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Deep learning-based image segmentation has allowed for the fully automated, accurate, and rapid analysis of musculoskeletal (MSK) structures from medical images. However, current approaches were either applied only to 2D cross-sectional images, addressed few structures, or were validated on small datasets, which limit the application in large-scale databases. This study aimed to validate an improved deep learning model for volumetric MSK segmentation of the hip and thigh with uncertainty estimation from clinical computed tomography (CT) images. Databases of CT images from multiple manufacturers/scanners, disease status, and patient positioning were used. The segmentation accuracy, and accuracy in estimating the structures volume and density, i.e., mean HU, were evaluated. An approach for segmentation failure detection based on predictive uncertainty was also investigated. The model has shown an overall improvement with respect to all segmentation accuracy and structure volume/density evaluation metrics. The predictive uncertainty yielded large areas under the receiver operating characteristic (AUROC) curves (AUROCs>=.95) in detecting inaccurate and failed segmentations. The high segmentation and muscle volume/density estimation accuracy, along with the high accuracy in failure detection based on the predictive uncertainty, exhibited the model's reliability for analyzing individual MSK structures in large-scale CT databases., Comment: 29 pages, 7+10supp figures, 8 tables

At millimeter wavelengths, the atmospheric emission is circularly polarized owing to the Zeeman splitting of molecular oxygen by the Earth's magnetic field. We report a measurement of the signal in the 150 GHz band using 3 years of observations of the \textsc{Polarbear} project. Although the detectors are sensitive to linear polarization, we can measure the circular polarization because a continuously rotating half-wave plate in the optics converts part of circular polarization into linear polarization. The atmospheric circular polarization signal appears as a modulated signal at twice the frequency of rotation of the half-wave plate. We reconstruct the azimuthal gradient of the circular polarization signal and measure the dependencies on the scanning azimuth and the detector bandpass. We compare the signal with a simulation based on atmospheric emission theory, the detector bandpass, and the half-wave plate leakage spectrum model. We find the ratio of the observed azimuthal slope to the simulated slope is $0.92 \pm 0.01\rm{(stat)} \pm 0.07\rm{(sys)}$, which demonstrates that our measurement is consistent with theoretical prediction. This result validates our understanding of the instrument and reinforces the feasibility of measuring the circular polarization using the imperfection of the half-wave plate. Quantifying atmospheric circular polarization is the first step toward conducting a search for cosmological circular polarization at these wavelengths., Comment: 22 pages, 11 figures

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chandra X-ray Observatory in 2000, 2009, and 2019 were utilized. The proposed multiepoch MLE was qualitatively and quantitatively demonstrated to provide accurate estimates of various motions, including shock waves and faint features, across all regions. To investigate asymmetric structures, such as singular components that deviate from the direction of expansion, the MLE method was extended to combine multiple computational domains and classify kinematic properties using the $k$-means algorithm. This approach allowed for the mapping of different physical states onto the image, and one classified component was suggested to interact with circumstellar material by comparison with infrared observations from the James Webb Space Telescope. Thus, this technique will help quantify the dynamics of SNRs and discover their unique evolution., Comment: Accepted for publication in ApJ. The paper is 20 pages long with 13 figures. Animations for Fig. 8 and Fig. 9, which provide a visual illustration of the results, are available in ApJ

The boolean satisfiability (SAT) problem asks whether there exists an assignment of boolean values to the variables of an arbitrary boolean formula making the formula evaluate to True. It is well-known that all NP-problems can be coded as SAT problems and therefore SAT is important both practically and theoretically. From both of these perspectives, better understanding the patterns and structure implicit in SAT data is of significant value. In this paper, we describe several advances that we believe will help open the door to such understanding: we introduce hardware accelerated algorithms for fast SAT problem generation, a geometric SAT encoding that enables the use of transformer architectures typically applied to vision tasks, and a simple yet effective technique we term head slicing for reducing sequence length representation inside transformer architectures. These advances allow us to scale our approach to SAT problems with thousands of variables and tens of thousands of clauses. We validate our architecture, termed Satisfiability Transformer (SaT), on the SAT prediction task with data from the SAT Competition (SATComp) 2022 problem sets. Prior related work either leveraged a pure machine learning approach, but could not handle SATComp-sized problems, or was hybrid in the sense of integrating a machine learning component in a standard SAT solving tool. Our pure machine learning approach achieves prediction accuracies comparable to recent work, but on problems that are an order of magnitude larger than previously demonstrated. A fundamental aspect of our work concerns the very nature of SAT data and its suitability for training machine learning models. We both describe experimental results that probe the landscape of where SAT data can be successfully used for learning and position these results within the broader context of complexity and learning., Comment: 22 pages

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.

In-context learning (ICL) has emerged as a powerful capability for large language models (LLMs) to adapt to downstream tasks by leveraging a few (demonstration) examples. Despite its effectiveness, the mechanism behind ICL remains underexplored. To better understand how ICL integrates the examples with the knowledge learned by the LLM during pre-training (i.e., pre-training knowledge) and how the examples impact ICL, this paper conducts a theoretical study in binary classification tasks. In particular, we introduce a probabilistic model extending from the Gaussian mixture model to exactly quantify the impact of pre-training knowledge, label frequency, and label noise on the prediction accuracy. Based on our analysis, when the pre-training knowledge contradicts the knowledge in the examples, whether ICL prediction relies more on the pre-training knowledge or the examples depends on the number of examples. In addition, the label frequency and label noise of the examples both affect the accuracy of the ICL prediction, where the minor class has a lower accuracy, and how the label noise impacts the accuracy is determined by the specific noise level of the two classes. Extensive simulations are conducted to verify the correctness of the theoretical results, and real-data experiments also align with the theoretical insights. Our work reveals the role of pre-training knowledge and examples in ICL, offering a deeper understanding of LLMs' behaviors in classification tasks.

Forward-backward stochastic differential equations (FBSDEs) have been generalized by introducing jumps for better capturing random phenomena, while the resulting FBSDEs are far more intricate than the standard one from every perspective. In this work, we establish a forward scheme for potentially high-dimensional FBSDEs with jumps, taking a similar approach to [Bender and Denk, 117 (2007), Stoch. Process. Their Appl., pp.1793-1812], with the aid of machine learning techniques for implementation. The developed forward scheme is built upon a recursive representation that decouples random jumps at every step and converges exponentially fast to the original FBSDE with jumps, often requiring only a few iterations to achieve sufficient accuracy, along with the error bound vanishing for lower jump intensities. The established framework also holds novelty in its neural network-based implementation of a wide class of forward schemes for FBSDEs, notably whether with or without jumps. We provide an extensive collection of numerical results, showcasing the effectiveness of the proposed recursion and its corresponding forward scheme in approximating high-dimensional FBSDEs with jumps (up to 100-dimension) without directly handling the random jumps., Comment: 26 pages, 9 figures, 7 tables

We present a measurement of the branching fraction and time-dependent charge-parity ($CP$) decay-rate asymmetries in $B^0 \to J/\psi \pi^0$ decays. The data sample was collected with the Belle~II detector at the SuperKEKB asymmetric $e^+e^-$ collider in 2019-2022 and contains $(387\pm 6)\times 10^6$ $B\overline{B}$ meson pairs from $\Upsilon(4S)$ decays. We reconstruct $392\pm 24$ signal decays and fit the $CP$ parameters from the distribution of the proper-decay-time difference of the two $B$ mesons. We measure the branching fraction to be $B(B^0 \to J/\psi \pi^0)=(2.02 \pm 0.12 \pm 0.10)\times 10^{-5}$ and the direct and mixing-induced $CP$ asymmetries to be $C_{CP}=0.13 \pm 0.12 \pm 0.03$ and $S_{CP}=-0.88 \pm 0.17 \pm 0.03$, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced $CP$ violation with a significance of $5.0$ standard deviations for the first time in this mode.

JASMINE is a Japanese planned space mission that aims to reveal the formation history of our Galaxy and discover habitable exoEarths. For these objectives, the JASMINE satellite performs high-precision astrometric observations of the Galactic bulge and high-precision transit monitoring of M-dwarfs in the near-infrared (1.0-1.6 microns in wavelength). For feasibility studies, we develop an image simulation software named JASMINE-imagesim, which produces realistic observation images. This software takes into account various factors such as the optical point spread function (PSF), telescope jitter caused by the satellite's attitude control error (ACE), detector flat patterns, exposure timing differences between detector pixels, and various noise factors. As an example, we report a simulation for the feasibility study of astrometric observations using JASMINE-imagesim. The simulation confirms that the required position measurement accuracy of 4 mas for a single exposure of 12.5-mag objects is achievable if the telescope pointing jitter uniformly dilutes the PSF across all stars in the field of view. On the other hand, the simulation also demonstrates that the combination of realistic pointing jitter and exposure timing differences in the detector can significantly degrade accuracy and prevent achieving the requirement. This means that certain countermeasures against this issue must be developed. This result implies that this kind of simulation is important for mission planning and advanced developments to realize more realistic simulations help us to identify critical issues and also devise effective solutions., Comment: 13 pages, 7 figures, 1 table

To embed structured knowledge within labels into feature representations, prior work (Zeng et al., 2022) proposed to use the Cophenetic Correlation Coefficient (CPCC) as a regularizer during supervised learning. This regularizer calculates pairwise Euclidean distances of class means and aligns them with the corresponding shortest path distances derived from the label hierarchy tree. However, class means may not be good representatives of the class conditional distributions, especially when they are multi-mode in nature. To address this limitation, under the CPCC framework, we propose to use the Earth Mover's Distance (EMD) to measure the pairwise distances among classes in the feature space. We show that our exact EMD method generalizes previous work, and recovers the existing algorithm when class-conditional distributions are Gaussian in the feature space. To further improve the computational efficiency of our method, we introduce the Optimal Transport-CPCC family by exploring four EMD approximation variants. Our most efficient OT-CPCC variant runs in linear time in the size of the dataset, while maintaining competitive performance across datasets and tasks.

In this paper, we generalize the deformations driven by the stress-energy tensor $T$ and investigate their relation to the flow equation for the background metric at the classical level. For a deformation operator $\mathcal O$ as a polynomial function of the stress-energy tensor, we develop a formalism that relates a deformed action to a flow equation for the metric in arbitrary spacetime dimensions. It is shown that in the $T\bar{T}$ deformation and the $\mathcal O(T)=\text{tr}[\textbf{T}]^m$ deformation, the flow equations for the metric allow us to directly obtain exact solutions in closed forms. We also demonstrate the perturbative approach to find the same results. As several applications of the $\mathcal O(T)=\text{tr}[\textbf{T}]^m$ deformation, we discuss the relation between the deformations and gravitational models. Besides, the deformations of Lagrangians for scalar field theories are performed., Comment: 23 pages, no figure, references added and typos corrected

Radio-quiet Seyfert galaxies have been detected in GeV gamma-rays by the Fermi Large Area Telescope (LAT), but the origin of much of this emission is unclear. We consider the nearby example, the Seyfert galaxy GRS 1734-292, which exhibits weak starburst and jet activities that are insufficient to explain the observed gamma-ray flux. With the first detailed multi-wavelength study of this source, we demonstrate that an active galactic nucleus (AGN) disk wind can account for its gamma-ray emission. Using a lepto-hadronic emission model based on a shocked ambient medium and a shocked wind region created by an AGN accretion disk wind, we identify two viable scenarios that are consistent with the Fermi-LAT data and multi-wavelength observations: a hadronic pp-dominated scenario and a leptonic external Compton-dominated scenario. Both of these show that future observations with the Cherenkov Telescope Array (CTA) and the Southern Wide-field Gamma-ray Observatory (SWGO) could detect TeV emission from a disk wind in GRS 1734-292. Such a detection would substantially improve our understanding of cosmic ray acceleration efficiency in AGN disk wind systems, and would establish radio-quiet Seyfert galaxies as cosmic ray accelerators capable of reaching ultra-high energies., Comment: 8 pages, 2 figures, 1 table

This study focuses on linear partial differential equation (PDE) systems that arise in topology optimization where the thickness of a structure is constrained. The thickness derived from the PDE is a fictitious one, and the key challenge of this work is to verify its equivalence to the intuitive, geometrically defined thickness. The main difficulty lies in that while intuitive thickness is determined solely by the shape, the thickness defined by the PDE depends not only on the shape but also on the entire domain and the diffusion coefficients used in solving the PDE. In this paper, we demonstrate that the thickness of an infinite, straight film as a simple shape with constant thickness is equivalent within a general domain. The proof involves constructing a reference solution within a special domain and evaluating the difference using the maximum (modulus) principle and an interior $H^1$ estimate. Additionally, we provide an estimate of the dependence of thickness on the diffusion coefficient.

We study $d$-wave superconductivity(SC) in the Hubbard model on the isotropic triangular lattice described by the hopping parameter $t$ and on-site Coulomb repulsion $U$ at zero temperature and half-filling using the variational cluster approximation. We found that the $d_{xy}$ SC is the ground state below the Mott insulator phase $U/t \lesssim 6$, and the energy of chiral $d+id$ SC is slightly higher than the $d_{xy}$ SC. The energy difference between the normal and $d_{xy}$ states is about $0.02t \sim 0.06t$ for $U/t \simeq 5$. This result is semi-quantitatively consistent with the SC transition temperature $T_K=3.9$ K of $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$, where $t$ is estimated to be about $0.06$ eV, and the predicted pairing symmetry $d_{xy}$ agrees with the STM observations. The energy difference between the $d+id$ and $d_{xy}$ is about $0.01t\sim 0.03t$ for $U/t \simeq 5$ so the transition from $d+id$ to $d_{xy}$, or some effects of $d+id$ in $d_{xy}$ phase may be observed in experiments for $\kappa$-(BEDT-TTF)$_2$Cu$_2$(CN)$_3$., Comment: 6 pages, 5 figures

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd$_3$Ga$_5$O$_{12}$. The electron paramagnetic resonance of Gd$^{3+}$ ions is tuned by a magnetic field into resonance with a Fabry--P\'erot cavity mode, resulting in the formation of spin--photon hybrid states, or Zeeman polaritons. We observe that the light--matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin--boson system is more compatible with the Dicke model and has advantages over boson--boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light--matter hybrids.

We perform the first search for $C\!P$ violation in ${D_{(s)}^{+}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}}$ decays. We use a combined data set from the Belle and Belle II experiments, which study $e^+e^-$ collisions at center-of-mass energies at or near the $\Upsilon(4S)$ resonance. We use 980 fb$^{-1}$ of data from Belle and 428 fb$^{-1}$ of data from Belle~II. We measure six $C\!P$-violating asymmetries that are based on triple products and quadruple products of the momenta of final-state particles, and also the particles' helicity angles. We obtain a precision at the level of 0.5% for $D^+\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays, and better than 0.3% for $D^+_{s}\to{}K_{S}^{0}K^{-}\pi^{+}\pi^{+}$ decays. No evidence of $C\!P$ violation is found. Our results for the triple-product asymmetries are the most precise to date for singly-Cabibbo-suppressed $D^+$ decays. Our results for the other asymmetries are the first such measurements performed for charm decays., Comment: 21 pages, 10 figures

Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to bridge this divide by integrating images and point clouds on a unified transformer model. This approach integrates the modality-specific properties of images and point clouds and achieves fundamental downstream tasks in image and 3D object recognition on a unified transformer model by learning visual-geometric representations. In this work, we introduce Formula-Supervised Visual-Geometric Pre-training (FSVGP), a novel synthetic pre-training method that automatically generates aligned synthetic images and point clouds from mathematical formulas. Through cross-modality supervision, we enable supervised pre-training between visual and geometric modalities. FSVGP also reduces reliance on real data collection, cross-modality alignment, and human annotation. Our experimental results show that FSVGP pre-trains more effectively than VisualAtom and PC-FractalDB across six tasks: image and 3D object classification, detection, and segmentation. These achievements demonstrate FSVGP's superior generalization in image and 3D object recognition and underscore the potential of synthetic pre-training in visual-geometric representation learning. Our project website is available at https://ryosuke-yamada.github.io/fdsl-fsvgp/., Comment: Accepted to ECCV2024

We report on the realization of extremely high-density ($>10^{14}$cm$^{-2}$) 2D electron gases in N-polar AlGaN/GaN heterostructures grown on sapphire substrates. By introduction the GaN/AlN superlattice (SL) back barrier between GaN buffer layer and AlGaN barrier layer we observed a giant enhancement of the 2D electron gas density at the GaN/Al$_{0.3}$Ga$_{0.7}$N interface from $3\times10^{13}$cm$^{-2}$ (without SL) to $1.4\times10^{14}$cm$^{-2}$ (with SL back barrier) that is only an order of magnitude below the intrinsic crystal limit of $\approx10^{15}$cm$^{-2}$. We found that the changes of 2D electron gas density with SL correlated well with changes of the wafer warp parameter which suggested the strain-induced 2D electron gas density. Simultaneously, the room temperature electron mobility was as high as 169 cm$^2$/Vs, which with the electron density of $1.4\times10^{14}$cm$^{-2}$ gives a very low sheet resistance of 264 $\Omega$/sq (one of the lowest reported so far for the N-polar 2D electron gas channel). The finding of the very high-density 2D electron gas with very low sheet resistance in N-polar III-nitride heterostructures can push the power performance N-polar GaN HEMTs into a new level which is unreachable by the conventional N-polar GaN HEMTs.

Biometric recognition systems, known for their convenience, are widely adopted across various fields. However, their security faces risks depending on the authentication algorithm and deployment environment. Current risk assessment methods faces significant challenges in incorporating the crucial factor of attacker's motivation, leading to incomplete evaluations. This paper presents a novel human-centered risk evaluation framework using conjoint analysis to quantify the impact of risk factors, such as surveillance cameras, on attacker's motivation. Our framework calculates risk values incorporating the False Acceptance Rate (FAR) and attack probability, allowing comprehensive comparisons across use cases. A survey of 600 Japanese participants demonstrates our method's effectiveness, showing how security measures influence attacker's motivation. This approach helps decision-makers customize biometric systems to enhance security while maintaining usability.

The equilibrium selection problem in the generalized Nash equilibrium problem (GNEP) has recently been studied as an optimization problem, defined over the set of all variational equilibria achievable first through a non-cooperative game among players. However, to make such a selection fairly for all players, we have to rely on an unrealistic assumption, that is, the availability of a reliable center not possible to cause any bias for all players. In this paper, we propose a new equilibrium selection achievable by solving a further GNEP, named the hierarchical Nash equilibrium problem (HNEP), within only the players. The HNEP covers existing optimization-based equilibrium selections as its simplest cases, while the general style of the HNEP can ensure a fair equilibrium selection without assuming any trusted center or randomness. We also propose an iterative algorithm for the HNEP as an application of the hybrid steepest descent method to a variational inequality newly defined over the fixed point set of a quasi-nonexpansive operator. Numerical experiments show the effectiveness of the proposed equilibrium selection via the HNEP.

We propose a proximal variable smoothing algorithm for nonsmooth optimization problem with sum of three functions involving weakly convex composite function. The proposed algorithm is designed as a time-varying forward-backward splitting algorithm with two steps: (i) a time-varying forward step with the gradient of a smoothed surrogate function, designed with the Moreau envelope, of the sum of two functions; (ii) the backward step with a proximity operator of the remaining function. For the proposed algorithm, we present a convergence analysis in terms of a stationary point by using a newly smoothed surrogate stationarity measure. As an application of the target problem, we also present a formulation of multiple-input-multiple-output (MIMO) signal detection with phase-shift keying. Numerical experiments demonstrate the efficacy of the proposed formulation and algorithm., Comment: 5 pages, 3 figures

Recent advances in robotic learning in simulation have shown impressive results in accelerating learning complex manipulation skills. However, the sim-to-real gap, caused by discrepancies between simulation and reality, poses significant challenges for the effective deployment of autonomous surgical systems. We propose a novel approach utilizing image translation models to mitigate domain mismatches and facilitate efficient robot skill learning in a simulated environment. Our method involves the use of contrastive unpaired Image-to-image translation, allowing for the acquisition of embedded representations from these transformed images. Subsequently, these embeddings are used to improve the efficiency of training surgical manipulation models. We conducted experiments to evaluate the performance of our approach, demonstrating that it significantly enhances task success rates and reduces the steps required for task completion compared to traditional methods. The results indicate that our proposed system effectively bridges the sim-to-real gap, providing a robust framework for advancing the autonomy of surgical robots in minimally invasive procedures., Comment: Accepted at 2024 IEEE International Symposium on Micro-NanoMechatronics and Human Science

Exceptional points (EPs) are spectral singularities in non-Hermitian systems where eigenvalues and their corresponding eigenstates coalesce simultaneously. In this study, we calculate scattering poles in an open spherical solid and propose a depth-first search-based method to identify EPs. Using the proposed method, we numerically identify multiple EPs in a parameter space and confirm the simultaneous degeneracy of scattering poles through numerical experiments. The proposed method and findings enable the exploration of applications in practical three-dimension models., Comment: 28 pages, 9 figures

Since the invention of the laser nonlinear effects such as filamentation, Rabi-cycling and collective emission have been explored in the optical regime leading to a wide range of scientific and industrial applications. X-ray free electron lasers (XFELs) have led to the extension of many optical techniques to X-rays for their advantages of angstrom scale spatial resolution and elemental specificity. One such example is XFEL driven population inversion of 1s core hole states resulting in inner-shell K${\alpha}$ (2p to 1s) X-ray lasing in elements ranging from neon to copper, which has been utilized for nonlinear spectroscopy and development of next generation X-ray laser sources. Here we show that strong lasing effects, similar to those observed in the optical regime, can occur at 1.5 to 2.1 angstrom wavelengths during high intensity (> ${10^{19}}$ W/cm${^{2}}$) XFEL driven inner-shell lasing and superfluorescence of copper and manganese. Depending on the temporal substructure of the XFEL pump pulses, the resulting inner-shell X-ray laser pulses can exhibit strong spatial inhomogeneities as well as spectral inhomogeneities and broadening. Through 3D Maxwell Bloch theory we show that the observed spatial inhomogeneities result from X-ray filamentation, and that the spectral broadening is driven by Rabi cycling with sub-femtosecond periods. These findings indicate that we have generated Angstrom-wavelength x-ray pulses (containing ${10^{6}}$ - ${10^{8}}$ photons) in the strong lasing regime, some of them with pulse lengths of less than 100 attoseconds.

3D point clouds enhanced the robot's ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (\eg HyperCD ), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses ({\em weighted CD}) that requires no parameter tuning. To this end, we propose a search scheme, {\em Loss Distillation via Gradient Matching}, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely {\em Landau CD}, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. {\it Our demo code is available at \url{https://github.com/Zhang-VISLab/IROS2024-LossDistillationWeightedCD}.}, Comment: 10 pages, 7 figures, 7 tables, this paper was accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

In modern materials science, effective and high-volume data management across leading-edge experimental facilities and world-class supercomputers is indispensable for cutting-edge research. Such facilities and supercomputers are typically utilized by a wide range of researchers across different fields and organizations in academia and industry. However, existing integrated systems that handle data from these resources have primarily focused just on smaller-scale cross-institutional or single-domain operations. As a result, they often lack the scalability, efficiency, agility, and interdisciplinarity, needed for handling substantial volumes of data from various researchers. In this paper, we introduce ARIM-mdx data system, a nationwide data platform for materials science in Japan. The platform involves 8 universities and institutes all over Japan through the governmental materials science project. Currently in its trial phase, the ARIM-mdx data system is utilized by over 800 researchers from around 140 organizations in academia and industry, being intended to gradually expand its reach. The system employs a hybrid architecture, combining a peta-scale dedicated storage system for security and stability with a high-performance academic cloud for efficiency and scalability. Through direct network connections between them, the system achieves 4.7x latency reduction compared to a conventional approach, resulting in near real-time interactive data analysis. It also utilizes specialized IoT devices for secure data transfer from equipment computers and connects to multiple supercomputers via an academic ultra-fast network, achieving 4x faster data transfer compared to the public Internet. The ARIM-mdx data system, as a pioneering nationwide data platform, has the potential to contribute to the creation of new research communities and accelerates innovations., Comment: Under Review

We developed a machine learning model using LightGBM, one of the most popular gradient boosting decision tree methods these days, to predict the Eddington tensor, or the second-order angular moment, for neutrino radiation transport in core-collapse supernova simulations. We use not only zero-th and first moments as in ordinary closure relations but also information on the background matter configuration extensively. For training the model we utilize some post-bounce snapshots from one of our previous Boltzmann radiation-hydrodynamics simulations. The Eddington tensor as well as the zero-th and first angular moments are calculated from the neutrino distribution function obtained in the simulation. LightGBM is light indeed and its high efficiency in training enables us to feed a large number of features and figure out which features are more important than others. We report in this paper the results of the training and validation as well as the generalization of our model: it can reproduce the Eddington factor better in general than the M1-closure relation, one of the most commonly employed algebraic closure relations at present; the generalization performance is also much improved from our previous model based on the deep neural network., Comment: Submitted to ApJ

Nonlinear electronic excitation in laser-irradiated silicon at finite electron temperatures is numerically investigated by first-principles calculations based on the time-dependent density functional theory. In bulk silicon at finite temperatures under near-infrared laser irradiation, we found that the absorbed energy is saturated when using a certain laser intensity even with a few-cycle pulse. Although one-photon processes of conduction-to-conduction and valence-to-valence transitions are dominant at such a laser intensity, the Pauli blocking inhibits further one-photon transition. With higher intensities, multi-photon excitation across the bandgap overwhelms the one-photon excitation and the saturable absorption disappears. At the surface of finite-temperature silicon, the Pauli blocking is suppressed by the symmetry breaking and the absorbed energy is relatively enhanced from the energy of the saturable absorption in the bulk region., Comment: 15 pages, 12 figures

In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, {thereby addressing the question of ``How important is SR training for SR models?''}. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models., Comment: Accepted to ECCV2024

We propose a straightforward and effective method for discretizing multi-dimensional diffusion processes as an extension of Milstein scheme. The new scheme is explicitly given and can be simulated using Gaussian variates, requiring the same number of random variables as Euler-Maruyama (EM) scheme. We show that the proposed scheme has a weak convergence rate of one, which is consistent with other classical schemes like EM/Milstein schemes but involves fewer leading-order error terms. Due to the reduction of the error terms, the proposed scheme is expected to provide a more accurate estimation than alternative first-order schemes. We demonstrate that the weak error of the new scheme is effectively reduced compared with EM/Milstein schemes when the diffusion coefficients involve a small parameter. We conduct simulation studies on Asian option pricing in finance to showcase that our proposed scheme significantly outperforms EM/Milstein schemes, while interestingly, we find no differences in the performance between EM and Milstein schemes.

We demonstrate the existence of smooth three-dimensional vector fields where the cross product between the vector field and its curl is balanced by the gradient of a smooth function, with toroidal level sets that are not invariant under continuous Euclidean isometries. This finding indicates the existence of steady compressible Euler flows, either influenced by an external potential energy or maintained by a density source in the continuity equation, that are foliated by asymmetric nested toroidal surfaces. Our analysis suggests that the primary obstacle in resolving Grad's conjecture regarding the existence of nontrivial magnetohydrodynamic equilibria arises from the incompressibility constraint imposed on the magnetic field., Comment: 21 pages, 2 figures

Nonreciprocal transport, characterized by its direction-selective nature, holds significant potential for applications in various devices. In this study, we investigate nonreciprocal heat transport in Majorana systems, specifically focusing on the Kitaev chiral spin liquid under external magnetic fields. Our theoretical examination focuses on effects of open boundaries in which the Majorana edge modes exist, and the inversion symmetry is broken, which leads to the Dzyaloshinskii-Moriya interaction (DMI). Through perturbation theory, we demonstrate that DMI induces asymmetric hopping, resulting in the asymmetry of the Majorana band. The results of nonreciprocal heat currents are presented for various directions of external magnetic fields, and we discuss the relation between the current and the field-directions. The potential exists to manipulate both of the directions and magnitude of the non-dissipative current by varying external magnetic fields and apply to non-dissipative heat transfer devices., Comment: 8 pages, 9 figures

We discuss the effects of electric fields along compact directions within (supersymmetric) gauge theory in $\mathbb{R}^{1,3}\times{\mathbb T}^2 (\times{\mathbb T}^2\times{\mathbb T}^2)$. The electric field along compact directions is equivalent to time-dependent homogeneous configuration of Wilson line moduli, which would be relevant to physics in the early universe. In particular, we consider models with and without background magnetic fluxes, which lead to completely different effects of the electric field due to the difference in the Kaluza-Klein (KK) level structure. We show that, in the case without magnetic fluxes, the deceleration of KK momenta may cause non-perturbative KK particle production dubbed as the KK Schwinger effect, whereas in the case with magnetic flux such KK particle production does not take place but flavor structure of low energy effective theory may be affected., Comment: 24+20 pages, 6 figures, v2:typos corrected, comments added

We report the discovery of a hyperluminous type 1 quasar (eFEDS J082826.9-013911; eFEDSJ0828-0139) at $z_{\rm spec}$ = 1.622 with a super-Eddington ratio ($\lambda_{\rm Edd}$). We perform the optical spectroscopic observations with KOOLS-IFU on the Seimei Telescope. The black hole mass ($M_{\rm BH}$) based on the single-epoch method with MgII $\lambda$2798 is estimated to be $M_{\rm BH} = (6.2 \pm 1.2) \times 10^8$ $M_{\odot}$. To measure the precise infrared luminosity ($L_{\rm IR}$), we obtain submillimeter data taken by SCUBA-2 on JCMT and conduct the spectral energy distribution analysis with X-ray to submillimeter data. We find that $L_{\rm IR}$ of eFEDSJ0828-0139 is $L_{\rm IR} = (6.8 \pm 1.8) \times 10^{13}$ $L_{\odot}$, confirming the existence of a hypeluminous infrared galaxy (HyLIRG). $\lambda_{\rm Edd}$ is estimated to be $\lambda_{\rm Edd} = 3.6 \pm 0.7$, making it one of the quasars with the highest BH mass accretion rate at cosmic noon., Comment: 9 pages, 3 figures, and 3 tables, accepted for publication in PASJ

Massive star-forming regions (MSFRs) are commonly associated with hub-filament systems (HFSs) and sites of cloud-cloud collision (CCC). Recent observational studies of some MSFRs suggest a possible connection between CCC and the formation of HFSs. To understand this connection, we analyzed the magneto-hydrodynamic simulation data from Inoue et al. (2018). This simulation involves the collision of a spherical turbulent molecular cloud with a plane-parallel sea of dense molecular gas at a relative velocity of about 10 km/s. Following the collision, the turbulent and non-uniform cloud undergoes shock compression, rapidly developing filamentary structures within the compressed layer. We found that CCC can lead to the formation of HFSs, which is a combined effect of turbulence, shock compression, magnetic field, and gravity. The collision between the cloud components shapes the filaments into a cone and drives inward flows among them. These inward flows merge at the vertex of the cone, rapidly accumulating high-density gas, which can lead to the formation of massive star(s). The cone acts as a mass-collecting machine, involving a non-gravitational early process of filament formation, followed by gravitational gas attraction to finalize the HFS. The gas distribution in the position-velocity (PV) and position-position spaces highlights the challenges in detecting two cloud components and confirming their complementary distribution if the colliding clouds have a large size difference. However, such CCC events can be confirmed by the PV diagrams presenting gas flow toward the vertex of the cone, which hosts gravitationally collapsing high-density objects, and by the magnetic field morphology curved toward the direction of the collision., Comment: 30 pages, 15 figures, 4 table, Accepted for publication in The Astrophysical Journal

A classical reconstruction of Wright's first-order logic of strict finitism is presented. Strict finitism is a constructive standpoint of mathematics that is more restrictive than intuitionism. Wright sketched the semantics of said logic in Wright (Realism, Meaning and Truth, chap 4, 2nd edition in 1993. Blackwell Publishers, Oxford, Cambridge, pp.107-75, 1982), in his strict finitistic metatheory. Yamada (J Philos Log. https://doi.org/10.1007/s10992-022-09698-w, 2023) proposed, as its classical reconstruction, a propositional logic of strict finitism under an auxiliary condition that makes the logic correspond with intuitionistic propositional logic. In this paper, we extend the propositional logic to a first-order logic that does not assume the condition. We will provide a sound and complete pair of a Kripke-style semantics and a natural deduction system, and show that if the condition is imposed, then the logic exhibits natural extensions of Yamada (2023)'s results., Comment: The author's final manuscript in 47 pages. The published version is 54 pages long due to its format. This abstract is that of the published version

Reduced-rank approach has been used for decades in robust linear estimation of both deterministic and random vector of parameters in linear model y=Hx+\sqrt{epsilon}n. In practical settings, estimation is frequently performed under incomplete or inexact model knowledge, which in the stochastic case significantly increases mean-square-error (MSE) of an estimate obtained by the linear minimum mean-square-error (MMSE) estimator, which is MSE-optimal among linear estimators in the theoretical case of perfect model knowledge. However, the improved performance of reduced-rank estimators over MMSE estimator in estimation under incomplete or inexact model knowledge has been established to date only by means of numerical simulations and arguments indicating that the reduced-rank approach may provide improved performance over MMSE estimator in certain settings. In this paper we focus on the high signal-to-noise ratio (SNR) case, which has not been previously considered as a natural area of application of reduced-rank estimators. We first show explicit sufficient conditions under which familiar reduced-rank MMSE and truncated SVD estimators achieve lower MSE than MMSE estimator if singular values of array response matrix H are perturbed. We then extend these results to the case of a generic perturbation of array response matrix H, and demonstrate why MMSE estimator frequently attains higher MSE than reduced-rank MMSE and truncated SVD estimators if H is ill-conditioned. The main results of this paper are verified in numerical simulations.

Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, in this paper we ask whether this is truly necessary. To this end, we search for a minimal, purely synthetic pre-training dataset that allows us to achieve performance similar to the 1 million images of ImageNet-1k. We construct such a dataset from a single fractal with perturbations. With this, we contribute three main findings. (i) We show that pre-training is effective even with minimal synthetic images, with performance on par with large-scale pre-training datasets like ImageNet-1k for full fine-tuning. (ii) We investigate the single parameter with which we construct artificial categories for our dataset. We find that while the shape differences can be indistinguishable to humans, they are crucial for obtaining strong performances. (iii) Finally, we investigate the minimal requirements for successful pre-training. Surprisingly, we find that a substantial reduction of synthetic images from 1k to 1 can even lead to an increase in pre-training performance, a motivation to further investigate ''scaling backwards''. Finally, we extend our method from synthetic images to real images to see if a single real image can show similar pre-training effect through shape augmentation. We find that the use of grayscale images and affine transformations allows even real images to ''scale backwards''., Comment: Accepted to ECCV2024

We systematically investigate the variability of polarized X-rays on a timescale of a few seconds in the low/hard state of the black hole binary Cygnus X-1. The correlation between polarization degrees and angles with X-Ray intensity was analyzed using data collected by the Imaging X-ray Polarimetry Explorer (IXPE) in June 2022. Given that X-Ray variability in the low/hard state of Cygnus X-1 is non-periodic, flux peaks were aggregated to suppress statistical fluctuations. We divided the temporal profiles of these aggregated flux peaks into seven time segments and evaluated the polarization for each segment. The results reveal that the polarization degree was 4.6\%$\pm$1.2 and 5.3\%$\pm$1.2 before and after the peak, respectively, but decreased to 3.4\%$\pm$1.1 and 2.7\%$\pm$1.1 in the segments including and immediately following the peak. Furthermore, the polarization angle exhibited a slight shift from approximately 30$^{\circ}$ to $\sim$40$^{\circ}$ before and after the peak. These findings suggest that the accretion disk contracts with increasing X-Ray luminosity, and the closer proximity of the X-Ray emitting gas to the black hole may lead to reduced polarization.

Highly efficient anti-Stokes (AS) photoluminescence (PL) is observed from halide perovskite quantum dots (QDs) due to their strong electron-phonon interactions. The AS PL is particularly intriguing as it suggests the potential for semiconductor optical cooling if the external quantum efficiency approaches 100%. However, the PL quantum efficiency in QDs is primarily dominated by multiparticle nonradiative Auger recombination processes under intense photoexcitation, which impose limits on the optical cooling gain. Here, we investigate the Auger recombination of dot-in-crystal perovskites. We quantitatively estimate the maximum optical cooling gain and the corresponding excitation intensity. We further conducted optical cooling experiments and demonstrate a maximum photo-cooling of approximately 9 K from room temperature. Additionally, we confirmed that increasing the excitation intensity leads to a transition from photo-cooling to photo-heating. These observations are consistent with our time-resolved measurements, offering insights into the potential and limitations of optical cooling in semiconductor QDs.

We study an inflection point inflation scenario where a flat direction of the minimal supersymmetric standard model (MSSM) is identified with the inflaton. We focus on the case where the flat direction (inflaton) has non-zero baryon number, and consider a non-thermal baryogenesis scenario where the decay of the inflaton at the reheating directly generates baryon asymmetry of the Universe. Specifically, we consider a udd flat direction that is lifted by a superpotential operator of dimension 6, and show that inflection point inflation with the udd flat direction can be compatible with cosmological observations and can account for the baryon asymmetry of the Universe., Comment: arXiv admin note: text overlap with arXiv:2405.00918

In this study, we developed a learning method for constructing a neural network system capable of memorizing data and recalling it without parameter updates. The system we built using this method is called the Appendable Memory system. The Appendable Memory system enables an artificial intelligence (AI) to acquire new knowledge even after deployment. It consists of two AIs: the Memorizer and the Recaller. This system is a key-value store built using neural networks. The Memorizer receives data and stores it in the Appendable Memory vector, which is dynamically updated when the AI acquires new knowledge. Meanwhile, the Recaller retrieves information from the Appendable Memory vector. What we want to teach AI in this study are the operations of memorizing and recalling information. However, traditional machine learning methods make AI learn features inherent in the learning dataset. We demonstrate that the systems we intend to create cannot be realized by current machine learning methods, that is, by merely repeating the input and output learning sequences with AI. Instead, we propose a method to teach AI to learn operations, by completely removing the features contained in the learning dataset. Specifically, we probabilized all the data involved in learning. This measure prevented AI from learning the features of the data. The learning method proposed in the study differs from traditional machine learning methods and provides fundamental approaches for building an AI system that can store information in a finite memory and recall it at a later date.

We revisit one-loop corrections to the power spectrum of curvature perturbations $\zeta$ in an inflationary scenario containing a transient ultra-slow-roll (USR) period. In Ref.[1], it was argued that one-loop corrections to the power spectrum of $\zeta$ can be larger than the tree-level one within the parameter region generating the seeds of primordial black holes during the USR epoch, which implies the breakdown of perturbation theory. We prove that this is not the case by using a master formula for one-loop corrections to the power spectrum obtained in Ref.[2]. We derive the same formula within the path-integral formalism, which is simpler than the original derivation in [2]. To show the smallness of one-loop corrections, the consistency relations and the effective constancy of tree-level mode functions of $\zeta$ for super-Hubble modes play essential roles, with which the master formula gives a simple expression for one-loop corrections. For concreteness, we provide a reduced set of interactions including the leading-order one, while establishing the consistency relations in a self-consistent manner. We also show how the consistency relations of various operators hold explicitly, which plays a key role in proving the absence of large one-loop corrections., Comment: 46 pages, 11 figures

A metamorphic robotic system (MRS) consists of anonymous modules, each of which autonomously moves in the 2D square grid by sliding and rotation with keeping connectivity among the modules. Existing literature considers distributed coordination among modules so that they collectively form a single MRS. In this paper, we consider distributed coordination for two MRSs. We first present a rendezvous algorithm that makes the two MRSs gather so that each module can observe all the other modules. Then, we present a merge algorithm that makes the two MRSs assemble and establish connectivity after rendezvous is finished. These two algorithms assume that each MRS consists of five modules, that do not have a common coordinate system. Finally, we show that five modules for each MRS is necessary to solve the rendezvous problem. To the best of our knowledge, our result is the first result on distributed coordination of multiple MRSs.

We present a measurement of $|V_{ub}|$ from a simultaneous study of the charmless semileptonic decays $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$, where $\ell = e, \mu$. This measurement uses a data sample of 387 million $B\overline{B}$ meson pairs recorded by the Belle~II detector at the SuperKEKB electron-positron collider between 2019 and 2022. The two decays are reconstructed without identifying the partner $B$ mesons. We simultaneously measure the differential branching fractions of $B^0\to\pi^- \ell^+ \nu_{\ell}$ and $B^+\to\rho^0 \ell^+\nu_{\ell}$ decays as functions of $q^2$ (momentum transfer squared). From these, we obtain total branching fractions $B(B^0\to\pi^- \ell^+ \nu_{\ell}) = (1.516 \pm 0.042 (\mathrm{stat}) \pm 0.059 (\mathrm{syst})) \times 10^{-4}$ and $B(B^+\to\rho^0 \ell^+\nu_{\ell}) = (1.625 \pm 0.079 (\mathrm{stat}) \pm 0.180 (\mathrm{syst})) \times 10^{-4}$. By fitting the measured $B^0\to\pi^- \ell^+ \nu_{\ell}$ partial branching fractions as functions of $q^2$, together with constraints on the non-perturbative hadronic contribution from lattice QCD calculations, we obtain $|V_{ub}|$ = $(3.93 \pm 0.09 \pm 0.13 \pm 0.19) \times 10^{-3}$. Here, the first uncertainty is statistical, the second is systematic, and the third is theoretical.

The evolution of giant molecular clouds (GMCs), the main sites of high-mass star formation, is an essential process to unravel the galaxy evolution. Using a GMC catalogue of M33 from ALMA-ACA survey, we classified 848 GMCs into three types based on the association with HII regions and their H$\alpha$ luminosities $\textit{L}$(H$\alpha$): Type I is associated with no HII regions; Type II with HII regions of $\textit{L}$(H$\alpha$) $<$ 10$^{37.5}$ erg s$^{-1}$; and Type III with HII regions of $\textit{L}$(H$\alpha$) $\geqq$ 10$^{37.5}$ erg s$^{-1}$. These criteria yield 224 Type I GMCs, 473 Type II GMCs, and 151 Type III GMCs. GMCs show changes in their physical properties according to the types; mass, radius, velocity dispersion, and $^{13}$CO detection rate of GMCs systematically increase from Type I to Type III, and additionally, Type III GMCs are closest to virial equilibrium. Type III GMCs show the highest spatial correlation with clusters younger than 10 Myr, Type II GMCs moderate correlation, and Type I GMCs are almost uncorrelated. We interpret that these types indicate an evolutionary sequence from Type I to Type II, and then to Type III with timescales of 4 Myr, 13 Myr, and 5 Myr, respectively, indicating the GMC lifetime of 22 Myr by assuming that Type II GMC has the same timescale as the Large Magellanic Cloud. The evolved GMCs concentrate on the spiral arms, while the younger GMCs are apart from the arm both to the leading and trailing sides. This indicated that GMCs collide with each other by the spiral potential, leading to the compression of GMCs and the triggering of high-mass star formation, which may support the dynamic spiral model. Overall, we suggest that the GMC evolution concept helps illuminate the galaxy evolution, including the spiral arm formation., Comment: 29 pages, 23 figures, 5 Tables, accepted for publication in PASJ