Extract coefficients of thermal expansion of TaN thin film by tuning the N2 gas flow in the PVD process

Coefficients of thermal expansion (CTE) mismatch between different materials is an essential and critical concern in semiconductor development. During the manufacturing processes, the thermal budget will induce residual stress, occurring the deformation of the material. In the worst case, the thin film or the elements would be broken or failed. [1] However, these mechanical properties are difficult to determine and measure. In this study, we used the simple micro-cantilever beams array as the test key which was fabricated by the MEMS process. We deposited the TaN thin film on the different length cantilevers and then exploited the double layer method and Stoney equation to analyze its mechanical properties [2] [3]. Figure 1 shows the micrograph of the SiO2 cantilever beam deposited with TaN thin film and whose geometry size is also measured by the SEM system. Please click Download on the upper right corner to see the full abstract

CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models

Self-supervised learning (SSL) is a powerful technique for learning representations from unlabeled data. Transformer based models such as HuBERT, which consist a feature extractor and transformer layers, are leading the field in the speech domain. SSL models are fine-tuned on a wide range of downstream tasks, which involves re-training the majority of the model for each task. Previous studies have introduced applying adapters, which are small lightweight modules commonly used in Natural Language Processing (NLP) to adapt pre-trained models to new tasks. However, such efficient tuning techniques only provide adaptation at the transformer layer, but failed to perform adaptation at the feature extractor. In this paper, we propose CHAPTER, an efficient tuning method specifically designed for SSL speech model, by applying CNN adapters at the feature extractor. Using this method, we can only fine-tune fewer than 5% of parameters per task compared to fully fine-tuning and achieve better and more stable performance. We empirically found that adding CNN adapters to the feature extractor can help the adaptation on emotion and speaker tasks. For instance, the accuracy of SID is improved from 87.71 to 91.56, and the accuracy of ER is improved by 5%.Comment: Submitted to ICASSP 2023. Under revie

Motor neuron-derived Thsd7a is essential for zebrafish vascular development via the Notch-dll4 signaling pathway.

BackgroundDevelopment of neural and vascular systems displays astonishing similarities among vertebrates. This parallelism is under a precise control of complex guidance signals and neurovascular interactions. Previously, our group identified a highly conserved neural protein called thrombospondin type I domain containing 7A (THSD7A). Soluble THSD7A promoted and guided endothelial cell migration, tube formation and sprouting. In addition, we showed that thsd7a could be detected in the nervous system and was required for intersegmental vessels (ISV) patterning during zebrafish development. However, the exact origin of THSD7A and its effect on neurovascular interaction remains unclear.ResultsIn this study, we discovered that zebrafish thsd7a was expressed in the primary motor neurons. Knockdown of Thsd7a disrupted normal primary motor neuron formation and ISV sprouting in the Tg(kdr:EGFP/mnx1:TagRFP) double transgenic zebrafish. Interestingly, we found that Thsd7a morphants displayed distinct phenotypes that are very similar to the loss of Notch-delta like 4 (dll4) signaling. Transcript profiling further revealed that expression levels of notch1b and its downstream targets, vegfr2/3 and nrarpb, were down-regulated in the Thsd7a morphants. These data supported that zebrafish Thsd7a could regulate angiogenic sprouting via Notch-dll4 signaling during development.ConclusionsOur results suggested that motor neuron-derived Thsd7a plays a significant role in neurovascular interactions. Thsd7a could regulate ISV angiogenesis via Notch-dll4 signaling. Thus, Thsd7a is a potent angioneurin involved in the development of both neural and vascular systems

Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.Comment: submitted to IEEE ICASSP 202

How to Estimate Model Transferability of Pre-Trained Speech Models?

In this work, we introduce a ``score-based assessment'' framework for estimating the transferability of pre-trained speech models (PSMs) for fine-tuning target tasks. We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates using the extracted representations. Our framework efficiently computes transferability scores without actual fine-tuning of candidate models or layers by making a temporal independent hypothesis. We evaluate some popular supervised speech models (e.g., Conformer RNN-Transducer) and self-supervised speech models (e.g., HuBERT) in cross-layer and cross-model settings using public data. Experimental results show a high Spearman's rank correlation and low $p$-value between our estimation framework and fine-tuning ground truth. Our proposed transferability framework requires less computational time and resources, making it a resource-saving and time-efficient approach for tuning speech foundation models.Comment: Accepted to Interspeech. Code will be release

Hedgehog overexpression leads to the formation of prostate cancer stem cells with metastatic property irrespective of androgen receptor expression in the mouse model

<p>Abstract</p> <p>Background</p> <p>Hedgehog signalling has been implicated in prostate tumorigenesis in human subjects and mouse models, but its effects on transforming normal basal/stem cells toward malignant cancer stem cells remain poorly understood.</p> <p>Methods</p> <p>We produced pCX-shh-IG mice that overexpress Hedgehog protein persistently in adult prostates, allowing for elucidation of the mechanism during prostate cancer initiation and progression. Various markers were used to characterize and confirm the transformation of normal prostate basal/stem cells into malignant cancer stem cells under the influence of Hedgehog overexpression.</p> <p>Results</p> <p>The pCX-shh-IG mice developed prostatic intraepithelial neoplasia (PIN) that led to invasive and metastatic prostate cancers within 90 days. The prostate cancer was initiated through activation of P63⁺basal/stem cells along with simultaneous activation of Hedgehog signalling members, suggesting that P63⁺/Patch1⁺and P63⁺/Smo⁺cells may serve as cancer-initiating cells and progress into malignant prostate cancer stem cells (PCSCs). In the hyperplastic lesions and tumors, the progeny of PCSCs differentiated into cells of basal-intermediate and intermediate-luminal characteristics, whereas rare ChgA⁺neuroendocrine differentiation was seen. Furthermore, in the metastatic loci within lymph nodes, kidneys, and lungs, the P63⁺PCSCs formed prostate-like glandular structures, characteristic of the primitive structures during early prostate development. Besides, androgen receptor (AR) expression was detected heterogeneously during tumor progression. The existence of P63⁺/AR^-, CK14⁺/AR^-and CD44⁺/AR^-progeny indicates direct procurement of AR^-malignant cancer trait.</p> <p>Conclusions</p> <p>These data support a cancer stem cell scenario in which Hedgehog signalling plays important roles in transforming normal prostate basal/stem cells into PCSCs and in the progression of PCSCs into metastatic tumor cells.</p

Mechanism and kinetics for both thermal and electrochemical reduction of N_2 catalysed by Ru(0001) based on quantum mechanics

The conversion of N_(2(g)) to NH_(3(g)) is an important industrial process that plays a vital role in sustaining the current human population. This chemical transformation relies heavily on the Haber–Bosch process (N2 thermal reduction, N_2TR), which requires enormous quantities of energy (2% of the world supply) and extreme conditions (200 atm and 500 °C). Alternatively, N_(2(g)) can be reduced to NH_(3(g)) through electrochemical means (N_2ER), which may be a less energy intensive and lower-capital approach since the H atoms come from H_2O not H_2. However, N_2ER efficiency is far from satisfactory. In order to provide the basis for developing a new generation of energy efficient processes, we report the detailed atomistic mechanism and kinetics for N_2ER on Ru(0001) along with a comparison to N2TR. We obtained these results using a new electrochemical model for quantum mechanics (QM) calculations to obtain free energy surfaces for all plausible reaction pathways for N_2ER under a constant electrode potential of 0.0 V_(SHE). For both processes, the elementary steps involve several steps of breaking of the NN bonds, hydrogenation of surface N_2H_X or NH_X, and NH_3 release. We find similar energetics for the NN cleavage steps for both systems. However, the hydrogenation steps are very different, leading to much lower free energy barriers for N_2ER compared to N_2TR. Thus, N_2ER favors an associative route where successive hydrogen atoms are added to N_2 prior to breaking the NN bonds rather than the dissociative route preferred by N_2TR, where the NN bonds are broken first followed by the addition of Hs. Our QM results provide the detailed free energy surfaces for N_2ER and N_2TR, suggesting a strategy for improving the efficiency of N_2ER

Mechanism and kinetics for both thermal and electrochemical reduction of N_2 catalysed by Ru(0001) based on quantum mechanics

The conversion of N_(2(g)) to NH_(3(g)) is an important industrial process that plays a vital role in sustaining the current human population. This chemical transformation relies heavily on the Haber–Bosch process (N2 thermal reduction, N_2TR), which requires enormous quantities of energy (2% of the world supply) and extreme conditions (200 atm and 500 °C). Alternatively, N_(2(g)) can be reduced to NH_(3(g)) through electrochemical means (N_2ER), which may be a less energy intensive and lower-capital approach since the H atoms come from H_2O not H_2. However, N_2ER efficiency is far from satisfactory. In order to provide the basis for developing a new generation of energy efficient processes, we report the detailed atomistic mechanism and kinetics for N_2ER on Ru(0001) along with a comparison to N2TR. We obtained these results using a new electrochemical model for quantum mechanics (QM) calculations to obtain free energy surfaces for all plausible reaction pathways for N_2ER under a constant electrode potential of 0.0 V_(SHE). For both processes, the elementary steps involve several steps of breaking of the NN bonds, hydrogenation of surface N_2H_X or NH_X, and NH_3 release. We find similar energetics for the NN cleavage steps for both systems. However, the hydrogenation steps are very different, leading to much lower free energy barriers for N_2ER compared to N_2TR. Thus, N_2ER favors an associative route where successive hydrogen atoms are added to N_2 prior to breaking the NN bonds rather than the dissociative route preferred by N_2TR, where the NN bonds are broken first followed by the addition of Hs. Our QM results provide the detailed free energy surfaces for N_2ER and N_2TR, suggesting a strategy for improving the efficiency of N_2ER

Characterization of membranous and cytoplasmic EGFR expression in human normal renal cortex and renal cell carcinoma

Metastatic renal cell carcinoma (RCC) is highly resistant to conventional systemic treatments, including chemotherapy, radiotherapy and hormonal therapies. Previous studies have shown over-expression of EGFR is associated with high grade tumors and a worse prognosis. Recent studies suggest anticancer therapies targeting the EGFR pathway have shown promising results in clinical trials of RCC patients. Therefore, characterization of the level and localization of EGFR expression in RCC is important for target-dependent therapy. In this study, we investigated the clinical significance of cellular localization of EGFR in human normal renal cortex and RCC. RCC and adjacent normal kidney tissues of 63 patients were obtained for characterization of EGFR expression. EGFR protein expression was assessed by immunohistochemistry on a scale from 0 to 300 (percentage of positive cells × staining intensity) and Western blotting. EGFR membranous staining was significantly stronger in RCC tumors than in normal tissues (P < 0.001). In contrast, EGFR cytoplasmic staining was significantly higher in normal than in tumor tissues (P < 0.001). The levels of membranous or cytoplasmic EGFR expression in RCC tissues were not correlated with sex, tumor grade, TNM stage or overall survival (P > 0.05). These results showed abundant expression of membranous EGFR in RCC, and abundant expression of cytoplasmic EGFR in normal tissues. EGFR expression in RCC was mostly located in the cell membrane, whereas the EGFR expression in normal renal tissues was chiefly seen in cytoplasm. Our results suggest different locations of EGFR expression may be associated with human renal tumorigenesis