Program to Enhance Leadership Literacy of Rural Kindergarten Principals in Guangxi

This study examines the leadership literacy of rural kindergarten principals in Guangxi, China, focusing on their administrative, pedagogical, and strategic planning competencies. Despite national initiatives to modernize early childhood education, rural school leaders face challenges in school administration, instructional leadership, and resource management. Using a three-phase research methodology, this study identifies key leadership literacy components, assesses existing and desired literacy levels, and develops a structured intervention program. A survey was conducted with 384 rural kindergarten principals, selected using Yamane’s (1973) sampling formula, to evaluate their leadership literacy levels. Data analysis was performed using SPSS, with a Modified Priority Needs Index (PNI modified) used to identify gaps. The study found significant discrepancies between existing and desired literacy levels, particularly in strategic planning and fostering student-centered learning. Expert validation confirmed the relevance and feasibility of the proposed leadership literacy program, incorporating the 70:20:10 learning model, which balances experiential learning, peer coaching, and formal training. The findings suggest that enhancing leadership literacy is essential for improving early childhood education administration in rural areas. This research contributes to educational policy by proposing a scalable leadership development framework. Future research should explore long-term program impacts and the potential of digital learning platforms to bridge rural-urban disparities in leadership training

Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation

This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a mesoscale model in increasingly realistic contexts from under a perfect model assumption and in the presence of significant model error with synthetic observations to real-world data assimilation in comparison to the three-dimensional variational (3DVar) method via both case study and month-long experiments. The EnKF is shown to be promising for future application in operational data assimilation practice. The EnKF with synthetic observations, which is implemented in the mesoscale model MM5, is very effective in keeping the analysis close to the truth under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the presence of significant model errors from physical parameterization schemes, the EnKF performs reasonably well though sometimes it can be significantly degraded compared to its performance under the perfect model assumption. Using a combination of different physical parameterization schemes in the ensemble (the so-called âÂÂmulti-schemeâ ensemble) can significantly improve filter performance due to the resulting better background error covariance and a smaller ensemble bias. The EnKF performs differently for different flow regimes possibly due to scale- and flow-dependent error growth dynamics and predictability. Real-data (including soundings, profilers and surface observations) are assimilated by directly comparing the EnKF and 3DVar and both are implemented in the Weather Research and Forecasting model. A case study and month-long experiments show that the EnKF is efficient in tracking observations in terms of both prior forecast and posterior analysis. The EnKF performs consistently better than 3DVar for the time period of interest due to the benefit of the EnKF from both using ensemble mean for state estimation and using a flow-dependent background error covariance. Proper covariance inflation and using a multi-scheme ensemble can significantly improve the EnKF performance. Using a multi-scheme ensemble results in larger improvement in thermodynamic variables than in other variables. The 3DVar system can benefit substantially from using a short-term ensemble mean for state estimate. Noticeable improvement is also achieved in 3DVar by including some flow dependence in its background error covariance

SememeASR: Boosting Performance of End-to-End Speech Recognition against Domain and Long-Tailed Data Shift with Sememe Semantic Knowledge

Recently, excellent progress has been made in speech recognition. However, pure data-driven approaches have struggled to solve the problem in domain-mismatch and long-tailed data. Considering that knowledge-driven approaches can help data-driven approaches alleviate their flaws, we introduce sememe-based semantic knowledge information to speech recognition (SememeASR). Sememe, according to the linguistic definition, is the minimum semantic unit in a language and is able to represent the implicit semantic information behind each word very well. Our experiments show that the introduction of sememe information can improve the effectiveness of speech recognition. In addition, our further experiments show that sememe knowledge can improve the model's recognition of long-tailed data and enhance the model's domain generalization ability.Comment: Accepted by INTERSPEECH 202

Cramer-Rao Bounds for Near-Field Sensing: A Generic Modular Architecture

A generic modular array architecture is proposed, featuring uniform/non-uniform subarray layouts that allows for flexible deployment. The bistatic near-field sensing system is considered, where the target is located in the near-field of the whole modular array and the far-field of each subarray. Then, the closed-form expressions of Cramer-Rao bounds (CRBs) for range and angle estimations are derived based on the hybrid spherical and planar wave model (HSPM). Simulation results validate the accuracy of the derived closed-form CRBs and demonstrate that: i) The HSPM with varying angles of arrival (AoAs) between subarrays can reduce the CRB for range estimation compared to the traditional HSPM with shared AoA; and ii) The proposed generic modular architecture with subarrays positioned closer to the edges can significantly reduce the CRBs compared to the traditional modular architecture with uniform subarray layout, when the array aperture is fixed

Enhancing the vocal range of single-speaker singing voice synthesis with melody-unsupervised pre-training

The single-speaker singing voice synthesis (SVS) usually underperforms at pitch values that are out of the singer's vocal range or associated with limited training samples. Based on our previous work, this work proposes a melody-unsupervised multi-speaker pre-training method conducted on a multi-singer dataset to enhance the vocal range of the single-speaker, while not degrading the timbre similarity. This pre-training method can be deployed to a large-scale multi-singer dataset, which only contains audio-and-lyrics pairs without phonemic timing information and pitch annotation. Specifically, in the pre-training step, we design a phoneme predictor to produce the frame-level phoneme probability vectors as the phonemic timing information and a speaker encoder to model the timbre variations of different singers, and directly estimate the frame-level f0 values from the audio to provide the pitch information. These pre-trained model parameters are delivered into the fine-tuning step as prior knowledge to enhance the single speaker's vocal range. Moreover, this work also contributes to improving the sound quality and rhythm naturalness of the synthesized singing voices. It is the first to introduce a differentiable duration regulator to improve the rhythm naturalness of the synthesized voice, and a bi-directional flow model to improve the sound quality. Experimental results verify that the proposed SVS system outperforms the baseline on both sound quality and naturalness

Direct observation of phase transition dynamics in suspensions of soft colloidal hydrogel particles

Due to the tunability of their softness and volume as a function of temperature, poly(N-isopropylacrylamide) (pNIPAm) hydrogel particles have emerged as a model system for soft colloidal spheres. By introducing AAc as comonomer, one can also tune the particle volume via pH. We report on the phase behavior of these stimuli-responsive colloids as measured with a microdialysis cell. This device, which integrates microfluidics with Particle Tracking Video-microscopy allows for simple and quick investigation of the phase behavior of suspensions the soft colloidal hydrogel as a function of pH as well as its packing density. In particular, we demonstrate the existence of an unusually broad liquid/crystal coexistence region as a function of effective particle volume fraction. Additionally, we reveal that nonequilibrium jammed states can be created in the coexistence region upon sudden large changes of pH. The phase diagram is indicative of complex interparticle interactions with weakly attractive components.https://digitalcommons.chapman.edu/sees_books/1002/thumbnail.jp

Neural Concatenative Singing Voice Conversion: Rethinking Concatenation-Based Approach for One-Shot Singing Voice Conversion

Any-to-any singing voice conversion (SVC) is confronted with the challenge of ``timbre leakage'' issue caused by inadequate disentanglement between the content and the speaker timbre. To address this issue, this study introduces NeuCoSVC, a novel neural concatenative SVC framework. It consists of a self-supervised learning (SSL) representation extractor, a neural harmonic signal generator, and a waveform synthesizer. The SSL extractor condenses audio into fixed-dimensional SSL features, while the harmonic signal generator leverages linear time-varying filters to produce both raw and filtered harmonic signals for pitch information. The synthesizer reconstructs waveforms using SSL features, harmonic signals, and loudness information. During inference, voice conversion is performed by substituting source SSL features with their nearest counterparts from a matching pool which comprises SSL features extracted from the reference audio, while preserving raw harmonic signals and loudness from the source audio. By directly utilizing SSL features from the reference audio, the proposed framework effectively resolves the ``timbre leakage" issue caused by previous disentanglement-based approaches. Experimental results demonstrate that the proposed NeuCoSVC system outperforms the disentanglement-based speaker embedding approach in one-shot SVC across intra-language, cross-language, and cross-domain evaluations

Modeling and Design of the Communication Sensing and Control Coupled Closed-Loop Industrial System

With the advent of 5G era, factories are transitioning towards wireless networks to break free from the limitations of wired networks. In 5G-enabled factories, unmanned automatic devices such as automated guided vehicles and robotic arms complete production tasks cooperatively through the periodic control loops. In such loops, the sensing data is generated by sensors, and transmitted to the control center through uplink wireless communications. The corresponding control commands are generated and sent back to the devices through downlink wireless communications. Since wireless communications, sensing and control are tightly coupled, there are big challenges on the modeling and design of such closed-loop systems. In particular, existing theoretical tools of these functionalities have different modelings and underlying assumptions, which make it difficult for them to collaborate with each other. Therefore, in this paper, an analytical closed-loop model is proposed, where the performances and resources of communication, sensing and control are deeply related. To achieve the optimal control performance, a co-design of communication resource allocation and control method is proposed, inspired by the model predictive control algorithm. Numerical results are provided to demonstrate the relationships between the resources and control performances.Comment: 6 pages, 3 figures, received by GlobeCom 202

Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization

As an economic crop, pepper satisfies people's spicy taste and has medicinal uses worldwide. To gain a better understanding of Capsicum evolution, domestication, and specialization, we present here the genome sequence of the cultivated pepper Zunla-1 (C. annuum L.) and its wild progenitor Chiltepin (C. annuum var. glabriusculum). We estimate that the pepper genome expanded similar to 0.3 Mya (with respect to the genome of other Solanaceae) by a rapid amplification of retrotransposons elements, resulting in a genome comprised of similar to 81% repetitive sequences. Approximately 79% of 3.48-Gb scaffolds containing 34,476 protein-coding genes were anchored to chromosomes by a high-density genetic map. Comparison of cultivated and wild pepper genomes with 20 resequencing accessions revealed molecular footprints of artificial selection, providing us with a list of candidate domestication genes. We also found that dosage compensation effect of tandem duplication genes probably contributed to the pungent diversification in pepper. The Capsicum reference genome provides crucial information for the study of not only the evolution of the pepper genome but also, the Solanaceae family, and it will facilitate the establishment of more effective pepper breeding programs