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We introduce Monte Carlo-based non-LTE line radiative transfer calculations in the 3D dust radiative transfer code SKIRT, which was originally set up as a dust radiative transfer code. By doing so, we develop a generic and powerful 3D radiative transfer code that can self-consistently generate spectra with molecular and atomic lines against the underlying continuum. We test the accuracy of the non-LTE line radiative transfer module in the extended SKIRT code using standard benchmarks. We find excellent agreement between the SKIRT results, the published benchmark results, and results obtained using the ray-tracing non-LTE line radiative transfer code MAGRITTE, which validates our implementation. We apply the extended SKIRT code on a 3D hydrodynamic simulation of a dusty AGN torus model and generate multi-wavelength images with CO rotational-line spectra against the underlying dust continuum. We find that the low-J CO emission traces the geometrically thick molecular torus, whereas the higher-J CO lines originate from the gas with high kinetic temperature located in the innermost regions of the torus. Comparing the calculations with and without dust radiative transfer, we find that higher-J CO lines are slightly attenuated by the surrounding cold dust when seen edge-on. This shows that atomic and molecular lines can experience attenuation, an effect that is particularly important for transitions at mid- and near-infrared wavelengths. Therefore, our self-consistent dust and non-LTE line radiative transfer calculations can help interpret the observational data from Herschel, ALMA, and JWST., Comment: 12 pages, 9 figures, Accepted for publication in A&A

We propose a method for constructing $\texttt{PREPARE}$ circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry by using variational optimization of quantum circuits solely on classical computers. The $\texttt{PREPARE}$ circuit generates a quantum state which encodes the coefficients of the terms in the Hamiltonian as probability amplitudes and plays a crucial role in the state-of-the-art efficient implementations of quantum phase estimation. We employ the automatic quantum circuit encoding algorithm [Shirakawa $\textit{et al.}$, arXiv:2112.14524] to construct $\texttt{PREPARE}$ circuits, which requires classical simulations of quantum circuits of $O(\log N)$ qubits with $N$ being the number of qubits of the Hamiltonian. The generated $\texttt{PREPARE}$ circuits do not need any ancillary qubit. We demonstrate our method by investigating the number of $T$-gates of the obtained $\texttt{PREPARE}$ circuits for quantum chemistry Hamiltonians of various molecules, which shows a constant-factor reduction compared to previous approaches that do not use ancillary qubits. Since the number of available logical qubits and $T$ gates will be limited at the early stage of the fault-tolerant quantum computing, the proposed method is particularly of use for performing the quantum phase estimation with such limited capability., Comment: 9 pages, 6 figures

We numerically studied the vortex dynamics at zero temperature in superfluid $^4$He confined between two parallel rough solid boundaries, one of which oscillates in a shear mode. This study was motivated by the experimental work by Barquist $et$ $al.$ which employed a microelectromechanical systems (MEMS) oscillator operating in superfluid $^4$He at a near-zero temperature. Their experiments suggest that the motion of the MEMS oscillator is damped by quantized vortices. In our study, we postulated that this damping effect was closely associated with vortex pinning phenomena and developed pinning models. Our primary objective is to understand the vortex dynamics in the presence of pinning and to provide insight into the experimental observations regarding the damping mechanism. We confirmed that Kelvin waves were excited in the pinned vortices when the oscillation frequency of the solid boundary matched with the mode frequency of the Kelvin wave. Additionally, we examined the formation and evolution of vortex tangles between the boundaries. The vortex tangle was suppressed in the presence of pinning, while the absence of pinning allowed to form well developed vortex tangle resulting in turbulence. Finally, by evaluating the tension of pinned vortices we extracted the damping force acting on the solid boundaries., Comment: 11 pages + supplement, 17 figures

Understanding and predicting the properties of solid-state materials from first-principles has been a great challenge for decades. Owing to the recent advances in quantum technologies, quantum computations offer a promising way to achieve this goal. Here, we demonstrate the first-principles calculation of a quasiparticle band structure on actual quantum computers. This is achieved by hybrid quantum-classical algorithms in conjunction with qubit-reduction and error-mitigation techniques. Our demonstration will pave the way to practical applications of quantum computers.

van der Waals (vdW) layered transition-metal chalcogenides are attracting significant attention owing to their fascinating physical properties. This group of materials consists of abundant members with various elements, having a variety of different structures. However, all vdW layered materials studied to date have been limited to crystalline materials, and the physical properties of vdW layered quasicrystals have not yet been reported. Here, we report on the discovery of superconductivity in a vdW layered quasicrystal of Ta1.6Te. The electrical resistivity, magnetic susceptibility, and specific heat of the Ta1.6Te quasicrystal fabricated by reaction sintering, unambiguously validated the occurrence of bulk superconductivity at a transition temperature of ~1 K. This discovery can pioneer new research on assessing the physical properties of vdW layered quasicrystals as well as two-dimensional quasicrystals; moreover, it paves the way toward new frontiers of superconductivity in thermodynamically stable quasicrystals, which has been the predominant challenge facing condensed matter physics since the discovery of quasicrystals almost four decades ago.

In a two-way contingency table analysis with explanatory and response variables, the analyst is interested in the independence of the two variables. However, if the test of independence does not show independence or clearly shows a relationship, the analyst is interested in the degree of their association. Various measures have been proposed to calculate the degree of their association, one of which is the proportional reduction in variation (PRV) measure which describes the PRV from the marginal distribution to the conditional distribution of the response. The conventional PRV measures can assess the association of the entire contingency table, but they can not accurately assess the association for each explanatory variable. In this paper, we propose a geometric mean type of PRV (geoPRV) measure that aims to sensitively capture the association of each explanatory variable to the response variable by using a geometric mean, and it enables analysis without underestimation when there is partial bias in cells of the contingency table. Furthermore, the geoPRV measure is constructed by using any functions that satisfy specific conditions, which has application advantages and makes it possible to express conventional PRV measures as geometric mean types in special cases., Comment: 16 pages

The variational quantum eigensolver (VQE) stands as a prominent quantum-classical hybrid algorithm for near-term quantum computers to obtain the ground states of molecular Hamiltonians in quantum chemistry. However, due to the non-commutativity of the Pauli operators in the Hamiltonian, the number of measurements required on quantum computers increases significantly as the system size grows, which may hinder practical applications of VQE. In this work, we present a protocol termed joint Bell measurement VQE (JBM-VQE) to reduce the number of measurements and speed up the VQE algorithm. Our method employs joint Bell measurements, enabling the simultaneous measurement of the absolute values of all expectation values of Pauli operators present in the Hamiltonian. In the course of the optimization, JBM-VQE estimates the absolute values of the expectation values of the Pauli operators for each iteration by the joint Bell measurement, while the signs of them are measured less frequently by the conventional method to measure the expectation values. Our approach is based on the empirical observation that the signs do not often change during optimization. We illustrate the speed-up of JBM-VQE compared to conventional VQE by numerical simulations for finding the ground states of molecular Hamiltonians of small molecules, and the speed-up of JBM-VQE at the early stage of the optimization becomes increasingly pronounced in larger systems. Our approach based on the joint Bell measurement is not limited to VQE and can be utilized in various quantum algorithms whose cost functions are expectation values of many Pauli operators., Comment: 14 pages, 8 figures

We prove sum representations of Appell-Lauricella functions over a finite field using confluent hypergeometric functions over the finite field. As an application, we also prove transformation formulas, summation formulas and reduction formulas for Appell-Lauricella functions over the finite field., Comment: 28 pages

In order to investigate the far-infrared excess detected from the west hot spot of the radio galaxy Pictor A with the Herschel observatory, a submillimeter photometry is performed with the Atacama Compact Array (ACA) of the Atacama Large Millimeter/submillimeter Array at Band 8 with the reference frequency of 405 GHz. A submillimeter source is discovered at the radio peak of the hot spot. Because the 405 GHz flux density of the source, $80.7\pm3.1$ mJy, agrees with the extrapolation of the synchrotron radio spectrum, the far-infrared excess is suggested to exhibit no major contribution at the ACA band. In contrast, by subtracting the power-law spectrum tightly constrained by the radio and ACA data, the significance of the excess in the Herschel band is well confirmed. No diffuse submillimeter emission is detected within the ACA field of view, and thus, the excess is ascribed to the west hot spot itself. In comparison to the previous estimate based on the Herschel data, the relative contribution of the far-infrared excess is reduced by a factor of $\sim 1.5$. The spectrum of the excess below the far-infrared band is determined to be harder than that of the diffusive shock acceleration. This strengthens the previous interpretation that the excess originates via the magnetic turbulence in the substructures within the hot spot. The ACA data are utilized to evaluate the magnetic field strength of the excess and of diffuse radio structure associated to the hot spot., Comment: 10 pages,4 figures, 3 tables, accepted for ApJ

Reliable estimates of Gross Primary Productivity (GPP), crucial for evaluating climate change initiatives, are currently only available from sparsely distributed eddy covariance tower sites. This limitation hampers access to reliable GPP quantification at regional to global scales. Prior machine learning studies on upscaling \textit{in situ} GPP to global wall-to-wall maps at sub-daily time steps faced limitations such as lack of input features at higher temporal resolutions and significant missing values. This research explored a novel upscaling solution using Temporal Fusion Transformer (TFT) without relying on past GPP time series. Model development was supplemented by Random Forest Regressor (RFR) and XGBoost, followed by the hybrid model of TFT and tree algorithms. The best preforming model yielded to model performance of 0.704 NSE and 3.54 RMSE. Another contribution of the study was the breakdown analysis of encoder feature importance based on time and flux tower sites. Such analysis enhanced the interpretability of the multi-head attention layer as well as the visual understanding of temporal dynamics of influential features., Comment: Accepted Oral Presentation at CVPR 2023 MultiEarth Workshop

Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea behind predictive pattern mining is to build a prediction model by considering substructures, such as subsets, subgraphs, and subsequences (referred to as patterns), present in the structured data as features of the model. The primary challenge in predictive pattern mining lies in the exponential growth of the number of patterns with the complexity of the structured data. In this study, we propose the Safe Pattern Pruning (SPP) method to address the explosion of pattern numbers in predictive pattern mining. We also discuss how it can be effectively employed throughout the entire model building process in practical data analysis. To demonstrate the effectiveness of the proposed method, we conduct numerical experiments on regression and classification problems involving sets, graphs, and sequences.

Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and one example is the quantum expectation estimation algorithm that accelerates the widely-used Monte Carlo method in fields such as finance. A previous study has shown that quantum generative adversarial networks(qGANs), a quantum circuit version of generative adversarial networks(GANs), can generate the probability distribution necessary for the quantum expectation estimation algorithm in shallow quantum circuits. However, a previous study has also suggested that the convergence speed and accuracy of the generated distribution can vary greatly depending on the initial distribution of qGANs' generator. In particular, the effectiveness of using a normal distribution as the initial distribution has been claimed, but it requires a deep quantum circuit, which may lose the advantage of qGANs. Therefore, in this study, we propose a novel method for generating an initial distribution that improves the learning efficiency of qGANs. Our method uses the classical process of label replacement to generate various probability distributions in shallow quantum circuits. We demonstrate that our proposed method can generate the log-normal distribution, which is pivotal in financial engineering, as well as the triangular distribution and the bimodal distribution, more efficiently than current methods. Additionally, we show that the initial distribution proposed in our research is related to the problem of determining the initial weights for qGANs., Comment: 14 pages, 5 figures, some typos were corrected

Human T-cell leukemia virus type 1 (HTLV-1) causes adult T-cell leukemia (ATL) and HTLV-1-associated myelopathy (HAM) after a long latent period in a fraction of infected individuals. These HTLV-1-infected cells typically have phenotypes similar to that of CD4${^+}$ T cells, but the cell status is not well understood. To extract the inherent information of HTLV-1-infected CD4$^+$ cells, we integratively analyzed the ATAC-seq and RNA-seq data of infected cells. Compared to CD4${^+}$ T cells from healthy donors, we found anomalous chromatin accessibility in HTLV-1-infected CD4${^+}$ cells derived from ATL cases in terms of location and sample-to-sample fluctuations in open chromatin regions. Further, by focusing on systematically selected genes near the open chromatin regions, all the gene expressions in ATL cases were found to be distinct from those of healthy CD4$^+$ T cells. Based on a further analysis of chromatin accessibility, we detected TLL1 (Tolloid Like 1) as one of the key genes that exhibit unique gene expressions in ATL cases. A luciferase assay indicated that TLL1 has a strong regulatory effect on TGF-$\beta$. Overall, this study provides results about the status of HTLV-1 infected cells, which are qualitatively consistent across the different scales of chromatin accessibility, transcription, and immunophenotype., Comment: 23 pages, 13 figures

In this study, we grow FeSe/SrTiO$_{3}$ with thicknesses of 4-19 nm using pulsed laser deposition and investigate their magneto-transport properties. The thinnest film (4 nm) exhibit negative Hall effect, indicating electron transfer into FeSe from the SrTiO$_{3}$ substrate. This is in agreement with reports on ultrathin FeSe/SrTiO$_{3}$ grown by molecular beam epitaxy. The upper critical field is found to exhibit large anisotropy ($\gamma >$ 11.9), estimated from the data near the transition temperature ($T_{\mathrm{c}}$). In particular, the estimated coherence lengths in the perpendicular direction are 0.15-0.27 nm, which are smaller than the c-axis length of FeSe, and are found to be almost independent of the total thicknesses of the films. These results indicate that superconductivity is confined at the interface of FeSe/SrTiO$_{3}$., Comment: 5pages, 3figures

We constructed a large-scale electromagnetic calorimeter prototype as a part of the Forward Calorimeter upgrade project (FoCal) for the ALICE experiment at the Large Hadron Collider (LHC). The prototype, also known as ``Mini FoCal'', consists of 20 layers of silicon pad sensors and tungsten alloy plates with printed circuit boards and readout electronics. The constructed detector was tested at the test beam facility of the Super Proton Synchrotron (SPS) at CERN. We obtain an energy resolution of about 4.3% for electron beams at both 150 and 250 GeV/$c$, which is consistent with realistic detector response simulations. Longitudinal profiles of electromagnetic shower were also measured and found to agree with the simulations. The same prototype detector was installed in the ALICE experimental area about 7.5m away from the interaction point. It was used to measure inclusive electromagnetic cluster energy distributions and neutral-pion candidate invariant mass distributions for pseudo-rapidity of $\eta$=3.7-4.5 in proton-proton collisions at $\sqrt{s}$ = 13 TeV at LHC. The measured distributions in different $\eta$ regions are similar to those obtained from PYTHIA simulations., Comment: 30 pages, 27 figures

We verify the existence of density functions of the running maximum of a stochastic differential equation (SDE) driven by a Brownian motion and a non-truncated stable process. This is proved by the existence of density functions of the running maximum of Wiener-Poisson functionals resulting from Bismut's approach to Malliavin calculus for jump processes., Comment: 21 pages

In this study, we investigate the bulk electronic structure of Pb-substituted LaO$_{0.5}$F$_{0.5}$BiS$_2$ single crystals, using two types of hard-x-ray spectroscopy. High-energy-resolution fluorescence-detected x-ray absorption spectroscopy revealed a spectral change at low temperatures. Using density functional theory (DFT) simulations, we find that the temperature-induced change originates from a structural phase transition, similar to the pressure-induced transition in LaO$_{0.5}$F$_{0.5}$BiS$_2$. This finding suggests that the mechanism of bulk superconductivity induced by Pb substitution is the same as that under high pressure. Furthermore, a novel low-valence state with a mixture of divalent and trivalent Bi ions is discovered using hard x-ray photoemission spectroscopy with the aid of DFT calculations., Comment: 8 pages, 6 figures, 1 table

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (XMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous XMR algorithms have employed techniques such as sharing bottom or incorporating inter-market similarity to optimize the performance of XMR. However, the existing approaches suffer from two crucial limitations: (1) They ignore the co-occurrences of items provided by data-rich markets. (2) They do not adequately tackle the issue of negative transfer stemming from disparities across diverse markets. In order to address these limitations, we propose a novel session-based model called Bert4XMR, which is able to model item co-occurrences across markets and mitigate negative transfer. Specifically, we employ the pre-training and fine-tuning paradigm to facilitate knowledge transfer across markets. Pre-training occurs on global markets to learn item co-occurrences, while fine-tuning happens in the target market for model customization. To mitigate potential negative transfer, we separate the item representations into market embeddings and item embeddings. Market embeddings model the bias associated with different markets, while item embeddings learn generic item representations. Extensive experiments conducted on seven real-world datasets illustrate our model's effectiveness. It outperforms the suboptimal model by an average of $4.82\%$, $4.73\%$, $7.66\%$, and $6.49\%$ across four metrics. Through the ablation study, we experimentally demonstrate that the market embedding approach helps prevent negative transfer, especially in data-scarce markets. Our implementations are available at https://github.com/laowangzi/Bert4XMR.

We investigated the inner buried nucleus of a nearby luminous infrared galaxy NGC 4418 using high-resolution spectroscopy of fundamental carbon monoxide (CO) ro-vibrational absorptions around $4.67 \mu$m for the first time. This method allowed us to examine the physical and kinematical properties in the hot inner region of this nucleus. We detected a series of both very deep (partly saturated) $^{12}$CO and moderately deep (optically thin) $^{13}$CO absorption lines and inferred a large column density ($N_\mathrm{H2}=(5\pm3)\times10^{23}$ cm$^{-2}$ in front of the $5 \mu$m photosphere) of warm ($T_\mathrm{ex}\simeq170$ K) molecular gas by assuming an isothermal plane-parallel slab illuminated by a compact background MIR-emitting source. We modeled that the warm CO absorber almost covers the central heating source and that it is an inner layer around the $5 \mu$m photosphere (at $r=$several pc) of a compact shroud of gas and dust ($d\sim100$ pc). The width of the absorption lines ($110$ km s$^{-1}$) and their small deviation from the systemic velocity ($<10$ km s$^{-1}$) are consistent with a warm and turbulent layer with little bulk motion in the radial direction., Comment: 18 pages, 3 figures, 2 tables, accepted for publication in ApJ

Ordinal response model is a popular and commonly used regression for ordered categorical data in a wide range of fields such as medicine and social sciences. However, it is empirically known that the existence of ``outliers'', combinations of the ordered categorical response and covariates that are heterogeneous compared to other pairs, makes the inference with the ordinal response model unreliable. In this article, we prove that the posterior distribution in the ordinal response model does not satisfy the posterior robustness with any link functions, i.e., the posterior cannot ignore the influence of large outliers. Furthermore, to achieve robust Bayesian inference in the ordinal response model, this article defines general posteriors in the ordinal response model with two robust divergences (the density-power and $\gamma$-divergences) based on the framework of the general posterior inference. We also provide an algorithm for generating posterior samples from the proposed posteriors. The robustness of the proposed methods against outliers is clarified from the posterior robustness and the index of robustness based on the Fisher-Rao metric. Through numerical experiments on artificial data and two real datasets, we show that the proposed methods perform better than the ordinary bayesian methods with and without outliers in the data for various link functions., Comment: 29 pages

This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using $\alpha$-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training., Comment: 32 pages, 13 figures