The Impact of Social Movement on Racial Diversification Initiatives: Evidence From the Movie Industry

The movie industry is facing rising advocacy for racially inclusive casting. However, it remains an open question whether the promised benefits of racial diversification will materialize. Using data from 540 movies nested in 258 sequels released from 2008 to 2021, we find that, on average, increasing the number of racial minority actors in the main cast depresses movie evaluations. More importantly, the negative effect of racial diversification attenuates after Black Lives Matter (#BLM), a new media enabled social movement. Further, incorporating insights from tokenism and discrimination theories, we probe the heterogeneity in the bias mitigation effects of #BLM and find movie type and the core production team’s credentials as important boundary conditions. The present research shows that a social movement that seeks to address racial inequality can, indeed, lead to meaningful changes in public opinions toward racial inclusive initiatives. It also provides perspectives for thinking about the mechanisms underlying such changes

Optimal Spatial-Temporal Triangulation for Bearing-Only Cooperative Motion Estimation

Vision-based cooperative motion estimation is an important problem for many multi-robot systems such as cooperative aerial target pursuit. This problem can be formulated as bearing-only cooperative motion estimation, where the visual measurement is modeled as a bearing vector pointing from the camera to the target. The conventional approaches for bearing-only cooperative estimation are mainly based on the framework distributed Kalman filtering (DKF). In this paper, we propose a new optimal bearing-only cooperative estimation algorithm, named spatial-temporal triangulation, based on the method of distributed recursive least squares, which provides a more flexible framework for designing distributed estimators than DKF. The design of the algorithm fully incorporates all the available information and the specific triangulation geometric constraint. As a result, the algorithm has superior estimation performance than the state-of-the-art DKF algorithms in terms of both accuracy and convergence speed as verified by numerical simulation. We rigorously prove the exponential convergence of the proposed algorithm. Moreover, to verify the effectiveness of the proposed algorithm under practical challenging conditions, we develop a vision-based cooperative aerial target pursuit system, which is the first of such fully autonomous systems so far to the best of our knowledge

Predictive Models for Disaggregate Stock Market Volatility

This paper incorporates the macroeconomic determinants into the forecasting model of industry-level stock return volatility in order to detect whether different macroeconomic factors can forecast the volatility of various industries. To explain different fluctuation characteristics among industries, we identified a set of macroeconomic determinants to examine their effects. The Clark and West (2007) test is employed to verify whether the new forecasting models, which vary among industries based on the in-sample results, can have better predictions than the two benchmark models. Our results show that default return and default yield have significant impacts on stock return volatility

Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis

Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generated images with respect to the text description, such as missing objects, mismatched attributes, and mislocated objects. One key reason for such inconsistencies is the inaccurate cross-attention to text in both the spatial dimension, which controls at what pixel region an object should appear, and the temporal dimension, which controls how different levels of details are added through the denoising steps. In this paper, we propose a new text-to-image algorithm that adds explicit control over spatial-temporal cross-attention in diffusion models. We first utilize a layout predictor to predict the pixel regions for objects mentioned in the text. We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object. The temporal attention control is further added by allowing the combination weights to change at each denoising step, and the combination weights are optimized to ensure high fidelity between the image and the text. Experiments show that our method generates images with higher fidelity compared to diffusion-model-based baselines without fine-tuning the diffusion model. Our code is publicly available at https://github.com/UCSB-NLP-Chang/Diffusion-SpaceTime-Attn.Comment: 20 pages, 16 figure

ReFlow-TTS: A Rectified Flow Model for High-fidelity Text-to-Speech

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compared with other diffusion based models. And the ReFlow-TTS with one step sampling achieves competitive performance compared with existing one-step TTS models.Comment: Accepted at ICASSP202

Polydatin up-regulates clara cell secretory protein to suppress phospholipase A2 of lung induced by LPS in vivo and in vitro

<p>Abstract</p> <p>Background</p> <p>Lung injury induced by lipopolysaccharide (LPS) remains one of the leading causes of morbidity and mortality in children. The damage to membrane phospholipids leads to the collapse of the bronchial alveolar epithelial barrier during acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). Phospholipase A₂(PLA₂), a key enzyme in the hydrolysis of membrane phospholipids, plays an important traumatic role in pulmonary inflammation, and Clara cell secretory protein (CCSP) is an endogenous inhibitor of PLA₂. Our previous study showed that polydatin (PD), a monocrystalline extracted from a traditional Chinese medicinal herb (Polygonum cuspidatum Sieb, et Zucc), reduced PLA₂activity and sPLA₂-IIA mRNA expression and mitigated LPS-induced lung injury. However, the potential mechanism for these effects has not been well defined. We have continued to investigate the effect of PD on LPS-induced expression of CCSP mRNA and protein in vivo and in vitro.</p> <p>Results</p> <p>Our results suggested that the CCSP mRNA level was consistent with its protein expression. CCSP expression was decreased in lung after LPS challenge. In contrast, PD markedly increased CCSP expression in a concentration-dependent manner. In particular, CCSP expression in PD-pretreated rat lung was higher than in rats receiving only PD treatment.</p> <p>Conclusion</p> <p>These results indicated that up-regulation of CCSP expression causing inhibition of PLA₂activation may be one of the crucial protective mechanisms of PD in LPS-induced lung injury.</p