Gender Recognition Using a Gaze-Guided Self-Attention Mechanism Robust Against Background Bias in Training Samples

We propose an attention mechanism in deep learning networks for gender recognition using the gaze distribution of human observers when they judge the gender of people in pedestrian images. Prevalent attention mechanisms spatially compute the correlation among values of all cells in an input feature map to calculate attention weights. If a large bias in the background of pedestrian images (e.g., test samples and training samples containing different backgrounds) is present, the attention weights learned using the prevalent attention mechanisms are affected by the bias, which in turn reduces the accuracy of gender recognition. To avoid this problem, we incorporate an attention mechanism called gaze-guided self-attention (GSA) that is inspired by human visual attention. Our method assigns spatially suitable attention weights to each input feature map using the gaze distribution of human observers. In particular, GSA yields promising results even when using training samples with the background bias. The results of experiments on publicly available datasets confirm that our GSA, using the gaze distribution, is more accurate in gender recognition than currently available attention-based methods in the case of background bias between training and test samples

Extracting discriminative features using task-oriented gaze maps measured from observers for personal attribute classification

We discuss how to reveal and use the gaze locations of observers who view pedestrian images for personal attribute classification. Observers look at informative regions when attempting to classify the attributes of pedestrians in images. Thus, we hypothesize that the regions in which observers’ gaze locations are clustered will contain discriminative features for the classifiers of personal attributes. Our method acquires the distribution of gaze locations from several observers while they perform the task of manually classifying each personal attribute. We term this distribution a task-oriented gaze map. To extract discriminative features, we assign large weights to the region with a cluster of gaze locations in the task-oriented gaze map. In our experiments, observers mainly looked at different regions of body parts when classifying each personal attribute. Furthermore, our experiments show that the gaze-based feature extraction method significantly improved the performance of personal attribute classification when combined with a convolutional neural network or metric learning technique

Expression transmission using exaggerated animation for Elfoid

We propose an expression transmission system using a cellular-phone-type teleoperated robot called Elfoid. Elfoid has a soft exterior that provides the look and feel of human skin, and is designed to transmit the speaker's presence to their communication partner using a camera and microphone.To transmit the speaker's presence, Elfoid sends not only the voice of the speaker but also the facial expression captured by the camera. In this research, facial expressions are recognized using a machine learning technique. Elfoid cannot, however, display facial expressions because of its compactness and a lack of sufficiently small actuator motors. To overcome this problem, facial expressions are displayed using Elfoid's head-mounted mobile projector. In an experiment, we built a prototype system and experimentally evaluated its subjective usability

Temperature effects on diffusion coefficient for 6-gingerol and 6-shogaol in subcritical water extraction

6-gingerol and 6-shogaol are the main constituents as anti-inflammatory or bioactive compounds from zingiber officinale Roscoe. These bioactive compounds have been proven for inflammatory disease, antioxidatives and anticancer. The effect of temperature on diffusion coefficient for 6-gingerol and 6-shogaol were studied in subcritical water extraction. The diffusion coefficient was determined by Fick's second law. By neglecting external mass transfer and solid particle in spherical form, a linear portion of Ln (1-(Ct/Co)) versus time was plotted in determining the diffusion coefficient. 6-gingerol obtained the higher yield at 130°C with diffusion coefficient of 8.582x10-11 m 2/s whilst for 6-shogaol, the higher yield and diffusion coefficient at 170°C and 19.417 × 10-11 m2/s

Solubilities and diffusion coefficients of high boiling compounds in supercritical carbon dioxide

金沢大学大学院自然科学研究科生産プロセスA brief introduction of the data sources and the applications of correlation methods for the solubilities and diffusion coefficients of hig-boiling compounds (mainly in solid state) in supercritical carbon dioxide are reviewed. Empirical equations, equations of state, solution models, and the Monte Carlo simulation for the calculation of solubilities in supercritical carbon dioxide are discussed. The application of empirical equation based on the Stokes-Einstein model, rough hard sphere theory, Schmidt number correlation, and molecular dynamics simulation for the calculation of diffusion coefficients in supercritical carbon dioxide at infinite dilution condition are reviewed. Further, the application of the Darken equation and the Leffler and Cullinan equation for the calculation of concentration dependence of diffusion coefficients in supercritical carbon dioxide is presented. © 2001 Elsevier Science Ltd. All rights reserved

Comparison of molecular models used in molecular dynamics simulation for tracer diffusion coefficients of naphthalene and dimethylnaphthalene isomers in supercritical carbon dioxide

金沢大学大学院自然科学研究科生産プロセスNVT ensemble molecular dynamics simulation was performed to calculate the tracer diffusion coefficients of naphthalene and dimethylnaphthalene isomers in supercritical carbon dioxide. Carbon dioxide was treated as a Lennard-Jones molecule (single site model) and solutes were treated as a rigid model of multi sites (united atom model) and a flexible model of all atoms (all atom model). The calculated results are compared with the experimental data and the calculated results by a single site model. The united atom model gives the best results to the experimental data among the three models. The calculated tracer diffusion coefficients by the all atom model show fairly good results without adjustable interaction parameters. © 2005 Elsevier B.V. All rights reserved

Planar CuO_2 hole density estimation in multilayered high-T_c cuprates

We report that planar CuO_2 hole densities in high-T_c cuprates are consistently determined by the Cu-NMR Knight shift. In single- and bi-layered cuprates, it is demonstrated that the spin part of the Knight shift K_s(300 K) at room temperature monotonically increases with the hole density $p$ from underdoped to overdoped regions, suggesting that the relationship of K_s(300 K) vs. p is a reliable measure to determine p. The validity of this K_s(300 K)-p relationship is confirmed by the investigation of the p-dependencies of hyperfine magnetic fields and of spin susceptibility for single- and bi-layered cuprates with tetragonal symmetry. Moreover, the analyses are compared with the NMR data on three-layered Ba_2Ca_2Cu_3O_6(F,O)_2, HgBa_2Ca_2Cu_3O_{8+delta}, and five-layered HgBa_2Ca_4Cu_5O_{12+delta}, which suggests the general applicability of the K_s(300 K)-p relationship to multilayered compounds with more than three CuO_2 planes. We remark that the measurement of K_s(300 K) enables us to separately estimate p for each CuO_2 plane in multilayered compounds, where doped hole carriers are inequivalent between outer CuO_2 planes and inner CuO_2 planes.Comment: 7 pages, 5 figures, 2 Tables, to be published in Physical Review

Evaluation of heterogeneity dose distributions for Stereotactic Radiotherapy (SRT): comparison of commercially available Monte Carlo dose calculation with other algorithms

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to compare dose distributions from three different algorithms with the x-ray Voxel Monte Carlo (XVMC) calculations, in actual computed tomography (CT) scans for use in stereotactic radiotherapy (SRT) of small lung cancers.</p> <p>Methods</p> <p>Slow CT scan of 20 patients was performed and the internal target volume (ITV) was delineated on Pinnacle³. All plans were first calculated with a scatter homogeneous mode (SHM) which is compatible with Clarkson algorithm using Pinnacle³treatment planning system (TPS). The planned dose was 48 Gy in 4 fractions. In a second step, the CT images, structures and beam data were exported to other treatment planning systems (TPSs). Collapsed cone convolution (CCC) from Pinnacle³, superposition (SP) from XiO, and XVMC from Monaco were used for recalculating. The dose distributions and the Dose Volume Histograms (DVHs) were compared with each other.</p> <p>Results</p> <p>The phantom test revealed that all algorithms could reproduce the measured data within 1% except for the SHM with inhomogeneous phantom. For the patient study, the SHM greatly overestimated the isocenter (IC) doses and the minimal dose received by 95% of the PTV (PTV95) compared to XVMC. The differences in mean doses were 2.96 Gy (6.17%) for IC and 5.02 Gy (11.18%) for PTV95. The DVH's and dose distributions with CCC and SP were in agreement with those obtained by XVMC. The average differences in IC doses between CCC and XVMC, and SP and XVMC were -1.14% (p = 0.17), and -2.67% (p = 0.0036), respectively.</p> <p>Conclusions</p> <p>Our work clearly confirms that the actual practice of relying solely on a Clarkson algorithm may be inappropriate for SRT planning. Meanwhile, CCC and SP were close to XVMC simulations and actual dose distributions obtained in lung SRT.</p