Search

A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{\Lambda\Lambda}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$\Lambda$ hypernuclear event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$\Lambda$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$\Lambda$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.

Utterances by L2 speakers can be unintelligible due to mispronunciation and improper prosody. In computer-aided language learning systems, textual feedback is often provided using a speech recognition engine. However, an ideal form of feedback for L2 speakers should be so fine-grained that it enables them to detect and diagnose unintelligible parts of L2 speakers' utterances. Inspired by language teachers who correct students' pronunciation through a voice-to-voice process, this pilot study utilizes a unique semi-parallel dataset composed of non-native speakers' (L2) reading aloud, shadowing of native speakers (L1) and their script-shadowing utterances. We explore the technical possibility of replicating the process of an L1 speaker's shadowing L2 speech using Voice Conversion techniques, to create a virtual shadower system. Experimental results demonstrate the feasibility of the VC system in simulating L1's shadowing behavior. The output of the virtual shadower system shows a reasonable similarity to the real L1 shadowing utterances in both linguistic and acoustic aspects., Comment: Accepted by APSIPA ASC 2024. arXiv admin note: text overlap with arXiv:2409.11742

We study superconductivity in Ammann-Beenker quasicrystals under magnetic field. By assuming an intrinsic $s$-wave pairing interaction and solving for mean-field equations self-consistently, we find gapless superconductivity in the quasicrystals at and near half filling. We show that gapless superconductivity originates in broken translational symmetry and confined states unique to the quasicrystals. When Rashba spin-orbit coupling is present, the quasicrystalline gapless superconductor can be topologically nontrivial and characterized by a nonzero pseudospectrum invariant given by a spectral localizer. The gapless topological superconducting phase exhibits edge states with near-zero energy. These findings suggest that quasicrystals can be a unique platform for realizing gapless superconductivity with nontrivial topology., Comment: 6 pages, 3 figures, and Supplemental Material (3 pages, 5 figures)

The electron antinuetrino flux limits are presented for the brightest gamma-ray burst (GRB) of all time, GRB221009A, over a range of 1.8\,-\,200\,MeV using the Kamioka Liquid Scintillator Anti Neutrino Detector (KamLAND). Using a variety of time windows to search for electron antineutrinos coincident with the GRB, we set an upper limit on the flux under the assumption of various neutrino source spectra. No excess was observed in any time windows ranging from seconds to days around the event trigger time. The limits are compared to the results presented by IceCube., Comment: 8 pages, 3 figures

In this study, we consider the problem of predicting task success for open-vocabulary manipulation by a manipulator, based on instruction sentences and egocentric images before and after manipulation. Conventional approaches, including multimodal large language models (MLLMs), often fail to appropriately understand detailed characteristics of objects and/or subtle changes in the position of objects. We propose Contrastive $\lambda$-Repformer, which predicts task success for table-top manipulation tasks by aligning images with instruction sentences. Our method integrates the following three key types of features into a multi-level aligned representation: features that preserve local image information; features aligned with natural language; and features structured through natural language. This allows the model to focus on important changes by looking at the differences in the representation between two images. We evaluate Contrastive $\lambda$-Repformer on a dataset based on a large-scale standard dataset, the RT-1 dataset, and on a physical robot platform. The results show that our approach outperformed existing approaches including MLLMs. Our best model achieved an improvement of 8.66 points in accuracy compared to the representative MLLM-based model., Comment: Accepted for presentation at CoRL2024

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics

We investigate static, axially symmetric spacetimes without naked singularities that are constructed by patching Weyl class spacetimes with the flat spacetimes. Once the exterior geometry is specified, the junction conditions determine the shape of a thin shell, which is the boundary between the two patched spacetimes, and the distribution of the energy and pressure on this shell, leaving a parameter representing the shell size free. We examine the cases where the exterior of the shell is given by Curzon--Chazy or Zipoy--Voorhees spacetimes. For each case, we find a lower bound on the shell size. Additionally, we find that the weak and null energy conditions are satisfied for any shell size, while the dominant energy condition is satisfied for sufficiently large shells. These results provide concrete examples of non-singular spacetimes with non-spherically symmetric exteriors that respect the energy conditions., Comment: 15 pages, 10 figures

The phase noise of low-noise oscillators is conventionally measured by the cross-spectrum method (CSM), which has a complicated setup. We developed an alternative method called zero-crossing analysis with a double recorder setup (ZCA-DRS) that has much simpler configuration, which we previously demonstrated to measure phase noise in the audible frequency range. In this study we conducted experiments using ZCA-DRS to measure phase noise in the radiofrequency range. A temperature-compensated crystal oscillator was used to generate a periodic signal at 27 MHz. The results demonstrated that the obtained single-side band spectrum was almost the same as that obtained by CSM. The measurement sensitivity was limited by the jitter of the internal clock and noise of the analog-to-digital converter. Thus, ZCA-DRS can be used as an alternative to CSM for phase noise measurement in the radiofrequency range., Comment: 8 pages, 10 figures

The precise determination of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements is very important, because it could be a clue to new physics beyond Standard Theory. This is particular true of $V_{ud}$, because it is the main contribution to the unitary condition of the CKM matrix elements. The level of accuracy for the test of the unitarity involving the element $V_{ud}$ is now of the order of $10^{-4}$. Because the precise data for $V_{ud}$ is usually extracted from super-allowed nuclear $\beta$ decay, it is quite significant to investigate the breaking of SU(3) flavor symmetry on the weak vector coupling constant in nuclear matter. The purpose of this paper is to investigate how the isoscalar scalar ($\sigma$) and the isovector scalar ($\delta$ or $a_0$) mean-fields affect the weak vector and axial-vector coupling constants for semileptonic baryon (neutron, $\Lambda$ or $\Xi^-$) decay in asymmetric nuclear matter. To do so, we use the quark-meson coupling (QMC) model, where nuclear matter consists of nucleons including quark degrees of freedom bound by the self-consistent exchange of scalar and vector mesons. We pay careful attention to the center of mass correction to the quark currents in matter. We then find that, for neutron $\beta$ decay in asymmetric nuclear matter, the defect of the vector coupling constant due to the $\delta$ field can be of the order of $10^{-4}$ at the nuclear saturation density, which is the same amount as the level of the current uncertainty in the measurements. It is also interesting that, in neutron-rich matter, there exists a certain low density at which isospin symmetry is restored, that is, the $u$-$d$ quark mass difference vanishes. We conclude that the effect of the isoscalar scalar and the isovector scalar fields should be considered in baryon semileptonic decays in nuclei., Comment: 49 pages, 13 figures, 6 tables

We introduce a pro-cdh topology on formal schemes and prove that the $\infty$-topos of pro-cdh sheaves of spaces has an optimal bound of homotopy dimension. This remedies a defect for a pro-cdh topology on schemes introduced in [KS23]. As an application, we give a topos-theoretic interpretation of Weibel's vanishing of negative K-theory and motivic cohomology of Elmanto and Morrow.

This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probability distribution into a complex probability distribution as an invertible function. By learning an invertible transformation between a complex multidimensional distribution, such as experimental data observed in collider experiments, and a multidimensional normal distribution, the normalizing flow model gains the ability to sample (or generate) pseudo experimental data from random numbers and to evaluate a log-likelihood value from multidimensional observed events. The normalizing flow model can also be extended to take multidimensional conditional variables as arguments. Thus, the normalizing flow model can be used as a generator and evaluator of pseudo experimental data conditioned by the BSM model parameters. The log-likelihood value, the output of the normalizing flow model, is a function of the conditional variables. Therefore, the model can quickly calculate gradients of the log-likelihood to the conditional variables. Following this property, it is expected that the most likely set of conditional variables that reproduce the experimental data, i.e. the optimal set of parameters for the BSM model, can be efficiently searched. This paper demonstrates this on a simple dataset and discusses its limitations and future extensions., Comment: Presented at International Symposium on Grids and Clouds (ISGC2024)

The increasing demand for high-quality 3D assets across various industries necessitates efficient and automated 3D content creation. Despite recent advancements in 3D generative models, existing methods still face challenges with optimization speed, geometric fidelity, and the lack of assets for physically based rendering (PBR). In this paper, we introduce 3DTopia-XL, a scalable native 3D generative model designed to overcome these limitations. 3DTopia-XL leverages a novel primitive-based 3D representation, PrimX, which encodes detailed shape, albedo, and material field into a compact tensorial format, facilitating the modeling of high-resolution geometry with PBR assets. On top of the novel representation, we propose a generative framework based on Diffusion Transformer (DiT), which comprises 1) Primitive Patch Compression, 2) and Latent Primitive Diffusion. 3DTopia-XL learns to generate high-quality 3D assets from textual or visual inputs. We conduct extensive qualitative and quantitative experiments to demonstrate that 3DTopia-XL significantly outperforms existing methods in generating high-quality 3D assets with fine-grained textures and materials, efficiently bridging the quality gap between generative models and real-world applications., Comment: Code https://github.com/3DTopia/3DTopia-XL Project Page https://3dtopia.github.io/3DTopia-XL/

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. 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

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

Radiance fields are powerful and, hence, popular models for representing the appearance of complex scenes. Yet, constructing them based on image observations gives rise to ambiguities and uncertainties. We propose a versatile approach for learning Gaussian radiance fields with explicit and fine-grained uncertainty estimates that impose only little additional cost compared to uncertainty-agnostic training. Our key observation is that uncertainties can be modeled as a low-dimensional manifold in the space of radiance field parameters that is highly amenable to Monte Carlo sampling. Importantly, our uncertainties are differentiable and, thus, allow for gradient-based optimization of subsequent captures that optimally reduce ambiguities. We demonstrate state-of-the-art performance on next-best-view planning tasks, including high-dimensional illumination planning for optimal radiance field relighting quality., Comment: Siggraph Asia 2024 conference

Spin amplification is the process that ideally increases the number of excited spins if there was one excited spin to begin with. Using optimal control techniques to find classical drive pulse shapes, we show that spin amplification can be done in the previously unexplored regime with amplification times comparable to the timescale set by the interaction terms in the Hamiltonian. This is an order of magnitude faster than the previous protocols and makes spin amplification possible even with significant decoherence and inhomogeneity in the spin system. The initial spin excitation can be delocalized over the entire ensemble, which is a more typical situation when a photon is collectively absorbed by the spins. We focus on the superconducting persistent-current artificial atoms as spins, but this approach can be applied to other kinds of strongly-interacting spins, including the Rydberg atoms., Comment: 14 pages, 10 figures

Evaluating speech intelligibility is a critical task in computer-aided language learning systems. Traditional methods often rely on word error rates (WER) provided by automatic speech recognition (ASR) as intelligibility scores. However, this approach has significant limitations due to notable differences between human speech recognition (HSR) and ASR. A promising alternative is to involve a native (L1) speaker in shadowing what nonnative (L2) speakers say. Breakdowns or mispronunciations in the L1 speaker's shadowing utterance can serve as indicators for assessing L2 speech intelligibility. In this study, we propose a speech generation system that simulates the L1 shadowing process using voice conversion (VC) techniques and latent speech representations. Our experimental results demonstrate that this method effectively replicates the L1 shadowing process, offering an innovative tool to evaluate L2 speech intelligibility. Notably, systems that utilize self-supervised speech representations (S3R) show a higher degree of similarity to real L1 shadowing utterances in both linguistic accuracy and naturalness., Comment: Submitted to ICASSP2025 Demo available: https://secondtonumb.github.io/publication_demo/ICASSP_2025/index.html

Matching users with mutual preferences is a critical aspect of services driven by reciprocal recommendations, such as job search. To produce recommendations in such scenarios, one can predict match probabilities and construct rankings based on these predictions. However, this direct match prediction approach often underperforms due to the extreme sparsity of match labels. Therefore, most existing methods predict preferences separately for each direction (e.g., job seeker to employer and employer to job seeker) and then aggregate the predictions to generate overall matching scores and produce recommendations. However, this typical approach often leads to practical issues, such as biased error propagation between the two models. This paper introduces and demonstrates a novel and practical solution to improve reciprocal recommendations in production by leveraging pseudo-match scores. Specifically, our approach generates dense and more directly relevant pseudo-match scores by combining the true match labels, which are accurate but sparse, with relatively inaccurate but dense match predictions. We then train a meta-model to output the final match predictions by minimizing the prediction loss against the pseudo-match scores. Our method can be seen as a best-of-both (BoB) approach, as it combines the high-level ideas of both direct match prediction and the two separate models approach. It also allows for user-specific weights to construct personalized pseudo-match scores, achieving even better matching performance through appropriate tuning of the weights. Offline experiments on real-world job search data demonstrate the superior performance of our BoB method, particularly with personalized pseudo-match scores, compared to existing approaches in terms of finding potential matches.

In this paper, we provide the semi-analytical solution of the temperature and flow fields of a fluid confined in a narrow space between two parallel plates. The temperature increase is triggered by photothermal effects of fluids and/or boundaries due to the absorption of a focused Gaussian beam irradiated perpendicular to the fluid film, and then the temperature variation induces the flow fields through a buoyancy force and/or thermo-osmotic slip. The semi-analytical solution to this optothermal fluidic system is validated by comparing with the results of numerical simulation, and is applied to typical optothermal fluidic problems. In particular, the optothermal trap of nanoparticles observed in our previous experiment [T. Tsuji, et al., Electrophoresis, 42, 2401 (2021)] is investigated in terms of thermophoretic force and flow drag that are obtained semi-analytically. The semi-analytical solution can be shared through open-source codes that are available to researchers without the background of fluid mechanics., Comment: 19 figures

We present our system (denoted as T05) for the VoiceMOS Challenge (VMC) 2024. Our system was designed for the VMC 2024 Track 1, which focused on the accurate prediction of naturalness mean opinion score (MOS) for high-quality synthetic speech. In addition to a pretrained self-supervised learning (SSL)-based speech feature extractor, our system incorporates a pretrained image feature extractor to capture the difference of synthetic speech observed in speech spectrograms. We first separately train two MOS predictors that use either of an SSL-based or spectrogram-based feature. Then, we fine-tune the two predictors for better MOS prediction using the fusion of two extracted features. In the VMC 2024 Track 1, our T05 system achieved first place in 7 out of 16 evaluation metrics and second place in the remaining 9 metrics, with a significant difference compared to those ranked third and below. We also report the results of our ablation study to investigate essential factors of our system., Comment: Accepted by IEEE SLT 2024. Our MOS prediction system (UTMOSv2) is available in https://github.com/sarulab-speech/UTMOSv2

Tactile perception is vital, especially when distinguishing visually similar objects. We propose an approach to incorporate tactile data into a Vision-Language Model (VLM) for visuo-tactile zero-shot object recognition. Our approach leverages the zero-shot capability of VLMs to infer tactile properties from the names of tactilely similar objects. The proposed method translates tactile data into a textual description solely by annotating object names for each tactile sequence during training, making it adaptable to various contexts with low training costs. The proposed method was evaluated on the FoodReplica and Cube datasets, demonstrating its effectiveness in recognizing objects that are difficult to distinguish by vision alone., Comment: 9 pages, 9 figures, accepted to IROS2024, project page: https://omron-sinicx.github.io/visuo-tactile-recognition/

Given a graph $G$ and two spanning trees $T$ and $T'$ in $G$, Spanning Tree Reconfiguration asks whether there is a step-by-step transformation from $T$ to $T'$ such that all intermediates are also spanning trees of $G$, by exchanging an edge in $T$ with an edge outside $T$ at a single step. This problem is naturally related to matroid theory, which shows that there always exists such a transformation for any pair of $T$ and $T'$. Motivated by this example, we study the problem of transforming a sequence of spanning trees into another sequence of spanning trees. We formulate this problem in the language of matroid theory: Given two sequences of bases of matroids, the goal is to decide whether there is a transformation between these sequences. We design a polynomial-time algorithm for this problem, even if the matroids are given as basis oracles. To complement this algorithmic result, we show that the problem of finding a shortest transformation is NP-hard to approximate within a factor of $c \log n$ for some constant $c > 0$, where $n$ is the total size of the ground sets of the input matroids., Comment: 16 pages, 5 figures

We propose GauFace, a novel Gaussian Splatting representation, tailored for efficient animation and rendering of physically-based facial assets. Leveraging strong geometric priors and constrained optimization, GauFace ensures a neat and structured Gaussian representation, delivering high fidelity and real-time facial interaction of 30fps@1440p on a Snapdragon 8 Gen 2 mobile platform. Then, we introduce TransGS, a diffusion transformer that instantly translates physically-based facial assets into the corresponding GauFace representations. Specifically, we adopt a patch-based pipeline to handle the vast number of Gaussians effectively. We also introduce a novel pixel-aligned sampling scheme with UV positional encoding to ensure the throughput and rendering quality of GauFace assets generated by our TransGS. Once trained, TransGS can instantly translate facial assets with lighting conditions to GauFace representation, With the rich conditioning modalities, it also enables editing and animation capabilities reminiscent of traditional CG pipelines. We conduct extensive evaluations and user studies, compared to traditional offline and online renderers, as well as recent neural rendering methods, which demonstrate the superior performance of our approach for facial asset rendering. We also showcase diverse immersive applications of facial assets using our TransGS approach and GauFace representation, across various platforms like PCs, phones and even VR headsets., Comment: Project Page: https://dafei-qin.github.io/TransGS.github.io/

In this note, after recalling a proof of the Macdonald identities for untwisted affine root systems, we derive the Macdonald identities for twisted affine root systems.

We explore cross-dialect text-to-speech (CD-TTS), a task to synthesize learned speakers' voices in non-native dialects, especially in pitch-accent languages. CD-TTS is important for developing voice agents that naturally communicate with people across regions. We present a novel TTS model comprising three sub-modules to perform competitively at this task. We first train a backbone TTS model to synthesize dialect speech from a text conditioned on phoneme-level accent latent variables (ALVs) extracted from speech by a reference encoder. Then, we train an ALV predictor to predict ALVs tailored to a target dialect from input text leveraging our novel multi-dialect phoneme-level BERT. We conduct multi-dialect TTS experiments and evaluate the effectiveness of our model by comparing it with a baseline derived from conventional dialect TTS methods. The results show that our model improves the dialectal naturalness of synthetic speech in CD-TTS., Comment: Accepted by IEEE SLT 2024

Young planetary systems represent an opportunity to investigate the early stages of (exo)planetary formation because the gravitational interactions have not yet significantly changed the initial configuration of the system. TOI-4562 b is a highly eccentric temperate Jupiter analogue orbiting a young F7V-type star of $<700$ Myr in age with an orbital period of $P_{orb} \sim 225$ days and an eccentricity of $e=0.76$, and is one of the largest known exoplanets to have formed in situ. We observed a new transit of TOI-4562 b using the 0.6-m Zeiss telescope at the Pico dos Dias Observatory (OPD/LNA) in Minas Gerais, Brazil, and combine our data with Transiting Exoplanet Survey Satellite (TESS) and archive data, with the aim being to improve the ephemerides of this interesting system. The $O-C$ diagram for the new ephemeris is consistent with the presence of a giant planet in an outer orbit around TOI-4562. TOI-4562 c is a planet with a mass of $M=5.77 M_{Jup}$, an orbital period of $P_{orb}= 3990$ days, and a semi-major axis of $a = 5.219$ AU. We report the discovery of TOI-4562 c, the exoplanet with the longest orbital period discovered to date via the transit timing variation (TTV) method. The TOI-4562 system is in the process of violent evolution with intense dynamical changes - judging by its young age and high eccentricity - and is therefore a prime target for studies of formation and evolution of planetary systems., Comment: 5 pages and 3 figures (+ appendix). Accepted for publication in Astronomy & Astrophysics, Letters to the Editor

Chiral superconductivity exhibits the formation of novel electron pairs that breaks the time-reversal symmetry and has been actively studied in various quantum materials in recent years. However, despite its potential to provide definitive information, effects of disorder in the crystal structure on the chiral superconductivity has not yet been clarified, and therefore the investigation using a solid-solution system is desirable. We report muon-spin-relaxation (muSR) results of layered pnictide BaPtAs_1-x_Sb_x_ with a honeycomb network composed of Pt and (As, Sb). We observed an increase of the zero-field muon-spin relaxation rate in the superconducting (SC) state at the Sb end of x=1.0, suggesting the occurrence of spontaneous magnetic field due to the time-reversal symmetry breaking in the SC state. On the other hand, spontaneous magnetic field was almost and completely suppressed for the As-Sb mixed samples of x=0.9 and 0.2, respectively, suggesting that the time-reversal symmetry breaking SC state in x=1.0 is sensitive to disorder. The magnetic penetration depth estimated from transverse-field muSR measurements at x=1.0 and 0.2 behaved like weak-coupling s-wave superconductivity. These seemingly incompatible zero-field and transverse-field muSR results of BaPtAs_1-x_Sb_x_ with x=1.0 could be understood in terms of chiral d-wave superconductivity with point nodes on the three-dimensional Fermi surface., Comment: 6 pages, 3 figures, 1 table

The Hawking-Moss (HM) bounce solution implies that the tunneling amplitude between vacua is uniquely determined by the vacuum energy at the initial vacuum and the top of a potential barrier, regardless of the field distance between them $\Delta \phi$. This implausible conclusion was carefully discussed in [E. J. Weinberg, Phys. Rev. Lett. 98, 251303, (2007)], and it was concluded that the conventional HM amplitude is not reliable for a transition to the top of distant local maxima (hereinafter referred to as the remote HM transition). We revisit this issue and study the impact of the quantum tunneling effect on the remote HM transition. We demonstrate that the amplitude for such a distant transition is indeed smaller than the conventional HM amplitude by employing the Lorentzian path integral in a simple setup. We consider a linear potential, which allows for analytic treatments, and evaluate the up-tunneling probability of a homogeneous scalar field in de Sitter spacetime. The Picard-Lefschetz theory is employed to identify the relevant Lefschetz thimble, representing the relevant tunneling trajectory. We then compare the resulting transition amplitude with the conventional HM amplitude. We find that when the field separation $|\Delta \phi|$ is larger, the quantum-tunneling amplitude, estimated by our Lorentzian path integral, is smaller than that of the conventional HM amplitude. This implies that the transition amplitude may be significantly suppressed if the thermal interpretation is not applicable and the quantum-tunneling effect is dominant for the remote HM transition., Comment: 13 pages, 5 figures

In this work, we propose a fashion item recommendation model that incorporates hyperbolic geometry into user and item representations. Using hyperbolic space, our model aims to capture implicit hierarchies among items based on their visual data and users' purchase history. During training, we apply a multi-task learning framework that considers both hyperbolic and Euclidean distances in the loss function. Our experiments on three data sets show that our model performs better than previous models trained in Euclidean space only, confirming the effectiveness of our model. Our ablation studies show that multi-task learning plays a key role, and removing the Euclidean loss substantially deteriorates the model performance., Comment: This work was presented at the CVFAD Workshop at CVPR 2024

We estimate the probability distribution for the spins of the primordial black holes (PBHs) that formed during an early matter-dominated era in the Universe. We employ the Zel'dovich approximation and focus on the linear-order effect of cosmological perturbations which causes the tidal torque. Assuming that the fluctuations obey Gaussian statistics, we apply the peak theory of random Gaussian variables to compute the root mean square (RMS) and the probability distribution of the non-dimensional Kerr parameter $a_{*}$ at their formation. The value of $a_{*}$ is evaluated through the angular momentum at the turn-around time. We find that the RMS $\bar{a}_{*}$ with a given amplitude of the fluctuation $\delta_{\rm{pk}}$ decreases with the amplitude. This behavior allows us to set the threshold value of the amplitude of the fluctuation through the under-extremal condition $\bar{a}_{*}<1$. Then we discuss the impact of spin and anisotropic collapse on the production rate of PBHs. We find that, for $\sigma_{H}\leq 10^{-3}$ with $\sigma_{\rm H}$ being the square root of the variance of the fluctuation at the horizon reentry, the suppression from the spin effect is dominant, while the effect of anisotropy becomes more important for $\sigma_{H}>10^{-3}$. Since $\bar{a}_{*}$ can be written as a function of $\nu:=\delta_{\rm{pk}}/\sigma_{\rm H}$, we can obtain the probability distribution of $\bar a_*$, $P(\bar a_*)$, through the probability distribution of $\nu$ characterized by a given power spectrum of the fluctuation. $P(\bar a_*)$ depends on $\sigma_{\rm H}$ and the parameter $\gamma$ that characterizes the width of the power spectrum. It is shown that, in the parameter regions of our interests, substantial values of PBH spins are expected in contrast to the PBH formation in a radiation-dominated universe., Comment: 25 pages, 7 figures

Joint injuries, and their long-term consequences, present a substantial global health burden. Wearable prophylactic braces are an attractive potential solution to reduce the incidence of joint injuries by limiting joint movements that are related to injury risk. Given human motion and ground reaction forces, we present a computational framework that enables the design of personalized braces by optimizing the distribution of microstructures and elasticity. As varied brace designs yield different reaction forces that influence kinematics and kinetics analysis outcomes, the optimization process is formulated as a differentiable end-to-end pipeline in which the design domain of microstructure distribution is parameterized onto a neural network. The optimized distribution of microstructures is obtained via a self-learning process to determine the network coefficients according to a carefully designed set of losses and the integrated biomechanical and physical analyses. Since knees and ankles are the most commonly injured joints, we demonstrate the effectiveness of our pipeline by designing, fabricating, and testing prophylactic braces for the knee and ankle to prevent potentially harmful joint movements.

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)

A localized Zeeman field, intensified at heterostructure interfaces, could play a crucial role in a broad area including spintronics and unconventional superconductors. Conventionally, the generation of a local Zeeman field is achieved through magnetic exchange coupling with a magnetic material. However, magnetic elements often introduce defects, which could weaken or destroy superconductivity. Alternatively, the coupling between a superconductor with strong spin-orbit coupling and a non-magnetic chiral material could serve as a promising approach to generate a spin active interface. In this study, we leverage an interface superconductor, namely induced superconductivity in noble metal surface states, to probe the spin active interface. Our results unveil an enhanced interface Zeeman field, which selectively closes the surface superconducting gap while preserving the bulk superconducting pairing. The chiral material, i.e. trigonal tellurium, also induces Andreev bound states (ABS) exhibiting spin polarization. The field dependence of ABS manifests a substantially enhanced interface Land\'e g-factor (g_eff ~ 12), thereby corroborating the enhanced interface Zeeman energy., Comment: 27 pages, 11 figures

Humans can exploit contacts anywhere on their body surface to manipulate large and heavy items, objects normally out of reach or multiple objects at once. However, such manipulation through contacts using the whole surface of the body remains extremely challenging to achieve on robots. This can be labelled as Whole-Body Contact-Rich Manipulation (WBCRM) problem. In addition to the high-dimensionality of the Contact-Rich Manipulation problem due to the combinatorics of contact modes, admitting contact creation anywhere on the body surface adds complexity, which hinders planning of manipulation within a reasonable time. We address this computational problem by formulating the contact and motion planning of planar WBCRM as hierarchical continuous optimization problems. To enable this formulation, we propose a novel continuous explicit representation of the robot surface, that we believe to be foundational for future research using continuous optimization for WBCRM. Our results demonstrate a significant improvement of convergence, planning time and feasibility - with, on the average, 99% less iterations and 96% reduction in time to find a solution over considered scenarios, without recourse to prone-to-failure trajectory refinement steps.

We present the substellar mass function of star-forming clusters ($\simeq$0.1 Myr old) in a low-metallicity environment ($\simeq$$-$0.7 dex). We performed deep JWST/NIRCam and MIRI imaging of two star-forming clusters in Digel Cloud 2, a star-forming region in the Outer Galaxy ($R_G \gtrsim 15$ kpc). The very high sensitivity and spatial resolution of JWST enable us to resolve cluster members clearly down to a mass detection limit of 0.02 $M_\odot$, enabling the first detection of brown dwarfs in low-metallicity clusters. Fifty-two and ninety-one sources were extracted in mass-$A_V$-limited samples in the two clusters, from which Initial mass functions (IMFs) were derived by model-fitting the F200W band luminosity function, resulting in IMF peak masses (hereafter $M_C$) $\log M_C / M_\odot \simeq -1.5 \pm 0.5$ for both clusters. Although the uncertainties are rather large, the obtained $M_C$ values are lower than those in any previous study ($\log M_C / M_\odot \sim -0.5$). Comparison with the local open clusters with similar ages to the target clusters ($\sim$$10^6$-$10^7$ yr) suggests a metallicity dependence of $M_C$, with lower $M_C$ at lower metallicities, while the comparison with globular clusters, similarly low metallicities but considerably older ($\sim$$10^{10}$ yr), suggests that the target clusters have not yet experienced significant dynamical evolution and remain in their initial physical condition. The lower $M_C$ is also consistent with the theoretical expectation of the lower Jeans mass due to the higher gas density under such low metallicity. The $M_C$ values derived from observations in such an environment would place significant constraints on the understanding of star formation., Comment: Accepted for publication in ApJ

A scalar field with a timelike gradient defines a preferred slicing. This occurs even in a non-cosmological setup in scalar-tensor theories such as khronometric theory. We study a black hole moving slowly relative to the preferred slicing defined by the scalar field. We consider a family of higher-order scalar-tensor theories that extend khronometric theory. The action is characterized by one constant parameter and one arbitrary function of the kinetic term of the scalar field. A slowly moving black hole is described by a dipole perturbation of a black hole at rest. We treat the metric and scalar-field perturbations consistently, derive a single master equation for the coupled system, and reconstruct the metric for a slowly moving black hole from the master variable. In this way, we revisit and generalize the previous studies. For generic theories in the family we consider, we find a solution that is regular outside the universal horizon and agnostic to the arbitrary function in the action., Comment: 13 pages, 3 figures

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

Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integer and continuous variables, and several integer handlings have been developed to inherit the optimization performance of the continuous optimization methods to mixed-integer optimization. In some applications, acceptable solutions are given by selecting possible points in the disjoint subspaces. This paper focuses on the optimization on sets of points and proposes an optimization method by extending the covariance matrix adaptation evolution strategy (CMA-ES), termed the CMA-ES on sets of points (CMA-ES-SoP). The CMA-ES-SoP incorporates margin correction that maintains the generation probability of neighboring points to prevent premature convergence to a specific non-optimal point, which is an effective integer-handling technique for CMA-ES. In addition, because margin correction with a fixed margin value tends to increase the marginal probabilities for a portion of neighboring points more than necessary, the CMA-ES-SoP updates the target margin value adaptively to make the average of the marginal probabilities close to a predefined target probability. Numerical simulations demonstrated that the CMA-ES-SoP successfully optimized the optimization problems on sets of points, whereas the naive CMA-ES failed to optimize them due to premature convergence., Comment: This paper has been accepted for presentation at PPSN2024

We present Sapiens, a family of models for four fundamental human-centric vision tasks -- 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction. Our models natively support 1K high-resolution inference and are extremely easy to adapt for individual tasks by simply fine-tuning models pretrained on over 300 million in-the-wild human images. We observe that, given the same computational budget, self-supervised pretraining on a curated dataset of human images significantly boosts the performance for a diverse set of human-centric tasks. The resulting models exhibit remarkable generalization to in-the-wild data, even when labeled data is scarce or entirely synthetic. Our simple model design also brings scalability -- model performance across tasks improves as we scale the number of parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks. We achieve significant improvements over the prior state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1 mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5% relative angular error. Project page: https://about.meta.com/realitylabs/codecavatars/sapiens., Comment: ECCV 2024 (Oral)

The potential of deep learning in clinical speech processing is immense, yet the hurdles of limited and imbalanced clinical data samples loom large. This article addresses these challenges by showcasing the utilization of automatic speech recognition and self-supervised learning representations, pre-trained on extensive datasets of normal speech. This innovative approach aims to estimate voice quality of patients with impaired vocal systems. Experiments involve checks on PVQD dataset, covering various causes of vocal system damage in English, and a Japanese dataset focusing on patients with Parkinson's disease before and after undergoing subthalamic nucleus deep brain stimulation (STN-DBS) surgery. The results on PVQD reveal a notable correlation (>0.8 on PCC) and an extraordinary accuracy (<0.5 on MSE) in predicting Grade, Breathy, and Asthenic indicators. Meanwhile, progress has been achieved in predicting the voice quality of patients in the context of STN-DBS., Comment: Accepted by Interspeech 2024

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space. For example, it might choose a set of furniture pieces (a bed and a drawer) from available items (bed, drawer, chair, etc.) for interior design sales. This setting is widespread in fields such as recommender systems and healthcare, yet OPE/L of CCB remains unexplored in the relevant literature. Typical OPE/L methods such as regression and importance sampling can be applied to the CCB problem, however, they face significant challenges due to high bias or variance, exacerbated by the exponential growth in the number of available subsets. To address these challenges, we introduce a concept of factored action space, which allows us to decompose each subset into binary indicators. This formulation allows us to distinguish between the ''main effect'' derived from the main actions, and the ''residual effect'', originating from the supplemental actions, facilitating more effective OPE. Specifically, our estimator, called OPCB, leverages an importance sampling-based approach to unbiasedly estimate the main effect, while employing regression-based approach to deal with the residual effect with low variance. OPCB achieves substantial variance reduction compared to conventional importance sampling methods and bias reduction relative to regression methods under certain conditions, as illustrated in our theoretical analysis. Experiments demonstrate OPCB's superior performance over typical methods in both OPE and OPL., Comment: accepted at RecSys2024

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

Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill the gap, we propose DisMix, a generative framework in which the pitch and timbre representations act as modular building blocks for constructing the melody and instrument of a source, and the collection of which forms a set of per-instrument latent representations underlying the observed mixture. By manipulating the representations, our model samples mixtures with novel combinations of pitch and timbre of the constituent instruments. We can jointly learn the disentangled pitch-timbre representations and a latent diffusion transformer that reconstructs the mixture conditioned on the set of source-level representations. We evaluate the model using both a simple dataset of isolated chords and a realistic four-part chorales in the style of J.S. Bach, identify the key components for the success of disentanglement, and demonstrate the application of mixture transformation based on source-level attribute manipulation.

Extracting geometry features from photographic images independently of surface texture and transferring them onto different materials remains a complex challenge. In this study, we introduce Harmonizing Attention, a novel training-free approach that leverages diffusion models for texture-aware geometry transfer. Our method employs a simple yet effective modification of self-attention layers, allowing the model to query information from multiple reference images within these layers. This mechanism is seamlessly integrated into the inversion process as Texture-aligning Attention and into the generation process as Geometry-aligning Attention. This dual-attention approach ensures the effective capture and transfer of material-independent geometry features while maintaining material-specific textural continuity, all without the need for model fine-tuning., Comment: Accepted at WACV2025

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

Elucidating fundamental limitations inherent in physical systems is a central subject in physics. For important thermodynamic operations such as information erasure, cooling, and copying, resources like time and energetic cost must be expended to achieve the desired outcome within a predetermined error margin. In this study, we introduce the concept of separated states, which consist of fully unoccupied and occupied states. This concept generalizes many critical states involved in relevant thermodynamic operations. We then uncover a three-way trade-off relation between time, cost, and error for a general class of thermodynamic operations aimed at creating separated states, simply expressed as $\tau\mathcal{C}\varepsilon_{\tau}\ge 1-\eta$. This fundamental relation is applicable to diverse thermodynamic operations, including information erasure, cooling, and copying. It provides a profound quantification of the unattainability principle in the third law of thermodynamics in a general form. Building upon this relation, we explore the quantitative limitations governing cooling operations, the preparation of separated states, and a no-go theorem for exact classical copying. Furthermore, we extend these findings to the quantum regime, encompassing both Markovian and non-Markovian dynamics. Specifically, within Lindblad dynamics, we derive a similar three-way trade-off relation that quantifies the cost of achieving a pure state with a given error. The generalization to general quantum dynamics involving a system coupled to a finite bath implies that heat dissipation becomes infinite as the quantum system is exactly cooled down to the ground state or perfectly reset to a pure state, thereby resolving an open question regarding the thermodynamic cost of information erasure., Comment: 21 pages, 6 figures

The classical Cepheid KQ Sco is a valuable anchor for the distance scale because of its long pulsation period ($28^{\rm d}.7$) and evidence implying membership in the open cluster UBC 1558. Analyses tied to Gaia DR3 astrometry, photometry, spectroscopy, radial velocities, and 2MASS-VVV photometry indicate a common distance of $2.15\pm0.15$ kpc (\citealt{lin21} DR3 corrections applied). Additional cluster Cepheid candidates requiring follow-up are identified, and it's suggested that a team of international researchers could maintain a cluster Cepheid database to guide the broader community to cases where consensus exists., Comment: Accepted for publication (PASP). Modifications include corrected survey IDs in Table 2

K. Saito (Publ. RIMS 21 (1), 1985, 75-179) has introduced a class of root systems called elliptic root systems which lies in the real vector space $F$ with a metric $I$ whose signature is of type $(l,2,0)$. He also classified the pair $(R,G)$ of an elliptic root system $R$ with one dimensional subspace $G$ of the radical of $I$, under the assumption that the quotient root system $R/G$ is reduced. Here, we classify the pairs $(R,G)$ where $R/G$ is non-reduced.

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