Deriving matrix of peptide-MHC interactions in diabetic mouse by genetic algorithm

Finding motifs that can elucidate rules that govern peptide binding to medically important receptors is important for screening targets for drugs and vaccines. This paper focuses on elucidation of peptide binding to I-A(g7) molecule of the non-obese diabetic (NOD) mouse - an animal model for insulin-dependent diabetes mellitus (IDDM). A number of proposed motifs that describe peptide binding to I-A(g7) have been proposed. These motifs results from independent experimental studies carried out on small data sets. Testing with multiple data sets showed that each of the motifs at best describes only a subset of the solution space, and these motifs therefore lack generalization ability. This study focuses on seeking a motif with higher generalization ability so that it can predict binders in all A(g7) data sets with high accuracy. A binding score matrix representing peptide binding motif to A(g7) was derived using genetic algorithm (GA). The evolved score matrix significantly outperformed previously reported

Global control and fast solid-state donor electron spin quantum computing

We propose a scheme for quantum information processing based on donor electron spins in semiconductors, with an architecture complementary to the original Kane proposal. We show that a naive implementation of electron spin qubits provides only modest improvement over the Kane scheme, however through the introduction of global gate control we are able to take full advantage of the fast electron evolution timescales. We estimate that the latent clock speed is 100-1000 times that of the nuclear spin quantum computer with the ratio T-2/T-ops approaching the 10(6) level.

Distance measures to compare real and ideal quantum processes

With growing success in experimental implementations it is critical to identify a gold standard for quantum information processing, a single measure of distance that can be used to compare and contrast different experiments. We enumerate a set of criteria that such a distance measure must satisfy to be both experimentally and theoretically meaningful. We then assess a wide range of possible measures against these criteria, before making a recommendation as to the best measures to use in characterizing quantum information processing.

The value of patches of intertidal seagrass to prawns depends on their proximity to mangroves

Penaeid prawns were sampled with a small seine net to test whether catches of postlarvae and juveniles in seagrass were affected by the distance of the seagrass (mainly Zostera capricorni) from mangroves and the density of the seagrass in a subtropical marine embayment. Sampling was replicated on the western and eastern sides of Moreton Bay, Queensland, Australia. Information on catches was combined with broad-scale spatial information on the distribution of habitats to estimate the contribution of four different categories of habitat (proximal dense seagrass, distal dense seagrass, proximal sparse seagrass, distal sparse seagrass) to the overall population of small prawns in these regions of Moreton Bay. The abundance of Penaeus plebejus and Metapenaeus bennettae was significantly and consistently greater in dense seagrass proximal to mangroves than in other types of habitat. Additionally, sparse seagrass close to mangroves supported more of these species than dense seagrass farther away, indicating that the role of spatial arrangement of habitats was more important than the effects of structural complexity alone. In contrast, the abundance of P. esculentus tended to be greatest in sparse seagrass distal from mangroves compared with the other habitats. The scaling up of the results from different seagrass types suggests that proximal seagrass beds on both sides of Moreton Bay provide by far the greatest contribution of juvenile M. bennettae and P. plebejus to the overall populations in the Bay.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Specification and validation of process constraints for flexible workflows

Workflow systems have traditionally focused on the so-called production processes which are characterized by pre-definition, high volume, and repetitiveness. Recently, the deployment of workflow systems in non-traditional domains such as collaborative applications, e-learning and cross-organizational process integration, have put forth new requirements for flexible and dynamic specification. However, this flexibility cannot be offered at the expense of control, a critical requirement of business processes. In this paper, we will present a foundation set of constraints for flexible workflow specification. These constraints are intended to provide an appropriate balance between flexibility and control. The constraint specification framework is based on the concept of pockets of flexibility which allows ad hoc changes and/or building of workflows for highly flexible processes. Basically, our approach is to provide the ability to execute on the basis of a partially specified model, where the full specification of the model is made at runtime, and may be unique to each instance. The verification of dynamically built models is essential. Where as ensuring that the model conforms to specified constraints does not pose great difficulty, ensuring that the constraint set itself does not carry conflicts and redundancy is an interesting and challenging problem. In this paper, we will provide a discussion on both the static and dynamic verification aspects. We will also briefly present Chameleon, a prototype workflow engine that implements these concepts. (c) 2004 Elsevier Ltd. All rights reserved.

How many variables can humans process?

The conceptual complexity of problems was manipulated to probe the limits of human information processing capacity. Participants were asked to interpret graphically displayed statistical interactions. In such problems, all independent variables need to be considered together, so that decomposition into smaller subtasks is constrained, and thus the order of the interaction. directly determines conceptual complexity. As the order of the interaction increases, the number of variables increases. Results showed a significant decline in accuracy and speed of solution from three-way to four-way interactions. Furthermore, performance on a five-way interaction was at chance level. These findings suggest that a structure defined on four variables is at the limit of human processing capacity.

Hierarchical indexing structure for efficient similarity search in video retrieval

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during k Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user-defined parameter delta. By adjusting delta, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance.

ICICLE: A semantic-based retrieval system for WWW images

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

Cluster-state quantum computation

This article is a short introduction to and review of the cluster-state model of quantum computation, in which coherent quantum information processing is accomplished via a sequence of single-qubit measurements applied to a fixed quantum state known as a cluster state. We also discuss a few novel properties of the model, including a proof that the cluster state cannot occur as the exact ground state of any naturally occurring physical system, and a proof that measurements on any quantum state which is linearly prepared in one dimension can be efficiently simulated on a classical computer, and thus are not candidates for use as a substrate for quantum computation.

Effects of reflex stimulus intensity and stimulus onset asynchrony on prepulse inhibition and perceived intensity of the blink-eliciting stimulus

Prepulse inhibition of the blink reflex is widely applied to investigate information processing deficits in schizophrenia and other psychiatric patient groups. The present experiment investigated the hypothesis that prepulse inhibition reflects a transient process that protects preattentive processing of the prepulse. Participants were presented with pairs of blinkeliciting noises, some preceded by a prepulse at a variable stimulus onset asynchrony (SOA), and were asked to rate the intensity of the second noise relative to the first. Inhibition of blink amplitude was greater for a 110-dB (A) noise than for a 95-dB(A) noise with a 120-ms SOA, whereas there was no difference with a 30-ms SOA. The perceived intensity was also lower for the 110-dB(A) noise than for the 95-dB(A) noise with the 120-ms SOA, but not with the 30-ms SOA. The parallel results support a relationship between prepulse inhibition of response amplitude and perceived intensity. However, the prepulse did not reduce intensity ratings relative to control trials in some conditions, suggesting that prepulse inhibition is not always associated with an attenuation of the perceived impact of the blink-eliciting stimulus.