The War Vol. 1, No. 20- November 7, 1812

This newspaper was published bi-weekly from June 1812 to September 1814 by S. Woodworth and Co. in New York. Editor Samuel Woodworth formed the content of the newspaper using official documents from both the American and British side with the intention to report the events of the war. Article topics in this issue include: Page 1: Letter from Maj. Gen. Van Rensselaer to Maj. Gen. Henry Dearborn describing in detail the battle of Queenston; Page 2: Letter from Maj. Gen. Van Rensselaer to Maj. Gen. Henry Dearborn describing in detail the battle of Queenston; report of U.S. war sloop Wasp capturing the British war ship Frolic and the subsequent capture of the Wasp by another British war ship, Poictiers; copy of statement by U.S. President James Madison detailing battles in Detroit, Queenston, and his plans for the war; Page 3: copy of statement by U.S. President James Madison detailing battles in Detroit, Queenston, and his plans for the war; Page 4: copy of statement by U.S. President James Madison detailing battles in Detroit, Queenston, and his plans for the war; U.S. President James Madison promotes Capt. Z. Taylor to rank of Major for his part in defense of Ft. Harrison; report of various Naval movements;

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

The Confederate Air Corps Certificate, 1958

A certificate from The Confederate Air Corps that reads, "To all who shall see these presents, greeting: Know yes, that in recognition of his having manifested an unusually high regard for black-eyed peas, turnip greens, hog jowl, sow belly, pot likkers, grits, chittlins, and good old corn squeezins, Dorothy Rungeling is, as of this date, hereby appointed to the rank of Colonel in the Confederate Air Corps. This officer will, by virtue of this appointment, therefore, be obliged to carefully and diligently discharge the duties of the office to which appointed by doing and performing all manner of things thereunto belonging. As evidence of his good faith in accepting this commission, the officer named herein will continue to praise the glories of the Deep South, consume a true gentleman's share of the fares mentioned above, pay respectful homage to our lovely Southern Belles, save his Confederate money, harass the carpetbaggers, and always remember that damnyankee is one word. As Secretary of this Corps, I strictly charge and require all officers of the air militia of the South to render such obedience and courtesies as are due an Officer of this distinguished rank and honored position. Done at the City of Montgomery, Alabama, the Cradle of the Confederacy, this Seventh day of July in the year One Thousand Nine Hundred and Fifty Eight." It is stamped with the name Thadeus P. Throckmorton - Secretary, Confederate Air Corps.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and deterministic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel metaheuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS metaheuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.

Feature Selection and Classification Using Age Layered Population Structure Genetic Programming

The curse of dimensionality is a major problem in the fields of machine learning, data mining and knowledge discovery. Exhaustive search for the most optimal subset of relevant features from a high dimensional dataset is NP hard. Sub–optimal population based stochastic algorithms such as GP and GA are good choices for searching through large search spaces, and are usually more feasible than exhaustive and determinis- tic search algorithms. On the other hand, population based stochastic algorithms often suffer from premature convergence on mediocre sub–optimal solutions. The Age Layered Population Structure (ALPS) is a novel meta–heuristic for overcoming the problem of premature convergence in evolutionary algorithms, and for improving search in the fitness landscape. The ALPS paradigm uses an age–measure to control breeding and competition between individuals in the population. This thesis uses a modification of the ALPS GP strategy called Feature Selection ALPS (FSALPS) for feature subset selection and classification of varied supervised learning tasks. FSALPS uses a novel frequency count system to rank features in the GP population based on evolved feature frequencies. The ranked features are translated into probabilities, which are used to control evolutionary processes such as terminal–symbol selection for the construction of GP trees/sub-trees. The FSALPS meta–heuristic continuously refines the feature subset selection process whiles simultaneously evolving efficient classifiers through a non–converging evolutionary process that favors selection of features with high discrimination of class labels. We investigated and compared the performance of canonical GP, ALPS and FSALPS on high–dimensional benchmark classification datasets, including a hyperspectral image. Using Tukey’s HSD ANOVA test at a 95% confidence interval, ALPS and FSALPS dominated canonical GP in evolving smaller but efficient trees with less bloat expressions. FSALPS significantly outperformed canonical GP and ALPS and some reported feature selection strategies in related literature on dimensionality reduction.