. We determine the gene co-expression network of Bacillus subtilis using Affymetrix GeneChip time series data and show how the inferred network topology can be linked to sequence-level information hard-wired in the organism's genome. We propose a systematic way for determining the correlation threshold at which two genes are assessed to be co-expressed by using the clustering coefficient and we expand the scope of the gene co-expression network by proposing the slope ratio metric as a means for incorporating directionality on the edges. We show through specific examples for B. subtilis that by incorporating expression level information in addition to the temporal expression patterns, we can uncover sequence-level biological insights. In particular, we are able to identify a number of cases where (i) the co-expressed genes are part of a single transcriptional unit or operon and (ii) the inferred directionality arises due to the presence of intra-operon transcription termination sites.Comment: 7 pages, 8 figures, accepted in Bioinformatic

Forecasting Energy Demand in Large Commercial Buildings Using Support Vector Machine Regression

As our society gains a better understanding of how humans have negatively impacted the environment, research related to reducing carbon emissions and overall energy consumption has become increasingly important. One of the simplest ways to reduce energy usage is by making current buildings less wasteful. By improving energy efficiency, this method of lowering our carbon footprint is particularly worthwhile because it reduces energy costs of operating the building, unlike many environmental initiatives that require large monetary investments. In order to improve the efficiency of the heating, ventilation, and air conditioning (HVAC) system of a Manhattan skyscraper, 345 Park Avenue, a predictive computer model was designed to forecast the amount of energy the building will consume. This model uses Support Vector Machine Regression (SVMR), a method that builds a regression based purely on historical data of the building, requiring no knowledge of its size, heating and cooling methods, or any other physical properties. SVMR employs time-delay coordinates as a representation of the past to create the feature vectors for SVM training. This pure dependence on historical data makes the model very easily applicable to different types of buildings with few model adjustments. The SVM regression model was built to predict a week of future energy usage based on past energy, temperature, and dew point temperature data