21052607
On Easily Testable and Reconfigurable Two-Dimensional Systolic Arrays
27298271
The Rule-Based Approach to Reconfiguration of 2-D Processor Arrays
The authors propose to set up assignment rules based on the available interconnection resources of 2-D processor arrays. The reconfiguration of 2-D processor arrays is guided by the assignment rules, such that logical cells are always connected through the available interconnection resources. The advantage of the proposed approach is the easy adaptation to various system requirements, by adjusting assignment rules. The proposed reconfiguration algorithm can be extended to arrays with clustered faulty cells, overcoming the weakness of fixed domain approaches. >
250579
Live neural network formations on electronic chips
Abstract Live neuron behavior on an electronic chip was recorded by Nippon Telephone & Telegraph (NTT) with a time-lapsed video under the microscope. The dissociated chick embryonic brain neurons (telencephalic neurons) were cultured, and individual neurons were placed on silicon glass plates deposited with metal oxide strips about 10 μm width for possible electronegativity neurite guidance. We have attempted to map biological neural networks (BNN) to artificial neural networks (ANN) to determine whether the connectivity patterns dictate the information processing efficiency, or vice versa. By an image processing technique, we discovered the smallest size of intelligent BNN from those NTT video tapes that happened to fail their neurite guidance experiments. The neurite growth connecting other neurons was accomplished intentionally through selective paring in time rather than mechanically following the external guidance. Such a dynamic interconnection has inspired a model of energy landscaping, of which a general convergence theorem that has unified Hopfield-Cohen-Grossberg and Werbos-Rumelhart theorems is given, and a nonlinear Hebbian learning is derived capable for self-determination of ANN architectures.
10981925
Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field
A geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm. >
31990528
Diagnosing Multiple Bridge Faults in Baseline Multistage Interconnection Networks
38004947
On-Line Detection of Errors in Homogeneous Multiprocessor Systems
6738575
Agent-oriented software modeling
Due to the increased applications of agents, agent-oriented software becomes large and complex. To support systematic developments of such software, the agent-oriented software development methodology needs to be developed. This paper focuses on the modeling phase of agent-oriented software development. For agent-oriented software modeling, the Agent Elicitation method, Intra and Inter Agent modeling method are proposed. Agent Elicitation shows how to extract agents from classes in the real world. Modeling methods of agent's characteristics-Goal, Belief, Plan and Capability-are proposed for Intra Agent modeling. Methods of agent's mobility and communication in multiagent systems are proposed for Inter Agent modeling.
20426558
The ANSER Project
6308143
Geometry of gene expression dynamics
MOTIVATION
A gene expression trajectory moves through a high dimensional space where each axis represents the mRNA abundance of a different gene. Genome wide gene expression has a dynamic structure, especially in studies of development and temporal response. Both visualization and analyses of such data require an explicit attention to the temporal structure.


RESULTS
Using three cell cycle trajectories from Saccharomyces cerevisiae to illustrate, we present several techniques which reveal the geometry of the data. We import phase-delay time plots from chaotic systems theory as a dynamic data visualization device and show how these plots capture important aspects of the trajectories. We construct an objective function to find an optimal two-dimensional projection of the cell cycle, demonstrate that the system returns to this plane after three different initial perturbations, and explore the conditions under which this geometric approach outperforms standard approaches such as singular value decomposition and Fourier analysis. Finally, we show how a geometric analysis can isolate distinct parts of the trajectories, in this case the initial perturbation versus the cell cycle.


CONTACT
junhyong.kim@yale.edu
33321332
Learning Information Extraction Rules for Protein Annotation from Unannotated Corpora
As the number of published papers on proteins increases rapidly, manual protein annotation for biological sequence databases faces the problem of catching up with the speed of publication. Automated information extraction for protein annotation offers a solution to this problem. Generally, information extraction tasks have relied on the availability of pre-defined templates as well as annotated corpora. However, in many real world applications, it is difficult to fulfill this requirement; only relevant sentences for target domains can be easily collected. At the same time, other resources can be harnessed to compensate for this difficulty: natural language processing provides reliable tools for syntactic text analysis, and in bio-medical domains, there is a large amount of background knowledge available, e.g., in the form of ontologies. In this paper, we present a method for learning information extraction rules without pre-defined templates or labor-intensive pre-annotation by exploiting various types of background knowledge in an inductive logic programming framework.
2757542
Comparing the performance of high-level middleware systems in shared and distributed memory parallel environments
The utilization of toolkits for writing parallel and/or distributed applications has been shown to greatly enhance developer's productivity. Such an approach hides many of the complexities associated with writing these applications, rather than relying solely on programming language aids and parallel library support, such as MPI or PVM. In this work, we evaluate three different middleware systems that have been used to implement a computation and I/O-intensive data analysis application from the domain of computer vision. This study shows the benefits and overheads associated with each of the middleware systems, in different homogeneous computational environments and with different workloads. Our results lead the way toward being able to make better decisions for tuning the application environment, for selecting the appropriate middleware, and also for designing more powerful middleware systems to efficiently build and run highly complex applications in both parallel and distributed computing environments.