Party Members’ Preferences and the Changing Nature of Party Representation: Does Representation Change when Parties become Mini-Republics?

Giovanni Sartori famously wrote that political parties do not need to be mini-republics, yet today parties in many parliamentary democracies are moving in this direction by giving their members direct votes over important decisions, including selecting party leaders and settling policy issues. This paper explores some of the implications of these changes. It asks whether the addition of membership rights affects the types of members who are attracted: do we find a bigger gap between the preferences of party members and of party voters in parties that are more plebiscitary, as literature on members' motivations might lead us to expect? The paper examines this question both cross-sectionally and longitudinally using opinion data from the European Social Survey and newly-available party organizational data from the Political Party Database project.

Back to Basics: Deriving Representation Changers Functionally

Many functional programs can be viewed as representation changers, that is, as functions that convert abstract values from one concrete representation to another. Examples of such programs include base-converters, binary adders and multipliers, and compilers. In this paper we give a number of different approaches to specifying representation changers (pointwise, functional, and relational), and present a simple technique that can be used to derive functional programs from the specifications.

Representation of odometry errors on occupancy grids

SOUZA, Anderson A. S. ; SANTANA, André M. ; BRITTO, Ricardo S. ; GONÇALVES, Luiz Marcos G. ; MEDEIROS, Adelardo A. D. Representation of Odometry Errors on Occupancy Grids. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

RICH AND EFFICIENT VISUAL DATA REPRESENTATION

Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.

Sparse Signal Representation in Digital and Biological Systems

Theories of sparse signal representation, wherein a signal is decomposed as the sum of a small number of constituent elements, play increasing roles in both mathematical signal processing and neuroscience. This happens despite the differences between signal models in the two domains. After reviewing preliminary material on sparse signal models, I use work on compressed sensing for the electron tomography of biological structures as a target for exploring the efficacy of sparse signal reconstruction in a challenging application domain. My research in this area addresses a topic of keen interest to the biological microscopy community, and has resulted in the development of tomographic reconstruction software which is competitive with the state of the art in its field. Moving from the linear signal domain into the nonlinear dynamics of neural encoding, I explain the sparse coding hypothesis in neuroscience and its relationship with olfaction in locusts. I implement a numerical ODE model of the activity of neural populations responsible for sparse odor coding in locusts as part of a project involving offset spiking in the Kenyon cells. I also explain the validation procedures we have devised to help assess the model's similarity to the biology. The thesis concludes with the development of a new, simplified model of locust olfactory network activity, which seeks with some success to explain statistical properties of the sparse coding processes carried out in the network.

A representation-free description of the Kasevich-Chu interferometer: a resolution of the redshift controversy

Motivated by a recent claim by Muller et al (2010 Nature 463 926-9) that an atom interferometer can serve as an atom clock to measure the gravitational redshift with an unprecedented accuracy, we provide a representation-free description of the Kasevich-Chu interferometer based on operator algebra. We use this framework to show that the operator product determining the number of atoms at the exit ports of the interferometer is a c-number phase factor whose phase is the sum of only two phases: one is due to the acceleration of the phases of the laser pulses and the other one is due to the acceleration of the atom. This formulation brings out most clearly that this interferometer is an accelerometer or a gravimeter. Moreover, we point out that in different representations of quantum mechanics such as the position or the momentum representation the phase shift appears as though it originates from different physical phenomena. Due to this representation dependence conclusions concerning an enhanced accuracy derived in a specific representation are unfounded.

Interest Representation as a Clash of Unequal Allies

Organized interests do not have direct control over the fate of their policy agendas in Congress. They cannot introduce bills, vote on legislation, or serve on House committees. If organized interests want to achieve virtually any of their legislative goals they must rely on and work through members of Congress. As an interest group seeks to move its policy agenda forward in Congress, then, one of the most important challenges it faces is the recruitment of effective legislative allies. Legislative allies are members of Congress who “share the same policy objective as the group” and who use their limited time and resources to advocate for the group’s policy needs (Hall and Deardorff 2006, 76). For all the financial resources that a group can bring to bear as it competes with other interests to win policy outcomes, it will be ineffective without the help of members of Congress that are willing to expend their time and effort to advocate for its policy positions (Bauer, Pool, and Dexter 1965; Baumgartner and Leech 1998b; Hall and Wayman 1990; Hall and Deardorff 2006; Hojnacki and Kimball 1998, 1999). Given the importance of legislative allies to interest group success, are some organized interests better able to recruit legislative allies than others? This question has received little attention in the literature. This dissertation offers an original theoretical framework describing both when we should expect some types of interests to generate more legislative allies than others and how interests vary in their effectiveness at mobilizing these allies toward effective legislative advocacy. It then tests these theoretical expectations on variation in group representation during the stage in the legislative process that many scholars have argued is crucial to policy influence, interest representation on legislative committees. The dissertation uncovers pervasive evidence that interests with a presence across more congressional districts stand a better chance of having legislative allies on their key committees. It also reveals that interests with greater amounts of leverage over jobs and economic investment will be better positioned to win more allies on key committees. In addition, interests with a policy agenda that closely overlaps with the jurisdiction of just one committee in Congress are more likely to have legislative allies on their key committees than are interests that have a policy agenda divided across many committee jurisdictions. In short, how groups are distributed across districts, the leverage that interests have over local jobs and economic investment, and how committee jurisdictions align with their policy goals affects their influence in Congress.

Dynamic model development of adiabatic compressed air energy storage

The share of variable renewable energy in electricity generation has seen exponential growth during the recent decades, and due to the heightened pursuit of environmental targets, the trend is to continue with increased pace. The two most important resources, wind and insolation both bear the burden of intermittency, creating a need for regulation and posing a threat to grid stability. One possibility to deal with the imbalance between demand and generation is to store electricity temporarily, which was addressed in this thesis by implementing a dynamic model of adiabatic compressed air energy storage (CAES) with Apros dynamic simulation software. Based on literature review, the existing models due to their simplifications were found insufficient for studying transient situations, and despite of its importance, the investigation of part load operation has not yet been possible with satisfactory precision. As a key result of the thesis, the cycle efficiency at design point was simulated to be 58.7%, which correlated well with literature information, and was validated through analytical calculations. The performance at part load was validated against models shown in literature, showing good correlation. By introducing wind resource and electricity demand data to the model, grid operation of CAES was studied. In order to enable the dynamic operation, start-up and shutdown sequences were approximated in dynamic environment, as far as is known, the first time, and a user component for compressor variable guide vanes (VGV) was implemented. Even in the current state, the modularly designed model offers a framework for numerous studies. The validity of the model is limited by the accuracy of VGV correlations at part load, and in addition the implementation of heat losses to the thermal energy storage is necessary to enable longer simulations. More extended use of forecasts is one of the important targets of development, if the system operation is to be optimised in future.

Spectral graph theory with applications to quantum adiabatic optimization

In this dissertation I draw a connection between quantum adiabatic optimization, spectral graph theory, heat-diffusion, and sub-stochastic processes through the operators that govern these processes and their associated spectra. In particular, we study Hamiltonians which have recently become known as ``stoquastic'' or, equivalently, the generators of sub-stochastic processes. The operators corresponding to these Hamiltonians are of interest in all of the settings mentioned above. I predominantly explore the connection between the spectral gap of an operator, or the difference between the two lowest energies of that operator, and certain equilibrium behavior. In the context of adiabatic optimization, this corresponds to the likelihood of solving the optimization problem of interest. I will provide an instance of an optimization problem that is easy to solve classically, but leaves open the possibility to being difficult adiabatically. Aside from this concrete example, the work in this dissertation is predominantly mathematical and we focus on bounding the spectral gap. Our primary tool for doing this is spectral graph theory, which provides the most natural approach to this task by simply considering Dirichlet eigenvalues of subgraphs of host graphs. I will derive tight bounds for the gap of one-dimensional, hypercube, and general convex subgraphs. The techniques used will also adapt methods recently used by Andrews and Clutterbuck to prove the long-standing ``Fundamental Gap Conjecture''.

Criteria for graphic representation in musical scores: Visual elements in musical scores

A interdisciplinaridade entre a música e as artes visuais tem sido explorado por conceituados teóricos e filósofos, embora não exista muito na área da interpretação visual do grafismo de partituras musicais. Este estudo investiga como os grafismos na notação e símbolos musicais afectam o intérprete na sua transformação em som, com referência especial a partituras contemporâneas, que utilizam notação menos convencional para a criação de uma interpretação por sugestão. Outras relações entre o som e o visual são exploradas, incluindo a sinestesia, a temporalidade e a relação entre obra de arte e público. O objectivo desta dissertação é a de constituir um estudo inovativo sobre partituras musicais contemporâneas, simultaneamente do ponto de vista musical e visual. Finalmente, também vai mais longe, incluindo desenhos da própria autora inspirados e motivados pela música. Estes já não cumprem uma função de notação convencional para o músico, embora existe uma constante possibilidade de uma reinterpretação. ABSTRACT; The inter-disciplinarity between music and visual art has been explored by leading theorists and philosophers, though very little exists in the area of the visual interpretation of graphic musical scores. This study looks at how the graphics of musical notation and symbols affect the performer in transforming them into sound, with particular reference to contemporary scores that use non¬conventional notation to create an interpretation through suggestion. Other sound-visual relationships are explored, including synaesthesia, temporality and the interconnection between work of art and audience or public. This dissertation aims to be an innovative study of contemporary musical scores, from a musical as well as visual perspective. Finally, it takes a step further with drawings of my own, directly inspired and motivated by the music. These no longer fulfil a conventionally notational function for the musician, yet the potential for a re-interpretation is ever-present.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.