We present an algorithm for creating digital micrography images, or micrograms , a special type of calligrams created from minuscule text. These attractive text-art works successfully combine beautiful images with readable meaningful text. Traditional micrograms are created by highly skilled artists and involve a huge amount of tedious manual work. We aim to simplify this process by providing a computerized digital micrography design tool. The main challenge in creating digital micrograms is designing textual layouts that simultaneously convey the input image, are readable and appealing. To generate such layout we use the streamlines of singularity free, low curvature, smooth vector fields, especially designed for our needs. The vector fields are computed using a new approach which controls field properties via a priori boundary condition design that balances the different requirements we aim to satisfy. The optimal boundary conditions are computed using a graph-cut approach balancing local and global design considerations. The generated layouts are further processed to obtain the final micrograms. Our method automatically generates engaging, readable micrograms starting from a vector image and an input text while providing a variety of optional high-level controls to the user.

The draft revision of the IEEE Standard for Floating- Point Arithmetic (IEEE P754) includes a definition for decimal floating-point (FP) in addition to the widely used binary FP specification. The decimal standard raises new concerns with regard to the verification of hardware- and software-based designs. The verification process normally emphasizes intricate corner cases and uncommon events. The decimal format introduces several new classes of such events in addition to those characteristic of binary FP. Our work addresses the following problem: Given a decimal floating-point operation, a constraint on the intermediate result, and a constraint on the representation selected for the result, find random inputs for the operation that yield an intermediate result compatible with these specifications. The paper supplies efficient analytic solutions for addition and for some cases of multiplication and division. We provide probabilistic algorithms for the remaining cases. These algorithms prove to be efficient in the actual implementation.

Test generation for datapath floating-point verification involves targeting intricate corner cases, which can often be solved only through complex constraint solving. In the process of calculating the result, we use an intermediate result whose significand comprises a finite number of bits and a sticky bit that is 0 if and only if the intermediate result is exact. We refer to all the bits beyond those represented in the final result as the invisible bits. We deal with corner cases that can only be defined via constraints on the intermediate result. Our work investigates the following problem: given a floating-point operation, and constraints on the invisible bits and the sticky bit, find two inputs for the operation that yield an intermediate result compatible with the constraints. The paper supplies a deterministic solution for addition and subtraction, and probabilistic solutions for multiplication and division. It also discusses the application of these algorithms to the verification of floating-point implementations.