985 resultados para Kernel function
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We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.
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The active site lysine residue, K256, involved in Schiffs base linkage with pyridoxal-5'-phosphate (PEP) in sheep liver recombinant serine hydroxymethyltransferase (rSHMT) was changed to glutamine or arginine by site-directed mutagenesis. The purified K256Q and K256R SHMTs had less than 0.1% of catalytic activity with serine and H(4)folate as substrates compared to rSHMT. The mutant enzymes also failed to exhibit the characteristic visible absorbance spectrum (lambda(max) 425 nm) and did not produce the quinonoid intermediate (lambda(max) 495 nm) upon the addition of glycine and H(4)folate. The mutant enzymes were unable to catalyze aldol cleavage of beta-phenylserine and transamination of D-alanine. These results suggested that the mutation of the lysine had resulted in the inability of the enzyme to bind to the cofactor. Therefore, the K256Q SHMT was isolated as a dimer and the K256R SHMT as a mixture of dimers and tetramers which were converted to dimers slowly. On the other hand, rSHMT was stable as a tetramer for several months, further confirming the role of PLP in maintenance of oligomeric structure. The mutant enzymes also failed to exhibit the increased thermal stability upon the addition of serine, normally observed with rSHMT. The enhanced thermal stability has been attributed to a change in conformation of the enzyme from open to closed form leading to reaction specificity. The mutant enzymes were unable to undergo this conformational change probably because of the absence of bound cofactor.
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The velocity distribution function for the steady shear flow of disks (in two dimensions) and spheres (in three dimensions) in a channel is determined in the limit where the frequency of particle-wall collisions is large compared to particle-particle collisions. An asymptotic analysis is used in the small parameter epsilon, which is naL in two dimensions and na(2)L in three dimensions, where; n is the number density of particles (per unit area in two dimensions and per unit volume in three dimensions), L is the separation of the walls of the channel and a is the particle diameter. The particle-wall collisions are inelastic, and are described by simple relations which involve coefficients of restitution e(t) and e(n) in the tangential and normal directions, and both elastic and inelastic binary collisions between particles are considered. In the absence of binary collisions between particles, it is found that the particle velocities converge to two constant values (u(x), u(y)) = (+/-V, O) after repeated collisions with the wall, where u(x) and u(y) are the velocities tangential and normal to the wall, V = (1 - e(t))V-w/(1 + e(t)), and V-w and -V-w, are the tangential velocities of the walls of the channel. The effect of binary collisions is included using a self-consistent calculation, and the distribution function is determined using the condition that the net collisional flux of particles at any point in velocity space is zero at steady state. Certain approximations are made regarding the velocities of particles undergoing binary collisions :in order to obtain analytical results for the distribution function, and these approximations are justified analytically by showing that the error incurred decreases proportional to epsilon(1/2) in the limit epsilon --> 0. A numerical calculation of the mean square of the difference between the exact flux and the approximate flux confirms that the error decreases proportional to epsilon(1/2) in the limit epsilon --> 0. The moments of the velocity distribution function are evaluated, and it is found that [u(x)(2)] --> V-2, [u(y)(2)] similar to V-2 epsilon and -[u(x)u(y)] similar to V-2 epsilon log(epsilon(-1)) in the limit epsilon --> 0. It is found that the distribution function and the scaling laws for the velocity moments are similar for both two- and three-dimensional systems.
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The prop-2-ynyloxy carbonyl function (POC) which can be cleaved under mild and neutral conditions in the presence of benzyltriethylammonium tetrathiomolybdate has been developed as a new protecting group for amines. (C) 1999 Elsevier Science Ltd. All rights reserved.
Resumo:
The natural frequencies of continuous systems depend on the governing partial differential equation and can be numerically estimated using the finite element method. The accuracy and convergence of the finite element method depends on the choice of basis functions. A basis function will generally perform better if it is closely linked to the problem physics. The stiffness matrix is the same for either static or dynamic loading, hence the basis function can be chosen such that it satisfies the static part of the governing differential equation. However, in the case of a rotating beam, an exact closed form solution for the static part of the governing differential equation is not known. In this paper, we try to find an approximate solution for the static part of the governing differential equation for an uniform rotating beam. The error resulting from the approximation is minimized to generate relations between the constants assumed in the solution. This new function is used as a basis function which gives rise to shape functions which depend on position of the element in the beam, material, geometric properties and rotational speed of the beam. The results of finite element analysis with the new basis functions are verified with published literature for uniform and tapered rotating beams under different boundary conditions. Numerical results clearly show the advantage of the current approach at high rotation speeds with a reduction of 10 to 33% in the degrees of freedom required for convergence of the first five modes to four decimal places for an uniform rotating cantilever beam.
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A novel series of vesicle-forming ion-paired amphiphiles, bis(hexadecyldimethylammonium)alkane dipalmitate (1a-1h), containing four chains were synthesized with two isolated headgroups. In each of these amphiphiles, the two headgroup charges are separated by a flexible polymethylene spacer chain -[(CH2)(m)]- of varying lengths (m) such that the length and the conformation of the spacer chain determine the intra-"monomer" headgroup separation. Transmission electron microscopy indicated that each of these forms bilayer membranes upon dispersion in aqueous media. The vesicular properties of these aggregates have been examined by differential scanning calorimetry and temperature-dependent fluorescence anisotropy measurements. Interestingly, their T-m values decreased with the increase in the m value. Thus while the apparent T-m of the lipid with m = 2 (1a) is 74.1 degrees C, the corresponding value observed for the lipid with m = 12 (1h) is 38.9 degrees C. The fluorescence anisotropy values (r) for 1b-1g were quite high (r similar to 0.3) compared to that of 1h (r similar to 0.23) at 20-30 degrees C in their gel states. On the other hand, the r value for vesicular 1b beyond melting was higher (0.1) compared to any of those for 1c-1h (similar to 0.04-0.06). X-ray diffraction of the cast films was performed to understand the nature and the thickness of these membrane organizations. The membrane widths ranged from 30 to 51 A as the m values varied. The entrapment of a small water-soluble solute, riboflavin, by the individual vesicular aggregates, and their sustenance: under an imposed transmembrane pH gradient have also been examined. These results show that all lipid vesicles entrap riboflavin and that generally the resistance to OH- permeation decreases with the increase in m value. Finally,all the above observations were comparatively analyzed, and on the basis of the calculated structures of these lipids, it was possible to conclude that membrane propel-ties can be modulated by spacer chain length variation of the ion-paired amphiphiles.
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We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.
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In this paper we consider the problem of learning an n × n kernel matrix from m(1) similarity matrices under general convex loss. Past research have extensively studied the m = 1 case and have derived several algorithms which require sophisticated techniques like ACCP, SOCP, etc. The existing algorithms do not apply if one uses arbitrary losses and often can not handle m > 1 case. We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case. One of the major contributions of the paper is to extend the well knownMirror Descent(MD) framework to handle Cartesian product of psd matrices. This novel extension leads to an algorithm, called EMKL, which solves the problem in O(m2 log n 2) iterations; in each iteration one solves an MKL involving m kernels and m eigen-decomposition of n × n matrices. By suitably defining a restriction on the objective function, a faster version of EMKL is proposed, called REKL,which avoids the eigen-decomposition. An alternative to both EMKL and REKL is also suggested which requires only an SVMsolver. Experimental results on real world protein data set involving several similarity matrices illustrate the efficacy of the proposed algorithms.
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A vast amount of literature has accumulated on the characterization of DNA methyltransferases. The HhaI DNA methyltransferase, a C5-cytosine methyltransferase, has been the subject of investigation for the last 2 decades. Biochemical and kinetic characterization have led to an understanding of the catalytic and kinetic mechanism of the methyltransfer reaction. The HhaI methyltransferase has also been subjected to extensive structural analysis, with the availability of 12 structures with or without a cofactor and a variety of DNA substrates. The mechanism of base flipping, first described for the HhaI methyltransferase, is conserved among all DNA methyltransferases and is also found to occur in numerous DNA repair enzymes. Studies with other methyltransferase reveal a significant structural and functional similarity among different types of methyltransferases. This review aims to summarize the available information on the HhaI DNA methyltransferase.
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An entirely different approach for localisation of winding deformation based on terminal measurements is presented. Within the context of this study, winding deformation means, a discrete and specific change externally imposed at a particular position on the winding. The proposed method is based on pre-computing and plotting the complex network-function loci e.g. driving-point impedance (DPI)] at a selected frequency, for a meaningful range of values for each element (increasing and decreasing) of the ladder network which represents the winding. This loci diagram is called the nomogram. After introducing a discrete change, amplitude and phase of DPI are measured. By plotting this single measurement on the nomogram, it is possible to estimate the location and identify the extent of change. In contrast to the existing approach, the proposed method is fast, non-iterative and yields reasonably good localisation. Experimental results for actual transformer windings (interleaved and continuous disc) are presented.
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In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.
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In Saccharomyces cerevisiae, Prp17p is required for the efficient completion of the second step of pre-mRNA splicing. The function and interacting factors for this protein have not been elucidated. We have performed a mutational analysis of yPrp17p to identify protein domains critical for function. A series of deletions were made throughout the region spanning the N-terminal 158 amino acids of the protein, which do not contain any identified structural motifs. The C-terminal portion (amino acids 160–455) contains a WD domain containing seven WD repeats. We determined that a minimal functional Prp17p consists of the WD domain and 40 amino acids N-terminal to it. We generated a three-dimensional model of the WD repeats in Prp17p based on the crystal structure of the [beta]-transducin WD domain. This model was used to identify potentially important amino acids for in vivo functional characterization. Through analysis of mutations in four different loops of Prp17p that lie between [beta] strands in the WD repeats, we have identified four amino acids, 235TETG238, that are critical for function. These amino acids are predicted to be surface exposed and may be involved in interactions that are important for splicing. Temperature-sensitive prp17 alleles with mutations of these four amino acids are defective for the second step of splicing and are synthetically lethal with a U5 snRNA loop I mutation, which is also required for the second step of splicing. These data reinforce the functional significance of this region within the WD domain of Prp17p in the second step of splicing.
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A methodology termed the “filtered density function” (FDF) is developed and implemented for large eddy simulation (LES) of chemically reacting turbulent flows. In this methodology, the effects of the unresolved scalar fluctuations are taken into account by considering the probability density function (PDF) of subgrid scale (SGS) scalar quantities. A transport equation is derived for the FDF in which the effect of chemical reactions appears in a closed form. The influences of scalar mixing and convection within the subgrid are modeled. The FDF transport equation is solved numerically via a Lagrangian Monte Carlo scheme in which the solutions of the equivalent stochastic differential equations (SDEs) are obtained. These solutions preserve the Itô-Gikhman nature of the SDEs. The consistency of the FDF approach, the convergence of its Monte Carlo solution and the performance of the closures employed in the FDF transport equation are assessed by comparisons with results obtained by direct numerical simulation (DNS) and by conventional LES procedures in which the first two SGS scalar moments are obtained by a finite difference method (LES-FD). These comparative assessments are conducted by implementations of all three schemes (FDF, DNS and LES-FD) in a temporally developing mixing layer and a spatially developing planar jet under both non-reacting and reacting conditions. In non-reacting flows, the Monte Carlo solution of the FDF yields results similar to those via LES-FD. The advantage of the FDF is demonstrated by its use in reacting flows. In the absence of a closure for the SGS scalar fluctuations, the LES-FD results are significantly different from those based on DNS. The FDF results show a much closer agreement with filtered DNS results. © 1998 American Institute of Physics.