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As the performance of Large-scale Vision Language Models (LVLMs) improves, they are increasingly capable of responding in multiple languages, and there is an expectation that the demand for explanations generated by LVLMs will grow. However, pre-training of Vision Encoder and the integrated training of LLMs with Vision Encoder are mainly conducted using English training data, leaving it uncertain whether LVLMs can completely handle their potential when generating explanations in languages other than English. In addition, multilingual QA benchmarks that create datasets using machine translation have cultural differences and biases, remaining issues for use as evaluation tasks. To address these challenges, this study created an extended dataset in multiple languages without relying on machine translation. This dataset that takes into account nuances and country-specific phrases was then used to evaluate the generation explanation abilities of LVLMs. Furthermore, this study examined whether Instruction-Tuning in resource-rich English improves performance in other languages. Our findings indicate that LVLMs perform worse in languages other than English compared to English. In addition, it was observed that LVLMs struggle to effectively manage the knowledge learned from English data.

Channel resolvability concerns the minimum resolution for approximating the channel output. We study the resolvability of classical-quantum channels in two settings, for the channel output generated from the worst input, and form the fixed independent and identically distributed (i.i.d.) input. The direct part of the worst-input setting is derived from sequential hypothesis testing as it involves of non-i.i.d.~inputs. The strong converse of the worst-input setting is obtained via the connection to identification codes. For the fixed-input setting, while the direct part follows from the known quantum soft covering result, we exploit the recent alternative quantum Sanov theorem to solve the strong converse., Comment: 20 pages, 3 figures. Comments are welcome!

We present the properties of a new organic $S=1$ chain system $m$-NO$_2$PhBNO (abbreviated BoNO). In this biradical system two unpaired electrons from aminoxyl groups are strongly ferromagnetically coupled ($|J_\text{FM}| \gtrsim 500$ K) which leads to the formation of an effective $S=1$ state for each molecule. The chains of BoNO diradicals propagate along the crystallographic $a$ axis. Temperature dependence of the $g$ factor and electron paramagnetic resonance (EPR) linewidth are consistent with a low-dimensional system with antiferromagnetic interactions. The EPR data further suggest that BoNO is the first known Haldane system with an almost isotropic $g$ factor ($2.0023 \pm 2 \unicode{x2030}$). The magnetization measurements in magnetic fields up to $40$ T and low-field susceptibility, together with $^1$H nuclear magnetic resonance (NMR) spectra, reveal a dominant intrachain antiferromagnetic exchange coupling of $J_\text{1D} = (11.3\pm0.1)$ K, and attainable critical magnetic fields of $\mu_0 H_\text{c1} \approx 2$ T and $\mu_0 H_\text{c2} \approx 33$ T. These measurements therefore suggest that BoNO is a unique Haldane system with extremely small magnetic anisotropy. Present results are crucial for a future in-depth NMR study of the low-temperature Tomonaga-Luttinger liquid (TLL) and magnetic field-induced phases, which can be performed in the entire phase space., Comment: 13 pages, 12 figures

Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A

Text generation commonly relies on greedy and beam decoding that limit the search space and degrade output quality. Minimum Bayes Risk (MBR) decoding can mitigate this problem by utilizing automatic evaluation metrics and model-generated pseudo-references. Previous studies have conducted empirical analyses to reveal the improvement by MBR decoding, and reported various observations. However, despite these observations, the theoretical relationship between them remains uncertain. To address this, we present a novel theoretical interpretation of MBR decoding from the perspective of bias-diversity decomposition. We decompose errors in the estimated quality of generated hypotheses in MBR decoding into two key factors: bias, which reflects the closeness between utility functions and human evaluations, and diversity, which represents the variation in the estimated quality of utility functions. Our theoretical analysis reveals the difficulty in simultaneously improving both bias and diversity, and highlights the effectiveness of increasing diversity to enhance MBR decoding performance. This analysis verifies the alignment between our theoretical insights and the empirical results reported in previous work. Furthermore, to support our theoretical findings, we propose a new metric, pseudo-bias, which approximates the bias term using gold references. We also introduce a new MBR approach, Metric-augmented MBR (MAMBR), which increases diversity by adjusting the behavior of utility functions without altering the pseudo-references. Experimental results across multiple NLP tasks show that the decomposed terms in the bias-diversity decomposition correlate well with performance, and that MAMBR improves text generation quality by modifying utility function behavior. Our code will be available at https://github.com/naist-nlp/mbr-bias-diversity.

The telecommunications industry's deployment of billions of kilometers of optical fiber has created a vast global network that can be exploited for additional applications such as environmental sensing, quantum networking and international clock comparisons. However, for reasons such as the unidirectionality of long-haul fiber links, telecom fiber networks cannot always be adapted for important applications beyond data transmission. Fortunately, new multicore optical fibers create the opportunity for application coexistence with data traffic, creating expansive multifunctional networks. Towards that end, we propose and demonstrate the faithful transfer of ultrastable optical signals through multicore fiber in a way that is compatible with the unidirectionality of long-haul fiber optic systems, demonstrating a fractional frequency instability of 3x10-19 at 10,000 seconds. This opens the door towards intercontinental optical clock comparisons, with applications in fundamental physics and the redefinition of the second.

We study Heisenberg scaling of quantum metrology in the viewpoint of population coding. Although Fisher information has been used for a figure of merit to characterize Heisenberg scaling in quantum metrology, several studies pointed out it does not work as a figure of merit because it does not reflect the global structure. As an alternative figure of merit, we propose the mutual information, which connects the number of distinguishable elements of the parameter space in the viewpoint of population coding. We show that several unitary models achieve Heisenberg scaling in this context.

Digital signature is a strong tool widely used in different industry areas since by using this technology, the identity of a signer can be safely authenticated by communications between the signers and verifiers. Although quantum digital signature has been extensively studied, the security still depends on a trusted third party under many existing protocols. Hence, to fit it in more actual applications, here we propose a brand new quantum digital signature protocol without a trusted third party only with qubit technology to further improve the security. We note that a quantum one-way function can work appropriately in digital signature due to the intrinsic non-cloning property for quantum states. Secret keys in the protocol are constituted by classical private keys and quantum public keys because we assume that no user is trusted in the protocol. We prove that the protocol has information-theoretical unforgeability. Moreover, it satisfies other important secure properties, including asymmetry, undeniability, and expandability.

A large part of human communication relies on nonverbal cues such as facial expressions, eye contact, and body language. Unlike language or sign language, such nonverbal communication lacks formal rules, requiring complex reasoning based on commonsense understanding. Enabling current Video Large Language Models (VideoLLMs) to accurately interpret body language is a crucial challenge, as human unconscious actions can easily cause the model to misinterpret their intent. To address this, we propose a dataset, BQA, a body language question answering dataset, to validate whether the model can correctly interpret emotions from short clips of body language comprising 26 emotion labels of videos of body language. We evaluated various VideoLLMs on BQA and revealed that understanding body language is challenging, and our analyses of the wrong answers by VideoLLMs show that certain VideoLLMs made significantly biased answers depending on the age group and ethnicity of the individuals in the video. The dataset is available.

Supervaluational fixed-point theories of formal truth aim to amend an important shortcoming of fixed-point theories based on the Strong Kleene logic, namely, accounting for the truth of classical validities. In a celebrated paper, Andrea Cantini proposed an axiomatization of one such supervaluational theory of truth, which he called VF, and which proved to be incredibly strong proof-theoretically speaking. However, VF only axiomatizes one in a collection of several supervaluational schemes, namely the scheme which requires truth to be consistent. In this paper, we provide axiomatic theories for the remaining supervaluational schemes, labelling these systems VF$^-$ (for the theory which drops the consistency requirement), and VFM (for the theory which requires not only consistency but also completeness, i.e., maximal consistency). We then carry out proof-theoretic analyses of both theories. Our results show that VF$^-$ is as strong as VF, but that VFM's strength decreases significantly, being only as strong as the well-known theory KF. Furthermore, we introduce and analyse proof-theoretically two variants of these theories: the schematic extension, in the sense of Feferman, of VFM; and a theory in-between VFM and VF, that we call VFW, and which drops the assumption of maximal consistency. The former is shown to match the strength of predicative analysis; for the latter, we show its proof-theoretical equivalence with ramified analysis up to the ordinal $\varphi_20$, thus standing halfway between VFM and VF.

We performed the longest numerical-relativity neutrino-radiation magnetohydrodynamics simulation for a binary neutron star merger that extends to $\approx1.5\mathrm{\,s}$ after the merger. We consider the binary model that undergoes the prompt collapse to a black hole after the merger with asymmetric mass 1.25$\,M_{\odot}$ and 1.65$\,M_{\odot}$ and SFHo equation of state. We find the Poynting flux-driven collimated outflow as well as the gravitational wave emission, neutrino emission, dynamical mass ejection, and post-merger mass ejection facilitated by magnetorotational instability-driven turbulent viscosity in a single self-consistent binary neutron star merger simulation. A magnetosphere dominated by the aligned global magnetic field penetrating the black hole develops along the black-hole spin axis after the turbulence in the remnant disk is enhanced. A jet with the Poynting flux with isotropic-equivalent luminosity of $\sim10^{49}\mathrm{\,erg/s}$ is launched, and the duration of the high luminosity is expected to be $O(1)\mathrm{\,s}$., Comment: 14 pages, 9 figures

In this paper, we study weakly-supervised laparoscopic image segmentation with sparse annotations. We introduce a novel Bayesian deep learning approach designed to enhance both the accuracy and interpretability of the model's segmentation, founded upon a comprehensive Bayesian framework, ensuring a robust and theoretically validated method. Our approach diverges from conventional methods that directly train using observed images and their corresponding weak annotations. Instead, we estimate the joint distribution of both images and labels given the acquired data. This facilitates the sampling of images and their high-quality pseudo-labels, enabling the training of a generalizable segmentation model. Each component of our model is expressed through probabilistic formulations, providing a coherent and interpretable structure. This probabilistic nature benefits accurate and practical learning from sparse annotations and equips our model with the ability to quantify uncertainty. Extensive evaluations with two public laparoscopic datasets demonstrated the efficacy of our method, which consistently outperformed existing methods. Furthermore, our method was adapted for scribble-supervised cardiac multi-structure segmentation, presenting competitive performance compared to previous methods. The code is available at https://github.com/MoriLabNU/Bayesian_WSS., Comment: Early acceptance at MICCAI 2024. Supplementary material included. Minor typo corrections in notation have been made

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

Portfolio selection problems that optimize expected utility are usually difficult to solve. If the number of assets in the portfolio is large, such expected utility maximization problems become even harder to solve numerically. Therefore, analytical expressions for optimal portfolios are always preferred. In our work, we study portfolio optimization problems under the expected utility criterion for a wide range of utility functions, assuming return vectors follow hyperbolic distributions. Our main result demonstrates that under this setup, the two-fund monetary separation holds. Specifically, an individual with any utility function from this broad class will always choose to hold the same portfolio of risky assets, only adjusting the mix between this portfolio and a riskless asset based on their initial wealth and the specific utility function used for decision making. We provide explicit expressions for this mutual fund of risky assets. As a result, in our economic model, an individual's optimal portfolio is expressed in closed form as a linear combination of the riskless asset and the mutual fund of risky assets. Additionally, we discuss expected utility maximization problems under exponential utility functions over any domain of the portfolio set. In this part of our work, we show that the optimal portfolio in any given convex domain of the portfolio set either lies on the boundary of the domain or is the unique globally optimal portfolio within the entire domain.

Developing the capacity to effectively search for requisite datasets is an urgent requirement to assist data users in identifying relevant datasets considering the very limited available metadata. For this challenge, the utilization of third-party data is emerging as a valuable source for improvement. Our research introduces a new architecture for data exploration which employs a form of Retrieval-Augmented Generation (RAG) to enhance metadata-based data discovery. The system integrates large language models (LLMs) with external vector databases to identify semantic relationships among diverse types of datasets. The proposed framework offers a new method for evaluating semantic similarity among heterogeneous data sources and for improving data exploration. Our study includes experimental results on four critical tasks: 1) recommending similar datasets, 2) suggesting combinable datasets, 3) estimating tags, and 4) predicting variables. Our results demonstrate that RAG can enhance the selection of relevant datasets, particularly from different categories, when compared to conventional metadata approaches. However, performance varied across tasks and models, which confirms the significance of selecting appropriate techniques based on specific use cases. The findings suggest that this approach holds promise for addressing challenges in data exploration and discovery, although further refinement is necessary for estimation tasks.

In this paper, we establish a scheme-theoretic analog of the works of Matsuki and Richardson--Springer on the symmetric subgroup orbit decomposition of the flag variety under a certain assumption that asserts local constancy of their combinatorial description of the classification of orbits at geometric points (the local constancy hypothesis). We also prove that scheme-theoretic models of orbits are affinely imbedded into the flag scheme under the same assumption (Beilinson--Bernstein's affinity theorem). Finally, we compute some classical examples to give scheme-theoretic consequences of the geometric point free and descent phenomena of orbit decompositions., Comment: Final version

The pursuit of quantum spin liquid (QSL) states in condensed matter physics has drawn attention to kagome antiferromagnets (AFM) where a two-dimensional corner-sharing network of triangles frustrates conventional magnetic orders. While quantum kagome AFMs based on Cu$^{2+}$ (3d$^9$, $s=\frac{1}{2}$) ions have been extensively studied, there is so far little work beyond copper-based systems. Here we present our bulk magnetization, specific heat and neutron scattering studies on single crystals of a new titanium fluoride Cs$_8$RbK$_3$Ti$_{12}$F$_{48}$ where Ti$^{3+}$ (3d$^1$, $s = \frac{1}{2}$) ions form a breathing quantum kagome antiferromagnet that does not order magnetically down to 1.5 K. Our comprehensive map of the dynamic response function $S(Q,\hbar\omega)$ acquired at 1.5 K where the heat capacity is T-linear reveals a dispersive continuum emanating from soft lines that extend along (100). The data indicate fractionalized spinon-like excitations with quasi-one-dimensional dispersion within a quasi-two-dimensional spin system.

We study geometry on curves passing through a Whitney umbrella by using a Darboux frame along it. We define three invariants by using Frenet-Serre type formula relating to the geodesic curvature, the normal curvature, and the geodesic torsion. We investigate the degrees of divergence and the top-terms of these invariants and their geometric meanings. We also consider a developable surface along the curve.

TOI-1266 is a benchmark system of two temperate ($<$ 450 K) sub-Neptune-sized planets orbiting a nearby M dwarf exhibiting a rare inverted architecture with a larger interior planet. In this study, we characterize transit timing variations (TTVs) in the TOI-1266 system using high-precision ground-based follow-up and new TESS data. We confirm the presence of a third exterior non-transiting planet, TOI-1266 d (P = 32.5 d, $M_d$ = 3.68$^{+1.05}_{-1.11} M_{\oplus}$), and combine the TTVs with archival radial velocity (RV) measurements to improve our knowledge of the planetary masses and radii. We find that, consistent with previous studies, TOI-1266 b ($R_b$ = 2.52 $\pm$ 0.08 $R_{\oplus}$, $M_b$ = 4.46 $\pm$ 0.69 $M_{\oplus}$) has a low bulk density requiring the presence of a hydrogen-rich envelope, while TOI-1266 c ($R_c$ = 1.98 $\pm$ 0.10 $R_{\oplus}$, $M_c$ = 3.17 $\pm$ 0.76 $M_{\oplus}$) has a higher bulk density that can be matched by either a hydrogen-rich or water-rich envelope. Our new dynamical model reveals that this system is arranged in a rare configuration with the inner and outer planets located near the 3:1 period ratio with a non-resonant planet in between them. Our dynamical fits indicate that the inner and outer planet have significantly nonzero eccentricities ($e_b + e_d = 0.076^{+0.029}_{-0.019}$), suggesting that TOI-1266 b may have an inflated envelope due to tidal heating. Finally, we explore the corresponding implications for the formation and long-term evolution of the system, which contains two of the most favorable cool ($<$ 500 K) sub-Neptunes for atmospheric characterization with JWST., Comment: 36 pages, 19 figures, 5 tables, submitted to The Astronomical Journal

We examine the giant graviton expansions of the Coulomb and Higgs indices for the M2-brane SCFTs to find the closed-form expressions for the indices that encode the spectra of the $1/4$-BPS M5-brane giant gravitons and the $1/3$-BPS orbifold M5-brane giant gravitons. Consequently, we get exact forms of the twisted indices for the 6d $(2,0)$ theories describing a stack of $N$ M5-branes which generalize the unrefined indices. We confirm that they are also beautifully expanded with respect to the indices for the M2-brane giant gravitons which are obtained from the Coulomb and Higgs indices for the M2-brane SCFTs upon the change of variables., Comment: 53 pages

A source code difference (diff) indicates changes made by comparing new and old source codes, and it can be utilized in code reviews to help developers understand the changes made to the code. Although many diff generation methods have been proposed, existing automatic methods may generate nonoptimal diffs, hindering reviewers from understanding the changes. In this paper, we propose an interactive approach to optimize diffs. Users can provide feedback for the points of a diff that should not be matched but are or parts that should be matched but are not. The edit graph is updated based on this feedback, enabling users to obtain a more optimal diff. We simulated our proposed method by applying a search algorithm to empirically assess the number of feedback instances required and the amount of diff optimization resulting from the feedback to investigate the potential of this approach. The results of 23 GitHub projects confirm that 92% of nonoptimal diffs can be addressed with less than four feedback actions in the ideal case., Comment: (C) 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Quantum state estimation is a fundamental task in quantum information theory, where one estimates real parameters continuously embedded in a family of quantum states. In the theory of quantum state estimation, the widely used Cram\'er Rao approach which considers local estimation gives the ultimate precision bound of quantum state estimation in terms of the quantum Fisher information. However practical scenarios need not offer much prior information about the parameters to be estimated, and the local estimation setting need not apply. In general, it is unclear whether the Cram\'er-Rao approach is applicable for global estimation instead of local estimation. In this paper, we find situations where the Cram\'er-Rao approach does and does not work for quantum state estimation problems involving a family of bosonic states in a non-IID setting, where we only use one copy of the bosonic quantum state in the large number of bosons setting. Our result highlights the importance of caution when using the results of the Cram\'er-Rao approach to extrapolate to the global estimation setting., Comment: 15 pages

Matching users with mutual preferences is a critical aspect of services driven by reciprocal recommendations, such as job search. To produce recommendations in such scenarios, one can predict match probabilities and construct rankings based on these predictions. However, this direct match prediction approach often underperforms due to the extreme sparsity of match labels. Therefore, most existing methods predict preferences separately for each direction (e.g., job seeker to employer and employer to job seeker) and then aggregate the predictions to generate overall matching scores and produce recommendations. However, this typical approach often leads to practical issues, such as biased error propagation between the two models. This paper introduces and demonstrates a novel and practical solution to improve reciprocal recommendations in production by leveraging pseudo-match scores. Specifically, our approach generates dense and more directly relevant pseudo-match scores by combining the true match labels, which are accurate but sparse, with relatively inaccurate but dense match predictions. We then train a meta-model to output the final match predictions by minimizing the prediction loss against the pseudo-match scores. Our method can be seen as a best-of-both (BoB) approach, as it combines the high-level ideas of both direct match prediction and the two separate models approach. It also allows for user-specific weights to construct personalized pseudo-match scores, achieving even better matching performance through appropriate tuning of the weights. Offline experiments on real-world job search data demonstrate the superior performance of our BoB method, particularly with personalized pseudo-match scores, compared to existing approaches in terms of finding potential matches.

A crucial activity in software maintenance and evolution is the comprehension of the changes performed by developers, when they submit a pull request and/or perform a commit on the repository. Typically, code changes are represented in the form of code diffs, textual representations highlighting the differences between two file versions, depicting the added, removed, and changed lines. This simplistic representation must be interpreted by developers, and mentally lifted to a higher abstraction level, that more closely resembles natural language descriptions, and eases the creation of a mental model of the changes. However, the textual diff-based representation is cumbersome, and the lifting requires considerable domain knowledge and programming skills. We present an approach, based on the concept of micro-change, to overcome these difficulties, translating code diffs into a series of pre-defined change operations, which can be described in natural language. We present a catalog of micro-changes, together with an automated micro-change detector. To evaluate our approach, we performed an empirical study on a large set of open-source repositories, focusing on a subset of our micro-change catalog, namely those related to changes affecting the conditional logic. We found that our detector is capable of explaining more than 67% of the changes taking place in the systems under study., Comment: 12 pages, 10 figures, ICSME 2024

The TESS mission searches for transiting exoplanets by monitoring the brightness of hundreds of thousands of stars across the entire sky. M-type planet hosts are ideal targets for this mission due to their smaller size and cooler temperatures, which makes it easier to detect smaller planets near or within their habitable zones. Additionally, M~dwarfs have a smaller contrast ratio between the planet and the star, making it easier to measure the planet's properties accurately. Here, we report the validation analysis of 13 TESS exoplanet candidates orbiting around M dwarfs. We studied the nature of these candidates through a multi-colour transit photometry transit analysis using several ground-based instruments (MuSCAT2, MuSCAT3, and LCO-SINISTRO), high-spatial resolution observations, and TESS light curves. We present the validation of five new planetary systems: TOI-1883b, TOI-2274b, TOI2768b, TOI-4438b, and TOI-5319b, along with compelling evidence of a planetary nature for TOIs 2781b and 5486b. We also present an empirical definition for the Neptune desert boundaries. The remaining six systems could not be validated due to large true radius values overlapping with the brown dwarf regime or, alternatively, the presence of chromaticity in the MuSCAT2 light curves.

We explore the stability and fate of gravitational triple systems comprising a central massive body and a tight binary of less massive pairs. Our present purpose is two fold; (1) to improve the Hill-type stability criterion for the binary in those systems, and (2) to examine the fate of the triple systems after the binary break-up, with particular attention to the effects of the eccentricities of the inner and outer orbits. We perform direct Newtonian N-body simulations over much longer integration times than previous studies, which is essential to determine the eventual fate of those systems statistically in a reliable fashion. We obtain an empirical fitting formula of the binary stability boundary that incorporates effects of the inner and outer eccentricities, the mutual inclination of the inner and outer orbits, and the mass ratios of the three bodies. We also find that those triple systems are stable for a much longer timescale after the binary break-up, and that their final fates (ejection of the outer body, merger to the central massive body, and collision of two less massive bodies) are very sensitive to the initial outer eccentricity., Comment: 22 pages, 14 figures, submitted to ApJ

We study two kinds of different problems. One is the multiple independence testing, which can be considered as a kind of generalization of quantum Stein's lemma. We test whether the quantum system is correlated to the classical system or is independent of it. Here, the null hypothesis is composed of states having the quantum system is correlated to the classical system in an arbitrarily varying form. The second problem is the problem of reliable communication over classical-quantum arbitrarily varying multiple access channels (CQ-AVMAC) and establishing its capacity region by giving multiple achievability techniques. We prove that each of these techniques is optimal by proving a converse. Further, for both these techniques, the decoder designed is a \emph{universal} decoder and can achieve any rate pair in the capacity region without time sharing and also these decoders do not depend on the channel and therefore they are universal. Our result covers the case when the channel parameter is continuous, which has not been studied even in the classical case. Further, both these techniques can be easily generalized to the case when there are $T (T>2)$ senders. The design of each of these decoders is based on the study of multiple independence testing. This approach allows us to study the problem of reliable communication over CQ-AVMAC from the point of view of hypothesis testing. Further, we also give a necessary and sufficient condition for the deterministic code capacity region of CQ-AVMAC to be non-empty., Comment: 60 pages

Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes., Comment: 14 pages, 8 figures, accepted in Medical Image Analysis 2024

Appropriate symmetry breaking generates an anomalous Hall (AH) effect, even in antiferromagnetic (AFM) materials. Itinerant magnets with $d$ electrons are typical examples that show a significant response. By contrast, the process by which a response emerges from $f$-electron AFM structures remains unclear. In this study, we show that an AFM material, Ce$_2$CuGe$_6$, yields a large AH conductivity (AHC) of $550$ $\Omega^{-1}$cm$^{-1}$, which exceeds the values previously reported in $d$-electron AFM materials. Observed features, including the scaling relation against electrical conductivity, suggest that this AH transport is induced cooperatively by both intrinsic and extrinsic mechanisms derived from the AFM structure., Comment: 5 pages, 4 figures

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

The north-south (NS) anisotropy of galactic cosmic rays (GCRs) is dominated by a diamagnetic drift flow of GCRs in the interplanetary magnetic field (IMF), allowing us to derive key parameters of cosmic-ray propagation, such as the density gradient and diffusion coefficient. We propose a new method to analyze the rigidity spectrum of GCR anisotropy and reveal a solar cycle variation of the NS anisotropy's spectrum using ground-based muon detectors in Nagoya, Japan, and Hobart, Australia. The physics-based correction method for the atmospheric temperature effect on muons is used to combine the different-site detectors free from local atmospheric effects. NS channel pairs in the multi-directional muon detectors are formed to enhance sensitivity to the NS anisotropy, and in this process, general graph matching in graph theory is introduced to survey optimized pairs. Moreover, Bayesian estimation with the Gaussian process allows us to unfold the rigidity spectrum without supposing any analytical function for the spectral shape. Thanks to these novel approaches, it has been discovered that the rigidity spectrum of the NS anisotropy is dynamically varying with solar activity every year. It is attributed to a rigidity-dependent variation of the radial density gradient of GCRs based on the nature of the diamagnetic drift in the IMF. The diffusion coefficient and mean-free-path length of GCRs as functions of the rigidity are also derived from the diffusion-convection flow balance. This analysis expands the estimation limit of the mean-free-path length into $\le200$ GV rigidity region from $<10$ GV region achieved by solar energetic particle observations., Comment: 22 pages, 10 figures, accepted for publication in The Astrophysical Journal

We present ground-based photometric observations of secondary eclipses of the hottest known planet KELT-9b using MuSCAT2 and Sinistro. We detect secondary eclipse signals in $i$ and $z_{\rm s}$ with eclipse depths of $373^{+74}_{-75}$ ppm and $638^{+199}_{-178}$, respectively. We perform an atmospheric retrieval on the emission spectrum combined with the data from HST/WFC3, Spitzer, TESS, and CHEOPS to obtain the temperature profile and chemical abundances, including TiO and VO, which have been thought to produce temperature inversion structures in the dayside of ultra-hot Jupiters. While we confirm a strong temperature inversion structure, we find low abundances of TiO and VO with mixing ratios of $\rm{log(TiO)}=-7.80^{+0.15}_{-0.30}$ and $\rm{log(VO)}=-9.60^{+0.64}_{-0.57}$, respectively. The low abundances of TiO and VO are consistent with theoretical predictions for such an ultra-hot atmosphere. In such low abundances, TiO and VO have little effect on the temperature structure of the atmosphere. The abundance of ${\rm e}^{-}$, which serves as a proxy for ${\rm H}^{-}$ ions in this study, is found to be high, with $\rm{log(e^-)}=-4.89\pm{0.06}$. These results indicate that the temperature inversion in KELT-9 b's dayside atmosphere is likely not caused by TiO/VO, but rather by the significant abundance of ${\rm H}^{-}$ ions. The best-fit model cannot fully explain the observed spectrum, and chemical species not included in the retrieval may introduce modeling biases. Future observations with broader wavelength coverage and higher spectral resolution are expected to provide more accurate diagnostics on the presence and abundances of TiO/VO. These advanced observations will overcome the limitations of current data from HST and photometric facilities, which are constrained by narrow wavelength coverage and instrumental systematics., Comment: 16 pages, 9 figures, 4 tables, Accepted for publication in PASJ

This paper proposes a new conceptual framework called Collective Predictive Coding as a Model of Science (CPC-MS) to formalize and understand scientific activities. Building on the idea of collective predictive coding originally developed to explain symbol emergence, CPC-MS models science as a decentralized Bayesian inference process carried out by a community of agents. The framework describes how individual scientists' partial observations and internal representations are integrated through communication and peer review to produce shared external scientific knowledge. Key aspects of scientific practice like experimentation, hypothesis formation, theory development, and paradigm shifts are mapped onto components of the probabilistic graphical model. This paper discusses how CPC-MS provides insights into issues like social objectivity in science, scientific progress, and the potential impacts of AI on research. The generative view of science offers a unified way to analyze scientific activities and could inform efforts to automate aspects of the scientific process. Overall, CPC-MS aims to provide an intuitive yet formal model of science as a collective cognitive activity.

This paper presents the global analysis of two extended decreases of the galactic cosmic ray intensity observed by world-wide networks of ground-based detectors in 2012. This analysis is capable of separately deriving the cosmic ray density (or omnidirectional intensity) and anisotropy each as a function of time and rigidity. A simple diffusion model along the spiral field line between Earth and a cosmic-ray barrier indicates the long duration of these events resulting from about 190$^\circ$ eastern extension of a barrier such as an IP-shock followed by the sheath region and/or the corotating interaction region (CIR). It is suggested that the coronal mass ejection merging and compressing the preexisting CIR at its flank can produce such the extended barrier. The derived rigidity spectra of the density and anisotropy both vary in time during each event period. In particular we find that the temporal feature of the ``phantom Forbush decrease'' reported in an analyzed period is dependent on rigidity, looking quite different at different rigidities. From these rigidity spectra of the density and anisotropy, we derive the rigidity spectrum of the average parallel mean-free-path of pitch angle scattering along the spiral field line and infer the power spectrum of the magnetic fluctuation and its temporal variation. Possible physical cause of the strong rigidity dependence of the ``phantom Forbush decrease'' is also discussed. These results demonstrate the high-energy cosmic rays observed at Earth responding to remote space weather., Comment: Accepted for publication in ApJ

The new gauge boson introduced in the minimal extension of the standard model (SM) by gauging the U(1)$_{\rm B-L}$ symmetry plays the role of dark matter when the U(1)$_{\rm B-L}$ gauge coupling is highly suppressed. This dark matter, named the F\'eeton dark matter is known to be efficiently created in the early universe by inflationary fluctuations with minimal gravity coupling, hence the framework, the gauged U(1)$_{\rm B-L}$ extended SM + inflation, solves the four major problems of the SM; neutrino masses/mixings, dark matter, baryon asymmetry of the universe, and the initial condition of the universe (inflation). We comprehensively study the phenomenology of the dark matter when it is heavier than the $e^- e^+$ threshold, namely twice the electron mass, considering the threshold effect on the dark matter decay into $e^- e^+$. The viable parameter region is found only in the threshold region, while the branching fraction of the decay into $e^- e^+$ (i.e., the $e^- e^+$ signal) never vanishes even at the threshold due to the effect. As a result, the pure U(1)$_{\rm B-L}$ extension without the kinetic mixing between the U(1)$_{\rm B-L}$ and hyper-charge gauge bosons have already been excluded by the present observation of the 511\,keV photon from the galactic center. So, the F\'eeton dark matter requires a non-zero kinetic mixing to be a viable dark matter candidate and will be efficiently explored by future MeV-gamma ray telescopes thanks to the non-vanishing decay process into $e^- e^+$.

Galaxies are known to be aligned toward specific directions within the large-scale structure. Such alignment signals become important for controlling the systematics of weak lensing surveys and for constraining galaxy formation and evolution scenarios. We measure the galaxy-ellipticity and ellipticity-ellipticity correlation functions for blue star-forming galaxies at $z=1.19$ and $z=1.47$ that are selected by detecting [OII] emission lines in narrow-band filters of the Hyper Suprime-Cam on the Subaru Telescope. Assuming that disk galaxies are thin and rotation-supported, we also measure the spin correlation function by estimating spin directions with ellipticities and position angles. Above $1 \; h^{-1}{\rm Mpc}$, we do not find significant signals for galaxy-ellipticity, ellipticity-ellipticity, or spin correlations at both redshifts. Below $1 \; h^{-1}{\rm Mpc}$, a weak deviation from zero is seen at $z=1.47$, implying weak spin-filament correlations, but it is not verified by the direct comparison between angles of spins and filaments. The linear alignment model fit yields the amplitude parameter $A_{\rm NLA}=1.38\pm2.32$ at $z=1.19$ and $0.45\pm2.09$ at $z=1.47$ ($95\%$ confidence levels). We discuss various observational and physical origins that affect the search for alignments of disk galaxies at high redshifts., Comment: 16 pages, 12 figures, submitted to PASJ

Machine learning is widely utilized across various industries. Identifying the appropriate machine learning models and datasets for specific tasks is crucial for the effective industrial application of machine learning. However, this requires expertise in both machine learning and the relevant domain, leading to a high learning cost. Therefore, research focused on extracting combinations of tasks, machine learning models, and datasets from academic papers is critically important, as it can facilitate the automatic recommendation of suitable methods. Conventional information extraction methods from academic papers have been limited to identifying machine learning models and other entities as named entities. To address this issue, this study proposes a methodology extracting tasks, machine learning methods, and dataset names from scientific papers and analyzing the relationships between these information by using LLM, embedding model, and network clustering. The proposed method's expression extraction performance, when using Llama3, achieves an F-score exceeding 0.8 across various categories, confirming its practical utility. Benchmarking results on financial domain papers have demonstrated the effectiveness of this method, providing insights into the use of the latest datasets, including those related to ESG (Environmental, Social, and Governance) data., Comment: 10 pages, 8 figures

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.

Renaming identifiers in source code is a common refactoring task in software development. When renaming an identifier, other identifiers containing words with the same naming intention related to the renaming should be renamed simultaneously. However, identifying these related identifiers can be challenging. This study introduces a technique called RENAS, which identifies and recommends related identifiers that should be renamed simultaneously in Java applications. RENAS determines priority scores for renaming candidates based on the relationships and similarities among identifiers. Since identifiers that have a relationship and/or have similar vocabulary in the source code are often renamed together, their priority scores are determined based on these factors. Identifiers with higher priority are recommended to be renamed together. Through an evaluation involving real renaming instances extracted from change histories and validated manually, RENAS demonstrated an improvement in the F1-measure by more than 0.11 compared with existing renaming recommendation approaches., Comment: ICSME 2024. (C) 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Background: Search-based refactoring involves searching for a sequence of refactorings to achieve specific objectives. Although a typical objective is improving code quality, a different perspective is also required; the searched sequence must undergo review before being applied and may not be applied if the review fails or is postponed due to no proper reviewers. Aim: Therefore, it is essential to ensure that the searched sequence of refactorings can be reviewed promptly by reviewers who meet two criteria: 1) having enough expertise and 2) being free of heavy workload. The two criteria are regarded as the review availability of the refactoring sequence. Method: We propose MORCoRA, a multi-objective search-based technique that can search for code quality improvable, semantic preserved, and high review availability possessed refactoring sequences and corresponding proper reviewers. Results: We evaluate MORCoRA on six open-source repositories. The quantitative analysis reveals that MORCoRA can effectively recommend refactoring sequences that fit the requirements. The qualitative analysis demonstrates that the refactorings recommended by MORCoRA can enhance code quality and effectively address code smells. Furthermore, the recommended reviewers for those refactorings possess high expertise and are available to review. Conclusions: We recommend that refactoring recommenders consider both the impact on quality improvement and the developer resources required for review when recommending refactorings., Comment: Preprint of an article accepted to be published in International Journal of Software Engineering and Knowledge Engineering, (C) 2024 World Scientific Publishing Company, https://www.worldscientific.com/worldscinet/ijseke

We generalize the generalized Arimoto-Blahut algorithm to a general function defined over Bregman-divergence system. In existing methods, when linear constraints are imposed, each iteration needs to solve a convex minimization. Exploiting our obtained algorithm, we propose a convex-optimization-free algorithm. This algorithm can be applied to classical and quantum rate-distortion theory. We numerically apply our method to the derivation of the optimal conditional distribution in the rate-distortion theory.

Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that integrates RadonPy, a Python library for fully automated polymer property calculations based on all-atom classical molecular dynamics into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we successfully synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and Abbe number.

To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch. This study demonstrates the scaling law of simulation-to-real (Sim2Real) transfer learning for several machine learning tasks in materials science. Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases. Observing the scaling behavior offers various insights for database development, such as determining the sample size necessary to achieve a desired performance, identifying equivalent sample sizes for physical and computational experiments, and guiding the design of data production protocols for downstream real-world tasks., Comment: 22 pages, 6 figures

The second law of thermodynamics is the cornerstone of physics, characterizing the convertibility between thermodynamic states through a single function, entropy. Given the universal applicability of thermodynamics, a fundamental question in quantum information theory is whether an analogous second law can be formulated to characterize the convertibility of resources for quantum information processing by a single function. In 2008, a promising formulation was proposed, linking resource convertibility to the optimal performance of a variant of the quantum version of hypothesis testing. Central to this formulation was the generalized quantum Stein's lemma, which aimed to characterize this optimal performance by a measure of quantum resources, the regularized relative entropy of resource. If proven valid, the generalized quantum Stein's lemma would lead to the second law for quantum resources, with the regularized relative entropy of resource taking the role of entropy in thermodynamics. However, in 2023, a logical gap was found in the original proof of this lemma, casting doubt on the possibility of such a formulation of the second law. In this work, we address this problem by developing alternative techniques to successfully prove the generalized quantum Stein's lemma under a smaller set of assumptions than the original analysis. Based on our proof, we reestablish and extend the second law of quantum resource theories, applicable to both static resources of quantum states and a fundamental class of dynamical resources represented by classical-quantum (CQ) channels. These results resolve the fundamental problem of bridging the analogy between thermodynamics and quantum information theory., Comment: 33 pages, 1 figure

Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fr\'echet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems. Traditionally, biological research focuses on average values from repeated experiments. However, EVA offers insights into molecular mechanisms by examining extreme data points. We introduce EVA's concepts with simulations and review its use in studying motor protein movements within cells, highlighting the importance of in vivo analysis due to the complex intracellular environment. We suggest EVA as a tool for extracting motor proteins' physical properties in vivo and discuss its potential in other biological systems. While there have been only a few applications of EVA to biological systems, it holds promise for uncovering hidden properties in extreme data, promoting its broader application in life sciences., Comment: Short review. 11 pages, 3 figures