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In the worldwide knowledge-based economy, intellectual property (IP) is recognised as a key business asset. In the digital economy, whether for large corporates, SMEs or start-ups, it is not uncommon that innovative technologies, devices (products) and services are built from a complex set of IP. As organisations increasingly employ open innovation processes, IP is likely to not only result from internal R&D, but also from external sources, such as R&D partners, complementors and even competitors. Building and maintaining IP-based competitive advantage then rests on the effective acquisition (internal and external), exploitation and enforcement of IP. When having to manage complex IP portfolios and actor relations, an IP strategy is useful to provide guidance to decision makers. While business executives often express the need for better IP management tools and IP strategy formulation approaches, the corresponding literature is surprisingly scarce. In this thesis, a roadmapping-based approach is proposed for IP strategy formulation. While roadmapping is a widely adopted approach for strategy formulation at all firm levels, it has hardly been discussed in the IP management literature. Therefore, based on the strategic roadmapping approach, the IP roadmapping framework and the practical process model were developed for especially IP strategy formulation. The proposed IP strategy formulation approach integrates different strategy levels, provides clear visibility and facilitates communication and consensus among stakeholders in order to increase the prospects for better decision-making. The IP strategy formulation process is business centric, starting with an identification of business strategy objectives that can be supported by the means of IP, for instance, the creating of entry barriers and minimisation of the threats from substitutes. Throughout the process, these IP objectives are gradually translated into specific IP actions. The roadmapping approach then allows to align the timing and sequencing of these IP actions in support of the organisation's business objectives. The sequence of the IP actions can then be synthesised into a coherent IP strategy. The roadmapping-based IP strategy formulation approach was developed using a procedural action research approach. The prototype has been developed based on literature review and 20 interviews in total with IP or roadmapping experts, and then the approach has been tested and refined through three action research cycles based on 12 interviews with 17 IP practitioners and roadmapping experts, and 14 roadmapping workshops involving IP managers, innovation experts and business executives of large international companies, SMEs and start-ups. Accordingly, this research contributes to both practice and theory. It not only provides a novel roadmapping approach which enables and facilitates structured conversations among executives, innovation, technology and IP experts, who often find it difficult to engage in conversations about IP decision making, but also contributes to the IP management literature in which there is a paucity of studies on IP strategy formulation in practice. It also contributes to the roadmapping literature proposing a novel use case for this widely adopted strategy formulation approach. Additionally, this research also contributes to the strategic management literature with elaborations on the concepts of IP strategy and strategic alignment. The next stage for this research will be disseminating the tool widely and evaluating its commercialisation value for different purposes. This is going to be completed through close collaboration with IfM ECS and one independent technical consulting firm specialising in matters relating to IP. In addition, to help equip entrepreneurs with the tools they need for IP strategy formulation, the materials developed through this research will be made available online for downloading. The hope is that the materials can enable companies to conduct IP strategy formulation exercises by themselves, but also that this openly accessible material will be picked up by entrepreneurship teaching programmes. By spreading good practice on IP strategy formulation, it is expected that the toolkit can help companies to overcome some of the obstacles and maximise value from their IP.

This study conceptualises Chinese language-major undergraduates' motivation to learn a third language (L3) from a self perspective. Two overarching aims were adopted to guide the research: how learners' L3 motivation was formulated and reformulated over the course of one year of learning an L3 and whether classroom intervention could help learners to construct their L3 motivation. This research adopted a longitudinal case study design and was situated in a state university in China. Students who had chosen to learn an L3 as their major and English majors who were required to learn an L3 participated in the research. To achieve the two principal research aims, the data collection process was divided into two phases. The goal of the first phase was to explore the development of my participants' L3 motivation without any intervention. Qualitative research methods were employed during this phase and data were gathered from open questionnaires, interviews, class observation and written journals. In the second phase, an intervention was carried out to explore how to construct my participants' ideal L3 selves. A quasi-experimental design was employed and mixed methods were adopted. Analysis was primarily guided by the L2 Motivational Self System and was carried out at both at class level and individual level. At a class level, findings suggest that both L3 majors' and English majors' L3 motivation was mainly constituted on the basis of their ideal L3 selves, ought-to L3 selves and L3 learning experiences, which were constantly constructed and reconstructed over the course of learning. Notably, learners' motivational trajectories did not display a homogenous pattern at the group level. In total, six different motivational patterns were identified, three from English majors and three from L3 majors. It was also interesting to identify that a few learners developed a type of multilingually oriented motivation during their L3 learning. Data collected at the second phase of fieldwork revealed that the intervention was effective in helping English majors to construct their ideal L3 selves but less useful in the case of L3 majors. At the individual level, six cases were analysed in depth to investigate how the trajectories identified at the group level developed temporally and contextually. The analysis shows that the construction of these learners' L3 motivation involved a complex interplay between their future L3 selves and current L3 learning experience. It was through this process that learners explored the relationship between the L3 and their self-identification, and attempted to develop their personal meaning of learning an L3. On the basis of the empirical evidence, this thesis argues that the construction of learners' L3 motivation hinges on exploration of the position of the L3 in their self-identification, and that class intervention might help learners to realise the importance of learning an L3 by helping them to develop their ideal L3 selves, at least in the case of non-L3-major students. Moreover, this study suggests that it is crucial for L3 learners in China to recognise the value of being multilingual, which plays an essential role in constructing their L3 selves and sustaining their L3 motivation.

The synergy between dynamical systems and deep learning (DL) has become an increasingly popular research topic because of the limitation of classic methods and the great potential of DL in addressing these challenges through the data-fitting and feature-extraction power of deep neural networks (DNNs). DNNs have demonstrated their ability to approximate highly complicated functions while enjoying good trainability, which can help dynamical system modeling with both the search of solution and the expressiveness of models. Furthermore, the feature extraction ability of DNNs have proven useful in identifying system states identification when the state cannot be defined from first-principles.Conversely, the study of dynamical systems has benefited DL. Viewing DNNs as discretization of ordinary differential equations (ODEs) inspires a novel family of models named neural differential equations which offer unique advantages in time series learning, especially the modeling of dynamical systems. From another perspective, viewing the optimization of deep learning models as a dynamical system on the loss landscape enables better analysis and enhancement of the optimization processes.This work focuses on this interplay. We develop novel deep learning methods to efficiently model dynamical systems, incorporating physical prior knowledge and meta-learning techniques. By analyzing the dynamics of the optimization process, we also design a novel variant of stochastic gradient descent to enhance the resilience of DNNs against weight perturbations, enabling their deployment on analog in-memory computing platforms where analog noise is inevitable. Through these investigations, we contribute to the growing body of research on the intersection of dynamical systems and deep learning, paving the way for innovative solutions to complex real-world problems.

My dissertation studies the political impacts of media and information technologies in American history. The first chapter employs novel data to examine the electric telegraph's impacts on political participation and news coverage in the mid-19th century America. I use proximity to daily newspapers with telegraphic connection to Washington to generate plausibly exogenous variation in access to telegraphed news from Washington. I find that access to Washington news with less delay increased presidential election turnout. Text analysis on historical newspapers shows that the improved access to news from Washington led newspapers to cover more national political news, including coverage of Congress, the presidency, and sectional divisions involving slavery. The results suggest that the telegraph made newspapers less parochial, facilitated a national conversation and increased political participation. The second chapter investigates the political impacts of the first populist radio personality in American history. Father Charles Coughlin blended populist demagoguery, anti-Semitism, and fascist sympathies to create a hugely popular radio program that attracted tens of millions of listeners throughout the 1930s. I digitized unique data on Father Coughlin’s radio network. Exploiting topography to generate plausibly exogenous variation in radio signal strength as well as another difference-in-differences strategy, I find strong evidence that Coughlin’s anti-Roosevelt broadcast reduced the support for Franklin D. Roosevelt in presidential elections. Moreover, Coughlin’s broadcast appeared to influence public sentiment concerning WWII and anti-Semitism. The third chapter studies whether media and information technologies can empower minorities in the resistance to oppression. Specifically, I assemble a novel dataset to study the effects of black radio on black political activism and participation during the civil rights movement. Exploiting plausibly exogenous variation in signal reception resulting from topographic factors, I find strong evidence that black radio increased black voter registration and the presence of NAACP chapters in the South during the early 1960s. I explore potential mechanisms and also provide evidence that black radio led to greater political power and economic benefits for Southern blacks, as measured by state aid transfers and legislative support for civil rights bills in Congress.

My thesis On Your Own is an 18-minute short fiction film focusing on the relationship between a daughter and her dying father. After his wife was killed in a car accident, Frank, as a single father, raises his daughter Giana who has Down syndrome on his own. Frank becomes informed that he is in the terminal stage of lung cancer. He teaches Giana how to live on her own and tries different ways to find a suitable facility to take care of Giana after his death. The film was shot in HD format using Black Magic Pocket Cinema Camera with Zeiss Lenses, edited in Adobe Premiere Pro CC and Pro Tools and color graded in DaVinci Resolve 11. In this paper, I will discuss my experience during the whole production processes, and explore goals and the authentic works from its original conceptions to the first official screening of my at Rochester Institute of Technology (RIT).

Atherosclerosis and specifically rupture of vulnerable plaques account for 23% of all deaths worldwide, far surpassing both infectious diseases and cancer. Plaque-based macrophages, often associated with lipid deposits, contribute to atherogenesis from initiation through progression, plaque rupture and ultimately, thrombosis. Therefore, the macrophage is an important early cellular marker related to vulnerability of atherosclerotic plaques. The objective of my research is to assess the ability of multiple optical imaging modalities to detect, and further characterize the distribution of macrophages (having taken up plasmonic gold nanoparticles as a contrast agent) and lipid deposits in atherosclerotic plaques. Tissue phantoms and macrophage cell cultures were used to investigate the capability of nanorose as an imaging contrast agent to target macrophages. Ex vivo aorta segments from a rabbit model of atherosclerosis after intravenous nanorose injection were imaged by optical coherence tomography (OCT), photothermal imaging (PTW) and two-photon luminescence microscopy (TPLM), respectively. OCT images depicted detailed surface structure of atherosclerotic plaques. PTW images identified nanorose-loaded macrophages (confirmed by co-registration of a TPLM image and corresponding RAM-11 stain on a histological section) associated with lipid deposits at multiple depths. TPLM images showed three-dimensional distribution of nanorose-loaded macrophages with a high spatial resolution. Imaging results suggest that superficial nanorose-loaded macrophages are distributed at shoulders on the upstream side of atherosclerotic plaques at the edges of lipid deposits. Combination of OCT with PTW or TPLM can simultaneously reveal plaque structure and composition, permitting assessment of plaque vulnerability during cardiovascular interventions.

The degradation process of a complex system may be affected by many unknown factors, such as unidentified fault modes, unmeasured operational conditions, engineering variance, environmental conditions, etc. These unknown factors not only complicate the degradation behaviors of the system, but also lower the quality of the collected data for modeling. Due to lack of knowledge and incomplete measurements, certain important context information (e.g. fault modes, operational conditions) of the collected data will be missing. Therefore historical data of the system with a large variety of degradation patterns will be mixed together. With such data, learning a global model for Remaining Useful Life (RUL) prediction becomes extremely hard. This has led us to look for advanced RUL prediction techniques beyond the traditional global models. In this thesis, a novel RUL prediction method inspired by the Instance Based Learning methodology, called Trajectory Similarity Based Prediction (TSBP), is proposed. In TSBP, the historical instances of a system with life-time condition data and known failure time are used to create a library of degradation models. For a test instance of the same system whose RUL is to be estimated, similarity between it and each of the degradation models is evaluated by computing the minimal weighted Euclidean distance defined on two degradation trajectories. Based on the known failure time, each of the degradation models will produce one RUL estimate for the test instance. The final RUL estimate can then be obtained by aggregating the multiple RUL estimates using a density estimation method. A case study using the turbofan engine degradation simulation data supplied by NASA Ames is provided to study the performance of TSBP. In this study, the TSBP method has demonstrated significant improvement in performance over a Neural Network based prediction method.