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We propose using the wave function generated by the quantum selected configuration interaction (QSCI) method as the trial wave function in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC). In the QSCI framework, electronic configurations are sampled from the quantum state realized on a quantum computer. These configurations serve as basis states for constructing an effective Hamiltonian, which is then diagonalized to obtain the corresponding eigenstate. Using this wave function, ph-AFQMC is performed to recover the dynamical electron correlation across the whole orbital space. The use of the QSCI trial wave function is expected to improve the feasibility of the quantum-classical (QC) hybrid quantum Monte Carlo approach [Nature, 603, 416 (2022)]. We call this integrated approach QC-QSCI-AFQMC, or QSCI-AFQMC for short. This method is validated across several molecular systems. For H2O and a linear H4 chain, we achieved chemical accuracy in most investigations relative to full configuration interaction while utilizing superconducting quantum computers at Osaka University and RIKEN. Additionally, the application of QSCI-AFQMC to the O-H bond dissociation in an organic molecule highlights the complementary synergy between capturing static correlation on quantum hardware and incorporating dynamical correlation via classical post-processing. For the N2, when QSCI-AFQMC is executed with a noiseless simulator, it ranks among the most accurate methods compared to various multireference electronic structure theories. Although the proposed method is demonstrated using small active spaces on current quantum devices, the concept is not limited to few-qubit problems. The QSCI-AFQMC can compete with state-of-the-art classical computational techniques, particularly in larger active spaces, displaying considerable potential for resolving classically intractable problems in quantum chemistry., Comment: 10 pages, 8 figures

Swarm User Interfaces allow dynamic arrangement of user environments through the use of multiple mobile robots, but their operational range is typically confined to a single plane due to constraints imposed by their two-wheel propulsion systems. We present corobos, a proof-of-concept design that enables these robots to cooperatively transition between table (horizontal) and wall (vertical) surfaces seamlessly, without human intervention. Each robot is equipped with a uniquely designed slope structure that facilitates smooth rotation when another robot pushes it toward a target surface. Notably, this design relies solely on passive mechanical elements, eliminating the need for additional active electrical components. We investigated the design parameters of this structure and evaluated its transition success rate through experiments. Furthermore, we demonstrate various application examples to showcase the potential of corobos in enhancing user environments., Comment: CHI 2025

Autonomous agents capable of diverse object manipulations should be able to acquire a wide range of manipulation skills with high reusability. Although advances in deep learning have made it increasingly feasible to replicate the dexterity of human teleoperation in robots, generalizing these acquired skills to previously unseen scenarios remains a significant challenge. In this study, we propose a novel algorithm, Gaze-based Bottleneck-aware Robot Manipulation (GazeBot), which enables high reusability of the learned motions even when the object positions and end-effector poses differ from those in the provided demonstrations. By leveraging gaze information and motion bottlenecks, both crucial features for object manipulation, GazeBot achieves high generalization performance compared with state-of-the-art imitation learning methods, without sacrificing its dexterity and reactivity. Furthermore, the training process of GazeBot is entirely data-driven once a demonstration dataset with gaze data is provided. Videos and code are available at https://crumbyrobotics.github.io/gazebot.

Managing technical debt (TD) is essential for maintaining long-term software projects. Nonetheless, the time and cost involved in technical debt management (TDM) are often high, which may lead practitioners to omit TDM tasks. The adoption of tools, and particularly the usage of automated solutions, can potentially reduce the time, cost, and effort involved. However, the adoption of tools remains low, indicating the need for further research on TDM automation. To address this problem, this study aims at understanding which TDM activities practitioners are discussing with respect to automation in TDM, what tools they report for automating TDM, and the challenges they face that require automation solutions. To this end, we conducted a mining software repositories (MSR) study on three websites of Stack Exchange (Stack Overflow, Project Management, and Software Engineering) and collected 216 discussions, which were analyzed using both thematic synthesis and descriptive statistics. We found that identification and measurement are the most cited activities. Furthermore, 51 tools were reported as potential alternatives for TDM automation. Finally, a set of nine main challenges were identified and clustered into two main categories: challenges driving TDM automation and challenges related to tool usage. These findings highlight that tools for automating TDM are being discussed and used; however, several significant barriers persist, such as tool errors and poor explainability, hindering the adoption of these tools. Moreover, further research is needed to investigate the automation of other TDM activities such as TD prioritization., Comment: Accepted for publication on the International Conference on Technical Debt 2025 (TechDebt'25)

We present an approach that uses the doubly occupied configuration interaction (DOCI) wave function as the trial wave functions in phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC). DOCI is a seniority-zero method focused on electron pairs. Although DOCI considers much fewer electron configurations than the complete active space (CAS) configuration interaction method, it efficiently captures the static correlation, while the consequent ph-AFQMC recovers the dynamical correlation across all orbitals. We also explore an orbital-optimized version (OO-DOCI) to further improve accuracy. We test this approach on several chemical systems, including single O-H bond breaking in water and polymer additives. In these cases, OO-DOCI-AFQMC closely matches CAS-based ph-AFQMC and even outperforms coupled-cluster singles, doubles, and perturbative triples. However, for strongly correlated systems, such as the carbon dimer and multi-bond dissociation in hydrogen systems and water, the method's accuracy drops. This suggests that seniority-zero space models may be insufficient as trial wave functions in ph-AFQMC for strongly correlated systems, suggesting the need for trial wave functions in an extended space. Despite such a limitation, our study demonstrates that DOCI- and OO-DOCI-based ph-AFQMC can reduce the steep cost of CAS approaches, offering a path to accurate multi-reference calculations for larger, more complex systems., Comment: 10 pages, 12 figures

Ultrashort burst laser pulses serve as powerful tools for precise laser processing, broadband ultrafast spectroscopy, and high-speed laser-scanning microscopy. However, the performance of conventional burst pulse generators is limited by constraints in the pulse time interval variability, pulse energy variability, pulse number variability, and overall system complexity. Here, we present a compact burst pulse generator that offers a broad tuning range for pulse time intervals, along with control over the number of pulses and pulse energies within the burst. It consists of four mirrors, two of which are parallel to each other, and outputs pulses that are equally spaced both temporally and spatially. We demonstrated the generation of a burst laser pulse by shaping a single ultrashort laser pulse into six pulses with time intervals ranging from femtoseconds to nanoseconds. The pulse time intervals and energies were consistent with theoretical results.

Robust Bayesian inference using density power divergence (DPD) has emerged as a promising approach for handling outliers in statistical estimation. While the DPD-based posterior offers theoretical guarantees for robustness, its practical implementation faces significant computational challenges, particularly for general parametric models with intractable integral terms. These challenges become especially pronounced in high-dimensional settings where traditional numerical integration methods prove inadequate and computationally expensive. We propose a novel sampling methodology that addresses these limitations by integrating the loss-likelihood bootstrap with a stochastic gradient descent algorithm specifically designed for DPD-based estimation. Our approach enables efficient and scalable sampling from DPD-based posteriors for a broad class of parametric models, including those with intractable integrals, and we further extend it to accommodate generalized linear models. Through comprehensive simulation studies, we demonstrate that our method efficiently samples from DPD-based posteriors, offering superior computational scalability compared to conventional methods, particularly in high-dimensional settings. The results also highlight its ability to handle complex parametric models with intractable integral terms.

This study presents asynchronous optical sampling (ASOPS) terahertz spectroscopy using commercially available Ti:Sapphire lasers without stabilizing repetition frequency. Our postprocessing algorithm using the multiplied repetition frequency difference as the calibration signal successfully corrected the jitter, thereby allowing broadband (2.5 THz) spectroscopy with a high spectral resolution (82 MHz). The robustness of the jitter correction based on the free-running laser setup and broadband electric circuits was rigorously examined under varying temperatures, thereby demonstrating reliable long-term operations over 60 h. This study expands the applicability of the ASOPS terahertz time-domain spectroscopy., Comment: 16 pages, 7 figures

Systematic Literature Reviews (SLRs) are a widely employed research method in software engineering. However, there are several problems with SLRs, including the enormous time and effort to conduct them and the lack of obvious impacts of SLR results on software engineering practices and industry projects. To address these problems, the concepts of \textit{sustainability} and \textit{sustainable SLR} have been proposed, aiming to raise awareness among researchers about the importance of dealing with SLR problems in a consistent way; however, practical and concrete actions are still lacking. This paper presents concrete directions towards sustainable SLRs. We first identified 18 ``green drivers'' (GD) that could directly impact SLR sustainability, and we distilled 25 sustainability indicators (SI) associated with the GD to assess SLRs regarding their sustainability. A preliminary evaluation was conducted on the ten top-cited SLRs in software engineering published over the last decade. From this analysis, we synthesized our insights into 12 leverage points for sustainability. Our results indicate that even in high-quality reviews, there are threats to sustainability, such as: flaws in the search process, lack of essential details in the documentation, weak collaboration with stakeholders, poor knowledge management, lack of use of supporting tools, and a dearth of practical insights for software engineering practitioners. The good news is that moving towards sustainable SLRs only requires some simple actions, which can pave the way for a profound change in the software engineering community's mindset about how to create and sustain SLRs.

We develop a formulation of global thermodynamics for equilibrium systems under the influence of gravity. The free energy for simple fluids is extended to include a dependence on $(T, V, N, mgL)$, where $L$ represents the vertical system length in the direction of gravity. A central idea in this formulation is to uniquely fix the reference point of the gravitational potential, ensuring a consistent thermodynamic framework. Using this framework, we derive the probability density of thermodynamic quantities, which allows us to define a variational function for determining equilibrium liquid-gas coexistence under gravity. The resulting free energy landscape, derived from the variational function, reveals the local stability of liquid-gas configurations. Specifically, the liquid phase resides at the lower portion of the system due to gravity, while the inverted configuration (with liquid on top) is also locally stable in this landscape. Furthermore, we characterize the transition between these liquid-gas configurations as a first-order phase transition using the thermodynamic free energy of $(T,V,N,mgL)$. Finally, we validate the predictions of global thermodynamics through molecular dynamics simulations, demonstrating the applicability and accuracy of the proposed framework., Comment: 42 pages, 17 figures

Expectation value estimation is ubiquitous in quantum algorithms. The expectation value of a Hamiltonian, which is essential in various practical applications, is often estimated by measuring a large number of Pauli strings on quantum computers and performing classical post-processing. In the case of $n$-qubit molecular Hamiltonians in quantum chemistry calculations, it is necessary to evaluate $O(n^4)$ Pauli strings, requiring a large number of measurements for accurate estimation. To reduce the measurement cost, we assess an existing idea that uses two copies of an $n$-qubit quantum state of interest and coherently measures them in the Bell basis, which enables the simultaneous estimation of the absolute values of expectation values of all the $n$-qubit Pauli strings. We numerically investigate the efficiency of energy estimation for molecular Hamiltonians of up to 12 qubits. The results show that, when the target precision is no smaller than tens of milli-Hartree, this method requires fewer measurements than conventional sampling methods. This suggests that the method may be useful for many applications that rely on expectation value estimation of Hamiltonians and other observables as well when moderate precision is sufficient., Comment: 14 pages, 12 figures

Quantum-selected configuration interaction (QSCI) utilizes an input quantum state on a quantum device to select important bases (electron configurations in quantum chemistry) which define a subspace in which to diagonalize a target Hamiltonian, i.e., perform selected configuration interaction, on classical computers. Previous proposals for preparing a good input state, which is crucial for the quality of QSCI, based on optimization of quantum circuits may suffer from optimization difficulty and require many runs of the quantum device. Here we propose using a time-evolved state by the target Hamiltonian (for some initial state) as an input of QSCI. Our proposal is based on the intuition that the time evolution by the Hamiltonian creates electron excitations of various orders when applied to the initial state. We numerically investigate the accuracy of the energy obtained by the proposed method for quantum chemistry Hamiltonians describing electronic states of small molecules. Numerical results reveal that our method can yield sufficiently accurate ground-state energies for the investigated molecules. Systematic analysis when increasing the number of qubits in a Hydrogen chain shows that the subspace size required for sufficiently accurate results is reasonable at system sizes that cannot be solved by naive classical diagonalization. Our proposal provides a systematic and optimization-free method to prepare the input state of QSCI and could contribute to practical applications of quantum computers in quantum chemistry calculations., Comment: 19 pages, 8 figures, 4 tables

In recent years, knowledge graphs have been integrated into recommender systems as item-side auxiliary information, enhancing recommendation accuracy. However, constructing and integrating structural user-side knowledge remains a significant challenge due to the improper granularity and inherent scarcity of user-side features. Recent advancements in Large Language Models (LLMs) offer the potential to bridge this gap by leveraging their human behavior understanding and extensive real-world knowledge. Nevertheless, integrating LLM-generated information into recommender systems presents challenges, including the risk of noisy information and the need for additional knowledge transfer. In this paper, we propose an LLM-based user-side knowledge inference method alongside a carefully designed recommendation framework to address these challenges. Our approach employs LLMs to infer user interests based on historical behaviors, integrating this user-side information with item-side and collaborative data to construct a hybrid structure: the Collaborative Interest Knowledge Graph (CIKG). Furthermore, we propose a CIKG-based recommendation framework that includes a user interest reconstruction module and a cross-domain contrastive learning module to mitigate potential noise and facilitate knowledge transfer. We conduct extensive experiments on three real-world datasets to validate the effectiveness of our method. Our approach achieves state-of-the-art performance compared to competitive baselines, particularly for users with sparse interactions., Comment: Accepted at AAAI 2025

We propose a novel paradigm for the QCD axion with high-quality Peccei-Quinn (PQ) symmetry on the basis of electric-magnetic duality in the conformal window of a supersymmetric gauge theory. PQ breaking fields, that contain the QCD axion, emerge in the magnetic theory and possess a large anomalous dimension, which leads to not only generation of an intermediate scale of spontaneous PQ breaking but also significant suppression of explicit PQ symmetry breaking operators. The high PQ quality and the absence of a Landau pole in the color gauge coupling are achieved. The parameter space to realize the correct abundance of the axion dark matter (DM) predicts explicit PQ violation which may be probed by future measurements of the neutron electric dipole moment. In the other viable parameter space, the lightest supersymmetric particle can become a DM candidate. Since the model naturally accommodates a mechanism to suppress the axion isocurvature fluctuation, it provides a complete solution to the strong CP problem as well as the identity of DM., Comment: 12 pages, 6 figures

This paper presents a 610 MHz radio survey covering 1.94 square degrees around the North Ecliptic Pole (NEP), which includes parts of the AKARI (ADF-N) and Euclid, Deep Fields North. The median 5-sigma sensitivity is 28 microJy beam per beam, reaching as low as 19 microJy per beam, with a synthesised beam of 3.6 x 4.1 arcsec. The catalogue contains 1675 radio components, with 339 grouped into multi-component sources and 284 isolated components likely part of double radio sources. Imaging, cataloguing, and source identification are presented, along with preliminary scientific results. From a non-statistical sub-set of 169 objects with multi-wavelength AKARI and other detections, luminous infrared galaxies (LIRGs) represent 66 percent of the sample, ultra-luminous infrared galaxies (ULIRGs) 4 percent, and sources with L_IR < 1011 L_sun 30 percent. In total, 56 percent of sources show some AGN presence, though only seven are AGN-dominated. ULIRGs require three times higher AGN contribution to produce high-quality SED fits compared to lower luminosity galaxies, and AGN presence increases with AGN fraction. The PAH mass fraction is insignificant, although ULIRGs have about half the PAH strength of lower IR-luminosity galaxies. Higher luminosity galaxies show gas and stellar masses an order of magnitude larger, suggesting higher star formation rates. For LIRGs, AGN presence increases with redshift, indicating that part of the total luminosity could be contributed by AGN activity rather than star formation. Simple cross-matching revealed 13 ROSAT QSOs, 45 X-ray sources, and 61 sub-mm galaxies coincident with GMRT radio sources.

This study aims to evaluate the sentiment of financial texts using large language models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge about firms on the sentiment measurement of texts by LLMs. Firstly, we compare the sentiment scores of financial texts by LLMs when the company name is explicitly included in the prompt versus when it is not. We define and quantify company-specific bias as the difference between these scores. Next, we construct an economic model to theoretically evaluate the impact of sentiment bias on investor behavior. This model helps us understand how biased LLM investments, when widespread, can distort stock prices. This implies the potential impact on stock prices if investments driven by biased LLMs become dominant in the future. Finally, we conduct an empirical analysis using Japanese financial text data to examine the relationship between firm-specific sentiment bias, corporate characteristics, and stock performance., Comment: Acceped IEEE BigData 2024 Special Sessionon Understanding New Markets by Data Science, Social Science, and Economics

This paper studies stable learning methods for generative models that enable high-quality data generation. Noise injection is commonly used to stabilize learning. However, selecting a suitable noise distribution is challenging. Diffusion-GAN, a recently developed method, addresses this by using the diffusion process with a timestep-dependent discriminator. We investigate Diffusion-GAN and reveal that data scaling is a key component for stable learning and high-quality data generation. Building on our findings, we propose a learning algorithm, Scale-GAN, that uses data scaling and variance-based regularization. Furthermore, we theoretically prove that data scaling controls the bias-variance trade-off of the estimation error bound. As a theoretical extension, we consider GAN with invertible data augmentations. Comparative evaluations on benchmark datasets demonstrate the effectiveness of our method in improving stability and accuracy.

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

We derive a bound on dark photon dark matter scenarios where the dark photon mass is generated through the Higgs mechanism, based on the requirement that symmetry breaking must occur sufficiently early in the universe. We emphasize that dark photon production occurs successfully when the dark Higgs field remains in the symmetric phase due to non-thermal trapping effects. For renormalizable Higgs potentials, our bound reads $$\frac{m_{\gamma'}}{q_H e_H}\;\gg \;60\,{\rm eV}\left(\frac{2\pi}{\lambda}\right)^{1/4}$$ where $m_{\gamma'}$ is the dark photon mass, $e_H$ is the gauge coupling, $q_H$ is the charge of the dark Higgs boson, and $\lambda$ is the Higgs quartic coupling}. This constraint holds independently of any complications arising from the Schwinger effect and vortex formation in the Higgsed phase. For more general Higgs potentials such as the Coleman-Weinberg type potential, our bound yields different forms. We argue that late-time symmetry breaking of the dark U(1) symmetry satisfying our bound has only a mild impact on both the abundance and momentum distribution of dark photon dark matter, and therefore does not pose any serious problem for the dark photon dark matter scenario., Comment: v3: 8 pages, 7 figures, version to appear in PLB

A measure of asymmetry is a quantification method that allows for the comparison of categorical evaluations before and after treatment effects or among different target populations, irrespective of sample size. We focus on square contingency tables that summarize survey results between two time points or cohorts, represented by the same categorical variables. We propose a measure to evaluate the degree of departure from a symmetry model using cosine similarity. This proposal is based on the Fisher-Rao distance, allowing asymmetry to be interpreted as a geodesic distance between two distributions. Various measures of asymmetry have been proposed, but visualizing the relationship of these quantification methods on a two-dimensional plane demonstrates that the proposed measure provides the geometrically simplest and most natural quantification. Moreover, the visualized figure indicates that the proposed method for measuring departures from symmetry is less affected by very few cells with extreme asymmetry. A simulation study shows that for square contingency tables with an underlying asymmetry model, our method can directly extract and quantify only the asymmetric structure of the model, and can more sensitively detect departures from symmetry than divergence-type measures.

By coupling to both the Higgs and electroweak gauge sectors, an axion can generate the matter-antimatter asymmetry in the universe via electroweak baryogenesis when the axion decay constant lies within the range of approximately $10^5$ and $10^7$GeV, corresponding to axion masses between the MeV and GeV scales. In this work, we explore the intriguing possibility that the axion interacts with a dark sector, particularly with dark photons through anomalous couplings. Notably, axion-coupled dark photons can play multiple roles, including $(i)$ suppressing the branching ratio of axion decay to Standard Model (SM) particles, which would otherwise conflict with the constraints from supernovae explosions, Big Bang nucleosynthesis, and neutron star merger events, $(ii)$ serving as a candidate for cold dark matter if they are massive and stable, and $(iii)$ contributing to dark radiation if they are ultralight. The axion decouples from the SM thermal bath when it becomes non-relativistic, facilitating the production of dark matter dark photons through the freeze-in mechanism, while dark radiation dark photons are thermally generated prior to the electroweak phase transition., Comment: 12 pages, 3 figures

Rapid advancements in artificial intelligence (AI) have made it crucial to integrate moral reasoning into AI systems. However, existing models and datasets often overlook regional and cultural differences. To address this shortcoming, we have expanded the JCommonsenseMorality (JCM) dataset, the only publicly available dataset focused on Japanese morality. The Extended JCM (eJCM) has grown from the original 13,975 sentences to 31,184 sentences using our proposed sentence expansion method called Masked Token and Label Enhancement (MTLE). MTLE selectively masks important parts of sentences related to moral judgment and replaces them with alternative expressions generated by a large language model (LLM), while re-assigning appropriate labels. The model trained using our eJCM achieved an F1 score of 0.857, higher than the scores for the original JCM (0.837), ChatGPT one-shot classification (0.841), and data augmented using AugGPT, a state-of-the-art augmentation method (0.850). Specifically, in complex moral reasoning tasks unique to Japanese culture, the model trained with eJCM showed a significant improvement in performance (increasing from 0.681 to 0.756) and achieved a performance close to that of GPT-4 Turbo (0.787). These results demonstrate the validity of the eJCM dataset and the importance of developing models and datasets that consider the cultural context.

Determining the inner structure of the molecular torus around an active galactic nucleus is essential for understanding its formation mechanism. However, spatially resolving the torus is difficult because of its small size. To probe the clump conditions in the torus, we therefore perform the systematic velocity-decomposition analyses of the gaseous CO rovibrational absorption lines ($v=0\to 1,\Delta J=\pm 1$) at $\lambda\sim 4.67 \mathrm{\mu{m}}$ observed toward four (ultra)luminous infrared galaxies using the high-resolution ($R\sim 5000\text{--}10000$) spectroscopy from the Subaru Telescope. We find that each transition has two to five distinct velocity components with different line-of-sight (LOS) velocities ($V_\mathrm{LOS}\sim -240\text{--}+100\mathrm{km\,s^{-1}}$) and dispersions ($\sigma_V\sim 15\text{--}190\mathrm{km\,s^{-1}}$); i.e., the components (a), (b), ..., beginning with the broadest one in each target, indicating that the tori have clumpy structures. By assuming a hydrostatic disk ($\sigma_V\propto R_\mathrm{rot}^{-0.5}$), we find that the tori have dynamic inner structures, with the innermost component (a) outflowing with velocity $|V_\mathrm{LOS}|\sim 160\text{--}240\mathrm{km\,s^{-1}}$, and the outer components (b) and (c) outflowing more slowly or infalling with $|V_\mathrm{LOS}|\lesssim 100\mathrm{km\,s^{-1}}$. In addition, we find that the innermost component (a) can be attributed to collisionally excited hot ($\gtrsim 530$K) and dense ($n_\mathrm{H_2}\gtrsim 10^6\mathrm{cm^{-3}}$) clumps, based on the level populations. Conversely, the outer component (b) can be attributed to cold ($\sim 30\text{--}140$K) clumps radiatively excited by a far-infrared-to-submillimeter background with a brightness temperature higher than $\sim 20\text{--}400$K. These observational results demonstrate the clumpy and dynamic structure of tori in the presence of background radiation., Comment: 43 pages, 15 figures, accepted for publication in ApJ

When the row and column variables consist of the same category in a two-way contingency table, it is specifically called a square contingency table. Since it is clear that the square contingency tables have an association structure, a primary objective is to examine symmetric relationships and transitions between variables. While various models and measures have been proposed to analyze these structures understanding changes between two variables in behavior at two-time points or cohorts, it is also necessary to require a detailed investigation of individual categories and their interrelationships, such as shifts in brand preferences. This paper proposes a novel approach to correspondence analysis (CA) for evaluating departures from symmetry in square contingency tables with nominal categories, using a power-divergence-type measure. The approach ensures that well-known divergences can also be visualized and, regardless of the divergence used, the CA plot consists of two principal axes with equal contribution rates. Additionally, the scaling is independent of sample size, making it well-suited for comparing departures from symmetry across multiple contingency tables. Confidence regions are also constructed to enhance the accuracy of the CA plot.

We investigated the temperature (T) dependence of the irreversibility field Hirr(T) in high-critical-temperature cuprate Bi2+xSr2-xCa1-yYyCu2O8+d (Bi-2212) single crystals over a wide range of hole doping level (p). Hirr(T) was evaluated by measuring the magnetization hysteresis loop. The value of Hirr(T) extrapolated to T = 0 K [Hirr(0)], is either equal to or sets the lower boundary for the upper critical field at T = 0 K [Hc2(0)]. Tc shows a parabolic p-dependence (peak at p = 0.16), whereas Hirr(0) increases monotonically with p by approximately one order of magnitude, from 19 T for the most underdoped sample (p = 0.065, Tc = 24 K) to 209 T for the most overdoped sample (p = 0.200, Tc = 75 K). The present results qualitatively agree with Hc2(0) values evaluated from the specific heat measurements. The observed p-dependence of Hirr(0) in Bi-2212 is distinct from those in YBa2Cu3O7-d and HgBa2CuO6+d, in which a pronounced dip structure appears in the underdoped region. Considering that the dip structures observed in these two systems are likely associated with the formation of competing orders (most likely field-induced charge orders), the present results indicate that the influence of the competing order in Bi-2212 is less prominent than that in the other two systems., Comment: 32 pages, 9 figures, 1 table

We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1

The AI traders in financial markets have sparked significant interest in their effects on price formation mechanisms and market volatility, raising important questions for market stability and regulation. Despite this interest, a comprehensive model to quantitatively assess the specific impacts of AI traders remains undeveloped. This study aims to address this gap by modeling the influence of AI traders on market price formation and volatility within a multi-agent framework, leveraging the concept of microfoundations. Microfoundations involve understanding macroeconomic phenomena, such as market price formation, through the decision-making and interactions of individual economic agents. While widely acknowledged in macroeconomics, microfoundational approaches remain unexplored in empirical finance, particularly for models like the GARCH model, which captures key financial statistical properties such as volatility clustering and fat tails. This study proposes a multi-agent market model to derive the microfoundations of the GARCH model, incorporating three types of agents: noise traders, fundamental traders, and AI traders. By mathematically aggregating the micro-structure of these agents, we establish the microfoundations of the GARCH model. We validate this model through multi-agent simulations, confirming its ability to reproduce the stylized facts of financial markets. Finally, we analyze the impact of AI traders using parameters derived from these microfoundations, contributing to a deeper understanding of their role in market dynamics., Comment: Accepted PRIMA2024

Band gap energy of an organic molecule can be reduced by intermolecular interaction enhancement, and thus, certain polycyclic aromatic hydrocarbons (PAHs), which are insulators with wide band gaps, are expected to undergo insulator-metal transitions by simple compression. Such a pressure-induced electronic transition can be exploited to transform non-metallic organic materials into states featuring intriguing electronic characteristics such as high-temperature superconductivity. Numerous attempts have been made to metalize various small PAHs, but so far only pressure-induced amorphization well below the megabar region was observed. The wide band gap energy of the small PAHs and low chemical stability under simple compression are the bottlenecks. We have investigated the band gap energy evolution and the crystal structural compression of the large PAH molecules, where the band gap energy is significantly reduced by increasing the number of {\pi}-electrons and improved chemical stability with fully benzenoid molecular structure. Herein, we present a pressure-induced transition in dicoronylene, C48H20, an insulator at ambient conditions that transforms into a semi-metallic state above 23.0 GPa with a three-order-of-magnitude reduction in resistivity. In-situ UV-visible absorption, transport property measurement, Raman spectroscopy, X-ray diffraction and density functional theory calculations were performed to provide tentative explanations to the alterations in its electronic structure at high pressure. The discovery of an electronic transition at pressures well below the megabar is a promising step towards realization of a single component purely hydrocarbon molecular metal in the near future., Comment: 22 pages, 5 figures

Simulating thermal-equilibrium properties at finite temperature is crucial for studying quantum many-body systems. Quantum computers are expected to enable us to simulate large systems at finite temperatures, overcoming challenges faced by classical computers, like the sign problem of the quantum Monte-Carlo technique. Conventional methods suitable for fault-tolerant quantum computing (FTQC) devices are designed for studying large-scale quantum many-body systems but require a large number of ancilla qubits and a deep quantum circuit with many basic gates, making them unsuitable for the early stage of the FTQC era, at which the availability of qubits and quantum gates is limited. In this paper, we propose a method suitable for quantum devices in this early stage to calculate the thermal-equilibrium expectation value of an observable at finite temperatures. Our proposal, named the Markov-chain Monte Carlo with sampled pairs of unitaries (MCMC-SPU) algorithm, involves sampling simple quantum circuits and generating the corresponding statistical ensembles. This approach addresses the issues of resource demand and the decay in probability associated with postselection of measurement outcomes on ancilla qubits. We validate our proposal with numerical simulation on the one-dimensional transverse-field Ising model as an illustrative example., Comment: 22 pages, 11 figures

This paper evaluated the effects of the Tokyo 2020 Olympic and Paralympic Games on traffic demand on the Metropolitan expressway. We constructed panel data for both passenger and freight vehicles' demand using longitudinal disaggregated trip records from the Metropolitan expressway. Subsequently, we established a demand function and used a difference-in-differences method to individually estimate the impacts of toll surcharges and other Olympics-related factors by leveraging the fact that the toll surcharges were not applied to freight vehicles. The results indicate that toll surcharges resulted in a decrease of 25.0 % for weekdays and 36.8 % for weekends/holidays in passenger vehicle demand on the Metropolitan expressway. The estimated toll elasticities are 0.345 for weekdays and 0.615 for weekends/holidays, respectively. Notably, analysis of the Olympics-related factor demonstrated that travel demand management (TDM) strategies effectively curbed demand on weekends/holidays with a reduction of 2.9 % in traffic demand. However, on weekdays, induced demand surpassed the reduction of demand by other TDM strategies than tolling, resulting in a 4.6 % increase in traffic demand. Additionally, We developed a zone-based demand function and investigate the spatial heterogeneity in toll elasticity. Our findings revealed small heterogeneity for weekdays (0.283 to 0.509) and large heterogeneity for weekends/holidays (0.484 to 0.935).

The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html

A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{\Lambda\Lambda}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{\Lambda\Lambda}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$\Lambda$ hypernuclear event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$\Lambda$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$\Lambda$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.

We present a detailed study of the MASTER OT J030227.28+191754.5 outburst in 2021-2022, reaching an amplitude of 10.2 mag and a duration of 60 d. The detections of (1) the double-peaked optical emission lines, and (2) the early and ordinary superhumps, established that MASTER OT J030227.28+191754.5 is an extremely energetic WZ Sge-type dwarf nova (DN). Based on the superhump observations, we obtained its orbital period and mass ratio as 0.05986(1) d and 0.063(1), respectively. These are within a typical range of low-mass-ratio DNe. According to the binary parameters derived based on the thermal-tidal instability model, our analyses showed that (1) the standard disk model requires an accretion rate $\simeq$ 10$^{20}$ g s$^{-1}$ to explain its peak optical luminosity and (2) large mass was stored in the disk at the outburst onset. These cannot be explained solely by the impact of its massive ($\gtrsim$ 1.15 M$_\odot$) primary white dwarf implied by Kimura et al. (2023). Instead, we propose that the probable origin of this enormously energetic DN outburst is the even lower quiescence viscosity than other WZ Sge-type DNe. This discussion is qualitatively valid for most possible binary parameter spaces unless the inclination is low ($\lesssim 40^\circ$) enough for the disk to be bright explaining the outburst amplitude. Such low inclinations, however, would not allow detectable amplitude of early superhumps in the current thermal-tidal instability model. The optical spectra at outburst maximum showed the strong emission lines of Balmer, He I, and He II series whose core is narrower than $\sim 800$ km s$^{-1}$. Considering its binary parameters, a Keplerian disk cannot explain this narrow component, but the presumable origin is disk winds., Comment: 23 pages, 13 figures, 2 tables. Accepted by PASJ. Part of the online supplemental information is included

In recent years, metal hydride research has become one of the driving forces of the high-pressure community, as it is believed to hold the key to superconductivity close to ambient temperature. While numerous novel metal hydride compounds have been reported and extensively investigated for their superconducting properties, little attention has been focused on the atomic and electronic states of hydrogen, the main ingredient in these novel compounds. Here, we present combined $^{1}H$- and $^{139}La$-NMR data on lanthanum superhydrides, $LaH_{x}$, ($x = 10.2 - 11.1$), synthesized after laser heating at pressures above 160 GPa. Strikingly, we found hydrogen to be in a highly diffusive state at room temperature, with diffusion coefficients in the order of $10^{-6}~cm^2s^{-1}$. We found that this diffusive state of hydrogen results in a dynamic de-hydrogenation of the sample over the course of several weeks, approaching a composition similar to its precursor materials. Quantitative measurements demonstrate that the synthesized superhydrides continuously decompose over time. Transport measurements underline this conclusion as superconducting critical temperatures were found to decrease significantly over time as well. This observation sheds new light on formerly unanswered questions on the long-term stability of metal superhydrides.

An experimental investigation of collisionless shock ion acceleration is presented using a multicomponent plasma and a high-intensity picosecond duration laser pulse. Protons are the only accelerated ions when a near-critical-density plasma is driven by a laser with a modest normalized vector potential. The results of particle-in-cell simulations imply that collisionless shock may accelerate protons alone selectively, which can be an important tool for understanding the physics of inaccessible collisionless shocks in space and astrophysical plasma.

The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0

Azide functionalization produced luminescent sp2-type defects on single-walled carbon nanotubes, by which defect photoluminescence appeared in near infrared regions (1116 nm). Changes in exciton properties were induced by localization effects at the defect sites, creating exciton-engineered nanomaterials based on the defect structure design.

We analyze the competition between strong correlations and dissipation in quantum impurity systems from the Kondo regime to the valence fluctuation regime by developing a slave-boson theory for a non-Hermitian Anderson impurity model with one-body loss. Notably, in the non-Hermitian Kondo regime, strong correlations qualitatively change the nature of dissipation through renormalization effects, where the effective one-body loss is suppressed and emergent many-body dissipation characterized by the complex-valued hybridization is generated. We unveil the mechanism of a dissipative quantum phase transition of the Kondo state on the basis of this renormalization effect, which counterintuitively enhances the lifetime of the impurity against loss. We also find a crossover from the non-Hermitian Kondo regime to the valence fluctuation regime dominated by one-body dissipation. Our results can be tested in a wide variety of setups such as quantum dots coupled to electronic leads and quantum point contacts in ultracold Fermi gases., Comment: 8+14 pages, 3+4 figures

Image-generative artificial intelligence (AI) has garnered significant attention in recent years. In particular, the diffusion model, a core component of generative AI, produces high-quality images with rich diversity. In this study, we proposed a novel computed tomography (CT) reconstruction method by combining the denoising diffusion probabilistic model with iterative CT reconstruction. In sharp contrast to previous studies, we optimized the fidelity loss of CT reconstruction with respect to the latent variable of the diffusion model, instead of the image and model parameters. To suppress the changes in anatomical structures produced by the diffusion model, we shallowed the diffusion and reverse processes and fixed a set of added noises in the reverse process to make it deterministic during the inference. We demonstrated the effectiveness of the proposed method through the sparse-projection CT reconstruction of 1/10 projection data. Despite the simplicity of the implementation, the proposed method has the potential to reconstruct high-quality images while preserving the patient's anatomical structures and was found to outperform existing methods, including iterative reconstruction, iterative reconstruction with total variation, and the diffusion model alone in terms of quantitative indices such as the structural similarity index and peak signal-to-noise ratio. We also explored further sparse-projection CT reconstruction using 1/20 projection data with the same trained diffusion model. As the number of iterations increased, the image quality improved comparable to that of 1/10 sparse-projection CT reconstruction. In principle, this method can be widely applied not only to CT but also to other imaging modalities., Comment: 20 pages, 10 figures

Conclusive crystal structure determination of the high pressure phases of hydrogen remains elusive due to lack of core electrons and vanishing wave vectors, rendering standard high-pressure experimental methods moot. Ab-initio DFT calculations have shown that structural polymorphism might be solely resolvable using high-resolution nuclear magnetic resonance (NMR) spectroscopy at mega-bar pressures, however technical challenges have precluded such experiments thus far. Here, we present in-situ high-pressure high-resolution NMR experiments in hydrogen phase III between 181 GPa and 208 GPa at room temperature. Our spectra suggest that at lower pressures phase III adopts a hexagonal P6122 crystal structure, transitioning into a monoclinic C2/c phase at about 197 GPa. The high resolution spectra are in excellent agreement with earlier structural and spectral predictions and underline the possibility of a subtle P6122 to C2/c phase transition in hydrogen phase III. These experiments show the importance of a combination of ab-initio calculations and low-Z sensitive spectral probes in high-pressure science in elucidating the structural complexity of the most abundant element in our universe.

Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative models - Variational Auto-Encoders (VAEs). Contrary to common beliefs and existing literature which suggest that noise injection towards training data can make models more robust, our preliminary experiments revealed that naive usage of noise augmentation technique did not substantially improve VAE robustness. In fact, it even degraded the quality of learned representations, making VAEs more susceptible to adversarial perturbations. This paper introduces a novel framework that enhances robustness by regularizing the latent space divergence between original and noise-augmented data. Through incorporating a paired probabilistic prior into the standard variational lower bound, our method significantly boosts defense against adversarial attacks. Our empirical evaluations demonstrate that this approach, termed Robust Augmented Variational Auto-ENcoder (RAVEN), yields superior performance in resisting adversarial inputs on widely-recognized benchmark datasets.

Simulation of quantum many-body systems is a promising application of quantum computers. However, implementing the time-evolution operator as a quantum circuit efficiently on near-term devices with limited resources is challenging. Standard approaches like Trotterization often require deep circuits, making them impractical. This paper proposes a hybrid quantum-classical algorithm called Local Subspace Variational Quantum Compilation (LSVQC) for compiling the time-evolution operator. The LSVQC uses variational optimization to reproduce the action of the target time-evolution operator within a physically reasonable subspace. Optimization is performed on small local subsystems based on the Lieb-Robinson bound, allowing for cost function evaluation using small-scale quantum devices or classical computers. Numerical simulations on a spin-lattice model and an $\mathit{\text{ab initio}}$ effective model of strongly correlated material Sr$_2$CuO$_3$ demonstrate the algorithm's effectiveness. It is shown that the LSVQC achieves a 95% reduction in circuit depth compared to Trotterization while maintaining accuracy. The subspace restriction also reduces resource requirements and improves accuracy. Furthermore, we estimate the gate count needed to execute the quantum simulations using the LSVQC on near-term quantum computing architectures in the noisy intermediate-scale or early fault-tolerant quantum computing era. Our estimation suggests that the acceptable physical gate error rate for the LSVQC can be significantly larger than for Trotterization., Comment: 29 pages, 14 figures

Liquid-gas phase coexistence in a boundary-driven diffusive system is studied by analyzing fluctuating hydrodynamics of a density field defined on a one-dimensional lattice with a space interval $\Lambda$. When an interface width $\ell$ is much larger than $\Lambda$, the discrete model becomes the standard fluctuating hydrodynamics, where the phase coexistence condition is given by the local equilibrium thermodynamics. In contrast, when $\ell < \Lambda$, the most probable density profile is determined by a new variational principle, where the chemical potential at the interface is found to deviate from the equilibrium coexistence chemical potential. This means that metastable states at equilibrium stably appear near the interface as the influence of the particle current. The variational function derived in the theoretical analysis is also found to be equivalent to the variational function formulated in an extended framework of thermodynamics called global thermodynamics. Finally, the validity of the theoretical result is confirmed by numerical simulations., Comment: 29 pages, 8 figures. Revised version