Since the past few years research has been directed towards the training of neural networks using unlabeled data or pairwise pseudo constraints known as unsupervised learning and semi-supervised learning respectively. In ...

Current structure-from-motion (SFM) pipelines integrated with multi-view stereo (MVS) module often applies traditional MVS algorithms. These algorithms cannot be well parallelized and can be slow when the data size and the ...

In this paper, we introduce a new type system based on linear typing, and show how it can be incorporated in a concurrent programming language to track ownership of promises. By tracking write operations on each promise, ...

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. A key challenge in LfD research is that users tend ...

X chromosome inactivation in female mammals ensures dosage compensation between sexes. Allele-specific DNA methylation is a known epigenetic signature of X chromosome inactivation where the active and inactive X chromosomes ...

Cheating is an issue that affects more than just the student doing it, no matter the format of the assessment being cheated on. Take-home exams provide more flexibility for the instructor and student than regular proctored ...

We present a space-efficient implementation of the quantum verification of matrix products (QVMP) algorithm and demonstrate its functionality by running it on the Aer simulator with two simulation methods: statevector and ...

Reinforcement learning (RL) algorithms have successfully learned control policies for quadruped locomotion such as walking, rotation, and basic navigation. We utilize Proximal Policy Optimization and iGibson to train a ...

IMUTube generates on-body sensor-based human activity recognition data by leveraging the abundance of videos on platforms like YouTube as a dataset for training human activity recognition models. This thesis deals with the ...

We study the problem of building and traversing a search tree for combinatorial optimization problems, which are generally exponential in time complexity and often characterize NP-Hard problems. A typical approach for such ...