Experiments on three-dimensional wire-frame object recognition

Some experimental results on the recognition of three-dimensional wire-frame objects are presented. In order to overcome the limitations of a recent model, which employs radial basis functions-based neural networks, we have proposed a hybrid learning system for object recognition, featuring: an optimization strategy (simulated annealing) in order to avoid local minima of an energy functional; and an appropriate choice of centers of the units. Further, in an attempt to achieve improved generalization ability, and to reduce the time for training, we invoke the principle of self-organization which utilises an unsupervised learning algorithm.

On improving the generalization of SVM classifier

The generalization performance of the SVM classifier depends mainly on the VC dimension and the dimensionality of the data. By reducing the VC dimension of the SVM classifier, its generalization performance is expected to increase. In the present paper, we argue that the VC dimension of SVM classifier can be reduced by applying bootstrapping and dimensionality reduction techniques. Experimental results showed that bootstrapping the original data and bootstrapping the projected (dimensionally reduced) data improved the performance of the SVM classifier.

No-reference image quality assessment using modified extreme learning machine classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity (HVS) factors such as edge amplitude, edge length, background activity and background luminance. Image quality assessment involves estimating the functional relationship between HVS features and subjective test scores. The quality of the compressed images are obtained without referring to their original images ('No Reference' metric). Here, the problem of quality estimation is transformed to a classification problem and solved using extreme learning machine (ELM) algorithm. In ELM, the input weights and the bias values are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for classification problems with imbalance in the number of samples per quality class depends critically on the input weights and the bias values. Hence, we propose two schemes, namely the k-fold selection scheme (KS-ELM) and the real-coded genetic algorithm (RCGA-ELM) to select the input weights and the bias values such that the generalization performance of the classifier is a maximum. Results indicate that the proposed schemes significantly improve the performance of ELM classifier under imbalance condition for image quality assessment. The experimental results prove that the estimated visual quality of the proposed RCGA-ELM emulates the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality metric and full-reference structural similarity image quality metric.

Validation-Based Sparse Gaussian Process Classifier Design

Gaussian processes (GPs) are promising Bayesian methods for classification and regression problems. Design of a GP classifier and making predictions using it is, however, computationally demanding, especially when the training set size is large. Sparse GP classifiers are known to overcome this limitation. In this letter, we propose and study a validation-based method for sparse GP classifier design. The proposed method uses a negative log predictive (NLP) loss measure, which is easy to compute for GP models. We use this measure for both basis vector selection and hyperparameter adaptation. The experimental results on several real-world benchmark data sets show better orcomparable generalization performance over existing methods.

Glass forming ability and stability: Ternary Cu bearing Ti, Zr, Hf alloys

Four Cu bearing alloys of nominal composition Zr25Ti25Cu50, Zr34Ti16Cu50, Zr25Hf25Cu50 and Ti25Hf25Cu50 have been rapidly solidified in order to produce ribbons. All the alloys become amorphous after meltspinning. In the Zr34Ti16Cu50 alloy localized precipitation of cF24 Cu5Zr phase can be observed in the amorphous matrix. The alloys show a tendency of phase separation at the initial stages of crystallization. The difference in crystallization behavior of these alloys with Ni bearing ternary alloys can be explained by atomic size, binary heat of mixing and Mendeleev number. It has been observed that both Laves and Anti-Laves phase forming compositions are suitable for glass formation. The structures of the phases, precipitated during rapid solidification and crystallization can be viewed in terms of Bernal deltahedra and Frank-Kasper polyhedra.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Use of structure-directed DNA ligands to probe the binding of recA protein to narrow and wide grooves of DNA and on its ability to promote homologous pairing

We have used circular dichroism and structure-directed drugs to identify the role of structural features, wide and narrow grooves in particular, required for the cooperative polymerization, recognition of homologous sequences, and the formation of joint molecules promoted by recA protein. The path of cooperative polymerization of recA protein was deduced by its ability to cause quantitative displacement of distamycin from the narrow groove of duplex DNA. By contrast, methyl green bound to the wide groove was retained by the nucleoprotein filaments comprised of recA protein-DNA. Further, the mode of binding of these ligands and recA protein to DNA was confirmed by DNaseI digestion. More importantly, the formation of joint molecules was prevented by distamycin in the narrow groove while methyl green in the wide groove had no adverse effect. Intriguingly, distamycin interfered with the production of coaggregates between nucleoprotein filaments of recA protein-M13 ssDNA and naked linear M13 duplex DNA, but not with linear phi X174 duplex DNA. Thus, these data, in conjunction with molecular modeling, suggest that the narrow grooves of duplex DNA provide the fundamental framework required for the cooperative polymerization of recA protein and alignment of homologous sequences. These findings and their significance are discussed in relation to models of homologous pairing between two intertwined DNA molecules.

Profiling k-Iteration Paths : A Generalization of the Ball-Larus Profiling Algorithm

The Ball-Larus path-profiling algorithm is an efficient technique to collect acyclic path frequencies of a program. However, longer paths -those extending across loop iterations - describe the runtime behaviour of programs better. We generalize the Ball-Larus profiling algorithm for profiling k-iteration paths - paths that can span up to to k iterations of a loop. We show that it is possible to number suchk-iteration paths perfectly, thus allowing for an efficient profiling algorithm for such longer paths. We also describe a scheme for mixed-mode profiling: profiling different parts of a procedure with different path lengths. Experimental results show that k-iteration profiling is realistic.

Generalization of Dynkin's Identity and Some Applications

Let X(t) be a right continuous temporally homogeneous Markov pro- cess, Tt the corresponding semigroup and A the weak infinitesimal genera- tor. Let g(t) be absolutely continuous and r a stopping time satisfying E.( S f I g(t) I dt) < oo and E.( f " I g'(t) I dt) < oo Then for f e 9iJ(A) with f(X(t)) right continuous the identity Exg(r)f(X(z)) - g(O)f(x) = E( 5 " g'(s)f(X(s)) ds) + E.( 5 " g(s)Af(X(s)) ds) is a simple generalization of Dynkin's identity (g(t) 1). With further restrictions on f and r the following identity is obtained as a corollary: Ex(f(X(z))) = f(x) + k! Ex~rkAkf(X(z))) + n-1E + (n ) )!.E,(so un-1Anf(X(u)) du). These identities are applied to processes with stationary independent increments to obtain a number of new and known results relating the moments of stopping times to the moments of the stopped processes.

Relative ability of ovine follicle stimulating hormone and its beta-subunit to generate antibodies having bioneutralization potential in nonhuman primates

The relative ability of ovine follicle stimulating hormone and its beta-subunit, two potential candidates for male contraceptive vaccine, to generate antibodies in monkeys capable of bioneutralizing follicle stimulating hormone was assessed using in vitro model systems. Antiserum against native ovine follicle stimulating hormone was found to be highly specific to the intact form with no cross-reactivity with either of the two subunits while the antiserum against beta-subunit of follicle stimulating hormone could bind to the beta-subunit in its free form as well as when it is combined with alpha-subunit to form the intact hormone. Both antisera could block the binding of the hormone to the receptor if the hormone was preincubated with the antibody. However, the follicle stimulating hormone beta-antisera could only inhibit the binding of the hormone partially (33 percent inhibition) if the antibody and receptor were mixed prior to the addition of the hormone, while antisera to the native follicle stimulating hormone could block the binding completely (100 percent inhibition) in the same experiment. Similarly antisera to the native follicle stimulating hormone was significantly effective in blocking (100 percent) response to follicle stimulating hormone but not the beta-subunit antisera (0 percent) as checked using an in vitro granulosa cell system. Thus the probability of obtaining antibodies of greater bioneutralization potential is much higher if intact hormone is used as an antigen rather than its beta-subunit as a vaccine.

Analysis,Insights and Generalization of a Fast Decentralized Relay Selection Mechanism

Relay selection for cooperative communications has attracted considerable research interest recently. While several criteria have been proposed for selecting one or more relays and analyzed, mechanisms that perform the selection in a distributed manner have received relatively less attention. In this paper, we analyze a splitting algorithm for selecting the single best relay amongst a known number of active nodes in a cooperative network. We develop new and exact asymptotic analysis for computing the average number of slots required to resolve the best relay. We then propose and analyze a new algorithm that addresses the general problem of selecting the best Q >= 1 relays. Regardless of the number of relays, the algorithm selects the best two relays within 4.406 slots and the best three within 6.491 slots, on average. Our analysis also brings out an intimate relationship between multiple access selection and multiple access control algorithms.

Ability of human chorionic gonadotropin beta-subunit to inhibit the steroidogenic response to lutropin

Ability of the beta-subunit of human chorionic gonadotropin to inhibit the response to lutropin (luteinizing hormone, LH) was tested in the immature rat ovarian system and pregnant-mare-serum-gonadotropin-primed rat ovarian system with progesterone production being used as the response. Human chorionic gonadotropin beta-subunit was found to inhibit human and ovine lutropin-stimulated progesterone production. At a constant dose of lutropin, inhibition was dependent on the concentration of beta-subunit. When concentration of the beta-subunit was kept constant at 5.0 microgram/ml and the concentration of lutropin was varied, the inhibition was maximum at the saturating concentration of the native hormone. The alpha-subunit of the human chorionic gonadotropin did not inhibit the response to lutropin. The lutropin/beta-subunit ratio required to produce an inhibition of response was much lower than that required to bring about an observable inhibition of binding.

The ability of prolactin to change the sensitivity of the pituitary of the lactating rat to luteinising hormone releasing hormonein vitro

The ability of prolactin to influence the responsiveness of the lactating rat pituitary to luteinising hormone releasing hormone has been examinedin vitro. The pituitary responsivenessin vivo to luteinising hormone releasing hormone decreased as a function of increase in the lactational stimulus. Prolactin inhibited the spontaneousin vitro release of luteinising hormone and follicle stimulating hormone to a small extent, from the pituitary of lactating rats with the suckling stimulus. However, it significantly inhibited the release of these two hormones from luteinising hormone releasing hormone-stimulated pituitaries. The responsiveness of pituitaries of rats deprived of their litter 24 h earlier, to luteinising hormone releasing hormone was also inhibited by prolactin, although minimal. It was concluded that prolactin could be influencing the functioning of the pituitary of the lactating rat by (a) partially suppressing the spontaneous release of gonadotropin and (b) inhibiting the responsiveness of the pituitary to luteinising hormone releasing hormone.