Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison.
Managing their complexity and facilitating their design drove part of the co-evolution of mode ...
The auxiliary power supply for medium voltage converters requires high insulation capability between the source and the load. Inductive power transfer technology, with an air gap between the primary and secondary coil, offers such high insulation capabilit ...
Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine ...
With growing awareness of the vulnerability of the near-Earth space region and the anticipated surge in satellite objects, efforts are underway to assess and implement various mitigation strategies. These aim to minimize the impact of space activities and ...
In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...
BiFeO3 is a ferroelectric with a Curie temperature of 830 C-degrees, however, its piezoelectric performance at high temperature remains unclear. The current work reveals a disappearance/recovery of piezoelectricity in BiFeO3 at elevated temperature and upo ...
The present invention concerns a thermal sensing device (1) and a sensory feedback system and method using such thermal sensing device, comprising at least one film (19) of electrically insulating polymer defining a global surface of the thermal sensing de ...
Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...
We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...
Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...
