Search

Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emittance of the accelerated muons in the horizontal and vertical planes were $0.85 \pm 0.25 ~\rm{(stat.)}~^{+0.22}_{-0.13} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad and $0.32\pm 0.03~\rm{(stat.)} ^{+0.05}_{-0.02} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad, respectively. The measured emittance values demonstrated phase space reduction by a factor of $2.0\times 10^2$ (horizontal) and $4.1\times 10^2$ (vertical) allowing good acceleration efficiency. These results pave the way to realize the first-ever muon accelerator for a variety of applications in particle physics, material science, and other fields.

Gamma rays consist of high-energy photons that selectively interact with nuclei, induce and mediate nuclear reactions and elementary particle interactions, and exceed x-rays in penetrating power and thus are indispensable for analysis and modification of dense or compressed object interior. Yet, the available gamma sources lack power and brightness which, if available, would revolutionize science and technology. The predicted laser-driven Gamma Flash (GF) would be the highest-power and the brightest terrestrial gamma source with a 30-40% laser-to-gamma energy conversion. It is based on inverse Compton scattering in a laser-solid interaction at irradiance typically above $10^{23}W/cm^2$. GF is one of the motivating goals for the most advanced laser facilities. However, till now GF remains overshadowed by simultaneously generated low-brightness Bremsstrahlung. Here we experimentally differentiate these two mechanisms and demonstrate a GF dominant regime producing several times the number of Bremsstrahlung photons. We found steep GF yield growth with the laser power and irradiance. Simulations revealed a Terawatt GF with nanometre source and an attosecond pulse train with a record brightness of $~10^{22}photons/mm^2mrad^2s0.1\%BW$ at up to tens of MeV. The small source size and high brightness paves the way towards spatially coherent gamma rays. At high photon energies, our regime is comparable in brightness to astrophysical Gamma Ray Bursts. We anticipate that the gamma ray source based on our findings will facilitate a breakthrough in research on future inertial fusion energy by enabling high-spatial-resolution time-resolved radiography of fuel mixing instabilities in extremely compressed targets. Such a new compact bright ultrafast gamma source could facilitate significant advances in time-resolved nuclear physics, homeland security, and nuclear waste management and non-proliferation., Comment: 36 pages, 11 figures

In this study, we propose a high-performance disparity (depth) estimation method using dual-pixel (DP) images with few parameters. Conventional end-to-end deep-learning methods have many parameters but do not fully exploit disparity constraints, which limits their performance. Therefore, we propose a lightweight disparity estimation method based on a completion-based network that explicitly constrains disparity and learns the physical and systemic disparity properties of DP. By modeling the DP-specific disparity error parametrically and using it for sampling during training, the network acquires the unique properties of DP and enhances robustness. This learning also allows us to use a common RGB-D dataset for training without a DP dataset, which is labor-intensive to acquire. Furthermore, we propose a non-learning-based refinement framework that efficiently handles inherent disparity expansion errors by appropriately refining the confidence map of the network output. As a result, the proposed method achieved state-of-the-art results while reducing the overall system size to 1/5 of that of the conventional method, even without using the DP dataset for training, thereby demonstrating its effectiveness. The code and dataset are available on our project site., Comment: Accepted to IEEE Winter Conference on Applications of Computer Vision (WACV) 2025

We propose a novel method to distinguish states of matter by identifying spontaneous symmetry breaking on extended objects, such as vortices, even in the absence of a bulk phase transition. As a specific example, we investigate the phase transition on superfluid vortices in the Higgs-confinement crossover using a $\mathrm{U}(1)_\mathrm{gauge} \times \mathrm{U}(1)_\mathrm{global}$ model. This model exhibits superfluidity of $\mathrm{U}(1)_\mathrm{global}$ symmetry and allows for a crossover between the Higgs and confinement regimes by varying the gauge coupling constant from weak to strong. We demonstrate that, on vortices, spontaneous breaking of the $\mathbb{Z}_2$ flavor symmetry occurs in the weak coupling (Higgs) regime, while it does not in the strong coupling (confinement) regime. We also confirm that those regimes are separated by a second-order phase transition through Monte Carlo simulations, whose universality class corresponds to the two-dimensional Ising model., Comment: 22 pages, 5 figures

In this note, we give a definitive basis for the dimension-eight operators leading to quartic -- but no cubic -- interactions among electroweak gauge bosons. These are often called anomalous quartic gauge couplings, or aQGCs. We distinguish in particular the CP-even ones from their CP-odd counterparts., Comment: LHC Effective Field Theory Working Group Note, 8 pages, 3 tables

The motion of chiral magnetic domain walls (DWs) driven by spin-orbit torque (SOT) has been extensively studied in heavy metal/ferromagnet heterostructures with perpendicular magnetic anisotropy. This study specifically focuses on SOT-driven DWs in near Bloch-states, which we refer to as ``quasi-Bloch DWs". These quasi-Bloch DWs exhibit slower motion compared to Neel-type DWs, offering potential for achieving highly controllable DW positions. Here, we investigate the characteristics of SOT-driven motion of quasi-Bloch DWs in perpendicularly magnetized ultra-thin films consisting of W/CoFeB/MgO. For analyzing the DW motion, we employ a one-dimensional model incorporating parameters derived from experimental data obtained from our samples. Our model successfully reproduces the experimental results, which reveal variations in the direction and threshold current density of DW motion among different samples. Through theoretical analysis, we unveil that the DW remains in quasi-Bloch states during motion, with SOT serving as the primary driving force rather than spin transfer torque (STT). The direction of motion is determined not only by the sign combination of Dzyaloshinskii-Moriya interaction (DMI) and spin Hall angle but also by the strength of DMI, STT, and extrinsic DW pinning. Furthermore, we provide analytical expressions for the threshold current density required for SOT-driven quasi-Bloch DW motion. These findings provide valuable insights for the design of future DW devices with specific film structures.

This paper addresses a system identification for linear periodically time-varying plants in the discrete-time setting. A system identification algorithm for linear, periodically time-varying plants is introduced based on a cyclic reformulation and a state coordinate transformation of the cycled system. By using our system identification algorithm, the high-accuracy model of the periodically time-varying plant can be obtained without using specific periodic input signals. The effectiveness of the proposed algorithm is demonstrated with numerical examples.

This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models. In our approach, trainable LoRA (low-rank adaptation) adapters are inserted between the last layer and every other layer to enhance the network expressive power while keeping the backward computation cost low. This architecture is well-suited to cache intermediate computation results of the forward pass and then can skip the forward computation of seen samples as training epochs progress. We implemented the combination of the proposed architecture and cache, denoted as Skip2-LoRA, and tested it on a $15 single board computer. Our results show that Skip2-LoRA reduces the fine-tuning time by 90.0% on average compared to the counterpart that has the same number of trainable parameters while preserving the accuracy, while taking only a few seconds on the microcontroller board., Comment: ASP-DAC 2025 (accepted)

We show that K3 surfaces in characteristic 2 can admit sets of $n$ disjoint smooth rational curves whose sum is divisible by 2 in the Picard group, for each $n=8,12,16,20$. More precisely, all values occur on supersingular K3 surfaces, with exceptions only at Artin invariants 1 and 10, while on K3 surfaces of finite height, only $n=8$ is possible., Comment: 28 pages

The multiplicities of positive and negative pions, kaons and unidentified hadrons produced in deep-inelastic scattering are measured in bins of the Bjorken scaling variable $x$, the relative virtual-photon energy $y$ and the fraction of the virtual-photon energy transferred to the final-state hadron $z$. Data were obtained by the COMPASS Collaboration using a 160 GeV muon beam of both electric charges and a liquid hydrogen target. These measurements cover the kinematic domain with photon virtuality $Q^2 > 1$ (GeV/$c)^2$, $0.004 < x < 0.4$, $0.1 < y < 0.7$ and $0.2 < z < 0.85$, in accordance with the kinematic domain used in earlier published COMPASS multiplicity measurements with an isoscalar target. The calculation of radiative corrections was improved by using the Monte Carlo generator DJANGOH, which results in up to 12\% larger corrections in the low-$x$ region., Comment: 19 pages, 29 figures

Let $p$ be a rational prime, $q>1$ a power of $p$ and $F=\mathbb{F}_q(t)$. For an integer $d\geq 2$, let $D$ be a central division algebra over $F$ of dimension $d^2$ which is split at $\infty$ and has invariant $\mathrm{inv}_x(D)=1/d$ at any place $x$ of $F$ at which $D$ ramifies. Let $X^D$ be the Drinfeld--Stuhler variety, the coarse moduli scheme of the algebraic stack over $F$ classifying $\mathscr{D}$-elliptic sheaves. In this paper, we establish various arithmetic properties of $\mathscr{D}$-elliptic sheaves to give an explicit criterion for the non-existence of rational points of $X^D$ over a finite extension of $F$ of degree $d$. As an application, for $d=2$, we present explicit infinite families of quadratic extensions of $F$ over which the curve $X^D$ violates the Hasse principle., Comment: 49 pages. This article supersedes arXiv:1908.08678

Objective: To propose a mathematical model for applying Ionization Detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach: Our model provides for selection of preferred ID parameters (I_p) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I_p and macroscopic RTP. Selection of I_p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen Ip: N_k; k = 2,3,...,10, the number of ionizations in clusters of k or more per particle, and F_k; k = 1,2,...,10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results: The preferred I_p were N_4 and F_5 for aerobic cells, N_5 and F_7 for hypoxic cells. Signifcant differences were found in cell survival for beams having the same LET or the preferred N_k. Conversely, there was no signi?cant difference for F_5 for aerobic cells and F_7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I_p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I_p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Signifcance: Our model provides a practical means to select preferred I_p from radiobiological data, and to convert I_p to the macroscopic cluster dose for particle RTP., Comment: 31 pages, 12 figures

Current audio-visual representation learning can capture rough object categories (e.g., ``animals'' and ``instruments''), but it lacks the ability to recognize fine-grained details, such as specific categories like ``dogs'' and ``flutes'' within animals and instruments. To address this issue, we introduce DETECLAP, a method to enhance audio-visual representation learning with object information. Our key idea is to introduce an audio-visual label prediction loss to the existing Contrastive Audio-Visual Masked AutoEncoder to enhance its object awareness. To avoid costly manual annotations, we prepare object labels from both audio and visual inputs using state-of-the-art language-audio models and object detectors. We evaluate the method of audio-visual retrieval and classification using the VGGSound and AudioSet20K datasets. Our method achieves improvements in recall@10 of +1.5% and +1.2% for audio-to-visual and visual-to-audio retrieval, respectively, and an improvement in accuracy of +0.6% for audio-visual classification., Comment: under review

Written texts reflect an author's perspective, making the thorough analysis of literature a key research method in fields such as the humanities and social sciences. However, conventional text mining techniques like sentiment analysis and topic modeling are limited in their ability to capture the hierarchical narrative structures that reveal deeper argumentative patterns. To address this gap, we propose a method that leverages large language models (LLMs) to extract and organize these structures into a hierarchical framework. We validate this approach by analyzing public opinions on generative AI collected by Japan's Agency for Cultural Affairs, comparing the narratives of supporters and critics. Our analysis provides clearer visualization of the factors influencing divergent opinions on generative AI, offering deeper insights into the structures of agreement and disagreement.

The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large-scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address the simultaneous prediction of both link presence and weight. Therefore, we introduce a novel model that dynamically predicts link presence and weight by dividing the task into two sub-tasks: predicting remittance ratios and forecasting the total remittance volume. We use a self-attention mechanism that combines temporal-topological neighborhood features to predict remittance ratios and use a separate model to forecast the total remittance volume. We achieve the final prediction by multiplying the outputs of these models. We validated our approach using two real-world datasets: a cryptocurrency network and bank transfer network.

Hypergraphs provide a robust framework for modeling complex systems with higher-order interactions. However, analyzing them in dynamic settings presents significant computational challenges. To address this, we introduce a novel method that adapts the cardinality-based gadget to convert hypergraphs into strongly connected weighted directed graphs, complemented by a symmetrized combinatorial Laplacian. We demonstrate that the harmonic mean of the conductance and edge expansion of the original hypergraph can be upper-bounded by the conductance of the transformed directed graph, effectively preserving crucial cut information. Additionally, we analyze how the resulting Laplacian relates to that derived from the star expansion. Our approach was validated through change point detection experiments on both synthetic and real datasets, showing superior performance over clique and star expansions in maintaining spectral information in dynamic settings. Finally, we applied our method to analyze a dynamic legal hypergraph constructed from extensive United States court opinion data.

Pre-training video transformers generally requires a large amount of data, presenting significant challenges in terms of data collection costs and concerns related to privacy, licensing, and inherent biases. Synthesizing data is one of the promising ways to solve these issues, yet pre-training solely on synthetic data has its own challenges. In this paper, we introduce an effective self-supervised learning framework for videos that leverages readily available and less costly static images. Specifically, we define the Pseudo Motion Generator (PMG) module that recursively applies image transformations to generate pseudo-motion videos from images. These pseudo-motion videos are then leveraged in masked video modeling. Our approach is applicable to synthetic images as well, thus entirely freeing video pre-training from data collection costs and other concerns in real data. Through experiments in action recognition tasks, we demonstrate that this framework allows effective learning of spatio-temporal features through pseudo-motion videos, significantly improving over existing methods which also use static images and partially outperforming those using both real and synthetic videos. These results uncover fragments of what video transformers learn through masked video modeling., Comment: ECCV 2024

Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity. However, a notable limitation is the slow inference caused by the multi-step reverse diffusion. Therefore, we propose FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of iterations from dozens to one while inheriting the high VC performance of the multi-step diffusion-based VC. We obtain the model using adversarial conditional diffusion distillation (ACDD), leveraging the ability of generative adversarial networks and diffusion models while reconsidering the initial states in sampling. Evaluations of one-shot any-to-any VC demonstrate that FastVoiceGrad achieves VC performance superior to or comparable to that of previous multi-step diffusion-based VC while enhancing the inference speed. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/., Comment: Accepted to Interspeech 2024. Project page: https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery; during live surgery; and when writing operation notes. The Pituitary Vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision; and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic Vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://doi.org/10.5522/04/26531686.

Hydrocarbon dust is one of the dominant components of interstellar dust, which mainly consists of polycyclic aromatic hydrocarbons and aliphatic hydrocarbons. While hydrocarbon dust is thought to be processed in interstellar radiation fields or shocks, detailed processing mechanisms are not completely understood yet. We investigate the processing of hydrocarbon dust by analyzing the relation between the luminosities emitted by hydrocarbon dust and the total infrared luminosities $(L_{\mathrm{IR}})$ for 138 star-forming galaxies at redshift $z\ <\ 0.3$. Using near-infrared 2.5-5 $\mathrm{\mu m}$ spectra obtained with AKARI, we derived the luminosities of the aromatic hydrocarbon feature at 3.3 $\mathrm{\mu m}$ ($L_\mathrm{aromatic}$) and the aliphatic hydrocarbon feature at 3.4-3.6 $\mathrm{\mu m}$ ($L_\mathrm{aliphatic}$). We also derived $L_\mathrm{IR}$ and the radiation field strength by modeling the spectral energy distributions of the 138 galaxies with AKARI, WISE and IRAS photometry data. We find that galaxies with higher $L_\mathrm{IR}$ tend to exhibit lower $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios. Furthermore, we find that there is an anti-correlation between $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios and the radiation field strength, and also that the galaxies with low $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratios are dominated by merger galaxies. These results support that hydrocarbon dust is processed through photodissociation in strong radiation fields and/or shocks during merging processes of galaxies; the $L_\mathrm{aliphatic}/L_\mathrm{aromatic}$ ratio is likely to decrease in such harsh interstellar conditions since the aliphatic bonds are known to be chemically weaker than the aromatic bonds., Comment: 10 pages, 11 figures, accepted for publication in PASJ

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of >10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of >8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 45 pages, 24 figures

Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often rely on fully annotated datasets. However, for lymph node segmentation, these datasets are typically small due to the extensive time and expertise required to annotate the numerous lymph nodes in 3D CT scans. Weakly-supervised learning, which leverages incomplete or noisy annotations, has recently gained interest in the medical imaging community as a potential solution. Despite the variety of weakly-supervised techniques proposed, most have been validated only on private datasets or small publicly available datasets. To address this limitation, the Mediastinal Lymph Node Quantification (LNQ) challenge was organized in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). This challenge aimed to advance weakly-supervised segmentation methods by providing a new, partially annotated dataset and a robust evaluation framework. A total of 16 teams from 5 countries submitted predictions to the validation leaderboard, and 6 teams from 3 countries participated in the evaluation phase. The results highlighted both the potential and the current limitations of weakly-supervised approaches. On one hand, weakly-supervised approaches obtained relatively good performance with a median Dice score of $61.0\%$. On the other hand, top-ranked teams, with a median Dice score exceeding $70\%$, boosted their performance by leveraging smaller but fully annotated datasets to combine weak supervision and full supervision. This highlights both the promise of weakly-supervised methods and the ongoing need for high-quality, fully annotated data to achieve higher segmentation performance., Comment: Submitted to MELBA

In this study, we investigated Rydberg atom-based microwave electrometry using polarization spectroscopy in a room-temperature vapor cell. By measuring Autler-Townes splitting in the electromagnetically induced transparency (EIT) spectrum, we determined that the minimum measurable microwave electric field is approximately five times lower than conventional EIT techniques. The results are well reproduced by a full optical Bloch equation model, which takes into account all the hyperfine levels involved. Subsequently, the EIT setup was used to characterize a custom microwave cylindrical lens, which increases the field at the focus by a factor of three, decreasing the minimum measurable microwave electric field by the same amount. Our results indicate that the combination of polarization spectroscopy and a microwave lens may enhance microwave electrometry.

The concept of capacitive-sharing readout, described in detail in a previous study, offers the possibility for the development of high-performance three-coordinates (X-Y-U)-strip readout for Micro Pattern Gaseous Detectors (MPGDs) using simple standard PCB fabrication techniques. Capacitive-sharing (X-Y-U)-strip readout allows simultaneous measurement of the Cartesian coordinates x and y of the position of the particles together with a third coordinate u along the diagonal axis in a single readout PCB. This provides a powerful tool to address multiple-hit ambiguity and enable pattern recognition capabilities in moderate particle flux environment of collider or fixed target experiments in high energy physics HEP) and nuclear physics (NP). We present in this paper the performance of a 10 cm {\times} 10 cm triple-GEM detector with capacitive-sharing (X-Y-U)-strip anode readout. Spatial resolutions of the order of {\sigma}^res_x = 71.6 {\pm} 0.8 {\mu}m for X-strips, {\sigma}^res_y = 56.2 {\pm} 0.9 {\mu}m for Y-strips and {\sigma}^res_u = 75.2 {\pm} 0.9 {\mu}m for U-strips have been obtained at a beam test at Thomas Jefferson National Accelerator Facility (Jefferson Lab). Modifications of the readout design of future prototypes to improve the spatial resolution and challenges in scaling to large-area MPGDs are discussed.

The neutron-rich unbound fluorine isotope $^{30}$F$_{21}$ has been observed for the first time by measuring its neutron decay at the SAMURAI spectrometer (RIBF, RIKEN) in the quasi-free proton knockout reaction of $^{31}$Ne nuclei at 235 MeV/nucleon. The mass and thus one-neutron-separation energy of $^{30}$F has been determined to be $S_n = -472\pm 58 \mathrm{(stat.)} \pm 33 \mathrm{(sys.)}$ keV from the measurement of its invariant-mass spectrum. The absence of a sharp drop in $S_n$($^{30}$F) shows that the ``magic'' $N=20$ shell gap is not restored close to $^{28}$O, which is in agreement with our shell-model calculations that predict a near degeneracy between the neutron $d$ and $fp$ orbitals, with the $1p_{3/2}$ and $1p_{1/2}$ orbitals becoming more bound than the $0f_{7/2}$ one. This degeneracy and reordering of orbitals has two potential consequences: $^{28}$O behaves like a strongly superfluid nucleus with neutron pairs scattering across shells, and both $^{29,31}$F appear to be good two-neutron halo-nucleus candidates., Comment: 9 pages, 2 figures, accepted for publication in Physical Review Letters

Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kickstart the first step in this direction by introducing Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithm-specific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into 5 hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits, or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing., Comment: 14 pages, 8 figures

Real-world physical systems, like composite materials and porous media, exhibit complex heterogeneities and multiscale nature, posing significant computational challenges. Computational homogenization is useful for predicting macroscopic properties from the microscopic material constitution. It involves defining a representative volume element (RVE), solving governing equations, and evaluating its properties such as conductivity and elasticity. Despite its effectiveness, the approach can be computationally expensive. This study proposes a tensor-train (TT)-based asymptotic homogenization method to address these challenges. By deriving boundary value problems at the microscale and expressing them in the TT format, the proposed method estimates material properties efficiently. We demonstrate its validity and effectiveness through numerical experiments applying the proposed method for homogenization of thermal conductivity and elasticity in two- and three-dimensional materials, offering a promising solution for handling the multiscale nature of heterogeneous systems., Comment: 25 pages, 7 figures

We consider a periodic higher-order nonlinear Schr\"odinger equation with the nonlinearity $u^k \partial_x u$, where $k$ is a natural number. We prove the norm inflation in a subspace of the Sobolev space $H^s(\mathbb{T})$ for any $s \in \mathbb{R}$. In particular, the Cauchy problem is ill-posed in $H^s(\mathbb{T})$ for any $s \in \mathbb{R}$., Comment: 16 pages

We investigate magneto-optical trap loss spectroscopy of Rydberg excited $^{85}$Rb ($66\leq n \leq 68~S_{1/2}$) atoms, placed inside a tailored microwave cavity. The cavity frequency at 13.053 GHz is in resonance with the $67S_{1/2} \rightarrow 66P_{3/2}$ transition, inducing a ladder multiphoton microwave Rydberg absorption and emission. The observed spectra are modeled with an extended Jaynes-Cumming formalism that accounts for multiphoton absorption from and emission into the cavity, the loss from the trap due to Rydberg excitation, and cavity imperfection. We calculate the average photons in each spectral feature and find evidence for fractional photon emission into the cavity modes. The microwave cavity Rydberg spectroscopy in this work should inform the application and technology development of Rydberg based sensors and hybrid Rydberg atom-superconducting resonator quantum gates.

In this paper, we propose a method to probe a Stochastic Gravitational Wave Background (SGWB) with Josephson Junction Switching Current Detector (JJSCD). The sensitivity for the shear can reach $h\simeq 10^{-19}$ realistically, $10^{-21}$ in the near future, $10^{-24}$ optimistically. If we utilize the enhancement factor from the ratio of the frequency, it is possible to reach further below the Big Bang Nucleosynthesis (BBN) bound. It will be interesting if we can access the region to discover a footprint of new physics., Comment: 22 pages, 8 figures

NanoTerasu, a new 3 GeV synchrotron light source in Japan, began user operation in April 2024. It provides high-brilliance soft to tender X-rays and covers a wide spectral range from ultraviolet to tender X-rays. Its compact storage ring with a circumference of 349 m is based on a four-bend achromat lattice to provide two straight sections in each cell for insertion devices with a natural horizontal emittance of 1.14 nm rad, which is small enough for soft X-rays users. The NanoTerasu accelerator incorporates several innovative technologies, including a full-energy injector C-band linear accelerator with a length of 110 m, an in-vacuum off-axis injection system, a four-bend achromat with B-Q combined bending magnets, and a TM020 mode accelerating cavity with built-in higher-order-mode dampers in the storage ring. This paper presents the accelerator machine commissioning over a half-year period and our model-consistent ring optics correction. The first user operation with a stored beam current of 160 mA is also reported. We summarize the storage ring parameters obtained from the commissioning. This is helpful for estimating the effective optical properties of synchrotron radiation at NanoTerasu., Comment: 30 pages, 24 figures, submitted to the journal

In recent years, the field of artificial intelligence has been rapidly developing. Among them, OpenAI's ChatGPT excels at natural language processing tasks and can also generate source code. However, the generated code often has problems with consistency and program rules. Therefore, in this research, we developed a system that tests the code generated by ChatGPT, automatically corrects it if it is inappropriate, and presents the appropriate code to the user. This study aims to address the challenge of reducing the manual effort required for the human feedback and modification process for generated code. When we ran the system, we were able to automatically modify the code as intended.

The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV/c$^2$. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV/c$^2$ particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP--nucleon interaction cross-sections below $1\times10^{-42}$ cm$^2$ is achievable for WIMP masses above 800 MeV/c$^2$. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV/c$^2$., Comment: submitted to Nature Communications

As warehouses are emphasizing space utilization and the ability to handle multi-line orders, multi-tote storage and retrieval (MTSR) autonomous mobile robot systems, where robots directly retrieve totes from high shelves, are becoming increasingly popular. This paper presents a novel shared-token, multi-class, semi-open queueing network model to account for multi-line orders with general distribution forms in MTSR systems. The numerical results obtained from solving the SOQN model are validated against discrete-event simulation, with most key performance metrics demonstrating high accuracy. In our experimental setting, results indicate a 12.5% reduction in the minimum number of robots needed to satisfy a specific order arrival rate using the closest retrieval sequence policy compared with the random policy. Increasing the number of tote buffer positions on a robot can greatly reduce the number of robots required in the warehouse., Comment: 8 pages, 5 figures. This paper has been accepted for publication in IEEE 20th International Conference on Automation Science and Engineering (IEEE CASE 2024)

Partial differential equations (PDEs) are crucial for modeling various physical phenomena such as heat transfer, fluid flow, and electromagnetic waves. In computer-aided engineering (CAE), the ability to handle fine resolutions and large computational models is essential for improving product performance and reducing development costs. However, solving large-scale PDEs, particularly for systems with spatially varying material properties, poses significant computational challenges. In this paper, we propose a quantum algorithm for solving second-order linear PDEs of non-conservative systems with spatially varying parameters, using the linear combination of Hamiltonian simulation (LCHS) method. Our approach transforms those PDEs into ordinary differential equations represented by qubit operators, through spatial discretization using the finite difference method. Then, we provide an algorithm that efficiently constructs the operator corresponding to the spatially varying parameters of PDEs via a logic minimization technique, which reduces the number of terms and subsequently the circuit depth. We also develop a scalable method for realizing a quantum circuit for LCHS, using a tensor-network-based technique, specifically a matrix product state (MPS). We validate our method with applications to the acoustic equation with spatially varying parameters and the dissipative heat equation. Our approach includes a detailed recipe for constructing quantum circuits for PDEs, leveraging efficient encoding of spatially varying parameters of PDEs and scalable implementation of LCHS, which we believe marks a significant step towards advancing quantum computing's role in solving practical engineering problems., Comment: 21 pages, 5 figures

The calcium isotopes are an ideal system to investigate the evolution of shell structure and magic numbers. Although the properties of surface nucleons in calcium have been well studied, probing the structure of deeply bound nucleons remains a challenge. Here, we report on the first measurement of unbound states in $^{53}$Ca and $^{55}$Ca, populated from \ts{54,56}Ca($p,pn$) reactions at a beam energy of around 216 MeV/nucleon at the RIKEN Radioactive Isotopes Beam Factory. The resonance properties, partial cross sections, and momentum distributions of these unbound states were analyzed. Orbital angular momentum $l$ assignments were extracted from momentum distributions based on calculations using the distorted wave impulse approximation (DWIA) reaction model. The resonances at excitation energies of 5516(41)\,keV in $^{53}$Ca and 6000(250)\,keV in $^{55}$Ca indicate a significant $l$\, =\,3 component, providing the first experimental evidence for the $\nu 0f_{7/2}$ single-particle strength of unbound hole states in the neutron-rich Ca isotopes. The observed excitation energies and cross-sections point towards extremely localized and well separated strength distributions, with some fragmentation for the $\nu 0f_{7/2}$ orbital in $^{55}$Ca. These results are in good agreement with predictions from shell-model calculations using the effective GXPF1Bs interaction and \textit{ab initio} calculations and diverge markedly from the experimental distributions in the nickel isotones at $Z=28$., Comment: 13 pages, 7 figures

In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a small amount of high-quality parallel data. To investigate the effectiveness of our proposed approach, we conducted continual pre-training with a 3.8B-parameter model and parallel data across eight different formats. We evaluate these methods on thirteen test sets for Japanese-to-English and English-to-Japanese translation. The results demonstrate that when utilizing parallel data in continual pre-training, it is essential to alternate between source and target sentences. Additionally, we demonstrated that the translation accuracy improves only for translation directions where the order of source and target sentences aligns between continual pre-training data and inference. In addition, we demonstrate that the LLM-based translation model is more robust in translating spoken language and achieves higher accuracy with less training data compared to supervised encoder-decoder models. We also show that the highest accuracy is achieved when the data for continual pre-training consists of interleaved source and target sentences and when tags are added to the source sentences., Comment: IWSLT2024, 18 pages

The magnetic skyrmions generated in a centrosymmetric crystal were recently first discovered in Gd2PdSi3. In light of this, we observe the electronic structure by angle-resolved photoemission spectroscopy (ARPES) and unveil its direct relationship with the magnetism in this compound. The Fermi surface and band dispersions are demonstrated to have a good agreement with the density functional theory (DFT) calculations carried out with careful consideration of the crystal superstructure. Most importantly, we find that the three-dimensional Fermi surface has extended nesting which matches well the q-vector of the magnetic order detected by recent scattering measurements. The consistency we find among ARPES, DFT, and the scattering measurements suggests the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction involving itinerant electrons to be the formation mechanism of skyrmions in Gd2PdSi3.

Surgical skill assessment is paramount for ensuring patient safety and enhancing surgical outcomes. This study addresses the need for efficient and objective evaluation methods by introducing ZEAL (surgical skill assessment with Zero-shot surgical tool segmentation with a unifiEd foundAtion modeL). ZEAL uses segmentation masks of surgical instruments obtained through a unified foundation model for proficiency assessment. Through zero-shot inference with text prompts, ZEAL predicts segmentation masks, capturing essential features of both instruments and surroundings. Utilizing sparse convolutional neural networks and segmentation masks, ZEAL extracts feature vectors for foreground (instruments) and background. Long Short-Term Memory (LSTM) networks encode temporal dynamics, modeling sequential data and dependencies in surgical videos. Combining LSTM-encoded vectors, ZEAL produces a surgical skill score, offering an objective measure of proficiency. Comparative analysis with conventional methods using open datasets demonstrates ZEAL's superiority, affirming its potential in advancing surgical training and evaluation. This innovative approach to surgical skill assessment addresses challenges in traditional supervised learning techniques, paving the way for enhanced surgical care quality and patient outcomes.

In this work, we precisely measure the transition energies between the Rydberg state n$\text{D}_{5/2}$ to the nearby Rydberg states $(\e{n}+2)\text{P}_{3/2}$, $(\e{n}-2)\text{F}_{7/2}$ for the range $41\leq \e{n}\leq46$. This was done by carrying out microwave spectroscopy via Electromagnetically Induced Transparency (EIT) in a room temperature vapor reference cell of Rubidium, which is similar to the experimental approach followed by Li et al. [Results in Physics \textbf{29}, 104728 (2021)]. This range is interesting because there is a quasi F\"orster resonance between the atomic pair $43\text{D}_{5/2}+43\text{D}_{5/2}$ and $45\text{P}_{3/2}+41\text{F}_{7/2}$. We compared the obtained results with numerically calculated transition energies based on previously tabulated quantum defect numbers by various research groups using both hot and ultra-cold atomic samples. Our data are more consistent with measurements made within ultra-cold atomic systems [Phys. Rev. A \textbf{67}, 052502 (2003), Phys. Rev. A \textbf{74}, 054502 (2006)].

We study the congruence of bitangent lines of an irreducible surface in the 3-dimensional projective space in arbitrary characteristic, with special attention to quartic surfaces with rational double points and, in particular, Kummer quartic surfaces., Comment: Minor revision

Large Language Models (LLMs) are gaining popularity in the field of robotics. However, LLM-based robots are limited to simple, repetitive motions due to the poor integration between language models, robots, and the environment. This paper proposes a novel approach to enhance the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC). The approach involves using a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. The system also employs a YOLO-based perception algorithm, providing visual cues to the LLM, which aids in planning feasible motions within the specific environment. Additionally, an HRC method is proposed by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance. Real-world experiments have been conducted using the Toyota Human Support Robot for manipulation tasks. The outcomes indicate that tasks requiring complex trajectory planning and reasoning over environments can be efficiently accomplished through the incorporation of human demonstrations., Comment: IEEE Robotics and Automation Letters

The electronic structure of the topmost layer in Sr2RuO4 in the close vicinity of the Fermi level is investigated by angle-resolved photoemission spectroscopy (ARPES) with a 7-eV laser. We find that the spin-orbit coupling (SOC) predicted as 100 meV by the density functional theory (DFT) calculations is enormously enhanced in a real material up to 250 meV, even more than that of bulk state (200 meV), by the electron-correlation effect increased by the octahedral rotation in the crystal structure. This causes the formation of highly orbital-mixing small Fermi pockets and reasonably explains why the orbital-selective Mott transition (OSMT) is not realized in perovskite oxides with crystal distortion. Interestingly, Hund's metal feature allows the quasiparticle generation only near EF, restricting the spectral gap opening derived by band hybridization within an extremely small binding energy (< 10 meV). Furthermore, it causes coherent-incoherent crossover, making the Fermi pockets disappear at elevated temperatures. The anomalous Fermi pockets are characterized by the dichotomy of the orbital-isolating Hund's coupling and the orbital-mixing SOC, which is key to understanding the nature of Sr2RuO4.

In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories. However, due to localization errors/uncertainties, trajectory deconfliction can fail even if trajectories are perfectly shared between agents. To address this issue, we first present PARM and PARM*, perception-aware, decentralized, asynchronous multiagent trajectory planners that enable a team of agents to navigate uncertain environments while deconflicting trajectories and avoiding obstacles using perception information. PARM* differs from PARM as it is less conservative, using more computation to find closer-to-optimal solutions. While these methods achieve state-of-the-art performance, they suffer from high computational costs as they need to solve large optimization problems onboard, making it difficult for agents to replan at high rates. To overcome this challenge, we present our second key contribution, PRIMER, a learning-based planner trained with imitation learning (IL) using PARM* as the expert demonstrator. PRIMER leverages the low computational requirements at deployment of neural networks and achieves a computation speed up to 5500 times faster than optimization-based approaches., Comment: 7 pages, 3 figures

Analogous to the charged electron-electron pair condensation in superconductors, an excitonic insulator (EI) represents Fermi surface instability due to spontaneous formation and condensation of charge-neutral electron-hole pair (exciton). Unlike in superconductors, however, the charge-neutral nature of exciton makes probing emergent EI phase via macroscopic physical properties generally difficult. Here, we propose a van der Waals coupled antiferromagnetic semiconductor GdGaI (GGI) as a new material category leading to emergent multi-q magnet intertwined with spontaneous exciton formation/condensation. Before excitonic band hybridization, a simple picture for the parent electronic state consists of electron (Gd-derived 5d) and hole (Ga-derived 4p) delocalized bands, together with Gd-derived 4f localized antiferromagnets with S = 7/2 classical nature. Through intra Gd atom 4f-5d Hund's coupling, a notable finding is the emergent minimum length scale (2a) Skyrmion-like spin texture resulting from spontaneous condensation/formation of spin-polarized exciton with BCS-BEC crossover phenomenology. This discovered platform is promising for realizing valuable quantum matter on the nanoscale; our finding will provide significant insight into designing the atomic scale topological magnetism out of itinerant systems., Comment: 23 pages, 10 figures

We studied the optical variability of 241 BL Lacs and 83 flat-spectrum radio quasars (FSRQ) from the 4LAC catalog using data from the Tomo-e Gozen Northern Sky Transient Survey, with $\sim$ 50 epochs per blazar on average. We excluded blazars whose optical variability may be underestimated due to the influence of their host galaxy, based on their optical luminosity ($L_O$). FSRQs with $\gamma$-ray photon index greater than 2.6 exhibit very low optical variability, and their distribution of standard deviation of repeated photometry is significantly different from that of the other FSRQs (KS test P value equal to $5 \times 10^{-6}$ ). Among a sample of blazars at any particular cosmological epoch, those with lower $\gamma$-ray luminosity ($L_\gamma$) tend to have lower optical variability, and those FSRQs with $\gamma$-ray photon index greater than 2.6 tend to have low $L_\gamma$. We also measured the structure function of optical variability and found that the amplitude of the structure function for FSRQs is higher than previously measured and higher than that of BL Lacs at multiple time lags. Additionally, the amplitude of the structure function of FSRQs with high $\gamma$-ray photon index is significantly lower than that of FSRQs with low $\gamma$-ray photon index. The structure function of FSRQs of high $\gamma$-ray photon index shows a characteristic timescale of more than 10 days, which may be the variability timescale of the accretion disk. In summary, we infer that the optical component of FSRQs with high $\gamma$-ray photon index may be dominated by the accretion disk., Comment: 22 pages, 13 figures