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Progress in gravitational-wave astronomy depends upon having sensitive detectors with good data quality. Since the end of the LIGO-Virgo-KAGRA third Observing run in March 2020, detector-characterization efforts have lead to increased sensitivity of the detectors, swifter validation of gravitational-wave candidates and improved tools used for data-quality products. In this article, we discuss these efforts in detail and their impact on our ability to detect and study gravitational-waves. These include the multiple instrumental investigations that led to reduction in transient noise, along with the work to improve software tools used to examine the detectors data-quality. We end with a brief discussion on the role and requirements of detector characterization as the sensitivity of our detectors further improves in the future Observing runs., Comment: 35 pages, 18 figures

In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate and rethink the training data from the perspectives of diversity and quality, {thereby addressing the question of ``How important is SR training for SR models?''}. To this end, we propose an automated image evaluation pipeline. With this, we stratify existing high-resolution image datasets and larger-scale image datasets such as ImageNet and PASS to compare their performances. We find that datasets with (i) low compression artifacts, (ii) high within-image diversity as judged by the number of different objects, and (iii) a large number of images from ImageNet or PASS all positively affect SR performance. We hope that the proposed simple-yet-effective dataset curation pipeline will inform the construction of SR datasets in the future and yield overall better models., Comment: Accepted to ECCV2024

This paper proposes a control-based framework for aligning large language models (LLMs) by leveraging a control barrier function (CBF) to ensure user-desirable text generation. The presented framework applies the safety filter, designed based on the CBF, to the output generation of the baseline LLM, i.e., the sequence of the token, with the aim of intervening in the generated text. The overall text-generation system is implemented with Llama 3 and a RoBERTa model, and the source code is available at https://github.com/Mya-Mya/CBF-LLM. The experiment demonstrates its control ability and effectiveness in reducing the number of interventions needed for user-specified alignment tasks.

We propose a quantum programming paradigm where all data are familiar classical data, and the only non-classical element is a random number generator that can return results with negative probability. Currently, the vast majority of quantum programming languages instead work with quantum data types made up of qubits. The description of their behavior relies on heavy linear algebra and many interdependent concepts and intuitions from quantum physics, which takes dedicated study to understand. We demonstrate that the proposed view of quantum programming explains its central concepts and constraints in more accessible, computationally relevant terms. This is achieved by systematically reducing everything to the existence of that negative-probability random generator, avoiding mention of advanced physics as much as possible. This makes quantum programming more accessible to programmers without a deep background in physics or linear algebra. The bulk of this paper is written with such an audience in mind. As a working vehicle, we lay out a simple quantum programming language under this paradigm, showing that not only can it express all quantum programs, it also naturally captures the semantics of measurement without ever mentioning qubits or collapse. The language is proved to be implementable and universal., Comment: 20 pages, 5 figures. Version submitted to APLAS 2024

Drug repurposing offers a promising avenue for accelerating drug development by identifying new therapeutic potentials of existing drugs. In this paper, we propose a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Our framework comprises several specialized agents: an AI Agent trains robust drug-target interaction (DTI) models; a Knowledge Graph Agent utilizes the drug-gene interaction database (DGIdb), DrugBank, Comparative Toxicogenomics Database (CTD), and Search Tool for Interactions of Chemicals (STITCH) to systematically extract DTIs; and a Search Agent interacts with biomedical literature to annotate and verify computational predictions. By integrating outputs from these agents, our system effectively harnesses diverse data sources, including external databases, to propose viable repurposing candidates. Preliminary results demonstrate the potential of our approach in not only predicting drug-disease interactions but also in reducing the time and cost associated with traditional drug discovery methods. This paper highlights the scalability of multi-agent systems in biomedical research and their role in driving innovation in drug repurposing. Our approach not only outperforms existing methods in predicting drug repurposing potential but also provides interpretable results, paving the way for more efficient and cost-effective drug discovery processes., Comment: 18 pages, 1 figure

We introduce a new method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy), aimed at learning with distant supervision, in which direct labels are unavailable. Unlike prior methods, our approach does not depend on symbolic solvers for reasoning about missing labels. Instead, it evaluates logical implications and constraints in a differentiable manner by embedding both neural network outputs and logic programs into matrices. This method facilitates more efficient learning under distant supervision. We evaluated our approach against existing methods while maintaining a constant volume of training data. The findings indicate that our method not only matches or exceeds the accuracy of other methods across various tasks but also speeds up the learning process. These results highlight the potential of our approach to enhance both accuracy and learning efficiency in NeSy applications., Comment: Updated Figure 1 and fixed the overlapping caption issue. 11 pages including the appendix. To be published in ECAI 2024

Learning from interpretation transition (LFIT) is a framework for learning rules from observed state transitions. LFIT has been implemented in purely symbolic algorithms, but they are unable to deal with noise or generalize to unobserved transitions. Rule extraction based neural network methods suffer from overfitting, while more general implementation that categorize rules suffer from combinatorial explosion. In this paper, we introduce a technique to leverage variable permutation invariance inherent in symbolic domains. Our technique ensures that the permutation and the naming of the variables would not affect the results. We demonstrate the effectiveness and the scalability of this method with various experiments. Our code is publicly available at https://github.com/phuayj/delta-lfit-2

We report the highest-redshift detection of [O I] 63 $\mu$m from a luminous quasar, J2054-0005, at $z=6.04$ based on the Atacama Large Millimeter/sub-millimeter Array Band 9 observations. The [O I] 63 $\mu$m line luminosity is $(4.5\pm1.5) \times 10^{9}~L_{\rm \odot}$, corresponding to the [O I] 63 $\mu$m-to-far-infrared luminosity ratio of $\approx 6.7\times10^{-4}$, which is consistent with the value obtained in the local universe. Remarkably, [O I] 63 $\mu$m is as bright as [C II] 158 $\mu$m, resulting in the [O I]-to-[C II] line luminosity ratio of $1.3\pm0.5$. Based on a careful comparison of the luminosity ratios of [O I] 63 $\mu$m, [C II] 158 $\mu$m, and dust continuum emission to models of photo-dissociation regions, we find that J2054-0005 has a gas density log($n_{\rm H}$/cm$^{-3}$)$=3.7\pm0.3$ and an incident far-ultraviolet radiation field of log($G/G_{\rm 0}$)$= 3.0\pm0.1$, showing that [O I] 63 $\mu$m serves as an important coolant of the dense and warm gas in J2054-0005. A close examination of the [O I] and [C II] line profiles suggests that the [O I] line may be partially self-absorbed, however deeper observations are needed to verify this conclusion. Regardless, the gas density and incident radiation field are in a broad agreement with the values obtained in nearby star-forming galaxies and objects with [O I] 63 $\mu$m observations at $z=1-3$ with the Herschel Space Observatory. These results demonstrate the power of ALMA high-frequency observations targeting [O I] 63 $\mu$m to examine the properties of photo-dissociation regions in high-redshift galaxies., Comment: Submitted to PASJ. 5 figures and 3 tables. Comments are very welcome

Massive star-forming regions (MSFRs) are commonly associated with hub-filament systems (HFSs) and sites of cloud-cloud collision (CCC). Recent observational studies of some MSFRs suggest a possible connection between CCC and the formation of HFSs. To understand this connection, we analyzed the magneto-hydrodynamic simulation data from Inoue et al. (2018). This simulation involves the collision of a spherical turbulent molecular cloud with a plane-parallel sea of dense molecular gas at a relative velocity of about 10 km/s. Following the collision, the turbulent and non-uniform cloud undergoes shock compression, rapidly developing filamentary structures within the compressed layer. We found that CCC can lead to the formation of HFSs, which is a combined effect of turbulence, shock compression, magnetic field, and gravity. The collision between the cloud components shapes the filaments into a cone and drives inward flows among them. These inward flows merge at the vertex of the cone, rapidly accumulating high-density gas, which can lead to the formation of massive star(s). The cone acts as a mass-collecting machine, involving a non-gravitational early process of filament formation, followed by gravitational gas attraction to finalize the HFS. The gas distribution in the position-velocity (PV) and position-position spaces highlights the challenges in detecting two cloud components and confirming their complementary distribution if the colliding clouds have a large size difference. However, such CCC events can be confirmed by the PV diagrams presenting gas flow toward the vertex of the cone, which hosts gravitationally collapsing high-density objects, and by the magnetic field morphology curved toward the direction of the collision., Comment: 30 pages, 15 figures, 4 table, Accepted for publication in The Astrophysical Journal

Representation Misdirection for Unlearning (RMU), which steers model representation in the intermediate layer to a target random representation, is an effective method for large language model (LLM) unlearning. Despite its high performance, the underlying cause and explanation remain underexplored. In this paper, we first theoretically demonstrate that steering forget representations in the intermediate layer reduces token confidence, causing LLMs to generate wrong or nonsense responses. Second, we investigate how the coefficient influences the alignment of forget-sample representations with the random direction and hint at the optimal coefficient values for effective unlearning across different network layers. Third, we show that RMU unlearned models are robust against adversarial jailbreak attacks. Last, our empirical analysis shows that RMU is less effective when applied to the middle and later layers in LLMs. To resolve this drawback, we propose Adaptive RMU -- a simple yet effective alternative method that makes unlearning effective with most layers. Extensive experiments demonstrate that Adaptive RMU significantly improves the unlearning performance compared to prior art while incurring no additional computational cost., Comment: 15 pages, 5 figures, 8 tables

Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set., Comment: 16 pages, 10 figures, submitted to A&A; Updated references; Updated author list

Missing data in tabular dataset is a common issue as the performance of downstream tasks usually depends on the completeness of the training dataset. Previous missing data imputation methods focus on numeric and categorical columns, but we propose a novel end-to-end approach called Table Transformers for Imputing Textual Attributes (TTITA) based on the transformer to impute unstructured textual columns using other columns in the table. We conduct extensive experiments on two Amazon Reviews datasets, and our approach shows competitive performance outperforming baseline models such as recurrent neural networks and Llama2. The performance improvement is more significant when the target sequence has a longer length. Additionally, we incorporated multi-task learning to simultaneously impute for heterogeneous columns, boosting the performance for text imputation. We also qualitatively compare with ChatGPT for realistic applications.

We explore the problem of explaining observations starting from a classically inconsistent theory by adopting a paraconsistent framework. We consider two expansions of the well-known Belnap--Dunn paraconsistent four-valued logic $\mathsf{BD}$: $\mathsf{BD}_\circ$ introduces formulas of the form $\circ\phi$ (the information on $\phi$ is reliable), while $\mathsf{BD}_\triangle$ augments the language with $\triangle\phi$'s (there is information that $\phi$ is true). We define and motivate the notions of abduction problems and explanations in $\mathsf{BD}_\circ$ and $\mathsf{BD}_\triangle$ and show that they are not reducible to one another. We analyse the complexity of standard abductive reasoning tasks (solution recognition, solution existence, and relevance / necessity of hypotheses) in both logics. Finally, we show how to reduce abduction in $\mathsf{BD}_\circ$ and $\mathsf{BD}_\triangle$ to abduction in classical propositional logic, thereby enabling the reuse of existing abductive reasoning procedures., Comment: This is an extended version of a paper with the same title appearing at the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024)

Pre-training and transfer learning are an important building block of current computer vision systems. While pre-training is usually performed on large real-world image datasets, in this paper we ask whether this is truly necessary. To this end, we search for a minimal, purely synthetic pre-training dataset that allows us to achieve performance similar to the 1 million images of ImageNet-1k. We construct such a dataset from a single fractal with perturbations. With this, we contribute three main findings. (i) We show that pre-training is effective even with minimal synthetic images, with performance on par with large-scale pre-training datasets like ImageNet-1k for full fine-tuning. (ii) We investigate the single parameter with which we construct artificial categories for our dataset. We find that while the shape differences can be indistinguishable to humans, they are crucial for obtaining strong performances. (iii) Finally, we investigate the minimal requirements for successful pre-training. Surprisingly, we find that a substantial reduction of synthetic images from 1k to 1 can even lead to an increase in pre-training performance, a motivation to further investigate ''scaling backwards''. Finally, we extend our method from synthetic images to real images to see if a single real image can show similar pre-training effect through shape augmentation. We find that the use of grayscale images and affine transformations allows even real images to ''scale backwards''., Comment: Accepted to ECCV2024

Visual question answering (VQA) is the task of providing accurate answers to natural language questions based on visual input. Programmatic VQA (PVQA) models have been gaining attention recently. These use large language models (LLMs) to formulate executable programs that address questions requiring complex visual reasoning. However, there are challenges in enabling LLMs to comprehend the usage of image processing modules and generate relevant code. To overcome these challenges, this paper introduces PyramidCoder, a novel prompting framework for PVQA models. PyramidCoder consists of three hierarchical levels, each serving a distinct purpose: query rephrasing, code generation, and answer aggregation. Notably, PyramidCoder utilizes a single frozen LLM and pre-defined prompts at each level, eliminating the need for additional training and ensuring flexibility across various LLM architectures. Compared to the state-of-the-art PVQA model, our approach improves accuracy by at least 0.5% on the GQA dataset, 1.4% on the VQAv2 dataset, and 2.9% on the NLVR2 dataset., Comment: Accepted to the IEEE International Conference on Image Processing (IEEE ICIP) 2024

Self-supervised learning has emerged as a key approach for learning generic representations from speech data. Despite promising results in downstream tasks such as speech recognition, speaker verification, and emotion recognition, a significant number of parameters is required, which makes fine-tuning for each task memory-inefficient. To address this limitation, we introduce ELP-adapter tuning, a novel method for parameter-efficient fine-tuning using three types of adapter, namely encoder adapters (E-adapters), layer adapters (L-adapters), and a prompt adapter (P-adapter). The E-adapters are integrated into transformer-based encoder layers and help to learn fine-grained speech representations that are effective for speech recognition. The L-adapters create paths from each encoder layer to the downstream head and help to extract non-linguistic features from lower encoder layers that are effective for speaker verification and emotion recognition. The P-adapter appends pseudo features to CNN features to further improve effectiveness and efficiency. With these adapters, models can be quickly adapted to various speech processing tasks. Our evaluation across four downstream tasks using five backbone models demonstrated the effectiveness of the proposed method. With the WavLM backbone, its performance was comparable to or better than that of full fine-tuning on all tasks while requiring 90% fewer learnable parameters.

Visual question answering aims to provide responses to natural language questions given visual input. Recently, visual programmatic models (VPMs), which generate executable programs to answer questions through large language models (LLMs), have attracted research interest. However, they often require long input prompts to provide the LLM with sufficient API usage details to generate relevant code. To address this limitation, we propose AdaCoder, an adaptive prompt compression framework for VPMs. AdaCoder operates in two phases: a compression phase and an inference phase. In the compression phase, given a preprompt that describes all API definitions in the Python language with example snippets of code, a set of compressed preprompts is generated, each depending on a specific question type. In the inference phase, given an input question, AdaCoder predicts the question type and chooses the appropriate corresponding compressed preprompt to generate code to answer the question. Notably, AdaCoder employs a single frozen LLM and pre-defined prompts, negating the necessity of additional training and maintaining adaptability across different powerful black-box LLMs such as GPT and Claude. In experiments, we apply AdaCoder to ViperGPT and demonstrate that it reduces token length by 71.1%, while maintaining or even improving the performance of visual question answering.

Employing the spin-wave formalism within and beyond the harmonic-oscillator approximation, we study the dynamic structure factors of spin-$\frac{1}{2}$ nearest-neighbor quantum Heisenberg antiferromagnets on two-dimensional quasiperiodic lattices with particular emphasis on a magnetic analog to the well-known confined states of a hopping Hamiltonian for independent electrons on a two-dimensional Penrose lattice. We present comprehensive calculations on the $\mathbf{C}_{5\mathrm{v}}$ Penrose tiling in comparison with the $\mathbf{C}_{8\mathrm{v}}$ Ammann-Beenker tiling, revealing their decagonal and octagonal antiferromagnetic microstructures. Their dynamic spin structure factors both exhibit linear soft modes emergent at magnetic Bragg wavevectors and have nearly or fairly flat scattering bands, signifying magnetic excitations localized in some way, at several different energies in a self-similar manner. In particular, the lowest-lying highly flat mode is distinctive of the Penrose lattice, which is mediated by its unique antiferromagnons confined within tricoordinated sites only, unlike their itinerant electron counterparts involving pentacoordinated as well as tricoordinated sites. Bringing harmonic antiferromagnons into higher-order quantum interaction splits the lowest-lying nearly flat scattering band in two, each mediated by further confined antiferromagnons, which is fully demonstrated and throughly visualized in the perpendicular as well as real spaces. We disclose superconfined antiferromagnons on the two-dimensional Penrose lattice., Comment: 26 pages including 15 figures and 3 tables

Brillouin optical correlation-domain reflectometry (BOCDR) is unique in its ability to measure distributed strain and temperature changes along a fiber under test (FUT) from a single end, offering random access and relatively high spatial resolution, making it promising for infrastructure monitoring. BOCDR achieves spatial resolution through frequency modulation of the laser output, and this modulation frequency determines the measurement position, necessitating accurate association of modulation frequencies with positions on the FUT. However, a practical method to precisely correlate modulation frequency values with FUT positions has not yet been proposed. This study introduces a method leveraging the change in Rayleigh noise spectrum with modulation frequency to accurately associate these frequencies with positions on the FUT. The effectiveness of this method is proved through distributed strain measurement., Comment: 5 pages, 9 figures

This paper addresses the challenges in computing the column moist static energy (MSE) budget in climate models. Residuals from such computations often match other major budget terms in magnitude, obscuring their contributions. This study introduces a methodology for accurately computing the column MSE budget in climate models, demonstrated using the GISS ModelE3. Multiple factors leading to significant residuals are identified, with the failure of the continuous calculus's chain rule upon discretization being the most critical. This failure causes the potential temperature equation to diverge from the enthalpy equation in discretized models. Consequently, in models using potential temperature as a prognostic variable, the MSE budget equation is fundamentally not upheld, requiring a tailored strategy to close the budget. This study introduces the ``process increment method'' for accurately computing the column MSE flux divergence. This method calculates the difference in the sum of column internal energy, geopotential, and latent heats before and after applying the dynamics scheme. Furthermore, the calculated column flux divergence is decomposed into its advective components. These computations enable precise MSE budget analysis. The most crucial finding is that vertical interpolation into pressure coordinates can introduce errors substantial enough to reverse the sign of vertical MSE advection in the warm pool regions. In ModelE3, accurately computed values show MSE import via vertical circulations, while values in pressure coordinates indicate export. This discrepancy may prompt a reevaluation of vertical advection as an exporting mechanism and underscores the importance of precise MSE budget calculations., Comment: Submitted to JAMES

We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has been a lack of comprehensive discussion about their behaviors. Our research uncovers that the majority of these measures are unable to handle various layout differences, primarily due to their dependencies on strict element matching, that is one-by-one matching of elements within the same category. To overcome this limitation, we propose a new similarity measure based on optimal transport, which facilitates a more flexible matching of elements. This approach allows us to quantify the similarity between any two layouts even those sharing no element categories, making our measure highly applicable to a wide range of layout generation tasks. For tasks such as unconditional layout generation, where FID is commonly used, we also extend our measure to deal with collection-level similarities between groups of layouts. The empirical result suggests that our collection-level measure offers more reliable comparisons than existing ones like FID and Max.IoU., Comment: 26 pages

Neutral atoms are among the leading platforms toward realizing fault-tolerant quantum computation (FTQC). However, scaling up a single neutral-atom device beyond $\sim 10^4$ atoms to meet the demands of FTQC for practical applications remains a challenge. To overcome this challenge, we clarify the criteria and technological requirements for further scaling based on multiple neutral atom quantum processing units (QPUs) connected through photonic networking links. Our quantitative analysis shows that nanofiber optical cavities have the potential as an efficient atom-photon interface to enable fast entanglement generation between atoms in distinct neutral-atom modules, allowing multiple neutral-atom QPUs to operate cooperatively without sacrificing computational speed. Using state-of-the-art millimeter-scale nanofiber cavities with the finesse of thousands, over a hundred atoms can be coupled to the cavity mode with an optical tweezer array, with expected single-atom cooperativity exceeding 100 for telecom-band transition of ytterbium atoms. This enables efficient time-multiplexed entanglement generation with a predicted Bell pair generation rate of 100 kHz while maintaining a small footprint for channel multiplexing. These proposals and results indicate a promising pathway for building large-scale multiprocessor fault-tolerant quantum computers using neutral atoms, nanofiber optical cavities, and fiber-optic networks.

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

NanoTerasu, a new 3 GeV synchrotron light source in Japan, began user operation in April 2024. It provides high-brilliance soft to tender X-rays and covers a wide spectral range from ultraviolet to tender X-rays. Its compact storage ring with a circumference of 349 m is based on a four-bend achromat lattice to provide two straight sections in each cell for insertion devices with a natural horizontal emittance of 1.14 nm rad, which is small enough for soft X-rays users. The NanoTerasu accelerator incorporates several innovative technologies, including a full-energy injector C-band linear accelerator with a length of 110 m, an in-vacuum off-axis injection system, a four-bend achromat with B-Q combined bending magnets, and a TM020 mode accelerating cavity with built-in higher-order-mode dampers in the storage ring. This paper presents the accelerator machine commissioning over a half-year period and our model-consistent ring optics correction. The first user operation with a stored beam current of 160 mA is also reported. We summarize the storage ring parameters obtained from the commissioning. This is helpful for estimating the effective optical properties of synchrotron radiation at NanoTerasu., Comment: 30 pages, 24 figures, submitted to the journal

The Mg-Y-Zn magnesium alloy system is commonly recognized for its remarkable combination of high strength and ductility, achieved even with minimal amounts of alloying elements. This exceptional performance is attributed to its unique microstructure, which includes Long-Period Stacking Ordered (LPSO) phases or the distinctive microstructure derived from the LPSO phase, referred to as the Mille-Feuille structure (MFS). This study systematically compares the traditional ingot metallurgy method with the rapid solidification technique, coupled with diverse heat treatments and extrusion processes. Microscopic analyses reveal variations in the presence of LPSO phases, Mille-Feuille structure, and grain size, leading to divergent mechanical and corrosion properties. The rapid solidification approach stands out, ensuring superior mechanical properties alongside a reasonable corrosion rate., Comment: preprint

Large-scale cosmic filaments connect galaxies, clusters and voids. They are permeated by magnetic fields with a variety of topologies. Cosmic rays with energies up to $10^{20}\;\!{\rm eV}$ can be produced in astrophysical environments associated with star-formation and AGN activities. The fate of these cosmic rays in filaments, which cannot be directly observed on Earth, are rarely studied. We investigate the high-energy processes associated with energetic particles (cosmic rays) in filaments, adopting an ecological approach that includes galaxies, clusters/superclusters and voids as key cosmological structures in the filament ecosystem. We derive the phenomenology for modelling interfaces between filaments and these structures, and investigate how the transfer and fate of energetic cosmic ray protons are affected by the magnetism of the interfaces. We consider different magnetic field configurations in filaments and assess the implications for cosmic ray confinement and survival against hadronic pion-producing and photo-pair interactions. Our analysis shows that the fate of the particles depends on the location of their origin within a filament ecosystem, and that filaments act as `highways', channelling cosmic rays between galaxies, galaxy clusters and superclusters. Filaments can also operate as cosmic `fly paper', capturing cosmic ray protons with energies up to $10^{18}\;\!{\rm eV}$ from cosmic voids. Our analysis predicts the presence of a population of $\sim 10^{12}-10^{16}\;\!{\rm eV}$ cosmic ray protons in filaments and voids accumulated continually over cosmic time. These protons do not suffer significant energy losses through photo-pair or pion-production, nor can they be cooled efficiently. Instead, they form a cosmic ray fossil record of the power generation history of the Universe., Comment: 34 pages, 6 figures, accepted for publication in Universe (MDPI)

We present Atacama Large Millimetre/submillimetre Array (ALMA) observations of the [O$_{\rm III}$] 88 $\mu$m emission of a sample of thirteen galaxies at $z$ = 6 to 7.6 selected as [C$_{\rm II}$]-emitting companion sources of quasars. To disentangle the origins of the luminous Oxygen line in the $z$ > 6 Universe, we looked at emission-line galaxies that are selected through an excellent star-formation tracer [C$_{\rm II}$] with star-formation rates between 9 and 162 M$_{\odot}$/yr. Direct observations reveal [O$_{\rm III}$] emission in just a single galaxy (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ = 2.3), and a stacked image shows no [O$_{\rm III}$] detection, providing deep upper limits on the L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ ratios in the $z > 6$ Universe (L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ < 1.2 at 3${\sigma}$). While the fidelity of this sample is high, no obvious optical/near-infrared counterpart is seen in the JWST imaging available for four galaxies. Additionally accounting for low-redshift CO emitters, line stacking shows that our sample-wide result remains robust: The enhanced L$_{\rm [O_{\rm III}]}$/L$_{\rm [C_{\rm II}]}$ reported in the first billion years of the Universe is likely due to the selection towards bright, blue Lyman-break galaxies with high surface star-formation rates or young stellar populations. The deep upper limit on the rest-frame 90 $\mu$m continuum emission (< 141 $\mu$Jy at 3${\sigma}$), implies a low average dust temperature (T$_{\rm dust}$ < 30K) and high dust mass (M$_{\rm dust}$ ~ 10$^8$ M$_{\odot}$). As more normal galaxies are explored in the early Universe, synergy between JWST and ALMA is fundamental to further investigate the ISM properties of the a broad range of samples of high-$z$ galaxies., Comment: 20 pages; 13 figures; accepted for publication in MNRAS

We study the major merger fraction along the massive galaxy quenching channel (traced with rest-frame $\mathrm{NUV}-r$ color) at $z=$ 0.2-0.7, aiming to examine the Cosmic Web Detachment (CWD) scenario of galaxy quenching. In this scenario, the major merger fraction is expected to be high in green valley galaxies as compared with those in star-forming and quiescent galaxies of similar stellar mass. We used photometry in the E-COSMOS field to select 1491 (2334) massive ($M_\ast>10^{9.5}$ $M_\odot$) galaxies with $m_i<22$ mag ($m_z<22$ mag) at $z=$ 0.2-0.4 ($z=$ 0.4-0.7) in the rest-frame color range of $0.8

Xtend is one of the two telescopes onboard the X-ray imaging and spectroscopy mission (XRISM), which was launched on September 7th, 2023. Xtend comprises the Soft X-ray Imager (SXI), an X-ray CCD camera, and the X-ray Mirror Assembly (XMA), a thin-foil-nested conically approximated Wolter-I optics. A large field of view of $38^{\prime}\times38^{\prime}$ over the energy range from 0.4 to 13 keV is realized by the combination of the SXI and XMA with a focal length of 5.6 m. The SXI employs four P-channel, back-illuminated type CCDs with a thick depletion layer of 200 $\mu$m. The four CCD chips are arranged in a 2$\times$2 grid and cooled down to $-110$ $^{\circ}$C with a single-stage Stirling cooler. Before the launch of XRISM, we conducted a month-long spacecraft thermal vacuum test. The performance verification of the SXI was successfully carried out in a course of multiple thermal cycles of the spacecraft. About a month after the launch of XRISM, the SXI was carefully activated and the soundness of its functionality was checked by a step-by-step process. Commissioning observations followed the initial operation. We here present pre- and post-launch results verifying the Xtend performance. All the in-orbit performances are consistent with those measured on ground and satisfy the mission requirement. Extensive calibration studies are ongoing., Comment: 10 pages, 8 figures. Proceedings of SPIE Astronomical Telescopes and Instrumentation 2024

XRISM (X-Ray Imaging and Spectroscopy Mission) is an astronomical satellite with the capability of high-resolution spectroscopy with the X-ray microcalorimeter, Resolve, and wide field-of-view imaging with the CCD camera, Xtend. The Xtend consists of the mirror assembly (XMA: X-ray Mirror Assembly) and detector (SXI: Soft X-ray Imager). The components of SXI include CCDs, analog and digital electronics, and a mechanical cooler. After the successful launch on September 6th, 2023 (UT) and subsequent critical operations, the mission instruments were turned on and set up. The CCDs have been kept at the designed operating temperature of $-110^\circ$C ~after the electronics and cooling system were successfully set up. During the initial operation phase, which continued for more than a month after the critical operations, we verified the observation procedure, stability of the cooling system, all the observation options with different imaging areas and/or timing resolutions, and operations for protection against South Atlantic Anomaly. We optimized the operation procedure and observation parameters including the cooler settings, imaging areas for the specific modes with higher timing resolutions, and event selection algorithm. We summarize our policy and procedure of the initial operations for SXI. We also report on a couple of issues we faced during the initial operations and lessons learned from them., Comment: 10 pages, 8 figures, Proceedings of SPIE Astronomical Telescopes + Instrumentation 2024

Multipole expansions have been often used for extracting cosmological information from anisotropic quantities in observation. However, which basis of the expansion is best suited to quantify the anisotropies is not a trivial question in any summary statistics. In this paper, using the Fisher matrix formalism, we investigate the information content in multipole moments of the power spectra of galaxy density and intrinsic ellipticity fields in linear theory from the Alcock-Paczynski effect and redshift-space distortions (RSD). We consider two expansion schemes, the associated Legendre basis as well as the standard Legendre basis conventionally used in literature. We find that the information in the multipoles of the intrinsic alignment (IA) power spectra in the associated Legendre basis converges more slowly to that in the full 2D power spectra than in the Legendre basis. This trend is particularly significant when we consider a high number density sample. In this high number density case, we show that the errors on the Hubble parameter obtained from the multipoles of the IA cross- and auto-power spectra in the associated Legendre basis are respectively about $6 \%$ and $10 \%$ larger than the full 2D case even when we use multipoles up to $\ell = 6$. Our results demonstrate that the choice of basis is arbitrary but changes the information content encoded in multipoles depending on the sample and statistics under consideration., Comment: 19 pages, 7 figures

We report an estimation of the injected mass composition of ultra-high energy cosmic rays (UHECRs) at energies higher than 10 EeV. The composition is inferred from an energy-dependent sky distribution of UHECR events observed by the Telescope Array surface detector by comparing it to the Large Scale Structure of the local Universe. In the case of negligible extra-galactic magnetic fields the results are consistent with a relatively heavy injected composition at E ~ 10 EeV that becomes lighter up to E ~ 100 EeV, while the composition at E > 100 EeV is very heavy. The latter is true even in the presence of highest experimentally allowed extra-galactic magnetic fields, while the composition at lower energies can be light if a strong EGMF is present. The effect of the uncertainty in the galactic magnetic field on these results is subdominant., Comment: 8 pages, 3 figures, accepted for publication in PRL

We use a new method to estimate the injected mass composition of ultrahigh cosmic rays (UHECRs) at energies higher than 10 EeV. The method is based on comparison of the energy-dependent distribution of cosmic ray arrival directions as measured by the Telescope Array experiment (TA) with that calculated in a given putative model of UHECR under the assumption that sources trace the large-scale structure (LSS) of the Universe. As we report in the companion letter, the TA data show large deflections with respect to the LSS which can be explained, assuming small extra-galactic magnetic fields (EGMF), by an intermediate composition changing to a heavy one (iron) in the highest energy bin. Here we show that these results are robust to uncertainties in UHECR injection spectra, the energy scale of the experiment and galactic magnetic fields (GMF). The assumption of weak EGMF, however, strongly affects this interpretation at all but the highest energies E > 100 EeV, where the remarkable isotropy of the data implies a heavy injected composition even in the case of strong EGMF. This result also holds if UHECR sources are as rare as $2 \times 10^{-5}$ Mpc$^{-3}$, that is the conservative lower limit for the source number density., Comment: 18 pages, 11 figures, accepted for publication in PRD

We present the number densities and physical properties of the bright galaxies spectroscopically confirmed at $z\sim7-14$. Our sample is composed of 53 galaxies at $z_\mathrm{spec}\sim7-14$, including recently-confirmed galaxies at $z_\mathrm{spec}=12.34-14.32$ with JWST, as well as new confirmations at $z_\mathrm{spec}=6.583-7.643$ with $-24< M_\mathrm{UV}< -21$ mag using ALMA and Keck. Our JWST/NIRSpec observations have also revealed that very bright galaxy candidates at $z\sim10-13$ identified from ground-based telescope images before JWST are passive galaxies at $z\sim3-4$, emphasizing the necessity of strict screening and spectroscopy in the selection of the brightest galaxies at $z>10$. The UV luminosity functions derived from these spectroscopic results are consistent with a double power-law function, showing tensions with theoretical models at the bright end. To understand the origin of the overabundance of bright galaxies, we investigate their morphologies using JWST/NIRCam high-resolution images obtained in various surveys including PRIMER and COSMOS-Web. We find that $\sim70\%$ of the bright galaxies at $z\sim7$ exhibit clumpy morphologies with multiple sub-components, suggesting merger-induced starburst activity, which is consistent with SED fitting results showing bursty star formation histories. At $z\gtrsim10$, bright galaxies are classified into two types of galaxies; extended ones with weak high-ionization emission lines, and compact ones with strong high-ionization lines including NIV]$\lambda$1486, indicating that at least two different processes (e.g., merger-induced starburst and compact star formation/AGN) are shaping the physical properties of the brightest galaxies at $z\gtrsim10$ and are responsible for their overabundance., Comment: 32 pages, 22 figures, submitted to ApJ

In-Context Learning (ICL) typically utilizes classification criteria from probabilities of manually selected label tokens. However, we argue that such token-based classification criteria lead to suboptimal decision boundaries, despite delicate calibrations through translation and constrained rotation. To address this problem, we propose Hidden Calibration, which renounces token probabilities and uses the nearest centroid classifier on the LM's last hidden states. In detail, we use the nearest centroid classification on the hidden states, assigning the category of the nearest centroid previously observed from a few-shot calibration set to the test sample as the predicted label. Our experiments on 3 models and 10 classification datasets indicate that Hidden Calibration consistently outperforms current token-based calibrations by about 20%. Our further analysis demonstrates that Hidden Calibration finds better classification criteria with less inter-categories overlap, and LMs provide linearly separable intra-category clusters with the help of demonstrations, which supports Hidden Calibration and gives new insights into the conventional ICL., Comment: 21 pages, 14 figures, 8 tables

In the trend towards the globalization of football and the increasing commercialization of professional football clubs, a methodology for calculating the firm value of clubs in non-western countries has yet to be established. This study reviews the valuation methods for the club firm values in Europe and North America and how values are calculated at the time of changing ownership of Japanese clubs and develops regression models with higher explanatory power than before to estimate the more accurate firm value of Japanese football clubs. A review of the existing literature on methods for calculating the firm value of professional sports clubs in Europe and North America, as well as financial statements and registers relating to changes of ownership of Japanese clubs, was conducted. After that, multiple regression analyses were conducted using the firm value of European clubs as the explained variable. From the literature review and the Japanese case studies, it has become clear that European clubs' standard valuation methods are based on revenue and other factors, while in Japan, valuation is based solely on the par value of stocks or net assets. Multiple regression analysis revealed that the firm value of European clubs over the past three years is best explained by revenue or player market value and the number of SNS followers. Two models with high explanatory power were developed. The estimated firm value using the revenue-based formula was higher than the one based on player market value. However, in the J.League, the former was more than three times higher than the latter, while the former was only 1.2 times higher for European clubs. The discrepancy relates to differences in European and J.League clubs' revenues and asset structures. In either formula, the firm value of J.League clubs exceeded the actual transaction price when the change of ownership occurred in the past.

Digital human models of motion sickness have been actively developed, among which models based on subjective vertical conflict (SVC) theory are the most actively studied. These models facilitate the prediction of motion sickness in various scenarios such as riding in a car. Most SVC theory models predict the motion sickness incidence (MSI), which is defined as the percentage of people who would vomit with the given specific motion stimulus. However, no model has been developed to describe milder forms of discomfort or specific symptoms of motion sickness, even though predicting milder symptoms is important for applications in automobiles and daily use vehicles. Therefore, the purpose of this study was to build a computational model of symptom progression of vestibular motion sickness based on SVC theory. We focused on a model of vestibular motion sickness with six degrees-of-freedom (6DoF) head motions. The model was developed by updating the output part of the state-of-the-art SVC model, termed the 6DoF-SVC (IN1) model, from MSI to the MIsery SCale (MISC), which is a subjective rating scale for symptom progression. We conducted an experiment to measure the progression of motion sickness during a straight fore-aft motion. It was demonstrated that our proposed method, with the parameters of the output parts optimized by the experimental results, fits well with the observed MISC.

We conduct X-ray reverberation mapping and spectral analysis of the radio galaxy Centaurus A to uncover its central structure. We compare the light curve of the hard X-ray continuum from Swift Burst Alert Telescope observations with that of the Fe K$\alpha$ fluorescence line, derived from the Nuclear Spectroscopic Telescope Array (NuSTAR), Suzaku, XMM-Newton, and Swift X-ray Telescope observations. The analysis of the light curves suggests that a top-hat transfer function, commonly employed in reverberation mapping studies, is improbable. Instead, the relation between these light curves can be described by a transfer function featuring two components: one with a lag of $0.19_{- 0.02}^{+ 0.10}~\mathrm{pc}/c$, and another originating at $r > 1.7~\mathrm{pc}$ that produces an almost constant light curve. Further, we analyze the four-epoch NuSTAR and six-epoch Suzaku spectra, considering the time lag of the reflection component relative to the primary continuum. This spectral analysis supports that the reflecting material is Compton-thin, with $N_{\mathrm{H}} = 3.14_{-0.74}^{+0.44} \times 10^{23}~ \mathrm{cm}^{-2}$. These results suggest that the Fe K$\alpha$ emission may originate from Compton-thin circumnuclear material located at sub-parsec scale, likely a dust torus, and materials at a greater distance., Comment: 19 pages, 13 figures, 7 tables, accepted for publication in PASJ

Vision-and-language navigation (VLN) aims to guide autonomous agents through real-world environments by integrating visual and linguistic cues. While substantial progress has been made in understanding these interactive modalities in ground-level navigation, aerial navigation remains largely underexplored. This is primarily due to the scarcity of resources suitable for real-world, city-scale aerial navigation studies. To bridge this gap, we introduce CityNav, a new dataset for language-goal aerial navigation using a 3D point cloud representation from real-world cities. CityNav includes 32,637 natural language descriptions paired with human demonstration trajectories, collected from participants via a new web-based 3D simulator developed for this research. Each description specifies a navigation goal, leveraging the names and locations of landmarks within real-world cities. We also provide baseline models of navigation agents that incorporate an internal 2D spatial map representing landmarks referenced in the descriptions. We benchmark the latest aerial navigation baselines and our proposed model on the CityNav dataset. The results using this dataset reveal the following key findings: (i) Our aerial agent models trained on human demonstration trajectories outperform those trained on shortest path trajectories, highlighting the importance of human-driven navigation strategies; (ii) The integration of a 2D spatial map significantly enhances navigation efficiency at city scale. Our dataset and code are available at https://water-cookie.github.io/city-nav-proj/, Comment: The first two authors are equally contributed

Prior research in computational argumentation has mainly focused on scoring the quality of arguments, with less attention on explicating logical errors. In this work, we introduce four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy's implicit logic. Using our templates, we conduct an annotation study on top of 400 fallacious arguments taken from LOGIC dataset and achieve a high agreement score (Krippendorf's alpha of 0.54) and reasonable coverage (0.83). Finally, we conduct an experiment for detecting the structure of fallacies and discover that state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy). To facilitate research on fallacies, we make our dataset and guidelines publicly available.

Young massive clusters (YMCs) are dense aggregates of young stars and are often speculated as potential precursors to globular clusters. However, the formation mechanism of massive and compact gas clumps that precede YMCs remains unknown. In this paper, we study the formation of such massive clumps via fast HI gas collisions (~100 km/s) as suggested by recent observations and their subsequent evolution into YMCs by using three-dimensional magnetohydrodynamics simulations involving self-gravity and detailed thermal/chemical processes. In particular, the impact of ionization feedback from stellar radiation is included in an approximate fashion where the temperature within the HII regions is elevated to 10,000 K, while supernova feedback is not included. We examine whether the resulting massive clumps can survive this ionization feedback and evolve into YMCs. Our simulations reveal the emergence of gas clumps that do not only possess substantial mass (~10^5 M_sun) but also sufficient compactness (~5 pc). Notably, these clumps exhibit significantly higher escape velocities compared to the sound speed of the HII region, indicating effective gravitational retention of gas against feedback-induced evaporation. Consequently, these conditions foster efficient star formation within the massive gas clumps, ultimately leading to their evolution into YMCs. We also perform simulations involving lower-velocity gas collisions, approximately 15 km/s, typical shock velocities induced by galactic superbubbles., Comment: 15 pages, 13 figures, and 1 table; Accepted for publication in ApJ

We present a search for neutrinoless double-beta ($0\nu\beta\beta$) decay of $^{136}$Xe using the full KamLAND-Zen 800 dataset with 745 kg of enriched xenon, corresponding to an exposure of $2.097$ ton yr of $^{136}$Xe. This updated search benefits from a more than twofold increase in exposure, recovery of photo-sensor gain, and reduced background from muon-induced spallation of xenon. Combining with the search in the previous KamLAND-Zen phase, we obtain a lower limit for the $0\nu\beta\beta$ decay half-life of $T_{1/2}^{0\nu} > 3.8 \times 10^{26}$ yr at 90% C.L., a factor of 1.7 improvement over the previous limit. The corresponding upper limits on the effective Majorana neutrino mass are in the range 28-122 meV using phenomenological nuclear matrix element calculations., Comment: arXiv admin note: text overlap with arXiv:2203.02139

The Black Hole Explorer (BHEX) is a next-generation space very long baseline interferometry (VLBI) mission concept that will extend the ground-based millimeter/submillimeter arrays into space. The mission, closely aligned with the science priorities of the Japanese VLBI community, involves an active engagement of this community in the development of the mission, resulting in the formation of the Black Hole Explorer Japan Consortium. Here we present the current Japanese vision for the mission, ranging from scientific objectives to instrumentation. The Consortium anticipates a wide range of scientific investigations, from diverse black hole physics and astrophysics studied through the primary VLBI mode, to the molecular universe explored via a potential single-dish observation mode in the previously unexplored 50-70\,GHz band that would make BHEX the highest-sensitivity explorer ever of molecular oxygen. A potential major contribution for the onboard instrument involves supplying essential elements for its high-sensitivity dual-band receiving system, which includes a broadband 300\,GHz SIS mixer and a space-certified multi-stage 4.5K cryocooler akin to those used in the Hitomi and XRISM satellites by the Japan Aerospace Exploration Agency. Additionally, the Consortium explores enhancing and supporting BHEX operations through the use of millimeter/submillimeter facilities developed by the National Astronomical Observatory of Japan, coupled with a network of laser communication stations operated by the National Institute of Information and Communication Technology., Comment: Submitted to SPIE Conference Series, 17 pages for the main text, 33 pages for the references, 4 figures, SPIE Astronomical Telescopes + Instrumentation 2024

Automatic generation of graphic designs has recently received considerable attention. However, the state-of-the-art approaches are complex and rely on proprietary datasets, which creates reproducibility barriers. In this paper, we propose an open framework for automatic graphic design called OpenCOLE, where we build a modified version of the pioneering COLE and train our model exclusively on publicly available datasets. Based on GPT4V evaluations, our model shows promising performance comparable to the original COLE. We release the pipeline and training results to encourage open development., Comment: To appear as an extended abstract (EA) in Workshop on Graphic Design Understanding and Generation (in CVPR2024), code: https://github.com/CyberAgentAILab/OpenCOLE

Unveiling the emergence and prevalence of massive/bright galaxies during the epoch of reionization and beyond, within the first 600 million years of the Universe, stands as a pivotal pursuit in astronomy. Remarkable progress has been made by JWST in identifying an immense population of bright galaxies, which hints at exceptionally efficient galaxy assembly processes. However, the underlying physical mechanisms propelling their rapid growth remain unclear. With this in mind, millimeter and submillimeter-wave spectroscopic observations of redshifted far-infrared spectral lines, particularly the [O III] 88 micron and [C II] 158 micron lines, offers a crucial pathway to address this fundamental query. To this end, we develop a dual-polarization sideband-separating superconductor-insulator-superconductor (SIS) mixer receiver, FINER, for the Large Millimeter Telescope (LMT) situated in Mexico. Harnessing advancements from ALMA's wideband sensitivity upgrade (WSU) technology, FINER covers radio frequencies spanning 120-360 GHz, delivering an instantaneous intermediate frequency (IF) of 3-21 GHz per sideband per polarization, which is followed by a set of 10.24 GHz-wide digital spectrometers. At 40% of ALMA's light-collecting area, the LMT's similar atmospheric transmittance and FINER's 5 times wider bandwidth compared to ALMA culminate in an unparalleled spectral scanning capability in the northern hemisphere, paving the way for finer spectral-resolution detection of distant galaxies., Comment: 12 pages, 8 figures, and 3 tables. Proceedings paper presented in SPIE Astronomical Telescope and Instrumentation 2024

Monitor of All-sky X-ray Image (MAXI) detected a superflare, releasing $5\times 10^{37}$ erg in 2$-$10 keV, of the RS CVn-type star IM Peg at 10:41 UT on 2023 July 23 with its Gas Slit Camera (GSC; 2$-$30 keV). We conducted X-ray follow-up observations of the superflare with Neutron Star Interior Composition ExploreR (NICER; 0.2$-$12 keV) starting at 16:52 UT on July 23 until 06:00 UT on August 2. NICER X-ray spectra clearly showed emission lines of the Fe XXV He$\alpha$ and Fe XXVI Ly$\alpha$ for $\sim 1.5$ days since the MAXI detection. The Fe XXV He$\alpha$ line was blue-shifted with its maximum Doppler velocity reaching $-2200 \pm 600$ $\mathrm{km \: s^{-1}}$, suggesting an upward-moving plasma during the flare, such as a coronal mass ejection (CME) and/or chromospheric evaporation. This is the first case that the Fe XXV He$\alpha$ line is blue-shifted during a stellar flare and its velocity overwhelmingly exceeds the escape velocity of the star ($-230$ $\mathrm{km \: s^{-1}}$). One hour before the most pronounced blueshift detection, a signature of reheating the flare plasma was observed. We discuss the origin of the blueshift, a CME or high-velocity chromospheric evaporation., Comment: 14 pages, 9 figures. Accepted for publication in The Astrophysical Journal Letters

The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as "diacritics in the wild," to unveil patterns and latent information across six diverse genres: news articles, novels, children's books, poetry, political documents, and ChatGPT outputs. We present a new annotated dataset that maps real-world partially diacritized words to their maximal full diacritization in context. Additionally, we propose extensions to the analyze-and-disambiguate approach in Arabic NLP to leverage these diacritics, resulting in notable improvements. Our contributions encompass a thorough analysis, valuable datasets, and an extended diacritization algorithm. We release our code and datasets as open source., Comment: Accepted to ACL 2024