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The James Webb Space Telescope (JWST) observations enable the exploration of active galactic nuclei (AGNs) with broad-line emission in the early universe. Despite their clear radiative and morphological signatures of AGNs in rest-frame optical bands, complementary evidence of AGN activity -- such as X-ray emission and UV/optical variability -- remains rarely detected. The weakness of X-rays and variability in these broad-line emitters challenges the conventional AGN paradigm, indicating that the accretion processes or environments around the central black holes (BHs) differ from those of low-redshift counterparts. In this work, we study the radiation spectra of super-Eddington accretion disks enveloped by high-density coronae. Radiation-driven outflows from the disk transport mass to the poles, resulting in moderately optically-thick, warm coronae formed through effective inverse Comptonization. This mechanism leads to softer X-ray spectra and larger bolometric correction factors for X-rays compared to typical AGNs, while being consistent with those of JWST AGNs and low-redshift super-Eddington accreting AGNs. In this scenario, UV/optical variability is suppressed due to photon trapping within super-Eddington disks, while X-ray emissions remain weak yet exhibit significant relative variability. These characteristics are particularly evident in high-redshift AGNs powered by lower-mass BHs with $\lesssim 10^{7-8}~M_\odot$, which undergo rapid mass accretion following overmassive evolutionary tracks relative to the BH-to-stellar mass correlation in the local universe., Comment: 10 pages, 5 figures, submitted to PASJ

Chirality pervades multiple scientific domains-physics, chemistry, biology, and astronomy-and profoundly influences their foundational principles. Recently, the chirality-induced spin selectivity (CISS) phenomenon has captured significant attention in physical chemistry due to its potential applications and intriguing underlying physics. Despite its prominence, the microscopic mechanisms of CISS remain hotly debated, hindering practical applications and further theoretical advancements. Here we challenge the established view that attributes CISS-related phenomena to current-induced spin polarization and electron transport across interfaces. We propose that molecular vibrations in chiral molecules primarily drive spin polarization, thereby governing CISS. Employing an electrochemical cell paired with a precisely engineered magnetic multilayer, we demonstrate that the magnetic interactions akin to interlayer exchange coupling are crucial for CISS. Our theoretical study suggests that molecular vibrations facilitate chirality-dependent spin polarization, which plays a pivotal role in CISS-related phenomena such as magnetoresistance and enantiomer separation using ferromagnets. These findings necessitate a paradigm shift in the design and analysis of systems in various scientific fields, extending the role of spin dynamics from traditional areas such as solid-state physics to chemical reactions, molecular biology, and even drug discovery.

The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-500 keV) compared to their estimation of 35% for the current uncertainty. Integral irradiation experiments suggest that the evaluated cross section of the JEFF-3.1 library overestimates the 242Pu(n,{\gamma}) cross section by 14% in the range between 1 keV and 1 MeV. In addition, the last measurement at LANSCE reported a systematic reduction of 20-30% in the 1-40 keV range relative to the evaluated libraries and previous data sets. In the present work this cross section has been determined up to 600 keV in order to solve the mentioned discrepancies. A 242Pu target of 95(4) mg enriched to 99.959% was irradiated at the n TOF-EAR1 facility at CERN. The capture cross section of 242Pu has been obtained between 1 and 600 keV with a systematic uncertainty (dominated by background subtraction) between 8 and 12%, reducing the current uncertainties of 35% and achieving the accuracy requested by the NEA in a large energy range. The shape of the cross section has been analyzed in terms of average resonance parameters using the FITACS code as implemented in SAMMY, yielding results compatible with our recent analysis of the resolved resonance region.The results are in good agreement with the data of Wisshak and K\"appeler and on average 10-14% below JEFF-3.2 from 1 to 250 keV, which helps to achieve consistency between integral experiments and cross section data. At higher energies our results show a reasonable agreement within uncertainties with both ENDF/B-VII.1 and JEFF-3.2. Our results indicate that the last experiment from DANCE underestimates the capture cross section of 242Pu by as much as 40% above a few keV., Comment: 20 pages, 19 figures, submitted to Phys. Rev. C

Many transformation groups on manifolds are simple, but their universal coverings are not. In the present paper, we study the concept of relatively simple group, that is, a group with the maximum proper normal subgroup. We show that many examples of universal coverings of transformation groups are relatively simple, including the universal covering $\widetilde{\mathrm{Ham}}(M,\omega)$ of the group of Hamiltonian diffeomorphisms of a closed symplectic manifold $(M,\omega)$. Tsuboi constructed a metric space $\mathcal{M}(G)$ for a simple group $G$. We generalize his construction to relatively simple groups, and study their large scale geometric structure. In particular, Tsuboi's metric space of $\widetilde{\mathrm{Ham}}(M, \omega)$ is not quasi-isometric to the half line for every closed symplectic manifold $(M,\omega)$., Comment: 37 pages, comments are welcome

Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have low sensitivity to neutrons, can quickly recover from the so-called $\gamma$-flash, and offer the highest possible detection sensitivity. In this paper, we present several steps toward such advanced systems. Specifically, we describe the performance of a high-sensitivity experimental setup at CERN n\_TOF EAR2. It consists of nine sTED detector modules in a compact cylindrical configuration, two conventional used large-volume C$_{6}$D$_{6}$ detectors, and one LaCl$_{3}$(Ce) detector. The performance of these detection systems is compared using $^{93}$Nb($n$,$\gamma$) data. We also developed a detailed \textsc{Geant4} Monte Carlo model of the experimental EAR2 setup, which allows for a better understanding of the detector features, including their efficiency determination. This Monte Carlo model has been used for further optimization, thus leading to a new conceptual design of a $\gamma$ detector array, STAR, based on a deuterated-stilbene crystal array. Finally, the suitability of deuterated-stilbene crystals for the future STAR array is investigaged experimentally utilizing a small stilbene-d12 prototype. The results suggest a similar or superior performance of STAR with respect to other setups based on liquid-scintillators, and allow for additional features such as neutron-gamma discrimination and a higher level of customization capability.

We simulate ultra-central collisions of prolate uranium-uranium nuclei at intermediate energies using the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to investigate the impact of momentum anisotropy on spatial geometric effects. By defining the quadrupole deformation parameter in momentum space $\beta_\text{p}$, we establish an ellipsoidal Fermi surface, aligning its rotational symmetry axis with the one in coordinate space. It is found that oblate momentum density enhances elliptic flow $v_2$, while prolate momentum density has the opposite effect, particularly pronounced in the outer, high transverse momentum $p_\text{t}$ region. Momentum anisotropy also causes differences in the initial momentum mean projection along the beam direction, with larger projections producing more pion mesons. Additionally, significant effects on mean square elliptic flow are observed in non-polarized collisions. We further examine the relationship between the $v_2$-$p_\text{t}$ slope and $\beta_\text{p}$, eliminating systematic errors through the two-system ratio. These findings provide important references for experimentalists in heavy-ion collisions and valuable feedback to theorists regarding nuclear structure.

The proportion of elderly people is increasing worldwide, particularly those living alone in Japan. As elderly people get older, their risks of physical disabilities and health issues increase. To automatically discover these issues at a low cost in daily life, sensor-based detection in a smart home is promising. As part of the effort towards early detection of abnormal behaviors, we propose a simulator-based detection systems for six typical anomalies: being semi-bedridden, being housebound, forgetting, wandering, fall while walking and fall while standing. Our detection system can be customized for various room layout, sensor arrangement and resident's characteristics by training detection classifiers using the simulator with the parameters fitted to individual cases. Considering that the six anomalies that our system detects have various occurrence durations, such as being housebound for weeks or lying still for seconds after a fall, the detection classifiers of our system produce anomaly labels depending on each anomaly's occurrence duration, e.g., housebound per day and falls per second. We propose a method that standardizes the processing of sensor data, and uses a simple detection approach. Although the validity depends on the realism of the simulation, numerical evaluations using sensor data that includes a variety of resident behavior patterns over nine years as test data show that (1) the methods for detecting wandering and falls are comparable to previous methods, and (2) the methods for detecting being semi-bedridden, being housebound, and forgetting achieve a sensitivity of over 0.9 with fewer than one false alarm every 50 days., Comment: 20 pages, 3 figures

Multimodal sensing systems are increasingly prevalent in various real-world applications. Most existing multimodal learning approaches heavily rely on training with a large amount of complete multimodal data. However, such a setting is impractical in real-world IoT sensing applications where data is typically collected by distributed nodes with heterogeneous data modalities, and is also rarely labeled. In this paper, we propose MMBind, a new framework for multimodal learning on distributed and heterogeneous IoT data. The key idea of MMBind is to construct a pseudo-paired multimodal dataset for model training by binding data from disparate sources and incomplete modalities through a sufficiently descriptive shared modality. We demonstrate that data of different modalities observing similar events, even captured at different times and locations, can be effectively used for multimodal training. Moreover, we propose an adaptive multimodal learning architecture capable of training models with heterogeneous modality combinations, coupled with a weighted contrastive learning approach to handle domain shifts among disparate data. Evaluations on ten real-world multimodal datasets highlight that MMBind outperforms state-of-the-art baselines under varying data incompleteness and domain shift, and holds promise for advancing multimodal foundation model training in IoT applications.

Code readability is an important indicator of software maintenance as it can significantly impact maintenance efforts. Recently, LLM (large language models) have been utilized for code readability evaluation. However, readability evaluation differs among developers, so personalization of the evaluation by LLM is needed. This study proposes a method which calibrates the evaluation, using collaborative filtering. Our preliminary analysis suggested that the method effectively enhances the accuracy of the readability evaluation using LLMs., Comment: 2 pages, 2 figures, 1 table

This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line between loudspeakers and a microphone. Because reflection and diffraction of sound by the human body cause subtle acoustic signal changes compared to sound obstruction, the existing model degrades its accuracy significantly when subjects deviate from this line, limiting its practicality in real-world scenarios. To overcome this limitation, we propose a novel method composed of a position discriminator and reverberation-resistant model. The former predicts the standing positions of subjects and applies adversarial learning to extract subject position-invariant features. The latter utilizes acoustic signals before the estimation target time as references to enhance robustness against the variations in sound arrival times due to diffraction and reflection. We construct an acoustic pose estimation dataset that covers diverse human locations and demonstrate through experiments that our proposed method outperforms existing approaches., Comment: Accepted at BMVC2024

Diffusion mechanism design, which investigate how to incentivise agents to invite as many colleagues to a multi-agent decision making as possible, is a new research paradigm at the intersection between microeconomics and computer science. In this paper we extend traditional facility location games into the model of diffusion mechanism design. Our objective is to completely understand to what extent of anonymity/voter-relevance we can achieve, along with strategy-proofness and Pareto efficiency when voters strategically invite collegues. We define a series of anonymity properties applicable to the diffusion mechanism design model, as well as parameterized voter-relevance properties for guaranteeing reasonably-fair decision making. We obtained two impossibility theorems and two existence theorems, which partially answer the question we have raised in the beginning of the paper, Comment: To appear in Proceedings of WALCOM-25

Among various topological insulators, Sn-doped Bi$_{1.1}$Sb$_{0.9}$Te$_{2}$S stands out for its exceptional properties. It has a wide energy gap and typically exhibits a well-isolated Dirac point and a Fermi level positioned within the gap. The samples we present display metallic-like low-temperature resistivity attributed to surface states, pronounced quantum oscillations observable even at 40 K, and a Fermi level located approximately 100 meV above the Dirac point. In this work, we report an unusual effect: a strong temperature dependence of the quantum oscillation frequency, which decreases by around 10\% between 2 and 40 K. This reduction significantly exceeds the expected effects of the Sommerfeld and topological corrections for Dirac quasi-particles, which could account for only one-eighth of the observed change. We attribute this change to the temperature-induced renormalization of the bulk band gap size due to electron-phonon interactions, which in turn affect the position of the surface Dirac point within the gap. Furthermore, we propose that in this compound, surface quantum oscillations can serve as a precise tool for investigating the low-temperature evolution of the bulk band gap size., Comment: 7 pages, 3 figures

We performed shot analyses of X-ray and optical sub-second flares observed during the low/hard state of the 2018 outburst in MAXI J1820$+$070. Optical shots were less spread than X-ray shots. The amplitude of X-ray shots was the highest at the onset of the outburst, and they faded at the transition to the intermediate state. The timescale of shots was $\sim$0.2 s, and we detected the abrupt spectral hardening synchronized with this steep flaring event. The time evolution of optical shots was not similar to that of X-ray shots. These results suggest that accreting gas blobs triggered a series of magnetic reconnections at the hot inner accretion flow in the vicinity of the black hole, which enhanced X-ray emission and generated flaring events. The rapid X-ray spectral hardening would be caused by this kind of magnetic activity. Also, the synchrotron emission not only at the hot flow but also at the jet plasma would contribute to the optical rapid variability. We also found that the low/hard state exhibited six different phases in the hardness-intensity diagram and the correlation plot between the optical flux and the X-ray hardness. The amplitude and duration of X-ray shots varied in synchrony with these phases. This time variation may provide key information about the evolution of the hot flow, the low-temperature outer disk, and the jet-emitting plasma., Comment: 16 pages, 7 figures, accepted for publication in PASJ

The modern Hopfield network, proposed by Krotov and Hopfield, is a mathematical generalization of the Hopfield network, which is a basic model of associative memory that employs higher-order interactions. This study introduces an open quantum model for discrete modern Hopfield networks that generalizes the open quantum Hopfield network. Our model integrates dissipative quantum spin systems, governed by quantum master equations, with classical hopping terms and additional quantum effects through a transverse field. We analytically examined the behavior of the stable fixed points and numerically determined the phase diagram. The results demonstrated qualitatively distinct behaviors from the open quantum Hopfield network, showing that the ferromagnetic and limit cycle phases have additional stable fixed points., Comment: 8pages, 6figures

We further develop the BPS/CFT correspondence between quiver W-algebras/$qq$-characters and partition functions of gauge origami. We introduce $qq$-characters associated with multi-dimensional partitions with nontrivial boundary conditions which we call Donaldson--Thomas (DT) $qq$-characters. They are operator versions of the equivariant DT vertices of toric Calabi--Yau three and four-folds. Moreover, we revisit the construction of the D8 $qq$-characters with no boundary conditions and give a quantum algebraic derivation of the sign rules of the magnificent four partition function. We also show that under the proper sign rules, the D6 and D8 $qq$-characters with no boundary conditions all commute with each other and discuss its physical interpretation., Comment: 66+13 pages

We present the first numerical calculation of the virtual photon polarization and assess the feasibility of measuring the polarization via the anisotropic decay $\gamma^{*} \rightarrow \mu\mu$ using the LHC-ALICE detector. In presence of intense magnetic fields generated in high-energy non-central heavy-ion collisions that exceed the critical magnetic field intensity of quantum electrodynamics (QED), prompt virtual photons are predicted to decay anisotropically into lepton pairs, which we call virtual photon polarization. Using a relativistic resistive magnetohydrodynamics model, we computed the time evolution of the magnetic field and used these results to estimate the averaged polarization by calculating the vacuum polarization under the influence of the magnetic field at specific times. The estimated polarization deviates from zero with a statistical significance of $0.07\sigma$ with the data statistics collected from 2010 to 2011 and $0.15\sigma$ with the one from 2015 to 2018. It is understandable that the magnetic field could not be detected through polarization due to low statistical significance. With the data collecting the ongoing ALICE run from 2023 to 2026, the statistics dramatically increase by the upgraded LHC and the new data processing system. Thereby we expect that the statistical significance could reach $\sim 1.7\sigma$, resulting in a promising probe for detecting the intense magnetic fields., Comment: 7 pages, 4 figures

One-proton emission from the $^{102}$Sb nucleus is discussed, assuming an inert $^{100}$Sn core and the valence proton and neutron. There are experimentally measured bound states in the $^{100}$Sn-neutron system, whereas no particle-bound $^{100}$Sn-proton state has been observed. With time-dependent three-body calculations, the $1^+$ ground state of $^{102}$Sb is suggested as a possible proton emitter. This conclusion is reached by assuming a weakening effect on the proton-neutron ($pn$) interaction with respect to a bare deuteron. An analogous phenomenon is necessary to reproduce the empirical binding energies of $^{42}$Sc and $^{18}$F. Continuous shift from the unbound to bound regions by changing the $pn$-interaction strength is demonstrated. The lower limit of lifetime is evaluated as $\tau \gtrsim 4.4 \times 10^{-18}$ seconds in the no-$pn$-interaction limit. However, the actual lifetime is expected as longer with a finite $pn$ interaction. Observation of a resonant state in $^{102}$Sb and its decay would provide a benchmark of the $pn$-pairing correlation., Comment: 6 pages, 6 figures, 4 tables

Out-of-distribution (OOD) detection is a critical issue for the stable and reliable operation of systems using a deep neural network (DNN). Although many OOD detection methods have been proposed, it remains unclear how the differences between in-distribution (ID) and OOD samples are generated by each processing step inside DNNs. We experimentally clarify this issue by investigating the layer dependence of feature representations from multiple perspectives. We find that intrinsic low dimensionalization of DNNs is essential for understanding how OOD samples become more distinct from ID samples as features propagate to deeper layers. Based on these observations, we provide a simple picture that consistently explains various properties of OOD samples. Specifically, low-dimensional weights eliminate most information from OOD samples, resulting in misclassifications due to excessive attention to dataset bias. In addition, we demonstrate the utility of dimensionality by proposing a dimensionality-aware OOD detection method based on alignment of features and weights, which consistently achieves high performance for various datasets with lower computational cost., Comment: This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in ECML PKDD 2024, and is available online at https://doi.org/10.1007/978-3-031-70341-6_9

The motion of two-dimensional incompressible and viscous fluids in doubly periodic domains is often used as a numerical model to investigate the dynamics and statistical properties of two-dimensional (2d) turbulence. In the study of 2d turbulence, it is important to describe interactions of coherent vortex structures of various sizes in turbulent flows, but identifying such vortex structures accurately from complex flow patterns is not easy. In this paper, we provide a classification theory for the topological structure of particle orbits generated by two-dimensional Hamiltonian flows on a flat torus $\mathbb{T}^2$, which is a mathematical model of 2d incompressible and viscous flows. Based on this theory, we show that the global orbit structure of every Hamiltonian flow is converted into a planar tree, called {\it a partially Cyclically-Ordered rooted Tree (COT)}, and its string expression (COT representation). Applying the conversion algorithm to snapshots of two-dimensional free-decaying turbulence and enstrophy cascade turbulence, we demonstrate that the topological structure of a complex flow pattern can be represented by a simple tree and a sequence of letters by the conversion algorithm, thereby extracting coherent vortex structures successfully from the viewpoint of topology.

We study a universal approximation property of ODENet and ResNet. The ODENet is a map from an initial value to the final value of an ODE system in a finite interval. It is considered a mathematical model of a ResNet-type deep learning system. We consider dynamical systems with vector fields given by a single composition of the activation function and an affine mapping, which is the most common choice of the ODENet or ResNet vector field in actual machine learning systems. We show that such an ODENet and ResNet with a restricted vector field can uniformly approximate ODENet with a general vector field., Comment: 14 pages

Acceleration of positive muons from thermal energy to $100~$keV has been demonstrated. Thermal muons were generated by resonant multi-photon ionization of muonium atoms emitted from a sheet of laser-ablated aerogel. The thermal muons were first electrostatically accelerated to $5.7~$keV, followed by further acceleration to 100 keV using a radio-frequency quadrupole. The transverse normalized emittance of the accelerated muons in the horizontal and vertical planes were $0.85 \pm 0.25 ~\rm{(stat.)}~^{+0.22}_{-0.13} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad and $0.32\pm 0.03~\rm{(stat.)} ^{+0.05}_{-0.02} ~\rm{(syst.)}~\pi~$mm$\cdot$mrad, respectively. The measured emittance values demonstrated phase space reduction by a factor of $2.0\times 10^2$ (horizontal) and $4.1\times 10^2$ (vertical) allowing good acceleration efficiency. These results pave the way to realize the first-ever muon accelerator for a variety of applications in particle physics, material science, and other fields.

Facility location games provide an abstract model of mechanism design. In such games, a mechanism takes a profile of $n$ single-peaked preferences over an interval as an input and determines the location of a facility on the interval. In this paper, we restrict our attention to distance-based single-peaked preferences and focus on a well-known class of parameterized mechanisms called ordered weighted average methods, which is proposed by Yager in 1988 and contains several practical implementations such as the standard average and the Olympic average. We comprehensively analyze their performance in terms of both incentives and fairness. More specifically, we provide necessary and sufficient conditions on their parameters to achieve strategy-proofness, non-obvious manipulability, individual fair share, and proportional fairness, respectively.

While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics, such as intracranial EEG resources. When provided with a single-channel EEG, how can we learn representations that are robust to multi-channels and scalable across varied tasks, such as seizure prediction? In this paper, we present SplitSEE, a structurally splittable framework designed for effective temporal-frequency representation learning in single-channel EEG. The key concept of SplitSEE is a self-supervised framework incorporating a deep clustering task. Given an EEG, we argue that the time and frequency domains are two distinct perspectives, and hence, learned representations should share the same cluster assignment. To this end, we first propose two domain-specific modules that independently learn domain-specific representation and address the temporal-frequency tradeoff issue in conventional spectrogram-based methods. Then, we introduce a novel clustering loss to measure the information similarity. This encourages representations from both domains to coherently describe the same input by assigning them a consistent cluster. SplitSEE leverages a pre-training-to-fine-tuning framework within a splittable architecture and has following properties: (a) Effectiveness: it learns representations solely from single-channel EEG but has even outperformed multi-channel baselines. (b) Robustness: it shows the capacity to adapt across different channels with low performance variance. Superior performance is also achieved with our collected clinical dataset. (c) Scalability: With just one fine-tuning epoch, SplitSEE achieves high and stable performance using partial model layers., Comment: This paper has been accepted by ICDM2024

Color Lie algebras, which were introduced by Ree, are a graded extension of Lie (super)algebras by an abelian group. We show that the color Lie algebras can be used to construct universal weight systems for knot invariants of of Vassiliev and Kontsevich. As a simple example, we take $\mathbb{Z}_2 \times \mathbb{Z}_2$ as the grading group and consider the four-dimensional color Lie algebra called $A1_{\epsilon}$. The weight system constructed from $A1_{\epsilon}$ is studied in some detail and some relations between the weights, such as the recurrence relation for chord diagrams, are derived. These relations show that the weight system from $A1_{\epsilon}$ is a hybrid of those from $sl(2)$ and $gl(1|1)$., Comment: 28 pages, many figures

Automatic rapport estimation in social interactions is a central component of affective computing. Recent reports have shown that the estimation performance of rapport in initial interactions can be improved by using the participant's personality traits as the model's input. In this study, we investigate whether this findings applies to interactions between friends by developing rapport estimation models that utilize nonverbal cues (audio and facial expressions) as inputs. Our experimental results show that adding Big Five features (BFFs) to nonverbal features can improve the estimation performance of self-reported rapport in dyadic interactions between friends. Next, we demystify how BFFs improve the estimation performance of rapport through a comparative analysis between models with and without BFFs. We decompose rapport ratings into perceiver effects (people's tendency to rate other people), target effects (people's tendency to be rated by other people), and relationship effects (people's unique ratings for a specific person) using the social relations model. We then analyze the extent to which BFFs contribute to capturing each effect. Our analysis demonstrates that the perceiver's and the target's BFFs lead estimation models to capture the perceiver and the target effects, respectively. Furthermore, our experimental results indicate that the combinations of facial expression features and BFFs achieve best estimation performances not only in estimating rapport ratings, but also in estimating three effects. Our study is the first step toward understanding why personality-aware estimation models of interpersonal perception accomplish high estimation performance., Comment: 9 pages, 5 figures

A strong coupling between electric polarization and elastic deformation in solids is an important factor in creating useful electromechanical nanodevices. Such coupling is typically allowed in insulating materials with inversion symmetry breaking as exemplified by the piezoelectric effect in ferroelectric materials. Therefore, materials with metallicity and centrosymmetry have tended to be out of scope in this perspective. Here, we report the observation of giant elastic deformation by the application of an alternating electric current in topological semimetals (V,Mo)Te2, regardless of the centrosymmetry. Considering the crystal and band structures and the asymmetric measurement configurations in addition to the absence of the electromechanical effect in a trivial semimetal TiTe2, the observed effect is discussed in terms of a Berry-phase-derived converse flexoelectric effect in metals. The observation of the flexoelectric effect in topological semimetals paves a way for a new type of nanoscale electromechanical sensors and energy harvesting., Comment: 11 pages, 4 figures

Magnetic structures of the noncentrosymmetric magnet Sr$_2$MnSi$_2$O$_7$ were examined through neutron diffraction for powder and single-crystalline samples, as well as magnetometry measurements. All allowed magnetic structures under the space group $P\bar{4}2_1 m$ with the magnetic wavevector $\vec{q}_{\rm m}=(0,0,1/2)$~r.l.u., were analysed via irreducible representation and magnetic space group approaches. The compound is refined to have in-plane magnetic moments within the magnetic space group $Cmc2_1.1^{\prime}_c$ (\#36.177) under zero field, which can be altered to $P2_1 2_1 2_1.1^{\prime}_c$ (\#19.28) above $\mu_0 H=0.067(5)$~T to align induced weak-ferromagnetic components within one layer on the $ab$-plane. All refined parameters are provided following the recent framework based upon the magnetic space group, which better conveys when exchanging crystallographic information for commensurate magnetic structures., Comment: 7 pages, 4 figures

To investigate the role of the excitons for the origin of the pressure-induced phase transition (BGT) from the black-colored insulator (BI) to the golden-yellow-colored metal (GM) of samarium monosulfide (SmS), optical reflectivity, Sm $3d$ X-ray absorption spectroscopy (XAS), and X-ray diffraction (XRD) with the creation of excitons by photoexcitation (PE) are reported. In the pump-probe reflectivity measurement, following a huge reflectivity change of about 22 %, three different relaxation times with a vibration component were observed. The fast component with the relaxation time ($\tau$) of less than 1 ps is due to the excitation and relaxation of electrons into the conduction band, and the slowest one with $\tau > {\rm several} 100$ ps originates from the appearance of the photo-induced (PI) state. The components with $\tau \sim 10$ ps and vibration originate from the appearance of the PI state and the interference between the reflection lights at the sample surface and the boundary between the BI and PI states, suggesting that the electronic structure of the PI phase is different from that of the BI state. XAS spectra indicate that the Sm mean valence is shifted from the Sm$^{2+}$ dominant to the intermediate between Sm$^{2+}$ and Sm$^{3+}$ by PE but did not change to that of the GM phase across BGT, consistent with the reflectivity data. The XRD result after PE shows that the PI state has much less lattice contraction than the GM phase. These results suggest that the BGT cannot be achieved solely by creating excitons after PE but requires other effects, such as a lattice contraction., Comment: 9 pages, 5 figures

We investigate T-duality transformation on an almost bi-hermitian space with torsion. By virtue of the Buscher rule, we completely describe not only the covariant derivative of geometrical objects but also the Nijenhuis tensor. We apply this description to string sigma model whose target space is a T-dualized system from an integrable bi-hermitian space with isometry. We find that hermiticity is not a sufficient condition to preserve integrability under T-duality transformations. However, in the presence of the K\"{a}hler condition, the T-dualized almost complex structures keep integrable even though they become no longer covariantly constant. We also observe that the form of $H$-flux is suitable for string compactification scenarios., Comment: 33 pages, references added, discussions improved

We present near-infrared high-dispersion spectroscopic observations of Europa using the Infrared Camera and Spectrograph (IRCS) onboard the Subaru Telescope, seeking direct evidence of water plumes on Europa and exploring spatial variations in plume activity. Using high spectral/spatial resolution and sensitivity of Subaru/IRCS, our observations have enabled a spatially resolved search for water plumes on Europa. Within our detection limits and time of observation, we found no evidence for the presence of water emission. For a rotation temperature of 50 K, we derived an upper limit on the H$_{2}$O abundance of 9.46$\times$10$^{19}$ - 5.92$\times$10$^{20}$ m$^{-2}$ in each divided slit area and 4.61$\times$10$^{19}$ m$^{-2}$ in the entire area covered by the slit. This upper limit lies below the inferred water abundance from previous UV observations by the Hubble Space Telescope (HST), while being less sensitive by a factor of three compared to the Keck telescope and by one order of magnitude or more than the James Webb Space Telescope (JWST) observations. Our results align with previous studies and demonstrate that using Subaru/IRCS is an effective strategy for searching for water plumes on Europa with high spatial resolution. Continued observations across different surface areas and orbital phases are essential to fully characterize Europa's plume activity and complement upcoming space missions., Comment: 8 pages, 7 figures. Accepted for publication in PASJ

Recent observations of core-collapse supernovae revealed that the existence of dense circumstellar matter (CSM) around their progenitors is ubiquitous. Interaction of supernova ejecta with such a dense CSM is a potential production sight of high-energy cosmic rays (CRs), gamma-rays, and neutrinos. We estimate the gamma-ray and neutrino signals from SN 2023ixf, a core-collapse supernova occurred in a nearby galaxy M101, which exhibits signatures of the interaction with the confined dense CSM. Using radiation-hydrodynamic simulation model calibrated by the optical and ultraviolet observations of SN 2023ixf, we find that the CRs cannot be accelerated in the early phase because the sharp velocity jump at the shock disappears due to strong radiation pressure. Roughly 4 days after the explosion, the collisionless sub-shock is formed in the CSM, which enables the CR production and leads to gamma-ray and neutrino emissions. The shock sweeps up the entire dense CSM roughly 9 days after the explosion, which ceases the high-energy radiation. Based on this scenario, we calculate the gamma-ray and neutrino signals, which have a peak around 9 days after the explosion. We can constrain the cosmic-ray production efficiency to be less than 30\% by comparing our prediction to the Fermi-LAT data. Future multi-messenger observations with an enlarged sample of nearby supernovae will provide a better constraint on the cosmic-ray production efficiency in the early phases of supernovae., Comment: 9 pages, 5 figures

Let $R$ be a commutative Noetherian ring of dimension $d$. In this paper, we first show that some power of the cohomology annihilator annihilates the $(d+1)$-th Ext modules for all finitely generated modules when either $R$ admits a dualizing complex or $R$ is local. Next, we study the Jacobian ideal of affine algebras over a field and equicharacteristic complete local rings, and characterize the equidimensionality of the ring in terms of the singular locus and the closed subsets defined by the cohomology annihilator and the Jacobian ideal., Comment: 16 pages

Data augmentation is known to contribute significantly to the robustness of machine learning models. In most instances, data augmentation is utilized during the training phase. Test-Time Augmentation (TTA) is a technique that instead leverages these data augmentations during the testing phase to achieve robust predictions. More precisely, TTA averages the predictions of multiple data augmentations of an instance to produce a final prediction. Although the effectiveness of TTA has been empirically reported, it can be expected that the predictive performance achieved will depend on the set of data augmentation methods used during testing. In particular, the data augmentation methods applied should make different contributions to performance. That is, it is anticipated that there may be differing degrees of contribution in the set of data augmentation methods used for TTA, and these could have a negative impact on prediction performance. In this study, we consider a weighted version of the TTA based on the contribution of each data augmentation. Some variants of TTA can be regarded as considering the problem of determining the appropriate weighting. We demonstrate that the determination of the coefficients of this weighted TTA can be formalized in a variational Bayesian framework. We also show that optimizing the weights to maximize the marginal log-likelihood suppresses candidates of unwanted data augmentations at the test phase.

This paper introduces an analog spiking neuron that utilizes time-domain information, i.e., a time interval of two signal transitions and a pulse width, to construct a spiking neural network (SNN) for a hardware-friendly physical reservoir computing (RC) on a complementary metal-oxide-semiconductor (CMOS) platform. A neuron with leaky integrate-and-fire is realized by employing two voltage-controlled oscillators (VCOs) with opposite sensitivities to the internal control voltage, and the neuron connection structure is restricted by the use of only 4 neighboring neurons on the 2-dimensional plane to feasibly construct a regular network topology. Such a system enables us to compose an SNN with a counter-based readout circuit, which simplifies the hardware implementation of the SNN. Moreover, another technical advantage thanks to the bottom-up integration is the capability of dynamically capturing every neuron state in the network, which can significantly contribute to finding guidelines on how to enhance the performance for various computational tasks in temporal information processing. Diverse nonlinear physical dynamics needed for RC can be realized by collective behavior through dynamic interaction between neurons, like coupled oscillators, despite the simple network structure. With behavioral system-level simulations, we demonstrate physical RC through short-term memory and exclusive OR tasks, and the spoken digit recognition task with an accuracy of 97.7% as well. Our system is considerably feasible for practical applications and also can be a useful platform for studying the mechanism of physical RC.