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US 7469228 B2 Abstract The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: performing a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; observing the generated efficient frontier; based on the observing, identifying an area of the efficient frontier in which there is a gap; and effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the efficient frontier being used in investment decisioning.
Claims(6) 1. A method for efficient frontier supplementation in multi-objective portfolio analysis, the method comprising:
generating an initial population of feasible solutions of portfolio allocations in a computing device;
generating a non-dominated solution set comprising a first efficient frontier in a portfolio performance space having at least three-dimensions using one of an evolutionary algorithm and optimization processing by using the computing device;
identifying at least one region having a gap in the at least three-dimensions of the first efficient frontier using a visualization tool;
interactively placing at least one target in the at least one region of the first efficient frontier using the visualization tool; and
providing the initial population of feasible solutions to a Target Objectives Genetic Algorithm (TOGA); and
performing TOGA processing in the at least one region of the first efficient frontier to generate supplemental solutions to the first efficient frontier and to create a second efficient frontier, the second efficient frontier being used in investment decisions.
2. The method of
3. The method of
accepting a set of target vectors; and
generating a series of chromosomes, evaluated on the basis of the accepted target vectors, over multiple generations.
4. The method of
5. The method of
6. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method steps for efficient frontier supplementation in multi-objective portfolio analysis, said method steps comprising:
generating an initial population of feasible solutions of portfolio allocations in a computing device;
generating a non-dominated solution set comprising a first efficient frontier in a portfolio performance space having at least three-dimensions using one of an evolutionary algorithm and optimization processing by using the computing device;
identifying at least one region having a gap in the at least three-dimensions of the first efficient frontier using a visualization tool;
interactively placing at least one target in the at least one region of the first efficient frontier using the visualization tool; and
providing the initial population of feasible solutions to a Target Objectives Genetic Algorithm (TOGA); and
performing TOGA processing in the at least one region of the first efficient frontier to generate supplemental solutions to the first efficient frontier and create a second efficient frontier, the second efficient frontier being used in investment decisions.
Description The invention is directed to portfolio optimization and techniques associated with optimization processing of asset portfolios, such as portfolios of securities, for example. Markowitz's portfolio theory, a foundation of modern finance, is based on a trade-off between a single return and a single risk measure. Variance or standard deviation of return is typically employed as a measure of risk. The goal of portfolio optimization is maximizing return and, at the same time, minimizing risk. This is typically treated as a two-objective optimization problem using the following problem formulation:
Alternatively, the portfolio optimization might be described with the following alternative portfolio optimization problem formulation:
As is known in the art, the efficient frontier can be obtained by varying the return target, or alternatively the risk target, and optimizing on the other measure, i.e., the measure that is not being varied. The resulting efficient frontier is a curve in a two-dimensional space as shown in However, there are major drawbacks to this approach. In modern portfolio management, portfolio managers not only care about the variation around mean, but also the risk of losing most of the portfolio's value due to rare events. In a normal situation, the portfolio value fluctuates around its mean due to market volatility and other risk drivers. However, a portfolio may lose a significant amount of value from a low-probability-high-impact event. This possibility calls for a need to use other risk measures, in addition to variance, for managing the portfolio risk. In accordance with further known aspects of portfolio processing, In a similar manner, in accordance with further known aspects of portfolio processing, In prior art techniques, various risk measures were developed for capturing tail risk, i.e., the risk that constitutes the tail of the distribution. Value at risk (VaR) has been widely adopted by financial institutions as a measure of financial exposure, and by regulators and rating agencies for determining the capital adequacy. Expected shortfall, another tail risk measure, is the average of losses exceeding some pre-described loss level. Portfolio managers may also deal with an optimization problem that involves multiple return measures. Some portfolio managers may be concerned with accounting incomes as well as economic returns. To incorporate multiple measures of risk and return, portfolio managers must consider a multi-objective optimization problem (rather than a two objective problem presented above). The general multi-objective optimization problem can be described by the following formulation:
Adding complexity to the problem, the risk measures are typically non-linear, and sometimes non-convex. In a problem with all linear objective functions, one can use a linear programming solver to efficiently obtain optimal solutions. If one or more of the objective functions is non-linear, a non-linear solver is required. For a problem with high dimensionality, but wherein the risk measure is non-linear but convex, a special technique may be utilized to reduce computational run time. In a prior application, U.S. application Ser. No. 10/390,689 filed Mar. 19, 2003 , which is incorporated herein by reference in its entirety, we described a method for solving such type of problem. The method describes providing a mathematical model of a relaxation of a problem; generating a sequence of additional constraints; and sequentially applying respective nonlinear risk functions to generate respective adjusted maximum return solutions to obtain an efficient frontier. In modern-day portfolio management problems, measuring and incorporating tail risk introduces more complexity. Some of them are nonlinear and non-convex. Some measures are even not in analytical forms. The needs of present portfolio analysis calls for special techniques to solve this class of problems. In accordance with another aspect of the invention, evolutionary algorithms will now be described. In one approach, Evolutionary Algorithms (EAs) include techniques based on a general paradigm of simulated natural evolution (B�ck 1996, Goldberg 1989). EAs perform their search by maintaining at any time t a population P(t)={P1(t), P2(t), . . . , Pp(t)} of individuals. �Genetic′� operators that model simplified rules of biological evolution are applied to create the new and desirably more superior (optimal) population P(t+1). This process continues until a sufficiently good population is achieved, or some other termination condition is satisfied. Each Pi(t)εP(t), represents via an internal data structure, a potential solution to the original problem. The choice of an appropriate data structure for representing solutions is very much an �art� than �science� due to the plurality of data structures suitable for a given problem. However, the choice of an appropriate representation is an important step in a successful application of EAs, and effort is required to select a data structure that is compact and can avoid creation of infeasible individuals. Closely linked to choice of representation of solutions, is choice of a fitness function ψ: P(t)→R. The fitness function assigns credit to candidate solutions. Individuals in a population are assigned fitness values according to some evaluation criterion. Fitness values measure how well individuals represent solutions to the problem. Highly fit individuals are more likely to create offspring by �recombination� or �mutation� operations, described below. Weak individuals are less likely to be picked for reproduction, and so they eventually die out. A mutation operator introduces genetic variations in the population by randomly modifying some of the building blocks of individuals. Evolutionary algorithms are essentially parallel by design, and at each evolutionary step a breadth search of increasingly optimal sub-regions of the search space is performed. Evolutionary search is a powerful technique of solving problems, and is applicable to a wide variety of practical problems that are nearly intractable with other conventional optimization techniques. Practical evolutionary search schemes do not guarantee convergence to the global optimum in a predetermined finite time, but they are often capable of finding very good and consistent approximate solutions. However, they are shown to asymptotically converge under mild conditions (Subbu and Sanderson 2000). Evolutionary algorithms have received a lot of attention for use in a single objective optimization and learning applications, and have been applied to various practical problems. In recent years, the relatively new area of evolutionary multi-objective optimization has grown considerably. Since evolutionary algorithms inherently work with a population of solutions, they are naturally suited for extension into the multi-objective optimization problem domain, which requires the search for and maintenance of multiple solutions during the search. This characteristic allows finding an entire set of Pareto optimal solutions in a single execution of the algorithm. Additionally, evolutionary algorithms are less sensitive to the shape or continuity of the Pareto front than traditional mathematical programming-based techniques. In the past decade, the field of evolutionary multi-objective decision-making has been significantly energized, due in part to the multitude of immediate real-life applications in academia and industry. Several researchers have proposed several evolutionary multi-objective optimization techniques, as is reviewed and summarized in Coello et al. 2002. However, the known techniques as discussed above fail to effectively and efficiently provide optimization processing to the extent possible. Further, techniques related to optimization processing fail to provide the tools needed to effectively work with the portfolios. The various systems and methods of the invention provide novel approaches to optimize portfolios. In one embodiment, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: performing a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; observing the generated efficient frontier; based on the observing, identifying an area of the efficient frontier in which there is a gap; and effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the efficient frontier being used in investment decisioning. In a further embodiment, the invention provides a system for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the system comprising: an efficient frontier generation portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a gap filling portion, the gap filling portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. In a further embodiment, the invention provides a computer readable medium for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the computer readable medium comprising: a first portion that performs a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; a visual tool by which a user observes the generated efficient frontier, based on the observing, the user identifying an area of the efficient frontier in which there is a gap; and a second portion, the second portion effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the supplemented efficient frontier being used in investment decisioning. The present invention can be more fully understood by reading the following detailed description together with the accompanying drawings, in which like reference indicators are used to designate like elements, and in which: The various embodiments of the invention are directed to systems and methods relating to multi-objective portfolio optimization and/or analysis for use in investment decisions, i.e., for use in investment decisioning, based on competing objectives and a plurality of constraints constituting a portfolio problem. For example, the invention relates to a situation wherein one or more decision makers, e.g. portfolio managers and investors, determine an optimal investment strategy that achieves a set of competing investment objectives characterized by more than two objectives without violating a plurality of investment constraints. For example, the more than two objectives might be more than two risk/return measures. The various inventions and embodiments as described herein provide the technical effect of offering various tools for use in investment decisioning, including manipulation and generation of efficient frontiers representing asset portfolios, for example. In the disclosure as set forth herein, various references are disclosed to provide background information and to show the state of the art, as described herein. The particulars of these references are set forth in a list thereafter, i.e., at the end of this disclosure. For example, in an invention described herein, we propose an approach for solving multiple-objective portfolio risk optimization problems with complex risk/return measures using evolutionary algorithms in conjunction with domain knowledge, as well as related techniques. The systems and methods described herein, in accordance with some embodiments, propose a method for solving portfolio risk optimization problems with complex multiple objectives. A complex objective may be defined as a function that is nonlinear and non-convex or not in an analytical form. The approach, as described below, is based on a powerful combination of evolutionary algorithms and heuristics that exploit problem domain knowledge. The invention is used for modern portfolio management, which involves trade-offs between multiple complex return and risk measures. Given a set of measures to be managed, the portfolio managers face a challenge of solving multi-objective optimization problems, which are not handled by conventional approaches. The invention and the systems and methods described herein can handle problems with more than two objectives and complex functions. Various systems and methods are described herein, in accordance with various inventions and embodiments. The inventions described herein and claimed may be used in conjunction with the inventions of the following eight (8) patent applications, each of which is incorporated herein by reference in its entirety:
The inventions as described herein may be used with the methods and systems described in the above applications, as well as other techniques that are known in the art. In U.S. application Ser. No. 10/390,689, we described a method for determining an efficient frontier, which comprises a collection of security allocations in a portfolio, with multiple, conflicting objectives in a multi-factor portfolio problem. The described method comprises providing a mathematical model of a relaxation of a problem; generating a sequence of additional constraints; and sequentially applying respective nonlinear risk functions to generate respective adjusted maximum return solutions to obtain an efficient frontier. In a portfolio optimization problem, there are typically several non-linear functions f. These non-linear functions are typically related to risk, but could also arise from other sources. The technique in U.S. application Ser. No. 10/390,689 involved the transformation of a convex nonlinear function f into an iterative sequence of linear constraints. As a result, a sequence of proxy linear constraints is used to obtain the efficient frontier between the multiple objectives of the problem. This approach is also called Sequential Linear Programming (SLP). The SLP approach as described in U.S. application Ser. No. 10/390,689 is used for problems with nonlinear, but convex constraints, by first relaxing the problem and eliminating the nonlinear constraints, and then successively building a set of linear constraints that approximate each nonlinear constraint in the region of the optimal solutions along the efficient frontier. A fundamental problem arises however when one or more of the nonlinear functions f is also nonconvex. In this event, the SLP approach will not guarantee convergence. In some portfolio optimization problems, the objective function may not have an analytical form. As a result, the SLP approach is not applicable. Conventional analytical optimization approaches are also not suitable for this type of problem. Evolutionary Algorithms As described herein, evolutionary or genetic algorithms are utilized in the various inventions and embodiments described herein. Genetic algorithms might be characterized as a subpart of evolutionary algorithms. Genetic algorithms (GAs) are a general-purpose optimization method based on the theory of natural selection. GAs make no assumptions about the search space, so they can be applied to a variety of optimization problems (Goldberg, 2002). However, GAs exchange applicability for speed�although they can be used on a wide variety of problems, they are typically slower to converge to a solution than algorithms designed for a specific problem. Genetic algorithms employ a vocabulary borrowed from genetics (Michalewicz, 1996). A �chromosome� is an encoding of an individual solution to an optimization problem. A chromosome is composed of an ordered series of �genes� (i.e., a specific gene always occupies the same position in a chromosome), with each gene corresponding to a specific characteristic of the problem. Typically, this ordered series of genes is a vector, but sometimes arrays are used. Further, it is noted that although there are some applications of diploid chromosomes (e.g., Green, 1994) the overwhelming majority of GA implementations employ haploid chromosomes (i.e., only one set of chromosomes per solution). Further, it is noted that a chromosome in its raw form (i.e., the encoded form that the GA manipulates) is known as the �genotype�; and the solution that the chromosome represents (i.e., the solution decoded from the chromosome) is known as the phenotype. Each gene has a set of �alleles�, which are valid values for that gene. A �population� is composed of a collection (typically of fixed size) of chromosomes. An iteration of the algorithm during which a new population is produced is known as a �generation�. The basic operation of a genetic algorithm is outlined in
As shown in There are many possibilities for the stopping criterion, including stopping after a fixed number of generations, stopping after a predefined fitness has been achieved, stopping when the rate of fitness improvement slows to a predefined level, and/or stopping when the population has converged to a single solution (e.g., when the maximum and mean population fitness is nearly the same). A fixed number of generations and convergence are the most popular stopping criteria. Selection chooses (frequently with replacement) chromosomes in the current population to be members of the following generation, either passed directly, or modified by crossover or mutation. The goal of selection is to favor relatively more fit chromosomes. Those not selected (presumably, the least fit) die out. The three most commonly used selection schemes (Goldberg and Deb, 1991) are proportionate selection, rank selection, and tournament selection. In proportionate selection (�roulette wheel� selection), the likelihood of selecting a chromosome is equal to the ratio of the fitness of the chromosome to the sum of the fitness of all chromosomes. One limitation of this method is that one comparatively very fit chromosome can very quickly overcome a population; rank and tournament selection are designed to overcome this problem. In rank selection, the population is sorted from best to worst fitness, and the probability of selection is some (linear or nonlinear) function of rank. In tournament selection, some small number of chromosomes (frequently two) are chosen at random, compared, and the fittest chromosome is selected. This process is repeated until sufficient chromosomes have been selected. Crossover is the exchange of portions of genetic material from a pair of �parent� chromosomes to produce a pair of �children�. A pair is typically used, although crossover schemes with greater than two parents do exist. There are many ways to perform crossover (Michalewicz, 1996). The simplest method is single point crossover, where the chromosomes are split at a randomly selected point, and genes to the left of the split from one chromosome are exchanged with genes to the right of the split from the other chromosome, and vice versa. For example, consider these two parent chromosomes, where the non-underlined chromosomes are from a first parent and the underlined chromosomes are from a second parent:
Accordingly, a newly generated population will be provided. The newly generated population is passed to the fitness evaluation stage, and the cycle continues until the stopping criterion is met. With further reference to the present novel concepts, The systems and methods of the invention may use convex crossover. Convex crossover is a powerful interior sampling method, which is guaranteed to produce feasible offspring solutions. Given parents P1, P2, it creates offspring O1=λP1+(1−λ)P2, O2=(1−λ)P1+λP2. An offspring Ok and Pk can be crossed over to produce more diverse offspring. Overview Hereinafter, various embodiments of the systems and methods of the invention will be described in further detail. Portfolio optimization conventionally involves trade-off between one return and one risk measure, for example. In a quest for better risk management and investment decisions, modern portfolio managers have to simultaneously take multiple measures of often competing interests into account. These competing measures may typically be risk and return measures. From a portfolio optimization perspective in particular, one needs to solve a multi-objective optimization problem to obtain the efficient frontier of a particular investment problem. The objective functions, in particular risk measures, are far more complicated than those in a conventional approach, e.g., variance. Conventional analytical optimization approaches are not suitable for solving modern portfolio optimization problems, which involve trade-offs of complex objectives. A complex objective is defined as a function that is nonlinear and non-convex, or not in an analytical form. In this invention in some embodiments, we propose a method for solving modern portfolio optimization problems with multiple objectives, some of which are complex functions. In accordance with one embodiment of the invention, the method uses evolutionary algorithms in conjunction with domain knowledge to determine the efficient frontier. The approach exploits domain knowledge via (1) the geometrical nature of the problem to explore and partially memorize the bounds of the feasible space and (2) the geometrical nature of the problem to design search operators that can efficiently explore the interiors of the feasible space, and (3) employs evolutionary algorithms for identifying the efficient frontier over multiple return and risk objectives, some of them being complex objectives. In accordance with one embodiment of the invention, As shown in The efficient frontier generation portion 30 generates multiple interim efficient frontiers. Specifically, the efficient frontier generation portion 30 includes a number of processing portions (202, 302, 312), as desired, that perform efficient frontier generation processes. The processing portions (202, 302, 312) may perform the efficient frontier generation processes using the same process or different processes. Further details of this processing are described below. As shown, the processing portions (202, 302, 312), as well as the other portions of As a result of the processing of portions (202, 302, 312) multiple interim efficient frontiers (32, 34, 36) are generated. After step 560 of As a result of the processing of step 400′, Thereafter, the process of As shown in It should be appreciated that the particular processing as shown in As shown Initialization of Population As described above, evolutionary algorithms, also known as genetic algorithms, are used to obtain solutions that are close to optimal by intelligently searching the feasible region. Evolutionary algorithms do this by implementing simplified models of natural evolution in a computer. In this approach populations of trial solutions compete for survival and succeed in proportion to their fitness, further participating in the production of offspring. The offspring with better genetic content (representation of a trial solution to the problem) survive and reproduce. After many generations of evolution, the best solutions are near optimal. The success of an evolutionary algorithm for an application depends in part on the computational efficiency of generating new solutions from parent solutions. The algorithm should be able to generate a new population of solutions from the previous generation in a reasonable amount of time. In the portfolio optimization problem generating a valid portfolio is not trivial. This is because a typical portfolio has to satisfy a few thousand linear constraints. Ensuring that all the portfolios in a generation satisfy all these constraints requires computational run time. With known techniques, this makes using an evolutionary algorithm for solving the problem impractical. We therefore developed a novel method to circumvent this problem. In our evolutionary algorithm, we start with a large set of valid portfolios. This initial points population is generated using a novel technique, as described below in accordance with one embodiment of the invention, and is large enough to sufficiently sample the feasible region of portfolios. We generate each offspring, i.e., a valid portfolio in the next generation by using a convex combination of two or more parent portfolios (linear combination with non-negative weights that sum to unity). This is a convex crossover operation and allows quick generation of new populations from prior generations. To be able to search the entire feasible region using this convex crossover operation the initial points population should include all the vertices or corner points of the polytope defined by the linear portfolio constraints. However, generating the corner vertices of a polytope is an NP-complete problem. That is, NP means �nondeterministic polynomial time�, and the term �nondeterministic� means that we need to guess what the solution might be. The solution can be tested in polynomial time, but the difficult aspect of such analysis is to guess right. NP-complete means �Nondeterministic Polynomial time-complete� and is a subset of NP, with the additional property that it is also NP-hard. Thus a solution for one NP-complete problem would solve all problems in NP. Such a problem cannot be solved to completion in a reasonable time for any typical real-world portfolio. We have therefore developed a new algorithm to generate initial points that will sufficiently cover the search space for our needs. We describe this algorithm in detail below. In accordance with one embodiment of the invention, the multi-objective portfolio optimization problem may be posed as:
Since a typical portfolio optimization problem can have thousands of constraints defined over a very high dimensional decision space, the feasible space P corresponding to valid portfolio investment strategies, though very large, will only have a small fraction of the volume of the smallest hypercube that encloses the feasible polytope, P. In this situation, starting out with a good idea of the extents of the feasible space P is a powerful feature. Such a feature also serves as a domain-specific guide to any evolutionary search over this space. In the absence of this information, an evolutionary search may consume significant time in either finding a feasible point, or in repairing an infeasible point to make it feasible. Any portfolio that satisfies the linear constraints above is a valid portfolio. The simplex or the interior point algorithm for solving linear programs (LP) can be used to solve the set of linear constraints. Both of these algorithms are commercially available. An LP solver is used to find the optimal decision vector x that solves the problem:
The space Ax≦b is a convex polytope P. Due to the geometrical nature of the problem and the linear programming algorithm, given a weight vector w, an extreme corner vertex of the feasible space defined by Ax≦b is always identified as the optimal decision vector. If a different weight vector w is used, the optimal decision vector will be some other extreme corner vertex x. We use this property to our advantage in generating the initial points population of solutions. By solving multiple linear programs, each with a different weight vector w, one could obtain different corner vertices of the feasible polytope, P. These vectors w, could be randomly generated. By repeating this process of random weight vector specification followed by the solution of the ensuing linear program a few thousand times one can generate many valid portfolios that are vertices of the feasible polytope. However this process does not ensure that the feasible region is sufficiently sampled for our needs. To ensure adequate sampling of the feasible region we reformulate the linear program as follows by adding new constraints.
As shown in Then, the process passes to step 120. In step 120, the linear program is solved multiple times by setting w equal to one of the linear constraints each time. Since in the resulting portfolio the linear constraint chosen for the objective function is activated, this process ensures that there is at least one portfolio where any specific constraint is activated. After step 120, the process passes to step 130. In step 130, the linear program is solved multiple times by setting w equal to a randomly generated vector. Then, the process proceeds to step 140, in accordance with this embodiment of the invention. In step 140, the value of one of the pis or qis is changed. Then, in step 150, a determination is made whether the entire range of pis and qis is fully covered, i.e., to the extent desired by the particular user. If no, then the process returns to step 120, and the processing is again performed as described above. Alternatively, if the entire range of pis and qis is fully covered, i.e., to the extent desired by the particular user, the process then passes to step 160. In step 160, the initialization processing is completed and the initial points population is output to the database 20, as shown in In accordance with one embodiment of the invention, the Sequential Linear Programming (SLP) algorithm that was described in U.S. application Ser. No. 10/390,689 may be used in defining the range of pis and qis for linear and convex objectives. As illustrated in The linear program portion 104 performs the linear program processing as shown in steps 120 and 130 of As illustrated in Pareto Sorting Evolutionary Algorithm (PSEA) The Pareto Sorting Evolutionary Algorithm (PSEA) is an approach to nonlinear non-convex multi-objective optimization, and provides an efficient method for portfolio risk optimization and planning, in accordance with one embodiment of the invention. Though the PSEA is in the family of Pareto-based multi-objective evolutionary algorithms reviewed in Coello et al. 2002, the process of the invention is a unique combination of heuristic techniques that is novel and unlike those in the prior art. The algorithm incorporates the basic evolutionary heuristics of selection, crossover (X-over), and diversity maintenance for identifying Pareto (efficient) frontiers in multi-objective optimization problems. The PSEA process of the invention works by systematically searching, memorizing, and improving populations of vectors (solutions), and performs multi-objective search via the evolution of populations of test solutions in an effort to attain the true Pareto frontier. A high-level view of the operation of the PSEA, a multi-objective optimization algorithm is shown in Given an n-dimensional measurable space whose elements can be partially ordered, a vector in this space x=(x1, x2, . . . , xn) is considered non-dominated if there exists no other vector z such that xi≦zi for all i, and xk<zk for at least one 1≦k≦n. As used in this relationship, the symbol ≦ may be interpreted as �the right-hand-side of the relationship �is better than� its left-hand-side� (or �better-than� relation) without loss of generality. A useful component of the PSEA is a dominance filter that is able to identify the subset of non-dominated vectors given a set of vectors, each of which corresponds to a feasible alternative, and given a set of ordering preferences corresponding to each objective (either �lower is better� or �higher is better�). In the context of portfolio optimization, each vector is a point in the multi-objective return-risk space, and is a unique image of a complete portfolio investment plan. A dominance filter partitions a set of vectors into two subsets, i.e., the non-dominated subset and the dominated subset. A selection operation for an evolutionary algorithm is one that is able to differentiate between the good solutions and weaker solutions, and employs a consistent means to retain the better solutions while discarding the weaker solutions. From the perspective of multi-objective search, a non-dominated solution is considered stronger than a dominated solution. The dominance filter is used to differentiate the stronger solutions from the weaker ones as the first step in a selection operation. The second step in a selection operation, the retention heuristic, may be implemented in a variety of ways. The most simple and intuitive selection operation is retention of only the non-dominated solutions. Given two objectives, the Pareto frontier is a curve in the space defined by the two objectives. When there are three objectives, the Pareto frontier is a surface in this space, and when there are more than three objectives, the Pareto frontier is volumetric. Since the PSEA works by systematic sampling of the Pareto frontier, one approach to identifying the entire Pareto frontier with reasonable fidelity is to enable the algorithm to maintain the entire Pareto frontier found up to a given generation of the search. However, this may require a very large population size, whose maintenance is computationally unattractive and inefficient as well. One novel way to alleviate this problem is to work with small and tractable population sizes while incorporating an �archive� that saves good non-dominated solutions found at any generation for future reference and use. In this latter method, the search algorithm navigates the search space using only a small population size, but commits any good solutions found to memory. However, there is no guarantee that a non-dominated solution found at a certain generation would also be globally non-dominated with respect to the existing solutions in the archive. Therefore, the final step of the PSEA is dominance filtering of the solutions in the archive to generate the set of globally non-dominated solutions across all the generations. Though the �archive� method is an efficient diversity preservation technique and is helpful for preservation of diversity across the entire Pareto frontier, it is not sufficient to ensure diversity in the populations during search. Diversity preservation in the populations themselves is an important aspect of an efficient search. In addition, diversity preservation in the populations has a significant impact on the diversity of the solutions in the archive. Diversity in populations may be enabled via three methods: (i) incorporation of the �crossover� operator, where two solutions (parent solutions) are mathematically combined to yield two new solutions (offspring solutions), (ii) incorporation of �new random solutions� in each generation of the search, and (iii) incorporation of a �non-crowding filter�, that is able to filter out solutions in a population that are heavily clustered and is able to reduce the propensity for clustering that causes reduced diversity. The operational details of the �Run PSEA� step 200 of The processing of In Step 2 the initial population of n individuals is passed through a dominance filter 211. As a result of the dominance filtering, in step 3, a non-dominated subset of cardinality (μ<n) is identified. In step 4 as shown in In Step 5, n/2 randomly matched pairs of parent solutions from the population 210 are combined to create n �offspring� solutions. In the context of portfolio risk optimization, since the feasible space is convex, and we already include a sample of the bounds of the convex feasible space via points from the solutions archive, convex combination of two parent solutions to yield two offspring solutions is a suitable crossover technique. Given two vectors x and y, the two offspring vectors generated via convex crossover are wx+(1−w)y, and (1−w)x+wy, where w is a real random number in the space [0, 1]. The n offspring solutions are passed through a dominance filter in Step 6. The dominance filter processing identifies the non-dominated subset of cardinality (λ<n), as shown in Step 7. Accordingly, as shown in After step 9, the process passes to step 10, in accordance with one embodiment of the invention. In Step 10, a new population 216 of n individuals is created. If n=μ+λ−α, then the filtered population from Step 9 becomes the new improved population. In the case that n>μ+λ−α, m individuals from the solutions archive 218 are randomly drawn such that n=m+μ+λ−α. The inclusion of these m individuals from the solutions archive 218 injects new points into the population enhancing its diversity. Alternatively, it may be the case that n<μ+λ−α. In this situation, we are faced with a problem of discarding some solutions from the set of μ+λ−α, and at the same time promoting diversity via the injection of new points. This may be achieved in two sub-steps. In the first sub-step, (n−p) solutions are randomly selected from the μ+λ−α solutions, where �p� is a number that is one tenth the magnitude of n, for example. In this processing, the value of �p� stays constant (e.g. �p� may be 5 solutions). In other words, β solutions (e.g. 20 solutions) are randomly discarded from the set of μ+λ−α solutions (e.g., which might number 65). To adjust the cardinality of the new population to n, p solutions are randomly injected from the solutions archive. The inclusion of these p individuals from the solutions archive injects new points into the population enhancing its diversity, and resulting in a desired number of 50 solutions, for example. The process of In Step 11 of Accordingly, in step 12 of In step 16, the process finalizes the resulting archive of filtered global non-dominated solutions. Accordingly, a near-Pareto-optimal frontier is generated. TOGA Processing As shown in While TOGA has been previously disclosed (Eklund 2002) in the context of multi-objective visible spectrum optimization for lighting applications, its implementation in the context of multifactor nonlinear non-convex multi-objective portfolio risk optimization, as described herein, is novel. Further, in accordance with embodiments of the invention, TOGA is incorporated into a broader framework for portfolio risk optimization. In accordance with some embodiments of the invention, this broader framework for portfolio risk optimization includes the linear programming-based initial points population generation approach (step 100 of
After step 330 of
In accordance with one embodiment of the invention, it is recommended that q be kept quite low (less than 0.1), to promote exchange of genetic material from different locations on the optimal front (i.e., so that universally good building blocks developed in one region can spread rapidly to other regions). However, because of the low likelihood of preserving the best chromosome from each target combination, elitism is an important feature of TOGA. Further, as shown in step 360, during each generation the chromosome with the maximum Fc for each of the c combinations is passed on, unmodified, to the next generation. Note that this might be the same chromosome for different target combinations, particularly during early generations. The method of mutation is not specified for TOGA. That is, mutation should occur, but the particulars of implementation are not dictated by the TOGA approach. With further reference to In further explanation of the TOGA processing, the selection and elitism strategies are the key elements in TOGA. By selecting from the perspective of each The Fusion Technique In accordance with further aspects of the invention, it may be the situation that the measures of return and risk are represented by functions that are arbitrarily non-convex and nonlinear. In this case, there is no computationally tractable algorithmic approach that can guarantee optimality of the identified efficient frontier. In such a situation, a single execution of any of the stochastic multi-objective optimization algorithms described above may be insufficient. However, in accordance with one embodiment of the invention, better coverage of the efficient frontier may be obtained by multiple executions and combination of the results of the executions of a combination of these algorithms. Accordingly, step 560 of It is noted that each of the multi-objective optimization algorithms (200, 300, 310) incorporates a different set of stochastic search heuristics. Accordingly, one such algorithm may therefore be better than the other algorithm for certain problems and in certain regions of the efficient frontier. Therefore, an efficient and robust method (for identification of the best possible efficient frontier) is to combine the results of multiple executions of each of these optimization algorithms, and perform a dominance filtering of the fused set of results. This process is described in That is, Once the optimization problem has been prepared as shown in steps 573 and 572, the process passes to steps (200, 300 and/or 310). That is, the process of Once the interim efficient frontiers 574 have been generated, the process passes to step 575. In step 575, the process of Further, it is appreciated that the user might view the generated efficient frontier (from step 575 or step 576) and determine that such generated efficient frontier is not sufficient. For example, the efficient frontier may not have the diversity that the user, e.g., a portfolio manager, desires or expects. Accordingly, the user might rerun the process of Further, it is not necessary that each of the techniques be used. For example, only PSEA processing and TOGA processing might be used. Each of the PSEA, TOGA and/or SLP processing feeds into the fused efficient frontiers. That is, in step 575 of In accordance with one embodiment of the invention, as shown in Hereinafter, further aspects of the fusion processing will be described in accordance with some embodiments of the invention. Fusion as described herein may be characterized as relying on two assumptions: (1) the evaluation of a given portfolio is deterministic, i.e., the performance of a portfolio It is possible to have situations in which we need to relax one of the above assumptions. In situations when we could no longer assume a deterministic portfolio evaluation, we could face two possible types of evaluation uncertainty: �stochastic' (defined by a continuous probability distribution) and �discrete-probabilistic.� Stochastic uncertainty is introduced when the performance of the assets included in a portfolio The fusion process will now account for this uncertainty and the set augmentation will be extended to include hyper-rectangles as well as single points. In such a case, it is important to also consider this uncertainty when using the space-decomposition-based dominance filter. Rather than selecting only non-dominated points in the set generated by the fusion, the filter will include a tolerance bound ε, which will retain ε-dominated points. These aspects of the fusion processing in accordance with one embodiment of the invention are described below. In In A different situation arises when the uncertainty cannot be represented by a continuous probability distribution but rather by discrete probability assignments to subsets of points in the performance space Instead of creating hyper-rectangles (as we did in the case of stochastic uncertainty), we now have a discrete subset of points, with a probabilistic assignment associated with each singleton point. For example the representation of portfolio
This representation can be interpreted as following: in the first assignment, we show that the first metric can take one of five possible values: yi,11 through yi,15. Each value assignment has an associated probability value. The sum of the probability values for the entire assignment is equal to one. In the last assignment, we show that the last metric can take one of two possible values yi,n1 and yi,n2. Next to each value we show its associated probability value. Note that we could represent the deterministic assignment illustrated in In the same example in After all assignments have been determined, we can perform a �probabilistic fusion,� which is derived from an extension of Dempster-Shafer (DS) rule of combination. DS rule has been defined (Shafer, 1967; Dempster, 1976) to implement the �intersection� of the probabilistic outcome of two or more sources. In a generalization of this rule, filed in a previous patent application (U.S. application Ser. No. 10/425,721, filed Apr. 30, 2003, which is incorporated herein by reference in its entirety, the inventors redefined DS rule of combination as the �outer product� of two probability distributions using the �scalar product� as an operator. In the same patent application, the inventors generalized DS rule of composition by allowing the outer product operator to be any Triangular T-norms (instead of the scalar product). This is illustrated in the table of We recall that Triangular norms (T-norms) and Triangular conorms (T-conorms) are the most general families of binary functions that satisfy the requirements of the intersection and union operators, respectively (Schweizer and Sklar, 1963; Bonissone 1987). T-norms T(x,y) and T-conorms S(x,y) are two-place functions that map the unit square into the unit interval, i.e., T: [0,1]�[0,1]→[0,1] and S: [0,1]�[0,1]→[0,1] They are monotonic, commutative and associative functions. Their corresponding boundary conditions, i.e., the evaluation of the T-norms and T-conorms at the extremes of the [0,1] interval, satisfy the truth tables of the logical AND and OR operators. They are related by the DeMorgan duality, which states that if N(x) is a negation operator, then the T-conorm S(x,y) can be defined as S(x,y)=N(T(N(x), N(y))). Given that the fusion, in accordance with some embodiments of the invention requires evaluating the �union� of the probabilistic outcome of two or more sources, we extend the previous work set out in U.S. application Ser. No. 10/425,721 (to P. Bonissone, et al. entitled �System And Process For A Fusion Classification For Insurance Underwriting Suitable For Use By An Automated System�) by using Triangular conorms S(x,y,) as the outer product operators. In Once the fusion is performed, we can use a threshold and only consider those points whose probability is greater than the threshold. This is analogous to the threshold-operation that yielded the confidence interval in the case of stochastic uncertainty. As a further extension, the probabilistic assignment illustrated in Up to this point we assumed that original assumption two was valid, i.e., we assumed that each process generating a set of non-dominated points was �equally reliable.� When this is not the case, we need to use a discounting mechanism that can provide such a differentiation. This discounting can be implemented in the framework of discrete-probabilistic evaluation by assigning a non-zero probability value to the union of all values. Space-Decomposition-Based Dominance Filtering Process In accordance with the embodiment of the invention shown in Accordingly, in some embodiments of the invention, what might be characterized as a fast version of dominance filtering is disclosed below, i.e., an approach to dominance filtering that has been observed by the inventors to typically be faster than known approaches. This implementation, as provided by one aspect of the invention, relies on intelligently decomposing the set of solutions to be partitioned, and working systematically on smaller subsets of the full set of solutions. Such a problem decomposition results in a significantly reduced problem complexity, since the cardinality (m), i.e., size, for these smaller sets is typically much smaller than the cardinality (size) of the entire solution set (m<<M). However, while the use of a dominance filter is described herein in accordance with some embodiments of the invention, it is noted that the use of this specific implementation may not be needed, and that one novelty of the inventive systems and methods described herein is in the use of a dominance filter with performance better than the traditional O(N M2) for improving the on-line performance of a multi-objective search algorithm, and more specifically for portfolio optimization. Therefore, any so called fast dominance filter that may implement any from a diverse set of heuristics for improving on the traditional filter's computational performance without compromising on the quality of results may be utilized for the purposes of achieving performance improvements. We refer to For purposes of illustration, the steps of As shown in The initial set of data is also illustrated by graph 1 of After step 420, the process passes to step 430. In step 430, a first dimension is selected for consideration. Accordingly, the considered dimension may be termed a �dimension under consideration.� As shown in After step 430, in which the dimension under consideration is selected, the process passes to step 440. In step 440, the process generates bins based on all the dimensions, other than the dimension under consideration. Thus, if dealing with a total of two dimensions, the binning will simply involve strips, as shown in Alternatively, if dealing with a total of three dimensions, the binning will involve decomposing the space into rectangles based on all the dimensions, other than the dimension under consideration. Alternatively, if dealing with a total of four dimensions, the binning will involve decomposing the space into hexahedra based on the three dimensions not under consideration. After step 440, the process passes to step 450. In step 450, the process determines the point in each of the formed bins with the greatest value in the dimension under consideration. For example, with reference to graph 2, this point with the greatest value would be the points 403, as shown in In step 460, the process of After step 465 of After step 475, the process returns to step 440. Thereafter, the process proceeds as described above, i.e., in processing of the further dimension. As the process proceeds through the dimensions, i.e., by making in turn each dimension the dimension under consideration, the number of points in the data set is progressively reduced. As described herein, a first dimension will be the dimension under consideration, and thereafter, a second dimension will be the dimension under consideration, for example. It should be understood that during processing with the �second dimension under consideration�, for example, the first dimension (which was previously under consideration) is treated as any other dimension, i.e., the first dimension is not, at the time, under consideration. Likewise, when a third dimension is under consideration, the first and third dimension will be treated as any other. At some point in the process of In step 480, the process performs a final dominance check on the remaining points. This final dominance check may be performed using known techniques. The set of remaining points subjected to the final dominance check will be in most cases substantially reduced, as compared to the number of points in the initial data set. After step 480, the process passes to step 490. In step 490, the fast dominance filtering process ends. As shown in In accordance with embodiments of the invention, the dominance filter provides a speedup. The magnitude of the speedup provided by the fast dominance filter varies of course based on the nature of the binning method, including the size of the bins, and the distribution of the initial set, for example. If the number of points dominated by the maximum points within the bins early in the process is a relatively large fraction of M, very good speedups are possible in the dominance filtering. The maximum speedups therefore arise when the raw data are relatively evenly distributed throughout the objectives space. Hereinafter, further aspects and subtitles of the dominance filtering process are described. The dominance filtering process 400 as described herein assumes that there is no uncertainty associated with the value of each portfolio on each dimension. While this assumption produces quite reasonable and usable results, it is assuredly false. Any metric that purports to measure the future value of a portfolio has some uncertainty associated with the prediction. To take that uncertainty into account for the dominance filtering, one needs only to extend the definition of dominance described above. That is, the definition of dominance may be extended to: Given an n-dimensional measurable space whose elements can be partially ordered, and some estimate of uncertainty, εi, a vector in this space x=(x1, x2, . . . , xn) is considered non-dominated if there exists no other vector z such that:
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text: The New Believer's Bible is uniquely designed to help the new Christian read, study, and understand the Bible. It includes features that help Christians develop and deepen their faith, while providing a foundation for their new life in Christ. Dimensions: 8.3 x 5.5 x 1.3 inches Type: Paper () Category: Bibles > Study Bible Translation: New Living Tran. Language: English ISBN / UPC: 9781414302546/1414302541 Publish Date: 7/28/2010 Item No: 156104 Vendor: Tyndale House Publishers |

subset: commoncrawl


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Fair enough, but personally I think the game's back-story is the real strength. It deserves to be made into an animated short or something.
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Recent Comments
Such a shame.
You know, they could have saved it by changing some things according to useful reviews on it.
Could have......
Yet another poorly executed Wii ware game. Blasphemous and attention seeking.
I'm still waiting for a game that does angels justice.
@oldgamesonly: In a good way or a bad way? The best aim for the service would to allow newcomers to experience the classics while refining the content just enough so it doesn't feel stale to the vets. By refined I mean rendering the game in HD resolutions, remastering the sound and improving the controls if need be; not a full HD remake, that would just ruin it.
Boarding the Normandy? No. Blasting it out of the sky? Yes!
Sorry, Mass Effect is one of the many RPGs I can stand and magic isn't even involved for once.
Wonderful! This reminds me of the Lego Island games from my childhood, so it's day one buy for me.
Looks great! One thing though, why are we playing as useless toads when we could be using Wario and Waluigi?!
Horray for Aussies!
Now that's out of the way, I am so glad that all my money hasn't gone to waste buying those few VC titles. Hopefully the Wii U will have an improved VC service i.e. More games, correct displays, remastered sound and controls, etc.
These tech specs aren't precise enough for me.
How powerful each component is down to the very last detail is the only thing that would satisfy me and I'm not surprised the battery life for the controller is so poor, lithium ion cells are outdated technology, they will lose charge even when there is no current draw. Maybe I can rig my controller to use a Lithium Polymer battery, providing I can do it safely and without voiding the warranty.
I think It was mostly due to a lack of interest and other games coming out around the same time. Most people who own a wii aren't even slightly interested in SS and the ones who would be were probably turned off by something like the graphics.
COD 1.753, Battlefield 3 and Skyrim were released around the same time, so that probably put a dent in the numbers as well.
I can't wait to get this digital bottle of awesomeness.
(I can't be the only one who read the subheading in a Gabe Newell voice, surly?)
Isn't this guy the one that never has a clue what he's talking about and no one cares what he thinks anyway?
Now that's interesting, this will probably inspire some sort of dedicated medical device for this purpose so doctors don't need to use console controls for medicine.
I think for the most part, Nintendo has done well with the wii, apart from the lack of 'Hardcore' games, I just hope they can combine traditional, motion, IR, and touch controls with superior graphic to create something fantastic.
I still don't understand how you can patent an idea or theory, it goes against all logic and common sense! It's because of these patent laws that people like me have such a hard time.
Looks like Nintendo has the moral high ground with this and a good thing too, this blasphemous crap should never have seen the light of day.
I think Nintendo has got the right idea to strike while the iron's hot and lay as much pressure on the Vita as possible, they just need to focus more on the western market with their videos and announcements.
Well If the 720 really is only 20% more powerful than the wii u, then I call lies!
The wii u's real capabilities are probably far more than a six year old, outdated chip.


text:
Having problems with your TV signal or Picture? Did you know that most TV issues can be solved by using our in-home reset tool? Just sign-in, click the Reset button, and get back to watching the show. >> Reset My Cable Box
Using MusicChoice Plus Playlists on Your Contour Receiver
Last Updated: Tue, 30 Sep 2014 > Related Articles
Learn how to build a playlist using MusicChoice on your Contour receiver.
MusicChoice Interactive Service requires a digital receiver with the Contour program guide. You can browse and search video playlists through a variety of music genres located On DEMAND.
This feature is not available on the Rovi program guide, or on the Contour App for iPad and Android tablets.
With the MusicChoice Playlist On DEMAND option you can:
- Automatically play up to 14 music videos in a row.
- Use the Search By feature to find a video by artist, song title, genre, or keyword.
Accessing MusicChoice Interactive Services
To access the MusicChoice playlist feature follow the steps below:
Searching for Playlist Content
To search through the loaded MusicChoice the interactive playlist feature:
Controlling a MusicChoice Playlist
Once you've selected and loaded your playlist you can use your remote control to:
Remote Button | Action |
PLAY | Play / resume continuous video playback. |
PAUSE | Pause the current video |
FFWD | Fast‐forward the current video (play resumes with next video in the playlist). |
REW | Rewind current video (play resumes at beginning of current video). |
STOP | Return to playlist menu. |
EXIT | Exit the MusicChoice Interactive Service and return to the last channel viewed. Note: You can also exit MusicChoice using the CH+, |
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- Technical Support Live Chat
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We want to ensure that making a return is as easy and hassle-free as possible! If for any reason you decide that your purchase just isn't for you, simply return the item within 30 days of receipt and we'll cover the cost of return shipping. Learn more about our Return Policy.
Unfortunately VWR VistaVision Upright Compound Microscopes 11389-128 Eyepieces is no longer carried by OpticsPlanet. You can also explore other items in the Microscopes category yourself to try and find the perfect replacement for you!
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Microscopes feature exceptional contrast and resolution, brilliant uniform illumination, and highly durable mechanical elements. The viewing head is of the Siedentopf design with single diopter adjustment. The 6V, 20W Koehler illumination provides uniform intensity and the 1.25 Numerical Aperture (N.A.) Abbe condenser with iris diaphragm allows precise centering of the light path. Four-function focusing includes focus stop to protect slides/specimens and tension adjustment to prevent slippage.
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All microscopes are supplied with 4x, 10x, 40xR, and 100xR (oil) objectives. The 40x and 100x oil objectives feature a retractable lens. The N.A. is 0.10 for 4x objectives, 0.25 for 10x objectives, and 0.65 for the 40xR and 100xR (oil) objectives.


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FWD 4dr i Sport
Starting at | Starting at 20 MPG City - 28 MPG Highway
Introduced in 2006, the CX-7 represents Mazda's first foray into the crossover SUV market and was designed on an original platform. The Mazda CX-7 slots between the smaller Tribute and larger CX-9. Since its debut, the CX-7 has gained in popularity and competes directly with Honda's CR-V, Toyota's RAV4 and Nissan's Murano. With the new choice of two engines and several trims, the CX-7 keeps the Mazda tradition of performance and practicality alive and running. MSRP starts at around $21,000, which is about $2,200 cheaper than the 2009 model. Mileage on the base i SV is estimated at 20 mpg city and 28 mpg highway.
The 2010 Mazda CX-7 offers a number of changes and upgrades from previous years. New for 2010 is the introduction of a new base trim, the 'i SV', which comes with a newly offered naturally aspirated engine
The 2010 Mazda CX-7 is offered in four different trims
Safety Ratings |
Driver Crash Grade |
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Rollover Resistance |
Side Impact Crash Test - Front |
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Braking & Traction |
4-Wheel ABS | Std |
4-Wheel Disc Brakes | Std |
Traction/Stability Control | Std |
Tire Pressure Monitoring System | Std |
Passenger Restraint |
Driver Air Bag | Std |
Passenger Air Bag | Std |
Side Air Bag | Std |
Side Head Air Bag | Std |
Rear Head side Air Bag | Std |
Child Safety Locks | Std |
Road Visibility |
Fog Lamps | Opt |
Electrochromic Rearview Mirror | Opt |
Intermittent Wipers | Std |
Variable Inter. Wipers | Std |
Accident Prevention |
Back-Up Camera | Opt |
Handsfree Wireless | Std |
Security |
Anti-theft System | Std |
Original Warranty |
Basic | 3 Years/36,000 Miles | |
Drivetrain | 5 Years/60,000 Miles | |
Roadside Assistance | 3 Years/36,000 Miles |
Mazda Certified Pre-Owned Warranty |
Each Mazda Certified Pre-Owned vehicle comes with a 12-Month / 12,000 Mile Vehicle Limited Warranty with no deductible on covered components, which begins when the factory warranty ends. If it's out of warranty, the 12-month/12,000 miles warranty begins on the certified retail date. Coverage begins on the limited powertrain warranty from the original retail sales date and covers 7-Years /100,000 miles, whichever comes first. |
Age/Mileage Eligibility | 2009-2015 model years and less than 80,000 miles |
Lease Term Certified | No |
Point Inspection | 150-Point Detailed Inspection Autocheck® Vehicle History Report with 3-year buyback protection from Experian, subject to all Experian & Experian Automotive buyback terms and conditions. |
Return/Exchange Program | No |
Roadside Assistance | 24hr Roadside Assistance for the life of the Powertrain Limited Warranty offers confidence on the road around the clock. |
Special Financing | Yes, see your Mazda dealer for details. |
Transferrable Warranty | Yes |
Warranty Deductible | $0 |
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Energizer Small Rubber LED Light is ideal as a non-conductive lighting tool for electrical applications. This Energizer flashlight is perfect for any job. It features Nichia LED that provides 11 lumens of bright light for up to 16 hours. Rubber LED Flash Light from Energizer features focusing lens that projects a precise spot. In addition, this flashlight from Energizer has built in stand and non-roll foot.


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Chair, Department of Criminal Justice
Research Methods, Incident Crime Analysis, Juvenile Delinquency, Social Psychology, Forensic Criminology, Private Security, Premise Liability.
Ph.D., University of Tennessee, 1977
Dr. William E. Thornton is Chair of the Department of Criminal Justice, Director of the Master of Criminal Justice program, and Professor of Sociology at Loyola University New Orleans. His teaching has covered a wide range of undergraduate and graduate courses and topics in the disciplines of sociology, criminal justice, criminology and security. Dr. Thornton is a forensic criminologist specializing in crime foreseeability, crime prevention, premises security litigation, and security assessment. He is author and co-author of several books, chapters, articles and other materials in the fields of criminology, juvenile delinquency, security, and social justice. He is a program planning and evaluation specialist and has done extensive evaluations on both private and public programs and organizations.
Dr. Thornton has served as a board member, commissioner, consultant or researcher on dozens of task forces, committees or studies in New Orleans, Louisiana and other states dealing with such issues as crime and delinquency, youth problems, jail and prison overcrowding, school crime and security, crime in public housing, drug use, casino gambling and crime, criminal court sentencing disparities, faith based correctional programs, community policing, and other specialized police units. He served as a crime/security consultant for the New Orleans Downtown Development District for several years helping to enhance business and tourism in the city by designing and implementing various crime prevention and public safety programs for businesses in the central business district.
Dr. Thornton is currently conducting research on the physical and social aspects of disasters in relation to different types of criminal activity including violent, property and white collar crimes. His most recent research involves the analysis of post-Katrina Latino migrant worker victimizations and the impact that these crimes have on day labor populations. Dr. Thornton is also writing a book dealing with public and private security and crime prevention, which will be used in college and university courses in the field of criminology and criminal justice.


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Genealogy, Technology And A Whole Lot More
Friday, December 24, 2010
A Barry Merry Christmas
One of Barry's fans made this, I think it's very cute and well done. I know, I know, my Mother calls him Woodstock and my family thinks I'm nuts, but I just can't help melting from the baby blues he's got.
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Click directly on any photo to see a larger image.
History of science... protects scientists from the sins of dogma—the arrogant belief that science is infallible, unchallenged and final.... It encourages young scientists not to worship what is already known but to question it. —Pangratios Papacosta |
A Bell Labs scientist studies a helium-neon laser to determine the relationship of power output to the length of the cavity, ca. 1963-1964. Courtesy AT&T Archives and History Center |
By unrolling before [the physics student] the continuous tradition through which the science of each epoch is nourished by the systems of past centuries, through which it is pregnant with the physics of the future; by mentioning to him the predictions that theory has formulated and experiment realized; ... [history] fortifies in him the conviction that physical theory is not merely an artificial system, suitable today and useless tomorrow, but that it is... an increasingly more clear reflection of realities. --Pierre Duhem |
Otto Stern (left) and Irving Langmuir in discussion during a conference at Como, Italy, circa 1927. AIP Emilio Segrè Visual Archives |
"We live today in a world in which poets and historians and men of affairs are proud that they wouldn't even begin to consider thinking --J. Robert Oppenheimer |
David Hilbert (left) and James Franck stop to discuss physics, circa 1926 AIP Emilio Segrè Visual Archives, Franck Collection |
From left to right: Lois and Julian Brodsky, Fred Dylla, Marc and Vivian Brodsky |


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On the next airing of DNR, we will be discussing the newest iteration of the Hidden Colors Series. Hidden Colors 3, The rules of Racism, as well as a retrospection of sorts on the series itself, and how its effected the community. We will also have a special guest call in, Dr. Kaba Hiawatha Kamene, to share his points of view as we break down the Rules of Racism… on the next Broadcast... Peace but @war
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Cyclic variation in onset of central retinal vein occlusion. | |
MedLine Citation:
PMID: 3814564 Owner: NLM Status: MEDLINE |
We studied the seasonal incidence of onset of symptoms of central retinal vein occlusion (CRVO) in 105 patients over a 5-year period. Their onset showed a significant cyclic variation (p less than 0.01), being most frequent in the months September through to February. We believe this seasonal variation may in part reflect seasonal changes in haemostasis and retinal perfusion, though other unidentified factors play a significant role. |
M J Lavin; B J Dhillon |
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Publication Detail:
Type: Journal Article |
Journal Detail:
Title: The British journal of ophthalmology Volume: 71 ISSN: 0007-1161 ISO Abbreviation: Br J Ophthalmol Publication Date: 1987 Jan |
Date Detail:
Created Date: 1987-04-02 Completed Date: 1987-04-02 Revised Date: 2009-11-18 |
Medline Journal Info:
Nlm Unique ID: 0421041 Medline TA: Br J Ophthalmol Country: ENGLAND |
Other Details:
Languages: eng Pagination: 18-20 Citation Subset: IM |
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Humans Periodicity Retinal Vein Occlusion / etiology* Seasons* Temperature |
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I'm interested in buying this phone and I the main reason for that is it's battery; So I'm wondering if anyone can share their experience. I currently have the Nexus 4 and I'm far from pleased, I want a phone that can last at least two days on a charge with some basic use (Facebook, Twitter, Internet, any game for 10 minutes, maybe some music for a couple of minutes). Is that possible with the G2?


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Rodriguez On World Cafe
Hear More From The Interview
Detroit singer-songwriter Rodriguez has finally found the fame that had eluded him for more than four decades. In the early '70s, he achieved cult status in South Africa, while at home, he remained virtually unknown. His public obscurity sparked rumors that he'd killed himself, but while he was alive and well, he'd retreated from music, earning a philosophy degree while working as a demolition man.
Searching for Sugar Man is a film documentary which chronicles the efforts of two South African fans trying to piece together information about Rodriguez's life. They finally find the folk-rocker doing manual labor, unaware of his music's profound popularity abroad. In this World Cafe session, you'll hear from Rodriguez himself, as well as the producers of his records, the people who found him and the director of the documentary about how it all happened.
This edition of World Cafe originally aired October 18, 2012.
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Total Challenge I: Director's Cut (Windows)
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5 point score based on user ratings. |
Total Challenge I Director's cut is an enhanced version of Blitzkrieg: Total Challenge. It adds improves upon the original with new models and textures, error corrections and some gameplay tweaks. Improvements of later editions in the Total Challenge series are also included such as improved line of sight and pathfinding.Screenshots
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Second out building
This gets a little crazy so please stay with me ,I aquired the land next to me it has two buildings on it and I need power in them.I should first tell you there was power run to them with a 3 wire system ( ground & netural bonded) I had the meter removed and begun running power from my main disconect. My first run (250 ft) with 2-2-2-4 al ,I then seperated the grounds and neturals and the bonding bar in the existing 50amp breaker box . and installed 2 ground rods 8ft apart.
Now the other building is another 250 ft away it has 3 #6 copper run to it set up the same way as the pole barn was with ground and netural bonded. My question is can I Ground that box with ground rods seperate the ground and neturals and just have the 2 hot and netural run back to my first building ?Or do I need to run a ground wire? The secound building was the main disconect it is a 2car garage that had a mobile home next to it (its gone) Advertisement |
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Second out buildingQuote:
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Marking (09-24-2012)
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Second out building
NOOO!!! Definitely not. That would be MUCH WORSE than just using the 3-wire feed with ground and neutral bonded. Here's the problem: The equipment grounding conductor (EGC, the wire that goes to the ground prong of a receptacle, or connects to the metal frame of a piece of equipment) has one main purpose. It exists to prevent the metal parts of an appliance from becoming electrified if a hot wire inside shorts out to the metal parts. It does this by tripping the circuit breaker. As soon as a hot wire contacts the grounded metal, lots of current flows through the hot wire, to the metal part, through the ground wire to the panel, across the neutral-ground bond, and back to the utility transformer via the service neutral conductor. Since way more current flows than the circuit breaker rating, the breaker trips and prevents the metal part from staying electrified. This is obviously important for safety. It also obviously requires a neutral-ground bond. If the panel is a subpanel with a 4-wire feeder, then the "ground" conductor in the feeder provides the return path for the fault current so it can reach the neutral-ground bond in the main panel.
What happens if there is no neutral-ground bond? The hot wire contacts a grounded metal part. Current flows back to the panel through the ground wire, where the ground wire is connected to all the other circuits' ground wires and to two ground rods - but not bonded to neutral. Each ground rod has an impedance of several tens of ohms (let's say 30), so both ground rods together are about 15 ohms. 120V divided by 15 ohms is 8 amps. So about 8 amps flows into the earth through the ground rods. The breaker obviously does not trip because that's not an overload. Now the ground rods, the earth around them, and every other piece of metal equipment connected to the grounding system are all electrified with 120V. If the plumbing system is bonded, then the plumbing is hot too. And it will all stay electrified until someone turns off the power (the metal case of the panel is hot, too). So you can see that the neutral-ground bond is REALLY important. A "grounding" system which is connected only to grounding electrodes but not bonded to neutral is much worse than no grounding system at all. Ground rods do not ever clear faults. They have way too much resistance to earth to ever be able to reliably trip a circuit breaker. They exist only to protect against indirect lightning damage and to stabilize the system voltage with respect to the earth. All of the electric shock protection provided by the "grounding" system is actually provided by BONDING of metal parts to the system neutral, not by connecting things to ground rods. If you insist on not running new wire, then the ONLY way to use what you've got is to run two hots and neutral, with a neutral-ground bond at the subpanel. This used to be code compliant up until a few years ago. It was probably code compliant when the feeder was installed, and there's no real need to change it. The four-wire feeder requirement fixes the big set of problems that occurs if that neutral conductor in a three-wire feeder ever comes loose. Last edited by mpoulton; 09-24-2012 at 01:27 PM. |
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Dr Oz played a game called The Booze To Lose game. A nice glass of red wine can be the perfect way to end a tough week, but not all alcoholic drinks are created equally according to Doctor Oz. So, what drinks should you have and which ones should you avoid?
Dr Oz: Bourbon Causes Worse Hangovers
Dr Oz asked which liquor gives the worst hangover: Gin, Vodka, or Bourbon? The answer is that Bourbon causes the worst hangovers, because its darker color comes from more of a waste product that develops as it ages and has a toxic effect that causes more hangovers. Bourbon has 37 time more of these toxic by-products than Gin, Vodka or clear alcohols.
Dr Oz: Diet Soda Is The Most Dangerous Mixer
Dr Oz asked which is the most dangerous mixer for a mixed alcoholic drink? Diet soda, orange juice, or regular soda? Dr Oz said that mixing diet soda with alcohol is the most dangerous because carbonation on its own causes alcohol to get absorbed much faster, plus the artificial sweeteners found in diet soda speed up the absorption even further.
Dr Oz: Potassium in Red Wine Prevents Kidney Stones
Dr Oz asked which wine has more potassium – Red wine or White wine? Dr Oz said that Red wine has more potassium and, therefore, you can prevent kidney stones by drinking Red wine!
Both contestants of Dr Oz's game won wine stem glasses and You Staying Younger, Dr Oz's book on aging.
Thank you for informing me of the theft to the offices of the Knights of Columbus and I am very sorry to hear that this happened. As I indicated to you, the building in between ours (owned by Bernstein Properties at 5028 Wisconsin Ave.) was burglarized on January 9th. During that incident, the burglar apparently remained in the building
the entire evening taking lap top computers, personal effects, and other valuables at their offices. Cameras in the building documented the burglary.I am sure that our three employees allocated to clean and maintain the offices are of the maximum confidence for that reason they have been working with us over three years and deal with one of the most important assets of our clients ("their car") and other valuables. For us honesty
is a very important trait.
Without putting any blame, you know that there is a gentleman that comes almost everyday to your offices that brings beer and snacks to the office
and remains there for several hours watching TV.
To clarify about our exterior cleaning, your father established that he would retain a plow contractor to clean the parking lot. Our scope of work would include: (a) Cleaning all sidewalks and stairs during winter, and clean part of the parking lot near the service stair. Clean all the exterior of any debris. (b) Mow grass of the whole area and maintain all the exterior area clean of debris and leaves during the rest of the year (including parking lot) Additionally, we have cut dead branches of the trees, mulched areas whenever necessary and sodded areas that required improvement, as you are our neighborhood and many times we have our workforce available.
I think that the rational of your father in employing us was to have our support whenever something was needed, therefore I have personally waited for some contractors, whenever needed and with our mechanic helped to fix the toilets. After Weichert took over their part of the cleaning contract, assumed by a relative of their manager, he requested us to remain taking care of the exterior of the building and your offices. We would like to remain providing said services to you, but feel free to fire us.


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MU's College of Agriculture, Food and Natural Resources offers several options in Eckles Hall for hungry people who want to stay on campus.
Buck's Ice Cream Place
Buck's Ice Cream Place, known for its Tiger Stripe ice cream, is always trying out new flavors. The shop, tucked beneath a black-and-gold striped awning, is near College Avenue and Hatch residence halls and is about a five minute walk from the Student Recreation Complex at the heart of campus. The shop, and everything behind the scenes, serves as teaching laboratory for students in the College of Agriculture, Food and Natural Resources.
Hours: noon — 5 p.m., Monday to Friday, Saturdays during warmer months
Culinary Café
Culinary Café offers a dinner series, which usually includes a four-course meal, staffed by upper-level student management teams in the Hotel and Restaurant Management program. The food, music and décor are all coordinated to a different theme. Examples from last spring include "International Chocolate Dinner," "Under the Tuscan Sun" and "Down Under."
Mizzou Meat Market
Mizzou Meat Market is a training laboratory for MU's Food Science and Animal Science programs. The store offers USDA approved cuts of beef, lamb and pork in a wide variety of cuts. For MU students with a passion for cooking meat, the market offers student charge orders.
Hours: noon — 5:30 p.m., Wednesdays and Fridays; special pick-up appointments available.


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Please could you help with the font in the Sage (UK) logo - the letters are all touching so none of the auto font identifications are working for me and I only have 4 letters for the question based identification sites.
Website: http://www.sage.co.uk/