A Novel Weight-Shared Multi-Stage CNN for Scale Robustness

Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness to the parallel shift of objects in images as well as their numerous parameters and the resulting high expression ability. However, CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This limits the performance improvement of the deep CNNs, but there is no established solution. This study focuses on scale transformation and proposes a network architecture called the weight-shared multi-stage network (WSMS-Net), which consists of multiple stages of CNNs. The proposed WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet and enables them to acquire robustness to object scaling. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for image classification tasks with only a minor increase in the number of parameters and computation time.Comment: accepted version, 13 page

Low Temperature Selective Laser Melting of High Temperature Plastic Powder

In a typical plastic laser sintering or melting system, powder bed temperature is maintained above the recrystallization temperature of the powder material to prevent the parts under process from warping until the whole layers are processed. Although this countermeasure can elegantly suppress the part warpage, heating the powder bed to such a high temperature causes many problems. In case of high temperature plastic such as polyetheretherketone (PEEK), bed temperature should be more than 300°C. Due to this requirement, machine cost is extremely high and powder recyclability is very low. The authors had introduced another countermeasure for the part warpage that anchors the in-process parts to a rigid base plate instead of heating the powder bed above the recrystallization temperature. In the current research, application of this method to PEEK powder is tested, and a simple test piece of which relative density is more than 90% was successfully obtained with preheating temperature of 200°C. In this paper, mechanical performances of obtained parts are presented, and several problems with the process of PEEK powder are discussed as well.Mechanical Engineerin

Convex Configurations on Nana-kin-san Puzzle

We investigate a silhouette puzzle that is recently developed based on the golden ratio. Traditional silhouette puzzles are based on a simple tile. For example, the tangram is based on isosceles right triangles; that is, each of seven pieces is formed by gluing some identical isosceles right triangles. Using the property, we can analyze it by hand, that is, without computer. On the other hand, if each piece has no special property, it is quite hard even using computer since we have to handle real numbers without numerical errors during computation. The new silhouette puzzle is between them; each of seven pieces is not based on integer length and right angles, but based on golden ratio, which admits us to represent these seven pieces in some nontrivial way. Based on the property, we develop an algorithm to handle the puzzle, and our algorithm succeeded to enumerate all convex shapes that can be made by the puzzle pieces. It is known that the tangram and another classic silhouette puzzle known as Sei-shonagon chie no ita can form 13 and 16 convex shapes, respectively. The new puzzle, Nana-kin-san puzzle, admits to form 62 different convex shapes

Neural Basis for the Ability of Atypical Antipsychotic Drugs to Improve Cognition in Schizophrenia

Cognitive impairments are considered to largely affect functional outcome in patients with schizophrenia, other psychotic illnesses, or mood disorders. Specifically, there is much attention to the role of psychotropic compounds acting on serotonin (5-HT) receptors in ameliorating cognitive deficits of schizophrenia. It is noteworthy that atypical antipsychotic drugs (AAPDs), e.g., clozapine, melperone, risperidone, olanzapine, quetiapine, aripiprazole, perospirone, blonanserin, and lurasidone, have variable affinities for these receptors. Among the 5-HT receptor subtypes, the 5-HT(1A) receptor is attracting particular interests as a potential target for enhancing cognition, based on preclinical and clinical evidence. The neural network underlying the ability of 5-HT(1A) agonists to treat cognitive impairments of schizophrenia likely includes dopamine, glutamate, and gamma-aminobutyric acid neurons. A novel strategy for cognitive enhancement in psychosis may be benefited by focusing on energy metabolism in the brain. In this context, lactate plays a major role, and has been shown to protect neurons against oxidative and other stressors. In particular, our data indicate chronic treatment with tandospirone, a partial 5-HT(1A) agonist, recover stress-induced lactate production in the prefrontal cortex of a rat model of schizophrenia. Recent advances of electrophysiological measures, e.g., event-related potentials, and their imaging have provided insights into facilitative effects on cognition of some AAPDs acting directly or indirectly on 5-HT(1A) receptors. These findings are expected to promote the development of novel therapeutics for the improvement of functional outcome in people with schizophrenia

The Minimum Mass of the First Stars and the Anthropic Principle

The lower limit of the mass of the first stars suggested recently may imply the formation of massive stars of mass greater than 8 solar mass irrespective of the details of the initial mass function. The production of heavy metals from the first stars will ensure a requisite for the existence of life without the anthropic principle.Comment: 3 Pages, to be published in Progress of Theoretical Physics 1997 January issu

Data Augmentation using Random Image Cropping and Patching for Deep CNNs

Deep convolutional neural networks (CNNs) have achieved remarkable results in image processing tasks. However, their high expression ability risks overfitting. Consequently, data augmentation techniques have been proposed to prevent overfitting while enriching datasets. Recent CNN architectures with more parameters are rendering traditional data augmentation techniques insufficient. In this study, we propose a new data augmentation technique called random image cropping and patching (RICAP) which randomly crops four images and patches them to create a new training image. Moreover, RICAP mixes the class labels of the four images, resulting in an advantage similar to label smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the shake-shake regularization model) by comparison with competitive data augmentation techniques such as cutout and mixup. RICAP achieves a new state-of-the-art test error of $2.19\%$ on CIFAR-10. We also confirmed that deep CNNs with RICAP achieve better results on classification tasks using CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft COCO.Comment: accepted version, 16 page