Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.Comment: 8 pages, 6 figures, Submitted to SLT 201

Hafnium oxide-based ferroelectric thin-film transistor with a-InGaZnO channel fabricated at temperatures \u3c= 350°C

HfO2-based ferroelectric materials integrated with oxide-based thin-film transistors have been considered as potential candidates for back-end-of-line compatible ferroelectric field-effect transistors, which can be vertically stacked on silicon CMOS circuits to realize high-density neural network applications. However, the formation of ferroelectric orthorhombic phase in HfO2-based materials usually requires an annealing temperature of 400°C or higher. In this work, ferroelectric thin-film transistors (Fe-TFTs) were developed by monolithically integrating HfZrO2 (HZO) ferroelectric capacitors with amorphous indium-gallium-zinc oxide (a-IGZO) TFTs at a maximum processing temperature of 350°C on a glass substrate. A butterfly-shaped C-V curve was clearly observed in the low-temperature annealed metal-HZO-metal capacitor, indicating the formation of ferroelectricity in the HZO layer, as shown in Fig. 1. The positive and negative coercive voltages were 3 V and -2.4 V, respectively. The dielectric constant was 20.65. The field-effect mobility, threshold voltage, subthreshold swing and on/off current ratio of the a-IGZO TFT extracted from the transfer characteristics shown in Fig. 2 were 6.15 cm2V-1s-1, 1.5 V, 0.1 V/dec and 4.3´107, respectively. Fig. 3 shows the transfer hysteresis curves of the low-temperature Fe-TFTs in a metal-ferroelectric-metal-insulator-semiconductor configuration. The Fe-TFTs exhibited large hysteresis memory windows of 2.8 V and 3.8 V when the area ratios between ferroelectric capacitors and gate insulators (AFE / ADE) were 1/8 and 1/12, respectively. The result shows a great potential for back-end-of-line compatible memory applications. Please click Download on the upper right corner to see the full abstract

The Potential Economic Impact of Avian Flu Pandemic on Taiwan

This study analyzes the potential consequences of an outbreak of avian influenza (H5N1) on Taiwan¡¦s macro economy and individual industries. Both the Input-Output (IO) Analysis Model and Computable General Equilibrium (CGE) Model are used to simulate the possible damage brought by lowering domestic consumption, export, and labor supply. The simulation results indicates that if the disease is confined within the poultry sector, then the impact on real GDP is around -0.1%~-0.4%. Once it becomes a human-to-human pandemic, the IO analysis suggests that the potential impacts on real GDP would be as much as -4.2%~-5.9% while labor demand would decrease 4.9%~6.4%. In the CGE analysis, which allows for resource mobility and substitutions through price adjustments, the real GDP and labor demand would contract 2.0%~2.4% and 2.2%~2.4%, respectively, and bringing down consumer prices by 3%. As for the individual sector, the outbreak will not only damage the poultry sector and its upstream and downstream industries, but also affect the service sectors including wholesale, retail, trade, air transportation, restaurants, as well as healthcare services. These results can be used to support public investment in animal disease control measures.Avian Flu Pandemic, Input-output Model, Computable General Equilibrium Model, Livestock Production/Industries,

Association of interieukin-18 gene polymorphism with asthma in Chinese patients

[[abstract]]Like other allergic diseases, asthma results from multiple conditions. Asthmatic beginning and severity are mediated by both environmental and genetic factors. In asthma studies, important work is realization of the genetic background and identification of genetic factors resulting in asthma development and phenomena. Here, we investigated whether interleukin (IL)-18 single nucleotide polymorphisms (SNPs) are involved in Chinese asthma patients. IL-18 (IL-18) SNP was detected by polymerase chain reaction (PCR)-based restriction analysis in 201 patients with asthma and 60 normal controls. Significant differences were found in the genotype distribution of IL-18 SNIP between asthma patients and controls (P = 0.000003). Allelic frequency of the IL-18 gene distinguished asthma patients from controls (P = 0.000066). The results revealed a significant difference between asthma patients and normal controls in IL-18 SNP and a statistical correlation between IL-18 polymorphisms (105A/C) and asthma formation. We concluded that Chinese who carry the C/C homozygote of the IL-18-105A/C gene polymorphism in coding regions may have a higher risk of developing asthma

DAHiTrA: Damage Assessment Using a Novel Hierarchical Transformer Architecture

This paper presents DAHiTrA, a novel deep-learning model with hierarchical transformers to classify building damages based on satellite images in the aftermath of hurricanes. An automated building damage assessment provides critical information for decision making and resource allocation for rapid emergency response. Satellite imagery provides real-time, high-coverage information and offers opportunities to inform large-scale post-disaster building damage assessment. In addition, deep-learning methods have shown to be promising in classifying building damage. In this work, a novel transformer-based network is proposed for assessing building damage. This network leverages hierarchical spatial features of multiple resolutions and captures temporal difference in the feature domain after applying a transformer encoder on the spatial features. The proposed network achieves state-of-the-art-performance when tested on a large-scale disaster damage dataset (xBD) for building localization and damage classification, as well as on LEVIR-CD dataset for change detection tasks. In addition, we introduce a new high-resolution satellite imagery dataset, Ida-BD (related to the 2021 Hurricane Ida in Louisiana in 2021, for domain adaptation to further evaluate the capability of the model to be applied to newly damaged areas with scarce data. The domain adaptation results indicate that the proposed model can be adapted to a new event with only limited fine-tuning. Hence, the proposed model advances the current state of the art through better performance and domain adaptation. Also, Ida-BD provides a higher-resolution annotated dataset for future studies in this field