Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. In peripheral areas such as the prediction of molecular properties, fully connected attention networks have been shown to be successful. However, unlike these finite atom arrangements, crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in *infinitely connected attention*. In this work, we show that this infinitely connected attention can lead to a computationally tractable formulation, interpreted as *neural potential summation*, that performs infinite interatomic potential summations in a deeply learned feature space. We then propose a simple yet effective Transformer-based encoder architecture for crystal structures called *Crystalformer*. Compared to an existing Transformer-based model, the proposed model requires only 29.4% of the number of parameters, with minimal modifications to the original Transformer architecture. Despite the architectural simplicity, the proposed method outperforms state-of-the-art methods for various property regression tasks on the Materials Project and JARVIS-DFT datasets.

In recent years, the threat of profiling attacks using deep learning has emerged. Successful attacks have been demonstrated against various types of cryptographic modules. However, the application of deep learning to side-channel attacks (SCAs) is often not adequately assessed because the labels that are widely used in SCAs, such as the Hamming weight (HW) and Hamming distance (HD), follow an imbalanced distribution. This study analyzes and solves the problems caused by dataset imbalance during training and inference. First, we state the reasons for the negative effect of data imbalance in classification for deep-learning-based SCAs and introduce the Kullback-Leibler (KL) divergence as a metric to measure this effect. Using the KL divergence, we demonstrate through analysis how the recently reported cross-entropy ratio loss function can solve the problem of imbalanced data. We further propose a method to solve dataset imbalance at the inference phase, which utilizes a likelihood function based on the key value instead of the HW/HD. The proposed method can be easily applied in deep-learning-based SCAs because it only needs an extra multiplication of the inverted binomial coefficients and inference results (i.e., the output probabilities) from the conventionally trained model. The proposed solution corresponds to data-augmentation techniques at the training phase, and furthermore, it better estimates the keys because the probability distributions of the training and test data are preserved. We demonstrate the validity of our analysis and the effectiveness of our solution through extensive experiments on two public databases.

Recently, a soft robot considering the contact of the human has attracted attention, and the zero stiffness torque control is required. Because a soft robot causes vibration at the load side, vibration suppression control of a soft robot is required. In this paper, the authors discuss position control and torque control of a soft robot by using a torsion torque sensor based on a resonance ratio control. Position control can be used to design parameters. However, torque control cannot be used to design parameters because the proportional gain Kp and resonance ratio gain Kr are not independent with regard to integral-proportional differential(I-PD) torque control. Therefore, in the case of torque control, resonance ratio control is equivalent to I-PD control.