The Low-order Central Moments UpWrite has been introduced in various contexts. We motivate and formalize this operation.

1 Department of Mathematics, Institute for Physical Science & Technology and Center for Scientific Computation And Mathematical Modeling (CSCAMM), University of Maryland College Park, MD 20742-3289 [email protected] 2 Department of Mathematics University of California Davis, CA 95616 [email protected] “Hyperbolic Problems: Theory, Numerics, Applications”, Proceedings of the 9th Inter...

Due to the difference between processor speed and memory speed, the latter has steadily appeared further away in cycles to the processor. Superscalar out-of-order processors cope with these increasing latencies by having more in-flight instructions from where to extract ILP. With coming latencies of 500 cycles and more, this will eventually derive in what we have called Kilo-Instruction Process...

a $p$-group $g$ is $p$-central if $g^{p}le z(g)$‎, ‎and $g$ is‎ ‎$p^{2}$-abelian if $(xy)^{p^{2}}=x^{p^{2}}y^{p^{2}}$ for all $x,yin‎ ‎g$‎. ‎we prove that for $g$ a finite $p^{2}$-abelian $p$-central ‎$p$-group‎, ‎excluding certain cases‎, ‎the order of $g$ divides the ‎order of $text{aut}(g)$‎.

In this paper a new parallel algorithm is presented for generation of all m{block partitions of n{element set, 1 m n. Computations run in an associative processor model. Objects are generated in lexicographic order, with O(1) time per object. The algorithm is well suited for column/masks generation in associative processors.

One of the problems in future processors will be the resource conflicts caused by several load/store units competing to access the same cache bank. The traditional approach for handling this case is by introducing buffers combined with a cross-bar. This approach suffers from (i) the nondeterministic latency of a load/store and (ii) the extra latency caused by the cross-bar and the buffer manage...

In this paper two new parallel algorithms are presented for generation of (n,k)-combinations. Computations run in associative processor models. Objects are generated in lexicographic order, with O(1) time per object, in two di erent representations. The rst algorithm uses the conventional representation of combinations while the second algorithm generates combinations in the form of binary vect...

Neuroplasticity is the ability of the central nervous system to remodel itself. In the last few decades, we have learned that neuroplasticity is not only possible but that it is also constantly occurring; the brain is always changing. Neuroplasticity is how we adapt to changing conditions, learn new facts, and develop new skills. If the brain is injured, it tries to repair itself with these nor...