Self-Supervised Sketch-to-Image Synthesis

Imagining a colored realistic image from an arbitrarily drawn sketch is one of the human capabilities that we eager machines to mimic. Unlike previous methods that either requires the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images that are both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycle-consistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE's superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on 1024*1024 resolution demonstrate a new state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful. Our code is available on https://github.com/odegeasslbc/Self-Supervised-Sketch-to-Image-Synthesis-PyTorch, and please visit https://create.playform.io/my-projects?mode=sketch for an online demo of our model.Comment: AAAI-202

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

Diffusion Guided Domain Adaptation of Image Generators

Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain? In this paper, we show that the classifier-free guidance can be leveraged as a critic and enable generators to distill knowledge from large-scale text-to-image diffusion models. Generators can be efficiently shifted into new domains indicated by text prompts without access to groundtruth samples from target domains. We demonstrate the effectiveness and controllability of our method through extensive experiments. Although not trained to minimize CLIP loss, our model achieves equally high CLIP scores and significantly lower FID than prior work on short prompts, and outperforms the baseline qualitatively and quantitatively on long and complicated prompts. To our best knowledge, the proposed method is the first attempt at incorporating large-scale pre-trained diffusion models and distillation sampling for text-driven image generator domain adaptation and gives a quality previously beyond possible. Moreover, we extend our work to 3D-aware style-based generators and DreamBooth guidance.Comment: Project website: https://styleganfusion.github.io

TIME: Text and Image Mutual-Translation Adversarial Networks

Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. While previous methods tackle the T2I problem as a uni-directional task and use pre-trained language models to enforce the image--text consistency, TIME requires neither extra modules nor pre-training. We show that the performance of G can be boosted substantially by training it jointly with D as a language model. Specifically, we adopt Transformers to model the cross-modal connections between the image features and word embeddings, and design an annealing conditional hinge loss that dynamically balances the adversarial learning. In our experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB and MS-COCO dataset (Inception Score of 4.91 and Fr\'echet Inception Distance of 14.3 on CUB), and shows promising performance on MS-COCO on image captioning and downstream vision-language tasks.Comment: AAAI-202

How Do Price and Quantity Promotions Affect Hedonic Purchases? An ERPs Study

Due to consuming hedonic products unnecessary to basic well-being, consumers need justifications for pleasure. However, different justifications have differential influences in promoting hedonic purchases, such as price and quantity promotions (PP and QP), the difference being that the latter requires purchasing additional units to get the same discount as the former. In the present study, even-related potentials (ERPs) was applied to reveal the timing of brain activities to further understand how promotion information consisting of promotion type (PP and QP) and discount depth, deep and shallow discounts (DD and SD) on hedonic products was processed. Behaviorally, consumers were more willing to purchase items in PP and DD conditions than QP and SD conditions, respectively, and spent more time making final purchase decisions in QP and DD condition or PP and SD condition compared to PP and DD condition. Neurophysiologically, DD automatically recruited more attentional resources than SD and led to a higher P2 amplitude. QP and DD condition or PP and SD condition evoked a larger N2 amplitude and enhanced perceptual conflict compared to PP and DD condition. During late stage, PP and DD elicited a more positive LPP amplitude in contrast to QP and SD, respectively, indicating that people have stronger purchase intention and positive affect in PP and DD contexts. These findings provided evidence for the differential influences between PP and QP and what ultimately made consumers buy hedonic products or not

Effectiveness of Post-Traumatic Growth Intervention to Promote Positive Post-Traumatic Traits in Chinese Breast Cancer Patients:A Randomized Controlled Trial

Objective: The purpose of this study was to evaluate the effectiveness of post-traumatic growth (PTG) model-based intervention to improve positive psychological traits in Chinese breast cancer patients. Design: A randomized control trial of a psychological group intervention based on PTG model. Methods: The Clinical Trial was registered on 17 August 2019 at Chinese Clinical Trials.gov with Registration number ChiCTR1900025264. A total of 92 patients with breast cancer were recruited. The participants were randomly assigned to the experimental group (n = 46) and the control group (n = 46). A six-session psychological group intervention based on PTG model was implemented in the experimental group, and a six-session health education was implemented in the control group. The outcomes were measured at baseline (pre-intervention), 3 weeks, 6 weeks after the intervention. The primary outcome was post-traumatic growth assessed by the Chinese version of the Post-Traumatic Growth Inventory (PTGI); Secondary outcomes included psychological resilience, family resilience, rumination, and self-disclosure. Results: A total of 87 patients with breast cancer completed this study, including 44 patients in the experimental group and 43 patients in the control group. There was no significant difference in baseline data of breast cancer patients between the two groups except for the treatment regimen (p &gt; 0.05). The two groups were compared after the intervention; the interaction effects between the total scores of post-traumatic growth, family resilience, and self-disclosure and the time term were statistically significant (p &lt; 0.05), indicating that the trend of change in total scores of post-traumatic growth, family resilience, and self-disclosure differed between the experimental and control groups over time, and the scores improved in the experimental group were significantly higher than those in the control group. The comparison of psychological resilience and total score of rumination at each time point was statistically significant (p &lt; 0.05), indicating that group intervention based on the PTG model could improve the psychological recovery ability and rumination level of the experimental group. Conclusion: The psychological group intervention based on the PTG model significantly improved post-traumatic growth, family resilience, and self-disclosure in patients with breast cancer. However, the impact on psychological resilience and rumination was relatively small. Long-term intervention is needed to further test the effect of the PTG model on psychological resilience and rumination.</p

Identifying patients at risk of prolonged hospital length of stay after total knee arthroplasty: A real-world study on the creation and validation of a cloud estimator

Accurate prediction of the length of stay for patients undergoing total knee arthroplasty (TKA) is critical for efficient medical resource allocation. This study aimed to create a user-friendly model to assist this estimation process. A secondary analysis was conducted on 2676 patients who underwent elective primary TKA at a tertiary academic medical center in Singapore from January 2013 to June 2014. The eligible patients (n = 2600) were randomly divided into a training cohort (n = 2081) and a validation cohort (n = 519), at a ratio of 4:1. A prolonged hospital stay was defined as exceeding six days. Multivariable logistic regression was used to develop a prediction model, and an online calculator was created to facilitate its application. The model's discrimination power, goodness-of-fit, and clinical applicability were evaluated. Additionally, models using other statistical methods were developed for performance comparison. The model includes predictors such as age, operation duration, history of cerebrovascular accidents, creatinine levels, procedure site, the American Society of Anesthesiologists Physical status, hemoglobin levels, and primary anesthesia type. The model demonstrated robust discrimination power with a C statistic of 0.70 (95% confidence interval, 0.64 to 0.75), satisfactory goodness-of-fit (Hosmer–Lemeshow test, P=0.286), and was applicable when thresholds were between 0.08 and 0.52, based on decision curve analysis. A predictive model was developed that can be used to identify patients who are likely to require an extended stay following TKA. This could assist in planning bed availability and guiding therapeutic decisions

Improving Negative-Prompt Inversion via Proximal Guidance

DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifier-free guidance (CFG) scales being used for enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control. Negative-prompt inversion (NPI) further offers a training-free closed-form solution of NTI. However, it may introduce artifacts and is still constrained by DDIM reconstruction quality. To overcome these limitations, we propose Proximal Negative-Prompt Inversion (ProxNPI), extending the concepts of NTI and NPI. We enhance NPI with a regularization term and reconstruction guidance, which reduces artifacts while capitalizing on its training-free nature. Our method provides an efficient and straightforward approach, effectively addressing real image editing tasks with minimal computational overhead.Comment: Code at https://github.com/phymhan/prompt-to-promp

Effect of Temperature on Electromagnetic Performance of Active Phased Array Antenna

Active phased array antennas (APAAs) can suffer from the effects of harsh thermal environments, which are caused by the large quantity of power generated by densely packed T/R modules and external thermal impacts. The situation may be worse in the case of limited room and severe thermal loads, due to heat radiation and a low temperature sink. The temperature field of the antenna can be changed. Since large numbers of temperature-sensitive electronic components exist in T/R modules, excitation current output can be significantly affected and the electromagnetic performance of APAAs can be seriously degraded. However, due to a lack of quantitative analysis, it is difficult to directly estimate the effect of temperature on the electromagnetic performance of APAAs. Therefore, this study investigated the electromagnetic performance of APAAs as affected by two key factors—the uniformly distributed temperature field and the temperature gradient field—based on different antenna shapes and sizes, to provide theoretical guidance for their thermal design