Deep transfer modeling for classification of Maize Plant Leaf Disease

Multimedia Tools and Applications - Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently...     

Structural plasticity in RNA and its role in the regulation of protein translation in coliphage Q beta

We have analyzed both conformational and functional changes caused by two large cis-acting deletions (delta 159 and delta 549) located within the read-through domain, a 850 nucleotide hairpin, in coliphage Q beta genomic RNA. Studies in vivo show that co-translational regulation of the viral coat and replicase genes has been uncoupled in viral genomes carrying deletion delta 159. Translational regulation is restored in deletion delta 549, a naturally evolved pseudorevertant. Structural analysis by computer modeling shows that structural features within the read-through domain of delta 159 RNA are less well determined than they are in the read-through domain of wild-type RNA, whereas predicted structure in the read-through domain of evolved pseudorevertant delta 549 is unusually well determined. Structural analysis by electron microscopy of the genomic RNAs shows that several long range helices at the base of the read-through domain, that suppress translational initiation of the viral replicase gene in the wild-type genome, have been destabilized in delta 159 RNA. In addition, the structure of local hairpins within the read-through region is more variable in delta 159 RNA than in wild-type RNA. Stable RNA secondary structure is restored in the read-through domain of delta 549 RNA. Our analyses suggest that structure throughout the read-through domain affects the regulation of viral replicase expression by altering the likelihood that long-range interactions at the base of the domain will form. We discuss possible kinetic and equilibrium models that can explain this effect, and argue that observed changes in structural plasticity within the read-through domain of the mutant genomes are key in understanding the process. During the course of these studies, we became aware of the importance of the information contained in the energy dot plot produced by the RNA secondary structure prediction program mfold. As a result, we have improved the graphical representation of this information through the use of color annotation in the predicted optimal folding. The method is presented here for the first time.     

Experience with Applying Formal Methods to Protocol Specification and System Architecture

In the last three years or so we at Enterprise Platforms Group at Intel Corporation have been applying formal methods to various problems that arose during the process of defining platform architectures for Intel's processor families. In this paper we give an overview of some of the problems we have worked on, the results we have obtained, and the lessons we have learned. The last topic is addressed mainly from the perspective of platform architects.     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

Predictive simulation of human walking transitions using an optimization formulation

A general optimization formulation for transition walking prediction using 3D skeletal model is presented. The formulation is based on a previously presented one-step walking formulation (Xiang et al., Int J Numer Methods Eng 79:667–695, 2009b). Two basic transitions are studied: walk-to-stand and slow-to-fast walk. The slow-to-fast transition is used to connect slow walk to fast walk by using a step-to-step transition formulation. In addition, the speed effects on the walk-to-stand motion are investigated. The joint torques and ground reaction forces (GRF) are recovered and analyzed from the simulation. For slow-to-fast walk transition, the predicted ground reaction forces in step transition is even larger than that of the fast walk. The model shows good correlation with the experimental data for the lower extremities except for the standing ankle profile. The optimal solution of transition simulation is obtained in a few minutes by using predictive dynamics method.     

Simultaneous analysis and design optimization of nonlinear response

The optimal structural design requiring nonlinear analysis and design sensitivity analysis can be an enormous computational task. It is extremely important to explore ways to reduce the computational effort so that more realistic and larger-scale structures can be optimized. The optimal design process is iterative requiring response analysis of the structure for each design improvement. A recent study has shown that up to 90 percent of the total computational effort is spent in computing the nonlinear response of the structure during the optimal design process. Thus, efficiency of the optimization process for nonlinear structures can be substantially improved if numerical effort for analyzing the structure can be reduced. This paper explores the idea of using design sensitivity coefficients (computed at each iteration to improve design) to predict displacement response of the structure at a changed design. The iterative procedure for nonlinear analysis of the structure is then started from the predicted response. This optimization procedure is called mixed and the original procedure where sensitivity information is not used is called the conventional approach. The numerical procedures for the two approaches are developed and implemented. They are compared on some truss type structures by including both geometric and material nonlinearities. Stress, strain, displacement, and buckling load constraints are imposed. The study shows the mixed method to be numerically stable and efficient.     

Phosphate sorption isotherms in relation to P nutrition of wheat (Triticum aestivum)

The phosphate sorption isotherms are needed to explain differential plant responses to P fertilization in soils. Laboratory and greenhouse experiments investigated the use of phosphorus sorption isotherms in relation to P fertilizer requirement of wheat in ten benchmark soils of Punjab, India. The modified Mitscherlich Equation (3) was used to describe plant response observed in different soils. Maximum obtainable yield (MOY) ranged from 11.6 g pot^–1 in Gurdaspur (I) sandy clay loam to 7.0 g pot^–1 in Nabha sandy clay loam. Response to P applied @ 25 mg P kg^–1 soil was maximum (77%) in Bathinda sand and minimum in Chuharpur clay loam (33%). The response curvature varied from 3.74 × 10^–2 in Nabha sandy clay loam to 4.43 × 10^–2 in Kanjli sandy loam. The soil solution P required to produce optimum yield (90% MOY) varied from 1.61 µg ml^–1 in Bathinda sand to 0.10 µg ml^–1 in Sadhugarh clay. Dry matter yield obtained at 0.2 µg ml^–1 solution P concentration ranged from 55% in Bathinda sand to 85% of MOY in Gurdaspur (II) clay loam. At the same solution P concentration (0.1 µg P ml^–1), dry matter yield was 91% in Sadhugarh clay, 80% in Gurdaspur (II) clay loam and, 43% of MOY in Bathinda sand and eventually coincided with the decreasing maximum buffer capacity (MBC) in these soils. At the same level of sorbed P (100 mg P kg^–1 soil) the yield was observed to be inversely proportional to MBC. The study, therefore, concludes that, soils should be grouped according to their P sorption characteristics and MBC before using critical soil solution P as a criterion for obtaining optimum yields.     

Micro-computed tomography scanning approaches to quantify,parameterize and visualize bioturbation activity in clogged streambeds: A proof of concept

Fine particle clogging and faunal bioturbation are two key processes co-occurring in the hyporheic zone that potentially affect hyporheic exchange through modifications in the sediment structure of streambeds. Clogging results from excessive fine sediment infiltration and deposition in rivers, and it is known to decrease matrix porosity and potentially reduce permeability. Faunal bioturbation activity may compensate for the negative effect of clogging by reworking the sediment, increasing porosity, and preventing further infiltration of fines. Although both processes of clogging and bioturbation have received significant attention in the literature separately, their combined effects on streambed sediment structure are not well understood, mostly due to the lack of a standard methodology for their assessment. Here, we illustrate a novel methodology using X-ray computed tomography (CT), as proof of concept, to investigate how, together, clogging and bioturbation affect streambed porosity in a controlled flow-through flume. By visualising gallery formations of an upward conveyor macroinvertebrate; Lumbriculus variegatus as a model species, we quantified bioturbation activity in a clogged streambed, focusing on orientation, depth, and volume at downwelling and upwelling areas of the flume. Gallery creation increased the porosity of the streambed sediment, suggesting a potential improvement in permeability and a possible offset of clogging effects. We illustrate the promising use of X-ray CT as a tool to assess bioturbation in clogged streambeds, and the potential role of bioturbation activity supporting hyporheic exchange processes in streambeds, warranting further studies to understand the extent of bioturbation impacts in natural systems.     

Global optimization methods for engineering applications: A review

A review of the methods for global optimization reveals that most methods have been developed for unconstrained problems. They need to be extended to general constrained problems because most of the engineering applications have constraints. Some of the methods can be easily extended while others need further work. It is also possible to transform a constrained problem to an unconstrained one by using penalty or augmented Lagrangian methods and solve the problem that way. Some of the global optimization methods find all the local minimum points while others find only a few of them. In any case, all the methods require a very large number of calculations. Therefore, the computational effort to obtain a global solution is generally substantial. The methods for global optimization can be divided into two broad categories: deterministic and stochastic. Some deterministic methods are based on certain assumptions on the cost function that are not easy to check. These methods are not very useful since they are not applicable to general problems. Other deterministic methods are based on certain heuristics which may not lead to the true global solution. Several stochastic methods have been developed as some variation of the pure random search. Some methods are useful for only discrete optimization problems while others can be used for both discrete and continuous problems. Main characteristics of each method are identified and discussed. The selection of a method for a particular application depends on several attributes, such as types of design variables, whether or not all local minima are desired, and availability of gradients of all the functions.Notation Number of equality constraints - () ^T A transpose of a vector - A A hypercubic cell in clustering methods - Distance between two adjacent mesh points - Probability that a uniform sample of sizeN contains at least one point in a subsetA ofS - A(v, x) Aspiration level function - A The set of points with cost function values less thanf(x _G ^* ) +. Same asA _f () - A _f () A set of points at which the cost function value is within off(x _G ^* ) - A () A set of points x with[f(x)] smaller than - A _N The set ofN random points - A _q The set of sample points with the cost function value f _q - _Q The contraction coefficient; –1 _Q 0 - _R The expansion coefficient; _E > 1 - _R The reflection coefficient; 0 < _R 1 - A _x () A set of points that are within the distance from x _G ^* - D Diagonal form of the Hessian matrix - det() Determinant of a matrix - d _j A monotonic function of the number of failed local minimizations - d _t Infinitesimal change in time - d _x Infinitesimal change in design - A small positive constant - (t) A real function called the noise coefficient - ₀ Initial value for(t) - exp() The exponential function - f ^(c) The record; smallest cost function value over X^(C) - [f(x)] Functional for calculating the volume fraction of a subset - Second-order approximation tof(x) - f(x) The cost function - An estimate of the upper bound of global minimum - f ^E The cost function value at x^E - f ^L The cost function value at x^L - f _opt The current best minimum function value - f ^P The cost function value at x ^P - f ^Q The cost function value at x ^Q - f _q A function value used to reduce the random sample - f ^R The cost function value at x ^R - f ^S The cost function value at x^S - f _{T F min} A common minimum cost function value for several trajectories - f _{TF opt} The best current minimum value found so far forf _{TF min} - f ^W The cost function value at x ^W - G Minimum number of points in a cell (A) to be considered full - The gamma function - A factor used to scale the global optimum cost in the zooming method - Minimum distance assumed to exist between two local minimum points - gi(x) Constraints of the optimization problem - H The size of the tabu list - H(x^*) The Hessian matrix of the cost function at x^* - h _j Half side length of a hypercube - h _m Minimum half side lengths of hypercubes in one row - I The unity matrix - ILIM A limit on the number of trials before the temperature is reduced - J The set of active constraints - K Estimate of total number of local minima - k Iteration counter - The number of times a clustering algorithm is executed - L Lipschitz constant, defined in Section 2 - L The number of local searches performed - _i The corresponding pole strengths - log () The natural logarithm - LS Local search procedure - M Number of local minimum points found inL searches - m Total number of constraints - m(t) Mass of a particle as a function of time - m() TheLebesgue measure of thea set - Average cost value for a number of random sample of points inS - N The number of sample points taken from a uniform random distribution - n Number of design variables - n(t) Nonconservative resistance forces - n _c Number of cells;S is divided inton _c cells - NT Number of trajectories - Pi (3.1415926) - P _i ^(j) Hypersphere approximating thej-th cluster at stagei - p(x ⁽ⁱ⁾⁾ Boltzmann-Gibbs distribution; the probability of finding the system in a particular configuration - pg A parameter corresponding to each reduced sample point, defined in (36) - Q An orthogonal matrix used to diagonalize the Hessian matrix - _i (i = 1, K) The relative size of thei-th region of attraction - r _i ^(j) Radius of thej-th hypersp here at stagei - R _x ^* Region of attraction of a local minimum x^* - r _j Radius of a hypersphere - r A critical distance; determines whether a point is linked to a cluster - R ⁿ A set ofn tuples of real numbers - A hyper rectangle set used to approximateS - S The constraint set - A user supplied parameter used to determiner - s The number of failed local minimizations - T The tabu list - t Time - T(x) The tunneling function - T _c (x) The constrained tunneling function - T _i The temperature of a system at a configurationi - TLIMIT A lower limit for the temperature - TR A factor between 0 and 1 used to reduce the temperature - u(x) A unimodal function - V(x) The set of all feasible moves at the current design - v(x) An oscillating small perturbation. - V(y⁽ⁱ⁾) Voronoi cell of the code point y⁽ⁱ⁾ - v^–1 An inverse move - v _k A move; the change from previous to current designs - w(t) Ann-dimensional standard. Wiener process - x Design variable vector of dimensionn - x^# A movable pole used in the tunneling method - x⁽⁰⁾ A starting point for a local search procedure - X^(c) A sequence of feasible points {x⁽¹⁾, x⁽²⁾,,x^(c)} - x(t) Design vector as a function of time - X^* The set of all local minimum points - x^* A local minimum point forf(x) - x^*(i) Poles used in the tunneling method - x _G ^* A global minimum point forf(x) - Transformed design space - The velocity vector of the particle as a function of time - Acceleration vector of the particle as a function of time - x ^C Centroid of the simplex excluding x ^L - x ^c A pole point used in the tunneling method - x ^E An expansion point of x ^R along the direction x ^C x ^R - x ^L The best point of a simplex - x ^P A new trial point - x ^Q A contraction point - x ^R A reflection point; reflection of x ^W on x ^C - x ^S The second worst point of a simplex - x ^W The worst point of a simplex - The reduced sample point with the smallest function value of a full cell - Y The set of code points - y ⁽ⁱ⁾ A code point; a point that represents all the points of thei-th cell - z A random number uniformly distributed in (0,1) - Z ^(c) The set of points x where [f ^(c) – ] is smaller thanf(x) - []₊ Max (0,) - | | Absolute value - The Euclidean norm - f[x(t)] The gradient of the cost function