Search

We present the first measurement of the missing energy due to nuclear effects in monoenergetic, muon neutrino charged-current interactions on carbon, originating from $K^+ \rightarrow \mu^+ \nu_\mu$ decay-at-rest ($E_{\nu_\mu}=235.5$ MeV), performed with the JSNS$^2$ liquid scintillator based experiment. Towards characterizing the neutrino interaction, ostensibly $\nu_\mu n \rightarrow \mu^- p$ or $\nu_\mu$$^{12}\mathrm{C}$ $\rightarrow \mu^-$$^{12}\mathrm{N}$, and in analogy to similar electron scattering based measurements, we define the missing energy as the energy transferred to the nucleus ($\omega$) minus the kinetic energy of the outgoing proton(s), $E_{m} \equiv \omega-\sum T_p$, and relate this to visible energy in the detector, $E_{m}=E_{\nu_\mu}~(235.5~\mathrm{MeV})-m_\mu~(105.7~\mathrm{MeV}) - E_{vis}$. The missing energy, which is naively expected to be zero in the absence of nuclear effects (e.g. nucleon separation energy, Fermi momenta, and final-state interactions), is uniquely sensitive to many aspects of the interaction, and has previously been inaccessible with neutrinos. The shape-only, differential cross section measurement reported, based on a $(77\pm3)$% pure double-coincidence KDAR signal (621 total events), provides an important benchmark for models and event generators at 100s-of-MeV neutrino energies, characterized by the difficult-to-model transition region between neutrino-nucleus and neutrino-nucleon scattering, and relevant for applications in nuclear physics, neutrino oscillation measurements, and Type-II supernova studies.

We theoretically investigate quasiparticle properties of a neutron immersed in an alpha condensate, which is one of the possible states of dilute symmetric nuclear matter. The resonant $p$-wave neutron-alpha scattering, which plays a crucial role in forming halo nuclei, is considered. This system is similar to a Bose polaron near the $p$-wave Feshbach resonance that can be realized in cold-atomic experiments. Calculating the low-momentum self-energy within the field-theoretical approach, we give an analytical formula for the effective mass of a polaronic neutron as a function of alpha condensation density. Two adjacent neutrons in a medium, each of which behaves like a stable polaron having an enhanced effective mass, can form a bound dineutron, with the help of $^1S_0$ neutron-neutron attraction. This is in contrast to the case of the vacuum, where a dineutron is known to be unbound. Moreover, adding an impurity-like neutron to symmetric nuclear matter can be regarded as a perturbation accompanied by an explicit breaking of Wigner's $ SU \left( 4 \right) $ symmetry. The relationship between the polaron energy and the Wigner term in nuclear mass formulas is discussed in the low-density limit. Our result would be useful for understanding many-body physics in astrophysical environments as well as the formation of multi-nucleon clusters in neutron-halo nuclei., Comment: 13 pages, 6 figures

The transparent formulation of explanation methods is essential for elucidating the predictions of neural networks, which are typically black-box models. Layer-wise Relevance Propagation (LRP) is a well-established method that transparently traces the flow of a model's prediction backward through its architecture by backpropagating relevance scores. However, the conventional LRP does not fully consider the existence of skip connections, and thus its application to the widely used ResNet architecture has not been thoroughly explored. In this study, we extend LRP to ResNet models by introducing Relevance Splitting at points where the output from a skip connection converges with that from a residual block. Our formulation guarantees the conservation property throughout the process, thereby preserving the integrity of the generated explanations. To evaluate the effectiveness of our approach, we conduct experiments on ImageNet and the Caltech-UCSD Birds-200-2011 dataset. Our method achieves superior performance to that of baseline methods on standard evaluation metrics such as the Insertion-Deletion score while maintaining its conservation property. We will release our code for further research at https://5ei74r0.github.io/lrp-for-resnet.page/, Comment: Accepted for presentation at ECCV2024

Soft robotics has emerged as the standard solution for grasping deformable objects, and has proven invaluable for mobile robotic exploration in extreme environments. However, despite this growth, there are no widely adopted computational design tools that produce quality, manufacturable designs. To advance beyond the diminishing returns of heuristic bio-inspiration, the field needs efficient tools to explore the complex, non-linear design spaces present in soft robotics, and find novel high-performing designs. In this work, we investigate a hierarchical design optimization methodology which combines the strengths of topology optimization and quality diversity optimization to generate diverse and high-performance soft robots by evolving the design domain. The method embeds variably sized void regions within the design domain and evolves their size and position, to facilitating a richer exploration of the design space and find a diverse set of high-performing soft robots. We demonstrate its efficacy on both benchmark topology optimization problems and soft robotic design problems, and show the method enhances grasp performance when applied to soft grippers. Our method provides a new framework to design parts in complex design domains, both soft and rigid.

FeSe$_{1-x}$S$_{x}$ superconductors are commonly considered layered van der Waals materials with negligible inter-layer coupling. Here, using inelastic neutron scattering to study spin excitations in single-crystal samples, we reveal that the magnetic coupling between adjacent Fe layers is not only significant, as it affects excitations up to \textcolor{black}{15} meV, but also ferromagnetic in nature, making the system different from most unconventional superconductors including iron pnictides. Our observation provides a new standpoint to understand the absence of magnetic order in FeSe$_{1-x}$S$_{x}$. Since intercalating between the Fe layers is known to enhance superconductivity and suppress the inter-layer coupling, superconductivity appears to be a more robust phenomenon in the two-dimensional limit than antiferromagnetic order.

This study introduces a groundbreaking approach to simultaneous interpretation by directly leveraging the predictive capabilities of Large Language Models (LLMs). We present a novel algorithm that generates real-time translations by predicting speaker utterances and expanding multiple possibilities in a tree-like structure. This method demonstrates unprecedented flexibility and adaptability, potentially overcoming the structural differences between languages more effectively than existing systems. Our theoretical analysis, supported by illustrative examples, suggests that this approach could lead to more natural and fluent translations with minimal latency. The primary purpose of this paper is to share this innovative concept with the academic community, stimulating further research and development in this field. We discuss the theoretical foundations, potential advantages, and implementation challenges of this technique, positioning it as a significant step towards democratizing multilingual communication., Comment: 7 pages

We investigate the phase structure and the equation of state (EoS) for dense two-color QCD (QC$_2$D) at low temperature ($T = 40$ MeV, $32^4$ lattice) for the purpose of extending our previous works~\cite{Iida:2019rah, Iida:2022hyy} at $T=80$ MeV ($16^4$ lattice). Indeed, a rich phase structure below the pseudo-critical temperature $T_c$ as a function of quark chemical potential $\mu$ has been revealed, but finite volume effects in a high-density regime sometimes cause a wrong understanding. Therefore, it is important to investigate the temperature dependence down to zero temperature with large-volume simulations. By performing $32^4$ simulations, we obtain essentially similar results to the previous ones, but we are now allowed to get a fine understanding of the phase structure via the temperature dependence. Most importantly, we find that the hadronic-matter phase, which is composed of thermally excited hadrons, shrinks with decreasing temperature and that the diquark condensate scales as $\langle qq \rangle \propto \mu^2$ in the BCS phase, a property missing at $T=80$ MeV. From careful analyses, furthermore, we confirm a tentative conclusion that the topological susceptibility is independent of $\mu$. We also show the temperature dependence of the pressure, internal energy, and sound velocity as a function of $\mu$. The pressure increases around the hadronic-superfluid phase transition more rapidly at the lower temperature, while the temperature dependence of the sound velocity is invisible. Breaking of the conformal bound is also confirmed thanks to the smaller statistical error., Comment: 29 pages, 11 figures, (v2) minor update, a reference added

Advances in artificial intelligence (AI) have been propelling the evolution of human-robot interaction (HRI) technologies. However, significant challenges remain in achieving seamless interactions, particularly in tasks requiring physical contact with humans. These challenges arise from the need for accurate real-time perception of human actions, adaptive control algorithms for robots, and the effective coordination between human and robotic movements. In this paper, we propose an approach to enhancing physical HRI with a focus on dynamic robot-assisted hand-object interaction (HOI). Our methodology integrates hand pose estimation, adaptive robot control, and motion primitives to facilitate human-robot collaboration. Specifically, we employ a transformer-based algorithm to perform real-time 3D modeling of human hands from single RGB images, based on which a motion primitives model (MPM) is designed to translate human hand motions into robotic actions. The robot's action implementation is dynamically fine-tuned using the continuously updated 3D hand models. Experimental validations, including a ring-wearing task, demonstrate the system's effectiveness in adapting to real-time movements and assisting in precise task executions., Comment: 8 pages, 10 figures

External pressure and associated changes in lattice structures are key to realizing exotic quantum phases such as high-$T_{\rm c}$ superconductivity. While applying external pressure is a standard method to induce novel lattice structures, its impact on organic molecular crystals has been less explored. Here we report a unique structural phase transition in (DMET-TTF)$_2$AuBr$_2$ under pressure. By combining advanced high-pressure techniques and $ab$ $initio$ calculations, we elucidate that (DMET-TTF)$_2$AuBr$_2$ undergoes a transition from a quasi-one-dimensional lattice to a nearly perfect rectangular lattice at 0.9 GPa. This transition leads to the realization of an antiferromagnetic Mott insulator with $T_{\rm N}=66$ K, the highest $T_{\rm N}$ in low-dimensional molecular crystal solids to date. Upon increasing the pressure, the antiferromagnetic ordering is suppressed, and a superconducting phase with $T_{\rm c}=4.8$ K emerges around 6 GPa. Our study reveals the significant impact of external pressure on lattice structures of organic molecular crystals and highlights the intricate relationship between geometrical frustration and superconductivity. Our findings also pave the way for realizing functional organic molecular crystals through changes in lattice structures by pressure., Comment: 11 pages, 7 figures, 3 Tables

Cross-lingual continual pre-training of large language models (LLMs) initially trained on English corpus allows us to leverage the vast amount of English language resources and reduce the pre-training cost. In this study, we constructed Swallow, an LLM with enhanced Japanese capability, by extending the vocabulary of Llama 2 to include Japanese characters and conducting continual pre-training on a large Japanese web corpus. Experimental results confirmed that the performance on Japanese tasks drastically improved through continual pre-training, and the performance monotonically increased with the amount of training data up to 100B tokens. Consequently, Swallow achieved superior performance compared to other LLMs that were trained from scratch in English and Japanese. An analysis of the effects of continual pre-training revealed that it was particularly effective for Japanese question answering tasks. Furthermore, to elucidate effective methodologies for cross-lingual continual pre-training from English to Japanese, we investigated the impact of vocabulary expansion and the effectiveness of incorporating parallel corpora. The results showed that the efficiency gained through vocabulary expansion had no negative impact on performance, except for the summarization task, and that the combined use of parallel corpora enhanced translation ability.

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion pages crawled between 2020 and 2023). This corpus consists of approximately 312.1 billion characters (approximately 173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (approximately 25.8 billion characters), mC4 (approximately 239.7 billion characters) and OSCAR 23.10 (approximately 74 billion characters). To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral 7B v0.1, and Mixtral 8x7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora., Comment: 17 pages

Giant diffusion, where the diffusion coefficient of a Brownian particle in a periodic potential with an external force is significantly enhanced by the external force, is a non-trivial non-equilibrium phenomenon. We propose a simple stochastic model of giant diffusion, which is based on a biased continuous-time random walk (CTRW). In this model, we introduce a flight time in the biased CTRW. We derive the diffusion coefficients of this model by the renewal theory and find that there is a maximum diffusion coefficient when the bias is changed. Giant diffusion is universally observed in the sense that there is a peak of the diffusion coefficient for any tilted periodic potentials and the degree of the diffusivity is greatly enhanced especially for low-temperature regimes. The biased CTRW models with flight times are applied to diffusion under three tilted periodic potentials. Furthermore, the temperature dependence of the maximum diffusion coefficient and the external force that attains the maximum are presented for diffusion under a tilted sawtooth potential., Comment: 13 pages, 7 figures

JSNS$^2$ searches for short baseline neutrino oscillations with a baseline of 24~meters and a target of 17~tonnes of the Gd-loaded liquid scintillator. The correct algorithm on the event reconstruction of events, which determines the position and energy of neutrino interactions in the detector, are essential for the physics analysis of the data from the experiment. Therefore, the performance of the event reconstruction is carefully checked with calibrations using $^{252}$Cf source. This manuscript describes the methodology and the performance of the event reconstruction.

Quadratic Assignment Problem (QAP) is a practical combinatorial optimization problems that has been studied for several years. Since it is NP-hard, solving large problem instances of QAP is challenging. Although heuristics can find semi-optimal solutions, the execution time significantly increases as the problem size increases. Recently, solving combinatorial optimization problems by deep learning has been attracting attention as a faster solver than heuristics. Even with deep learning, however, solving large QAP is still challenging. In this paper, we propose the deep reinforcement learning model called the two-stage graph pointer network (GPN) for solving QAP. Two-stage GPN relies on GPN, which has been proposed for Euclidean Traveling Salesman Problem (TSP). First, we extend GPN for general TSP, and then we add new algorithms to that model for solving QAP. Our experimental results show that our two-stage GPN provides semi-optimal solutions for benchmark problem instances from TSPlib and QAPLIB., Comment: 7 pages, 7 figures

JSNS$^2$ (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\bar{\nu}_{\mu} \rightarrow \bar{\nu}_e$ appearance oscillations using neutrinos with muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD) is essential because the JSNS$^2$ detector is located above ground, on the third floor of the building. We have achieved 95$\%$ rejection of neutron events while keeping 90$\%$ of signal, electron-like events using a data driven likelihood method., Comment: arXiv admin note: text overlap with arXiv:2111.07482, arXiv:2308.02722

Inexact interior-point methods (IPMs) are a type of interior-point methods that inexactly solve the linear equation system for obtaining the search direction. On the other hand,arc-search IPMs approximate the central path with an ellipsoidal arc obtained by solving two linear equation systems in each iteration, while conventional line-search IPMs solve one linear system, therefore, the improvement due to the inexact solutions of the linear equation systems can be more beneficial in arc-search IPMs than conventional IPMs. In this paper, we propose an inexact infeasible arc-search interior-point method.We establish that the proposed method is a polynomial-time algorithm through its convergence analysis. The numerical experiments with the conjugate gradient method show that the proposed method can reduce the number of iterations compared to an existing method for benchmark problems; the numbers of iterations are reduced to two-thirds for more than 70% of the problems., Comment: 25 pages, 3 figures

We present a framework for studying transverse knots and symplectic surfaces utilizing the Seiberg-Witten monopole equation. Our primary approach involves investigating an equivariant Seiberg-Witten theory introduced by Baraglia-Hekmati on branched covers, incorporating invariant contact/symplectic structures. Within this framework, we introduce a novel slice-torus invariant denoted as $q_M(K)$. This invariant can be viewed as the Seiberg-Witten analog of Hendricks-Lipshitz-Sarker's $q_\tau$ invariant, with a signature correction term. One property of the invariant $q_M(K)$ is an adjunction equality for properly embedded connected symplectic surfaces in the symplectic filling $D^4\# m \overline{\mathbb{C}P}^2$. The proof of this equality utilizes the equivariant version of the homotopical contact invariant introduced by the authors, leading to a transverse knot invariant. Another ingredient of the proof involves constructing invariant symplectic structures on branched covering spaces branched along properly embedded symplectic surfaces in symplectic fillings. As an application of the invariant $q_M(K)$, we determine the value of any slice-torus invariant within a permissible deviation of $2$ for squeezed knots concordant to certain Montesinos knots. Additionally, we provide an obstruction to realizing second homology classes of $D^4 \#m \overline{\mathbb{C}P}^2$ as connected embedded symplectic surfaces with transverse knot boundary or connected embedded Lagrangian surfaces with collarable Legendrian knot boundary. Moreover, we introduce a new obstruction to certain Montesinos knots being quasipositive, which is described only in terms of slice genera and their signatures., Comment: 58 pages, typo corrected

Generating annotations for bird's-eye-view (BEV) segmentation presents significant challenges due to the scenes' complexity and the high manual annotation cost. In this work, we address these challenges by leveraging the abundance of unlabeled data available. We propose the Perspective Cue Training (PCT) framework, a novel training framework that utilizes pseudo-labels generated from unlabeled perspective images using publicly available semantic segmentation models trained on large street-view datasets. PCT applies a perspective view task head to the image encoder shared with the BEV segmentation head, effectively utilizing the unlabeled data to be trained with the generated pseudo-labels. Since image encoders are present in nearly all camera-based BEV segmentation architectures, PCT is flexible and applicable to various existing BEV architectures. PCT can be applied to various settings where unlabeled data is available. In this paper, we applied PCT for semi-supervised learning (SSL) and unsupervised domain adaptation (UDA). Additionally, we introduce strong input perturbation through Camera Dropout (CamDrop) and feature perturbation via BEV Feature Dropout (BFD), which are crucial for enhancing SSL capabilities using our teacher-student framework. Our comprehensive approach is simple and flexible but yields significant improvements over various baselines for SSL and UDA, achieving competitive performances even against the current state-of-the-art., Comment: 13 pages, 5 figures; Accepted to IROS 2024

We show that the knot group of any knot in any integer homology sphere admits a non-abelian representation into $SU(3)$ such that meridians are mapped to matrices whose eigenvalues are the three distinct third roots of unity. This answers the $N=3$ case of a question posed by Xie and the first author. We also characterize when a $PU(3)$-bundle admits a flat connection. The key ingredient in the proofs is a study of the ring structure of $U(3)$ instanton Floer homology of $S^1\times \Sigma_g$. In an earlier paper, Xie and the first author stated the so-called eigenvalue conjecture about this ring, and in this paper we partially resolve this conjecture. This allows us to establish a surface decomposition theorem for $U(3)$ instanton Floer homology of sutured manifolds, and then obtain the mentioned topological applications. Along the way, we prove a structure theorem for $U(3)$ Donaldson invariants, which is the counterpart of Kronheimer and Mrowka's structure theorem for $U(2)$ Donaldson invariants. We also prove a non-vanishing theorem for the $U(3)$ Donaldson invariants of symplectic manifolds., Comment: 69 pages, 1 figure

The existence of cosmic dark matter and neutrino properties are long-standing problems in cosmology and particle physics. These problems have been investigated by using radiation detectors. We will discuss the application of inorganic crystal scintillators to studies on dark matter and neutrino properties. A large volume and high-purity inorganic crystal is a promising detector for investigating dark matter and neutrino.

Uncovering neutrinoless double beta decay (0$\nu$2$\beta$) is crucial for confirming neutrinos' Majorana characteristics. The decay rate of 0$\nu\beta\beta$ is theoretically uncertain, influenced by nuclear matrix elements that vary across nuclides. To reduce this uncertainty, precise measurement of the half-life of neutrino-emitting double beta decay (2$\nu$2$\beta$) in different nuclides is essential. We have launched the PIKACHU (Pure Inorganic scintillator experiment in KAmioka for CHallenging Underground sciences) project to fabricate high-purity Ce-doped Gd$_{3}$Ga$_{3}$Al$_{2}$O$_{12}$ (GAGG) single crystals and use them to study the double beta decay of $^{160}$Gd. Predictions from two theoretical models on nuclear matrix element calculations for 2$\nu$2$\beta$ in $^{160}$Gd show a significant discrepancy in estimated half-lives, differing by approximately an order of magnitude. If the lower half-life estimation holds true, detecting 2$\nu$2$\beta$ in $^{160}$Gd could be achievable with a sensitivity enhancement slightly more than an order of magnitude compared to prior investigations using Ce-doped Gd$_2$SiO$_5$ (GSO) crystal. We have successfully developed GAGG crystals with purity levels surpassing previous standards through refined purification and selection of raw materials. Our experiments with these crystals indicate the feasibility of reaching sensitivities exceeding those of earlier studies. This paper discusses the ongoing development and scintillator performance evaluation of High-purity GAGG crystals, along with the anticipated future prospects of the PIKACHU experiment., Comment: 19 pages, 10 figures

We theoretically investigate a non-relativistic trace anomaly and its impact on the low-temperature equation of state in spatially one-dimensional three-component fermionic systems with a three-body interaction, which exhibit a non-trivial three-body crossover from a bound trimer gas to dense fermionic matter with increasing density. By applying the $G$-matrix approach to the three-body interaction, we obtain the analytical expression for the ground-state equation of state relevant to the high-density degenerate regime and thereby address how the three-body contact or, equivalently, the trace anomaly emerges. The analytical results are compared with the recent quantum Monte Carlo data. Our study of the trace anomaly and the sound speed could have some relevance to the physics of hadron-quark crossover in compact stars., Comment: 7 pages, 4 figures

In current accelerators, numerous parameters and monitored values are to be adjusted and evaluated, respectively. In addition, fine adjustments are required to achieve the target performance. Therefore, the conventional accelerator-operation method, in which experts manually adjust the parameters, is reaching its limits. We are currently investigating the use of machine learning for accelerator tuning as an alternative to expert-based tuning. In recent years, machine-learning algorithms have progressed significantly in terms of speed, sensitivity, and application range. In addition, various libraries are available from different vendors and are relatively easy to use. Herein, we report the results of electron-beam tuning experiments using Bayesian optimization, a tree-structured Parzen estimator, and a covariance matrix-adaptation evolution strategy. Beam-tuning experiments are performed at the KEK $e^-$/$e^+$ injector Linac to maximize the electron-beam charge and reduce the energy-dispersion function. In each case, the performance achieved is comparable to that of a skilled expert.

Spontaneous Hall conductivity has recently been reported in the triangular lattice antiferromagnet Co$_{1/3}$TaS$_2$ under a zero magnetic field. This phenomenon originates from the distinctive noncoplanar triple-Q magnetic ground state, possessing uniform real-space Berry curvature characterized by scalar spin chirality. We investigated the physical properties of Co$_{1/3}$TaS$_2$ by judiciously controlling the composition, revealing a drastic change in its bulk properties, even by slight variations in cobalt composition, despite the same crystal structure. For $0.299 < x < 0.325$, Co$_x$TaS$_2$ keeps all the characteristics of the ground state consistent with the previous studies -- two antiferromagnetic phase transitions at $T_{N1}$ and $T_{N2} (< T_{N1})$, a large spontaneous Hall conductivity (${\sigma}_{xy} (H=0)$), and a weak ferromagnetic moment along the c-axis. However, samples with $x > 0.330$ exhibit distinct bulk properties, including the absence of both ${\sigma}_{xy} (H=0)$ and the weak ferromagnetic moment. Our neutron diffraction data reveal that Co$_x$TaS$_2$ with $x > 0.330$ develops coplanar helical magnetic order with $q_{m1} = (1/3, 0, 0)$. This is entirely different from what has been seen in $x < 0.325$, explaining the observed composition dependence., Comment: 15 pages, 4 figures, accepted for publication in Phys. Rev. B