All maximal-pairs in step-leap representation of melodic sequence

This paper proposes an efficient pattern extraction algorithm that can be applied on melodic sequences that are represented as strings of abstract intervallic symbols; the melodic representation introduces special “binary don’t care” symbols for intervals that may belong to two partially overlapping intervallic categories. As a special case the well established “step–leap” representation is examined. In the step–leap representation, each melodic diatonic interval is classified as a step (±s), a leap (±l) or a unison (u). Binary don’t care symbols are used to represent the possible overlapping between the various abstract categories e.g. *=s, *=l and #=-s, #=-l. We propose an O(n+d(n-d)+z)-time algorithm for computing all maximal-pairs in a given sequence x=x[1..n], where x contains d occurrences of binary don’t cares and z is the number of reported maximal-pairs.

Image matching using TI multi-wavelet transform

A multi-resolution image matching technique based on multiwavelets followed by a coarse to fine strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed by a geometric topological refining approach along with the uniqueness constraint whereas the illuminative variation is dealt by using windowed normalized correlation.

Introductory computer programming as a means for extending spatial and temporal understanding

Should computer programming be taught within schools of architecture?

Incorporating even low-level computer programming within architectural education curricula is a matter of debate but we have found it useful to do so for two reasons: as an introduction or at least a consolidation of the realm of descriptive geometry and in providing an environment for experimenting in morphological time-based change.

Mathematics and descriptive geometry formed a significant proportion of architectural education until the end of the 19th century. This proportion has declined in contemporary curricula, possibly at some cost for despite major advances in automated manufacture, Cartesian measurement is still the principal ‘language’ with which to describe building for construction purposes. When computer programming is used as a platform for instruction in logic and spatial representation, the waning interest in mathematics as a basis for spatial description can be readdressed using a left-field approach. Students gain insights into topology, Cartesian space and morphology through programmatic form finding, as opposed to through direct manipulation.

In this context, it matters to the architect-programmer how the program operates more than what it does. This paper describes an assignment where students are given a figurative conceptual space comprising the three Cartesian axes with a cube at its centre. Six Phileban solids mark the Cartesian axial limits to the space. Any point in this space represents a hybrid of one, two or three transformations from the central cube towards the various Phileban solids. Students are asked to predict the topological and morphological outcomes of the operations. Through programming, they become aware of morphogenesis and hybridisation. Here we articulate the hypothesis above and report on the outcome from a student group, whose work reveals wider learning opportunities for architecture students in computer programming than conventionally assumed.

Understanding science

"Understanding Science is about breakdown of meaning, breakdown of relationship, trying to exist in that space between meaninglessness and understanding, at its cusp, its node, its no-man’s land.. It is a melting pot of more than just fragments of images, there are clusters of things, ideas, sounds, words, that swim in and out of your attention. I wanted this film to be a dense multidimensional collage of automatic writing, sound poetry and abstracting strings of images." http://www.innersense.com.au/mif/debruyn_films.html

Proceedings : 10th International Multimedia Modeling Conference : MMM 2004 : Brisbane, Australia, 5-7 January 2004

The International Multimedia Modelling conference series is an annual forum to discuss the efficient representation, processing, interaction, integration, communication, and retrieval of multimedia information.
In particular, the 10th International Multimedia Modelling Conference (MMM2004) concentrates on common modelling frameworks for integrating the diverse fields of visual, audio, video, and
virtual world information.
MMM2004 deals with emerging Multimedia Modelling topics including:
• Multimedia Databases
• Audio Processing, Coding and Encryption
• Network Games and Animation
• Video Applications
• Multimedia Frameworks and QoS
• Topological and 3D Geometric Modelling
• Image Applications
• Image Retrieval
• Modelling / Editing / Virtual Environment
• Video Retrieval and Browsing

Stereo correspondence estimation using multiwavelets scale-space representation-based multiresolution analysis

A multiresolution technique based on multiwavelets scale-space representation for stereo correspondence estimation is presented. The technique uses the well-known coarse-to-fine strategy, involving the calculation of stereo correspondences at the coarsest resolution level with consequent refinement up to the finest level. Vector coefficients of the multiwavelets transform modulus are used as corresponding features, where modulus maxima defines the shift invariant high-level features (multiscale edges) with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps. Illuminative variation that can exist between the perspective views of the same scene is controlled using scale normalization at each decomposition level by dividing the details space coefficients with approximation space. The problems of ambiguity, explicitly, and occlusion, implicitly, are addressed by using a geometric topological refinement procedure. Geometric refinement is based on a symbolic tagging procedure introduced to keep only the most consistent matches in consideration. Symbolic tagging is performed based on probability of occurrence and multiple thresholds. The whole procedure is constrained by the uniqueness and continuity of the corresponding stereo features. The comparative performance of the proposed algorithm with eight famous existing algorithms, presented in the literature, is shown to validate the claims of promising performance of the proposed algorithm.

Improved 3D thinning algorithms for skeleton extraction

In this study, we focused on developing a novel 3D Thinning algorithm to extract one-voxel wide skeleton from various 3D objects aiming at preserving the topological information. The 3D Thinning algorithm was testified on computer-generated and real 3D reconstructed image sets acquired from TEMT and compared with other existing 3D Thinning algorithms. It is found that the algorithm has conserved medial axes and simultaneously topologies very well, demonstrating many advantages over the existing technologies. They are versatile, rigorous, efficient and rotation invariant.

Stereo image matching using wavelet scale-space representation

A multi-resolution technique for matching a stereo pair of images based on translation invariant discrete multi-wavelet transform is presented. The technique uses the well known coarse to fine strategy, involving the calculation of matching points at the coarsest level with consequent refinement up to the finest level. Vector coefficients of the wavelet transform modulus are used as matching features, where modulus maxima defines the shift invariant high-level features (multiscale edges) with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps. Illuminative variation that can exist between the perspective views of the same scene is controlled using scale normalization at each decomposition level by dividing the details space coefficients with approximation space and then using normalized correlation. The problem of ambiguity, explicitly, and occlusion, implicitly, is addressed by using a geometric topological refinement procedure and symbolic tagging.

High functional coherence in k-partite protein cliques of protein interaction networks

We introduce a new topological concept called k-partite protein cliques to study protein interaction (PPI) networks. In particular, we examine functional coherence of proteins in k-partite protein cliques. A k-partite protein clique is a k-partite maximal clique comprising two or more nonoverlapping protein subsets between any two of which full interactions are exhibited. In the detection of PPI’s k-partite maximal cliques, we propose to transform PPI networks into induced K-partite graphs with proteins as vertices where edges only exist among the graph’s partites. Then, we present a k-partite maximal clique mining (MaCMik) algorithm to enumerate k-partite maximal cliques from K-partite graphs. Our MaCMik algorithm is applied to a yeast PPI network. We observe that there does exist interesting and unusually high functional coherence in k-partite protein cliques—most proteins in k-partite protein cliques, especially those in the same partites, share the same functions. Therefore, the idea of k-partite protein cliques suggests a novel approach to characterizing PPI networks, and may help function prediction for unknown proteins.

Structural and functional properties of human multidrug resistance protein 1 (MRP1/ABCC1)

Multidrug ABC transporters such as P-glycoprotein (P-gp/MDR1/ABCB1) and multidrug resistance protein 1 (MRP1/ABCC1) play an important role in the extrusion of drugs from the cell and their overexpression can be a cause of failure of anticancer and antimicrobial chemotherapy. Recently, the mouse P-gp/Abcb1a structure has been determined and this has significantly enhanced our understanding of the structure-activity relationship (SAR) of mammalian ABC transporters. This paper highlights our current knowledge on the structural and functional properties and the SAR of human MRP1/ABCC1. Although the crystal structure of MRP1/ABCC1 has yet to be resolved, the current topological model of MRP1/ABCC1 contains two transmembrane domains (TMD1 and TMD2) each followed by a nucleotide binding domain (NBD) plus a third NH2-terminal TMD0. MRP1/ABCC1 is expressed in the liver, kidney, intestine, brain and other tissues. MRP1/ABCC1 transports a structurally diverse array of important endogenous substances (e.g. leukotrienes and estrogen conjugates) and xenobiotics and their metabolites, including various conjugates, anticancer drugs, heavy metals, organic anions and lipids. Cells that highly express MRP1/ABCC1 confer resistance to a variety of natural product anticancer drugs such as vinca alkaloids (e.g. vincristine), anthracyclines (e.g. etoposide) and epipodophyllotoxins (e.g. doxorubicin and mitoxantrone). MRP1/ABCC1 is associated with tumor resistance which is often caused by an increased efflux and decreased intracellular accumulation of natural product anticancer drugs and other anticancer agents. However, most compounds that efficiently reverse P-gp/ABCB1-mediated multidrug resistance have only low affinity for MRP1/ABCC1 and there are only a few effective and relatively specific MRP1/ABCC1 inhibitors available. A number of site-directed mutagenesis studies, biophysical and photolabeling studies, SAR and QSAR, molecular docking and homology modeling studies have documented the role of multiple residues in determining the substrate specificity and inhibitor selectivity of MRP1/ABCC1. Most of these residues are located in the TMs of TMD1 and TMD2, in particular TMs 4, 6, 7, 8, 10, 11, 14, 16, and 17, or in close proximity to the membrane/cytosol interface of MRP1/ABCC1. The exact transporting mechanism of MRP1/ABCC1 is unclear. MRP1/ABCC1 and other multidrug transporters are front-line mediators of drug resistance in cancers and represent important therapeutic targets in future chemotherapy. The crystal structure of human MRP1/ABCC1 is expected to be resolved in the near future and this will provide an insight into the SAR of MRP1/ABCC1 and allow for rational design of anticancer drugs and potent and selective MRP1/ABCC1 inhibitors.

Semantic and layered protein function prediction from PPI networks

Background The past few years have seen a rapid development in novel high-throughput technologies that have created large-scale data on protein-protein interactions (PPI) across human and most model species. This data is commonly represented as networks, with nodes representing proteins and edges representing the PPIs. A fundamental challenge to bioinformatics is how to interpret this wealth of data to elucidate the interaction of patterns and the biological characteristics of the proteins. One significant purpose of this interpretation is to predict unknown protein functions. Although many approaches have been proposed in recent years, the challenge still remains how to reasonably and precisely measure the functional similarities between proteins to improve the prediction effectiveness.

Results We used a Semantic and Layered Protein Function Prediction (SLPFP) framework to more effectively predict unknown protein functions at different functional levels. The framework relies on a new protein similarity measurement and a clustering-based protein function prediction algorithm. The new protein similarity measurement incorporates the topological structure of the PPI network, as well as the protein's semantic information in terms of known protein functions at different functional layers. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed framework in predicting unknown protein functions.

Conclusion The proposed framework has a higher prediction accuracy compared with other similar approaches. The prediction results are stable even for a large number of proteins. Furthermore, the framework is able to predict unknown functions at different functional layers within the Munich Information Center for Protein Sequence (MIPS) hierarchical functional scheme. The experimental results demonstrated that the new protein similarity measurement reflects more reasonably and precisely relationships between proteins.

Differentiating malware from cleanware using behavioural analysis

This paper proposes a scalable approach for distinguishing malicious files from clean files by investigating the behavioural features using logs of various API calls. We also propose, as an alternative to the traditional method of manually identifying malware files, an automated classification system using runtime features of malware files. For both projects, we use an automated tool running in a virtual environment to extract API call features from executables and apply pattern recognition algorithms and statistical methods to differentiate between files. Our experimental results, based on a dataset of 1368 malware and 456 cleanware files, provide an accuracy of over 97% in distinguishing malware from cleanware. Our techniques provide a similar accuracy for classifying malware into families. In both cases, our results outperform comparable previously published techniques.

Architecture of macromolecular network of soft functional materials : from structure to function

An enhanced macromolecular nanofiber network and its implications have been developed by employing the understanding of its formation with an emphasis on its topological aspect. Using agarose aqueous solution as a typical example, the macromolecular nanofiber network of soft functional materials has been clearly visualized for the first time using the developed technique of field emission scanning electronic microscopy coupled with flash-freeze-drying. Both the systematic kinetic study and the image evidence indicates that the nanofiber network in soft functional materials such as agarose turns out to form through a self-expitaxial nucleation-controlled process. This new understanding enables us to engineer ultra functions of soft materials via nanofiber network architecture, which in turn opens up a new direction in nano fabrication.

Real-time observation of fiber network formation in molecular organogel : supersaturation-dependent microstructure and its related rheological property

Low-molecular mass organic gelators self-organizing into three-dimensional fiber networks within organic solvents have attracted much attention in recent years. However, to date, how the microstructure of fiber network is formed in a gelation process and the key factors that govern the topological structure of a gel network remain to be determined. In this work, we address these issues by investigating the in situ formation of the gel networks in the N-lauroyl-l-glutamic acid di-n-butylamide (GP-1)/propylene glycol (PG) system. By using optical microscopy, the time evolution of the gel network microstructure was investigated under various supersaturation conditions. It is found that supersaturation is one of the key factors that govern the topological structure of a gel network. In particular, the creation of the junctions turns out to be supersaturation-dependent. The rheological experiments further revealed the correlation between topological structure and mechanical properties. It suggests that the rheological properties can be effectively modified by tuning the microstructure topology of the gel network. Our results reported here provide new physical insight into the formation kinetics of a molecular gel. Furthermore, this work could be important in constructing and engineering a supramolecular structure for the purpose of applications.

Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.