Learning of sequential movements in the monkey: process of learning and retention of memory

1. To characterize procedural learning and memory, we devised a behavioral paradigm that allows us to examine the process of learning of new procedures, repeatedly and without serious difficulties for primate subjects. We trained two monkeys to perform a sequential button press task. Upon pressing of a home key, 2 of 16 (4 x 4 matrix) light-emitting diode (LED) buttons (called "set") were illuminated simultaneously, and the monkey had to press them in a predetermined order that he had to find out by trial-and-error. A total of five sets (called "hyperset") was presented in a fixed order for completion of a trial; an error at any set aborted the trial. A given hyperset was repeated as a block of experiment until 20 successful trials were performed. Monkeys PI and BO experienced 313 and 92 hypersets, respectively. Most of these hypersets were experienced only once (1 block of experiment); the others (28 hypersets for monkey PI and 14 hypersets for monkey BO) were chosen for extensive practice. 2. The learning, indicated as the decrease in the number of trials to criterion and the decrease in the performance time, proceeded at three levels: 1) short-term and sequence-selective learning that occurred by repeating a particular hyperset during a block of experiment; our monkeys learned, to some degree, to perform a new hyperset within a short period (< 5 min); 2) long-term and sequence-selective learning that took place for each hyperset across days; by daily practice, they further improved their skills for performing the particular hyperset; and 3) long-term and sequence-unselective learning that was indicated by the improvement of performance for new hypersets; the monkeys were required to learn many hypersets, each just once (a block of trials), in which they performed gradually better with more experiences in the 2 x 5 task. 3. To examine whether the memory was retained for a long period, we had the monkey learn 12 hypersets sufficiently, then we stopped the training and retested them after 1 or 6 mo. After the 1-mo interruption the performance was significantly better than that for new hypersets. After the 6-mo interruption the performance was not different from new hypersets in terms of the number of trials but was significantly better than new hypersets in terms of the performance time. The results suggest that motor memory (measured by performance time) can be retained longer than procedural memory (measured by the number of trials).     

A scalable interest-oriented peer-to-peer pub/sub network

There has been a big challenge in structured peer-to-peer overlay network research area. Generally, a structured overlay network involves nodes evenly or based on their resource availabilities, and gathers nodes?? resources to achieve some bigger tasks. The challenge here is to gather resources based on nodes?? interests, and only interested nodes are involved in a certain task. Toward this challenge, we propose a new scheme to a peer-to-peer publish/subscribe network. Publish/subscribe represents a new paradigm for distributed content delivery. It provides an alternative to address-based communication due to its ability to decouple communication between the source and the destination. We propose a Bloom filter based mapping scheme to map IDs to nodes?? interests in addition to new interest proximity metric to forward events and to build nodes?? routing tables. We also propose a new approach called ??shared interest approach?? for network discovery. To evaluate the algorithms proposed in this work, we conducted simulations in both static and dynamic settings, and found a low false positive rate. We also discuss about a well-known application called Twitter, and show how our scheme would work in a real environment.     

Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus

We present and analyze an unsupervised method for Word Sense Disambiguation (WSD). Our work is based on the method presented by McCarthy et al. in 2004 for finding the predominant sense of each word in the entire corpus. Their maximization algorithm allows weighted terms (similar words) from a distributional thesaurus to accumulate a score for each ambiguous word sense, i.e., the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous word. This list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a thesaurus. In the method of McCarthy et al., every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where the ambiguous word occurs. Our method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous word. We obtain a top precision of 77.54% of accuracy versus 67.10% of the original method tested on SemCor. We also analyze the effect of the number of weighted terms in the tasks of finding the Most Frecuent Sense (MFS) and WSD, and experiment with several corpora for building the Word Space Model.     

Automatic 3D model reconstruction of cutting tools from a single camera

Collision detection by machining simulation requires the 3D models of rotating cutters. However, the 3D models of a cutter and holder are not always available. In this paper, a new method is proposed to design an automatic vision-based 3D modeling system, which is able to quickly reconstruct the 3D model of a cutter and holder when they are installed onto the spindle. Only a single camera is mounted on the machine tool to capture the image of the rotating cutter and holder. By viewing the rotating cutter and holder as an object of surface of revolution, the contour of the imaged cutter and holder can be used to reconstruct the 3D model as a stack of circular cross-sections. Then the complete generating function of the cutter and holder can be recovered from the cross-sections. Finally, the 3D model of the cutter is built by rotating the generating function around the spindle axis. The effectiveness and accuracy of the proposed method are verified by experiments on-machine using 12 kinds of cutters and holders, which can satisfy the requirement of collision detection.     

An accurate adaptive NURBS curve interpolator with real-time flexible acceleration/deceleration control

Parametric interpolation has been widely used in CNC machining because of its advantages over the traditional linear or circular interpolation. Many researchers focused on this field and have made great progress in the specific one, NURBS curve interpolation. These works greatly improved the CNC machining with constant feedrate, confined chord error and limited acceleration/deceleration. However, during CNC machining process, mechanical shocks to machine tool caused by the undesired acceleration/deceleration profile will dramatically deteriorate the surface accuracy and quality of the machined parts. This is, in most occasions, very harmful to machine tools. In this paper, an accurate adaptive NURBS curve interpolator is proposed with consideration of acceleration–deceleration control. The proposed design effectively reduces the machining shocks by constraining the machine tool jerk dynamically. Meanwhile, the constant feedrate is maintained during most time of machining process, and thus high accuracy is achieved while the feedrate profile is greatly smoothed. In order to deal with the sudden change of the acceleration/deceleration around the corner with large curvature, a real-time flexible acceleration/deceleration control scheme is introduced to adjust the feedrate correspondingly. Case study has been taken to verify the feasibility and advantages of the proposed design.     

Challenge: Concept of system life and its application to robotics

Artifact systems created by humans interact with the surrounding natural world and have a large-scale influence on our human lives. The most creditable concept to this critical issue of scientific and technological development seems to be “System Life” that is an innovative competence to be embodied into any artifact system for creating harmony in the world of natural entities and artifact systems interacting with each other. System life is defined as a seamless system of sensing, processing, activating and expressing mechanisms governed by system life information. This paper introduces a design approach of robots possessing system life. First, this paper presents the concept of system life comparing it with the conventional design methodology of intelligent systems. Second, the paper introduces an intelligent control methodology using the cubic neural network that the author developed in order to cope with unpredicted failures. Finally, the paper presents various intelligent robots, a skiing robot, autonomous soccer robots, a game playing robot, as new concrete artifact systems designed using the system life concept.     

Ultra light-weight and high-resolution X-ray mirrors using DRIE and X-ray LIGA techniques for space X-ray telescopes

We are developing novel ultra light-weight and high-resolution X-ray micro pore optics for space X-ray telescopes. In our method, curvilinear micro pore structures are firstly fabricated by silicon deep reactive ion etching (DRIE) or X-ray LIGA processes. Secondly, side walls of the micro structures are smoothed by magnetic field assisted finishing and/or hydrogen annealing techniques for high reflectivity mirrors. Thirdly, to focus parallel X-ray lights from astronomical objects, these structures are elastically or plastically bent into a spherical shape. Fourthly, the bent structures are stacked to form a multi-stage X-ray telescope. In this paper, we report on fabrication and X-ray reflection tests of silicon and nickel X-ray mirrors using the DRIE and LIGA processes, respectively. For the first time, X-ray reflections were confirmed on both of the mirrors. Estimated rms roughnesses were 5 nm and 3 nm for the silicon and nickel mirrors, respectively.     

Negation-Limited Complexity of Parity and Inverters

In negation-limited complexity, one considers circuits with a limited number of NOT gates, being motivated by the gap in our understanding of monotone versus general circuit complexity, and hoping to better understand the power of NOT gates. We give improved lower bounds for the size (the number of AND/OR/NOT) of negation-limited circuits computing Parity and for the size of negation-limited inverters. An inverter is a circuit with inputs x ₁,…,x _n and outputs ¬ x ₁,…,¬ x _n . We show that: (a) for n=2 ^r ?1, circuits computing Parity with r?1 NOT gates have size at least 6n?log?₂(n+1)?O(1), and (b) for n=2 ^r ?1, inverters with r NOT gates have size at least 8n?log?₂(n+1)?O(1). We derive our bounds above by considering the minimum size of a circuit with at most r NOT gates that computes Parity for sorted inputs x ₁≥???≥x _n . For an arbitrary r, we completely determine the minimum size. It is 2n?r?2 for odd n and 2n?r?1 for even n for ?log?₂(n+1)??1≤r≤n/2, and it is ?3n/2??1 for r≥n/2. We also determine the minimum size of an inverter for sorted inputs with at most r NOT gates. It is 4n?3r for ?log?₂(n+1)?≤r≤n. In particular, the negation-limited inverter for sorted inputs due to Fischer, which is a core component in all the known constructions of negation-limited inverters, is shown to have the minimum possible size. Our fairly simple lower bound proofs use gate elimination arguments in a somewhat novel way.     

Analysis of Humanoid Appearances in Human–Robot Interaction

Identifying the extent to which the appearance of a humanoid robot affects human behavior toward it is important. We compared participant impressions of and behaviors toward two real humanoid robots in simple human-robot interactions. These two robots, which have different appearances but are controlled to perform the same recorded utterances and motions, are adjusted by a motion-capturing system. We conducted an experiment with 48 human participants who individually interacted with the two robots and also with a human for reference. The results revealed that different appearances did not affect participant verbal behaviors, but they did affect such nonverbal behaviors as distance and delay of response. These differences are explained by two factors: impressions and attributions.     

Evolutionary Learning of a Fuzzy Behavior Based Controller for a Nonholonomic Mobile Robot in a Class of Dynamic Environments

This paper presents an approach for evolving optimum behaviors for a nonholonomic mobile robot in a class of dynamic environments. A new evolutionary algorithm reflecting some powerful features in the natural evolutionary process to have flexibility to deal with changes in the environment is used to evolve optimum behaviors. Furthermore, a fuzzy set based multi-objective fitness evaluation function is adopted in the evolutionary algorithm. The multi-objective evaluation function is designed so that it allows incorporating complex linguistic features that a human observer would desire in the behaviors of the mobile robot movements. To illustrate the effectiveness of the proposed method, simulation results are compared using a conventional evolutionary algorithm.