Multivariate Gaussian Mixture Model Research Articles

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

Multidimensional Alternating Kernel Method for cortical layer segmentation in 3D reconstructed histology

The neocortex of the brain can be divided into six layers each with a distinct cell composition and connectivity pattern. Recently, sensory deprivation, including congenital deafness, has been shown to alter cortical structure (e.g. the cortical thickness) of the feline auditory cortex with variable and inconsistent results. Thus, understanding these complex changes will require further study of the constituent cortical layers in three-dimensional space. Further progress crucially depends on the use of objective computational techniques that can reliably characterize spatial properties of the complex cortical structure. Here a method for cortical laminar segmentation is derived and applied to the three-dimensional cortical areas reconstructed from a series of histological sections from four feline brains. In this approach, the Alternating Kernel Method was extended to fit a multi-variate Gaussian mixture model to a feature space consisting of both staining intensity and a biologically plausible equivolumetric depth map.This research method•Extends the Alternating Kernel Method to multi-dimensional feature spaces.•Uses it to segment the cortical layers in reconstructed histology volume. Segmentation features include staining intensity and a biologically plausible equivolumetric depth map.•Validates results in auditory cortical areas of feline brains, two with normal hearing and two with congenital deafness.

A note on estimating multivariate Gaussian mixtures with unknown number of components

. In this article, a new penalized likelihood method is proposed for finite multivariate Gaussian mixture models to conduct order selection and model estimation. The method is proved to achieve order selection consistency and have root-n convergence rate. Asymptotic normality is established for the proposed estimator. A modified EM algorithm is developed for computation. Extensive simulations and a real data analysis are conducted to illustrate the performance of our method.

Probabilistic Optimal Power Flow Computation Using Stratified Quadrature Rule

In this paper, the probabilistic optimal power flow (POPF) problem is modeled as a black box uncertainty quantification problem. If distribution functions of POPF inputs are unknown, only samples are available, the multivariate Gaussian mixture model is suggested to recover the joint cumulative distribution function of POPF inputs, and a probability selection method is introduced to relate POPF inputs to an independent standard normal vector. Then, moment matching equations are proposed to characterize the uncertainty effects of POPF inputs on outputs, whereby some properties of the POPF problem are revealed, and a stratified quadrature rule (SQR) is proposed for POPF computation by combining a Hadamard matrix based quadrature rule and a discrete sine transformation matrix based quadrature rule. This newly developed SQR has a computational burden linear with the number of POPF inputs, and performs well in estimating means and standard deviations POPF outputs. Finally, case studies are conducted on a modified IEEE 118-bus system to illustrate the accuracy and efficiency of the proposed method.

TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally.

Affordable method for measuring fluorescence using Gaussian distributions and bounded MESE.

We present an accurate and low-cost method for measuring fluorescence in materials. Our method outputs an estimate of the material's Donaldson matrix, which is a commonly used two-dimensional spectral characterization of its fluorescence and reflectance properties. To find the estimate, only a few measurements of the material's reflectance under a few illuminants are needed, which we demonstrate using low-cost optical components. Internally, our algorithm is based on representing each Donaldson matrix with a multivariate Gaussian mixture model and its diagonal with a bounded MESE (maximum entropy spectral estimate). It parametrizes and constrains the estimate in a robust and simple way, allowing the use of gradient-descent optimization. We evaluate our algorithm on a combination of real and synthetic data, and four examples of distinct optical components. We reach significantly lower errors than the current state of the art on the exact same inputs, our estimates do not suffer from artifacts such as oscillations of the spectra, and they are stable and robust.

Measure of Similarity between GMMs Based on Autoencoder-Generated Gaussian Component Representations

A novel similarity measure between Gaussian mixture models (GMMs), based on similarities between the low-dimensional representations of individual GMM components and obtained using deep autoencoder architectures, is proposed in this paper. Two different approaches built upon these architectures are explored and utilized to obtain low-dimensional representations of Gaussian components in GMMs. The first approach relies on a classical autoencoder, utilizing the Euclidean norm cost function. Vectorized upper-diagonal symmetric positive definite (SPD) matrices corresponding to Gaussian components in particular GMMs are used as inputs to the autoencoder. Low-dimensional Euclidean vectors obtained from the autoencoder’s middle layer are then used to calculate distances among the original GMMs. The second approach relies on a deep convolutional neural network (CNN) autoencoder, using SPD representatives to generate embeddings corresponding to multivariate GMM components given as inputs. As the autoencoder training cost function, the Frobenious norm between the input and output layers of such network is used and combined with regularizer terms in the form of various pieces of information, as well as the Riemannian manifold-based distances between SPD representatives corresponding to the computed autoencoder feature maps. This is performed assuming that the underlying probability density functions (PDFs) of feature-map observations are multivariate Gaussians. By employing the proposed method, a significantly better trade-off between the recognition accuracy and the computational complexity is achieved when compared with other measures calculating distances among the SPD representatives of the original Gaussian components. The proposed method is much more efficient in machine learning tasks employing GMMs and operating on large datasets that require a large overall number of Gaussian components.

Motion Planning of Manipulator by Points-Guided Sampling Network

This paper proposes a network called points-guided sampling net (PGSN) to guide the sampling process in sampling-based motion planner by utilizing the geometric information of obstacles. The geometric information is extracted from the point cloud of obstacles. By analyzing the properties of the point cloud, we propose a VAE feature extraction net that incorporates the variational autoencoder (VAE) framework with unique architectures designed for point clouds. Furthermore, we design a multi-modal sampling net to model the probability distribution of the states based on training trajectories taken from different environments. Based on PGSN, we propose a sampling-based motion planning algorithm called the point-guided rapidly-exploring random tree (PG-RRT). Three experiments are conducted to verify the proposed PGSN: Exp I shows the proposed VAE feature extraction net can successfully extract geometric features from the inputted point cloud; Exp II verifies the multi-modal sampling net successfully chooses corresponding mode with respect to extracted features; Exp III demonstrates the efficacy of our PG-RRT algorithm by showing PG-RRT outperforms other algorithms. Moreover, we provide theoretical analysis and insights towards understanding our model. Note to Practitioners—Obstacles cause lots of the sampling space invalid, thus the traditional sampling-based motion planning (SBMP) algorithm is usually unable to generate a trajectory within a reasonable short period of time. To improve the success rate and efficiency of SBMP, this paper proposes a novel deep neural network called points-guided sampling net (PGSN). PGSN is designed to exploit: (1) environmental point clouds and (2) training trajectories from multiple environments with different obstacles. In the first step, the point clouds include important geometric information. To utilize this information, we adopt a variational autoencoder approach which combines an encoder and a decoder together to extract geometric features more accurately from point clouds. In the second step, trajectories from multiple environments have a multi-modal property which can be represented by a truncated multivariate Gaussian mixture model. We propose a multi-modal sampling net to learn optimal parameters of this model from the training trajectories, and to select corresponding mode based on the extracted features. Experiments demonstrate that the proposed algorithm is feasible and can achieve higher success rate than the state-of-the-art methods. Our method uses a single frame of point cloud to improve efficiency, therefore multiple point clouds from different perspective maybe needed when objects occlude with each other.

부유선별공정 모니터링을 위한 다변수 가우시안 혼합모델기반 색상 분석

Froth flotation is a type of mineral processing that selectively separates useful minerals from theBRfroth produced by pulp aeration. The physical features of the froth contain information to understandBRthe status of the process of froth flotation. In particular, by obtaining the color feature of the froth, itBRis possible to predict not only the type of useful mineral but also the grade of the mineral. Therefore,BRit is very important to obtain high-precision color analysis results in froth flotation monitoring. In thisBRpaper, we propose a color analysis method based on multivariate Gaussian mixture models to guaranteeBRthe cyclicity with a period of 360 degrees in hue component of color. In order to verify the proposedBRmethod, we present the color analysis results of images which contain around 0º (360º ) of hue andBRimages obtained by froth flotation. Through the experimental results, it shows that the color analysisBRresults obtained by the proposed method are more precise.BR

A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.

Red Dragon: a redshift-evolving Gaussian mixture model for galaxies

ABSTRACT Precision-era optical cluster cosmology calls for a precise definition of the red sequence (RS), consistent across redshift. To this end, we present the Red Dragon algorithm: an error-corrected multivariate Gaussian mixture model (GMM). Simultaneous use of multiple colours and smooth evolution of GMM parameters result in a continuous RS and blue cloud (BC) characterization across redshift, avoiding the discontinuities of red fraction inherent in swapping RS selection colours. Based on a mid-redshift spectroscopic sample of SDSS galaxies, an RS defined by Red Dragon selects quiescent galaxies (low specific star formation rate) with a balanced accuracy of over $90{{\ \rm per\ cent}}$. This approach to galaxy population assignment gives more natural separations between RS and BC galaxies than hard cuts in colour–magnitude or colour–colour spaces. The Red Dragon algorithm is publicly available at bitbucket.org/wkblack/red-dragon-gamma/.

Unsupervised Appearance Map Abstraction for Indoor Visual Place Recognition With Mobile Robots

Visual Place Recognition (VPR), the task of identifying the place where an image has been taken from, is at the core of important robotic problems as relocalization, loop-closure detection or topological navigation. Even for indoors, the focus of this work, VPR is challenging for a number of reasons, including real-time performance when dealing with large image databases ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim10^{4}$</tex-math></inline-formula> ) (probably captured by different robots), or the avoidance of Perceptual Aliasing in environments with repetitive structures and scenes. In this letter, we tackle these issues by proposing an off-line mapping technique that abstracts a dense database of georeferenced images without particular order into a Multivariate Gaussian Mixture Model, by creating soft clusters in terms of their similarity in both pose and appearance. This abstract representation is obtained through an Expectation-Maximization algorithm and plays the role of a simplified map. Since querying this map yields a probability of being in a cluster, we exploit this “belief” within a Bayesian filter that regards previous query images and a topological map between clusters to perform more robust VPR. We evaluate our proposal in two different indoor datasets, demonstrating comparable VPR precision to querying the full database while incurring in shorter query times and handling Perceptual Aliasing for sequential navigation.

An Adaptive Framework for Anomaly Detection in Time-Series Audio-Visual Data

Anomaly detection is an integral part of a number of surveillance applications. However, most of the existing anomaly detection models are statically trained on pre-recorded data from a single source, thus making multiple assumptions about the surrounding environment. As a result, their usefulness is limited to controlled scenarios. In this paper, we fuse information from live streams of audio and video data to detect anomalies in the captured environment. We train a deep learning-based teacher-student network using video, image, and audio information. The pre-trained visual network in the teacher model distills its information to the image and audio networks in the student model. Features from image and audio networks are combined and compressed using principal component analysis. Thus, the teacher-student network produces an image-audio-based light-weight joint representation of the data. The data dynamics are learned in a multivariate adaptive Gaussian mixture model. Empirical results from two audio-visual datasets demonstrate the effectiveness of joint representation over single modalities in the adaptive anomaly detection framework. The proposed framework outperforms the state-of-the-art methods by an average of 15.00 % and 14.52 % in AUC values for dataset 1 and dataset 2, respectively.

An Artificial Intelligence Approach in Classifying Acute Myeloid Leukemia and Myelodysplastic Syndrome for Minimal Residual Disease Detection with Post Induction Prognosis Analysis Using Flow Cytometry

IntroductionFlow cytometry (FC) is a reliable tool for minimal residual disease (MRD) detection in acute leukemia. However, manual interpretation inevitably suffers from individual idiosyncrasy even by experienced physicians, which could affect diagnostic reproducibility and objectivity. With recently advanced artificial intelligence (AI) technology, we aim to develop an automated FC data interpretation algorithm to support physicians in objectively detecting MRD for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS).MethodsFrom 2009 to 2017, 4350 AML or MDS FC data samples for MRD detection at National Taiwan University Hospital were enrolled, from two different machines (3090 from Calibur and 1260 from CantoII-Senior). A formula of inter-machine value calibration was provided by the manufacturer. A 12-tube test was performed for each sample: 100000 cells measured in 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) per tube.The whole dataset was randomly divided into Training set 1, Training set 2 and Validation set, taking 64%, 16% and 20% samples respectively. The Training set 1 was for developing AI algorithm, and the Training set 2 for parameter tuning. Final concordance was estimated with the blinded Validation set. Algorithms for three paired comparisons (AML vs. normal, MDS vs. normal, and abnormal (AML+MDS) vs. normal) were developed independently, according to previous manual analysis.For algorithm development, the recorded numerical values of the 6 aforementioned fluorescent channels of each tube were used as raw feature attributes. The probabilistic distributions of these attributes was modeled as multivariate Gaussian mixture model by a sub-dictionary learning approach. A probabilistic derivation was then exploited to compute per-sample L2-normalized vectorized representations. Lastly, representations of each tube were then concatenated to be the final feature input to the supervised machine learning classifier, i.e., a support vector machine with linear kernel. ANOVA-based feature selection was also conducted.For clinical validation of AI-assisted FC analysis, clinical characters and outcomes from 120 AML patients with available FC data after standard induction were analyzed, including their overall survival (OS) and progression-free survival (PFS).ResultsAs in Table 1, AI algorithm achieved promising concordance rates of 88.2%, 92.5%, and 87.6% in pair-wise recognition tasks, i.e., AML vs normal, MDS vs normal, and abnormal vs normal, respectively. Similar results were noted for the Validation set, with the rates of 87.9%, 87.9%, and 85.1%. The areas under receiver operating characteristic curve (AUCs) were 0.903, 0.784 and 0.886, respectively. Concordance was reduced when data from different machines were aggregated even with conversion formula, indicating that algorithm may need adjustment for different machines.It is particularly noteworthy that only 7 seconds were required for AI to interpret one sample, i.e., at least 130 folds faster than current manual work.Moreover, even with only 1 tube data, high concordance was maintained. When comparing abnormal vs. normal in Calibur data, algorithm from all 12 tube data yielded an AUC of 0.935, which remained unchanged as tube number decreased. In fact, the AUC with only 1 tube data (CD13, CD16, CD45, FSC and SSC) was 0.915. This was also noted in CantoII-Senior data (AUC 0.832 and 0.790 for 12 and 1 tube), and across all other comparisons. The findings implicate that AI may reduce laboratory works currently required for high-fidelity manual analysis.For the 120 AML cases with available post-induction FC data, 45 were classified as abnormal (i.e., positive MRD) and 75 as normal by AI algorithm. As in Figure 1, patients with abnormal FC by AI had a significantly worse PFS (median 7.6 vs. 19.0 months, p<0.0001), and OS (median 19.2 months vs. not reached, p<0.0001) than those with normal FC. Multivariate analysis with Cox hazard proportional model confirmed the outcome predicting significance for AI interpretation.ConclusionsWe demonstrated reliable accuracies and efficient prognostic stratification for AI-assisted MRD detection in AML/MDS, with high-speed interpretation and labor saving. Further integration with other laboratory or genomic data into AI algorithm may facilitate more precise outcome prediction and risk-adapted intervention for these patients. [Display omitted] DisclosuresKo:Celgene International Sàrl: Research Funding. Li:Celgene International Sàrl: Research Funding. Hou:Celgene International Sàrl: Research Funding. Tien:Celgene International Sàrl: Research Funding. Tang:Celgene International Sàrl: Research Funding.

An Artificial Intelligence Approach for B Lymphoblastic Leukemia Minimal Residual Disease Detection and Clinical Prognosis Prediction Using Flow Cytometry Data

IntroductionDetection of minimal residual disease (MRD) of B-cell acute lymphoblastic leukemia (B-ALL) measured by flow cytometry (FC) has been shown to be an important prognostic indicator in B-ALL. However, the current analysis is based on the manual gating by the reviewers; therefore, objectivity could be compromised, and decentralization of the B-ALL MRD detection is difficult to be established. We then aimed to develop an objective diagnostic tool via artificial intelligence (AI) technique for FC data interpretation in order to provide efficient and objective B-ALL MRD detection.MethodsRetrospective FC data of patients with adult B-ALL from 2009 to 2016 in National Taiwan University Hospital were enrolled. A total of 1,073 unique bone marrow FC samples were collected for analysis. Each of the samples had a 10-tube test done on 100,000 cells - each cell was measured in 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) within one tube.The flow conclusion of each FC sample was categorized into “positive MRD” and “no MRD” groups according to previous manual interpretation, and the whole dataset was randomly divided into 3 groups: Training set (n=718), Accuracy set (n=141) and Validation set (n=178). The Training set was used to train the algorithm, and the Accuracy set was used to evaluate the accuracies achieved and optimize algorithmic parameters. The final optimized algorithm was then evaluated blindly in Validation set, with the concordance rate comparing the manual and the AI conclusions in distinguishing “positive MRD” from “no MRD”.For the algorithm development, the recorded numerical values of the 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) of each tube (100,000 cells) were considered as raw feature attributes. The probabilistic distributions of these attributes were first modeled as multivariate Gaussian mixture model using a sub-dictionary learning approach. Then a probabilistic derivation was exploited to compute per-sample L2-normalized vectorized representations. Lastly, representations of each tube were then concatenated to be the final feature input to the supervised machine learning classifier, in this case, a support vector machine with linear kernel. In addition, ANOVA-based feature selection was also conducted throughout the experiments.For outcome correlation analysis, we included 35 adult B-ALL patients with available FC data after induction therapy, record their clinical parameters and measured their overall survival (OS) and progression-free survival (PFS), with median follow-up 40.0 months. The Kaplan-Meier curves were constructed to estimated OS and RFS.ResultsThe primary algorithm concordance estimated using the Accuracy set was 90.8% and the area under the receiver operating characteristic curve (AUC) is 0.944 (Table1). The calibrated algorithm, adjusted with training in Accuracy set, can produce a concordance rate of 88.2% with manual interpretation on Validation set, and the AUC is 0.919 (Table 1).It is noteworthy that it only took 7 seconds to analyze one FC sample using the AI algorithm while it took approximately 15 to 30 minutes for manual gating by trained professionals. Furthermore, the concordance of the algorithm developed from 10 tube-data remained almost unchanged when the tube number decreased. In fact, algorithm from 1 tube still yields an AUC of 0.874 (vs. 0.919 from 12 tubes), indicating that AI algorithm may reduce laboratory testing currently applied in standard FC processing.For outcome correlation, B-ALL patients with no MRD by AI algorithm had significant longer OS compared to those with positive MRD (NR vs 20.0 months, p=0.0051). Moreover, patients with no MRD also had significant longer RFS compared to those with positive MRD (8.9 vs 5.2 months, p=0.019), (Figure.1).ConclusionsThis is the first study using AI approach to develop FC data diagnostic algorithms with prognosis implications for B-ALL, demonstrating the advantages of reliability, high efficacy and time saving. Incorporating other test results into AI algorithm may lead to more precise prognostic prediction in the future and potentially guide therapeutic strategy for B-ALL. [Display omitted] DisclosuresKo:Celgene International Sàrl: Research Funding. Li:Celgene International Sàrl: Research Funding. Tien:Celgene International Sàrl: Research Funding. Tang:Celgene International Sàrl: Research Funding.

An Artificial Intelligence Approach for Predicting Allogeneic Hematopoietic Stem Cell Transplantation Outcome By Detecting Pre-Transplant Minimal Residual Disease in Acute Myeloid Leukemia Using Flow Cytometry Data

IntroductionDisease status before allogeneic hematopoietic stem cell transplantation (allo-HSCT) is an important prognostic marker for recipients with acute myeloid leukemia (AML), and flow cytometry (FC)-assisted minimal residual disease (MRD) detection is of well-known clinical significance in this setting. However, current interpretation requires experienced manual gating, which is time consuming as well as suffering from inter-physician idiosyncrasies. The reproducibility and objectivity could then be compromised. In this study, we aimed to develop automated FC data interpretation algorithm using artificial intelligence (AI) technology in supporting physicians to conduct rapid and reliable pre-HSCT MRD detection for AML patients.MethodsRetrospective FC data of AML or myelodysplastic syndrome (MDS) from 2009 to 2017 for MRD detection at the National Taiwan University Hospital were used for establishing AI diagnostic strategy. Totally 4350 data samples from two different FC machines (3090 from FASCalibur and 1260 from FASCantoII-Senior) were included. Each of the samples had a 12-tube test on 100000 cells, measured in 6 fluorescent channels (FSC, SSC, FITC, PE, PerCP, APC) within 1 tube.The whole dataset was randomly divided to the Training set and the Validation set, taking 80% and 20% samples respectively. The Training set was used to develop and tune the AI algorithm, and final concordance was estimated on the blinded Validation set. Algorithms for pair-wise recognition (AML vs. normal, MDS vs. normal and abnormal (AML+MDS) vs. normal) were developed independently, according to previous manual interpretation of FC data.The recorded numerical values of the 6 fluorescent channels of each tube were considered as raw feature attributes. The data was first statistically-modelled with a sub-dictionary approach in learning a multivariate Gaussian mixture model. Then a probabilistic derivation based on Fisher scoring was exploited to compute the L2-normalized fixed-dimensional input representations. The raw feature attributes were then encoded into a Fisher representational vector. Lastly, vectors of each tube were concatenated as the final high-dimensional input to the supervised machine learning classifier, i.e., a support vector machine with linear kernel. In addition, ANOVA-based feature selection was also conducted throughout the experiments.For outcome predicting analysis, we included 94 AML patients with available pre-allo-HSCT FC data. Their clinical parameters, progression-free survival (PFS) and overall survival (OS) after allo-HSCT are recorded and analyzed with a median follow-up of 39.9 months.ResultsFor the Calibur Training set (n=2,649), the concordance rate of AI algorithm with manual analysis in differentiating AML vs. normal, MDS vs. normal, and abnormal vs normal was as high as 91.8%, 94.0%, and 90.8%, respectively. Similar rates were noted for the CantoII-Senior Training set (n=1,041): 88.0%, 85.5%, and 84.4%. The final concordance rates for the overall Validation set was 87.9%, 87.9% and 85.1%, respectively. Surprisingly, the algorithm developed from only single tube (CD13, CD16, CD45, FSC and SSC) can achieve almost identical concordance rates as to that from all 12 tubes, both for Calibur data (estimated by the area under receiver operating characteristic curve (AUC), 0.932 vs. 0.935) and CantoII-Senior data (AUC 0.790 vs. 0.832). Another point to note is that the AI system is 100 times faster than the trained professionals in interpreting one FC data (around 7 secs vs 15-30 mins).The clinical parameters for 94 AML patients underwent HSCT were illustrated (Table 1). As noted, 38 were classified to have abnormal pre-HSCT FC data by AI system (indicating residual disease) and 56 were normal. Those with normal FC had significantly longer post-HSCT OS compared to those with abnormal FC (median OS NR vs 6.5 months, p<0.001) (Figure 1a). Pre-HSCT normal FC also predicted longer post-HSCT PFS (median PFS NR vs 4.5 months, p<0.0001) (Figure. 1b). Multi-variate analysis with Cox Hazard Proportional model confirmed the prognostic significance of positive MRD by AI for OS and RFS.ConclusionsThis study demonstrated that AI could be an efficient and reliable diagnostic and prognosis prediction tool for AML. In the future, we like to incorporate other test results simultaneously measured for those patients as our next phase of advancing the AI system. [Display omitted] DisclosuresLi:Celgene International Sàrl: Research Funding. Ko:Celgene International Sàrl: Research Funding. Hou:Celgene International Sàrl: Research Funding. Lin:Celgene International Sàrl: Research Funding. Tien:Celgene International Sàrl: Research Funding. Tang:Celgene International Sàrl: Research Funding.

Bayesian mixture modelling for glass refractive index measurement

Glass is a common type of physical evidence in forensic science. Broken glass recovered from a suspect may have similar physical characteristics to glass collected at a crime scene and therefore can be used as evidence. Statistical treatment of this evidence involves computing a measure of the weight of evidence. This may be done in a Bayesian framework that incorporates information from the circumstances of the crime. One of the most crucial quantities in this calculation is the assessment of the relative rarity of the characteristics of the glass, essentially the probability distribution used to model the physical characteristics of recovered glass. Typical characteristics used in casework are the elemental composition of glass and the refractive index measurement. There is a considerable body of scientific literature devoted to the modelling of this information. For example a kernel density estimation has been used to model the background population of glass based on the refractive index measurement and a multivariate Gaussian finite mixture model has been used to model the elemental composition of glass. In this paper, we present an alternative approach, the Dirichlet Process Mixture Model, to model the glass refractive index measurement in a Bayesian methodology. A key advantage is that using this method allows us to model the probability density distribution of refractive index measurements in a more flexible way.

MRI reconstruction based on Bayesian group sparse representation

Compressed sensing (CS) theory speeds up the magnetic resonance imaging (MRI) by undersampling the k-space data. Utilizing a proper sparse representation for image and incorporating prior information are vital to yield a high-quality reconstruction in CS-MRI. In this work, the multivariate Gaussian scale mixture (GSM) model is developed to precisely characterize to the statistical properties of sparse coefficients of group formed by similar patches, and a Bayesian group sparse representation (BGSR) is derived from maximum a posterior (MAP) estimation. The efficient multiclass orthogonal dictionaries learning is further integrated in BGSR driven CS-MRI reconstruction to enable high sparsity as well as performance enhancement. The solution is obtained by a use of alternating direction method of multipliers (ADMM) iteration, and a closed-form solution for each subproblem is separately deduced. The experimental results show that the proposed method provides a superior visual quality and performance indexes over the regularization based CS-MRI methods. Hence, the proposed method can be utilized to further accelerate MRI and produce the highly accurate reconstruction for subsequent image processing as well as clinical diagnosis. Furthermore, this work can be extended to other imaging applications and provide some references for Bayesian probabilistic model based CS-MRI reconstruction.

Marine Acoustic Zones of Australia

Underwater sound is modelled and mapped for purposes ranging from localised environmental impact assessments of individual offshore developments to large-scale marine spatial planning. As the area to be modelled increases, so does the computational effort. The effort is more easily handled if broken down into smaller regions that could be modelled separately and their results merged. The goal of our study was to split the Australian maritime Exclusive Economic Zone (EEZ) into a set of smaller acoustic zones, whereby each zone is characterised by a set of environmental parameters that vary more across than within zones. The environmental parameters chosen reflect the hydroacoustic (e.g., water column sound speed profile), geoacoustic (e.g., sound speeds and absorption coefficients for compressional and shear waves), and bathymetric (i.e., seafloor depth and slope) parameters that directly affect the way in which sound propagates. We present a multivariate Gaussian mixture model, modified to handle input vectors (sound speed profiles) of variable length, and fitted by an expectation-maximization algorithm, that clustered the environmental parameters into 20 maritime acoustic zones corresponding to 28 geographically separated locations. Mean zone parameters and shape files are available for download. The zones may be used to map, for example, underwater sound from commercial shipping within the entire Australian EEZ.

A one-step Bayesian inversion framework for 3D reservoir characterization based on a Gaussian mixture model — A Norwegian Sea demonstration

We have developed a one-step approach for Bayesian prediction and uncertainty quantification of lithology/fluid classes, petrophysical properties, and elastic attributes conditional on prestack 3D seismic amplitude-variation-with-offset data. A 3D Markov random field prior model is assumed for the lithology/fluid classes to ensure spatially coupled lithology/fluid class predictions in the lateral and vertical directions. Conditional on the lithology/fluid classes, we consider Gauss-linear petrophysical and rock-physics models including depth trends. Then, the marginal prior models for the petrophysical properties and elastic attributes are multivariate Gaussian mixture models. The likelihood model is assumed to be Gauss-linear to allow for analytic computation. A recursive algorithm that translates the Gibbs formulation of the Markov random field into a set of vertical Markov chains is proposed. This algorithm provides a proposal density in a Markov chain Monte Carlo algorithm such that efficient simulation from the posterior model of interest in three dimensions is feasible. The model is demonstrated on real data from a Norwegian Sea gas reservoir. We evaluate the model at the location of a blind well, and we compare results from the proposed model with results from a set of 1D models in which each vertical trace is inverted independently. At the blind well location, we obtain at most a 60% reduction in the root-mean-square error for the proposed 3D model compared to the model without lateral spatial coupling.