Rapidly-evolving smart grid technologies add complexity for electric utilities working to maintain a balanced network and ...

Texas A&M researchers received a grant from the U.S. Army Research Laboratory to address the limitations of applying artificial intelligence tools in military scenarios.

Previous works predominantly focus on inter-contract parallel execution, but they fail to address the inherent limitations of each smart contract execution performance. In this paper, we propose PaVM, ...

The increasing need to process large, high-dimensional datasets and the substantial computational power required have made the use of distributed cloud servers essential. These servers provide ...

Efficiently exploiting thread-level parallelism has been challenging. Many parallel applications are not sufficiently balanced or CPU-bound to take advantage of the increasing number of cores and the ...

In parallel and distributed environments, however, load balancing and communication, including disk and network input/output (I/O), can easily dominate computation. These factors greatly increase the ...

To build hippocampal memory prosthesis for restoring memory functions, we previously developed and implemented a multi-input multi-output (MIMO) nonlinear dynamic model of the hippocampus. This model ...

With the widespread use of heterogeneous computing platforms in the AI field, neural network models on heterogeneous computing platforms are experiencing problems such as low execution efficiency and ...

A photovoltaic grid integration planning method that balances economy and safety is proposed for the site selection and capacity determination of distributed photovoltaic power sources accessed to the ...

Distributed learning and adaptation have received significant interest and found wide-ranging applications in machine learning and signal processing. While various approaches, such as shared-memory ...

For future networks, it is highly demanding to satisfy a wide range of time-sensitive and computation-intensive services. This is a very challenging task, since it requires a combination of aspects ...

Asynchronous Proximal Policy Optimization (APPO) has emerged as a crucial framework for achieving scalability in distributed reinforcement learning. In this paper, we propose an enhanced APPO ...