The effect of lutein supplementation on visual fatigue: A psychophysiological analysis

We used psychophysiological technology to examine the effect of an oral supplement, a combination of lutein, zeaxanthin and blackcurrant extract (LUT), on visual fatigue, within the context of a randomized, double-blind, placebo-controlled cross-over trial. The LUT supplement and placebo samples were randomly assigned to thirteen participants, who took the samples for two LUT (and vice versa) for another 2 week. Each participant completed visual proof reading tasks for 2 h during each of four testing sessions. Saccade tests were administered before and after the proof reading task, during which the participants moved their eyes back and forth between two targets positioned in the center of two checkerboards. We recorded EEG, EOG, heart rate, and facial muscle potential/performance during the saccade tests. Blood pressure was measured and subjective fatigue and stress scores were collected before and after the proof reading task. We averaged EEG starting at saccade offset in order to analyze eye fixation related potentials (EFRP). Our results suggested that the proof reading task induced visual fatigue. An analysis of EFRP and other psychophysiological data revealed significant differences between the LUT and placebo conditions. These results suggest that supplementation with LUT could help to reduce symptoms of visual fatigue.     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

Audio-visual speech recognition integrating 3D lip information obtained from the Kinect

Audio-visual speech recognition (AVSR) has shown impressive improvements over audio-only speech recognition in the presence of acoustic noise. However, the problems of region-of-interest detection and feature extraction may influence the recognition performance due to the visual speech information obtained typically from planar video data. In this paper, we deviate from the traditional visual speech information and propose an AVSR system integrating 3D lip information. The Microsoft Kinect multi-sensory device was adopted for data collection. The different feature extraction and selection algorithms were applied to planar images and 3D lip information, so as to fuse the planar images and 3D lip feature into the visual-3D lip joint feature. For automatic speech recognition (ASR), the fusion methods were investigated and the audio-visual speech information was integrated into a state-synchronous two stream Hidden Markov Model. The experimental results demonstrated that our AVSR system integrating 3D lip information improved the recognition performance of traditional ASR and AVSR system in acoustic noise environments.     

Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer

Artificial market simulations have the potential to be a strong tool for studying rapid and large market fluctuations and designing financial regulations. High-frequency traders, that exchange multiple assets simultaneously within a millisecond, are said to be a cause of rapid and large market fluctuations. For such a large-scale problem, this paper proposes a software or computing platform for large-scale and high-frequency artificial market simulations (Plham: /pl\(\Lambda\)m). The computing platform, Plham, enables modeling financial markets composed of various brands of assets and a large number of agents trading on a short timescale. The design feature of Plham is the separation of artificial market models (simulation models) from their execution (execution models). This allows users to define their simulation models without parallel computing expertise and to choose one of the execution models they need. This computing platform provides a prototype execution model for parallel simulations, which exploits the variety in trading frequency among traders, that is, the fact that some traders do not require up-to-date information of markets changing in millisecond order. We evaluated a prototype implementation on the K computer using up to 256 computing nodes.     

Path-planning and navigation of a mobile robot as discrete optimization problems

There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Minimizing False Positives of a Decision Tree Classifier for Intrusion Detection on the Internet

Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation function yields better solutions than the leaf rewriting approach.

Shear viscosity of the B phase of superfluid3He

The shear viscosity (T) in the Balian-Werthamer (BW) state of superfluid ³ He is calculated variationally throughout the region 0t 1(t=T/T _c) from the transport equation for Bogoliubov quasiparticles. Coherence factors are treated exactly in the calculation of the collision integral. The numerical result for =_s= _s(T)/_n(T_c) agree very well with experiment in the range 0.8t1.0. Analytic expressions = 0.577 (1–1.0008t) and =1–(23/64) [=(T)/k _B T] are obtained in the low-temperature region and in the vicinity ofT _c, respectively. From the numerical analysis it is shown that the latter equation is valid only in the temperature range 0.9997t1.0.Supported by the Research Institute for Fundamental Physics, Kyoto University.     

Copolymerization of Vinyl Chloride and Ethylene with Cp<Superscript>*</Superscript>Ti( OCH<Subscript>3</Subscript>)<Subscript>3</Subscript>/MAO Catalyst

Summary Copolymerization of vinyl chloride (VC) and ethylene with Cp^*Ti( OCH₃)₃/MAO catalyst was investigated. The Cp^*Ti( OCH₃)₃/MAO catalyst initiated the copolymerization of VC with ethylene, although the copolymer yields were low. In the ¹³C NMR spectra of the copolymers, the peaks based on junction part between VC and ethylene was observed, but the signals were small. From DSC measurement of the copolymers, only one glass transition temperature was observed. Thus, it is clear that the copolymerization with Cp^*Ti( OCH₃)₃/MAO catalysts gave the copolymer, and the copolymer consisting of block sequence rather than random copolymer. Received: 13 November 2002/Revised: 6 January 2003/Accepted: 10 January 2003 Correspondence to Kiyoshi Endo     

Grain refinement and superplasticity in a magnesium alloy processed by equal-channel angular pressing

The extrusion/equal channel angular pressing (EX-ECAP) processing procedure, in which magnesium-based alloys are subjected to extrusion followed by ECAP, was applied to a Mg-7.5 pct Al-0.2 pct Zr alloy prepared by casting. Microstructural inspection showed the EX-ECAP process was effective in reducing the grain size from ∼21 μm after extrusion to an as-pressed grain size of ∼0.8 μm. It is shown through static annealing that these ultrafine grains are reasonably stable up to 473 K, but grain growth occurs at higher temperatures. Tensile specimens were cut from the billets prepared by EX-ECAP and testing showed these specimens exhibited superplasticity at relatively low temperatures with maximum elongations up to >700 pct. By processing through EX-ECAP to a higher imposed strain and thereby increasing the area fraction of high-angle boundaries, it is demonstrated that there is a potential for achieving high-strain-rate superplasticity. This article is based on a presentation made at the Symposium entitled “Phase Transformations and Deformation in Magnesium Alloys,” which occurred during the Spring TMS meeting, March 14–18, 2004, in Charlotte, NC, under the auspices of the ASM-MSCTS Phase Transformations Committee.     

Achieving enhanced ductility in a dilute magnesium alloy through severe plastic deformation

Experiments were conducted to evaluate the utility of a new processing procedure developed for Mg-based alloys in which samples are subjected to a two-step processing route of extrusion followed by equal-channel angular pressing (designated as EX-ECAP). The experiments were conducted using a Mg-0.6 wt pct Zr alloy and, for comparison purposes, samples of pure Mg. It is shown that the potential for successfully using ECAP increases in both materials when adopting the EX-ECAP procedure. For the Mg-Zr alloy, the use of EX-ECAP produces a grain size of ∼1.4 μm when the pressing is undertaken at 573 K. By contrast, using EX-ECAP with pure Mg at 573 K produces a grain size of ∼26 μm. Tensile testing of the Mg-Zr alloy at 523 and 573 K after processing by EX-ECAP revealed the occurrence of significantly enhanced ductilities with maximum elongations of ∼300 to 400 pct.