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We study how to construct explicit deformations of generic smooth maps from closed $n$--dimensional manifolds $M$ with $n \geq 4$ to the $2$--sphere $S^2$ and show that every smooth map $M \to S^2$ is homotopic to a $C^\infty$ stable map with at most one cusp point and with only folds of the middle absolute index. Furthermore, if $n$ is even, such a $C^\infty$ stable map can be so constructed that the restriction to the singular point set is a topological embedding. As a corollary, we show that for $n \geq 4$ even, there always exists a $C^\infty$ stable map $M \to \mathbb{R}^2$ with at most one cusp point such that the restriction to the singular point set is a topological embedding. As another corollary, we give a new proof to the existence of an open book structure on odd dimensional manifolds which extends a given one on the boundary, originally due to Quinn. Finally, using the open book structure thus constructed, we show that $k$--connected $n$--dimensional manifolds always admit a fold map into $\mathbb{R}^2$ without folds of absolute indices $i$ with $1 \leq i \leq k$, for $n \geq 7$ odd and $1 \leq k < (n-3)/2$., Comment: 16 pages, 19 figures

This paper will examine the cognitive processes involved in palpation in order to develop an appropriate remote palpation system. In a conventional remote palpation system, the tactile condition of the patient is conveyed to the doctors using a force feedback system. A clarification of the cognitive process during palpation suggests that the purpose of palpation is to formulate a clear idea about the patient's medical problems using the tactile sensation as a trigger to combine the results of other assessments, past experience and memory, and patient reactions to the doctor's touch. This is in contrast to the objective of acquiring the detailed tactile condition of the affected body part. In order to demonstrate this purpose, we will describe the two significant signal pathways for the perception of tactile sensation, both in doctors and patients. The perception of doctors progresses as the result of active touch to the affected part, thereby implying that the simultaneous stimulation of kinaesthetic and tactile sensation is necessary. Conversely, the tactile sensation experienced by patients is the result of passive touch, which evokes a more subjective and emotional response. Patients both explicitly and implicitly perceive the stimulation, and doctors use these perceptions as reactions of the pain to the doctors' touch. This paper proposes the fundamental concept of a remote palpation system, ``Palpation Reality beyond Real'', to achieve the purpose of palpation. Palpation reality implies a system in which the whole cognitive process progresses at the same level or better than palpation in the standard examination, rather than presenting the real tactile sensation.

In this study, we introduce YODAS (YouTube-Oriented Dataset for Audio and Speech), a large-scale, multilingual dataset comprising currently over 500k hours of speech data in more than 100 languages, sourced from both labeled and unlabeled YouTube speech datasets. The labeled subsets, including manual or automatic subtitles, facilitate supervised model training. Conversely, the unlabeled subsets are apt for self-supervised learning applications. YODAS is distinctive as the first publicly available dataset of its scale, and it is distributed under a Creative Commons license. We introduce the collection methodology utilized for YODAS, which contributes to the large-scale speech dataset construction. Subsequently, we provide a comprehensive analysis of speech, text contained within the dataset. Finally, we describe the speech recognition baselines over the top-15 languages., Comment: ASRU 2023

The Reeb space is a topological structure which is a generalization of the notion of the Reeb graph to multi-fields. Its effectiveness has been established in revealing topological features in data across diverse computational domains which cannot be identified using the Reeb graph or other scalar-topology-based methods. Approximations of Reeb spaces such as the Mapper and the Joint Contour Net have been developed based on quantization of the range. However, computing the topologically correct Reeb space dispensing the range-quantization is a challenging problem. In the current paper, we develop an algorithm for computing a correct net-like approximation corresponding to the Reeb space of a generic piecewise-linear (PL) bivariate field based on a multi-dimensional Reeb graph (MDRG). First, we prove that the Reeb space is homeomorphic to its MDRG. Subsequently, we introduce an algorithm for computing the MDRG of a generic PL bivariate field through the computation of its Jacobi set and Jacobi structure, a projection of the Jacobi set into the Reeb space. This marks the first algorithm for MDRG computation without requiring the quantization of bivariate fields. Following this, we compute a net-like structure embedded in the corresponding Reeb space using the MDRG and the Jacobi structure. We provide the proof of correctness and complexity analysis of our algorithm.

This paper is concerned with the stability analysis problem of feedback systems with rectified linear unit (ReLU) nonlinearities. Such feedback systems arise when we model dynamical (recurrent) neural networks (NNs) and NN-driven control systems where all the activation functions of NNs are ReLUs. In this study, we focus on the semialgebraic set representation characterizing the input-output properties of ReLUs. This allows us to employ a novel copositive multiplier in the framework of the integral quadratic constraint and, thus, to derive a linear matrix inequality (LMI) condition for the stability analysis of the feedback systems. However, the infeasibility of this LMI does not allow us to obtain any conclusion on the system's stability due to its conservativeness. This motivates us to consider its dual LMI. By investigating the structure of the dual solution, we derive a rank condition on the dual variable certificating that the system at hand is never stable. In addition, we construct a hierarchy of dual LMIs allowing for improved instability detection. We illustrate the effectiveness of the proposed approach by several numerical examples., Comment: 8 pages, 5 figures

Collecting high-quality studio recordings of audio is challenging, which limits the language coverage of text-to-speech (TTS) systems. This paper proposes a framework for scaling a multilingual TTS model to 100+ languages using found data without supervision. The proposed framework combines speech-text encoder pretraining with unsupervised training using untranscribed speech and unspoken text data sources, thereby leveraging massively multilingual joint speech and text representation learning. Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth). With just 15 minutes of transcribed, found data, we can reduce the intelligibility difference to 1% or less from the ground-truth, and achieve naturalness scores that match the ground-truth in several languages., Comment: To appear in ICASSP 2024. Demo page: https://google.github.io/tacotron/publications/extending_tts/

This article proposes to integrate two Reeb graphs with the information of their isosurfaces' inclusion relation. As computing power evolves, there arise numerical data that have small-scale physics inside larger ones -- for example, small clouds in a simulation can be contained inside an atmospheric layer, which is further contained in an enormous hurricane. Extracting such inclusions between isosurfaces is a challenge for isosurfacing: the user would have to explore the vast combinations of isosurfaces $(f_1^{-1}(l_1), f_2^{-1}(l_2))$ from scalar fields $f_i: M(n) \to \mathbb{R}$, $i = 1, 2$, where $M$ is an $n$-dimensional domain manifold and $f_i$ are physical quantities, to find inclusion of one isosurface within another. For this, we propose the \textit{Reeb complement}, a topological space that integrates two Reeb graphs with the inclusion relation. The Reeb complement has a natural partition that classifies equivalent containment of isosurfaces. This is a handy characteristic that lets the Reeb complement serve as an overview of the inclusion relationship in the data. We also propose level-of-detail control of the inclusions through simplification of the Reeb complement. We demonstrate that the relationship of two independent scalar fields can be extracted by taking the product of Reeb graphs (which we call the Reeb product) and by then subtracting the projection of the Reeb space, which opens up a new possibility for feature analysis., Comment: 17 pages, 12 figures

We consider links of complex isolated hypersurface singularities in $\mathbb{C}^{n+1}$ and study differentiable maps defined by restricting holomorphic functions to the links. We give an explicit example in which such a restriction gives a fold map into the plane $\mathbb{C} = \mathbb{R}^2$ whose singular value set consists of concentric circles., Comment: 9 pages, 3 figures

While subjective assessments have been the gold standard for evaluating speech generation, there is a growing need for objective metrics that are highly correlated with human subjective judgments due to their cost efficiency. This paper proposes reference-aware automatic evaluation methods for speech generation inspired by evaluation metrics in natural language processing. The proposed SpeechBERTScore computes the BERTScore for self-supervised dense speech features of the generated and reference speech, which can have different sequential lengths. We also propose SpeechBLEU and SpeechTokenDistance, which are computed on speech discrete tokens. The evaluations on synthesized speech show that our method correlates better with human subjective ratings than mel cepstral distortion and a recent mean opinion score prediction model. Also, they are effective in noisy speech evaluation and have cross-lingual applicability., Comment: Accepted by Interspeech 2024. An extended version with Appendix. Code: https://github.com/Takaaki-Saeki/DiscreteSpeechMetrics

This paper proposes a method for extracting a lightweight subset from a text-to-speech (TTS) corpus ensuring synthetic speech quality. In recent years, methods have been proposed for constructing large-scale TTS corpora by collecting diverse data from massive sources such as audiobooks and YouTube. Although these methods have gained significant attention for enhancing the expressive capabilities of TTS systems, they often prioritize collecting vast amounts of data without considering practical constraints like storage capacity and computation time in training, which limits the available data quantity. Consequently, the need arises to efficiently collect data within these volume constraints. To address this, we propose a method for selecting the core subset~(known as \textit{core-set}) from a TTS corpus on the basis of a \textit{diversity metric}, which measures the degree to which a subset encompasses a wide range. Experimental results demonstrate that our proposed method performs significantly better than the baseline phoneme-balanced data selection across language and corpus size.

The Reeb space of a continuous function is the space of connected components of the level sets. In this paper we characterize those smooth functions on closed manifolds whose Reeb spaces have the structure of a finite graph. We also give several explicit examples of smooth functions on closed manifolds such that they themselves or their Reeb spaces have some interesting properties., Comment: 11 pages, 6 figures, continuation of the previous paper "Reeb spaces of smooth functions on manifolds"

This paper describes an inverse analysis method using neural networks on optical spectroscopy, and its application to the quantitative optical constant evaluation. The present method consists of three subprocesses. First, measurable UV-visible spectroscopic quantities were calculated as functions of the optical constants of the solid based on the Tomlin equations [J. Phys. D 1 1667 (1968)] by carefully eliminating the unpractical combinations of optical constants. Second, the back-propagation neural network is trained using the calculated relationships between the measurable quantities and the optical constants. Finally, the trained network is utilized to determine the optical constants from measured responses. The conventional (Newton-Raphson) method tends to require the judgement of a well-experienced analyst, while machine learning shows automatically human-free performance in data conversion., Comment: 13 pages; 6 figures; accepted for publication in Jpn. J. Appl. Phys. as a regular article

We present a study of a directional search for Dark Matter boosted forward when scattered by cosmic-ray nuclei, using a module of the NEWSdm experiment. The boosted Dark Matter flux at the edge of the Earth's atmosphere is expected to be pointing to the Galactic Center, with a flux 15 to 20 times larger than in the transverse direction. The module of the NEWSdm experiment consists of a 10 kg stack of Nano Imaging Trackers, i.e.~newly developed nuclear emulsions with AgBr crystal sizes down to a few tens of nanometers. The module is installed on an equatorial telescope. The relatively long recoil tracks induced by boosted Dark Matter, combined with the nanometric granularity of the emulsion, result in an extremely low background. This makes an installation at the INFN Gran Sasso laboratory, both on the surface and underground, viable. A comparison between the two locations is made. The angular distribution of nuclear recoils induced by boosted Dark Matter in the emulsion films at the surface laboratory is expected to show an excess with a factor of 3.5 in the direction of the Galactic Center. This excess allows for a Dark Matter search with directional sensitivity. The surface laboratory configuration prevents the deterioration of the signal in the rock overburden and it emerges as the most powerful approach for a directional observation of boosted Dark Matter with high sensitivity. We show that, with this approach, a 10 kg module of the NEWSdm experiment exposed for one year at the Gran Sasso surface laboratory can probe Dark Matter masses between 1 keV/c$^2$ and 1 GeV/c$^2$ and cross-section values down to $10^{-30}$~cm$^2$ with a directional sensitive search., Comment: 15 pages, 14 figures, updated references, clarified discussion in intro section. Accepted in JCAP

On a daily basis we stir tee or coffee with a spoon and leave it to rest. We know empirically the larger the stickiness, viscosity, of the fluid, more rapidly its velocity slows down. It is surprising, therefore, that the variation, the decay rate of the velocity, has not been utilized for measuring (kinematic) viscosity of fluids. This study shows that a spectroscopy decomposing a velocity field into fluid modes (Stokes eigenmodes) allows us to measure accurately the kinematic viscosity. The method, Fluid Mode Spectroscopy (FMS), is based on the fact that each Stokes eigenmode has its inherent decay rate of eigenvalue and that the dimensionless rate of the slowest decaying mode (SDM) is constant, dependent only on the normalized shape of a fluid container, obtained analytically for some shapes including cylindrical containers. The FMS supplements major conventional measuring methods with each other, particularly useful for measuring relatively low kinematic viscosity and for a direct measurement of viscosity at zero shear rate without extrapolation. The method is validated by the experiments of water poured into an open cylindrical container, as well as by the corresponding numerical simulations., Comment: 21 pagese, 7 figures