Two-dimensional HP-adaptive Algorithm for Continuous Approximations of Material Data Using Space Projection

In this paper we utilize the concept of the L2 and H1 projections used toadaptively generate a continuous approximation of an input material data inthe finite element (FE) base. This approximation, along with a correspondingFE mesh, can be used as material data for FE solvers. We begin with a brieftheoretical background, followed by description of the hp-adaptive algorithmadopted here to improve gradually quality of the projections. We investigatealso a few distinct sample problems, apply the aforementioned algorithms andconclude with numerical results evaluation

ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Our approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of tasks. We train this lightweight adapter architecture using images paired with corresponding free-text radiology reports from the MIMIC-CXR dataset. ELIXR achieved state-of-the-art performance on zero-shot chest X-ray (CXR) classification (mean AUC of 0.850 across 13 findings), data-efficient CXR classification (mean AUCs of 0.893 and 0.898 across five findings (atelectasis, cardiomegaly, consolidation, pleural effusion, and pulmonary edema) for 1% (~2,200 images) and 10% (~22,000 images) training data), and semantic search (0.76 normalized discounted cumulative gain (NDCG) across nineteen queries, including perfect retrieval on twelve of them). Compared to existing data-efficient methods including supervised contrastive learning (SupCon), ELIXR required two orders of magnitude less data to reach similar performance. ELIXR also showed promise on CXR vision-language tasks, demonstrating overall accuracies of 58.7% and 62.5% on visual question answering and report quality assurance tasks, respectively. These results suggest that ELIXR is a robust and versatile approach to CXR AI

International evaluation of an AI system for breast cancer screening.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.Professor Fiona Gilbert receives funding from the National Institute for Health Research (Senior Investigator award)

Adaptive strategies for multiscale problems

Promotor: Maciej Paszyński.Recenzent: Krzysztof Banaś, Luis Emilio García Castillo.Niepublikowana praca doktorska.Tyt. z ekranu tyt.Praca doktorska. AGH University of Science and Technology in Krakow. Faculty of Computer Science, Electronics and Telecommunications. Department of Computer Science, 2015.Zawiera bibliogr.Dostępna również w wersji drukowanej.Tryb dostępu: Internet.Digital material representation, application of space projections, adaptive algorithms for solving weak forms of PDEs, application of continuous representation of material data, multi-frontal direct solvers and graph grammar systems, algorithms, self-adaptive algorithm for source data pre-processor, self-adaptive algorithm for source data pre-processor using global projection solver, properties of self-adaptive algorithm of material data adaptive pre-processor using global projection solver, new algorithm for adaptive source data pre-processor based on projection-based interpolation, properties and applicability of the modified algorithms of material data adaptive pre-processor, numerical experiment, generalization on meshes with four-faced elements, elimination subtree re-use algorithm for multi-frontal direct solver, classical algorithms of the multi-frontal solver, algorithm of elimination subtrees reuse algorithm for regular mesh, properties and applications of elimination subtree re-use algorithm, faces, edges, vertices, benefits from the optimization, algorithm of elimination subtrees reuse for irregular meches, graph grammar based formulation of the multi-frontal solver algorithm with reuse technique, numerical experiments, adaptive algorithm of automatic scale change for multi-scale methods, one dimensional multi-scale model of Step-and-Flash Imprint Lithography, defining discreet problem, definition of continuous problem, adaptive algorithm of automatic scale change for multiscale model

Linear computational cost graph grammar based direct solver for 3D adaptive finite element method simulations

In this paper we present a new graph grammar based direct solver algorithm delivering linear O(N) computational cost and linear O(N) memory usage for adaptive finite element method simulations. Classical direct solvers on regular grids deliver O(N1.5) complexity for 2D problems and O(N2) in 3D ones. The linear computational cost of our solver is obtained by generating graph representation of the adaptive mesh and by utilizing dynamic construction prescribing the solver algorithm as graph grammar productions

Graph transformation systems for modeling three dimensional finite element method : part I

In this paper we present several graph transformation systems modeling three dimensional h-adaptive Finite Element Method (3D h-FEM) algorithms with tetrahedral finite elements. In our approach a computational mesh is represented by a composite graph and mesh operations are expressed by the graph transformation rules. Each graph transformation system is responsible for different kind of operations. In particular, there is a graph transformation system expressing generation of an initial mesh, generating element matrices and elimination trees for interfacing with direct solver algorithm, a graph transformation system deciding which elements have to be further refined, as well as a graph transformation system responsible for execution of mesh refinements. These graph transformation systems are tested using a graph transformation tool (called GRAGRA), which provides a graphical environment for defining graphs, graph transformation rules and graph transformation systems. In this paper we illustrate the concepts by using an exemplary derivation for a three dimensional projection problem, based on a set of graph transformation rules

Graph transformation systems for modeling three dimensional finite element method : part II

In this paper we introduce formal definitions for several graph transformation systems modeling three dimensional h-adaptive Finite Element Method (3D h-FEM) algorithms with tetrahedral finite elements. We introduce a composite graph representation of the computational mesh and graph transformation rules expressing the mesh operations. In particular, there are graph transformation rules expressing the generation of the initial mesh consisting with tetrahedral finite elements, graph transformation rules expressing the construction of an elimination tree for interfacing with multi-frontal direct solver algorithm, graph transformation rules selecting sub-graph representing finite elements for further refinements, graph transformation rules responsible for execution of mesh refinements. We also discuss several benefits of using graph transformation system instead of classical FEM approach, including the benefits from the viewpoint of multi-frontal direct solvers

Hypergraph grammar based adaptive linear computational cost projection solvers for two and three dimensional modeling of brain

In this paper we present a hypergraph grammar model for transformation of two and three dimensional grids. The hypergraph grammar concerns the proces of generation of uniform grids with two or three dimensional rectangular or hexahedral elements, followed by the proces of h refinements, namely breaking selected elements into four or eight son elements, in two or three dimensions, respectively. The hypergraph grammar presented in this paper expresses also the two solver algorithms. The first one is the projection based interpolation solver algorithm used for computing H^{1} or L^{2} projections of MRI scan of human head, in two and three dimensions. The second one is the multi-frontal direct solver utilized in the loop of the Euler scheme for solving the non-stationary problem modeling the three dimensional heat transport in the human head generated by the cellphone usage