UWSpeech: Speech to Speech Translation for Unwritten Languages

Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target speech from text, or directly to target speech with target text for auxiliary training. However, those methods cannot be applied to unwritten target languages, which have no written text or phoneme available. In this paper, we develop a translation system for unwritten languages, named as UWSpeech, which converts target unwritten speech into discrete tokens with a converter, and then translates source-language speech into target discrete tokens with a translator, and finally synthesizes target speech from target discrete tokens with an inverter. We propose a method called XL-VAE, which enhances vector quantized variational autoencoder (VQ-VAE) with cross-lingual (XL) speech recognition, to train the converter and inverter of UWSpeech jointly. Experiments on Fisher Spanish-English conversation translation dataset show that UWSpeech outperforms direct translation and VQ-VAE baseline by about 16 and 10 BLEU points respectively, which demonstrate the advantages and potentials of UWSpeech

JMJD3 promotes survival of diffuse large B-cell lymphoma subtypes via distinct mechanisms.

JMJD3 (Jumonji domain containing-3), a histone H3 Lys27 (H3K27) demethylase, has been reported to be involved in the antigen-driven differentiation of germinal center B-cells. However, insight into the mechanism of JMJD3 in DLBCL (Diffuse large B-cell lymphoma) progression remains poorly understood. In this study, we investigated the subtype-specific JMJD3-dependent survival effects in DLBCL. Our data showed that in the ABC subtype, silencing-down of JMJD3 inhibited interferon regulatory factor 4 (IRF4) expression in a demethylase activity-dependent fashion. IRF4 reciprocally stimulated expression of JMJD3, forming a positive feedback loop that promoted survival in these cells. Accordingly, IRF4 expression was sufficient to rescue the pro-apoptotic effect of JMJD3 suppression in the ABC, but not in the GCB subtype. In contrast, ectopic overexpression of BCL-2 completely offset JMJD3-mediated survival in the GCB DLBCL cells. In vivo, treatment with siRNA to JMJD3 reduced tumor volume concordant with increased apoptosis in either subtype. This suggests it is a common target, though the distinctive signaling axes regulating DCBCL survival offer different strategic options for treating DLBCL subtypes

Adaptive Robust Guidance Scheme Based on the Sliding Mode Control in an Aircraft Pursuit-Evasion Problem

In this chapter, a robust guidance scheme utilizing a line-of-sight (LOS) observation is presented. Initial relative speed and distance, and error boundaries of them are estimated in accordance with the interceptor-target relative motion kinematics. A robust guidance scheme based on the sliding mode control (SMC) is developed, which requires the boundaries of the target maneuver, and inevitably has jitter phenomenon. For solving above-mentioned problems, an estimation to the target acceleration’s boundary is developed for enhancing robustness of the guidance scheme and the Lyapunov stabilization is analyzed. The proposed robust guidance scheme’s brief characteristic is to reduce the effect of relative speed and distance, to reduce the effect of target maneuverability on the guidance precision, and to strengthen the influence of line-of-sight angular velocity. The proposed scheme’s performances are validated by the simulations of different target maneuvers under two worst-case conditions

Systematic review: Factors influencing creativity in the design discipline and assessment criteria

Using psychological instrument to measure creativity is getting popular in design research. However, unlike quantifying general creativity using divergent thinking, the complexity and interdisciplinarity of the design discipline have made it difficult to explore research on design creativity. Therefore, to better quantify and measure design creativity, 31 relevant studies were retrieved by Google Scholar and the University of London Common Research in this article. This study summarizes the factors that influence design creativity in different design disciplines, the rules for setting the internal dimensions, and the valid instruments for measuring design creativity. The factors affecting design creativity can be divided into internal factors (aesthetic, spatial ability, and ambiguity tolerance) and external factors (environment and visual stimulation). Among these factors, different instruments and evaluation criteria considerably impact the result, while the measurement of design creativity is still not mature enough. A single scale evaluation or creative task evaluation cannot comprehensively evaluate the design creativity, which consists of aesthetic, functional, and technical aspects. In addition, the reference value of ordinary creativity remains to be further discussed in design. Under some professional design fields, the effect of widely recognized factors closely related to creativity, such as divergent thinking, imagination, and personality, is insignificant

Water and nitrogen availability co-control ecosystem CO2 exchange in a semiarid temperate steppe

Both water and nitrogen (N) availability have significant effects on ecosystem CO2 exchange (ECE), which includes net ecosystem productivity (NEP), ecosystem respiration (ER) and gross ecosystem photosynthesis (GEP). How water and N availability influence ECE in arid and semiarid grasslands is still uncertain. A manipulative experiment with additions of rainfall, snow and N was conducted to test their effects on ECE in a semiarid temperate steppe of northern China for three consecutive years with contrasting natural precipitation. ECE increased with annual precipitation but approached peak values at different precipitation amount. Water addition, especially summer water addition, had significantly positive effects on ECE in years when the natural precipitation was normal or below normal, but showed trivial effect on GEP when the natural precipitation was above normal as effects on ER and NEP offset one another. Nitrogen addition exerted non-significant or negative effects on ECE when precipitation was low but switched to a positive effect when precipitation was high, indicating N effect triggered by water availability. Our results indicate that both water and N availability control ECE and the effects of future precipitation changes and increasing N deposition will depend on how they can change collaboratively in this semiarid steppe ecosystem

Treadmill exercise suppressed stress-induced dendritic spine elimination in mouse barrel cortex and improved working memory via BDNF/TrkB pathway

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Retrieval of Optical Constant and Particle Size Distribution of Particulate Media Using the PSO-Based Neural Network Algorithm

An improved neural network algorithm was proposed and applied to the inverse radiative problems. A multi-strategy particle swarm optimization was applied to improve the performance of the back propagation multi-layer feed-forward neural network algorithm. Three commonly used particle size distribution (PSD) functions in a one-dimensional particle system were retrieved using the proposed algorithm. In addition, the optical constant was also estimated, and the measurement errors were considered. Results show that the proposed algorithm can be applied to the retrieval of PSDs and optical constant even with measurement errors. Finally, the proposed algorithm was applied to the simultaneous estimation of the PSDs and optical constant using the multi-wavelength and multi-thickness method

SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint

Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the paired lyric and melody data are limited, which affects the generation quality of the two tasks, considering a lot of paired training data are needed due to the weak correlation between lyric and melody; 2) Strict alignments are required between lyric and melody, which relies on specific alignment modeling. In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. Specifically, 1) we extend the original sentence-level MASS pre-training to song level to better capture long contextual information in music, and use a separate encoder and decoder for each modality (lyric or melody); 2) we leverage sentence-level attention mask and token-level attention constraint during training to enhance the alignment between lyric and melody. During inference, we use a dynamic programming strategy to obtain the alignment between each word/syllable in lyric and note in melody. We pre-train SongMASS on unpaired lyric and melody datasets, and both objective and subjective evaluations demonstrate that SongMASS generates lyric and melody with significantly better quality than the baseline method without pre-training or alignment constraint