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Antiferromagnets without parity-time ($\mathcal{PT}$) symmetry offer novel perspectives in the field of functional magnetic materials. Among them, those with ferromagnetic-like responses are promising candidates for future applications such as antiferromagnetic (AF) memory; however, examples showing large effects are extremely limited. In this study, we show that the orthorhombic system TaMnP exhibits a large anomalous Hall conductivity (AHC) $\sim360-370$ $\Omega^{-1}$cm$^{-1}$ in spite of the small net magnetization $\sim10^{-2}$ $\mu_B$/Mn. Our neutron scattering experiment and the observation of the AH effect indicated that a magnetic structure of TaMnP was dominated by an AF component represented by $B_{3g}$ with the propagation vector $q=0$. Furthermore, we confirmed that the obtained AHC is among the largest observed in AF materials at zero fields. Additionally, our first-principles calculations revealed that the spin-orbit interaction originating in the nonmagnetic Ta-$5d$ electrons significantly contributes to enhancing Berry curvatures in the momentum space. We found that the magnetic fields along all the crystal axes triggered the AF domain switching, indicating the possibility of controlling the AF domain using the small net magnetization, which is symmetrically different., Comment: 8 pages, 6 figures

Diffusion-based voice conversion (VC) techniques such as VoiceGrad have attracted interest because of their high VC performance in terms of speech quality and speaker similarity. However, a notable limitation is the slow inference caused by the multi-step reverse diffusion. Therefore, we propose FastVoiceGrad, a novel one-step diffusion-based VC that reduces the number of iterations from dozens to one while inheriting the high VC performance of the multi-step diffusion-based VC. We obtain the model using adversarial conditional diffusion distillation (ACDD), leveraging the ability of generative adversarial networks and diffusion models while reconsidering the initial states in sampling. Evaluations of one-shot any-to-any VC demonstrate that FastVoiceGrad achieves VC performance superior to or comparable to that of previous multi-step diffusion-based VC while enhancing the inference speed. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/., Comment: Accepted to Interspeech 2024. Project page: https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/fastvoicegrad/

Electronic flying qubits offer an interesting alternative to photonic qubits: electrons propagate slower, hence easier to control in real time, and Coulomb interaction enables direct entanglement between different qubits. While their coherence time is limited, picosecond-scale control would make them competitive in terms of number of possible coherent operations. The key challenge lies in achieving the dynamical regime, where the injected plasmonic pulse width is shorter than the quantum device dimensions. Here we reach this new regime in a quantum nanoelectronic system by injecting ultrashort single electron plasmonic pulses into a 14-micrometer-long Mach-Zehnder interferometer. Our findings reveal that quantum coherence is preserved for ultrashort plasmonic pulses, exhibiting enhanced contrast of coherent oscillations compared to the DC regime. Moreover, this coherence remains robust even under large bias voltages. This milestone demonstrates the feasibility of flying qubits as a promising alternative to localized qubit architectures, offering reduced hardware footprint, increased connectivity, and potential for scalable quantum information processing.

Nuclear mass measurements have recently been extended conspicuously to proton-rich region in the upper $fp$ shell. The new data are utilized to study isospin symmetry breaking phenomena}using Coulomb displacement energy (CDE) and triplet displacement energy (TDE) as probes. The new mass data, either measured for the first time or with greatly improved accuracy, removed several previously found ``anomalies" in the systematical behavior in the $fp$ shell. Remarkably, more regular odd-even staggering patterns can be established in both CDE and TDE, calling for a uniform explanation in terms of isospin-nonconserving (INC) forces across the $sd$, $f_{7/2}$, and upper $fp$ shells. By extending the large-scale shell-model calculation [Phys. Rev. Lett. \textbf{110}, 172505 (2013)] to the upper $fp$-shell region, we found that, in order to describe the new data, the same INC force is required as previously used for the $f_{7/2}$ shell. Especially, we propose the $T=1$ TDE for those triplet nuclei, that have $pp$, $nn$, and $pn$ pairs on top of a common even-even $N=Z$ core, to be a good indicator for the isotensor component of isospin violating interactions, which is estimated here to be 150 keV., Comment: 7 pages, 2 figures

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

The region of $N\approx Z\approx 40$ has long been associated with strongly deformed nuclear configurations. The presence of this strong deformation was recently confirmed through lifetime measurements in $N\approx Z$ Sr and Zr nuclei. Theoretically, however, these nuclei present a challenge due to the vast valence space required to incorporate all deformation driving interactions. Recent state-of-the-art predictions indicate a near axial prolate deformation for $N=Z$ and $N=Z+2$ nuclei between $N=Z=36$ and $N=Z=40$. In this work we investigate the shores of this island of deformation through a sub-barrier Coulomb excitation study of the $N=Z+4$ nucleus, \textsuperscript{80}Sr. Extracting a spectroscopic quadrupole moment of $Q_s(2^+_1) = 0.45^{+0.83}_{-0.88}$~eb, we find that \textsuperscript{80}Sr is inconsistent with significant axial prolate deformation. This indicates that the predicted region of strong prolate deformation around $N=Z=40$ is tightly constrained to the quartet of nuclei: \textsuperscript{76,78}Sr and \textsuperscript{78,80}Zr., Comment: Submitted to Physics Letters B

Solar energetic events are caused by the release of magnetic energy accumulated in the solar atmosphere. To understand their initiating physical mechanisms, the dynamics of the coronal magnetic fields must be studied. Unfortunately, the dominant mechanisms are still unclear due to lack of direct measurements. Numerical simulations based on magnetohydrodynamics (MHD) can reproduce the dynamical evolution of solar coronal magnetic field providing a useful tool to explore flare initiation. Data-driven MHD simulations, in which the time-series observational data of the photospheric magnetic field is used as the simulation boundary condition, can explore different mechanisms. To investigate the accumulation of free magnetic energy through to a solar eruption, we simulated the first of several large flares in NOAA Active Region 11283. We used a data-driven model (Kaneko et al 2021) that was governed by zero-beta MHD, focusing on the free magnetic energy accumulation prior to the M5.3 flare (September 6, 01:59 UT, 2011). We reproduced the flare-associated eruption following the formation of twisted magnetic fields, or a magnetic flux rope (MFR), formed by photospheric motions at its footpoints. We found that the eruption was first triggered by the growth of the torus instability. The erupting MFR caused magnetic reconnections with neighboring magnetic field lines located along the direction of the eruption. Using the simulation results and an axial-radial decay index centered on the MFR, we find a natural explanation for the inclination of the eruption and a possible approach to predict the direction of solar eruptive events., Comment: Accepted for publication in Astrophys. J

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

Expressibility is a crucial factor of a Parameterized Quantum Circuit (PQC). In the context of Variational Quantum Algorithms (VQA) based Quantum Machine Learning (QML), a QML model composed of highly expressible PQC and sufficient number of qubits is theoretically capable of approximating any arbitrary continuous function. While much research has explored the relationship between expressibility and learning performance, as well as the number of layers in PQCs, the connection between expressibility and PQC structure has received comparatively less attention. In this paper, we analyze the connection between expressibility and the types of quantum gates within PQCs using a Gradient Boosting Tree model and SHapley Additive exPlanations (SHAP) values. Our analysis is performed on 1,615 instances of PQC derived from 19 PQC topologies, each with 2-18 qubits and 1-5 layers. The findings of our analysis provide guidance for designing highly expressible PQCs, suggesting the integration of more RX or RY gates while maintaining a careful balance with the number of CNOT gates. Furthermore, our evaluation offers an additional evidence of expressibility saturation, as observed by previous studies., Comment: 8 pages, 9 figures

Life systems are complex and hierarchical, with diverse components at different scales, yet they sustain themselves, grow, and evolve over time. How can a theory of such complex biological states be developed? Here we note that for a hierarchical biological system to be robust, it must achieve consistency between micro-scale (e.g. molecular) and macro-scale (e.g. cellular) phenomena. This allows for a universal theory of adaptive change in cells based on biological robustness and consistency between cellular growth and molecular replication. Here, we show how adaptive changes in high-dimensional phenotypes (biological states) are constrained to low-dimensional space, leading to the derivation of a macroscopic law for cellular states. The theory is then extended to evolution, leading to proportionality between evolutionary and environmental responses, as well as proportionality between phenotypic variances due to noise and due to genetic changes. The universality of the results across several models and experiments is demonstrated. Then, by further extending the theory of evolutionary dimensional reduction to multicellular systems, the relationship between multicellular development and evolution, in particular the developmental hourglass, is demonstrated. Finally, the possibility of collapse of dimensional reduction under nutrient limitation is discussed., Comment: Review as a proceeding paper for IUPAB conference

We investigate the ground-state properties of a correlated model for the double-chain structure in cuprates. We consider the $t$-$J$ model, in which the nearest-neighbor spin interaction $J_1$ is smaller than the next-nearest-neighbor interaction $J_2$ corresponding to the CuO double-chain structure. We vary $J_1$ from antiferromagnetic to ferromagnetic values and calculate the correlation functions including the superconducting pair correlation function. Employing the density-matrix renormalization group method, we show that the ground state for antiferromagnetic $J_1$ exhibits the hallmarks of the Luther-Emery liquid phase, in which the spin-singlet pair and charge-density-wave correlations exhibit power-law decays against distance, and the spin correlation function decays exponentially. Its signatures are gradually dismissed as $J_1$ approaches the ferromagnetic regime. Our findings suggest that the antiferromagnetic double-chain structure without ferromagnetic bonds is favorable for superconductivity., Comment: 7 pages, 7 figures

Recent advancements in circuit quantum electrodynamics have enabled precise manipulation and detection of the single energy quantum in quantum systems. A quantum circuit refrigerator (QCR) is capable of electrically cooling the excited population of quantum systems, such as superconducting resonators and qubits, through photon-assisted tunneling of quasi-particles within a superconductor-insulator-normal metal junction. In this study, we demonstrated instantaneous QCR in the quantum regime. We performed the time-resolved measurement of the QCR-induced cooling of photon number inside the superconducting resonator by harnessing a qubit as a photon detector. From the enhanced photon loss rate of the resonator estimated from the amount of the AC Stark shift, the QCR was shown to have a cooling power of approximately 300 aW. Furthermore, even below the single energy quantum, the QCR can reduce the number of photons inside the resonator with 100 ns pulse from thermal equilibrium. Numerical calculations based on the Lindblad master equation successfully reproduced these experimental results., Comment: 15 pages, 9 figures, and 1 table

How does social complexity depend on population size and cultural transmission? Kinship structures in traditional societies provide a fundamental illustration, where cultural rules between clans determine people's marriage possibilities. Here we propose a simple model of kinship interactions that considers kin and in-law cooperation and sexual rivalry. In this model, multiple societies compete. Societies consist of multiple families with different cultural traits and mating preferences. These values determine interactions and hence the growth rate of families and are transmitted to offspring with mutations. Through a multilevel evolutionary simulation, family traits and preferences are grouped into multiple clans with inter-clan mating preferences. It illustrates the emergence of kinship structures as the spontaneous formation of interdependent cultural associations. Emergent kinship structures are characterized by the cycle length of marriage exchange and the number of cycles in society. We numerically and analytically clarify their parameter dependence. The relative importance of cooperation versus rivalry determines whether attraction or repulsion exists between families. Different structures evolve as locally stable attractors. The probabilities of formation and collapse of complex structures depend on the number of families and the mutation rate, showing characteristic scaling relationships. It is now possible to explore macroscopic kinship structures based on microscopic interactions, together with their environmental dependence and the historical causality of their evolution. We propose the basic causal mechanism of the formation of typical human social structures by referring to ethnographic observations and concepts from statistical physics and multilevel evolution. Such interdisciplinary collaboration will unveil universal features in human societies., Comment: 9 pages, 6 figures

We investigate the ground state of the SU($4$) Heisenberg model on a square lattice with spatial anisotropy on each plaquette bond using the tensor-network method based on infinite projected entangled pair states. We find that the SU($4$) singlet ground state appears in the strongly anisotropic limit, whereas N\'eel and valence-bond crystal orders coexist in the nearly isotropic limit. By examining the intermediate parameter region, we identify a phase transition between these phases. The nature of the phase transition is likely to be of first order, and the transition point is estimated to be around $J'/J\approx 0.85(5)$, where $J$ and $J'$ are the interaction strengths of intra- and interplaquette bonds, respectively. We also calculate the anisotropy dependence of singlet correlations on a plaquette bond, which will be useful for future experiments of ultracold atoms in optical lattices., Comment: 10 pages, 13 figures

Warning: This paper contains examples of stereotypes and biases. Large Language Models (LLMs) exhibit considerable social biases, and various studies have tried to evaluate and mitigate these biases accurately. Previous studies use downstream tasks as prompts to examine the degree of social biases for evaluation and mitigation. While LLMs' output highly depends on prompts, previous studies evaluating and mitigating bias have often relied on a limited variety of prompts. In this paper, we investigate the sensitivity of LLMs when changing prompt variations (task instruction and prompt, few-shot examples, debias-prompt) by analyzing task performance and social bias of LLMs. Our experimental results reveal that LLMs are highly sensitive to prompts to the extent that the ranking of LLMs fluctuates when comparing models for task performance and social bias. Additionally, we show that LLMs have tradeoffs between performance and social bias caused by the prompts. Less bias from prompt setting may result in reduced performance. Moreover, the ambiguity of instances is one of the reasons for this sensitivity to prompts in advanced LLMs, leading to various outputs. We recommend using diverse prompts, as in this study, to compare the effects of prompts on social bias in LLMs.

In the pursuit of advanced energy storage solutions, the crystal structure of ionic conductors plays a pivotal role in facilitating ion transport. The conventional structural design principle that compounds with the body-centered cubic (BCC) anionic frameworks have high ionic conductivity is well known. We have extended the conventional design principle by uncovering that many of the anionic frameworks of Ag-ion conductors are characterized by tetrahedrally packed (TP) structures. Leveraging our findings, we have virtually screened TP framework compounds, uncovering their intrinsic potential for superior ionic conductivity through first-principles molecular dynamics simulations. Our design principle is applicable to Ag$^+$ and other mobile ions, including Li$^+$ and F$^-$. We proposed the Met2Ion method to generate ionic crystal structures using metal crystal structures as templates and demonstrated that new ionic conductors with TP frameworks can be discovered. This work paves the way for the discovery and development of next-generation energy storage materials with enhanced performance., Comment: 19 pages, 4 figures

The accelerating growth of the Internet of Things (IoT) and its integration with Low-Earth Orbit (LEO) satellites demand efficient, reliable, and scalable communication protocols. Among these, the Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) modulation, tailored for LEO satellite IoT communications, sparks keen interest. This work presents a joint approach to enhancing the scalability of LR-FHSS, addressing the demand for massive connectivity. We deepen into Frequency Hopping Sequence (FHS) mechanisms within LR-FHSS, spotlighting the potential of leveraging Wide-Gap sequences. Concurrently, we introduce two novel demodulator allocation strategies, namely, ``Early-Decode" and ``Early-Drop," to optimize the utilization of LoRa-specific gateway decoding resources. Our research further validates these findings with extensive simulations, offering a comprehensive look into the future potential of LR-FHSS scalability in IoT settings.

We report the detection of the CO(2-1) emission line with a spatial resolution of 0.9 arcsec ($3.5 \mathrm{kpc}$) from the host galaxy of the fast radio burst (FRB), FRB 20191001A at $z=0.2340$, using the Atacama Large Millimeter/submillimeter Array. This is the first detection of spatially resolved CO emission from the host galaxy of an FRB at a cosmological distance. The inferred molecular gas mass of the host galaxy is $(2.3\pm0.4)\times10^{10} \mathrm{M_\odot}$, indicating that it is gas-rich, as evidenced by the measured molecular gas fraction $\mu_\mathrm{gas}=0.50\pm0.22$. This molecular-gas mass and the star formation rate of the host, $\mathrm{SFR}=8.06\pm2.42 \mathrm{M_\odot yr^{-1}}$, differ from those observed in the other FRB host galaxies with the average $M_\mathrm{gas}=9.6\times10^8 \mathrm{M_\odot}$ and $\mathrm{SFR}=0.90 \mathrm{M_\odot yr^{-1}}$. This lends further credibility to the hypothesis that FRBs may originate from single or multiple progenitors across a diverse range of galaxy environments. Based on the observed velocity field modeling, we find that the molecular gas disk is dominated by an ordered circular rotation, despite the fact that the host galaxy has a gas-rich companion galaxy with a projected separation of $\sim 25 \mathrm{kpc}$. The formation of the FRB's progenitor might not have been triggered by this interaction. We derive the 3$\sigma$ upper limit of the molecular gas column density at the FRB detection site to be $< 2.1\times 10^{21} \mathrm{cm^{-2}}$ with a 3$\sigma$ upper limit., Comment: 10 pages, 7 figures, 3 tables

Humans' trust in AI constitutes a pivotal element in fostering a synergistic relationship between humans and AI. This is particularly significant in the context of systems that leverage AI technology, such as autonomous driving systems and human-robot interaction. Trust facilitates appropriate utilization of these systems, thereby optimizing their potential benefits. If humans over-trust or under-trust an AI, serious problems such as misuse and accidents occur. To prevent over/under-trust, it is necessary to predict trust dynamics. However, trust is an internal state of humans and hard to directly observe. Therefore, we propose a prediction model for trust dynamics using dynamic structure equation modeling, which extends SEM that can handle time-series data. A path diagram, which shows causalities between variables, is developed in an exploratory way and the resultant path diagram is optimized for effective path structures. Over/under-trust was predicted with 90\% accuracy in a drone simulator task,, and it was predicted with 99\% accuracy in an autonomous driving task. These results show that our proposed method outperformed the conventional method including an auto regression family.

We consider the dunking problem: a solid body at uniform temperature $T_{\text i}$ is placed in a environment characterized by farfield temperature $T_\infty$ and spatially uniform time-independent heat transfer coefficient. We permit heterogeneous material composition: spatially dependent density, specific heat, and thermal conductivity. Mathematically, the problem is described by a heat equation with Robin boundary conditions. The crucial parameter is the Biot number -- a nondimensional heat transfer (Robin) coefficient; we consider the limit of small Biot number. We introduce first-order and second-order asymptotic approximations (in Biot number) for several quantities of interest, notably the spatial domain average temperature as a function of time; the first-order approximation is simply the standard engineering `lumped' model. We then provide asymptotic error estimates for the first-order and second-order approximations for small Biot number, and also, for the first-order approximation, alternative strict bounds valid for all Biot number. Companion numerical solutions of the heat equation confirm the effectiveness of the error estimates for small Biot number. The second-order approximation and the first-order and second-order error estimates depend on several functional outputs associated to an elliptic partial differential equation; the latter is derived from Biot-sensitivity analysis of the heat equation eigenproblem in the limit of small Biot number. Most important is $\phi$, the only functional output required for the first-order error estimates; $\phi$ admits a simple physical interpretation in terms of conduction length scale. We investigate the domain and property dependence of $\phi$: most notably, we characterize spatial domains for which the standard lumped-model error criterion -- Biot number (based on volume-to-area length scale) small -- is deficient.

Rydberg atoms in an optical tweezer array have been used as a quantum simulator of the spin-$1/2$ antiferromagnetic Ising model with longitudinal and transverse fields. We suggest how to implement the next-nearest-neighbor (NNN) interaction whose sign is opposite to that of the nearest neighbor one in the Rydberg atom systems. We show that this can be achieved by weakly coupling one Rydberg state with another Rydberg state. We further study the surface criticality associated with the first-order quantum phase transition between the antiferromagnetic and paramagnetic phases, which emerges due to the sign-inverted NNN interaction. From the microscopic model, we derive a Ginzburg-Landau (GL) equation, which describes static and dynamic properties of the antiferromagnetic order parameter near the transition. Using both analytical GL theory and numerical method based on a mean-field theory, we calculate the order parameter in the proximity of a boundary of the system in order to show that the healing length of the order parameter logarithmically diverges, signaling the surface criticality., Comment: 8 pages, 6 figures

Geometry-agnostic system identification is a technique for identifying the geometry and physical properties of an object from video sequences without any geometric assumptions. Recently, physics-augmented continuum neural radiance fields (PAC-NeRF) has demonstrated promising results for this technique by utilizing a hybrid Eulerian-Lagrangian representation, in which the geometry is represented by the Eulerian grid representations of NeRF, the physics is described by a material point method (MPM), and they are connected via Lagrangian particles. However, a notable limitation of PAC-NeRF is that its performance is sensitive to the learning of the geometry from the first frames owing to its two-step optimization. First, the grid representations are optimized with the first frames of video sequences, and then the physical properties are optimized through video sequences utilizing the fixed first-frame grid representations. This limitation can be critical when learning of the geometric structure is difficult, for example, in a few-shot (sparse view) setting. To overcome this limitation, we propose Lagrangian particle optimization (LPO), in which the positions and features of particles are optimized through video sequences in Lagrangian space. This method allows for the optimization of the geometric structure across the entire video sequence within the physical constraints imposed by the MPM. The experimental results demonstrate that the LPO is useful for geometric correction and physical identification in sparse-view settings., Comment: Accepted to CVPR 2024. Project page: https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/lpo/

Large language models (LLMs) have demonstrated remarkable performances on a wide range of natural language tasks. Yet, LLMs' successes have been largely restricted to tasks concerning words, sentences, or documents, and it remains questionable how much they understand the minimal units of text, namely characters. In this paper, we examine contemporary LLMs regarding their ability to understand character composition of words, and show that most of them fail to reliably carry out even the simple tasks that can be handled by humans with perfection. We analyze their behaviors with comparison to token level performances, and discuss the potential directions for future research., Comment: ICML 2024 Workshop on Large Language Models and Cognition

Text-to-video generation task has witnessed a notable progress, with the generated outcomes reflecting the text prompts with high fidelity and impressive visual qualities. However, current text-to-video generation models are invariably focused on conveying the visual elements of a single scene, and have so far been indifferent to another important potential of the medium, namely a storytelling. In this paper, we examine text-to-video generation from a storytelling perspective, which has been hardly investigated, and make empirical remarks that spotlight the limitations of current text-to-video generation scheme. We also propose an evaluation framework for storytelling aspects of videos, and discuss the potential future directions., Comment: To appear at CVPR 2024 Workshop on AI for Content Creation (AI4CC)

With the development of lattice QCD in recent years, a determination of decay rates for inclusive semileptonic decays from lattice correlators now seems viable. We report on the calculation of the inclusive semileptonic decay of the $D_s$ meson on the lattice. We simulate the $D_s \rightarrow X_s\ell\nu_\ell$ process with M\"obius domain-wall charm and strange quarks, whose masses were approximately tuned to the physical values. We cover the whole kinematical region. We present a general overview on how the inclusive decays are treated on the lattice and discuss ongoing challenges, such as the estimation of systematic errors and future prospects of the project., Comment: 7 pages, 2 figures, Presented at the 12th Workshop on the CKM Unitarity Triangle, 18-22 September 2023, Santiago de Compostela

The digital frequency domain multiplexing (DfMux) technique is widely used for astrophysical instruments with large detector arrays. Detailed detector characterization is required for instrument calibration and systematics control. We conduct the TES complex electrothermal-feedback (ETF) response measurement with the DfMux readout system as follows. By injecting a single sideband signal, we induce modulation in TES power dissipation over a frequency range encompassing the detector response. The modulated current signal induced by TES heating effect is measured, allowing for the ETF response characterization of the detector. With the injection of an upper sideband, the TES readout current shows both an upper and a lower sideband. We model the upper and lower sideband complex ETF response and verify the model by fitting to experimental data. The model not only can fit for certain physical parameters of the detector, such as loop gain, temperature sensitivity, current sensitivity, and time constant, but also enables us to estimate the systematic effect introduced by the multiplexed readout. The method is therefore useful for in-situ detector calibration and for estimating systematic effects during astronomical telescope observations, such as those performed by the upcoming LiteBIRD satellite., Comment: 9 pages, 4 figures, accepted to Journal of Low Temperature Physics

We investigated the magnetic helicity of the triple-$q$ magnetic structure of the triangular skyrmion lattice in the ``A-phase" of EuPtSi for a magnetic field along the [111] axis by resonant x-ray diffraction using a circularly polarized beam. We show that all three Fourier components of the triple-$q$ structure are perpendicular to the respective $q$ vectors and have the same helicity. They are connected by the rotation operations about the [111] axis. The helicity is the same as that of the single-$q$ helimagnetic phase at low fields, suggesting that the antisymmetric exchange interaction inherent in the chiral structure supports the formation of the triangular skyrmion lattice. We also observe that the helical plane in the helimagnetic phase is tilted to the magnetic field to form a conical structure before the first-order transition to the skyrmion lattice phase., Comment: 9 pages, 8 figures, Accepted for publication in Phys. Rev. B

HoNiSi$_{3}$ is an intermetallic compound characterized by two successive antiferromagnetic transitions at $T_{N1} = 6.3$ K and $T_{N2} = 10.4$ K. Here, its zero-field microscopic magnetic structure is inferred from resonant x-ray magnetic diffraction experiments on a single crystalline sample that complement previous bulk magnetic susceptibility data. For $T < T_{N2}$, the primitive magnetic unit cell matches the chemical cell. The magnetic structure features ferromagnetic {\it ac} planes stacked in an antiferromagnetic $\uparrow \downarrow \uparrow \downarrow$ pattern. For $T_{N1} < T < T_{N2}$, the ordered magnetic moment points along $\vec{a}$, and for $T < T_{N1}$ a component along $\vec{c}$ also orders. A symmetry analysis indicates that the magnetic structure for $T

Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. This poses the risk of leaking inappropriate data such as benchmarks, personal information, and copyrighted texts in the training data. Membership Inference Attacks (MIA), which determine whether a given text is included in the model's training data, have been attracting attention. Previous studies of MIAs revealed that likelihood-based classification is effective for detecting leaks in LLMs. However, the existing methods cannot be applied to some proprietary models like ChatGPT or Claude 3 because the likelihood is unavailable to the user. In this study, we propose a Sampling-based Pseudo-Likelihood (\textbf{SPL}) method for MIA (\textbf{SaMIA}) that calculates SPL using only the text generated by an LLM to detect leaks. The SaMIA treats the target text as the reference text and multiple outputs from the LLM as text samples, calculates the degree of $n$-gram match as SPL, and determines the membership of the text in the training data. Even without likelihoods, SaMIA performed on par with existing likelihood-based methods.

We report the results of the CO $\textit J$=1-0 and SiO $\textit J$=2-1 mapping observations towards the broad-velocity-width molecular feature CO 16.134-0.553 with the Nobeyama Radio Observatory 45 m telescope. The high quality CO map shows that the 5-pc size broad-velocity-width feature bridges two separate velocity components at $\textit V_{\rm{LSR}}$$\quad$$\simeq$ 40 km s$^{-1}$ and 65 km s$^{-1}$ in the position-velocity space. The kinetic power of CO 16.134-0.553 amounts to $7.8\times10^2$ $\textit L$$_\odot$ whereas no apparent driving sources were identified. Prominent SiO emission was detected from the broad-velocity-width feature and its root in the $\textit V_{\rm{LSR}}$$\quad$$\simeq$ 40 km s$^{-1}$ component. In the CO Galactic plane survey data, CO 16.134-0.553 appears to correspond to the Galactic eastern rim of a 15-pc diameter expanding CO shell. An $1\deg$-diameter H I emission void and $4\deg$-long vertical H I filament were also found above and below the CO shell, respectively. We propose that the high-velocity plunge of a dark matter subhalo with a clump of baryonic matter was responsible for the formation of the H I void, CO 16.134-0.553/CO shell, and the H I filament., Comment: 7 pages, 6 figures, 2 table, accepted for publication in ApJ

We review recent progress from lattice QCD for the determination of the Cabibbo-Kobayashi-Maskawa matrix elements., Comment: Presented at the 12th Workshop on the CKM Unitarity Triangle, 18-22 September 2023, Santiago de Compostela; v2: references added

This paper introduces the physics-inspired synthesized underwater image dataset (PHISWID), a dataset tailored for enhancing underwater image processing through physics-inspired image synthesis. Deep learning approaches to underwater image enhancement typically demand extensive datasets, yet acquiring paired clean and degraded underwater ones poses significant challenges. While several underwater image datasets have been proposed using physics-based synthesis, a publicly accessible collection has been lacking. Additionally, most underwater image synthesis approaches do not intend to reproduce atmospheric scenes, resulting in incomplete enhancement. PHISWID addresses this gap by offering a set of paired ground-truth (atmospheric) and synthetically degraded underwater images, showcasing not only color degradation but also the often-neglected effects of marine snow, a composite of organic matter and sand particles that considerably impairs underwater image clarity. The dataset applies these degradations to atmospheric RGB-D images, enhancing the dataset's realism and applicability. PHISWID is particularly valuable for training deep neural networks in a supervised learning setting and for objectively assessing image quality in benchmark analyses. Our results reveal that even a basic U-Net architecture, when trained with PHISWID, substantially outperforms existing methods in underwater image enhancement. We intend to release PHISWID publicly, contributing a significant resource to the advancement of underwater imaging technology.

The ac Harman method has been used for the direct estimation of dimensionless thermoelectric figure of merit (zT) through ac/dc resistance measurements. However, accurate zT estimation with a four-probe configuration is difficult owing to the occurrence of a thermal phase-delay in the heat flow with a low frequency current. This study reports an exact solution for zT estimation by solving the heat conduction equation. The analysis can explain the reverse heat flow, which is the main source of the error in the four-probe configuration, and the experimentally obtained behavior of the frequency dependence of zT of (Bi,Sb)$_2$Te$_3$. Approximately 20 % of the error is caused by a thermal phase-delay, unless an appropriate current frequency and voltage-terminal position are chosen. Thus, an accurate zT evaluation using a four-probe configuration at any voltage terminal position is achieved. These findings can lead to interesting thermoelectric metrology and could serve as a powerful tool to search for promising thermoelectric materials., Comment: 10 pages, 7 figures

We define a hypergeometric series in $m$ variables with $p+(p-1)m$ parameters, which reduces to the generalized hypergeometric series $_pF_{p-1}$ when $m=1$, and to Lauricella's hypergeometric series $F_C$ in $m$ variables when $p=2$. We give a system of hypergeometric differential equations annihilating the series. Under some non-integral conditions on parameters, we give an Euler type integral representation of the series, and linearly independent $p^m$ solutions to this system around a point near to the origin. We show that this system is of rank $p^m$, and determine its singular locus., Comment: 22 pages, no figure

Numerically computing physical quantities of time-evolved states in quantum many-body systems is a challenging task in general. Here, we propose a method that utilizes reliable short-time data of physical quantities to accurately forecast long-time behavior. The method is based on the dynamic mode decomposition (DMD), which is commonly used in fluid dynamics. The effectiveness and applicability of the DMD in quantum many-body systems such as the Ising model in the transverse field at the critical point are studied, even when the input data exhibits complicated features such as multiple oscillatory components and a power-law decay with long-ranged quantum entanglements unlike fluid dynamics. It is demonstrated that the present method enables accurate forecasts at time as long as nearly an order of magnitude longer than that of the short-time training data. Effects of noise on the accuracy of the forecast are also investigated, because they are important especially when dealing with the experimental data. We find that a few percent of noise does not affect the prediction accuracy destructively., Comment: 18 pages, 24 figures

Antiferromagnetic (AF) order of U$_{2}$Pt$_{6}$Ga$_{15}$ with the ordering temperature $T_{\rm N}$ = 26 K was investigated by resonant X-ray scattering and neutron diffraction on single crystals. This compound possesses a unique crystal structure in which uranium ions form honeycomb layers and then stacks along the $c$-axis with slight offset, which gives rise to a stacking disorder. The AF order can be described with the propagation vector of $q = (1/6, 1/6, 0)$ in the hexagonal notation. The ordered magnetic moments orient perpendicular to the honeycomb layers, indicating a collinear spin structure consistent with Ising-like anisotropy. The magnetic reflections are found to be broadened along $c^*$ indicating that the stacking disorder results in anisotropic correlation lengths. The semi-quantitative analysis of neutron diffraction intensity, combined with group theory considerations based on the crystallographic symmetry, suggests a zig-zag type magnetic structure for the AF ground state, in which the AF coupling runs perpendicular to the stacking offset, characterized as $q = (1, 0, 0)_{\rm orth}$. The realization of the zig-zag magnetic structure implies the presence of frustrating intralayer exchange interactions involving both ferromagnetic (FM) first-neighbor and AF second and third-neighbor interactions in this compound., Comment: 17 pages, 10 figures, accepted for Phys. Rev. B

The Simons Array (SA) project is a ground-based Cosmic Microwave Background (CMB) polarization experiment. The SA observes the sky using three telescopes, and POLARBEAR-2A (PB-2A) is the receiver system on the first telescope. For the ground-based experiment, atmospheric fluctuation is the primary noise source that could cause polarization leakage. In the PB-2A receiver system, a continuously rotating half-wave plate (HWP) is used to mitigate the polarization leakage. However, due to the rapid modulation of the polarization signal, the uncertainty in the time constant of the detector results in an uncertainty in the polarization angle. For PB-2A, the time constant of each bolometer needs to be calibrated at the sub-millisecond level to avoid introducing bias to the polarization signal. We have developed a new calibrator system that can be used to calibrate the time constants of the detectors. In this study, we present the design of the calibration system and the preliminary results of the time constant calibration for PB-2A., Comment: Proceedings of the 15th Asia Pacific Physics Conference (APPC15)

A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fact motivates us to train a GAN-based vocoder on limited data. A promising solution is to augment the training data to avoid overfitting. However, a standard discriminator is unconditional and insensitive to distributional changes caused by data augmentation. Thus, augmented speech (which can be extraordinary) may be considered real speech. To address this issue, we propose an augmentation-conditional discriminator (AugCondD) that receives the augmentation state as input in addition to speech, thereby assessing the input speech according to the augmentation state, without inhibiting the learning of the original non-augmented distribution. Experimental results indicate that AugCondD improves speech quality under limited data conditions while achieving comparable speech quality under sufficient data conditions. Audio samples are available at https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/augcondd/., Comment: Accepted to ICASSP 2024. Project page: https://www.kecl.ntt.co.jp/people/kaneko.takuhiro/projects/augcondd/

Large Language Models (LLMs) are trained on massive web-crawled corpora. This poses risks of leakage, including personal information, copyrighted texts, and benchmark datasets. Such leakage leads to undermining human trust in AI due to potential unauthorized generation of content or overestimation of performance. We establish the following three criteria concerning the leakage issues: (1) leakage rate: the proportion of leaked data in training data, (2) output rate: the ease of generating leaked data, and (3) detection rate: the detection performance of leaked versus non-leaked data. Despite the leakage rate being the origin of data leakage issues, it is not understood how it affects the output rate and detection rate. In this paper, we conduct an experimental survey to elucidate the relationship between the leakage rate and both the output rate and detection rate for personal information, copyrighted texts, and benchmark data. Additionally, we propose a self-detection approach that uses few-shot learning in which LLMs detect whether instances are present or absent in their training data, in contrast to previous methods that do not employ explicit learning. To explore the ease of generating leaked information, we create a dataset of prompts designed to elicit personal information, copyrighted text, and benchmarks from LLMs. Our experiments reveal that LLMs produce leaked information in most cases despite less such data in their training set. This indicates even small amounts of leaked data can greatly affect outputs. Our self-detection method showed superior performance compared to existing detection methods.

Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse unsupervised representation learning with cascaded VoxelHop units, 2) decoder: coarse to fine segmentation prediction with voxel-wise classification and local refinement. Experiments are conducted on the publicly available ISBI-2013 dataset, as well as on a larger private one. Experimental analysis shows that our proposed PSHop is effective, robust and lightweight in the tasks of prostate gland and zonal segmentation, achieving a Dice Similarity Coefficient (DSC) of 0.873 for the gland segmentation task. PSHop achieves a competitive performance comparatively to other deep learning methods, while keeping the model size and inference complexity an order of magnitude smaller., Comment: 11 pages, 5 figures, 5 tables

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity., Comment: 13 pages, 7 figures, 5 tables

This paper presents a stochastic/robust nonlinear model predictive control (NMPC) to enhance the robustness of legged locomotion against contact uncertainties. We integrate the contact uncertainties into the covariance propagation of stochastic/robust NMPC framework by leveraging the guard saltation matrix and an extended Kalman filter-like covariance update. We achieve fast stochastic/robust NMPC computation by utilizing the zero-order stochastic/robust NMPC algorithm with additional improvements in computational efficiency concerning the feedback gains. We conducted numerical experiments and demonstrate that the proposed method can accurately forecast future state covariance and generate trajectories that satisfies constraints even in the presence of the contact uncertainties. Hardware experiments on the perceptive locomotion of a wheeled-legged robot were also carried out, validating the feasibility of the proposed method in a real-world system with limited on-board computation., Comment: 8 pages, 8 figures

Unsupervised intrinsic image decomposition (IID) is the process of separating a natural image into albedo and shade without these ground truths. A recent model employing light detection and ranging (LiDAR) intensity demonstrated impressive performance, though the necessity of LiDAR intensity during inference restricts its practicality. Thus, IID models employing only a single image during inference while keeping as high IID quality as the one with an image plus LiDAR intensity are highly desired. To address this challenge, we propose a novel approach that utilizes only an image during inference while utilizing an image and LiDAR intensity during training. Specifically, we introduce a partially-shared model that accepts an image and LiDAR intensity individually using a different specific encoder but processes them together in specific components to learn shared representations. In addition, to enhance IID quality, we propose albedo-alignment loss and image-LiDAR conversion (ILC) paths. Albedo-alignment loss aligns the gray-scale albedo from an image to that inferred from LiDAR intensity, thereby reducing cast shadows in albedo from an image due to the absence of cast shadows in LiDAR intensity. Furthermore, to translate the input image into albedo and shade style while keeping the image contents, the input image is separated into style code and content code by encoders. The ILC path mutually translates the image and LiDAR intensity, which share content but differ in style, contributing to the distinct differentiation of style from content. Consequently, LIET achieves comparable IID quality to the existing model with LiDAR intensity, while utilizing only an image without LiDAR intensity during inference.

We prove the prime geodesic theorem for the Picard manifold whose error term shrinks as the hybrid subconvex exponent $\theta$ for quadratic Dirichlet $L$-functions over Gaussian integers decreases. Our rate of decay is faster than that of Balkanova-Frolenkov (2022) and Kaneko (2022), and leads to the exponent $\frac{202}{139}+\varepsilon$ in the prime geodesic theorem under the generalised Lindel\"{o}f hypothesis $\theta = 0$, improving upon the existing conditional results. This provides further solid evidence towards the validity of the conjectural exponent $1+\varepsilon$., Comment: 14 pages. LaTeX2e

The Crab Nebula, also known as Tau A, is a polarized astronomical source at millimeter wavelengths. It has been used as a stable light source for polarization angle calibration in millimeter-wave astronomy. However, it is known that its intensity and polarization vary as a function of time at a variety of wavelengths. Thus, it is of interest to verify the stability of the millimeter-wave polarization. If detected, polarization variability may be used to better understand the dynamics of Tau A, and for understanding the validity of Tau~A as a calibrator. One intriguing application of such observation is to use it for the search of axion-light particles (ALPs). Ultralight ALPs couple to photons through a Chern-Simons term, and induce a temporal oscillation in the polarization angle of linearly polarized sources. After assessing a number of systematic errors and testing for internal consistency, we evaluate the variability of the polarization angle of the Crab Nebula using 2015 and 2016 observations with the 150 GHz POLARBEAR instrument. We place a median 95% upper bound of polarization oscillation amplitude $A < 0.065^\circ$ over the oscillation frequencies from $0.75~\mathrm{year}^{-1}$ to $0.66~\mathrm{hour}^{-1}$. Assuming that no sources other than ALP are causing Tau A's polarization angle variation, that the ALP constitutes all the dark matter, and that the ALP field is a stochastic Gaussian field, this bound translates into a median 95% upper bound of ALP-photon coupling $g_{a\gamma\gamma} < 2.16\times10^{-12}\,\mathrm{GeV}^{-1}\times(m_a/10^{-21} \mathrm{eV})$ in the mass range from $9.9\times10^{-23} \mathrm{eV}$ to $7.7\times10^{-19} \mathrm{eV}$. This demonstrates that this type of analysis using bright polarized sources is as competitive as those using the polarization of cosmic microwave background in constraining ALPs., Comment: 24 pages, 19 figures, 5 tables. Submitted to PRD

SGR J1935+2154 has truly been the most prolific magnetar over the last decade: It has been entering into burst active episodes once every 1-2 years since its discovery in 2014, it emitted the first Galactic fast radio burst associated with an X-ray burst in 2020, and has emitted hundreds of energetic short bursts. Here, we present the time-resolved spectral analysis of 51 bright bursts from SGR J1935+2154. Unlike conventional time-resolved X-ray spectroscopic studies in the literature, we follow a two-step approach to probe true spectral evolution. For each burst, we first extract spectral information from overlapping time segments, fit them with three continuum models, and employ a machine learning based clustering algorithm to identify time segments that provide the largest spectral variations during each burst. We then extract spectra from those non-overlapping (clustered) time segments and fit them again with the three models: the cutoff power-law model, the sum of two blackbody functions, and the model considering the emission of a modified black body undergoing resonant cyclotron scattering, which is applied systematically at this scale for the first time. Our novel technique allowed us to establish the genuine spectral evolution of magnetar bursts. We discuss the implications of our results and compare their collective behavior with the average burst properties of other magnetars., Comment: Accepted for publication in ApJ

Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. However, the likelihood, a measure of LLM's plausibility for a sentence, can vary due to superficial differences in sentences, such as word order and sentence structure. It is therefore possible that there might be a likelihood bias if LLMs are used for evaluation: they might overrate sentences with higher likelihoods while underrating those with lower likelihoods. In this paper, we investigate the presence and impact of likelihood bias in LLM-based evaluators. We also propose a method to mitigate the likelihood bias. Our method utilizes highly biased instances as few-shot examples for in-context learning. Our experiments in evaluating the data-to-text and grammatical error correction tasks reveal that several LLMs we test display a likelihood bias. Furthermore, our proposed method successfully mitigates this bias, also improving evaluation performance (in terms of correlation of models with human scores) significantly., Comment: 4 main pages