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Grip, walk, crawl, and jump. Soft robots are integrated functional structures composed of compliant mechanisms, whose activity spans various industrial applications such as surgery, healthcare, surveillance, and even planetary exploration. One of their promising mobility mechanism is snap-buckling; the instability mode of flexible structures passing from one equilibrium state to another can instantaneously generate large power for its motion. Predicting their performance with even simple geometry requires disentangling material, geometric nonlinearity, and contact, thereby still being a challenging problem to date. Here, we study the jumping dynamics of hemispherical elastic shells driven by snap-buckling, as a model system of soft jumping mechanisms, combining experiments, simulations, and analytical theory. We find that the contact transition dynamics trigger the jumping phenomenon upon snap-buckling by constructing the analytical predictions with shell elasticity in excellent agreement with both experiments and simulations. Despite the simple geometry of the shell, its dynamical performance primarily relies on a complex interplay between elasticity, geometry, and contact friction. By elucidating the dynamics of the building blocks of soft robots that undergo large deformations, we can build their predictive experimental and numerical framework. Our research paves the way for designing soft robots suitable for the required loading conditions or structural requirements without empirical methods., Comment: Main text: 16 pages, 4 figures. Supplementary Information: 7 pages, 3 figures

GaN is expected to be a key material for next-generation electronics due to its interesting properties. However, the current collapse poses a challenge to the application of GaN FETs to electronic devices. In this study, we investigate the formation of quantum dots in GaN FETs under the current collapse. By comparing the Coulomb diamond between standard measurements and those under current collapse, we find that the gate capacitance is significantly decreased by the current collapse. This suggests that the current collapse changes the distribution of trapped electrons at the device surface, which is reported in the previous study by operando X-ray spectroscopy. Also, we show external control of quantum dot formation, previously challenging in an FET structure, by using current collapse., Comment: 11 pages, 4 figures

We report the $p+p$ and $p+d$ differential cross sections measured in the SeaQuest experiment for $J/\psi$ and $\psi\left(2S\right)$ production at 120 GeV beam energy covering the forward $x$-Feynman ($x_F$) range of $0.5 < x_F <0.9$. The measured cross sections are in good agreement with theoretical calculations based on the nonrelativistic QCD (NRQCD) using the long-distance matrix elements deduced from a recent global analysis of proton- and pion-induced charmonium production data. The $\sigma_{\psi\left(2S\right)} / \sigma_{J/\psi}$ cross section ratios are found to increase as $x_F$ increases, indicating that the $q \bar{q}$ annihilation process has larger contributions in the $\psi\left(2S\right)$ production than the $J/\psi$ production. The $\sigma_{pd}/2\sigma_{pp}$ cross section ratios are observed to be significantly different for the Drell-Yan process and $J/\psi$ production, reflecting their different production mechanisms. We find that the $\sigma_{pd}/2\sigma_{pp}$ ratios for $J/\psi$ production at the forward $x_F$ region are sensitive to the $\bar{d}/ \bar{u}$ flavor asymmetry of the proton sea, analogous to the Drell-Yan process. The transverse momentum ($p_T$) distributions for $J/\psi$ and $\psi\left(2S\right)$ production are also presented and compared with data collected at higher center-of-mass energies., Comment: 8 pages, 7 figures, with 1 page of supplementary material which has 2 figures

We report on an observation of the difference between northern and southern skies of the ultrahigh energy cosmic ray energy spectrum with a significance of ${\sim}8\sigma$. We use measurements from the two largest experiments$\unicode{x2014}$the Telescope Array observing the northern hemisphere and the Pierre Auger Observatory viewing the southern hemisphere. Since the comparison of two measurements from different observatories introduces the issue of possible systematic differences between detectors and analyses, we validate the methodology of the comparison by examining the region of the sky where the apertures of the two observatories overlap. Although the spectra differ in this region, we find that there is only a $1.8\sigma$ difference between the spectrum measurements when anisotropic regions are removed and a fiducial cut in the aperture is applied., Comment: 8 pages, 6 figures

The tree-structured stick-breaking process (TS-SBP) mixture model is a non-parametric Bayesian model that can represent tree-like hierarchical structures among the mixture components. For TS-SBP mixture models, only a Markov chain Monte Carlo (MCMC) method has been proposed and any variational Bayesian (VB) methods has not been proposed. In general, MCMC methods are computationally more expensive than VB methods. Therefore, we require a large computational cost to learn the TS-SBP mixture model. In this paper, we propose a learning algorithm with less computational cost for the TS-SBP mixture of Gaussians by using the VB method under an assumption of finite tree width and depth. When constructing such VB method, the main challenge is efficient calculation of a sum over all possible trees. To solve this challenge, we utilizes a subroutine in the Bayes coding algorithm for context tree models. We confirm the computational efficiency of our VB method through an experiments on a benchmark dataset.

With the growth of internet of things (IoT) devices, cyberattacks, such as distributed denial of service, that exploit vulnerable devices infected with malware have increased. Therefore, vendors and users must keep their device firmware updated to eliminate vulnerabilities and quickly handle unknown cyberattacks. However, it is difficult for both vendors and users to continually keep the devices safe because vendors must provide updates quickly and the users must continuously manage the conditions of all deployed devices. Therefore, to ensure security, it is necessary for a system to adapt autonomously to changes in cyberattacks. In addition, it is important to consider network-side security that detects and filters anomalous traffic at the gateway to comprehensively protect those devices. This paper proposes a self-adaptive anomaly detection system for IoT traffic, including unknown attacks. The proposed system comprises a honeypot server and a gateway. The honeypot server continuously captures traffic and adaptively generates an anomaly detection model using real-time captured traffic. Thereafter, the gateway uses the generated model to detect anomalous traffic. Thus, the proposed system can adapt to unknown attacks to reflect pattern changes in anomalous traffic based on real-time captured traffic. Three experiments were conducted to evaluate the proposed system: a virtual experiment using pre-captured traffic from various regions across the world, a demonstration experiment using real-time captured traffic, and a virtual experiment using a public dataset containing the traffic generated by malware. The experimental results indicate that a system adaptable in real time to evolving cyberattacks is a novel approach for ensuring the comprehensive security of IoT devices against both known and unknown attacks., Comment: 12 pages, 43 figures

With a growing interest in quantum technology globally, there is an increasing need for accessing relevant physical systems for education and research. In this paper we introduce a commercially available on-site quantum computer utilizing superconducting technology, offering insights into its fundamental hardware and software components. We show how this system can be used in education to teach quantum concepts and deepen understanding of quantum theory and quantum computing. It offers learning opportunities for future talent and contributes to technological progress. Additionally, we demonstrate its use in research by replicating some notable recent achievements., Comment: 47 pages and 23 figures. Several typos corrected. Reference [50] added

Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are bagging and boosting. Bagging independently constructs decision trees without evaluating their combination performance and averages them afterward. Boosting constructs decision trees sequentially, only evaluating a combination performance of a new decision tree and the fixed past decision trees at each step. Therefore, neither method directly constructs nor evaluates a combination of decision trees for the final prediction. When the final prediction is based on a combination of decision trees, it is natural to evaluate the appropriateness of the combination when constructing them. In this study, we propose a new algorithmic framework that constructs decision trees simultaneously and evaluates their combination performance throughout the construction process. Our framework repeats two procedures. In the first procedure, we construct new candidates of combinations of decision trees to find a proper combination of decision trees. In the second procedure, we evaluate each combination performance of decision trees under some criteria and select a better combination. To confirm the performance of the proposed framework, we perform experiments on synthetic and benchmark data.

A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-tree is recently proposed. It solves the problem of overfitting caused by overly deepened trees. Moreover, the meta-tree guarantees statistical optimality based on Bayes decision theory. Therefore, the meta-tree is expected to perform better than the decision tree. In contrast to a single decision tree, it is known that ensembles of decision trees, which are typically constructed boosting algorithms, are more effective in improving predictive performance. Thus, it is expected that ensembles of meta-trees are more effective in improving predictive performance than a single meta-tree, and there are no previous studies that construct multiple meta-trees in boosting. Therefore, in this study, we propose a method to construct multiple meta-trees using a boosting approach. Through experiments with synthetic and benchmark datasets, we conduct a performance comparison between the proposed methods and the conventional methods using ensembles of decision trees. Furthermore, while ensembles of decision trees can cause overfitting as well as a single decision tree, experiments confirmed that ensembles of meta-trees can prevent overfitting due to the tree depth.

Autism spectrum disorders are complex, polygenic and heterogenous neurodevelopmental conditions, imposing a substantial economic burden. Genetics are influenced by the environment, specifically the social experience during the critical neurodevelopmental period. Despite efficacy of early behavior interventions targeted specific behaviors in some autistic children, there is no sustainable treatment for the two core symptoms: deficits in social interaction and communication, and stereotyped or restrained behaviors or interests. In this study, we investigated the impact of the social environment on both wild-type (WT) and Shank3 knockout (KO) mice, a mouse model that reproduces core autism-like symptoms. Our findings revealed that WT mice raised in an enriched social environment maintained social interest towards new conspecifics across multiple trials. Additionally, we observed that 2 hours or chronic social isolation induced social deficits or enhanced social interaction and olfactory neuron responses in WT animals, respectively. Notably, chronic social isolation restored both social novelty and olfactory deficits, and normalized self-grooming behavior in Shank3 KO mice. These results novel insights for the implementation of behavioral intervention and inclusive classrooms programs for children with ASD.

This paper describes an overview of the techniques of Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team has developed a dataset generator for the training of a robot vision system and an open-source development environment running on a human support robot simulator. The robot system comprises self-developed libraries including those for motion synthesis and open-source software works on the robot operating system. The team aims to realize a home service robot that assists humans in a home, and continuously attend the competition to evaluate the developed system. The brain-inspired artificial intelligence system is also proposed for service robots which are expected to work in a real home environment.

By the Majorana representation, for any $d > 1$ there is a one-one correspondence between a quantum state of dimension $d$ and $d-1$ qubits represented as $d-1$ points in the Bloch sphere. Using the theory of symmetry class of tensors, we present a simple scheme for constructing $d-1$ points on the Bloch sphere and the corresponding $d-1$ qubits representing a $d$-dimensional quantum state. Additionally, we demonstrate how the inner product of two $d$-dimensional quantum states can be expressed as a permanent of a matrix related to their $(d-1)$-qubit state representations. Extension of the result to mixed states is also considered., Comment: 6 pages

In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are usually used only to represent prediction functions to be constructed from given data. In contrast, some studies, including this paper, used the trees to represent stochastic data observation processes behind given data. Moreover, they derived the statistically optimal prediction, which is robust against overfitting, based on the Bayesian decision theory by assuming a prior distribution for the trees. However, these studies still have a problem in computing this Bayes optimal prediction because it involves an infeasible summation for all division patterns of a feature space, which is represented by the trees and some parameters. In particular, an open problem is a summation with respect to combinations of division axes, i.e., the assignment of features to inner nodes of the tree. We solve this by a Markov chain Monte Carlo method, whose step size is adaptively tuned according to a posterior distribution for the trees.