Culture, Psychology, and Education

In my view, the study of culture provides three main contributions to our understanding of human behavior and mental processes. First there is great knowledge to impart about cultural similarities and differences in behavior, and these form the basis for improving psychological theories. Second the study of culture is a prime example of critical thinking in the field, as cross-cultural research begs the question about whether our notions of truth and psychological principles are applicable to people beyond those whom were studied. Third research on intercultural adjustment provides us with clues about possible psychological constructs that may be universally necessary for adjusting to life well in a pluralistic and diverse environment. I discuss these contributions, and reframe thinking about the goals of education focusing on these skills

Gas, Iron and Gravitational Mass in Galaxy Clusters: The General Lack of Cluster Evolution at z < 1.0

We have analyzed the ASCA data of 29 nearby clusters of galaxies systematically, and obtained temperatures, iron abundances, and X-ray luminosities of their intracluster medium (ICM). We also estimate ICM mass using the beta model, and then evaluate iron mass contained in the ICM and derive the total gravitating mass. This gives the largest and most homogeneous information about the ICM derived only by the ASCA data. We compare these values with those of distant clusters whose temperatures, abundances, and luminosities were also measured with ASCA, and find no clear evidence of evolution for the clusters at z<1.0. Only the most distant cluster at z=1.0, AXJ2019.3+1127, has anomalously high iron abundance, but its iron mass in the ICM may be among normal values for the other clusters, because the ICM mass may be smaller than the other clusters. This may suggest a hint of evolution of clusters at z ~ 1.0.Comment: 23 pages including 5 figures. Using PASJ2.sty, and PASJ95.sty. Accepted by PAS

Analyzing Input and Output Representations for Speech-Driven Gesture Generation

This paper presents a novel framework for automatic speech-driven gesture generation, applicable to human-agent interaction including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech as input and produces gestures as output, in the form of a sequence of 3D coordinates. Our approach consists of two steps. First, we learn a lower-dimensional representation of human motion using a denoising autoencoder neural network, consisting of a motion encoder MotionE and a motion decoder MotionD. The learned representation preserves the most important aspects of the human pose variation while removing less relevant variation. Second, we train a novel encoder network SpeechE to map from speech to a corresponding motion representation with reduced dimensionality. At test time, the speech encoder and the motion decoder networks are combined: SpeechE predicts motion representations based on a given speech signal and MotionD then decodes these representations to produce motion sequences. We evaluate different representation sizes in order to find the most effective dimensionality for the representation. We also evaluate the effects of using different speech features as input to the model. We find that mel-frequency cepstral coefficients (MFCCs), alone or combined with prosodic features, perform the best. The results of a subsequent user study confirm the benefits of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code is available at https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode

Phase structure of a spherical surface model on fixed connectivity meshes

An elastic surface model is investigated by using the canonical Monte Carlo simulation technique on triangulated spherical meshes. The model undergoes a first-order collapsing transition and a continuous surface fluctuation transition. The shape of surfaces is maintained by a one-dimensional bending energy, which is defined on the mesh, and no two-dimensional bending energy is included in the Hamiltonian.Comment: 13 pages with 9 figure