arXiv subjects

Yusuke Sakai

Publications and source records attributed to Yusuke Sakai.

70 records · Page 4Linked to original sources

arXiv2024

Kinematics of Supernova Remnants Using Multiepoch Maximum Likelihood Estimation: Chandra Observation of Cassiopeia A as an Example

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chandra X-ray Observatory in 2000, 2009, and 2019 were utilized. The proposed multiepoch MLE was qualitatively and quantitatively demonstrated to provide accurate estimates of various motions, including shock waves and faint features, across all regions. To investigate asymmetric structures, such as singular components that deviate from the direction of expansion, the MLE method was extended to combine multiple computational domains and classify kinematic properties using the $k$-means algorithm. This approach allowed for the mapping of different physical states onto the image, and one classified component was suggested to interact with circumstellar material by comparison with infrared observations from the James Webb Space Telescope. Thus, this technique will help quantify the dynamics of SNRs and discover their unique evolution.

astro-ph.HE↗

arXiv2024

Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templates

The natural language understanding (NLU) performance of large language models (LLMs) has been evaluated across various tasks and datasets. The existing evaluation methods, however, do not take into account the variance in scores due to differences in prompts, which leads to unfair evaluation and comparison of NLU performance. Moreover, evaluation designed for specific prompts is inappropriate for instruction tuning, which aims to perform well with any prompt. It is therefore necessary to find a way to measure NLU performance in a fair manner, considering score variance between different instruction templates. In this study, we provide English and Japanese cross-lingual datasets for evaluating the NLU performance of LLMs, which include multiple instruction templates for fair evaluation of each task, along with regular expressions to constrain the output format. Furthermore, we propose the Sharpe score as an evaluation metric that takes into account the variance in scores between templates. Comprehensive analysis of English and Japanese LLMs reveals that the high variance among templates has a significant impact on the fair evaluation of LLMs.

cs.CL↗

arXiv2024

Centroid-Based Efficient Minimum Bayes Risk Decoding

Minimum Bayes risk (MBR) decoding achieved state-of-the-art translation performance by using COMET, a neural metric that has a high correlation with human evaluation. However, MBR decoding requires quadratic time since it computes the expected score between a translation hypothesis and all reference translations. We propose centroid-based MBR (CBMBR) decoding to improve the speed of MBR decoding. Our method clusters the reference translations in the feature space, and then calculates the score using the centroids of each cluster. The experimental results show that our CBMBR not only improved the decoding speed of the expected score calculation 5.7 times, but also outperformed vanilla MBR decoding in translation quality by up to 0.5 COMET in the WMT'22 En$\leftrightarrow$Ja, En$\leftrightarrow$De, En$\leftrightarrow$Zh, and WMT'23 En$\leftrightarrow$Ja translation tasks.

cs.CL↗

arXiv2024

Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?

Knowledge graphs (KGs) consist of links that describe relationships between entities. Due to the difficulty of manually enumerating all relationships between entities, automatically completing them is essential for KGs. Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG. Traditional embedding-based KGC methods, such as RESCAL, TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using only the knowledge from training data. In contrast, the recent Pre-trained Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training. Therefore, PLM-based KGC can estimate missing links between entities by reusing memorized knowledge from pre-training without inference. This approach is problematic because building KGC models aims to infer unseen links between entities. However, conventional evaluations in KGC do not consider inference and memorization abilities separately. Thus, a PLM-based KGC method, which achieves high performance in current KGC evaluations, may be ineffective in practical applications. To address this issue, we analyze whether PLM-based KGC methods make inferences or merely access memorized knowledge. For this purpose, we propose a method for constructing synthetic datasets specified in this analysis and conclude that PLMs acquire the inference abilities required for KGC through pre-training, even though the performance improvements mostly come from textual information of entities and relations.

cs.CL↗

arXiv2024

mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans

It is very challenging to curate a dataset for language-specific knowledge and common sense in order to evaluate natural language understanding capabilities of language models. Due to the limitation in the availability of annotators, most current multilingual datasets are created through translation, which cannot evaluate such language-specific aspects. Therefore, we propose Multilingual CommonsenseQA (mCSQA) based on the construction process of CSQA but leveraging language models for a more efficient construction, e.g., by asking LM to generate questions/answers, refine answers and verify QAs followed by reduced human efforts for verification. Constructed dataset is a benchmark for cross-lingual language-transfer capabilities of multilingual LMs, and experimental results showed high language-transfer capabilities for questions that LMs could easily solve, but lower transfer capabilities for questions requiring deep knowledge or commonsense. This highlights the necessity of language-specific datasets for evaluation and training. Finally, our method demonstrated that multilingual LMs could create QA including language-specific knowledge, significantly reducing the dataset creation cost compared to manual creation. The datasets are available at https://huggingface.co/datasets/yusuke1997/mCSQA.

cs.CL↗

arXiv2024

Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair

In Simultaneous Machine Translation (SiMT) systems, training with a simultaneous interpretation (SI) corpus is an effective method for achieving high-quality yet low-latency systems. However, it is very challenging to curate such a corpus due to limitations in the abilities of annotators, and hence, existing SI corpora are limited. Therefore, we propose a method to convert existing speech translation corpora into interpretation-style data, maintaining the original word order and preserving the entire source content using Large Language Models (LLM-SI-Corpus). We demonstrate that fine-tuning SiMT models in text-to-text and speech-to-text settings with the LLM-SI-Corpus reduces latencies while maintaining the same level of quality as the models trained with offline datasets. The LLM-SI-Corpus is available at \url{https://github.com/yusuke1997/LLM-SI-Corpus}.

cs.CL↗

arXiv2024

Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences

Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This study presents a comparative analysis of two CNN architectures: one-dimensional (1D) and two-dimensional (2D) along with an ensemble model combining both. We trained these models to detect gravitational wave signals from binary black hole (BBH) mergers, neutron star-black hole (NSBH) mergers, and binary neutron star (BNS) mergers within real detector noise. Our investigation entailed a comprehensive evaluation of the detection performance of each model type across different signal classes. To understand the models' decision-making processes, we employed feature map visualization and attribution analysis. The findings revealed that while the 1D model showed superior performance in detecting BBH signals, the 2D model excelled in identifying NSBH and BNS signals. Notably, the ensemble model outperformed both individual models across all signal types, demonstrating enhanced detection capabilities. Additionally, input feature visualization indicated distinct areas of focus in the data for the 1D and 2D models, emphasizing the effectiveness of their combination.

gr-qc↗

arXiv2023

Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae

In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived from three-dimensional numerical simulations of waveforms, which are injected onto real noise data from the third observing run of both Advanced LIGO and Advanced Virgo. To gain insights into the decision-making process of the model, we apply class activation mapping techniques to visualize the regions in the input image that are significant for the model's prediction. The class activation maps reveal that the model's predictions predominantly rely on specific features within the input spectrograms, namely, the $g$-mode and low-frequency modes. The visualization of convolutional neural network models provides interpretability to enhance their reliability and offers guidance for improving detection efficiency.

astro-ph.IM↗

arXiv2023

Formation of first star clusters under the supersonic gas flow -- I. Morphology of the massive metal-free gas cloud

We performed $42$ simulations of the first star formation with initial supersonic gas flows relative to the dark matter at the cosmic recombination era. Increasing the initial streaming velocities led to delayed halo formation and increased halo mass, enhancing the mass of the gravitationally shrinking gas cloud. For more massive gas clouds, the rate of temperature drop during contraction, in other words, the structure asymmetry, becomes more significant. When the maximum and minimum gas temperature ratios before and after contraction exceed about ten, the asymmetric structure of the gas cloud prevails, inducing fragmentation into multiple dense gas clouds. We continued our simulations until $10^5$ years after the first dense core formation to examine the final fate of the massive star-forming gas cloud. Among the $42$ models studied, we find the simultaneous formation of up to four dense gas clouds, with a total mass of about $2254\,M_\odot$. While the gas mass in the host halo increases with increasing the initial streaming velocity, the mass of the dense cores does not change significantly. The star formation efficiency decreases by more than one order of magnitude from $ε_{\rm III} \sim 10^{-2}$ to $10^{-4}$ when the initial streaming velocity, normalised by the root mean square value, increases from 0 to 3.

astro-ph.GA↗

arXiv2023

Universal Automatic Phonetic Transcription into the International Phonetic Alphabet

This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA). Transcription of spoken languages into IPA is an essential yet time-consuming process in language documentation, and even partially automating this process has the potential to drastically speed up the documentation of endangered languages. Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use training data from seven languages from CommonVoice 11.0, transcribed into IPA semi-automatically. Although this training dataset is much smaller than Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or better results. Furthermore, we show that the quality of our universal speech-to-IPA models is close to that of human annotators.

cs.CL↗

arXiv2023

Richardson-Lucy deconvolution with a spatially Variant point-spread function of Chandra: Supernova Remnant Cassiopeia A as an Example

Richardson-Lucy (RL) deconvolution is one of the classical methods widely used in X-ray astronomy and other areas. Amid recent progress in image processing, RL deconvolution still leaves much room for improvement under a realistic situations. One direction is to include the positional dependence of a point-spread function (PSF), so-called RL deconvolution with a spatially variant PSF (RL$_{\rm{sv}}$). Another is the method of estimating a reliable number of iterations and their associated uncertainties. We developed a practical method that incorporates the RL$_{\rm{sv}}$ algorithm and the estimation of uncertainties. As a typical example of bright and high-resolution images, the Chandra X-ray image of the supernova remnant Cassiopeia~A was used in this paper. RL$_{\rm{sv}}$ deconvolution enables us to uncover the smeared features in the forward/backward shocks and jet-like structures. We constructed a method to predict the appropriate number of iterations by using statistical fluctuation of the observed images. Furthermore, the uncertainties were estimated by error propagation from the last iteration, which was phenomenologically tested with the observed data. Thus, our method is a practically efficient framework to evaluate the time evolution of the remnants and their fine structures embedded in high-resolution X-ray images.

astro-ph.HE↗

arXiv2023

FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks

Recently, many convolutional neural networks (CNNs) for classification by time domain data of multisignals have been developed. Although some signals are important for correct classification, others are not. When data that do not include important signals for classification are taken as the CNN input layer, the calculation, memory, and data collection costs increase. Therefore, identifying and eliminating nonimportant signals from the input layer are important. In this study, we proposed features gradient-based signals selection algorithm (FG-SSA), which can be used for finding and removing nonimportant signals for classification by utilizing features gradient obtained by the calculation process of grad-CAM. When we define N as the number of signals, the computational complexity of the proposed algorithm is linear time O(N), that is, it has a low calculation cost. We verified the effectiveness of the algorithm using the OPPORTUNITY Activity Recognition dataset, which is an open dataset comprising acceleration signals of human activities. In addition, we checked the average 6.55 signals from a total of 15 acceleration signals (five triaxial sensors) that were removed by FG-SSA while maintaining high generalization scores of classification. Therefore, the proposed algorithm FG-SSA has an effect on finding and removing signals that are not important for CNN-based classification.

eess.SP↗

arXiv2022

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

gr-qc↗

arXiv2022

Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time--frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time--frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.

gr-qc↗

arXiv2015

ALMA imaging study of methyl formate (HCOOCH$_{3}$) in the torsionally excited states towards Orion KL

We recently reported the first identification of rotational transitions of methyl formate (HCOOCH$_{3}$) in the second torsionally excited state toward Orion Kleinmann-Low (KL) observed with the Nobeyama 45 m telescope. In combination with the identified transitions of methyl formate in the ground state and the first torsional excited state, it was found that there is a difference in rotational temperature and vibrational temperature, where the latter is higher. In this study, high spatial resolution analysis by using Atacama Large Millimeter/Submillimeter Array (ALMA) science verification data was carried out to verify and understand this difference. Toward the Compact Ridge, two different velocity components at 7.3 and 9.1 km s$^{-1}$ were confirmed, while a single component at 7.3 km s$^{-1}$ was identified towards the Hot Core. The intensity maps in the ground, first, and second torsional excited states have quite similar distributions. Using extensive ALMA data, we determined the rotational and vibrational temperatures for the Compact Ridge and Hot Core by the conventional rotation diagram method. The rotational temperature and vibrational temperatures agree for the Hot Core and for one component of the Compact Ridge. At the 7.3 km s$^{-1}$ velocity component for the Compact Ridge, the rotational temperature was found to be higher than the vibrational temperature. This is different from what we obtained from the results by using the single-dish observation. The difference might be explained by the beam dilution effect of the single-dish data and/or the smaller number of observed transitions within the limited range of energy levels ($\leq$30 K) of $E_u$ in the previous study.

astro-ph.SR↗

arXiv2012

Reflective terahertz time-domain spectroscopy measurement on the stripe-ordered superconductor La$_{1.84-y}$Nd$_y$Sr$_{0.16}$CuO$_4$

We measured reflectivity on static stripe-ordered La$_{1.84-y}$Nd$_y$Sr$_{0.16}$CuO$_4$ (LNSCO) $y$\,=\,0.1, 0.2, 0.3, and 0.4 by means of reflective terahertz time-domain spectroscopy (THz-TDS) with electric field polarized along c-axis, in which one can obtain lower frequency information than the conventional Fourier transform type spectrometer. Recently, two-dimensional superconducting (2DSC) property \cite{tajima,schafgans,li}, intra-layer perfect conductivity not accompanied with inter-layer superconducting behavior, was reported on LNSCO ($x$\,=0.15\,,\,$y$\,$\geq$\,0.2). We observed the existence of Josephson plasma edge, which suggests that 2DSC is not realized in these compounds.

cond-mat.supr-con↗