Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

Generating function of color information detection using genetic programming

This article proposes a function for color information detection using genetic programming (GP). In image-processing, object detection is one of the important processes. In cases where the object has a complex color domain, detection becomes more difficult. We generated a detection function for a complex color domain by using GP. The detection function deals with one pixel of an input image, and it obtains an output image by processing for all pixels. We aimed at a reduction in the time taken by a human to consider an image-processing system design. We applied the generation of GP to detect a target color region in actual images. The results show that the detection function has sufficient ability for these detections.     

Ligand density-dependent influence of arginine–glycine–aspartate functionalized gold nanoparticles on osteogenic and adipogenic differentiation of mesenchymal stem cells

Extracellular matrix (ECM) plays a very important role in regulating cell function and fate. It is highly desirable to fabricate biomimetic models to investigate the role of ECM in stem cell differentiation. In this study, arginine–glycine–aspartate (RGD)-modified gold nanoparticles (Au NPs) with tunable surface ligand density were prepared to mimic the ECM microenvironment. Their effect on osteogenic and adipogenic differentiation of human mesenchymal stem cells (MSCs) was investigated. The biomimetic Au NPs were taken up by MSCs in a ligand density-dependent manner. The biomimetic NPs with a high RGD density had an inhibitive effect on the alkaline phosphatase (ALP) activity, calcium deposition, and osteogenic marker gene expression of MSCs. Their effect on oil droplet formation and adipogenic marker gene expression was negative when RGD density was low, while their effect was promotive when RGD density was high. The biomimetic Au NPs regulated the osteogenic and adipogenic differentiation of MSCs mainly through affecting the focal adhesion and cytoskeleton. This study highlights the roles of biomimetic NPs on stem cell differentiation that could provide a meaningful strategy in fabricating functional biomaterials for tissue engineering and biomedical applications.

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Negative regulation by interleukin-3 (IL-3) of mouse early B-cell progenitors and stem cells in culture: transduction of the negative signals by betac and betaIL-3 proteins of IL-3 receptor and absence of negative regulation by granulocyte-macrophage colony-stimulating factor

The receptors for interleukin-3 (IL-3), granulocyte-macrophage colony-stimulating factor (GM-CSF), and IL-5 share a common signaling subunit betac. However, in the mouse, there is an additional IL-3 signaling protein, betaIL-3, which is specific for IL-3. We have previously reported that IL-3 abrogates the lymphoid potentials of murine lymphohematopoietic progenitors and the reconstituting ability of hematopoietic stem cells. We used bone marrow cells from betac- and betaIL-3-knock-out mice to examine the relative contributions of the receptor proteins to the negative regulation by IL-3. First, we tested the effects of IL-3 on lymphohematopoietic progenitors by using lineage-negative (Lin-) marrow cells of 5-fluorouracil (5-FU)-treated mice in the two-step methylcellulose culture we reported previously. Addition of IL-3 to the combination of steel factor (SF, c-kit ligand) and IL-11 abrogated the B-lymphoid potential of the marrow cells of both types of knock-out mice as well as wild-type mice. Next, we investigated the effects of IL-3 on in vitro expansion of the hematopoietic stem cells. We cultured Lin-Sca-1-positive, c-kit-positive marrow cells from 5-FU-treated mice in suspension in the presence of SF and IL-11 with or without IL-3 for 7 days and tested the reconstituting ability of the cultured cells by transplanting the cells into lethally irradiated Ly-5 congenic mice together with "compromised" marrow cells. Presence of IL-3 in culture abrogated the reconstituting ability of the cells from both types of knock-out mice and the wild-type mice. In contrast, addition of GM-CSF to the suspension culture abrogated neither B-cell potential nor reconstituting abilities of the cultured cells of wild-type mice. These observations may have implications in the choice of cytokines for use in in vitro expansion of human hematopoietic stem cells and progenitors.     

Translational lemmas for DLOGTIME-uniform circuits,alternating TMs,and PRAMs

We present translational lemmas for the three standard models of parallel computation, and apply them to obtain tight hierarchy results. It is shown that, for arbitrarily small rational constant , (i) there is a language which can be accepted by a -uniform circuit family of depth and size but not by any -uniform circuit family of depth and size , (ii) there is a language which can be accepted by a -time -space ATM with l worktapes but not by any -time -space ATM with the same l worktapes if the number of tape symbols is fixed, and (iii) there is a language which can be accepted by a -time PRAM with processors but not by any -time PRAM with processors. Here, c > 0, d ≥ 1, r ₁ > 1, and r ₂ ≥ 1 are arbitrary rational constants, and l ≥ 2 is an arbitrary integer. Preliminary versions of different parts of this paper appeared in Proc. MCU 2004 (LNCS 3354) and Proc. FCT 2005 (LNCS 3623).     

Simultaneous rate-synchrony codes in populations of spiking neurons

Firing rates and synchronous firing are often simultaneously relevant signals, and they independently or cooperatively represent external sensory inputs, cognitive events, and environmental situations such as body position. However, how rates and synchrony comodulate and which aspects of inputs are effectively encoded, particularly in the presence of dynamical inputs, are unanswered questions. We examine theoretically how mixed information in dynamic mean input and noise input is represented by dynamic population firing rates and synchrony. In a subthreshold regime, amplitudes of spatially uncorrelated noise are encoded up to a fairly high input frequency, but this requires both rate and synchrony output channels. In a suprathreshold regime, means and common noise amplitudes can be simultaneously and separately encoded by rates and synchrony, respectively, but the input frequency for which this is possible has a lower limit.     

Learning commutative deterministic finite state automata in polynomial time

We consider the problem of learning the commutative subclass of regular languages in the on-line model of predicting {0,1∼-valued functions from examples and reinforcements due to Littlestone [7,4]. We show that the entire class of commutative deterministic finite state automata (CDFAs) of an arbitrary alphabet sizek is predictable inO(s ^k) time with the worst case number of mistakes bounded above byO(s ^kk logs), wheres is the number of states in the target DFA. As a corollary, this result implies that the class of CDFAs is also PAC-learnable from random labeled examples in timeO(s ^k) with sample complexity, using a different class of representations. The mistake bound of our algorithm is within a polynomial, for a fixed alphabet size, of the lower boundO(s+k) we obtain by calculating the VC-dimension of the class. Our result also implies the predictability of the class of finite sets of commutative DFAs representing the finite unions of the languages accepted by the respective DFAs. Part of this work was supported by the Office of Naval Research under contract number N00014-87-K-0401 while the author was at the Department of Computer and Information Science, University of Pennsylvania, and N0014-86-K-0454 while at the Department of Computer and Information Sciences, U.C. Santa Cruz. The author’s email address is abe@IBL.CL.nec.co.jp