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A versatile robot working in a domestic environment based on a deep neural network (DNN) is currently attracting attention. One of the roles expected for domestic robots is caregiving for a human. In particular, we focus on repositioning care because repositioning plays a fundamental role in supporting the health and quality of life of individuals with limited mobility. However, generating motions of the repositioning care, avoiding applying force to non-target parts and applying appropriate force to target parts, remains challenging. In this study, we proposed a DNN-based architecture using visual and somatosensory attention mechanisms that can generate dual-arm repositioning motions which involve different sequential policies of interaction force; contact-less reaching and contact-based assisting motions. We used the humanoid robot Dry-AIREC, which features the capability to adjust joint impedance dynamically. In the experiment, the repositioning assistance from the supine position to the sitting position was conducted by Dry-AIREC. The trained model, utilizing the proposed architecture, successfully guided the robot's hand to the back of the mannequin without excessive contact force on the mannequin and provided adequate support and appropriate contact for postural adjustment.

In recent years, studies have been actively conducted on combining large language models (LLM) and robotics; however, most have not considered end-to-end feedback in the robot-motion generation phase. The prediction of deep neural networks must contain errors, it is required to update the trained model to correspond to the real environment to generate robot motion adaptively. This study proposes an integration method that connects the robot-motion learning model and LLM using shared latent variables. When generating robot motion, the proposed method updates shared parameters based on prediction errors from both sensorimotor attention points and task language instructions given to the robot. This allows the model to search for latent parameters appropriate for the robot task efficiently. Through simulator experiments on multiple robot tasks, we demonstrated the effectiveness of our proposed method from two perspectives: position generalization and language instruction generalization abilities., Comment: 7 pages, 8 figures, accepted at IROS 2024

Ice I has three forms depending on the stacking arrangements of its layers: hexagonal ice Ih, cubic ice Ic, and stacking disordered ice Isd. Below ~60 K, amorphous water becomes metastable, and the formation of any form of ice I is often implicitly precluded. Using a newly developed low-temperature reflection high-energy electron diffraction (RHEED) technique, we show that crystalline ice with cubic stacking sequences (i.e., ice Ic) formed through Ne sublimation from a solid H2O/Ne (1:1000 ratio) matrix at 13 K. The extent of staking disorder (disordered cubic and hexagonal stacking sequences) in the ice formed by Ne matrix sublimation is smaller than that in vapor-deposited ice Isd prepared at 143 K and below the limit of detection of low-temperature RHEED. Dependence of the resulting ice structures on the thickness of the H2O/Ne matrix shows that amorphous water first forms in the early stages of Ne sublimation and the cubic stacking sequence subsequently takes place. As the cubic ice Ic formed here at a much lower temperature (13 K) than previously observed (typically above 78 K), Ne matrix sublimation represents a novel route to the formation of cubic ice Ic under low-temperature and low-pressure conditions.

Infrared (IR) spectra of vapor-deposited amorphous water at low temperatures show two weak peaks at around 3720 and 3696 cm-1 assigned to free-OH stretching modes of two- and three-coordinated water molecules (so-called dangling OH bonds), respectively, on the ice surface. A recent James Webb Space Telescope (JWST) observation first succeeded in detection of a potential dangling OH feature at 3664 cm-1 for ices in molecular clouds, highlighting the importance of dangling OH bonds in interstellar ice chemistry. A lack of band strengths of these features at low temperatures restricts the quantification of dangling OH bonds from IR spectra, hindering development of a molecular-level understanding of the surface structure and chemistry of ice. Using IR multiple-angle incidence resolution spectrometry, we quantified the band strengths of two- and three-coordinated dangling OH features in amorphous water at 20 K as being 4.6 plus-minus 1.6 times 10-18 and 9.1 plus-minus 1.0 times 10-18 cm molecule-1, respectively. These values are more than an order of magnitude lower than band strengths of bulk water molecules in ice and liquid water and are similar to those of H2O monomers confined in solid matrices. Adsorption of carbon monoxide with dangling OH bonds results in the appearance of a new broad dangling OH feature at 3680-3620 cm-1, with a band strength of 1.8 plus-minus 0.1 times 10-17 cm molecule-1. The band strengths of dangling OH features determined in this study advance our understanding of the surface structure of interstellar ice analogs and recent IR observations of the JWST.

Saturated fatty acids are abundant organic compounds in oceans and sea sprays. Their photochemical reactions induced by solar radiation have recently been discovered as an abiotic source of volatile organic compounds, which serve as precursors of secondary organic aerosols. However, photoabsorption of wavelengths longer than 250 nm in liquid saturated fatty acids remains unexplained, despite being first reported in 1931. Here we demonstrate that the previously reported absorption of wavelengths longer than 250 nm by liquid nonanoic acid [CH3(CH2)7COOH)] originates from traces of impurities (0.1% at most) intrinsically contained in nonanoic acid reagents. Absorption cross sections of nonanoic acid newly obtained here indicate that the upper limit of its photolysis rate is three-to-five orders of magnitude smaller than those for atmospherically relevant carbonyl compounds.

We construct the marginal loosely trapped surface (marginal LTS) for the Kerr spacetime with a small Kerr parameter perturbatively, where the LTS condition is saturated. An LTS is a surface that specifies the strong gravity region, which is a generalization of the photon sphere in the Schwarzschild spacetime. It turns out that there are an infinite number of marginal LTSs. At the leading order of the small Kerr parameter, all of the marginal LTSs have the same area. However, one can see that the maximal marginal LTS among them is uniquely determined at the higher order., Comment: 7 pages

We study the symmetry of superconductivity around the All-In-All-Out (AIAO) spin ordered phase in a nearly half-filled pyrochlore electronic system. The electronic system is described by the Hubbard model with an anti-symmetric spin-orbit coupling. The AIAO spin ordered state is formed by arranging tetrahedral hedgehog spin-clusters uniformly in the lattice system, where 12 types of spin-clusters are allowed in a unit cell. All spin-cluster susceptibilities are calculated within the random phase approximation with respect to the onsite electron interaction. It is shown that, for the negative spin-orbit coupling coefficient, the AIAO spin-cluster susceptibility diverges at q=0, while other types of spin-cluster susceptibilities are not developed and are structureless. By calculating the superconducting transition mediated by AIAO spin fluctuation, it is found that a spin-triplet superconductivity appears, while there is no signal of superconducting transition for the spin-singlet one. We suggest that the spin-triplet superconductivity appears around the AIAO spin ordered phase in the pyrochlore lattice.

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

Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community still needs to fully understand the hallucination issues within these models. To address this challenge, we introduce a comprehensive diagnostic benchmark, ToolBH. Specifically, we assess the LLM's hallucinations through two perspectives: depth and breadth. In terms of depth, we propose a multi-level diagnostic process, including (1) solvability detection, (2) solution planning, and (3) missing-tool analysis. For breadth, we consider three scenarios based on the characteristics of the toolset: missing necessary tools, potential tools, and limited functionality tools. Furthermore, we developed seven tasks and collected 700 evaluation samples through multiple rounds of manual annotation. The results show the significant challenges presented by the ToolBH benchmark. The current advanced models Gemini-1.5-Pro and GPT-4o only achieve a total score of 45.3 and 37.0, respectively, on a scale of 100. In this benchmark, larger model parameters do not guarantee better performance; the training data and response strategies also play a crucial role in tool-enhanced LLM scenarios. Our diagnostic analysis indicates that the primary reason for model errors lies in assessing task solvability. Additionally, open-weight models suffer from performance drops with verbose replies, whereas proprietary models excel with longer reasoning.

Magnon-polariton is a synthetic quasi-particle of quantized spinwaves in magnets coupled with photons. The recent reporting of ultrastrong photon-to-magnon coupling in magnetic heterostructures was demonstrated at cryogenic temperatures in terms of reciprocal electromagnetic responses. Here, we report that magnon-polaritons in a three-dimensional metamaterial lacking time-reversal and space-inversion symmetries -- magnetochiral magnon-polaritons -- demonstrate ultrastrong coupling and directional non-reciprocity at room temperature. Ultrastrong coupling is due to a direct coupling between microwave photons confined in the chiral meta-atom and magnons in the magnetic meta-atom. Our results reveal a crucial step in identifying cavity-free, deepstrongly-coupled, non-reciprocal magnon-polaritons for hybrid quantum systems and quasi-particle "chemistry" using metamaterials., Comment: 23 pages, 7 figures, 1 table

We give an alternative proof of a fact that a finite continuous non-decreasing submodular set function on a measurable space can be expressed as a supremum of measures dominated by the function, if there exists a class of sets which is totally ordered with respect to inclusion and generates the sigma-algebra of the space. The proof is elementary in the sense that the measure attaining the supremum in the claim is constructed by a standard extension theorem of measures. As a consequence, a uniquness of the supremum attaining measure also follows. A Polish space is an examples of the measurable space which has a class of totally ordered sets that generates the Borel sigma-algebra., Comment: 9 pages

The genus non-increasing totally positive unknotting number is the minimum number of crossing changes that transform a knot into the unknot, such that all the crossing changes are positive-to-negative crossing changes that do not increase the genus. We show that the genus non-increasing totally positive unknotting number can be arbitrary large for genus one knots., Comment: 10 pages, 3 Figures

The nature of the interaction between active galactic nuclei (AGNs) and their host galaxies remains an unsolved question. Therefore, conducting an AGN census is valuable to AGN research. Nevertheless, a significant fraction of AGNs are obscured by their environment, which blocks UV and optical emissions due to the dusty torus surrounding the central supermassive black hole (SMBH). To overcome this challenge, mid-infrared (IR) surveys have emerged as a valuable tool for identifying obscured AGNs, as the obscured light is re-emitted in this range. With its high sensitivity, the James Webb Space Telescope (JWST) uncovered more fainter objects than previous telescopes. By applying the SED fitting, this work investigates AGN candidates in JWST Cosmic Evolution Early Release Science (CEERS) fields. We identified 42 candidates, 30 of them are classified as composites ($0.2\leq f_{\rm AGN, IR}< 0.5$), and 12 of them are AGNs ($f_{\rm AGN, IR}\geq 0.5$). We report the AGN luminosity contributions and AGN number fractions as a function of redshift and total infrared luminosity, showing that previously reported increasing relations are not apparent in our sample due to the sample size. We also extend the previous results on ultra-luminous infrared galaxies (ULIRGs, $L_{\rm TIR}\geq 10^{12} L_{\odot}$) to less luminous AGNs, highlighting the power of JWST., Comment: 15 pages, 20 figures, 4 tables. Accepted for publication in MNRAS. The 3 min summary: https://www.youtube.com/watch?v=mWUebbgUOh8

We select blue-horizontal branch stars (BHBs) from the internal data release of the Hyper Suprime-Cam Subaru Strategic Program to reveal the global structure of the Milky Way (MW) stellar halo. The data are distributed over $\sim 1,100$~deg$^2$ area in the range of $18.5r_{\rm b}$; the statistical difference between these fitting profiles is small. Both profiles are found to show prolate shapes having axial ratios of $q=1.47^{+0.30}_{-0.33}$ and $1.56^{+0.34}_{-0.23}$, respectively. We also find a signature of the so-called "splashback radius" for the candidate BHBs, which can reach as large as $r \sim 575$~kpc, although it is still inconclusive owing to rather large distance errors in this faintest end of the sample. Our results suggest that the MW stellar halo consists of the two overlapping components: the {\it in situ} inner halo showing a relatively steep radial density profile and the {\it ex situ} outer halo with a shallower profile, being characteristic of a component formed from accretion of small stellar systems., Comment: 15 pages, 8 figures, 5 tables, submitted to PASJ. arXiv admin note: substantial text overlap with arXiv:1904.04966

Deep learning holds a big promise for optimizing existing peptides with more desirable properties, a critical step towards accelerating new drug discovery. Despite the recent emergence of several optimized Antimicrobial peptides(AMP) generation methods, multi-objective optimizations remain still quite challenging for the idealism-realism tradeoff. Here, we establish a multi-objective AMP synthesis pipeline (MoFormer) for the simultaneous optimization of multi-attributes of AMPs. MoFormer improves the desired attributes of AMP sequences in a highly structured latent space, guided by conditional constraints and fine-grained multi-descriptor.We show that MoFormer outperforms existing methods in the generation task of enhanced antimicrobial activity and minimal hemolysis. We also utilize a Pareto-based non-dominated sorting algorithm and proxies based on large model fine-tuning to hierarchically rank the candidates. We demonstrate substantial property improvement using MoFormer from two perspectives: (1) employing molecular simulations and scoring interactions among amino acids to decipher the structure and functionality of AMPs; (2) visualizing latent space to examine the qualities and distribution features, verifying an effective means to facilitate multi-objective optimization AMPs with design constraints

The calculations of the first and the second ionization potentials of lawrencium and lutetium and the electron affinity of lawrencium are performed within the relativistic coupled cluster framework. These results are corrected by including the contributions of extrapolation to the complete basis set limit and higher order contributions due to relativity and electron correlation. The excellent agreement between our predictions of the ionization potentials of Lu and Lr and experimental values supports the accuracy of our predictions of the second ionization potential and the electron affinity of Lr.

Analyzing the motion of multiple biological agents, be it cells or individual animals, is pivotal for the understanding of complex collective behaviors. With the advent of advanced microscopy, detailed images of complex tissue formations involving multiple cell types have become more accessible in recent years. However, deciphering the underlying rules that govern cell movements is far from trivial. Here, we present a novel deep learning framework to estimate the underlying equations of motion from observed trajectories, a pivotal step in decoding such complex dynamics. Our framework integrates graph neural networks with neural differential equations, enabling effective prediction of two-body interactions based on the states of the interacting entities. We demonstrate the efficacy of our approach through two numerical experiments. First, we used a simulated data from a toy model to tune the hyperparameters. Based on the obtained hyperparameters, we then applied this approach to a more complex model that describes interacting cells of cellular slime molds. Our results show that the proposed method can accurately estimate the function of two-body interactions, thereby precisely replicating both individual and collective behaviors within these systems., Comment: 14 pages, 4 figures

The exploration of outflows in protobinary systems presents a challenging yet crucial endeavour, offering valuable insights into the dynamic interplay between protostars and their evolution. In this study, we examine the morphology and dynamics of jets and outflows within the IRAS\,4A protobinary system. This analysis is based on ALMA observations of SiO(5--4), H$_2$CO(3$_{0,3}$--2$_{0,3}$), and HDCO(4$_{1,4}$--3$_{1,3}$) with a spatial resolution of $\sim$150\,au. Leveraging an astrochemical approach involving the use of diverse tracers beyond traditional ones has enabled the identification of novel features and a comprehensive understanding of the broader outflow dynamics. Our analysis reveals the presence of two jets in the redshifted emission, emanating from IRAS\,4A1 and IRAS\,4A2, respectively. Furthermore, we identify four distinct outflows in the region for the first time, with each protostar, 4A1 and 4A2, contributing to two of them. We characterise the morphology and orientation of each outflow, challenging previous suggestions of bends in their trajectories. The outflow cavities of IRAS\,4A1 exhibit extensions of 10$''$ and 13$''$ with position angles (PA) of 0$^{\circ}$ and -12$^{\circ}$, respectively, while those of IRAS\,4A2 are more extended, spanning 18$''$ and 25$''$ with PAs of 29$^{\circ}$ and 26$^{\circ}$. We propose that the misalignment of the cavities is due to a jet precession in each protostar, a notion supported by the observation that the more extended cavities of the same source exhibit lower velocities, indicating they may stem from older ejection events.

Text-to-Table aims to generate structured tables to convey the key information from unstructured documents. Existing text-to-table datasets are typically oriented English, limiting the research in non-English languages. Meanwhile, the emergence of large language models (LLMs) has shown great success as general task solvers in multi-lingual settings (e.g., ChatGPT), theoretically enabling text-to-table in other languages. In this paper, we propose a Chinese text-to-table dataset, CT-Eval, to benchmark LLMs on this task. Our preliminary analysis of English text-to-table datasets highlights two key factors for dataset construction: data diversity and data hallucination. Inspired by this, the CT-Eval dataset selects a popular Chinese multidisciplinary online encyclopedia as the source and covers 28 domains to ensure data diversity. To minimize data hallucination, we first train an LLM to judge and filter out the task samples with hallucination, then employ human annotators to clean the hallucinations in the validation and testing sets. After this process, CT-Eval contains 88.6K task samples. Using CT-Eval, we evaluate the performance of open-source and closed-source LLMs. Our results reveal that zero-shot LLMs (including GPT-4) still have a significant performance gap compared with human judgment. Furthermore, after fine-tuning, open-source LLMs can significantly improve their text-to-table ability, outperforming GPT-4 by a large margin. In short, CT-Eval not only helps researchers evaluate and quickly understand the Chinese text-to-table ability of existing LLMs but also serves as a valuable resource to significantly improve the text-to-table performance of LLMs., Comment: 10 pages

Migration is a major decision to leave one place and move to another, and involves job and life changes. The migration flow of people provides relational information across places about which is better to live by ``voting with their feet'' (Tiebout, 1956; Douglas, 1997). From the people's votes, in a ``democratic'' process, we quantify a descriptive statistic of liveable cities by a potential of migration flow in Combinatorial Hodge theory. As a case study, we measure the liveability of municipalities in Japan for specific populations such as families with small children and women of reproductive age. Using these potentials as dependent variables, we perform a regression analysis to identify the factors relevant to liveability. Additionally, using the aformentioned theoretical framework, we analytically derive the expression of the utility as a function of given flow data, which was numerically estimated in previous studies (Douglas & Wall, 1993; Douglas, 1997; Douglas & Wall, 2000; Wall, 2001; Nakajima & Tabuchi, 2011). The proposed method extracts a consistent metric of interval scale from the non-transitive, pairwise comparison between locations and provides valuable statistics for urban planning by policymakers.

We theoretically investigate nonequilibrium spin fluctuations in a ferromagnet induced by a light pulse. Using a Lindblad equation consistent with the Landau-Lifshitz-Gilbert equation, we compute the autocorrelation function of magnetization. Our analysis reveals that this function comprises both thermal and nonequilibrium components. To examine the latter in detail, we introduce a Fano factor similar to nonequilibrium current noise in electronic circuits. We demonstrate that this factor encapsulates insights into the transfer of spin units to the environment. Our findings lay the groundwork for nonequilibrium spin noise spectroscopy, offering valuable insights into spin relaxation dynamics., Comment: 11 pages, 3 figures

Concerns have arisen regarding the unregulated utilization of artificial intelligence (AI) outputs, potentially leading to various societal issues. While humans routinely validate information, manually inspecting the vast volumes of AI-generated results is impractical. Therefore, automation and visualization are imperative. In this context, Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. Simultaneously, software auto-tuning (AT) technology has emerged, aiming to reduce the man-hours required for performance tuning in numerical calculations. AT is a potent tool for cost reduction during parameter optimization and high-performance programming for numerical computing. The synergy between AT mechanisms and AI technology is noteworthy, with AI finding extensive applications in AT. However, applying AI to AT mechanisms introduces challenges in AI model explainability. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations: performance parameter tuning of accuracy-guaranteed numerical calculations and sparse iterative algorithm., Comment: This article has been submitted to Special Session: Performance Optimization and Auto-Tuning of Software on Multicore/Manycore Systems (POAT), In conjunction with IEEE MCSoC-2024 (Dec 16-19, 2024, Days Hotel & Suites by Wyndham Fraser Business Park, Kuala Lumpur)

Iron-based superconductor Sr$_2$VFeAsO$_3$ is composed of alternate stacking of a superconducting FeAs layer and an insulating vanadium-oxide layer with a perovskite-type structure. Electronic orders stemming from the spin and orbital degrees of freedom of V $3d$ electrons can arise in the vanadium-oxide layer, but such orders have not been confirmed so far. Here, we systematically investigate the electronic state of Sr$_2$VFeAsO$_{3-\delta}$ with oxygen deficiency and demonstrate the phase diagram of Sr$_2$VFeAsO$_{3-\delta}$ as a function of the $c$-axis lattice parameter, which has turned out to be a suitable measure of the amount of oxygen deficiency. We found a magnetic and structural anomaly at $\sim 100$ K with a thermal hysteresis, which is manifested with the introduction of oxygen deficiency. The presence of orthorhombic distortion was revealed below the temperature at which the anomaly appears, suggestive of V orbital ordering involving the $d_{xz}$ and $d_{yz}$ orbitals. It seems that substantial fluctuations associated with the orthorhombic distortion significantly influence the electronic state of the FeAs layer. Our findings indicate that the vanadium-oxide layer plays a significant role in the electronic state of Sr$_2$VFeAsO$_{3-\delta}$., Comment: 10 pages, 9 figures

Eukaryotic cells perform chemotaxis by determining the direction of chemical gradients based on stochastic sensing of concentrations at the cell surface. To examine the efficiency of this process, previous studies have investigated the limit of estimation accuracy for gradients. However, these studies assume that the cell shape and gradient are constant, and do not consider how adaptive regulation of cell shape affects the estimation limit. Dynamics of cell shape during gradient sensing is biologically ubiquitous and can influence the estimation by altering the way the concentration is measured, and cells may strategically regulate their shape to improve estimation accuracy. To address this gap, we investigate the estimation limits in dynamic situations where cells change shape adaptively depending on the sensed signal. We approach this problem by analyzing the stationary solution of the Bayesian nonlinear filtering equation. By applying diffusion approximation to the ligand-receptor binding process and the Laplace method for the posterior expectation under a high signal-to-noise ratio regime, we obtain an analytical expression for the estimation limit. This expression indicates that estimation accuracy can be improved by elongating perpendicular to the estimated direction, which is also confirmed by numerical simulations. Our analysis provides a basis for clarifying the interplay between estimation and control in gradient sensing and sheds light on how cells optimize their shape to enhance chemotactic efficiency., Comment: 14 pages, 5 figures

The Hayabusa 2 spacecraft sampled approximately 5.4 g of asteroid material from the Cb-type asteroid Ryugu. Initial analysis of the Ryugu materials revealed a mineralogical, chemical, and isotopic kinship to the CI chondrites. The pristine nature of Ryugu makes the returned samples ideal for constraining the composition of the Solar System. However, some elements (e.g., P, Ca, Mn, and rare earth elements) show large relative dispersions compared to the other elements in the returned materials studied so far, most likely due to the presence of aqueously formed secondary minerals (e.g., carbonates, phosphates) in Ryugu. Therefore, the estimation of the Solar System composition using currently available Ryugu data is challenging due to the so-called nugget effect of carbonates, phosphates, and possibly other accessory minerals. The nugget effect can be mitigated by analyzing a homogenized, relatively large amount of sample. We estimate that for approximately 0.1 g of Ryugu sample, the dispersion (2SD) of the bulk Mn/Cr and Rb/Sr ratios are +/-13% and +/-15%, respectively, while they will be improved to be better than +/-5% for approximately 1 g of homogenized Ryugu sample. To further constrain the Solar System composition and to evaluate if previous estimates based on CI chondrites stored in museums for decades to centuries are reliable, it is strongly recommended to determine the chemical and isotopic compositions of Ryugu using a homogenized sample prepared from relatively large (approx. 1 g) returned material. Determining Ryugu reference compositions will be used by multidisciplinary communities, including Earth and planetary sciences, astronomy, physics, and chemistry., Comment: 13 pages, 5 figures, 2 Tables

Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for recommender systems (VQ4Rec), examining their applications from multiple perspectives. Further, it provides a thorough introduction to research efforts in diverse recommendation scenarios, including efficiency-oriented approaches and quality-oriented approaches. Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems. We hope this survey can pave the way for future researchers in the recommendation community and accelerate their exploration in this promising field.

In this paper, support vector machine (SVM) performance was assessed utilizing a quantum-inspired complementary metal-oxide semiconductor (CMOS) annealer. The primary focus during performance evaluation was the accuracy rate in binary classification problems. A comparative analysis was conducted between SVM running on a CPU (classical computation) and executed on a quantum-inspired annealer. The performance outcome was evaluated using a CMOS annealing machine, thereby obtaining an accuracy rate of 93.7% for linearly separable problems, 92.7% for non-linearly separable problem 1, and 97.6% for non-linearly separable problem 2. These results reveal that a CMOS annealing machine can achieve an accuracy rate that closely rivals that of classical computation.

Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, which allows us to treat the dynamics in CRNs as transitions between chemicals. We also define thermodynamic-like quantities on the S-graph from the introduced transitions and investigate their properties, including the relationship between specieswise forces, activities, and conventional thermodynamic quantities. Remarkably, we demonstrate that this formulation can be developed for a class of irreversible CRNs, while for reversible CRNs, it is related to conventional thermodynamic quantities associated with reactions. The behavior of these specieswise quantities is numerically validated using an oscillating system (Brusselator). Our work provides a novel methodology for studying dynamics on S-graphs, paving the way for a deeper understanding of the intricate interplay between the structure and dynamics of chemical reaction networks., Comment: 8 pages, 4 figures

Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent complex conversational sessions for dense retrieval. To achieve this, we propose a simple and effective dual-learning approach that adapts LLM for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning on high-quality conversational instruction tuning data. Extensive experiments on five conversational search benchmarks demonstrate that ChatRetriever substantially outperforms existing conversational dense retrievers, achieving state-of-the-art performance on par with LLM-based rewriting approaches. Furthermore, ChatRetriever exhibits superior robustness in handling diverse conversational contexts. Our work highlights the potential of adapting LLMs for retrieval with complex inputs like conversational search sessions and proposes an effective approach to advance this research direction.

We consider a situation that multiple monitoring applications (each with a different sensor-monitor pair) compete for a common service resource such as a communication link. Each sensor reports the latest state of its own time-varying information source to its corresponding monitor, incurring queueing and processing delays at the shared resource. The primary performance metric of interest is the age of information (AoI) of each sensor-monitor pair, which is defined as the elapsed time from the generation of the information currently displayed on the monitor. Although the multi-source first-come first-served (FCFS) M/GI/1 queue is one of the most fundamental model to describe such competing sensors, its exact analysis has been an open problem for years. In this paper, we show that the Laplace-Stieltjes transform (LST) of the stationary distribution of the AoI in this model, as well as the mean AoI, is given by a simple explicit formula, utilizing the double Laplace transform of the transient workload in the M/GI/1 queue.

A method that enables an industrial robot to accomplish the peg-in-hole task for holes in concrete is proposed. The proposed method involves slightly detaching the peg from the wall, when moving between search positions, to avoid the negative influence of the concrete's high friction coefficient. It uses a deep neural network (DNN), trained via reinforcement learning, to effectively find holes with variable shape and surface finish (due to the brittle nature of concrete) without analytical modeling or control parameter tuning. The method uses displacement of the peg toward the wall surface, in addition to force and torque, as one of the inputs of the DNN. Since the displacement increases as the peg gets closer to the hole (due to the chamfered shape of holes in concrete), it is a useful parameter for inputting in the DNN. The proposed method was evaluated by training the DNN on a hole 500 times and attempting to find 12 unknown holes. The results of the evaluation show the DNN enabled a robot to find the unknown holes with average success rate of 96.1% and average execution time of 12.5 seconds. Additional evaluations with random initial positions and a different type of peg demonstrate the trained DNN can generalize well to different conditions. Analyses of the influence of the peg displacement input showed the success rate of the DNN is increased by utilizing this parameter. These results validate the proposed method in terms of its effectiveness and applicability to the construction industry., Comment: Published in 2021 IEEE International Conference on Robotics and Automation (ICRA) on 30 May 2021

This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect. It explores how decoy results alter users' interactions on search engine result pages, focusing on metrics like click-through likelihood, browsing time, and perceived document usefulness. By analyzing user interaction logs from multiple datasets, the study demonstrates that decoy results significantly affect users' behavior and perceptions. Furthermore, it investigates how different levels of task difficulty and user knowledge modify the decoy effect's impact, finding that easier tasks and lower knowledge levels lead to higher engagement with target documents. In terms of IR system evaluation, the study introduces the DEJA-VU metric to assess systems' susceptibility to the decoy effect, testing it on specific retrieval tasks. The results show differences in systems' effectiveness and vulnerability, contributing to our understanding of cognitive biases in search behavior and suggesting pathways for creating more balanced and bias-aware IR evaluations.

Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the specimen and reporting the corresponding findings, which are extremely burdensome tasks. In this study, we propose a report-generation technique for lung cytology images. In total, 71 benign and 135 malignant pulmonary cytology specimens were collected. Patch images were extracted from the captured specimen images, and the findings were assigned to each image as a dataset for report generation. The proposed method consists of a vision model and a text decoder. In the former, a convolutional neural network (CNN) is used to classify a given image as benign or malignant, and the features related to the image are extracted from the intermediate layer. Independent text decoders for benign and malignant cells are prepared for text generation, and the text decoder switches according to the CNN classification results. The text decoder is configured using a Transformer that uses the features obtained from the CNN for report generation. Based on the evaluation results, the sensitivity and specificity were 100% and 96.4%, respectively, for automated benign and malignant case classification, and the saliency map indicated characteristic benign and malignant areas. The grammar and style of the generated texts were confirmed as correct and in better agreement with gold standard compared to existing LLM-based image-captioning methods and single-text-decoder ablation model. These results indicate that the proposed method is useful for pulmonary cytology classification and reporting., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Transition state theory is a standard framework for predicting the rate of a chemical reaction. Although the transition state theory has been successfully applied to numerous chemical reaction analyses, many experimental and theoretical studies have reported chemical reactions with a reactivity which cannot be explained by the transition state theory due to dynamic effects. Dynamical systems theory provides a theoretical framework for elucidating dynamical mechanisms of such chemical reactions. In particular, reactive islands are essential phase space structures revealing dynamical reaction patterns. However, the numerical computation of reactive islands in a reaction system of many degrees of freedom involves an intrinsic challenge -- the curse of dimensionality. In this paper, we propose a dimensionality reduction algorithm for computing reactive islands in a reaction system of many degrees of freedom. Using the supervised principal component analysis, the proposed algorithm projects reactive islands into a low-dimensional phase space with preserving the dynamical information on reactivity as much as possible. The effectiveness of the proposed algorithm is examined by numerical experiments for H\'enon-Heiles systems extended to many degrees of freedom. The numerical results indicate that our proposed algorithm is effective in terms of the quality of reactivity prediction and the clearness of the boundaries of projected reactive islands. The proposed algorithm is a promising elemental technology for practical applications of dynamical systems analysis to real chemical systems.

Understanding deaths and life-death boundaries of cells is a fundamental challenge in biological sciences. In this study, we present a theoretical framework for investigating cell death. We conceptualize cell death as a controllability problem within dynamical systems, and compute the life-death boundary through the development of ``stoichiometric rays''. This method utilizes enzyme activity as control parameters, exploiting the inherent property of enzymes to enhance reaction rates without shifting equilibrium states. This approach facilitates the efficient evaluation of the global controllability of models. We demonstrate the utility of our framework using its application to a toy metabolic model, where we delineate the life-death boundary. The formulation of cell death through mathematical principles provides a foundation for the theoretical study of cellular mortality.

The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data. The multi-spectral MRSI can effectively separate spectral peaks of multiple substances and is useful to measure spatial distributions of substances. However it is difficult to measure time-varying substance distributions directly by ordinary full sampling because the measurement requires a significantly long time. In this study, we propose a novel method to reconstruct the spatio-temporal distributions of substances from randomly undersampled multi-spectral MRSI data on the basis of compressed sensing (CS) and the partially separable function model with base spectra of substances. In our method, we have employed spatio-temporal sparsity and temporal smoothness of the substance distributions as prior knowledge to perform CS. The effectiveness of our method has been evaluated using phantom data sets of glass tubes filled with glucose or lactate solution in increasing amounts over time and animal data sets of a tumor-bearing mouse to observe the metabolic dynamics involved in the Warburg effect in vivo. The reconstructed results are consistent with the expected behaviors, showing that our method can reconstruct the spatio-temporal distribution of substances with a temporal resolution of four seconds which is extremely short time scale compared with that of full sampling. Since this method utilizes only prior knowledge naturally assumed for the spatio-temporal distributions of substances and is independent of the number of the spectral and spatial dimensions or the acquisition sequence of MRSI, it is expected to contribute to revealing the underlying substance dynamics in MRSI data already acquired or to be acquired in the future.

Mid-infrared (MIR) light from galaxies is sensitive to dust-obscured star-formation activities because it traces the characteristic emission of dust heated by young, massive stars. By constructing the MIR luminosity functions (LFs), we are able to quantify the overall dusty star formation history and the evolution of galaxies over cosmic time. In this work, we report the first rest-frame MIR LFs at 7.7, 10, 12.8, 15, 18, and 21 $\mu$m as well as the total IR LF from the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science (CEERS) survey. We identify 506 galaxies at $z=0-5.1$ in the CEERS survey that also have optical photometry from the Hubble Space Telescope. With the unprecedented sensitivity of the JWST, we probe the faintest end of the LFs at $z=0-1$ down to $L^* \sim 10^7 L_\odot$, $\sim 2$ orders of magnitude fainter than those from the previous generation of IR space telescopes. Our findings connect well with and continue the faint end of the MIR LFs from the deepest observations in past works. As a proxy of star formation history, we present the MIR-based luminosity density up to $z\simeq4.0$, marking the first probe of the early Universe by JWST MIRI., Comment: 22 pages, 22 figures, 7 tables. Accepted for publication in MNRAS. A summary video can be found at https://youtu.be/TRb6bjmGfOU

Estimation of conditional average treatment effects (CATEs) is an important topic in sciences. CATEs can be estimated with high accuracy if distributed data across multiple parties can be centralized. However, it is difficult to aggregate such data owing to privacy concerns. To address this issue, we proposed data collaboration double machine learning, a method that can estimate CATE models with privacy preservation of distributed data, and evaluated the method through simulations. Our contributions are summarized in the following three points. First, our method enables estimation and testing of semi-parametric CATE models without iterative communication on distributed data. Semi-parametric CATE models enable estimation and testing that is more robust to model mis-specification than parametric models. Second, our method enables collaborative estimation between multiple time points and different parties. Third, our method performed equally or better than other methods in simulations using synthetic, semi-synthetic and real-world datasets., Comment: 51 pages, 11 figures

A link in $S^{3}$ is a fully positive braid link if it is the closure of a positive braid that contains at least one full-twist. We show that a fully positive braid link is a satellite link if and only if it is the satellite of a fully positive braid link $C$ such that the pattern is a positive braid that contains sufficiently many full twists, where the number of necessary full twists only depends on $C$. As an application, we give a characterization of the unknot by the property that certain braided satellite is a (fully) positive braid knot., Comment: 14 pages, 3 figures. v2, errors concerning the number of necessary full twsits to get a full twist braid are corrected

We report 850 $\mu$m continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations (BISTRO) large program on the James Clerk Maxwell Telescope (JCMT). These data reveal a well-structured non-uniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30 deg from north to east. Field strengths estimates and a virial analysis for the major clumps indicate that NGC 2264C is globally dominated by gravity while in 2264D magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type-I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type-II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and the longitudinal collapsing, driven by the region's global gravity., Comment: Accepted for publication in the Astrophysical Journal. 43 pages, 32 figures, and 4 tables (including Appendix)

The role of spin fluctuations near magnetic phase transitions is crucial for generating various exotic phenomena, including anomalies in the extraordinary Hall effect, excess spin-current generation through the spin-Hall effect (SHE), and enhanced spin-pumping, amongst others. In this study, we experimentally investigate the temperature dependence of spin-orbit torques (SOTs) generated by Mn3Ni0.35Cu0.65N (MNCN), a member of the noncollinear antiferromagnetic family that exhibits unconventional magnetotransport properties. Our work uncovers a strong and nontrivial temperature dependence of SOTs, peaking near the N\'eel temperature of MNCN, which cannot be explained by conventional intrinsic and extrinsic scattering mechanisms of the SHE. Notably, we measure a maximum SOT efficiency of 30%, which is substantially larger than that of commonly studied nonmagnetic materials such as Pt. Theoretical calculations confirm a negligible SHE and a strong orbital Hall effect that can explain the observed SOTs. We propose a previously unidentified mechanism wherein fluctuating antiferromagnetic moments trigger the generation of substantial orbital currents near the N\'eel temperature due to the emergence of scalar spin chirality. Our findings present an approach for enhancing SOTs, which holds promise for magnetic memory applications by leveraging antiferromagnetic spin fluctuations to amplify both orbital and spin currents.

It has been an unanswered question how many dusty galaxies have been undetected from the state-of-the-art observational surveys. JWST enables us to detect faint IR galaxies that have prominent polycyclic aromatic hydrocarbon (PAH) features in the mid-IR wavelengths. PAH is a valuable tracer of star formation and dust properties in the mid-infrared wavelength. The JWST Cosmic Evolution Early Release Science (CEERS) fields provide us with wavelength coverage from 7.7 to 21 $\mu$m using six photometric bands of the mid-infrared instrument (MIRI). We have identified galaxies dominated by mid-IR emission from PAHs, termed PAH galaxies. From our multi-band photometry catalogue, we selected ten PAH galaxies displaying high flux ratios of $\log(S_{15}/S_{10}) > 0.8$. The SED fitting analysis indicates that these galaxies are star-forming galaxies with total IR luminosities of $10^{10}$ $\sim$ $10^{11.5}$ $L_{\odot}$ at z $\sim 1$. The morphology of PAH galaxies does not show any clear signatures of major merging or interaction within the MIRI resolution. The majority of them are on the star-formation main sequence at $z \sim 1$. Our result demonstrates that JWST can detect PAH emissions from normal star-forming galaxies at $z \sim 1$, in addition to ultra-luminous infrared galaxies (ULIRGs) or luminous infrared galaxies (LIRGs)., Comment: 12 pages, 20 figures, 4 tables. Accepted by MNRAS. A summary video is at https://www.youtube.com/watch?v=UtPaVTFM4f8&ab_channel=NTHUCosmology

Anchor-bolt insertion is a peg-in-hole task performed in the construction field for holes in concrete. Efforts have been made to automate this task, but the variable lighting and hole surface conditions, as well as the requirements for short setup and task execution time make the automation challenging. In this study, we introduce a vision and proprioceptive data-driven robot control model for this task that is robust to challenging lighting and hole surface conditions. This model consists of a spatial attention point network (SAP) and a deep reinforcement learning (DRL) policy that are trained jointly end-to-end to control the robot. The model is trained in an offline manner, with a sample-efficient framework designed to reduce training time and minimize the reality gap when transferring the model to the physical world. Through evaluations with an industrial robot performing the task in 12 unknown holes, starting from 16 different initial positions, and under three different lighting conditions (two with misleading shadows), we demonstrate that SAP can generate relevant attention points of the image even in challenging lighting conditions. We also show that the proposed model enables task execution with higher success rate and shorter task completion time than various baselines. Due to the proposed model's high effectiveness even in severe lighting, initial positions, and hole conditions, and the offline training framework's high sample-efficiency and short training time, this approach can be easily applied to construction., Comment: Published in IEEE Robotics and Automation Letters on 08 February 2023

We delve into growing open chemical reaction systems (CRSs) characterized by autocatalytic reactions within a variable volume, which changes in response to these reactions. Understanding the thermodynamics of such systems is crucial for comprehending biological cells and constructing protocells, as it sheds light on the physical conditions necessary for their self-replication. Building on our recent work, where we developed a thermodynamic theory for growing CRSs featuring basic autocatalytic motifs with regular stoichiometric matrices, we now expand this theory to include scenarios where the stoichiometric matrix has a nontrivial left kernel space. This extension introduces conservation laws, which limit the variations in chemical species due to reactions, thereby confining the system's possible states to those compatible with its initial conditions. By considering both thermodynamic and stoichiometric constraints, we clarify the environmental and initial conditions that dictate the CRSs' fate-whether they grow, shrink, or reach equilibrium. We also find that the conserved quantities significantly influence the equilibrium state achieved by a growing CRS. These results are derived independently of specific thermodynamic potentials or reaction kinetics, therefore underscoring the fundamental impact of conservation laws on the growth of the system., Comment: 26 pages, 9 figures

We report a $^{139}$La-NMR study of polycrystalline samples of multi($n$)-layered nickelates, La$_3$Ni$_2$O$_{7-\delta}$ ($n=2$) and La$_4$Ni$_3$O$_{10-\delta}$ ($n=3$), at ambient pressure. Measurements of the nuclear magnetic resonance (NMR) spectra and nuclear spin relaxation rate ($1/T_1$) indicate the emergence of a density wave order with a gap below $T^*\sim150$ K for La$_3$Ni$_2$O$_{7-\delta}$ and $\sim130$ K for La$_4$Ni$_3$O$_{10-\delta}$. The finite value of $1/T_1$ below $T^*$ indicates metallic ground states with the remaining density of states at the Fermi level ($E_{\rm F}$) under the density wave order. These features are attributed to multiple $d$ electron bands with different characteristics. Above $T^*$, the gradual decrease in $1/T_1T$ upon cooling implies the presence of a band with flat dispersion near $E_{\rm F}$. From our microscopic probes, we point out that these nickelates ($n=2$ and $3$) possess similar electronic states despite the difference in the formal valence of the Ni-$d$ electron states, which provides a basis for understanding the novel high-$T_{\rm c}$ superconductivity under high pressures., Comment: 5+1 pages, 3+1 figures