Understanding the effects of carbocyclic sugars constrained to north and south conformations on RNA nanodesign

Relatively new types of the modified nucleotides, namely carbocyclic sugars that are constrained to north or south (C2' or C3' exo) conformations, can be used for RNA nanoparticle design to control their structures and stability by rigidifying nucleotides and altering the helical properties of RNA duplexes. Two RNA structures, an RNA dodecamer and an HIV kissing loop complex where several nucleotides were replaced with north or south constrained sugars, were studied by molecular dynamics (MD) simulations. The substituted south constrained nucleotides in the dodecamer widened the major groove and narrowed and deepened the minor groove thus inducing local conformational changes that resemble a B-form DNA helix. In the HIV kissing loop complex, north and south constrained nucleotides were substituted into flanking bases and stems. The modified HIV kissing loop complex showed a lower RMSD value than the normal kissing loop complex. The overall twist angle was also changed and its standard deviation was reduced. In addition, the modified RNA dodecamer and HIV kissing loop complex were characterized by principal component analysis (PCA) and steered molecular dynamics (SMD). PCA results showed that the constrained sugars stabilized the overall motions. The results of the SMD simulations indicated that as the backbone δ angles were increased by elongation, more force was applied to the modified RNA due to the constrained sugar analogues.     

Reconstruction of structure and texture of city building facades

An important problem in creating photorealistic 3D city maps is to interpret and clear building facades from foreground objects. Modern image recovery algorithms either yield poor quality and require much time or need substantial interaction with the user. In this paper, a new algorithm for recovering texture of building facades, which is based on information on regularity of their structures, is presented. The structure is described by a set of nonintersecting lattices of similar rectangular fragments. An algorithm for searching such structures is proposed. The algorithms are compared with the existing ones on available image databases.     

A bayesian statistical algorithm for RNA secondary structure prediction.

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.     

Discovering predictive ensembles for transfer learning and meta-learning

Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.     

Finding short structural motifs for re-construction of proteins 3D structure

With a view to find useful building blocks (short structural motifs) for reconstruction of 3D structure of proteins, we propose a modified neural gas learning algorithm that we call structural neural gas (SNG) algorithm. The SNG is applied on a benchmark protein data set and its performance is compared with a well known algorithm from the literature (two stage clustering algorithm (TSCA)). The SNG algorithm is found to generate better building blocks compared to TSCA. We have also compared the performance of SNG algorithm with that of a recently reported Incremental Structural Mountain Clustering Method (ISMCM). In general, ISMCM is found to use more building blocks to yield results comparable to that of SNG algorithm. We demonstrate the superiority of SNG over TSCA both in terms of local-fit and global-fit errors using fragments of length five, six, and seven. We also use a graphical means for comparison of the performance of the two algorithms.     

Extended stigmergy in collective construction

We've described a scheme by which a swarm of robots can automatically assemble solid structures of square building blocks in two dimensions according to a high-level user-specified design. We discuss the use of extended stigmergy: augmenting the basic notion of stigmergy by increasing the capabilities of environmental elements. In this construction setting, the elements are the building blocks, the basic information they carry is the simple fact of their presence at a location, and extensions include cases where they can store additional information, perform computations, and/or communicate with physically attached neighbors. Benefits can include increased robustness and faster completion of a desired structure. We review three variants of that construction scheme that use extended stigmergy to different degrees. We focus on analysis of the algorithms and their comparative performance, mainly theoretical but also through simulation experiments.     

An implementation of hyper-resolution

In this paper the data structures and algorithms which were used to implement hyper-resolution are presented. The algorithms, which do not generate hyper-resolvents by creating sequences of P1-resolvents, have been used to obtain proofs of

THEOREM 1. Let G be a group such that x³ = e for all xG. If h is defined as h(x, y) = xyx′y′ forx, yG, then for all x, yG, h(h(x, y), y) = e (the identity).

THEOREM 2. Let R be a ring such that x² = x for all xR. Then R is commutative.

THEOREM 3. Every subgroup of index 2 is normal.

The data structures have been designed so that only a single copy of any literal or term is retained, no matter how often it occurs in the clauses kept. The main advantage of this approach is not the resulting savings in storage, but instead the fact that simultaneously matching a set of literals generates an entire set of hyper-resolvents.A method of extracting a set of “candidates for unification with a given literal” from the data structures is also presented. The result of using this method is a substantial reduction in the number of times a complete unification of two literals must be attempted.The initial results obtained from the program suggest that many resolution algorithms besides hyper-resolution could be enhanced by the use of similar data structures and algorithms.     

Two-View Orthographic Epipolar Geometry: Minimal and Optimal Solvers

We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn’t generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.     

Case-based reasoning assisted explanation of genetic algorithm results

Abstract

This paper describes a system for explaining solutions generated by genetic algorithms (GAs) using tools developed for case-based reasoning (CBR). In addition, this work empirically supports the building block hypothesis (BBH) which states that genetic algorithms work by combining good sub-solutions called building blocks into complete solutions. Since the space of possible building blocks and their combinations is extremely large, solutions found by GAs are often opaque and cannot be easily explained. Ironically, much of the knowledge required to explain such solutions is implicit in the processing done by the GA. Our system extracts and processes historical information from the GA by using knowledge acquisition and analysis tools developed for case-based reasoning. If properly analysed, the resulting knowledge base can be used: to shed light on the nature of the search space; to explain how a solution evolved; to discover its building blocks; and to justify why it works. Such knowledge about the search space can be used to tune the GA in various ways. As well as being a useful explanatory tool for GA researchers, our system serves as an empirical test of the building block hypothesis. The fact that it works so well lends credence to the theory that GAs work by exploiting common genetic building blocks.     

Microfluidic spatial growth of vertically aligned ZnO nanostructures by soft lithography for antireflective patterning

Hierarchical assemblies of nanostructured building blocks on conducting substrates are significant for construction of functional devices. Microfluidics is powerful but less exploited tool for spatial organization or growth of functionally sophisticated nanostructures with precise control. In this paper, we introduce a simple but unique strategy for the hydrothermal synthesis and patterned assembly of ZnO nanostructures within microchannels by soft lithography technique. Optical/antireflection properties of such hierarchically structured nanostructures are studied.     

Minimum cost service composition in service overlay networks

In service computing, it is often desirable to find the service composition solution for a given service composition request such that the total cost of the service composition solution is minimized. In this paper, we study the problem of finding the minimum cost service composition (MCSC) for a general service composition request which is represented by a directed acyclic graph (DAG). We first prove that the general case of the MCSC problem is NP-Hard. We then show that optimal solutions can be found in polynomial time for some special structured service composition requests. To this end, we derive a sufficient condition on the service composition request graph and propose corresponding algorithms to find the optimal solutions in polynomial time. Using such algorithms as building blocks, we propose heuristic algorithms to decompose the general service composition request graph into service composition request subgraphs with optimal structures. Simulation results demonstrate the effectiveness of the proposed heuristic algorithms.     

Interactive Reconfiguration of Urban Layouts

The layout of an urban space is a complex collection of man-made structures arranged in parcels, city blocks, and neighborhoods. An editor for interactively reconfiguring city layouts exploits geographical information system (GIS) data and provides tools to expand, scale, replace, and move parcels and blocks while efficiently exploiting their connectivity and zoning. The ability to create, extend, and change a model of a large-scale urban environment is useful for a variety of computer graphics applications. For example, it lets urban planning applications simulate changes to city layouts or newly proposed neighborhoods, create hypothetical views of an urban area after applying development and growth algorithms, and show architects the results of using common building blocks to design a new city layout.     

Software pipelining of loops by the method of modulo scheduling

Software pipelining is an efficient method of loop optimization that allows for parallelism of operations related to different loop iterations. Currently, most commercial compilers use loop pipelining methods based on modulo scheduling algorithms. This paper reviews these algorithms and considers algorithmic solutions designed for overcoming the negative effects of software pipelining of loops (a significant growth in code size and increase in the register pressure) as well as methods making it possible to use some hardware features of a target architecture. The paper considers global-scheduling mechanisms allowing one to pipeline loops containing a few basic blocks and loops in which the number of iterations is not known before the execution.     

进化算法中的模式定理及建筑块

探讨了进化算法中的模式定理及建筑块理论．通过引入模式进化、模式进化能力、适度模式等概念，以标准遗传算法为例，证明了在变异算子独立的条件下，进化算法中模式的构成与多点交叉和变异的顺序无关，然后证明了具有强进化能力的模式，将以指数阶增长．该文的模式理论有别于Holland等人提出的模式理论，特别是在交叉算子上采用了多点交叉算子，给出了相应的公式；并从这一推导过程论证了建筑块假设的合理性，可以称之为建筑块理论．     

Building blocks, cohort genetic algorithms, and hyperplane-defined functions

Building blocks are a ubiquitous feature at all levels of human understanding, froin perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples.     

A general criterion and an algorithmic framework for learning in multi-agent systems

We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one’s rewards in the presence of other agents who might also be learning (using the same or other learning algorithms). This new criterion takes in as a parameter the class of opponents. We then provide a modular approach for achieving effective agent-centric learning; the approach consists of a number of basic algorithmic building blocks, which can be instantiated and composed differently depending on the environment setting (for example, 2- versus n-player games) as well as the target class of opponents. We then provide several specific instances of the approach: an algorithm for stationary opponents, and two algorithms for adaptive opponents with bounded memory, one algorithm for the n-player case and another optimized for the 2-player case. We prove our algorithms correct with respect to the formal criterion, and furthermore show the algorithms to be experimentally effective via comprehensive computer testing.     

Automatic identification of informative sections of Web pages

Web pages - especially dynamically generated ones - contain several items that cannot be classified as the "primary content," e.g., navigation sidebars, advertisements, copyright notices, etc. Most clients and end-users search for the primary content, and largely do not seek the noninformative content. A tool that assists an end-user or application to search and process information from Web pages automatically, must separate the "primary content sections" from the other content sections. We call these sections as "Web page blocks" or just "blocks." First, a tool must segment the Web pages into Web page blocks and, second, the tool must separate the primary content blocks from the noninformative content blocks. In this paper, we formally define Web page blocks and devise a new algorithm to partition an HTML page into constituent Web page blocks. We then propose four new algorithms, ContentExtractor, FeatureExtractor, K-FeatureExtractor, and L-Extractor. These algorithms identify primary content blocks by 1) looking for blocks that do not occur a large number of times across Web pages, by 2) looking for blocks with desired features, and by 3) using classifiers, trained with block-features, respectively. While operating on several thousand Web pages obtained from various Web sites, our algorithms outperform several existing algorithms with respect to runtime and/or accuracy. Furthermore, we show that a Web cache system that applies our algorithms to remove noninformative content blocks and to identify similar blocks across Web pages can achieve significant storage savings.     

Software Structure for Display Management Systems

Display management software usually provides support for: creating displays on a CRT (cathode ray tube) or other output device; handling user inputs from keyboards, light-pens, and other input devices; and communicating with an application program in terms of these outputs and inputs. We propose a design for a family of display management systems which provide device-independent, high-level support for applications programming. The design involves structuring the display management software as a set of modules, with three levels of interfaces. The key features of this design include: 1) separation of display specifications from the actual data to be displayed; 2) structured output and input specifications; 3) asynchronous, message passing inter-faces; and 4) a software structure based on hiding details of data structures, algorithms and device orders.     

Urban building reconstruction from raw LiDAR point data

We present a method for automatic reconstruction of the volumetric structures of urban buildings, directly from raw LiDAR point clouds. Given the large-scale LiDAR data from a group of urban buildings, we take advantage of the “divide-and-conquer” strategy to decompose the entire point clouds into a number of subsets, each of which corresponds to an individual building. For each urban building, we determine its upward direction and partition the corresponding point data into a series of consecutive blocks, achieved by investigating the distributions of feature points of the building along the upward direction. Next, we propose a novel algorithm, Spectral Residual Clustering (SRC), to extract the primitive elements within the contours of blocks from the sectional point set, which is formed by registering the series of consecutive slicing points. Subsequently, we detect the geometric constraints among primitive elements through individual fitting, and perform constrained fitting over all primitive elements to obtain the accurate contour. On this basis, we execute 3D modeling operations, like extrusion, lofting or sweeping, to generate the 3D models of blocks. The final accurate 3D models are generated by applying the union Boolean operations over the block models. We evaluate our reconstruction method on a variety of raw LiDAR scans to verify its robustness and effectiveness.     

Incremental design of scalable interconnection networks using basic building blocks

In this paper, we present an incremental design of scalable interconnection networks in multicomputer systems using basic building blocks. Both network topologies and routing algorithms are considered. We use wormhole-routed small-scale 2D meshes as basic building blocks. The minimum requirement to expand these networks is a single building block. This implies that the network does not have to maintain the regular 2D mesh topology. Some new topologies are introduced: incomplete meshes based on those adaptive routing algorithms designed from the turn model and extended incomplete meshes based on XY routing. We show that the original routing algorithm can be adopted to send a message between any source and destination without using store-and-forward and causing deadlock. The way that the network is constructed incrementally requires no or a very small amount of rewiring and keeps high bisection density and short diameter of the network. The design methods can be used to economically and incrementally build expandable and scalable parallel computers.