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Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials., Comment: 17 pages, 12 figures

Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensive, posing a significant barrier compared to 2D vision. Recent advances in generative models have achieved unprecedented realism in synthetic data production, and recent research shows the potential for using generated data as training data. Here, naturally raising the question: Can synthetic 3D data generated by generative models be used as expanding limited 3D datasets? In response, we present a synthetic 3D dataset expansion method, Textguided Geometric Augmentation (TeGA). TeGA is tailored for language-image-3D pretraining, which achieves SoTA in zero-shot 3D classification, and uses a generative textto-3D model to enhance and extend limited 3D datasets. Specifically, we automatically generate text-guided synthetic 3D data and introduce a consistency filtering strategy to discard noisy samples where semantics and geometric shapes do not match with text. In the experiment to double the original dataset size using TeGA, our approach demonstrates improvements over the baselines, achieving zeroshot performance gains of 3.0% on Objaverse-LVIS, 4.6% on ScanObjectNN, and 8.7% on ModelNet40. These results demonstrate that TeGA effectively bridges the 3D data gap, enabling robust zero-shot 3D classification even with limited real training data and paving the way for zero-shot 3D vision application.

In a two-dimensional (2D) spin system, the XY model, characterized by planar rotational symmetry, exhibits a unique phenomenon known as the Berezinskii-Kosterlitz-Thouless (BKT) transition. In contrast, the clock model, which introduces discrete rotational symmetry, exhibits the BKT transition at two different temperatures due to this discreteness. In this study, we numerically investigate the BKT transition for XY and six-state clock models over various two-dimensional lattices. We employ two primary methods: the Monte Carlo method, which analyzes the size dependence of the ratio of the correlation functions for two different distances, and a machine-learning approach to classify the different phases -- namely, the low-temperature ordered phase, the intermediate BKT phase, and the high-temperature disordered phase. We identify the BKT transition temperatures for the XY and six-state models on honeycomb, kagome, and diced lattices. Combined with the previously calculated data for the triangular lattice, we then compare these values with the second-order phase transition temperatures of the 2D Ising model, for which exact solutions are known. Our results indicate that the ratio of the BKT transition temperatures for each lattice relative to the Ising model transition temperatures are close, although the values are not universal., Comment: accepted for publication in J. Phys. A

This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postprocessing and quantum annealing. Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization, necessitating robust and efficient noise-removal strategies. The proposed method evaluates filtered training subsets based on validation loss, iteratively refines loss estimates through surrogate model-based BBO with postprocessing, and leverages quantum annealing to efficiently sample diverse training subsets with low validation error. Experiments on a noisy majority bit task demonstrate the method's ability to prioritize the removal of high-risk mislabeled instances. Integrating D-Wave's clique sampler running on a physical quantum annealer achieves faster optimization and higher-quality training subsets compared to OpenJij's simulated quantum annealing sampler or Neal's simulated annealing sampler, offering a scalable framework for enhancing dataset quality. This work highlights the effectiveness of the proposed method for supervised learning tasks, with future directions including its application to unsupervised learning, real-world datasets, and large-scale implementations.

To realize practical quantum computers, a large number of quantum bits (qubits) will be required. Semiconductor spin qubits offer advantages such as high scalability and compatibility with existing semiconductor technologies. However, as the number of qubits increases, manual qubit tuning becomes infeasible, motivating automated tuning approaches. In this study, we use U-Net, a neural network method for object detection, to identify charge transition lines in experimental charge stability diagrams. The extracted charge transition lines are analyzed using the Hough transform to determine their positions and angles. Based on this analysis, we obtain the transformation matrix to virtual gates. Furthermore, we identify the single-electron regime by clustering the Hough transform outputs. We also show the single-electron regime within the virtual gate space. These sequential processes are performed automatically. This approach will advance automated control technologies for large-scale quantum devices., Comment: 9 pages, 6 figures

Zinc oxide (ZnO) has garnered much attention as a promising material for quantum devices due to its unique characteristics. To utilize the potential of ZnO for quantum devices, the development of fundamental technological elements such as high-speed readout and charge sensing capabilities has become essential. In this study, we address these challenges by demonstrating radio-frequency (rf) reflectometry and charge sensing in ZnO quantum dots, thus advancing the potential for qubit applications. A device is fabricated on a high-quality ZnO heterostructure, featuring gate-defined target and sensor quantum dots. The sensor dot, integrated into an rf resonator circuit, enables the detection of single-electron charges in the target dots. Using this setup, the formation of few-electron double quantum dots is observed by obtaining their charge stability diagram. Also, a charge stability diagram with a gate pulse sequence is measured. We discuss the strong electron correlation in ZnO, which leads to nearly degenerate spin-singlet and -triplet two-electron states in the (0, 2) charge state, and the perspectives on spin-state readout., Comment: 6 pages, 4 figures

The $M1$ transition strengths between excited $2^+$ states of the neutron-rich, radioactive nuclide $^{132}$Te have been studied through direct lifetime measurements using the Doppler-shift attenuation method in a two-neutron transfer reaction on a $^{130}$Te target. An unambiguous identification of the lowest-lying mixed-symmetry $2^+$ state has been achieved on the basis of the large $B(M1;2^+_2\rightarrow2^+_1$)=0.18(2) $\mu_\mathrm{N}^2$ transition strength, in agreement with shell-model calculations. Results are compared to the shell model, and the analysis of both, data and calculations, unambiguously identifies the second-excited $2^+$ state of $^{132}$Te as the one-quadrupole phonon mixed-symmetry state of this isotope. A lowering of the energy and $B(M1;2^+_\mathrm{ms}\rightarrow 2^+_1)$ strength within the $N$=80 isotones toward the $Z$=50 shell closure is observed, which goes alongside with the lowering of the $E2$ collectivity approaching the magic proton shell.

We discuss selection rules of chiral matters in type IIA intersecting and IIB magnetized D-brane models on toroidal orbifolds. Since the chiral matters on toroidal orbifolds are labeled by a certain conjugacy class of the gauged orbifold group, the selection rules involve non-trivial fusion rules. We find that the representation of the chiral matters is described by a $D_4$ flavor symmetry for an even number of magnetic fluxes or winding numbers at tree level. Furthermore, the $D_4$ symmetry still remains even when we take into account loop effects. We also study non-perturbative effects such as D-brane instantons., Comment: 30 pages, 2 figures

Prospective memory (PM), defining the currently conceived intention of a future action, is crucial for daily functioning, particularly in aging populations. This study develops and validates a virtual reality prospective memory training (VR-PMT) system that integrates visual imagery training (VIT) and virtual reality training (VRT) to enhance the PM abilities of users. The framework is designed to progressively challenge users by simulating real-life PM tasks in a controlled VR environment. The VIT component is designed to improve the generation and utilization of visual imagery by users, while the VRT component provides PM tasks based on time and event cues within a virtual environment.The framework was evaluated on ten healthy adults (university students and elderly participants) over nine weeks. During the initial session, the baseline PM abilities of the participants were assessed using the memory for intentions screening test (MIST). The subsequent sessions alternated between VIT and VRT with increasing task complexity. The MIST scores were significantly positively correlated with task achievement, confirming the efficacy of the system. Imagery abilities were also strongly correlated with task performance, underscoring the importance of visual imagery in PM training.Usability and user experiences, evaluated on the Jikaku-sho Shirabe questionnaire and the user experience questionnaire, indicated an overall positive user experience but higher fatigue levels in elderly participants. This study demonstrates that the VR--PMT system effectively trains and assesses PM abilities by integrating VIT and VRT, supporting its potential for broader applications in clinical settings., Comment: This manuscript has been submitted to IEEE Access

Carbyne, a one-dimensional (1D) carbon allotrope with alternating triple and single bonds, has the highest known mechanical strength but is unstable to bending, limiting synthesis to short linear chains. Encapsulation within carbon nanotubes (CNTs) stabilizes carbyne, forming confined carbyne (CC), thus enabling further research concerning attractive 1D physics and materials properties of carbyne. While CC has been synthesized in multi-walled CNTs (MWCNTs) using the arc-discharge method and in double-walled CNTs (DWCNTs) via high-temperature high-vacuum (HTHV) method, synthesis in single-walled CNTs (SWCNTs) has been challenging due to their fragility under such conditions. In this work, we report a low-temperature method to synthesize CC inside SWCNTs (CC@SWCNT). By annealing SWCNTs containing ammonium deoxycholate (ADC) at 400{\deg}C, ADC is converted into CC without damaging the SWCNTs. Raman spectroscopy revealed a strong CC phonon (CC-mode) peak at 1860-1870 cm^-1, much stronger than the SWCNT G-band peak, confirming a high fraction of CC in the resulting material. The Raman mapping result showed the uniformity of the CC-mode signal across the entire film sample, proving the high efficiency of this method in synthesizing CC in every SWCNT of appropriate size. Notably, the CC-mode peaks of CC@SWCNT (above 1860 cm^-1) are higher than those reported in previous CC@CNT samples (mostly less than 1856 cm^-1). This is attributed to larger SWCNT diameters (over 0.95 nm) used in this study, compared to the typical 0.6-0.8 nm range. Larger diameters result in reduced confinement, allowing carbyne to closely resemble free-standing carbyne while remaining stabilized. This low-temperature synthesis of long-chain, nearly free-standing carbyne within large-diameter SWCNTs offers new opportunities for exploring 1D physics and the unique properties of carbyne for potential applications.

Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The resultant demand for comprehensive RAW image datasets contrasts with the labor-intensive process of creating specific datasets for individual sensors. To address this, we propose a novel diffusion-based method for generating RAW images guided by RGB images. Our approach integrates an RGB-guidance module for feature extraction from RGB inputs, then incorporates these features into the reverse diffusion process with RGB-guided residual blocks across various resolutions. This approach yields high-fidelity RAW images, enabling the creation of camera-specific RAW datasets. Our RGB2RAW experiments on four DSLR datasets demonstrate state-of-the-art performance. Moreover, RAW-Diffusion demonstrates exceptional data efficiency, achieving remarkable performance with as few as 25 training samples or even fewer. We extend our method to create BDD100K-RAW and Cityscapes-RAW datasets, revealing its effectiveness for object detection in RAW imagery, significantly reducing the amount of required RAW images., Comment: Accepted at WACV 2025

We demonstrate that a broad class of modular inflation models predicts the emergence of new physics within an energy range of approximately $10^{15}\, \mathrm{GeV}$ to $10^{17} \, \mathrm{GeV}$. This prediction arises by comparing the moduli-dependent species scale with observational constraints on inflation. Specifically, we illustrate this within the context of $SL(2, \mathbb{Z})$-modular inflation models by re-expressing inflationary observables in terms of the species scale. We further discuss the implications of this approach for generic Calabi-Yau threefolds, showing that this reformulation allows us to directly constrain the fundamental parameters related to the geometry of extra dimensions, specifically the second Chern numbers., Comment: 6 pages, 1 figure

The Roberts linkage is recognized for enabling long-period pendulum motion in a compact format. Utilizing this characteristic, we are developing a three-point Roberts linkage for vibration isolation systems, with an eye towards its potential contribution to the development of next-generation interferometric gravitational wave antennas. In this article, we derived the equations to determine the essential parameters when using this linkage as a vibration isolation system, namely the equivalent pendulum length and the relationship between translational motion of the center of mass and rigid body rotation, from size parameters. Additionally, we analyzed the behavior in response to various errors., Comment: This article has been accepted by Review of Scientific Instruments. After it is published, it will be found at https://publishing.aip.org/resources/librarians/products/journals/

Exceptional points (EPs) in the propagation states give rise to the emergence of intriguing properties with the divergence of the group velocity. However, there have been no experimental reports due to the necessity of maintaining high levels of fabrication precision and the requisite high group velocity contrast. In our study, we propose a design of photonic crystal waveguide with glide and time reversal symmetry, and derive an effective Hamiltonian for edge states to realize fast-light edge states. We adopt a systematic method to generate EPs in edge states by introducing non-Hermitian perturbations to Dirac points guaranteed by glide symmetry, which ensures that EP modes are free from out-of-plane radiation losses. Then, our study reveals the conditions for the exact EP restoration and provides an analytical solution to offset the EP smoothing due to symmetry breaking, which drastically reduces the group velocity contrast. A good symmetry property of the photonic crystal waveguide allows us to derive the effective Hamiltonian as a simple form, and the EPs can be restored by adjusting the real part of the permittivity. Furthermore, we design a feasible photonic crystal slab waveguide incorporating graphene as the absorbing material, and numerically demonstrate a group velocity reaching $v_g = 3.3c$ near the EP, which is up to 25 times that of the original structure. Thanks to the short periodicity of photonic crystals, it's possible to reach the speed of light in vacuum with group velocity contrasts on the order of one digit. Our study paves an innovative way to manipulate the group velocity of light., Comment: 17 pages, 11 figures

Recently, the navigation of mobile robots in unknown environments has become a particularly significant research topic. Previous studies have primarily employed real-time environmental mapping using cameras and LiDAR, along with self-localization and path generation based on those maps. Additionally, there is research on Sim-to-Real transfer, where robots acquire behaviors through pre-trained reinforcement learning and apply these learned actions in real-world navigation. However, strictly the observe action and modelling of unknown environments that change unpredictably over time with accuracy and precision is an extremely complex endeavor. This study proposes a simple navigation algorithm for traversing unknown environments by utilizes the number of swarm robots. The proposed algorithm assumes that the robot has only the simple function of sensing the direction of the goal and the relative positions of the surrounding robots. The robots can navigate an unknown environment by simply continuing towards the goal while bypassing surrounding robots. The method does not need to sense the environment, determine whether they or other robots are stuck, or do the complicated inter-robot communication. We mathematically validate the proposed navigation algorithm, present numerical simulations based on the potential field method, and conduct experimental demonstrations using developed robots based on the sound fields for navigation., Comment: 11 pages, 22 figures

We present X-ray observations of the upper atmospheric density disturbance caused by the explosive eruption of the Hunga Tonga-Hunga Ha'apai (HTHH) volcano on 15 January 2022. From 14 January to 16 January, the Chinese X-ray astronomy satellite, Insight-HXMT, was observing the supernova remnant Cassiopeia A. The X-ray data obtained during Earth's atmospheric occultations allowed us to measure neutral densities in the altitude range of ~90-150 km. The density profiles above 110 km altitude obtained before the major eruption are in reasonable agreement with expectations by both GAIA and NRLMSIS 2.0 models. In contrast, after the HTHH eruption, a severe density depletion was found up to ~1,000 km away from the epicenter, and a relatively weak depletion extending up to ~7,000 km for over 8 hr after the eruption. In addition, density profiles showed wavy structures with a typical length scale of either ~20 km (vertical) or ~1,000 km (horizontal). This may be caused by Lamb waves or gravity waves triggered by the volcanic eruption., Comment: 14 pages, 4 figures, accepted for publication in Geophysical Research Letters

We study the modular symmetry of localized modes on fixed points of $T^2/\mathbb{Z}_2$ orbifold. First, we find that the localized modes with even (odd) modular weight generally have $\Delta(6n^2)$ ($\Delta'(6n^2)$) modular flavor symmetry. Moreover, when we consider an additional Ansatz, the localized modes with even (odd) modular weight generally enjoy $S_3$ ($S'_4$) modular flavor symmetry, and we show the concrete wave functions of the localized modes., Comment: 18 pages

We discuss the stabilization of multiple moduli by utilizing Siegel modular forms in the framework of $Sp(2g,\mathbb{Z})$ modular invariant theories. We derive the stationary conditions at CP-conserving fixed points for a generic modular- and CP-invariant scalar potential. The stabilization of multiple moduli is explicitly demonstrated in $Sp(4,\mathbb{Z})$ and $Sp(6,\mathbb{Z})$ modular invariant scalar potentials. Furthermore, it turns out that there exists a light axion when the moduli are stabilized nearby a fixed point., Comment: 31 pages, 3 figures

We propose a reinforcement learning-based search strategy to explore new physics beyond the Standard Model. The reinforcement learning, which is one of machine learning methods, is a powerful approach to find model parameters with phenomenological constraints. As a concrete example, we focus on a minimal axion model with a global $U(1)$ flavor symmetry. Agents of the learning succeed in finding $U(1)$ charge assignments of quarks and leptons solving the flavor and cosmological puzzles in the Standard Model, and find more than 150 realistic solutions for the quark sector taking renormalization effects into account. For the solutions found by the reinforcement learning-based analysis, we discuss the sensitivity of future experiments for the detection of an axion which is a Nambu-Goldstone boson of the spontaneously broken $U(1)$. We also examine how fast the reinforcement learning-based searching method finds the best discrete parameters in comparison with conventional optimization methods. In conclusion, the efficient parameter search based on the reinforcement learning-based strategy enables us to perform a statistical analysis of the vast parameter space associated with the axion model from flavor., Comment: 39 pages, 4 figures

Phenomenological aspects of non-invertible symmetries, in particular the flavor structure of quarks and leptons, are studied. We start with a $\mathbb{Z}_M$ discrete symmetry and gauge $\mathbb{Z}_2$ so as to obtain a non-invertible symmetry. We study which Yukawa textures can be derived from the non-invertible symmetries. Various textures can be realized and some of them cannot be realized by a conventional symmetry. For example, the nearest neighbor interaction texture as well as other interesting textures of quarks and leptons are obtained., Comment: 14 pages

Soil microbial communities are known to be robust against perturbations such as nutrition inputs, which appears as an obstacle for the soil improvement. On the other hand, its adaptable aspect has been also reported. Here we propose simple measures for these seemingly contradicting features of soil microbial communities, robustness and plasticity, based on the distribution of the populations. The first measure is the similarity in the population balance, i.e. the shape of the distribution function, which is found to show resilience against the nutrition inputs. The other is the similarity in the composition of the species measured by the rank order of the population, which shows an adaptable response during the population balance is recovering. These results clearly show that the soil microbial system is robust (or, homeostatic) in its population balance, while the composition of the species is rather plastic and adaptable., Comment: 9 pages, 3 figures, with supplementary information

We study the flavor structures of zero-modes, which are originated from the modular symmetry on $T^2_1\times T^2_2$ and its orbifold with magnetic fluxes. We introduce the constraint on the moduli parameters by $\tau_2=N\tau_1$, where $\tau_i$ denotes the complex structure moduli on $T^2_i$. Such a constraint can be derived from the moduli stabilization. The modular symmetry of $T^2_1 \times T^2_2$ is $SL(2,\mathbb{Z})_{\tau_1} \times SL(2,\mathbb{Z})_{\tau_2} \subset Sp(4,\mathbb{Z})$ and it is broken to $\Gamma_0(N) \times \Gamma^0(N)$ by the moduli constraint. The wave functions represent their covering groups. We obtain various flavor groups in these models., Comment: 20 pages

This paper addresses user-specific dialogs. In contrast to previous research on personalized dialogue focused on achieving virtual user dialogue as defined by persona descriptions, user-specific dialogue aims to reproduce real-user dialogue beyond persona-based dialogue. Fine-tuning using the target user's dialogue history is an efficient learning method for a user-specific model. However, it is prone to overfitting and model destruction due to the small amount of data. Therefore, we propose a learning method for user-specific models by combining parameter-efficient fine-tuning with a pre-trained dialogue model that includes user profiles. Parameter-efficient fine-tuning adds a small number of parameters to the entire model, so even small amounts of training data can be trained efficiently and are robust to model destruction. In addition, the pre-trained model, which is learned by adding simple prompts for automatically inferred user profiles, can generate speech with enhanced knowledge of the user's profile, even when there is little training data during fine-tuning. In experiments, we compared the proposed model with large-language-model utterance generation using prompts containing users' personal information. Experiments reproducing real users' utterances revealed that the proposed model can generate utterances with higher reproducibility than the compared methods, even with a small model.

This paper proposes a new conceptual framework called Collective Predictive Coding as a Model of Science (CPC-MS) to formalize and understand scientific activities. Building on the idea of collective predictive coding originally developed to explain symbol emergence, CPC-MS models science as a decentralized Bayesian inference process carried out by a community of agents. The framework describes how individual scientists' partial observations and internal representations are integrated through communication and peer review to produce shared external scientific knowledge. Key aspects of scientific practice like experimentation, hypothesis formation, theory development, and paradigm shifts are mapped onto components of the probabilistic graphical model. This paper discusses how CPC-MS provides insights into issues like social objectivity in science, scientific progress, and the potential impacts of AI on research. The generative view of science offers a unified way to analyze scientific activities and could inform efforts to automate aspects of the scientific process. Overall, CPC-MS aims to provide an intuitive yet formal model of science as a collective cognitive activity.

This paper examines the connections among various approaches to understanding concepts in philosophy, cognitive science, and machine learning, with a particular focus on their mathematical nature. By categorizing these approaches into Abstractionism, the Similarity Approach, the Functional Approach, and the Invariance Approach, the study highlights how each framework provides a distinct mathematical perspective for modeling concepts. The synthesis of these approaches bridges philosophical theories and contemporary machine learning models, providing a comprehensive framework for future research. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence., Comment: 25 pages, 3 figures

We discuss non-invertible symmetries in toroidal compactifications of higher dimensional Yang-Mills theory with magnetic fluxes, which can be regarded as an effective action of type IIB string theory with magnetized D-branes. Specifically, we derive fusion rules of discrete isometry operators, which are invariant under the $\mathbb{Z}_N$ orbifold twist. It turns out that degenerate chiral zero-modes non-trivially transform under the non-invertible symmetries in the context of quantum mechanics. Hence, non-invertible symmetries correspond to flavor symmetries of chiral zero modes and determine their flavor structure. We explicitly show the representations and selection rules of chiral zero modes on their wave functions. These zero modes may correspond to generations of quarks and leptons in four-dimensional low-energy effective field theory., Comment: 35 pages

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

Detection of single-electron charges in solid-state nanodevices is a key technique in semiconductor quantum bit readout for quantum information processing and probing electronic properties of nanostructures. This detection is achieved using quantum dot charge sensors, with its speed enhanced by high-speed RF reflectometry. Recently, real-time processing of data from RF reflectometry has attracted much attention to quantum information processing. In this paper, we propose a sequential method based on Bayes' theorem for estimating the charge state and compare its performance with the averaging approach and threshold judgment. When the noise variance differs between the empty and occupied states, the Bayesian approach demonstrates a lower error score, facilitating the extraction of more data points in real-time charge state estimation. Additionally, the Bayesian approach outperforms the averaging method and threshold judgment in terms of error rates for charge state estimation, even during charge transitions. This technique is broadly applicable to single-electron detection and holds substantial utility for quantum bit readout and the operation of nanoprobes that utilize single-electron detection., Comment: 19 pages, 8 figures

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for lattice-guided growth of dense, aligned MoS2 NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm and longitudinal axis parallel to the zigzag direction, being also extensible to the growth of WS2 NRs and MoS2-WS2 hetero-nanoribbons. Growth is influenced by both substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis., Comment: 41 pages, 27 figures

Xtend is one of the two telescopes onboard the X-ray imaging and spectroscopy mission (XRISM), which was launched on September 7th, 2023. Xtend comprises the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. A large field of view of $38^{\prime}\times38^{\prime}$ over the energy range from 0.4 to 13 keV is realized by the combination of the SXI and XMA with a focal length of 5.6 m. The SXI employs four P-channel, back-illuminated type CCDs with a thick depletion layer of 200 $\mu$m. The four CCD chips are arranged in a 2$\times$2 grid and cooled down to $-110$ $^{\circ}$C with a single-stage Stirling cooler. Before the launch of XRISM, we conducted a month-long spacecraft thermal vacuum test. The performance verification of the SXI was successfully carried out in a course of multiple thermal cycles of the spacecraft. About a month after the launch of XRISM, the SXI was carefully activated and the soundness of its functionality was checked by a step-by-step process. Commissioning observations followed the initial operation. We here present pre- and post-launch results verifying the Xtend performance. All the in-orbit performances are consistent with those measured on ground and satisfy the mission requirement. Extensive calibration studies are ongoing., Comment: 10 pages, 8 figures. Proceedings of SPIE Astronomical Telescopes and Instrumentation 2024

XRISM (X-Ray Imaging and Spectroscopy Mission) is an astronomical satellite with the capability of high-resolution spectroscopy with the X-ray microcalorimeter, Resolve, and wide field-of-view imaging with the CCD camera, Xtend. The Xtend consists of the mirror assembly (XMA: X-ray Mirror Assembly) and detector (SXI: Soft X-ray Imager). The components of SXI include CCDs, analog and digital electronics, and a mechanical cooler. After the successful launch on September 6th, 2023 (UT) and subsequent critical operations, the mission instruments were turned on and set up. The CCDs have been kept at the designed operating temperature of $-110^\circ$C ~after the electronics and cooling system were successfully set up. During the initial operation phase, which continued for more than a month after the critical operations, we verified the observation procedure, stability of the cooling system, all the observation options with different imaging areas and/or timing resolutions, and operations for protection against South Atlantic Anomaly. We optimized the operation procedure and observation parameters including the cooler settings, imaging areas for the specific modes with higher timing resolutions, and event selection algorithm. We summarize our policy and procedure of the initial operations for SXI. We also report on a couple of issues we faced during the initial operations and lessons learned from them., Comment: 10 pages, 8 figures, Proceedings of SPIE Astronomical Telescopes + Instrumentation 2024

We study the stabilization of a twisted modulus in Type IIB flux compactifications on a mirror of the rigid Calabi-Yau threefold. By analyzing the effective action of twisted and untwisted moduli, we find that three-form fluxes satisfying the tadpole cancellation conditions lead to supersymmetric AdS vacua. We also investigate swampland conjectures on this non-geometric background., Comment: 16 pages, 2 figures