Data augmentation has been widely used to improve generalizability of machine learning models. However, comparatively little work studies data for graphs. This is largely due the complex, non-Euclidean structure graphs, which limits possible manipulation operations. Augmentation operations commonly in vision and language have no analogs Our graph neural networks (GNNs) context improving semi-su...

The so-called modified transform (T) graphs are described in the paper, which are a graph analogy to wellknown voltage and current incidence matrices. The procedure is shown how to pass from T-graphs to the classical Mason-Coates (MC) flow graph. The explanation is illustrated with examples of circuits containing operational amplifiers and current conveyors. Key-Words: Signal flow graph, flow g...

In object-oriented testing literature, a class is considered to be a basic w i t of testing. A m,ajor characteristic of classes is the interaction between data m.cm<bers and mem,ber functionw. This interaction is representcd as dejhitions and uses of data members in member functions and can be properly modeled with f i ~ i , i t ~ state machines (FSM). I n this paper, we discuss how FSMs cun be...

ABSTRACT— Digital signal processing algorithms are described by iterative data-flow graphs where nodes represent computations and edges represent communications. In this paper we propose a novel method to determine the iteration bound, which is the fundamental lower bound of the iteration period of a processing algorithm, by using the minimum cycle mean algorithm to achieve a lower polynomial t...

In reconfigurable systems, reconfiguration latency has a significant impact on the system performance. In this work, a temporal partitioning algorithm is presented to partition data flow graphs for reconfigurable computing systems. Life-time of a node in a data flow graph represents the number of times it executes during the application run. A new factor, called residing eligibility, inspired f...