SparseM: A Sparse Matrix Package for R *

Directory of Open Access Journals (Sweden)

Roger Koenker

2003-02-01

Full Text Available SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.

X-ray computed tomography using curvelet sparse regularization.

Science.gov (United States)

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

Spectrum recovery method based on sparse representation for segmented multi-Gaussian model

Science.gov (United States)

Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan

2016-09-01

Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

Ionizing radiation used in medical diagnostics as a source of radiation exposure of the patient with occupational diseases. Analysis and problems

International Nuclear Information System (INIS)

Apostolova, D.B.; Paskalev, Z.D.

2001-01-01

X-rays in medical diagnostic are the major source of Bulgarian population exposure to ionizing radiations. Diagnostic X-ray is the most diagnostic application and is used in a wide variety of examinations. The modern concept for radiation protection of patients in diagnostic radiology is based on two main principles: justification of the examinations and radiation protection optimization. It is pointed out that the collective effective dose of radiation may be considerably reduced by decreasing the number of clinically unwarranted X-ray examination of storage and delivery of diagnostic information and adopting a system for physical and technical quality control of the X-ray equipment. The aim of this investigation is assessment of the collective effective doses for the patients with occupational diseases exposed to ionizing radiation by radiological diagnostics. The study covers the period of 1990 through 1999. A total of 3293 patients, treated in the Department of Occupational Toxicology, Clinic of Occupational Diseases, Medical University - Sofia, were examined with X-ray and KT (cervical and lumbar spine, chest, skull, stomach, extremities, pelvis, brain). Most of the observed patients were with predominantlyheavy metals poisonings and a few with other chemical agents poisonings. Number of patients with radiological examinations was 1938, number of examination per capita was 0,59 and the total number of radiological examinations was 2536. The average number of radiological examination for one patient was 1,36, the most number of radiological examinations for one patient was 4. The collective effective dose for an examined patient was 1803 man.mSv. Our results shown the essential of the raising ensure that the medical exposure of patients be the minimum necessary to achieve the required diagnostic objective. (author)

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan

2014-07-06

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu; Bibi, Adel Aamer; Ghanem, Bernard

2016-01-01

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

In Defense of Sparse Tracking: Circulant Sparse Tracker

KAUST Repository

Zhang, Tianzhu

2016-12-13

Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

SDSS-IV MaNGA: the impact of diffuse ionized gas on emission-line ratios, interpretation of diagnostic diagrams and gas metallicity measurements

Science.gov (United States)

Zhang, Kai; Yan, Renbin; Bundy, Kevin; Bershady, Matthew; Haffner, L. Matthew; Walterbos, René; Maiolino, Roberto; Tremonti, Christy; Thomas, Daniel; Drory, Niv; Jones, Amy; Belfiore, Francesco; Sánchez, Sebastian F.; Diamond-Stanic, Aleksandar M.; Bizyaev, Dmitry; Nitschelm, Christian; Andrews, Brett; Brinkmann, Jon; Brownstein, Joel R.; Cheung, Edmond; Li, Cheng; Law, David R.; Roman Lopes, Alexandre; Oravetz, Daniel; Pan, Kaike; Storchi Bergmann, Thaisa; Simmons, Audrey

2017-04-01

Diffuse ionized gas (DIG) is prevalent in star-forming galaxies. Using a sample of 365 nearly face-on star-forming galaxies observed by Mapping Nearby Galaxies at APO, we demonstrate how DIG in star-forming galaxies impacts the measurements of emission-line ratios, hence the interpretation of diagnostic diagrams and gas-phase metallicity measurements. At fixed metallicity, DIG-dominated low ΣHα regions display enhanced [S II]/Hα, [N II]/Hα, [O II]/Hβ and [O I]/Hα. The gradients in these line ratios are determined by metallicity gradients and ΣHα. In line ratio diagnostic diagrams, contamination by DIG moves H II regions towards composite or low-ionization nuclear emission-line region (LI(N)ER)-like regions. A harder ionizing spectrum is needed to explain DIG line ratios. Leaky H II region models can only shift line ratios slightly relative to H II region models, and thus fail to explain the composite/LI(N)ER line ratios displayed by DIG. Our result favours ionization by evolved stars as a major ionization source for DIG with LI(N)ER-like emission. DIG can significantly bias the measurement of gas metallicity and metallicity gradients derived using strong-line methods. Metallicities derived using N2O2 are optimal because they exhibit the smallest bias and error. Using O3N2, R23, N2 = [N II]/Hα and N2S2Hα to derive metallicities introduces bias in the derived metallicity gradients as large as the gradient itself. The strong-line method of Blanc et al. (IZI hereafter) cannot be applied to DIG to get an accurate metallicity because it currently contains only H II region models that fail to describe the DIG.

Intercomparison of ionization chambers in standard X-ray beams, at radiotherapy, diagnostic radiology and radioprotection levels; Intercomparacao de camaras de ionizacao em feixes padroes de raios X, niveis radioterapia, radiodiagnostico e radioprotecao

Energy Technology Data Exchange (ETDEWEB)

Bessa, Ana Carolina Moreira de

2006-07-01

Since the calibration of radiation measurement instruments and the knowledge of their major characteristics are very important subjects, several different types of ionization chambers were intercompared in terms of their calibration coefficients and their energy dependence, in radiotherapy, diagnostic radiology and radioprotection standard beams. An intercomparison of radionuclide calibrators for nuclear medicine was performed, using three radionuclides: {sup 67}Ga, {sup 201}Tl and {sup 99m}Tc; the results obtained were all within the requirements of the national standard CNEN-NE-3.05. In order to complete the range of radiation qualities of the Calibration Laboratory of IPEN, standard radiation beam qualities, radiation protection and low energy radiation therapy levels, were established, according international recommendations. Three methodologies for the calibration of unsealed ionization chambers in X-ray beams were studied and compared. A set of Victoreen ionization chambers, specially designed for use in laboratorial intercomparisons, was submitted to characterization tests. The performance of these Victoreen ionization chambers showed that they are suitable for use in radioprotection beams, because the results obtained agree with international recommendations. However, these Victoreen ionization chambers can be used in radiotherapy and diagnostic radiology beams only with some considerations, since their performance in these beams, especially in relation to the energy dependence and stabilization time tests, did not agree with the international recommendations for dosimeters used in radiotherapy and diagnostic radiology beams. This work presents data on the performance of several types of ionization chambers in different X-ray beams, that may be useful for choosing the appropriate instrument for measurements in ionizing radiation beams. (author)

Management in the protection from ionizing radiation

International Nuclear Information System (INIS)

Radunovic, Miodrag; Nikolic, Krsto; Rakic, Goran

2008-01-01

There are numerous types and forms of endangering working and living environment, ranging from natural disasters to nuclear accidents. Challenges of the New Age determined that most of the countries reviewed its strategic decisions in the system of protection from ionizing radiation and nuclear safety and defined in a new way the threats, which could considerably imperil health of the population and national interests as well. Excessive radiation of the population became a serious and actual problem in the era of increasingly mass application of ionizing radiation, especially in medicine. The goal of this work is to reduce the risk through using knowledge and existing experiences, in particular when it comes to ionizing radiation in medicine. Optimization of the protection in radiology actually means an effort to find the compromise between quality information provided by diagnostics procedure and quality effects of therapy procedure on one side and dose of radiation received by patients on the other. Criteria for the quality management in the protection from ionizing radiation used in diagnostic radiology was given by the European Commission: European Guidelines on Quality Criteria for Diagnostic Radiographic Images, EUR, 16260. (author)

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu

2015-06-01

Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

Assessment of health risks from exposure to ionizing radiation

International Nuclear Information System (INIS)

Beebe, G.W.

1982-01-01

Rapid development in the assessment of health risks from exposure to ionizing radiation has produced an impressive array of risk differentials of presumed biologic significance. In the human data these differentials involve: (1) the variety of cancer, especially its size; (2) host factors, especially age; (3) time following exposure; (4) magnitude of dose; and (5) type of radiation. From experimental work we may presume that dose-rate also plays a role, especially for sparsely ionizing radiation. Current research is extending the scope of differentials with respect to these and other variables, including cell type and concomitant environmental risk factors, and testing dose-response models suggested by experimental and theoretical work. As facts to be explained, differentials in risk may lead to hypotheses to be explored experimentally and improve our understanding of how ionizing radiation causes cancer. 74 references

Chemical inhibition of cell recovery after irradiation with sparsely and densely ionizing radiation

Energy Technology Data Exchange (ETDEWEB)

Evastratova, Ekaterina S.; Petin, Vladislav [A. Tsyb Medical Radiological Research Centre-branch of the National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Obninsk (Russian Federation); Kim, Jin Hong; Kim, Jin Kyu [Korea Atomic Energy Research Institute, Advanced Radiation Technology Institute (ARTI), Jeongeup (Korea, Republic of); Lim, Youg Khi [Dept. of Radiological Science, Gachon University, Incheon (Korea, Republic of)

2017-02-15

The dependence of cell survival on exposure dose and the duration of the liquid holding recovery (LHR) was obtained for diploid yeast cells irradiated with ionizing radiation of different linear energy transfer (LET) and recovering from radiation damage without and with various concentrations of cisplatin - the most widely used anticancer drug. The ability of yeast cells to recover from radiation damage was less effective after cell exposure to high-LET radiation, when cells were irradiated without drug. The increase in cisplatin concentration resulted in the disappearance of this difference whereas the fraction of irreversible damage was permanently enlarged independently of radiation quality. The probability of cell recovery was shown to be constant for various conditions of irradiation and recovery. A new mechanism of cisplatin action was suggested according with which the inhibition of cell recovery after exposure to ionizing radiations was completely explained by the production of irreversible damage.

Chemical inhibition of cell recovery after irradiation with sparsely and densely ionizing radiation

International Nuclear Information System (INIS)

Evastratova, Ekaterina S.; Petin, Vladislav; Kim, Jin Hong; Kim, Jin Kyu; Lim, Youg Khi

2017-01-01

The dependence of cell survival on exposure dose and the duration of the liquid holding recovery (LHR) was obtained for diploid yeast cells irradiated with ionizing radiation of different linear energy transfer (LET) and recovering from radiation damage without and with various concentrations of cisplatin - the most widely used anticancer drug. The ability of yeast cells to recover from radiation damage was less effective after cell exposure to high-LET radiation, when cells were irradiated without drug. The increase in cisplatin concentration resulted in the disappearance of this difference whereas the fraction of irreversible damage was permanently enlarged independently of radiation quality. The probability of cell recovery was shown to be constant for various conditions of irradiation and recovery. A new mechanism of cisplatin action was suggested according with which the inhibition of cell recovery after exposure to ionizing radiations was completely explained by the production of irreversible damage

Investigation of the applicability of a special parallel-plate ionization chamber for x-ray beam dosimetry

International Nuclear Information System (INIS)

Perini, Ana P.; Neves, Lucio P.; Caldas, Linda V.E.

2014-01-01

Diagnostic x-rays are the greatest source of exposition to ionizing radiation of the population worldwide. In order to obtain accurate and lower-cost dosimeters for quality control assurance of medical x-ray facilities, a special ionization chamber was designed at the Calibration Laboratory of the IPEN, for dosimetry in diagnostic radiology beams. For the chamber characterization some tests were undertaken. Monte Carlo simulations were proposed to evaluate the distribution of the deposited energy in the sensitive volume of the ionization chamber and the collecting electrode effect on the chamber response. According to the obtained results, this special ionization chamber presents potential use for dosimetry of conventional diagnostic radiology beams. - Highlights: • An ionization chamber with a novel design was characterized for x-ray beam dosimetry. • This ionization chamber was evaluated in diagnostic radiology qualities. • The characterization tests results were within the recommended limits. • Monte Carlo simulations were employed to evaluate the design of the dosimeter. • The developed prototype is a good alternative for calibration laboratories and clinics

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions

NARCIS (Netherlands)

Eck, A.; Zintgraf, L.M.; de Groot, E.F.J.; de Meij, T.G.J.; Cohen, T.S.; Savelkoul, P.H.M.; Welling, M.; Budding, A.E.

2017-01-01

Background The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

Directory of Open Access Journals (Sweden)

Zhi Gao

2018-05-01

Full Text Available Light detection and ranging (LiDAR sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs and unmanned aerial vehicles (UAVs to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

Science.gov (United States)

Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

2018-05-06

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan

2017-06-28

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

[Special application of matrix-assisted laser desorption ionization time-of-flight mass spectrometry in clinical microbiological diagnostics].

Science.gov (United States)

Nagy, Erzsébet; Abrók, Marianna; Bartha, Noémi; Bereczki, László; Juhász, Emese; Kardos, Gábor; Kristóf, Katalin; Miszti, Cecilia; Urbán, Edit

2014-09-21

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry as a new possibility for rapid identification of bacteria and fungi revolutionized the clinical microbiological diagnostics. It has an extreme importance in the routine microbiological laboratories, as identification of the pathogenic species rapidly will influence antibiotic selection before the final determination of antibiotic resistance of the isolate. The classical methods for identification of bacteria or fungi, based on biochemical tests, are influenced by many environmental factors. The matrix-assisted laser desorption ionization time-of-flight mass spectrometry is a rapid method which is able to identify a great variety of the isolated bacteria and fungi based on the composition of conserved ribosomal proteins. Recently several other applications of the method have also been investigated such as direct identification of pathogens from the positive blood cultures. There are possibilities to identify bacteria from the urine samples in urinary tract infection or from other sterile body fluids. Using selective enrichment broth Salmonella sp from the stool samples can be identified more rapidly, too. The extended spectrum beta-lactamase or carbapenemase production of the isolated bacteria can be also detected by this method helping the antibiotic selection in some cases. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry based methods are suitable to investigate changes in deoxyribonucleic acid or ribonucleic acid, to carry out rapid antibiotic resistance determination or other proteomic analysis. The aim of this paper is to give an overview about present possibilities of using this technique in the clinical microbiological routine procedures.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Qing Ye

2015-01-01

Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

2014-01-01

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

Sparse structure regularized ranking

KAUST Repository

Wang, Jim Jing-Yan

2014-04-17

Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

Turbulent flows over sparse canopies

Science.gov (United States)

Sharma, Akshath; García-Mayoral, Ricardo

2018-04-01

Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.

Cognitive aspect of diagnostic errors.

Science.gov (United States)

Phua, Dong Haur; Tan, Nigel C K

2013-01-01

Diagnostic errors can result in tangible harm to patients. Despite our advances in medicine, the mental processes required to make a diagnosis exhibits shortcomings, causing diagnostic errors. Cognitive factors are found to be an important cause of diagnostic errors. With new understanding from psychology and social sciences, clinical medicine is now beginning to appreciate that our clinical reasoning can take the form of analytical reasoning or heuristics. Different factors like cognitive biases and affective influences can also impel unwary clinicians to make diagnostic errors. Various strategies have been proposed to reduce the effect of cognitive biases and affective influences when clinicians make diagnoses; however evidence for the efficacy of these methods is still sparse. This paper aims to introduce the reader to the cognitive aspect of diagnostic errors, in the hope that clinicians can use this knowledge to improve diagnostic accuracy and patient outcomes.

Modelling ultraviolet-line diagnostics of stars, the ionized and the neutral interstellar medium in star-forming galaxies

Science.gov (United States)

Vidal-García, A.; Charlot, S.; Bruzual, G.; Hubeny, I.

2017-09-01

We combine state-of-the-art models for the production of stellar radiation and its transfer through the interstellar medium (ISM) to investigate ultraviolet-line diagnostics of stars, the ionized and the neutral ISM in star-forming galaxies. We start by assessing the reliability of our stellar population synthesis modelling by fitting absorption-line indices in the ISM-free ultraviolet spectra of 10 Large Magellanic Cloud clusters. In doing so, we find that neglecting stochastic sampling of the stellar initial mass function in these young (∼10-100 Myr), low-mass clusters affects negligibly ultraviolet-based age and metallicity estimates but can lead to significant overestimates of stellar mass. Then, we proceed and develop a simple approach, based on an idealized description of the main features of the ISM, to compute in a physically consistent way the combined influence of nebular emission and interstellar absorption on ultraviolet spectra of star-forming galaxies. Our model accounts for the transfer of radiation through the ionized interiors and outer neutral envelopes of short-lived stellar birth clouds, as well as for radiative transfer through a diffuse intercloud medium. We use this approach to explore the entangled signatures of stars, the ionized and the neutral ISM in ultraviolet spectra of star-forming galaxies. We find that, aside from a few notable exceptions, most standard ultraviolet indices defined in the spectra of ISM-free stellar populations are prone to significant contamination by the ISM, which increases with metallicity. We also identify several nebular-emission and interstellar-absorption features, which stand out as particularly clean tracers of the different phases of the ISM.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.; Bensmail, H.; Yao, N.; Gao, Xin

2013-01-01

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Discriminative sparse coding on multi-manifolds

KAUST Repository

Wang, J.J.-Y.

2013-09-26

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

Evaluation of fast highly undersampled contrast-enhanced MR angiography (sparse CE-MRA) in intracranial applications - initial study

International Nuclear Information System (INIS)

Gratz, Marcel; Quick, Harald H.; Schlamann, Marc; Goericke, Sophia; Maderwald, Stefan

2017-01-01

To assess the image quality of sparsely sampled contrast-enhanced MR angiography (sparse CE-MRA) providing high spatial resolution and whole-head coverage. Twenty-three patients scheduled for contrast-enhanced MR imaging of the head, (N = 19 with intracranial pathologies, N = 9 with vascular diseases), were included. Sparse CE-MRA at 3 Tesla was conducted using a single dose of contrast agent. Two neuroradiologists independently evaluated the data regarding vascular visibility and diagnostic value of overall 24 parameters and vascular segments on a 5-point ordinary scale (5 = very good, 1 = insufficient vascular visibility). Contrast bolus timing and the resulting arterio-venous overlap was also evaluated. Where available (N = 9), sparse CE-MRA was compared to intracranial Time-of-Flight MRA. The overall rating across all patients for sparse CE-MRA was 3.50 ± 1.07. Direct influence of the contrast bolus timing on the resulting image quality was observed. Overall mean vascular visibility and image quality across different features was rated good to intermediate (3.56 ± 0.95). The average performance of intracranial Time-of-Flight was rated 3.84 ± 0.87 across all patients and 3.54 ± 0.62 across all features. Sparse CE-MRA provides high-quality 3D MRA with high spatial resolution and whole-head coverage within short acquisition time. Accurate contrast bolus timing is mandatory. (orig.)

Evaluation of fast highly undersampled contrast-enhanced MR angiography (sparse CE-MRA) in intracranial applications - initial study

Energy Technology Data Exchange (ETDEWEB)

Gratz, Marcel; Quick, Harald H. [University of Duisburg-Essen, Erwin L. Hahn Institute for MR Imaging, Essen (Germany); University Hospital Essen, High Field and Hybrid MR Imaging, Essen (Germany); Schlamann, Marc [University Hospital Giessen and Marburg GmbH, Neuroradiology, Giessen (Germany); University Hospital Essen, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Goericke, Sophia [University Hospital Essen, Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen (Germany); Maderwald, Stefan [University of Duisburg-Essen, Erwin L. Hahn Institute for MR Imaging, Essen (Germany)

2017-03-15

To assess the image quality of sparsely sampled contrast-enhanced MR angiography (sparse CE-MRA) providing high spatial resolution and whole-head coverage. Twenty-three patients scheduled for contrast-enhanced MR imaging of the head, (N = 19 with intracranial pathologies, N = 9 with vascular diseases), were included. Sparse CE-MRA at 3 Tesla was conducted using a single dose of contrast agent. Two neuroradiologists independently evaluated the data regarding vascular visibility and diagnostic value of overall 24 parameters and vascular segments on a 5-point ordinary scale (5 = very good, 1 = insufficient vascular visibility). Contrast bolus timing and the resulting arterio-venous overlap was also evaluated. Where available (N = 9), sparse CE-MRA was compared to intracranial Time-of-Flight MRA. The overall rating across all patients for sparse CE-MRA was 3.50 ± 1.07. Direct influence of the contrast bolus timing on the resulting image quality was observed. Overall mean vascular visibility and image quality across different features was rated good to intermediate (3.56 ± 0.95). The average performance of intracranial Time-of-Flight was rated 3.84 ± 0.87 across all patients and 3.54 ± 0.62 across all features. Sparse CE-MRA provides high-quality 3D MRA with high spatial resolution and whole-head coverage within short acquisition time. Accurate contrast bolus timing is mandatory. (orig.)

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin

2015-04-03

© 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

Sparse distributed memory overview

Science.gov (United States)

Raugh, Mike

1990-01-01

The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

In-place sparse suffix sorting

DEFF Research Database (Denmark)

Prezza, Nicola

2018-01-01

information regarding the lexicographical order of a size-b subset of all n text suffixes is often needed. Such information can be stored space-efficiently (in b words) in the sparse suffix array (SSA). The SSA and its relative sparse LCP array (SLCP) can be used as a space-efficient substitute of the sparse...... suffix tree. Very recently, Gawrychowski and Kociumaka [11] showed that the sparse suffix tree (and therefore SSA and SLCP) can be built in asymptotically optimal O(b) space with a Monte Carlo algorithm running in O(n) time. The main reason for using the SSA and SLCP arrays in place of the sparse suffix...... tree is, however, their reduced space of b words each. This leads naturally to the quest for in-place algorithms building these arrays. Franceschini and Muthukrishnan [8] showed that the full suffix array can be built in-place and in optimal running time. On the other hand, finding sub-quadratic in...

Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

Science.gov (United States)

Moody, Daniela; Wohlberg, Brendt

2018-01-02

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

Effect of ionizing radiation on advanced life support medications

International Nuclear Information System (INIS)

Sullivan, D.J.; Hubbard, L.B.; Broadbent, M.V.; Stewart, P.; Jaeger, M.

1987-01-01

Advanced life support medications stored in emergency department stretcher areas, diagnostic radiology rooms, and radiotherapy suites are exposed to ionizing radiation. We hypothesized that radiation may decrease the potency and thus the shelf life of medications stored in these areas. Atropine, dopamine, epinephrine, and isoproterenol were exposed to a wide range of ionizing radiation. The potency of the four drugs was unaffected by levels of radiation found in ED stretcher areas and high-volume diagnostic radiograph rooms (eg, chest radiograph, computed tomography, fluoroscopy). The potency of atropine may be reduced by gamma radiation in high-use radiotherapy suites. However, dopamine, epinephrine, and isoproterenol were unaffected by high doses of gamma radiation. Atropine, dopamine, epinephrine, and isoproterenol may be safely kept in ED stretcher areas and diagnostic radiology rooms without loss of potency over the shelf life of the drugs

Similarity regularized sparse group lasso for cup to disc ratio computation.

Science.gov (United States)

Cheng, Jun; Zhang, Zhuo; Tao, Dacheng; Wong, Damon Wing Kee; Liu, Jiang; Baskaran, Mani; Aung, Tin; Wong, Tien Yin

2017-08-01

Automatic cup to disc ratio (CDR) computation from color fundus images has shown to be promising for glaucoma detection. Over the past decade, many algorithms have been proposed. In this paper, we first review the recent work in the area and then present a novel similarity-regularized sparse group lasso method for automated CDR estimation. The proposed method reconstructs the testing disc image based on a set of reference disc images by integrating the similarity between testing and the reference disc images with the sparse group lasso constraints. The reconstruction coefficients are then used to estimate the CDR of the testing image. The proposed method has been validated using 650 images with manually annotated CDRs. Experimental results show an average CDR error of 0.0616 and a correlation coefficient of 0.7, outperforming other methods. The areas under curve in the diagnostic test reach 0.843 and 0.837 when manual and automatically segmented discs are used respectively, better than other methods as well.

Discrete Sparse Coding.

Science.gov (United States)

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

Induction of hepatocyte polyploidization in rats of different age by ionizing radiation of different LET

International Nuclear Information System (INIS)

Gil'yano, N.Ya.; Malinovskij, O.V.; Khair, M.B.

1992-01-01

A decrease in the effectiveness of neutron-irradiation with respect to fusion of nonproliferating hepatocytes of animals with age was shown by the method of flow cytometry. There was an inverse relationship between the effectiveness of induction of non-proliferating hepatocytes fusion and neutron energy. The process of hepatocyte fusion induced by neutrons was inhibited by uranyl acetate. No age-dependent changes were noted in the induction of polyploidization of proliferating hepatocytes by sparsely ionizing radiation. A hypothesis is proposed concerning a membrane nature of the target responsible for hepatocyte polyploidization induced by densely ionizing radiation. (authors). 8 refs., 4 figs., 5 tabs

Induction of hepatocyte polyploidization in rats of different age by ionizing radiation of different LET

International Nuclear Information System (INIS)

Gil'yano, N.Ya.; Malinovskij, O.V.; Khair, M.B.

1990-01-01

A decrease in the effectiveness of neutron-irradiation with respect to fusion of nonproliferating hepatocytes of animals with age was shown by the method of flow cytometry. There was an inverse relationship between the effectiveness of induction of non-proliferating hepatocytes fusion and neutron energy. The process of hepatocyte fusion induced by neutrons was inhibited by uranyl acetate. No age-dependent changes were noted in the induction of polyploidization of proliferating hepatocytes by sparsely ionizing radiation. A hypothesis is proposed concerning a membrane nature of the target responsible for hepatocyte polyploidization induced by densely ionizing radiation

Health Risks of Diagnostic Radiology

International Nuclear Information System (INIS)

Al-Oraby, M.N.A.

2014-01-01

Exposure to ionizing radiation during diagnostic radiologic procedures carries small but real risks. Children, young adults and pregnant women are especially vulnerable. Exposure of patients to diagnostic energy levels of ionizing radiation should be kept to the minimum necessary to provide useful clinical information and allay patients concerns about radiation-related risks. Computerized Tomography (CT) accounts for two thirds of the cumulative patient dose from diagnostic radiological procedures and the cumulative dose from CT is rising as technological advances increase the number of indications and the capabilities of CT. Carcinogenesis and teratogenesis are the main concerns with ionizing radiation. The risk increases as the radiation dose increases. There is no minimum threshold and the risk is cumulative: a dose of 1 mSv once a year for 10 years is equivalent to a single dose of 10 mSv. Whenever practical, choose an imaging test that uses less radiation or no radiation and lengthen the periods between follow-up imaging tests. Some patients may avoid screening mammography because of fear of radiation-induced cancer, yet this test uses a very small radiation dose (0.6 mSv, much less than the annual dose from background radiation, 3.6 mSv). (author)

Solving Sparse Polynomial Optimization Problems with Chordal Structure Using the Sparse, Bounded-Degree Sum-of-Squares Hierarchy

NARCIS (Netherlands)

Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim

The sparse bounded degree sum-of-squares (sparse-BSOS) hierarchy of Weisser, Lasserre and Toh [arXiv:1607.01151,2016] constructs a sequence of lower bounds for a sparse polynomial optimization problem. Under some assumptions, it is proven by the authors that the sequence converges to the optimal

Biomarkers of exposition to ionizing radiation and hematology parameters in fitness for work. Case Report

International Nuclear Information System (INIS)

Djokovic, J.; Milacic, S.; Rakic, B.; Pajic, J.; Petrovic, D.; Vuckovic, J.

2009-01-01

Ionizing radiation is frequently used in medicine, especially during diagnostic procedures. The workers who are exposed to radiation have obligation for periodic check up. Presented case shows changes in hematological parameters and biomarkers of exposition to ionizing radiation (chromosome aberrations, structural changes and micronucleus test). The aim of this case report is to indicate metodology of diagnostic procedures for chronicle radiation syndrome. (author) [sr

Impact of volume and surface processes on the pre-ionization of dielectric barrier discharges: advanced diagnostics and fluid modeling

Science.gov (United States)

Nemschokmichal, Sebastian; Tschiersch, Robert; Höft, Hans; Wild, Robert; Bogaczyk, Marc; Becker, Markus M.; Loffhagen, Detlef; Stollenwerk, Lars; Kettlitz, Manfred; Brandenburg, Ronny; Meichsner, Jürgen

2018-05-01

The phenomenology and breakdown mechanism of dielectric barrier discharges are strongly determined by volume and surface memory effects. In particular, the pre-ionization provided by residual species in the volume or surface charges on the dielectrics influences the breakdown behavior of filamentary and diffuse discharges. This was investigated by advanced diagnostics such as streak camera imaging, laser photodetachment of negative ions and laser photodesorption of electrons from dielectric surfaces in correlation with 1D fluid modeling. The streak camera images show that an increasing number of residual charges in the volume changes the microdischarge breakdown in air-like gas mixtures from a cathode-directed streamer to a simultaneous propagation of cathode- and anode-directed streamers. In contrast, seed electrons are important for the pre-ionization if the density of residual charges in the volume is low. One source of seed electrons are negative ions, whose density exceeds the electron density during the pre-phase of diffuse helium-oxygen barrier discharges as indicated by the laser photodetachment experiments. Electrons desorbed from the cathodic dielectric have an even larger influence. They induce a transition from the glow-like to the Townsend-like discharge mode in nominally pure helium. Apart from analyzing the importance of the pre-ionization for the breakdown mechanism, the opportunities for manipulating the lateral structure and discharge modes are discussed. For this purpose, the intensity and diameter of a diffuse discharge in helium are controlled by an illuminated semiconducting barrier. Contribution to the Topical Issue "Fundamentals of Complex Plasmas", edited by Jürgen Meichsner, Michael Bonitz, Holger Fehske, Alexander Piel.

Ionizing radiations used in medical diagnostics as a source of radiation exposure of the Bulgarian population

International Nuclear Information System (INIS)

Ingilizova, K.; Vasilev, G.

1998-01-01

X-ray and radionuclide application in medical diagnosing is the major sources of Bulgarian population exposure to ionizing radiations exceeding the radiation background. The number of X-ray examination on a nationwide scale shows an increase from 1600 thousand annually in 1950 to 10300 thousand in 1980 and decreases to about 4700 thousand annually for the period 1992-1993. The frequency for the above mentioned time intervals varies in the range 0.22 to 1.17 examinations per capita annually and decreases to 0.56. The roentgenoscopy to roentgenography ratio varies from 2.5:1 to 0.9:1 (1975) and increases to 2.0:1 (1993). The number of radioisotope examinations increased from 34 thousand in 1970 to 170 thousand annually in 1985 and decreased to about thousand annually in 1992-1993 with a number of studies per capita varying from 0.004 to 0.020 and decreasing to 0.010. In 1993 the annual collective effective dose due to X-ray diagnostics amounts to about 7000 man-Sv/a which exceeds the radiation background exposure by 76%. Radioisotope diagnostics in the period reviewed accounted for nearly 700 man-Sv/a with an exposure exceeding the radiation background by 7.7%. The major problems relating to patient protection and benefit/risk ratio improvement are discussed. (author)

Bayesian Inference Methods for Sparse Channel Estimation

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand

2013-01-01

This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

Effects of low doses of ionizing radiation

International Nuclear Information System (INIS)

Anon.

2008-01-01

Ionizing radiation of cosmic or terrestrial origin is part of the environment in which all living things have evolved since the creation of the universe. The artificial radioactivity generated by medical diagnostic and treatment techniques, some industrial activities, radioactive fallout, etc. has now been added to this natural radioactivity. This article reviews the biological effects of the low doses of ionizing radiation to which the population is thus exposed. Their carcinogenic risk cannot simply be extrapolated from what we know about high-dose exposure. (author)

When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

KAUST Repository

Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

2017-01-01

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays

Improved Sparse Channel Estimation for Cooperative Communication Systems

Directory of Open Access Journals (Sweden)

Guan Gui

2012-01-01

Full Text Available Accurate channel state information (CSI is necessary at receiver for coherent detection in amplify-and-forward (AF cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS and least absolute shrinkage and selection operator (LASSO, are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.

Sparse Image Reconstruction in Computed Tomography

DEFF Research Database (Denmark)

Jørgensen, Jakob Sauer

In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

Patterns of anaphylaxis after diagnostic workup

DEFF Research Database (Denmark)

Oropeza, Athamaica Ruiz; Bindslev-Jensen, Carsten; Broesby-Olsen, Sigurd

2017-01-01

BACKGROUND: Most published studies on anaphylaxis are retrospective or register based. Data on subsequent diagnostic work-up are sparse. We aimed to characterize patients seen with suspected anaphylaxis at the emergency care setting (ECS), after subsequent diagnostic work-up at our Allergy Center...... (AC). METHODS: Prospective study including patients from the ECS, Odense University Hospital, during May 2013-April 2014. Possible anaphylaxis cases were daily identified based on a broad search profile including history and symptoms in patient records, diagnostic codes and pharmacological treatments....... At the AC, all patients were evaluated according to international guidelines. RESULTS: Among 226 patients with suspected anaphylaxis, the diagnosis was confirmed in 124 (54.9%) after diagnostic work-up; 118 of the 124 fulfilled WAO/EAACI criteria of anaphylaxis at the ECS, while 6 were found among 46...

Sparse Regression by Projection and Sparse Discriminant Analysis

KAUST Repository

Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

2015-01-01

predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths

Clinical dosimetry in diagnostic and interventional radiology

International Nuclear Information System (INIS)

Dimcheva, M.; Sergieva, S.; Jovanovska, A.

2012-01-01

Full text: Introduction: Diagnostic and interventional procedures involving x-rays are the most significant contributor to total population dose form man made sources of ionizing radiation. Purpose and aim: X-ray imaging generally covers a diverse range of examination types, many of which are increasing in frequency and technical complexity. Materials and methods: The European Directives 96/29 and 97/43 EURATOM stress the importance of accurate dosimetry and require calibration of all measuring equipment related to application of ionizing radiation in medicine. Results: The paper gives and overview of current system of dosimetry of ionizing radiations that is relevant for metrology and clinical applications. It also reflects recently achieved international harmonization in the field promoted by International Atomic Energy Agency (IAEA). Discussion: Objectives of clinical dose measurements in diagnostic and interventional radiology are multiple, as assessment of equipment performance, or assessment of risk emerging from use of ionizing radiation Conclusion: Therefore, from the clinical point of view, the requirements for dosimeters and procedures to assess dose to standard dosimetry phantoms and patients in clinical diverse modalities, as computed tomography are presented

Sparse decompositions in 'incoherent' dictionaries

DEFF Research Database (Denmark)

Gribonval, R.; Nielsen, Morten

2003-01-01

a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...

Performance of a pencil ionization chamber in various radiation beams

International Nuclear Information System (INIS)

Maia, A.F.; Caldas, L.V.E.

2003-01-01

Pencil ionization chambers were recommended for use exclusively in the computed tomography (CT) dosimetry, and, from the start, they were developed only with this application in view. In this work, we studied the behavior of a pencil ionization chamber in various radiation beams with the objective of extending its application. Stability tests were performed, and calibration coefficients were obtained for several standard radiation qualities of the therapeutical and diagnostic levels. The results show that the pencil ionization chamber can be used in several radiation beams other than those used in CT

Data analysis in high-dimensional sparse spaces

DEFF Research Database (Denmark)

Clemmensen, Line Katrine Harder

classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

Ionizing and non-ionizing radiations

International Nuclear Information System (INIS)

1994-01-01

The monograph is a small manual to get a knowledge of ionizing and non-ionizing radiations. The main chapters are: - Electromagnetic radiations - Ionizing and non-ionizing radiations - Non-ionizing electromagnetic radiations - Ionizing electromagnetic radiation - Other ionizing radiations - Ionizing radiation effects - The Nuclear Safety Conseil

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2018-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse-grid isogeometric solver

KAUST Repository

Beck, Joakim

2018-02-28

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

Exposure of the french population to ionizing radiation link to medical diagnosis act in 2007

International Nuclear Information System (INIS)

Etard, C.; Aubert, B.; Sinno-Tellier, S.

2010-01-01

The objective of this report is to update and complete the data relative to the medical exposure of the French population to diagnostic imaging examinations for the year 2007. The last published data correspond to the year 2002. The information supplied by this report precise: the medical exposure to diagnostic imaging examinations by imaging modality (conventional radiology, scanner, nuclear medicine, and diagnostic interventional imaging), by anatomical area, by age, and according to the sex of the patient and it also the part of the French population (strength, age, sex) who actually benefited of diagnostic acts using ionizing radiation in 2007. In 2007, 74.6 millions of diagnostic acts using ionizing radiation have been realised in france. These acts induce for the year 2007 to an efficient average dose of 1.3 MSv. (N.C.)

Supervised Transfer Sparse Coding

KAUST Repository

Al-Shedivat, Maruan

2014-07-27

A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

Science.gov (United States)

Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

2015-12-01

A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

Administration of ionizing radiation to human subjects in medical research

International Nuclear Information System (INIS)

1985-01-01

Any administration of ionizing radiation to human subjects for the purposes of diagnostic or therapeutic research involving either irradiation or the administration of radionuclides, should be undertaken only after approval by an institutional ethics committee. The ethics committee should obtain advice from a person experienced in radiation protection before granting approval. The research proposal must conform to regulatory requirements relating to the use of ionizing radiation

Parallel Sparse Matrix - Vector Product

DEFF Research Database (Denmark)

Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd

This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...

Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.

Energy Technology Data Exchange (ETDEWEB)

Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2018-01-01

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

Sparse approximation with bases

CERN Document Server

2015-01-01

This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications.  The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...

Efficient convolutional sparse coding

Science.gov (United States)

Wohlberg, Brendt

2017-06-20

Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Directory of Open Access Journals (Sweden)

Chang Li

2016-07-01

Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy.

Science.gov (United States)

Fayad, Laura M; Mugera, Charles; Soldatos, Theodoros; Flammang, Aaron; del Grande, Filippo

2013-07-01

We demonstrate the clinical use of an MR angiography sequence performed with sparse k-space sampling (MRA), as a method for dynamic contrast-enhanced (DCE)-MRI, and apply it to the assessment of sarcomas for treatment response. Three subjects with sarcomas (2 with osteosarcoma, 1 with high-grade soft tissue sarcomas) underwent MRI after neoadjuvant therapy/prior to surgery, with conventional MRI (T1-weighted, fluid-sensitive, static post-contrast T1-weighted sequences) and DCE-MRI (MRA, time resolution = 7-10 s, TR/TE 2.4/0.9 ms, FOV 40 cm(2)). Images were reviewed by two observers in consensus who recorded image quality (1 = diagnostic, no significant artifacts, 2 = diagnostic, 75 % with good response, >75 % with poor response). DCE-MRI findings were concordant with histological response (arterial enhancement with poor response, no arterial enhancement with good response). Unlike conventional DCE-MRI sequences, an MRA sequence with sparse k-space sampling is easily integrated into a routine musculoskeletal tumor MRI protocol, with high diagnostic quality. In this preliminary work, tumor enhancement characteristics by DCE-MRI were used to assess treatment response.

III. Penning ionization, associative ionization and chemi-ionization processes

International Nuclear Information System (INIS)

Cermak, V.

1975-01-01

Physical mechanisms of three important ionization processes in a cold plasma and the methods of their experimental study are discussed. An apparatus for the investigation of the Penning ionization using ionization processes of long lived metastable rare gas atoms is described. Methods of determining interaction energies and ionization rates from the measured energy spectra of the originating electrons are described and illustrated by several examples. Typical associative ionization processes are listed and the ionization rates are compared with those of the Penning ionization. Interactions with short-lived excited particles and the transfer of excitation without ionization are discussed. (J.U.)

Image fusion using sparse overcomplete feature dictionaries

Science.gov (United States)

Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

2015-10-06

Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

Manifold regularization for sparse unmixing of hyperspectral images.

Science.gov (United States)

Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin

2016-01-01

Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

Enhancing Scalability of Sparse Direct Methods

International Nuclear Information System (INIS)

Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan

2007-01-01

TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers

Regression with Sparse Approximations of Data

DEFF Research Database (Denmark)

Noorzad, Pardis; Sturm, Bob L.

2012-01-01

We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

Sparse adaptive filters for echo cancellation

CERN Document Server

Paleologu, Constantin

2011-01-01

Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

Parallel sparse direct solver for integrated circuit simulation

CERN Document Server

Chen, Xiaoming; Yang, Huazhong

2017-01-01

This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...

Biclustering via Sparse Singular Value Decomposition

KAUST Repository

Lee, Mihee

2010-02-16

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.

Risks Associated with Ionizing Radiations

International Nuclear Information System (INIS)

Cascon, Adriana

2009-01-01

Medical use of ionizing radiations implies certain risks which are widely balanced by their diagnostic and therapeutic benefits. Nevertheless, knowledge about these risks and how to diagnose and prevent them minimizes their disadvantages and optimizes the quality and safety of the method. This article describes the aspects related to skin dose (nonstochastic effects), the importance of dose limit, the physiopathology of biological damage and, finally, the prevention measures. [es

Ionization-potential depression and other dense plasma statistical property studies - Application to spectroscopic diagnostics.

Science.gov (United States)

Calisti, Annette; Ferri, Sandrine; Mossé, Caroline; Talin, Bernard

2017-02-01

The radiative properties of an emitter surrounded by a plasma, are modified through various mechanisms. For instance the line shapes emitted by bound-bound transitions are broadened and carry useful information for plasma diagnostics. Depending on plasma conditions the electrons occupying the upper quantum levels of radiators no longer exist as they belong to the plasma free electron population. All the charges present in the radiator environment contribute to the lowering of the energy required to free an electron in the fundamental state. This mechanism is known as ionization potential depression (IPD). The knowledge of IPD is useful as it affects both the radiative properties of the various ionic states and their populations. Its evaluation deals with highly complex n-body coupled systems, involving particles with different dynamics and attractive ion-electron forces. A classical molecular dynamics (MD) code, the BinGo-TCP code, has been recently developed to simulate neutral multi-component (various charge state ions and electrons) plasma accounting for all the charge correlations. In the present work, results on IPD and other dense plasma statistical properties obtained using the BinGo-TCP code are presented. The study focuses on aluminum plasmas for different densities and several temperatures in order to explore different plasma coupling conditions.

Robust Face Recognition Via Gabor Feature and Sparse Representation

Directory of Open Access Journals (Sweden)

Hao Yu-Juan

2016-01-01

Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

Sparse Learning with Stochastic Composite Optimization.

Science.gov (United States)

Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

2017-06-01

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

Shearlets and Optimally Sparse Approximations

DEFF Research Database (Denmark)

Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q

2012-01-01

Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

Ionizing radiation in the education of medicine

International Nuclear Information System (INIS)

Ivanova, N.

2016-01-01

Physics is a fundamental science that finds its applications in all areas of our lives. Its application in modern medicine is undeniable. In today’s medical practice special attention is dedicated to the use of ionizing radiation. The wide range of modern science and technology offers enormous possibilities for creation and implementation of new equipment using adequate doses of ionizing radiation. For accurate medical diagnostics and effective treatment of patients, this type of equipment must provide the necessary information to the physicians. On the other hand, the physicians should possess enough knowledge in the relative field of medicine. This paper contains information about the knowledge communicated to the students of the graduate program Medical Physics and Biophysics in the discipline Medicine in the first year of graduate study at the Medical University “Prof. Dr. Paraskev Stoyanov” of Varna. Firstly, we discuss the topics in the lectures of these two disciplines, concerning knowledge about ionizing radiation. Secondly, the respective laboratory exercises are described that illustrate the lectures in the graduate programs Medical Physics and Biophysics. Keywords: ionizing radiation, education, medicine, medical physics, biophysics

Multilevel sparse functional principal component analysis.

Science.gov (United States)

Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

2014-01-29

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

2017-01-01

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

A sparse version of IGA solvers

KAUST Repository

Beck, Joakim

2017-07-30

Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

LARGE-SCALE SHOCK-IONIZED AND PHOTOIONIZED GAS IN M83: THE IMPACT OF STAR FORMATION

International Nuclear Information System (INIS)

Hong, Sungryong; Calzetti, Daniela; Dopita, Michael A.; Blair, William P.; Whitmore, Bradley C.; Bond, Howard E.; Balick, Bruce; Carollo, Marcella; Disney, Michael J.; Frogel, Jay A.; Hall, Donald; Holtzman, Jon A.; Kimble, Randy A.; McCarthy, Patrick J.; O'Connell, Robert W.; Paresce, Francesco; Saha, Abhijit; Silk, Joseph I.; Trauger, John T.; Walker, Alistair R.

2011-01-01

We investigate the ionization structure of the nebular gas in M83 using the line diagnostic diagram, [O III](5007 A)/Hβ versus [S II](6716 A+6731 A)/Hα, with the newly available narrowband images from the Wide Field Camera 3 (WFC3) of the Hubble Space Telescope (HST). We produce the diagnostic diagram on a pixel-by-pixel (0.''2 x 0.''2) basis and compare it with several photo- and shock-ionization models. We select four regions from the center to the outer spiral arm and compare them in the diagnostic diagram. For the photoionized gas, we observe a gradual increase of the log ([O III]/Hβ) ratios from the center to the spiral arm, consistent with the metallicity gradient, as the H II regions go from super-solar abundance to roughly solar abundance from the center out. Using the diagnostic diagram, we separate the photoionized from the shock-ionized component of the gas. We find that the shock-ionized Hα emission ranges from ∼2% to about 15%-33% of the total, depending on the separation criteria used. An interesting feature in the diagnostic diagram is a horizontal distribution around log ([O III]/Hβ) ∼ 0. This feature is well fit by a shock-ionization model with 2.0 Z sun metallicity and shock velocities in the range of 250-350 km s -1 . A low-velocity shock component, -1 , is also detected and is spatially located at the boundary between the outer ring and the spiral arm. The low-velocity shock component can be due to (1) supernova remnants located nearby, (2) dynamical interaction between the outer ring and the spiral arm, and (3) abnormal line ratios from extreme local dust extinction. The current data do not enable us to distinguish among those three possible interpretations. Our main conclusion is that, even at the HST resolution, the shocked gas represents a small fraction of the total ionized gas emission at less than 33% of the total. However, it accounts for virtually all of the mechanical energy produced by the central starburst in M83.

Language Recognition via Sparse Coding

Science.gov (United States)

2016-09-08

explanation is that sparse coding can achieve a near-optimal approximation of much complicated nonlinear relationship through local and piecewise linear...training examples, where x(i) ∈ RN is the ith example in the batch. Optionally, X can be normalized and whitened before sparse coding for better result...normalized input vectors are then ZCA- whitened [20]. Em- pirically, we choose ZCA- whitening over PCA- whitening , and there is no dimensionality reduction

Interview - a method for diagnostic and attitude formation to ionizing radiation

International Nuclear Information System (INIS)

Katsarova, N.; Katsarov, V.; Belcheva, Yu.

2009-01-01

The aim of this paper is to present the experimental methodological model used for analyses of attitude changes. The analysis has been performed using two inquiries about ionizing radiation applications between 48 high level school students in Plovdiv, Bulgaria

Ionization Energy: Implications of Preservice Teachers' Conceptions

Science.gov (United States)

Tan, Kim Chwee Daniel; Taber, Keith S.

2009-01-01

The results from a study to explore pre-service teachers' understanding of ionization energy, a topic that features in A-level (grade 11 and 12) chemistry courses. in Singapore , is described. A previous study using a two-tier multiple choice diagnostic test has shown that Singapore A-level students have considerable difficulty understanding the…

Sparse seismic imaging using variable projection

NARCIS (Netherlands)

Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan

2013-01-01

We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using

Tunable Sparse Network Coding for Multicast Networks

DEFF Research Database (Denmark)

Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant

2014-01-01

This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity...... a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting simulation results are provided showing the trade-off between decoding complexity and completion time....

Mammalian Tissue Response to Low Dose Ionizing Radiation: The Role of Oxidative Metabolism and Intercellular Communication

Energy Technology Data Exchange (ETDEWEB)

Azzam, Edouard I

2013-01-16

The objective of the project was to elucidate the mechanisms underlying the biological effects of low dose/low dose rate ionizing radiation in organs/tissues of irradiated mice that differ in their susceptibility to ionizing radiation, and in human cells grown under conditions that mimic the natural in vivo environment. The focus was on the effects of sparsely ionizing cesium-137 gamma rays and the role of oxidative metabolism and intercellular communication in these effects. Four Specific Aims were proposed. The integrated outcome of the experiments performed to investigate these aims has been significant towards developing a scientific basis to more accurately estimate human health risks from exposures to low doses ionizing radiation. By understanding the biochemical and molecular changes induced by low dose radiation, several novel markers associated with mitochondrial functions were identified, which has opened new avenues to investigate metabolic processes that may be affected by such exposure. In particular, a sensitive biomarker that is differentially modulated by low and high dose gamma rays was discovered.

Sparse PCA with Oracle Property.

Science.gov (United States)

Gu, Quanquan; Wang, Zhaoran; Liu, Han

In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

Structural Sparse Tracking

KAUST Repository

Zhang, Tianzhu; Yang, Ming-Hsuan; Ahuja, Narendra; Ghanem, Bernard; Yan, Shuicheng; Xu, Changsheng; Liu, Si

2015-01-01

candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs

Technique detection software for Sparse Matrices

Directory of Open Access Journals (Sweden)

KHAN Muhammad Taimoor

2009-12-01

Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.

Technical innovation in dynamic contrast-enhanced magnetic resonance imaging of musculoskeletal tumors: an MR angiographic sequence using a sparse k-space sampling strategy

International Nuclear Information System (INIS)

Fayad, Laura M.; Mugera, Charles; Grande, Filippo del; Soldatos, Theodoros; Flammang, Aaron

2013-01-01

We demonstrate the clinical use of an MR angiography sequence performed with sparse k-space sampling (MRA), as a method for dynamic contrast-enhanced (DCE)-MRI, and apply it to the assessment of sarcomas for treatment response. Three subjects with sarcomas (2 with osteosarcoma, 1 with high-grade soft tissue sarcomas) underwent MRI after neoadjuvant therapy/prior to surgery, with conventional MRI (T1-weighted, fluid-sensitive, static post-contrast T1-weighted sequences) and DCE-MRI (MRA, time resolution = 7-10 s, TR/TE 2.4/0.9 ms, FOV 40 cm 2 ). Images were reviewed by two observers in consensus who recorded image quality (1 = diagnostic, no significant artifacts, 2 = diagnostic, 75 % with good response, >75 % with poor response). DCE-MRI findings were concordant with histological response (arterial enhancement with poor response, no arterial enhancement with good response). Unlike conventional DCE-MRI sequences, an MRA sequence with sparse k-space sampling is easily integrated into a routine musculoskeletal tumor MRI protocol, with high diagnostic quality. In this preliminary work, tumor enhancement characteristics by DCE-MRI were used to assess treatment response. (orig.)

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-11-23

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Sparse Representations of Hyperspectral Images

KAUST Repository

Swanson, Robin J.

2015-01-01

Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed

2018-04-08

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

Structure-aware Local Sparse Coding for Visual Tracking

KAUST Repository

Qi, Yuankai

2018-01-24

Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.

Project, construction and characterization of ionization chambers for use as standard systems in X and gamma radiation beams

International Nuclear Information System (INIS)

Perini, Ana Paula

2013-01-01

Ionization chambers present some advantages in relation to other dosimeters: easiness of handling, low energy dependence and high precision. The advantages associated to ionization chambers and the large number of diagnostic radiology exams and therapeutic treatments motivated the development of this PhD program. In this project ionization chambers were developed and characterized to be applied in diagnostic radiology and therapy beam dosimetry, with high precision and performance, in compliance with international recommendations. They were assembled in a simple way, utilizing low-cost national materials, so they can be reproduced and applied at calibration laboratories. The project of these ionization chambers presents some differences in relation to commercial ionization chambers, as the materials utilized and geometrical arrangements. Besides the development of the ionization chambers to be utilized in standard X-ray beam dosimetry as work standard systems, two graphite parallel-plate ionization chambers were developed and characterized to be applied as reference standard systems for determining the air kerma rates of gamma radiation sources. Comparing the air kerma rates determined with the reference standard of the Calibration Laboratory of IPEN, a Farmer ionization chamber, with the values of the air kerma rates obtained with the graphite ionization chambers, the maximum differences obtained were only 1.7% and 1.2% for the G1 and G2 graphite ionization chambers, respectively. Moreover, these ionization chambers presented correction factors close to 1.000, which is ideal for an ionization chamber be characterized as a reference standard system. (author)

Sparse Frequency Waveform Design for Radar-Embedded Communication

Directory of Open Access Journals (Sweden)

Chaoyun Mai

2016-01-01

Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.

Patient dosimetry in diagnostic radiology

International Nuclear Information System (INIS)

Rweyemamu, M.

2013-04-01

The objective of this project was to review patient dosimetry aiming at reducing the patient dose during diagnostic procedures while maintaining the best image quality in order to protect patients from ionizing radiation. CT examination was selected in this study to represent imaging protocols with high patient doses used in diagnostic radiology. Dosimetric parameters in CT which are CTDI, CTDIW, DLP, MSAD, organ dose and effective dose were discussed. Parameters such as tube current, tube voltage, filtration, scan volume and slice thickness were found to affect patient dose, therefore proper management of these factors was recommended. For optimization of protection of the patient, application of the “as low as reasonably achievable” (ALARA) principle was recommended as an important key for avoiding overexposure and minimizing patient doses. Also it was recommended that CT examinations should be performed if and only if is the only suitable option when weighed against other options which do not involve ionizing radiation exposure. (author)

Massive Asynchronous Parallelization of Sparse Matrix Factorizations

Energy Technology Data Exchange (ETDEWEB)

Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)

2018-01-08

Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

Diagnostic Medical Imaging in Pediatric Patients and Subsequent Cancer Risk.

Science.gov (United States)

Mulvihill, David J; Jhawar, Sachin; Kostis, John B; Goyal, Sharad

2017-11-01

The use of diagnostic medical imaging is becoming increasingly more commonplace in the pediatric setting. However, many medical imaging modalities expose pediatric patients to ionizing radiation, which has been shown to increase the risk of cancer development in later life. This review article provides a comprehensive overview of the available data regarding the risk of cancer development following exposure to ionizing radiation from diagnostic medical imaging. Attention is paid to modalities such as computed tomography scans and fluoroscopic procedures that can expose children to radiation doses orders of magnitude higher than standard diagnostic x-rays. Ongoing studies that seek to more precisely determine the relationship of diagnostic medical radiation in children and subsequent cancer development are discussed, as well as modern strategies to better quantify this risk. Finally, as cardiovascular imaging and intervention contribute substantially to medical radiation exposure, we discuss strategies to enhance radiation safety in these areas. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Storage of sparse files using parallel log-structured file system

Science.gov (United States)

Bent, John M.; Faibish, Sorin; Grider, Gary; Torres, Aaron

2017-11-07

A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a single patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir

2013-11-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Image understanding using sparse representations

CERN Document Server

Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas

2014-01-01

Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin

Sparse regularization for force identification using dictionaries

Science.gov (United States)

Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng

2016-04-01

The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

Gauging Metallicity of Diffuse Gas under an Uncertain Ionizing Radiation Field

Science.gov (United States)

Chen, Hsiao-Wen; Johnson, Sean D.; Zahedy, Fakhri S.; Rauch, Michael; Mulchaey, John S.

2017-06-01

Gas metallicity is a key quantity used to determine the physical conditions of gaseous clouds in a wide range of astronomical environments, including interstellar and intergalactic space. In particular, considerable effort in circumgalactic medium (CGM) studies focuses on metallicity measurements because gas metallicity serves as a critical discriminator for whether the observed heavy ions in the CGM originate in chemically enriched outflows or in more chemically pristine gas accreted from the intergalactic medium. However, because the gas is ionized, a necessary first step in determining CGM metallicity is to constrain the ionization state of the gas which, in addition to gas density, depends on the ultraviolet background radiation field (UVB). While it is generally acknowledged that both the intensity and spectral slope of the UVB are uncertain, the impact of an uncertain spectral slope has not been properly addressed in the literature. This Letter shows that adopting a different spectral slope can result in an order of magnitude difference in the inferred CGM metallicity. Specifically, a harder UVB spectrum leads to a higher estimated gas metallicity for a given set of observed ionic column densities. Therefore, such systematic uncertainties must be folded into the error budget for metallicity estimates of ionized gas. An initial study shows that empirical diagnostics are available for discriminating between hard and soft ionizing spectra. Applying these diagnostics helps reduce the systematic uncertainties in CGM metallicity estimates.

Gauging Metallicity of Diffuse Gas under an Uncertain Ionizing Radiation Field

Energy Technology Data Exchange (ETDEWEB)

Chen, Hsiao-Wen; Zahedy, Fakhri S. [Department of Astronomy and Astrophysics, The University of Chicago, 5640 S Ellis Avenue, Chicago, IL 60637 (United States); Johnson, Sean D. [Department of Astrophysics, Princeton University, Princeton, NJ (United States); Rauch, Michael; Mulchaey, John S., E-mail: hchen@oddjob.uchicago.edu [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101 (United States)

2017-06-20

Gas metallicity is a key quantity used to determine the physical conditions of gaseous clouds in a wide range of astronomical environments, including interstellar and intergalactic space. In particular, considerable effort in circumgalactic medium (CGM) studies focuses on metallicity measurements because gas metallicity serves as a critical discriminator for whether the observed heavy ions in the CGM originate in chemically enriched outflows or in more chemically pristine gas accreted from the intergalactic medium. However, because the gas is ionized, a necessary first step in determining CGM metallicity is to constrain the ionization state of the gas which, in addition to gas density, depends on the ultraviolet background radiation field (UVB). While it is generally acknowledged that both the intensity and spectral slope of the UVB are uncertain, the impact of an uncertain spectral slope has not been properly addressed in the literature. This Letter shows that adopting a different spectral slope can result in an order of magnitude difference in the inferred CGM metallicity. Specifically, a harder UVB spectrum leads to a higher estimated gas metallicity for a given set of observed ionic column densities. Therefore, such systematic uncertainties must be folded into the error budget for metallicity estimates of ionized gas. An initial study shows that empirical diagnostics are available for discriminating between hard and soft ionizing spectra. Applying these diagnostics helps reduce the systematic uncertainties in CGM metallicity estimates.

Sparse inpainting and isotropy

Energy Technology Data Exchange (ETDEWEB)

Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom); Marinucci, Domenico; Cammarota, Valentina [Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, Roma, 00133 (Italy); Wandelt, Benjamin D., E-mail: s.feeney@imperial.ac.uk, E-mail: marinucc@axp.mat.uniroma2.it, E-mail: jason.mcewen@ucl.ac.uk, E-mail: h.peiris@ucl.ac.uk, E-mail: wandelt@iap.fr, E-mail: cammarot@axp.mat.uniroma2.it [Kavli Institute for Theoretical Physics, Kohn Hall, University of California, 552 University Road, Santa Barbara, CA, 93106 (United States)

2014-01-01

Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

IZI: INFERRING THE GAS PHASE METALLICITY (Z) AND IONIZATION PARAMETER (q) OF IONIZED NEBULAE USING BAYESIAN STATISTICS

Energy Technology Data Exchange (ETDEWEB)

Blanc, Guillermo A. [Observatories of the Carnegie Institution for Science, 813 Santa Barbara Street, Pasadena, CA 91101 (United States); Kewley, Lisa; Vogt, Frédéric P. A.; Dopita, Michael A. [Research School of Astronomy and Astrophysics, Australian National University, Cotter Road, Weston, ACT 2611 (Australia)

2015-01-10

We present a new method for inferring the metallicity (Z) and ionization parameter (q) of H II regions and star-forming galaxies using strong nebular emission lines (SELs). We use Bayesian inference to derive the joint and marginalized posterior probability density functions for Z and q given a set of observed line fluxes and an input photoionization model. Our approach allows the use of arbitrary sets of SELs and the inclusion of flux upper limits. The method provides a self-consistent way of determining the physical conditions of ionized nebulae that is not tied to the arbitrary choice of a particular SEL diagnostic and uses all the available information. Unlike theoretically calibrated SEL diagnostics, the method is flexible and not tied to a particular photoionization model. We describe our algorithm, validate it against other methods, and present a tool that implements it called IZI. Using a sample of nearby extragalactic H II regions, we assess the performance of commonly used SEL abundance diagnostics. We also use a sample of 22 local H II regions having both direct and recombination line (RL) oxygen abundance measurements in the literature to study discrepancies in the abundance scale between different methods. We find that oxygen abundances derived through Bayesian inference using currently available photoionization models in the literature can be in good (∼30%) agreement with RL abundances, although some models perform significantly better than others. We also confirm that abundances measured using the direct method are typically ∼0.2 dex lower than both RL and photoionization-model-based abundances.

IZI: INFERRING THE GAS PHASE METALLICITY (Z) AND IONIZATION PARAMETER (q) OF IONIZED NEBULAE USING BAYESIAN STATISTICS

International Nuclear Information System (INIS)

Blanc, Guillermo A.; Kewley, Lisa; Vogt, Frédéric P. A.; Dopita, Michael A.

2015-01-01

We present a new method for inferring the metallicity (Z) and ionization parameter (q) of H II regions and star-forming galaxies using strong nebular emission lines (SELs). We use Bayesian inference to derive the joint and marginalized posterior probability density functions for Z and q given a set of observed line fluxes and an input photoionization model. Our approach allows the use of arbitrary sets of SELs and the inclusion of flux upper limits. The method provides a self-consistent way of determining the physical conditions of ionized nebulae that is not tied to the arbitrary choice of a particular SEL diagnostic and uses all the available information. Unlike theoretically calibrated SEL diagnostics, the method is flexible and not tied to a particular photoionization model. We describe our algorithm, validate it against other methods, and present a tool that implements it called IZI. Using a sample of nearby extragalactic H II regions, we assess the performance of commonly used SEL abundance diagnostics. We also use a sample of 22 local H II regions having both direct and recombination line (RL) oxygen abundance measurements in the literature to study discrepancies in the abundance scale between different methods. We find that oxygen abundances derived through Bayesian inference using currently available photoionization models in the literature can be in good (∼30%) agreement with RL abundances, although some models perform significantly better than others. We also confirm that abundances measured using the direct method are typically ∼0.2 dex lower than both RL and photoionization-model-based abundances

Supersonic propagation of ionization waves in an underdense, laser-produced plasma

International Nuclear Information System (INIS)

Constantin, C.; Back, C.A.; Fournier, K.B.; Gregori, G.; Landen, O.L.; Glenzer, S.H.; Dewald, E.L.; Miller, M.C.

2005-01-01

A laser-driven supersonic ionization wave propagating through a millimeter-scale plasma of subcritical density up to 2-3 keV electron temperatures was observed. Propagation velocities initially ten times the sound speed were measured by means of time-resolved x-ray imaging diagnostics. The measured ionization wave trajectory is modeled analytically and by a two-dimensional radiation-hydrodynamics code. The comparison to the modeling suggests that nonlocal heat transport effects may contribute to the attenuation of the heat-wave propagation

Object tracking by occlusion detection via structured sparse learning

KAUST Repository

Zhang, Tianzhu

2013-06-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

Sparse Vector Distributions and Recovery from Compressed Sensing

DEFF Research Database (Denmark)

Sturm, Bob L.

It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....

A possibility of local measurements of ion temperature in a high-temperature plasma by laser induced ionization

International Nuclear Information System (INIS)

Kantor, M

2012-01-01

A new diagnostic for local measurements of ion temperature and drift velocity in fusion plasmas is proposed in the paper. The diagnostic is based on laser induced ionization of excited hydrogen and deuterium atoms from the levels which ionization energy less than the laser photon energy. A high intensive laser beam ionizes nearly all the excited atoms in the beam region resulting in a quench of spontaneous line emission of the appropriate optical transitions. The measurements of the quenching emission have been used in the past for local measurements of hydrogen atom density in tokamak plasma. The idea of the new diagnostic is spectral resolution of the quenching emission. The measured spectrum relates directly to the velocity distribution of the excited atoms. This distribution is strongly coupled to the distribution of the hydrogen atoms at the ground state. So, the spectral resolution of quenching emission is a way of local measurements of the temperature and drift velocity of hydrogen atoms in plasma. The temperature of hydrogen atoms is well coupled to the local ion temperature as long as the mean free path of the atoms is shorter than the ion gradient length in plasma. In this case the new diagnostic can provide local measurements of ion temperature in plasma. The paper considers technical capabilities of the diagnostic, physical restrictions of its application and interpretation of the measurements.

A rapid novel derivatization of amphetamine and methamphetamine using 2,2,2-trichloroethyl chloroformate for gas chromatography electron ionization and chemical ionization mass spectrometric analysis.

Science.gov (United States)

Dasgupta, A; Spies, J

1998-05-01

Amphetamine and methamphetamine are commonly abused central nervous system stimulants. We describe a rapid new derivatization of amphetamine and methamphetamine using 2,2,2-trichloroethyl chloroformate for gas chromatography-mass spectrometric analysis. Amphetamine and methamphetamine, along with N-propyl amphetamine (internal standard), were extracted from urine using 1-chlorobutane. The derivatization with 2,2,2-trichloroethyl chloroformate can be achieved at room temperature in 10 minutes. The electron ionization mass spectrum of amphetamine 2,2,2-trichloroethyl carbamate showed two weak molecular ions at m/z 309 and 311, but showed diagnostic strong peaks at m/z 218, 220, and 222. In contrast, chemical ionization of the mass spectrum of amphetamine 2,2,2-trichloroethyl carbamate showed strong (M + 1) ions at m/z 310 and 312 and other strong diagnostic peaks at m/z 274 and 276. The major advantages of this derivative are the presence of a diagnostic cluster of peaks due to the isotopic effect of three chlorine atoms (isotopes 35 and 37) in the derivatized molecule and the relative ease of its preparation. We also observed strong molecular ions for derivatized methamphetamine in the chemical ionization mass spectrum, but the molecular ions were very weak in the electron ionization mass spectrum. We used the scan mode of mass spectrometry in all analyses. When using a urine standard containing 1,000 ng/mL of amphetamine (a 7.4-micromol/L concentration) and methamphetamine (a 6.7-micromol/L concentration), the within-run precisions were 4.8% for amphetamine and 3.6% for methamphetamine. The corresponding between-run precisions were 5.3% for amphetamine and 6.7% for methamphetamine. The assay was linear for amphetamine and methamphetamine concentrations of 250 to 5,000 ng/mL (amphetamine, 1.9-37.0 micromol/L; methamphetamine, 1.7-33.6 micromol/L). The detection limit was 100 ng/mL (amphetamine, 0.74 micromol/L; methamphetamine, 0.67 micromol/L) using the scan mode

Exhaustive Search for Sparse Variable Selection in Linear Regression

Science.gov (United States)

Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato

2018-04-01

We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

Densely ionizing radiation affects DNA methylation of selective LINE-1 elements

Energy Technology Data Exchange (ETDEWEB)

Prior, Sara; Miousse, Isabelle R. [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Nzabarushimana, Etienne [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Department of Bioinformatics, School of Informatics and Computing, Indiana University, Bloomington, IN 47405 (United States); Pathak, Rupak [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Skinner, Charles; Kutanzi, Kristy R. [Department of Environmental and Occupational Health, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Allen, Antiño R. [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Raber, Jacob [Departments of Behavioral Neuroscience, Neurology, and Radiation Medicine, Division of Neuroscience, ONPRC, Oregon Health & Science University, Portland, OR 97239 (United States); Tackett, Alan J. [Department of Biochemistry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Hauer-Jensen, Martin [Division of Radiation Health, Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR 72205 (United States); Nelson, Gregory A. [Department of Basic Sciences, Division of Radiation Research, Loma Linda University, Loma Linda, CA 92350 (United States); and others

2016-10-15

Long Interspersed Nucleotide Element 1 (LINE-1) retrotransposons are heavily methylated and are the most abundant transposable elements in mammalian genomes. Here, we investigated the differential DNA methylation within the LINE-1 under normal conditions and in response to environmentally relevant doses of sparsely and densely ionizing radiation. We demonstrate that DNA methylation of LINE-1 elements in the lungs of C57BL6 mice is dependent on their evolutionary age, where the elder age of the element is associated with the lower extent of DNA methylation. Exposure to 5-aza-2′-deoxycytidine and methionine-deficient diet affected DNA methylation of selective LINE-1 elements in an age- and promoter type-dependent manner. Exposure to densely IR, but not sparsely IR, resulted in DNA hypermethylation of older LINE-1 elements, while the DNA methylation of evolutionary younger elements remained mostly unchanged. We also demonstrate that exposure to densely IR increased mRNA and protein levels of LINE-1 via the loss of the histone H3K9 dimethylation and an increase in the H3K4 trimethylation at the LINE-1 5′-untranslated region, independently of DNA methylation. Our findings suggest that DNA methylation is important for regulation of LINE-1 expression under normal conditions, but histone modifications may dictate the transcriptional activity of LINE-1 in response to exposure to densely IR. - Highlights: • DNA methylation of LINE-1 elements is dependent on their evolutionary age. • Densely ionizing radiation affects DNA methylation of selective LINE-1 elements. • Radiation-induced reactivation of LINE-1 is DNA methylation-independent. • Histone modifications dictate the transcriptional activity of LINE-1.

Densely ionizing radiation affects DNA methylation of selective LINE-1 elements

International Nuclear Information System (INIS)

Prior, Sara; Miousse, Isabelle R.; Nzabarushimana, Etienne; Pathak, Rupak; Skinner, Charles; Kutanzi, Kristy R.; Allen, Antiño R.; Raber, Jacob; Tackett, Alan J.; Hauer-Jensen, Martin; Nelson, Gregory A.

2016-01-01

Long Interspersed Nucleotide Element 1 (LINE-1) retrotransposons are heavily methylated and are the most abundant transposable elements in mammalian genomes. Here, we investigated the differential DNA methylation within the LINE-1 under normal conditions and in response to environmentally relevant doses of sparsely and densely ionizing radiation. We demonstrate that DNA methylation of LINE-1 elements in the lungs of C57BL6 mice is dependent on their evolutionary age, where the elder age of the element is associated with the lower extent of DNA methylation. Exposure to 5-aza-2′-deoxycytidine and methionine-deficient diet affected DNA methylation of selective LINE-1 elements in an age- and promoter type-dependent manner. Exposure to densely IR, but not sparsely IR, resulted in DNA hypermethylation of older LINE-1 elements, while the DNA methylation of evolutionary younger elements remained mostly unchanged. We also demonstrate that exposure to densely IR increased mRNA and protein levels of LINE-1 via the loss of the histone H3K9 dimethylation and an increase in the H3K4 trimethylation at the LINE-1 5′-untranslated region, independently of DNA methylation. Our findings suggest that DNA methylation is important for regulation of LINE-1 expression under normal conditions, but histone modifications may dictate the transcriptional activity of LINE-1 in response to exposure to densely IR. - Highlights: • DNA methylation of LINE-1 elements is dependent on their evolutionary age. • Densely ionizing radiation affects DNA methylation of selective LINE-1 elements. • Radiation-induced reactivation of LINE-1 is DNA methylation-independent. • Histone modifications dictate the transcriptional activity of LINE-1.

A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

Czech Academy of Sciences Publication Activity Database

Benzi, M.; Tůma, Miroslav

1998-01-01

Roč. 19, č. 3 (1998), s. 968-994 ISSN 1064-8275 R&D Projects: GA ČR GA201/93/0067; GA AV ČR IAA230401 Keywords : large sparse systems * interative methods * preconditioning * approximate inverse * sparse linear systems * sparse matrices * incomplete factorizations * conjugate gradient -type methods Subject RIV: BA - General Mathematics Impact factor: 1.378, year: 1998

Ionizing radiation regulations and the dental practitioner: 1. The nature of ionizing radiation and its use in dentistry.

Science.gov (United States)

Rout, John; Brown, Jackie

2012-04-01

Legislation governing the use of ionizing radiation in the workplace and in medical treatment first became law in 1985 and 1988, being superseded by the Ionizing Radiations Regulations 1999 (IRR99) and the Ionizing Radiation (Medical Exposure) Regulations 2000, (IR(ME)R 2000), respectively. This legislation ensures a safe environment in which to work and receive treatment and requires that those involved in the radiographic process must be appropriately trained for the type of radiographic practice they perform. A list of the topics required is detailed in Schedule 2 of IR(ME)R 2000 and is paraphrased in Table 1, with the extent and amount of knowledge required depending on the type of radiographic practice undertaken. Virtually all dental practitioners undertake radiography as part of their clinical practice. Legislation requires that users of radiation, including dentists and members of the dental team, understand the basic principles of radiation physics, hazards and protection, and are able to undertake dental radiography safely with the production of high quality, diagnostic images.

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul

2012-12-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

Quantification of localized vertebral deformities using a sparse wavelet-based shape model.

Science.gov (United States)

Zewail, R; Elsafi, A; Durdle, N

2008-01-01

Medical experts often examine hundreds of spine x-ray images to determine existence of various pathologies. Common pathologies of interest are anterior osteophites, disc space narrowing, and wedging. By careful inspection of the outline shapes of the vertebral bodies, experts are able to identify and assess vertebral abnormalities with respect to the pathology under investigation. In this paper, we present a novel method for quantification of vertebral deformation using a sparse shape model. Using wavelets and Independent component analysis (ICA), we construct a sparse shape model that benefits from the approximation power of wavelets and the capability of ICA to capture higher order statistics in wavelet space. The new model is able to capture localized pathology-related shape deformations, hence it allows for quantification of vertebral shape variations. We investigate the capability of the model to predict localized pathology related deformations. Next, using support-vector machines, we demonstrate the diagnostic capabilities of the method through the discrimination of anterior osteophites in lumbar vertebrae. Experiments were conducted using a set of 150 contours from digital x-ray images of lumbar spine. Each vertebra is labeled as normal or abnormal. Results reported in this work focus on anterior osteophites as the pathology of interest.

Greedy vs. L1 convex optimization in sparse coding

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2015-01-01

Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... solutions. Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

A sparse matrix based full-configuration interaction algorithm

International Nuclear Information System (INIS)

Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

2008-01-01

We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

The utility of sparse 2D fully electronically steerable focused ultrasound phased arrays for thermal surgery: a simulation study

International Nuclear Information System (INIS)

Ellens, Nicholas; Pulkkinen, Aki; Song Junho; Hynynen, Kullervo

2011-01-01

Sparse arrays are widely used in diagnostic ultrasound for their strong performance and relative technical simplicity. This simulation study assessed the efficacy of phased arrays of varied sparseness for thermal surgery, especially with regard to power consumption and near-field heating. It employs a linear ultrasound propagation model and a semi-analytical solution to the Pennes' bioheat transfer equation. The basic design had 4912 cylindrical transducers (500 kHz) arranged on a flat 12 cm disk (1.5 mm spacing). This array was compared to randomly-thinned sparse arrays with 75%, 50% and 25% populations. Temperature elevations of 60 and 70 deg. C were induced in sonication times of 5-20 s, at foci spanning depths of 50-150 mm and radii of 0-60 mm. The sparse arrays produced nearly indistinguishable focal patterns but, averaged across the foci, required 132%, 200% and 393% of the power of the full array, respectively, applied through fewer transducer elements. Comparable results were found at 1 MHz from equivalent arrays. Simulated lesions were formed (thermal dose ≥ 240 equivalent minutes at 43 deg. C (T 43 )) and 'transition' and 'unsafe' regions (both defined as 5 min 43 < 240 min) were identified, the former immediately surrounding the lesion and the latter anywhere else. At a depth of 100 mm, sparse arrays were found to produce comparable lesions to the full array at the focus, but 'unsafe', over-heated near-field regions after some ablated lesion volume: about 12 mL for the 25% array, around 100 mL for the 50% array, while the 75% and full arrays produced 150 mL lesions safely.

An in-depth study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2016-01-01

Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes. While much emphasis is put on discriminative dictionary construction, there are no comparative studies of sparse codes regarding abnormality...... are carried out from various angles to better understand the applicability of sparse codes, including computation time, reconstruction error, sparsity, detection accuracy, and their performance combining various detection methods. The experiment results show that combining OMP codes with maximum coordinate...

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

Sparse reconstruction using distribution agnostic bayesian matching pursuit

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

Evaluation of a tissue equivalent ionization chamber in X-ray beams

Energy Technology Data Exchange (ETDEWEB)

Perini, Ana Paula; Neves, Lucio Pereira; Santos, William de Souza; Caldas, Linda V.E., E-mail: aperini@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil); Frimaio, Audrew [Seal Technology Ind. Com. Ltda, Sao Paulo, SP (Brazil); Costa, Paulo R. [Universidade de Sao Paulo (USP/IF), Sao Paulo, SP (Brazil). Inst. de Fisica

2014-07-01

Tissue equivalent materials present a variety of uses, including routine quality assurance and quality control programs in both diagnostic and therapeutic physics. They are frequently used in research facilities to measure doses delivered to patients undergoing various clinical procedures. This work presents the development and evaluation of a tissue equivalent ionization chamber, with a sensitive volume of 2.3 cm{sup 3}, for routine use in X-rays beams. This ionization chamber was developed at the Calibration Laboratory/IPEN. The new tissue equivalent material was developed at the Physics Institute of the University of Sao Paulo. In order to evaluate the dosimetric performance of the new ionization chamber, several tests described by international standards were undertaken, and all results were within the recommended limits. (author)

Evaluation of a tissue equivalent ionization chamber in X-ray beams

International Nuclear Information System (INIS)

Perini, Ana Paula; Neves, Lucio Pereira; Santos, William de Souza; Caldas, Linda V.E.; Frimaio, Audrew; Costa, Paulo R.

2014-01-01

Tissue equivalent materials present a variety of uses, including routine quality assurance and quality control programs in both diagnostic and therapeutic physics. They are frequently used in research facilities to measure doses delivered to patients undergoing various clinical procedures. This work presents the development and evaluation of a tissue equivalent ionization chamber, with a sensitive volume of 2.3 cm 3 , for routine use in X-rays beams. This ionization chamber was developed at the Calibration Laboratory/IPEN. The new tissue equivalent material was developed at the Physics Institute of the University of Sao Paulo. In order to evaluate the dosimetric performance of the new ionization chamber, several tests described by international standards were undertaken, and all results were within the recommended limits. (author)

The design of diagnostic medical facilities using ionizing radiation

International Nuclear Information System (INIS)

1988-03-01

This Code, setting out the general principles of radiological protection as applied to diagnostic radiation facilities in hospitals and clinics, is intended as a guide to architects and to works departments concerned with their design and construction, and with the modification of existing units

User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.

Science.gov (United States)

Reddy, C. J.

2000-01-01

PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.

Children's exposure to ionizing radiations linked with diagnostic procedures in 2010 in France

International Nuclear Information System (INIS)

2013-01-01

The objective of this study was to characterize the medical exposure of the French pediatric population to ionizing radiations (IR) in 2010. It only includes diagnostic procedures. Data are mainly provided by the French Health Insurance (CNAM-TS), through a representative sample of about 1% of the French population (the so-called 'EGB' sample). In 2010, more than 100,000 children from 0 to 15 years old were included in this sample. About 600 examinations per 1000 children were performed: 55% are radiological examinations and 42% dental. CT examinations are rather rare (about 2%). Nuclear medicine and interventional radiology represent less than 1% of the examinations. Children from 10 to 15 years old and babies from new born to 1 year old are the most examined. Exposure of girls and boys are rather similar. From 10 to 15 years old, dental and limbs examinations are the most frequent. Chest and pelvic examinations are the most frequent examinations performed on babies. CT pediatric examinations concern mainly the head and the neck. In 2010, a third of the French children has been exposed to at least one examination using IR. The mean and median effective doses were respectively equal to 0.65 mSv and 0.025 mSv. These values were respectively 5.7 mSv and 1.7 mSv for the children exposed to at least one CT examination (about 1% of the studied population). This study brings reference data on pediatric exposure to IR, and makes them available for public health and epidemiological purposes. This analysis should be periodically carried out to assess the evolution of the pediatric exposure. (authors)

Parallel transposition of sparse data structures

DEFF Research Database (Denmark)

Wang, Hao; Liu, Weifeng; Hou, Kaixi

2016-01-01

Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel tr...... transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor....

Numerical solution of large sparse linear systems

International Nuclear Information System (INIS)

Meurant, Gerard; Golub, Gene.

1982-02-01

This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr

Annotated bibliography of highly ionized atoms of importance to plasmas

International Nuclear Information System (INIS)

Schmieder, R.W.

1975-04-01

A bibliography is presented of the literature on highly ionized atoms which have relevance to plasmas. The bibliography is annotated with keywords, and indexed by subjects and authors. It should be of greatest use to researchers working on the problems of impurity cooling and diagnostics of CTR plasmas. (U.S.)

Ultraviolet/Optical Emission of the Ionized Gas in AGN: Diagnostics of the Ionizing Source and Gas Properties

Energy Technology Data Exchange (ETDEWEB)

Feltre, Anna [Univ Lyon, Univ Lyon1, Ens de Lyon, Centre National de la Recherche Scientifique, Centre de Recherche Astrophysique de Lyon UMR5574, Saint-Genis-Laval (France); Sorbonne Universités, UPMC-Centre National de la Recherche Scientifique, UMR7095, Institut d' Astrophysique de Paris, Paris (France); Charlot, Stephane [Sorbonne Universités, UPMC-Centre National de la Recherche Scientifique, UMR7095, Institut d' Astrophysique de Paris, Paris (France); Mignoli, Marco [INAF-Osservatorio Astronomico di Bologna, Bologna (Italy); Bongiorno, Angela [INAF-Osservatorio Astronomico di Roma, Monteporzio Catone (Italy); Calura, Francesco [INAF-Osservatorio Astronomico di Bologna, Bologna (Italy); Chevallard, Jacopo [Scientific Support Office, Directorate of Science and Robotic Exploration, European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Noordwijk (Netherlands); Curtis-Lake, Emma [Sorbonne Universités, UPMC-Centre National de la Recherche Scientifique, UMR7095, Institut d' Astrophysique de Paris, Paris (France); Gilli, Roberto [INAF-Osservatorio Astronomico di Bologna, Bologna (Italy); Plat, Adele, E-mail: anna.feltre@univ-lyon1.fr [Sorbonne Universités, UPMC-Centre National de la Recherche Scientifique, UMR7095, Institut d' Astrophysique de Paris, Paris (France)

2017-11-02

Spectroscopic studies of active galactic nuclei (AGN) are powerful means of probing the physical properties of the ionized gas within them. In particular, near future observational facilities, such as the James Webb Space Telescope (JWST), will allow detailed statistical studies of rest-frame ultraviolet and optical spectral features of the very distant AGN with unprecedented accuracy. In this proceedings, we discuss the various ways of exploiting new dedicated photoionization models of the narrow-line emitting regions (NLR) of AGN for the interpretation of forthcoming revolutionary datasets.

Sparse Source EEG Imaging with the Variational Garrote

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai

2013-01-01

EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions...

Low-count PET image restoration using sparse representation

Science.gov (United States)

Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli

2018-04-01

In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.

[In vivo mutagenicity and clastogenicity of ionizing radiation in nuclear medicine

International Nuclear Information System (INIS)

1989-01-01

The overall goals of our research remains to investigate the mutagenic and clastogenic effects of exposure to low levels of ionizing radiation in human lymphocytes. We are studying hospital patients referred to a nuclear medicine department for diagnostic cardiac imaging and nuclear medicine technologists who administer radionuclides

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

Science.gov (United States)

Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang

2017-07-01

It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

Ultraviolet/Optical Emission of the Ionized Gas in AGN: Diagnostics of the Ionizing Source and Gas Properties

Directory of Open Access Journals (Sweden)

Anna Feltre

2017-11-01

Full Text Available Spectroscopic studies of active galactic nuclei (AGN are powerful means of probing the physical properties of the ionized gas within them. In particular, near future observational facilities, such as the James Webb Space Telescope (JWST, will allow detailed statistical studies of rest-frame ultraviolet and optical spectral features of the very distant AGN with unprecedented accuracy. In this proceedings, we discuss the various ways of exploiting new dedicated photoionization models of the narrow-line emitting regions (NLR of AGN for the interpretation of forthcoming revolutionary datasets.

A sparse electromagnetic imaging scheme using nonlinear landweber iterations

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting

Optical remote diagnostics of atmospheric propagating beams of ionizing radiation

Science.gov (United States)

Karl JR., Robert R.

1990-03-06

Data is obtained for use in diagnosing the characteristics of a beam of ionizing radiation, such as charged particle beams, neutral particle beams, and gamma ray beams. In one embodiment the beam is emitted through the atmosphere and produces nitrogen fluorescence during passage through air. The nitrogen fluorescence is detected along the beam path to provide an intensity from which various beam characteristics can be calculated from known tabulations. Optical detecting equipment is preferably located orthogonal to the beam path at a distance effective to include the entire beam path in the equipment field of view.

Scalable group level probabilistic sparse factor analysis

DEFF Research Database (Denmark)

Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard

2017-01-01

Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

Fast wavelet based sparse approximate inverse preconditioner

Energy Technology Data Exchange (ETDEWEB)

Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

1996-12-31

Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

Exposure from diagnostic nuclear medicine procedures

International Nuclear Information System (INIS)

Iacob, O.; Diaconescu, C.; Isac, R.

2002-01-01

According to our last national study on population exposures from natural and artificial sources of ionizing radiation, 16% of overall annual collective effective dose represent the contribution of diagnostic medical exposures. Of this value, 92% is due to diagnostic X-ray examinations and only 8% arise from diagnostic nuclear medicine procedures. This small contribution to collective dose is mainly the result of their lower frequency compared to that of the X-ray examinations, doses delivered to patients being, on average, ten times higher. The purpose of this review was to reassess the population exposure from in vivo diagnostic nuclear medicine procedures and to evaluate the temporal trends of diagnostic usage of radiopharmaceuticals in Romania. The current survey is the third one conducted in the last decade. As in the previous ones (1990 and 1995), the contribution of the Radiation Hygiene Laboratories Network of the Ministry of Health and Family in collecting data from nuclear medicine departments in hospitals was very important

Local posterior concentration rate for multilevel sparse sequences

NARCIS (Netherlands)

Belitser, E.N.; Nurushev, N.

2017-01-01

We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one

Integration of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in blood culture diagnostics: a fast and effective approach.

Science.gov (United States)

Klein, Sabrina; Zimmermann, Stefan; Köhler, Christine; Mischnik, Alexander; Alle, Werner; Bode, Konrad A

2012-03-01

Sepsis is a major cause of mortality in hospitalized patients worldwide, with lethality rates ranging from 30 to 70 %. Sepsis is caused by a variety of different pathogens, and rapid diagnosis is of outstanding importance, as early and adequate antimicrobial therapy correlates with positive clinical outcome. In recent years, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) fingerprinting has become a powerful tool in microbiological diagnostics. The direct identification of micro-organisms in a positive blood culture by MALDI-TOF MS can shorten the diagnostic procedure significantly. Therefore, the aim of the present study was to evaluate whether identification rates could be improved by using the new Sepsityper kit from Bruker Daltonics for direct isolation and identification of bacteria from positive blood cultures by MALDI-TOF MS compared with the use of conventional separator gel columns, and to integrate the MALDI-TOF MS-based identification method into the routine course of blood culture diagnostics in the setting of a microbiological laboratory at a university hospital in Germany. The identification of Gram-negative bacteria by MALDI-TOF MS was significantly better using the Sepsityper kit compared with a separator gel tube-based method (99 and 68 % correct identification, respectively). For Gram-positive bacteria, only 73 % were correctly identified by MALDI-TOF with the Sepsityper kit and 59 % with the separator gel tube assay. A major problem of both methods was the poor identification of Gram-positive grape-like clustered cocci. As differentiation of Staphylococcus aureus from coagulase-negative staphylococci is of clinical importance, a PCR was additionally established that was capable of identifying S. aureus directly from positive blood cultures, thus closing this diagnostic gap. Another benefit of the PCR approach is the possibility of directly detecting the genes responsible for meticillin

Study of the strongly ionized medium in active galactic n ('Warm Absorber'): multi-wavelength modelling and plasma diagnostics in the X-ray spectral range

International Nuclear Information System (INIS)

Porquet, Delphine

1999-01-01

The so-called 'Warm Absorber' medium is observed in the central region of Active Galactic Nuclei and particularly in Seyfert l galaxies. lt is mainly characterized by O(VII) and O(VIII) absorption edges detected in the soft X-rays. Its study (modelization and observation) is an important key tool to understand Active Galactic Nuclei. The work presented here consists in modelling the Warm Absorber, and in developing X-ray spectroscopy diagnostics to constrain the physical parameters of any hot medium such as the Warm Absorber. The physical parameters of the Warm Absorber (density, temperature, ionization processes..) are difficult to determine only on the basis of present X-ray data. In particular, the value of the density cannot be derived only from the modelling of the resonance lines and of the soft X-ray absorption edges since there are almost insensitive to the density in the range of values expected for the Warm Absorber. lt is why we have developed diagnostic methods based on a multi-wavelength approach. The modelling is made with two complementary computational codes: PEGAS, and IRIS which takes into account the most accurate atomic data. With these two codes, we have modelled several types of plasma ionisation processes (photoionized plasmas and/or collisional). Results for the Warm Absorber were compared to multi-wavelength observations (mainly the optical iron coronal lines [Fe X] 6375 Angstroms, [Fe XI] 7892 Angstroms, and [Fe XIV] 5303 Angstroms). The proposed method has allowed to show that the Warm Absorber could be responsible of the emission of these lines totally or partially. All models of the Warm Absorber producing coronal line equivalent widths larger than observed were ruled out. This strongly constrains the physical parameters of the Warm Absorber, and particularly its density (n H ≥10 10 cm -3 ). The new generation of X-ray satellites (Chandra/AXAF, XMM...) will produce spectra at high spectral resolution and high sensitivity

Sparse modeling of spatial environmental variables associated with asthma.

Science.gov (United States)

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Ionizing collisions: a new diagnostic for Bose-Einstein condensates of metastable helium

International Nuclear Information System (INIS)

Sirjean, O.

2003-06-01

At this writing, metastable helium (23S1) is the only example of Bose-Einstein condensation of an atom in an excited electronic state. The corresponding internal energy permits efficient and fast electronic detection of the atoms using a micro-channel plate detector (MCP). Moreover, this energy is responsible for ionizing collisions inside the magnetically trapped cloud (Penning ionization). These ions are also easily detected by the MCP. This thesis begins by describing the characteristics of the MCP detector. Next, the experimental procedure to achieve Bose-Einstein condensation is presented. These preliminaries are followed by a description of the experiments performed in order to determine the origin of the ions produced and by a presentation of some of the new experimental possibilities provided by the ion signal. For clouds with a low enough density, ions are mainly produced by collisions with the residual gas, and the signal is proportional to the number of trapped atoms. For clouds with a sufficiently high density, for example close to the condensation threshold, ions are mainly produced by 2- and 3-body collisions. In this case, the ion signal is also related to the density of the cloud. Depending on the density, the signal gives a real-time and 'non-destructive' measurement of these different characteristics. In particular, we have shown it is a valuable indicator of the onset of condensation, because it signals the sudden increase of density which then occurs. By studying the ion rate versus the density and the number of atoms for pure condensates and for thermal clouds at critical temperature, we have measured the collision rate constants for these ionizing processes. Our results are in agreement with theoretical predictions. (author)

Analog system for computing sparse codes

Science.gov (United States)

Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell

2010-08-24

A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

Use of non-ionizing electromagnetic fields for the treatment of cancer.

Science.gov (United States)

Jimenez, Hugo; Blackman, Carl; Lesser, Glenn; Debinski, Waldemar; Chan, Michael; Sharma, Sambad; Watabe, Kounosuke; Lo, Hui-Wen; Thomas, Alexandra; Godwin, Dwayne; Blackstock, William; Mudry, Albert; Posey, James; O'Connor, Rodney; Brezovich, Ivan; Bonin, Keith; Kim-Shapiro, Daniel; Barbault, Alexandre; Pasche, Boris

2018-01-01

Cancer treatment and treatment options are quite limited in circumstances such as when the tumor is inoperable, in brain cancers when the drugs cannot penetrate the blood-brain-barrier, or when there is no tumor-specific target for generation of effective therapeutic antibodies. Despite the fact that electromagnetic fields (EMF) in medicine have been used for therapeutic or diagnostic purposes, the use of non-ionizing EMF for cancer treatment is a new emerging concept. Here we summarize the history of EMF from the 1890's to the novel and new innovative methods that target and treat cancer by non-ionizing radiation.

Occlusion detection via structured sparse learning for robust object tracking

KAUST Repository

Zhang, Tianzhu

2014-01-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

Directory of Open Access Journals (Sweden)

Xiangwei Xing

2014-01-01

Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

Gas chromatography coupled to atmospheric pressure ionization mass spectrometry (GC-API-MS): review.

Science.gov (United States)

Li, Du-Xin; Gan, Lin; Bronja, Amela; Schmitz, Oliver J

2015-09-03

Although the coupling of GC/MS with atmospheric pressure ionization (API) has been reported in 1970s, the interest in coupling GC with atmospheric pressure ion source was expanded in the last decade. The demand of a "soft" ion source for preserving highly diagnostic molecular ion is desirable, as compared to the "hard" ionization technique such as electron ionization (EI) in traditional GC/MS, which fragments the molecule in an extensive way. These API sources include atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI), atmospheric pressure laser ionization (APLI), electrospray ionization (ESI) and low temperature plasma (LTP). This review discusses the advantages and drawbacks of this analytical platform. After an introduction in atmospheric pressure ionization the review gives an overview about the history and explains the mechanisms of various atmospheric pressure ionization techniques used in combination with GC such as APCI, APPI, APLI, ESI and LTP. Also new developments made in ion source geometry, ion source miniaturization and multipurpose ion source constructions are discussed and a comparison between GC-FID, GC-EI-MS and GC-API-MS shows the advantages and drawbacks of these techniques. The review ends with an overview of applications realized with GC-API-MS. Copyright © 2015. Published by Elsevier B.V.

Highly-Accelerated Real-Time Cardiac Cine MRI Using k-t SPARSE-SENSE

Science.gov (United States)

Feng, Li; Srichai, Monvadi B.; Lim, Ruth P.; Harrison, Alexis; King, Wilson; Adluru, Ganesh; Dibella, Edward VR.; Sodickson, Daniel K.; Otazo, Ricardo; Kim, Daniel

2012-01-01

For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (~2.5mm × 2.5mm) and temporal resolution (~40ms), to produce high-quality real-time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular (LV) function. In this work, we present an 8-fold accelerated real-time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE). Compared with reference, breath-hold cine MRI, our 8-fold accelerated real-time cine MRI produced significantly worse qualitative grades (1–5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both 8-fold accelerated real-time cine and breath-hold cine MRI yielded comparable LV function measurements, with coefficient of variation cine MRI with k-t SPARSE-SENSE is a promising modality for rapid imaging of myocardial function. PMID:22887290

Highly accelerated real-time cardiac cine MRI using k-t SPARSE-SENSE.

Science.gov (United States)

Feng, Li; Srichai, Monvadi B; Lim, Ruth P; Harrison, Alexis; King, Wilson; Adluru, Ganesh; Dibella, Edward V R; Sodickson, Daniel K; Otazo, Ricardo; Kim, Daniel

2013-07-01

For patients with impaired breath-hold capacity and/or arrhythmias, real-time cine MRI may be more clinically useful than breath-hold cine MRI. However, commercially available real-time cine MRI methods using parallel imaging typically yield relatively poor spatio-temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm(2)) and temporal resolution (∼40 ms), to produce high-quality real-time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real-time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k-t SPARSE-SENSE). Compared with reference, breath-hold cine MRI, our eightfold accelerated real-time cine MRI produced significantly worse qualitative grades (1-5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real-time cine and breath-hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation cine MRI with k-t SPARSE-SENSE is a promising modality for rapid imaging of myocardial function. Copyright © 2012 Wiley Periodicals, Inc.

A New Diagnostic Diagram of Ionization Sources for High-redshift Emission Line Galaxies

Science.gov (United States)

Zhang, Kai; Hao, Lei

2018-04-01

We propose a new diagram, the kinematics–excitation (KEx) diagram, which uses the [O III] λ5007/Hβ line ratio and the [O III] λ5007 emission line width (σ [O III]) to diagnose the ionization source and physical properties of active galactic nuclei (AGNs) and star-forming galaxies (SFGs). The KEx diagram is a suitable tool to classify emission line galaxies at intermediate redshift because it uses only the [O III] λ5007 and Hβ emission lines. We use the main galaxy sample of SDSS DR7 and the Baldwin‑Phillips‑Terlevich (BPT) diagnostic to calibrate the diagram at low redshift. The diagram can be divided into three regions: the KEx-AGN region, which consists mainly of pure AGNs, the KEx-composite region, which is dominated by composite galaxies, and the KEx-SFG region, which contains mostly SFGs. LINERs strongly overlap with the composite and AGN regions. AGNs are separated from SFGs in this diagram mainly because they preferentially reside in luminous and massive galaxies and have higher [O III]/Hβ than SFGs. The separation between AGNs and SFGs is even cleaner thanks to the additional 0.15/0.12 dex offset in σ [O III] at fixed luminosity/stellar mass. We apply the KEx diagram to 7866 galaxies at 0.3 Survey, and compare it to an independent X-ray classification scheme using Chandra observations. X-ray AGNs are mostly located in the KEx-AGN region, while X-ray SFGs are mostly located in the KEx-SFG region. Almost all Type 1 AGNs lie in the KEx-AGN region. These tests support the reliability of this classification diagram for emission line galaxies at intermediate redshift. At z ∼ 2, the demarcation line between SFGs and AGNs is shifted by ∼0.3 dex toward higher values of σ [O III] due to evolution effects.

Learning sparse generative models of audiovisual signals

OpenAIRE

Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

2008-01-01

This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

NMR Metabolomics in Ionizing Radiation

Energy Technology Data Exchange (ETDEWEB)

Hu, Jian Z.; Xiao, Xiongjie; Hu, Mary Y.

2016-09-08

Ionizing radiation is an invisible threat that cannot be seen, touched or smelled and exist either as particles or waves. Particle radiation can take the form of alpha, beta or neutrons, as well as high energy space particle radiation such as high energy iron, carbon and proton radiation, etc. (1) Non-particle radiation includes gamma- and x-rays. Publically, there is a growing concern about the adverse health effects due to ionizing radiation mainly because of the following facts. (a) The X-ray diagnostic images are taken routinely on patients. Even though the overall dosage from a single X-ray image such as a chest X-ray scan or a CT scan, also called X-ray computed tomography (X-ray CT), is low, repeated usage can cause serious health consequences, in particular with the possibility of developing cancer (2, 3). (b) Human space exploration has gone beyond moon and is planning to send human to the orbit of Mars by the mid-2030s. And a landing on Mars will follow.

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir

2013-05-01

A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-05-04

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla

2014-01-06

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays

Science.gov (United States)

Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.

2004-01-01

Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2014-01-01

Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

Methodology of ionizing radiation measurement, from x-ray equipment, for radiation protection

International Nuclear Information System (INIS)

Caballero, Katia C.S.; Borges, Jose C.

1996-01-01

Most of X-rays beam used for diagnostic, are short exposure time (milliseconds). Exception are those used in fluoroscopy. measuring instruments (area monitors with ionizing chambers or Geiger tubes) used in hospitals and clinics, in general, have characteristic answer time not adequate to X-rays beams length in time. Our objective was to analyse instruments available commercially, to prepare a measuring methodology for direct and secondary beams, in order to evaluate protection barriers for beams used in diagnostic radiology installations. (author)

A comprehensive study of sparse codes on abnormality detection

DEFF Research Database (Denmark)

Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

2017-01-01

Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...

Support agnostic Bayesian matching pursuit for block sparse signals

KAUST Repository

Masood, Mudassir; Al-Naffouri, Tareq Y.

2013-01-01

priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal

Evaluation of illnesses associated with occupational exposure to ionizing radiation

International Nuclear Information System (INIS)

Frometa Suarez, I.

1997-01-01

A retrospective study by the Institute of Occupational Medicine is presented of all cases of pathological indications of ionizing radiation exposure during the period 1990-1995. It describes the incidence of theses diseases and their relationship with other factors. It has shown the predominance of pathologies of the haemolymphopoietic system in individuals who work in radiological diagnostics

The physics of the ionized media

International Nuclear Information System (INIS)

Gresillon, D.; Virmont, J.

1988-01-01

The 1988 progress report of the laboratory of the Ionized Media Physics (Polytechnic School, France), is presented. The most important results are obtained on the field of waves: the study of the conversion of a proper mode into another one, by means of the electromagnetic wave scattering. The research program involves the following topics: plasma nonlinear physics, fluctuations and transport phenomena in magnetic fusion plasmas, plasmas and negatif ion beams, beam and plasma radiations, atomic physics and spectroscopic plasma diagnostics, The published papers, the congress communications, the thesis and the patents are listed [fr

Selectivity and sparseness in randomly connected balanced networks.

Directory of Open Access Journals (Sweden)

Cengiz Pehlevan

Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

KAUST Repository

Desmal, Abdulla

2015-07-29

A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.

Vector sparse representation of color image using quaternion matrix analysis.

Science.gov (United States)

Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong

2015-04-01

Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

Implementation of metrology in diagnostic radiology at National Laboratory of Ionization Radiation Metrology

International Nuclear Information System (INIS)

Peixoto, J.G.P.

1992-01-01

Studies aiming the calibration implementation of measuring instruments used in radiodiagnosis are presented. Considerations about x-ray beam qualities, dosimetric standards and the results from energy dependence of some ionization chambers for x-ray beams of 40 to 150 kV are also shown. C.G.C.)

Fast convolutional sparse coding using matrix inversion lemma

Czech Academy of Sciences Publication Activity Database

Šorel, Michal; Šroubek, Filip

2016-01-01

Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

Radiative Rates for Forbidden Transitions in Doubly-Ionized Fe-Peak Elements

Science.gov (United States)

Fivet, Vanessa; Quinet, P.; Bautista, M.

2012-05-01

Accurate and reliable atomic data for lowly-ionized Fe-peak species (Sc, Ti, V, Cr, Mn, Fe, Co, Ni and Cu) are of paramount importance for the analysis of the high resolution astrophysical spectra currently available. The third spectra of several iron group elements have been observed in different galactic sources like Herbig-Haro objects in the Orion Nebula [1] and stars like Eta Carinae [2]. However, forbidden transitions between low-lying metastable levels of doubly-ionized iron-peak ions have been very little investigated so far and radiative rates for those lines remain sparse or inexistent. We are carrying out a systematic study of the electronic structure of doubly-ionized iron-peak elements. The magnetic dipole (M1) and electric quadrupole (E2) transition probabilities are computed using the pseudo-relativistic Hartree-Fock (HFR) code of Cowan [3] and the central Thomas-Fermi-Dirac potential approximation implemented in AUTOSTRUCTURE [4]. This multi-platform approach allows for consistency checks and intercomparison and has proven very successful in the study of the complex Fe-peak species where many different effects contribute [5]. References [1] A. Mesa-Delgado et al., MNRAS 395 (2009) 855 [2] S. Johansson et al., A&A 361 (2000) 977 [3] R.D. Cowan, The Theory of Atomic Structure and Spectra, Berkeley: Univ. California Press (1981) [4] N.R. Badnell, J. Phys. B: At. Mol. Opt. Phys. 30 (1997) 1 [5] M. Bautista et al., ApJ 718 (2010) L189

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Binary Sparse Phase Retrieval via Simulated Annealing

Directory of Open Access Journals (Sweden)

Wei Peng

2016-01-01

Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

Confidence of model based shape reconstruction from sparse data

DEFF Research Database (Denmark)

Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.

2010-01-01

Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...

Pediatric providers and radiology examinations. Knowledge and comfort levels regarding ionizing radiation and potential complications of imaging

Energy Technology Data Exchange (ETDEWEB)

Wildman-Tobriner, Benjamin; Maxfield, Charles M. [Duke University Hospital, Department of Radiology, Durham, NC (United States); Parente, Victoria M. [Duke University Hospital, Department of Pediatrics, Durham, NC (United States)

2017-12-15

Pediatric providers should understand the basic risks of the diagnostic imaging tests they order and comfortably discuss those risks with parents. Appreciating providers' level of understanding is important to guide discussions and enhance relationships between radiologists and pediatric referrers. To assess pediatric provider knowledge of diagnostic imaging modalities that use ionizing radiation and to understand provider concerns about risks of imaging. A 6-question survey was sent via email to 390 pediatric providers (faculty, trainees and midlevel providers) from a single academic institution. A knowledge-based question asked providers to identify which radiology modalities use ionizing radiation. Subjective questions asked providers about discussions with parents, consultations with radiologists, and complications of imaging studies. One hundred sixty-nine pediatric providers (43.3% response rate) completed the survey. Greater than 90% of responding providers correctly identified computed tomography (CT), fluoroscopy and radiography as modalities that use ionizing radiation, and ultrasound and magnetic resonance imaging (MRI) as modalities that do not. Fewer (66.9% correct, P<0.001) knew that nuclear medicine utilizes ionizing radiation. A majority of providers (82.2%) believed that discussions with radiologists regarding ionizing radiation were helpful, but 39.6% said they rarely had time to do so. Providers were more concerned with complications of sedation and cost than they were with radiation-induced cancer, renal failure or anaphylaxis. Providers at our academic referral center have a high level of basic knowledge regarding modalities that use ionizing radiation, but they are less aware of ionizing radiation use in nuclear medicine studies. They find discussions with radiologists helpful and are concerned about complications of sedation and cost. (orig.)

Ionization impact on molecular clouds and star formation: Numerical simulations and observations

International Nuclear Information System (INIS)

Tremblin, Pascal

2012-01-01

At all the scales of Astrophysics, the impact of the ionization from massive stars is a crucial issue. At the galactic scale, the ionization can regulate star formation by supporting molecular clouds against gravitational collapse and at the stellar scale, indications point toward a possible birth place of the Solar System close to massive stars. At the molecular cloud scale, it is clear that the hot ionized gas compresses the surrounding cold gas, leading to the formation of pillars, globules, and shells of dense gas in which some young stellar objects are observed. What are the formation mechanisms of these structures? Are the formation of these young stellar objects triggered or would have they formed anyway? Do massive stars have an impact on the distribution of the surrounding gas? Do they have an impact on the mass distribution of stars (the initial mass function, IMF)? This thesis aims at shedding some light on these questions, by focusing especially on the formation of the structures between the cold and the ionized gas. We present the state of the art of the theoretical and observational works on ionized regions (H II regions) and we introduce the numerical tools that have been developed to model the ionization in the hydrodynamic simulations with turbulence performed with the HERACLES code. Thanks to the simulations, we present a new model for the formation of pillars based on the curvature and collapse of the dense shell on itself and a new model for the formations of cometary globules based on the turbulence of the cold gas. Several diagnostics have been developed to test these new models in the observations. If pillars are formed by the collapse of the dense shell on itself, the velocity spectrum of a nascent pillar presents a large spectra with a red-shifted and a blue-shifted components that are caused by the foreground and background parts of the shell that collapse along the line of sight. If cometary globules emerge because of the turbulence of

Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Zongze Wu

2015-08-01

Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

KAUST Repository

Sicat, Ronell Barrera

2014-12-31

This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

Relative effectiveness of ionizing radiations in relation to LET and the influence of oxygen

International Nuclear Information System (INIS)

Barendsen, G.W.

1966-01-01

For the investigation of the mechanism by which effects of ionizing radiations in living cells are initiated an important consideration is the comparison of responses caused by radiations which differ with regard to their ionization density. Many biological effects of ionizing radiations on living cells and organisms are produced more efficiently by radiations with a high as compared with a low linear energy transfer (LET). The assumption has generally been made that the nature and yield of ionizations and excitations produced by ionizing particles in biological material depend only to a relatively small extent on the charge and energy of the particles. Consequently differences in effectiveness per unit dose between various radiations must be due to differences in the spatial distributions of the ionizations produced in the irradiated objects. he high relative effectiveness of densely as compared with sparsely ionizing radiations, observed for various biological systems, implies that interaction occurs between primary effects of ionizations, e. g. chemical changes of various molecules produced close together, and that this interaction is required for, or at least enhances, the production of biological damage. As discussed previously by Pollard, Howard-Flanders and Brustad for inactivation of enzymes and reproductive death of bacteria and yeast cells, investigations of the relation between the relative biological effectiveness (RBE) and LET may provide information about the number of ionizations which are required and the dimensions of the value in which the effects must be produced to initiate the sequence of biophysical, biochemical and biological changes which finally results in the observed effect, e.g. death of a cell. This type of analysis has also been applied to data obtained from irradiations of cultured human cells with α-particles and deuterons of different energies (Barendsen). An important characteristic of any interpretation of radiobiological

Sinogram denoising via simultaneous sparse representation in learned dictionaries

International Nuclear Information System (INIS)

Karimi, Davood; Ward, Rabab K

2016-01-01

Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster. (paper)

Ordering sparse matrices for cache-based systems

International Nuclear Information System (INIS)

Biswas, Rupak; Oliker, Leonid

2001-01-01

The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

A flexible framework for sparse simultaneous component based data integration

Directory of Open Access Journals (Sweden)

Van Deun Katrijn

2011-11-01

Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

A flexible framework for sparse simultaneous component based data integration.

Science.gov (United States)

Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven

2011-11-15

High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

Collective radiation dose from diagnostic x-ray examination in nine ...

African Journals Online (AJOL)

Bernt Lindtjorn

Conclusion: Although the use of ionizing radiation for diagnostic medical procedures is an acceptable ... It is estimated that the adoption of rare earth screen technology might reduce the ... coated in a smooth layer on a plastic support or card.

Proteome-based bacterial identification using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS): A revolutionary shift in clinical diagnostic microbiology.

Science.gov (United States)

Nomura, Fumio

2015-06-01

Rapid and accurate identification of microorganisms, a prerequisite for appropriate patient care and infection control, is a critical function of any clinical microbiology laboratory. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is a quick and reliable method for identification of microorganisms, including bacteria, yeast, molds, and mycobacteria. Indeed, there has been a revolutionary shift in clinical diagnostic microbiology. In the present review, the state of the art and advantages of MALDI-TOF MS-based bacterial identification are described. The potential of this innovative technology for use in strain typing and detection of antibiotic resistance is also discussed. This article is part of a Special Issue entitled: Medical Proteomics. Copyright © 2014 Elsevier B.V. All rights reserved.

P-SPARSLIB: A parallel sparse iterative solution package

Energy Technology Data Exchange (ETDEWEB)

Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)

1994-12-31

Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.

Pediatric providers and radiology examinations: knowledge and comfort levels regarding ionizing radiation and potential complications of imaging.

Science.gov (United States)

Wildman-Tobriner, Benjamin; Parente, Victoria M; Maxfield, Charles M

2017-12-01

Pediatric providers should understand the basic risks of the diagnostic imaging tests they order and comfortably discuss those risks with parents. Appreciating providers' level of understanding is important to guide discussions and enhance relationships between radiologists and pediatric referrers. To assess pediatric provider knowledge of diagnostic imaging modalities that use ionizing radiation and to understand provider concerns about risks of imaging. A 6-question survey was sent via email to 390 pediatric providers (faculty, trainees and midlevel providers) from a single academic institution. A knowledge-based question asked providers to identify which radiology modalities use ionizing radiation. Subjective questions asked providers about discussions with parents, consultations with radiologists, and complications of imaging studies. One hundred sixty-nine pediatric providers (43.3% response rate) completed the survey. Greater than 90% of responding providers correctly identified computed tomography (CT), fluoroscopy and radiography as modalities that use ionizing radiation, and ultrasound and magnetic resonance imaging (MRI) as modalities that do not. Fewer (66.9% correct, Pionizing radiation. A majority of providers (82.2%) believed that discussions with radiologists regarding ionizing radiation were helpful, but 39.6% said they rarely had time to do so. Providers were more concerned with complications of sedation and cost than they were with radiation-induced cancer, renal failure or anaphylaxis. Providers at our academic referral center have a high level of basic knowledge regarding modalities that use ionizing radiation, but they are less aware of ionizing radiation use in nuclear medicine studies. They find discussions with radiologists helpful and are concerned about complications of sedation and cost.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. © 2013 Elsevier Ltd.

Feature selection and multi-kernel learning for sparse representation on a manifold.

Science.gov (United States)

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-03-01

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

CERN Document Server

Cheng, Hong

2015-01-01

This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

Design Patterns for Sparse-Matrix Computations on Hybrid CPU/GPU Platforms

Directory of Open Access Journals (Sweden)

Valeria Cardellini

2014-01-01

Full Text Available We apply object-oriented software design patterns to develop code for scientific software involving sparse matrices. Design patterns arise when multiple independent developments produce similar designs which converge onto a generic solution. We demonstrate how to use design patterns to implement an interface for sparse matrix computations on NVIDIA GPUs starting from PSBLAS, an existing sparse matrix library, and from existing sets of GPU kernels for sparse matrices. We also compare the throughput of the PSBLAS sparse matrix–vector multiplication on two platforms exploiting the GPU with that obtained by a CPU-only PSBLAS implementation. Our experiments exhibit encouraging results regarding the comparison between CPU and GPU executions in double precision, obtaining a speedup of up to 35.35 on NVIDIA GTX 285 with respect to AMD Athlon 7750, and up to 10.15 on NVIDIA Tesla C2050 with respect to Intel Xeon X5650.

An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2014-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method, breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM algorithm has to handle extra...... irregularity from three aspects: (1) the number of the nonzero entries in the result sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the result sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input....... Load balancing builds on the number of the necessary arithmetic operations on the nonzero entries and is guaranteed in all stages. Compared with the state-of-the-art GPU SpGEMM methods in the CUSPARSE library and the CUSP library and the latest CPU SpGEMM method in the Intel Math Kernel Library, our...

Comparison of Methods for Sparse Representation of Musical Signals

DEFF Research Database (Denmark)

Endelt, Line Ørtoft; la Cour-Harbo, Anders

2005-01-01

by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....

Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction

Directory of Open Access Journals (Sweden)

Dong Zhang

2017-01-01

Full Text Available Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

Time-resolved spectroscopy of nonequilibrium ionization in laser-produced plasmas

International Nuclear Information System (INIS)

Marjoribanks, R.S.

1988-01-01

The highly transient ionization characteristic of laser-produced plasmas at high energy densities has been investigated experimentally, using x-ray spectroscopy with time resolution of less than 20 ps. Spectroscopic diagnostics of plasma density and temperature were used, including line ratios, line profile broadening and continuum emission, to characterize the plasma conditions without relying immediately on ionization modeling. The experimentally measured plasma parameters were used as independent variables, driving an ionization code, as a test of ionization modeling, divorced from hydrodynamic calculations. Several state-of-the-art streak spectrographs, each recording a fiducial of the laser peak along with the time-resolved spectrum, characterized the laser heating of thin signature layers of different atomic numbers imbedded in plastic targets. A novel design of crystal spectrograph, with a conically curved crystal, was developed. Coupled with a streak camera, it provided high resolution (λ/ΔΛ > 1000) and a collection efficiency roughly 20-50 times that of planar crystal spectrographs, affording improved spectra for quantitative reduction and greater sensitivity for the diagnosis of weak emitters. Experimental results were compared to hydrocode and ionization code simulations, with poor agreement. The conclusions question the appropriateness of describing electron velocity distributions by a temperature parameter during the time of laser illumination and emphasis the importance of characterizing the distribution more generally

Discussion of CoSA: Clustering of Sparse Approximations

Energy Technology Data Exchange (ETDEWEB)

Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

Point-of-care rare cell cancer diagnostics.

Science.gov (United States)

Issadore, David

2015-01-01

The sparse cells that are shed from tumors into peripheral circulation are an increasingly promising resource for noninvasive monitoring of cancer progression, early diagnosis of disease, and serve as a tool for improving our understanding of cancer metastasis. However, the extremely sparse concentration of circulating tumor cells (CTCs) in blood (~1-100 CTC in 7.5 mL of blood) as well as their heterogeneous biomarker expression has limited their detection using conventional laboratory techniques. To overcome these challenges, we have developed a microfluidic chip-based micro-Hall detector (μHD), which can directly measure single, immunomagnetically tagged cells in whole blood. The μHD can detect individual cells even in the presence of vast numbers of blood cells and unbound reactants, and does not require any washing or purification steps. Furthermore, this cost-effective, single-cell analytical technique is well suited for miniaturization into a mobile platform for low-cost point-of-care use. In this chapter, we describe the methodology used to design, fabricate, and apply these chips to cancer diagnostics.

The Increase in Animal Mortality Risk following Exposure to Sparsely Ionizing Radiation Is Not Linear Quadratic with Dose

OpenAIRE

Haley, Benjamin M.; Paunesku, Tatjana; Grdina, David J.; Woloschak, Gayle E.

2015-01-01

Introduction The US government regulates allowable radiation exposures relying, in large part, on the seventh report from the committee to estimate the Biological Effect of Ionizing Radiation (BEIR VII), which estimated that most contemporary exposures- protracted or low-dose, carry 1.5 fold less risk of carcinogenesis and mortality per Gy than acute exposures of atomic bomb survivors. This correction is known as the dose and dose rate effectiveness factor for the life span study of atomic bo...

Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

Directory of Open Access Journals (Sweden)

Ying Chen

2014-05-01

Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Directory of Open Access Journals (Sweden)

Lu Bing

2017-01-01

Full Text Available We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL. After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM. Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification.

Science.gov (United States)

Bing, Lu; Wang, Wei

2017-01-01

We propose a novel method based on sparse representation for breast ultrasound image classification under the framework of multi-instance learning (MIL). After image enhancement and segmentation, concentric circle is used to extract the global and local features for improving the accuracy in diagnosis and prediction. The classification problem of ultrasound image is converted to sparse representation based MIL problem. Each instance of a bag is represented as a sparse linear combination of all basis vectors in the dictionary, and then the bag is represented by one feature vector which is obtained via sparse representations of all instances within the bag. The sparse and MIL problem is further converted to a conventional learning problem that is solved by relevance vector machine (RVM). Results of single classifiers are combined to be used for classification. Experimental results on the breast cancer datasets demonstrate the superiority of the proposed method in terms of classification accuracy as compared with state-of-the-art MIL methods.

Joint sparse representation for robust multimodal biometrics recognition.

Science.gov (United States)

Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

2014-01-01

Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

Dentistry 4. X-ray diagnostics

International Nuclear Information System (INIS)

2014-01-01

DIN pocketbook 267/4 gives an overview of the normative requirements of the new X-Ray and Radiation Protection Ordinance, which has been in effect since 1 November 2011. This DIN pocketbook is intended for anyone charged with professional responsibility for the use of ionizing radiation in dentistry, operators and users of x-ray devices, radiation protection officers, accredited experts, manufacturers as well as for anyone with an interest in radiation protection or optimal radiological diagnostics. It contains standards relating to the following areas: acceptance and constancy testing; devices for evaluating findings (monitors, film viewing devices), films, printers; archiving, designating, labelling. Adherence to the standards makes it possible to avoid distractive artefacts in x-ray images and optimise the quality of x-ray diagnostics in dentistry.

Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

KAUST Repository

Zhang, Tianzhu

2014-06-19

Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

Long term effects of exposure to ionizing irradiation on periodontal health status – the Tinea Capitis cohort study

Directory of Open Access Journals (Sweden)

Siegal eSadetzki

2015-10-01

Full Text Available Studies among long term survivors of childhood cancer who had received high dose irradiation therapy of 4-60 Gy, demonstrated acute and chronic dental effects including periodontal diseases. However, the possible effects of low to moderate doses of radiation on dental health are sparse. The aim of this study is to investigate the association between childhood exposure to low-moderate doses of ionizing radiation and periodontal health following 50 years from the exposure. The study population included 253 irradiated subjects (treated for Tinea Capitis in the 1950s and, 162 non-irradiated subjects, treated for Tinea Capitis in the 1950s. The estimated dose to the teeth was 0.2-0.4Gy. Dental examination was performed according to the Community Periodontal Index (CPI. Socio-economic and health behavior variables were obtained through a personal questionnaire. Periodontal disease was operationally defined as deep periodontal pockets. A multivariate logistic regression model was used for the association of irradiation status and other independent variables with periodontal status.The results showed that among the irradiated subjects, 23% (95% CI 18%-28% demonstrated complete edentulousness or insufficient teeth for CPI scoring as compared to 13% (95% CI 8%-19% among the non-irradiated subjects (p=0.01. Periodontal disease was detected among 54% of the irradiated subjects as compared to 40% of the non-irradiated (p=0.008. Controlling for education and smoking, the ORs for the association between radiation and periodontal disease were 1.61 (95% CI 1.01-2.57 and 1.95 (95% CI 1.1-3.5 for ever never and per 1 Gy absorbed in the salivary gland, respectively. In line with other studies, a protective effect for periodontal diseases among those with high education and an increased risk for ever smokers were observed. In conclusion, childhood exposure to low-moderate doses of ionizing radiation might be associated with later outcomes of dental health. The

Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir

2015-08-12

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Efficient collaborative sparse channel estimation in massive MIMO

KAUST Repository

Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

2015-01-01

We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

Novel diagnostics for dust in space, Laboratory and fusion plasmas

International Nuclear Information System (INIS)

Castaldo, C.

2011-01-01

In situ diagnostics for mobile dust, based on dust impact ionization phenomena, as well as silica aerogel dust collectors are discussed for applications to space and fusion plasmas. The feasibility of an electro-optical probe to detect hypervelocity (>1 km/s) dust particles in tokamaks is evaluated. For quiescent plasmas, a diagnostic of submicron dust based on measurements of plasma fluctuation spectra can be used (copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

Sparse dictionary learning of resting state fMRI networks.

Science.gov (United States)

Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

2012-07-02

Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

2013-01-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

Low-Rank Sparse Coding for Image Classification

KAUST Repository

Zhang, Tianzhu

2013-12-01

In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

Suitability of tunneling ionization produced plasmas for the plasma beat wave accelerator

International Nuclear Information System (INIS)

Leeman, W.P.; Clayton, C.E.; Marsh, K.A.; Dyson, A.; Joshi, C.

1991-01-01

Tunneling ionization can be thought of as the high intensity, low frequency limit of multi-photon ionization (MPI). Extremely uniform plasmas were produced by the latter process at Rutherford lab for beat wave excitation experiments using a 0.5 μm laser. Plasmas with 100% ionization were produced with densities exceeding 10 17 cm -3 . The experiment uses a CO 2 laser (I max ∼ 5 x 10 14 W/cm 2 ) which allows the formation of plasmas via the tunneling process. For the experiments the authors need plasmas with densities in the range of 5 to 10 x 10 16 cm -3 . Using Thomson scattering as a diagnostic they have explored the density and temperature regime of tunneling ionization produced plasmas. They find that plasmas with densities up to 10 16 cm -3 can indeed be produced and that these plasmas are hot. Beyond this density strong refraction of laser radiation occurs due to the radial profile of the plasma. Implications of this work to the Beat Wave Accelerator program will be discussed

Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

DEFF Research Database (Denmark)

Han, Xixuan; Clemmensen, Line Katrine Harder

2015-01-01

We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

A performance study of sparse Cholesky factorization on INTEL iPSC/860

Science.gov (United States)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Spatially resolved density and ionization measurements of shocked foams using x-ray fluorescence

Energy Technology Data Exchange (ETDEWEB)

MacDonald, M. J.; Keiter, P. A.; Montgomery, D. S.; Scott, H. A.; Biener, M. M.; Fein, J. R.; Fournier, K. B.; Gamboa, E. J.; Kemp, G. E.; Klein, S. R.; Kuranz, C. C.; LeFevre, H. J.; Manuel, M. J. -E.; Wan, W. C.; Drake, R. P.

2016-09-28

We present experiments at the Trident laser facility demonstrating the use of x-ray fluorescence (XRF) to simultaneously measure density, ionization state populations, and electron temperature in shocked foams. An imaging x-ray spectrometer obtained spatially resolved measurements of Ti K-α emission. Density profiles were measured from K-α intensity. Ti ionization state distributions and electron temperatures were inferred by fitting K-α spectra to spectra from CRETIN simulations. This work shows that XRF provides a powerful tool to complement other diagnostics to make equation of state measurements of shocked materials containing a suitable tracer element.

Fast sparsely synchronized brain rhythms in a scale-free neural network.

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dsparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of

Fast sparsely synchronized brain rhythms in a scale-free neural network

Science.gov (United States)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically homogeneous

l1- and l2-Norm Joint Regularization Based Sparse Signal Reconstruction Scheme

Directory of Open Access Journals (Sweden)

Chanzi Liu

2016-01-01

Full Text Available Many problems in signal processing and statistical inference involve finding sparse solution to some underdetermined linear system of equations. This is also the application condition of compressive sensing (CS which can find the sparse solution from the measurements far less than the original signal. In this paper, we propose l1- and l2-norm joint regularization based reconstruction framework to approach the original l0-norm based sparseness-inducing constrained sparse signal reconstruction problem. Firstly, it is shown that, by employing the simple conjugate gradient algorithm, the new formulation provides an effective framework to deduce the solution as the original sparse signal reconstruction problem with l0-norm regularization item. Secondly, the upper reconstruction error limit is presented for the proposed sparse signal reconstruction framework, and it is unveiled that a smaller reconstruction error than l1-norm relaxation approaches can be realized by using the proposed scheme in most cases. Finally, simulation results are presented to validate the proposed sparse signal reconstruction approach.

Image fusion via nonlocal sparse K-SVD dictionary learning.

Science.gov (United States)

Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

2016-03-01

Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Yongzhi Qu

2017-05-01

Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

Laser-enhanced ionization spectroscopy around the ionization limit

International Nuclear Information System (INIS)

Axner, O.; Berglind, T.; Sjoestroem, S.

1986-01-01

Laser-induced photoionization and Laser-Enhanced collision Ionization (LEI) of Na, Tl, and Li in flames are detected by measuring the production of charges following a laser excitation. The ionization signal is investigated for excitations of the atoms from lower lying states both to Rydberg states close to the ionization limit, as well as to continuum states, i.e. the process of collision ionization is compared with that of photoionization. The qualitative behaviour of the ionization signal when scanning across the ionization limit is studied. It is shown that the ionization signal has a smooth behaviour when passing from bound states into continuum states. The laser-induced photoionization signal strength of atoms in flames is both calculated and measured and a good agreement is obtained. A calculation of wavelength dependent photoionization signal strengths for a number of elements is also presented. Photoionization is used to determine flame- and geometry-dependent parameters. An implication of photoionization in connection with LEI spectrometry for trace element analysis is that there will be a significant increase in background noise if the sample contains high concentrations of easily photoionizing elements and short wavelength light is used. (orig.)

The study of practices in planed diagnostic medical exposure

International Nuclear Information System (INIS)

Popescu, Irina-Anca; Perju, Nicoleta Ana-Maria; Cobzeanu, Camelia

2011-01-01

The exposure of population to ionizing radiations in medical diagnostic purposes represents a planed exposure procedure, medically justified, having a direct impact on patient health state. A justification of exposure, with a result that can confirm a clinical diagnostic, implies further important steps in treatment decisions. Optimization in patients radiological protection is the result of observing the reference levels recommendations, which maintains a reasonable individual exposure to ionizing radiation in medical purpose. In this paper we investigated the justification of 4189 exposures of patients who underwent planed diagnostic medical investigation over 36 months in a radiological unit. The most frequent investigation concerned the spinal column in 38.3% of total exposures-mainly at lumbar level (63.0% and 24.1%, respectively of total number of exposures), followed by limb bones (20.6%) and thorax (26.9%). Justification of practices included: rheumatic pains in 45.8% of exposures followed by traumatic injuries (20.6%), pleural and pulmonary pathology (19.3%), malignant processes (12.3%), ear-nose-throat investigations (1.1%) and car accidents (0.9%). The females over 40 years old were the group with the highest number of medical exposures, with 54.5% of total practices. This study revealed that the number of medical exposures justification is almost equal with non-justified examinations, confirming a not so good correlation between clinical diagnostic and the required radiological investigation. The percentages of justified versus non-justified practices indicated by specialist physicians and general practitioners were slightly equal - 59.3% vs. 40.7%, 56.9% vs. 43.1%, respectively. The analysis of data concluded that either specialist/general physicians must evaluate more rigorously the patients and all clinical signs in order to reduce as reasonable as possible the non-justified medical exposures to ionizing radiations, and thus to avoid financial and

In-Storage Embedded Accelerator for Sparse Pattern Processing

OpenAIRE

Jun, Sang-Woo; Nguyen, Huy T.; Gadepally, Vijay N.; Arvind

2016-01-01

We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fracti...

Process Knowledge Discovery Using Sparse Principal Component Analysis

DEFF Research Database (Denmark)

Gao, Huihui; Gajjar, Shriram; Kulahci, Murat

2016-01-01

As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets...... SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach...

Massively parallel sparse matrix function calculations with NTPoly

Science.gov (United States)

Dawson, William; Nakajima, Takahito

2018-04-01

We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

Deformable segmentation via sparse representation and dictionary learning.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

2012-10-01

"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

Sparseness- and continuity-constrained seismic imaging

Science.gov (United States)

Herrmann, Felix J.

2005-04-01

Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.

Combinatorial Algorithms for Computing Column Space Bases ThatHave Sparse Inverses

Energy Technology Data Exchange (ETDEWEB)

Pinar, Ali; Chow, Edmond; Pothen, Alex

2005-03-18

This paper presents a combinatorial study on the problem ofconstructing a sparse basis forthe null-space of a sparse, underdetermined, full rank matrix, A. Such a null-space is suitable forsolving solving many saddle point problems. Our approach is to form acolumn space basis of A that has a sparse inverse, by selecting suitablecolumns of A. This basis is then used to form a sparse null-space basisin fundamental form. We investigate three different algorithms forcomputing the column space basis: Two greedy approaches that rely onmatching, and a third employing a divide and conquer strategy implementedwith hypergraph partitioning followed by the greedy approach. We alsodiscuss the complexity of selecting a column basis when it is known thata block diagonal basis exists with a small given block size.

Diagnostics and structure

International Nuclear Information System (INIS)

Vial, J.C.

1986-01-01

The structure of prominences and the diagnostic techniques used to evaluate their physical parameters are discussed. These include electron temperature, various densities (n sub p, n sub e, n sub l), ionization degree, velocities, and magnetic field vector. UV and radio measurements have already evidenced the existence of different temperature regions, corresponding to different geometrical locations, e.g., the so called Prominence-Corona (P-C) interface. Velocity measurements are important for considering formation and mass balance of prominences but there are conflicting velocity measurements which have led to the basic question: what structure is actually observed at a given wavelength; what averaging is performed within the projected slit area during the exposure time? In optically thick lines, the question of the formation region of the radiation along the line of sight is also not a trivial one. The same is true for low resolution measurements of the magnetic field. Coupling diagnostics with structure is now a general preoccupation

Image Super-Resolution Algorithm Based on an Improved Sparse Autoencoder

Directory of Open Access Journals (Sweden)

Detian Huang

2018-01-01

Full Text Available Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

[Ionizing and non-ionizing radiation (comparative risk estimations)].

Science.gov (United States)

Grigor'ev, Iu G

2012-01-01

The population has widely used mobile communication for already more than 15 years. It is important to note that the use of mobile communication has sharply changed the conditions of daily exposure of the population to EME We expose our brain daily for the first time in the entire civilization. The mobile phone is an open and uncontrollable source of electromagnetic radiation. The comparative risk estimation for the population of ionizing and non-ionizing radiation was carried out taking into account the real conditions of influence. Comparison of risks for the population of ionizing and non-ionizing radiation leads us to a conclusion that EMF RF exposure in conditions of wide use of mobile communication is potentially more harmful than ionizing radiation influence.

Diagnosis and prognosis of Ostheoarthritis by texture analysis using sparse linear models

DEFF Research Database (Denmark)

Marques, Joselene; Clemmensen, Line Katrine Harder; Dam, Erik

We present a texture analysis methodology that combines uncommitted machine-learning techniques and sparse feature transformation methods in a fully automatic framework. We compare the performances of a partial least squares (PLS) forward feature selection strategy to a hard threshold sparse PLS...... algorithm and a sparse linear discriminant model. The texture analysis framework was applied to diagnosis of knee osteoarthritis (OA) and prognosis of cartilage loss. For this investigation, a generic texture feature bank was extracted from magnetic resonance images of tibial knee bone. The features were...... used as input to the sparse algorithms, which dened the best features to retain in the model. To cope with the limited number of samples, the data was evaluated using 10 fold cross validation (CV). The diagnosis evaluation using sparse PLS reached a generalization area-under-the-ROC curve (AUC) of 0...

Efficient implementations of block sparse matrix operations on shared memory vector machines

International Nuclear Information System (INIS)

Washio, T.; Maruyama, K.; Osoda, T.; Doi, S.; Shimizu, F.

2000-01-01

In this paper, we propose vectorization and shared memory-parallelization techniques for block-type random sparse matrix operations in finite element (FEM) applications. Here, a block corresponds to unknowns on one node in the FEM mesh and we assume that the block size is constant over the mesh. First, we discuss some basic vectorization ideas (the jagged diagonal (JAD) format and the segmented scan algorithm) for the sparse matrix-vector product. Then, we extend these ideas to the shared memory parallelization. After that, we show that the techniques can be applied not only to the sparse matrix-vector product but also to the sparse matrix-matrix product, the incomplete or complete sparse LU factorization and preconditioning. Finally, we report the performance evaluation results obtained on an NEC SX-4 shared memory vector machine for linear systems in some FEM applications. (author)

Assessment of the radiation risk from diagnostic radiology

International Nuclear Information System (INIS)

Streffer, C.; Mueller, W.U.

1995-01-01

In any assessment of radiation risks from diagnostic radiology the main concern is the possible induction of cancer. It now appears to be beyond all doubt that ionizing rays invite the development of cancer in humans. The radiation doses encountered in diagnostic radiology generally vary from 1 to 50 mSv. For this dose range, no measured values are available to ascertain cancer risks from ionizing rays. The effects of such doses must therefore be extrapolated from higher dose levels under consideration of given dose-effect relationships. All relevant figures for diagnostic X-ray measures are therefore mathematically determined approximate values. The stochastic radiation risk following non-homogeneous radiation exposure is assessed on the basis of the effective dose. This dose was originally introduced to ascertain the risk from radioactive substances incorporated at the working place. A secondary intention was to trigger further developmental processes in radiation protection. Due to the difficulties previously outlined and the uncertainties surrounding the determination and assessment of the effective dose from diagnostic X-ray procedures, this dose should merely be used for technological refinements and comaprisons of examination procedures. It appears unreasonable that the effective doses determined for the individual examinations are summed up to obtain a collective effective dose and to multiply this with a risk factor so as to give an approximation of the resulting deaths from cancer. A reasonable alternative is to inform patients subjected to X-ray examinations about the associated radiation dose and to estimate form this the magnitude of the probable radiation risk. (orig./MG) [de

A Projected Conjugate Gradient Method for Sparse Minimax Problems

DEFF Research Database (Denmark)

Madsen, Kaj; Jonasson, Kristjan

1993-01-01

A new method for nonlinear minimax problems is presented. The method is of the trust region type and based on sequential linear programming. It is a first order method that only uses first derivatives and does not approximate Hessians. The new method is well suited for large sparse problems...... as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... with the method are presented. In fact, we find that the number of iterations required is comparable to that of state-of-the-art quasi-Newton codes....

Identification of MIMO systems with sparse transfer function coefficients

Science.gov (United States)

Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios

2012-12-01

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

Directory of Open Access Journals (Sweden)

Rami Zewail

2017-08-01

Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient

KAUST Repository

Nobile, Fabio

2016-03-18

In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature and interpolation on unbounded sets. We also consider several profit indicators that are suitable to drive the adaptation process. We then use such algorithm to solve an important test case in Uncertainty Quantification problem, namely the Darcy equation with lognormal permeability random field, and compare the results with those obtained with the quasi-optimal sparse grids based on profit estimates, which we have proposed in our previous works (cf. e.g. Convergence of quasi-optimal sparse grids approximation of Hilbert-valued functions: application to random elliptic PDEs). To treat the case of rough permeability fields, in which a sparse grid approach may not be suitable, we propose to use the adaptive sparse grid quadrature as a control variate in a Monte Carlo simulation. Numerical results show that the adaptive sparse grids have performances similar to those of the quasi-optimal sparse grids and are very effective in the case of smooth permeability fields. Moreover, their use as control variate in a Monte Carlo simulation allows to tackle efficiently also problems with rough coefficients, significantly improving the performances of a standard Monte Carlo scheme.

Sparse principal component analysis in medical shape modeling

Science.gov (United States)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Charge transfer and ionization involving argon ions and neutral hydrogen

International Nuclear Information System (INIS)

Errea, L F; Illescas, Clara; Mendez, L; Pons, B; Riera, A; Suarez, J

2006-01-01

We present classical trajectory Monte Carlo (CTMC) calculations of total and partial cross sections for capture and ionization in Ar 18+ , Ar 17+ , Ar 16+ +H(1s) collisions in the 30-300 keV amu -1 impact energy range. We specially focus on capture into high-lying states of the projectile, which are of paramount importance for diagnostics of fusion plasmas involving Ar q+ seeding. (letter to the editor)

Inference algorithms and learning theory for Bayesian sparse factor analysis

International Nuclear Information System (INIS)

Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

2009-01-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Inference algorithms and learning theory for Bayesian sparse factor analysis

Energy Technology Data Exchange (ETDEWEB)

Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

2009-12-01

Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

Universal Regularizers For Robust Sparse Coding and Modeling

OpenAIRE

Ramirez, Ignacio; Sapiro, Guillermo

2010-01-01

Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

Science.gov (United States)

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Efficient coordinated recovery of sparse channels in massive MIMO

KAUST Repository

Masood, Mudassir

2015-01-01

This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

Studies of ground-state dynamics in isolated species by ionization-detected stimulated Raman techniques

Energy Technology Data Exchange (ETDEWEB)

Felker, P.M. [Univ. of California, Los Angeles (United States)

1993-12-01

First, the author aims to develop methods of nonlinear Raman spectroscopy for application in studies of sparse samples. Second, the author wishes to apply such methods to structural and dynamical studies of species (molecules, complexes, and clusters) in supersonic molecular beams. In the past year, the author has made progress in several areas. The first pertains to the application of mass-selective ionization-detected stimulated Raman spectroscopies (IDSRS) to the size-specific vibrational spectroscopy of solute-solvent{sub n} clusters. The second involves the application of IDSRS methods to studies of jet-cooled benzene clusters. The third pertains to the use of IDSRS methods in the study of intermolecular vibrational transitions in van der Waals complexes.

Ionization effects in electronic inner-shells of ionized atoms

International Nuclear Information System (INIS)

Shchornak, G.

1983-01-01

A review of the atomic physics of ionization atoms has been presented. Interaction and structure effects in atomic shells, correlated to the occurrence of vacancies in several subshells of the atom have been considered. The methods of calculations of atomic states and wave functions have been reviewed. The energy shift of characteristic X-rays is discussed as a function of the ionization stage of the atom. The influence of inner and outer-shell vacancies on the energy of the X-rays is shown in detail. The influence of chemical effects on the parameters of X-rays is also taken into account. Further on, the change of transition probabilities in radiative and non-radiative transitions by changing stage of ionization is discussed; and among them the leading part of Auger and Coster-Kronig transitions by the arearrangement of the atomic states is shown. The influence of non-radiative electronic transitions on ionization cross-sections for multiple ionization is discussed. Using these results, ionization cross-sections for direct and indirect processes for several ionization stages are given

Robust Fringe Projection Profilometry via Sparse Representation.

Science.gov (United States)

Budianto; Lun, Daniel P K

2016-04-01

In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

Influence of ionizing radiation on human body

Directory of Open Access Journals (Sweden)

Zygmunt Zdrojewicz

2016-06-01

Full Text Available This article describes positive and negative aspects of ionizing radiation and its effects on human body. Being a part of various medical procedures in medicine, ionising radiation has become an important aspect for both medical practitioners and patients. Commonly used in treatment, diagnostics and interventional radiology, its medical usage follows numerous rules, designed to reduce excessive exposure to ionizing radiation. Its widespread use makes it extremely important to research and confirm effects of various doses of radiation on patients of all ages. Two scientific theories, explaining radiation effects on human organism, stand in contrast: commonly accepted LNT-hypothesis and yet to be proven hormesis theory. Despite the fact that the current radiation protection standards are based on the linear theory (LNT-hypothesis, the hormesis theory arouses more and more interest, and numerous attempts are made to prove its validity. Further research expanding the knowledge on radiation hormesis can change the face of the future. Perhaps such researches will open up new possibilities for the use of ionizing radiation, as well as enable the calculation of the optimal and personalised radiation dose for each patient, allowing us to find a new “golden mean”. The authors therefore are careful and believe that these methods have a large future, primarily patient’s good should however be kept in mind.

The Medical Exposure to Ionizing Radiation and Protection of the Patient in Medical Imaging Procedures for Diagnostic and Therapeutic Purposes (Excluding Radiotherapy) using X-Rays in Israel - Risk - Cost and Benefit

International Nuclear Information System (INIS)

Ben-Shlomo, A.

1998-10-01

Diagnostic and therapeutic radiology is playing a major role in modern medicine. The utilization of devices emitting ionizing radiation for medical diagnostic and therapeutic purposes is classified into three categories: a. Radiotherapy procedures for the treatment of malignant and benign tumors. b. Nuclear medicine procedures using radiopharmaceuticals that are introduced into the patient's body for diagnostic and therapeutic purposes. c. Diagnostic and therapeutic x-ray imaging procedures. This group includes conventional radiography, conventional fluoroscopy, cardiac catheterization, angiography, CT, mammography, dental, and fluoroscopy operation procedures. A survey was carried out on a sample of three major Israeli hospitals in order to: 1. Determine the status of radiation protection of patients in Israel with regard to the use of x-rays in medical imaging and interventional radiology. 2. Assess the extent of exposure of the population to medical x-rays, and assess the collective risk in Israel in this relation (based on Icr-60). 3. Carry out a cost-benefit optimization procedure related to the means that should be used to reduce the exposure of Israeli patients under x-ray procedures. 4. Establish a of practical recommendations to reduce the x-ray radiation exposure of patients and to increase the image quality. 5. Establish a number of basic rules to be utilized by health policy makers in Israel

The Medical Exposure to Ionizing Radiation and Protection of the Patient in Medical Imaging Procedures for Diagnostic and Therapeutic Purposes (Excluding Radiotherapy) using X-Rays in Israel - Risk - Cost and Benefit

Energy Technology Data Exchange (ETDEWEB)

Ben-Shlomo, A

1998-10-01

Diagnostic and therapeutic radiology is playing a major role in modern medicine. The utilization of devices emitting ionizing radiation for medical diagnostic and therapeutic purposes is classified into three categories: a. Radiotherapy procedures for the treatment of malignant and benign tumors. b. Nuclear medicine procedures using radiopharmaceuticals that are introduced into the patient's body for diagnostic and therapeutic purposes. c. Diagnostic and therapeutic x-ray imaging procedures. This group includes conventional radiography, conventional fluoroscopy, cardiac catheterization, angiography, CT, mammography, dental, and fluoroscopy operation procedures. A survey was carried out on a sample of three major Israeli hospitals in order to: 1. Determine the status of radiation protection of patients in Israel with regard to the use of x-rays in medical imaging and interventional radiology. 2. Assess the extent of exposure of the population to medical x-rays, and assess the collective risk in Israel in this relation (based on Icr-60). 3. Carry out a cost-benefit optimization procedure related to the means that should be used to reduce the exposure of Israeli patients under x-ray procedures. 4. Establish a of practical recommendations to reduce the x-ray radiation exposure of patients and to increase the image quality. 5. Establish a number of basic rules to be utilized by health policy makers in Israel.

Sparse DOA estimation with polynomial rooting

DEFF Research Database (Denmark)

Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren

2015-01-01

Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve highresol......Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...... highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods...

A General Sparse Tensor Framework for Electronic Structure Theory.

Science.gov (United States)

Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin

2017-03-14

Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.

Gas chromatography coupled to atmospheric pressure ionization mass spectrometry (GC-API-MS): Review

International Nuclear Information System (INIS)

Li, Du-Xin; Gan, Lin; Bronja, Amela; Schmitz, Oliver J.

2015-01-01

Although the coupling of GC/MS with atmospheric pressure ionization (API) has been reported in 1970s, the interest in coupling GC with atmospheric pressure ion source was expanded in the last decade. The demand of a “soft” ion source for preserving highly diagnostic molecular ion is desirable, as compared to the “hard” ionization technique such as electron ionization (EI) in traditional GC/MS, which fragments the molecule in an extensive way. These API sources include atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI), atmospheric pressure laser ionization (APLI), electrospray ionization (ESI) and low temperature plasma (LTP). This review discusses the advantages and drawbacks of this analytical platform. After an introduction in atmospheric pressure ionization the review gives an overview about the history and explains the mechanisms of various atmospheric pressure ionization techniques used in combination with GC such as APCI, APPI, APLI, ESI and LTP. Also new developments made in ion source geometry, ion source miniaturization and multipurpose ion source constructions are discussed and a comparison between GC-FID, GC-EI-MS and GC-API-MS shows the advantages and drawbacks of these techniques. The review ends with an overview of applications realized with GC-API-MS. - Highlights: • Atmospheric pressure ion sources (APCI, ESI, APPI, APLC etc) enable the coupling of LC-based high-end MS to GC. • APIs show advantages in selectivity and sensitivity compared with EI in GC-MS. • Accurate mass database in GC-APCI/MS is emerging as an alternative to GC-EI/MS database.

Gas chromatography coupled to atmospheric pressure ionization mass spectrometry (GC-API-MS): Review

Energy Technology Data Exchange (ETDEWEB)

Li, Du-Xin; Gan, Lin; Bronja, Amela [University of Duisburg-Essen, Applied Analytical Chemistry, Universitaetsstr. 5-7, 45141 Essen (Germany); Schmitz, Oliver J., E-mail: oliver.schmitz@uni-due.de [University of Duisburg-Essen, Applied Analytical Chemistry, Universitaetsstr. 5-7, 45141 Essen (Germany)

2015-09-03

Although the coupling of GC/MS with atmospheric pressure ionization (API) has been reported in 1970s, the interest in coupling GC with atmospheric pressure ion source was expanded in the last decade. The demand of a “soft” ion source for preserving highly diagnostic molecular ion is desirable, as compared to the “hard” ionization technique such as electron ionization (EI) in traditional GC/MS, which fragments the molecule in an extensive way. These API sources include atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI), atmospheric pressure laser ionization (APLI), electrospray ionization (ESI) and low temperature plasma (LTP). This review discusses the advantages and drawbacks of this analytical platform. After an introduction in atmospheric pressure ionization the review gives an overview about the history and explains the mechanisms of various atmospheric pressure ionization techniques used in combination with GC such as APCI, APPI, APLI, ESI and LTP. Also new developments made in ion source geometry, ion source miniaturization and multipurpose ion source constructions are discussed and a comparison between GC-FID, GC-EI-MS and GC-API-MS shows the advantages and drawbacks of these techniques. The review ends with an overview of applications realized with GC-API-MS. - Highlights: • Atmospheric pressure ion sources (APCI, ESI, APPI, APLC etc) enable the coupling of LC-based high-end MS to GC. • APIs show advantages in selectivity and sensitivity compared with EI in GC-MS. • Accurate mass database in GC-APCI/MS is emerging as an alternative to GC-EI/MS database.

Low-rank and sparse modeling for visual analysis

CERN Document Server

Fu, Yun

2014-01-01

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

Comparison between two pencil-type ionization chambers with sensitive volume length of 30 cm

International Nuclear Information System (INIS)

Castro, Maysa C. de; Xavier, Marcos; Silva, Natalia F.; Caldas, Linda V.E.

2016-01-01

Computed tomography (CT) for imaging procedures has been growing due to advances in the equipment technology, providing a higher dose to the patient, in relation to other diagnostic radiology tests, resulting in a concern for the patients. The dosimetry in CT is carried out with a pencil-type ionization chamber with sensitive volume length of 10 cm. Studies have shown the underestimation of the dose values. In this work two ionization chambers with the sensitive volume length of 30 cm were developed. They were submitted to the main characterization tests; the results showed to be within the international recommended limits. (author)

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

Science.gov (United States)

She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie

2014-02-01

To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

Development of a high resolution cylindrical crystal spectrometer for line shape and spectral diagnostics of x-rays emitted from - hot - plasmas. Final report, June 1, 1976-December 31, 1983

International Nuclear Information System (INIS)

Kaellne, E.G.

1984-01-01

The development, installation and evaluation of a high resolution X-ray spectroscopic diagnostics are reported. The approach has been to optimize spectrometer throughput to enable single shot plasma diagnostics with good time resolution and to ensure sufficient energy resolution to allow line profile analysis. These goals have been achieved using a new X-ray geometry combined with a new position sensitive X-ray detector. These diagnostics have been used at Alcator C to detect X-ray emission of highly ionized impurity elements as well as argon seed elements specially introduced into the plasma for this diagnostic. Temporally resolved ion temperature profiles have been obtained from the recorded X-ray spectra simultaneously with other plasma parameters such as electron temperature, ionization temperature and ionization stage distribution. Radial profiles have also been measured. The developed X-ray diagnostics thus serve as a major multiparameter probe of the central core of the plasma with complementary informtion on radial profiles

Real-time SPARSE-SENSE cardiac cine MR imaging: optimization of image reconstruction and sequence validation.

Science.gov (United States)

Goebel, Juliane; Nensa, Felix; Bomas, Bettina; Schemuth, Haemi P; Maderwald, Stefan; Gratz, Marcel; Quick, Harald H; Schlosser, Thomas; Nassenstein, Kai

2016-12-01

Improved real-time cardiac magnetic resonance (CMR) sequences have currently been introduced, but so far only limited practical experience exists. This study aimed at image reconstruction optimization and clinical validation of a new highly accelerated real-time cine SPARSE-SENSE sequence. Left ventricular (LV) short-axis stacks of a real-time free-breathing SPARSE-SENSE sequence with high spatiotemporal resolution and of a standard segmented cine SSFP sequence were acquired at 1.5 T in 11 volunteers and 15 patients. To determine the optimal iterations, all volunteers' SPARSE-SENSE images were reconstructed using 10-200 iterations, and contrast ratios, image entropies, and reconstruction times were assessed. Subsequently, the patients' SPARSE-SENSE images were reconstructed with the clinically optimal iterations. LV volumetric values were evaluated and compared between both sequences. Sufficient image quality and acceptable reconstruction times were achieved when using 80 iterations. Bland-Altman plots and Passing-Bablok regression showed good agreement for all volumetric parameters. 80 iterations are recommended for iterative SPARSE-SENSE image reconstruction in clinical routine. Real-time cine SPARSE-SENSE yielded comparable volumetric results as the current standard SSFP sequence. Due to its intrinsic low image acquisition times, real-time cine SPARSE-SENSE imaging with iterative image reconstruction seems to be an attractive alternative for LV function analysis. • A highly accelerated real-time CMR sequence using SPARSE-SENSE was evaluated. • SPARSE-SENSE allows free breathing in real-time cardiac cine imaging. • For clinically optimal SPARSE-SENSE image reconstruction, 80 iterations are recommended. • Real-time SPARSE-SENSE imaging yielded comparable volumetric results as the reference SSFP sequence. • The fast SPARSE-SENSE sequence is an attractive alternative to standard SSFP sequences.

Security-enhanced phase encryption assisted by nonlinear optical correlation via sparse phase

International Nuclear Information System (INIS)

Chen, Wen; Chen, Xudong; Wang, Xiaogang

2015-01-01

We propose a method for security-enhanced phase encryption assisted by a nonlinear optical correlation via a sparse phase. Optical configurations are established based on a phase retrieval algorithm for embedding an input image and the secret data into phase-only masks. We found that when one or a few phase-only masks generated during data hiding are sparse, it is possible to integrate these sparse masks into those phase-only masks generated during the encoding of the input image. Synthesized phase-only masks are used for the recovery, and sparse distributions (i.e., binary maps) for generating the incomplete phase-only masks are considered as additional parameters for the recovery of secret data. It is difficult for unauthorized receivers to know that a useful phase has been sparsely distributed in the finally generated phase-only masks for secret-data recovery. Only when the secret data are correctly verified can the input image obtained with valid keys be claimed as targeted information. (paper)

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Nature of the ionizing source of the nuclear gas in NGC 1052

International Nuclear Information System (INIS)

Keel, W.C.; Miller, J.S.

1983-01-01

We examine the ionization and physical state of the emission-line region in the nucleus of elliptical galaxy NGC 1052. The [O III] lambda4363/lambda5007 ratio, frequently used as a diagnostic for ionization mechanisms, is very poorly determined because of difficulties in matching the underlying stellar continuum spectrum, which is unusual in having very strong lines for the galaxy luminosity. Within these limitations, we find the [O III] temperature to be only marginally compatible with shock models, and the overall emission spectrum to be better fitted by photoionization models with a very dilute flat-spectrum central source. In any event, the case for NGC 1052 as a shock-heated nucleus is not strong

Suppression of E. multilocularis hydatid cysts after ionizing radiation exposure.

Directory of Open Access Journals (Sweden)

Xin Zhou

Full Text Available BACKGROUND: Heavy-ion therapy has an advantage over conventional radiotherapy due to its superb biological effectiveness and dose conformity in cancer therapy. It could be a potential alternate approach for hydatid cyst treatment. However, there is no information currently available on the cellular and molecular basis for heavy-ion irradiation induced cell death in cystic echinococcosis. METHODODOLOGY/PRINCIPAL FINDINGS: LD50 was scored by protoscolex death. Cellular and ultrastructural changes within the parasite were studied by light and electron microscopy, mitochondrial DNA (mtDNA damage and copy number were measured by QPCR, and apoptosis was determined by caspase 3 expression and caspase 3 activity. Ionizing radiation induced sparse cytoplasm, disorganized and clumped organelles, large vacuoles and devoid of villi. The initial mtDNA damage caused by ionizing radiation increased in a dose-dependent manner. The kinetic of DNA repair was slower after carbon-ion radiation than that after X-rays radiation. High dose carbon-ion radiation caused irreversible mtDNA degradation. Cysts apoptosis was pronounced after radiation. Carbon-ion radiation was more effective to suppress hydatid cysts than X-rays. CONCLUSIONS: These studies provide a framework to the evaluation of attenuation effect of heavy-ion radiation on cystic echinococcosis in vitro. Carbon-ion radiation is more effective to suppress E. multilocularis than X-rays.

Litigations in diagnostic radiology

International Nuclear Information System (INIS)

Patil, Ranjit

2014-01-01

There are various regulatory bodies at the international and national level, which lay down norms for radiation protection. These are the International Commission for Radiation Protection (ICRP) the National Commission for Radiation Protection (NCRP) in America, and the Atomic Energy Regulatory Board (AERB) in India. These bodies recommend norms on various radiation issues. Radiography and radiology are two key tools for diagnosing and treating diseases. Recently there are concerns about the effect of ionizing radiation on man and the frequent use of diagnostic radiographs. The professionals are expected to conduct their actions according to guidelines which reflect new information and changing technology in diagnostic radiography. Failure to do so may have severe legal consequences. Patient protection is a matter of normal course but knowledge and awareness of the legal issues is important to avoid legal hassles. Implications of the radiation protection guidelines are discussed. (author)

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

Science.gov (United States)

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

KAUST Repository

Desmal, Abdulla

2015-04-13

A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

Deriving the coronal hole electron temperature: electron density dependent ionization / recombination considerations

International Nuclear Information System (INIS)

Doyle, John Gerard; Perez-Suarez, David; Singh, Avninda; Chapman, Steven; Bryans, Paul; Summers, Hugh; Savin, Daniel Wolf

2010-01-01

Comparison of appropriate theoretically derived line ratios with observational data can yield estimates of a plasma's physical parameters, such as electron density or temperature. The usual practice in the calculation of the line ratio is the assumption of excitation by electrons/protons followed by radiative decay. Furthermore, it is normal to use the so-called coronal approximation, i.e. one only considers ionization and recombination to and from the ground-state. A more accurate treatment is to include ionization/recombination to and from metastable levels. Here, we apply this to two lines from adjacent ionization stages, Mg IX 368 A and Mg X 625 A, which has been shown to be a very useful temperature diagnostic. At densities typical of coronal hole conditions, the difference between the electron temperature derived assuming the zero density limit compared with the electron density dependent ionization/recombination is small. This, however, is not the case for flares where the electron density is orders of magnitude larger. The derived temperature for the coronal hole at solar maximum is around 1.04 MK compared to just below 0.82 MK at solar minimum.

On the Automatic Parallelization of Sparse and Irregular Fortran Programs

Directory of Open Access Journals (Sweden)

Yuan Lin

1999-01-01

Full Text Available Automatic parallelization is usually believed to be less effective at exploiting implicit parallelism in sparse/irregular programs than in their dense/regular counterparts. However, not much is really known because there have been few research reports on this topic. In this work, we have studied the possibility of using an automatic parallelizing compiler to detect the parallelism in sparse/irregular programs. The study with a collection of sparse/irregular programs led us to some common loop patterns. Based on these patterns new techniques were derived that produced good speedups when manually applied to our benchmark codes. More importantly, these parallelization methods can be implemented in a parallelizing compiler and can be applied automatically.

Collective dose estimation in Portuguese population due to medical exams of diagnostic radiology and nuclear medicine

International Nuclear Information System (INIS)

Teles, Pedro; Vaz, Pedro; Paulo, Graciano; Santos, Joana; Pascoal, Ana; Lanca, Isabel; Matela, Nuno; Sousa, Patrick; Carvoeiras, Pedro; Parafita, Rui; Simaozinho, Paula

2013-01-01

In order to assess the exposure of the Portuguese population to ionizing radiation due to medical examinations of diagnostic radiology and nuclear medicine, a working group, consisting of 40 institutions, public and private, was created to evaluation the coletive dose in the Portuguese population in 2010. This work was conducted in collaboration with the Dose Datamed European consortium, which aims to assess the exposure of the European population to ionizing radiation due to 20 diagnostic radiology examinations most frequent in Europe (the 'TOP 20') and nuclear medicine examinations. We obtained an average value of collective dose of ≈ 1 mSv/caput, which puts Portugal in the category of countries medium to high exposure to Europe. We hope that this work can be a starting point to bridge the persistent lack of studies in the areas referred to in Portugal, and to enable the characterization periodic exposure of the Portuguese population to ionizing radiation in the context of medical applications

Thyroid cancer due to biological effects of ionizing radiation

International Nuclear Information System (INIS)

Galvão, T.; Castro, N.; Teixeira, D.; Matuo, R.

2017-01-01

Thyroid cancer is considered the most common in the region of the head and neck. It can be caused by spontaneous mutations, but also by ionizing radiation. The effect of ionizing radiation on the thyroid has been studied for several decades. The exact cause of the cancer is not known, but people with certain risk factors are more vulnerable, such as exposure to radiation, family history and age over 40 years. The thyroid is susceptible to the effects of radiation and is involved in the field of diagnostic or therapeutic irradiation, and may present functional and structural changes. Radiation can act in different ways, such as inhibiting or activating specific functions of the follicular epithelium, reducing the number of functioning follicles, altering vascularization or vascular permeability and inducing immune reactions. These morphological and histological changes may be related to the development of thyroid cancer

Split-Bregman-based sparse-view CT reconstruction

Energy Technology Data Exchange (ETDEWEB)

Vandeghinste, Bert; Vandenberghe, Stefaan [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Goossens, Bart; Pizurica, Aleksandra; Philips, Wilfried [Ghent Univ. (Belgium). Image Processing and Interpretation Research Group (IPI); Beenhouwer, Jan de [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Wilrijk (Belgium). The Vision Lab; Staelens, Steven [Ghent Univ. (Belgium). Medical Image and Signal Processing (MEDISIP); Antwerp Univ., Edegem (Belgium). Molecular Imaging Centre Antwerp

2011-07-01

Total variation minimization has been extensively researched for image denoising and sparse view reconstruction. These methods show superior denoising performance for simple images with little texture, but result in texture information loss when applied to more complex images. It could thus be beneficial to use other regularizers within medical imaging. We propose a general regularization method, based on a split-Bregman approach. We show results for this framework combined with a total variation denoising operator, in comparison to ASD-POCS. We show that sparse-view reconstruction and noise regularization is possible. This general method will allow us to investigate other regularizers in the context of regularized CT reconstruction, and decrease the acquisition times in {mu}CT. (orig.)

Sparse Modeling Reveals miRNA Signatures for Diagnostics of Inflammatory Bowel Disease.

Directory of Open Access Journals (Sweden)

Matthias Hübenthal

Full Text Available The diagnosis of inflammatory bowel disease (IBD still remains a clinical challenge and the most accurate diagnostic procedure is a combination of clinical tests including invasive endoscopy. In this study we evaluated whether systematic miRNA expression profiling, in conjunction with machine learning techniques, is suitable as a non-invasive test for the major IBD phenotypes (Crohn's disease (CD and ulcerative colitis (UC. Based on microarray technology, expression levels of 863 miRNAs were determined for whole blood samples from 40 CD and 36 UC patients and compared to data from 38 healthy controls (HC. To further discriminate between disease-specific and general inflammation we included miRNA expression data from other inflammatory diseases (inflammation controls (IC: 24 chronic obstructive pulmonary disease (COPD, 23 multiple sclerosis, 38 pancreatitis and 45 sarcoidosis cases as well as 70 healthy controls from previous studies. Classification problems considering 2, 3 or 4 groups were solved using different types of penalized support vector machines (SVMs. The resulting models were assessed regarding sparsity and performance and a subset was selected for further investigation. Measured by the area under the ROC curve (AUC the corresponding median holdout-validated accuracy was estimated as ranging from 0.75 to 1.00 (including IC and 0.89 to 0.98 (excluding IC, respectively. In combination, the corresponding models provide tools for the distinction of CD and UC as well as CD, UC and HC with expected classification error rates of 3.1 and 3.3%, respectively. These results were obtained by incorporating not more than 16 distinct miRNAs. Validated target genes of these miRNAs have been previously described as being related to IBD. For others we observed significant enrichment for IBD susceptibility loci identified in earlier GWAS. These results suggest that the proposed miRNA signature is of relevance for the etiology of IBD. Its diagnostic

A framework for general sparse matrix-matrix multiplication on GPUs and heterogeneous processors

DEFF Research Database (Denmark)

Liu, Weifeng; Vinter, Brian

2015-01-01

General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle...... extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the resulting sparse matrix dominate the execution time, and (3) load balancing must account for sparse data...... memory space and efficiently utilizes the very limited on-chip scratchpad memory. Parallel insert operations of the nonzero entries are implemented through the GPU merge path algorithm that is experimentally found to be the fastest GPU merge approach. Load balancing builds on the number of necessary...

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

Science.gov (United States)

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Two-dimensional sparse wavenumber recovery for guided wavefields

Science.gov (United States)

Sabeti, Soroosh; Harley, Joel B.

2018-04-01

The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

Sparse matrix test collections

Energy Technology Data Exchange (ETDEWEB)

Duff, I.

1996-12-31

This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.

Electron capture and ionization in collisions of multiply charged ions with H(2s)

International Nuclear Information System (INIS)

Errea, L F; Guzman, F; Illescas, Clara; Mendez, L; Pons, B; Riera, A; Suarez, J

2007-01-01

We present total cross sections for electron capture and ionization in collisions of B 5+ and Ne 10+ with H(2s), calculated using two methods: the semiclassical close-coupling molecular formalism and the eikonal-CTMC method. We have evaluated partial cross sections for capture into excited n-levels, required in plasma diagnostics

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias

2015-08-12

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure

KAUST Repository

Labschutz, Matthias; Bruckner, Stefan; Groller, M. Eduard; Hadwiger, Markus; Rautek, Peter

2015-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

Science.gov (United States)

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Ring magnetron ionizer

International Nuclear Information System (INIS)

Alessi, J.G.

1986-01-01

A ring magnetron D - charge exchange ionizer has been built and tested. An H - current of 500 μA was extracted with an estimated H 0 density in the ionizer of 10 12 cm -3 . This exceeds the performance of ionizers presently in use on polarized H - sources. The ionizer will soon be tested with a polarized atomic beam

Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

Directory of Open Access Journals (Sweden)

Qi Yuan(Alan

2010-01-01

Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

The use of MR in cardiological diagnostics

International Nuclear Information System (INIS)

Smith, Hans-Joergen

2004-01-01

Image diagnostics is playing an important role in cardiology, and magnetic resonance tomography (MR) is one of many methods used in examinations of the heart. Based on studies of the literature and his own experience the author surveys the potential of MR in today's and tomorrow's diagnostics of heart diseases. Among the image diagnostic methods MR is the one that can give the most extensive information about the heart's anatomy and function. In a non-invasive way and without the use of ionizing radiation, MR can represent the anatomy in selectable planes, visualize and quantify the heart's pumping function and functioning of the cardiac valves, and give detailed information about the regional contractility, blood flow and viability of myocard. MR is capable of giving important and to some extent unique contributions to heart diseases, both congenital and contracted heart disease. Because of failing availability and competence MR is still little used in cardiological diagnostics, but the method undoubtedly has the potential to play a very important role in the future

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

Directory of Open Access Journals (Sweden)

Li Jun-Bao

2017-06-01

Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Compact data structure and scalable algorithms for the sparse grid technique

KAUST Repository

Murarasu, Alin

2011-01-01

The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs. Copyright © 2011 ACM.

Multisnapshot Sparse Bayesian Learning for DOA

DEFF Research Database (Denmark)

Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki

2016-01-01

The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...

Continuous speech recognition with sparse coding

CSIR Research Space (South Africa)

Smit, WJ

2009-04-01

Full Text Available generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process...

Study in the plasma with non-equilibrium ionization state by relative intensities in K-spectra of multicharged ions

International Nuclear Information System (INIS)

Bojko, V.A.; Skobelev, I.Yu.; Faenov, A.Ya.

1984-01-01

The pressure of the K-spectra formation of multicharge h-, He-, Li-like ions in a plasma with an arbitrary ionization state are considered. It is shown that comparison of experimental and theoretical data on the intensities of f a number of spectral lines belonging to such ions allows one to determine both the plasma electron temperature and ion distribution versus the ionization degre ees. The proposed method of plasma diagnostics is used for measuring parameters of the expanding laser-produced magnesium plasme

A density functional for sparse matter

DEFF Research Database (Denmark)

Langreth, D.C.; Lundqvist, Bengt; Chakarova-Kack, S.D.

2009-01-01

forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators......Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional...... theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed...

Sparse learning of stochastic dynamical equations

Science.gov (United States)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

A novel method to design sparse linear arrays for ultrasonic phased array.

Science.gov (United States)

Yang, Ping; Chen, Bin; Shi, Ke-Ren

2006-12-22

In ultrasonic phased array testing, a sparse array can increase the resolution by enlarging the aperture without adding system complexity. Designing a sparse array involves choosing the best or a better configuration from a large number of candidate arrays. We firstly designed sparse arrays by using a genetic algorithm, but found that the arrays have poor performance and poor consistency. So, a method based on the Minimum Redundancy Linear Array was then adopted. Some elements are determined by the minimum-redundancy array firstly in order to ensure spatial resolution and then a genetic algorithm is used to optimize the remaining elements. Sparse arrays designed by this method have much better performance and consistency compared to the arrays designed only by a genetic algorithm. Both simulation and experiment confirm the effectiveness.

Multiple instance learning tracking method with local sparse representation

KAUST Repository

Xie, Chengjun

2013-10-01

When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

The 1989 progress report: Physics of the Ionized Media

International Nuclear Information System (INIS)

Gresillon, D.; Virmont, J.

1989-01-01

The 1989 progress report of the laboratory of Physics of the Ionized Media of the Polytechnic School (France) is presented. The research projects were carried out in the following fields: plasma waves localization, wave beatings, collective scattering, fluctuation and transport in magnetic fusion plasmas, the construction of ALTAIR (French acronym for local analysis of anomalous transport by infrared), sources of negative ion beams, z-pinch and laser plasma diagnostics, computer codes on plasma dynamics. The published papers, the conferences and the Laboratory staff are listed [fr

Identifying and managing the risks of medical ionizing radiation in endourology.

Science.gov (United States)

Yecies, Todd; Averch, Timothy D; Semins, Michelle J

2018-02-01

The risks of exposure to medical ionizing radiation is of increasing concern both among medical professionals and the general public. Patients with nephrolithiasis are exposed to high levels of ionizing radiation through both diagnostic and therapeutic modalities. Endourologists who perform a high-volume of fluoroscopy guided procedures are also exposed to significant quantities of ionizing radiation. The combination of judicious use of radiation-based imaging modalities, application of new imaging techniques such as ultra-low dose computed tomography (CT) scan, and modifying use of current technology such as increasing ultrasound and pulsed fluoroscopy utilization offers the possibility of significantly reducing radiation exposure. We present a review of the literature regarding the risks of medical ionizing radiation to patients and surgeons as it pertains to the field of endourology and interventions that can be performed to limit this exposure. A review of the current state of the literature was performed using MEDLINE and PubMed. Interventions designed to limit patient and surgeon radiation exposure were identified and analyzed. Summaries of the data were compiled and synthesized in the body of the text. While no level 1 evidence exists demonstrating the risk of secondary malignancy with radiation exposure, the preponderance of evidence suggests a dose and age dependent increase in malignancy risk from ionizing radiation. Patients with nephrolithiasis were exposed to an average effective dose of 37mSv over a 2 year period. Multiple evidence-based interventions to limit patient and surgeon radiation exposure and associated risk were identified. Current evidence suggest an age and dose dependent risk of secondary malignancy from ionizing radiation. Urologists must act in accordance with ALARA principles to safely manage nephrolithiasis while minimizing radiation exposure.

Constancy check of beam quality in conventional diagnostic X-ray equipment

International Nuclear Information System (INIS)

Costa, Alessandro M.; Badin, Romulo S.; Leite, Marina S.; Caldas, Linda V.E.

2008-01-01

A tandem ionization chamber was developed for quality control programs of X-ray equipment used in conventional radiography and mammography. A methodology for the use of the tandem chamber in the constancy check of diagnostic X-ray beam qualities was established. The application at a medical X-ray imaging facility of this established methodology is presented. The use of the tandem chamber in the constancy check of diagnostic X-ray beam qualities is a useful method to control the performance of the X-ray equipment

Sparse linear models: Variational approximate inference and Bayesian experimental design

International Nuclear Information System (INIS)

Seeger, Matthias W

2009-01-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Sparse linear models: Variational approximate inference and Bayesian experimental design

Energy Technology Data Exchange (ETDEWEB)

Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

2009-12-01

A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

Discriminative object tracking via sparse representation and online dictionary learning.

Science.gov (United States)

Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

2014-04-01

We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

High speed manyframe optical methods for plasma diagnostics

International Nuclear Information System (INIS)

Erokhin, A.A.; Shikanov, A.S.; Sklizkov, G.V.; Zakharenkov, Yu.A.; Zorev, N.N.

1979-01-01

A complex of active optical plasma and strong ionized shock wave diagnostics is described. The complex consisted of a specially developed high speed manyframe systems of shadow, schlieren and interferometric photography. The comparison of results obtained by a simultaneous registration of investigated object by means of different optical methods allowed us to determine optimal employment range for the methods. The sensitivity, temporal and space resolution of each optical method under conditions of high probe radiation refraction are discussed. The application boundaries of these methods for ionized shock wave investigation were found to depend on the shock wave front width. The methods described were used for the study of laser-produced plasma phenomena, occuring in the experiments on powerful nine-channel laser installation ''Kalmar''. (author)

Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation

KAUST Repository

Murarasu, Alin; Weidendorfer, Josef

2012-01-01

bring a substantial contribution to the speedup. By identifying common patterns in the input data, we propose new algorithms for sparse grid interpolation that accelerate the state-of-the-art non-specialized version. Sparse grid interpolation

Jointly-check iterative decoding algorithm for quantum sparse graph codes

International Nuclear Information System (INIS)

Jun-Hu, Shao; Bao-Ming, Bai; Wei, Lin; Lin, Zhou

2010-01-01

For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with a standard belief-propagation (BP) algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms the standard BP algorithm with an obvious performance improvement. (general)

Rotational image deblurring with sparse matrices

DEFF Research Database (Denmark)

Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos

2014-01-01

We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...

Normalization for sparse encoding of odors by a wide-field interneuron.

Science.gov (United States)

Papadopoulou, Maria; Cassenaer, Stijn; Nowotny, Thomas; Laurent, Gilles

2011-05-06

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Sparse Representation Denoising for Radar High Resolution Range Profiling

Directory of Open Access Journals (Sweden)

Min Li

2014-01-01

Full Text Available Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

The Real-Valued Sparse Direction of Arrival (DOA Estimation Based on the Khatri-Rao Product

Directory of Open Access Journals (Sweden)

Tao Chen

2016-05-01

Full Text Available There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR product called the L1-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV. The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

Surface-ionization field mass-spectrometry studies of nonequilibrium surface ionization

International Nuclear Information System (INIS)

Blashenkov, Nikolai M; Lavrent'ev, Gennadii Ya

2007-01-01

The ionization of polyatomic molecules on tungsten and tungsten oxide surfaces is considered for quasiequilibrium or essentially nonequilibrium conditions (in the latter case, the term nonequilibrium surface ionization is used for adsorbate ionization). Heterogeneous reactions are supposed to proceed through monomolecular decay of polyatomic molecules or fragments of multimolecular complexes. The nonequilibrium nature of these reactions is established. The dependences of the current density of disordered ions on the surface temperature, electric field strength, and ionized particle energy distribution are obtained in analytical form. Heterogeneous dissociation energies, the ionization potentials of radicals, and the magnitude of reaction departure from equilibrium are determined from experimental data, as are energy exchange times between reaction products and surfaces, the number of molecules in molecular complexes, and the number of effective degrees of freedom in molecules and complexes. In collecting the data a new technique relying on surface-ionization field mass-spectrometry was applied. (instruments and methods of investigation)

Integrative analysis of multiple diverse omics datasets by sparse group multitask regression

Directory of Open Access Journals (Sweden)

Dongdong eLin

2014-10-01

Full Text Available A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: 1 treat the biomarker identification in each single study as a task and then combine them by multitask learning; 2 group variables from all studies for identifying significant genes; 3 enforce sparse constraint on groups of variables to overcome the ‘small sample, but large variables’ problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

Science.gov (United States)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems

OpenAIRE

Bethune, Iain; Gloess, Andeas; Hutter, Juerg; Lazzaro, Alfio; Pabst, Hans; Reid, Fiona

2017-01-01

Multiplication of two sparse matrices is a key operation in the simulation of the electronic structure of systems containing thousands of atoms and electrons. The highly optimized sparse linear algebra library DBCSR (Distributed Block Compressed Sparse Row) has been specifically designed to efficiently perform such sparse matrix-matrix multiplications. This library is the basic building block for linear scaling electronic structure theory and low scaling correlated methods in CP2K. It is para...

Fast Solution in Sparse LDA for Binary Classification

Science.gov (United States)

Moghaddam, Baback

2010-01-01

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic

Ionization balance for Ti and Cr ions: effects of uncertainty in dielectronic recombination rate

International Nuclear Information System (INIS)

Seon, Kwang-Il; Nam, Uk-Won; Park, Il H

2003-01-01

The available electron-impact ionization cross sections for Ti and Cr ions are reviewed, and calculations of the ionization balance for the ions under coronal equilibrium are presented. The calculated ionic abundance fractions are compared with those of previous works. The effects of modelling uncertainty in dielectronic recombination on isoelectronic line ratios, which are formed using the same spectral line from two elements of slightly different atomic numbers, are discussed concentrating on high temperature ranges. Also discussed are the effects of modelling uncertainty on inter-ionization stage line ratios formed from adjacent ionization stages. It is demonstrated that the modelling uncertainty in dielectronic recombination tends to cancel out only when the isoelectronic line ratio of He-like ions is considered, and that the sensitivity of the isoelectronic line ratios to the modelling uncertainty tends to increase for less ionized stages. It is also found that the interstage line ratios are less sensitive to the typical ∼20% uncertainties of dielectronic rates than the isoelectronic line ratios, and that the interstage line ratio of He-to Li-like ions in Ti and Cr plasmas is a better choice for a temperature diagnostic in the temperature ranges from ∼0.6 to ∼1.5 keV in which Li-like ions have maximum ionic abundances

Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Chunhui Zhao

2015-09-01

Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

Robust visual tracking via multiscale deep sparse networks

Science.gov (United States)

Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo

2017-04-01

In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

Efficient MATLAB computations with sparse and factored tensors.

Energy Technology Data Exchange (ETDEWEB)

Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)

2006-12-01

In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.

Subspace Based Blind Sparse Channel Estimation

DEFF Research Database (Denmark)

Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

2012-01-01

The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

Stability of special ionizing chambers for using in programs of quality control in radiotherapy and radiodiagnostic

International Nuclear Information System (INIS)

Afonso, Luciana C.; Caldas, Linda V.E.; Costa, Alessandro M. da

2004-01-01

In this work the response stability of two special parallel-plate ionization chambers, developed at the Calibration Laboratory of IPEN, were tested. The chambers are face doubled, with internal collecting electrodes of different materials (graphite and aluminium), in tandem system, and with air volumes of 0.6 cm 3 and 2.5 cm 3 , for radiotherapy and diagnostic radiology levels, respectively. The results showed that the chambers kept constant their metrological characteristics presenting their usefulness for quality control programs in radiotherapy and diagnostic radiology. (author)

Sparse grid techniques for particle-in-cell schemes

Science.gov (United States)

Ricketson, L. F.; Cerfon, A. J.

2017-02-01

We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

Feature selection and multi-kernel learning for sparse representation on a manifold

KAUST Repository

Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

2014-01-01

combination of some basic items in a dictionary. Gao etal. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity

Group sparse canonical correlation analysis for genomic data integration.

Science.gov (United States)

Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping

2013-08-12

The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

Information filtering in sparse online systems: recommendation via semi-local diffusion.

Science.gov (United States)

Zeng, Wei; Zeng, An; Shang, Ming-Sheng; Zhang, Yi-Cheng

2013-01-01

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.

Codesign of Beam Pattern and Sparse Frequency Waveforms for MIMO Radar

Directory of Open Access Journals (Sweden)

Chaoyun Mai

2015-01-01

Full Text Available Multiple-input multiple-output (MIMO radar takes the advantages of high degrees of freedom for beam pattern design and waveform optimization, because each antenna in centralized MIMO radar system can transmit different signal waveforms. When continuous band is divided into several pieces, sparse frequency radar waveforms play an important role due to the special pattern of the sparse spectrum. In this paper, we start from the covariance matrix of the transmitted waveform and extend the concept of sparse frequency design to the study of MIMO radar beam pattern. With this idea in mind, we first solve the problem of semidefinite constraint by optimization tools and get the desired covariance matrix of the ideal beam pattern. Then, we use the acquired covariance matrix and generalize the objective function by adding the constraint of both constant modulus of the signals and corresponding spectrum. Finally, we solve the objective function by the cyclic algorithm and obtain the sparse frequency MIMO radar waveforms with desired beam pattern. The simulation results verify the effectiveness of this method.

High-SNR spectrum measurement based on Hadamard encoding and sparse reconstruction

Science.gov (United States)

Wang, Zhaoxin; Yue, Jiang; Han, Jing; Li, Long; Jin, Yong; Gao, Yuan; Li, Baoming

2017-12-01

The denoising capabilities of the H-matrix and cyclic S-matrix based on the sparse reconstruction, employed in the Pixel of Focal Plane Coded Visible Spectrometer for spectrum measurement are investigated, where the spectrum is sparse in a known basis. In the measurement process, the digital micromirror device plays an important role, which implements the Hadamard coding. In contrast with Hadamard transform spectrometry, based on the shift invariability, this spectrometer may have the advantage of a high efficiency. Simulations and experiments show that the nonlinear solution with a sparse reconstruction has a better signal-to-noise ratio than the linear solution and the H-matrix outperforms the cyclic S-matrix whether the reconstruction method is nonlinear or linear.

Comparison of sparse point distribution models

DEFF Research Database (Denmark)

Erbou, Søren Gylling Hemmingsen; Vester-Christensen, Martin; Larsen, Rasmus

2010-01-01

This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior m...

Microplasma discharge vacuum ultraviolet photoionization source for atmospheric pressure ionization mass spectrometry.

Science.gov (United States)

Symonds, Joshua M; Gann, Reuben N; Fernández, Facundo M; Orlando, Thomas M

2014-09-01

In this paper, we demonstrate the first use of an atmospheric pressure microplasma-based vacuum ultraviolet (VUV) photoionization source in atmospheric pressure mass spectrometry applications. The device is a robust, easy-to-operate microhollow cathode discharge (MHCD) that enables generation of VUV photons from Ne and Ne/H(2) gas mixtures. Photons were detected by excitation of a microchannel plate detector and by analysis of diagnostic sample ions using a mass spectrometer. Reactive ions, charged particles, and metastables produced in the discharge were blocked from entering the ionization region by means of a lithium fluoride window, and photoionization was performed in a nitrogen-purged environment. By reducing the output pressure of the MHCD, we observed heightened production of higher-energy photons, making the photoionization source more effective. The initial performance of the MHCD VUV source has been evaluated by ionizing model analytes such as acetone, azulene, benzene, dimethylaniline, and glycine, which were introduced in solid or liquid phase. These molecules represent species with both high and low proton affinities, and ionization energies ranging from 7.12 to 9.7 eV.

Galaxy redshift surveys with sparse sampling

International Nuclear Information System (INIS)

Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.

2013-01-01

Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V survey ∼ 10Gpc 3 ) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V survey , we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V survey (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V survey (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

Science.gov (United States)

Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

2015-12-01

Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

Uniform sparse bounds for discrete quadratic phase Hilbert transforms

Science.gov (United States)

Kesler, Robert; Arias, Darío Mena

2017-09-01

For each α \\in T consider the discrete quadratic phase Hilbert transform acting on finitely supported functions f : Z → C according to H^{α }f(n):= \\sum _{m ≠ 0} e^{iα m^2} f(n - m)/m. We prove that, uniformly in α \\in T , there is a sparse bound for the bilinear form for every pair of finitely supported functions f,g : Z→ C . The sparse bound implies several mapping properties such as weighted inequalities in an intersection of Muckenhoupt and reverse Hölder classes.

Sparse Matrix for ECG Identification with Two-Lead Features

Directory of Open Access Journals (Sweden)

Kuo-Kun Tseng

2015-01-01

Full Text Available Electrocardiograph (ECG human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

MICROWAVE INTERACTIONS WITH INHOMOGENEOUS PARTIALLY IONIZED PLASMA

Energy Technology Data Exchange (ETDEWEB)

Kritz, A. H.

1962-11-15

Microwave interactions with inhomogeneous plasmas are often studied by employing a simplified electromagnetic approach, i.e., by representing the effects of the plasma by an effective dielectric coefficient. The problems and approximations associated with this procedure are discussed. The equation describing the microwave field in an inhomogeneous partially ionized plasma is derived, and the method that is applied to obtain the reflected, transmitted, and absorbed intensities in inhomogeneous plasmas is presented. The interactions of microwaves with plasmas having Gaussian electron density profiles are considered. The variation of collision frequency with position is usually neglected. In general, the assumption of constant collision frequency is not justified; e.g., for a highly ionized plasma, the electron density profile determines, in part, the profile of the electron-ion collision frequency. The effect of the variation of the collision frequency profile on the interaction of microwaves with inhomogeneous plasmas is studied in order to obtain an estimate of the degree of error that may result when constant collision frequency is assumed instead of a more realistic collision frequency profile. It is shown that the degree of error is of particular importance when microwave analysis is used as a plasma diagnostic. (auth)

A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms

KAUST Repository

Buse, Gerrit

2012-06-01

The name sparse grids denotes a highly space-efficient, grid-based numerical technique to approximate high-dimensional functions. Although employed in a broad spectrum of applications from different fields, there have only been few tries to use it in real time visualization (e.g. [1]), due to complex data structures and long algorithm runtime. In this work we present a novel approach inspired by principles of I/0-efficient algorithms. Locally applied coefficient permutations lead to improved cache performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations on modern multi-core systems by a factor of 37 for a grid size of 127 million points. For larger problems the speedup is even increasing, and with execution times below 1 s, sparse grids are well-suited for visualization applications. Furthermore, we point out how a broad class of sparse grid algorithms can benefit from our approach. © 2012 IEEE.

Ionization

International Nuclear Information System (INIS)

2002-01-01

This document reprints the text of the French by-law from January 8, 2002 relative to the approval and to the controls and verifications of facilities devoted to the ionizing of food products for human beings and animals. The by-law imposes the operators of such facilities to perform measurements and dosimetric verifications all along the ionization process. (J.S.)

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

Science.gov (United States)

Jakeman, J. D.; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

International Nuclear Information System (INIS)

Jakeman, J.D.; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchical surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation

Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

Science.gov (United States)

Vishnukumar, S.; Wilscy, M.

2017-12-01

In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

Ionizing radiation

International Nuclear Information System (INIS)

Kruger, J.

1989-01-01

Ionizing radiation results in biological damage that differs from other hazardous substances and is highly dangerous to man. Ionizing radiation cannot be perceived by man's sense organs and the biological damage cannot be detected immediately afterwards (except in very high doses). Every human being is exposed to low doses of radiation. The structure of the atom; sources of ionizing radiation; radiation units; biological effects; norms for radiation protection; and the national control in South Africa are discussed. 1 fig., 5 refs

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics

Science.gov (United States)

Will, Sebastian; Otto, Christina; Miladi, Milad; Möhl, Mathias; Backofen, Rolf

2015-01-01

Motivation: RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of O(n6). Subsequently, numerous faster ‘Sankoff-style’ approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity (≥ quartic time). Results: Breaking this barrier, we introduce the novel Sankoff-style algorithm ‘sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)’, which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff’s original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics. Availability and implementation: SPARSE is freely available at http://www.bioinf.uni-freiburg.de/Software/SPARSE. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25838465

Radiation doses from medical diagnostic procedures in Canada

Energy Technology Data Exchange (ETDEWEB)

Aldrich, J E; Lentle, B C; Vo, C [British Columbia Univ., Vancouver, BC (Canada). Dept. of Radiology

1997-03-01

This document sets out to record and analyze the doses incurred in Canada from medical procedures involving the use of ionizing radiation in a typical year. Excluded are those doses incurred during therapeutic irradiation, since they differ in scale to such a large degree and because they are used almost exclusively in treating cancer. In this we are following a precedent set by the United Nations Scientific Committee on the Effects of Ionizing Radiation. Although the International Commission on Radiological Protection (ICRP) notes that dose limits should not be applied to medical exposures, it also observes that doses in different settings for the same procedure may vary by as much as two orders of magnitude, and that there are considerable opportunities for dose reductions in diagnostic radiology. Because these data do not stand in isolation the report also encompasses a review of the relevant literature and some background comment on the evolving technology of the radiological sciences. Because there is a somewhat incomplete perception of the changes taking place in diagnostic methods we have also provided some introductory explanations of the relevant technologies. In addition, there is an analysis of at least some of the limitations on the completeness of the data which are reported here. (author).

Radiation doses from medical diagnostic procedures in Canada

International Nuclear Information System (INIS)

Aldrich, J.E.; Lentle, B.C.; Vo, C.

1997-03-01

This document sets out to record and analyze the doses incurred in Canada from medical procedures involving the use of ionizing radiation in a typical year. Excluded are those doses incurred during therapeutic irradiation, since they differ in scale to such a large degree and because they are used almost exclusively in treating cancer. In this we are following a precedent set by the United Nations Scientific Committee on the Effects of Ionizing Radiation. Although the International Commission on Radiological Protection (ICRP) notes that dose limits should not be applied to medical exposures, it also observes that doses in different settings for the same procedure may vary by as much as two orders of magnitude, and that there are considerable opportunities for dose reductions in diagnostic radiology. Because these data do not stand in isolation the report also encompasses a review of the relevant literature and some background comment on the evolving technology of the radiological sciences. Because there is a somewhat incomplete perception of the changes taking place in diagnostic methods we have also provided some introductory explanations of the relevant technologies. In addition, there is an analysis of at least some of the limitations on the completeness of the data which are reported here. (author)

Electron ionization and the Compton effect in double ionization of helium

International Nuclear Information System (INIS)

Samson, J.

1994-01-01

The author discusses ionization phenomena in helium, both photoionization and electron ionization. In particular he compares double ionization cross sections with total cross sections, as a function of electron energy, and photon energy. Data is discussed over the energy range up to 10 keV

Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery

Science.gov (United States)

Kim, Daeun; Haldar, Justin P.

2016-01-01

This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368

SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2015-01-01

minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two

Multiuser TOA Estimation Algorithm in DS-CDMA Sparse Channel for Radiolocation

Science.gov (United States)

Kim, Sunwoo

This letter considers multiuser time delay estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) algorithm is considered, and the continuous time delay of each path is estimated without requiring a priori known data sequences.

Effects of sparse sampling schemes on image quality in low-dose CT

International Nuclear Information System (INIS)

Abbas, Sajid; Lee, Taewon; Cho, Seungryong; Shin, Sukyoung; Lee, Rena

2013-01-01

Purpose: Various scanning methods and image reconstruction algorithms are actively investigated for low-dose computed tomography (CT) that can potentially reduce a health-risk related to radiation dose. Particularly, compressive-sensing (CS) based algorithms have been successfully developed for reconstructing images from sparsely sampled data. Although these algorithms have shown promises in low-dose CT, it has not been studied how sparse sampling schemes affect image quality in CS-based image reconstruction. In this work, the authors present several sparse-sampling schemes for low-dose CT, quantitatively analyze their data property, and compare effects of the sampling schemes on the image quality.Methods: Data properties of several sampling schemes are analyzed with respect to the CS-based image reconstruction using two measures: sampling density and data incoherence. The authors present five different sparse sampling schemes, and simulated those schemes to achieve a targeted dose reduction. Dose reduction factors of about 75% and 87.5%, compared to a conventional scan, were tested. A fully sampled circular cone-beam CT data set was used as a reference, and sparse sampling has been realized numerically based on the CBCT data.Results: It is found that both sampling density and data incoherence affect the image quality in the CS-based reconstruction. Among the sampling schemes the authors investigated, the sparse-view, many-view undersampling (MVUS)-fine, and MVUS-moving cases have shown promising results. These sampling schemes produced images with similar image quality compared to the reference image and their structure similarity index values were higher than 0.92 in the mouse head scan with 75% dose reduction.Conclusions: The authors found that in CS-based image reconstructions both sampling density and data incoherence affect the image quality, and suggest that a sampling scheme should be devised and optimized by use of these indicators. With this strategic

Pulse-Width-Modulation of Neutral-Point-Clamped Sparse Matrix Converter

DEFF Research Database (Denmark)

Loh, P.C.; Blaabjerg, Frede; Gao, F.

2007-01-01

input current and output voltage can be achieved with minimized rectification switching loss, rendering the sparse matrix converter as a competitive choice for interfacing the utility grid to (e.g.) defense facilities that require a different frequency supply. As an improvement, sparse matrix converter...... with improved waveform quality. Performances and practicalities of the designed schemes are verified in simulation and experimentally using an implemented laboratory prototype with some representative results captured and presented in the paper....

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

Directory of Open Access Journals (Sweden)

Jacquelyn A Shelton

Full Text Available Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule, the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (nonlinear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Efficient sparse matrix-matrix multiplication for computing periodic responses by shooting method on Intel Xeon Phi

Science.gov (United States)

Stoykov, S.; Atanassov, E.; Margenov, S.

2016-10-01

Many of the scientific applications involve sparse or dense matrix operations, such as solving linear systems, matrix-matrix products, eigensolvers, etc. In what concerns structural nonlinear dynamics, the computations of periodic responses and the determination of stability of the solution are of primary interest. Shooting method iswidely used for obtaining periodic responses of nonlinear systems. The method involves simultaneously operations with sparse and dense matrices. One of the computationally expensive operations in the method is multiplication of sparse by dense matrices. In the current work, a new algorithm for sparse matrix by dense matrix products is presented. The algorithm takes into account the structure of the sparse matrix, which is obtained by space discretization of the nonlinear Mindlin's plate equation of motion by the finite element method. The algorithm is developed to use the vector engine of Intel Xeon Phi coprocessors. It is compared with the standard sparse matrix by dense matrix algorithm and the one developed by Intel MKL and it is shown that by considering the properties of the sparse matrix better algorithms can be developed.

Compressed sensing & sparse filtering

CERN Document Server

Carmi, Avishy Y; Godsill, Simon J

2013-01-01

This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related app

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Science.gov (United States)

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Diagnostic imaging procedures during pregnancy: what are the fetal risks?

International Nuclear Information System (INIS)

Taylor, K.

2008-01-01

An important facet of health care is the counsel of patients seeking a better understanding of their medical treatment. One of the most challenging scenarios is the management of female patients exposed to ionizing radiation while pregnant. It requires careful consideration of both maternal benefit and fetal risk. Given the increased frequency of diagnostic examinations involving ionizing radiation, this situation has become commonplace. This paper reviews current literature discussing the risk associated with prenatal exposure to ionizing radiation. The fetal dose received during common radiological procedures is reported in order to emphasize that these doses do not exceed threshold levels for deterministic effects. The definitive cancer risk associated with radiation exposure in utero has yet to be established. This paper will also show that physicians who deal with pregnant women are generally uninformed or misinformed of the doses and risks associated with the exams that they prescribe. This lack of information could be leading to inappropriate advice and actions with respect to patient care. (author)

Diagnostic imaging procedures during pregnancy: what are the fetal risks?

Energy Technology Data Exchange (ETDEWEB)

Taylor, K. [McMaster Univ., Hamilton, Ontario (Canada)

2008-07-01

An important facet of health care is the counsel of patients seeking a better understanding of their medical treatment. One of the most challenging scenarios is the management of female patients exposed to ionizing radiation while pregnant. It requires careful consideration of both maternal benefit and fetal risk. Given the increased frequency of diagnostic examinations involving ionizing radiation, this situation has become commonplace. This paper reviews current literature discussing the risk associated with prenatal exposure to ionizing radiation. The fetal dose received during common radiological procedures is reported in order to emphasize that these doses do not exceed threshold levels for deterministic effects. The definitive cancer risk associated with radiation exposure in utero has yet to be established. This paper will also show that physicians who deal with pregnant women are generally uninformed or misinformed of the doses and risks associated with the exams that they prescribe. This lack of information could be leading to inappropriate advice and actions with respect to patient care. (author)

Dose-shaping using targeted sparse optimization

International Nuclear Information System (INIS)

Sayre, George A.; Ruan, Dan

2013-01-01

Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E tot sparse ), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L 1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot sparse improves

Dose-shaping using targeted sparse optimization.

Science.gov (United States)

Sayre, George A; Ruan, Dan

2013-07-01

Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method. In designing the energy minimization objective (E tot (sparse)), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot (sparse) improves tradeoff between

Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

Science.gov (United States)

Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

Ionization detector

International Nuclear Information System (INIS)

Solomon, E.E.

1980-01-01

A safe and reliable apparatus for detecting products of combustion and aerosols in the atmosphere was developed which uses a beta source. It is easy to adjust for optimum performance. The ionization detector comprises a double chamber; one of the chambers is the basic sensing chamber. The sensing chamber is ported to both the secondary chambers to account for slow ambient changes in the atmosphere outside of the chamber. The voltages from the ionization chamber are adjusted with electrodes in each chamber. The ionization chamber contains baffles to direct the air to be sensed as well as an electrostatic screen. A unique electronic circuit provides an inexpensive and reliable means for detecting the signal change which occurs in the ionization chamber. The decision level of the alarm circuit can be adjusted to allow for any desired sensitivity. (D.N.)

Feature based omnidirectional sparse visual path following

OpenAIRE

Goedemé, Toon; Tuytelaars, Tinne; Van Gool, Luc; Vanacker, Gerolf; Nuttin, Marnix

2005-01-01

Goedemé T., Tuytelaars T., Van Gool L., Vanacker G., Nuttin M., ''Feature based omnidirectional sparse visual path following'', Proceedings IEEE/RSJ international conference on intelligent robots and systems - IROS2005, pp. 1003-1008, August 2-6, 2005, Edmonton, Alberta, Canada.

A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms

KAUST Repository

Buse, Gerrit; Pfluger, Dirk; Murarasu, Alin; Jacob, Riko

2012-01-01

performance and facilitate the use of vector registers for our sparse grid benchmark problem hierarchization. Based on the compact data structure proposed for regular sparse grids in [2], we developed a new algorithm that outperforms existing implementations

Education and training in radiological protection for diagnostic and interventional procedures ICRP 113 in brief

International Nuclear Information System (INIS)

Salama, S.; Gomaa, M. A.; Alshoufi, J.H.

2013-01-01

The international commission on radiological protection (ICRP) is the primary body in protection against ionizing radiation. Among its latest publication is ICRP publication 113 e ducation and training in radiological protection for diagnostic and interventional procedures . This document introduces diagnostic and interventional medical procedures using ionizing radiations in deep details. The document is approved by the commission in October 2010 and translated into Arabic at December 2011. This work is a continuation of the efforts series to translate some of the most important of the radiological protection references into the Arabic; aiming to maximize the benefit. The previous translation include WHO handbook on indoor radon: a public health perspective, issued by world health organization 2009 and Radiation Protection in Medicine, ICRP Publication 105 2007 that translated into Arabic with support of Arab atomic energy authority at 2011.

New methods for sampling sparse populations

Science.gov (United States)

Anna Ringvall

2007-01-01

To improve surveys of sparse objects, methods that use auxiliary information have been suggested. Guided transect sampling uses prior information, e.g., from aerial photographs, for the layout of survey strips. Instead of being laid out straight, the strips will wind between potentially more interesting areas. 3P sampling (probability proportional to prediction) uses...

Biodosimetry of ionizing radiation by selective painting of prematurely condensed chromosomes in human lymphocytes

Science.gov (United States)

Durante, M.; George, K.; Yang, T. C.

1997-01-01

Painting of interphase chromosomes can be useful for biodosimetric purposes in particular cases such as radiation therapy, accidental exposure to very high radiation doses and exposure to densely ionizing radiation, for example during space missions. Biodosimetry of charged-particle radiation is analyzed in the present paper. Target cells were human peripheral blood lymphocytes irradiated in vitro with gamma rays, protons and iron ions. After exposure, lymphocytes were incubated for different times to allow repair of radiation-induced damage and then fused to mitotic hamster cells to promote premature condensation in the interphase chromosomes. Chromosome spreads were then hybridized with whole-chromosome DNA probes labeled with fluorescent stains. Dose-response curves for the induction of chromatin fragments shortly after exposure, as well as the kinetics of rejoining and misrejoining, were not markedly dependent on linear energy transfer. However, after exposure to heavy ions, more aberrations were scored in the interphase cells after incubation for repair than in metaphase samples harvested at the first postirradiation mitosis. On the other hand, no significant differences were observed in the two samples after exposure to sparsely ionizing radiation. These results suggest that interphase chromosome painting can be a useful tool for biodosimetry of particle radiation.

Balanced and sparse Tamo-Barg codes

KAUST Repository

Halbawi, Wael; Duursma, Iwan; Dau, Hoang; Hassibi, Babak

2017-01-01

We construct balanced and sparse generator matrices for Tamo and Barg's Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

Balanced and sparse Tamo-Barg codes

KAUST Repository

Halbawi, Wael

2017-08-29

We construct balanced and sparse generator matrices for Tamo and Barg\\'s Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

Characteristic parameters analysis on diagnostic X-ray beams for dosemeter calibration

International Nuclear Information System (INIS)

Oliveira, Paulo Marcio Campos de

2008-01-01

Ionizing radiation metrology is the base to achieve reliable dose measurements in ali areas; it is also part of the framework that is established to assure radiation protection procedures in order to avoid or minimize the harmful biological effect that may be caused by ionizing radiation. A well done metrology means the use of reliable instruments that comply with standard performance requirements worldwide accepted. Those instruments are expected to be calibrated by Metrology Laboratories under well defined conditions. The International Electrotechnical Commission (IEC) in Standard 61267 established the reference radiations for medical diagnostic x-ray equipment that are recommended to be used for calibrating dosimetric systems for diagnostic dosimetry. In this work, X-ray beam qualities were established in a Calibration Laboratory and their characteristics were analyzed through the measurement of beam parameters like inherent tube filtration, beam uniformity and field size, energy spectra and peak voltage for additional filtration with 94.425 por cent and 99.999 por cent purity filters. Also, the first half-value layer and the homogeneity coefficient were measured for the three RQR 2, RQR 6 and RQR 10 IEC beam qualities and they were analyzed according to the IEC standard. Air-kerma measurements were carried out with an ionization chamber that had its reliability confirmed through repetition and reproducibility reading tests. In 50 sets of measurements the maximum standard deviation found of 10 successive readings was 0.19 %; the maximum shift of the reading mean value at a fixed geometry condition was 0.80 % with an overall standard deviation of 0.23 %. Results showed that the use of different purity filters did not cause a relevant influence on the beam energy spectra. An ionization chamber was also calibrated against a standard dosimeter in ali implemented reference radiations and the relevant sources of uncertainties were estimated. Calibration could be done

The application of sparse linear prediction dictionary to compressive sensing in speech signals

Directory of Open Access Journals (Sweden)

YOU Hanxu

2016-04-01

Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.

Ionizing radiation in environment

International Nuclear Information System (INIS)

Jandl, J.; Petr, I.

1988-01-01

The basic terms are explained such as the atom, radioactivity, nuclear reaction, interaction of ionizing radiation with matter, etc. The basic dosimetric variables and units and properties of radionuclides and ionizing radiation are given. Natural and artificial sources of ionizing radiation are discussed with regard to the environment and the propagation and migration of radionuclides is described in the environment to man. The impact is explained of ionizing radiation on the cell and the somatic and genetic effects of radiation on man are outlined. Attention is devoted to protection against ionizing radiation and to radiation limits, also to the detection, dosimetry and monitoring of ionizing radiation in the environment. (M.D.). 92 figs., 40 tabs. 74 refs

Mutation rules and the evolution of sparseness and modularity in biological systems.

Directory of Open Access Journals (Sweden)

Tamar Friedlander

Full Text Available Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules with distinct functionality. Recent work suggests that modularity can evolve in a variety of circumstances, including goals that vary in time such that they share the same subgoals (modularly varying goals, or when connections are costly. Here, we studied the origin of modularity and sparseness focusing on the nature of the mutation process, rather than on connection cost or variations in the goal. We use simulations of evolution with different mutation rules. We found that commonly used sum-rule mutations, in which interactions are mutated by adding random numbers, do not lead to modularity or sparseness except for in special situations. In contrast, product-rule mutations in which interactions are mutated by multiplying by random numbers--a better model for the effects of biological mutations--led to sparseness naturally. When the goals of evolution are modular, in the sense that specific groups of inputs affect specific groups of outputs, product-rule mutations also lead to modular structure; sum-rule mutations do not. Product-rule mutations generate sparseness and modularity because they tend to reduce interactions, and to keep small interaction terms small.

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

Science.gov (United States)

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Characterization and Simulation of a New Design Parallel-Plate Ionization Chamber for CT Dosimetry at Calibration Laboratories

Science.gov (United States)

Perini, Ana P.; Neves, Lucio P.; Maia, Ana F.; Caldas, Linda V. E.

2013-12-01

In this work, a new extended-length parallel-plate ionization chamber was tested in the standard radiation qualities for computed tomography established according to the half-value layers defined at the IEC 61267 standard, at the Calibration Laboratory of the Instituto de Pesquisas Energéticas e Nucleares (IPEN). The experimental characterization was made following the IEC 61674 standard recommendations. The experimental results obtained with the ionization chamber studied in this work were compared to those obtained with a commercial pencil ionization chamber, showing a good agreement. With the use of the PENELOPE Monte Carlo code, simulations were undertaken to evaluate the influence of the cables, insulator, PMMA body, collecting electrode, guard ring, screws, as well as different materials and geometrical arrangements, on the energy deposited on the ionization chamber sensitive volume. The maximum influence observed was 13.3% for the collecting electrode, and regarding the use of different materials and design, the substitutions showed that the original project presented the most suitable configuration. The experimental and simulated results obtained in this work show that this ionization chamber has appropriate characteristics to be used at calibration laboratories, for dosimetry in standard computed tomography and diagnostic radiology quality beams.

The Roles of Sparse Direct Methods in Large-scale Simulations

Energy Technology Data Exchange (ETDEWEB)

Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

2005-06-27

Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research.

The Roles of Sparse Direct Methods in Large-scale Simulations

International Nuclear Information System (INIS)

Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

2005-01-01

Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research

Conditions for licensing workers exposed to ionizing radiation

International Nuclear Information System (INIS)

2007-01-01

This entrance speaking on conditions of license workers in the areas of employment ionizing radiation addresses two aspects, the first aspect: industrial applications: speak for the workers in this area by a supervisor to portray industrial and industrial photographer and a supervisor sounding wells and a Nuclear Gauges Supervisor and the previous and subsequent Practices of the law The second aspect: about the medical applications and describes the general conditions of the licenses in this area and those working in this area of professional diagnostic radiology and nuclear medicine technician and technician treatment of radiotherapy and radiation protection officers at large and small institutions

Sparse logistic principal components analysis for binary data

KAUST Repository

Lee, Seokho

2010-09-01

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

Passive Spectroscopic Diagnostics for Magnetically-confined Fusion Plasmas

International Nuclear Information System (INIS)

Stratton, B.C.; Bitter, M.; Hill, K.W.; Hillis, D.L.; Hogan, J.T.

2007-01-01

Spectroscopy of radiation emitted by impurities and hydrogen isotopes plays an important role in the study of magnetically-confined fusion plasmas, both in determining the effects of impurities on plasma behavior and in measurements of plasma parameters such as electron and ion temperatures and densities, particle transport, and particle influx rates. This paper reviews spectroscopic diagnostics of plasma radiation that are excited by collisional processes in the plasma, which are termed 'passive' spectroscopic diagnostics to distinguish them from 'active' spectroscopic diagnostics involving injected particle and laser beams. A brief overview of the ionization balance in hot plasmas and the relevant line and continuum radiation excitation mechanisms is given. Instrumentation in the soft X-ray, vacuum ultraviolet, ultraviolet, visible, and near-infrared regions of the spectrum is described and examples of measurements are given. Paths for further development of these measurements and issues for their implementation in a burning plasma environment are discussed.

Passive Spectroscopic Diagnostics for Magnetically-confined Fusion Plasmas

Energy Technology Data Exchange (ETDEWEB)

Stratton, B. C.; Biter, M.; Hill, K. W.; Hillis, D. L.; Hogan, J. T.

2007-07-18

Spectroscopy of radiation emitted by impurities and hydrogen isotopes plays an important role in the study of magnetically-confined fusion plasmas, both in determining the effects of impurities on plasma behavior and in measurements of plasma parameters such as electron and ion temperatures and densities, particle transport, and particle influx rates. This paper reviews spectroscopic diagnostics of plasma radiation that are excited by collisional processes in the plasma, which are termed 'passive' spectroscopic diagnostics to distinguish them from 'active' spectroscopic diagnostics involving injected particle and laser beams. A brief overview of the ionization balance in hot plasmas and the relevant line and continuum radiation excitation mechanisms is given. Instrumentation in the soft X-ray, vacuum ultraviolet, ultraviolet, visible, and near-infrared regions of the spectrum is described and examples of measurements are given. Paths for further development of these measurements and issues for their implementation in a burning plasma environment are discussed.

Calculations of elastic, ionization and total cross sections for inert gases upon electron impact: threshold to 2 keV

Energy Technology Data Exchange (ETDEWEB)

Vinodkumar, Minaxi [V P and R P T P Science College, Vallabh Vidyanagar 388 120, Gujarat (India); Limbachiya, Chetan [P S Science College, Kadi 382 715, Gujarat (India); Antony, Bobby [Department of Environmental, Earth and Atmospheric Sciences, University of Massachusetts Lowell, 265 Riverside Street, Lowell, MA 01854-5045 (United States); Joshipura, K N [Department of Physics, Sardar Patel University, Vallabh Vidyanagar 388 120, Gujarat (India)

2007-08-28

In this paper we report comprehensive calculations of total elastic (Q{sub el}), total ionization (Q{sub ion}) and total (complete) cross sections (Q{sub T}) for the impact of electrons on inert gases (He, Ne, Ar, Kr and Xe) at energies from about threshold to 2000 eV. We have employed the spherical complex optical potential (SCOP) formalism to evaluate Q{sub el} and Q{sub T} and used the complex spherical potential-ionization contribution (CSP-ic) method to derive Q{sub ion}. The dependence of Q{sub T} on polarizability and incident energy is presented for these targets through an analytical formula. Mutual comparison of various cross sections is provided to show their relative contribution to the total cross sections Q{sub T}. Comparison of Q{sub T} for all these targets is carried out to present a general theoretical picture of collision processes. The present calculations also provide information, hitherto sparse, on the excitation processes of these atomic targets. These results are compared with available experimental and other theoretical data and overall good agreement is observed.

Radiation protection of patients in diagnostic radiology: implementation of a management system optimization

International Nuclear Information System (INIS)

Corpas Rivera, L.; Devesa Pardo, F. J.; Gamez Jimenez, J. L.; Vallejo Carrascal, C.; Garcia de Diego, A. A.; Amador Vela-Hidalgo, J. J.

2011-01-01

The enforcement of quality in diagnostic radiology (Royal Decree 1976/1999 laying down the criteria for quality in diagnostic radiology and Royal Decree 815/2001 to justify the use of ionizing radiations for medical exposure, etc.) and recommendations and European regulations on the matter, is done by carrying out the optimization of the doses received, based on image quality in a continuous process of monitoring of such dose from the dose reference Values ??(VRD ) that the system has allowed to establish for each technique.

Coronal temperature diagnostics from high-resolution soft X-ray spectra

Science.gov (United States)

Strong, K. T.; Claflin, E. S.; Lemen, J. R.; Linford, G. A.

1988-01-01

The problem of deriving the temperature of the coronal plasma from soft X-ray spectra is discussed. Spectral atlas scans of the soft X-ray spectrum from the Flat Crystal Spectrometer on the Solar Maximum Mission are compared with theoretical predictions of the relative intensities of some of the brighter lines to determine which line intensity ratios give the most reliable temperature diagnostics. The techniques considered include line widths, He-like G ratios, intensity ratios, and ratios of lines formed by different elements. It is found that the best temperature diagnostics come from the ratios of lines formed by successive ionization stages of the same element.

Current-voltage characteristic of parallel-plane ionization chamber with inhomogeneous ionization

International Nuclear Information System (INIS)

Stoyanov, D G

2007-01-01

The balances of particles and charges in the volume of parallel-plane ionization chamber are considered. Differential equations describing the distribution of current densities in the chamber volume are obtained. As a result of the differential equations solution an analytical form of the current-voltage characteristic of parallel-plane ionization chamber with inhomogeneous ionization in the volume is obtained

Current-voltage characteristic of parallel-plane ionization chamber with inhomogeneous ionization

Energy Technology Data Exchange (ETDEWEB)

Stoyanov, D G [Faculty of Engineering and Pedagogy in Sliven, Technical University of Sofia, 59, Bourgasko Shaussee Blvd, 8800 Sliven (Bulgaria)

2007-08-15

The balances of particles and charges in the volume of parallel-plane ionization chamber are considered. Differential equations describing the distribution of current densities in the chamber volume are obtained. As a result of the differential equations solution an analytical form of the current-voltage characteristic of parallel-plane ionization chamber with inhomogeneous ionization in the volume is obtained.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

Energy Technology Data Exchange (ETDEWEB)

Pilipchuk, L. A., E-mail: pilipchik@bsu.by [Belarussian State University, 220030 Minsk, 4, Nezavisimosti avenue, Republic of Belarus (Belarus); Pilipchuk, A. S., E-mail: an.pilipchuk@gmail.com [The Natural Resources and Environmental Protestion Ministry of the Republic of Belarus, 220004 Minsk, 10 Kollektornaya Street, Republic of Belarus (Belarus)

2015-11-30

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure.

Sparse linear systems: Theory of decomposition, methods, technology, applications and implementation in Wolfram Mathematica

International Nuclear Information System (INIS)

Pilipchuk, L. A.; Pilipchuk, A. S.

2015-01-01

In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure

Multi-Layer Sparse Representation for Weighted LBP-Patches Based Facial Expression Recognition

Directory of Open Access Journals (Sweden)

Qi Jia

2015-03-01

Full Text Available In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

Science.gov (United States)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm

IONIZATION IN ATMOSPHERES OF BROWN DWARFS AND EXTRASOLAR PLANETS. V. ALFVÉN IONIZATION

International Nuclear Information System (INIS)

Stark, C. R.; Helling, Ch.; Rimmer, P. B.; Diver, D. A.

2013-01-01

Observations of continuous radio and sporadic X-ray emission from low-mass objects suggest they harbor localized plasmas in their atmospheric environments. For low-mass objects, the degree of thermal ionization is insufficient to qualify the ionized component as a plasma, posing the question: what ionization processes can efficiently produce the required plasma that is the source of the radiation? We propose Alfvén ionization as a mechanism for producing localized pockets of ionized gas in the atmosphere, having sufficient degrees of ionization (≥10 –7 ) that they constitute plasmas. We outline the criteria required for Alfvén ionization and demonstrate its applicability in the atmospheres of low-mass objects such as giant gas planets, brown dwarfs, and M dwarfs with both solar and sub-solar metallicities. We find that Alfvén ionization is most efficient at mid to low atmospheric pressures where a seed plasma is easier to magnetize and the pressure gradients needed to drive the required neutral flows are the smallest. For the model atmospheres considered, our results show that degrees of ionization of 10 –6 -1 can be obtained as a result of Alfvén ionization. Observable consequences include continuum bremsstrahlung emission, superimposed with spectral lines from the plasma ion species (e.g., He, Mg, H 2 , or CO lines). Forbidden lines are also expected from the metastable population. The presence of an atmospheric plasma opens the door to a multitude of plasma and chemical processes not yet considered in current atmospheric models. The occurrence of Alfvén ionization may also be applicable to other astrophysical environments such as protoplanetary disks

An algorithm for 3D target scatterer feature estimation from sparse SAR apertures

Science.gov (United States)

Jackson, Julie Ann; Moses, Randolph L.

2009-05-01

We present an algorithm for extracting 3D canonical scattering features from complex targets observed over sparse 3D SAR apertures. The algorithm begins with complex phase history data and ends with a set of geometrical features describing the scene. The algorithm provides a pragmatic approach to initialization of a nonlinear feature estimation scheme, using regularization methods to deconvolve the point spread function and obtain sparse 3D images. Regions of high energy are detected in the sparse images, providing location initializations for scattering center estimates. A single canonical scattering feature, corresponding to a geometric shape primitive, is fit to each region via nonlinear optimization of fit error between the regularized data and parametric canonical scattering models. Results of the algorithm are presented using 3D scattering prediction data of a simple scene for both a densely-sampled and a sparsely-sampled SAR measurement aperture.

An Improved Information Hiding Method Based on Sparse Representation

Directory of Open Access Journals (Sweden)

Minghai Yao

2015-01-01

Full Text Available A novel biometric authentication information hiding method based on the sparse representation is proposed for enhancing the security of biometric information transmitted in the network. In order to make good use of abundant information of the cover image, the sparse representation method is adopted to exploit the correlation between the cover and biometric images. Thus, the biometric image is divided into two parts. The first part is the reconstructed image, and the other part is the residual image. The biometric authentication image cannot be restored by any one part. The residual image and sparse representation coefficients are embedded into the cover image. Then, for the sake of causing much less attention of attackers, the visual attention mechanism is employed to select embedding location and embedding sequence of secret information. Finally, the reversible watermarking algorithm based on histogram is utilized for embedding the secret information. For verifying the validity of the algorithm, the PolyU multispectral palmprint and the CASIA iris databases are used as biometric information. The experimental results show that the proposed method exhibits good security, invisibility, and high capacity.

Project, construction and characterization of ionization chambers for use as standard systems in X and gamma radiation beams; Projeto, construcao e caracterizacao de camaras de ionizacao para utilizacao como sistemas padroes em feixes de radiacao X e gama

Energy Technology Data Exchange (ETDEWEB)

Perini, Ana Paula

2013-07-01

Ionization chambers present some advantages in relation to other dosimeters: easiness of handling, low energy dependence and high precision. The advantages associated to ionization chambers and the large number of diagnostic radiology exams and therapeutic treatments motivated the development of this PhD program. In this project ionization chambers were developed and characterized to be applied in diagnostic radiology and therapy beam dosimetry, with high precision and performance, in compliance with international recommendations. They were assembled in a simple way, utilizing low-cost national materials, so they can be reproduced and applied at calibration laboratories. The project of these ionization chambers presents some differences in relation to commercial ionization chambers, as the materials utilized and geometrical arrangements. Besides the development of the ionization chambers to be utilized in standard X-ray beam dosimetry as work standard systems, two graphite parallel-plate ionization chambers were developed and characterized to be applied as reference standard systems for determining the air kerma rates of gamma radiation sources. Comparing the air kerma rates determined with the reference standard of the Calibration Laboratory of IPEN, a Farmer ionization chamber, with the values of the air kerma rates obtained with the graphite ionization chambers, the maximum differences obtained were only 1.7% and 1.2% for the G1 and G2 graphite ionization chambers, respectively. Moreover, these ionization chambers presented correction factors close to 1.000, which is ideal for an ionization chamber be characterized as a reference standard system. (author)

Biological Effects of Ionizing Radiation

Science.gov (United States)

Ingram, M.; Mason, W. B.; Whipple, G. H.; Howland, J. W.

1952-04-07

This report presents a review of present knowledge and concepts of the biological effects of ionizing radiations. Among the topics discussed are the physical and chemical effects of ionizing radiation on biological systems, morphological and physiological changes observed in biological systems subjected to ionizing radiations, physiological changes in the intact animal, latent changes following exposure of biological systems to ionizing radiations, factors influencing the biological response to ionizing radiation, relative effects of various ionizing radiations, and biological dosimetry.

Sparse reconstruction by means of the standard Tikhonov regularization

International Nuclear Information System (INIS)

Lu Shuai; Pereverzev, Sergei V

2008-01-01

It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

Sparse modeling applied to patient identification for safety in medical physics applications

Science.gov (United States)

Lewkowitz, Stephanie

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration. The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and different tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherently sparse in some bases, due to their inherent structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competitive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by ℓ1 pooling

Ionization chambers

International Nuclear Information System (INIS)

Boag, J.W.

1987-01-01

Although a variety of solid-state and chemical methods for measuring radiation dose have been developed in recent decades and calorimetry can now provide an absolute standard of reference, ionization dosimetry retains its position as the most widely used, most convenient, and, in most situations, most accurate method of measuring either exposure or absorbed dose. The ionization chamber itself is the central element in this system of dosimetry. In this chapter the principles governing the construction and operation of ionization chambers of various types are examined. Since the ionization chambers now in general use are nearly all of commercial manufacture, the emphasis is on operating characteristics and interpretation of measurements rather than on details of construction, although some knowledge of the latter is often required when applying necessary corrections to the measured quantities. Examples are given of the construction of typical chambers designed for particular purposes, and the methods of calibrating them are discussed

Sparse logistic principal components analysis for binary data

KAUST Repository

Lee, Seokho; Huang, Jianhua Z.; Hu, Jianhua

2010-01-01

with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated

Elemental composition and ionization state of the solar atmosphere and solar wind

International Nuclear Information System (INIS)

Joselyn, J.A.C.

1978-01-01

Abundance measurements have always proved useful in generating and refining astrophysical theories. Some of the classical problems of astrophysics involve determining the relative abundances of elements in the atmosphere of a star from observations of its line spectrum, and then synthesizing the physical processes which would produce such abundances. Theories of the formation of the solar system are critically tested by their ability to explain observed abundances, and, elemental abundances can serve as tracers, helping to determine the origin and transport of ions. Since the solar wind originates at the sun, it can act as a diagnostic probe of solar conditions. In particular, measurements of the composition of the solar wind should be related to the solar composition. And, assuming ionization equilibrium, measurements of the relative abundances of the ionization states in the solar wind should infer coronal temperatures and temperature gradients. However, most spherically symmetric models of the solar wind are unable to explain the relationship between the composition estimated from solar observations and as measured at 1 AU; and, recent observations of significant flow speeds in the transition region raise doubts about the validity of the assumption of ionization equilibrium

Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT

Directory of Open Access Journals (Sweden)

Shaoyan Hua

2014-01-01

Full Text Available Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT. In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed.

IFMIF-LIPAc Beam Diagnostics. Profiling and Loss Monitoring Systems

International Nuclear Information System (INIS)

Egberts, J.

2012-01-01

The IFMIF accelerator will accelerate two 125 mA continuous wave (cw) deuteron beams up to 40 MeV and blasts them onto a liquid lithium target to release neutrons. The very high beam power of 10 MW pose unprecedented challenges for the accelerator development. Therefore, it was decided to build a prototype accelerator, the Linear IFMIF Prototype Accelerator (LIPAc), which has the very same beam characteristic, but is limited to 9 MeV only. In the frame of this thesis, diagnostics devices for IFMIF and LIPAc have been developed. The diagnostics devices consist of beam loss monitors and interceptive as well as non-interceptive profile monitors. For the beam loss monitoring system, ionization chambers and diamond detectors have been tested and calibrated for neutron and γ radiation in the energy range expected at LIPAc. During these tests, for the first time, diamond detectors were successfully operated at cryogenic temperatures. For the interceptive profilers, thermal simulations were performed to ensure safe operation. For the non-interceptive profiler, Ionization Profile Monitors (IPMs) were developed. A prototype has been built and tested, and based on the findings, the final IPMs were designed and built. To overcome the space charge of accelerator beam, a software algorithm was written to reconstruct the actual beam profile. (author) [fr

Non-equilibrium hydrogen ionization in 2D simulations of the solar atmosphere

Science.gov (United States)

Leenaarts, J.; Carlsson, M.; Hansteen, V.; Rutten, R. J.

2007-10-01

Context: The ionization of hydrogen in the solar chromosphere and transition region does not obey LTE or instantaneous statistical equilibrium because the timescale is long compared with important hydrodynamical timescales, especially of magneto-acoustic shocks. Since the pressure, temperature, and electron density depend sensitively on hydrogen ionization, numerical simulation of the solar atmosphere requires non-equilibrium treatment of all pertinent hydrogen transitions. The same holds for any diagnostic application employing hydrogen lines. Aims: To demonstrate the importance and to quantify the effects of non-equilibrium hydrogen ionization, both on the dynamical structure of the solar atmosphere and on hydrogen line formation, in particular Hα. Methods: We implement an algorithm to compute non-equilibrium hydrogen ionization and its coupling into the MHD equations within an existing radiation MHD code, and perform a two-dimensional simulation of the solar atmosphere from the convection zone to the corona. Results: Analysis of the simulation results and comparison to a companion simulation assuming LTE shows that: a) non-equilibrium computation delivers much smaller variations of the chromospheric hydrogen ionization than for LTE. The ionization is smaller within shocks but subsequently remains high in the cool intershock phases. As a result, the chromospheric temperature variations are much larger than for LTE because in non-equilibrium, hydrogen ionization is a less effective internal energy buffer. The actual shock temperatures are therefore higher and the intershock temperatures lower. b) The chromospheric populations of the hydrogen n = 2 level, which governs the opacity of Hα, are coupled to the ion populations. They are set by the high temperature in shocks and subsequently remain high in the cool intershock phases. c) The temperature structure and the hydrogen level populations differ much between the chromosphere above photospheric magnetic elements

On Sparse Multi-Task Gaussian Process Priors for Music Preference Learning

DEFF Research Database (Denmark)

Nielsen, Jens Brehm; Jensen, Bjørn Sand; Larsen, Jan

In this paper we study pairwise preference learning in a music setting with multitask Gaussian processes and examine the effect of sparsity in the input space as well as in the actual judgments. To introduce sparsity in the inputs, we extend a classic pairwise likelihood model to support sparse...... simulation shows the performance on a real-world music preference dataset which motivates and demonstrates the potential of the sparse Gaussian process formulation for pairwise likelihoods....

Linear Regression on Sparse Features for Single-Channel Speech Separation

DEFF Research Database (Denmark)

Schmidt, Mikkel N.; Olsson, Rasmus Kongsgaard

2007-01-01

In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech...... mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both...

The ionizing treatment of food

International Nuclear Information System (INIS)

1998-01-01

This book of proceedings contains the talks given by the members of the Society of chemical experts of France (SECF) and by various specialists of the ionizing treatment during the scientific days of September 25-26, 1997. The aim of this meeting was to reconsider the effects of ionization from a scientific point of view and apart from the polemics generated by this domain. The following topics were discussed successively: source and characterization of a ionizing treatment, biological effects of ionization on food and the expected consequences, the ionizing treatment and the reduction of the vitamin C content of fruits and vegetables, is it safe to eat irradiated food?, the organoleptic modifications of food after ionization, quality assurance of dosimetry measurements in an industrial installation of food ionization, the French and European regulations in food ionization, the detection of irradiated foodstuffs, processed food and complex lipid matrices, sterilization of dishes for immuno-depressed patients using ionization. (J.S.)

Quasi optimal and adaptive sparse grids with control variates for PDEs with random diffusion coefficient

KAUST Repository

Tamellini, Lorenzo

2016-01-05

In this talk we discuss possible strategies to minimize the impact of the curse of dimensionality effect when building sparse-grid approximations of a multivariate function u = u(y1, ..., yN ). More precisely, we present a knapsack approach , in which we estimate the cost and the error reduction contribution of each possible component of the sparse grid, and then we choose the components with the highest error reduction /cost ratio. The estimates of the error reduction are obtained by either a mixed a-priori / a-posteriori approach, in which we first derive a theoretical bound and then tune it with some inexpensive auxiliary computations (resulting in the so-called quasi-optimal sparse grids ), or by a fully a-posteriori approach (obtaining the so-called adaptive sparse grids ). This framework is very general and can be used to build quasi-optimal/adaptive sparse grids on bounded and unbounded domains (e.g. u depending on uniform and normal random distributions for yn), using both nested and non-nested families of univariate collocation points. We present some theoretical convergence results as well as numerical results showing the efficiency of the proposed approach for the approximation of the solution of elliptic PDEs with random diffusion coefficients. In this context, to treat the case of rough permeability fields in which a sparse grid approach may not be suitable, we propose to use the sparse grids as a control variate in a Monte Carlo simulation.

Sparse Channel Estimation Including the Impact of the Transceiver Filters with Application to OFDM

DEFF Research Database (Denmark)

Barbu, Oana-Elena; Pedersen, Niels Lovmand; Manchón, Carles Navarro

2014-01-01

Traditionally, the dictionary matrices used in sparse wireless channel estimation have been based on the discrete Fourier transform, following the assumption that the channel frequency response (CFR) can be approximated as a linear combination of a small number of multipath components, each one......) and receive (demodulation) filters. Hence, the assumption of the CFR being sparse in the canonical Fourier dictionary may no longer hold. In this work, we derive a signal model and subsequently a novel dictionary matrix for sparse estimation that account for the impact of transceiver filters. Numerical...... results obtained in an OFDM transmission scenario demonstrate the superior accuracy of a sparse estimator that uses our proposed dictionary rather than the classical Fourier dictionary, and its robustness against a mismatch in the assumed transmit filter characteristics....

Sparse data structure design for wavelet-based methods

Directory of Open Access Journals (Sweden)

Latu Guillaume

2011-12-01

Full Text Available This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An answer will be given to the question: how to implement and efficiently use the discrete wavelet transform in computer applications? A focus will be made on time-evolution problems, and use of wavelet-based scheme for adaptively solving partial differential equations (PDE. One crucial issue is that the benefits of the adaptive method in term of algorithmic cost reduction can not be wasted by overheads associated to sparse data management.

Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

KAUST Repository

Desmal, Abdulla; Bagci, Hakan

2014-01-01

with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm

'Saddle-point' ionization

International Nuclear Information System (INIS)

Gay, T.J.; Hale, E.B.; Irby, V.D.; Olson, R.E.; Missouri Univ., Rolla; Berry, H.G.

1988-01-01

We have studied the ionization of rare gases by protons at intermediate energies, i.e., energies at which the velocities of the proton and the target-gas valence electrons are comparable. A significant channel for electron production in the forward direction is shown to be 'saddle-point' ionization, in which electrons are stranded on or near the saddle-point of electric potential between the receding projectile and the ionized target. Such electrons yield characteristic energy spectra, and contribute significantly to forward-electron-production cross sections. Classical trajectory Monte Carlo calculations are found to provide qualitative agreement with our measurements and the earlier measurements of Rudd and coworkers, and reproduce, in detail, the features of the general ionization spectra. (orig.)

Heating and ionization in MHD shock waves propagating into partially ionized plasma

International Nuclear Information System (INIS)

Bighel, L.; Collins, A.R.; Cramer, N.F.; Watson-Munro, C.N.

1975-09-01

A model of the structure of MHD switch-on shocks propagating in a partially ionized plasma, in which the primary dissipation mechanism is friction between ions and neutrals, is here compared favourably with experimental results. Four degrees of upstream ionization were studied, ranging from almost complete to very small ionization. (author)

Heating and ionization in MHD shock wave propagating into partially ionized plasma

International Nuclear Information System (INIS)

Bighel, L.; Collins, A.R.; Cramer, N.F.; Watson-Munro, C.N.

1975-09-01

A model of the structure of MHD switch-on shocks propagating in a partially ionized plasma, in which the primary dissipation mechanism is friction between ions and neutrals, is here compared favourably with experimental results. Four degrees of upstream ionization were studied, ranging from almost complete to very small ionization. (author)

Five-photon ionization of atomic hydrogen at wavelengths around the threshold for four-photon ionization

International Nuclear Information System (INIS)

Gontier, Y.; Trahin, M.; Wolff-Rottke, B.; Rottke, H.; Welge, K.H.; Feldmann, D.

1992-01-01

Theoretical and experimental studies show the strong influence of the three-photon nearly resonant 2p state on four- and five-photon ionization of atomic hydrogen near the threshold for four-photon ionization. Changes in five-photon ionization occur when the four-photon ionization channel opens. The angular distributions of photoelectrons from five-photon ionization of H are studied at five wavelengths which cover the range from four-photon resonance with high-lying Rydberg states (n≥10) to direct four-photon ionization into the continuum. The role of resonances in this ionization process is discussed. A fair agreement is found in comparing experimental and theoretical results

Equipment for handling ionization chamber

International Nuclear Information System (INIS)

Altmann, J.

1988-01-01

The device consists of an ionization channel with an ionization chamber, of a support ring, axial and radial bearings, a sleeve, a screw gear and an electric motor. The ionization chamber is freely placed on the bottom of the ionization channel. The bottom part of the channel deviates from the vertical axis. The support ring propped against the axial bearing in the sleeve is firmly fixed to the top part of the ionization channel. The sleeve is fixed to the reactor lid. Its bottom part is provided with a recess for the radial bearing which is propped against a screw wheel firmly connected to the ionization channel. In measuring neutron flux, the screw wheel is rotated by the motor, thus rotating the whole ionization channel such that the ionization chamber is displaced into the reactor core.(J.B.). 1 fig

Foodstuffs preservation by ionization

International Nuclear Information System (INIS)

1991-12-01

This document contains all the papers presented at the meeting on foodstuffs preservation by ionization. These papers deal especially with the food ionization process, its development and the view of the food industry on ionization. Refs and figs (F.M.)

Ambient ionization mass spectrometry: A tutorial

Energy Technology Data Exchange (ETDEWEB)

Huang, Min-Zong; Cheng, Sy-Chi; Cho, Yi-Tzu [Department of Chemistry, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); Shiea, Jentaie, E-mail: jetea@fac.nsysu.edu.tw [Department of Chemistry, National Sun Yat-Sen University, Kaohsiung, Taiwan (China); Cancer Center, Kaohsiung Medical University, Kaohsiung, Taiwan (China)

2011-09-19

Highlights: {yields} Ambient ionization technique allows the direct analysis of sample surfaces with little or no sample pretreatment. {yields} We sort ambient ionization techniques into three main analytical strategies, direct ionization, direct desorption/ionization, and two-step ionization. {yields} The underlying principles of operation, ionization processes, detecting mass ranges, sensitivity, and representative applications of these techniques are described and compared. - Abstract: Ambient ionization is a set of mass spectrometric ionization techniques performed under ambient conditions that allows the direct analysis of sample surfaces with little or no sample pretreatment. Using combinations of different types of sample introduction systems and ionization methods, several novel techniques have been developed over the last few years with many applications (e.g., food safety screening; detection of pharmaceuticals and drug abuse; monitoring of environmental pollutants; detection of explosives for antiterrorism and forensics; characterization of biological compounds for proteomics and metabolomics; molecular imaging analysis; and monitoring chemical and biochemical reactions). Electrospray ionization and atmospheric pressure chemical ionization are the two main ionization principles most commonly used in ambient ionization mass spectrometry. This tutorial paper provides a review of the publications related to ambient ionization techniques. We describe and compare the underlying principles of operation, ionization processes, detecting mass ranges, sensitivity, and representative applications of these techniques.

Ambient ionization mass spectrometry: A tutorial

International Nuclear Information System (INIS)

Huang, Min-Zong; Cheng, Sy-Chi; Cho, Yi-Tzu; Shiea, Jentaie

2011-01-01

Highlights: → Ambient ionization technique allows the direct analysis of sample surfaces with little or no sample pretreatment. → We sort ambient ionization techniques into three main analytical strategies, direct ionization, direct desorption/ionization, and two-step ionization. → The underlying principles of operation, ionization processes, detecting mass ranges, sensitivity, and representative applications of these techniques are described and compared. - Abstract: Ambient ionization is a set of mass spectrometric ionization techniques performed under ambient conditions that allows the direct analysis of sample surfaces with little or no sample pretreatment. Using combinations of different types of sample introduction systems and ionization methods, several novel techniques have been developed over the last few years with many applications (e.g., food safety screening; detection of pharmaceuticals and drug abuse; monitoring of environmental pollutants; detection of explosives for antiterrorism and forensics; characterization of biological compounds for proteomics and metabolomics; molecular imaging analysis; and monitoring chemical and biochemical reactions). Electrospray ionization and atmospheric pressure chemical ionization are the two main ionization principles most commonly used in ambient ionization mass spectrometry. This tutorial paper provides a review of the publications related to ambient ionization techniques. We describe and compare the underlying principles of operation, ionization processes, detecting mass ranges, sensitivity, and representative applications of these techniques.

Interferometric interpolation of sparse marine data

KAUST Repository

Hanafy, Sherif M.

2013-10-11

We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.

Dose-shaping using targeted sparse optimization

Energy Technology Data Exchange (ETDEWEB)

Sayre, George A.; Ruan, Dan [Department of Radiation Oncology, University of California - Los Angeles School of Medicine, 200 Medical Plaza, Los Angeles, California 90095 (United States)

2013-07-15

Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E{sub tot}{sup sparse}), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L{sub 1} norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E{sub tot

High Order Tensor Formulation for Convolutional Sparse Coding

KAUST Repository

Bibi, Adel Aamer; Ghanem, Bernard

2017-01-01

Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

A Novel Design of Sparse Prototype Filter for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks

Directory of Open Access Journals (Sweden)

Wei Xu

2018-05-01

Full Text Available Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks.

Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

KAUST Repository

Buse, Gerrit

2014-01-01

In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤ 10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the International Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation.

Noniterative MAP reconstruction using sparse matrix representations.

Science.gov (United States)

Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

2009-09-01

We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

Science.gov (United States)

Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

2013-01-01

Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

Ionization of food products

International Nuclear Information System (INIS)

Vasseur, J.P.

1991-01-01

After general remarks on foods preservation, on international works and on ionization future prospects, main irradiation sources are described. Recalls on radioactivity, on radiation-matter interaction, on toxicology of ionized foods and on ionized foods detection are given. Ionization applications to various products are reviewed, especially in: - Poultry meat - Fishing products - Fresh fruits and vegetables - Dry fruits and vegetables - spices, tea, infusion - prepacked products... An evaluation of economics and sociocultural impacts is presented in connection with recent experiments [fr

Pain related to cancer treatments and diagnostic procedures: a no man's land?

Science.gov (United States)

Ripamonti, C I; Bossi, P; Santini, D; Fallon, M

2014-06-01

While guidelines are available for the management of cancer-related pain, little attention is given to the assessment and treatment of pain caused by treatments and diagnostic procedures in cancer patients. We evaluated the literature on pain related to cancer treatment and diagnostic procedures within a critical analysis. The data available are sparse, suggesting that little attention has been directed at this important aspect of oncology. This points to potentially suboptimal patient management. Appropriate studies are necessary in order to understand the incidence and appropriate management of pain, both during and/or after oncological treatments and diagnostic procedures. At the same time, Health Care Professionals should have heightened awareness of the causes and treatment of pain with the aim of anticipating and managing pain most appropriately for each individual patient. This is clearly an important component of holistic patient care before, during, and after oncological treatment. © The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification

Directory of Open Access Journals (Sweden)

Benesty Jacob

2007-01-01

Full Text Available A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS algorithm and the efficient implementation of the multidelay adaptive filtering (MDF algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

2012-01-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

Science.gov (United States)

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

Chemical data on ionizing and non-ionizing angiographic contrast materials

International Nuclear Information System (INIS)

Bonati, F.

1980-01-01

The cardiovascular effects of ionizing and non-ionizing contrast media are compared in experimental animals and in isolated heart preparations. The following parameters were recorded: peripheric arterial diastolic pressure, heart rate, duration of asystolic period, respiratory rate, contractility of the myocardium (dp/dt, LVSP, Vsub(max), EDV, ESV, SV). The observed changes are mainly due to the higher osmotic activity of the contrast media, as similar alterations were recorded after the injection of hyperosmotic glucose solution. It is concluded that administration of non-ionizing contrast media results in significantly less cardiovascular side effects. (L.E.)

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA for L p -Regularization Using the Multiple Sub-Dictionary Representation

Directory of Open Access Journals (Sweden)

Yunyi Li

2017-12-01

Full Text Available Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 , which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA. Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

Shape prior modeling using sparse representation and online dictionary learning.

Science.gov (United States)

Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N

2012-01-01

The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.

Mapping visual stimuli to perceptual decisions via sparse decoding of mesoscopic neural activity.

Science.gov (United States)

Sajda, Paul

2010-01-01

In this talk I will describe our work investigating sparse decoding of neural activity, given a realistic mapping of the visual scene to neuronal spike trains generated by a model of primary visual cortex (V1). We use a linear decoder which imposes sparsity via an L1 norm. The decoder can be viewed as a decoding neuron (linear summation followed by a sigmoidal nonlinearity) in which there are relatively few non-zero synaptic weights. We find: (1) the best decoding performance is for a representation that is sparse in both space and time, (2) decoding of a temporal code results in better performance than a rate code and is also a better fit to the psychophysical data, (3) the number of neurons required for decoding increases monotonically as signal-to-noise in the stimulus decreases, with as little as 1% of the neurons required for decoding at the highest signal-to-noise levels, and (4) sparse decoding results in a more accurate decoding of the stimulus and is a better fit to psychophysical performance than a distributed decoding, for example one imposed by an L2 norm. We conclude that sparse coding is well-justified from a decoding perspective in that it results in a minimum number of neurons and maximum accuracy when sparse representations can be decoded from the neural dynamics.

Multi-Frequency Polarimetric SAR Classification Based on Riemannian Manifold and Simultaneous Sparse Representation

Directory of Open Access Journals (Sweden)

Fan Yang

2015-07-01

Full Text Available Normally, polarimetric SAR classification is a high-dimensional nonlinear mapping problem. In the realm of pattern recognition, sparse representation is a very efficacious and powerful approach. As classical descriptors of polarimetric SAR, covariance and coherency matrices are Hermitian semidefinite and form a Riemannian manifold. Conventional Euclidean metrics are not suitable for a Riemannian manifold, and hence, normal sparse representation classification cannot be applied to polarimetric SAR directly. This paper proposes a new land cover classification approach for polarimetric SAR. There are two principal novelties in this paper. First, a Stein kernel on a Riemannian manifold instead of Euclidean metrics, combined with sparse representation, is employed for polarimetric SAR land cover classification. This approach is named Stein-sparse representation-based classification (SRC. Second, using simultaneous sparse representation and reasonable assumptions of the correlation of representation among different frequency bands, Stein-SRC is generalized to simultaneous Stein-SRC for multi-frequency polarimetric SAR classification. These classifiers are assessed using polarimetric SAR images from the Airborne Synthetic Aperture Radar (AIRSAR sensor of the Jet Propulsion Laboratory (JPL and the Electromagnetics Institute Synthetic Aperture Radar (EMISAR sensor of the Technical University of Denmark (DTU. Experiments on single-band and multi-band data both show that these approaches acquire more accurate classification results in comparison to many conventional and advanced classifiers.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

Science.gov (United States)

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

Ionizing radiations

International Nuclear Information System (INIS)

Anon.

1999-01-01

This is an update about the radiological monitoring in base nuclear installations. A departmental order of the 23. march 1999 (J.O.28. april, p.6309) determines the enabling rules by the Office of Protection against Ionizing Radiations of person having at one's disposal the results with names of individual exposure of workers put through ionizing radiations. (N.C.)

Development of an EMC3-EIRENE Synthetic Imaging Diagnostic

Science.gov (United States)

Meyer, William; Allen, Steve; Samuell, Cameron; Lore, Jeremy

2017-10-01

2D and 3D flow measurements are critical for validating numerical codes such as EMC3-EIRENE. Toroidal symmetry assumptions preclude tomographic reconstruction of 3D flows from single camera views. In addition, the resolution of the grids utilized in numerical code models can easily surpass the resolution of physical camera diagnostic geometries. For these reasons we have developed a Synthetic Imaging Diagnostic capability for forward projection comparisons of EMC3-EIRENE model solutions with the line integrated images from the Doppler Coherence Imaging diagnostic on DIII-D. The forward projection matrix is 2.8 Mpixel by 6.4 Mcells for the non-axisymmetric case we present. For flow comparisons, both simple line integral, and field aligned component matrices must be calculated. The calculation of these matrices is a massive embarrassingly parallel problem and performed with a custom dispatcher that allows processing platforms to join mid-problem as they become available, or drop out if resources are needed for higher priority tasks. The matrices are handled using standard sparse matrix techniques. Prepared by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. DOE, Office of Science, Office of Fusion Energy Sciences. LLNL-ABS-734800.

Sparse canonical correlation analysis: new formulation and algorithm.

Science.gov (United States)

Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei

2013-12-01

In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

System and method for acquiring and inverting sparse-frequency data

KAUST Repository

Alkhalifah, Tariq Ali

2017-01-01

A method of imaging an object includes generating a plurality of mono-frequency waveforms and applying the plurality of mono-frequency waveforms to the object to be modeled. In addition, sparse mono-frequency data is recorded in response to the plurality of mono-frequency waveforms applied to the object to be modeled. The sparse mono-frequency data is cross-correlated with one or more source functions each having a frequency approximately equal to each of the plurality of mono-frequency waveforms to obtain monochromatic frequency data. The monochromatic frequency data is utilized in an inversion to converge a model to a minimum value.

System and method for acquiring and inverting sparse-frequency data

KAUST Repository

Alkhalifah, Tariq Ali

2017-11-30

A method of imaging an object includes generating a plurality of mono-frequency waveforms and applying the plurality of mono-frequency waveforms to the object to be modeled. In addition, sparse mono-frequency data is recorded in response to the plurality of mono-frequency waveforms applied to the object to be modeled. The sparse mono-frequency data is cross-correlated with one or more source functions each having a frequency approximately equal to each of the plurality of mono-frequency waveforms to obtain monochromatic frequency data. The monochromatic frequency data is utilized in an inversion to converge a model to a minimum value.

Study of the performance of diagnostic radiology instruments during calibration

International Nuclear Information System (INIS)

Freitas, Rodrigo N. de; Vivolo, Vitor; Potiens, Maria da Penha A.

2008-01-01

Full text: The instruments used in diagnostic radiology measurements represent 8 % of the tested instruments by the calibration laboratory of IPEN annually (approximately 1600 in 2007). Considering that the calibration of this kind of instrument is performed biannually it is possible to conclude that almost 300 instruments are being used to measure the air kerma in diagnostic radiology clinics to determine the in beam values (in front of the patient), attenuated measurements (behind the patient) and scattered radiation. This work presents the results of the calibration of the instruments used in mammography, computed tomography, dental and conventional diagnostic radiology dosimetry, performed during the period of 2005 to 2007. Their performances during the calibrations measurements were evaluated. Although at the calibration laboratory there are three available series of radiation quality to this type of calibration (RQR, N and M, according to standards IEC 61267 and ISO 4037-1.), the applications can be assorted (general radiology, computed tomography, mammography, radiation protection and fluoroscopy). Depending on its design and behaviour , one kind of instrument can be used for one or more type of applications. The instruments normally used for diagnostic radiology measurements are ionization chambers with volumes varying from 3 to 1800 cm 3 , and can be cylindrical, spherical or plane parallel plates kind. They usually are sensitive to photon particles, with energies greater than 15 keV and can be used up to 1200 keV. In this work they were tested in X radiation fields from 25 to 150 kV, in specific qualities depending on the utilization of the instrument. The calibration results of 390 instruments received from 2005 to 2007 were analyzed. About 20 instruments were not able to be calibrated due to bad functioning. The calibration coefficients obtained were between 0.88 and 1.24. The uncertainties were always less than ± 3.6% to instruments used in scattered

Dynamic Representations of Sparse Graphs

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Fagerberg, Rolf

1999-01-01

We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....

Recursive nearest neighbor search in a sparse and multiscale domain for comparing audio signals

DEFF Research Database (Denmark)

Sturm, Bob L.; Daudet, Laurent

2011-01-01

We investigate recursive nearest neighbor search in a sparse domain at the scale of audio signals. Essentially, to approximate the cosine distance between the signals we make pairwise comparisons between the elements of localized sparse models built from large and redundant multiscale dictionaries...

Cancer risks following diagnostic and therapeutic radiation exposure in children

Energy Technology Data Exchange (ETDEWEB)

Kleinerman, Ruth A. [National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, EPS 7044, Rockville, MD (United States)

2006-09-15

The growing use of interventional and fluoroscopic imaging in children represents a tremendous benefit for the diagnosis and treatment of benign conditions. Along with the increasing use and complexity of these procedures comes concern about the cancer risk associated with ionizing radiation exposure to children. Children are considerably more sensitive to the carcinogenic effects of ionizing radiation than adults, and children have a longer life expectancy in which to express risk. Numerous epidemiologic cohort studies of childhood exposure to radiation for treatment of benign diseases have demonstrated radiation-related risks of cancer of the thyroid, breast, brain and skin, as well as leukemia. Many fewer studies have evaluated cancer risk following diagnostic radiation exposure in children. Although radiation dose for a single procedure might be low, pediatric patients often receive repeated examinations over time to evaluate their conditions, which could result in relatively high cumulative doses. Several cohort studies of girls and young women subjected to multiple diagnostic radiation exposures have been informative about increased mortality from breast cancer with increasing radiation dose, and case-control studies of childhood leukemia and postnatal diagnostic radiation exposure have suggested increased risks with an increasing number of examinations. Only two long-term follow-up studies of cancer following cardiac catheterization in childhood have been conducted, and neither reported an overall increased risk of cancer. Most cancers can be induced by radiation, and a linear dose-response has been noted for most solid cancers. Risks of radiation-related cancer are greatest for those exposed early in life, and these risks appear to persist throughout life. (orig.)

Cancer risks following diagnostic and therapeutic radiation exposure in children

International Nuclear Information System (INIS)

Kleinerman, Ruth A.

2006-01-01

The growing use of interventional and fluoroscopic imaging in children represents a tremendous benefit for the diagnosis and treatment of benign conditions. Along with the increasing use and complexity of these procedures comes concern about the cancer risk associated with ionizing radiation exposure to children. Children are considerably more sensitive to the carcinogenic effects of ionizing radiation than adults, and children have a longer life expectancy in which to express risk. Numerous epidemiologic cohort studies of childhood exposure to radiation for treatment of benign diseases have demonstrated radiation-related risks of cancer of the thyroid, breast, brain and skin, as well as leukemia. Many fewer studies have evaluated cancer risk following diagnostic radiation exposure in children. Although radiation dose for a single procedure might be low, pediatric patients often receive repeated examinations over time to evaluate their conditions, which could result in relatively high cumulative doses. Several cohort studies of girls and young women subjected to multiple diagnostic radiation exposures have been informative about increased mortality from breast cancer with increasing radiation dose, and case-control studies of childhood leukemia and postnatal diagnostic radiation exposure have suggested increased risks with an increasing number of examinations. Only two long-term follow-up studies of cancer following cardiac catheterization in childhood have been conducted, and neither reported an overall increased risk of cancer. Most cancers can be induced by radiation, and a linear dose-response has been noted for most solid cancers. Risks of radiation-related cancer are greatest for those exposed early in life, and these risks appear to persist throughout life. (orig.)

Nonuniform Sparse Data Clustering Cascade Algorithm Based on Dynamic Cumulative Entropy

Directory of Open Access Journals (Sweden)

Ning Li

2016-01-01

Full Text Available A small amount of prior knowledge and randomly chosen initial cluster centers have a direct impact on the accuracy of the performance of iterative clustering algorithm. In this paper we propose a new algorithm to compute initial cluster centers for k-means clustering and the best number of the clusters with little prior knowledge and optimize clustering result. It constructs the Euclidean distance control factor based on aggregation density sparse degree to select the initial cluster center of nonuniform sparse data and obtains initial data clusters by multidimensional diffusion density distribution. Multiobjective clustering approach based on dynamic cumulative entropy is adopted to optimize the initial data clusters and the best number of the clusters. The experimental results show that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm and it effectively improves the clustering accuracy of nonuniform sparse data by about 5%.

2D sparse array transducer optimization for 3D ultrasound imaging

International Nuclear Information System (INIS)

Choi, Jae Hoon; Park, Kwan Kyu

2014-01-01

A 3D ultrasound image is desired in many medical examinations. However, the implementation of a 2D array, which is needed for a 3D image, is challenging with respect to fabrication, interconnection and cabling. A 2D sparse array, which needs fewer elements than a dense array, is a realistic way to achieve 3D images. Because the number of ways the elements can be placed in an array is extremely large, a method for optimizing the array configuration is needed. Previous research placed the target point far from the transducer array, making it impossible to optimize the array in the operating range. In our study, we focused on optimizing a 2D sparse array transducer for 3D imaging by using a simulated annealing method. We compared the far-field optimization method with the near-field optimization method by analyzing a point-spread function (PSF). The resolution of the optimized sparse array is comparable to that of the dense array.

Sparse and smooth canonical correlation analysis through rank-1 matrix approximation

Science.gov (United States)

Aïssa-El-Bey, Abdeldjalil; Seghouane, Abd-Krim

2017-12-01

Canonical correlation analysis (CCA) is a well-known technique used to characterize the relationship between two sets of multidimensional variables by finding linear combinations of variables with maximal correlation. Sparse CCA and smooth or regularized CCA are two widely used variants of CCA because of the improved interpretability of the former and the better performance of the later. So far, the cross-matrix product of the two sets of multidimensional variables has been widely used for the derivation of these variants. In this paper, two new algorithms for sparse CCA and smooth CCA are proposed. These algorithms differ from the existing ones in their derivation which is based on penalized rank-1 matrix approximation and the orthogonal projectors onto the space spanned by the two sets of multidimensional variables instead of the simple cross-matrix product. The performance and effectiveness of the proposed algorithms are tested on simulated experiments. On these results, it can be observed that they outperform the state of the art sparse CCA algorithms.

Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

Science.gov (United States)

He, Xingyu; Tong, Ningning; Hu, Xiaowei

2018-01-01

Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation-maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

Structure-aware Local Sparse Coding for Visual Tracking

KAUST Repository

Qi, Yuankai; Qin, Lei; Zhang, Jian; Zhang, Shengping; Huang, Qingming; Yang, Ming-Hsuan

2018-01-01

with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we

Aliasing-free wideband beamforming using sparse signal representation

NARCIS (Netherlands)

Tang, Z.; Blacquière, G.; Leus, G.

2011-01-01

Sparse signal representation (SSR) is considered to be an appealing alternative to classical beamforming for direction-of-arrival (DOA) estimation. For wideband signals, the SSR-based approach constructs steering matrices, referred to as dictionaries in this paper, corresponding to different

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

Science.gov (United States)

Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-01-01

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

Face Image Retrieval of Efficient Sparse Code words and Multiple Attribute in Binning Image

Directory of Open Access Journals (Sweden)

Suchitra S

2017-08-01

Full Text Available ABSTRACT In photography, face recognition and face retrieval play an important role in many applications such as security, criminology and image forensics. Advancements in face recognition make easier for identity matching of an individual with attributes. Latest development in computer vision technologies enables us to extract facial attributes from the input image and provide similar image results. In this paper, we propose a novel LOP and sparse codewords method to provide similar matching results with respect to input query image. To improve accuracy in image results with input image and dynamic facial attributes, Local octal pattern algorithm [LOP] and Sparse codeword applied in offline and online. The offline and online procedures in face image binning techniques apply with sparse code. Experimental results with Pubfig dataset shows that the proposed LOP along with sparse codewords able to provide matching results with increased accuracy of 90%.

Paul Ion Trap as a Diagnostic for Plasma Focus

Science.gov (United States)

Sadat Kiai, S. M.; Adlparvar, S.; Zirak, A.; Alhooie, Samira; Elahi, M.; Sheibani, S.; Safarien, A.; Farhangi, S.; Dabirzadeh, A. A.; Khalaj, M. M.; Mahlooji, M. S.; KaKaei, S.; Talaei, A.; Kashani, A.; Tajik Ahmadi, H.; Zahedi, F.

2010-02-01

The plasma discharge contamination by high and low Z Impurities affect the rate of nuclear fusion reaction products, specially when light particles have to be confined. These impurities should be analyzed and can be fairly controlled. This paper reports on the development of a Paul ion trap with ion sources by impact electron ionization as a diagnostic for the 10 kJ Iranian sunshine plasma focus device. Preliminary results of the residual gas are analyzed and presented.

Carcinogenic risk in diagnostic nuclear medicine: biological and epidemiological considerations

International Nuclear Information System (INIS)

Overbeek, F.; Pauwels, E.K.J.; Broerse, J.J.

1994-01-01

During the last decade new data have become available on the mechanism of carcinogenesis and on cancer induction by ionizing radiation. This review concentrates on these two items in relation to the use of radiopharmaceuticals in diagnostic nuclear medicine. On the basis of reports of expert committees, the concept of radiation risk is elucidated for high and low doses. Mortality risk factors due to ionizing radiation are put in perspective to other risks. The extra risk for patients who undergo a scintigraphic examination for fatal cancer is very small and is of the order of 1.4 x 10 -4 . It is most unlikely that this figure can even be verified by actual measurement since the majority of nuclear medicine patients will die of other causes before the radiogenic cancer manifests itself. (orig.)

ℓ0 -based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation

Science.gov (United States)

Xu, Xia; Shi, Zhenwei; Pan, Bin

2018-07-01

Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually ℓ0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with ℓ0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the ℓ0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of hyperspectral images into SMoSU. By considering the spectral correlation in hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.

Biological effects of low-level ionizing and non-ionizing radiation

International Nuclear Information System (INIS)

Upton, A.C.

1986-01-01

Early in this century it was recognized that large doses of ionizing radiation could injure almost any tissue in the body, but small doses were generally thought to be harmless. By the middle of the century however it came to be suspected that even the smallest doses of ionizing radiation to the gonads might increase the risk of hereditary disease in subsequently-conceived offspring. Since then the hypothesis that carcinogenic and teratogenic effects also have no threshold has been adopted for purposes of radiological protection. It is estimated nevertheless that the risks that may be associated with natural background levels of ionizing irradiation are too small to be detectable. Hence validation of such risk estimates will depend on further elucidation of the dose-effect relationships and mechanisms of the effects in question, through studies at higher dose levels. In contrast to the situation with ionizing radiation, exposure to natural background levels of ultraviolet radiation has been implicated definitively in the etiology of skin cancers in fair-skinned individuals. Persons with inherited effects in DNA repair capacity are particularly susceptible. Non-ionizing radiations of other types can also affect health at high dose levels, but whether they can cause injury at low levels of exposure is not known

Sparse Linear Solver for Power System Analysis Using FPGA

National Research Council Canada - National Science Library

Johnson, J. R; Nagvajara, P; Nwankpa, C

2005-01-01

.... Numerical solution to load flow equations are typically computed using Newton-Raphson iteration, and the most time consuming component of the computation is the solution of a sparse linear system...

Sparse Machine Learning Methods for Understanding Large Text Corpora

Data.gov (United States)

National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...

Radiation protection of the patient during medical uses of ionizing radiation in the GDR

International Nuclear Information System (INIS)

Arndt, D.

1987-01-01

Section 18 of the new Radiation Protection Ordinance of the GDR defines basic principles for the radiation protection of patients undergoing diagnostic examinations or treatments with ionizing radiation, including, for example, the requirements that necessary exposures should be justifiable in terms of the benefit to be expected and that doses administered should be limited to as low an amount as possible. An outline is given of these principles, their importance and enforcement. (author)

Differentiation of isomeric 2-aryldimethyltetrahydro-5-quinolinones by electron ionization and electrospray ionization mass spectrometry.

Science.gov (United States)

Kumar, Ch Dinesh; Chary, V Naresh; Dinesh, A; Reddy, P S; Srinivas, K; Gayatri, G; Sastry, G N; Prabhakar, S

2011-10-15

A series of isomeric 2-aryl-6,6-dimethyltetrahydro-5-quinolinones (set I) and 2-aryl-7,7-dimethyltetrahydro-5-quinolinones (set II) were studied under positive ion electron ionization (EI) and electrospray ionization (ESI) techniques. Under EI conditions, the molecular ions were found to be less stable in set I isomers, and they resulted in abundant fragment ions, i.e., [M-CH(3)](+), [M-CO](+.), [M-HCO](+), [M-(CH(3),CO)](+), and [M-(CH(3),CH(2)O)](+), when compared with set II isomers. In addition, the set I isomers showed specific fragment ions corresponding to [M-OH](+) and [M-OCH(3)](+). The retro-Diels-Alder (RDA) product ion was always higher in set II isomers. The ESI mass spectra produced [M + H](+) ions, and their decomposition showed favorable loss of CH(3) radical, CH(4) and C(2)H(6) molecules in set I isomers. The set II isomers, however, showed predominant RDA product ions, and specific loss of H(2)O. The selectivity in EI and ESI was attributed to the instability of set I isomers by the presence of a gem-dimethyl group at the α-position, and it was supported by the data from model compounds without a gem-dimethyl group. Density functional theory (DFT) calculations successfully corroborated the fragmentation pathways for diagnostic ions. This study revealed the effect of a gem-dimethyl group located at the α-position to the carbonyl having aromatic/unsaturated carbon on the other side of the carbonyl group. Copyright © 2011 John Wiley & Sons, Ltd.

Better Size Estimation for Sparse Matrix Products

DEFF Research Database (Denmark)

Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus

2010-01-01

We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected t...

Shared decision-making: is it time to obtain informed consent before radiologic examinations utilizing ionizing radiation? Legal and ethical implications.

Science.gov (United States)