206622161
The Reverse-Exchange Interconnection Network
Properties of the reverse-exchange interconnection network are used to develop a reconfiguration scheme and a two-pass structure for enhancing the efficiency of a class of multistage interconnection networks. Functional relationships among a class of multistage interconnection networks are first derived. According to the functional relationships, we propose a reconfiguration scheme which enables a network to accomplish various interconnection functions of other networks. Then the admissible permutations along with related recursive control algorithms of the reverse-exchange interconnection network are specified through a set of theorems. Using the reverse-exchange property, we also prove that the algorithms actually work. Finally, we prove that arbitrary permutations can be realized in two passes (or 2 · 1og2N switching steps where N is the network size). By taking advantage of Benes network control algorithms, a way to control the two-pass structure is also developed.
26391037
Adaptive Checkpointing and Rollback in Multiprocessor Systems
5170715
Distributed Instruction Set Computer
The Distributed Instruction Set Computer, or DISC for short, is an experimental computer system for fine-grained parallel processing. DISC employs a new parallel instruction set, an Early Binding and Scheduling data tagging scheme, and a distributed control mechanism to explore a software dataflow control method in a multiple-functional-unit system. 
With "zero" system control overhead, multiple instructions are executed in parallel and/or out of order at the highest speed of n instructions/cycle, where n is the number of functional units. The quantitative simulation result indicates that a DISC system with 16 functional units can deliverer a maximal 7.7X performance speedup over a single-functional-unit system at the same clock speed. 
Exploring a new parallel instruction set and distributed control mechanism, DISC represents three major breakthroughs in the domain of fine-grained parallel processing: (1) Fast multiple instruction issuing mechanism; (2) Parallel and/or out-of-order execution; (3) Software dataflow control scheme.
19123449
Operating System Kernel for a Reconfigurable Multiprocessor System
16106791
A microprocessor-controlled asynchronous circuit switching network
This paper describes an asynchronous circuit switching network for multiple-processor systems. Several circuit switching networks for various applications have been proposed and constructed. However, there are problems associated with these networks. The asynchronous circuit switching network possesses several features that can solve these problems. A three-stage fully connected topology is utilized to construct the network. Each switching element is functionally and physically identical and this facilitates a cost-effective LSI implementation and software development. The control structure of the switching element and the routing algorithm are re-organized to fit the asynchronous operation. The network takes advantage of low-cost microcomputers to do the distributed routing control and to implement the communication protocols. The graceful degradation characteristic in the network is provided by independent multiple paths existing between any pair of the source and the destination. A network monitor is incorporated to facilitate an adaptive routing strategy and to have the fault diagnostic capability. Three alternatives for the switching element implementation are described to demonstrate the hardware and software tradeoffs.
7443931
Limitation of superscalar microprocessor performance
Today’s microprocessors are striving to get the highest possible performance. A new breed of microprocessors called superscalar which execute multiple instructions per clock cycle. The major problem of executing multiple instructions in a scalar program is the handling of data dependencies. If data dependencies are not effectively handled, it is difficult to achieve an execution rate of more than one instruction per clock cycle. In this study, we use a novel multi-bit control for handling of data dependencies for out-of-order execution and completion of instructions. This paper presents the maximum performance of the superscalar microprocessor using two simulation models and one calculation model. The first simulation model uses real benchmark programs, and the second model uses queuing networks. The calculation model derives the formula for deficiencies from the peak performance.
471313
Dependence network modeling for biomarker identification
MOTIVATION
Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data.


RESULTS
Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction.


AVAILABILITY
See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/
490124
Modeling Psychological Refractory Period (PRP) and Practice Effect on PRP with Queuing Networks and Reinforcement Learning Algorithms
PRP (Psychological Refractory Period) is a basic but important form of human information processing in dual-task situations. This article describes a queuing network model of PRP that successfully modeled PRP without the need of setting up complex lock/unlock performance strategies employed in the EPIC model of PRP or drawing complex scheduling charts employed in the ACT-R/PM model of PRP. Further, by integrating queuing networks with reinforcement learning algorithms, the queuing network model successfully simulated practice effect on PRP, which has not been modeled in existing PRP models. The current research indicates that depending on an individual’s degree of practice, cognition can be either serial or parallel at the level of production or response selection. Extensions of queuing network model in modeling other tasks and its easiness in modeling concurrent tasks in practice are also discussed.
205225795
ProClass protein family database
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PROSITE patterns and PIR superfamilies. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of more than 120 000 sequence entries, approximately 60% of which is classified into about 3500 families. To maximize family information retrieval, the database provides links to various protein family/domain and structural class databases and contains multiple motif alignments of all PROSITE patterns as well as global alignments of PIR superfamilies. The motif sequences are retrieved from both PIR-International and SWISS-PROT databases, including a large number of new members detected by our GeneFIND family identification system. ProClass can be used to support full-scale genomic annotation, because of its high classification rate. The ProClass database is available for on-line search and record retrieval from our WWW server at http://diana.uthct.edu/proclass.html
8586136
Emotion recognition using acoustic features and textual content
The paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input. After principle component analysis (PCA), 14 principle components are selected for discriminative representation. In this representation, each principle component is the combination of the 33 original acoustic features and forms a feature subspace. Support vector machines (SVMs) are adopted to classify the emotional states. In text analysis, all emotional keywords and emotion modification words are manually defined. The emotion intensity levels of emotional keywords and emotion modification words are estimated from a collected emotion corpus. The final emotional state is determined based on the emotion outputs from the acoustic and textual approaches. The experimental result shows that the emotion recognition accuracy of the integrated system is better than each of the two individual approaches.
40955091
Hash channels
It is believed that in authentication protocols where no random numbers are involved, it is hard to introduce subliminal channels. In this paper we show that subliminal channels exist in digital signature schemes where no random numbers are used. The source where the subliminal channels exist is widely acceptable hash functions with their outputs being of some randomness. Because the subliminal channels are established by using hash functions, the subliminal channels are called hash channels in this paper. This indicates that there exist subliminal channels in any authentication protocols that use hash functions.