Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization

We study the question of reconstructing two signals $f$ and $g$ from their convolution $y = f\ast g$. This problem, known as {\em blind deconvolution}, pervades many areas of science and technology, including astronomy, medical imaging, optics, and wireless communications. A key challenge of this intricate non-convex optimization problem is that it might exhibit many local minima. We present an efficient numerical algorithm that is guaranteed to recover the exact solution, when the number of measurements is (up to log-factors) slightly larger than the information-theoretical minimum, and under reasonable conditions on $f$ and $g$. The proposed regularized gradient descent algorithm converges at a geometric rate and is provably robust in the presence of noise. To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions. Moreover, numerical experiments do not only provide empirical verification of our theory, but they also demonstrate that our method yields excellent performance even in situations beyond our theoretical framework

RISE-Based Adaptive Control with Mass-Inertia Parameter Estimation for Aerial Transportation of Multi-Rotor UAVs

This paper proposes an adaptive tracking strategy with mass-inertia estimation for aerial transportation problems of multi-rotor UAVs. The dynamic model of multi-rotor UAVs with disturbances is firstly developed with a linearly parameterized form. Subsequently, a cascade controller with the robust integral of the sign of the error (RISE) terms is applied to smooth the control inputs and address bounded disturbances. Then, adaptive estimation laws for mass-inertia parameters are designed based on a filter operation. Such operation is introduced to extract estimation errors exploited to theoretically guarantee the finite-time (FT) convergence of estimation errors. Finally, simulations are conducted to verify the effectiveness of the designed controller. The results show that the proposed method provides better tracking and estimation performance than traditional adaptive controllers based on sliding mode control algorithms and gradient-based estimation strategies

Knowledge Domains and Skills that Facilitate Knowledge Sharing in Project Management - A Case Study in the Chinese Construction Industry

The aim of this thesis is to identify different sets of skills that facilitate the knowledge sharing practice of project managers within the context of a construction project. This aim stems from a gap identified in the knowledge sharing literature concerning the individual skills that contribute to knowledge sharing by project managers in the applied setting of construction projects. In order to achieve the research aim, an exploratory qualitative study was conducted following a combination of Grounded Theory and case study as the research method. The study focuses specifically on a construction project in China. The construction industry in China has been experiencing an increasing development as a result of the national economy’s sustained growth and continuing urbanisation trends, but it is still confronted with challenges in knowledge sharing practice especially concerning the role of project manager, who performs the high level control of projects. Grounded Theory is the main method and a case study provides the appropriate context for the research. Empirical data were collected through a total of twenty-one interviews at a five-star hotel construction project, located in Hebei Province, eastern China. Following the constant comparison method, iterations in data analysis contributed to the development of an integrative framework. The framework indicates knowledge pertaining to five domains, including risk, planning, implementation, people, and business strategies and operations, needs to be shared by project managers. It also illustrates three sets of skills that contribute to the practice of sharing knowledge. Social cognitive skills assist project managers in interpreting differences in knowledge and achieving mutual understanding; interpersonal skills facilitate knowledge sharing through creating a positive project environment; strategic orientation skills contribute to reaching agreement among participating organisations and stakeholders. Furthermore, the framework reveals the specific relationships between the knowledge domains and skills, within the three phases of the construction project. In addition, findings suggest that the sharing of knowledge and the application of skills are of a dynamic and relational nature. The project is a collective and interactive process where knowledge pertaining to different domains needs to be dynamically shared and skills need to be dynamically applied. The knowledge domains and skills do not operate independently but overlap and interact over the duration of the project. Moreover, they are open to different interpretations according to various positions of actors within the project. This thesis contributes to an enhanced theoretical understanding of skills for knowledge sharing in the specific context of construction projects. It also delivers practical guidance for project managers on how to develop and apply the skills in these knowledge sharing practice

OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?

This paper presents OxfordTVG-HIC (Humorous Image Captions), a large-scale dataset for humour generation and understanding. Humour is an abstract, subjective, and context-dependent cognitive construct involving several cognitive factors, making it a challenging task to generate and interpret. Hence, humour generation and understanding can serve as a new task for evaluating the ability of deep-learning methods to process abstract and subjective information. Due to the scarcity of data, humour-related generation tasks such as captioning remain under-explored. To address this gap, OxfordTVG-HIC offers approximately 2.9M image-text pairs with humour scores to train a generalizable humour captioning model. Contrary to existing captioning datasets, OxfordTVG-HIC features a wide range of emotional and semantic diversity resulting in out-of-context examples that are particularly conducive to generating humour. Moreover, OxfordTVG-HIC is curated devoid of offensive content. We also show how OxfordTVG-HIC can be leveraged for evaluating the humour of a generated text. Through explainability analysis of the trained models, we identify the visual and linguistic cues influential for evoking humour prediction (and generation). We observe qualitatively that these cues are aligned with the benign violation theory of humour in cognitive psychology.Comment: Accepted by ICCV 202

Enablers for embedding big data solutions in smart factories: an empirical investigation

This study provides insight into the enablers that assist organizations in implementing big data solutions in their smart factory development, as well as the interrelationships between these enablers from an information system (IS) perspective. The research followed an inductive qualitative approach. Twenty-two in-depth semi-structured interviews were conducted with experienced consultants and IT managers from SAP Consultancy Company, and general managers and engineers from Xiamen Intretech Inc., a leading manufacturing company in adopting big data solutions in smart factory. Following thematic analysis approach, three sets of enablers including organization, technology and external environment were identified together with the interrelationships between them. This paper extends the current understanding of smart factory and big data solutions in information system research through offering an empirical investigation of different enablers in this context. The findings also provide recommendations for practitioners to increase the possibilities of success when implementing big data solutions in smart factory context

Midwest Crop Farmers’ Perceptions of the U.S.-China Trade War

The trade dispute between the United States and China that began in 2018 quickly reached an unprecedented level. As of June 2019, several rounds of talks failed to prevent the United States from imposing tariffs on more than $250 billion worth of Chinese products