Water governance and poverty: a framework for analysis

YesIn this paper we present a framework for understanding water governance, through which we critique some of the assumptions underlying the current consensus on good governance. Specifically, we suggest that current approaches are based on incomplete or partial understandings of the concepts of governance. We question the idea that governance can be identified as an abstract set of principles, without the need for contextualisation and localisation. In particular, we suggest that there is a general lack of understanding of the way local interactions shape and influence governance processes. Finally, and with specific reference to the MDGs and the water sector, we question the implicit assumption that `good¿ governance is necessarily pro-poor governance. The paper addresses these issues through a critical discussion of governance, from which we develop a framework for conceptualising water governance. The framework draws on theories of governance, institutions and structuration, but is also informed by recent empirical research and experiences from the field. We apply the framework to a specific case in Southwestern Tanzania and raise a number of issues and challenges for further research

How institutions elude design: river basin management and sustainable livelihoods.

YesThis paper challenges ideas that it is possible to `get the institutions right¿ in the management of natural resources. It engages with the literature and policy specifying `design principles¿ for robust institutions and uses data from a river basin management project in Usangu, Tanzania, to illustrate the complexity of institutional evolution. The paper draws on emerging `post-institutionalist¿ perspectives to reject over-formalised managerial approaches in favour of those that accept the dynamic nature of institutional formation, and accommodate a variety of partial and contingent solutions. Data from Usangu suggests that external `crafting¿ is inevitably problematic because, to a certain extent, institutions elude design

Performance management system enhancement and maintenance

The research described in this report concludes a two-year effort to develop a Performance Management System (PMS) for the NCC computers. PMS provides semi-automated monthly reports to NASA and contractor management on the status and performance of the NCC computers in the TDRSS program. Throughout 1984, PMS was tested, debugged, extended, and enhanced. Regular PMS monthly reports were produced and distributed. PMS continues to operate at the NCC under control of Bendix Corp. personnel

Flat Directions in Three-Generation Free-Fermionic String Models

In quasi-realistic string models that contain an anomalous U(1) the non-zero Fayet-Iliopoulos term triggers the shifting of the original vacuum to a new one along some flat direction, so that SUSY is preserved but the gauge group is partially broken. The phenomenological study of these models thus requires as a first step the mapping of the space of flat directions. We investigate F- and D-flat directions in several three-generation SU(3)_C x SU(2)_L x U(1)_Y free-fermionic string models and discuss the typical scenarios that generically arise. When they exist, we systematically construct the flat directions that preserve hypercharge, only break Abelian group factors, and can be proven to remain F-flat to all orders in the non-renormalizable superpotential.Comment: 59 page

Free Fermionic Heterotic Model Building and Root Systems

We consider an alternative derivation of the GSO Projection in the free fermionic construction of the weakly coupled heterotic string in terms of root systems, as well as the interpretation of the GSO Projection in this picture. We then present an algorithm to systematically and efficiently generate input sets (i.e. basis vectors) in order to study Landscape statistics with minimal computational cost. For example, the improvement at order 6 is approximately 10^{-13} over a traditional brute force approach, and improvement increases with order. We then consider an example of statistics on a relatively simple class of models.Comment: Standard Latex, 12 page

Learning Functions Generated by Randomly Initialized MLPs and SRNs

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) and two benchmark functions are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - PSO-QI is introduced. PSO-QI is a standard particle swarm optimization (PSO) algorithm with the addition of a quantum step utilizing the probability density property of a quantum particle. The results from PSO-QI are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but PSO-QI provides learning capabilities of these functions by MLPs and SRNs compared to BP and PSO

Phenomenology of A Three-Family Standard-like String Model

We discuss the phenomenology of a three-family supersymmetric Standard-like Model derived from the orientifold construction, in which the ordinary chiral states are localized at the intersection of branes at angles. In addition to the Standard Model group, there are two additional U(1)' symmetries, one of which has family non-universal and therefore flavor changing couplings, and a quasi-hidden non-abelian sector which becomes strongly coupled above the electroweak scale. The perturbative spectrum contains a fourth family of exotic (SU(2)- singlet) quarks and leptons, in which, however, the left-chiral states have unphysical electric charges. It is argued that these decouple from the low energy spectrum due to hidden sector charge confinement, and that anomaly matching requires the physical left-chiral states to be composites. The model has multiple Higgs doublets and additional exotic states. The moduli-dependent predictions for the gauge couplings are discussed. The strong coupling agrees with experiment for reasonable moduli, but the electroweak couplings are too small.Comment: 22 pages, 4 figure

Learning Nonlinear Functions with MLPs and SRNs

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results

Initial Systematic Investigations of the Landscape of Low Layer NAHE Extensions

The discovery that the number of physically consistent string vacua is on the order of 10^500 has prompted several statistical studies of string phenomenology. Contained here is one such study that focuses on the Weakly Coupled Free Fermionic Heterotic String (WCFFHS) formalism. Presented are systematic extensions of the well-known NAHE (Nanopoulos, Antoniadis, Hagelin, Ellis) set of basis vectors, which have been shown to produce phenomenologically realistic models. Statistics related to the number of U(1)'s, gauge group factors, non-Abelian singlets, ST SUSYs, as well as the gauge groups themselve are discussed for the full range of models produced as well as models containing GUT groups only. Prior results of other large-scale investigations are compared with these regarding the aforementioned quantities. Statistical coupling between the gauge groups and the number of ST SUSYs is also discussed, and it was found that for order-3 extensions there are more models with enhanced ST SUSY when there is an exceptional group present. Also discussed are some three-generation GUT models found in the data sets. These models are unique because they come from basis vectors which still have a geometric interpretation -- there are no "rank-cuts" in these models.Comment: 65 Pages, 31 Tables, 31 Figure