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Inflow control is essential for most fluid mechanics experiments. Although vertically falling soap film flows have been extensively used in the last four decades to study two-dimensional flows, its inflow stability has not yet been discussed in detail. In this article, aiming to improve the inflow stability of the system, we discuss how flow driving systems dominate the inflow states and the statistics of soap film flow properties. We report experimental measurements of inflow rates using different flow driving methods, followed by soap film velocity measurements by Laser Doppler Velocimetry (LDV). The widely-used method of a constant-pressure-head reservoir exhibits a continuous drop of inflow rate. In addition, even when the flow is not disturbed, the soap film displays high magnitudes of velocity fluctuations. The mean square of velocity fluctuation was measured at 4.6% of the mean. We also test other methods without the reservoir, where the flow is directly controlled by a pump; two staggered syringe pumps are able to maintain inflow stability over days and reduce the velocity fluctuation to 0.5% of the mean. From these measurements, we conclude that the drop of inflow rate might be caused by micro/milli-scale air bubbles, which cannot be completely removed in the system. A method to control the inflow actively is necessary in a soap film setup, when a stable and long standing film flow is needed.

Cosmic very small dust grains (VSGs) contain 100 to 10,000 atoms, making it a mesoscopic system with specific thermal and optical characteristics due to the finite number of atoms within each grain. This paper focuses on graphite VSGs which contain free electrons. The energy level statistics devised by Kubo (1962, J.Phys.Soc.Jpn., 17, 975-986) were used for the first time to understand the thermal properties of free electrons in graphite VSGs. We showed that the shape irregularity of the grains allows graphite VSGs to absorb or emit photons at sub-millimeter wavelengths or longer; otherwise, the frequency is limited to above a few THz. Additionally, we considered the decrease in Debye temperature due to the surface effect. VSGs have an extremely small volume, resulting in limited thermal energy storage, especially at low temperatures. Since a VSG is able to emit a photon with energy smaller than its internal energy, this determines the maximum frequency of the emitted photon. We developed a Monte-Carlo simulation code to track the thermal history of a dust grain, considering the stochastic heating from the absorption of ambient photons and radiative cooling. This approach was applied to the interstellar environment to compute the spectral energy distributions from the interstellar graphite dust grains. The results showed that graphite VSGs emit not only the mid-infrared excess emission, but also a surplus emission from sub-millimeter to millimeter wavelengths., Comment: 16 pages, 10 figures, 1 table

We investigate a novel scenario involving asymmetric keV-range dark matter (DM) in the form of right-handed (sterile) neutrinos. Based on the Fermi-Dirac distribution, we demonstrate that asymmetric fermionic DM forms a Fermi degenerate gas, making it potentially colder than symmetric fermionic DM. This setup simultaneously accounts for the Universe's baryon asymmetry through tiny Yukawa interactions with Standard Model leptons and the Higgs field, and the homochirality of amino acids via decay into circularly polarized photons. This scenario can be investigated through soft X-ray searches conducted by current and upcoming space missions. The helical X-rays is a smoking-gun signal of our scenario. Additionally, we propose a new mechanism to suppress DM thermal production by introducing a light modulus, which may also benefit cosmology involving generic right-handed neutrinos with large mixing., Comment: 22pages, 3figures, comments are welcome

Publication bias (PB) is one of the serious issues in meta-analysis. Many existing methods dealing with PB are based on the normal-normal (NN) random-effects model assuming normal models in both the within-study and the between-study levels. For rare-event meta-analysis where the data contain rare occurrences of event, the standard NN random-effects model may perform poorly. Instead, the generalized linear mixed effects model (GLMM) using the exact within-study model is recommended. However, no method has been proposed for dealing with PB in rare-event meta-analysis using the GLMM. In this paper, we propose sensitivity analysis methods for evaluating the impact of PB on the GLMM based on the famous Copas-Heckman-type selection model. The proposed methods can be easily implemented with the standard software coring the nonlinear mixed-effects model. We use a real-world example to show how the usefulness of the proposed methods in evaluating the potential impact of PB in meta-analysis of the log-transformed odds ratio based on the GLMM using the non-central hypergeometric or binomial distribution as the within-study model. An extension of the proposed method is also introduced for evaluating PB in meta-analysis of proportion based on the GLMM with the binomial within-study model.

Cross-lingual continual pre-training of large language models (LLMs) initially trained on English corpus allows us to leverage the vast amount of English language resources and reduce the pre-training cost. In this study, we constructed Swallow, an LLM with enhanced Japanese capability, by extending the vocabulary of Llama 2 to include Japanese characters and conducting continual pre-training on a large Japanese web corpus. Experimental results confirmed that the performance on Japanese tasks drastically improved through continual pre-training, and the performance monotonically increased with the amount of training data up to 100B tokens. Consequently, Swallow achieved superior performance compared to other LLMs that were trained from scratch in English and Japanese. An analysis of the effects of continual pre-training revealed that it was particularly effective for Japanese question answering tasks. Furthermore, to elucidate effective methodologies for cross-lingual continual pre-training from English to Japanese, we investigated the impact of vocabulary expansion and the effectiveness of incorporating parallel corpora. The results showed that the efficiency gained through vocabulary expansion had no negative impact on performance, except for the summarization task, and that the combined use of parallel corpora enhanced translation ability.

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion pages crawled between 2020 and 2023). This corpus consists of approximately 312.1 billion characters (approximately 173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (approximately 25.8 billion characters), mC4 (approximately 239.7 billion characters) and OSCAR 23.10 (approximately 74 billion characters). To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral 7B v0.1, and Mixtral 8x7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora., Comment: 17 pages

In this study, we present a systematic computational investigation to analyze the long debated crystal stability of two well known aspirin polymorphs, labeled as Form I and Form II. Specifically, we developed a strategy to collect training configurations covering diverse interatomic interactions between representative functional groups in the aspirin crystals. Utilizing a state-of-the-art neural network interatomic potential (NNIP) model, we developed an accurate machine learning potential to simulate aspirin crystal dynamics under finite temperature conditions with $\sim$0.46 kJ/mol/molecule accuracy. Employing the trained NNIP model, we performed thermodynamic integration to assess the free energy difference between aspirin Forms I and II, accounting for the anharmonic effects in a large supercell consisting of 512 molecules. For the first time, our results convincingly demonstrated that Form I is more stable than Form II at 300 K, ranging from 0.74 to 1.83 kJ/mol/molecule, aligning with the experimental observations. Unlike the majority of previous simulations based on (quasi)harmonic approximations in a small super cell, which often found the degenerate energies between aspirin I and II, our findings underscore the importance of anharmonic effects in determining polymorphic stability ranking. Furthermore, we proposed the use of rotational degrees of freedom of methyl and ester/phenyl groups in the aspirin crystal, as characteristic motions to highlight rotational entropic contribution that favors the stability of Form I. Beyond the aspirin polymorphism, we anticipate that such entropy-driven stabilization can be broadly applicable to many other organic systems and thus our approach, suggesting our approach holds a great promise for stability studies in small molecule drug design.

Meta-analysis is a powerful tool to synthesize findings from multiple studies. The normal-normal random-effects model is widely used to account for between-study heterogeneity. However, meta-analysis of sparse data, which may arise when the event rate is low for binary or count outcomes, poses a challenge to the normal-normal random-effects model in the accuracy and stability in inference since the normal approximation in the within-study model may not be good. To reduce bias arising from data sparsity, the generalized linear mixed model can be used by replacing the approximate normal within-study model with an exact model. Publication bias is one of the most serious threats in meta-analysis. Several quantitative sensitivity analysis methods for evaluating the potential impacts of selective publication are available for the normal-normal random-effects model. We propose a sensitivity analysis method by extending the likelihood-based sensitivity analysis with the t-statistic selection function of Copas to several generalized linear mixed-effects models. Through applications of our proposed method to several real-world meta-analysis and simulation studies, the proposed method was proven to outperform the likelihood-based sensitivity analysis based on the normal-normal model. The proposed method would give useful guidance to address publication bias in meta-analysis of sparse data.

We develop an efficient real-time simulation method for the spin-charge coupled system in the velocity gauge. This method enables us to compute the real-time simulation for the two-dimensional system with the complex spin texture. We focus on the effect of the collective excitation of the localized spins on the electronic transport properties of the non-trivial topological state in real space. To investigate this effect, we calculate the linear optical conductivity by calculating the real-time evolution of the Kondo lattice model on the triangular lattice, which hosts an all-in/all-out magnetic structure. In the linear conductivity spectra, we observe multiple peaks below the bandgap regime, attributed to the resonant contributions of collective modes similar to the skyrmionic system, alongside broadband modifications resulting from off-resonant spin dynamics. The result shows that the collective excitation, similar to the skyrmionic system, influences the optical response of the electron systems based on symmetry analysis. We elucidate the interference between the contributions from the different spin excitations to the optical conductivity in the multiple spin texture, pointing out the mode-dependent electrical activity. We show the complex interplay between the complex spin texture and the itinerant electrons in the two-dimensional spin-charge coupled system., Comment: 10 pages, 6 figures, 2 tables

We estimate ([M/H], [$\alpha$/M]) for 48 million giants and dwarfs in low dust extinction regions from the Gaia DR3 XP spectra by using tree-based machine-learning models trained on APOGEE DR17 and metal-poor star sample of Li et al. The root mean square error of our estimation is 0.0890 dex for [M/H] and 0.0436 dex for [$\alpha$/M], when we evaluate our models with the test data. Our estimation is most reliable for giants and metal-rich stars, because the training data are dominated by such stars. The high-[$\alpha$/M] stars and low-[$\alpha$/M] stars selected by our ([M/H], [$\alpha$/M]) show different kinematical properties for giants and low-temperature dwarfs. We further investigate how our machine-learning models extract information on ([M/H], [$\alpha$/M]). Intriguingly, our models seem to extract information on [$\alpha$/M] from Na D lines (589 nm) and Mg I line (516 nm). This result is understandable given the observed correlation between Na and Mg abundances in the literature. The catalog of ([M/H], [$\alpha$/M]) as well as their associated uncertainties will be publicly available online., Comment: 29 pages, 27 figures, 2 tables. To be submitted to ApJ. Comments are welcome. Catalog available at https://zenodo.org/records/10902172

A newly identified magnetic phase called altermagnet is being actively studied because of its unprecedented spin-dependent phenomena. Among the candidate materials, CrSb has a particularly high ordering temperature and a large spin-splitting energy, but its transport properties have remained unexplored. In this study, we report the magnetotransport properties of CrSb measured on single crystals. We found that the Hall resistivity shows a nonlinear dependence on the magnetic field at low temperatures. From symmetry-based considerations, however, this behavior can not be attributed to an anomalous Hall effect, but to a multicarrier effect. A multicarrier fitting to the in-plane conductivity tensor revealed the presence of carriers with high mobility in CrSb, which is an advantage for efficient spin current generation.

Fixation of structural parts to concrete is a repetitive, heavy-duty, and time-consuming task that requires automation due to the lack of skilled construction workers. Previously developed automation techniques have not achieved the complete fixation of structural parts and are difficult to implement in narrow construction environments. In this study, we propose a construction robot system that enables the complete installation of structural parts to concrete and can be easily introduced to unstructured and narrow construction environments. The system includes two arms that simultaneously position and fix the structural parts, and custom tools that reduce the reaction force applied to the robots so that smaller robots can be used with lower payloads. Due to the modular design of the proposed system, it can be transported in parts for easy introduction to the construction environment. We also propose a procedure for fixing structural parts. Experimental results demonstrate that the custom tools make it possible to use smaller robots without moment overload in the robot joints. Moreover, the results show that the proposed robot system and fixation procedure enable automatic fixation of a structural part to concrete., Comment: Published in 2023 IEEE/SICE International Symposium on System Integration (SII) on 17 January 2023

In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium molecular dynamics (HNEMD) method based on the Green-Kubo formula. Allegro can construct more advanced atomic descriptors than conventional ones, and can be applied to multicomponent and large-scale systems, providing a significant advantage in estimating the thermal conductivity of anharmonic materials, such as thermoelectric materials. In addition, the spectral heat current (SHC) method, recently developed for the HNEMD framework (HNEMD-SHC), allows the calculation of not only the total thermal conductivity but also its frequency components. The verification of the heat flux and the demonstration of HNEMD-SHC method are performed for the extremely anharmonic low-temperature phase of Ag$_2$Se.

Curved free shear layers emerge in many engineering problems involving complex flow geometries, such as the flow over a backward facing step, flows with wall injection in a boundary layer, the flow inside side-dump combustors, or wakes generated by vertical axis wind turbines, among others. Previous studies involving centrifugal instabilities have mainly focused on wall-flows where Taylor instabilities between two rotating concentric cylinders or G\"{o}rtler vortices in boundary layers, resulting from the imbalance between the centrifugal forces and the radial pressure gradients, are generated. Curved free shear layer flows, however, have not received sufficient attention, especially in the nonlinear regime. The present work investigates the development of centrifugal instabilities evolving in a curved free shear layer flow in the nonlinear compressible regime. The compressible Navier-Stokes equations are reduced to the nonlinear boundary region equations (BRE) in a high Reynolds number asymptotic framework wherein the streamwise wavelengths of the disturbances are assumed to be much larger than the spanwise and wall-normal counterparts. We study the effect of the freestream Mach number $\boldsymbol{M_\infty}$, the shear layer thickness $\boldsymbol{\delta}$, the amplitude of the incoming disturbance $\boldsymbol{A}$, and the relative velocity difference across the shear layer $\boldsymbol{\Delta V}$ on the development of these centrifugal instabilities., Comment: 17 pages

We address the linear-mode analysis performed near an equilibrium configuration in the fluid rest frame with a dynamical magnetic field perturbed on a constant configuration. We develop a simple and general algorithm for an analytic solution search that works on an order-by-order basis in the derivative expansion. This method can be applied to general sets of hydrodynamic equations. Applying our method to the first-order relativistic magnetohydrodynamics, we demonstrate that the method finds a complete set of solutions. We obtain two sets of analytic solutions for the four and two coupled modes with seven dissipative transport coefficients. The former set has been missing in the literature for a long time due to the difficulties originating from coupled degrees of freedom and strong anisotropy provided by a magnetic field. The newly developed method resolves these difficulties. We also find that the small-momentum expansions of the solutions break down when the momentum direction is nearly perpendicular to an equilibrium magnetic field due to the presence of another small quantity, that is, a trigonometric function representing the anisotropy. We elaborate on the angle dependence of the solutions and provide alternative series representations that work near the right angle. This identifies the origin of a discrepancy found in recent works. Finally, we discuss the issues of causality and stability based on our analytic solutions and recent developments in the literature., Comment: 18 pages, 2 figures. The version published in PRD

For ongoing studies of the role of rotation in stellar evolution, we require large catalogs of rotation periods for testing and refining gyrochronology. While there is a wealth of data from the Kepler and K2 missions, TESS presents both an opportunity and a challenge: despite its all-sky coverage, rotation periods remain hard to detect. We analyzed individual TESS sectors to detect short-period stellar rotation, using only parameters measured from light curves for a robust and unbiased method of evaluating detections. We used random forest classifiers for vetting, trained on a large corpus of period measurements in KELT data from the Oelkers et al. (2018) catalog and using TESS full-frame image light curves generated by eleanor (Feinstein et al. 2019). Finally, using data from the first 26 sectors of TESS, we analyzed 432,704 2-minute cadence single-sector light curves for FGKM dwarfs. We detected 16,800 periods in individual sector light curves, covering 10,909 distinct targets, and we present a catalog of the median period for each target as measured by a Lomb-Scargle periodogram., Comment: 23 pages, 15 figures, accepted for publication in The Astronomical Journal

We prove the solvability and nilpotency of Kac--Paljutkin's finite quantum group and Sekine quantum groups and we classify the solvable series of Kac--Paljutkin's finite quantum group via Cohen--Westreich's Burnside theorem. Some semisimple quasitriangular Hopf algebras of dimensions $2pq$ are also studied. In Appendix A, we give a direct computation of the universal $R$-matrices for Kac--Paljutkin's $8$-dimensional finite quantum group., Comment: 47 pages

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active candidates. However, the validity of its results is threatened by the publication bias issue. Existing methods to handle the publication bias issue in the standard pairwise meta-analysis are hard to extend to this area with the complicated data structure and the underlying assumptions for pooling the data. In this paper, we aimed to provide a flexible inverse probability weighting (IPW) framework along with several t-type selection functions to deal with the publication bias problem in the NMA context. To solve these proposed selection functions, we recommend making use of the additional information from the unpublished studies from multiple clinical trial registries. A comprehensive numerical study and a real example showed that our methodology can help obtain more accurate estimates and higher coverage probabilities, and improve other properties of an NMA (e.g., ranking the interventions).

The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to obtain the SROC curves along with the corresponding area under the curves in the worst case where the maximum possible PB under a range of marginal selection probabilities is considered. We apply the proposed method to a real-world meta-analysis to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.