The lattice constant and coefficient of expansion of chromium

The most important use of chromium, other than as an alloying element in the manufacture of stainless steel, is for electroplating; to form a coating on other metals for corrosion prevention in order to procure longer life, and to achieve a decorative effect. The physical properties of chromium are important in the effectiveness of its uses, and its lattice constant, as well as coefficient of thermal expansion, seem worthy of exact determination. A number of research workers, over a span of thirty years, have spent considerable effort in determining the aforementioned constants. However, their results do not check, and the degree of accuracy differs from person to person. Chromium near 37° C was identified by M. E. Fine as showing discontinuous changes of coefficient of expansion, Young\u27s modulus, internal friction, electrical resistivity and thermoelectric power. Although the X-ray diffraction pattern gave no clue, a difference in the thermal expansity has been found. D. MacNair determined the expansity of chromium by means of an interferometric dilatometer, with the result that near 38° C the thermal expansity curve went through an inflection point, corresponding to a minimum in the coefficient of expansion found by Fine. The purpose of this research is to check the results obtained by MacNair and Fine concerning the expansion of chromium, and to determine the exact lattice constant and thermal expansion of this metal by the X-ray powder method, using different samples, and at different temperatures within the range of 10° to 50° C --Introduction, pages 1-2

Complexity Scaling for Speech Denoising

Computational complexity is critical when deploying deep learning-based speech denoising models for on-device applications. Most prior research focused on optimizing model architectures to meet specific computational cost constraints, often creating distinct neural network architectures for different complexity limitations. This study conducts complexity scaling for speech denoising tasks, aiming to consolidate models with various complexities into a unified architecture. We present a Multi-Path Transform-based (MPT) architecture to handle both low- and high-complexity scenarios. A series of MPT networks present high performance covering a wide range of computational complexities on the DNS challenge dataset. Moreover, inspired by the scaling experiments in natural language processing, we explore the empirical relationship between model performance and computational cost on the denoising task. As the complexity number of multiply-accumulate operations (MACs) is scaled from 50M/s to 15G/s on MPT networks, we observe a linear increase in the values of PESQ-WB and SI-SNR, proportional to the logarithm of MACs, which might contribute to the understanding and application of complexity scaling in speech denoising tasks.Comment: Submitted to ICASSP202

Experiences of Chinese American Psychology Trainees in Multicultural Education

Although research has established that students of color have unique experiences in their multicultural training, few studies have examined the experiences and needs of specific subgroups of students of color. This study examined Chinese American psychology trainees’ experiences in multicultural education. Qualitative data was collected from individual semi-structured interviews with Chinese American doctoral students (N = 6). Interpretative phenomenological analysis was used to understand participants’ perceptions of their experiences in multicultural courses. Data analysis resulted in four themes: the (1) burden of being minoritized, (2) Chinese American identity inflection points, being (3) sidelined by whiteness, and (4) recommendations for curricular modification. Experiences unique to Chinese American trainees were uncovered, such as the burden of being in racial isolation, navigating Chinese American racial and cultural identity development, and having their educational needs be sidelined by whiteness. Participants described curricular and pedagogical shifts that would better support their learning in multicultural courses. Education and training changes that would support Chinese American psychology trainees in their multicultural education are discussed

DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis

This paper introduces an improved duration informed attention neural network (DurIAN-E) for expressive and high-fidelity text-to-speech (TTS) synthesis. Inherited from the original DurIAN model, an auto-regressive model structure in which the alignments between the input linguistic information and the output acoustic features are inferred from a duration model is adopted. Meanwhile the proposed DurIAN-E utilizes multiple stacked SwishRNN-based Transformer blocks as linguistic encoders. Style-Adaptive Instance Normalization (SAIN) layers are exploited into frame-level encoders to improve the modeling ability of expressiveness. A denoiser incorporating both denoising diffusion probabilistic model (DDPM) for mel-spectrograms and SAIN modules is conducted to further improve the synthetic speech quality and expressiveness. Experimental results prove that the proposed expressive TTS model in this paper can achieve better performance than the state-of-the-art approaches in both subjective mean opinion score (MOS) and preference tests