DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP

Kernelized Offline Contextual Dueling Bandits

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we take advantage of the fact that often the agent can choose contexts at which to obtain human feedback in order to most efficiently identify a good policy, and introduce the offline contextual dueling bandit setting. We give an upper-confidence-bound style algorithm for this setting and prove a regret bound. We also give empirical confirmation that this method outperforms a similar strategy that uses uniformly sampled contexts

Exploration via Planning for Information about the Optimal Trajectory

Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.Comment: Conference paper at Neurips 2022. Code available at https://github.com/fusion-ml/trajectory-information-rl. arXiv admin note: text overlap with arXiv:2112.0524

Sample-Efficient Reinforcement Learning with Applications in Nuclear Fusion

 In many practical applications of reinforcement learning (RL), it is expensive to observe state transitions from the environment. In the problem of plasma control for nuclear fusion, the motivating example of this thesis, determining the next state for a given state-action pair requires querying an expensive transition function which can lead to many hours of computer simulation or dollars of scientific research. Such expensive data collection prohibits application of standard RL algorithms which usually require a large number of observations to learn. In this thesis, I address the problem of efficiently learning a policy from a relatively modest number of observations, motivated by the application of automated decision making and control to nuclear fusion. The first section presents four approaches developed to evaluate the prospective value of data in learning a good policy and discusses their performance, guarantees, and application. These approaches address the problem through the lenses of information theory, decision theory, the optimistic value gap, and learning from comparative feedback. We apply this last method to reinforcement learning from human feedback for the alignment of large language models. The second presents work which uses physical prior knowledge about the dynamics to more quickly learn an accurate model. Finally, I give an introduction to the problem setting of nuclear fusion, present recent work optimizing the design of plasma current rampdowns at the DIII-Dtokamak, and discuss future applications of AI in fusion </p

DATA FUSION OF MULTISPECTRAL REMOTE SENSING MEASUREMENTS USING WAVELET TRANSFORM

MEHTA, VIRAJ KIRANKUMAR. Data fusion of multispectral remote sensing measurements using wavelet transform. (Under the direction of Dr. Hamid Krim.) This thesis focuses on fusion of multispectral data available from remote sensing instruments. The aim is to develop fast and memory efficient algorithms that may be used for real-time implementation aboard satellites. Multiple channel data from the SSM/I instrument are used for experiments. Starting with a Bayesian estimation formulation of the data fusion problem, an attempt is made to take advantage of the sparseness resulting from wavelet transforms to optimize computational efficiency. After generating the necessary statistical models for the data to be estimated, a preconditioning whitening filter, which simplifies the choice of the required wavelet transform, is developed. The significant gains obtained by a compact representation in wavelet basis are shown. An input grid transformation leading to channel filters is then used to construct a real-time implementation of the optimal estimator. Simulated results of such a system are then used to demonstrate the achieved improvement in field resolution. In conclusion, a direction for future work is laid out for improving the estimatio

Congenital Tonic Pupils Associated With Congenital Central Hypoventilation Syndrome and Hirschsprung Disease

Autonomic dysfunction can be associated with pupillary abnormalities. We describe a rare association of tonic pupils, congenital central hypoventilation syndrome, and Hirschsprung disease in a newborn with a mutation in the PHOX2B gene, a key regulator of neural crest cells. Hirschsprung disease is characterized by the congenital absence of neural crest-derived intrinsic ganglion cells. Tonic pupils may result from an abnormality of the ciliary ganglion, another structure of neural crest origin. The close association of these conditions in this child suggests a common abnormality in neural crest migration and differentiation

3D printed microfluidic devices: a review focused on four fundamental manufacturing approaches and implications on the field of healthcare

In the last few years, 3D printing has emerged as a promising alternative for the fabrication of microfluidic devices, overcoming some of the limitations associated with conventional soft-lithography. Stereolithography (SLA), extrusion-based technology, and inkjet 3D printing are three of the widely used 3D printing technologies owing to their accessibility and affordability. Microfluidic devices can be 3D printed by employing a manufacturing approach from four fundamental manufacturing approaches classified as (1) direct printing approach, (2) mold-based approach, (3) modular approach, and (4) hybrid approach. To evaluate the feasibility of 3D printing technologies for fabricating microfluidic devices, a review focused on 3D printing fundamental manufacturing approaches has been presented. Using a broad spectrum of additive manufacturing materials, 3D printed microfluidic devices have been implemented in various fields, including biological, chemical, and material synthesis. However, some crucial challenges are associated with the same, including low resolution, low optical transparency, cytotoxicity, high surface roughness, autofluorescence, non-compatibility with conventional sterilization methods, and low gas permeability. The recent research progress in materials related to additive manufacturing has aided in overcoming some of these challenges. Lastly, we outline possible implications of 3D printed microfluidics on the various fields of healthcare such as in vitro disease modeling and organ modeling, novel drug development, personalized treatment for cancer, and cancer drug screening by discussing the current state and future outlook of 3D printed ‘organs-on-chips,’ and 3D printed ‘tumor-on-chips.’ We conclude the review by highlighting future research directions in this field