Multiphoton ionization for hydrogen plasma diagnostics
International Nuclear Information System (INIS)
Bonnie, J.H.M.
1987-01-01
In this thesis the processes leading to the formation of negative ions (H - ) in hydrogen discharges are studied. These ions enable efficient production of a beam of fast neutral particles. Such beams are applied in nuclear fusion research. A model has been generally accepted in which H - is formed by means of dissociative attachment (DA) of electrons to vibrationally excited hydrogen molecules [H 2 (υ'')] molecule: when υ'' is low, electron emission is most probable, but when υ'' is high, H - production dominates. A necessary preliminary to the DA process is the presence of sufficient [H 2 (υ'')] molecules with υ'' > 4. By determining the densities of hydrogen molecules in the various vibrational levels as a function of the various discharge parameters (scaling laws), insight can be gained into the extent to which the DA process contributes to H - formation. Since the de-excitation of [H 2 (υ'')] molecules by H atoms is expected to have a large cross section, it is also relevant to determine the scaling laws for atomic hydrogen. This thesis gives an account of the development of an experimental setup for obtaining such measurements, and reports the first results achieved. In view of the anticipated density of the vibrationally excited molecules and the detection limit considered feasible, the diagnostic chosen was resonance-enhanced multiphoton ionization (REMPI). The principle is based on state-selective ionization with REMPI of particles effusing from the discharge chamber through an aperture in the wall. The ions produced in the REMPI-process are then detected. The use of both an electric and a magnetic field makes it possible to distinguish the REMPI ions from those originating elsewhere, such as plasma ions or photodesorption ions. 145 refs.; 25 figs.; 6 tabs
Cumulative ionizing radiation during coronary diagnostic and interventional procedures
International Nuclear Information System (INIS)
Oyarzun C, Carlos; Ramirez, Alfredo
2001-01-01
The diagnostic ability of ionizing radiation is well known and has led to the development of high speed, high resolution axial tomography, for which we must assume that the patient population is being exposed to ionizing radiation that could become great. In cardiology, especially, we attend patients that have to undergo two or three angio graph, diagnostic and other therapeutic procedures, accumulating radiation doses that are ignored and are not recorded. The skin doses are shown that were received by 18 patients in procedures that included coronary angiography and that were measured with cesium thermoluminescent detectors, varying from 54 to 877 mSv. per procedure. We propose that a personal register be set up in Chile to record the magnitude of the radiation received by a patient (CO)
International Nuclear Information System (INIS)
Myasnik, M.N.; Morozov, I.I.; Petin, V.G.
1980-01-01
The dependence of the photoreactivation effect in E coli Bsub(s-1) cells on LET and energy of sparsely ionizing radiation was studied. The photoreactivation was shown to be absent after densely ionizing radiation (α-particles of 239 Pu; fast neutron Esub(n) = 0.85 MeV) and after sparsely ionizing radiation with energies below 200 keV. In those cases where photoreactivation took place, the photoreactivable sector was found to increase with the voltage of the radiation. (author)
Computational evaluation of a pencil ionization chamber in a standard diagnostic radiology beam
International Nuclear Information System (INIS)
Mendonca, Dalila Souza Costa; Neves, Lucio Pereira; Perini, Ana Paula; Belinato, Walmir
2016-01-01
In this work a pencil ionization chamber was evaluated. This evaluation consisted in the determination of the influence of the ionization chamber components in its response. For this purpose, the Monte Carlo simulations and the spectrum of the standard diagnostic radiology beam (RQR5) were utilized. The results obtained, showed that the influence of the ionization chamber components presented no significant influence on the chamber response. Therefore, this ionization chamber is a good alternative for dosimetry in diagnostic radiology. (author)
UV laser ionization and electron beam diagnostics for plasma lenses
International Nuclear Information System (INIS)
Govil, R.; Volfbeyn, P.; Leemans, W.
1995-04-01
A comprehensive study of focusing of relativistic electron beams with overdense and underdense plasma lenses requires careful control of plasma density and scale lengths. Plasma lens experiments are planned at the Beam Test Facility of the LBL Center for Beam Physics, using the 50 MeV electron beam delivered by the linac injector from the Advanced Light Source. Here we present results from an interferometric study of plasmas produced in tri-propylamine vapor with a frequency quadrupled Nd:YAG laser at 266 nm. To study temporal dynamics of plasma lenses we have developed an electron beam diagnostic using optical transition radiation to time resolve beam size and divergence. Electron beam ionization of the plasma has also been investigated
Forbidden lines of highly ionized ions for localized plasma diagnostics
International Nuclear Information System (INIS)
Hinnov, E.; Fonck, R.; Suckewer, S.
1980-06-01
Numerous optically forbidden lines resulting from magnetic dipole transitions in low-lying electron configurations of highly ionized Fe, Ti and Cr atoms have been identified in PLT and PDX tokamak discharges, and applied for localized diagnostics in the high-temperature (0.5 to 3.0 keV) interior of these plasmas. The measurements include determination of local ion densities and their variation in time, and of ion motions (ion temperature, plasma rotations) through Doppler effect of the lines. These forbidden lines are particularly appropriate for such measurements because under typical tokamak conditions their emissivities are quite high (10 11 to 10 14 photons/cm 3 -sec), and their relatively long wavelengths allow the use of intricate optical techniques and instrumentation. The spatial location of the emissivity is directly measurable, and tends to occur near radii where the ionization potential of the ion in question is equal to the local electron temperature. In future larger and presumably higher-temperature tokamaks analogous measurements with somewhat heavier atoms, particularly krypton, and perhaps zirconium appear both feasible and desirable
Density diagnostics of ionized outflows in active galacitc nuclei
Mao, J.; Kaastra, J.; Mehdipour, M.; Raassen, T.; Gu, L.
2017-10-01
Ionized outflows in Active Galactic Nuclei are thought to influence their nuclear and local galactic environment. However, the distance of outflows with respect to the central engine is poorly constrained, which limits our understanding of the kinetic power by the outflows. Therefore, the impact of AGN outflows on their host galaxies is uncertain. Given the density of the outflows, their distance can be immediately obtained by the definition of the ionization parameter. Here we carry out a theoretical study of density diagnostics of AGN outflows using absorption lines from metastable levels in Be-like to F-like ions. With the new self-consistent photoionization model (PION) in the SPEX code, we are able to calculate ground and metastable level populations. This enable us to determine under what physical conditions these levels are significantly populated. We then identify characteristic transitions from these metastable levels in the X-ray band. Firm detections of absorption lines from such metastable levels are challenging for current grating instruments. The next generation of spectrometers like X-IFU onboard Athena will certainly identify the presence/absence of these density- sensitive absorption lines, thus tightly constraining the location and the kinetic power of AGN outflows.
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
Electron Beam Diagnostics in Plasmas Based on Electron Beam Ionization
Leonhardt, Darrin; Leal-Quiros, Edbertho; Blackwell, David; Walton, Scott; Murphy, Donald; Fernsler, Richard; Meger, Robert
2001-10-01
Over the last few years, electron beam ionization has been shown to be a viable generator of high density plasmas with numerous applications in materials modification. To better understand these plasmas, we have fielded electron beam diagnostics to more clearly understand the propagation of the beam as it travels through the background gas and creates the plasma. These diagnostics vary greatly in sophistication, ranging from differentially pumped systems with energy selective elements to metal 'hockey pucks' covered with thin layers of insulation to electrically isolate the detector from the plasma but pass high energy beam electrons. Most importantly, absolute measurements of spatially resolved beam current densities are measured in a variety of pulsed and continuous beam sources. The energy distribution of the beam current(s) will be further discussed, through experiments incorporating various energy resolving elements such as simple grids and more sophisticated cylindrical lens geometries. The results are compared with other experiments of high energy electron beams through gases and appropriate disparities and caveats will be discussed. Finally, plasma parameters are correlated to the measured beam parameters for a more global picture of electron beam produced plasmas.
Oncology Patient Perceptions of the Use of Ionizing Radiation in Diagnostic Imaging.
Steele, Joseph R; Jones, Aaron K; Clarke, Ryan K; Giordano, Sharon H; Shoemaker, Stowe
2016-07-01
To measure the knowledge of oncology patients regarding use and potential risks of ionizing radiation in diagnostic imaging. A 30-question survey was developed and e-mailed to 48,736 randomly selected patients who had undergone a diagnostic imaging study at a comprehensive cancer center between November 1, 2013 and January 31, 2014. The survey was designed to measure patients' knowledge about use of ionizing radiation in diagnostic imaging and attitudes about radiation. Nonresponse bias was quantified by sending an abbreviated survey to patients who did not respond to the original survey. Of the 48,736 individuals who were sent the initial survey, 9,098 (18.7%) opened it, and 5,462 (11.2%) completed it. A total of 21.7% of respondents reported knowing the definition of ionizing radiation; 35.1% stated correctly that CT used ionizing radiation; and 29.4% stated incorrectly that MRI used ionizing radiation. Many respondents did not understand risks from exposure to diagnostic doses of ionizing radiation: Of 3,139 respondents who believed that an abdominopelvic CT scan carried risk, 1,283 (40.9%) believed sterility was a risk; 669 (21.3%) believed heritable mutations were a risk; 657 (20.9%) believed acute radiation sickness was a risk; and 135 (4.3%) believed cataracts were a risk. Most patients and caregivers do not possess basic knowledge regarding the use of ionizing radiation in oncologic diagnostic imaging. To ensure health literacy and high-quality patient decision making, efforts to educate patients and caregivers should be increased. Such education might begin with information about effects that are not risks of diagnostic imaging. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.
Optical remote diagnostics of atmospheric propagating beams of ionizing radiation
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.
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
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
Counseling Patients Exposed to Ionizing Radiation in Diagnostic Radiology During Pregnancy
International Nuclear Information System (INIS)
Brnic, Z.; Leder, N.I.; Popic Ramac, J.; Vidjak, V.; Knezevic, Z.
2013-01-01
There are many false assumptions regarding influence of radiation on pregnant patients and fetus during diagnostic procedures in spite of scientific facts based on studies (both in general population and among physicians). These false assumptions are mostly based on the idea that every diagnostic procedure that uses ionizing radiation is a cause for serious concern and consideration for artificial abortion as a possible solution. We have analysed the data of counselling of pregnant patients exposed to ionizing radiation during diagnostic procedures in University Hospital Merkur, during a period of four years. In this period we had 26 patients come in counselling due to exposure to ionizing radiation during pregnancy. Results show that most of these patients have been exposed to radiation between 2nd and 3rd week of gestation (36 %), between 4th and 5th week - 32 %; before 2nd week - 24%; and after 6th week of gestation less than 8 %. Average doses were: up to 0.01 cGy in 46.2 % patients; 0.01 - 0.15 cGy in 19.2 % patients; 0.2 - 1 cGy in 26.9 % and 1 cGy or more in 7.7 % of patients. No one of the counselled patients had a medical indication for abortion, even though in a small percentage of patients abortion was a personal subjective decision. Considering that there are no Croatian guidelines for counselling patients exposed to ionizing radiation during pregnancy, recommendation is to use International Commission on Radiological Protection (ICRP) guidelines for management of pregnant patients exposed to ionizing radiation.(author)
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...
International Nuclear Information System (INIS)
Costa, Alessandro Martins da
2003-01-01
A quality control program of X-ray equipment used in diagnostic radiology and radiotherapy requires the check of the beam qualities constancy in terms of the half-value layers. In this work, two special double-faced parallel-plate ionization chambers were developed with inner electrodes of different materials, in tandem system. The different energy response of the two faces of each chamber allowed the development of tandem systems useful for the check of beam qualities constancy. The main application of these ionization chambers will be in quality control programs of diagnostic and therapeutic X-ray equipment for confirmation of half-value layers previously determined by the conventional method. Moreover, the tandem chambers may also be utilized for measurements of air kerma values (and air kerma rates) in kilo voltage X-radiation fields used for diagnostic and therapeutic procedures. The chambers were studied in relation to their operational characteristics, and they were calibrated in X-ray beams in accordance to international recommendations. They presented a very good level of performance. In this developed system no absorbers or special set-ups are necessary. A methodology of use of the chambers in the quality control of diagnostic and therapeutic X-ray systems was established, with the elaboration of the respective procedures. (author)
Multi-probe ionization chamber system for nuclear-generated plasma diagnostics
International Nuclear Information System (INIS)
Choi, W.Y.; Ellis, W.H.
1990-01-01
This paper reports on the pulsed ionization chamber (PIC) plasma diagnostic system used in studies of nuclear seeded plasma kinetics upgraded to increase the capabilities and extend the range of plasma parameter measurements to higher densities and temperatures. The PIC plasma diagnostic chamber has been provided with additional measurement features in the form of conductivity and Langmuir probes, while the overall experimental system has been fully automated, with computerized control, measurement, data acquisition and analysis by means of IEEE-488 (GPIB) bus control and data transfer protocols using a Macintosh series microcomputer. The design and use of a simple TTL switching system enables remote switching among the various GPIB instruments comprising the multi-probe plasma diagnostic system using software, without the need for a microprocessor. The new system will be used to extend the present study of nuclear generated plasma in He, Ar, Xe, fissionable UF 6 and other fluorine containing gases
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.)
Li, F.; Nie, Z.; Wu, Y. P.; Guo, B.; Zhang, X. H.; Huang, S.; Zhang, J.; Cheng, Z.; Ma, Y.; Fang, Y.; Zhang, C. J.; Wan, Y.; Xu, X. L.; Hua, J. F.; Pai, C. H.; Lu, W.; Mori, W. B.
2018-04-01
We report the transverse phase space diagnostics for electron beams generated through ionization injection in a laser-plasma accelerator. Single-shot measurements of both ultimate emittance and Twiss parameters are achieved by means of permanent magnetic quadrupole. Beams with emittance of μm rad level are obtained in a typical ionization injection scheme, and the dependence on nitrogen concentration and charge density is studied experimentally and confirmed by simulations. A key feature of the transverse phase space, matched beams with Twiss parameter α T ≃ 0, is identified according to the measurement. Numerical simulations that are in qualitative agreement with the experimental results reveal that a sufficient phase mixing induced by an overlong injection length leads to the matched phase space distribution.
Directory of Open Access Journals (Sweden)
Benjamin M Haley
Full Text Available 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 bomb survivors (DDREFLSS. It was calculated by applying a linear-quadratic dose response model to data from Japanese atomic bomb survivors and a limited number of animal studies.We argue that the linear-quadratic model does not provide appropriate support to estimate the risk of contemporary exposures. In this work, we re-estimated DDREFLSS using 15 animal studies that were not included in BEIR VII's original analysis. Acute exposure data led to a DDREFLSS estimate from 0.9 to 3.0. By contrast, data that included both acute and protracted exposures led to a DDREFLSS estimate from 4.8 to infinity. These two estimates are significantly different, violating the assumptions of the linear-quadratic model, which predicts that DDREFLSS values calculated in either way should be the same.Therefore, we propose that future estimates of the risk of protracted exposures should be based on direct comparisons of data from acute and protracted exposures, rather than from extrapolations from a linear-quadratic model. The risk of low dose exposures may be extrapolated from these protracted estimates, though we encourage ongoing debate as to whether this is the most valid approach. We also encourage efforts to enlarge the datasets used to estimate the risk of protracted exposures by including both human and animal data, carcinogenesis outcomes, a wider range of exposures, and by making more radiobiology data publicly accessible. We believe that these steps will contribute to better estimates
Danehkar, Ashkbiz; Karovska, Margarita; Maksym, Walter Peter; Montez, Rodolfo
2018-01-01
The planetary nebula NGC 5189 shows one of the most spectacular morphological structures among planetary nebulae with [WR]-type central stars. Using high-angular resolution HST/WFC3 imaging, we discovered inner, low-ionization structures within a region of 0.3 parsec × 0.2 parsec around the central binary system. We used Hα, [O III], and [S II] emission line images to construct line-ratio diagnostic maps, which allowed us to spatially resolve two distinct low-ionization envelopes within the inner, ionized gaseous environment, extending over a distance of 0.15 pc from the central binary. Both the low-ionization envelopes appear to be expanding along a NE to SW symmetric axis. The SW envelope appears smaller than its NE counterpart. Our diagnostic maps show that highly-ionized gas surrounds these low-ionization envelopes, which also include filamentary and clumpy structures. These envelopes could be a result of a powerful outburst from the central interacting binary, when one of the companions (now a [WR] star) was in its AGB evolutionary stage, with a strong mass-loss generating dense circumstellar shells. Dense material ejected from the progenitor AGB star is likely heated up as it propagates along a symmetric axis into the previously expelled low-density material. Our new diagnostic methodology is a powerful tool for high-angular resolution mapping of low-ionization structures in other planetary nebulae with complex structures possibly caused by past outbursts from their progenitors.
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)
International Nuclear Information System (INIS)
Bessa, Ana Carolina Moreira de
2007-01-01
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 inter compared 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: 67 Ga, 201 Tl and 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)
International Nuclear Information System (INIS)
Bessa, Ana Carolina Moreira de
2006-01-01
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: 67 Ga, 201 Tl and 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)
International Nuclear Information System (INIS)
Bosch de Basea, M.; Cardis, E.; Vrijheid, M.
2011-01-01
Ionizing radiation for diagnostic purposes are an indispensable tool in modern medicine. The increasing use of computed tomography (CT) in children and adolescents is of concern both from the point of view of radio-protection and public health. children are more sensitive to the carcinogenic effects of ionizing radiation than adults. Children also have a higher life expectancy to show any detrimental effect. Furthermore, because of their smaller mass, CT scans in children receiving higher doses of radiation in specific organs in adults.
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.
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.
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.
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)
A New Diagnostic Diagram of Ionization Sources for High-redshift Emission Line Galaxies
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.
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
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.)
Özenci, Volkan; Patel, Robin; Ullberg, Måns; Strålin, Kristoffer
2018-01-18
Although there are several US Food and Drug Administration (FDA)-approved/cleared molecular microbiology diagnostics for direct analysis of patient samples, all are single target or panel-based tests. There is no FDA-approved/cleared diagnostic for broad microbial detection. Polymerase chain reaction (PCR)/electrospray ionization-mass spectrometry (PCR/ESI-MS), commercialized as the IRIDICA system (Abbott) and formerly PLEX-ID, had been under development for over a decade and had become CE-marked and commercially available in Europe in 2014. Capable of detecting a large number of microorganisms, it was under review at the FDA when, in April 2017, Abbott discontinued it. This turn of events represents not only the loss of a potential diagnostic tool for infectious diseases but may be a harbinger of similar situations with other emerging and expensive microbial diagnostics, especially genomic tests. © The Author(s) 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
Energy Technology Data Exchange (ETDEWEB)
Sharifi, B., E-mail: babak_sharifi88@yahoo.com [Graduate University of Advanced Technology, Kerman (Iran, Islamic Republic of); Zamani Zeinali, H. [Application of Radiation Research School, Nuclear Science and Technology Research Institute, AEOI, Karaj (Iran, Islamic Republic of); Soltani, J.; Negarestani, A. [Graduate University of Advanced Technology, Kerman (Iran, Islamic Republic of); Shahvar, A. [Application of Radiation Research School, Nuclear Science and Technology Research Institute, AEOI, Karaj (Iran, Islamic Republic of)
2015-01-11
Medical diagnostic equipment, like diagnostic radiology and mammography require a dosimeter with high accuracy for dosimetry of the diagnostic X-ray beam. Ionization chambers are suitable instruments for dosimetry of diagnostic-range X-ray beams because of their appropriate response and high reliability. This work introduces the design and fabrication of a new parallel plate ionization chamber with a PMMA body, graphite-coated PMMA windows (0.5 mm thick) and a graphite-foil central electrode (0.1 mm thick, 0.7 g/cm{sup 3} dense). This design improves upon the response characteristics of existing designs through the specific choice of materials as well as the appropriate size and arrangement of the ionization chamber components. The results of performance tests conducted at the Secondary Standard Dosimetry laboratory in Karaj-Iran demonstrated the short and long-term stability, the low leakage current, the low directional dependence, and the high ion collection efficiency of the design. Furthermore, the FLUKA Monte Carlo simulations confirmed the low effect of central electrode on this new ionization chamber response. The response characteristics of the parallel plate ionization chamber presented in this work makes the instrument suitable for use as a standard dosimeter in laboratories.
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)
International Nuclear Information System (INIS)
Cardoso, Ricardo de Souza; Bossio, Francisco; Quaresma, Daniel da Silva; Peixoto, Jose Guilherme Pereira
2013-01-01
Activities radiotherapy, diagnostic radiology and radiation protection, require knowledge of physical and dosimetric parameters, to be applied safely. Aiming to meet demand in Brazil, the National Laboratory of Metrology of Ionising Radiation - LNMRI - is deploying the primary standard for the calibration of secondary standard chambers, used in quality control in hospitals, clinics and industries. (author)
Energy Technology Data Exchange (ETDEWEB)
Radtke, R; Guenther, K [Akademie der Wissenschaften der DDR, Berlin. Zentralinstitut fuer Elektronenphysik
1976-05-11
A diagnostic technique for the determination of pressure, temperature and its radial distribution, the strength of the electric field and the current of a wall-stabilized pulse hydrogen arc at a pressure of 10 atm and a maximum power of 120 kW/cm arc length is developed.
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.
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.
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.
International Nuclear Information System (INIS)
Wakeford, R.
2008-01-01
A statistical association between childhood leukaemia and an abdominal X-ray examination of the pregnant mother was first reported in 1956 from a case-control study of childhood cancer mortality conducted in Great Britain. This study, later called the Oxford Survey of Childhood Cancers (OSCC), was continued and eventually showed a highly statistically significant ∼50% proportional increase in the risk of childhood leukaemia associated with antenatal diagnostic radiography. The association has been confirmed by many case-control studies carried out around the world, the appropriately combined results of which show a highly statistically significant increase in risk that is compatible with the OSCC finding. There is no doubt about the reality of the statistical association, but a causal interpretation has been questioned. On balance, however, the evidence points to low-level irradiation of the fetus increasing the risk of leukaemia in childhood, with an excess relative risk coefficient of around 50 Gy -1 (equivalent to an excess absolute risk coefficient of about 3% Gy -1 ), although the uncertainty associated with these coefficients is considerable and they are likely to be overestimates. In contrast to exposure in utero, the evidence from case-control studies for an association between childhood leukaemia and postnatal exposure to medical diagnostic irradiation is equivocal and sometimes conflicting. Since standard radiation risk models predict that low-level exposure in the early years of life should produce an increased risk of childhood leukaemia that is roughly similar to that arising from fetal exposure, this absence of persuasive evidence is likely to be due to various problems with the studies. This is unfortunate given the rise in relatively high dose diagnostic procedures (e.g. paediatric CT scans) that would be predicted to materially increase the relative risk of childhood leukaemia. (authors)
Pre-ionization and spectroscopic diagnostic of plasma generated and confined by magnetic fields
International Nuclear Information System (INIS)
Honda, R.Y.
1980-01-01
A θ-pinch system has been constructed with pre-heating devices with a total energy of 2 kJ. During this experiment a He Plasma was studied using the following three different diagnostics. a) Magnetic Probes b) Visible Spectroscopy using the Optical Multichannel Analyser - OMA c) Image Converter Camera. The experimental results have been checked with existing theoretical models. The electrical characteristics of the system were determined with the magnetic probe. The Doppler and Stark broadening effects of the λ o = 4686 (angstrom) (HeII) have been used to determine the ionic temperature and electronic density respectively. The time evolution of these parameters was obtained using the OMA. The dynamics of the plasma were observed by high speed photography. Instabilities in the plasma columm have been observed. Good agreement between the experimental and theoretical values was obtained. (author) [pt
Sparse structure regularized ranking
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
National Aeronautics and Space Administration — Densely ionizing radiation is a major component of the space radiation environment and has potentially greater carcinogenic effect compared to sparsely ionizing...
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)
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.
DEFF Research Database (Denmark)
Donné, A.J.H.; Costley, A.E.; Barnsley, R.
2007-01-01
of the measurements—time and spatial resolutions, etc—will in some cases be more stringent. Many of the measurements will be used in the real time control of the plasma driving a requirement for very high reliability in the systems (diagnostics) that provide the measurements. The implementation of diagnostic systems...... on ITER is a substantial challenge. Because of the harsh environment (high levels of neutron and gamma fluxes, neutron heating, particle bombardment) diagnostic system selection and design has to cope with a range of phenomena not previously encountered in diagnostic design. Extensive design and R......&D is needed to prepare the systems. In some cases the environmental difficulties are so severe that new diagnostic techniques are required. The starting point in the development of diagnostics for ITER is to define the measurement requirements and develop their justification. It is necessary to include all...
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.
Sparse structure regularized ranking
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.
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
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.
Sparse distributed memory overview
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.
Efficient convolutional sparse coding
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.
Sparse approximation with bases
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...
Supervised Convolutional Sparse Coding
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.
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)
Supervised Transfer Sparse Coding
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.
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.
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
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.
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.
Compressed sensing & sparse filtering
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.Â Apart from compressed sensing this book contains other related app
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.
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.
Denning, Peter J.
1989-01-01
Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.
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...
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...
Sparse Classification - Methods & Applications
DEFF Research Database (Denmark)
Einarsson, Gudmundur
for analysing such data carry the potential to revolutionize tasks such as medical diagnostics where often decisions need to be based on only a few high-dimensional observations. This explosion in data dimensionality has sparked the development of novel statistical methods. In contrast, classical statistics...
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-12-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-04-11
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang
2017-01-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Turbulent flows over sparse canopies
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.
Sparse Regression by Projection and Sparse Discriminant Analysis
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
In Defense of Sparse Tracking: Circulant Sparse Tracker
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
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.
Language Recognition via Sparse Coding
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
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
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
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.
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....
Sparse Representations of Hyperspectral Images
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.
Sparse Representations of Hyperspectral Images
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.
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
X-ray computed tomography using curvelet sparse regularization.
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.
Image understanding using sparse representations
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
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.)
Sparse PCA with Oracle Property.
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.
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.
Sparse Regression by Projection and Sparse Discriminant Analysis
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.
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
Energy Technology Data Exchange (ETDEWEB)
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)
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
... diuretics Thrombocytosis (high platelet count) Tumors Vitamin A excess Vitamin D excess Lower-than-normal levels may be due to: Hypoparathyroidism Malabsorption Osteomalacia Pancreatitis Renal failure Rickets Vitamin D deficiency Alternative Names Free calcium; Ionized calcium ...
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.)
International Nuclear Information System (INIS)
Arnaud, M.
1985-07-01
In low density, thin plasmas (such as stellar coronae, interstellar medium, intracluster medium) the ionization process is governed by collision between electrons and ions in their ground state. In view of the recent improvements we thought an updating of ionization rates was really needed. The work is based on both experimental data and theoretical works and give separate estimates for the direct and autoionization rates
International Nuclear Information System (INIS)
Jilbert, P.H.
1975-01-01
The invention concerns ionization chambers with particular reference to air-equivalent ionization chambers. In order to ensure that similar chambers have similar sensitivities and responses the surface of the chamber bounding the active volume carries a conducting material, which may be a colloidal graphite, arranged in the form of lines so that the area of the conducting material occupies only a small proportion of the area of said surface. (U.S.)
Arnold, Nicholas; Loch, Stuart; Ballance, Connor; Thomas, Ed
2017-10-01
Low temperature plasmas (Te ADAS) code suite to calculate a level-resolved, generalized collisional-radiative (GCR) model for line emission in low temperature argon plasmas. By combining our theoretical model with experimental electron temperature, density, and spectral measurements from the Auburn Linear eXperiment for Instability Studies (ALEXIS), we have developed diagnostic techniques to measure metastable fraction, electron temperature, and electron density. In the future we hope to refine our methods, and extend our model to plasmas other than ALEXIS. Supported by the U.S. Department of Energy. Grant Number: DE-FG02-00ER54476.
Sparse Matrices in Frame Theory
DEFF Research Database (Denmark)
Lemvig, Jakob; Krahmer, Felix; Kutyniok, Gitta
2014-01-01
Frame theory is closely intertwined with signal processing through a canon of methodologies for the analysis of signals using (redundant) linear measurements. The canonical dual frame associated with a frame provides a means for reconstruction by a least squares approach, but other dual frames...... yield alternative reconstruction procedures. The novel paradigm of sparsity has recently entered the area of frame theory in various ways. Of those different sparsity perspectives, we will focus on the situations where frames and (not necessarily canonical) dual frames can be written as sparse matrices...
Diffusion Indexes with Sparse Loadings
DEFF Research Database (Denmark)
Kristensen, Johannes Tang
The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared...... it to be an important alternative to PC....
Sparse Linear Identifiable Multivariate Modeling
DEFF Research Database (Denmark)
Henao, Ricardo; Winther, Ole
2011-01-01
and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...
Programming for Sparse Minimax Optimization
DEFF Research Database (Denmark)
Jonasson, K.; Madsen, Kaj
1994-01-01
We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...
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....
Peel, Trisha N; Cole, Nicolynn C; Dylla, Brenda L; Patel, Robin
2015-03-01
Identification of pathogen(s) associated with prosthetic joint infection (PJI) is critical for patient management. Historically, many laboratories have not routinely identified organisms such as coagulase-negative staphylococci to the species level. The advent of matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) has enhanced clinical laboratory capacity for accurate species-level identification. The aim of this study was to describe the species-level identification of microorganisms isolated from periprosthetic tissue and fluid specimens using MALDI-TOF MS alongside other rapid identification tests in a clinical microbiology laboratory. Results of rapid identification of bacteria isolated from periprosthetic joint fluid and/or tissue specimens were correlated with clinical findings at Mayo Clinic, Rochester, Minnesota, between May 2012 and May 2013. There were 178 PJI and 82 aseptic failure (AF) cases analyzed, yielding 770 organisms (median, 3/subject; range, 1-19/subject). MALDI-TOF MS was employed for the identification of 455 organisms (59%) in 197 subjects (123 PJIs and 74 AFs), with 89% identified to the species level using this technique. Gram-positive bacteria accounted for 68% and 93% of isolates in PJI and AF, respectively. However, the profile of species associated with infection compared to specimen contamination differed. Staphylococcus aureus and Staphylococcus caprae were always associated with infection, Staphylococcus epidermidis and Staphylococcus lugdunensis were equally likely to be a pathogen or a contaminant, whereas the other coagulase-negative staphylococci were more frequently contaminants. Most streptococcal and Corynebacterium isolates were pathogens. The likelihood that an organism was a pathogen or contaminant differed with the prosthetic joint location, particularly in the case of Propionibacterium acnes. MALDI-TOF MS is a valuable tool for the identification of bacteria isolated from patients
International Nuclear Information System (INIS)
Newton, W.
1984-01-01
The purpose of this article is to simplify some of the relevant points of legislation, biological effects and protection for the benefit of the occupational health nurse not familiar with the nuclear industries. The subject is dealt with under the following headings; Understanding atoms. What is meant by ionizing radiation. Types of ionizing radiation. Effects of radiation: long and short term somatic effects, genetic effects. Control of radiation: occupational exposure, women of reproductive age, medical aspects, principles of control. The occupational health nurse's role. Emergency arrangements: national arrangements for incidents involving radiation, action to be taken by the nurse. Decontamination procedures: external and internal contamination. (U.K.)
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.)
International Nuclear Information System (INIS)
Dennis, J.A.
1982-01-01
The subject is discussed under the headings: characteristics of ionizing radiations; biological effects; comparison of radiation and other industrial risks; principles of protection; cost-benefit analysis; dose limits; the control and monitoring of radiation; reference levels; emergency reference levels. (U.K.)
Tobias, C. A.; Grigoryev, Y. G.
1975-01-01
The biological effects of ionizing radiation encountered in space are considered. Biological experiments conducted in space and some experiences of astronauts during space flight are described. The effects of various levels of radiation exposure and the determination of permissible dosages are discussed.
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...
Image fusion using sparse overcomplete feature dictionaries
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.
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...
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
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
International Nuclear Information System (INIS)
Quaresma, D.S.; Peixoto, J.G.P.; Pereira, M.A.G.
2007-01-01
This work has studied the parameters for the construction of an invasive high voltage meter for the National Reference Laboratory of the Brazilian Net Calibration in Diagnostic Radiology, the National Laboratory of Metrology of the Ionizing Radiation - LNMRI. This study took into consideration the necessity of quality control of the of X-rays equipment required by Ministry of Health - MS, through the regulation N.453. To satisfy the demands of the MS, the recommendation of the norm IEC 61676 was analyzed by using the quantity of Practical Peak Voltage (PPV) in the measurements of the voltage discharge applied to the X-rays tubes, the infra structures of metrology available in the country to offer tracking the components of the high voltage meter through INMETRO and the difficulty of adaptation of the high voltage meter analyser III U in relation to the Pan tak HF160 equipment in which respect the connection of the high voltage cable and the voltage limitations due to the electric configuration of the high voltage generator of the constant potential Pantak HF160 equipment. (author)
Wang, Zhenzhong; Geng, Jianliang; Dai, Yi; Xiao, Wei; Yao, Xinsheng
2015-01-01
The broad applications and mechanism explorations of traditional Chinese medicine prescriptions (TCMPs) require a clear understanding of TCMP chemical constituents. In the present study, we describe an efficient and universally applicable analytical approach based on ultra-performance liquid chromatography coupled to electrospray ionization tandem quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q/TOF-MS) with the MSE (E denotes collision energy) data acquisition mode, which allowed the rapid separation and reliable determination of TCMP chemical constituents. By monitoring diagnostic ions in the high energy function of MSE, target peaks of analogous compounds in TCMPs could be rapidly screened and identified. “Re-Du-Ning” injection (RDN), a eutherapeutic traditional Chinese medicine injection (TCMI) that has been widely used to reduce fever caused by viral infections in clinical practice, was studied as an example. In total, 90 compounds, including five new iridoids and one new sesquiterpene, were identified or tentatively characterized by accurate mass measurements within 5 ppm error. This analysis was accompanied by MS fragmentation and reference standard comparison analyses. Furthermore, the herbal sources of these compounds were unambiguously confirmed by comparing the extracted ion chromatograms (EICs) of RDN and ingredient herbal extracts. Our work provides a certain foundation for further studies of RDN. Moreover, the analytical approach developed herein has proven to be generally applicable for profiling the chemical constituents in TCMPs and other complicated mixtures. PMID:25875968
Neural Network for Sparse Reconstruction
Directory of Open Access Journals (Sweden)
Qingfa Li
2014-01-01
Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.
Diffusion Indexes With Sparse Loadings
DEFF Research Database (Denmark)
Kristensen, Johannes Tang
2017-01-01
The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model...... in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online....
Sparse and stable Markowitz portfolios.
Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace
2009-07-28
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.
SPARSE FARADAY ROTATION MEASURE SYNTHESIS
International Nuclear Information System (INIS)
Andrecut, M.; Stil, J. M.; Taylor, A. R.
2012-01-01
Faraday rotation measure synthesis is a method for analyzing multichannel polarized radio emissions, and it has emerged as an important tool in the study of Galactic and extragalactic magnetic fields. The method requires the recovery of the Faraday dispersion function from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we discuss a recovery method that assumes a sparse approximation of the Faraday dispersion function in an overcomplete dictionary of functions. We discuss the general case when both thin and thick components are included in the model, and we present the implementation of a greedy deconvolution algorithm. We illustrate the method with several numerical simulations that emphasize the effect of the covered range and sampling resolution in the Faraday depth space, and the effect of noise on the observed data.
International Nuclear Information System (INIS)
1977-01-01
An improved ionization chamber type X-ray detector comprises a heavy gas at high pressure disposed between an anode and a cathode. An open grid structure is placed next to the anode and is maintained at a voltage intermediate between the cathode and anode potentials. The electric field which is produced by positive ions drifting towards the cathode is thus shielded from the anode. Current measuring circuits connected to the anode are, therefore, responsive only to electron current flow within the chamber and the recovery time of the chamber is shortened. The grid structure also serves to shield the anode from electrical currents which might otherwise be induced by mechanical vibrations in the ionization chamber structure
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
Sparse seismic imaging using variable projection
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
International Nuclear Information System (INIS)
Steele, D.S.
1987-01-01
An ionization detector having an array of detectors has, for example, grounding pads positioned in the spaces between some detectors (data detectors) and other detectors (reference detectors). The grounding pads are kept at zero electric potential, i.e. grounded. The grounding serves to drain away electrons and thereby prevent an unwanted accumulation of charge in the spaces, and cause the electric field lines to be more perpendicular to the detectors in regions near the grounding pads. Alternatively, no empty space is provided there being additional, grounded, detectors provided between the data and reference detectors. (author)
Orthogonal sparse linear discriminant analysis
Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun
2018-03-01
Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.
Spectra of sparse random matrices
International Nuclear Information System (INIS)
Kuehn, Reimer
2008-01-01
We compute the spectral density for ensembles of sparse symmetric random matrices using replica. Our formulation of the replica-symmetric ansatz shares the symmetries of that suggested in a seminal paper by Rodgers and Bray (symmetry with respect to permutation of replica and rotation symmetry in the space of replica), but uses a different representation in terms of superpositions of Gaussians. It gives rise to a pair of integral equations which can be solved by a stochastic population-dynamics algorithm. Remarkably our representation allows us to identify pure-point contributions to the spectral density related to the existence of normalizable eigenstates. Our approach is not restricted to matrices defined on graphs with Poissonian degree distribution. Matrices defined on regular random graphs or on scale-free graphs, are easily handled. We also look at matrices with row constraints such as discrete graph Laplacians. Our approach naturally allows us to unfold the total density of states into contributions coming from vertices of different local coordinations and an example of such an unfolding is presented. Our results are well corroborated by numerical diagonalization studies of large finite random matrices
Discriminative sparse coding on multi-manifolds
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
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.
Stamm, Ivonne; Hailer, Mandy; Depner, Barbara; Kopp, Peter A; Rau, Jörg
2013-03-01
Yersinia enterocolitica is the main cause of yersiniosis in Europe, one of the five main bacterial gastrointestinal diseases of humans. Beside pigs, companion animals, especially dogs and cats, were repeatedly discussed in the past as a possible source of pathogenic Y. enterocolitica. To investigate the presence and types of Y. enterocolitica in companion animals, a total of 4,325 diagnostic fecal samples from dogs and 2,624 samples from cats were tested. The isolates obtained were differentiated by using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and Fourier transform infrared spectroscopy (FT-IR). Isolated Y. enterocolitica strains were bioserotyped. The detection of the ail gene by PCR and confirmation by FT-IR were used as a pathogenicity marker. Y. enterocolitica strains were isolated from 198 (4.6%) of the dog and 8 (0.3%) of the cat fecal samples investigated. One hundred seventy-nine isolates from dogs were analyzed in detail. The virulence factor Ail was detected in 91.6% of isolates. Isolates of biotype 4 (54.7%) and, to a lesser extent, biotypes 2 (23.5%), 3 (11.2%), and 5 (2.2%) were detected. The remaining 8.4% of strains belonged to the ail-negative biotype 1A. All 7 isolates from cats that were investigated in detail were ail positive. These results indicate that companion animals could be a relevant reservoir for a broad range of presumptively human-pathogenic Y. enterocolitica types. MALDI-TOF MS and FT-IR proved to be valuable methods for the rapid identification of Y. enterocolitica, especially in regard to the large number of samples that were investigated in a short time frame.
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
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
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.
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.
International Nuclear Information System (INIS)
2009-01-01
After having recalled some fundamental notions and measurement units related to ionizing radiations, this document describes various aspects of natural and occupational exposures: exposure modes and sources, exposure levels, biological effects, health impacts. Then, it presents prevention principles aimed at, in an occupational context of use of radiation sources (nuclear industry excluded), reducing and managing these exposures: risk assessment, implementation of safety from the front end. Some practical cases illustrate the radiation protection approach. The legal and regulatory framework is presented: general notions, worker exposure, measures specific to some worker categories (pregnant and breast feeding women, young workers, temporary workers). A last part describes what is to be done in case of incident or accident (dissemination of radioactive substances from unsealed sources, anomaly occurring when using a generator or a sealed source, post-accident situation)
Structure-based bayesian sparse reconstruction
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
Biclustering via Sparse Singular Value Decomposition
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.
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....
SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS
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
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
Learning sparse generative models of audiovisual signals
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...
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.
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
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.
Sparse Learning with Stochastic Composite Optimization.
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).
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...
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
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...
SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS
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.
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.
Deep ensemble learning of sparse regression models for brain disease diagnosis.
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.
Sparse regularization for force identification using dictionaries
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.
Analog system for computing sparse codes
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.
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....
Structure-based bayesian sparse reconstruction
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.
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.
Cognitive aspect of diagnostic errors.
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.
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...
Multilevel sparse functional principal component analysis.
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.
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...
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...
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...
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...
Feature based omnidirectional sparse visual path following
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.
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...
A sparse-grid isogeometric solver
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.
A sparse version of IGA solvers
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.
A sparse-grid isogeometric solver
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 version of IGA solvers
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.
New methods for sampling sparse populations
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...
Ionization Energy: Implications of Preservice Teachers' Conceptions
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…
Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.
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.
Spectrum recovery method based on sparse representation for segmented multi-Gaussian model
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.
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
A sparse electromagnetic imaging scheme using nonlinear landweber iterations
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
Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids
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
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 reconstruction using distribution agnostic bayesian matching pursuit
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
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.)
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...
Sparse learning of stochastic dynamical equations
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.
Sparseness- and continuity-constrained seismic imaging
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.
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...
Robust Fringe Projection Profilometry via Sparse Representation.
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.
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.)
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.)
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)
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
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
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)
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)
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.
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
Noniterative MAP reconstruction using sparse matrix representations.
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.
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 view of Kanerva's sparse distributed memory
Denning, P. J.
1986-01-01
Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.
Wavelets for Sparse Representation of Music
DEFF Research Database (Denmark)
Endelt, Line Ørtoft; Harbo, Anders La-Cour
2004-01-01
We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...
Sparse dynamics for partial differential equations.
Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D; Osher, Stanley
2013-04-23
We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms.
Abnormal Event Detection Using Local Sparse Representation
DEFF Research Database (Denmark)
Ren, Huamin; Moeslund, Thomas B.
2014-01-01
We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality measurem...... is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness....
Partitioning sparse rectangular matrices for parallel processing
Energy Technology Data Exchange (ETDEWEB)
Kolda, T.G.
1998-05-01
The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.
Functional fixedness in a technologically sparse culture.
German, Tim P; Barrett, H Clark
2005-01-01
Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.
Parallel preconditioning techniques for sparse CG solvers
Energy Technology Data Exchange (ETDEWEB)
Basermann, A.; Reichel, B.; Schelthoff, C. [Central Institute for Applied Mathematics, Juelich (Germany)
1996-12-31
Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.
Ionizing radiation in hospitals
International Nuclear Information System (INIS)
Blok, K.; Ginkel, G. van; Leun, K. van der; Muller, H.; Oude Elferink, J.; Vesseur, A.
1985-10-01
This booklet dels with the risks of the use of ionizing radiation for people working in a hospital. It is subdivided in three parts. Part 1 treats the properties of ionizing radiation in general. In part 2 the various applications are discussed of ionizing radiation in hospitals. Part 3 indicates how a not completely safe situation may be improved. (H.W.). 14 figs.; 4 tabs
Dosimetry of ionizing radiation
International Nuclear Information System (INIS)
Musilek, L.; Seda, J.; Trousil, J.
1992-01-01
The publication deals with a major field of ionizing radiation dosimetry, viz., integrating dosimetric methods, which are the basic means of operative dose determination. It is divided into the following sections: physical and chemical effects of ionizing radiation; integrating dosimetric methods for low radiation doses (film dosimetry, nuclear emulsions, thermoluminescence, radiophotoluminescence, solid-state track detectors, integrating ionization dosemeters); dosimetry of high ionizing radiation doses (chemical dosimetric methods, dosemeters based on the coloring effect, activation detectors); additional methods applicable to integrating dosimetry (exoelectron emission, electron spin resonance, lyoluminescence, etc.); and calibration techniques for dosimetric instrumentation. (Z.S.). 422 refs
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
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...
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.
Interferometric interpolation of sparse marine data
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.
Balanced and sparse Tamo-Barg codes
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.
Atmospheric inverse modeling via sparse reconstruction
Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten
2017-10-01
Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.
Balanced and sparse Tamo-Barg codes
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.
Parallel sparse direct solver for integrated circuit simulation
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...
Improvements in ionization chambers
International Nuclear Information System (INIS)
Whetten, N.R.; Zubal, C.
1980-01-01
A method of reducing mechanical vibrations transmitted to the parallel plate electrodes of ionization chamber x-ray detectors, commonly used in computerized x-ray axial tomography systems, is described. The metal plate cathodes and anodes are mounted in the ionizable gas on dielectric sheet insulators consisting of a composite of silicone resin and glass fibres. (UK)
International Nuclear Information System (INIS)
Mallory, J.; Turlej, Z.
1981-01-01
Dual ionization chambers are provided for use with an electronic smoke detector. The chambers are separated by electrically-conductive partition. A single radiation source extends through the partition into both chambers, ionizing the air in each. The mid-point current of the device may be balanced by adjusting the position of the source
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
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
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.
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
International Nuclear Information System (INIS)
Schrader-Reichhardt, U.; Markus, B.
1978-01-01
Experiments were performed with tetraploid asynchronous and synchronous Chinese hamster ovary fibroblasts. Irradiations were done with 15 MeV electrons at two different irradiation depths and with X-rays of 200 and 29 kV. For several diploid Chinese hamster cell lines it has been shown in literature that S-cells are much more sensitive to combined treatment of hyperthermia and radiation than G1-cells. With our tetraploid CHO fibroblasts we could find no additional enhancement of S-phase killing, converting radioresistant S-cells to the most radiation sensitive phase. (orig./AJ) [de
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...
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...
Introduction to ionizing radiation physics
International Nuclear Information System (INIS)
Musilek, L.
1979-01-01
Basic properties are described of the atom, atomic nucleus and of ionizing radiation particles; nuclear reactions, ionizing radiation sources and ionizing radiation interaction with matter are explained. (J.P.)
Biological Effects of Ionizing Radiation
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 Bayesian Learning for Nonstationary Data Sources
Fujimaki, Ryohei; Yairi, Takehisa; Machida, Kazuo
This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.
Narrowband interference parameterization for sparse Bayesian recovery
Ali, Anum
2015-09-11
This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.
Modern algorithms for large sparse eigenvalue problems
International Nuclear Information System (INIS)
Meyer, A.
1987-01-01
The volume is written for mathematicians interested in (numerical) linear algebra and in the solution of large sparse eigenvalue problems, as well as for specialists in engineering, who use the considered algorithms in the investigation of eigenoscillations of structures, in reactor physics, etc. Some variants of the algorithms based on the idea of a gradient-type direction of movement are presented and their convergence properties are discussed. From this, a general strategy for the direct use of preconditionings for the eigenvalue problem is derived. In this new approach the necessity of the solution of large linear systems is entirely avoided. Hence, these methods represent a new alternative to some other modern eigenvalue algorithms, as they show a slightly slower convergence on the one hand but essentially lower numerical and data processing problems on the other hand. A brief description and comparison of some well-known methods (i.e. simultaneous iteration, Lanczos algorithm) completes this volume. (author)
Sparse random matrices: The eigenvalue spectrum revisited
International Nuclear Information System (INIS)
Semerjian, Guilhem; Cugliandolo, Leticia F.
2003-08-01
We revisit the derivation of the density of states of sparse random matrices. We derive a recursion relation that allows one to compute the spectrum of the matrix of incidence for finite trees that determines completely the low concentration limit. Using the iterative scheme introduced by Biroli and Monasson [J. Phys. A 32, L255 (1999)] we find an approximate expression for the density of states expected to hold exactly in the opposite limit of large but finite concentration. The combination of the two methods yields a very simple geometric interpretation of the tails of the spectrum. We test the analytic results with numerical simulations and we suggest an indirect numerical method to explore the tails of the spectrum. (author)
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.
Fan, Jianqing; Rigollet, Philippe; Wang, Weichen
High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.
Miniature Laboratory for Detecting Sparse Biomolecules
Lin, Ying; Yu, Nan
2005-01-01
A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.
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
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.
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.
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
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...
Support agnostic Bayesian matching pursuit for block sparse signals
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
Local posterior concentration rate for multilevel sparse sequences
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
Joint Group Sparse PCA for Compressed Hyperspectral Imaging.
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.
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...
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....
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.
International Nuclear Information System (INIS)
Alexeev, V.I.; Emelyanov, I.Y.; Ivanov, V.M.; Konstantinov, L.V.; Lysikov, B.V.; Postnikov, V.V.; Rybakov, J.V.
1976-01-01
A miniature ionization chamber having a gas-filled housing which accommodates a guard electrode made in the form of a hollow perforated cylinder is described. The cylinder is electrically associated with the intermediate coaxial conductor of a triaxial cable used as the lead-in of the ionization chamber. The gas-filled housing of the ionization chamber also accommodates a collecting electrode shaped as a rod electrically connected to the center conductor of the cable and to tubular members. The rod is disposed internally of the guard electrode and is electrically connected, by means of jumpers passing through the holes in the guard electrode, to the tubular members. The tubular members embrace the guard electrode and are spaced a certain distance apart along its entire length. Arranged intermediate of these tubular members are spacers secured to the guard electrode and fixing the collecting electrode throughout its length with respect to the housing of the ionization chamber
What is ''ionizing radiation''?
International Nuclear Information System (INIS)
Tschurlovits, M.
1997-01-01
The scientific background of radiation protection and hence ''ionizing radiation'' is undergoing substantial regress since a century. Radiations as we are concerned with are from the beginning defined based upon their effects rather than upon the physical origin and their properties. This might be one of the reasons why the definition of the term ''ionizing radiation'' in radiation protection is still weak from an up to date point of view in texts as well as in international and national standards. The general meaning is unambiguous, but a numerical value depends on a number of conditions and the purpose. Hence, a clear statement on a numerical value of the energy threshold beyond a radiation has to be considered as ''ionizing'' is still missing. The existing definitions are, therefore, either correct but very general or theoretical and hence not applicable. This paper reviews existing definitions and suggests some issues to be taken into account for possible improvement of the definition of ''ionizing radiation''. (author)
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.)
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.
International Nuclear Information System (INIS)
Ried, L.
1982-01-01
A new device is claimed for detecting particles in a gas. The invention comprises a low cost, easy to assemble, and highly accurate particle detector using a single ionization chamber to contain a reference region and a sensing region. The chamber is designed with the radioactive source near one electrode and the second electrode located at a distance less than the distance of maximum ionization from the radioactive source
Relaxations to Sparse Optimization Problems and Applications
Skau, Erik West
Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we
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
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)
Sparse alignment for robust tensor learning.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming
2014-10-01
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.
Regression analysis of sparse asynchronous longitudinal data.
Cao, Hongyuan; Zeng, Donglin; Fine, Jason P
2015-09-01
We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.
Duplex scanning using sparse data sequences
DEFF Research Database (Denmark)
Møllenbach, S. K.; Jensen, Jørgen Arendt
2008-01-01
reconstruction of the missing samples possible. The periodic pattern has the length T = M + A samples, where M are for B-mode and A for velocity estimation. The missing samples can now be reconstructed using a filter bank. One filter bank reconstructs one missing sample, so the number of filter banks corresponds...... to M. The number of sub filters in every filter bank is the same as A. Every sub filter contains fractional delay (FD) filter and an interpolation function. Many different sequences can be selected to adapt the B-mode frame rate needed. The drawback of the method is that the maximum velocity detectable......, the fprf and the resolution are 15 MHz, 3.5 kHz, and 12 bit sample (8 kHz and 16 bit for the Carotid artery). The resulting data contains 8000 RF lines with 128 samples at a depth of 45 mm for the vein and 50 mm for Aorta. Sparse sequences are constructed from the full data sequences to have both...
Transformer fault diagnosis using continuous sparse autoencoder.
Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou
2016-01-01
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.
Joint Sparse Recovery With Semisupervised MUSIC
Wen, Zaidao; Hou, Biao; Jiao, Licheng
2017-05-01
Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.
SLAP, Large Sparse Linear System Solution Package
International Nuclear Information System (INIS)
Greenbaum, A.
1987-01-01
1 - Description of program or function: SLAP is a set of routines for solving large sparse systems of linear equations. One need not store the entire matrix - only the nonzero elements and their row and column numbers. Any nonzero structure is acceptable, so the linear system solver need not be modified when the structure of the matrix changes. Auxiliary storage space is acquired and released within the routines themselves by use of the LRLTRAN POINTER statement. 2 - Method of solution: SLAP contains one direct solver, a band matrix factorization and solution routine, BAND, and several interactive solvers. The iterative routines are as follows: JACOBI, Jacobi iteration; GS, Gauss-Seidel Iteration; ILUIR, incomplete LU decomposition with iterative refinement; DSCG and ICCG, diagonal scaling and incomplete Cholesky decomposition with conjugate gradient iteration (for symmetric positive definite matrices only); DSCGN and ILUGGN, diagonal scaling and incomplete LU decomposition with conjugate gradient interaction on the normal equations; DSBCG and ILUBCG, diagonal scaling and incomplete LU decomposition with bi-conjugate gradient iteration; and DSOMN and ILUOMN, diagonal scaling and incomplete LU decomposition with ORTHOMIN iteration
Object tracking by occlusion detection via structured sparse learning
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.
Manifold regularization for sparse unmixing of hyperspectral images.
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.
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
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...
Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays
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.
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...
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)
The critical ionization velocity
International Nuclear Information System (INIS)
Raadu, M.A.
1980-06-01
The critical ionization velocity effect was first proposed in the context of space plasmas. This effect occurs for a neutral gas moving through a magnetized plasma and leads to rapid ionization and braking of the relative motion when a marginal velocity, 'the critical velocity', is exceeded. Laboratory experiments have clearly established the significance of the critical velocity and have provided evidence for an underlying mechanism which relies on the combined action of electron impact ionization and a collective plasma interaction heating electrons. There is experimental support for such a mechanism based on the heating of electrons by the modified two-stream instability as part of a feedback process. Several applications to space plasmas have been proposed and the possibility of space experiments has been discussed. (author)
International Nuclear Information System (INIS)
Olson, C.L.
1975-01-01
In a recently proposed linear collective accelerator, ions are accelerated in a steep, moving potential well created at the head of an intense relativistic electron beam. The steepness of the potential well and its motion are controlled by the external ionization of a suitable background gas. Calculations concerning optimum choices for the background gas and the ionization method are presented; a two-step photoionization process employing Cs vapor is proposed. In this process, a super-radiant light source is used to excite the gas, and a UV laser is used to photoionize the excited state. The appropriate line widths and coupled ionization growth rate equations are discussed. Parameter estimates are given for a feasibility experiment, for a 1 GeV proton accelerator, and for a heavy ion accelerator (50 MeV/nucleon uranium). (auth)
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.)
Ambient ionization mass spectrometry
International Nuclear Information System (INIS)
Lebedev, A T
2015-01-01
Ambient ionization mass spectrometry emerged as a new scientific discipline only about ten years ago. A considerable body of information has been reported since that time. Keeping the sensitivity, performance and informativity of classical mass spectrometry methods, the new approach made it possible to eliminate laborious sample preparation procedures and triggered the development of miniaturized instruments to work directly in the field. The review concerns the theoretical foundations and design of ambient ionization methods. Their advantages and drawbacks, as well as prospects for application in chemistry, biology, medicine, environmetal analysis, etc., are discussed. The bibliography includes 194 references
Liquid ionizing radiaion detector
International Nuclear Information System (INIS)
deGaston, A.N.
1979-01-01
A normally nonconducting liquid such as liquid hydrocarbon is encased between a pair of electrodes in an enclosure so that when the liquid is subjected to ionizing radiation, the ion pairs so created measurably increase the conductivity of the fluid. The reduced impedance between the electrodes is detectable with a sensitive ohm-meter and indicates the amount of ionizing radiation. The enclosure, the electrodes and the fluid can be constructed of materials that make the response of the detector suitable for calibrating a large range of radiation energy levels. The detector is especially useful in medical applications where tissue equivalent X ray detectors are desired
Radiation dependent ionization model
International Nuclear Information System (INIS)
Busquet, M.
1991-01-01
For laser created plasma simulation, hydrodynamics codes need a non-LTE atomic physics package for both EOS and optical properties (emissivity and opacity). However in XRL targets as in some ICF targets, high Z material can be found. In these cases radiation trapping can induce a significant departure from the optically thin ionization description. The authors present a method to change an existing LTE code into a non-LTE code with coupling of ionization to radiation. This method has very low CPU cost and can be used in 2D simulations
Ionizing Radiation Processing Technology
International Nuclear Information System (INIS)
Rida Tajau; Kamarudin Hashim; Jamaliah Sharif; Ratnam, C.T.; Keong, C.C.
2017-01-01
This book completely brief on the basic concept and theory of ionizing radiation in polymers material processing. Besides of that the basic concept of polymerization addition, cross-linking and radiation degradation also highlighted in this informative book. All of the information is from scientific writing based on comprehensive scientific research in polymerization industry which using the radiation ionizing. It is very useful to other researcher whose study in Nuclear Sciencea and Science of Chemical and Material to use this book as a guideline for them in future scientific esearch.
International Nuclear Information System (INIS)
Hashmi, N.; Van Der Houven Van Oordt, A.J.
1975-01-01
An ion source in which an apertured or foraminous electrode having a multiplicity of openings is spaced from one or more active surfaces of an ionisation electrode, the active surfaces comprising a material capable of ionising by contact ionization a substance to be ionized supplied during operation to the active surface or surfaces comprises means for producing during operation a magnetic field which enables a stable plasma to be formed in the space between the active surface or surfaces and the apertured electrode, the field strength of the magnetic field being preferably in the range between 2 and 8 kilogauss. (U.S.)
High Order Tensor Formulation for Convolutional Sparse Coding
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
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
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
Multiple instance learning tracking method with local sparse representation
Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei
2013-01-01
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
Low-rank sparse learning for robust visual tracking
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
Robust visual tracking via multi-task sparse learning
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
Sparse Machine Learning Methods for Understanding Large Text Corpora
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...
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Sicat, Ronell Barrera; Kruger, Jens; Moller, Torsten; Hadwiger, Markus
2014-01-01
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
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...
Support agnostic Bayesian matching pursuit for block sparse signals
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.
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.
Sparse logistic principal components analysis for binary data
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
Sparse reconstruction using distribution agnostic bayesian matching pursuit
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.
Occlusion detection via structured sparse learning for robust object tracking
Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra
2014-01-01
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
Object tracking by occlusion detection via structured sparse learning
Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra
2013-01-01
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
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....
Sparse encoding of automatic visual association in hippocampal networks
DEFF Research Database (Denmark)
Hulme, Oliver J; Skov, Martin; Chadwick, Martin J
2014-01-01
Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can...
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)
Efficient collaborative sparse channel estimation in massive MIMO
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.
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
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.
Efficient collaborative sparse channel estimation in massive MIMO
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.
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.
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
In-Storage Embedded Accelerator for Sparse Pattern Processing
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...
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.)
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
Occlusion detection via structured sparse learning for robust object tracking
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.
Exhaustive Search for Sparse Variable Selection in Linear Regression
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.
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.
Structure-aware Local Sparse Coding for Visual Tracking
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.
Vector sparse representation of color image using quaternion matrix analysis.
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.
Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging
Desmal, Abdulla
2016-03-01
synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.
International Nuclear Information System (INIS)
Beerens, H.
1986-01-01
Irradiated foods and feed might be identified with two kinds of tests: 1. biochemical: detection of specific products are not yet available 2. microbiological: when a microbial species dissapears from a sample of food i.e. it is not detectable after enrichment (for instance Coliforms in hamburgers) it is likely that the sample has been ionized [fr
International Nuclear Information System (INIS)
Bakken, J.A.; Denes, P.; Piroue, P.A.; Stickland, D.P.; Sumner, R.L.; Taylor, C.; Barone, L.; Borgia, B.; Diemoz, M.; Dionisi, C.; Falciano, S.; Ferroni, F.; Gratta, G.; Longo, E.; Luminari, L.; Morganti, S.; Valente, E.; Blaising, J.J.; Boutigny, D.; Coignet, G.; Karyotakis, Y.; Sauvage, G.; Schneegans, M.; Vivargent, M.; Extermann, P.; Morand, G.; Ossmann, J.; Ruckstuhl, W.; Schaad, T.P.; Lecoq, P.; Walk, W.; Li, P.J.; Micke, M.; Micke, U.; Schmitz, D.
1988-01-01
We report on a precise measurement of the energy loss through ionization by pions in bismuth germanate performed at several values of the incident particles momentum with a prototype of the L3 electromagnetic calorimeter. The experimental results are in good agreement with theoretical predictions showing the relativistic rise modified by density effect. (orig.)
Ionizing radiation from tobacco
International Nuclear Information System (INIS)
Westin, J.B.
1987-01-01
Accidents at nuclear power facilities seem inevitably to bring in their wake a great deal of concern on the part of both the lay and medical communities. Relatively little attention, however, is given to what may be the largest single worldwide source of effectively carcinogenic ionizing radiation: tobacco. The risk of cancer deaths from the Chernobyl disaster are tobacco smoke is discussed
CERN PhotoLab
1973-01-01
Inner structure of an ionization beam scanner, a rather intricate piece of apparatus which permits one to measure the density distribution of the proton beam passing through it. On the outside of the tank wall there is the coil for the longitudinal magnetic field, on the inside, one can see the arrangement of electrodes creating a highly homogeneous transverse electric field.
Basic ionizing radiation symbol
International Nuclear Information System (INIS)
1987-01-01
A description is given of the standard symbol for ionizing radiation and of the conditions under which it should not be used. The Arabic equivalent of some English technical terms in this subject is given in one page. 1 ref., 1 fig
Dartnell, Lewis R
2011-01-01
Ionizing radiation is a ubiquitous feature of the Cosmos, from exogenous cosmic rays (CR) to the intrinsic mineral radioactivity of a habitable world, and its influences on the emergence and persistence of life are wide-ranging and profound. Much attention has already been focused on the deleterious effects of ionizing radiation on organisms and the complex molecules of life, but ionizing radiation also performs many crucial functions in the generation of habitable planetary environments and the origins of life. This review surveys the role of CR and mineral radioactivity in star formation, generation of biogenic elements, and the synthesis of organic molecules and driving of prebiotic chemistry. Another major theme is the multiple layers of shielding of planetary surfaces from the flux of cosmic radiation and the various effects on a biosphere of violent but rare astrophysical events such as supernovae and gamma-ray bursts. The influences of CR can also be duplicitous, such as limiting the survival of surface life on Mars while potentially supporting a subsurface biosphere in the ocean of Europa. This review highlights the common thread that ionizing radiation forms between the disparate component disciplines of astrobiology. © Mary Ann Liebert, Inc.
Ionization chamber smoke detectors
International Nuclear Information System (INIS)
1988-03-01
One kind of smoke detector, the ionization-type, is regulated by the Atomic Energy Control Board (AECB) because it uses a radioactive substance in its mechanism. Radioactivity and radiation are natural phenomena, but they are not very familiar to the average householder. This has led to a number of questions being asked of the AECB. These questions and AECB responses are outlined
Choe, Hyonmin; Deirmengian, Carl A.; Hickok, Noreen J.; Morrison, Tiffany N.; Tuan, Rocky S.
2015-01-01
Orthopaedic infections are complex conditions that require immediate diagnosis and accurate identification of the causative organisms to facilitate appropriate management. Conventional methodologies for diagnosis of these infections sometimes lack accuracy or sufficient rapidity. Current molecular diagnostics are an emerging area of bench-to-bedside research in orthopaedic infections. Examples of promising molecular diagnostics include measurement of a specific biomarker in the synovial fluid...
Ionization of Interstellar Hydrogen
Whang, Y. C.
1996-09-01
Interstellar hydrogen can penetrate through the heliopause, enter the heliosphere, and may become ionized by photoionization and by charge exchange with solar wind protons. A fluid model is introduced to study the flow of interstellar hydrogen in the heliosphere. The flow is governed by moment equations obtained from integration of the Boltzmann equation over the velocity space. Under the assumption that the flow is steady axisymmetric and the pressure is isotropic, we develop a method of solution for this fluid model. This model and the method of solution can be used to study the flow of neutral hydrogen with various forms of ionization rate β and boundary conditions for the flow on the upwind side. We study the solution of a special case in which the ionization rate β is inversely proportional to R2 and the interstellar hydrogen flow is uniform at infinity on the upwind side. We solve the moment equations directly for the normalized density NH/NN∞, bulk velocity VH/VN∞, and temperature TH/TN∞ of interstellar hydrogen as functions of r/λ and z/λ, where λ is the ionization scale length. The solution is compared with the kinetic theory solution of Lallement et al. The fluid solution is much less time-consuming than the kinetic theory solutions. Since the ionization rate for production of pickup protons is directly proportional to the local density of neutral hydrogen, the high-resolution solution of interstellar neutral hydrogen obtained here will be used to study the global distribution of pickup protons.
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
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
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
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
Sparse modeling of spatial environmental variables associated with asthma.
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.
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
Leukemia and ionizing radiation revisited
Energy Technology Data Exchange (ETDEWEB)
Cuttler, J.M. [Cuttler & Associates Inc., Vaughan, Ontario (Canada); Welsh, J.S. [Loyola University-Chicago, Dept. or Radiation Oncology, Stritch School of Medicine, Maywood, Illinois (United States)
2016-03-15
A world-wide radiation health scare was created in the late 19508 to stop the testing of atomic bombs and block the development of nuclear energy. In spite of the large amount of evidence that contradicts the cancer predictions, this fear continues. It impairs the use of low radiation doses in medical diagnostic imaging and radiation therapy. This brief article revisits the second of two key studies, which revolutionized radiation protection, and identifies a serious error that was missed. This error in analyzing the leukemia incidence among the 195,000 survivors, in the combined exposed populations of Hiroshima and Nagasaki, invalidates use of the LNT model for assessing the risk of cancer from ionizing radiation. The threshold acute dose for radiation-induced leukemia, based on about 96,800 humans, is identified to be about 50 rem, or 0.5 Sv. It is reasonable to expect that the thresholds for other cancer types are higher than this level. No predictions or hints of excess cancer risk (or any other health risk) should be made for an acute exposure below this value until there is scientific evidence to support the LNT hypothesis. (author)
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.
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...
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)
Epidemiology and ionizing radiations
International Nuclear Information System (INIS)
Bourguignon, M.; Masse, R.; Slama, R.; Spira, A.; Timarche, M.; Laurier, D.; Billon, S.; Rogel, A.; Telle Lamberton, M.; Catelinois, O.; Thierry, I.; Grosche, B.; Ron, E.; Vathaire, F. de; Cherie Challine, L.; Donadieu, J.; Pirard, Ph.; Bloch, J.; Setbon, M.
2004-01-01
The ionizing radiations have effects on living being. The determinist effects appear since a threshold of absorbed dose of radiation is reached. In return, the stochastic effects of ionizing radiations are these ones whom apparition cannot be described except in terms of probabilities. They are in one hand, cancers and leukemia, on the other hand, lesions of the genome potentially transmissible to the descendants. That is why epidemiology, defined by specialists as the science that studies the frequency and distribution of illness in time and space, the contribution of factors that determine this frequency and this distribution among human populations. This issue gathers and synthesizes the knowledge and examines the difficulties of methodologies. It allows to give its true place to epidemiology. (N.C.)
International Nuclear Information System (INIS)
Houston, J.M.
1977-01-01
An improved ionization chamber type x-ray detector comprises a heavy gas at high pressure disposed between an anode and a cathode. An open grid structure is disposed adjacent the anode and is maintained at a voltsge intermediate between the cathode and anode potentials. The electric field which is produced by positive ions drifting toward the cathode is thus shielded from the anode. Current measuring circuits connected to the anode are, therefore, responsive only to electron current flow within the chamber and the recovery time of the chamber is shortened. The grid structure also serves to shield the anode from electrical currents which might otherwise be induced by mechanical vibrations in the ionization chamber structure
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions
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
International Nuclear Information System (INIS)
Berkey, E.; Reed, W.A. III; Hickam, W.M.
1977-01-01
Sensor to detect thermally ionizable elements or molucules in air, water vapour or oxygen or to be used as alkali leak detector in vacuum systems, e.g. in the pipe system of a liquid-metal cooled FBR. The sensor consists of an filament made of thorium-containing iridium as cathode with a temperature upto 1000 0 C and an anode sheet of molybdenum, nickel or stainless steal. (ORU) [de
International Nuclear Information System (INIS)
Manero Amoros, F.
1962-01-01
In the present paper the working principles of a gridded ionization chamber are given, and all the different factors that determine its resolution power are analyzed in detail. One of these devices, built in the Physics Division of the JEN and designed specially for use in measurements of alpha spectroscopy, is described. finally the main applications, in which the chamber can be used, are shown. (Author) 17 refs
Image fusion via nonlocal sparse K-SVD dictionary learning.
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.
Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.
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.
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
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.
Low-count PET image restoration using sparse representation
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.
Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.
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.
DEFF Research Database (Denmark)
Jørgensen, Jan Trøst; Hersom, Maria
2016-01-01
of disease mechanisms, things are slowly changing. Within the last few years, we have seen an increasing number of predictive biomarker assays being developed to guide the use of targeted cancer drugs. This type of assay is called companion diagnostics and is developed in parallel to the drug using the drug-diagnostic...... co-development model. The development of companion diagnostics is a relatively new discipline and in this review, different aspects will be discussed including clinical and regulatory issues. Furthermore, examples of drugs, such as the ALK and PD-1/PD-L1 inhibitors, that have been approved recently....... Despite having discussed personalized medicine for more than a decade, we still see that most drug prescriptions for severe chronic diseases are largely based on 'trial and error' and not on solid biomarker data. However, with the advance of molecular diagnostics and a subsequent increased understanding...
Pregnancy and ionizing radiation
International Nuclear Information System (INIS)
Plataniotis, Th.N.; Nikolaou, K.I.; Syrgiamiotis, G.V.; Dousi, M.; Panou, Th.; Georgiadis, K.; Bougias, C.
2008-01-01
Full text: In this report there will be presented the effects of ionizing radiation at the fetus and the necessary radioprotection. The biological results on the fetus, caused by the irradiation, depend on the dose of ionizing radiation that it receives and the phase of its evolution. The imminent effects of the irradiation can cause the fetus death, abnormalities and mental retardation, which are the result of overdose. The effects are carcinogenesis and leukemia, which are relative to the acceptable irradiating dose at the fetus and accounts about 0,015 % per 1 mSv. The effects of ionizing radiation depend on the phase of the fetus evolution: 1 st phase (1 st - 2 nd week): presence of low danger; 2 nd phase (3 rd - 8 th week): for doses >100 mSv there is the possibility of dysplasia; 3 rd phase (8 th week - birth): this phase concerns the results with a percentage 0,015 % per 1 mSv. We always must follow some rules of radioprotection and especially at Classical radiation use of necessary protocols (low dose), at Nuclear Medicine use of the right radioisotope and the relative field of irradiation for the protection of the adjacent healthy tissues and at Radiotherapy extreme caution is required regarding the dose and the treatment. In any case, it is forbidden to end a pregnancy when the pregnant undergoes medical exams, in which the uterus is in the beam of irradiation. The radiographer must always discuss the possibility of pregnancy. (author)
International Nuclear Information System (INIS)
Fischer, P.G.
1983-01-01
The still growing use of non-ionizing radiation such as ultraviolet radiation laser light, ultrasound and infrasound, has induced growing interest in the effects of these types of radiation on the human organism, and in probable hazards emanating from their application. As there are up to now no generally approved regulations or standards governing the use of non-ionizing radiation and the prevention of damage, it is up to the manufacturers of the relevant equipment to provide for safety in the use of their apparatus. This situation has led to a feeling of incertainty among manufacturers, as to how which kind of damage should be avoided. Practice has shown that there is a demand for guidelines stating limiting values, for measuring techniques clearly indicating safety thresholds, and for safety rules providing for safe handling. The task group 'Non-ionizing radiation' of the Radiation Protection Association started a programme to fulfill this task. Experts interested in this work have been invited to exchange their knowledge and experience in this field, and a collection of loose leaves will soon be published giving information and recommendations. (orig./HP) [de
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.
Similarity regularized sparse group lasso for cup to disc ratio computation.
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.
Highly-Accelerated Real-Time Cardiac Cine MRI Using k-t SPARSE-SENSE
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.
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.
Quantification of localized vertebral deformities using a sparse wavelet-based shape model.
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.
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.
Joint sparse representation for robust multimodal biometrics recognition.
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.
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.
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....
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
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.
Massively parallel sparse matrix function calculations with NTPoly
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.
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
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.
Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging
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
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.
Identification of MIMO systems with sparse transfer function coefficients
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.
A General Sparse Tensor Framework for Electronic Structure Theory.
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.
Sparse dictionary learning of resting state fMRI networks.
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.
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.
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.
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.
Energy Technology Data Exchange (ETDEWEB)
Zaveryaev, V [Kurchatov Institute, Moscow (Russian Federation); others, and
2012-09-15
The success in achieving peaceful fusion power depends on the ability to control a high temperature plasma, which is an object with unique properties, possibly the most complicated object created by humans. Over years of fusion research a new branch of science has been created, namely plasma diagnostics, which involves knowledge of almost all fields of physics, from electromagnetism to nuclear physics, and up-to-date progress in engineering and technology (materials, electronics, mathematical methods of data treatment). Historically, work on controlled fusion started with pulsed systems and accordingly the methods of plasma parameter measurement were first developed for short lived and dense plasmas. Magnetically confined hot plasmas require the creation of special experimental techniques for diagnostics. The diagnostic set is the most scientifically intensive part of a plasma device. During many years of research operation some scientific tasks have been solved while new ones arose. New tasks often require significant changes in the diagnostic system, which is thus a very flexible part of plasma machines. Diagnostic systems are designed to solve several tasks. As an example here are the diagnostic tasks for the International Thermonuclear Experimental Reactor - ITER: (1) Measurements for machine protection and basic control; (2) Measurements for advanced control; (3) Additional measurements for performance evaluation and physics. Every new plasma machine is a further step along the path to the main goal - controlled fusion - and nobody knows in advance what new phenomena will be met on the way. So in the planning of diagnostic construction we should keep in mind further system upgrading to meet possible new scientific and technical challenges. (author)
Universal Regularizers For Robust Sparse Coding and Modeling
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...
Uniform sparse bounds for discrete quadratic phase Hilbert transforms
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 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 electromagnetic imaging using nonlinear iterative shrinkage thresholding
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
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.
Sparse grid techniques for particle-in-cell schemes
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.
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
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.
Low-rank and sparse modeling for visual analysis
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
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.
Ionization of anisothermal plasmas
International Nuclear Information System (INIS)
Dennery, F.M.
1994-01-01
During this last mid-century, only the temperature of electrons has been involved in the Saha's mass action law, whatever be the other ionic and neutral ones in any isothermal or anisothermal plasma. In order to set aside this underlying paradox in the case of argon ionization, it is necessary to improve this equation of partial equilibrium after having defined: - the basic Gibbs-Duhem's relations for such a polythermal mixture, - the inhomogeneous equilibrium issued from chemical reactions according to Le Chatelier's principle. (author). 3 refs
International Nuclear Information System (INIS)
Sevcik, J.
1976-01-01
Most measuring devices used in gas chromatography consist of detectors that measure the ionization current. The process is based on the collision of a moving high-energy particle with a target particle that is ionised while an electron is freed. The discussion of the conditions of the collision reaction, the properties of the colliding particles, and the intensity of the applied field point to a unified classification of ionisation detectors. Radioactive sources suitable for use in these detectors are surveyed. The slow-down mechanism, recombination and background current effect are discussed
Multiple chamber ionization detector
International Nuclear Information System (INIS)
Solomon, E.E.
1982-01-01
An ionization smoke detector employs a single radiation source in a construction comprising at least two chambers with a center or node electrode. The radioactive source is associated with this central electrode, and its positioning may be adjusted relative to the electrode to alter the proportion of the source that protrudes into each chamber. The source may also be mounted in the plane of the central electrode, and positioned relative to the center of the electrode. The central electrode or source may be made tiltable relative to the body of the detector
Personnel ionizing radiation dosimeter
International Nuclear Information System (INIS)
Williams, R.A.
1975-01-01
A dosimeter and method for use by personnel working in an area of mixed ionizing radiation fields for measuring and/or determining the effective energy of x- and gamma radiation; beta, x-, and gamma radiation dose equivalent to the surface of the body; beta, x-, and gamma radiation dose equivalent at a depth in the body; the presence of slow neutron, fast neutron dose equivalent; and orientation of the person wearing the dosimeter to the source of radiation is disclosed. Optionally integrated into this device and method are improved means for determining neutron energy spectrum and absorbed dose from fission gamma and neutron radiation resulting from accidental criticality
Doubly resonant multiphoton ionization
International Nuclear Information System (INIS)
Crance, M.
1978-01-01
A particular case of doubly resonant multiphoton ionization is theoretically investigated. More precisely, two levels quasi-resonant with two successive harmonics of the field frequency are considered. The method used is based on the effective operator formalism first introduced for this problem by Armstrong, Beers and Feneuille. The main result is to show the possibility of observing large interference effects on the width of the resonances. Moreover this treatment allows us to make more precise the connection between effective operator formalism and standard perturbation theory
International Nuclear Information System (INIS)
Barnett, C.F.; Brisson, D.A.; Greco, S.E.
1978-01-01
During the past year the far-infrared or submillimeter diagnostic research program resulted in three major developments: (1) an optically pumped 0.385-μm D 2 O-laser oscillator-amplifier system was operated at a power level of 1 MW with a line width of less than 50 MHz; (2) a conical Pyrex submillimeter laser beam dump with a retention efficiency greater than 10 4 was developed for the ion temperature Thompson scattering experiment; and (3) a new diagnostic technique was developed that makes use of the Faraday rotation of a modulated submillimeter laser beam to determine plasma current profile. Measurements of the asymmetric distortion of the H/sub α/ (6563 A) spectral line profile show that the effective toroidal drift velocity, dv/sub two vertical bars i/dT/sub i/, may be used as an indicator of plasma quality and as a complement to other ion temperature diagnostics
Plasma production via field ionization
Directory of Open Access Journals (Sweden)
C. L. O’Connell
2006-10-01
Full Text Available Plasma production via field ionization occurs when an incoming particle beam is sufficiently dense that the electric field associated with the beam ionizes a neutral vapor or gas. Experiments conducted at the Stanford Linear Accelerator Center explore the threshold conditions necessary to induce field ionization by an electron beam in a neutral lithium vapor. By independently varying the transverse beam size, number of electrons per bunch, or bunch length, the radial component of the electric field is controlled to be above or below the threshold for field ionization. Additional experiments ionized neutral xenon and neutral nitric oxide by varying the incoming beam’s bunch length. A self-ionized plasma is an essential step for the viability of plasma-based accelerators for future high-energy experiments.
Biology of ionizing radiation effects
International Nuclear Information System (INIS)
Ferradini, C.; Pucheault, J.
1983-01-01
The present trends in biology of ionizing radiation are reviewed. The following topics are investigated: interaction of ionizing radiations with matter; the radiolysis of water and aqueous solutions; properties of the free radicals intervening in the couples O 2 /H 2 O and H 2 O/H 2 ; radiation chemistry of biological compounds; biological effects of ionizing radiations; biochemical mechanisms involving free radicals as intermediates; applications (biotechnological applications, origins of life) [fr
News about ionized food identification
International Nuclear Information System (INIS)
Raffi, J.
1995-01-01
The ionizing radiations are used to clean food and increase their preservation life. If a lot of countries permits ionized products commercialization, others are opposed to it. To control the commercial exchanges, check the applied treatment aim and give to the consumers a better information, several ionized food identification methods were perfected and several are about to be recognized as european standards. 4 refs. 3 figs, 1 tab
Efficient coordinated recovery of sparse channels in massive MIMO
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.
Sparse Generalized Fourier Series via Collocation-based Optimization
2014-11-01
Theory 51, 12 (2005) 4203– 4215. [6] P. CONSTANTINE , M. ELDRED AND E. PHIPPS, Sparse pseu- dospectral approximation method. Comput. Methods Appl. Mech...Visition XVI: Algorithms, Techniques, Active Vision , and Materials Handling, 224 (1997). [15] J. SHEN AND L. WANG, Some recent advances on spectral methods
A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri
2014-01-01
This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...
Robust visual tracking via structured multi-task sparse learning
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 structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary
Behavior of greedy sparse representation algorithms on nested supports
DEFF Research Database (Denmark)
Mailhé, Boris; Sturm, Bob L.; Plumbley, Mark
2013-01-01
is not locally nested: there is a dictionary and supports Γ ⊃ Γ′ such that OMP can recover all signals with support Γ, but not all signals with support Γ′. We also show that the support recovery optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s...
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.
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.
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.
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.
Efficient coordinated recovery of sparse channels in massive MIMO
Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.
2015-01-01
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
Low-rank sparse learning for robust visual tracking
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.
Structure-aware Local Sparse Coding for Visual Tracking
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
A Practical View on Tunable Sparse Network Coding
DEFF Research Database (Denmark)
Sørensen, Chres Wiant; Shahbaz Badr, Arash; Cabrera Guerrero, Juan Alberto
2015-01-01
Tunable sparse network coding (TSNC) constitutes a promising concept for trading off computational complexity and delay performance. This paper advocates for the use of judicious feedback as a key not only to make TSNC practical, but also to deliver a highly consistent and controlled delay perfor...
Parallel and Scalable Sparse Basic Linear Algebra Subprograms
DEFF Research Database (Denmark)
Liu, Weifeng
and heterogeneous processors. The thesis compares the proposed methods with state-of-the-art approaches on six homogeneous and five heterogeneous processors from Intel, AMD and nVidia. Using in total 38 sparse matrices as a benchmark suite, the experimental results show that the proposed methods obtain significant...
SparseBeads data: benchmarking sparsity-regularized computed tomography
DEFF Research Database (Denmark)
Jørgensen, Jakob Sauer; Coban, Sophia B.; Lionheart, William R. B.
2017-01-01
-regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels...
Hierarchical Bayesian sparse image reconstruction with application to MRFM.
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.
Multiple instance learning tracking method with local sparse representation
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.
Fast sparse matrix-vector multiplication by partitioning and reordering
Yzelman, A.N.
2011-01-01
The thesis introduces a cache-oblivious method for the sparse matrix-vector (SpMV) multiplication, which is an important computational kernel in many applications. The method works by permuting rows and columns of the input matrix so that the resulting reordered matrix induces cache-friendly
Sobol indices for dimension adaptivity in sparse grids
Dwight, R.P.; Desmedt, S.G.L.; Shoeibi Omrani, P.
2016-01-01
Propagation of random variables through computer codes of many inputs is primarily limited by computational expense. The use of sparse grids mitigates these costs somewhat; here we show how Sobol indices can be used to perform dimension adaptivity to mitigate them further. The method is compared to
Discriminative object tracking via sparse representation and online dictionary learning.
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.
Fast Estimation of Optimal Sparseness of Music Signals
DEFF Research Database (Denmark)
la Cour-Harbo, Anders
2006-01-01
We want to use a variety of sparseness measured applied to ‘the minimal L1 norm representation' of a music signal in an overcomplete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal L1 norm representation is rather slow, and we...
Sparse principal component analysis in medical shape modeling
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.
Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard
2014-01-01
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
Non-Cartesian MRI scan time reduction through sparse sampling
Wajer, F.T.A.W.
2001-01-01
Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI signal can not be taken at will, but lie along k-space trajectories determined by the magnetic field gradients. MRI
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
DEFF Research Database (Denmark)
Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan
2012-01-01
We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures...
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.
Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
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.
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
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.
EEG Source Reconstruction using Sparse Basis Function Representations
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Hansen, Lars Kai
2014-01-01
-validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility...
Aliasing-free wideband beamforming using sparse signal representation
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
Resonance ionization spectroscopy 1990
International Nuclear Information System (INIS)
Parks, J.E.; Omenetto, N.
1991-01-01
The Fifth International Symposium on Resonance Ionization Spectroscopy (RIS) and its Applications was held in Varese, Italy, 16-21 September 1990. Interest in RIS and its applications continues to grow, and RIS is expanding into a more diverse and mature field of study. This maturity was evident in this meeting both in the basic science and understanding of RIS processes and in the number of new and improved applications and techniques. The application of RIS techniques to molecular detection problems made remarkable progress since the last meeting two years ago. Subtle effects pertaining to isotopic discrimination received more theoretical attention, and there now seems to be good understanding of these effects, which can lead to correction procedures and/or methods to avoid isotopic effects. RIS applications were presented in which significant, real world problems were addressed, demonstrating its capability to solve problems that previously could not be accurately solved by other more traditional techniques. The contributions to the conference are grouped under the following major topic headings: physics applications of rare atoms; laser ionization mechanisms - spectroscopy; atomic, molecular and ion sources; molecular RIS; atomic RIS - Rydberg states; environmental trace analysis; biological and medical applications; state selected chemistry; new laser sources and techniques; ultra-high resolution and isotopic selectivity; surface and bulk analysis. (Author)
Hygiene of ionizing radiations
International Nuclear Information System (INIS)
Legare, I.-M.; Conceicao Cunha, M. da
1976-01-01
The concepts of quality factor and rem are introduced and a table of biological effects of external ionizing radiation sources is presented. Natural exposures, with tables of background radiation sources and of doses due to cosmic rays on high altitude areas and their populations are treated, as well as medical exposures; artificial background; fallout; scientific, industrial and other sources. The maximum and limit doses for man are given and tables of maximum admissible doses of ionizing radiations for 16-18 year old workers professionaly exposed, for professionals eventually subjected to radiation in their work and for people eventually exposed. Professional protection is discussed and tables are given of half-value layer of water, concrete, iron and lead for radiations of different energies, as well as the classification of exposure zones to the radiations and of maximum acceptable contamination for surfaces. The basic safety standards for radiation protection are summarized; tables are given also with emergency references for internal irradiation. Procedures with patients which received radioisotopes are discussed. At last, consideration is given to the problem of radioactive wastes in connection with the medical use of radionuclides [pt
Foundations of ionizing radiation dosimetry
International Nuclear Information System (INIS)
Denisenko, O.N.; Pereslegin, I.A.
1985-01-01
Foundations of dosimetry in application to radiotherapy are presented. General characteristics of ionizing radiations and main characteristics of ionizing radiation sources, mostly used in radiotherapy, are given. Values and units for measuring ionizing radiation (activity of a radioactive substance, absorbed dose, exposure dose, integral dose and dose equivalent are considered. Different methods and instruments for ionizing radiation dosimetry are discussed. The attention is paid to the foundations of clinical dosimetry (representation of anatomo-topographic information, choice of radiation conditions, realization of radiation methods, corrections for a configuration and inhomogeneity of a patient's body, account of biological factors of radiation effects, instruments of dose field formation, control of irradiation procedure chosen)
DEFF Research Database (Denmark)
Feldt-Rasmussen, Ulla; Dobrovolny, Robert; Nazarenko, Irina
2011-01-01
Fabry disease, an X-linked lysosomal storage disorder, results from the deficient activity of a-galactosidase A (a-Gal A). In affected males, the clinical diagnosis is confirmed by the markedly decreased a-Gal A activity. However, in female heterozygotes, the a-Gal A activity can range from low t...... on enzyme replacement therapy. Thus, gene dosage analyses can detect large deletions (>50bp) in suspect heterozygotes for X-linked and autosomal dominant diseases that are "sequencing cryptic," resolving molecular diagnostic dilemmas....... to normal due to random X-chromosomal inactivation, and diagnostic confirmation requires identification of the family's a-Gal A gene mutation. In a young female who had occasional acroparesthesias, corneal opacities, and 15 to 50% of the lower limit of normal leukocyte a-Gal A activity, a-Gal A sequencing...... in two expert laboratories did not identify a confirmatory mutation, presenting a diagnostic dilemma. A renal biopsy proved diagnostic and renewed efforts to detect an a-Gal A mutation. Subsequent gene dosage analyses identified a large a-Gal A deletion confirming her heterozygosity, and she was started...
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
Deformable segmentation via sparse representation and dictionary learning.
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.
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
[Ionizing and non-ionizing radiation (comparative risk estimations)].
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.
Applications of ionizing radiations
International Nuclear Information System (INIS)
2014-01-01
Developments in standard applications and brand new nuclear technologies, with high impact on the future of the agriculture, medicine, industry and the environmental preservation. The Radiation Technology Center (CTR) mission is to apply the radiation and radioisotope technologies in Industry, Health, Agriculture, and Environmental Protection, expanding the scientific knowledge, improving human power resources, transferring technology, generating products and offering services for the Brazilian society. The CTR main R and D activities are in consonance with the IPEN Director Plan (2011-2013) and the Applications of Ionizing Radiation Program, with four subprograms: Irradiation of Food and Agricultural Products; Radiation and Radioisotopes Applications in Industry and Environment; Radioactive Sources and Radiation Applications in Human Health; and Radioactive Facilities and Equipment for the Applications of Nuclear Techniques
International Nuclear Information System (INIS)
Golnik, N.; Zielczynski, M.
1999-01-01
The paper presents the design of ionization chamber devoted for the determination of the absolute value of the absorbed dose in tissue-equivalent material. The special attention was paid to ensure that the volume of the active gas cavity was constant and well known. A specific property of the chamber design is that the voltage insulators are 'invisible' from any point of the active volume. Such configuration ensures a very good time stability of the electrical field and defines the active volume. The active volume of the chamber was determined with accuracy of 0.3%. This resulted in accuracy of 0.8% in determination of the absorbed dose in the layer of material adherent to the gas cavity. The chamber was applied for calibration purposes at radiotherapy facility in Joint Institute for Nuclear Research in Dubna (Russia) and in the calibration laboratory of the Institute of Atomic Energy in Swierk. (author)
Applications of ionizing radiations
Energy Technology Data Exchange (ETDEWEB)
NONE
2014-07-01
Developments in standard applications and brand new nuclear technologies, with high impact on the future of the agriculture, medicine, industry and the environmental preservation. The Radiation Technology Center (CTR) mission is to apply the radiation and radioisotope technologies in Industry, Health, Agriculture, and Environmental Protection, expanding the scientific knowledge, improving human power resources, transferring technology, generating products and offering services for the Brazilian society. The CTR main R and D activities are in consonance with the IPEN Director Plan (2011-2013) and the Applications of Ionizing Radiation Program, with four subprograms: Irradiation of Food and Agricultural Products; Radiation and Radioisotopes Applications in Industry and Environment; Radioactive Sources and Radiation Applications in Human Health; and Radioactive Facilities and Equipment for the Applications of Nuclear Techniques.
Thacker, Louis H.
1990-01-01
An ionizing radiation detector is provided which is based on the principle of analog electronic integration of radiation sensor currents in the sub-pico to nano ampere range between fixed voltage switching thresholds with automatic voltage reversal each time the appropriate threshold is reached. The thresholds are provided by a first NAND gate Schmitt trigger which is coupled with a second NAND gate Schmitt trigger operating in an alternate switching state from the first gate to turn either a visible or audible indicating device on and off in response to the gate switching rate which is indicative of the level of radiation being sensed. The detector can be configured as a small, personal radiation dosimeter which is simple to operate and responsive over a dynamic range of at least 0.01 to 1000 R/hr.
Transport processes in ionized gases
International Nuclear Information System (INIS)
Kremer, G.M.
1997-01-01
Based on kinetic theory of gases and on the combined of Chapman-Enskog and Grad, the laws of Ohm, Fourier and Navier-Stokes are derived for a non-relativistic fully ionized gas. Moreover, the combined method is applied to the BGK model of the relativistic Boltzmann equation and the Ohm's law is derived for a relativistic fully ionized gas. (author)
Amorphous silicon ionizing particle detectors
Street, Robert A.; Mendez, Victor P.; Kaplan, Selig N.
1988-01-01
Amorphous silicon ionizing particle detectors having a hydrogenated amorphous silicon (a--Si:H) thin film deposited via plasma assisted chemical vapor deposition techniques are utilized to detect the presence, position and counting of high energy ionizing particles, such as electrons, x-rays, alpha particles, beta particles and gamma radiation.
Worldwide exposures to ionizing radiation
International Nuclear Information System (INIS)
Bennett, B.G.
1993-01-01
All of mankind is exposed to ionizing radiation from natural sources, from human practices that release natural and artificial radionuclides to the environment, and from medical radiation procedures. This paper reviews the assessment in the UNSCEAR 1993 Report of the exposures of human populations worldwide to the various sources of ionizing radiation
International Nuclear Information System (INIS)
Strasser, A.; Raffi, J.; Hasselmann, C.
1997-01-01
Treatment of food with ionizing radiation is increasingly being recognized as a means of reducing food-borne illnesses and associated medical and other costs. In addition, the process may contribute to food security by preventing post-harvest losses, thereby making more food available to more people, eventually at lower cost. An ever increasing number of countries has approved the irradiation of a long and growing list of different food items, groups of classes, ranging from spices to grains to fruit and vegetables to meats and poultry and seafood. However, perception by consumers has been controversial and concerns have been expressed, particularly related to the safety of irradiated food. Therefore, the toxicological aspects of irradiated food are addressed in this dossier. It should be recognized that food irradiation is perhaps the most thoroughly investigated food processing technology. According to the World Health Organization 'irradiated food produced in accordance with established Good Manufacturing Practice can be considered safe and nutritionally adequate'. A recent evaluation by a WHO/FAO/IAEA study group (Geneva, Sept. 1997) even came to the conclusion, 'that as long as sensory qualities of food are retained and harmful microorganisms are destroyed, the actual amount of ionizing radiation applied is of secondary consideration'. Thus, also treatment of food with doses greater than the currently recommended upper level of 10 kGy by the Codex Alimentarius Commission will not lead to changes in the composition of the food that, from a toxicological point of view, would have an adverse effect on human health. (author)
Feature selection and multi-kernel learning for sparse representation on a manifold
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
Building Input Adaptive Parallel Applications: A Case Study of Sparse Grid Interpolation
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
A Non-static Data Layout Enhancing Parallelism and Vectorization in Sparse Grid Algorithms
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
Ionization detection system for aerosols
International Nuclear Information System (INIS)
Jacobs, M.E.
1977-01-01
This invention relates to an improved smoke-detection system of the ionization-chamber type. In the preferred embodiment, the system utilizes a conventional detector head comprising a measuring ionization chamber, a reference ionization chamber, and a normally non-conductive gas triode for discharging when a threshold concentration of airborne particulates is present in the measuring chamber. The improved system utilizes a measuring ionization chamber which is modified to minimize false alarms and reductions in sensitivity resulting from changes in ambient temperature. In the preferred form of the modification, an annular radiation shield is mounted about the usual radiation source provided to effect ionization in the measuring chamber. The shield is supported by a bimetallic strip which flexes in response to changes in ambient temperature, moving the shield relative to the source so as to vary the radiative area of the source in a manner offsetting temperature-induced variations in the sensitivity of the chamber. 8 claims, 7 figures
Group sparse canonical correlation analysis for genomic data integration.
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
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)
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.)
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
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...
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
Porting of the DBCSR library for Sparse Matrix-Matrix Multiplications to Intel Xeon Phi systems
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...
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.)
High-Order Sparse Linear Predictors for Audio Processing
DEFF Research Database (Denmark)
Giacobello, Daniele; van Waterschoot, Toon; Christensen, Mads Græsbøll
2010-01-01
Linear prediction has generally failed to make a breakthrough in audio processing, as it has done in speech processing. This is mostly due to its poor modeling performance, since an audio signal is usually an ensemble of different sources. Nevertheless, linear prediction comes with a whole set...... of interesting features that make the idea of using it in audio processing not far fetched, e.g., the strong ability of modeling the spectral peaks that play a dominant role in perception. In this paper, we provide some preliminary conjectures and experiments on the use of high-order sparse linear predictors...... in audio processing. These predictors, successfully implemented in modeling the short-term and long-term redundancies present in speech signals, will be used to model tonal audio signals, both monophonic and polyphonic. We will show how the sparse predictors are able to model efﬁciently the different...
Sparse Covariance Matrix Estimation by DCA-Based Algorithms.
Phan, Duy Nhat; Le Thi, Hoai An; Dinh, Tao Pham
2017-11-01
This letter proposes a novel approach using the [Formula: see text]-norm regularization for the sparse covariance matrix estimation (SCME) problem. The objective function of SCME problem is composed of a nonconvex part and the [Formula: see text] term, which is discontinuous and difficult to tackle. Appropriate DC (difference of convex functions) approximations of [Formula: see text]-norm are used that result in approximation SCME problems that are still nonconvex. DC programming and DCA (DC algorithm), powerful tools in nonconvex programming framework, are investigated. Two DC formulations are proposed and corresponding DCA schemes developed. Two applications of the SCME problem that are considered are classification via sparse quadratic discriminant analysis and portfolio optimization. A careful empirical experiment is performed through simulated and real data sets to study the performance of the proposed algorithms. Numerical results showed their efficiency and their superiority compared with seven state-of-the-art methods.
Sample size reduction in groundwater surveys via sparse data assimilation
Hussain, Z.
2013-04-01
In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.
High Order Tensor Formulation for Convolutional Sparse Coding
Bibi, Adel Aamer
2017-12-25
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 independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.
Sample size reduction in groundwater surveys via sparse data assimilation
Hussain, Z.; Muhammad, A.
2013-01-01
In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.
Sparse logistic principal components analysis for binary data
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.
Statistical mechanics of semi-supervised clustering in sparse graphs
International Nuclear Information System (INIS)
Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E
2011-01-01
We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears
A sparse electromagnetic imaging scheme using nonlinear landweber iterations
Desmal, Abdulla
2015-10-26
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 the nonlinear forward scattering operator into a sequence of linear ill-posed operations (for example using the Born iterative method) and applying sparsity constraints to the linear minimization problem of each iteration through the use of L0/L1-norm penalty term (A. Desmal and H. Bagci, IEEE Trans. Antennas Propag, 7, 3878–3884, 2014, and IEEE Trans. Geosci. Remote Sens., 3, 532–536, 2015). It has been shown that these techniques produce more accurate and sharper images than their counterparts which solve a minimization problem constrained with smoothness promoting L2-norm penalty term. But these existing techniques are only applicable to investigation domains involving weak scatterers because the linearization process breaks down for high values of dielectric permittivity.
Semi-blind sparse image reconstruction with application to MRFM.
Park, Se Un; Dobigeon, Nicolas; Hero, Alfred O
2012-09-01
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.
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.
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 canonical correlation analysis: new formulation and algorithm.
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.
Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm
Desmal, Abdulla
2017-04-03
An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.
Multi scales based sparse matrix spectral clustering image segmentation
Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin
2018-04-01
In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.
Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.
Ayinde, Babajide O; Zurada, Jacek M
2017-09-01
This paper proposes new techniques for data representation in the context of deep learning using agglomerative clustering. Existing autoencoder-based data representation techniques tend to produce a number of encoding and decoding receptive fields of layered autoencoders that are duplicative, thereby leading to extraction of similar features, thus resulting in filtering redundancy. We propose a way to address this problem and show that such redundancy can be eliminated. This yields smaller networks and produces unique receptive fields that extract distinct features. It is also shown that autoencoders with nonnegativity constraints on weights are capable of extracting fewer redundant features than conventional sparse autoencoders. The concept is illustrated using conventional sparse autoencoder and nonnegativity-constrained autoencoders with MNIST digits recognition, NORB normalized-uniform object data and Yale face dataset. Copyright © 2017 Elsevier Ltd. All rights reserved.
Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery
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
Limited-memory trust-region methods for sparse relaxation
Adhikari, Lasith; DeGuchy, Omar; Erway, Jennifer B.; Lockhart, Shelby; Marcia, Roummel F.
2017-08-01
In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.
Obtaining sparse distributions in 2D inverse problems
Reci, A; Sederman, Andrew John; Gladden, Lynn Faith
2017-01-01
The mathematics of inverse problems has relevance across numerous estimation problems in science and engineering. L1 regularization has attracted recent attention in reconstructing the system properties in the case of sparse inverse problems; i.e., when the true property sought is not adequately described by a continuous distribution, in particular in Compressed Sensing image reconstruction. In this work, we focus on the application of L1 regularization to a class of inverse problems; relaxat...
Sparse optimization for inverse problems in atmospheric modelling
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Branda, Martin
2016-01-01
Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf
Example-Based Image Colorization Using Locality Consistent Sparse Representation.
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.
Reliability of Broadcast Communications Under Sparse Random Linear Network Coding
Brown, Suzie; Johnson, Oliver; Tassi, Andrea
2018-01-01
Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kin...
Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids
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.
Iterative algorithms for large sparse linear systems on parallel computers
Adams, L. M.
1982-01-01
Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.
Distributed coding of multiview sparse sources with joint recovery
DEFF Research Database (Denmark)
Luong, Huynh Van; Deligiannis, Nikos; Forchhammer, Søren
2016-01-01
In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computati...... transform (SIFT) descriptors extracted from multiview images shows that our method leads to bit-rate saving of up to 43% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources....
Optimal deep neural networks for sparse recovery via Laplace techniques
Limmer, Steffen; Stanczak, Slawomir
2017-01-01
This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...
Shape prior modeling using sparse representation and online dictionary learning.
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.
Color normalization of histology slides using graph regularized sparse NMF
Sha, Lingdao; Schonfeld, Dan; Sethi, Amit
2017-03-01
Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The
Dentate Gyrus circuitry features improve performance of sparse approximation algorithms.
Directory of Open Access Journals (Sweden)
Panagiotis C Petrantonakis
Full Text Available Memory-related activity in the Dentate Gyrus (DG is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2-4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm "Iterative Soft Thresholding" (IST by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG.
Superresolving Black Hole Images with Full-Closure Sparse Modeling
Crowley, Chelsea; Akiyama, Kazunori; Fish, Vincent
2018-01-01
It is believed that almost all galaxies have black holes at their centers. Imaging a black hole is a primary objective to answer scientific questions relating to relativistic accretion and jet formation. The Event Horizon Telescope (EHT) is set to capture images of two nearby black holes, Sagittarius A* at the center of the Milky Way galaxy roughly 26,000 light years away and the other M87 which is in Virgo A, a large elliptical galaxy that is 50 million light years away. Sparse imaging techniques have shown great promise for reconstructing high-fidelity superresolved images of black holes from simulated data. Previous work has included the effects of atmospheric phase errors and thermal noise, but not systematic amplitude errors that arise due to miscalibration. We explore a full-closure imaging technique with sparse modeling that uses closure amplitudes and closure phases to improve the imaging process. This new technique can successfully handle data with systematic amplitude errors. Applying our technique to synthetic EHT data of M87, we find that full-closure sparse modeling can reconstruct images better than traditional methods and recover key structural information on the source, such as the shape and size of the predicted photon ring. These results suggest that our new approach will provide superior imaging performance for data from the EHT and other interferometric arrays.
Robust visual tracking via multiscale deep sparse networks
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.
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.
Two-dimensional sparse wavenumber recovery for guided wavefields
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.
Low-Rank Sparse Coding for Image Classification
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.
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.
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.
Low-Rank Sparse Coding for Image Classification
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.
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.
Radiation, ionization, and detection in nuclear medicine
International Nuclear Information System (INIS)
Gupta, Tapan K.
2013-01-01
Up-to-date information on a wide range of topics relating to radiation, ionization, and detection in nuclear medicine. In-depth coverage of basic radiophysics relating to diagnosis and therapy. Extensive discussion of instrumentation and radiation detectors. Detailed information on mathematical modelling of radiation detectors. Although our understanding of cancer has improved, the disease continues to be a leading cause of death across the world. The good news is that the recent technological developments in radiotherapy, radionuclide diagnostics and therapy, digital imaging systems, and detection technology have raised hope that cancer will in the future be combatted more efficiently and effectively. For this goal to be achieved, however, safe use of radionuclides and detailed knowledge of radiation sources are essential. Radiation, Ionization, and Detection in Nuclear Medicine addresses these subjects and related issues very clearly and elaborately and will serve as the definitive source of detailed information in the field. Individual chapters cover fundamental aspects of nuclear radiation, including dose and energy, sources, and shielding; the detection and measurement of radiation exposure, with detailed information on mathematical modelling; medical imaging; the different types of radiation detector and their working principles; basic principles of and experimental techniques for deposition of scintillating materials; device fabrication; the optical and electrical behaviors of radiation detectors; and the instrumentation used in nuclear medicine and its application. The book will be an invaluable source of information for academia, industry, practitioners, and researchers.
Radiation, ionization, and detection in nuclear medicine
Energy Technology Data Exchange (ETDEWEB)
Gupta, Tapan K. [Radiation Monitoring Devices Research, Nuclear Medicine, Watertown, MA (United States)
2013-08-01
Up-to-date information on a wide range of topics relating to radiation, ionization, and detection in nuclear medicine. In-depth coverage of basic radiophysics relating to diagnosis and therapy. Extensive discussion of instrumentation and radiation detectors. Detailed information on mathematical modelling of radiation detectors. Although our understanding of cancer has improved, the disease continues to be a leading cause of death across the world. The good news is that the recent technological developments in radiotherapy, radionuclide diagnostics and therapy, digital imaging systems, and detection technology have raised hope that cancer will in the future be combatted more efficiently and effectively. For this goal to be achieved, however, safe use of radionuclides and detailed knowledge of radiation sources are essential. Radiation, Ionization, and Detection in Nuclear Medicine addresses these subjects and related issues very clearly and elaborately and will serve as the definitive source of detailed information in the field. Individual chapters cover fundamental aspects of nuclear radiation, including dose and energy, sources, and shielding; the detection and measurement of radiation exposure, with detailed information on mathematical modelling; medical imaging; the different types of radiation detector and their working principles; basic principles of and experimental techniques for deposition of scintillating materials; device fabrication; the optical and electrical behaviors of radiation detectors; and the instrumentation used in nuclear medicine and its application. The book will be an invaluable source of information for academia, industry, practitioners, and researchers.
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)
Photoionization effects in ionization fronts
International Nuclear Information System (INIS)
Arrayas, Manuel; Fontelos, Marco A; Trueba, Jose L
2006-01-01
In this paper we study the effects of photoionization processes on the propagation of both negative and positive ionization fronts in streamer discharge. We show that negative fronts accelerate in the presence of photoionization events. The appearance and propagation of positive ionization fronts travelling with constant velocity is explained as the result of the combined effects of photoionization and electron diffusion. The photoionization range plays an important role in the selection of the velocity of the ionization front as we show in this work
Photoionization effects in ionization fronts
Energy Technology Data Exchange (ETDEWEB)
Arrayas, Manuel [Departamento de Electromagnetismo, Universidad Rey Juan Carlos, Tulipan s/n, 28933 Mostoles, Madrid (Spain); Fontelos, Marco A [Departamento de Matematicas, Instituto de Matematicas y Fisica Fundamental, Consejo Superior de Investigaciones CientIficas, C/Serrano 123, 28006 Madrid (Spain); Trueba, Jose L [Departamento de Electromagnetismo, Universidad Rey Juan Carlos, Tulipan s/n, 28933 Mostoles, Madrid (Spain)
2006-12-21
In this paper we study the effects of photoionization processes on the propagation of both negative and positive ionization fronts in streamer discharge. We show that negative fronts accelerate in the presence of photoionization events. The appearance and propagation of positive ionization fronts travelling with constant velocity is explained as the result of the combined effects of photoionization and electron diffusion. The photoionization range plays an important role in the selection of the velocity of the ionization front as we show in this work.
Ionizing radiation promotes protozoan reproduction
International Nuclear Information System (INIS)
Luckey, T.D.
1986-01-01
This experiment was performed to determine whether ionizing radiation is essential for maximum growth rate in a ciliated protozoan. When extraneous ionizing radiation was reduced to 0.15 mrad/day, the reproduction rate of Tetrahymena pyriformis was significantly less (P less than 0.01) than it was at near ambient levels, 0.5 or 1.8 mrad/day. Significantly higher growth rates (P less than 0.01) were obtained when chronic radiation was increased. The data suggest that ionizing radiation is essential for optimum reproduction rate in this organism
Nonlinear modulation of ionization waves
International Nuclear Information System (INIS)
Bekki, Naoaki
1981-01-01
In order to investigate the nonlinear characteristics of ionization waves (moving-striations) in the positive column of glow discharge, a nonlinear modulation of ionization waves in the region of the Pupp critical current is analysed by means of the reductive perturbation method. The modulation of ionization waves is described by a nonlinear Schroedinger type equation. The coefficients of the equation are evaluated using the data of the low pressure Argon-discharge, and the simple solutions (plane wave and envelope soliton type solutions) are presented. Under a certain condition an envelope soliton is propagated through the positive column. (author)
International Nuclear Information System (INIS)
Gregg, E.C.; Bakale, G.
1976-01-01
Application of pulsed-conductivity techniques to ionization phenomena in liquids has yielded new results on electron transport and electron reactions in nonpolar liquids which we have extrapolated to biological systems to develop a novel model of direct radiation damage to mammalian cells that involves the unsolvated electron as the key reactant. Among these new results are electron attachment rate constants of thirty-five substituted nitrobenzene compounds measured in nonpolar solvents which when combined with product anion lifetimes are correlated with cellular radiosensitization efficiencies. From this study we found that electron attachment rates are dependent upon the electron mobility in the solvents and upon the dipole moment of the electron-accepting nitrobenzene compounds. The model also drawn upon energy-dependent electron attachment rates which we have measured in cryogenic liquids, and we have measured in the same solvents associative detachment rate constants and electron momentum transfer cross sections. In addition to these studies of electronic processes in liquids, we have measured ion mobilities of lecithin and chlorophyll in nonpolar solvents and conclude that these solutes form inverse micelles under certain conditions. Formation of these micelles permits electron transport through the lipid micellar walls and electron attachment to electron-accepting polar solutes inside the lipid vesicles to be studied
International Nuclear Information System (INIS)
Bakale, G.
1990-01-01
During the 1987--1990 reporting period, studies were conducted that entailed the direct measurement of the transport and reaction properties of excess electrons in nonpolar liquids through the use of pulse-conductivity techniques. The results obtained from these studies should be applicable toward the development of a better understanding of the primary ionizing event in liquids as well as to providing physico-chemical information that is pertinent to electron-transfer processes that are ubiquitous in biological systems. Progress was also made in developing a better understanding of electron attachment reactions in liquids through measurements of the electron attachment rate constants, k e s, of a variety of electron-attaching solutes. The effects of several functional groups substituted at different positions on benzene were studied in liquid cyclohexane and isooctane. The electron-attaching properties of chemicals having well characterized carcinogenic properties were studied in cyclohexane to determine if the measure of electron-accepting potential that k e provides can elucidate the role that electrons play in the initiation step of carcinogenesis. The k e s that were measured indicate that the k e -carcinogenicity correlation that was observed can be used to complement short-term carcinogen-screening bioassays to identify potential carcinogens. 115 refs., 6 tabs
Fast Solution in Sparse LDA for Binary Classification
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
Energy Technology Data Exchange (ETDEWEB)
Scriba, P C; Boerner, W; Emrich, S; Gutekunst, R; Herrmann, J; Horn, K; Klett, M; Krueskemper, H L; Pfannenstiel, P; Pickardt, C R
1985-03-01
None of the in-vitro and in-vivo methods listed permits on unambiguous diagnosis when applied alone, owing to the fact that similar or even identical findings are obtained for various individual parameters in different thyroid diseases. Further, especially the in-vitro tests are also subject to extrathyroidal effects which may mask the typical findings. The limited and varying specificity and sensitivity of the tests applied, as well as the falsification of results caused by the patients' idiosyncracies and the methodology, make it necessary to interpret and evaluate the in-vivo and in-vitro findings only if the clinical situation (anamnesis and physical examination) is known. For maximum diagnostic quality of the tests, the initial probability of the assumed type of thyroid disease must be increased (formulation of the clinical problem). The concepts of exclusion diagnosis and identification must be distinguished as well as the diagnosis of functional disturbances on the one hand and of thyroid diseases on the other. Both of this requires a qualified, specific and detailed anamnesis and examination procedure, and the clinical examination remains the obligatory basis of clinical diagnostics. In case of inexplicable discrepancies between the clinical manifestations and the findings obtained with specific methods, or between the findings obtained with a specific method, the patient should be referred to an expert institution, or the expert institution should be consulted.
International Nuclear Information System (INIS)
Scriba, P.C.; Boerner, W.; Emrich, S.; Gutekunst, R.; Herrmann, J.; Horn, K.; Klett, M.; Krueskemper, H.L.; Pfannenstiel, P.; Pickardt, C.R.; Reiners, C.; Reinwein, D.; Schleusener, H.
1985-01-01
None of the in-vitro and in-vivo methods listed permits on unambiguous diagnosis when applied alone, owing to the fact that similar or even identical findings are obtained for various individual parameters in different thyroid diseases. Further, especially the in-vitro tests are also subject to extrathyroidal effects which may mask the typical findings. The limited and varying specificity and sensitivity of the tests applied, as well as the falsification of results caused by the patients' idiosyncracies and the methodology, make it necessary to interpret and evaluate the in-vivo and in-vitro findings only if the clinical situation (anamnesis and physical examination) is known. For maximum diagnostic quality of the tests, the initial probability of the assumed type of thyroid disease must be increased (formulation of the clinical problem). The concepts of exclusion diagnosis and identification must be distinguished as well as the diagnosis of functional disturbances on the one hand and of thyroid diseases on the other. Both of this requires a qualified, specific and detailed anamnesis and examination procedure, and the clinical examination remains the obligatory basis of clinical diagnostics. In case of inexplicable discrepancies between the clinical manifestations and the findings obtained with specific methods, or between the findings obtained with a specific method, the patient should be referred to an expert institution, or the expert institution should be consulted. (orig./MG) [de
Cai, Yang
2014-01-01
Part I. FundamentalsIntroductionWhat is Ambient Diagnostics?Diagnostic ModelsMultimedia IntelligenceCrowd SourcingSoft SensorsScience of SimplicityPersonal DiagnosesBasic AlgorithmsBasic ToolsSummaryProblemsTransformationEarly Discoveries of Heartbeat PatternsTransforms, Features, and AttributesSequential FeaturesSpatiotemporal FeaturesShape FeaturesImagery FeaturesFrequency Domain FeaturesMulti-Resolution FeaturesSummaryProblemsPattern RecognitionSimilarities and DistancesClustering MethodsClassification MethodsClassifier Accuracy MeasuresSummaryProblemsPart II. Multimedia IntelligenceSound RecognitionMicrophone AppsModern Acoustic Transducers (Microphones)Frequency Response CharacteristicsDigital Audio File FormatsHeart Sound SensingLung Sound SensingSnore MeterSpectrogram (STFT)Ambient Sound AnalysisSound RecognitionRecognizing Asthma SoundPeak ShiftFeature CompressionRegroupingNoise IssuesFuture ApplicationsSummaryProblemsColor SensorsColor SensingHuman Color VisionColor SensorsColor Matching ExperimentsC...
Physics of partially ionized plasmas
Krishan, Vinod
2016-01-01
Plasma is one of the four fundamental states of matter; the other three being solid, liquid and gas. Several components, such as molecular clouds, diffuse interstellar gas, the solar atmosphere, the Earth's ionosphere and laboratory plasmas, including fusion plasmas, constitute the partially ionized plasmas. This book discusses different aspects of partially ionized plasmas including multi-fluid description, equilibrium and types of waves. The discussion goes on to cover the reionization phase of the universe, along with a brief description of high discharge plasmas, tokomak plasmas and laser plasmas. Various elastic and inelastic collisions amongst the three particle species are also presented. In addition, the author demonstrates the novelty of partially ionized plasmas using many examples; for instance, in partially ionized plasma the magnetic induction is subjected to the ambipolar diffusion and the Hall effect, as well as the usual resistive dissipation. Also included is an observation of kinematic dynam...
Resonance Ionization Laser Ion Sources
Marsh, B
2013-01-01
The application of the technique of laser resonance ionization to the production of singly charged ions at radioactive ion beam facilities is discussed. The ability to combine high efficiency and element selectivity makes a resonance ionization laser ion source (RILIS) an important component of many radioactive ion beam facilities. At CERN, for example, the RILIS is the most commonly used ion source of the ISOLDE facility, with a yearly operating time of up to 3000 hours. For some isotopes the RILIS can also be used as a fast and sensitive laser spectroscopy tool, provided that the spectral resolution is sufficiently high to reveal the influence of nuclear structure on the atomic spectra. This enables the study of nuclear properties of isotopes with production rates even lower than one ion per second and, in some cases, enables isomer selective ionization. The solutions available for the implementation of resonance laser ionization at radioactive ion beam facilities are summarized. Aspects such as the laser r...
Storage of sparse files using parallel log-structured file system
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.
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
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.
Safe use of ionizing radiations
Energy Technology Data Exchange (ETDEWEB)
1973-01-01
Based on the ''Code of Practice for the protection of persons against ionizing radiations arising from medical and dental use'' (CIS 74-423), this handbook shows how hospital staff can avoid exposing themselves and others to these hazards. It is designed particularly for junior and student nurses. Contents: ionizing radiations, their types and characteristics; their uses and dangers; basic principles in their safe use; safe use in practice; explanation of terms.
Biomedical applications of ionizing radiation
International Nuclear Information System (INIS)
Rosiak, J.M.; Pietrzak, M.
1997-01-01
Application of ionizing radiation for sterilization of medical devices, hygienization of cosmetics products as well as formation of biomaterials have been discussed. The advantages of radiation sterilization over the conventional methods have been indicated. The properties of modern biomaterials, hydrogels as well as some ways of their formation and modification under action of ionizing radiation were presented. Some commercial biomaterials of this kind produced in accordance with original Polish methods by means of radiation technique have been pointed out. (author)
Diagnostic accuracy of organ electrodermal diagnostics | Szopinski ...
African Journals Online (AJOL)
Objective. To estimate the diagnostic accuracy as well as the scope of utilisation of a new bio-electronic method of organ diagnostics. Design. Double-blind comparative study of the diagnostic results obtained by means of organ electrodermal diagnostics (OED) and clinical diagnoses, as a criterion standard. Setting.
Modeling ionization by helicon waves
International Nuclear Information System (INIS)
Degeling, A.W.; Boswell, R.W.
1997-01-01
The response of the electron distribution function in one dimension to a traveling wave electric field is modeled for parameters relevant to a low-pressure helicon wave plasma source, and the resulting change in the ionization rate calculated. This is done by calculating the trajectories of individual electrons in a given wave field and assuming no collisions to build up the distribution function as the distance from the antenna is increased. The ionization rate is calculated for argon by considering the ionization cross section and electron flux at a specified position and time relative to the left-hand boundary, where the distribution function is assumed to be Maxwellian and the wave travels to the right. The simulation shows pulses in the ionization rate that move away from the antenna at the phase velocity of the wave, demonstrating the effect of resonant electrons trapped in the wave close-quote s frame of reference. It is found that the ionization rate is highest when the phase velocity of the wave is between 2 and 3x10 6 m/s, where the electrons interacting strongly with the wave (i.e., electrons with velocities inside the wave close-quote s open-quotes trapping widthclose quotes) have initial energies just below the ionization threshold. Results from the model are compared with experimental data and show reasonable qualitative agreement. copyright 1997 American Institute of Physics
International Nuclear Information System (INIS)
Shea, T.J.; Cameron, P.; Doolittle, L.; Power, J.
2000-01-01
The Spallation Neutron Source (SNS) Project is a collaborative effort to build the next generation neutron science facility at Oak Ridge, TN. The facility will deliver a 2 MW proton beam to a liquid mercury target. Neutrons from this target will be moderated and sent to several state-of-the-art instruments. Six national laboratories are involved in SNS construction. Berkeley (LBNL) will build the front end that produces a 2.5 MeV, 52 mA H-beam. Los Alamos (LANL) is responsible for the 1 GeV linac with a superconducting section provided by Thomas Jefferson (JLab). Brookhaven (BNL) is building the transfer lines and accumulator ring. Oak Ridge (ORNL) and Argonne (ANL) have responsibility for the target and instruments. All activities are coordinated by the SNS project office at Oak Ridge. The high beam power, a desired availability of 95%, and an aggressive commissioning schedule lead to some interesting challenges in beam diagnostics
Hellweg, Christine E.; Langen, Britta; Klimow, Galina; Ruscher, Roland; Schmitz, Claudia; Baumstark-Khan, Christa; Reitz, Günther
2009-10-01
Radiation is a potentially limiting factor for manned long-term space missions. Prolonged exposure to galactic cosmic rays may shorten the healthy life-span after return to Earth due to cancer induction. During the mission, a solar flare can be life threatening. For better risk estimation and development of appropriate countermeasures, the study of the cellular radiation response is necessary. Since apoptosis may be a mechanism the body uses to eliminate damaged cells, the induction by cosmic radiation of the nuclear anti-apoptotic transcription factor nuclear factor κB (NF-κB) could influence the cancer risk of astronauts exposed to cosmic radiation by improving the survival of radiation-damaged cells. In previous studies using a screening assay for the detection of NF-κB-dependent gene induction (HEK-pNF-κB-d2EGFP/Neo cells), the activation of this transcription factor by heavy ions was shown [Baumstark-Khan, C., Hellweg, C.E., Arenz, A., Meier, M.M. Cellular monitoring of the nuclear factor kappa B pathway for assessment of space environmental radiation. Radiat. Res. 164, 527-530, 2005]. Studies with NF-κB inhibitors can map functional details of the NF-κB pathway and the influence of radiation-induced NF-κB activation on various cellular outcomes such as survival or cell cycle arrest. In this work, the efficacy and cytotoxicity of four different NF-κB inhibitors, caffeic acid phenethyl ester (CAPE), capsaicin, the proteasome inhibitor MG-132, and the cell permeable peptide NF-κB SN50 were analyzed using HEK-pNF-κB-d2EGFP/Neo cells. In the recommended concentration range, only CAPE displayed considerable cytotoxicity. CAPE and capsaicin partially inhibited NF-κB activation by the cytokine tumor necrosis factor α. MG-132 completely abolished the activation and was therefore used for experiments with X-rays. NF-κB SN-50 could not reduce NF-κB dependent expression of the reporter destabilized Enhanced Green Fluorescent Protein (d2EGFP). MG-132 entirely suppressed the X-ray induced NF-κB activation in HEK-pNF-κB-d2EGFP/Neo cells. In conclusion, the degradation of the inhibitor of NF-κB (IκB) in the proteasome is essential for X-ray induced NF-κB activation, and MG-132 will be useful in studies of the NF-κB pathway involvement in the cellular response to heavy ion exposure and other space-relevant radiation qualities.
Social biases determine spatiotemporal sparseness of ciliate mating heuristics.
Clark, Kevin B
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present
Deploying temporary networks for upscaling of sparse network stations
Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane
2016-10-01
Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.
Social biases determine spatiotemporal sparseness of ciliate mating heuristics
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The
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.
Fast Convolutional Sparse Coding in the Dual Domain
Affara, Lama Ahmed
2017-09-27
Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns
Aoyagi, Toshio; Nomura, Masaki
1999-08-01
Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.
Sparse inverse covariance estimation with the graphical lasso.
Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert
2008-07-01
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
Fast Convolutional Sparse Coding in the Dual Domain
Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter
2017-01-01
Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.
Blind spectrum reconstruction algorithm with L0-sparse representation
International Nuclear Information System (INIS)
Liu, Hai; Zhang, Zhaoli; Liu, Sanyan; Shu, Jiangbo; Liu, Tingting; Zhang, Tianxu
2015-01-01
Raman spectrum often suffers from band overlap and Poisson noise. This paper presents a new blind Poissonian Raman spectrum reconstruction method, which incorporates the L 0 -sparse prior together with the total variation constraint into the maximum a posteriori framework. Furthermore, the greedy analysis pursuit algorithm is adopted to solve the L 0 -based minimization problem. Simulated and real spectrum experimental results show that the proposed method can effectively preserve spectral structure and suppress noise. The reconstructed Raman spectra are easily used for interpreting unknown chemical mixtures. (paper)
Calculation of the inverse data space via sparse inversion
Saragiotis, Christos
2011-01-01
The inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.