Search

We present a pioneering approach to tracking analysis within the COMET Phase-I experiment, which aims to search for the charged lepton flavor violating $\mu\to e$ conversion process in a muonic atom, at J-PARC, Japan. This paper specifically introduces the extraction of signal electron trajectories in the COMET Phase-I cylindrical drift chamber (CDC) amidst a high background hit rate, with more than $40\,\%$ occupancy of the total CDC cells, utilizing deep learning techniques of semantic segmentation. Our model achieved remarkable results, with a purity rate of $98\,\%$ and a retention rate of $90\,\%$ for CDC cells with signal hits, surpassing the design-goal performance of $90\,\%$ for both metrics. This study marks the initial application of deep learning to COMET tracking, paving the way for more advanced techniques in future research., Comment: 26 pages, 18 figures

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

We present a technique for dynamically projecting 3D content onto human hands with short perceived motion-to-photon latency. Computing the pose and shape of human hands accurately and quickly is a challenging task due to their articulated and deformable nature. We combine a slower 3D coarse estimation of the hand pose with high speed 2D correction steps which improve the alignment of the projection to the hands, increase the projected surface area, and reduce perceived latency. Since our approach leverages a full 3D reconstruction of the hands, any arbitrary texture or reasonably performant effect can be applied, which was not possible before. We conducted two user studies to assess the benefits of using our method. The results show subjects are less sensitive to latency artifacts and perform faster and with more ease a given associated task over the naive approach of directly projecting rendered frames from the 3D pose estimation. We demonstrate several novel use cases and applications., Comment: Project page: https://yoterel.github.io/casper-project-page/

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

Jacobson developed a counterpart of Galois theory for purely inseparable field extensions in positive characteristic. In his theory, a certain type of derivations replace the role of the generators of Galois groups. This article provides a convenient criterion for determining such derivations in dimension two. We also present examples demonstrating the efficiency of our criterion., Comment: 4 pages

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

Noise-induced phenomena in high-dimensional dynamical systems were investigated from a random dynamical systems point of view. In a class of generalized H\'enon maps, which are randomly perturbed delayed logistic maps, with monotonically increasing noise levels, we observed (i) an increase in the number of positive Lyapunov exponents from 4 to 5, and the emergence of characteristic periods at the same time, and (ii) a decrease in the number of positive Lyapunov exponents from 4 to 3, and an increase in Kolmogorov--Sinai entropy at the same time. Our results imply that simple concepts of noise-induced phenomena, such as noise-induced chaos and/or noise-induced order, may not describe those analogue in high dimensional dynamical systems, owing to coexistence of noise-induced chaos and noise-induced order.

Although the melting temperature, $T_{m}$, of a solid can be calculated based on first-principles molecular dynamics (FP-MD) simulations, systematic assessments of the accuracy of the resulting values have not yet been reported. FP-MD simulations require significant computational resources and hence an examination of the effect of cell size on convergence is difficult. In addition, calculation of the energy of a liquid is not a trivial problem because of energy dissipation effects. The present work attempts to resolve these problems, and thus allow the accuracy of $T_{m}$ values obtained from FP-MD simulations to be assessed for typical semiconductors, metals, and oxides. With the exception of Si, the $T_{m}$ value was overestimated in all cases. This overestimation can be reduced by increasing the cell size, although the convergence is slow unless the potential is very shallow. For oxides, this overestimation may not be removed by increasing the cell size. The LDA/GGA error of overbinding affects the melting enthalpy and thereby $T_{m}$. In order to fully capture the energy dissipation nature of liquids, adiabatic MD simulations are required, and such simulations have been performed in the present study.

Atomically Precise Manufacturing (APM) refers to the assembly of materials with atomic precision, representing a highly advanced technology with significant potential. However, the development of APM remains in its early stages, with applications largely confined to specialized fields and lacking cohesion within a unified discipline. The current literature on APM is often dominated by older, speculative papers that discuss its immense potential risks and benefits without sufficient grounding in the latest advancements or practical limitations that exist today. This paper aims to bridge this gap by providing a comprehensive assessment of current APM and near-APM technologies, as well as using the barriers to further progress to predict future developments. Through this analysis, we seek to establish a clearer understanding of the present state of the technology and then use these insights to predict the future trajectory of APM. By doing so, we aim to create a more grounded discourse on APM and its potential risks and benefits, while also guiding future research on the necessary regulations and safety considerations for this emerging field., Comment: 28 pages

Werewolf is an incomplete information game, which has several challenges when creating a computer agent as a player given the lack of understanding of the situation and individuality of utterance (e.g., computer agents are not capable of characterful utterance or situational lying). We propose a werewolf agent that solves some of those difficulties by combining a Large Language Model (LLM) and a rule-based algorithm. In particular, our agent uses a rule-based algorithm to select an output either from an LLM or a template prepared beforehand based on the results of analyzing conversation history using an LLM. It allows the agent to refute in specific situations, identify when to end the conversation, and behave with persona. This approach mitigated conversational inconsistencies and facilitated logical utterance as a result. We also conducted a qualitative evaluation, which resulted in our agent being perceived as more human-like compared to an unmodified LLM. The agent is freely available for contributing to advance the research in the field of Werewolf game.

We study non-Hermitian Josephson junctions formed by multiple superconductors and discover the emergence of exceptional points entirely determined by the interplay of the distinct superconducting phases and non-Hermiticity due to normal reservoirs. In particular, in Josephson junctions with three and four superconductors, we find stable lines and surfaces of exceptional points protected by non-Hermitian topology and highly tuneable by the superconducting phases. We also discover that, in Josephson junctions formed by laterally coupled superconductors, exceptional points can result from hybridized Andreev bound states and lead to the enhancement of supercurrents controlled by dissipation. Our work unveils the potential of multi-terminal Josephson junctions for realizing higher dimensional topological non-Hermitian superconducting phenomena., Comment: 7 pages, 5 figures

This paper proposes a method for extracting speaker embedding for each speaker from a variable-length recording containing multiple speakers. Speaker embeddings are crucial not only for speaker recognition but also for various multi-speaker speech applications such as speaker diarization and target-speaker speech processing. Despite the challenges of obtaining a single speaker's speech without pre-registration in multi-speaker scenarios, most studies on speaker embedding extraction focus on extracting embeddings only from single-speaker recordings. Some methods have been proposed for extracting speaker embeddings directly from multi-speaker recordings, but they typically require preparing a model for each possible number of speakers or involve complicated training procedures. The proposed method computes the embeddings of multiple speakers by focusing on different parts of the frame-wise embeddings extracted from the input multi-speaker audio. This is achieved by recursively computing attention weights for pooling the frame-wise embeddings. Additionally, we propose using the calculated attention weights to estimate the number of speakers in the recording, which allows the same model to be applied to various numbers of speakers. Experimental evaluations demonstrate the effectiveness of the proposed method in speaker verification and diarization tasks., Comment: Accepted to IEEE SLT 2024

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

In this paper, we propose a circuit design for implementing quantum walks on complex networks. Quantum walks are powerful tools for various graph-based applications such as spatial search, community detection, and node classification. Although many quantum-walk-based graph algorithms have been extensively studied, specific quantum circuits for implementing these algorithms have not yet been provided. To address this issue, we present a circuit design for implementing the discrete-time quantum walk on complex networks. We investigate the functionality of our circuit using the small-sized Watts-and-Strogatz model as the complex network model, comparing it with theoretical calculations. This work offers a new approach to constructing quantum circuits for implementing quantum walks on arbitrary complex networks., Comment: 2pages, 2figures, This paper will be published in the proceedings of the IEEE International Conference on Quantum Computing and Engineering 2024 (QCE24)

Achieving practical quantum speedup with limited resources is a crucial challenge in both academic and industrial communities. To address this, a partially fault-tolerant quantum computing architecture called ``space-time efficient analog rotation quantum computing architecture (STAR architecture)'' has been recently proposed. This architecture focuses on minimizing resource requirements while maximizing the precision of non-Clifford gates, essential for universal quantum computation. However, non-deterministic processes such as the repeat-until-success (RUS) protocol and state injection can introduce significant computational overhead. Therefore, optimizing the logical circuit to minimize this overhead by using efficient fault-tolerant operations is essential. This paper presents an efficient method for simulating the time evolution of the 2D Hubbard model Hamiltonian, a promising application of the STAR architecture. We present two techniques, parallel injection protocol and adaptive injection region updating, to reduce unnecessary time overhead specific to our architecture. By integrating these with the existing fSWAP technique, we develop an efficient Trotter-based time evolution operation for the 2D Hubbard model. Our analysis reveals an acceleration of over 10 times compared to naive serial compilation. This optimized compilation enables us to estimate the computational resources required for quantum phase estimation of the 2D Hubbard model. For devices with a physical error rate of $p_{\rm phys} = 10^{-4}$, we estimate that approximately $6.5 \times 10^4$ physical qubits are required to achieve faster ground state energy estimation of the $8\times8$ Hubbard model compared to classical computation., Comment: 22 pages, 33 figures

Achieving quantum speedups in practical tasks remains challenging for current noisy intermediate-scale quantum (NISQ) devices. These devices always encounter significant obstacles such as inevitable physical errors and the limited scalability of current near-term algorithms. Meanwhile, assuming a typical architecture for fault-tolerant quantum computing (FTQC), realistic applications inevitably require a vast number of qubits, typically exceeding $10^6$, which seems far beyond near-term realization. In this work, to bridge the gap between the NISQ and FTQC eras, we propose an alternative approach to achieve practical quantum advantages on early-FTQC devices. Our framework is based on partially fault-tolerant logical operations to minimize spatial overhead and avoids the costly distillation techniques typically required for executing non-Clifford gates. To this end, we develop a space-time efficient state preparation protocol to generate an ancillary non-Clifford state consumed for implementing an analog rotation gate with an arbitrary small angle $\theta$ and a remarkably low worst-case error rate below $\mathcal{O}(|\theta| p_{\text{ph}})$, where $p_{\text{ph}}$ is the physical error rate. Furthermore, we propose several error suppression schemes tailored to our preparation protocol, which are essential to minimize the overhead for mitigating errors. Based on this framework, we present several promising applications that leverage the potential of our framework, including the Trotter simulation and quantum phase estimation (QPE). Notably, we demonstrate that our framework allows us to perform the QPE for $(8\times 8)$-site Hubbard model with fewer than $4.9\times 10^4$ qubits and an execution time of 9 days (or 12 minutes with full parallelization) under $p_{\text{ph}}=10^{-4}$, which is significantly faster than recent classical estimation with tensor network techniques (DMRG and PEPS).

We present a detailed study of the MASTER OT J030227.28+191754.5 outburst in 2021-2022, reaching an amplitude of 10.2 mag and a duration of 60 d. The detections of (1) the double-peaked optical emission lines, and (2) the early and ordinary superhumps, established that MASTER OT J030227.28+191754.5 is an extremely energetic WZ Sge-type dwarf nova (DN). Based on the superhump observations, we obtained its orbital period and mass ratio as 0.05986(1) d and 0.063(1), respectively. These are within a typical range of low-mass-ratio DNe. According to the binary parameters derived based on the thermal-tidal instability model, our analyses showed that (1) the standard disk model requires an accretion rate $\simeq$ 10$^{20}$ g s$^{-1}$ to explain its peak optical luminosity and (2) large mass was stored in the disk at the outburst onset. These cannot be explained solely by the impact of its massive ($\gtrsim$ 1.15 M$_\odot$) primary white dwarf implied by Kimura et al. (2023). Instead, we propose that the probable origin of this enormously energetic DN outburst is the even lower quiescence viscosity than other WZ Sge-type DNe. This discussion is qualitatively valid for most possible binary parameter spaces unless the inclination is low ($\lesssim 40^\circ$) enough for the disk to be bright explaining the outburst amplitude. Such low inclinations, however, would not allow detectable amplitude of early superhumps in the current thermal-tidal instability model. The optical spectra at outburst maximum showed the strong emission lines of Balmer, He I, and He II series whose core is narrower than $\sim 800$ km s$^{-1}$. Considering its binary parameters, a Keplerian disk cannot explain this narrow component, but the presumable origin is disk winds., Comment: 23 pages, 13 figures, 2 tables. Accepted by PASJ. Part of the online supplemental information is included

Well-calibrated predictions of user preferences are essential for many applications. Since recommender systems typically select the top-N items for users, calibration for those top-N items, rather than for all items, is important. We show that previous calibration methods result in miscalibrated predictions for the top-N items, despite their excellent calibration performance when evaluated on all items. In this work, we address the miscalibration in the top-N recommended items. We first define evaluation metrics for this objective and then propose a generic method to optimize calibration models focusing on the top-N items. It groups the top-N items by their ranks and optimizes distinct calibration models for each group with rank-dependent training weights. We verify the effectiveness of the proposed method for both explicit and implicit feedback datasets, using diverse classes of recommender models., Comment: accepted at RecSys 2024

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

Speckle patterns manifesting from the interaction of coherent X-rays with matter offer a glimpse into the dynamics of nanoscale domains that underpin many emergent phenomena in quantum materials. While the dynamics of the average structure can be followed with time-resolved X-ray diffraction, the ultrafast evolution of local structures in nonequilibrium conditions have thus far eluded detection due to experimental limitations, such as insufficient X-ray coherent flux. Here we demonstrate a nonequilibrium speckle visibility experiment using a split-and-delay setup at an X-ray free-electron laser. Photoinduced electronic domain fluctuations of the magnetic model material Fe$_{3}$O$_{4}$ reveal changes of the trimeron network configuration due to charge dynamics that exhibit liquid-like fluctuations, analogous to a supercooled liquid phase. This suggests that ultrafast dynamics of electronic heterogeneities under optical stimuli are fundamentally different from thermally-driven ones.

In this paper, we give an explicit construction of higher Chow cycles of type $(2,1)$ on $K3$ surfaces obtained as quadruple coverings of the projective plane ramified along smooth quartics. The construction uses a pair of bitangents of the quartics. We prove that the higher Chow cycles generate a rank 2 subgroup in the indecomposable part of the higher Chow group for very general members, by using a specialization argument and an explicit computation of the regulator map., Comment: 18 pages, 5 figures

In this paper, we propose QWalkVec, a quantum walk-based node embedding method. A quantum walk is a quantum version of a random walk that demonstrates a faster propagation than a random walk on a graph. We focus on the fact that the effect of the depth-first search process is dominant when a quantum walk with a superposition state is applied to graphs. Simply using a quantum walk with its superposition state leads to insufficient performance since balancing the depth-first and breadth-first search processes is essential in node classification tasks. To overcome this disadvantage, we formulate novel coin operators that determine the movement of a quantum walker to its neighboring nodes. They enable QWalkVec to integrate the depth-first search and breadth-first search processes by prioritizing node sampling. We evaluate the effectiveness of QWalkVec in node classification tasks conducted on four small-sized real datasets. As a result, we demonstrate that the performance of QWalkVec is superior to that of the existing methods on several datasets. Our code will be available at \url{https://github.com/ReiSato18/QWalkVec}., Comment: This paper is accepted at 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2024). This paper is a slightly revised version

The axion kinetic misalignment mechanism (KMM) opens the possibility of explaining dark matter for almost any axion mass and decay constant that are not accessible by the standard misalignment mechanism, in particular at low values of the axion decay constant (i.e. large coupling). This is a new opportunity for most axion experiments which could be sensitive to dark matter and probe new regimes of axion cosmology. We scrutinise UV completions that lead to the KMM mechanism. These mainly rely on the early dynamics of the axion partner, the radial mode of the complex scalar field, from which the axion inherits kinetic energy. The damping of the radial-mode energy density is then a necessary ingredient. We study in detail thermal damping from interactions in the plasma. A minimal and rather natural implementation consists of a KSVZ-type model with a nearly-quadratic potential for the radial mode extended by U(1)-breaking higher-dimensional operators. Furthermore, we study Higgs portal interactions as an alternative damping mechanism and improve upon previously proposed implementations based on quartic potentials. These implementations can lead to the QCD axion being dark matter and in the reach of IAXO, while MADMAX, IAXO and ALPS II can be sensitive to a generic Axion-Like-Particle (ALP) as dark matter. Such models typically feature a kination era. We also show that ALP dark matter from KMM points to a particular realization of inflation., Comment: 51 pages, 21 figures (main text without references) + 47 pages, 25 figures (appendices)

To issue early warnings and rapidly initiate disaster responses after tsunami damage, various tsunami inundation forecast systems have been deployed worldwide. Japan's Cabinet Office operates a forecast system that utilizes supercomputers to perform tsunami propagation and inundation simulation in real time. Although this real-time approach is able to produce significantly more accurate forecasts than the conventional database-driven approach, its wider adoption was hindered because it was specifically developed for vector supercomputers. In this paper, we migrate the simulation code to modern CPUs and GPUs in a minimally invasive manner to reduce the testing and maintenance costs. A directive-based approach is employed to retain the structure of the original code while achieving performance portability, and hardware-specific optimizations including load balance improvement for GPUs are applied. The migrated code runs efficiently on recent CPUs, GPUs and vector processors: a six-hour tsunami simulation using over 47 million cells completes in less than 2.5 minutes on 32 Intel Sapphire Rapids CPUs and 1.5 minutes on 32 NVIDIA H100 GPUs. These results demonstrate that the code enables broader access to accurate tsunami inundation forecasts., Comment: 12 pages, 15 figures, accepted at IEEE CLUSTER 2024

We present a bounded model checking algorithm for signal temporal logic (STL) that exploits mixed-integer linear programming (MILP). A key technical element is our novel MILP encoding of the STL semantics; it follows the idea of stable partitioning from the recent work on SMT-based STL model checking. Assuming that our (continuous-time) system models can be encoded to MILP -- typical examples are rectangular hybrid automata (precisely) and hybrid dynamics with closed-form solutions (approximately) -- our MILP encoding yields an optimization-based model checking algorithm that is scalable, is anytime/interruptible, and accommodates parameter mining. Experimental evaluation shows our algorithm's performance advantages especially for complex STL formulas, demonstrating its practical relevance e.g. in the automotive domain., Comment: Extended version of the paper accepted by 36th International Conference on Computer-Aided Verification (CAV), 2024

We study the effect of diffuse surface scattering on the edge modes in two-dimensional chiral superconductors with time-reversal symmetry-breaking Cooper pairs, each carrying angular momentum $m \hbar$ ($m = 1,2,3, \cdots$). To elucidate the diffuse scattering effect, we formulate the inverse lifetime $\Gamma = \hbar/\tau$ corresponding to the broadening of the surface density of states (SDOS) for the edge mode. This derivation uses random S-matrix theory, which allows us to describe the surface effect in a unified way from the specular to the diffuse limit within the quasiclassical theory framework of superconductivity. We find that $\Gamma$ in the chiral states with $m \geq 2$ is larger than that in the chiral $p$-wave state ($m = 1$) because of the multiple edge mode branches in former superconducting states (the number of which equals $m$). Diffuse scattering between the different branches causes a significant broadening of SDOS. In contrast, the edge mode in the chiral $p$-wave state is robust to diffuse scattering because only a single edge mode branch exists. We also discuss SDOS at the diffuse limit, where the description in terms of $\Gamma$ is not useful. The diffuse scattering effect on SDOS can be understood qualitatively in terms of destructive interference analogous to that caused by impurity scattering in unconventional superconductors., Comment: 10 pages, 7 figures

Contemporary memory systems contain a variety of memory types, each possessing distinct characteristics. This trend empowers applications to opt for memory types aligning with developer's desired behavior. As a result, developers gain flexibility to tailor their applications to specific needs, factoring in attributes like latency, bandwidth, and power consumption. Our research centers on the aspect of power consumption within memory systems. We introduce an approach that equips developers with comprehensive insights into the power consumption of individual memory types. Additionally, we propose an ordered hierarchy of memory types. Through this methodology, developers can make informed decisions for efficient memory usage aligned with their unique requirements., Comment: ICPADS 2023

Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise, and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 14 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint., Comment: 11 pages, 7 figures, 4 tables

Results are presented for the measurement of large-scale anisotropies in the arrival directions of ultra-high-energy cosmic rays detected at the Pierre Auger Observatory during 19 years of operation, prior to AugerPrime, the upgrade of the Observatory. The 3D dipole amplitude and direction are reconstructed above $4\,$EeV in four energy bins. Besides the established dipolar anisotropy in right ascension above $8\,$EeV, the Fourier amplitude of the $8$ to $16\,$EeV energy bin is now also above the $5\sigma$ discovery level. No time variation of the dipole moment above $8\,$EeV is found, setting an upper limit to the rate of change of such variations of $0.3\%$ per year at the $95\%$ confidence level. Additionally, the results for the angular power spectrum are shown, demonstrating no other statistically significant multipoles. The results for the equatorial dipole component down to $0.03\,$EeV are presented, using for the first time a data set obtained with a trigger that has been optimized for lower energies. Finally, model predictions are discussed and compared with observations, based on two source emission scenarios obtained in the combined fit of spectrum and composition above $0.6\,$EeV.

Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that integrates RadonPy, a Python library for fully automated polymer property calculations based on all-atom classical molecular dynamics into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we successfully synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and Abbe number.

We study spin polarization induced by electric fields in an isotropic chiral metal with boundaries using the Boltzmann formalism. We calculate both spin polarization in bulk in the linear response and antiparallel spin polarization near the boundary in the quadratic response against electric fields. We also work out spin polarization induced by the fluctuating electric fields, i.e., noises. Based on these results, we address chirality-induced spin selectivity (CISS) without biased inputs, CISS at room temperature, and the enantio-selective interaction between chiral materials and a magnetic substrate., Comment: 6 pages, 3 figures, +Supplemental Material(2pages)

Assessing centrality in network systems is critical for understanding node importance and guiding decision-making processes. In dynamic networks, incorporating a controllability perspective is essential for identifying key nodes. This paper focuses on the Volumetric Controllability Score (VCS) and Average Energy Controllability Score (AECS) as centrality measures for selecting control nodes in linear dynamical network systems. We prove the uniqueness of VCS and AECS for any linear time-invariant system, enhancing their applicability beyond previously recognized cases. This ensures their interpretability, comparability, and reproducibility across different researchers. Our analysis reveals substantial differences between VCS and AECS in linear systems with symmetric and skew-symmetric transition matrices. Moreover, numerical experiments using real-world data indicate that VCS and AECS assessments in brain networks, modeled as Laplacian dynamics, exhibit contrasting trends.

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150-500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.

Identifying reaction coordinates (RCs) from many collective variable candidates have been of great challenge in understanding reaction mechanisms in complex systems. Machine learning approaches, especially the deep neural network (DNN), have become a powerful tool and are actively applied. On the other hand, selecting the hyperparameters that define the DNN model structure remains to be a nontrivial and tedious task. Here we develop the hyperparameter tuning approach to determine the parameters in an automatic fashion from the trajectory data. The DNN models are constructed to obtain a RC from many collective variables that can adequately describe the changes of the committor from the reactant to product. The approach is applied to study the isomerization of alanine dipeptide in vacuum and in water. We find that multiple DNN models with similar accuracy can be obtained, indicating that hyperparameter space is multimodal. Nevertheless, despite the differences in the DNN model structure, the key features extracted using the explainable AI (XAI) tools show that all RC share the same character. In particular, the reaction in vacuum is characterized by the dihedral angles $\phi$ and $\theta$. The reaction in water also involves these dihedral angles as key features, and in addition, the electrostatic potential from the solvent to the hydrogen (H${}_{18}$) plays a role at about the transition state. The current study thus shows that, in contrast to the diversity in the DNN models, suitably optimized DNN models share the same features and share the common mechanism for the reaction of interest., Comment: 10 pages, 10 figures, 2 table for main text, 9 pages for supplementary material

This study proposes an adaptive experimental design framework for channel simulation-based base station (BS) design that supports joint optimization of transmission power and placement. We consider a system in which multiple transmitters provide wireless services over a shared frequency band. Our objective is to maximize the average throughput within an area of interest. System operators can design the system parameters prior to deployment by iterating through channel simulations and updating parameters. However, accurate channel simulations are computationally expensive, thus it is preferable to configure the system using a limited number of simulation iterations. We develop a solver for this problem based on Bayesian optimization (BO), a black-box optimization method. Numerical results demonstrate that our proposed framework can achieve 18-22% higher throughput performance compared to conventional placement and power optimization strategies., Comment: 5 pages, 7 figures, 1 table. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Quantum spin Hall insulators, which possess a non-trivial $\mathbb{Z}_2$ topological phase, have attracted great attention for two decades. It is generally believed that when an even number of layers of the quantum spin Hall insulators are stacked, the $\mathbb{Z}_2$ topological phase becomes unstable due to $\mathbb{Z}_2$ nature. While several researchers report counterexamples of the instability, there is no systematic understanding. In this work, we show that the $\mathbb{Z}_2$ topological phase is robust against the stacking if it has an additional chiral symmetry, in terms of the level repulsion in the corresponding non-Hermitian system. We demonstrate this by mapping a one-dimensional class DIII superconductor with $\mathbb{Z}_2$ topology to the corresponding non-Hermitian system in AII$^\dagger$ with $\mathbb{Z}_2$ point-gap topology., Comment: 11 pages, 11 figures

While most vision tasks are essentially visual in nature (for recognition), some important tasks, especially in the medical field, also require quantitative analysis (for quantification) using quantitative images. Unlike in visual analysis, pixel values in quantitative images correspond to physical metrics measured by specific devices (e.g., a depth image). However, recent work has shown that it is sometimes possible to synthesize accurate quantitative values from visual ones (e.g., depth from visual cues or defocus). This research aims to improve quantitative image synthesis (QIS) by exploring pretraining and image resolution scaling. We propose a benchmark for evaluating pretraining performance using the task of QIS-based bone mineral density (BMD) estimation from plain X-ray images, where the synthesized quantitative image is used to derive BMD. Our results show that appropriate pretraining can improve QIS performance, significantly raising the correlation of BMD estimation from 0.820 to 0.898, while others do not help or even hinder it. Scaling-up the resolution can further boost the correlation up to 0.923, a significant enhancement over conventional methods. Future work will include exploring more pretraining strategies and validating them on other image synthesis tasks., Comment: SASHIMI, 2024 (MICCAI workshop). 13 pages, 3 figures

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

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 show that the holomorph of a cyclic group of order $n$ is isomorphic to its own automophism group when $n$ is twice of a power of an odd prime.

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.

A hallmark of certain topology, including the Chern number, is the obstruction to constructing exponentially localized Wannier functions in the bulk bands. Conversely, other types of topology do not necessarily impose Wannier obstructions. Remarkably, such Wannier-localizable topological insulators can host boundary states that are detachable from the bulk bands. In our accompanying Letter (D. Nakamura et al., arXiv:2407.09458), we demonstrate that non-Hermitian topology underlies detachable boundary states in Hermitian topological insulators and superconductors, thereby establishing their tenfold classification based on internal symmetry. Here, using $K$-theory, we elucidate the relationship between Wannier localizability and detachability of topological boundary states. From the boundary perspective, we classify intrinsic and extrinsic non-Hermitian topology, corresponding to nondetachable and detachable topological boundary states, respectively. From the bulk perspective, on the other hand, we classify Wannier localizability through the homomorphisms of topological phases from the tenfold Altland-Zirnbauer symmetry classes to the threefold Wigner-Dyson symmetry classes. Notably, these two approaches from the boundary and bulk perspectives lead to the same classification. We clarify this agreement and develop a unified understanding of the bulk-boundary correspondence on the basis of $K$-theory., Comment: 12 pages, 1 figure, 4 tables; see also arXiv:2407.09458

Plants display physical displacements during their growth due to photosynthesis, which converts light into chemical energy. This can be interpreted as plants acting as actuators with a built-in power source. This paper presents a method to create plant robots that move and perform tasks by harnessing the actuation output of plants: displacement and force generated from the growing process. As the target plant, radish sprouts are employed, and their displacement and force are characterized, followed by the calculation of power and energy densities. Based on the characterization, two different plant robots are designed and fabricated: a rotational robot and a gripper. The former demonstrates ground locomotion, achieving a travel distance of 14.6 mm with an average speed of 0.8 mm/h. The latter demonstrates the picking and placing of an object with a 0.1-g mass by the light-controlled open-close motion of plant fingers. A good agreement between the experimental and model values is observed in the specific data of the mobile robot, suggesting that obtaining the actuation characteristics of plants can enable the design and prediction of behavior in plant robots. These results pave the way for the realization of novel types of environmentally friendly and sustainable robots., Comment: 12 pages, 4 figures

This work tackles a significant challenge in dynamo theory: the possibility of long-term amplification and maintenance of an axisymmetric magnetic field. We introduce a novel model that allows for non-trivial axially-symmetric steady-state solutions for the magnetic field, particularly when the dynamo operates primarily within a "nearly-spherical" toroidal volume inside a fluid shell surrounding a solid core. In this model, Ohm's law is generalized to include the restoring friction force, which aligns the velocity of the shell with the rotational speed of the inner core and outer mantle. Our findings reveal that, in this context, Cowling's theorem and the neutral point argument are modified, leading to magnetic energy growth for a suitable choice of toroidal flow. The global equilibrium magnetic field that emerges from our model exhibits a dipolar character., Comment: 10 pages, 1 figure

In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety. Traditional datasets focus on driver or vehicle behavior, often neglecting pedestrian perspectives. To fill this gap, we introduce the WTS dataset, highlighting detailed behaviors of both vehicles and pedestrians across over 1.2k video events in hundreds of traffic scenarios. WTS integrates diverse perspectives from vehicle ego and fixed overhead cameras in a vehicle-infrastructure cooperative environment, enriched with comprehensive textual descriptions and unique 3D Gaze data for a synchronized 2D/3D view, focusing on pedestrian analysis. We also pro-vide annotations for 5k publicly sourced pedestrian-related traffic videos. Additionally, we introduce LLMScorer, an LLM-based evaluation metric to align inference captions with ground truth. Using WTS, we establish a benchmark for dense video-to-text tasks, exploring state-of-the-art Vision-Language Models with an instance-aware VideoLLM method as a baseline. WTS aims to advance fine-grained video event understanding, enhancing traffic safety and autonomous driving development., Comment: ECCV24. Website: https://woven-visionai.github.io/wts-dataset-homepage/

2D Janus TMDC layers with broken mirror symmetry exhibit giant Rashba splitting and unique excitonic behavior. For their 1D counterparts, the Janus nanotubes possess curvature, which introduce an additional degree of freedom to break the structural symmetry. This could potentially enhance these effects or even give rise to novel properties. In addition, Janus MSSe nanotubes (M=W, Mo), with diameters surpassing 4 nm and Se positioned externally, consistently demonstrate lower energy states than their Janus monolayer counterparts. However, there have been limited studies on the preparation of Janus nanotubes, due to the synthesis challenge and limited sample quality. Here we first synthesized MoS2 nanotubes based on SWCNT-BNNT heterostructure and then explored the growth of Janus MoSSe nanotubes from MoS2 nanotubes with the assistance of H2 plasma at room temperature. The successful formation of the Janus structure was confirmed via Raman spectroscopy, and microscopic morphology and elemental distribution of the grown samples were further characterized. The synthesis of Janus MoSSe nanotubes based on SWCNT-BNNT enables the further exploration of novel properties in Janus TMDC nanotubes.

Simulation of quantum many-body systems is a promising application of quantum computers. However, implementing the time-evolution operator as a quantum circuit efficiently on near-term devices with limited resources is challenging. Standard approaches like Trotterization often require deep circuits, making them impractical. This paper proposes a hybrid quantum-classical algorithm called Local Subspace Variational Quantum Compilation (LSVQC) for compiling the time-evolution operator. The LSVQC uses variational optimization to reproduce the action of the target time-evolution operator within a physically reasonable subspace. Optimization is performed on small local subsystems based on the Lieb-Robinson bound, allowing for cost function evaluation using small-scale quantum devices or classical computers. Numerical simulations on a spin-lattice model and an $\mathit{\text{ab initio}}$ effective model of strongly correlated material Sr$_2$CuO$_3$ demonstrate the algorithm's effectiveness. It is shown that the LSVQC achieves a 95% reduction in circuit depth compared to Trotterization while maintaining accuracy. The subspace restriction also reduces resource requirements and improves accuracy. Furthermore, we estimate the gate count needed to execute the quantum simulations using the LSVQC on near-term quantum computing architectures in the noisy intermediate-scale or early fault-tolerant quantum computing era. Our estimation suggests that the acceptable physical gate error rate for the LSVQC can be significantly larger than for Trotterization., Comment: 29 pages, 14 figures

The amount of Magnetic resonance imaging data has grown tremendously recently, creating an urgent need to accelerate data processing, which requires substantial computational resources and time. In this preliminary study, we applied FMRIB Software Library commands on T1-weighted and diffusion-weighted images of a single young adult using the Fugaku supercomputer. The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.

Single-stranded RNA viruses co-assemble their capsid with the genome and variations in capsid structures can have significant functional relevance. In particular, viruses need to respond to a dehydrating environment to prevent genomic degradation and remain active upon rehydration. Theoretical work has predicted low-energy buckling transitions in icosahedral capsids which could protect the virus from further dehydration. However, there has been no direct experimental evidence, nor molecular mechanism, for such behaviour. Here we observe this transition using X-ray single particle imaging of MS2 bacteriophages after aerosolization. Using a combination of machine learning tools, we classify hundreds of thousands of single particle diffraction patterns to learn the structural landscape of the capsid morphology as a function of time spent in the aerosol phase. We found a previously unreported compact conformation as well as intermediate structures which suggest an incoherent buckling transition which does not preserve icosahedral symmetry. Finally, we propose a mechanism of this buckling, where a single 19-residue loop is destabilised, leading to the large observed morphology change. Our results provide experimental evidence for a mechanism by which viral capsids protect themselves from dehydration. In the process, these findings also demonstrate the power of single particle X-ray imaging and machine learning methods in studying biomolecular structural dynamics., Comment: 10 pages, 4 figures plus 9 pages supplementary information