cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis
Creators: name: Lee, Huai-Ping
name: Lin, Ming C.
name: Niethammer, Marc
name: Krajcevski, Pavel
name: Foskey, Mark
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2012

description: Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound or MR images) and known external forces. Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.
MOESM4 of Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models
Creators: name: Meyer, Ninon
name: Moreno, Ricardo
name: Reyna-Hurtado, Rafael
name: Signer, Johannes
name: Balkenhol, Niko
Publisher: name: figshare
Year: 2020
Subjects: subject: Molecular Biology
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified

description: Additional file 4. Methods- Probability of occupancy and proportion of the study area.
TwinMARM: Two-Stage Multiscale Adaptive Regression Methods for Twin Neuroimaging Data
Creators: name: Li, Yimei
name: Gilmore, John H.
name: Lin, Weili
name: Wang, Jiaping
name: Zhu, Hongtu
name: Styner, Martin
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2012

description: Twin imaging studies have been valuable for understanding the relative contribution of the environment and genes on brain structures and their functions. Conventional analyses of twin imaging data include three sequential steps: spatially smoothing imaging data, independently fitting a structural equation model at each voxel, and finally correcting for multiple comparisons. However, conventional analyses are limited due to the same amount of smoothing throughout the whole image, the arbitrary choice of smoothing extent, and the decreased power in detecting environmental and genetic effects introduced by smoothing raw images. The goal of this article is to develop a two-stage multiscale adaptive regression method (TwinMARM) for spatial and adaptive analysis of twin neuroimaging and behavioral data. The first stage is to establish the relationship between twin imaging data and a set of covariates of interest, such as age and gender. The second stage is to disentangle the environmental and genetic influences on brain structures and their functions. In each stage, TwinMARM employs hierarchically nested spheres with increasing radii at each location and then captures spatial dependence among imaging observations via consecutively connected spheres across all voxels. Simulation studies show that our TwinMARM significantly outperforms conventional analyses of twin imaging data. Finally, we use our method to detect statistically significant effects of genetic and environmental variations on white matter structures in a neonatal twin study.
Meta-analysis of the technological context
Creators: name: Pescarin, Sofia
name: Bertini, Marco
name: Bevilacqua, Luca
name: Damiano, Rossana
name: Fanini, Bruno
name: Furferi, Rocco
name: Gentile, Manuel
name: Montanari, Roberto
name: Thun-Hohenstein, Ursula
Publisher: name: Zenodo
Year: 2023
Subjects: Project CHANGES
PNRR
Partenariato Esteso 5
Project deliverable
Work Package 3

description: This deliverable introduces the technological context referred to virtual technologies applied to museums and art collections. It is a living document and will be updated whenever necessary to comply with additional information and requirements from the Project Changes coordinator.
MOESM1 of Integrative analysis of time course metabolic data and biomarker discovery
Creators: name: Takoua Jendoubi
name: Ebbels, Timothy
Publisher: name: figshare
Year: 2020
Subjects: subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Pharmacology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Cancer

description: Additional file 1 Worked example. We illustrate a fully reproducible application of the iCARH package to a publicly available dataset from [55].
title: Vocal action: from training towards performance
Creators: name: Behrens, Electa
Publisher: name: University of Kent
Year: 2022

description: This thesis describes and analyzes a practice as research study (2008 - 2011) which comprised performances, ongoing workshops and a symposium. Its focus is the (re)examination of voice training for the contemporary performer exploring what existing methods are sti11 relevant, what gaps exist in current training, and how these might be bridged. The methodology is that of a feedback loop in which theoretical research informs practical explorations (conducted by the researcher on herself) which inform practical applications (in which the research is applied with other performers in a pedagogical context). The inquiry is divided into two main topics: training for Vocal Presence and for Vocal Composition. In the first, the theoretical base is that of Konstantin Stanislavski's method of physical actions and its interpretations by Jerzy Grotowski and Ingemar Lindh, with a further link made to the work of positive psychologist Mihaly Czilcszentmihalyi. In the second, the starting points are the work of John Britton and Anne Bogart's Viewpoints. Within both areas, these theories and their related practices are explored through the creation of two perfonnance pieces: lhe sound of m/y/our name ... and One By One and then applied in the creation of a method of vocal (re)actions. This work was presented through a practice as research symposium and is documented on DVD and in an interactive website. This thesis argues for the importance of mapping the connections between different aesthetic and cultural methods for voice training. For the performer, it proposes a non-aesthetic-specific practical method and terminology, which can be used to work both within and across different vocal techniques as well as between body and voice. It thus aims to broaden the repertoire of approaches to voice work for vocal training and for devising performance.
A Locally Adaptive Regularization Based on Anisotropic Diffusion for Deformable Image Registration of Sliding Organs
Creators: name: Aylward, Stephen R.
name: Pace, Danielle F.
name: Niethammer, Marc
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2013

description: We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.
Construction and visualization of optimal confidence sets for frequentist distributional forecasts
Creators: name: Harris, David
name: Martin, Gael M.
name: Perera, Indeewara
name: Poskitt, Don S.
Publisher: name: Monash University
Year: 2022
Subjects: Econometric and statistical methods
Econometrics not elsewhere classified

description: The focus of this paper is on the quantification of sampling variation in frequentist probabilistic forecasts. We propose a method of constructing confidence sets that respects the functional nature of the forecast distribution, and use animated graphics to visualize the impact of parameter uncertainty on the location, dispersion and shape of the distribution. The confidence sets are derived via the inversion of a Wald test and are asymptotically uniformly most accurate and, hence, optimal in this sense. A wide range of linear and non-linear time series models - encompassing long memory, state space and mixture specifications - is used to demonstrate the procedure, based on artificially generated data. An empirical example in which distributional forecasts of both financial returns and its stochastic volatility are produced is then used to illustrate the practical importance of accommodating sampling variation in the manner proposed.
Inferring Group-Wise Consistent Multimodal Brain Networks via Multi-View Spectral Clustering
Creators: name: The University of North Carolina at Chapel Hill University Libraries
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2013

description: Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional MRI (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain sub-networks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks - DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.
Perihelia reduction and global Kolmogorov tori in the planetary problem
Creators: name: Pinzari, Gabriella
Publisher: name: arXiv
Year: 2015
Subjects: subject: Dynamical Systems (math.DS)
subject: FOS: Mathematics
subject: 34D10, 34C20, 70E55, 70F10, 70F15, 70F07, 37J10, 37J15, 37J25, 37J35, 37J40, 70K45

description: We prove the existence of an almost full measure set of $(3n-2)$--dimensional quasi periodic motions in the planetary problem with $(1+n)$ masses, with eccentricities arbitrarily close to the Levi-Civita limiting value and relatively high inclinations. This extends previous results, where smallness of eccentricities and inclinations was assumed. The question had been previously considered by V.I.Arnold [2, Ch III, §1, n. 6, p. 128] in the 60s, for the particular case of the planar three--body problem, where, due to the limited number of degrees of freedom, it was enough to use the invariance of the system by the SO(3) group. The proof exploits nice parity properties of a new set of coordinates for the planetary problem, which reduces completely the number of degrees of freedom for the system (in particular, its degeneracy due to rotations) and, moreover, is well fitted to its reflection invariance. It allows the explicit construction of an associated close to be integrable system, replacing Birkhoff normal form, common tool of previous literature.
description: 78 pages. This research is supported partly (up to February 28, 2016) by ERC Ideas-Project 306414 "Hamiltonian PDEs and small divisor problems: a dynamical systems approach" and partly (since March 1, 2016) by the ERC Project 677793 "Stable and Chaotic Motions in the Planetary Problem." Final peer reviewed manuscript accepted for publication in Memoirs of the American Mathematical Society
Longitudinal Image Registration With Temporally-Dependent Image Similarity Measure
Creators: name: Davis, Brad
name: Csapo, Istvan
name: Sanchez, Mar
name: Styner, Martin
name: Shi, Yundi
name: Niethammer, Marc
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2013

description: Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology and often require spatial correspondence between images achieved through image registration. Beside morphological changes, image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, (i) local similarity measures, (ii) methods estimating intensity transformations between images, and (iii) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration that estimates a temporal model of intensity change using all available images simultaneously.
Robust Anatomical Correspondence Detection by Hierarchical Sparse Graph Matching
Creators: name: Shen, Dinggang
name: Jiang, Jianguo
name: Guo, Yanrong
name: Wu, Guorong
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2013

description: Robust anatomical correspondence detection is a key step in many medical image applications such as image registration and motion correction. In the computer vision field, graph matching techniques have emerged as a powerful approach for correspondence detection. By considering potential correspondences as graph nodes, graph edges can be used to measure the pairwise agreement between possible correspondences. In this paper, we present a novel, hierarchical graph matching method with sparsity constraint to further augment the power of conventional graph matching methods in establishing anatomical correspondences, especially for the cases of large inter-subject variations in medical applications. Specifically, we first propose to measure the pairwise agreement between potential correspondences along a sequence of intensity profiles which reduces the ambiguity in correspondence matching. We next introduce the concept of sparsity on the fuzziness of correspondences to suppress the distraction from misleading matches, which is very important for achieving the accurate, one-to-one correspondences. Finally, we integrate our graph matching method into a hierarchical correspondence matching framework, where we use multiple models to deal with the large inter-subject anatomical variations and gradually refine the correspondence matching results between the tentatively deformed model images and the underlying subject image. Evaluations on both synthetic data and public hand X-ray images indicate that the proposed hierarchical sparse graph matching method yields the best correspondence matching performance in terms of both accuracy and robustness when compared with several conventional graph matching methods.
Sparse Patch-Based Label Propagation for Accurate Prostate Localization in CT Images
Creators: name: Shen, Dinggang
name: Liao, Shu
name: Gao, Yaozong
name: Lian, Jun
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2013

description: In this paper, we propose a new prostate computed tomography (CT) segmentation method for image guided radiation therapy. The main contributions of our method lie in the following aspects. 1) Instead of using voxel intensity information alone, patch-based representation in the discriminative feature space with logistic sparse LASSO is used as anatomical signature to deal with low contrast problem in prostate CT images. 2) Based on the proposed patch-based signature, a new multi-atlases label fusion method formulated under sparse representation framework is designed to segment prostate in the new treatment images, with guidance from the previous segmented images of the same patient. This method estimates the prostate likelihood of each voxel in the new treatment image from its nearby candidate voxels in the previous segmented images, based on the nonlocal mean principle and sparsity constraint. 3) A hierarchical labeling strategy is further designed to perform label fusion, where voxels with high confidence are first labeled for providing useful context information in the same image for aiding the labeling of the remaining voxels. 4) An online update mechanism is finally adopted to progressively collect more patient-specific information from newly segmented treatment images of the same patient, for adaptive and more accurate segmentation. The proposed method has been extensively evaluated on a prostate CT image database consisting of 24 patients where each patient has more than 10 treatment images, and further compared with several state-of-the-art prostate CT segmentation algorithms using various evaluation metrics. Experimental results demonstrate that the proposed method consistently achieves higher segmentation accuracy than any other methods under comparison.
Incremental Learning With Selective Memory (ILSM): Towards Fast Prostate Localization for Image Guided Radiotherapy
Creators: name: The University of North Carolina at Chapel Hill University Libraries
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: Image-guided radiotherapy (IGRT) requires fast and accurate localization of the prostate in 3-D treatment-guided radiotherapy, which is challenging due to low tissue contrast and large anatomical variation across patients. On the other hand, the IGRT workflow involves collecting a series of computed tomography (CT) images from the same patient under treatment. These images contain valuable patient-specific information yet are often neglected by previous works. In this paper, we propose a novel learning framework, namely incremental learning with selective memory (ILSM), to effectively learn the patient-specific appearance characteristics from these patient-specific images. Specifically, starting with a population-based discriminative appearance model, ILSM aims to “personalize” the model to fit patient-specific appearance characteristics. The model is personalized with two steps: backward pruning that discards obsolete population-based knowledge and forward learning that incorporates patient-specific characteristics. By effectively combining the patient-specific characteristics with the general population statistics, the incrementally learned appearance model can localize the prostate of a specific patient much more accurately. This work has three contributions: 1) the proposed incremental learning framework can capture patient-specific characteristics more effectively, compared to traditional learning schemes, such as pure patient-specific learning, population-based learning, and mixture learning with patient-specific and population data; 2) this learning framework does not have any parametric model assumption, hence, allowing the adoption of any discriminative classifier; and 3) using ILSM, we can localize the prostate in treatment CTs accurately (DSC ∼0.89) and fast (∼4 s), which satisfies the real-world clinical requirements of IGRT.
Learning to Rank Atlases for Multiple-Atlas Segmentation
Creators: name: Sanroma, Gerard
name: Shen, Dinggang
name: Gao, Yaozong
name: Wu, Guorong
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.
Bloofi: Multidimensional Bloom Filters
Creators: name: Crainiceanu, Adina
name: Lemire, Daniel
Publisher: name: arXiv
Year: 2015
Subjects: subject: Databases (cs.DB)
subject: Data Structures and Algorithms (cs.DS)
subject: FOS: Computer and information sciences

description: Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cloud environment, each cloud provider could encode the information using Bloom filters and share the Bloom filters with a central coordinator. The problem of interest is not only whether a given element is in any of the sets represented by the Bloom filters, but which of the existing sets contain the given element. This problem cannot be solved by just constructing a Bloom filter on the union of all the sets. Instead, we effectively have a multidimensional Bloom filter problem: given an element, we wish to receive a list of candidate sets where the element might be. To solve this problem, we consider 3 alternatives. Firstly, we can naively check many Bloom filters. Secondly, we propose to organize the Bloom filters in a hierarchical index structure akin to a B+ tree, that we call Bloofi. Finally, we propose another data structure that packs the Bloom filters in such a way as to exploit bit-level parallelism, which we call Flat-Bloofi. Our theoretical and experimental results show that Bloofi and Flat-Bloofi provide scalable and efficient solutions alternatives to search through a large number of Bloom filters.
Local Metric Learning in 2D/3D Deformable Registration With Application in the Abdomen
Creators: name: Zhao, Qingyu
name: Mageras, Gig
name: Chou, Chen-Rui
name: Pizer, Stephen
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: In image-guided radiotherapy (IGRT) of disease sites subject to respiratory motion, soft tissue deformations can affect localization accuracy. We describe the application of a method of 2D/3D deformable registration to soft tissue localization in abdomen. The method, called registration efficiency and accuracy through learning a metric on shape (REALMS), is designed to support real-time IGRT. In a previously developed version of REALMS, the method interpolated 3D deformation parameters for any credible deformation in a deformation space using a single globally-trained Riemannian metric for each parameter. We propose a refinement of the method in which the metric is trained over a particular region of the deformation space, such that interpolation accuracy within that region is improved. We report on the application of the proposed algorithm to IGRT in abdominal disease sites, which is more challenging than in lung because of low intensity contrast and nonrespiratory deformation. We introduce a rigid translation vector to compensate for nonrespiratory deformation, and design a special region-of-interest around fiducial markers implanted near the tumor to produce a more reliable registration. Both synthetic data and actual data tests on abdominal datasets show that the localized approach achieves more accurate 2D/3D deformable registration than the global approach.
PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration
Creators: name: Akbari, Hamed
name: Pohl, Kilian M.
name: Kwon, Dongjin
name: Bilello, Michel
name: Niethammer, Marc
name: Davatzikos, Christos
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2014

description: We propose a new method for deformable registration of pre-operative and post-recurrence brain MR scans of glioma patients. Performing this type of intra-subject registration is challenging as tumor, resection, recurrence, and edema cause large deformations, missing correspondences, and inconsistent intensity profiles between the scans. To address this challenging task, our method, called PORTR, explicitly accounts for pathological information. It segments tumor, resection cavity, and recurrence based on models specific to each scan. PORTR then uses the resulting maps to exclude pathological regions from the image-based correspondence term while simultaneously measuring the overlap between the aligned tumor and resection cavity. Embedded into a symmetric registration framework, we determine the optimal solution by taking advantage of both discrete and continuous search methods. We apply our method to scans of 24 glioma patients. Both quantitative and qualitative analysis of the results clearly show that our method is superior to other state-of-the-art approaches.
Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data
Creators: name: Shan, Liang
name: Charles, H. Cecil
name: Zhu, Hongtu
name: Niethammer, Marc
name: Wirth, Wolfgang
name: Huang, Chao
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Magnetic resonance imaging (MRI) has become an important imaging technique for quantifying the spatial location and magnitude/direction of longitudinal cartilage morphology changes in patients with osteoarthritis (OA). Although several analytical methods, such as subregion-based analysis, have been developed to refine and improve quantitative cartilage analyses, they can be suboptimal due to two major issues: the lack of spatial correspondence across subjects and time and the spatial heterogeneity of cartilage progression across subjects. The aim of this paper is to present a statistical method for longitudinal cartilage quantification in OA patients, while addressing these two issues. The 3D knee image data is preprocessed to establish spatial correspondence across subjects and/or time. Then, a Gaussian hidden Markov model (GHMM) is proposed to deal with the spatial heterogeneity of cartilage progression across both time and OA subjects. To estimate unknown parameters in GHMM, we employ a pseudo-likelihood function and optimize it by using an expectation-maximization (EM) algorithm. The proposed model can effectively detect diseased regions in each OA subject and present a localized analysis of longitudinal cartilage thickness within each latent subpopulation. Our GHMM integrates the strengths of two standard statistical methods including the local subregion-based analysis and the ordered value approach. We use simulation studies and the Pfizer longitudinal knee MRI dataset to evaluate the finite sample performance of GHMM in the quantification of longitudinal cartilage morphology changes. Our results indicate that GHMM significantly outperforms several standard analytical methods.
Investigation of the formation of volatile fission products using furnace and gamma detectors
Creators: name: Zen, Andy Zeren
name: 0000-0002-3403-2399
Publisher: name: The University of Texas at Austin
Year: 2021
Subjects: Volatile fission products
STOMP
Rhenium

description: The objective of this thesis was to investigate the formation of volatile fission products resulting from an underground nuclear explosion to determine possible new signatures for monitoring purposes. The experiment was performed by irradiating a sample of rhenium (Re) wire using the reactor at the Nuclear Engineering Teaching Lab (NETL). The sample was then loaded into a stainless-steel column which was suspended within a furnace and heated at 400-500°C. The activity of the Re was monitored using two external gamma detectors placed at different heights along the column, and the resulting gamma counts were used to determine the movement of volatile products are they were produced. The experiments discussed in this thesis were conducted to determine the viability of the furnace setup as a method of producing volatile compounds, using Re wire as a preliminary sample due to its ability to produce volatile Re₂O₇ at a relative low temperatures. The Re wire was not successfully volatized using the experimental methods presented, however it does not conclude that the furnace setup cannot be used to produce detectable volatile signatures. In addition to the Re wire experiment, a review of parameters for the Subsurface Transport Over Multiple Phases (STOMP) simulator was performed in order to assist future experiments on which parameters to focus future studies
Low-Rank Atlas Image Analyses in the Presence of Pathologies
Creators: name: Niethammer, Marc
name: Aylward, Stephen
name: McCormick, Matt
name: Kwitt, Roland
name: Singh, Nikhil
name: Liu, Xiaoxiao
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes.
MOESM5 of Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models
Creators: name: Meyer, Ninon
name: Moreno, Ricardo
name: Reyna-Hurtado, Rafael
name: Signer, Johannes
name: Balkenhol, Niko
Publisher: name: figshare
Year: 2020
Subjects: subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified

description: Additional file 5. Methods - Core areas of two groups of species.
Mitigation methods for piperazine oxidation in post-combustion carbon capture
Creators: name: Wu, Yuying
name: 0000-0003-0780-6413
Publisher: name: The University of Texas at Austin
Year: 2022
Subjects: CO₂
Piperazine
Oxidation
Carbon treating
N₂ sparging

description: Piperazine is a promising second-generation solvent for amine scrubbing in post-combustion CO₂ capture. However, the oxidative degradation of PZ can cause environmental problems and economic loss. This work presents the effects of two mitigation methods: carbon treating and N₂ sparging, on the PZ oxidation in long-term operations. The species and their respective quantities adsorbed by the activated carbon were tested in a bench-scale device. The carbon was then tested in the High Temperature Oxidation Reactor (HTOR), where the solvent oxidizes at a reasonably fast rate. Pilot plant campaigns were also performed at the UT Austin SRP and the National Carbon Capture Center (NCCC) and the effects were verified. When dissolved Fe is removed by carbon treating or other methods, available soluble Fe in the system dissolves and replaces the dissolved Fe. Therefore, all available Fe and ligands need to be removed for the mitigation to be effective. The sources of soluble Fe include fly ash and the corrosion of stainless steel, and the ligands are degradation products. All PZ degraded solvents have two absorbance peaks at 320 nm and 538 nm. The 320 nm peak is caused by dissolved metals, especially Fe, complexed by degradation products. The 320 nm peak is related to the amine degradation level and can be used as a simple and efficient method to estimate the amine degradation rate. The pilot plant data suggest that NO₂ can oxidize PZ significantly, possibly through radical reactions. 0.01 mmol/kg-hr absorption of NO₂ increased the PZ oxidation rate from 1.2 mmol/kg-hr to 2.5 mmol/kg-hr.
Decomposable Norm Minimization with Proximal-Gradient Homotopy Algorithm
Creators: name: Eghbali, Reza
name: Fazel, Maryam
Publisher: name: arXiv
Year: 2015
Subjects: subject: Optimization and Control (math.OC)
subject: FOS: Mathematics

description: We study the convergence rate of the proximal-gradient homotopy algorithm applied to norm-regularized linear least squares problems, for a general class of norms. The homotopy algorithm reduces the regularization parameter in a series of steps, and uses a proximal-gradient algorithm to solve the problem at each step. Proximal-gradient algorithm has a linear rate of convergence given that the objective function is strongly convex, and the gradient of the smooth component of the objective function is Lipschitz continuous. In many applications, the objective function in this type of problem is not strongly convex, especially when the problem is high-dimensional and regularizers are chosen that induce sparsity or low-dimensionality. We show that if the linear sampling matrix satisfies certain assumptions and the regularizing norm is decomposable, proximal-gradient homotopy algorithm converges with a \emph{linear rate} even though the objective function is not strongly convex. Our result generalizes results on the linear convergence of homotopy algorithm for $l_1$-regularized least squares problems. Numerical experiments are presented that support the theoretical convergence rate analysis.
MOESM6 of Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models
Creators: name: Meyer, Ninon
name: Moreno, Ricardo
name: Reyna-Hurtado, Rafael
name: Signer, Johannes
name: Balkenhol, Niko
Publisher: name: figshare
Year: 2020
Subjects: subject: Molecular Biology
subject: Evolutionary Biology
subject: FOS: Biological sciences
subject: Ecology
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified

description: Additional file 6. Results - GPS relocations and steps for each individual.
MOESM7 of Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models
Creators: name: Meyer, Ninon
name: Moreno, Ricardo
name: Reyna-Hurtado, Rafael
name: Signer, Johannes
name: Balkenhol, Niko
Publisher: name: figshare
Year: 2020
Subjects: subject: Medicine
subject: Molecular Biology
subject: Ecology
subject: FOS: Biological sciences
subject: Sociology
subject: FOS: Sociology
subject: 69999 Biological Sciences not elsewhere classified

description: Additional file 7. Results - Determination of the optimal scale for forest cover, forest loss, and density of villages.
Galaxy formation in the Planck cosmology II. Star formation histories and post-processing magnitude reconstruction
Creators: name: Shamshiri, Sorour
name: Thomas, Peter A.
name: Henriques, Bruno M
name: Tojeiro, Rita
name: Lemson, Gerard
name: Oliver, Seb J.
name: Wilkins, Stephen
Publisher: name: arXiv
Year: 2015
Subjects: subject: Astrophysics of Galaxies (astro-ph.GA)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: FOS: Physical sciences

description: We adapt the L-Galaxies semi-analytic model to follow the star-formation histories (SFH) of galaxies -- by which we mean a record of the formation time and metallicities of the stars that are present in each galaxy at a given time. We use these to construct stellar spectra in post-processing, which offers large efficiency savings and allows user-defined spectral bands and dust models to be applied to data stored in the Millennium data repository. We contrast model SFHs from the Millennium Simulation with observed ones from the VESPA algorithm as applied to the SDSS-7 catalogue. The overall agreement is good, with both simulated and SDSS galaxies showing a steeper SFH with increased stellar mass. The SFHs of blue and red galaxies, however, show poor agreement between data and simulations, which may indicate that the termination of star formation is too abrupt in the models. The mean star-formation rate (SFR) of model galaxies is well-defined and is accurately modelled by a double power law at all redshifts: SFR proportional to $1/(x^{-1.39}+x^{1.33})$, where $x=(t_a-t)/3.0\,$Gyr, $t$ is the age of the stars and $t_a$ is the loopback time to the onset of galaxy formation; above a redshift of unity, this is well approximated by a gamma function: SFR proportional to $x^{1.5}e^{-x}$, where $x=(t_a-t)/2.0\,$Gyr. Individual galaxies, however, show a wide dispersion about this mean. When split by mass, the SFR peaks earlier for high-mass galaxies than for lower-mass ones, and we interpret this downsizing as a mass-dependence in the evolution of the quenched fraction: the SFHs of star-forming galaxies show only a weak mass dependence.
description: Accepted version of the paper, to appear in MNRAS. Compared to the original version, contains more detail on the post-processing of magnitudes, including a table of rms magnitude errors. SFHs available on Millennium database http://gavo.mpa-garching.mpg.de/MyMillennium/
MOESM8 of Towards the restoration of the Mesoamerican Biological Corridor for large mammals in Panama: comparing multi-species occupancy to movement models
Creators: name: Meyer, Ninon
name: Moreno, Ricardo
name: Reyna-Hurtado, Rafael
name: Signer, Johannes
name: Balkenhol, Niko
Publisher: name: figshare
Year: 2020
Subjects: subject: Molecular Biology
subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: Marine Biology

description: Additional file 8. Results - Species-specific coefficients for environmental variables estimated with occupancy modeling.
LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations
Creators: name: Shen, Dinggang
name: Shi, Feng
name: Cheng, Jian
name: Wang, Li
name: Yap, Pew-Thian
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2015

description: Image super-resolution (SR) aims to recover high-resolution images from their low-resolution counterparts for improving image analysis and visualization. Interpolation methods, widely used for this purpose, often result in images with blurred edges and blocking effects. More advanced methods such as total variation (TV) retain edge sharpness during image recovery. However, these methods only utilize information from local neighborhoods, neglecting useful information from remote voxels. In this paper, we propose a novel image SR method that integrates both local and global information for effective image recovery. This is achieved by, in addition to TV, low-rank regularization that enables utilization of information throughout the image. The optimization problem can be solved effectively via alternating direction method of multipliers (ADMM). Experiments on MR images of both adult and pediatric subjects demonstrate that the proposed method enhances the details in the recovered high-resolution images, and outperforms methods such as the nearest-neighbor interpolation, cubic interpolation, iterative back projection (IBP), non-local means (NLM), and TV-based up-sampling.
The Integrated Virtual Environment Rehabilitation Treadmill System
Creators: name: Brooks, Frederick P.
name: Lewek, Michael D.
name: Kassler, Laura
name: Feasel, Jeff
name: Whitton, Mary C.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2011

description: Slow gait speed and interlimb asymmetry are prevalent in a variety of disorders. Current approaches to locomotor retraining emphasize the need for appropriate feedback during intensive, task-specific practice. This paper describes the design and feasibility testing of the integrated virtual environment rehabilitation treadmill (IVERT) system intended to provide real-time, intuitive feedback regarding gait speed and asymmetry during training. The IVERT system integrates an instrumented, split-belt treadmill with a front-projection, immersive virtual environment. The novel adaptive control system uses only ground reaction force data from the treadmill to continuously update the speeds of the two treadmill belts independently, as well as to control the speed and heading in the virtual environment in real time. Feedback regarding gait asymmetry is presented 1) visually as walking a curved trajectory through the virtual environment and 2) proprioceptively in the form of different belt speeds on the split-belt treadmill. A feasibility study involving five individuals with asymmetric gait found that these individuals could effectively control the speed of locomotion and perceive gait asymmetry during the training session. Although minimal changes in overground gait symmetry were observed immediately following a single training session, further studies should be done to determine the IVERT’s potential as a tool for rehabilitation of asymmetric gait by providing patients with congruent visual and proprioceptive feedback.
Cardinal Interpolation With General Multiquadrics
Creators: name: Hamm, Keaton
name: Ledford, Jeff
Publisher: name: arXiv
Year: 2015
Subjects: subject: Classical Analysis and ODEs (math.CA)
subject: FOS: Mathematics

description: This paper studies the cardinal interpolation operators associated with the general multiquadrics, $ϕ_{α,c}(x) = (\|x\|^2+c^2)^α$, $x\in\mathbb{R}^d$. These operators take the form $$\mathscr{I}_{α,c}\mathbf{y}(x) = \sum_{j\in\mathbb{Z}^d}y_jL_{α,c}(x-j),\quad\mathbf{y}=(y_j)_{j\in\mathbb{Z}^d},\quad x\in\mathbb{R}^d,$$ where $L_{α,c}$ is a fundamental function formed by integer translates of $ϕ_{α,c}$ which satisfies the interpolatory condition $L_{α,c}(k) = δ_{0,k},\; k\in\mathbb{Z}^d$. We consider recovery results for interpolation of bandlimited functions in higher dimensions by limiting the parameter $c\to\infty$. In the univariate case, we consider the norm of the operator $\mathscr{I}_{α,c}$ acting on $\ell_p$ spaces as well as prove decay rates for $L_{α,c}$ using a detailed analysis of the derivatives of its Fourier transform, $\widehat{L_{α,c}}$.
description: Current version contains corrected definition of modified Bessel function and corresponding proof of Lemma 1 from the published version
An SVD investigation of modeling scatter in multiple energy windows for improved SPECT images
Creators: name: Kadrmas, D.J.
name: Frey, E.C.
name: Tsui, B.M.W.
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 1996

description: In this work singular value decomposition (SVD) techniques are used to investigate how the use of low energy photons and multiple energy windows affects the noise properties of Tc-99m SPECT imaging. We have previously shown that, when modeling scatter in the projector and backprojector of iterative reconstruction algorithms, simultaneous reconstruction from multiple energy window data can result in very different noise characteristics. Further, the properties depend upon the width and number of energy windows used. To investigate this further, we have generated photon transport matrices using models for scatter, an elliptical phantom containing cold rods of various sizes, and a number of multiple energy window acquisition schemes. Transfer matrices were also generated for the cases of perfect scatter rejection and ideal scatter subtraction. The matrices were decomposed using SVD, and signal power and projection space variance spectra were computed using the basis formed by the left singular vectors. Results indicate very different noise levels for the various energy window combinations. The perfect scatter rejection case resulted in the lowest variance spectrum, and reconstruction-based scatter compensation performed better than the scatter subtraction case. When including lower energy photons in reconstruction-based scatter compensation, using a series of multiple energy windows outperformed a single large energy window. One multiple window combination is presented which achieves a lower variance spectrum than the standard 20% energy window, indicating the potential for using low energy photons to improve the noise characteristics of SPECT images.
Finding a home for arms control : the origins of the Arms Control and Disarmament Agency (1945-1961)
Creators: name: Buchleiter, Jonathan Alan
name: 0000-0001-5201-455X
Publisher: name: The University of Texas at Austin
Year: 2021
Subjects: Disarmament
Arms control
Nuclear weapons
Truman
Eisenhower
Kennedy
ACDA

description: The advent of nuclear weapons in 1945 left US policymakers simultaneously heartened and horrified. Atomic bombs epitomized the apex of American power at the end of World War II, but the destructiveness of these new weapons also posed grave dangers to the US and the world. This paper explores US policymakers’ efforts to rein in atomic energy, first through proposed frameworks for international control and later through multilateral and bilateral limitations on nuclear weapons. Early efforts struggled to gain traction due to both persistent international tensions and an insufficient institutional framework to develop and promote arms control measures within the growing national security establishment. I argue that addressing the deficiencies of US arms control and disarmament policy helped enable diplomatic successes when conditions for compromise arose during the Cold War. Establishing the semi-autonomous Arms Control and Disarmament Agency with a sizable budget, sufficient staff, and empowered director served as a critical step to research and negotiation arms control and nonproliferation agreements during the 1960s and 1970s
DBRepo: A Semantic Digital Repository for Relational Databases
Creators: name: Weise, Martin
name: Staudinger, Moritz
name: Michlits, Cornelia
name: Gergely, Eva
name: Stytsenko, Kirill
name: Ganguly, Raman
name: Rauber, Andreas
Publisher: name: Zenodo
Year: 2022
Subjects: Semantics
Digital Repository
Relational Databases

description: This presentation was held on June 15th, 2022 virtually at the 17th International Digital Curation Conference