CFD Evaluation of Blood Flow in an Improved Blalock-Taussig Shunt Using Patient Specific Geometries

Blalock-Taussig (BT) Shunt is a palliative surgical procedure used during a Norwood surgery on a newborn baby suffering from cyanotic heart defects. The BT Shunt can increase blood flow in patients’ pulmonary artery which can ease the “Blue Baby Syndrome.” Currently used BT Shunts do not produce a balanced flow distribution to the pulmonary arteries (PAs) which can cause high wall shear stress (WSS) and blood flow separation resulting in blood clots. A modified BT Shunt was designed to partially solve this problem. In our previous work [1], the modified BT Shunt was shown by numerical simulations to have the ability to better control the flow distribution between Innominate Artery (IA) and PA with lower and gradually varying WSS and with improved flow balance to the pulmonary artery at the T-junction of the shunt. The goal of this paper is to computationally evaluate the flow in the modified BT shunt model between innominate and pulmonary artery using a patient specific aorta model. The simulations are performed using the commercial CFD software ANSYS Fluent. The improved modified BT shunt is connected between IA and PA. A change in the length of the shunt can be made to fit it under different conditions of actual patients. In numerical simulations, a full geometry of patient’s aorta is considered. Results for different lengths of the shunt are compared to determine the length that generates the lowest WSS and improved flow distribution to the PAs. It was found that the length of nearly 26mm creates lower WSS and flow rate difference between the two sides of PA at the T-junction attachment of the shunt. A sophisticated computational model was created using SolidWorks and Blender software to create the realistic geometry which included the IA, PA and modified BT shunt. The numerical simulations provide details of the flow field including velocity and pressure field, WSS, and blood damage. Several parameters in shunt design weigh heavily in reducing the thrombosis. This study demonstrates how CFD can be effectively utilized in the design of a medical device such as BT shunt to improve the clinical outcomes in patients

Neural Vector Fields: Generalizing Distance Vector Fields by Codebooks and Zero-Curl Regularization

Recent neural networks based surface reconstruction can be roughly divided into two categories, one warping templates explicitly and the other representing 3D surfaces implicitly. To enjoy the advantages of both, we propose a novel 3D representation, Neural Vector Fields (NVF), which adopts the explicit learning process to manipulate meshes and implicit unsigned distance function (UDF) representation to break the barriers in resolution and topology. This is achieved by directly predicting the displacements from surface queries and modeling shapes as Vector Fields, rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods do. In this way, our approach is capable of encoding both the distance and the direction fields so that the calculation of direction fields is differentiation-free, circumventing the non-trivial surface extraction step. Furthermore, building upon NVFs, we propose to incorporate two types of shape codebooks, \ie, NVFs (Lite or Ultra), to promote cross-category reconstruction through encoding cross-object priors. Moreover, we propose a new regularization based on analyzing the zero-curl property of NVFs, and implement this through the fully differentiable framework of our NVF (ultra). We evaluate both NVFs on four surface reconstruction scenarios, including watertight vs non-watertight shapes, category-agnostic reconstruction vs category-unseen reconstruction, category-specific, and cross-domain reconstruction

Neural Vector Fields: Implicit Representation by Explicit Learning

Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3D representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UDFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as \textit{Vector Fields}. Rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https://github.com/Wi-sc/NVF.Comment: Accepted by CVPR2023. Video: https://www.youtube.com/watch?v=GMXKoJfmHr

On the Evaluation of Generative Models in Distributed Learning Tasks

The evaluation of deep generative models including generative adversarial networks (GANs) and diffusion models has been extensively studied in the literature. While the existing evaluation methods mainly target a centralized learning problem with training data stored by a single client, many applications of generative models concern distributed learning settings, e.g. the federated learning scenario, where training data are collected by and distributed among several clients. In this paper, we study the evaluation of generative models in distributed learning tasks with heterogeneous data distributions. First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data. We prove that the model rankings according to the FID-all and FID-avg scores could be inconsistent, which can lead to different optimal generative models according to the two aggregate scores. Next, we consider the kernel inception distance (KID) and similarly define the KID-avg and KID-all aggregations. Unlike the FID case, we prove that KID-all and KID-avg result in the same rankings of generative models. We perform several numerical experiments on standard image datasets and training schemes to support our theoretical findings on the evaluation of generative models in distributed learning problems.Comment: 17 pages, 10 figure

A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the $\textit{status quo}$ of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/

AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators

Many natural language processing (NLP) tasks rely on labeled data to train machine learning models to achieve high performance. However, data annotation can be a time-consuming and expensive process, especially when the task involves a large amount of data or requires specialized domains. Recently, GPT-3.5 series models have demonstrated remarkable few-shot and zero-shot ability across various NLP tasks. In this paper, we first claim that large language models (LLMs), such as GPT-3.5, can serve as an excellent crowdsourced annotator by providing them with sufficient guidance and demonstrated examples. To make LLMs to be better annotators, we propose a two-step approach, 'explain-then-annotate'. To be more precise, we begin by creating prompts for every demonstrated example, which we subsequently utilize to prompt a LLM to provide an explanation for why the specific ground truth answer/label was chosen for that particular example. Following this, we construct the few-shot chain-of-thought prompt with the self-generated explanation and employ it to annotate the unlabeled data. We conduct experiments on three tasks, including user input and keyword relevance assessment, BoolQ and WiC. The annotation results from GPT-3.5 surpasses those from crowdsourced annotation for user input and keyword relevance assessment. Additionally, for the other two tasks, GPT-3.5 achieves results that are comparable to those obtained through crowdsourced annotation

Low-dimensional perovskite nanoplatelet synthesis using in situ photophysical monitoring to establish controlled growth.

Perovskite nanoparticles have attracted the attention of research groups around the world for their impressive photophysical properties, facile synthesis and versatile surface chemistry. Here, we report a synthetic route that takes advantage of a suite of soluble precursors to generate CsPbBr3 perovskite nanoplatelets with fine control over size, thickness and optical properties. We demonstrate near unit cell precision, creating well characterized materials with sharp, narrow emission lines at 430, 460 and 490 nm corresponding to nanoplatelets that are 2, 4, and 6 unit cells thick, respectively. Nanoplatelets were characterized with optical spectroscopy, atomic force microscopy, scanning electron microscopy and transmission electron microscopy to explicitly correlate growth conditions, thickness and resulting photophysical properties. Detailed in situ photoluminescence spectroscopic studies were carried out to understand and optimize particle growth by correlating light emission with nanoplatelet growth across a range of synthetic conditions. It was found that nanoplatelet thickness and emission wavelength increase as the ratio of oleic acid to oleyl amine or the reaction temperature is increased. Using this information, we control the lateral size, width and corresponding emission wavelength of the desired nanoplatelets by modulating the temperature and ratios of the ligand