Queer enough? Contested terrains of identity deployment in the context of gay and lesbian public activism in Poland

Gay and lesbian prides and marches are of crucial relevance to the way in which non-heterosexual lives are imagined internationally despite regional and national differences. Quite often, these events are connected not only with increased activist mobilisation, but also with great controversy, which is the case of Poland, where gay and lesbian marches have been attacked by right-wing protesters and cancelled by right-wing city authorities on a number of occasions. Overall, the scholars analysing these events have largely focused on the macro-context of the marches, paying less attention to the movement actors behind these events. The contribution of this thesis lies not only in filling a gap when it comes to research on sexual minorities in Eastern Europe/Poland, but also in its focus on micro-level movement processes and engagement with theories of collective identity and citizenship. Furthermore, this thesis challenges the inscription of Eastern European/Polish movements into the narrative of victimhood and delayed development when compared to LGBT movements in the Global North. This thesis is grounded in qualitative research including participant observation of public activist events as well as forty semi-structured interviews with the key organisers of gay and lesbian marches in Warsaw, Poznan and Krakow between 2001 and 2007, and five of these interviews were further accompanied by photo-elicitation (self-directed photography) methods. Starting from the processes whereby from 2001 onwards, marches, pride parades and demonstrations became the most visible and contested activity of the Polish lesbian and gay movement, this thesis examines how the activists redefined the meanings of citizenship in the post-transformation context, by incorporating the theme of sexual minorities' rights. Using Bernstein's (1997, 2002, 2005, 2008) concept of identity deployment, I show how and when movement actors use identity tactically, depending on their goals. Specifically, in the context of movement-media interactions, I examine the ways in which the activists use marches to challenge the negative representations of sexual minorities in Poland. I also broaden Bernstein's framework to include the discussion of emotion work as relevant to public LGBT activism in Poland. Later, I discuss how the emotions of protests allowed the activists to inscribe their efforts into the "revolutionary" narrative of the Polish Solidarity movement and by extension, the frame of citizenship. Finally, this thesis engages with the dilemmas of identity deployment strategies, and seeks to problematise the dichotomy between identity-based gay and lesbian assimilationist strategies and the anti-identity queer politics.

Development and deployment of an autonomous micro-drilling system for cochleostomy

This thesis describes the design and development of an autonomous micro-drilling system capable of accurately controlling the penetration of complaint tissues and its application to the drilling of the cochleostomy; a key stage in the cochlea implant procedure. The drilling of the cochleostomy is a precision micro-surgical task in which the control of the burr penetration through the outer bone tissue of the cochlea is vital to prevent damage to the structures within and requires a high degree of skill to perform successfully. The micro-drilling system demonstrates that the penetration of the cochlea can be achieved consistently and accurately. Breakthrough can be detected and controlled to within 20µm of the distal surface and the hole completed without perforation of the underlying endosteal membrane, leaving the membranous cochlea intact. This device is the first autonomous surgical tool successfully deployed in the operating theatre. The system is unique due to the way in which it uses real-time data from the cutting tool to derive the state of the tool-tissue interaction. Being a smart tool it uses this state information to actively control the way in which the drilling process progresses. This sensor guided strategy enables the tool to self-reference to the deforming tissue and navigate without the need for pre-operative scan data. It is this capability that enables the system to operate in circumstances where the tissue properties and boundary conditions are unknown, without the need to restrain the patient.

Stabilization of self-coherent OFDM with injection locked laser

With the rebirth of coherent detection, various algorithms have come forth to alleviate phase noise, one of the main impairments for coherent receivers. These algorithms provide stable compensation, however they limit the DSP. With this key issue in mind, Fabry Perot filter based self coherent optical OFDM was analyzed which does not require phase noise compensation reducing the complexity in DSP at low OSNR. However, the performance of such a receiver is limited due to ASE noise at the carrier wavelength, especially since an optical amplifier is typically employed with the filter to ensure sufficient carrier power. Subsequently, the use of an injection-locked laser (ILL) to retrieve the frequency and phase information from the extracted carrier without the use of an amplifier was recently proposed. In ILL based system, an optical carrier is sent along with the OFDM signal in the transmitter. At the receiver, the carrier is extracted from the OFDM signal using a Fabry-Perot tunable filter and an ILL is used to significantly amplify the carrier and reduce intensity and phase noise. In contrast to CO-OFDM, such a system supports low-cost broad linewidth lasers and benefits with lower complexity in the DSP as no carrier frequency estimation and correction along with phase noise compensation is required.

Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates, p < 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.

Supporting decision-making for self-adaptive systems:from goal models to dynamic decision networks

Context/Motivation - Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total fulfilment of functional (goals) and non-functional requirements (softgoals). Different goalrealization strategies can have different effects on softgoals which are specified with weighted contribution-links. The final decision about what strategy to use is based, among other reasons, on a utility function that takes into account the weighted sum of the different effects on softgoals. Questions/Problems - One of the main challenges about decisionmaking in self-adaptive systems is to deal with uncertainty during runtime. New techniques are needed to systematically revise the current model when empirical evidence becomes available from the deployment. Principal ideas/results - In this paper we enrich the decision-making supported by goal models by using Dynamic Decision Networks (DDNs). Goal realization strategies and their impact on softgoals have a correspondence with decision alternatives and conditional probabilities and expected utilities in the DDNs respectively. Our novel approach allows the specification of preferences over the softgoals and supports reasoning about partial satisfaction of softgoals using probabilities. We report results of the application of the approach on two different cases. Our early results suggest the decision-making process of SASs can be improved by using DDNs. © 2013 Springer-Verlag.

A modelling and simulation environment for self-aware and self-expressive systems

Self-awareness and self-expression are promising architectural concepts for embedded systems to be equipped with to match them with dedicated application scenarios and constraints in the avionic and space-flight industry. Typically, these systems operate in largely undefined environments and are not reachable after deployment for a long time or even never ever again. This paper introduces a reference architecture as well as a novel modelling and simulation environment for self-aware and self-expressive systems with transaction level modelling, simulation and detailed modelling capabilities for hardware aspects, precise process chronology execution as well as fine timing resolutions. Furthermore, industrial relevant system sizes with several self-aware and self-expressive nodes can be handled by the modelling and simulation environment.

Self-organizing Map and Cellular Automata Combined Technique for Advanced Mesh Generation in Urban and Architectural Design

This paper presents a technique for building complex and adaptive meshes for urban and architectural design. The combination of a self-organizing map and cellular automata algorithms stands as a method for generating meshes otherwise static. This intends to be an auxiliary tool for the architect or the urban planner, improving control over large amounts of spatial information. The traditional grid employed as design aid is improved to become more general and flexible.

Improved Cryptoanalysis of the Self-shrinking P-adic Cryptographic Generator

The Self-shrinking p-adic cryptographic generator (SSPCG) is a fast software stream cipher. Improved cryptoanalysis of the SSPCG is introduced. This cryptoanalysis makes more precise the length of the period of the generator. The linear complexity and the cryptography resistance against most recently used attacks are invesigated. Then we discuss how such attacks can be avoided. The results show that the sequence generated by a SSPCG has a large period, large linear complexity and is stable against the cryptographic attacks. This gives the reason to consider the SSPSG as suitable for critical cryptographic applications in stream cipher encryption algorithms.

Stabilization of self-coherent OFDM with injection locked laser

With the rebirth of coherent detection, various algorithms have come forth to alleviate phase noise, one of the main impairments for coherent receivers. These algorithms provide stable compensation, however they limit the DSP. With this key issue in mind, Fabry Perot filter based self coherent optical OFDM was analyzed which does not require phase noise compensation reducing the complexity in DSP at low OSNR. However, the performance of such a receiver is limited due to ASE noise at the carrier wavelength, especially since an optical amplifier is typically employed with the filter to ensure sufficient carrier power. Subsequently, the use of an injection-locked laser (ILL) to retrieve the frequency and phase information from the extracted carrier without the use of an amplifier was recently proposed. In ILL based system, an optical carrier is sent along with the OFDM signal in the transmitter. At the receiver, the carrier is extracted from the OFDM signal using a Fabry-Perot tunable filter and an ILL is used to significantly amplify the carrier and reduce intensity and phase noise. In contrast to CO-OFDM, such a system supports low-cost broad linewidth lasers and benefits with lower complexity in the DSP as no carrier frequency estimation and correction along with phase noise compensation is required.

Use of Start Pages for Building a Mashup Personal Learning Environment to Support Self-Organized Learners

The paper explores the functionalities of eight start pages and considers their usefulness when used as a mashable platform for deployment of personal learning environments (PLE) for self-organized learners. The Web 2.0 effects and eLearning 2.0 strategies are examined from the point of view of how they influence the methods of gathering and capturing data, information and knowledge, and the learning process. Mashup technology is studied in order to see what kind of components can be used in PLE realization. A model of a PLE for self-organized learners is developed and it is used to prototype a personal learning and research environment in the start pages Netvibes, Pageflakes and iGoogle.

Self-aware and self-expressive camera networks

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31-OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

Self-adapting parallel metric-space search engine for variable query loads

This research focuses on automatically adapting a search engine size in response to fluctuations in query workload. Deploying a search engine in an Infrastructure as a Service (IaaS) cloud facilitates allocating or deallocating computer resources to or from the engine. Our solution is to contribute an adaptive search engine that will repeatedly re-evaluate its load and, when appropriate, switch over to a dierent number of active processors. We focus on three aspects and break them out into three sub-problems as follows: Continually determining the Number of Processors (CNP), New Grouping Problem (NGP) and Regrouping Order Problem (ROP). CNP means that (in the light of the changes in the query workload in the search engine) there is a problem of determining the ideal number of processors p active at any given time to use in the search engine and we call this problem CNP. NGP happens when changes in the number of processors are determined and it must also be determined which groups of search data will be distributed across the processors. ROP is how to redistribute this data onto processors while keeping the engine responsive and while also minimising the switchover time and the incurred network load. We propose solutions for these sub-problems. For NGP we propose an algorithm for incrementally adjusting the index to t the varying number of virtual machines. For ROP we present an ecient method for redistributing data among processors while keeping the search engine responsive. Regarding the solution for CNP, we propose an algorithm determining the new size of the search engine by re-evaluating its load. We tested the solution performance using a custom-build prototype search engine deployed in the Amazon EC2 cloud. Our experiments show that when we compare our NGP solution with computing the index from scratch, the incremental algorithm speeds up the index computation 2{10 times while maintaining a similar search performance. The chosen redistribution method is 25% to 50% faster than other methods and reduces the network load around by 30%. For CNP we present a deterministic algorithm that shows a good ability to determine a new size of search engine. When combined, these algorithms give an adapting algorithm that is able to adjust the search engine size with a variable workload.

Hybrid multi-objective optimization and hybridized self-organizing response surface method

Numerical optimization is a technique where a computer is used to explore design parameter combinations to find extremes in performance factors. In multi-objective optimization several performance factors can be optimized simultaneously. The solution to multi-objective optimization problems is not a single design, but a family of optimized designs referred to as the Pareto frontier. The Pareto frontier is a trade-off curve in the objective function space composed of solutions where performance in one objective function is traded for performance in others. A Multi-Objective Hybridized Optimizer (MOHO) was created for the purpose of solving multi-objective optimization problems by utilizing a set of constituent optimization algorithms. MOHO tracks the progress of the Pareto frontier approximation development and automatically switches amongst those constituent evolutionary optimization algorithms to speed the formation of an accurate Pareto frontier approximation. Aerodynamic shape optimization is one of the oldest applications of numerical optimization. MOHO was used to perform shape optimization on a 0.5-inch ballistic penetrator traveling at Mach number 2.5. Two objectives were simultaneously optimized: minimize aerodynamic drag and maximize penetrator volume. This problem was solved twice. The first time the problem was solved by using Modified Newton Impact Theory (MNIT) to determine the pressure drag on the penetrator. In the second solution, a Parabolized Navier-Stokes (PNS) solver that includes viscosity was used to evaluate the drag on the penetrator. The studies show the difference in the optimized penetrator shapes when viscosity is absent and present in the optimization. In modern optimization problems, objective function evaluations may require many hours on a computer cluster to perform these types of analysis. One solution is to create a response surface that models the behavior of the objective function. Once enough data about the behavior of the objective function has been collected, a response surface can be used to represent the actual objective function in the optimization process. The Hybrid Self-Organizing Response Surface Method (HYBSORSM) algorithm was developed and used to make response surfaces of objective functions. HYBSORSM was evaluated using a suite of 295 non-linear functions. These functions involve from 2 to 100 variables demonstrating robustness and accuracy of HYBSORSM.

A gender study investigating physics self-efficacy

The underrepresentation of women in physics has been well documented and a source of concern for both policy makers and educators. My dissertation focuses on understanding the role self-efficacy plays in retaining students, particularly women, in introductory physics. I use an explanatory mixed methods approach to first investigate quantitatively the influence of self-efficacy in predicting success and then to qualitatively explore the development of self-efficacy. In the initial quantitative studies, I explore the utility of self-efficacy in predicting the success of introductory physics students, both women and men. Results indicate that self-efficacy is a significant predictor of success for all students. I then disaggregate the data to examine how self-efficacy develops differently for women and men in the introductory physics course. Results show women rely on different sources of self-efficacy than do men, and that a particular instructional environment, Modeling Instruction, has a positive impact on these sources of self-efficacy. In the qualitative phase of the project, this dissertation focuses on the development of self-efficacy. Using the qualitative tool of microanalysis, I introduce a methodology for understanding how self-efficacy develops moment-by-moment using the lens of self-efficacy opportunities. I then use the characterizations of self-efficacy opportunities to focus on a particular course environment and to identify and describe a mechanism by which Modeling Instruction impacts student self-efficacy. Results indicate that the emphasizing the development and deployment of models affords opportunities to impact self-efficacy. The findings of this dissertation indicate that introducing key elements into the classroom, such as cooperative group work, model development and deployment, and interaction with the instructor, create a mechanism by which instructors can impact the self-efficacy of their students. Results from this study indicate that creating a model to impact the retention rates of women in physics should include attending to self-efficacy and designing activities in the classroom that create self-efficacy opportunities.