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Showing posts from April, 2024

About technological innovation

Throughout my three decades in academia, I've observed a continuous loop of conversations highlighting the vital role of innovation, reform, and the imperative for pioneering work in research and development (R&D). The principle of "High Risk and High Return" has been a central theme, with a strong push for long-term visions over short-term gains. However, this consistent focus has led to a widespread sense of exhaustion, fueled by the concerning observation that most innovative efforts remain trapped within academic circles, barely touching the wider industrial landscape. Academics making breakthroughs often have limited capacity to steer these innovations towards becoming successful commercial products. Industry-academia collaborations struggle to bear fruit due to a variety of subtle and complex reasons, leading numerous promising innovations to become mere line items on a curriculum vitae, devoid of practical implementation. Finding effective R&D policies that...

Utilizing Implicit Neural Representations for Solving Ill-posed Inverse Problems

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Recently, the field of medical imaging has witnessed numerous attempts aimed at producing high-resolution images with significantly insufficient measured data. These endeavors are motivated by a variety of objectives, such as reducing data acquisition times, enhancing cost efficiency, minimizing invasiveness, and elevating patient comfort, among other factors. Nevertheless, these efforts necessitate tackling severely ill-posed inverse problems, due to the significant imbalance between the number of unknown variables (needed for desired resolution) and the number of available equations (derived from measured data). For a clearer understanding, let's examine a linear system represented by \mathbf{A}I = \mathbf{b}_{I} + \mathbf{\epsilon} , where \mathbf{A} represents an m \times n matrix with a highly underdetermined scenario ( m \ll n ). This matrix \mathbf{A} serves as a linearized forward model. In this formulation, I is an n-dimensional vector representing the imag...