Diffusion Framework Research Articles

Modeling vacancy-induced porosity in compositionally-graded complex alloys

A phase-field model of the void evolution is developed to investigate the vacancy-induced Kirkendall porosity within diffusion couples under isothermal annealing conditions. This model encapsulates a comprehensive computational framework for multicomponent diffusion, non-equilibrium vacancy diffusion, voids nucleation, and growth — integrating considerations for surface anisotropy and morphological instability. This novel thermodynamically consistent methodology combines the phase-field model with the vacancy diffusion model and uses thermodynamic and atomic mobility parameters defined by CALPHAD approach. The generalized nature of the method enables us to include both non-equilibrium and equilibrium vacancies, covering all cases of non-ideal/ideal sinks and sources for vacancies, whether at dislocations or void surfaces. Moreover, the model can deal with compositionally complex alloys such as Ni-based superalloys. Along with the full model, a fast variant is also suggested, providing long-time simulations at the experimentally relevant time and space scales valuable for both academic and industrial contexts. The model’s efficacy was demonstrated through precise simulation of voids evolution in a model AlCoCrTa/Ni diffusion couple, mirroring the experimental outcomes observed in the CMSX-10/Ni diffusion couple at 1525 K after 192 h of annealing. This encompassed accurate replication of diffusion profiles and tomographic analysis of voids. A very good agreement with the experimental data was observed, particularly in the distribution of pore volume-fractions and the delineation of void shapes.

DiSCO: Diffusion Schrödinger Bridge for Molecular Conformer Optimization

The generation of energetically optimal 3D molecular conformers is crucial in cheminformatics and drug discovery. While deep generative models have been utilized for direct generation in Euclidean space, this approach encounters challenges, including the complexity of navigating a vast search space. Recent generative models that implement simplifications to circumvent these challenges have achieved state-of-the-art results, but this simplified approach unavoidably creates a gap between the generated conformers and the ground-truth conformational landscape. To bridge this gap, we introduce DiSCO: Diffusion Schrödinger Bridge for Molecular Conformer Optimization, a novel diffusion framework that enables direct learning of nonlinear diffusion processes in prior-constrained Euclidean space for the optimization of 3D molecular conformers. Through the incorporation of an SE(3)-equivariant Schrödinger bridge, we establish the roto-translational equivariance of the generated conformers. Our framework is model-agnostic and offers an easily implementable solution for the post hoc optimization of conformers produced by any generation method. Through comprehensive evaluations and analyses, we establish the strengths of our framework, substantiating the application of the Schrödinger bridge for molecular conformer optimization. First, our approach consistently outperforms four baseline approaches, producing conformers with higher diversity and improved quality. Then, we show that the intermediate conformers generated during our diffusion process exhibit valid and chemically meaningful characteristics. We also demonstrate the robustness of our method when starting from conformers of diverse quality, including those unseen during training. Lastly, we show that the precise generation of low-energy conformers via our framework helps in enhancing the downstream prediction of molecular properties. The code is available at https://github.com/Danyeong-Lee/DiSCO.

A Generalized Neural Diffusion Framework on Graphs

Recent studies reveal the connection between GNNs and the diffusion process, which motivates many diffusion based GNNs to be proposed. However, since these two mechanisms are closely related, one fundamental question naturally arises: Is there a general diffusion framework that can formally unify these GNNs? The answer to this question can not only deepen our understanding of the learning process of GNNs, but also may open a new door to design a broad new class of GNNs. In this paper, we propose a general diffusion equation framework with the fidelity term, which formally establishes the relationship between the diffusion process with more GNNs. Meanwhile, with this framework, we identify one characteristic of graph diffusion networks, i.e., the current neural diffusion process only corresponds to the first-order diffusion equation. However, by an experimental investigation, we show that the labels of high-order neighbors actually appear monophily property, which induces the similarity based on labels among high-order neighbors without requiring the similarity among first-order neighbors. This discovery motives to design a new high-order neighbor-aware diffusion equation, and derive a new type of graph diffusion network (HiD-Net) based on the framework. With the high-order diffusion equation, HiD-Net is more robust against attacks and works on both homophily and heterophily graphs. We not only theoretically analyze the relation between HiD-Net with high-order random walk, but also provide a theoretical convergence guarantee. Extensive experimental results well demonstrate the effectiveness of HiD-Net over state-of-the-art graph diffusion networks.

ContactGen: Contact-Guided Interactive 3D Human Generation for Partners

Among various interactions between humans, such as eye contact and gestures, physical interactions by contact can act as an essential moment in understanding human behaviors. Inspired by this fact, given a 3D partner human with the desired interaction label, we introduce a new task of 3D human generation in terms of physical contact. Unlike previous works of interacting with static objects or scenes, a given partner human can have diverse poses and different contact regions according to the type of interaction. To handle this challenge, we propose a novel method of generating interactive 3D humans for a given partner human based on a guided diffusion framework (ContactGen in short). Specifically, we newly present a contact prediction module that adaptively estimates potential contact regions between two input humans according to the interaction label. Using the estimated potential contact regions as complementary guidances, we dynamically enforce ContactGen to generate interactive 3D humans for a given partner human within a guided diffusion model. We demonstrate ContactGen on the CHI3D dataset, where our method generates physically plausible and diverse poses compared to comparison methods.

Latent Diffusion Transformer for Probabilistic Time Series Forecasting

The probability prediction of multivariate time series is a notoriously challenging but practical task. This research proposes to condense high-dimensional multivariate time series forecasting into a problem of latent space time series generation, to improve the expressiveness of each timestamp and make forecasting more manageable. To solve the problem that the existing work is hard to extend to high-dimensional multivariate time series, we present a latent multivariate time series diffusion framework called Latent Diffusion Transformer (LDT), which consists of a symmetric statistics-aware autoencoder and a diffusion-based conditional generator, to implement this idea. Through careful design, the time series autoencoder can compress multivariate timestamp patterns into a concise latent representation by considering dynamic statistics. Then, the diffusion-based conditional generator is able to efficiently generate realistic multivariate timestamp values on a continuous latent space under a novel self-conditioning guidance which is modeled in a non-autoregressive way. Extensive experiments demonstrate that our model achieves state-of-the-art performance on many popular high-dimensional multivariate time series datasets.

MedSegDiff-V2: Diffusion-Based Medical Image Segmentation with Transformer

The Diffusion Probabilistic Model (DPM) has recently gained popularity in the field of computer vision, thanks to its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, which have demonstrated impressive capabilities and sparked much discussion within the community. Recent investigations have further unveiled the utility of DPM in the domain of medical image analysis, as underscored by the commendable performance exhibited by the medical image segmentation model across various tasks. Although these models were originally underpinned by a UNet architecture, there exists a potential avenue for enhancing their performance through the integration of vision transformer mechanisms. However, we discovered that simply combining these two models resulted in subpar performance. To effectively integrate these two cutting-edge techniques for the Medical image segmentation, we propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities. Through comprehensive evaluation, our approach demonstrates superiority over prior state-of-the-art (SOTA) methodologies. Code is released at https://github.com/KidsWithTokens/MedSegDiff.

A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation

Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases. Recently, deep generative models have exhibited remarkable potential for generating therapeutic peptides, but they only utilize sequence or structure information alone, which hinders the performance in generation. In this study, we propose a Multi-Modal Contrastive Diffusion model (MMCD), fusing both sequence and structure modalities in a diffusion framework to co-generate novel peptide sequences and structures. Specifically, MMCD constructs the sequence-modal and structure-modal diffusion models, respectively, and devises a multi-modal contrastive learning strategy with inter-contrastive and intra-contrastive in each diffusion timestep, aiming to capture the consistency between two modalities and boost model performance. The inter-contrastive aligns sequences and structures of peptides by maximizing the agreement of their embeddings, while the intra-contrastive differentiates therapeutic and non-therapeutic peptides by maximizing the disagreement of their sequence/structure embeddings simultaneously. The extensive experiments demonstrate that MMCD performs better than other state-of-the-art deep generative methods in generating therapeutic peptides across various metrics, including antimicrobial/anticancer score, diversity, and peptide-docking.

Diffusiophoresis of Macromolecules within the Framework of Multicomponent Diffusion.

Diffusiophoresis is the isothermal migration of a colloidal particle through a liquid caused by a cosolute concentration gradient. Although diffusiophoresis was originally introduced using hydrodynamics, it can also be described by employing the framework of multicomponent diffusion. This not only enables the extraction of diffusiophoresis coefficients from measured multicomponent-diffusion coefficients but also their theoretical interpretation using fundamental thermodynamic and transport parameters. This review discusses the connection of diffusiophoresis with the 2 × 2 diffusion-coefficient matrix of ternary liquid mixtures. Specifically, diffusiophoresis is linked to the cross-term diffusion coefficient characterizing diffusion of colloidal particles due to cosolute concentration gradient. The other cross-term, which describes cosolute diffusion due to the concentration gradient of colloidal particles, is denoted as osmotic diffusion. Representative experimental results on diffusiophoresis and osmotic diffusion for polyethylene glycol and lysozyme in the presence of aqueous salts and osmolytes are described. These data were extracted from ternary diffusion coefficients measured using precision Rayleigh interferometry at 25 °C. The preferential-hydration and electrophoretic mechanisms responsible for diffusiophoresis are examined. The connection of diffusiophoresis and osmotic diffusion to preferential-interaction coefficients, Onsager reciprocal relations, Donnan equilibrium and Nernst-Planck equations are also discussed.

Diffusion models for spatio-temporal-spectral fusion of homogeneous Gaofen-1 satellite platforms

Due to hardware technology limitations, satellite sensors are unable to capture images with high temporal, spatial, and spectral resolutions simultaneously. However, the Gaofen-1 satellite overcomes this challenge by incorporating 2-meter panchromatic, 8-meter multispectral, and 16-meter wide-field cameras, allowing for the integration of images from these sensors. To address this issue, we propose a study on the spatio-temporal-spectral fusion method for Gaofen-1 images, aiming to achieve more comprehensive structures. Inspired by the diffusion model, which learns the data distribution of the target image, we propose a new network utilizing an enhanced diffusion framework. The network incorporates both structural and spectral constraints to guide the fusion process. This work represents the first application of the diffusion model to spatio-temporal-spectral fusion, specifically synthesizing the 2-meter multispectral images with dense temporal resolution. To assess fusion quality, we have developed a benchmark dataset. During the validation stage, we evaluate the radiometric deviation, structural similarity, and spectral fidelity between the fused 2-meter multispectral images and the reference images. Both visual and quantitative assessments demonstrate that our newly proposed method work well for the Gaofen-1 fusion.

Asymptotic analysis of conversion-limited phase separation

Liquid–liquid phase separation plays a major role in the formation and maintenance of various membrane-less subcellular structures in the cytoplasm and nucleus of cells. Biological condensates contain enhanced concentrations of proteins and RNA, many of which can be continually exchanged with the surrounding medium. Coarsening is an important step in the kinetics of phase separation, whereby an emulsion of polydisperse condensates transitions to a single condensate in thermodynamic equilibrium with a surrounding dilute phase. A key feature of biological phase separation is the co-existence of multiple condensates over significant time scales, which is consistent with experimental observations showing a slowing of coarsening rates. It has recently been proposed that one rate limiting step could be the slow interfacial conversion of a molecular constituent between the dilute and dense phases. In this paper, we analyse conversion-limited phase separation within the framework of diffusion in singularly perturbed domains, which exploits the fact that biological condensates tend to be much smaller than the size of a cell. Using matched asymptotic analysis, we solve the quasi-static diffusion equation for the concentration in the dilute phase, and then derive kinetic equations for the slow growth/shrinkage of the condensates. This provides a systematic way of obtaining corrections to mean-field theory that take into account the geometry of the cell and the locations of all the condensates.

Atmosphere and ocean energy transport in extreme warming scenarios

Extreme scenarios of global warming out to 2300 from the SSP5-8.5 extension scenario are analyzed in three state-of-the-art climate models, including two models with climate sensitivity greater than 4.5°C. The result is some of the largest warming amounts ever seen in simulations run over the historical record and into the future. The simulations exhibit between 9.3 and 17.5°C global mean temperature change between pre-Industrial and the end of the 23rd century. The extremely large changes in global temperature allow exploration of fundamental questions in climate dynamics, such as the determination of moisture and energy transports, and their relation to global atmosphere-ocean circulation. Three models performed simulations of SSP5-8.5 to 2300: MRI-ESM2-0, IPSL-CM6A-LR, and CanESM5. We analyze these simulations to improve understanding of climate dynamics, rather than as plausible futures. In the model with the most warming, CanESM5, the moisture content of the planet more than doubles, and the hydrologic cycle increases in intensity. In CanESM5 and IPSL-CM6A-LR nearly all sea ice is eliminated in both summer and winter in both hemispheres. In all three models, the Hadley circulation weakens, the tropopause height rises, and storm tracks shift poleward, to varying degrees. We analyze the moist static energy transports in the simulations using a diffusive framework. The dry static energy flux decreases to compensate for the increased moisture transport; however the compensation is imperfect. The total atmospheric transport increases but not as quickly as expected with a constant diffusivity. The decrease in eddy intensity plays an important role in determining the energy transports, as do the pattern of cloud feedbacks and the strength of ocean circulations.

Navigating Sustainable Practices in E-commerce: A Systematic Literature Review with Insights from the ADO Framework

Purpose: The convergence of e-commerce and sustainability sparks an investigation into their implications and approaches. Despite the efficiency promised by online shopping, apprehensions regarding packaging waste and energy use emphasize the necessity for detailed scrutiny of the environmental impact of e-commerce and its contribution to sustainable consumption in the ever-evolving global market. Design/Methodology/Approach: This research paper aims to investigate the connection between sustainability and e-commerce, employing the (ADO) i.e. Adoption, Diffusion, and Optimization (ADO) framework to analyse antecedents, decisions, and outcomes. The research explores theoretical foundations, such as Industrial Ecology, B2C business models, and the Resource-Advantage Theory, to reveal the multifaceted relationship between e-commerce and sustainability. Strategic decisions, varying from impacts on energy consumption to the integration of sustainable practices in e-commerce, are studied, shedding light on the multifaceted landscape of e-commerce sustainability. Determinate outcomes, including enhanced sustainability factors, positive impacts on commitment and e-word of mouth, and noteworthy contributions to UN Sustainable Development Goals, emphasize the transformative potential of e-commerce. Findings/Result: The study assessment emphasizes for the continued integration of sustainability into e-commerce strategies, urging all stakeholders to navigate complexities and adopt eco-friendly practices for a better sustainable digital future. The discussion synthesizes diverse perspectives, highlighting the dynamic nature of e-commerce and its capacity to positively influence societal and environmental objectives. Originality Value: The paper has used ADO approach which ensures the novelty of the research for the topic under study. Paper Type: Research paper

Implementing a Standardized Phase-of-Care Craniotomy Education Program.

Leaders at a community hospital in the southeastern United States sought to add a craniotomy program to meet the needs of the local patient population. Perioperative and critical-care nurses required specific knowledge and skills to care for patients undergoing craniotomy procedures. The facility's education team applied adult learning theories and an innovation diffusion framework when developing an evidence-based craniotomy education program. A clinical nurse specialist conducted a gap analysis and readiness assessment to determine the nurses' knowledge, skills, and competence to care for patients; attitudes and behaviors toward implementation of the new program; and preferred learning methods. The three-tiered phase-of-care education program included didactic learning sessions, shadowing experiences, hands-on experience with equipment, and simulation sessions. The program focused on effective collaboration and care transitions. Nurses were satisfied with the program, and they continue to participate in ongoing quarterly simulation sessions with case study scenarios to enhance their skills.

Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns

Excitable systems give rise to important phenomena such as heat waves, epidemics and cardiac arrhythmias. Understanding, forecasting and controlling such systems requires reliable mathematical representations. For cardiac tissue, computational models are commonly generated in a reaction–diffusion framework based on detailed measurements of ionic currents in dedicated single-cell experiments.Here, we show that recorded movies at the tissue-level of stochastic pacing in a single variable are sufficient to generate a mathematical model. Via exponentially weighed moving averages, we create additional state variables, and use simple polynomial regression in the augmented state space to quantify excitation wave dynamics. A spatial gradient-sensing term replaces the classical diffusion as it is more robust to noise. Our pipeline for model creation is demonstrated for an in-silico model and optical voltage mapping recordings of cultured human atrial myocytes and only takes a few minutes.Our findings have the potential for widespread generation, use and on-the-fly refinement of personalised computer models for non-linear phenomena in biology and medicine, such as predictive cardiac digital twins.

Morphological Relaxation of Strained Epitaxial Films for Stripe‐Geometry Devices

AbstractOne of the major issues when reducing the channel length in strained transistors is the stress relaxation that significantly degrades the carriers mobility. A new morphological evolution is reported here, describing the relaxation of a strained epitaxial film deposited on a pattern characterized by stripe geometry, both paradigmatic of two‐dimensional (2D) like systems, and characteristic of many devices. The thermodynamic surface diffusion framework accounting for elasticity and capillarity is investigated. The former is solved in two dimensions thanks to the Airy formalism. The resulting dynamical Schrödinger‐like equation governing the film shape evolution is then solved thanks to a decomposition on eigenmodes. It reveals different developments depending upon the system's geometric parameters and the time scale. Mass transfer occurs toward the relaxed areas and creates a ridge at the nanolayers edges, controlled by the geometry and the scale of the structure. These results allow to refine the potential control of electronic properties via the geometry of a system.

Physics-Informed DeepMRI: k-Space Interpolation Meets Heat Diffusion.

Recently, diffusion models have shown considerable promise for MRI reconstruction. However, extensive experimentation has revealed that these models are prone to generating artifacts due to the inherent randomness involved in generating images from pure noise. To achieve more controlled image reconstruction, we reexamine the concept of interpolatable physical priors in k-space data, focusing specifically on the interpolation of high-frequency (HF) k-space data from low-frequency (LF) k-space data. Broadly, this insight drives a shift in the generation paradigm from random noise to a more deterministic approach grounded in the existing LF k-space data. Building on this, we first establish a relationship between the interpolation of HF k-space data from LF k-space data and the reverse heat diffusion process, providing a fundamental framework for designing diffusion models that generate missing HF data. To further improve reconstruction accuracy, we integrate a traditional physics-informed k-space interpolation model into our diffusion framework as a data fidelity term. Experimental validation using publicly available datasets demonstrates that our approach significantly surpasses traditional k-space interpolation methods, deep learning-based k-space interpolation techniques, and conventional diffusion models, particularly in HF regions. Finally, we assess the generalization performance of our model across various out-of-distribution datasets. Our code are available at https://github.com/ZhuoxuCui/Heat-Diffusion.

The evolution of complex metarhizium-insect-plant interactions

Metarhizium species interact with plants, insects, and microbes within a diffuse co-evolutionary framework that benefits soil health, biodiversity, and plant growth. The insect host ranges of these fungi vary greatly. Specialization to a narrow host range usually occurs in the tropics with its stable insect populations, and is characterized by the rapid evolution of existing protein sequences, sexual recombination, and small genomes. Host-generalists are associated with temperate regions and ephemeral insect populations. Their mutualistic plant-colonizing lifestyle increases survival when insects are rare, while facultative entomopathogenicity feeds both the fungi and plants when insects are common. Host-generalists have lost meiosis and associated genome defense mechanisms, enabling gene duplications to diversify functions related to plant colonization and host exploitation. Horizontal gene transfer events via transposons have also contributed to host range changes, while parasexuality combines beneficial mutations within individual clones of host-generalists. There is also a lot of genetic variation in insect populations and an increasing understanding that both pathogen virulence and insect immunity are linked with stress responses. Thus, susceptibility to host-generalists can vary due to non-specific resistance to multiple stressors, multipurpose physical and chemical barriers, and heterogeneity in physiological and behavioral factors, such as sleep.

HiDiff: Hybrid Diffusion Framework for Medical Image Segmentation.

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.

A unified conditional diffusion framework for dual protein targets based bioactive molecule generation

A unified conditional diffusion framework for dual protein targets based bioactive molecule generation

Fisher and Shannon Functionals for Hyperbolic Diffusion.

The complexity measure for the distribution in space-time of a finite-velocity diffusion process is calculated. Numerical results are presented for the calculation of Fisher's information, Shannon's entropy, and the Cramér-Rao inequality, all of which are associated with a positively normalized solution to the telegrapher's equation. In the framework of hyperbolic diffusion, the non-local Fisher's information with the x-parameter is related to the local Fisher's information with the t-parameter. A perturbation theory is presented to calculate Shannon's entropy of the telegrapher's equation at long times, as well as a toy model to describe the system as an attenuated wave in the ballistic regime (short times).