Learning Approach to Optical Tomography

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We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning app ...

Researchers have invested much effort into developing kinetic models due to their ability to capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed and realistic representation of cellular processes. Hist ...

The optical domain presents potential avenues for enhancing both computing and communication due to its inherent properties of bandwidth, parallelism, and energy efficiency. This research focuses on harnessing 3-Dimensional (3D) diffractive optics for nove ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Here, we introduce a design, fabrication, and control methodology for large amplitude torsional microactuators powered by ultrasound. The microactuators are 3D printed from two polymers with drastically different elastic moduli as a monolithic compliant me ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Phase contrast imaging (PCI) is an established and powerful technique for measuring density fluctuations in plasmas and has been successfully applied to several fusion devices. Rooted in a concept first developed for microscopy, PCI belongs to the category ...

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Evaluating the visual quality of distorted images is a crucial task in the field of image compression, as artifacts may significantly impair the appeal and fidelity of images, therefore reducing the user experience. As assessing the visual quality of image ...

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

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Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Learning Approach to Optical Tomography

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An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games

The Dawn of High-throughput and Genome-scale Kinetic Modeling: Recent Advances and Future Directions

3D diffractive optics for linear interconnects and nonlinear processing

Aggregating Spatial and Photometric Context for Photometric Stereo

Incorporating Projective Geometry into Deep Learning

3D printed large amplitude torsional microactuators powered by ultrasound

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Tangentially viewing phase contrast imaging for turbulence measurements in toroidal fusion devices

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An adaptive phase field approach to 3D internal crack growth in rocks

Topics in statistical physics of high-dimensional machine learning

A neurometabolic mechanism involving dmPFC/dACC lactate in physical effort-based decision-making

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

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