NAISR: A 3D Neural Additive Model for Interpretable Shape Representation

Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate. Such a method would be of high utility to researchers to discover knowledge hidden in a population of shapes. We propose a 3D Neural Additive Model for Interpretable Shape Representation (NAISR) which describes individual shapes by deforming a shape atlas in accordance to the effect of disentangled covariates. Our approach captures shape population trends and allows for patient-specific predictions through shape transfer. NAISR is the first approach to combine the benefits of deep implicit shape representations with an atlas deforming according to specified covariates. Although our driving problem is the construction of an airway atlas, NAISR is a general approach for modeling, representing, and investigating shape populations. We evaluate NAISR with respect to shape reconstruction, shape disentanglement, shape evolution, and shape transfer for the pediatric upper airway. Our experiments demonstrate that NAISR achieves competitive shape reconstruction performance while retaining interpretability.Comment: 20 page

VPFusion: Joint 3D Volume and Pixel-Aligned Feature Fusion for Single and Multi-view 3D Reconstruction

We introduce a unified single and multi-view neural implicit 3D reconstruction framework VPFusion. VPFusion attains high-quality reconstruction using both - 3D feature volume to capture 3D-structure-aware context, and pixel-aligned image features to capture fine local detail. Existing approaches use RNN, feature pooling, or attention computed independently in each view for multi-view fusion. RNNs suffer from long-term memory loss and permutation variance, while feature pooling or independently computed attention leads to representation in each view being unaware of other views before the final pooling step. In contrast, we show improved multi-view feature fusion by establishing transformer-based pairwise view association. In particular, we propose a novel interleaved 3D reasoning and pairwise view association architecture for feature volume fusion across different views. Using this structure-aware and multi-view-aware feature volume, we show improved 3D reconstruction performance compared to existing methods. VPFusion improves the reconstruction quality further by also incorporating pixel-aligned local image features to capture fine detail. We verify the effectiveness of VPFusion on the ShapeNet and ModelNet datasets, where we outperform or perform on-par the state-of-the-art single and multi-view 3D shape reconstruction methods

Multi-scaling analysis of turbulent boundary layers over an isothermally heated flat plate with zero pressure gradient

A meticulous investigation into turbulent boundary layers over an isothermally heated flat plate with zero pressure gradient has been conducted. Eight distinct turbulence models, including algebraic yPlus, standard k-ω, standard k-ε, length-velocity, Spalart-Allmaras, low Reynolds number k-ε, shear stress transport, and v2-f turbulence models, are carefully chosen for numerical simulation alongside thermal energy and Reynolds-Averaged Navier-Stokes equations. A comparative analysis has determined that the Spalart-Allmaras model exhibits remarkable agreement with the results from direct numerical simulation, making it a reliable tool for predicting turbulent heat transfer and fluid flow, particularly at higher Prandtl and Reynolds numbers. Subsequently, a multi-scale investigation employs a comprehensive four-layer structure scheme and encompasses various momentum thickness Reynolds numbers of 1432, 2522, and 4000, and Prandtl numbers of 0.71, 2, and 5. The subsequent investigation reveals the governing non-dimensional numbers' substantial impact on the distribution and magnitude of mean thermal and flow characteristics. Notably, the scaling of mean thermal and momentum fields discloses the existence of a meso or intermediate layer characterized by a logarithmic nature unique to itself. The multi-scaling analysis of the flow field demonstrates greater conformity with the selected scaling variables primarily relying on the Reynolds number. Furthermore, the scaling of the energy field yields compelling outcomes within the inner and intermediate layers. However, according to the four-layer theory, minor discrepancies are observed in the outer layer when using the current scaling

Conjugate mixed convection heat transfer with internal heat generation in a lid-driven enclosure with spinning solid cylinder

The current study investigates conjugate mixed convection heat transmission with internal heat generation in a square enclosure driven by a sliding lid and a solid cylinder with a heat-conducting surface at its center. The enclosure has a stationary bottom wall that is kept at a constant hot temperature and a cold upper wall that moves consistently. The solid cylinder rotates both clockwise and counterclockwise at different angular speeds. Two-dimensional steady continuity, momentum, thermal energy equations, and boundary and interface conditions are solved using a commercial CFD tool based on the finite element method. By choosing Reynolds, Grashof, and Richardson numbers, as well as varying the rotating cylinder's speed and direction under three different scenarios incorporating volumetric heat generation, parametric modeling of the mixed convection regime is carried out. The streamline and isotherm plots are used to illustrate qualitative findings. In contrast, the average Nusselt number, normalized Nusselt number, average drag coefficient, and average fluid temperature are used to assess quantitative thermal performance measures. This study reveals that the system's thermal performance is less dependent on the solid cylinder's rotational speed and direction. It successfully depicts the heat transfer enhancement with increasing Reynolds and Grashof numbers. A thorough study of the current facts can lead to the best choice of regulating parameters

Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models

Lung cancer stands as the leading cause of death in the United States, attributed to factors such as the spontaneous growth of malignant tumors in the lungs that can metastasize to other parts of the body, posing severe threats. Notably, smoking emerges as a predominant external factor contributing to lung problems and ultimately leading to lung cancer. Nevertheless, early detection presents a pivotal strategy for preventing this lethal disease. Leveraging machine learning, we aspire to develop robust algorithms capable of predicting lung cancer at its nascent stage. Such a model could prove instrumental in aiding physicians in making informed decisions during the diagnostic process, determining whether a patient necessitates an intensive or standard level of diagnosis. This approach holds the potential to significantly reduce treatment costs, as physicians can tailor the treatment plan based on accurate predictions, thereby avoiding unnecessary and costly interventions. Our goal is to establish a sustainable model that accurately predicts the disease, and our findings reveal that XGBoost outperformed other models, achieving an impressive accuracy level of 96.92%. In comparison, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machine achieved accuracies of 93.50%, 92.32%, 67.41%, and 88.02%, respectively