Joint Generative Modeling of Scene Graphs and Images via Diffusion Models

In this paper, we present a novel generative task: joint scene graph - image generation. While previous works have explored image generation conditioned on scene graphs or layouts, our task is distinctive and important as it involves generating scene graphs themselves unconditionally from noise, enabling efficient and interpretable control for image generation. Our task is challenging, requiring the generation of plausible scene graphs with heterogeneous attributes for nodes (objects) and edges (relations among objects), including continuous object bounding boxes and discrete object and relation categories. We introduce a novel diffusion model, DiffuseSG, that jointly models the adjacency matrix along with heterogeneous node and edge attributes. We explore various types of encodings for the categorical data, relaxing it into a continuous space. With a graph transformer being the denoiser, DiffuseSG successively denoises the scene graph representation in a continuous space and discretizes the final representation to generate the clean scene graph. Additionally, we introduce an IoU regularization to enhance the empirical performance. Our model significantly outperforms existing methods in scene graph generation on the Visual Genome and COCO-Stuff datasets, both on standard and newly introduced metrics that better capture the problem complexity. Moreover, we demonstrate the additional benefits of our model in two downstream applications: 1) excelling in a series of scene graph completion tasks, and 2) improving scene graph detection models by using extra training samples generated from DiffuseSG

Surgical Management of Urolithiasis in Patients After Urinary Diversion

Objective: To present our experience in surgical management of urolithiasis in patients after urinary diversion. Patients and Methods: Twenty patients with urolithiasis after urinary diversion received intervention. Percutaneous nephrolithotomy, percutaneous based antegrade ureteroscopy with semi-rigid or flexible ureteroscope, transurethral reservoir lithotripsy, percutaneous pouch lithotripsy and open operation were performed in 8, 3, 2, 6, and 1 patients, respectively. The operative finding and complications were retrospectively collected and analyzed. Results: The mean stone size was 4.5±3.1 (range 1.5-11.2) cm. The mean operation time was 82.0±11.5 (range 55-120) min. Eighteen patients were rendered stone free with a clearance of 90%. Complications occurred in 3 patients (15%). Two patients (10%) had postoperative fever greater than 38.5°C, and one patient (5%) suffered urine extravasations from percutaneous tract. Conclusions: The percutaneous based procedures, including percutaneous nephrolithotomy, antegrade ureteroscopy with semi-rigid ureteroscope or flexible ureteroscope from percutaneous tract, and percutaneous pouch lithotripsy, provides a direct and safe access to the target stones in patients after urinary diversion, and with high stone free rate and minor complications. The surgical management of urolithiasis in patients after urinary diversion requires comprehensive evaluation and individualized consideration depending upon the urinary diversion type, stone location, stone burden, available resource and surgeon experience

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.MethodsWe developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.ResultsUsing the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.ConclusionThe GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy

Coordinate-descent adaptation over networks

This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gradient vector. The selection of which coordinates to use varies randomly from iteration to iteration and from agent to agent across the network. Such schemes are useful in reducing computational complexity in power-intensive large data applications. The results show that the steady-state performance of the learning strategy is not affected, while the convergence rate suffers some degradation. The results provide yet another indication of the resilience and robustness of adaptive distributed strategies.AS

Numerical Study of the Effects of Topography and Urbanization on the Local Atmospheric Circulations over the Beijing-Tianjin-Hebei, China

The effects of the topography and urbanization on the local atmospheric circulations over the Beijing-Tianjin-Hebei (BTH) region were studied by the weather research and forecasting (WRF) model, as well as the interactions among these local atmospheric circulations. It was found that, in the summer day time, the multiscale thermally induced local atmospheric circulations may exist and interact in the same time over the BTH region; the topography played a role in the strengthening of the sea breeze circulations; after sunset, the inland progress of sea breeze was slowed down by the opposite mountain breeze; when the land breeze circulation dominated the Bohai bay, the mountain breeze circulation can couple with the land breeze circulation to form a large circulation ranging from the coastline to the mountains. And the presence of cities cannot change the general state of the sea-land breeze (SLB) circulation and mountain-valley breeze (MVB) circulation but acted to modify these local circulations slightly. Meanwhile, the development of the urban heat island (UHI) circulation was also strongly influenced by the nearby SLB circulation and MVB circulation

Simulating Flow and Dispersion by Using WRF-CFD Coupled Model in a Built-Up Area of Shenyang, China

Results are presented from a series of numerical studies designed to investigate the atmospheric boundary layer structure, ambient wind, and pollutant source location and their impacts on the wind field and pollutant distribution within the built-up areas of Shenyang, China. Two models, namely, Open Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study. Then the high resolution computational fluid dynamics (CFD) numerical experiments were performed under the typical simulated atmospheric boundary conditions. It was found that the atmospheric boundary structure played a crucial role in the pollution within the building cluster, which determined the potential turbulent diffusion ability of the atmospheric surface layer; the change of the ambient wind direction can significantly affect the dispersion pattern of pollutants, which was a more sensitive factor than the ambient wind speed; under a given atmospheric state, the location of the pollution sources would dramatically determine the pollution patterns within built-up areas. The WRF-CFD numerical evaluation is a reliable method to understand the complicated flow and dispersion within built-up areas