papers

Publications (7)

cs.DC2023

Minuet: Accelerating 3D Sparse Convolutions on GPUs

Jiacheng Yang, Christina Giannoula, Jun Wu +3

Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity of the input point clo…

cond-mat.soft2020

The interplay between spatial and heliconical bond order in twist-bend nematic materials

Rony Saha, Chenrun Feng, Chris Welch +6

The nanostructure of two novel sulfur containing dimer materials has been investigated experimentally by hard and by resonant tender X-ray scattering techniques. On cooling the dim…

cs.LG2021

RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads

James Gleeson, Srivatsan Krishnan, Moshe Gabel +3

Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications. Unfortunately, system-level bottlenecks in RL work…

cond-mat.soft2026

Landau theory applied to antiferroelectric ordering in ferroelectric nematic liquid crystals

Manisha Badu, Arjun Ghimire, Milon +6

The polarization and density modulation associated with antiferroelectric ordering is studied experimentally as a function of temperature in two ferroelectric nematic liquid crysta…

cond-mat.soft2025

Director-layer dynamics in the antiferroelectric smectic-ZA phase of a ferroelectric nematic liquid crystal

Arjun Ghimire, Bijaya Basnet, Hao Wang +7

A dynamic light scattering study of director-layer fluctuations in the antiferroelectric smectic-ZA phase of the ferroelectric nematic liquid crystal DIO is reported. The dynamics…

cs.LG2022

Optimizing Data Collection in Deep Reinforcement Learning

James Gleeson, Daniel Snider, Yvonne Yang +3

Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-u…

stat.ML2008

Prediction with Restricted Resources and Finite Automata

Finn Macleod, James Gleeson

We obtain an index of the complexity of a random sequence by allowing the role of the measure in classical probability theory to be played by a function we call the generating mech…