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pro vyhledávání: '"A., Shashikant"'
While 2D pose estimation has advanced our ability to interpret body movements in animals and primates, it is limited by the lack of depth information, constraining its application range. 3D pose estimation provides a more comprehensive solution by in
Externí odkaz:
http://arxiv.org/abs/2501.01174
Autor:
Dhole, Aniket Shashikant
This MS thesis outlines my contributions to the closed loop control and system integration of two robotic platforms: 1) Aerobat, a flapping wing robot stabilized by air jets, and 2) Harpy, a bipedal robot equipped with dual thrusters. Both systems sh
Externí odkaz:
http://arxiv.org/abs/2501.01443
Our study sets forth a carbon based two-dimensional (2D) kagome topological insulator without containing any metal atoms, that aligns the Fermi level with the Dirac point without the need for doping, overcoming a significant bottleneck issue observed
Externí odkaz:
http://arxiv.org/abs/2412.11516
Autor:
Tian, Tian, Timmerman, Lucas R, Kumar, Shashikant, Comer, Ben, Medford, Andrew J, Suryanarayana, Phanish
Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides advantages in ha
Externí odkaz:
http://arxiv.org/abs/2411.18024
Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. However, increasingly prevalent delay-intolerant real-time workloads ch
Externí odkaz:
http://arxiv.org/abs/2411.07628
The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud
Externí odkaz:
http://arxiv.org/abs/2410.23881
In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled a
Externí odkaz:
http://arxiv.org/abs/2410.10285
Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading without QoS violations is challenging in distributed and unreliable edg
Externí odkaz:
http://arxiv.org/abs/2410.06715
Autor:
Chu, Xiaoyu, Hofstätter, Daniel, Ilager, Shashikant, Talluri, Sacheendra, Kampert, Duncan, Podareanu, Damian, Duplyakin, Dmitry, Brandic, Ivona, Iosup, Alexandru
HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding
Externí odkaz:
http://arxiv.org/abs/2409.08949
We present a formalism for developing cyclic and helical symmetry-informed machine learned force fields (MLFFs). In particular, employing the smooth overlap of atomic positions descriptors with the polynomial kernel method, we derive cyclic and helic
Externí odkaz:
http://arxiv.org/abs/2408.07554