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.

Bending continuous structures with SMAs: a novel robotic fish design

In this paper, we describe our research on bio-inspired locomotion systems using deformable structures and smart materials, concretely shape memory alloys (SMAs). These types of materials allow us to explore the possibility of building motor-less and gear-less robots. A swimming underwater fish-like robot has been developed whose movements are generated using SMAs. These actuators are suitable for bending the continuous backbone of the fish, which in turn causes a change in the curvature of the body. This type of structural arrangement is inspired by fish red muscles, which are mainly recruited during steady swimming for the bending of a flexible but nearly incompressible structure such as the fishbone. This paper reviews the design process of these bio-inspired structures, from the motivations and physiological inspiration to the mechatronics design, control and simulations, leading to actual experimental trials and results. The focus of this work is to present the mechanisms by which standard swimming patterns can be reproduced with the proposed design. Moreover, the performance of the SMA-based actuators’ control in terms of actuation speed and position accuracy is also addressed.

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.

A computer program for the analysis of the dynamic bending-torsion coupling in bridges using a mini-computer

The analysis of modes and natural frequencies is of primary interest in the computation of the response of bridges. In this article the transfer matrix method is applied to this problem to provide a computer code to calculate the natural frequencies and modes of bridge-like structures. The Fortran computer code is suitable for running on small computers and results are presented for a railway bridge.

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.

Discussion of "Design Footing Area with Biaxial Bending" by Ramon Jarquio and Victor Jarquio (October, 1983)

The authors present a very interesting criterion for choosing a rectangular foundation.The writers should like to point out that the obtention of minimum area can be reduced to the problem of finding the minimum of x*+y*, subjected to the condition x*.y*=k2 whose solution is evidently x*=y*=k

Influence of the Second Bending Mode on the Response of High-Speed Bridges at Resonance

This paper deals with the assessment of the contribution of the second bending mode to the dynamic behavior of simply supported railway bridges. Traditionally the contributions of modes higher than the fundamental have been considered of little importance for the computation of the magnitudes of interest to structural engineers (vertical deflections, bending moments, etc.). Starting from the dimensionless equations of motion of a simply supported beam subjected to moving loads, the key parameters governing the dynamic behavior are identified. Then, a parametric study over realistic ranges of values of those parameters is conducted, and the influence of the second mode examined in detail. The main purpose is to decide whether the second mode should be taken into account for the determination of the maximum displacement and acceleration in high-speed bridges. In addition, the reasons that cause the contribution of the second bending mode to be relevant in some situations are highlighted, particularly with regard to the computation of the maximum acceleration.

A comparative analysis between FEM and Boundary Methods in bending of Plates

Two different methods of analysis of plate bending, FEM and BM are discussed in this paper. The plate behaviour is assumed to be represented by using the linear thin plate theory where the Poisson-Kirchoff assumption holds. The BM based in a weighted mean square error technique produced good results for the problem of plate bending. The computational effort demanded in the BM is smaller than the one needed in a FEM analysis for the same level of accuracy. The general application of the FEM cannot be matched by the BM. Particularly, different types of geometry (plates of arbitrary geometry) need a similar but not identical treatment in the BM. However, this loss of generality is counterbalanced by the computational efficiency gained in the BM in the solution achievement

Analysis of plate bending by means of high order finite hyperelements

The possibilities and limitations of high order hyperelements in plate bending analysis are discussed. Explicit shape functions for some types of triangular elements are given. These elements are applied to simple cases to assess their computational efficiency.

A C1 finite element for Kirchhoff plate bending

After a short introduction the possibilities and limitations of polynomial simple elements with C1 continuity are discussed with reference to plate bending analysis. A family of this kind of elements is presented.. These elements are applied to simple cases in order to assess their computational efficiency. Finally some conclusions are shown, and future research is also proposed.

Bending reinforcement of timber beams with composite carbon fiber and basalt fiber materials

En el presente trabajo se lleva a cabo un estudio basado en datos obtenidos experimentalmente mediante el ensayo a flexión de vigas de madera de pino silvestre reforzadas con materiales compuestos. Las fibras que componen los tejidos utilizados para la ejecución de los refuerzos son de basalto y de carbono. En el caso de los compuestos de fibra de basalto se aplican en distintos gramajes, y los de carbono en tejido unidireccional y bidireccional. El material compuesto se realizó in situ, simultáneamente a la ejecución del refuerzo. Se aplicaron en una y en dos capas, según el caso, y la forma de colocación fue en ?U?, adhiriéndose al canto inferior y a las caras laterales de la viga mediante resina o mortero epoxi. Se analiza el comportamiento de las vigas según las variables de refuerzo aplicadas y se comparan con los resultados de vigas ensayadas sin reforzar. Con este trabajo queda demostrado el buen funcionamiento del FRP de fibra de basalto aplicado en el refuerzo de vigas de madera y de los tejidos de carbono bidireccionales con respecto a los unidireccionales.

Learning Bayesian networks from data by the incremental compilation of new network polynomials

Probabilistic graphical models are a huge research field in artificial intelligence nowadays. The scope of this work is the study of directed graphical models for the representation of discrete distributions. Two of the main research topics related to this area focus on performing inference over graphical models and on learning graphical models from data. Traditionally, the inference process and the learning process have been treated separately, but given that the learned models structure marks the inference complexity, this kind of strategies will sometimes produce very inefficient models. With the purpose of learning thinner models, in this master thesis we propose a new model for the representation of network polynomials, which we call polynomial trees. Polynomial trees are a complementary representation for Bayesian networks that allows an efficient evaluation of the inference complexity and provides a framework for exact inference. We also propose a set of methods for the incremental compilation of polynomial trees and an algorithm for learning polynomial trees from data using a greedy score+search method that includes the inference complexity as a penalization in the scoring function.