SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation

Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data. However, in comparison to their non-invariant counterparts, we have found that these invariant models encounter greater learning challenges since 1) their effective target distributions exhibit more modes; 2) their optimal one-step denoising scores are the score functions of Gaussian mixtures with more components. Motivated by this analysis, we propose a non-invariant diffusion model, called $\textit{SwinGNN}$, which employs an efficient edge-to-edge 2-WL message passing network and utilizes shifted window based self-attention inspired by SwinTransformers. Further, through systematic ablations, we identify several critical training and sampling techniques that significantly improve the sample quality of graph generation. At last, we introduce a simple post-processing trick, $\textit{i.e.}$, randomly permuting the generated graphs, which provably converts any graph generative model to a permutation-invariant one. Extensive experiments on synthetic and real-world protein and molecule datasets show that our SwinGNN achieves state-of-the-art performances. Our code is released at https://github.com/qiyan98/SwinGNN

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

Numerička studija izrađena pomoću ChemKin za rasplinjavanje vodene pare ugljene prašine i transformacije žive unutar rasplinjača s vodenom parom

Zero-emission coal (ZEC) technology has been actively studied recently. It aims to achieve zero emission of CO2 and other pollutants and the efficiency of this system can reach no less than 70%. Hydro-gasification of pulverized coal is a core process of ZEC. However, the mechanism of gasification and transformation of mercury speciation in the hydro-gasification is has not been understood precisely up until now. This restrains the ZEC’s commercialization. The purpose of this paper is to study the mechanism of hydro-gasification and mercury speciation transformation for coal in the gasifier with high temperature and pressure. Detailed chemical kinetics mechanism (CKM) has been proposed for hydro-gasification for pulverized coal in an entrained flow hydro-gasifier. The effects have been studied for different reaction conditions on hydro-gasification products and evolution of Hg in terms of the chemical reaction kinetics method. The CKM mechanism includes 130 elementary reactions and is solved with commercially available software, ChemKin. The calculation results are validated against the experimental data from literature and meaningful predictions are finally obtained. In addition, the chemical equilibrium calculation (CEC) is also used for predictions. Although the CEC method assumes all the reactions have reached chemical equilibrium, which is not the case in industrial reality, the calculation results are of value as reference.Tehnologija korištenja ugljena bez emisija (ZEC) se od nedavno aktivno proučava. Njezin cilj je postizanje nulte stope emisija CO2 te ostalih štetnih tvari dok efikasnost sustava mora biti minimalno 70%. Rasplinjavanje ugljene prašine vodenom parom je temeljni proces ZEC-a. Međutim, mehanizam rasplinjavanja i transformacije žive u rasplinjavanju vodenom parom još nije u potpunosti shvaćeno. To ograničava mogućnost komercijalne primjene ZEC-a. Cilj ovog rada je proučavanje mehanizama rasplinjavanja vodenom parom i transformacije žive za rasplinjavanje ugljena u rasplinjaču s visokim temperaturama i tlakom. Predloženi su detaljni kemijski kinetički mehanizmi (CKM) za rasplinjavanje ugljene prašine u fluidiziranom sloju sa zajedničkim tokom tvari. Proučeni su utjecaji raznih uvjeta pod kojim su se odvijale reakcije na produkte rasplinjavanja i evoluciju žive u uvjetima kemijskih reakcija kinetičke metode. CMK mehanizam sadrži 130 elementarnih reakcija i rješava se s komercijalno dostupnim programom, ChemKin. Rezultati simulacije se uspoređuju s eksperimentalnim iz literature te su konačno dobivena smislena predviđanja. Jednadžbe kemijske ravnoteže (CEC) su također korištene za predviđanja. Iako CEC metoda pretpostavlja da su sve reakcije postigle ravnotežu, što nije uvijek slučaj u industriji, rezultati tog proračuna mogu poslužiti kao referenca

Large Distance Modification of Newtonian Potential and Structure Formation in Universe

In this paper, we study the effects of super-light brane world perturbative modes on structure formation in our universe. As these modes modify the large distance behavior of Newtonian potential, they effect the clustering of a system of galaxies. So, we explicitly calculate the clustering of galaxies interacting through such a modified Newtonian potential. We use a suitable approximation for analyzing this system of galaxies, and discuss the validity of such approximations. We observe that such corrections also modify the virial theorem for such a system of galaxies.Comment: 13 pages, 3 captioned figure

Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models

Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a long paragraph (up to 512 words), these generation models still struggle to achieve strong alignment and are unable to generate images depicting complex scenes. In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation. At its core is using a large language model (e.g., Llama V2) to encode long-form text, followed by fine-tuning with LORA to alignthe text-image feature spaces in the generation task. To facilitate the training of long-text semantic alignment, we also curated a high-quality paragraph-image pair dataset, namely ParaImage. This dataset contains a small amount of high-quality, meticulously annotated data, and a large-scale synthetic dataset with long text descriptions being generated using a vision-language model. Experiments demonstrate that ParaDiffusion outperforms state-of-the-art models (SD XL, DeepFloyd IF) on ViLG-300 and ParaPrompts, achieving up to 15% and 45% human voting rate improvements for visual appeal and text faithfulness, respectively. The code and dataset will be released to foster community research on long-text alignment.Comment: The project website is at: https://weijiawu.github.io/ParaDiffusionPage/. Code: https://github.com/weijiawu/ParaDiffusio

Diagnostic and Prognostic Performance of MicroRNA-25, Carbohydrate Antigen 19-9, Carcinoembryonic Antigen, and Carbohydrate Antigen 125 in Pancreatic Ductal Adenocarcinoma

Background: Pancreatic cancer is a malignancy with high mortality due to the difficulties in early detection. We investigated and compared the diagnostic and prognostic performance of several blood biomarkers, including microRNA-25 (miR-25), carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA), and carbohydrate antigen 125 (CA125). Methods: A retrospective study was conducted at the Chinese People’s Liberation Army General Hospital from May 2014 to September 2018. Serum specimens were collected, and miR-25 expression levels were measured using real-time quantitative polymerase chain reaction. Serum CA19-9, CEA, and CA125 levels were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analyses including nonparametric test, receiver operator characteristic (ROC) curves, Kaplan-Meier analysis, and subsequent log-rank test were performed with PRISM 5.0 software. Univariate and multivariate analyses were performed with the R software. P<0.05 was considered statistically significant.Results: A total of 250 individuals were recruited, including 75 with pancreatic ductal adenocarcinoma (PDAC), 75 with benign lesions, and 100 healthy controls. miR-25, CA19-9, CEA, and CA125 exhibited an area under the curve (AUC) of 0.88, 0.91, 0.81, and 0.76 with a sensitivity of 78.7%, 74.7%, 37.3%, and 35.7% and specificity of 91.5%, 97.0%, 98.2%, and 98.3%, respectively. The combination of miR-25 and CA19-9 further increased the sensitivity to 93.3% with a specificity of 88.5%. Stage-dependent sensitivity was observed with CA19-9, CEA, and CA125. miR-25 levels significantly stratified the prognosis by median level (4,989.97 copies/mL). CA19-9, CEA, and CA125 levels significantly stratified the prognosis by median levels. Univariate and subsequent multivariate analyses identified tumor (T) stage, CA19-9, and CA125 as independent risk factors for PDAC prognosis.Conclusion: The combination of miR-25 and CA19-9 significantly enhanced the detection sensitivity of PDAC. T stage, CA19-9, and CA125 levels were independent risk factors for PDAC prognosis

Transcriptomics combined with physiological analysis reveals the mechanism of cadmium uptake and tolerance in Ligusticum chuanxiong Hort. under cadmium treatment

IntroductionLigusticum chuanxiong Hort. is a widely used medicinal plant, but its growth and quality can be negatively affected by contamination with the heavy metal cadmium (Cd). Despite the importance of understanding how L. chuanxiong responds to Cd stress, but little is currently known about the underlying mechanisms.MethodsTo address this gap, we conducted physiological and transcriptomic analyses on L. chuanxiong plants treated with different concentrations of Cd2+ (0 mg·L−1, 5 mg·L−1, 10 mg·L−1, 20 mg·L−1, and 40 mg·L−1).ResultsOur findings revealed that Cd stress inhibited biomass accumulation and root development while activating the antioxidant system in L. chuanxiong. Root tissues were the primary accumulation site for Cd in this plant species, with Cd being predominantly distributed in the soluble fraction and cell wall. Transcriptomic analysis demonstrated the downregulation of differential genes involved in photosynthetic pathways under Cd stress. Conversely, the plant hormone signaling pathway and the antioxidant system exhibited positive responses to Cd regulation. Additionally, the expression of differential genes related to cell wall modification was upregulated, indicating potential enhancements in the root cell wall’s ability to sequester Cd. Several differential genes associated with metal transport proteins were also affected by Cd stress, with ATPases, MSR2, and HAM3 playing significant roles in Cd passage from the apoplast to the cell membrane. Furthermore, ABC transport proteins were found to be key players in the intravesicular compartmentalization and efflux of Cd.DiscussionIn conclusion, our study provides preliminary insights into the mechanisms underlying Cd accumulation and tolerance in L. chuanxiong, leveraging both physiological and transcriptomic approaches. The decrease in photosynthetic capacity and the regulation of plant hormone levels appear to be major factors contributing to growth inhibition in response to Cd stress. Moreover, the upregulation of differential genes involved in cell wall modification suggests a potential mechanism for enhancing root cell wall capabilities in isolating and sequestering Cd. The involvement of specific metal transport proteins further highlights their importance in Cd movement within the plant