E-kernel: an embedding kernel on the IBM victor V256 multiprocessorfor program mapping and network reconfiguration

We present the design of E-kernel, an embedding kernel on the Victor V256 message-passing partitionable multiprocessor, developed for the support of program mapping and network reconfiguration. E-kernel supports the embedding of a new network topology onto Victor's 2D mesh and also the embedding of a task graph onto the 2D mesh network or the reconfigured network. In the current implementation, the reconfigured network can be a line or an even-size ring, and the task graphs meshes or tori of a variety of dimensions and shapes or graphs with similar topologies. For application programs having these task graph topologies and that are designed according to the communication model of E-kernel, they can be run without any change on partitions connected by the 2D mesh, line, or ring. Further, E-kernel attempts the communication optimization of these programs on the different networks automatically, thus making both the network topology and the communication optimization attempt completely transparent to the application programs. Many of the embeddings used in E-kernel are optimal or asymptotically optimal (with respect to minimum dilation cost). The implementation of E-kernel translated some of the many theoretical results in graph embeddings into practical tools for program mapping and network reconfiguration in a parallel system. E-kernel is functional on Victor V256. Measurements of E-kernel's performance on V256 are also included     

Using recurrent neural networks for adaptive communication channelequalization

Nonlinear adaptive filters based on a variety of neural network models have been used successfully for system identification and noise-cancellation in a wide class of applications. An important problem in data communications is that of channel equalization, i.e., the removal of interferences introduced by linear or nonlinear message corrupting mechanisms, so that the originally transmitted symbols can be recovered correctly at the receiver. In this paper we introduce an adaptive recurrent neural network (RNN) based equalizer whose small size and high performance makes it suitable for high-speed channel equalization. We propose RNN based structures for both trained adaptation and blind equalization, and we evaluate their performance via extensive simulations for a variety of signal modulations and communication channel models. It is shown that the RNN equalizers have comparable performance with traditional linear filter based equalizers when the channel interferences are relatively mild, and that they outperform them by several orders of magnitude when either the channel's transfer function has spectral nulls or severe nonlinear distortion is present. In addition, the small-size RNN equalizers, being essentially generalized IIR filters, are shown to outperform multilayer perceptron equalizers of larger computational complexity in linear and nonlinear channel equalization cases.     

Low temperature wafer direct bonding

A pronounced increase of interface energy of room temperature bonded hydrophilic Si/Si, Si/SiO₂, and SiO₂/SiO ₂ wafers after storage in air at room temperature, 150°C for 10-400 h has been observed. The increased number of OH groups due to a reaction between water and the strained oxide and/or silicon at the interface at temperatures below 110°C and the formation of stronger siloxane bonds above 110°C appear to be the main mechanisms responsible for the increase in the interface energy. After prolonged storage, interface bubbles are detectable by an infrared camera at the Si/Si bonding seam. Desorbed hydrocarbons as well as hydrogen generated by a reaction of water with silicon appear to be the major contents in the bubbles. Design guidelines for low temperature wafer direct bonding technology are proposed     

MEETING THE IT CHALLENGES OF BUSINESS The Changing Mandate for MBAs

What is the implication for business when information technology (IT) changes in the workplace without a commensurate change in the composition of business programs educating tomorrow's employees? A survey of MBA graduates forms the basis of this article on the IT skills needed in the marketplace.     

Algebra of algorithms and Kaluzhnin's graphschemas

This research was supported under project No. 6.02.02/128-93 as part of the state scientific-technical program on future information technologies and systems by the Ukrainian State Committee, of Science and Technology.     

Fuzzy sets-a convenient fiction for modeling vagueness andpossibility

This paper is a reply to Laviolette and Seaman's critical discussion of fuzzy set theory. Rather than questioning the interest of the Bayesian approach to uncertainty, some reasons why Bayesian find the idea of a fuzzy set not palatable are laid bare. Some links between fuzzy sets and probability that Laviolette and Seaman seem not to be aware of are pointed out. These links suggest that, contrary to the claim sometimes found in the literature, probability theory is not a special case of fuzzy set theory. The major objection to Laviolette and Seaman is that they found their critique on as very limited view of fuzzy sets, including debatable papers, while they fail to account for significant works pertaining to axiomatic derivation of fuzzy set connectives, possibility theory, fuzzy random variables, among others     

Comments on “Editorial: fuzzy models-what are they andwhy?”

In the editorial by J.C. Bezdek (ibid., p.1), an example is presented to demonstrate differences between fuzzy membership and probability. The authors argue that probability can be used in a way much more closely analogous to this use of fuzzy membership, weakening the argument for the latter     

Components of random generation by normal subjects and patients with dysexecutive syndrome

The study presents a hypothesis on how randomness could be simulated by human subjects. Three sources of deviation from randomness are predicted: (1) the preferred application of overlearned production schemata for producing sequences of digits, (2) a wrong concept of randomness, and (3) the impossibility to monitor for redundancies of higher- than those of first-order. Deviations of random generation of digits produced by healthy subjects, patients with chronic frontal lobe damage, and patients with Parkinson's disease from random sequences produced by a computer program can be explained by the differential influence of these factors. Whereas incorrect concepts of randomness and limits on monitoring capacity distinguished all sequences produced by humans from actual random sequences, persistence on a single production strategy distinguished brain-damaged patients from controls. Random generation of digits appears to be a theoretically transparent and clinically useful test of executive function.     

Polymorphonuclear neutrophil granulocyte chemotactic hyperresponsiveness in a case of canine acromegaly

Growth hormone (GH) has recently been shown to affect polymorphonuclear neutrophil granulocyte (PMN) function and to be secreted by mononuclear cells, indicating that the hormone may be active in an immunophysiologic network, acting as an endo- or paracrine priming agent. The purpose of the present study was to evaluate the chemotactic responsiveness of canine peripheral PMN in a dog with acromegaly, caused by spontaneous, progesterone-induced hypersecretion of GH and, secondary to this, a seven-fold increase in insulin-like growth factor I (IGF-I). The chemotactic responsiveness towards zymosan-activated serum (ZAS) and leukotriene B4 (LTB4) was evaluated at a time when the dog suffered from acromegaly and again 57 days after corrective surgery (ovariohysterectomy). The experiments showed that PMN from the patient exhibited enhanced chemotactic migration that appeared to be associated with the hypersomatotropic condition as judged from the reversibility of the phenomenon. The glucose intolerance and elevated serum alkaline phosphatase that were observed in the acromegalic dog were also shown to be reversible following surgery.