Windowed-Wigner Representations, Interferences and Operators

2010 Mathematics Subject Classification: 42B10, 47A07, 35S05.

First-Order Conditions for Optimization Problems with Quasiconvex Inequality Constraints

2000 Mathematics Subject Classification: 90C46, 90C26, 26B25, 49J52.

Robust and high current cold electron source based on carbon nanotube field emitters and electron multiplier microchannel plate

The aim of this research was to demonstrate a high current and stable field emission (FE) source based on carbon nanotubes (CNTs) and electron multiplier microchannel plate (MCP) and design efficient field emitters. In recent years various CNT based FE devices have been demonstrated including field emission displays, x-ray source and many more. However to use CNTs as source in high powered microwave (HPM) devices higher and stable current in the range of few milli-amperes to amperes is required. To achieve such high current we developed a novel technique of introducing a MCP between CNT cathode and anode. MCP is an array of electron multipliers; it operates by avalanche multiplication of secondary electrons, which are generated when electrons strike channel walls of MCP. FE current from CNTs is enhanced due to avalanche multiplication of secondary electrons and in addition MCP also protects CNTs from irreversible damage during vacuum arcing. Conventional MCP is not suitable for this purpose due to the lower secondary emission properties of their materials. To achieve higher and stable currents we have designed and fabricated a unique ceramic MCP consisting of high SEY materials. The MCP was fabricated utilizing optimum design parameters, which include channel dimensions and material properties obtained from charged particle optics (CPO) simulation. Child Langmuir law, which gives the optimum current density from an electron source, was taken into account during the system design and experiments. Each MCP channel consisted of MgO coated CNTs which was chosen from various material systems due to its very high SEY. With MCP inserted between CNT cathode and anode stable and higher emission current was achieved. It was ∼25 times higher than without MCP. A brighter emission image was also evidenced due to enhanced emission current. The obtained results are a significant technological advance and this research holds promise for electron source in new generation lightweight, efficient and compact microwave devices for telecommunications in satellites or space applications. As part of this work novel emitters consisting of multistage geometry with improved FE properties were was also developed.

On Provable Algorithms for Determination of Continuous Protein Interdomain Motions from Residual Dipolar Couplings

Dynamics of biomolecules over various spatial and time scales are essential for biological functions such as molecular recognition, catalysis and signaling. However, reconstruction of biomolecular dynamics from experimental observables requires the determination of a conformational probability distribution. Unfortunately, these distributions cannot be fully constrained by the limited information from experiments, making the problem an ill-posed one in the terminology of Hadamard. The ill-posed nature of the problem comes from the fact that it has no unique solution. Multiple or even an infinite number of solutions may exist. To avoid the ill-posed nature, the problem needs to be regularized by making assumptions, which inevitably introduce biases into the result.

Here, I present two continuous probability density function approaches to solve an important inverse problem called the RDC trigonometric moment problem. By focusing on interdomain orientations we reduced the problem to determination of a distribution on the 3D rotational space from residual dipolar couplings (RDCs). We derived an analytical equation that relates alignment tensors of adjacent domains, which serves as the foundation of the two methods. In the first approach, the ill-posed nature of the problem was avoided by introducing a continuous distribution model, which enjoys a smoothness assumption. To find the optimal solution for the distribution, we also designed an efficient branch-and-bound algorithm that exploits the mathematical structure of the analytical solutions. The algorithm is guaranteed to find the distribution that best satisfies the analytical relationship. We observed good performance of the method when tested under various levels of experimental noise and when applied to two protein systems. The second approach avoids the use of any model by employing maximum entropy principles. This 'model-free' approach delivers the least biased result which presents our state of knowledge. In this approach, the solution is an exponential function of Lagrange multipliers. To determine the multipliers, a convex objective function is constructed. Consequently, the maximum entropy solution can be found easily by gradient descent methods. Both algorithms can be applied to biomolecular RDC data in general, including data from RNA and DNA molecules.

El Estado legislando, relevando, mapeando. Una etnografía de la implementación del Programa de Relevamiento Territorial de Comunidades Indígenas en Salta (Argentina)

Tomando como punto de partida mi colaboración en la recolección y la sistematización de datos vinculados al relevamiento territorial en distintas comunidades guaraníes del noroeste salteño, en apoyo al Programa de Relevamiento Territorial de Comunidades Indígenas (Re.Te.CI) y en el marco de la Ley de Emergencia en materia de posesión y propiedad comunitaria indígena (Ley 26.160), el presente artículo describe y analiza distintos momentos de la implementación del programa. Fundamentalmente, exploro las formas particulares que el mismo asumió en la provincia de Salta focalizándome en el Departamento General San Martín, siendo esta la región de mayor diversidad étnica de la provincia. Inspirada en la propuesta de Ferguson y Gupta (2002) de pensar al Estado como “una experiencia vivida” utilizo materiales etnográficos de tres comunidades indígenas donde se aplicó el programa de relevamiento territorial y me centro en una de ellas para pensar las formas que adopta la relación entre el Estado y los pueblos indígenas.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.

Problems in Algebraic Vision

Thesis (Ph.D.)--University of Washington, 2016-08

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

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.

T-duality without isometry via extended gauge symmetries of 2D sigma models

Target space duality is one of the most profound properties of string theory. However it customarily requires that the background fields satisfy certain invariance conditions in order to perform it consistently; for instance the vector fields along the directions that T-duality is performed have to generate isometries. In the present paper we examine in detail the possibility to perform T-duality along non-isometric directions. In particular, based on a recent work of Kotov and Strobl, we study gauged 2D sigma models where gauge invariance for an extended set of gauge transformations imposes weaker constraints than in the standard case, notably the corresponding vector fields are not Killing. This formulation enables us to follow a procedure analogous to the derivation of the Buscher rules and obtain two dual models, by integrating out once the Lagrange multipliers and once the gauge fields. We show that this construction indeed works in non-trivial cases by examining an explicit class of examples based on step 2 nilmanifolds.

Quantifying the impacts of urban wind sheltering on the building energy consumption

Common building energy modeling approaches do not account for the influence of surrounding neighborhood on the energy consumption patterns. This thesis develops a framework to quantify the neighborhood impact on a building energy consumption based on the local wind flow. The airflow in the neighborhood is predicted using Computational Fluid Dynamics (CFD) in eight principal wind directions. The developed framework in this study benefits from wind multipliers to adjust the wind velocity encountering the target building. The input weather data transfers the adjusted wind velocities to the building energy model. In a case study, the CFD method is validated by comparing with on-site temperature measurements, and the building energy model is calibrated using utilities data. A comparison between using the adjusted and original weather data shows that the building energy consumption and air system heat gain decreased by 5% and 37%, respectively, while the cooling gain increased by 4% annually.

Reading keys in the communication of wine in China. Wine marketing strategies

Mestrado Vinifera Euromaster - Instituto Superior de Agronomia - UL

Polyhedral results for the Cardinality Constrained Multi-cycle Problem (CCMcP) and the Cardinality Constrained Cycles and Chains Problem (CCCCP)

In recent years, there has been studies on the cardinality constrained multi-cycle problems on directed graphs, some of which considered chains co-existing on the same digraph whilst others did not. These studies were inspired by the optimal matching of kidneys known as the Kidney Exchange Problem (KEP). In a KEP, a vertex on the digraph represents a donor-patient pair who are related, though the kidney of the donor is incompatible to the patient. When there are multiple such incompatible pairs in the kidney exchange pool, the kidney of the donor of one incompatible pair may in fact be compatible to the patient of another incompatible pair. If Donor A’s kidney is suitable for Patient B, and vice versa, then there will be arcs in both directions between Vertex A to Vertex B. Such exchanges form a 2-cycle. There may also be cycles involving 3 or more vertices. As all exchanges in a kidney exchange cycle must take place simultaneously, (otherwise a donor can drop out from the program once his/her partner has received a kidney from another donor), due to logistic and human resource reasons, only a limited number of kidney exchanges can occur simultaneously, hence the cardinality of these cycles are constrained. In recent years, kidney exchange programs around the world have altruistic donors in the pool. A sequence of exchanges that starts from an altruistic donor forms a chain instead of a cycle. We therefore have two underlying combinatorial optimization problems: Cardinality Constrained Multi-cycle Problem (CCMcP) and the Cardinality Constrained Cycles and Chains Problem (CCCCP). The objective of the KEP is either to maximize the number of kidney matches, or to maximize a certain weighted function of kidney matches. In a CCMcP, a vertex can be in at most one cycle whereas in a CCCCP, a vertex can be part of (but in no more than) a cycle or a chain. The cardinality of the cycles are constrained in all studies. The cardinality of the chains, however, are considered unconstrained in some studies, constrained but larger than that of cycles, or the same as that of cycles in others. Although the CCMcP has some similarities to the ATSP- and VRP-family of problems, there is a major difference: strong subtour elimination constraints are mostly invalid for the CCMcP, as we do allow smaller subtours as long as they do not exceed the size limit. The CCCCP has its distinctive feature that allows chains as well as cycles on the same directed graph. Hence, both the CCMcP and the CCCCP are interesting and challenging combinatorial optimization problems in their own rights. Most existing studies focused on solution methodologies, and as far as we aware, there is no polyhedral studies so far. In this paper, we will study the polyhedral structure of the natural arc-based integer programming models of the CCMcP and the CCCCP, both containing exponentially many constraints. We do so to pave the way for studying strong valid cuts we have found that can be applied in a Lagrangean relaxation-based branch-and-bound framework where at each node of the branch-and-bound tree, we may be able to obtain a relaxation that can be solved in polynomial time, with strong valid cuts dualized into the objective function and the dual multipliers optimised by subgradient optimisation.

Input–output structures of the Australian construction industry

The Australian construction industry continues to play an important role in the national economy. Analysis using input–output tables makes it possible to understand the role of the construction industry in Australia’s economy and its relationships to other major industries over years. This study applies several economic indicators to investigate the construction industry’s contributions to gross national product and gross national income, as well as its backward and forward linkage indicators, and its output and input multipliers. The paper also investigates the purchases of goods and services by the construction industry from other sectors and its sales to other industries over the analysis period. Findings from this research may help policymakers to better understand the economic linkages between the construction industry and other major industries, and the structural changes in its inputs and outputs in relation to these others.