A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers

We present the first surrogate model for gravitational waveforms from the coalescence of precessing binary black holes. We call this surrogate model NRSur4d2s. Our methodology significantly extends recently introduced reduced-order and surrogate modeling techniques, and is capable of directly modeling numerical relativity waveforms without introducing phenomenological assumptions or approximations to general relativity. Motivated by GW150914, LIGO's first detection of gravitational waves from merging black holes, the model is built from a set of $276$ numerical relativity (NR) simulations with mass ratios $q \leq 2$, dimensionless spin magnitudes up to $0.8$, and the restriction that the initial spin of the smaller black hole lies along the axis of orbital angular momentum. It produces waveforms which begin $\sim 30$ gravitational wave cycles before merger and continue through ringdown, and which contain the effects of precession as well as all $\ell \in \{2, 3\}$ spin-weighted spherical-harmonic modes. We perform cross-validation studies to compare the model to NR waveforms \emph{not} used to build the model, and find a better agreement within the parameter range of the model than other, state-of-the-art precessing waveform models, with typical mismatches of $10^{-3}$. We also construct a frequency domain surrogate model (called NRSur4d2s_FDROM) which can be evaluated in $50\, \mathrm{ms}$ and is suitable for performing parameter estimation studies on gravitational wave detections similar to GW150914.Comment: 34 pages, 26 figure

Exploitation of infrared polarimetric imagery for passive remote sensing applications

Polarimetric infrared imagery has emerged over the past few decades as a candidate technology to detect manmade objects by taking advantage of the fact that smooth materials emit strong polarized electromagnetic waves, which can be remotely sensed by a specialized camera using a rotating polarizer in front of the focal plate array in order to generate the so-called Stokes parameters: S0, S1, S2, and DoLP. Current research in this area has shown the ability of using such variations of these parameters to detect smooth manmade structures in low contrast contrast scenarios. This dissertation proposes and evaluates novel anomaly detection methods for long-wave infrared polarimetric imagery exploitation suited for surveillance applications requiring automatic target detection capability. The targets considered are manmade structures in natural clutter backgrounds under unknown illumination and atmospheric effects. A method based on mathematical morphology is proposed with the intent to enhance the polarimetric Stokes features of manmade structures found in the scene while minimizing its effects on natural clutter. The method suggests that morphology-based algorithms are capable of enhancing the contrast between manmade objects and natural clutter backgrounds, thus, improving the probability of correct detection of manmade objects in the scene. The second method departs from common practices in the polarimetric research community (i.e., using the Stokes vector parameters as input to algorithms) by using instead the raw polarization component imagery (e.g., 0°, 45°, 90°, and 135°) and employing multivariate mathematical statistics to distinguish the two classes of objects. This dissertation unequivocally shows that algorithms based on this new direction significantly outperform the prior art (algorithms based on Stokes parameters and their variants). To support this claim, this dissertation offers an exhaustive data analysis and quantitative comparative study, among the various competing algorithms, using long-wave infrared polarimetric imagery collected outdoor, over several days, under varying weather conditions, geometry of illumination, and diurnal cycles

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

Industrial automation and control in hazardous nuclear environments

textThis report discusses the design and implementation of an automated system for use in geometrically-constrained, hazardous glovebox environments. This system’s purpose is to reduce a hemispherical plutonium pit into smaller pieces that fit inside of a crucible. The size reduction of plutonium pits supports stockpile stewardship efforts by the United States Department of Energy. The automation of this process increases the safety of radiation workers by handling radioactive nuclear material. This decreases glovebox worker dose and exposure to tools, sharps, and fines. This effort examines the hardware and software framework developed to support the use of a Port Deployed Manipulator (PDM) for a contact task. This research effort uses a 7 Degree-of-Freedom (DOF) PDM and a micropunch to reduce hemispherical pit surrogates. Formulation of the material reduction execution algorithm involved addressing a variety of topics related to industrial automation: 1. Collision detection and object recognition based on user-specified parameters. 2. Joint torque monitoring 3. Online motion planning for contact tasks 4. Object-in-hand industrial manufacturing 5. Grasping and handling of nuclear material 6. Software compliance via robust nonlinear control methods A high-bandwidth collision detection algorithm involving joint torque monitoring was developed to increase robot safety during operation. The motion planning algorithm developed for this effort takes variable geometric properties to be used with a range of hemishells. The algorithm’s feasibility was validated on a hardware test bed in a laboratory setting. Hardware cold tests conclude that mechanical compliance is sufficient for task completion. However, software compliance would increase performance, ef- ficiency, and safety during task execution. Two different nonlinear force control laws (feedback linearization and sliding mode control) that minimize object shear forces were developed using a simplified material reduction simulation. It is recommended that glovebox automation research continue to increase worker safety throughout the DOE complex.Mechanical Engineerin

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models

Surrogate model of hybridized numerical relativity binary black hole waveforms

Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to be both fast and accurate. However, NR-based surrogate models are limited by the training waveforms' length, which is typically about 20 orbits before merger. We remedy this by hybridizing the NR waveforms using both post-Newtonian and effective one body waveforms for the early inspiral. We present NRHybSur3dq8, a surrogate model for hybridized nonprecessing numerical relativity waveforms, that is valid for the entire LIGO band (starting at $20~\text{Hz}$) for stellar mass binaries with total masses as low as $2.25\,M_{\odot}$. We include the $\ell \leq 4$ and $(5,5)$ spin-weighted spherical harmonic modes but not the $(4,1)$ or $(4,0)$ modes. This model has been trained against hybridized waveforms based on 104 NR waveforms with mass ratios $q\leq8$, and $|\chi_{1z}|,|\chi_{2z}| \leq 0.8$, where $\chi_{1z}$ ($\chi_{2z}$) is the spin of the heavier (lighter) BH in the direction of orbital angular momentum. The surrogate reproduces the hybrid waveforms accurately, with mismatches $\lesssim 3\times10^{-4}$ over the mass range $2.25M_{\odot} \leq M \leq 300 M_{\odot}$. At high masses ($M\gtrsim40M_{\odot}$), where the merger and ringdown are more prominent, we show roughly two orders of magnitude improvement over existing waveform models. We also show that the surrogate works well even when extrapolated outside its training parameter space range, including at spins as large as 0.998. Finally, we show that this model accurately reproduces the spheroidal-spherical mode mixing present in the NR ringdown signal.Comment: Matches PRD version. Model publicly available at https://zenodo.org/record/2549618#.XJvMrutKii4. 18 pages, 12 figure

Interpretable Deep Learning Predicts the Molecular Endometrial Cancer Classification from H&E Images: A Combined Analysis of the Portec Randomized Clinical Trials

Background: Endometrial Cancer (EC) is molecularly classified into the POLE- mutated (POLE mut), mismatch-repair deficient (MMRd), p53 abnormal (p53abn) and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable DL pipeline for image-based prediction of the four-class molecular EC classification (im4MEC), to identify morpho-molecular correlates and refine EC prognostication. Methods: Diagnostic hematoxylin and Eosin (H&E)-stained slides from 2028 EC patients of the combined PORTEC-1-2,-3 randomized trials and four clinical cohorts were included. im4MEC combined self-supervised learning and an attention mechanism to achieve optimal performance. Tiles with highest attention scores were reviewed to identify morpho-molecular correlates. Predictions of a nuclear classification DL model served to derive interpretable morphological features and their relative contribution in the profiling of each molecular class. Prognostic refinement was explored through morphological and Kaplan-Meier-based survival analyses. Findings: im4MEC achieved a macro-average area under the receiver-operating-characteristic curve (AUROC) on cross-validation of 0·874±0·189, and 0·876 on the independent test set PORTEC-3 with highest performance of 0·928 among p53abn EC. Overall recurrence by image-based molecular class was significantly different in PORTEC-3 (p=1·e-04). Top-attended tiles indicated a significant association between dense lymphocyte infiltrates and POLE mut and MMRd EC, high nuclear atypia and p53abn EC, and an overlap of morphological representations between POLE mut and MMRd EC. im4MEC highlighted low tumor-stroma ratio as a potential novel characteristic feature of NSMP EC. p53abn cases predicted as imMMRd showed inflammatory morphology and better prognosis; NSMP predicted as imp53abn showed nuclear atypia and worse prognosis; MMRd predicted as im POLE mut had excellent prognosis. Interpretation: We present the first interpretable DL model for H&E-based prediction of the molecular EC classification. im4MEC robustly identified morpho-molecular correlates and enables further prognostic refinement of EC patients. Trial Registration: The PORTEC clinical trials are registered at clinicaltrials.gov PORTEC-3 with identifier: NCT00411138 (this trial is used for as independent testset for our model) PORTEC-2 with identifier: NCT00376844, PORTEC-1 was published in the Lancet in 2000 (attached) and ran 1990-1997. It was not registered in clinicaltrials.gov Funding: The Hanarth Foundation. V.H. Koelzer reports grants from the Swiss Federal Institute of Technology Strategic Focus Area: Personalized Health and Related Technologies PHRT and the Promedica Foundation (F-87701-41-01) during the conduct of the study. Declaration of Interest: All authors declare that they have no conflicts of interest in relation to this paper. Ethical Approval: The protocol was approved by the Protocol Review Committee of the Dutch Cancer Society and by the medical ethics committees of the University Hospital Rotterdam/Daniel den Hoed Cancer Centre (DDHCC) and of the participating centres. Keywords: deep learning, Endometrial Cancer, Molecular classification, Morphological features, Prognostic refinement, POLEmut EC, MMRd EC, NSMP EC, p53abn EC, whole slide images, Histopathology image