Of course, the count function should be fixed.

Sequential data in our everyday life is often hierarchically structured.

In many real-world scenarios, such as meetings, multiple speakers are present with an unknown number of participants, and their utterances often overlap. We address these multi-speaker challenges by a novel attention-based encoder-decoder method augmented with special speaker class tokens obtained by speaker clustering. During inference, we select multiple recognition hypotheses conditioned on predicted speaker cluster tokens, and these hypotheses are merged by agglomerative hierarchical clustering (AHC) based on the normalized edit distance. The clustered hypotheses result in the multi-speaker transcriptions with the appropriate number of speakers determined by AHC. Our experiments on the LibriMix dataset demonstrate that our proposed method was particularly effective in complex 3-mix environments, achieving a 55% relative error reduction on clean data and a 36% relative error reduction on noisy data compared with conventional serialized output training.

Piezoelectric ultrasonic motors perform the advantages of compact design, faster reaction time, and simpler setup compared to other motion units such as pneumatic and hydraulic motors, especially its non-ferromagnetic property makes it a perfect match in MRI-compatible robotics systems compared to traditional DC motors. Hollow shaft motors address the advantages of being lightweight and comparable to solid shafts of the same diameter, low rotational inertia, high tolerance to rotational imbalance due to low weight, and tolerance to high temperature due to low specific mass. This article presents a prototype of a hollow cylindrical ultrasonic motor (HCM) to perform direct drive, eliminate mechanical non-linearity, and reduce the size and complexity of the actuator or end effector assembly. Two equivalent HCMs are presented in this work, and under 50g prepressure on the rotor, it performed 383.3333rpm rotation speed and 57.3504mNm torque output when applying 282$V_{pp}$ driving voltage.

Characterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that aim to identify "hard" samples. However, there is a lack of consensus regarding the definition and evaluation of "hardness". Unfortunately, current HCMs have only been evaluated on specific types of hardness and often only qualitatively or with respect to downstream performance, overlooking the fundamental quantitative identification task. We address this gap by presenting a fine-grained taxonomy of hardness types. Additionally, we propose the Hardness Characterization Analysis Toolkit (H-CAT), which supports comprehensive and quantitative benchmarking of HCMs across the hardness taxonomy and can easily be extended to new HCMs, hardness types, and datasets. We use H-CAT to evaluate 13 different HCMs across 8 hardness types. This comprehensive evaluation encompassing over 14K setups uncovers strengths and weaknesses of different HCMs, leading to practical tips to guide HCM selection and future development. Our findings highlight the need for more comprehensive HCM evaluation, while we hope our hardness taxonomy and toolkit will advance the principled evaluation and uptake of data-centric AI methods.

Scientists often want to learn about cause and effect from hierarchical data, collected from subunits nested inside units. Consider students in schools, cells in patients, or cities in states. In such settings, unit-level variables (e.g. each school's budget) may affect subunit-level variables (e.g. the test scores of each student in each school) and vice versa. To address causal questions with hierarchical data, we propose hierarchical causal models, which extend structural causal models and causal graphical models by adding inner plates. We develop a general graphical identification technique for hierarchical causal models that extends do-calculus. We find many situations in which hierarchical data can enable causal identification even when it would be impossible with non-hierarchical data, that is, if we had only unit-level summaries of subunit-level variables (e.g. the school's average test score, rather than each student's score). We develop estimation techniques for hierarchical causal models, using methods including hierarchical Bayesian models. We illustrate our results in simulation and via a reanalysis of the classic "eight schools" study.

The Hair clip mechanism (HCM) is an in-plane prestressed bistable mechanism proposed in our previous research [1]~[5] to enhance the functionality of soft robotics. HCMs have several advantages, such as high rigidity, high mobility, good repeatability, and design and fabrication simplicity, compared to existing soft and compliant robotics. Using our experience with fish robots, this work delves into designing a novel HCM robotic propulsion system made from PETG plastic, carbon fiber-reinforced plastic (CFRP), and steel. Detailed derivation and verification of the HCM theory are given, and the influence of key parameters like dimensions, material types, and servo motor specifications are summarized. The designing algorithm offers insight into HCM robotics. It enables us to search for suitable components, operate robots at a desired frequency, and achieve high-frequency and high-speed undulatory swimming for fish robots.

When juxtaposed with conventional soft and compliant robotic systems, HCMs exhibit pronounced rigidity, augmented mobility, reproducible repeatability, and an effective design and fabrication paradigm. In this research, we investigate the feasibility of utilizing carbon fiber-reinforced plastic (CFRP) as the foundational material for an HCM-based fish robot, herein referred to as "CarbonFish." Our objective centers on realizing high-frequency undulatory motion, thereby laying the groundwork for accelerated aquatic locomotion in subsequent models. We proffer an exhaustive design and fabrication schema underpinned by mathematical principles. Preliminary evaluations of our single-actuated CarbonFish have evidenced an undulation frequency approaching 10 Hz, suggesting its potential to outperform other biologically inspired aquatic entities as well as real fish. Keywords: soft fish robot, compliant mechanism, bistability, undulation swimming Main Text Introduction Soft and compliant robotics represents an advancing domain in robotics research, emphasizing the design and development of robots utilizing soft and deformable materials.