The estimation of stochastic context-free grammars using the Inside-Outside algorithm

Using an entropy argument, it is shown that stochastic context-free grammars (SCFG's) can model sources with hidden branching processes more efficiently than stochastic regular grammars (or equivalently HMM's). However, the automatic estimation of SCFG's using the Inside-Outside algorithm is limited in practice by its O(n3) complexity. In this paper, a novel pre-training algorithm is described which can give significant computational savings. Also, the need for controlling the way that non-terminals are allocated to hidden processes is discussed and a solution is presented in the form of a grammar minimization procedure. © 1990.

How are internal models of unstable tasks formed?

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

Water budgeting studies on the hatchery and nursery rearing practices for the common carp, Cyprinus carpio (Linnaeus, 1758)

Aquaculture systems are an integral element of rural development and therefore should be environment friendly as well as socially and economically designed. From the economic standpoint, one of the major constraints for the development of sustainable aquaculture includes externalities generated by competition in access to a limited resource. This study was conducted as an investigation into the water requirement for the hatchery and nursery production phases of common carp, Cyprinus carpio (Linnaeus, 1758) at the Maharashtra State Fish Seed Farm at Khopoli in Raigad Dist. of Maharashtra during the winter months from November to February. The water budgeting study involves the quantification of water used in every stage of production in hatchery and nursery systems and aimed at becoming a foundation for the minimization of water during production without affecting the yield; thereby conserving water and upholding the theme of sustainable aquaculture. The total water used in a single operation cycle was estimated to be 11,25,040 L [sic]. Out of the total water consumed, 4.74% water was used in the pre-operational management steps, 4.48% was consumed during breeding, 62.72% was consumed in the hatching phase, 21.50% was used for hatchery rearing and 6.56% was consumed during conditioning. In the nursery ponds, the water gain was primarily the regulated inflow coming through the irrigation channel. The total quantum of water used in the nursery rearing was 31,60,800 L [sic]. The initial filling and regulated inflow formed 42.60% and 57.40% respectively of water gain, while evaporation, seepage and discharge contributed 20.71%, 36.46% and 42.82% respectively to the water loss. The total water expended for the entire operation was 1,21,61,120 L [sic]. Water expense occurred to produce a single spawn in the hatchery system was calculated and found to be 0.56 L while the water expended to produce one fry was calculated as 4.86 L. The study fulfills the hydrological equation described by Winter (1981) and Boyd (1985). It also validates the water budget simulation model that can be used for forecasting water requirements for aquaculture ponds (Nath and Bolte, 1998).

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Comparative studies on parallel and antiparallel duplex and triplex DNA

Parallel strand models for base sequences d(A)(10). d(T)(10), d(AT)(5) . d(TA)(5), d(G(5)C(5)). d(C(5)G(5)), d(GC)(5) . d(CG)(5) and d(CTATAGGGAT). d(GATATCCCTA), where reverse Watson-Crick A-T pairing with two H-bonds and reverse Watson-Crick G-C pairing with one H-bond or with two H-bonds were adopted, and three models of d(T)(14). d(A)(14). d(T)(14) triple helix with different strand orientations were built up by molecular architecture and energy minimization. Comparisons of parallel duplex models with their corresponding B-DNA models and comparisons among the three triple helices showed: (i) conformational energies of parallel AT duplex models were a little lower, while for GC duplex models they were about 8% higher than that of their corresponding B-DNA models; (ii) the energy differences between parallel and B-type duplex models and among the three triple helices arose mainly from base stacking energies, especially for GC base pairing; (iii) the parallel duplexes with one H-bond G-C pairs were less stable than those with two H-bonds G-C pairs. The present paper includes a brief discussion about the effect of base stacking and base sequences on DNA conformations. (C) 1997 Academic Press Limited.

Molecular modeling on some human interleukins sharing gamma c of interleukin-2 receptor, and structure-function relationships

Five models for human interleukin-7 (HIL-7), HIL-9, HIL-13, HIL-15 and HIL-17 have been generated by SYBYL software package. The primary models were optimized using molecular dynamics and molecular mechanics methods. The final models were optimized using a steepest descent algorithm and a subsequent conjugate gradient method. The complexes with these interleukins and the common gamma chain of interleukin-2 receptor (IL-2R) were constructed and subjected to energy minimization. We found residues, such as Gln127 and Tyr103, of the common gamma chain of IL-2R are very important. Other residues, e.g. Lys70, Asn128 and Glu162, are also significant. Four hydrophobic grooves and two hydrophilic sites converge at the active site triad of the gamma chain. The binding sites of these interleukins interaction with the common gamma chain exist in the first helical and/or the fourth helical domains. Therefore, we conclude that these interleukins binds to the common gamma chain of IL-2R by the first and the fourth helix domain. Especially at the binding sites of some residues (lysine, arginine, asparagine, glutamic acid and aspartic acid), with a discontinuous region of the common gamma chain of IL-2R, termed the interleukins binding sites (103-210). The study of these sites can be important for the development of new drugs. (C) 2000 Elsevier Science B.V. All rights reserved.

Rhinopithecus bieti at Tacheng, Yunnan: Diet and daytime activities

We collected data on diet and activity budget in a group of Rhinopithecus bieti at Tacheng (99degrees 18'E, 27degrees 36' N, between 2,700 - 3,700 m asl), Yunnan, from March 1999 to December 2000. We mainly recorded species-parts eaten with feeding scores from scanning state behaviors of one-male units in tree-crowns. We also conducted microscopic analysis of feces collected monthly. The subjects consumed 59 plant species, belonging to 42 genera in 28 families, of which 90 species-parts were distributed as follows: 21 in Winter, 38 in spring, 39 in Summer, 47 in autumn. Conversely, the group annually spent, on average, 35% of daytime feeding, 33% resting, 15% moving, and 13% in social activities. Seasonal changes are apparent in daytime budget and food item-related feeding time in tree-crowns, food remains in feces, and the number of species-parts eaten. Correlations within and between food items and time budget clearly indicate maximization of foraging effectiveness and minimization of energy expenditure. In consideration of reports from northern and southern groups, that which underlay the specific adaptation to the habitat appeared to be similar to those of other colobines. Thus, the ultimate factors for survival of the species are more hopeful than expected.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Application of the multi-objective Alliance algorithm to a benchmark aerodynamic optimization problem

This paper introduces a new version of the multiobjective Alliance Algorithm (MOAA) applied to the optimization of the NACA 0012 airfoil section, for minimization of drag and maximization of lift coefficients, based on eight section shape parameters. Two software packages are used: XFoil which evaluates each new candidate airfoil section in terms of its aerodynamic efficiency, and a Free-Form Deformation tool to manage the section geometry modifications. Two versions of the problem are formulated with different design variable bounds. The performance of this approach is compared, using two indicators and a statistical test, with that obtained using NSGA-II and multi-objective Tabu Search (MOTS) to guide the optimization. The results show that the MOAA outperforms MOTS and obtains comparable results with NSGA-II on the first problem, while in the other case NSGA-II is not able to find feasible solutions and the MOAA is able to outperform MOTS. © 2013 IEEE.

A self-adaptive genetic algorithm applied to multi-objective optimization of an airfoil

Genetic algorithms (GAs) have been used to tackle non-linear multi-objective optimization (MOO) problems successfully, but their success is governed by key parameters which have been shown to be sensitive to the nature of the particular problem, incorporating concerns such as the numbers of objectives and variables, and the size and topology of the search space, making it hard to determine the best settings in advance. This work describes a real-encoded multi-objective optimizing GA (MOGA) that uses self-adaptive mutation and crossover, and which is applied to optimization of an airfoil, for minimization of drag and maximization of lift coefficients. The MOGA is integrated with a Free-Form Deformation tool to manage the section geometry, and XFoil which evaluates each airfoil in terms of its aerodynamic efficiency. The performance is compared with those of the heuristic MOO algorithms, the Multi-Objective Tabu Search (MOTS) and NSGA-II, showing that this GA achieves better convergence.

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges superlinearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression. © 2013 Society for Industrial and Applied Mathematics.

Adaptive combustion instability control with saturation

The control of a class of combustion systems, suceptible to damage from self-excited combustion oscillations, is considered. An adaptive stable controller, called Self-Tuning Regulator (STR), has recently been developed, which meets the apparently contradictory challenge of relying as little as possible on a particular combustion model while providing some guarantee that the controller will cause no harm. The controller injects some fuel unsteadily into the burning region, thereby altering the heat release, in response to an input signal detecting the oscillation. This paper focuses on an extension of the STR design, when, due to stringent emission requirements and to the danger of flame extension, the amount of fuel used for control is limited in amplitude. A Lyapunov stability analysis is used to prove the stability of the modified STR when the saturation constraint is imposed. The practical implementation of the modified STR remains straightforward, and simulation results, based on the nonlinear premixed flame model developed by Dowling, show that in the presence of a saturation constraint, the self-excited oscillations are damped more rapidly with the modified STR than with the original STR. © 2001 by S. Evesque. Published by the American Institute of Aeronautics and Astronautics, Inc.

Cloning, expression and characterization of aerolysin from Aeromonas hydrophila in Escherichia coli

Aerolysin is a toxin (protein in nature) secreted by the strains of Aeromonas spp. and plays all important role in the virulence of Aeromonas strains. It has also found several applications such as for detection of glycosylphosphatidylinositol (GPI)-anchored proteins etc. A. hydrophila is a ubiquitous Gram-negative bacterium which causes frequent harm to the aquaculture. To obtain a significant amount of recombinant aerolysin in the active form, in this study, we expressed the aerolysin in E. Coli Under the control of T7 RNase promoter. The coding region (AerA-W) of the aerA gene of A. hydrophila XS91-4-1. excluding partial coding region of the signal peptide was cloned into the vector pET32a and then transformed into E. coli b121. After optimizing the expression conditions, the recombinant protein AerA-W was expressed in a soluble form and purified using His-Bind resin affinity chromatography. Recombinant aerolysin showed hemolytic activity in the agar diffusive hemolysis test. Western blot analysis demonstrated good antigenicity of the recombinant protein.