Brain-mediated Transfer Learning of Convolutional Neural Networks

The human brain can effectively learn a new task from a small number of samples, which indicate that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability

Controlling topological defects and contractile flow in confined nematic cell population

Topological defects in an orientation field play a vital role for controlling the collective motion of nematic cell populations within epithelia and tissue. In this study, we study the geometric control of the collective motion in a nematic cell population to further explore the interplay between topology and dynamics in active nematics. By applying spatial constraints consisting of two or three overlapping circle boundaries, we demonstrate an ordered pairing of half-integer topological defects in a confined cell population. The defects facing each other can induce a contractile cellular flow at broad geometric conditions. This robust contractile flow contributes to mechanical stimulation while altering the cell nucleus, which may be relevant to geometry-dependent morphogenesis.Comment: 8 pages, 5 figure

Geometry-Induced Dynamics of Confined Chiral Active Matter

Controlling the motion of active matter is a central issue that has recently garnered significant attention in fields ranging from non-equilibrium physics to chemical engineering and biology. Distinct methods for controlling active matter have been developed, and physical confinement to limited space and active matter with broken rotational symmetry (chirality) are two prominent mechanisms. However, the interplay between pattern formation due to physical constraints and the ordering by chiral motion needs to be better understood. In this study, we conduct numerical simulations of chiral self-propelled particles under circular boundary confinement. The collective motion of confined self-propelled particles can take drastically different forms depending on their chirality. The balance of orientation changes between particle interaction and the boundary wall is essential for generating ordered collective motion. Our results clarify the role of the steric boundary effect in controlling chiral active matter.Comment: 13 pages, 8 figures. Updated the list of reference

Visual Place Recognition From Eye Reflection

The cornea in the human eye reflects incoming environmental light, which means we can obtain information about the surrounding environment from the corneal reflection in facial images. In recent years, as the quality of consumer cameras increases, this has caused privacy concerns in terms of identifying the people around the subject or where the photo is taken. This paper investigates the security risk of eye corneal reflection images: specifically, visual place recognition from eye reflection images. First, we constructed two datasets containing pairs of scene and corneal reflection images. The first dataset is taken in a virtual environment. We showed pre-captured scene images in a 180-degree surrounding display system and took corneal reflections from subjects. The second dataset is taken in an outdoor environment. We developed several visual place recognition algorithms, including CNN-based image descriptors featuring a naive Siamese network and AFD-Net combined with entire image feature representations including VLAD and NetVLAD, and compared the results. We found that AFD-Net+VLAD performed the best and was able to accurately determine the scene in 73.08% of the top-five candidate scenes. These results demonstrate the potential to estimate the location at which a facial picture was taken, which simultaneously leads to a) positive applications such as the localization of a robot while conversing with persons and b) negative scenarios including the security risk of uploading facial images to the public

Spatial Variability and Seasonal Change of Radioactive Caesium Concentration in Grassland Vegetation

The damage from the Fukushima Daiichi Nuclear Power Plant caused by the Great East Japan Earthquake and tsunami on March 11, 2011 resulted in serious radioactive pollution over Eastern Japan. The distribution of radioactive fallout was largely determined by wind and rainfall patterns in March 2011. Distribution patterns were not necessarily in accordance with the distance from the nuclear power plant. In Iwate Prefecture (160 to 340 km north of the nuclear power plant), the amount of fallout of radioactive material in the southern region was greater than in the northern, but the distribution pattern was complex (Fig. 1, Tsuiki and Maeda 2012a; 2012b). In the southern region, the radioactive caesium (Cs) concentrations of herbage plants exceeded the provisional safety standard for dairy and fattening cattle feed, and the livestock industry has been seriously affected in numerous ways e.g. needing to dispose of polluted forage, grazing prohibitions, declines in beef prices, suspension of vending, and blanket testing of beef cattle. In this study, the spatial variability of radioactive Cs concentration in soil and vegetation was evaluated on grasslands in 2012