Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion

Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image content and identity. While previous approaches like DreamBooth and Textual Inversion have proposed model or latent representation personalization to maintain the content, their reliance on multiple reference images and complex training limits their practicality. In this paper, we present a simple yet highly effective approach to personalization using highly personalized (HiPer) text embedding by decomposing the CLIP embedding space for personalization and content manipulation. Our method does not require model fine-tuning or identifiers, yet still enables manipulation of background, texture, and motion with just a single image and target text. Through experiments on diverse target texts, we demonstrate that our approach produces highly personalized and complex semantic image edits across a wide range of tasks. We believe that the novel understanding of the text embedding space presented in this work has the potential to inspire further research across various tasks

The effects of pre-pregnancy body mass index and gestational weight gain on perinatal outcomes in Korean women: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The purpose of the study was to evaluate the effects of maternal pre-pregnancy body mass index (BMI) and gestational weight gain on perinatal outcomes in a population of Korean women.</p> <p>Methods</p> <p>We retrospectively reviewed the medical records of 2,454 women who had received antenatal care at Seoul St. Mary's Hospital from January 2007 to December 2009. We used World Health Organization definitions for Asian populations of underweight (BMI < 18.5), normal (BMI equal or higher 18.5 and < 23), overweight (BMI equal or higher 23 and < 25), and obese (BMI equal or higher 25). We analyzed perinatal outcomes according to the pre-pregnancy BMI and weight gain during pregnancy, and calculated the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) from multiple logistic regression models by considering maternal age, parity, number of fetuses, length of gestation, and medical history.</p> <p>Results</p> <p>Among obese women, the adjusted ORs for gestational diabetes, hypertensive disorder, and incompetent internal os of cervix were 4.46, 2.53, and 3.70 (95% CI = 2.63-7.59, 1.26-5.07, and 1.50-9.12), respectively, and the adjusted ORs for neonatal complications such as macrosomia and low Apgar score were 2.08 and 1.98 (95% CI = 1.34-3.22 and 1.19-3.29), respectively, compared with normal weight women. However, there was no positive linear association between gestational weight gain and obstetric outcomes. In normal weight women, maternal and neonatal complications were significantly increased with inadequate weight gain during pregnancy (p < 0.0001 and = 0.0180, respectively), and we observed similar results in underweight women (p = 0.0136 and 0.0004, respectively).</p> <p>Conclusions</p> <p>This study shows that pre-pregnancy overweight and obesity are more closely related to the adverse obstetric outcomes than excess weight gain during pregnancy. In addition, inadequate weight gain during pregnancy can result in significant complications.</p

Optimal Multiuser Diversity in Multi-Cell MIMO Uplink Networks: User Scaling Law and Beamforming Design

We introduce a distributed protocol to achieve multiuser diversity in a multicell multiple-input multiple-output (MIMO) uplink network, referred to as a MIMO interfering multiple-access channel (IMAC). Assuming both no information exchange among base stations (BS) and local channel state information at the transmitters for the MIMO IMAC, we propose a joint beamforming and user scheduling protocol, and then show that the proposed protocol can achieve the optimal multiuser diversity gain, i.e., KM log (SNR log N), as long as the number of mobile stations (MSs) in a cell, N, scales faster than SNRKM-L/1-epsilon for a small constant epsilon &gt; 0, where M, L, K, and SNR denote the number of receive antennas at each BS, the number of transmit antennas at each MS, the number of cells, and the signal-to-noise ratio, respectively. Our result indicates that multiuser diversity can be achieved in the presence of intra-cell and inter-cell interference even in a distributed fashion. As a result, vital information on how to design distributed algorithms in interference-limited cellular environments is provided