Editorial

Higher-Order and Symbolic Computation -     

Codevelopmental learning between human and humanoid robot using a dynamic neural-network model.

This paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural-network model, which is inspired by human parietal cortex functions. A humanoid robot, with a recurrent neural network that has a hierarchical structure, learns to manipulate objects. Robots learn tasks in repeated self-trials with the assistance of human interaction, which provides physical guidance until the tasks are mastered and learning is consolidated within the neural networks. Experimental results and the analyses showed the following: 1) codevelopmental shaping of task behaviors stems from interactions between the robot and a tutor; 2) dynamic structures for articulating and sequencing of behavior primitives are self-organized in the hierarchically organized network; and 3) such structures can afford both generalization and context dependency in generating skilled behaviors.     

Bottom-up pyramid cellular acceptors with three-dimensional layers

In 1997, C.R. Dyer and A. Rosenfeld introduced an acceptor on a two-dimensional pattern (or tape), called the pyramid cellular acceptor, and demonstrated that many useful recognition tasks are executed by pyramid cellular acceptors in time proportional to the logarithm of the diameter of the input. They also introduced a bottom-up pyramid cellular acceptor which is a restricted version of the pyramid cellular acceptor, and proposed some interesting open problems of the bottom-up pyramid cellular acceptors. On the other hand, we think that the study of threedimensional automata has been meaningful as the computational model of three-dimensional information processing such as computer vision, robotics, and so forth. In this paper, we investigate about bottom-up pyramid cellular acceptors with three-dimensional layers, and show their some accepting powers. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Making Java work for microcontroller applications

The authors investigate complete system development using a Java machine aimed at FPGA devices. A new design strategy targets a single FPGA chip, within which the dedicated Java microcontroller-FemtoJava-is synthesized     

Minimum cost source location problem with vertex-connectivity requirements in digraphs

Given a digraph (or an undirected graph) G=(V,E) with a set V of vertices v with nonnegative real costs w(v), and a set E of edges and a positive integer k, we deal with the problem of finding a minimum cost subset SV such that, for each vertex vV−S, there are k vertex-disjoint paths from S to v. In this paper, we show that the problem can be solved by a greedy algorithm in time in a digraph (or in time in an undirected graph), where n=|V| and m=|E|. Based on this, given a digraph and two integers k and ℓ, we also give a polynomial time algorithm for finding a minimum cost subset SV such that for each vertex vV−S, there are k vertex-disjoint paths from S to v as well as ℓ vertex-disjoint paths from v to S.     

Group weighted finite transducers

We consider weighted finite transducers over arbitrary groups, that is finite transducers having a counter in which, at any step, a value of the group is stored but no information regarding the content of this counter is available until the computation is finished. The computation is valid if the counter value is the neutral element of the group. We generalize here some results from [8] and [17]. Received: 28 January 2001 / 5 June 2001     

A high-field adiabatic fast passage ultracold neutron spin flipper for the UCNA experiment

The UCNA collaboration is making a precision measurement of the β asymmetry (A) in free neutron decay using polarized ultracold neutrons (UCN). A critical component of this experiment is an adiabatic fast passage neutron spin flipper capable of efficient operation in ambient magnetic fields on the order of 1 T. The requirement that it operate in a high field necessitated the construction of a free neutron spin flipper based, for the first time, on a birdcage resonator. The design, construction, and initial testing of this spin flipper prior to its use in the first measurement of A with UCN during the 2007 run cycle of the Los Alamos Neutron Science Center's 800 MeV proton accelerator is detailed. These studies determined the flipping efficiency of the device, averaged over the UCN spectrum present at the location of the spin flipper, to be ε=0.9985(4).     

Characterization of microbial community structures and their activities in single anaerobic granules by beta imaging, microsensors and fluorescence in situ hybridization

The spatial distribution of microorganisms and their in situ activities in anaerobic granules were investigated by fluorescence in situ hybridization (FISH), beta imaging and microsensors. FISH results revealed a layered structure of microorganisms in the granule, where Chloroflexi was present in the outermost layer, Smithella spp. and Syntrophobacter spp. were found in a depth of ca. 100 μm, and Archaea was restricted to the inner layer (below ca. 300 μm from the surface). Substrate uptake patterns elucidated by beta imaging demonstrated that glucose uptake was highest at 50 μm depth, whereas propionate uptake had a peak at 200 μm depth. In addition, microsensor measurements revealed that acid was produced mainly at 100 μm depth and H(2) production was detected at a depth from 100 to 200 μm. H(2) consumption and corresponding CH(4) production were found below 200 μm from the surface. Direct comparison of these results implied sequential degradation of complex organic compounds in anaerobic granules; Chloroflexi contributed to fermentation of organic compounds and acid production in the outermost layer, volatile fatty acids were oxidized and H(2) was produced mainly by Smithella spp. and Syntrophobacter spp. at a depth from 100 to 200 μm, and Archaea produced CH(4) below ca. 300 μm from the surface.     

Case learning for CBR-based collision avoidance systems

With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.