Efficiently scheduling deep learning jobs on large-scale GPU clusters is crucial for job
performance, system throughput, and hardware utilization. It is getting ever more challenging as …

Cloud platform provides great flexibility and cost-efficiency for end-users and cloud operators.
However, low resource utilization in modern datacenters brings huge wastes of hardware …

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses
general competences of foundation models as well as privacy-preserving capabilities of …

The design of bipolar plate flow fields significantly impacts reactant distribution and mass
transfer capabilities in fuel cells, thereby influencing overall cell performance. Herein, …

Titanium is widely used in the aerospace industry, however, its degradation of mechanical
properties and oxidation resistance at high temperatures and the low machinability limit its …

For instrumented spherical indentation, the presence of equibiaxial residual stress in a
material will lead the indentation load–depth curve to shift upward or downward. The load …

In this study, residual stress in Sn-3.0Ag-0.5Cu lead-free solder after annealing at varying
temperature and duration was investigated by nanoindentation and uniaxial tensile …

With the development of deep learning techniques, the combination of deep learning with
image compression has drawn lots of attention. Recently, learned image compression …

The efficacy of proton exchange membrane fuel cells is critically influenced by flow field
design. While machine learning (ML) has gained traction in fuel cell flow field (FCFF) …

To enhance the electrochemical performance and water management of proton exchange
membrane fuel cells (PEMFCs) under high-load conditions, this study proposes a zonal gas …