Training-free Diffusion Model Adaptation for Variable-Sized Text-to-Image Synthesis

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and evaluated on the images with fixed sizes. However, users are demanding for various images with specific sizes and various aspect ratio. This paper focuses on adapting text-to-image diffusion models to handle such variety while maintaining visual fidelity. First we observe that, during the synthesis, lower resolution images suffer from incomplete object portrayal, while higher resolution images exhibit repetitively disordered presentation. Next, we establish a statistical relationship indicating that attention entropy changes with token quantity, suggesting that models aggregate spatial information in proportion to image resolution. The subsequent interpretation on our observations is that objects are incompletely depicted due to limited spatial information for low resolutions, while repetitively disorganized presentation arises from redundant spatial information for high resolutions. From this perspective, we propose a scaling factor to alleviate the change of attention entropy and mitigate the defective pattern observed. Extensive experimental results validate the efficacy of the proposed scaling factor, enabling models to achieve better visual effects, image quality, and text alignment. Notably, these improvements are achieved without additional training or fine-tuning techniques.Comment: Accepted by NeurIPS 2023. 23 pages, 13 figure

Application of improved support vector machine regression analysis for medium- and long-term vibration trend prediction

Aircraft engine fault diagnosis plays a crucial role in cost-effective operations of aircraft engines. However, successful detection of signals due to vibrations in multiple transmission channels is not always easy to accomplish, and traditional tests for nonlinearity are not always capable of capturing the dynamics. Here we applied a new method of smooth support vector machine regression (SSVMR) to better fit complicated dynamic systems. Since quadratic loss functions are less sensitive, the constrained quadratic optimization could be transferred to the unconstrained optimization so that the number of constraint conditions could be reduced. Meanwhile, the problem of slow operation speed and large memory space requirement associated with quadratic programming could be solved. Based on observed input and output data, the equivalent dynamic model of aircraft engineers was established, and model verification was done using historical vibration data. The results showed that SSVMR had fast operation speed and high predictive precision, and thus could be applied to provide early warning if engine vibration exceeds the required standard

Rotor fault classification technique and precision analysis with kernel principal component analysis and multi-support vector machines

To solve the diagnosis problem of fault classification for aero-engine vibration over standard during test, a fault diagnosis classification approach based on kernel principal component analysis (KPCA) feature extraction and multi-support vector machines (SVM) is proposed, which extracted the feature of testing cell standard fault samples through exhausting the capability of nonlinear feature extraction of KPCA. By computing inner product kernel functions of original feature space, the vibration signal of rotor is transformed from principal low dimensional feature space to high dimensional feature spaces by this nonlinear map. Then, the nonlinear principal components of original low dimensional space are obtained by performing PCA on the high dimensional feature spaces. During muti-SVM training period, as eigenvectors, the nonlinear principal components are separated into training set and test set, and penalty parameter and kernel function parameter are optimized by adopting genetic optimization algorithm. A high classification accuracy of training set and test set is sustained and over-fitting and under-fitting are avoided. Experiment results indicate that this method has good performance in distinguishing different aero-engine fault mode, and is suitable for fault recognition of a high speed rotor

Src kinase up-regulates the ERK cascade through inactivation of protein phosphatase 2A following cerebral ischemia

<p>Abstract</p> <p>Background</p> <p>The regulation of protein phosphorylation requires a balance in the activity of protein kinases and protein phosphatases. Our previous data indicates that Src can increase ERK activity through Raf kinase in response to ischemic stimuli. This study examined the molecular mechanisms by which Src activates ERK cascade through protein phosphatases following cerebral ischemia.</p> <p>Results</p> <p>Ischemia-induced Src activation is followed by phosphorylation of PP2A at Tyr307 leading to its inhibition in the rat hippocampus. SU6656, a Src inhibitor, up-regulates PP2A activity, resulting in a significant decreased activity in ERK and its targets, CREB and ERα. In addition, the PP2A inhibitor, cantharidin, led to an up-regulation of ERK activity and was able to counteract Src inhibition during ischemia.</p> <p>Conclusion</p> <p>Src induces up-regulation of ERK activity and its target transcription factors, CREB and ERα, through attenuation of PP2A activity. Therefore, activation of ERK is the result of a crosstalk between two pathways, Raf-dependent positive regulators and PP2A-dependent negative regulators.</p

Reducing Objectification Could Tackle Stigma in the COVID-19 Pandemic: Evidence from China

Stigmatization associated with the coronavirus disease 2019 (COVID-19) is expected to be a complex issue and to extend into the later phases of the pandemic, which impairs social cohesion and relevant individuals\u27 well-being. Identifying contributing factors and learning their roles in the stigmatization process may help tackle the problem. This study quantitatively assessed the severity of stigmatization against three different groups of people: people from major COVID-19 outbreak sites, those who had been quarantined, and healthcare workers; explored the factors associated with stigmatization within the frameworks of self-categorization theory and core social motives; and proposed solutions to resolve stigma. The cross-sectional online survey was carried out between April 21 and May 7, 2020, using a convenience sample, which yielded 1,388 valid responses. Employing data analysis methods like multivariate linear regression and moderation analysis, this study yields some main findings: (1) those from major COVID-19 outbreak sites received the highest level of stigma; (2) factors most closely associated with stigmatization, in descending order, are objectification and epidemic proximity in an autonomic aspect and fear of contracting COVID-19 in a controllable aspect; and (3) superordinate categorization is a buffering moderator in objectification-stigmatization relationship. These findings are important for further understanding COVID-19-related stigma, and they can be utilized to develop strategies to fight against relevant discrimination and bias. Specifically, reinforcing superordinate categorization by cultivating common in-group identity, such as volunteering and donating for containment of the pandemic, could reduce objectification and, thus, alleviate stigma