Any-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images

Stable diffusion, a generative model used in text-to-image synthesis, frequently encounters resolution-induced composition problems when generating images of varying sizes. This issue primarily stems from the model being trained on pairs of single-scale images and their corresponding text descriptions. Moreover, direct training on images of unlimited sizes is unfeasible, as it would require an immense number of text-image pairs and entail substantial computational expenses. To overcome these challenges, we propose a two-stage pipeline named Any-Size-Diffusion (ASD), designed to efficiently generate well-composed images of any size, while minimizing the need for high-memory GPU resources. Specifically, the initial stage, dubbed Any Ratio Adaptability Diffusion (ARAD), leverages a selected set of images with a restricted range of ratios to optimize the text-conditional diffusion model, thereby improving its ability to adjust composition to accommodate diverse image sizes. To support the creation of images at any desired size, we further introduce a technique called Fast Seamless Tiled Diffusion (FSTD) at the subsequent stage. This method allows for the rapid enlargement of the ASD output to any high-resolution size, avoiding seaming artifacts or memory overloads. Experimental results on the LAION-COCO and MM-CelebA-HQ benchmarks demonstrate that ASD can produce well-structured images of arbitrary sizes, cutting down the inference time by 2x compared to the traditional tiled algorithm

Air-to-Air Collaborative Learning: A Multi-Task Orchestration in Federated Aerial Computing

—Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without exchanging them. In addition, EC systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. Existing research lack the potential to federate edge resources and manage corresponding service entities running across multiple drones, thus resulting to suboptimal performance. Therefore, we introduce AerialEdge, a federated learning-based orchestration framework for a federated aerial EC system. We propose a federated multi-output linear regression models to estimate multi-task resource requirements and execution time, to select the closest drone deployment having congruent resource availability and flight time to execute ready tasks at any given time. For better utilization of resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent containerized tasks that coschedules and co-locates tasks tightly on nodes to fully utilize available resources. Extensive experiments on real-world datatrace from Alibaba cluster trace with information on task dependencies show the effectiveness, fast executions, and resource efficiency of our approach

ALIP: Adaptive Language-Image Pre-training with Synthetic Caption

Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image-text pairs collected from the web. However, the presence of intrinsic noise and unmatched image-text pairs in web data can potentially affect the performance of representation learning. To address this issue, we first utilize the OFA model to generate synthetic captions that focus on the image content. The generated captions contain complementary information that is beneficial for pre-training. Then, we propose an Adaptive Language-Image Pre-training (ALIP), a bi-path model that integrates supervision from both raw text and synthetic caption. As the core components of ALIP, the Language Consistency Gate (LCG) and Description Consistency Gate (DCG) dynamically adjust the weights of samples and image-text/caption pairs during the training process. Meanwhile, the adaptive contrastive loss can effectively reduce the impact of noise data and enhances the efficiency of pre-training data. We validate ALIP with experiments on different scales of models and pre-training datasets. Experiments results show that ALIP achieves state-of-the-art performance on multiple downstream tasks including zero-shot image-text retrieval and linear probe. To facilitate future research, the code and pre-trained models are released at https://github.com/deepglint/ALIP.Comment: 15pages, 10figures, ICCV202

AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

Emerging edge computing (EC) systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. To this end, existing EC research has proposed several multiple drone-based edge deployments for various purposes, such as data caching, task offloading, real-time video analytics, and computer vision. However, none of them consider the ability of seamlessly integrating edge resources running across multiple drones in a single pool, to holistically manage and control these resources as well as to eliminate vendor lock-in situations. This paper presents an intelligent resource scheduling solution for a federated aerial EC system, called AirEdge, which jointly considers task dependencies, heterogeneous resource demand and drones’ flight time. We propose a multi-task execution time estimation and a dispatching policy, to select the closest drone deployment having congruent flight time and resource availability to execute ready tasks at any given time. For the utilization of the drones’ attached edge resources, we propose a variant bin-packing optimization approach through gangscheduling of multi-dependent tasks that co-locates tasks tightly on nodes to fully utilize available resources. Experiments on realworld data-trace from Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) and resource demands show the effectiveness, fast executions, and resource efficiency of our approac

Cardiovascular Response of Aged Outpatients With Systemic Diseases During Tooth Extraction: A Single-Center Retrospective Observational Study

BackgroundAged people are maintaining many natural teeth due to improved oral health. However, compromised general health and poor oral hygiene habits at earlier ages resulted in poor status of preserved teeth. Therefore, tooth extraction is required in many aged people. More knowledge is needed because there are many risk factors during the surgery in frail aged adults. The aim of this study was to evaluate the cardiovascular response of such a population during tooth extraction and analyze risk factors to provide clinical guidance.MethodsA retrospective study was performed on aged patients with systemic diseases who underwent tooth extraction. Data regarding demographic profiles and cardiovascular parameters of heart rate and blood pressure were collected preoperative, when local anesthesia was administered, at the beginning of tooth extraction, 5 min after tooth extraction, and postoperative. The effects of risk factors, including age, sex, and systemic diseases on these parameters were analyzed with a multilevel model.ResultsHeart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) of aged patients increased significantly when performing local anesthesia and tooth extraction. During the operation, the older patients (β = 2.011, P = 0.005) and the diabetics (β = 3.902, P < 0.0001) were associated with higher SBP, while those with more tooth extractions exhibited higher HR (β = 0.893, P = 0.007). Women patients showed both significantly elevated HR (β = 1.687, P < 0.0001) and SBP (β = 2.268, P < 0.0001). However, for coronary artery disease patients, HR (β = −2.747, P < 0.0001) and blood pressure [SBP (β = −4.094, P < 0.0001) and DBP (β = −0.87, P = 0.016)] were markedly lower than those of patients without a diagnosis of coronary artery disease.ConclusionCardiovascular response of aged outpatients with systemic diseases during tooth extraction is quite significant. Age, sex, systemic diseases, and the number of tooth extraction could be risk factors closely associated with cardiovascular response. The findings might provide safety guidance for dentists on tooth extraction in this population

A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistic priors and degradation models, which are difficult to meet the requirements of real-world applications in practice. In recent years, face restoration has witnessed great progress after stepping into the deep learning era. However, there are few works to study deep learning-based face restoration methods systematically. Thus, this paper comprehensively surveys recent advances in deep learning techniques for face restoration. Specifically, we first summarize different problem formulations and analyze the characteristic of the face image. Second, we discuss the challenges of face restoration. Concerning these challenges, we present a comprehensive review of existing FR methods, including prior based methods and deep learning-based methods. Then, we explore developed techniques in the task of FR covering network architectures, loss functions, and benchmark datasets. We also conduct a systematic benchmark evaluation on representative methods. Finally, we discuss future directions, including network designs, metrics, benchmark datasets, applications,etc. We also provide an open-source repository for all the discussed methods, which is available at https://github.com/TaoWangzj/Awesome-Face-Restoration.Comment: 21 pages, 19 figure

Fused Deposition Modeling PEEK Implants for Personalized Surgical Application: From Clinical Need to Biofabrication

Three-dimensional printing (3DP) technology is suitable for manufacturing personalized orthopedic implants for reconstruction surgery. Compared with traditional titanium, polyether-ether-ketone (PEEK) is the ideal material for 3DP orthopedic implants due to its various advantages, including thermoplasticity, thermal stability, high chemical stability, and radiolucency suitable elastic modulus. However, it is challenging to develop a well-designed method and manufacturing technique to meet the clinical needs because it requires elaborate details and interplays with clinical work. Furthermore, establishing surgical standards for new implants requires many clinical cases and an accumulation of surgical experience. Thus, there are few case reports on using 3DP PEEK implants in clinical practice. Herein, we formed a team with a lot of engineers, scientists, and doctors and conducted a series of studies on the 3DP PEEK implants for chest wall reconstruction. First, the thoracic surgeons sort out the specific types of chest wall defects. Then, the engineers designed the shape of the implant and performed finite element analysis for every implant. To meet the clinical needs and mechanical requirements of implants, we developed a new fused deposition modeling technology to make personalized PEEK implants. Overall, the thoracic surgeons have used 114 personalized 3DP PEEK implants to reconstruct the chest wall defect and further established the surgical standards of the implants as part of the Chinese clinical guidelines. The surface modification technique and composite process are developed to overcome the new clinical problems of implant-related complications after surgery. Finally, the major challenges and possible solutions to translating 3DP PEEK implants into a mature and prevalent clinical product are discussed in the paper

Intelligent Predictive Beamforming for Integrated Sensing and Communication Based Vehicular-to-Infrastructure Systems

Integrated Sensing and Communication (ISAC) has become a promising paradigm for next-generation wireless communications, which are capable of jointly performing sensing and communication operations. In ISAC systems, sensing accuracy and transmission rate are two major metrics to be targeted. In this paper, we propose a predictive beamforming approach based on the multi-dimensional feature extraction network (MDFEN) for vehicle-to-infrastructure (V2I) systems. In particular, in order to achieve high precision and low latency beamforming, the roadside unit (RSU) will perform angle parameter estimation and prediction based on the ISAC signal echoes. Furthermore, our predictive beamforming approach based on the multidimensional feature extraction network (MDFEN) is capable of improving the efficient beam alignment by exploiting the joint spatio-temporal characteristics of the received signals at the RSU side. Simulation results demonstrate that the proposed approach achieves a higher accuracy in angle tracking compared to convolutional neural network and long short-term memory models. At the same time, the system is capable of obtaining a higher transmission rate

MALAT1 Activates the P53 Signaling Pathway by Regulating MDM2 to Promote Ischemic Stroke

Background/Aims: This study focused on evaluating the effect of MALAT1 and MDM2 on ischemic stroke through regulation of the p53 signaling pathway. Materials: Bioinformatics analysis was performed to identify abnormally expressed lncRNAs, mRNAs and their associated pathways. Oxygen-glucose deprivation/reoxygenation (OGD/R) in cells and middle cerebral artery occlusion/reperfusion (MCAO/R) in mice were performed to simulate an ischemic stroke environment. Western blot and qRT-PCR were used to examine lncRNA expression and mRNA levels. Fluorescence in situ hybridization (FISH) LncRNA was used to locate mRNA. MTT and flow cytometry were performed to examine cell proliferation and apoptosis. Finally, immunohistochemistry was used to observe the expression of genes in vivo. Results: MALAT1 and MDM2, which exhibit strong expression in stroke tissues, were subjected to bioinformatics analysis, and the p53 pathway was chosen for further study. MALAT1, MDM2 and p53 signaling pathway-related proteins were all up regulated in OGD/R cells. Furthermore, Malat1, Mdm2 and p53 pathway related-proteins were also up regulated in MCAO/R mice. Both MALAT1 and MDM2 were localized in the nuclei. Down regulation of MALAT1 and MDM2 enhanced cell proliferation ability and reduced apoptosis, resulting in decreased infarct size in MCAO/R brains. Conclusion: These results indicate that MALAT1/MDM2/p53 signaling pathway axis may provide more effective clinical therapeutic strategy for patients with ischemic stroke