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We report the H, C, and N isotopic compositions of microscale (0.2 to 2$\mu$m) organic matter in samples of asteroid Ryugu and the Orgueil CI carbonaceous chondrite. Three regolith particles of asteroid Ryugu, returned by the Hayabusa2 spacecraft, and several fragments of Orgueil were analyzed by NanoSIMS isotopic imaging. The isotopic distributions of the Ryugu samples from two different collection spots are closely similar to each other and to the Orgueil samples, strengthening the proposed Ryugu-CI chondrite connection. Most individual sub-$\mu$m organic grains have isotopic compositions within error of bulk values, but 2-8% of them are outliers exhibiting large isotopic enrichments or depletions in D, $^{15}$N, and/or $^{13}$C. The H, C and N isotopic compositions of the outliers are not correlated with each other: while some C-rich grains are both D- and $^{15}$N-enriched, many are enriched or depleted in one or the other system. This most likely points to a diversity in isotopic fractionation pathways and thus diversity in the local formation environments for the individual outlier grains. The observation of a relatively small population of isotopic outlier grains can be explained either by escape from nebular and/or parent body homogenization of carbonaceous precursor material or addition of later isotopic outlier grains. The strong chemical similarity of isotopically typical and isotopically outlying grains, as reflected by synchrotron x-ray absorption spectra, suggests a genetic connection and thus favors the former, homogenization scenario. However, the fact that even the least altered meteorites show the same pattern of a small population of outliers on top of a larger population of homogenized grains indicates that some or most of the homogenization occurred prior to accretion of the macromolecular organic grains into asteroidal parent bodies., Comment: Accepted for publication in Earth and Planetary Science Letters. 8 figures (plus 3 supplementary figs), two tables

Although the MeV gamma-ray band is a promising energy-band window in astrophysics, the current situation of MeV gamma-ray astronomy significantly lags behind those of the other energy bands in angular resolution and sensitivity. An electron-tracking Compton camera (ETCC), a next-generation MeV detector, is expected to revolutionize the situation. An ETCC tracks each Compton-recoil electron with a gaseous electron tracker and determines the incoming direction of each gamma-ray photon; thus, it has a strong background rejection power and yields a better angular resolution than classical Compton cameras. Here, we study ETCC events in which the Compton-recoil electrons do not deposit all energies to the electron tracker but escape and hit the surrounding pixel scintillator array (PSA). We developed an analysis method for this untapped class of events and applied it to laboratory and simulation data. We found that the energy spectrum obtained from the simulation agreed with that of the actual data within a factor of 1.2. We then evaluated the detector performance using the simulation data. The angular resolution for the new-class events was found to be twice as good as in the previous study at the energy range 1.0--2.0~MeV, where both analyses overlap. We also found that the total effective area is dominated by the contribution of the double-hit events above an energy of 1.5~MeV. Notably, applying this new method extends the sensitive energy range with the ETCC from 0.2--2.1 MeV in the previous studies to up to 3.5~MeV. Adjusting the PSA dynamic range should improve the sensitivity in even higher energy gamma-rays. The development of this new analysis method would pave the way for future observations by ETCC to fill the MeV-band sensitivity gap in astronomy., Comment: 11 pages, 8 figures, Accepted for publication in NIM A

The enhancement of the parity-violating asymmetry in the vicinity of $p$-wave compound nuclear resonances was observed for a variety of medium-heavy nuclei. The enhanced parity-violating asymmetry can be understood using the $s$-$p$ mixing model. The $s$-$p$ mixing model predicts several neutron energy-dependent angular correlations between the neutron momentum $\vec k_n$, neutron spin $\vec\sigma_n$, $\gamma$-ray momentum $\vec k_\gamma$, and $\gamma$-ray polarization $\vec\sigma_\gamma$ in the $(n,\gamma)$ reaction. In this paper, the improved value of the transverse asymmetry of $\gamma$-ray emissions, corresponding to a correlation term $\vec{\sigma}_n\cdot(\vec k_n\times\vec k_\gamma)$ in the $^{139}\mathrm{La}(\vec n,\gamma)^{140}\mathrm{La}$ reaction, and the transverse asymmetries in the transitions to several low excited states of $^{140}\mathrm{La}$ are reported., Comment: 5 pages, 5 figures, 2 tables

To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray diffraction (XRD) is an effective technique for analysing crystal structures and other microstructural features that have information that can explain material properties. Therefore, the fully automated extraction of peak features from XRD data without the bias of an analyst is a significant challenge. This study aimed to establish an efficient and robust approach for constructing peak feature tables that follow ML standards (ML-ready) from XRD data. We challenge peak feature extraction in the situation where only the peak function profile is known a priori, without knowledge of the measurement material or crystal structure factor. We utilized Bayesian estimation to extract peak features from XRD data and subsequently performed Bayesian regression analysis with feature selection to predict the material property. The proposed method focused only on the tops of peaks within localized regions of interest (ROIs) and extracted peak features quickly and accurately. This process facilitated the rapid extracting of major peak features from the XRD data and the construction of an ML-ready feature table. We then applied Bayesian linear regression to the maximum energy product $(BH)_{max}$, using the extracted peak features as the explanatory variable. The outcomes yielded reasonable and robust regression results. Thus, the findings of this study indicated that \textit{004} peak height and area were important features for predicting $(BH)_{max}$.

In order to provide a cloud service of optical quantum computing, it is inevitable to stabilize the optical system for many hours. It is advantageous to construct a fiber-based system, which does not require spatial alignment. However, fiber-based systems are instead subject to fiber-specific instabilities. For instance, there are phase drifts due to ambient temperature changes and external disturbances, and polarization fluctuations due to the finite polarization extinction ratio of fiber components. Here, we report the success of measuring squeezed light with a fiber system for 24 hours. To do this, we introduce stabilization mechanics to suppress fluctuations in the fiber system, and integrated controller to automatically align the entire system. The squeezed light at the wavelength of 1545.3 nm is measured every 2 minutes, where automated alignments are inserted every 30 minutes. The squeezing levels with the average of -4.42 dB are recorded with an extremely small standard deviation of 0.08 dB over 24 hours.

Differentially private triangle counting in graphs is essential for analyzing connection patterns and calculating clustering coefficients while protecting sensitive individual information. Previous works have relied on either central or local models to enforce differential privacy. However, a significant utility gap exists between the central and local models of differentially private triangle counting, depending on whether or not a trusted server is needed. In particular, the central model provides a high accuracy but necessitates a trusted server. The local model does not require a trusted server but suffers from limited accuracy. Our paper introduces a crypto-assisted differentially private triangle counting system, named CARGO, leveraging cryptographic building blocks to improve the effectiveness of differentially private triangle counting without assumption of trusted servers. It achieves high utility similar to the central model but without the need for a trusted server like the local model. CARGO consists of three main components. First, we introduce a similarity-based projection method that reduces the global sensitivity while preserving more triangles via triangle homogeneity. Second, we present a triangle counting scheme based on the additive secret sharing that securely and accurately computes the triangles while protecting sensitive information. Third, we design a distributed perturbation algorithm that perturbs the triangle count with minimal but sufficient noise. We also provide a comprehensive theoretical and empirical analysis of our proposed methods. Extensive experiments demonstrate that our CARGO significantly outperforms the local model in terms of utility and achieves high-utility triangle counting comparable to the central model., Comment: Accepted by ICDE 2024

In recent years, large language models (LLMs) have rapidly proliferated and have been utilized in various tasks, including research in dialogue systems. We aimed to construct a system that not only leverages the flexible conversational abilities of LLMs but also their advanced planning capabilities to reduce the speaking load on human interlocutors and efficiently plan trips. Furthermore, we propose a method that divides the complex task of a travel agency into multiple subtasks, managing each as a separate phase to effectively accomplish the task. Our proposed system confirmed a certain level of success by achieving fourth place in the Dialogue Robot Competition 2023 preliminaries rounds. We report on the challenges identified through the competition., Comment: This paper is part of the proceedings of the Dialogue Robot Competition 2023

The integration of Large Language Models (LLMs) like ChatGPT into the workflows of geotechnical engineering has a high potential to transform how the discipline approaches problem-solving and decision-making. This paper delves into the innovative application of LLMs in geotechnical engineering, as explored in a hands-on workshop held in Tokyo, Japan. The event brought together a diverse group of 20 participants, including students, researchers, and professionals from academia, industry, and government sectors, to investigate practical uses of LLMs in addressing specific geotechnical challenges. The workshop facilitated the creation of solutions for four different practical geotechnical problems as illustrative examples, culminating in the development of an academic paper. The paper discusses the potential of LLMs to transform geotechnical engineering practices, highlighting their proficiency in handling a range of tasks from basic data analysis to complex, multimodal problem-solving. It also addresses the challenges in implementing LLMs, particularly in achieving high precision and accuracy in specialized tasks, and underscores the need for expert oversight. The findings demonstrate LLMs' effectiveness in enhancing efficiency, data processing, and decision-making in geotechnical engineering, suggesting a paradigm shift towards more integrated, data-driven approaches in this field. This study not only showcases the potential of LLMs in a specific engineering domain, but also sets a precedent for their broader application in interdisciplinary research and practice, where the synergy of human expertise and artificial intelligence redefines the boundaries of problem-solving., Comment: Supplementary information will be available upon request

Kagome-lattice materials offer novel playgrounds of exploring topologically nontrivial states of electrons under influence of many-body interactions. A noncollinear kagome antiferromagnet Mn$_3$Sn has attracted particular interest for application in spintronics owing to the large anomalous Hall effect related to the Weyl dispersion near the Fermi energy. In addition, strong electronic correlation suggesting the Kondo physics has also been implied. However, the effect of correlation on the band topology and their interplay remains elusive. Here, we investigate nonequilibrium Hall transport in a photoexcited Mn$_3$Sn using time-resolved terahertz Faraday rotation spectroscopy. In equilibrium, Mn$_3$Sn is a bad metal close to the Mott-Ioffe-Regal limit with low carrier mobility, and thus only the anomalous Hall effect is discerned. By contrast, intense photoexcitation beyond an approximate threshold gives rise to a clear cyclotron resonance, namely the normal Hall effect, indicating the emergence of unusual carriers with 50 times lighter effective mass and 40 times less scattering. The lifetime of high-mobility carriers as long as a few tens of picoseconds and a threshold-like behavior for the pump fluence are hardly explained by contribution of photoexcited hot carriers. Instead, the emergence of unusual carriers may be accounted for by dielectric screening of the on-site Coulomb interaction by high-density delocalized photocarriers. A possible role of electronic correlation in equilibrium transport in Mn$_3$Sn beyond the single-particle picture is discussed., Comment: 28 pages, 6 figures