QSD IV : 2+1 Euclidean Quantum Gravity as a model to test 3+1 Lorentzian Quantum Gravity

The quantization of Lorentzian or Euclidean 2+1 gravity by canonical methods is a well-studied problem. However, the constraints of 2+1 gravity are those of a topological field theory and therefore resemble very little those of the corresponding Lorentzian 3+1 constraints. In this paper we canonically quantize Euclidean 2+1 gravity for arbitrary genus of the spacelike hypersurface with new, classically equivalent constraints that maximally probe the Lorentzian 3+1 situation. We choose the signature to be Euclidean because this implies that the gauge group is, as in the 3+1 case, SU(2) rather than SU(1,1). We employ, and carry out to full completion, the new quantization method introduced in preceding papers of this series which resulted in a finite 3+1 Lorentzian quantum field theory for gravity. The space of solutions to all constraints turns out to be much larger than the one as obtained by traditional approaches, however, it is fully included. Thus, by suitable restriction of the solution space, we can recover all former results which gives confidence in the new quantization methods. The meaning of the remaining "spurious solutions" is discussed.Comment: 35p, LATE

Data analysis of the stock management of a manufacturing company

One of the tasks of stock management is determine the safety stock of a product. The aim of the safety stock is to avoid out-of-stock situations when demand increases unexpectedly. In most of manufacturing companies, the safety stock of a material is established by supply planners and they usually do not have a clear method to do it, they simply decide it from previous experience. Thus, the objective of this project is to perform a neural network capable of substituting this task developed by a human brain. To do so, an international manufacturing company provided real data to test the results. The research is divided into two parts. In the first trial a linear regression model is fitted to find the significant variables of the data given, and then a neural network is implemented with only the relevant inputs. Secondly, several supply planners are interviewed in order to adopt the variables they use to decide the safety stock as inputs of the neural network, in addition, the dataset is separated into three groups of products according to their similarity, and one neural network is implemented for each group of products. The results obtained in both parts are not good enough, that is, the neural networks built cannot replace the job done by a supply planner. However, it is found that the more similar the products of the dataset are, the easier it is for the neural network to predict their safety stock. In fact, the best neural network performed can accurately determine the safety stock of some materials, even though the total error is too high to consider capable of substituting the decision making of a human brain

Data analysis of the stock management of a manufacturing company

One of the tasks of stock management is determine the safety stock of a product. The aim of the safety stock is to avoid out-of-stock situations when demand increases unexpectedly. In most of manufacturing companies, the safety stock of a material is established by supply planners and they usually do not have a clear method to do it, they simply decide it from previous experience. Thus, the objective of this project is to perform a neural network capable of substituting this task developed by a human brain. To do so, an international manufacturing company provided real data to test the results. The research is divided into two parts. In the first trial a linear regression model is fitted to find the significant variables of the data given, and then a neural network is implemented with only the relevant inputs. Secondly, several supply planners are interviewed in order to adopt the variables they use to decide the safety stock as inputs of the neural network, in addition, the dataset is separated into three groups of products according to their similarity, and one neural network is implemented for each group of products. The results obtained in both parts are not good enough, that is, the neural networks built cannot replace the job done by a supply planner. However, it is found that the more similar the products of the dataset are, the easier it is for the neural network to predict their safety stock. In fact, the best neural network performed can accurately determine the safety stock of some materials, even though the total error is too high to consider capable of substituting the decision making of a human brain

Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity

This paper examines the effect of recommender systems on the diversity of sales. Two anecdotal views exist about such effects. Some believe recommenders help consumers discover new products and thus increase sales diversity. Others believe recommenders only reinforce the popularity of already popular products. This paper seeks to reconcile these seemingly incompatible views. We explore the question in two ways. First, modeling recommender systems analytically allows us to explore their path dependent effects. Second, turning to simulation, we increase the realism of our results by combining choice models with actual implementations of recommender systems. Our main result is that some well known recommenders can lead to a reduction in sales diversity. Because common recommenders (e.g., collaborative filters) recommend products based on sales and ratings, they cannot recommend products with limited historical data, even if they would be rated favorably. In turn, these recommenders can create a rich-get-richer effect for popular products and vice-versa for unpopular ones. This bias toward popularity can prevent what may otherwise be better consumer-product matches. That diversity can decrease is surprising to consumers who express that recommendations have helped them discover new products. In line with this, we show it is possible for individual-level diversity to increase but aggregate diversity to decrease. Recommenders can push each person to new products, but they often push similar users toward the same products. We show how basic design choices affect the outcome, and thus managers can choose recommender designs that are more consistent with their sales goals and consumers' preferences

Annual Report of the University, 1968-1969, Volumes 1 & 2

At least once every ten years the University of New Mexico has a chance to see itself as others sec it. The opportunity is provided by its accrediting agency. Routinely every decade, the North Central Association of Colleges and Secondary Schools sends a team of scholars and administrators to campus to determine whether the University is maintaining the prerequisites to continuing accreditation as a doctoral degree granting institution. While the examination is scheduled routinely, it is by no means a routine visit. The visitation team probes such areas as curricula, library, finances, administration, day-to-day operations, and long-range plans. Its report, much like that of an auditor, helps provide operational guidelines for succeeding years. The University of New Mexico in 1969 underwent its decennial examination by the North Central Association. The team of visitors prepared a comprehensive report touching on many areas vital to the University\u27s future. Findings of the committee and the University\u27s responses to them serve as the basis for this annual report of the President

Automated Systems in the Aviation and Aerospace Industries

A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.National Aviation Universit