40Ar/39Ar analysis for incremental heating experiments at DSDP Holes 77-537 and 77-538A

Drilling penetrated pre-Mesozoic crystalline basement beneath abbreviated sedimentary sequences overlying fault blocks in the southeastern Gulf of Mexico. At Hole 538A, located on Catoche Knoll, a foliated, regional metamorphic association of variably mylonitic felsic gneisses and interlayered amphibolite is intruded by post-tectonic diabase dikes. Hornblende from the amphibolite displays internally discordant 40Ar/39Ar age spectra, suggesting initial post-metamorphic cooling at about 500 Ma followed by a mild thermal disturbance at about 200 Ma. Biotite from the gneiss yields a plateau age of 348 Ma, which is interpreted to result from incorporation of extraneous argon components when the biotite system was opened during the about 200 Ma thermal overprint. A whole-rich diabase sample from Hole 538A records a crystallization age of 190.4 ± 3.4 Ma. A lower grade phyllitic metasedimentary sequence was penetrated at Hole 537, drilled about 30 km northwest of Catoche Knoll. Whole-rock phyllite samples display internally discordant 40Ar/39Ar age spectra, but plateau segments clearly document an early Paleozoic metamorphism at about 500 Ma. The age and lithologic character of the basement terrane penetrated at Holes 537 and 538A suggest that the drilled fault blocks are underlain by attenuated fragments of continental crust of "Pan-African" affinity. This supports pre-Mesozoic tectonic reconstructions that locate Yucatan in the present Gulf recess during the amalgamation of Pangea.

Probabilistic versus incremental presynaptic learning in biological plausible synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version

Incremental analysis of constraint logic programs

Global analyzers traditionally read and analyze the entire program at once, in a nonincremental way. However, there are many situations which are not well suited to this simple model and which instead require reanalysis of certain parts of a program which has already been analyzed. In these cases, it appears inecient to perform the analysis of the program again from scratch, as needs to be done with current systems. We describe how the xed-point algorithms used in current generic analysis engines for (constraint) logic programming languages can be extended to support incremental analysis. The possible changes to a program are classied into three types: addition, deletion, and arbitrary change. For each one of these, we provide one or more algorithms for identifying the parts of the analysis that must be recomputed and for performing the actual recomputation. The potential benets and drawbacks of these algorithms are discussed. Finally, we present some experimental results obtained with an implementation of the algorithms in the PLAI generic abstract interpretation framework. The results show signicant benets when using the proposed incremental analysis algorithms.

Optimized algorithms for the incremental analysis of logic programs

Global analysis of logic programs can be performed effectively by the use of one of several existing efficient algorithms. However, the traditional global analysis scheme in which all the program code is known in advance and no previous analysis information is available is unsatisfactory in many situations. Incrementa! analysis of logic programs has been shown to be feasible and much more efficient in certain contexts than traditional (non-incremental) global analysis. However, incremental analysis poses additional requirements on the fixpoint algorithm used. In this work we identify these requirements, present an important class of strategies meeting the requirements, present sufficient a priori conditions for such strategies, and propose, implement, and evalúate experimentally a novel algorithm for incremental analysis based on these ideas. The experimental results show that the proposed algorithm performs very efficiently in the incremental case while being comparable to (and, in some cases, considerably better than) other state-of-the-art analysis algorithms even for the non-incremental case. We argüe that our discussions, results, and experiments also shed light on some of the many tradeoffs involved in the design of algorithms for logic program analysis.

An efficient, parametric fixpoint algorithm for incremental analysis of java bytecode

Abstract interpretation has been widely used for the analysis of object-oriented languages and, more precisely, Java source and bytecode. However, while most of the existing work deals with the problem of finding expressive abstract domains that track accurately the characteristics of a particular concrete property, the underlying fixpoint algorithms have received comparatively less attention. In fact, many existing (abstract interpretation based) fixpoint algorithms rely on relatively inefficient techniques to solve inter-procedural call graphs or are specific and tied to particular analyses. We argue that the design of an efficient fixpoint algorithm is pivotal to support the analysis of large programs. In this paper we introduce a novel algorithm for analysis of Java bytecode which includes a number of optimizations in order to reduce the number of iterations. Also, the algorithm is parametric in the sense that it is independent of the abstract domain used and it can be applied to different domains as "plug-ins". It is also incremental in the sense that, if desired, analysis data can be saved so that only a reduced amount of reanalysis is needed after a small program change, which can be instrumental for large programs. The algorithm is also multivariant and flowsensitive. Finally, another interesting characteristic of the algorithm is that it is based on a program transformation, prior to the analysis, that results in a highly uniform representation of all the features in the language and therefore simplifies analysis. Detailed descriptions of decompilation solutions are provided and discussed with an example.

Incremental analysis of logic programs

Global analyzers traditionally read and analyze the entire program at once, in a non-incremental way. However, there are many situations which are not well suited to this simple model and which instead require reanalysis of certain parts of a program which has already been analyzed. In these cases, it appears inefficient to perform the analysis of the program again from scratch, as needs to be done with current systems. We describe how the fixpoint algorithms in current generic analysis engines can be extended to support incremental analysis. The possible changes to a program are classified into three types: addition, deletion, and arbitrary change. For each one of these, we provide one or more algorithms for identifying the parts of the analysis that must be recomputed and for performing the actual recomputation. The potential benefits and drawbacks of these algorithms are discussed. Finally, we present some experimental results obtained with an implementation of the algorithms in the PLAI generic abstract interpretation framework. The results show significant benefits when using the proposed incremental analysis algorithms.

Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.