University of Aizu

In recent years, the rise of advanced artificial intelligence technologies has had a profound impact on many fields, including education and research. One such technology is ChatGPT, a powerful large language model developed by OpenAI. This technology offers exciting opportunities for students and educators, including personalized feedback, increased accessibility, interactive conversations, lesson preparation, evaluation, and new ways to teach complex concepts. However, ChatGPT poses different threats to the traditional education and research system, including the possibility of cheating on online exams, human-like text generation, diminished critical thinking skills, and difficulties in evaluating information generated by ChatGPT. This study explores the potential opportunities and threats that ChatGPT poses to overall education from the perspective of students and educators. Furthermore, for programming learning, we explore how ChatGPT helps students improve their programming skills. To demonstrate this, we conducted different coding-related experiments with ChatGPT, including code generation from problem descriptions, pseudocode generation of algorithms from texts, and code correction. The generated codes are validated with an online judge system to evaluate their accuracy. In addition, we conducted several surveys with students and teachers to find out how ChatGPT supports programming learning and teaching. Finally, we present the survey results and analysis.

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

The magic numbers in exotic nuclei are discussed, and their novel origin is shown to be the spin-isospin dependent part of the nucleon-nucleon interaction in nuclei. The importance and robustness of this mechanism is shown in terms of meson exchange, G-matrix, and QCD theories. In neutron-rich exotic nuclei, magic numbers such as $N\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}8$, 20, etc. can disappear, while $N\phantom{\rule{0ex}{0ex}}=\phantom{\rule{0ex}{0ex}}6$, 16, etc. arise, affecting the structure of the lightest exotic nuclei to nucleosynthesis of heavy elements.

Hayabusa2 at the asteroid Ryugu Asteroids fall to Earth in the form of meteorites, but these provide little information about their origins. The Japanese mission Hayabusa2 is designed to collect samples directly from the surface of an asteroid and return them to Earth for laboratory analysis. Three papers in this issue describe the Hayabusa2 team's study of the near-Earth carbonaceous asteroid 162173 Ryugu, at which the spacecraft arrived in June 2018 (see the Perspective by Wurm). Watanabe et al. measured the asteroid's mass, shape, and density, showing that it is a “rubble pile” of loose rocks, formed into a spinning-top shape during a prior period of rapid spin. They also identified suitable landing sites for sample collection. Kitazato et al. used near-infrared spectroscopy to find ubiquitous hydrated minerals on the surface and compared Ryugu with known types of carbonaceous meteorite. Sugita et al. describe Ryugu's geological features and surface colors and combined results from all three papers to constrain the asteroid's formation process. Ryugu probably formed by reaccumulation of rubble ejected by impact from a larger asteroid. These results provide necessary context to understand the samples collected by Hayabusa2, which are expected to arrive on Earth in December 2020. Science , this issue p. 268 , p. 272 , p. 252 ; see also p. 230

Internet of Things (IoT) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of the future events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoT data to the data center for centralized model training. To address these issues, federated learning has been proposed to let nodes use the local data to train models, which are then aggregated to synthesize a global model. Most of the existing work has focused on designing learning algorithms with provable convergence time, but other issues, such as incentive mechanism, are unexplored. Although incentive mechanisms have been extensively studied in network and computation resource allocation, yet they cannot be applied to federated learning directly due to the unique challenges of information unsharing and difficulties of contribution evaluation. In this article, we study the incentive mechanism for federated learning to motivate edge nodes to contribute model training. Specifically, a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. Finally, numerical experiments have been implemented to evaluate the efficiency of the proposed DRL-based incentive mechanism.

Novel simple properties of the monopole component of effective nucleon-nucleon interactions are presented, leading to the so-called monopole-based universal interaction. Shell structures are shown to change as functions of N and Z, consistent with experiments. Some key cases of this shell evolution are discussed, clarifying the effects of central and tensor forces. The validity of the present tensor force is examined in terms of the low-momentum interaction V(lowk) and the Q(box) formalism.

The effective interaction GXPF1 for shell-model calculations in the full $pf$ shell is tested in detail from various viewpoints such as binding energies, electromagnetic moments and transitions, and excitation spectra. The semimagic structure is successfully described for $N$ or $Z=28$ nuclei, $^{53}\mathrm{Mn}$, $^{54}\mathrm{Fe}$, $^{55}\mathrm{Co}$, and $^{56,57,58,59}\mathrm{Ni}$, suggesting the existence of significant core excitations in low-lying nonyrast states as well as in high spin yrast states. The results of $N=Z$ odd-odd nuclei, $^{54}\mathrm{Co}$ and $^{58}\mathrm{Cu}$, also confirm the reliability of GXPF1 interaction in the isospin dependent properties. Studies of shape coexistence suggest an advantage of Monte Carlo shell model over conventional calculations in cases where full-space calculations still remain too large to be practical.

Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because downand upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from 3 × 3 to 7 × 7 and then 15 × 15) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder- decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions.

Hayabusa2 at the asteroid Ryugu Asteroids fall to Earth in the form of meteorites, but these provide little information about their origins. The Japanese mission Hayabusa2 is designed to collect samples directly from the surface of an asteroid and return them to Earth for laboratory analysis. Three papers in this issue describe the Hayabusa2 team's study of the near-Earth carbonaceous asteroid 162173 Ryugu, at which the spacecraft arrived in June 2018 (see the Perspective by Wurm). Watanabe et al. measured the asteroid's mass, shape, and density, showing that it is a “rubble pile” of loose rocks, formed into a spinning-top shape during a prior period of rapid spin. They also identified suitable landing sites for sample collection. Kitazato et al. used near-infrared spectroscopy to find ubiquitous hydrated minerals on the surface and compared Ryugu with known types of carbonaceous meteorite. Sugita et al. describe Ryugu's geological features and surface colors and combined results from all three papers to constrain the asteroid's formation process. Ryugu probably formed by reaccumulation of rubble ejected by impact from a larger asteroid. These results provide necessary context to understand the samples collected by Hayabusa2, which are expected to arrive on Earth in December 2020. Science , this issue p. 268 , p. 272 , p. eaaw0422 ; see also p. 230

We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions.

Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. The cooperation and specialization among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.

The structure of neutron-rich nuclei in the $N\ensuremath{\sim}20$ region is studied by the Monte Carlo shell model based on the quantum Monte Carlo diagonalization method. We present a comprehensive description of even-even isotopes of O, Ne, Mg, and Si. It is demonstrated that, for different nuclei, various particle-hole excitations from the $\mathrm{sd}$ to $\mathrm{pf}$ shell are mixed in different ways, producing distinct effects sometimes, for instance, in ${}^{28}\mathrm{Ne}.$ The monopole interaction is examined and modified, resulting in the shell gap changing from nucleus to nucleus. The drip line of O isotopes is then reproduced. The interplay between the $T=0$ and $T=1$ monopole interactions is discussed from the viewpoint of the potential energy surface and the effective single-particle energy. The extension of the neutron drip line of Ne isotopes is explained, and the boundary of the ``island of inversion'' is shown to be rather indistinct.

Traditional standalone embedded system is limited in their functionality, flexibility, and scalability. Fog computing platform, characterized by pushing the cloud services to the network edge, is a promising solution to support and strengthen traditional embedded system. Resource management is always a critical issue to the system performance. In this paper, we consider a fog computing supported software-defined embedded system, where task images lay in the storage server while computations can be conducted on either embedded device or a computation server. It is significant to design an efficient task scheduling and resource management strategy with minimized task completion time for promoting the user experience. To this end, three issues are investigated in this paper: 1) how to balance the workload on a client device and computation servers, i.e., task scheduling, 2) how to place task images on storage servers, i.e., resource management, and 3) how to balance the I/O interrupt requests among the storage servers. They are jointly considered and formulated as a mixed-integer nonlinear programming problem. To deal with its high computation complexity, a computation-efficient solution is proposed based on our formulation and validated by extensive simulation based studies.

We present a new effective interaction for shell-model calculations in the model space consisting of the single-particle orbits $1{p}_{3/2}$, $0{f}_{5/2}$, $1{p}_{1/2}$, and $0{g}_{9/2}$. Starting with a realistic interaction based on the Bonn-C potential, 133 two-body matrix elements and four single-particle energies are modified empirically so as to fit $400$ experimental energy data out of $69$ nuclei with mass numbers $A=63~96$. The systematics of binding energies, electromagnetic moments and transitions, and low-lying energy levels are described. The soft $Z=28$ closed core is observed, in contrast to the stable $N=50$ shell closure. The new interaction is applied to systematic studies of three different chains of nuclei, Ge isotopes around $N=40$, $N=Z$ nuclei with $A=64~70$, and $N=49$ odd-odd nuclei, focusing especially on the role of the ${g}_{9/2}$ orbit. The irregular behavior of the ${0}_{2}^{+}$ state in Ge isotopes is understood as a result of detailed balance between the $N=40$ single-particle energy gap and the collective effects. The development of the band structure in $N=Z$ nuclei is interpreted in terms of successive excitations of nucleons into the ${g}_{9/2}$ orbit. The triaxial/$\ensuremath{\gamma}$-soft structure in $^{64}\mathrm{Ge}$ and the prolate/oblate shape coexistence in $^{68}\mathrm{Se}$ are predicted, showing a good correspondence with the experimental data. The isomeric states in $^{66}\mathrm{As}$ and $^{70}\mathrm{Br}$ are obtained with the structure of an aligned proton-neutron pair in the ${g}_{9/2}$ orbit. Low-lying energy levels in $N=49$ odd-odd nuclei can be classified as proton-neutron pair multiplets, implying that the obtained single-particle structure in this neutron-rich region appears to be appropriate. These results demonstrate that, in spite of the modest model space, the new interaction turns out to describe rather well properties related to the ${g}_{9/2}$ orbit in various cases, including moderately deformed nuclei.

With the recent development in information and communication technology, more and more smart devices penetrate into people's daily life to promote the life quality. As a growing healthcare trend, medical cyber-physical systems (MCPSs) enable seamless and intelligent interaction between the computational elements and the medical devices. To support MCPSs, cloud resources are usually explored to process the sensing data from medical devices. However, the high quality-of-service of MCPS challenges the unstable and long-delay links between cloud data center and medical devices. To combat this issue, mobile edge cloud computing, or fog computing, which pushes the computation resources onto the network edge (e.g., cellular base stations), emerges as a promising solution. We are thus motivated to integrate fog computation and MCPS to build fog computing supported MCPS (FC-MCPS). In particular, we jointly investigate base station association, task distribution, and virtual machine placement toward cost-efficient FC-MCPS. We first formulate the problem into a mixed-integer non-linear linear program and then linearize it into a mixed integer linear programming (LP). To address the computation complexity, we further propose an LP-based two-phase heuristic algorithm. Extensive experiment results validate the high-cost efficiency of our algorithm by the fact that it produces near optimal solution and significantly outperforms a greedy algorithm.

Big data are widely recognized as being one of the most powerful drivers to promote productivity, improve efficiency, and support innovation. It is highly expected to explore the power of big data and turn big data into big values. To answer the interesting question whether there are inherent correlations between the two tendencies of big data and green challenges, a recent study has investigated the issues on greening the whole life cycle of big data systems. This paper would like to discover the relations between the trend of big data era and that of the new generation green revolution through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

We study the dynamic ac conductivity of a nonequilibrium two-dimensional electron-hole system in optically pumped graphene. Considering the contribution of both interband and intraband transitions, we demonstrate that at sufficiently strong pumping the population inversion in graphene can lead to the negative net ac conductivity in the terahertz range of frequencies. This effect might be used in graphene-based coherent sources of terahertz radiation.

Hayabusa2 at the asteroid Ryugu Asteroids fall to Earth in the form of meteorites, but these provide little information about their origins. The Japanese mission Hayabusa2 is designed to collect samples directly from the surface of an asteroid and return them to Earth for laboratory analysis. Three papers in this issue describe the Hayabusa2 team's study of the near-Earth carbonaceous asteroid 162173 Ryugu, at which the spacecraft arrived in June 2018 (see the Perspective by Wurm). Watanabe et al. measured the asteroid's mass, shape, and density, showing that it is a “rubble pile” of loose rocks, formed into a spinning-top shape during a prior period of rapid spin. They also identified suitable landing sites for sample collection. Kitazato et al. used near-infrared spectroscopy to find ubiquitous hydrated minerals on the surface and compared Ryugu with known types of carbonaceous meteorite. Sugita et al. describe Ryugu's geological features and surface colors and combined results from all three papers to constrain the asteroid's formation process. Ryugu probably formed by reaccumulation of rubble ejected by impact from a larger asteroid. These results provide necessary context to understand the samples collected by Hayabusa2, which are expected to arrive on Earth in December 2020. Science , this issue p. 268 , p. 272 , p. 252 ; see also p. 230

The head-related transfer function (HRTF) varies with range as well as with azimuth and elevation. To better understand its close-range behavior, a theoretical and experimental investigation of the HRTF for an ideal rigid sphere was performed. An algorithm was developed for computing the variation in sound pressure at the surface of the sphere as a function of direction and range to the sound source. The impulse response was also measured experimentally. The results may be summarized as follows. First, the experimental measurements were in close agreement with the theoretical solution. Second, the variation of low-frequency interaural level difference with range is significant for ranges smaller than about five times the sphere radius. Third, the impulse response reveals the source of the ripples observed in the magnitude response, and provides direct evidence that the interaural time difference is not a strong function of range. Fourth, the time delay is well approximated by well-known ray-tracing formula due to Woodworth and Schlosberg. Finally, except for this time delay, the HRTF for the ideal sphere appears to be minimum-phase, permitting exact recovery of the impulse response from the magnitude response in the frequency domain.

An effective interaction is derived for use in the full $\mathrm{pf}$ basis. Starting from a realistic G-matrix interaction, 195 two-body matrix elements and four single-particle energies are determined by fitting to 699 energy data in the mass range 47--66. The derived interaction successfully describes various structures of $\mathrm{pf}$-shell nuclei. As examples, systematics of the energies of the first ${2}^{+}$ states in the Ca, Ti, Cr, Fe, and Ni isotope chains and energy levels of ${}^{56,57,58}\mathrm{Ni}$ are presented. The appearance of a new magic number 34 is seen.