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JASMINE is a Japanese planned space mission that aims to reveal the formation history of our Galaxy and discover habitable exoEarths. For these objectives, the JASMINE satellite performs high-precision astrometric observations of the Galactic bulge and high-precision transit monitoring of M-dwarfs in the near-infrared (1.0-1.6 microns in wavelength). For feasibility studies, we develop an image simulation software named JASMINE-imagesim, which produces realistic observation images. This software takes into account various factors such as the optical point spread function (PSF), telescope jitter caused by the satellite's attitude control error (ACE), detector flat patterns, exposure timing differences between detector pixels, and various noise factors. As an example, we report a simulation for the feasibility study of astrometric observations using JASMINE-imagesim. The simulation confirms that the required position measurement accuracy of 4 mas for a single exposure of 12.5-mag objects is achievable if the telescope pointing jitter uniformly dilutes the PSF across all stars in the field of view. On the other hand, the simulation also demonstrates that the combination of realistic pointing jitter and exposure timing differences in the detector can significantly degrade accuracy and prevent achieving the requirement. This means that certain countermeasures against this issue must be developed. This result implies that this kind of simulation is important for mission planning and advanced developments to realize more realistic simulations help us to identify critical issues and also devise effective solutions., Comment: 13 pages, 7 figures, 1 table

This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordinal Archetypal Analysis (OAA), bypasses the two-step process of transforming ordinal data into continuous scales and operates directly on the ordinal data. We extend traditional AA methods to handle the subjective nature of questionnaire-based data, acknowledging individual differences in scale perception. We introduce the Response Bias Ordinal Archetypal Analysis (RBOAA), which learns individualized scales for each subject during optimization. The effectiveness of these methods is demonstrated on synthetic data and the European Social Survey dataset, highlighting their potential to provide deeper insights into human behavior and perception. The study underscores the importance of considering response bias in cross-national research and offers a principled approach to analyzing ordinal data through Archetypal Analysis., Comment: Accepted at Machine Learning and Signal Processing 2024

We present a distant automatic speech recognition (DASR) system developed for the CHiME-8 DASR track. It consists of a diarization first pipeline. For diarization, we use end-to-end diarization with vector clustering (EEND-VC) followed by target speaker voice activity detection (TS-VAD) refinement. To deal with various numbers of speakers, we developed a new multi-channel speaker counting approach. We then apply guided source separation (GSS) with several improvements to the baseline system. Finally, we perform ASR using a combination of systems built from strong pre-trained models. Our proposed system achieves a macro tcpWER of 21.3 % on the dev set, which is a 57 % relative improvement over the baseline., Comment: 5 pages, 4 figures, CHiME8 challenge

I describe in this paper how the shallow water waves of Airy, produced by submarine earthquake, gentle on a deep open sea traveling by the cruising speed, get instantaneous $ +\infty$ or $-\infty$ traveling speed in approaching the shallow water near the coasts at the points of the surface on x=X, near the crest or the trough, where the tangent of itself and the tangent of the seabed coincide, rushing as inland tunamis ( near the crest) or as offshore tunamis (near the trough).

This paper introduces a novel approach called sentence-wise speech summarization (Sen-SSum), which generates text summaries from a spoken document in a sentence-by-sentence manner. Sen-SSum combines the real-time processing of automatic speech recognition (ASR) with the conciseness of speech summarization. To explore this approach, we present two datasets for Sen-SSum: Mega-SSum and CSJ-SSum. Using these datasets, our study evaluates two types of Transformer-based models: 1) cascade models that combine ASR and strong text summarization models, and 2) end-to-end (E2E) models that directly convert speech into a text summary. While E2E models are appealing to develop compute-efficient models, they perform worse than cascade models. Therefore, we propose knowledge distillation for E2E models using pseudo-summaries generated by the cascade models. Our experiments show that this proposed knowledge distillation effectively improves the performance of the E2E model on both datasets., Comment: Accepted to Interspeech2024. Dataset: https://huggingface.co/datasets/komats/mega-ssum

This paper describes NAIST's submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-{German, Japanese, Chinese} speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance., Comment: IWSLT 2024 system paper

Large language models (LLMs) have been successfully applied for rescoring automatic speech recognition (ASR) hypotheses. However, their ability to rescore ASR hypotheses of casual conversations has not been sufficiently explored. In this study, we reveal it by performing N-best ASR hypotheses rescoring using Llama2 on the CHiME-7 distant ASR (DASR) task. Llama2 is one of the most representative LLMs, and the CHiME-7 DASR task provides datasets of casual conversations between multiple participants. We investigate the effects of domain adaptation of the LLM and context carry-over when performing N-best rescoring. Experimental results show that, even without domain adaptation, Llama2 outperforms a standard-size domain-adapted Transformer-LM, especially when using a long context. Domain adaptation shortens the context length needed with Llama2 to achieve its best performance, i.e., it reduces the computational cost of Llama2., Comment: 5 pages

The extraction of variable definitions from scientific and technical papers is essential for understanding these documents. However, the characteristics of variable definitions, such as the length and the words that make up the definition, differ among fields, which leads to differences in the performance of existing extraction methods across fields. Although preparing training data specific to each field can improve the performance of the methods, it is costly to create high-quality training data. To address this challenge, this study proposes a new method that generates new definition sentences from template sentences and variable-definition pairs in the training data. The proposed method has been tested on papers about chemical processes, and the results show that the model trained with the definition sentences generated by the proposed method achieved a higher accuracy of 89.6%, surpassing existing models.

Aiming to simulate the radiation damage effect on a dual alpha+beta phase Ti-6Al-4V alloy utilized as high-intensity accelerator beam window material, a series of irradiation experiments were conducted with a 2.8 MeV-Fe^2+ ion beam in several dpa regions at room temperature. The nano-indentation hardness increased steeply at 1 dpa and unchanged up to 10 dpa, due to the saturation of defect clusters and tangled dislocations in the dominant alpha-phase matrix with a size of 2~3 nm and a density of about 1x10^23 m^-3. In contrast in the intergranular beta-phase, larger loops of 20~30 nm diameter were observed with much less density of about 5x10^20 m^-3. The diffraction pattern showed rectilinear diffuse streaks between the beta-phase reflections, corresponding to the omega-phase precursor, without dose dependency in its intensity. FFT/I-FFT analysis of the HREM revealed a sub-nanometer-sized lattice disorder with local fluctuations, not discrete but continuous, and homogeneously distributed within the matrix beta-phase stably against the irradiation. The significantly low dislocation density and the absence of phase transformation in the beta-phase matrix could be attributed either to the strong sink effect expected for this distinctive sub-nanometer-sized homogeneous lattice disorder or to the anomalous point defect recombination induced by the high mobility of vacancies, both of which are originated from the metastable omega-phase precursors specifically formed in the beta(BCC) phase of group-4 transition metals., Comment: 36 pages, 15 figures

We continue to study the unbounded $\mathfrak{sl}_3$-laminations [IK22], with a focus on their structures at punctures. A key ingredient is their relation to the root data of $\mathfrak{sl}_3$. After giving a classification of signed $\mathfrak{sl}_3$-webs around a puncture, we describe the tropicalization of the Goncharov--Shen's Weyl group action in detail. We also clarify the relationship with several other approaches by Shen--Sun--Weng [SSW23] and Fraser--Pylyavskyy [FP21]. Finally, we discuss a formulation of unbounded $\mathfrak{g}$-laminations for a general semisimple Lie algebra $\mathfrak{g}$ in brief., Comment: 57 pages, 26 figures. v2: added a comment on the Roger--Yang relations in p.42. arXiv admin note: text overlap with arXiv:2204.08947

The cluster algebra associated with an acyclic quiver has a special mutation loop $\tau$, called the cluster Donaldson--Thomas (DT) transformation, related to the Auslander--Reiten translation. In this paper, we characterize the finite-tame-wild trichotomy for acyclic quivers by the sign stability of $\tau$ introduced in [IK21] and its cluster stretch factor. As an application, we compute several kinds of entropies of $\tau$ and other mutation loops. In particular, we show that the algebraic and categorical entropies of $\tau$ are commonly given by the logarithm of the spectral radius of the Coxeter matrix associated with the quiver, and that any mutation loop of finite or tame acyclic quivers have zero algebraic entropy., Comment: 23 pages, 3 figures

Confidence estimation, in which we estimate the reliability of each recognized token (e.g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications. In this study, we perform confidence estimation for end-to-end (E2E) ASR hypotheses. Recent E2E ASR systems show high performance (e.g., around 5% token error rates) for various ASR tasks. In such situations, confidence estimation becomes difficult since we need to detect infrequent incorrect tokens from mostly correct token sequences. To tackle this imbalanced dataset problem, we employ a bidirectional long short-term memory (BLSTM)-based model as a strong binary-class (correct/incorrect) sequence labeler that is trained with a class balancing objective. We experimentally confirmed that, by utilizing several types of ASR decoding scores as its auxiliary features, the model steadily shows high confidence estimation performance under highly imbalanced settings. We also confirmed that the BLSTM-based model outperforms Transformer-based confidence estimation models, which greatly underestimate incorrect tokens., Comment: Accepted to ICASSP 2021

We propose a skein model for the quantum cluster algebras of surface type with coefficients. We introduce a skein algebra $\mathscr{S}_{\Sigma,\mathbb{W}}^{A}$ of a walled surface $(\Sigma,\mathbb{W})$, and prove that it has a quantum cluster structure. The walled surfaces naturally generalize the marked surfaces with multi-laminations, which have been used to describe the quantum cluster algebras of geometric type for marked surfaces by Fomin--Thurston [FT18]. Moreover, we give skein theoretic interpretation for some of quasi-homomorphisms [Fra16] between these quantum cluster algebras., Comment: 43 pages; v2: Major revision, introduction refreshed, arguments concerning the minimal position moved to Section 5, the Fomin--Thurston's description of the normalized cluster algebras moved to Section 4.2, Examples 2.13 and 2.14 added

Simultaneous translation is a task in which the translation begins before the end of an input speech segment. Its evaluation should be conducted based on latency in addition to quality, and for users, the smallest possible amount of latency is preferable. Most existing metrics measure latency based on the start timings of partial translations and ignore their duration. This means such metrics do not penalize the latency caused by long translation output, which delays the comprehension of users and subsequent translations. In this work, we propose a novel latency evaluation metric for simultaneous translation called \emph{Average Token Delay} (ATD) that focuses on the duration of partial translations. We demonstrate its effectiveness through analyses simulating user-side latency based on Ear-Voice Span (EVS). In our experiment, ATD had the highest correlation with EVS among baseline latency metrics under most conditions., Comment: Extended version of the paper (doi: 10.21437/Interspeech.2023-933) which appeared in INTERSPEECH 2023

The inner hundred parsecs of the Milky Way hosts the nearest supermassive black hole, largest reservoir of dense gas, greatest stellar density, hundreds of massive main and post main sequence stars, and the highest volume density of supernovae in the Galaxy. As the nearest environment in which it is possible to simultaneously observe many of the extreme processes shaping the Universe, it is one of the most well-studied regions in astrophysics. Due to its proximity, we can study the center of our Galaxy on scales down to a few hundred AU, a hundred times better than in similar Local Group galaxies and thousands of times better than in the nearest active galaxies. The Galactic Center (GC) is therefore of outstanding astrophysical interest. However, in spite of intense observational work over the past decades, there are still fundamental things unknown about the GC. JWST has the unique capability to provide us with the necessary, game-changing data. In this White Paper, we advocate for a JWST NIRCam survey that aims at solving central questions, that we have identified as a community: i) the 3D structure and kinematics of gas and stars; ii) ancient star formation and its relation with the overall history of the Milky Way, as well as recent star formation and its implications for the overall energetics of our galaxy's nucleus; and iii) the (non-)universality of star formation and the stellar initial mass function. We advocate for a large-area, multi-epoch, multi-wavelength NIRCam survey of the inner 100\,pc of the Galaxy in the form of a Treasury GO JWST Large Program that is open to the community. We describe how this survey will derive the physical and kinematic properties of ~10,000,000 stars, how this will solve the key unknowns and provide a valuable resource for the community with long-lasting legacy value., Comment: This White Paper will be updated when required (e.g. new authors joining, editing of content). Most recent update: 24 Oct 2023

Markerless methods for animal posture tracking have been rapidly developing recently, but frameworks and benchmarks for tracking large animal groups in 3D are still lacking. To overcome this gap in the literature, we present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. We train a pose estimator to infer 2D keypoints and bounding boxes of multiple pigeons, then triangulate the keypoints to 3D. For identity matching of individuals in all views, we first dynamically match 2D detections to global identities in the first frame, then use a 2D tracker to maintain IDs across views in subsequent frames. We achieve comparable accuracy to a state of the art 3D pose estimator in terms of median error and Percentage of Correct Keypoints. Additionally, we benchmark the inference speed of 3D-MuPPET, with up to 9.45 fps in 2D and 1.89 fps in 3D, and perform quantitative tracking evaluation, which yields encouraging results. Finally, we showcase two novel applications for 3D-MuPPET. First, we train a model with data of single pigeons and achieve comparable results in 2D and 3D posture estimation for up to 5 pigeons. Second, we show that 3D-MuPPET also works in outdoors without additional annotations from natural environments. Both use cases simplify the domain shift to new species and environments, largely reducing annotation effort needed for 3D posture tracking. To the best of our knowledge we are the first to present a framework for 2D/3D animal posture and trajectory tracking that works in both indoor and outdoor environments for up to 10 individuals. We hope that the framework can open up new opportunities in studying animal collective behaviour and encourages further developments in 3D multi-animal posture tracking.

Japan Astrometry Satellite Mission for INfrared Exploration (JASMINE) is a planned M-class science space mission by the Institute of Space and Astronautical Science, the Japan Aerospace Exploration Agency. JASMINE has two main science goals. One is the Galactic archaeology with Galactic Center Survey, which aims to reveal the Milky Way's central core structure and formation history from Gaia-level (~25 $\mu$as) astrometry in the Near-Infrared (NIR) Hw-band (1.0-1.6 $\mu$m). The other is the Exoplanet Survey, which aims to discover transiting Earth-like exoplanets in the habitable zone from NIR time-series photometry of M dwarfs when the Galactic center is not accessible. We introduce the mission, review many science objectives, and present the instrument concept. JASMINE will be the first dedicated NIR astrometry space mission and provide precise astrometric information of the stars in the Galactic center, taking advantage of the significantly lower extinction in the NIR. The precise astrometry is obtained by taking many short-exposure images. Hence, the JASMINE Galactic center survey data will be valuable for studies of exoplanet transits, asteroseismology, variable stars and microlensing studies, including discovery of (intermediate mass) black holes. We highlight a swath of such potential science, and also describe synergies with other missions., Comment: 50 pages, 22 figures, accepted for publication in PASJ

Simultaneous speech translation (SimulST) translates partial speech inputs incrementally. Although the monotonic correspondence between input and output is preferable for smaller latency, it is not the case for distant language pairs such as English and Japanese. A prospective approach to this problem is to mimic simultaneous interpretation (SI) using SI data to train a SimulST model. However, the size of such SI data is limited, so the SI data should be used together with ordinary bilingual data whose translations are given in offline. In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline. The proposed method trains a single model using the mixed data with style tags that tell the model to generate SI- or offline-style outputs. Experiment results show improvements of BLEURT in different latency ranges, and our analyses revealed the proposed model generates SI-style outputs more than the baseline., Comment: Accepted to IWSLT2023 scientific paper

End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model. This approach is promising because it, in contrast to the conventional cascade approach, can utilize full acoustical information and mitigate to the propagation of transcription errors. However, due to the high cost of collecting speech-summary pairs, an E2E SSum model tends to suffer from training data scarcity and output unnatural sentences. To overcome this drawback, we propose for the first time to integrate a pre-trained language model (LM), which is highly capable of generating natural sentences, into the E2E SSum decoder via transfer learning. In addition, to reduce the gap between the independently pre-trained encoder and decoder, we also propose to transfer the baseline E2E SSum encoder instead of the commonly used automatic speech recognition encoder. Experimental results show that the proposed model outperforms baseline and data augmented models., Comment: Accepted by Interspeech 2023