The Healthy Farms, Food and Communities Act: Policy Initiatives for the 2002 Farm Bill And the First Decade of the 21st Century

This policy document includes a legislative initiative to be incorporated into the 2002 Farm Bill, and a broader set of policy principles and legislation endorsed by CFSC. Both policy platforms create the basis for furthering the goals of healthy farms, healthy food, and, ultimately, healthy communities

A semantic characterization of the well-typed formulae of λ-calculus

AbstractA model-theoretic operation is characterized that preserves the property of being a model of typed λ-calculus (i.e., the result of applying it to a model of typed λ-calculus is another model of typed λ-calculus). An expression is well-typed iff the class of its models is closed under this operation

Foundations of Generalism: Symmetries, Non-individuals and Ontological Nihilism

The topic of this thesis is the metaphysical theory of generalism: the view that the world is constituted by purely general facts. Whilst the connection may not be immediately obvious, generalism is also touted as a qualitative metaphysics: a theory that seeks to elevate, in some important metaphysical sense, the notion of qualities (i.e. properties and relations) over that of objects. As such, generalism is just as well individuated by its categorial commitments—its commitment to the fundamentality of certain metaphysical categories—as it is by its construal of fundamental facts. My aim in this thesis is to make explicit these connections, providing a proper explication of the generalist position, as well as its motivations and its apparent consequences. Beyond this, the thesis can also be read as an extended argument in favour of individualism: the view that holds, contrary to generalism, that the category of individual, or object, is at least as fundamental as that of property and relation. The subtitle of this thesis, ‘symmetries, non-individuals and ontological nihilism’, alludes to the topic addressed by each of the three chapters. In chapter 1 I explicate and critique the generalist’s primary argument against individualism, one based on the notion of a symmetry. In chapter 2 I investigate the tenability of a position dubbed ‘quantifier generalism’, a position that, I argue, can be further explicated through the notion of a non-individual. And in chapter 3 I turn to the most widely-discussed form of generalism found in the literature: algebraic generalism, a (purported) form of ontological nihlism

Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

It is unknown what kind of biases modern in the wild face datasets have because of their lack of annotation. A direct consequence of this is that total recognition rates alone only provide limited insight about the generalization ability of a Deep Convolutional Neural Networks (DCNNs). We propose to empirically study the effect of different types of dataset biases on the generalization ability of DCNNs. Using synthetically generated face images, we study the face recognition rate as a function of interpretable parameters such as face pose and light. The proposed method allows valuable details about the generalization performance of different DCNN architectures to be observed and compared. In our experiments, we find that: 1) Indeed, dataset bias has a significant influence on the generalization performance of DCNNs. 2) DCNNs can generalize surprisingly well to unseen illumination conditions and large sampling gaps in the pose variation. 3) Using the presented methodology we reveal that the VGG-16 architecture outperforms the AlexNet architecture at face recognition tasks because it can much better generalize to unseen face poses, although it has significantly more parameters. 4) We uncover a main limitation of current DCNN architectures, which is the difficulty to generalize when different identities to not share the same pose variation. 5) We demonstrate that our findings on synthetic data also apply when learning from real-world data. Our face image generator is publicly available to enable the community to benchmark other DCNN architectures.Comment: Accepted to CVPR 2018 Workshop on Analysis and Modeling of Faces and Gestures (AMFG

An assessment of land cover changes using GIS and remote sensing : a case study of the uMhlathuze Municipality, KwaZulu-Natal, South Africa.

Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.Rapid growth of cities is a global phenomenon exerting much pressure on land resources and causing associated environmental and social problems. Sustainability of land resources has become a central issue since the Earth Summit in Rio de Janeiro in 1992. A better understanding of the processes and patterns of land cover change will aid urban planners and decision makers in guiding more environmentally conscious development. The objective of this study was firstly, to determine the location and extent of land use and land cover changes in the uMhlathuze municipality, KwaZulu-Natal, South Africa between 1992 and 2002, and secondly, to predict the likely expansion of urban areas for the year 2012. The uMhlathuze municipality has experienced rapid urban growth since 1976 when the South African Ports and Railways Administration built a deep water harbour at Richards Bay, a town within the municipality. Three Landsat satellite images were obtained for the years, 1992, 1997 and 2002. These images were classified into six classes representing the dominant land covers in the area. A post classification change detection technique was used to determine the extent and location of the changes taking place during the study period. Following this, a GIS-based land cover change suitability model, GEOMOD2, was used to determine the likely distribution of urban land cover in the year 2012. The model was validated using the 2002 image. Sugarcane was found to expand by 129% between 1992 and 1997. Urban land covers increased by an average of 24%, while forestry and woodlands decreased by 29% between 1992 and 1997. Variation in rainfall on the study years and diversity in sugarcane growth states had an impact on the classification accuracy. Overall accuracy in the study was 74% and the techniques gave a good indication of the location and extent of changes taking place in the study site, and show much promise in becoming a useful tool for regional planners and policy makers

Morphable Face Models - An Open Framework

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model

Räumliche und zeitliche Visualisierung als Smart-City-Planungswerkzeug

Die steigende Bevölkerung und der starke Zuzug in die urbanen Ballungsräume ist eine große Herausforderung für die Akteure der Planungswelt. Um ressourcenschonende Planungen voranzutreiben, ist eine innere Entwicklung der urbanen Systeme zielführend. Dabei ist neben der Aufspürung und Nutzung von Flächenreserven, die Nutzung und der Ausbau bestehender Versorgungsinfrastruktursysteme eine Möglichkeit für nachhaltige Entwicklungen. Dies stellt eine komplexe Planungsaufgabe für Planer und Entscheidungsträger dar, die das Zusammenwirken von Planungsakteuren unterschiedlichster Domänen erfordert. Innerhalb des interdisziplinären Forschungsprojektes URBEM (Urbanes Energie- und Mobilitätssystem) wurde ein visuelles Planungs- und Entscheidungsunterstützungswerkzeug, die URBEMVisualisierung, entwickelt. Diese webbasierte Umgebung bietet eine Arbeits- und Kommunikationsplattform für Domänenexperten und Stakeholder zur Unterstützung komplexer Planungsprozesse. Die URBEMVisualisierung erlaubt domänenspezifische Simulationsergebnisse räumlich zu verorten, visuelle Übersichten zu generieren und ein urbanes Gesamtsystem mit Hilfe der räumlichen Überlagerung von Informationen unterschiedlichster Versorgungsträgerstrukturen im Bereich Energie und Mobilität zu untersuchen. Dies bietet den Planern eine Grundlage um Probleme im Raum und in der Zeit fest zu machen und gezielte Maßnahmen zur Entwicklung smarter Lebensräume aufzuzeigen. Die Möglichkeiten der URBEM Visualisierung werden im folgenden Beitrag anhand von Modellergebnissen aus der Domäne Mobilität illustriert

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis

Computer vision tasks are difficult because of the large variability in the data that is induced by changes in light, background, partial occlusion as well as the varying pose, texture, and shape of objects. Generative approaches to computer vision allow us to overcome this difficulty by explicitly modeling the physical image formation process. Using generative object models, the analysis of an observed image is performed via Bayesian inference of the posterior distribution. This conceptually simple approach tends to fail in practice because of several difficulties stemming from sampling the posterior distribution: high-dimensionality and multi-modality of the posterior distribution as well as expensive simulation of the rendering process. The main difficulty of sampling approaches in a computer vision context is choosing the proposal distribution accurately so that maxima of the posterior are explored early and the algorithm quickly converges to a valid image interpretation. In this work, we propose to use a Bayesian Neural Network for estimating an image dependent proposal distribution. Compared to a standard Gaussian random walk proposal, this accelerates the sampler in finding regions of the posterior with high value. In this way, we can significantly reduce the number of samples needed to perform facial image analysis.Comment: Accepted to the Bayesian Deep Learning Workshop at NeurIPS 201