High temperature dispersion strengthening of NiAl

A potential high temperature strengthening mechanism for alloys based on the intermetallic compound NiAl was investigated. This study forms part of an overall program at NASA Lewis Research Center for exploring the potential of alloys based on NiAl for high temperature applications. An alloy containing 2.26 at% Nb and produced by hot extrusion of blended powders was examined in detail using optical and electron microscopy. Interdiffusion between the blended Nb and NiAl powders results in the formation of intermediate phases. A fine dispersion of precipitates of a hexagonal, ordered NiAlNb phases in a matrix of NiAl can be produced and this results in strengthening of the alloy by interfering with dislocation motion at high temperature. These precipitates are, however, found to coarsen during the high temperature (1300 K) deformation at slow strain rates and this may impose some limitatioins on the use of this strengthening mechanism

High temperature properties of equiatomic FeAl with ternary additions

The aluminide intermetallic compounds are considered potential structural materials for aerospace applications. The B2 binary aluminide FeAl has a melting point in excess of 1500 K, is of simple cubic structure, exits over a wide range of composition with solubility for third elements and is potentially self-protecting in extreme environments. The B2 FeAl compound has been alloyed with 1 to 5 at % ternary additions of Si, Ti, Zr, Hf, Cr, Ni, Co, Nb, Ta, Mo, W, and Re. The alloys were prepared by blending a third elemental powder with prealloyed binary FeAl powder. Consolidation was by hot extrusion at 1250 K. Annealing studies on the extruded rods showed that the third element addition can be classified into three categories based upon the amount of homogenization and the extent of solid solutioning. Constant strain rate compression tests were performed to determine the flow stress as a function of temperature and composition. The mechanical strength behavior was dependent upon the third element homogenization classification

Alloys based on NiAl for high temperature applications

The NiAl alloys for potential high temperature applications were studied. Alloys were prepared by powder metallurgy techniques. Flow stress values at slow strain rates and high temperatures were measured. Some ternary alloying additions (Hf, Ta and Nb) were identified. The mechanism of strengthening in alloys containing these additions appears to be a form of particle dislocation interaction. The effects of grain size and stoichiometry in binary alloys are also presented

Study of the de Almeida-Thouless (AT) line in the one-dimensional diluted power-law XY spin glass

We study the AT line in the one-dimensional power-law diluted XY spin glass model, in which the probability that two spins separated by a distance $r$ interact with each other, decays as $1/r^{2\sigma}$. We develop a heat bath algorithm to equilibrate XY spins; using this in conjunction with the standard parallel tempering and overrelaxation sweeps, we carry out large scale Monte Carlo simulations. For $\sigma=0.6$ which is in the mean-field regime, we find clear evidence for an AT line. For $\sigma = 0.75$, there is evidence from finite size scaling studies for an AT transition but for $\sigma = 0.85$, the evidence for a transition is non-existent. We have also studied these systems at fixed temperature varying the field and discovered that at both $\sigma = 0.75$ and at $\sigma =0.85$ there is evidence of an AT transition! Confusingly, the correlation length and spin glass susceptibility as a function of the field are both entirely consistent with the predictions of the droplet picture and hence the non-existence of an AT line. The evidence from our simulations points to the complete absence of the AT line in dimensions outside the mean-field region and to the correctness of the droplet picture. Previous simulations which suggested there was an AT line can be attributed to the consequences of studying systems which are just too small. The collapse of our data to the droplet scaling form is poor for $\sigma = 0.75$ and to some extent also for $\sigma = 0.85$, when the correlation length becomes of the order of the length of the system, due to the existence of excitations which only cost a free energy of $O(1)$, just as envisaged in the TNT picture of the ordered state of spin glasses. However, for the case of $\sigma = 0.85$ we can provide evidence that for larger system sizes, droplet scaling will prevail even when the correlation length is comparable to the system size.Comment: 23 pages, 15 figure

High frame-rate cardiac ultrasound imaging with deep learning

Cardiac ultrasound imaging requires a high frame rate in order to capture rapid motion. This can be achieved by multi-line acquisition (MLA), where several narrow-focused received lines are obtained from each wide-focused transmitted line. This shortens the acquisition time at the expense of introducing block artifacts. In this paper, we propose a data-driven learning-based approach to improve the MLA image quality. We train an end-to-end convolutional neural network on pairs of real ultrasound cardiac data, acquired through MLA and the corresponding single-line acquisition (SLA). The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.Comment: To appear in the Proceedings of MICCAI, 201

Fast Nonlinear Vector Quantile Regression

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable $\mathrm{Y}$ given explanatory features $\boldsymbol{\mathrm{X}}$. A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate distributions. Recently, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal transport. Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target $\boldsymbol{\mathrm{Y}}$ given the features $\boldsymbol{\mathrm{X}}$; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist. In this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR; (ii) We propose {vector monotone rearrangement}, a method which ensures the quantile functions estimated by VQR are monotone functions; (iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels; (iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications.Comment: 35 pages, 15 figures, code: https://github.com/vistalab-technion/vq

Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos

Surgical tool presence detection is an important part of the intra-operative and post-operative analysis of a surgery. State-of-the-art models, which perform this task well on a particular dataset, however, perform poorly when tested on another dataset. This occurs due to a significant domain shift between the datasets resulting from the use of different tools, sensors, data resolution etc. In this paper, we highlight this domain shift in the commonly performed cataract surgery and propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor that addresses the problem of distribution shift without requiring any labels from another domain. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains while reducing redundancy and the need for higher batch sizes, thus improving cross-dataset performance. The use of BFAL is a novel approach to address the challenge of domain shift in cataract surgery data. Extensive experiments are conducted on two cataract surgery datasets and it is shown that the proposed method outperforms the state-of-the-art UDA methods by 6%. The code can be found at https://github.com/JayParanjape/Barlow-AdaptorComment: MICCAI 202

GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos

Automated surgical step recognition is an important task that can significantly improve patient safety and decision-making during surgeries. Existing state-of-the-art methods for surgical step recognition either rely on separate, multi-stage modeling of spatial and temporal information or operate on short-range temporal resolution when learned jointly. However, the benefits of joint modeling of spatio-temporal features and long-range information are not taken in account. In this paper, we propose a vision transformer-based approach to jointly learn spatio-temporal features directly from sequence of frame-level patches. Our method incorporates a gated-temporal attention mechanism that intelligently combines short-term and long-term spatio-temporal feature representations. We extensively evaluate our approach on two cataract surgery video datasets, namely Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods. These results validate the suitability of our proposed approach for automated surgical step recognition. Our code is released at: https://github.com/nisargshah1999/GLSFormerComment: Accepted to MICCAI 2023 (Early Accept

A deep learning enabler for non-intrusive reduced order modeling of fluid flows

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various deep neural network architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equations, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly non-intrusive, since any prior information about the underlying governing equations is not required for generating the reduced order model. Our \textit{a posteriori} analysis shows that the proposed data-driven approach is remarkably accurate, and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.Comment: 36 pages, 21 figure