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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.

The cellular structure is considered to be a key as a criterion in initiation, propagation, and quenching of terrestrial detonation. While a few studies on type Ia supernovae, which are known to involve detonation, have addressed the importance of the cellular structure, further detailed treatment will benefit enhanced understanding of the explosion outcomes. In the present study, we bridge this gap in the astrophysics and engineering fields, focusing on the detonation in a heliumrich white dwarf envelope as the triggering process for the so-called double-detonation model. The cellular structures are quantified via high-resolution two-dimensional simulations. We demonstrate that widely-accepted terrestrial-experimental criteria for quenching and initiation of detonation can indeed explain the results of previous hydrodynamic simulations very well. The present study highlights the potential of continuing to apply the insight from terrestrial detonation experiments to astrophysical problems, specifically the long unresolved problem of the explosion mechanism of type Ia supernovae., Comment: accepted for publication in PRL

We report the discovery of a characteristic trend in the intensity ratios of SiO emissions across the Central Molecular Zone (CMZ) of our Galaxy. Using the Nobeyama Radio Observatory 45-m telescope, we conducted large-scale, high-sensitivity imaging observations in molecular lines including SiO $J$=2$-$1 and CS $J$=2$-$1. By identifying SiO-emitting clouds and examining their intensity ratios relative to the other molecular lines, we unveiled a parabolic-like trend showing lower ratios near the Galactic nucleus, Sgr A$^*$, with gradual increases toward the edges of the CMZ. This pattern suggests a possible outburst of the nucleus within the last $\sim 10^5$ yr, which may have propagated through the entire CMZ with strong shocks. Alternatively, the observed trend may also be attributed to the destruction of small dust grains by high-energy photons. Our results can potentially lead to a new perspective on the history of nuclear activity and its impact on the surrounding molecular environment., Comment: 9 pages, 4 figures, accepted for publication in the Astrophysical Journal Letters

Asymmetric Numeral Systems (ANS) proposed by Jarek Duda are high-performance distortionless data compression schemes that can achieve almost the same compression performance as arithmetic codes with less arithmetic operations than arithmetic coding. The ANS is widely used in various practical systems like Facebook, Apple, Google, Dropbox, Microsoft, and Pixar, due to their high performance, but many researchers still lack much knowledge about the ANS. This paper thoroughly explains the encoding and decoding algorithms of the ANS, and theoretically analyzes the average code length achievable by the ANS., Comment: This paper is an English translation version of an invited paper published in the IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition)

A prototypical charge-transfer type Mott insulator NiS2 pyrite exhibits a metal-insulator transition with bandwidth control. Recent discoveries on surface-specific electronic states on other 3d transition-metal disulfide pyrites motivate us to further investigate the surface of NiS2, where metallic surface conduction is discussed. Here, the spectroscopic-imaging scanning-tunneling-microscopy observations revealed that the surface is not metallic, contrary to the expectation. Instead, the Mott gap is closed near step edges, suggesting possible electrical conduction from one-dimensional channels. The edge anomaly was observed irrespective of its magnetic order and is limited to the insulator phases., Comment: 5 pages, 5 figures

This study proposes a radar-based heartbeat measurement method that uses the absolute value of the second derivative of the complex radar signal, rather than its phase, and the variational mode extraction method, which is a type of mode decomposition algorithm. We show that the proposed second-derivative-based approach can amplify the heartbeat component in radar signals effectively and also confirm that use of the variational mode extraction method represents an efficient way to emphasize the heartbeat component amplified via the second-derivative-based approach. We demonstrate estimation of the heart interbeat intervals using the proposed approach in combination with the topology method, which is an accurate interbeat interval estimation method. The performance of the proposed method is evaluated quantitatively using data obtained from eleven participants that were measured using a millimeter-wave radar system. When compared with conventional methods based on the phase of the complex radar signal, our proposed method can achieve higher accuracy when estimating the heart interbeat intervals; the correlation coefficient for the proposed method was increased by 0.20 and the root-mean-square error decreased by 23%., Comment: 8 pages, 9 figures, 3 tables. This work is going to be submitted to the IEEE for possible publication

X-ray polarization is a powerful tool for unveiling the anisotropic characteristics of high-energy celestial objects. We present a novel imaging reconstruction method designed for hard X-ray polarimeters employing a Si CMOS sensor and coded apertures, which function as a photoelectron tracker and imaging optics, respectively. Faced with challenges posed by substantial artifacts and background noise in the coded aperture imaging associated with the conventional balanced correlation method, we adopt the Expectation-Maximization (EM) algorithm as the foundation of our imaging reconstruction method. The newly developed imaging reconstruction method is validated with imaging polarimetry and a series of X-ray beam experiments. The method demonstrates the capability to accurately reproduce an extended source comprising multiple segments with distinct polarization degrees. Comparative analysis exhibits a significant enhancement in imaging reconstruction accuracy compared to the balanced correlation method, with the background noise levels reduced to 17%. The outcomes of this study enhance the feasibility of Cube-Sat imaging polarimetry missions in the hard X-ray band, as the combination of Si CMOS sensors and coded apertures is a promising approach for realizing it., Comment: 12 pages, 5 figures, 1 table, accepted for publication in NIM-A

Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) $\mathrm{Mn}_{1-x} \mathrm{Ge}_x \mathrm{Bi}_2 \mathrm{Te}_4$ have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10$\%$ to 75$\%$. It was shown that when Ge concentration increases then the bulk band gap decreases and reaches zero plateau in the concentration range of 45$\%$-60$\%$ while non-topological surface states (TSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40$\%$. DFT calculations of $\mathrm{Mn}_{1-x} \mathrm{Ge}_x \mathrm{Bi}_2 \mathrm{Te}_4$ band structure were carried out to identify the nature of observed band dispersion features and to analyze a possibility of magnetic Weyl semimetal state formation in this system. These calculations were performed for both antiferromagnetic (AFM) and ferromagnetic (FM) ordering types while the spin-orbit coupling (SOC) strength was varied or a strain (compression or tension) along the $c$-axis was applied. Calculations show that two different series of topological phase transitions (TPTs) may be implemented in this system depending on the magnetic ordering. At AFM ordering transition between TI and trivial insulator phase goes through the Dirac semimetal state, whereas for FM phase such route admits three intermediate states instead of one (TI - Dirac semimetal - Weyl semimetal - Dirac semimetal - trivial insulator).

We conduct X-ray reverberation mapping and spectral analysis of the radio galaxy Centaurus A to uncover its central structure. We compare the light curve of the hard X-ray continuum from Swift Burst Alert Telescope observations with that of the Fe K$\alpha$ fluorescence line, derived from the Nuclear Spectroscopic Telescope Array (NuSTAR), Suzaku, XMM-Newton, and Swift X-ray Telescope observations. The analysis of the light curves suggests that a top-hat transfer function, commonly employed in reverberation mapping studies, is improbable. Instead, the relation between these light curves can be described by a transfer function featuring two components: one with a lag of $0.19_{- 0.02}^{+ 0.10}~\mathrm{pc}/c$, and another originating at $r > 1.7~\mathrm{pc}$ that produces an almost constant light curve. Further, we analyze the four-epoch NuSTAR and six-epoch Suzaku spectra, considering the time lag of the reflection component relative to the primary continuum. This spectral analysis supports that the reflecting material is Compton-thin, with $N_{\mathrm{H}} = 3.14_{-0.74}^{+0.44} \times 10^{23}~ \mathrm{cm}^{-2}$. These results suggest that the Fe K$\alpha$ emission may originate from Compton-thin circumnuclear material located at sub-parsec scale, likely a dust torus, and materials at a greater distance., Comment: 19 pages, 13 figures, 7 tables, accepted for publication in PASJ

In this investigation, we compute the nuclear matrix elements (NMEs) relevant to the light neutrino-exchange mechanism governing neutrinoless double beta ($0\nu\beta\beta$) decay in $^{136}$Xe. Our method is based on the nonclosure approach within the interacting nuclear shell model framework. This approach considers the genuine effects arising from the excitation energies of two hundred states for each spin-parity of the intermediary nucleus $^{136}$Cs. All computations are performed using the effective shell model Hamiltonian GCN5082. To understand the impact of nuclear structure on $0\nu\beta\beta$ decay, we explore the dependence of the NME on various factors, including the number of intermediate states and their spin-parity characteristics. We identify an optimal closure energy of approximately 3.7 MeV for the $0\nu\beta\beta$ decay of $^{136}$Xe that reproduces the nonclosure NME using the closure approach. The calculated total NME for the light neutrino-exchange $0\nu\beta\beta$ decay of $^{136}$Xe is 2.06 with the CD-Bonn short-range correlation (SRC). These results can be valuable for future experimental investigations into the $0\nu\beta\beta$ decay of $^{136}$Xe., Comment: 10 pages, and 3 figures

The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, ranging from scientific objectives to instrumentation. The Consortium anticipates a wide range of scientific investigations, from diverse black hole physics and astrophysics studied through the primary VLBI mode, to the molecular universe explored via a potential single-dish observation mode in the previously unexplored 50-70\,GHz band that would make BHEX the highest-sensitivity explorer ever of molecular oxygen. A potential major contribution for the onboard instrument involves supplying essential elements for its high-sensitivity dual-band receiving system, which includes a broadband 300\,GHz SIS mixer and a space-certified multi-stage 4.5K cryocooler akin to those used in the Hitomi and XRISM satellites by the Japan Aerospace Exploration Agency. Additionally, the Consortium explores enhancing and supporting BHEX operations through the use of millimeter/submillimeter facilities developed by the National Astronomical Observatory of Japan, coupled with a network of laser communication stations operated by the National Institute of Information and Communication Technology., Comment: Submitted to SPIE Conference Series, 17 pages for the main text, 33 pages for the references, 4 figures, SPIE Astronomical Telescopes + Instrumentation 2024

We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important problem since PU data naturally arise in real-world applications such as outlier detection and information retrieval. Existing PU learning methods require many PU data, but sufficient data are often unavailable in practice. The proposed method minimizes the test classification risk after the model is adapted to PU data by using related tasks that consist of positive, negative, and unlabeled data. We formulate the adaptation as an estimation problem of the Bayes optimal classifier, which is an optimal classifier to minimize the classification risk. The proposed method embeds each instance into a task-specific space using neural networks. With the embedded PU data, the Bayes optimal classifier is estimated through density-ratio estimation of PU densities, whose solution is obtained as a closed-form solution. The closed-form solution enables us to efficiently and effectively minimize the test classification risk. We empirically show that the proposed method outperforms existing methods with one synthetic and three real-world datasets., Comment: 21 pages

In vitro neuronal culture is an important research platform in cellular and network neuroscience. However, neurons cultured on a homogeneous scaffold form dense, randomly connected networks and display excessively synchronized activity; this phenomenon has limited their applications in network-level studies, such as studies of neuronal ensembles, or coordinated activity by a group of neurons. Herein, we develop polydimethylsiloxane-based microfluidic devices to create small neuronal networks exhibiting a hierarchically modular structure resembling the connectivity observed in the mammalian cortex. The strength of intermodular coupling was manipulated by varying the width and height of the microchannels that connect the modules. Using fluorescent calcium imaging, we observe that the spontaneous activity in networks with smaller microchannels (2.2$-$5.5 $\mu$m$^2$) had lower synchrony and exhibit a threefold variety of neuronal ensembles. Optogenetic stimulation demonstrates that a reduction in intermodular coupling enriches evoked neuronal activity patterns and that repeated stimulation induces plasticity in neuronal ensembles in these networks. These findings suggest that cell engineering technologies based on microfluidic devices enable in vitro reconstruction of the intricate dynamics of neuronal ensembles, thus providing a robust platform for studying neuronal ensembles in a well-defined physicochemical environment., Comment: 30 pages, 6 figures, 6 supplementary figures

Semi-supervised anomaly detection, which aims to improve the performance of the anomaly detector by using a small amount of anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that unlabeled data are mostly normal. They train the anomaly detector to minimize the anomaly scores for the unlabeled data, and to maximize those for the anomaly data. However, in practice, the unlabeled data are often contaminated with anomalies. This weakens the effect of maximizing the anomaly scores for anomalies, and prevents us from improving the detection performance. To solve this problem, we propose the positive-unlabeled autoencoder, which is based on positive-unlabeled learning and the anomaly detector such as the autoencoder. With our approach, we can approximate the anomaly scores for normal data using the unlabeled and anomaly data. Therefore, without the labeled normal data, we can train the anomaly detector to minimize the anomaly scores for normal data, and to maximize those for the anomaly data. In addition, our approach is applicable to various anomaly detectors such as the DeepSVDD. Experiments on various datasets show that our approach achieves better detection performance than existing approaches., Comment: Under revirew. Code is available at https://github.com/takahashihiroshi/puae

We are developing an imaging polarimeter by combining a fine-pixel CMOS image sensor with a coded aperture mask as part of the cipher project, aiming to achieve X-ray polarimetry in the energy range of $10$$\unicode{x2013}$$30~\mathrm{keV}$. A successful proof-of-concept experiment was conducted using a fine-pixel CMOS sensor with a $2.5~\mathrm{\mu m}$ pixel size. In this study, we conducted beam experiments to assess the modulation factor (MF) of the CMOS sensor with a $1.5~\mathrm{\mu m}$ pixel size manufactured by Canon and to determine if there was any improvement in the MF. The measured MF was $8.32\% \pm 0.34\%$ at $10~\mathrm{keV}$ and $16.10\% \pm 0.68\%$ at $22~\mathrm{keV}$, exceeding those of the $2.5~\mathrm{\mu m}$ sensor in the $6$$\unicode{x2013}$$22~\mathrm{keV}$ range. We also evaluated the quantum efficiency of the sensor, inferring a detection layer thickness of $2.67 \pm 0.48~{\rm \mu m}$. To develop a more sensitive polarimeter, a sensor with a thicker detection layer, smaller pixel size, and reduced thermal diffusion effect is desirable., Comment: 18 pages, 8 figures, 5 tables, accepted for publication in NIM-A

We investigate the electronic structures of noncentrosymmetric Weyl semimetals RAlSi (R: Ce and Nd) using soft x-ray angle-resolved photoemission spectroscopy. We find that the photoelectron intensity distribution observed in the momentum-resolved electronic bands is highly sensitive to the covered Brillouin zone (BZ) due to the zone-selection effect arising from the nonsymmorphic crystal structure of RAlSi. Our data reconstruct the photoelectron distributions varied according to the zone-selection effect, and reveal comprehensive information about the electronic band structures reproduced by band calculations. This detailed information enables us to experimentally trace the Weyl-cone dispersion throughout three-dimensional momentum space, providing valuable insights into the unique properties of RAlSi.

In this paper, a parallel symmetric eigensolver with very small matrices in massively parallel processing is considered. We define very small matrices that fit the sizes of caches per node in a supercomputer. We assume that the sizes also fit the exa-scale computing requirements of current production runs of an application. To minimize communication time, we added several communication avoiding and communication reducing algorithms based on Message Passing Interface (MPI) non-blocking implementations. A performance evaluation with up to full nodes of the FX10 system indicates that (1) the MPI non-blocking implementation is 3x as efficient as the baseline implementation, (2) the hybrid MPI execution is 1.9x faster than the pure MPI execution, (3) our proposed solver is 2.3x and 22x faster than a ScaLAPACK routine with optimized blocking size and cyclic-cyclic distribution, respectively., Comment: This article was submitted to Parallel Computing in December 9, 2013.This article was also published in IPSJ SIG Notes, Vol. 2015-HPC-148, Vol.2, pp.1-17 (February 23, 2015). (a non-reviewed technical report)

Hierarchically modular organization is a canonical network topology that is evolutionarily conserved in the nervous systems of animals. Within the network, neurons form directional connections defined by the growth of their axonal terminals. However, this topology is dissimilar to the network formed by dissociated neurons in culture because they form randomly connected networks on homogeneous substrates. In this study, we fabricated microfluidic devices to reconstitute hierarchically modular neuronal networks in culture (in vitro) and investigated how non-random structures, such as directional connectivity between modules, affect global network dynamics. Embedding directional connections in a pseudo-feedforward manner suppressed excessive synchrony in cultured neuronal networks and enhanced the integration-segregation balance. Modeling the behavior of biological neuronal networks using spiking neural networks (SNNs) further revealed that modularity and directionality cooperate to shape such network dynamics. Finally, we demonstrate that for a given network topology, the statistics of network dynamics, such as global network activation, correlation coefficient, and functional complexity, can be analytically predicted based on eigendecomposition of the transition matrix in the state-transition model. Hence, the integration of bioengineering and cell culture technologies enables us not only to reconstitute complex network circuitry in the nervous system but also to understand the structure-function relationships in biological neuronal networks by bridging theoretical modeling with in vitro experiments., Comment: 42 pages, 5 figures, 8 supplementary figures, 2 supplementary tables

A typical assumption in state-of-the-art self-localization models is that an annotated training dataset is available in the target workspace. However, this does not always hold when a robot travels in a general open-world. This study introduces a novel training scheme for open-world distributed robot systems. In our scheme, a robot ("student") can ask the other robots it meets at unfamiliar places ("teachers") for guidance. Specifically, a pseudo-training dataset is reconstructed from the teacher model and thereafter used for continual learning of the student model. Unlike typical knowledge transfer schemes, our scheme introduces only minimal assumptions on the teacher model, such that it can handle various types of open-set teachers, including uncooperative, untrainable (e.g., image retrieval engines), and blackbox teachers (i.e., data privacy). Rather than relying on the availability of private data of teachers as in existing methods, we propose to exploit an assumption that holds universally in self-localization tasks: "The teacher model is a self-localization system" and to reuse the self-localization system of a teacher as a sole accessible communication channel. We particularly focus on designing an excellent student/questioner whose interactions with teachers can yield effective question-and-answer sequences that can be used as pseudo-training datasets for the student self-localization model. When applied to a generic recursive knowledge distillation scenario, our approach exhibited stable and consistent performance improvement., Comment: 7 pages, 3 figures, technical report

The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks (DNNs) for learning such a mapping, enabling the learning of solution operators of partial differential equations (PDEs). However, these works still struggle to learn dynamics that obeys the laws of physics. This paper proposes Energy-consistent Neural Operators (ENOs), a general framework for learning solution operators of PDEs that follows the energy conservation or dissipation law from observed solution trajectories. We introduce a novel penalty function inspired by the energy-based theory of physics for training, in which the energy functional is modeled by another DNN, allowing one to bias the outputs of the DNN-based solution operators to ensure energetic consistency without explicit PDEs. Experiments on multiple physical systems show that ENO outperforms existing DNN models in predicting solutions from data, especially in super-resolution settings.

Producing a polarized lanthanum (La) target with high polarization and long relaxation time is crucial for realizing time-reversal violation experiments using polarized neutron beams. We use a LaAlO3 crystal doped with a small amount of Nd3+ ions for the polarized lanthanum target. Optimizing the amount of Nd3+ ions is considerably important because the achievable polarization and relaxation time strongly depend on this amount. We established a fundamental method to grow single crystals of Nd3+:LaAlO3 using an optical floating zone method that employs halogen lamps and evaluated the crystals with the dynamic nuclear polarization (DNP) method for polarizing nuclear spins. Two crystal samples were grown by ourselves and evaluated with the DNP at 1.3 K and 2.3 T for the first time except for the target materials of protons. The enhancement of NMR signals for 139La and 27Al was successfully observed, and the enhancement factors were eventually 3.5+-0.3 and 13+-3 for the samples with Nd3+ ions of 0.05 and 0.01 mol%, respectively. These enhancement factors correspond to absolute vector polarizations of 0.27+-0.02% (Nd 0.05 mol%) and 1.4+-0.3% (Nd 0.01 mol%). Although the obtained polarizations are still low, they are acceptable as a first step. The combination scheme of the crystal growth and evaluation of the crystals is found to be effectively applicable for optimizing the amount of Nd3+ ions for improving the performance of the polarized target., Comment: 18 pages, 7 figures, Resubmitted to Review of Scientific Instruments

Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict such events related to human activities. However, point processes present two problems in predicting events related to human activities. First, recent high-performance point process models require the input of sufficient numbers of events collected over a long period (i.e., long sequences) for training, which are often unavailable in realistic situations. Second, the long-term predictions required in real-world applications are difficult. To tackle these problems, we propose a novel meta-learning approach for periodicity-aware prediction of future events given short sequences. The proposed method first embeds short sequences into hidden representations (i.e., task representations) via recurrent neural networks for creating predictions from short sequences. It then models the intensity of the point process by monotonic neural networks (MNNs), with the input being the task representations. We transfer the prior knowledge learned from related tasks and can improve event prediction given short sequences of target tasks. We design the MNNs to explicitly take temporal periodic patterns into account, contributing to improved long-term prediction performance. Experiments on multiple real-world datasets demonstrate that the proposed method has higher prediction performance than existing alternatives.