Directory of Open Access Journals (Sweden)
Tamer Dawod
2015-01-01
Full Text Available Purpose: This work investigated the accuracy of prowess treatment planning system (TPS in dose calculation in a homogenous phantom for symmetric and asymmetric field sizes using collapse cone convolution / superposition algorithm (CCCS. Methods: The measurements were carried out at source-to-surface distance (SSD set to 100 cm for 6 and 10 MV photon beams. Data for a full set of measurements for symmetric fields and asymmetric fields, including inplane and crossplane profiles at various depths and percentage depth doses (PDDs were obtained during measurements on the linear accelerator.Results: The results showed that the asymmetric collimation dose lead to significant errors (up to approximately 7% in dose calculations if changes in primary beam intensity and beam quality. It is obvious that the most difference in the isodose curves was found in buildup and the penumbra regions. Conclusion: The results showed that the dose calculation using Prowess TPS based on CCCS algorithm is generally in excellent agreement with measurements.
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Thrower, Sara L., E-mail: slloupot@mdanderson.org [The University of Texas Graduate School of Biomedical Sciences at Houston, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Shaitelman, Simona F.; Bloom, Elizabeth [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Salehpour, Mohammad; Gifford, Kent [Department of Radiation Physics, The University of Texas Graduate School of Biomedical Sciences at Houston, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
2016-08-01
Purpose: To compare the treatment plans for accelerated partial breast irradiation calculated by the new commercially available collapsed cone convolution (CCC) and current standard TG-43–based algorithms for 50 patients treated at our institution with either a Strut-Adjusted Volume Implant (SAVI) or Contura device. Methods and Materials: We recalculated target coverage, volume of highly dosed normal tissue, and dose to organs at risk (ribs, skin, and lung) with each algorithm. For 1 case an artificial air pocket was added to simulate 10% nonconformance. We performed a Wilcoxon signed rank test to determine the median differences in the clinical indices V90, V95, V100, V150, V200, and highest-dosed 0.1 cm{sup 3} and 1.0 cm{sup 3} of rib, skin, and lung between the two algorithms. Results: The CCC algorithm calculated lower values on average for all dose-volume histogram parameters. Across the entire patient cohort, the median difference in the clinical indices calculated by the 2 algorithms was <10% for dose to organs at risk, <5% for target volume coverage (V90, V95, and V100), and <4 cm{sup 3} for dose to normal breast tissue (V150 and V200). No discernable difference was seen in the nonconformance case. Conclusions: We found that on average over our patient population CCC calculated (<10%) lower doses than TG-43. These results should inform clinicians as they prepare for the transition to heterogeneous dose calculation algorithms and determine whether clinical tolerance limits warrant modification.
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Oyewale, S [Cancer Centers of Southwest Oklahoma, Lawton, OK (United States); Pokharel, S [21st Century Oncology, Naples, FL (United States); Rana, S [ProCure Proton Therapy Center, Oklahoma City, OK (United States)
2015-06-15
Purpose: To compare the percentage depth dose (PDD) computational accuracy of Adaptive Convolution (AC) and Collapsed Cone Convolution (CCC) algorithms in the presence of air gaps. Methods: A 30×30×30 cm{sup 3} solid water phantom with two 5cm air gaps was scanned with a CT simulator unit and exported into the Phillips Pinnacle™ treatment planning system. PDDs were computed using the AC and CCC algorithms. Photon energy of 6 MV was used with field sizes of 3×3 cm{sup 2}, 5×5 cm{sup 2}, 10×10 cm{sup 2}, 15×15 cm{sup 2}, and 20×20 cm{sup 2}. Ionization chamber readings were taken at different depths in water for all the field sizes. The percentage differences in the PDDs were computed with normalization to the depth of maximum dose (dmax). The calculated PDDs were then compared with measured PDDs. Results: In the first buildup region, both algorithms overpredicted the dose for all field sizes and under-predicted for all other subsequent buildup regions. After dmax in the three water media, AC under-predicted the dose for field sizes 3×3 and 5×5 cm{sup 2} and overpredicted for larger field sizes, whereas CCC under-predicted for all field sizes. Upon traversing the first air gap, AC showed maximum differences of –3.9%, −1.4%, 2.0%, 2.5%, 2.9% and CCC had maximum differences of −3.9%, −3.0%,–3.1%, −2.7%, −1.8% for field sizes 3×3, 5×5, 10×10, 15×15, and 20×20 cm{sup 2} respectively. Conclusion: The effect of air gaps causes a significant difference in the PDDs computed by both the AC and CCC algorithms in secondary build-up regions. AC computed larger values for the PDDs except at smaller field sizes. For CCC, the size of the errors in prediction of the PDDs has an inverse relationship with respect to field size. These effects should be considered in treatment planning where significant air gaps are encountered.
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Ma, Y; Lacroix, F; Lavallee, M; Beaulieu, L
2014-01-01
Purpose: To evaluate the commercially released Collapsed Cone convolution-based(CCC) dose calculation module of the Elekta OncentraBrachy(OcB) treatment planning system(TPS). Methods: An allwater phantom was used to perform TG43 benchmarks with single source and seventeen sources, separately. Furthermore, four real-patient heterogeneous geometries (chestwall, lung, breast and prostate) were used. They were selected based on their clinical representativity of a class of clinical anatomies that pose clear challenges. The plans were used as is(no modification). For each case, TG43 and CCC calculations were performed in the OcB TPS, with TG186-recommended materials properly assigned to ROIs. For comparison, Monte Carlo simulation was run for each case with the same material scheme and grid mesh as TPS calculations. Both modes of CCC (standard and high quality) were tested. Results: For the benchmark case, the CCC dose, when divided by that of TG43, yields hot-n-cold spots in a radial pattern. The pattern of the high mode is denser than that of the standard mode and is representative of angular dicretization. The total deviation ((hot-cold)/TG43) is 18% for standard mode and 11% for high mode. Seventeen dwell positions help to reduce “ray-effect”, with the total deviation to 6% (standard) and 5% (high), respectively. For the four patient cases, CCC produces, as expected, more realistic dose distributions than TG43. A close agreement was observed between CCC and MC for all isodose lines, from 20% and up; the 10% isodose line of CCC appears shifted compared to that of MC. The DVH plots show dose deviations of CCC from MC in small volume, high dose regions (>100% isodose). For patient cases, the difference between standard and high modes is almost undiscernable. Conclusion: OncentraBrachy CCC algorithm marks a significant dosimetry improvement relative to TG43 in real-patient cases. Further researches are recommended regarding the clinical implications of the above
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Ma, Y; Lacroix, F; Lavallee, M [CHUQ Hotel-Dieu De Quebec, Quebec, QC (Canada); Beaulieu, L [CHUQ Hotel-Dieu De Quebec, Quebec, QC (Canada); Centre Hospitalier University de Quebec, Quebec, QC (Canada)
2014-06-15
Purpose: To evaluate the commercially released Collapsed Cone convolution-based(CCC) dose calculation module of the Elekta OncentraBrachy(OcB) treatment planning system(TPS). Methods: An allwater phantom was used to perform TG43 benchmarks with single source and seventeen sources, separately. Furthermore, four real-patient heterogeneous geometries (chestwall, lung, breast and prostate) were used. They were selected based on their clinical representativity of a class of clinical anatomies that pose clear challenges. The plans were used as is(no modification). For each case, TG43 and CCC calculations were performed in the OcB TPS, with TG186-recommended materials properly assigned to ROIs. For comparison, Monte Carlo simulation was run for each case with the same material scheme and grid mesh as TPS calculations. Both modes of CCC (standard and high quality) were tested. Results: For the benchmark case, the CCC dose, when divided by that of TG43, yields hot-n-cold spots in a radial pattern. The pattern of the high mode is denser than that of the standard mode and is representative of angular dicretization. The total deviation ((hot-cold)/TG43) is 18% for standard mode and 11% for high mode. Seventeen dwell positions help to reduce “ray-effect”, with the total deviation to 6% (standard) and 5% (high), respectively. For the four patient cases, CCC produces, as expected, more realistic dose distributions than TG43. A close agreement was observed between CCC and MC for all isodose lines, from 20% and up; the 10% isodose line of CCC appears shifted compared to that of MC. The DVH plots show dose deviations of CCC from MC in small volume, high dose regions (>100% isodose). For patient cases, the difference between standard and high modes is almost undiscernable. Conclusion: OncentraBrachy CCC algorithm marks a significant dosimetry improvement relative to TG43 in real-patient cases. Further researches are recommended regarding the clinical implications of the above
Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification
Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi
2017-03-01
In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.
Classification of teeth in cone-beam CT using deep convolutional neural network.
Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi
2017-01-01
Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification. Copyright © 2016 Elsevier Ltd
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Morrison, Hali; Menon, Geetha; Sloboda, Ron
2016-01-01
Purpose: To investigate the accuracy of model-based dose calculations using a collapsed-cone algorithm for COMS eye plaques loaded with I-125 seeds. Methods: The Nucletron SelectSeed 130.002 I-125 seed and the 12 mm COMS eye plaque were incorporated into a research version of the Oncentra® Brachy v4.5 treatment planning system which uses the Advanced Collapsed-cone Engine (ACE) algorithm. Comparisons of TG-43 and high-accuracy ACE doses were performed for a single seed in a 30×30×30 cm 3 water box, as well as with one seed in the central slot of the 12 mm COMS eye plaque. The doses along the plaque central axis (CAX) were used to calculate the carrier correction factor, T(r), and were compared to tabulated and MCNP6 simulated doses for both the SelectSeed and IsoAid IAI-125A seeds. Results: The ACE calculated dose for the single seed in water was on average within 0.62 ± 2.2% of the TG-43 dose, with the largest differences occurring near the end-welds. The ratio of ACE to TG-43 calculated doses along the CAX (T(r)) of the 12 mm COMS plaque for the SelectSeed was on average within 3.0% of previously tabulated data, and within 2.9% of the MCNP6 simulated values. The IsoAid and SelectSeed T(r) values agreed within 0.3%. Conclusions: Initial comparisons show good agreement between ACE and MC doses for a single seed in a 12 mm COMS eye plaque; more complicated scenarios are being investigated to determine the accuracy of this calculation method.
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Morrison, Hali; Menon, Geetha; Sloboda, Ron [Cross Cancer Institute, Edmonton, AB, and University of Alberta, Edmonton, AB, Cross Cancer Institute, Edmonton, AB, and University of Alberta, Edmonton, AB, Cross Cancer Institute, Edmonton, AB, and University of Alberta, Edmonton, AB (Canada)
2016-08-15
Purpose: To investigate the accuracy of model-based dose calculations using a collapsed-cone algorithm for COMS eye plaques loaded with I-125 seeds. Methods: The Nucletron SelectSeed 130.002 I-125 seed and the 12 mm COMS eye plaque were incorporated into a research version of the Oncentra® Brachy v4.5 treatment planning system which uses the Advanced Collapsed-cone Engine (ACE) algorithm. Comparisons of TG-43 and high-accuracy ACE doses were performed for a single seed in a 30×30×30 cm{sup 3} water box, as well as with one seed in the central slot of the 12 mm COMS eye plaque. The doses along the plaque central axis (CAX) were used to calculate the carrier correction factor, T(r), and were compared to tabulated and MCNP6 simulated doses for both the SelectSeed and IsoAid IAI-125A seeds. Results: The ACE calculated dose for the single seed in water was on average within 0.62 ± 2.2% of the TG-43 dose, with the largest differences occurring near the end-welds. The ratio of ACE to TG-43 calculated doses along the CAX (T(r)) of the 12 mm COMS plaque for the SelectSeed was on average within 3.0% of previously tabulated data, and within 2.9% of the MCNP6 simulated values. The IsoAid and SelectSeed T(r) values agreed within 0.3%. Conclusions: Initial comparisons show good agreement between ACE and MC doses for a single seed in a 12 mm COMS eye plaque; more complicated scenarios are being investigated to determine the accuracy of this calculation method.
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Cawston-Grant, Brie; Morrison, Hali; Sloboda, Ron; Menon, Geetha [Cross Cancer Institute, University of Alberta, Edmonton (Canada)
2016-08-15
Purpose: To present an investigation of the Advanced Collapsed-cone Engine (ACE) in Oncentraê Brachy (OcB) v4.5 using a tissue equivalent phantom modeling scalp brachytherapy (BT) treatments. Methods: A slab phantom modeling the skin, skull, brain and mold was used. A dose of 400cGy was prescribed to just above the skull layer using TG-43 and was delivered using an HDR afterloader. Measurements were made using Gafchromic™ EBT3 film at four depths within the phantom. The TG-43 planned and film measured doses were compared to the standard (sACE) and high (hACE) accuracy ACE options in OcB between the surface and below the skull. Results: The average difference between the TG-43 calculated and film measured doses was −11.25±3.38% when there was no air gap between the mold and skin; sACE and hACE doses were on average lower than TG-43 calculated doses by 3.41±0.03% and 2.45±0.03%, respectively. With a 3mm air gap between the mold and skin, the difference between the TG-43 calculated and measured doses was −8.28±5.76%; sACE and hACE calculations yielded average doses 1.87±0.03% and 1.78±0.04% greater than TG-43, respectively. Conclusions: TG-43, sACE, and hACE were found to overestimate doses below the skull layer compared to film. With a 3mm air gap between the mold and skin, sACE and hACE more accurately predicted the film dose to the skin surface than TG-43. More clinical variations and their implications are currently being investigated.
Sanchez-Garcia, Manuel; Gardin, Isabelle; Lebtahi, Rachida; Dieudonné, Arnaud
2015-10-21
Two collapsed cone (CC) superposition algorithms have been implemented for radiopharmaceutical dosimetry of photon emitters. The straight CC (SCC) superposition method uses a water energy deposition kernel (EDKw) for each electron, positron and photon components, while the primary and scatter CC (PSCC) superposition method uses different EDKw for primary and once-scattered photons. PSCC was implemented only for photons originating from the nucleus, precluding its application to positron emitters. EDKw are linearly scaled by radiological distance, taking into account tissue density heterogeneities. The implementation was tested on 100, 300 and 600 keV mono-energetic photons and (18)F, (99m)Tc, (131)I and (177)Lu. The kernels were generated using the Monte Carlo codes MCNP and EGSnrc. The validation was performed on 6 phantoms representing interfaces between soft-tissues, lung and bone. The figures of merit were γ (3%, 3 mm) and γ (5%, 5 mm) criterions corresponding to the computation comparison on 80 absorbed doses (AD) points per phantom between Monte Carlo simulations and CC algorithms. PSCC gave better results than SCC for the lowest photon energy (100 keV). For the 3 isotopes computed with PSCC, the percentage of AD points satisfying the γ (5%, 5 mm) criterion was always over 99%. A still good but worse result was found with SCC, since at least 97% of AD-values verified the γ (5%, 5 mm) criterion, except a value of 57% for the (99m)Tc with the lung/bone interface. The CC superposition method for radiopharmaceutical dosimetry is a good alternative to Monte Carlo simulations while reducing computation complexity.
Sanchez-Garcia, Manuel; Gardin, Isabelle; Lebtahi, Rachida; Dieudonné, Arnaud
2015-10-01
Two collapsed cone (CC) superposition algorithms have been implemented for radiopharmaceutical dosimetry of photon emitters. The straight CC (SCC) superposition method uses a water energy deposition kernel (EDKw) for each electron, positron and photon components, while the primary and scatter CC (PSCC) superposition method uses different EDKw for primary and once-scattered photons. PSCC was implemented only for photons originating from the nucleus, precluding its application to positron emitters. EDKw are linearly scaled by radiological distance, taking into account tissue density heterogeneities. The implementation was tested on 100, 300 and 600 keV mono-energetic photons and 18F, 99mTc, 131I and 177Lu. The kernels were generated using the Monte Carlo codes MCNP and EGSnrc. The validation was performed on 6 phantoms representing interfaces between soft-tissues, lung and bone. The figures of merit were γ (3%, 3 mm) and γ (5%, 5 mm) criterions corresponding to the computation comparison on 80 absorbed doses (AD) points per phantom between Monte Carlo simulations and CC algorithms. PSCC gave better results than SCC for the lowest photon energy (100 keV). For the 3 isotopes computed with PSCC, the percentage of AD points satisfying the γ (5%, 5 mm) criterion was always over 99%. A still good but worse result was found with SCC, since at least 97% of AD-values verified the γ (5%, 5 mm) criterion, except a value of 57% for the 99mTc with the lung/bone interface. The CC superposition method for radiopharmaceutical dosimetry is a good alternative to Monte Carlo simulations while reducing computation complexity.
Cawston-Grant, Brie; Morrison, Hali; Menon, Geetha; Sloboda, Ron S
2017-05-01
Model-based dose calculation algorithms have recently been incorporated into brachytherapy treatment planning systems, and their introduction requires critical evaluation before clinical implementation. Here, we present an experimental evaluation of Oncentra ® Brachy Advanced Collapsed-cone Engine (ACE) for a multichannel vaginal cylinder (MCVC) applicator using radiochromic film. A uniform dose of 500 cGy was specified to the surface of the MCVC using the TG-43 dose formalism under two conditions: (a) with only the central channel loaded or (b) only the peripheral channels loaded. Film measurements were made at the applicator surface and compared to the doses calculated using TG-43, standard accuracy ACE (sACE), and high accuracy ACE (hACE). When the central channel of the applicator was used, the film measurements showed a dose increase of (11 ± 8)% (k = 2) above the two outer grooves on the applicator surface. This increase in dose was confirmed with the hACE calculations, but was not confirmed with the sACE calculations at the applicator surface. When the peripheral channels were used, a periodic azimuthal variation in measured dose was observed around the applicator. The sACE and hACE calculations confirmed this variation and agreed within 1% of each other at the applicator surface. Additionally for the film measurements with the central channel used, a baseline dose variation of (10 ± 4)% (k = 2) of the mean dose was observed azimuthally around the applicator surface, which can be explained by offset source positioning in the central channel. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
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Cawston-Grant, Brie; Morrison, Hali; Sloboda, Ron; Menon, Geetha
2016-01-01
Purpose: To present an investigation of the Advanced Collapsed-cone Engine (ACE) in Oncentraê Brachy (OcB) v4.5 using a tissue equivalent phantom modeling scalp brachytherapy (BT) treatments. Methods: A slab phantom modeling the skin, skull, brain and mold was used. A dose of 400cGy was prescribed to just above the skull layer using TG-43 and was delivered using an HDR afterloader. Measurements were made using Gafchromic™ EBT3 film at four depths within the phantom. The TG-43 planned and film measured doses were compared to the standard (sACE) and high (hACE) accuracy ACE options in OcB between the surface and below the skull. Results: The average difference between the TG-43 calculated and film measured doses was −11.25±3.38% when there was no air gap between the mold and skin; sACE and hACE doses were on average lower than TG-43 calculated doses by 3.41±0.03% and 2.45±0.03%, respectively. With a 3mm air gap between the mold and skin, the difference between the TG-43 calculated and measured doses was −8.28±5.76%; sACE and hACE calculations yielded average doses 1.87±0.03% and 1.78±0.04% greater than TG-43, respectively. Conclusions: TG-43, sACE, and hACE were found to overestimate doses below the skull layer compared to film. With a 3mm air gap between the mold and skin, sACE and hACE more accurately predicted the film dose to the skin surface than TG-43. More clinical variations and their implications are currently being investigated.
Gorbach, Natalia; Plechova, Anastasiya; Portnyagin, Maxim
2017-04-01
Zhupanovsky volcano, situated 70 km north from Petropavlovsk-Kamchatsky city, resumed its activity in October 2013 [3]. In 2014 and in the first half of 2015, episodic explosions with ash plumes rising up to 6-8 km above sea level occurred on Priemish cone - one of four cones on the Zhupanovsky volcanic edifice [1]. In July 2015 after a series of seismic and explosive events, the southern sector of the active cone collapsed. The landslide and lahar deposits resulted from the collapse formed a large field on the volcano slopes [2]. In November 2015 and January-March 2016, a series of powerful explosions took place sending ash up to 8-10 km above sea level. No pure magmatic, effusive or extrusive, activity has been observed on Zhupanovsky in 2013-2016. We have studied the composition, morphology and textural features of ash particles produced by the largest explosive events of Zhupanovsky in the period from October 2013 to March 2016. The main components of the ash were found to be hydrothermally altered particles and lithics, likely originated by the defragmentation of rocks composing the volcanic edifice. Juvenile glass fragments occur in very subordinate quantities. The maximum amount of glass particles (up to 7%) was found in the ash erupted in January-March 2016, after the cone collapse. We suggest that the phreatic to phreatomagmatic explosive activity of Zhupanovsky volcano in 2013-2016 was initially caused by the intrusion of a new magma batch under the volcano. The intrusion and associated degassing of magma led to heating, overpressure and instability in the hydrothermal system of the volcano, causing episodic, predominantly phreatic explosions. Decompression of the shallow magmatic and hydrothermal system of the volcano due to the cone collapse in July 2015 facilitated a larger involvement of the magmatic component in the eruption and more powerful explosions. [1] Girina O.A. et al., 2016 Geophysical Research Abstracts Vol. 18, EGU2016-2101, doi: 10
Kaselis, Andrius; Treinys, Rimantas; Vosyliūtė, Rūta; Šatkauskas, Saulius
2014-03-01
Regeneration of embryonic and adult dorsal root ganglion (DRG) sensory axons is highly impeded when they encounter neuronal growth cone-collapsing factor semaphorin3A (Sema3A). On the other hand, increasing evidence shows that DRG axon's regeneration can be stimulated by nerve growth factor (NGF). In this study, we aimed to evaluate whether increased NGF concentrations can counterweight Sema3A-induced inhibitory responses in 15-day-old mouse embryo (E15) DRG axons. The DRG explants were grown in Neurobasal-based medium with different NGF concentrations ranging from 0 to 100 ng/mL and then treated with Sema3A at constant 10 ng/mL concentration. To evaluate interplay between NGF and Sema3A number of DRG axons, axon outgrowth distance and collapse rate were measured. We found that the increased NGF concentrations abolish Sema3A-induced inhibitory effect on axon outgrowth, while they have no effect on Sema3A-induced collapse rate.
Tedgren, Åsa Carlsson; Plamondon, Mathieu; Beaulieu, Luc
2015-07-07
The aim of this work was to investigate how dose distributions calculated with the collapsed cone (CC) algorithm depend on the size of the water phantom used in deriving the point kernel for multiple scatter. A research version of the CC algorithm equipped with a set of selectable point kernels for multiple-scatter dose that had initially been derived in water phantoms of various dimensions was used. The new point kernels were generated using EGSnrc in spherical water phantoms of radii 5 cm, 7.5 cm, 10 cm, 15 cm, 20 cm, 30 cm and 50 cm. Dose distributions derived with CC in water phantoms of different dimensions and in a CT-based clinical breast geometry were compared to Monte Carlo (MC) simulations using the Geant4-based brachytherapy specific MC code Algebra. Agreement with MC within 1% was obtained when the dimensions of the phantom used to derive the multiple-scatter kernel were similar to those of the calculation phantom. Doses are overestimated at phantom edges when kernels are derived in larger phantoms and underestimated when derived in smaller phantoms (by around 2% to 7% depending on distance from source and phantom dimensions). CC agrees well with MC in the high dose region of a breast implant and is superior to TG43 in determining skin doses for all multiple-scatter point kernel sizes. Increased agreement between CC and MC is achieved when the point kernel is comparable to breast dimensions. The investigated approximation in multiple scatter dose depends on the choice of point kernel in relation to phantom size and yields a significant fraction of the total dose only at distances of several centimeters from a source/implant which correspond to volumes of low doses. The current implementation of the CC algorithm utilizes a point kernel derived in a comparatively large (radius 20 cm) water phantom. A fixed point kernel leads to predictable behaviour of the algorithm with the worst case being a source/implant located well within a patient
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Stathakis, Sotirios [Department of Radiation Oncology, University of Texas Health Science Center San Antonio, 7979 Wurzbach Rd, San Antonio, TX 78229 (United States)], E-mail: stathakis@uthscsa.edu; Esquivel, Carlos; Gutierrez, Alonso N.; Shi, ChengYu; Papanikolaou, Niko [Department of Radiation Oncology, University of Texas Health Science Center San Antonio, 7979 Wurzbach Rd, San Antonio, TX 78229 (United States)
2009-10-15
Purpose: In this paper, we present an alternative to the originally proposed technique for the delivery of spatially fractionated radiation therapy (GRID) using multi-leaf collimator (MLC) shaped fields. We employ the MLC to deliver various pattern GRID treatments to large solid tumors and dosimetrically characterize the GRID fields. Methods and materials: The GRID fields were created with different open to blocked area ratios and with variable separation between the openings using a MLC. GRID designs were introduced into the Pinnacle{sup 3} treatment planning system, and the dose was calculated in a water phantom. Ionization chamber and film measurements using both Kodak EDR2 and Gafchromic EBT film were performed in a SolidWater phantom to determine the relative output of each GRID design as well as its spatial dosimetric characteristics. Results: Agreement within 5.0% was observed between the Pinnacle{sup 3} predicted dose distributions and the measurements for the majority of experiments performed. A higher magnitude of discrepancy (15%) was observed using a high photon beam energy (18 MV) and small GRID opening. Skin dose at the GRID openings was higher than the corresponding open field by a factor as high as three for both photon energies and was found to be independent of the open-to-blocked area ratio. Conclusion: In summary, we reaffirm that the MLC can be used to deliver spatially fractionated GRID therapy and show that various GRID patterns may be generated. The Pinnacle{sup 3} TPS can accurately calculate the dose of the different GRID patterns in our study to within 5% for the majority of the cases based on film and ion chamber measurements. Disadvantages of MLC-based GRID therapy are longer treatment times and higher surface doses.
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Hackett, S; Asselen, B van; Wolthaus, J; Kotte, A; Bol, G; Lagendijk, J; Raaymakers, B [University Medical Center, Utrecht (Netherlands); Feist, G [Elekta Instrument AB, Stockholm (Sweden); Pencea, S [Elekta Inc., Atlanta, GA (United States); Akhiat, H [Elekta BV, Best (Netherlands)
2016-06-15
Purpose: Treatment plans for the MR-linac, calculated in Monaco v5.19, include direct simulation of the effects of the 1.5T B{sub 0}-field. We tested the feasibility of using a collapsed-cone (CC) algorithm in Oncentra, which does not account for effects of the B{sub 0}-field, as a fast online, independent 3D check of dose calculations. Methods: Treatment plans for six patients were generated in Monaco with a 6 MV FFF beam and the B{sub 0}-field. All plans were recalculated with a CC model of the same beam. Plans for the same patients were also generated in Monaco without the B{sub 0}-field. The mean dose (Dmean) and doses to 10% (D10%) and 90% (D90%) of the volume were determined, as percentages of the prescribed dose, for target volumes and OARs in each calculated dose distribution. Student’s t-tests between paired parameters from Monaco plans and corresponding CC calculations were performed. Results: Figure 1 shows an example of the difference between dose distributions calculated in Monaco, with the B{sub 0}-field, and the CC algorithm. Figure 2 shows distributions of (absolute) difference between parameters for Monaco plans, with the B{sub 0}-field, and CC calculations. The Dmean and D90% values for the CTVs and PTVs were significantly different, but differences in dose distributions arose predominantly at the edges of the target volumes. Inclusion of the B{sub 0}-field had little effect on agreement of the Dmean values, as illustrated by Figure 3, nor on agreement of the D10% and D90% values. Conclusion: Dose distributions recalculated with a CC algorithm show good agreement with those calculated with Monaco, for plans both with and without the B{sub 0}-field, indicating that the CC algorithm could be used to check online treatment planning for the MRlinac. Agreement for a wider range of treatment sites, and the feasibility of using the γ-test as a simple pass/fail criterion, will be investigated.
Fluence-convolution broad-beam (FCBB) dose calculation
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Lu Weiguo; Chen Mingli, E-mail: wlu@tomotherapy.co [TomoTherapy Inc., 1240 Deming Way, Madison, WI 53717 (United States)
2010-12-07
IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N{sup 3}) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization.
Fundamentals of convolutional coding
Johannesson, Rolf
2015-01-01
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field * Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding * Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes * Distance properties of convolutional codes * Includes a downloadable solutions manual
Fast Convolution Module (Fast Convolution Module)
National Research Council Canada - National Science Library
Bierens, L
1997-01-01
This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...
... biopsy; Pap smear - cone biopsy; HPV - cone biopsy; Human papilloma virus - cone biopsy; Cervix - cone biopsy; Colposcopy - cone biopsy Images Female reproductive anatomy Cold cone biopsy Cold cone removal References Baggish ...
International Nuclear Information System (INIS)
Prokopec, T.; Sornborger, A.; Brandenberger, R.H.
1992-01-01
We study single-texture collapse using a leapfrog discretization method on a 30x30x30 spatial lattice. We investigate the influence of boundary conditions, physical size of the lattice, type of space-time background (flat, i.e., nonexpanding, vs radiation-dominated and matter-dominated universes), and spatial distribution of the initial texture configuration on collapse time and critical winding. For a spherically symmetric initial configuration of size equal to the horizon size on a lattice containing 12 (30) horizon volumes, the critical winding is found to be 0.621±0.001 (0.602±0.003) (flat case), 0.624±0.002 (0.604±0.005) (radiation era), 0.628±0.002 (0.612±0.003) (matter era). The larger the physical size of the lattice (in units of the horizon size), the smaller is the critical winding, and in the limit of an infinite lattice, we argue that the critical winding approaches 0.5. For radially asymmetric cases, contraction of one axis ( /Ipancake case) slightly reduces collapse time and critical winding, and contraction of two axes (d/Icigar case) reduces collapse time and critical winding significantly
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.
International Nuclear Information System (INIS)
Miller, R.H.; Smith, B.F.
1979-01-01
The self-consistent dynamical development of six stellar systems, started from rotating spherical configurations, has been studied by means of a fully three-dimensional n-body integration. The six examples had different initial angular velocities and velocity dispersions. All settled down into prolate bars rotating about a short axis within two initial rotation periods. The bars are long-lived, robust, and stable. Bars are the natural form toward which rapidly rotating stellar dynamical systems develop, instead of the flattened axisymmetric disks that had been expected.The early stages of each collapse are reasonably well described by a theoretical model according to which a collapse passes through a sequence of rigidly rotating, uniform-density spheroids. The first significant departures from spheroidal form were axisymmetric in all cases. Rings formed in some examples, sheets in others, with transition cases between these extremes. Nonaxisymmetry forms developed from these intermediate stages
Convolution based profile fitting
International Nuclear Information System (INIS)
Kern, A.; Coelho, A.A.; Cheary, R.W.
2002-01-01
Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based
Convolution copula econometrics
Cherubini, Umberto; Mulinacci, Sabrina
2016-01-01
This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.
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.
Multithreaded implicitly dealiased convolutions
Roberts, Malcolm; Bowman, John C.
2018-03-01
Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.
Ultrafast convolution/superposition using tabulated and exponential kernels on GPU
Energy Technology Data Exchange (ETDEWEB)
Chen Quan; Chen Mingli; Lu Weiguo [TomoTherapy Inc., 1240 Deming Way, Madison, Wisconsin 53717 (United States)
2011-03-15
Purpose: Collapsed-cone convolution/superposition (CCCS) dose calculation is the workhorse for IMRT dose calculation. The authors present a novel algorithm for computing CCCS dose on the modern graphic processing unit (GPU). Methods: The GPU algorithm includes a novel TERMA calculation that has no write-conflicts and has linear computation complexity. The CCCS algorithm uses either tabulated or exponential cumulative-cumulative kernels (CCKs) as reported in literature. The authors have demonstrated that the use of exponential kernels can reduce the computation complexity by order of a dimension and achieve excellent accuracy. Special attentions are paid to the unique architecture of GPU, especially the memory accessing pattern, which increases performance by more than tenfold. Results: As a result, the tabulated kernel implementation in GPU is two to three times faster than other GPU implementations reported in literature. The implementation of CCCS showed significant speedup on GPU over single core CPU. On tabulated CCK, speedups as high as 70 are observed; on exponential CCK, speedups as high as 90 are observed. Conclusions: Overall, the GPU algorithm using exponential CCK is 1000-3000 times faster over a highly optimized single-threaded CPU implementation using tabulated CCK, while the dose differences are within 0.5% and 0.5 mm. This ultrafast CCCS algorithm will allow many time-sensitive applications to use accurate dose calculation.
Prevention of gravitational collapse
International Nuclear Information System (INIS)
Moffat, J.W.; Taylor, J.G.
1981-01-01
We apply a new theory of gravitation to the question of gravitational collapse to show that collapse is prevented in this theory under very reasonable conditions. This result also extends to prevent ultimate collapse of the Universe. (orig.)
Convolutional coding techniques for data protection
Massey, J. L.
1975-01-01
Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.
International Nuclear Information System (INIS)
Suh, D. C.; Im, J. G.; Park, J. H.; Han, M. C.
1987-01-01
The computed tomographic (CT) findings of labor collapse are analysed in an attempt to evaluate the patterns of labor collapse and to get the helpful signs in differentiation between benign and malignant causes of collapse. 43 cases of labor collapse with or without endobronchial obstruction were reviewed. In 29 of 43 cases the collapses were caused by lung cancer. Benign causes of labor collapse included tuberculosis(10), broncholith(2), organizing pneumonia(1) and hamartoma(1). The helpful signs favoring malignant cause of the labor collapse were proximal bulging of the collapsed lobe, low density mass within the collapsed lung, and endobronchial lesion. Above described differential findings were especially applicable in cases of upper lobe collapse
Hirschman, Isidore Isaac
2005-01-01
In studies of general operators of the same nature, general convolution transforms are immediately encountered as the objects of inversion. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level undergraduates and graduate students. It may be read easily by anyone with a working knowledge of real and complex variable theory. Topics include the finite and non-finite kernels, variation diminishing transforms, asymptotic behavior of kernels, real inversion theory, representation theory, the Weierstrass transform, and
Separating Underdetermined Convolutive Speech Mixtures
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan
2006-01-01
a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...
Strongly-MDS convolutional codes
Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R
Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the
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.
Dealiased convolutions for pseudospectral simulations
International Nuclear Information System (INIS)
Roberts, Malcolm; Bowman, John C
2011-01-01
Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.
Vezzoli, Luigina; Corazzato, Claudia
2016-05-01
In the upper part of the Stromboli volcano, in the Le Croci and Bastimento areas, two dyke-like bodies of volcanic breccia up to two-metre thick crosscut and intrude the products of Vancori and Neostromboli volcanoes. We describe the lithofacies association of these unusual volcaniclastic dykes, interpret the setting of dyke-forming fractures and the emplacement mechanism of internal deposits, and discuss their probable relationships with the explosive eruption and major lateral collapse events that occurred at the end of the Neostromboli period. The dyke volcaniclastic deposits contain juvenile magmatic fragments (pyroclasts) suggesting a primary volcanic origin. Their petrographic characteristics are coincident with the Neostromboli products. The architecture of the infilling deposits comprises symmetrically-nested volcaniclastic units, separated by sub-vertical boundaries, which are parallel to the dyke margins. The volcanic units are composed of distinctive lithofacies. The more external facies is composed of fine and coarse ash showing sub-vertical laminations, parallel to the contact wall. The central facies comprises stratified, lithic-rich breccia and lapilli-tuff, whose stratification is sub-horizontal and convolute, discordant to the dyke margins. Only at Le Croci dyke, the final unit shows a massive tuff-breccia facies. The volcaniclastic dykes experienced a polyphasic geological evolution comprising three stages. The first phase consisted in fracturing, explosive intrusion related to magma rising and upward injection of magmatic fluids and pyroclasts. The second phase recorded the dilation of fractures and their role as pyroclastic conduits in an explosive eruption possibly coeval with the lateral collapse of the Neostromboli lava cone. Finally, in the third phase, the immediately post-eruption mass-flow remobilization of pyroclastic deposits took place on the volcano slopes.
Energy Technology Data Exchange (ETDEWEB)
Aguirre-Diaz, Gerardo J [Centro de Geociencias, Universidad Nacional Autonoma de Mexico, Campus Juriquilla, Queretaro, Qro., 76230 (Mexico)], E-mail: ger@geociencias.unam.mx
2008-10-01
Three main types of collapse calderas can be defined, 1) summit caldera: those formed at the top of large volcanoes, 2) classic caldera: semi-circular to irregular-shaped large structures, several km in diameter and related to relatively large-volume pyroclastic products, and 3) graben caldera: explosive volcano-tectonic collapse structures from which large-volume, ignimbrite-forming eruptions occurred through several fissural vents along the graben master faults and the intra-graben block faults. These in turn can collapse at least with three styles: 1) Piston: when the collapse occurs as a single crustal block; 2) Trap-door: when collapse occurs unevenly along one side while the opposite side remains with no collapse; 3) Piece-meal: when collapse occurs as broken pieces of the crust on top of the magma chamber.
Design of convolutional tornado code
Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu
2017-09-01
As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.
Indian Academy of Sciences (India)
general setting of the space CL(X, Y ) of all continuous linear mappings from a normed cone (X, p) to a normed cone (Y, q), extending several well-known results related to open continuous linear mappings between normed linear spaces. Keywords. Normed cone; extended quasi-metric; continuous linear mapping; bicom-.
Experimental study of current loss and plasma formation in the Z machine post-hole convolute
Directory of Open Access Journals (Sweden)
M. R. Gomez
2017-01-01
Full Text Available The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H_{2}O, H_{2}, and hydrocarbons. Plasma densities increase from 1×10^{16} cm^{−3} (level of detectability just before peak current to over 1×10^{17} cm^{−3} at stagnation (tens of ns later. The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35–50 cm/μs. Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.
Solutions to Arithmetic Convolution Equations
Czech Academy of Sciences Publication Activity Database
Glöckner, H.; Lucht, L.G.; Porubský, Štefan
2007-01-01
Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007
Mechanisms of cascade collapse
International Nuclear Information System (INIS)
Diaz de la Rubia, T.; Smalinskas, K.; Averback, R.S.; Robertson, I.M.; Hseih, H.; Benedek, R.
1988-12-01
The spontaneous collapse of energetic displacement cascades in metals into vacancy dislocation loops has been investigated by molecular dynamics (MD) computer simulation and transmission electron microscopy (TEM). Simulations of 5 keV recoil events in Cu and Ni provide the following scenario of cascade collapse: atoms are ejected from the central region of the cascade by replacement collision sequences; the central region subsequently melts; vacancies are driven to the center of the cascade during resolidification where they may collapse into loops. Whether or not collapse occurs depends critically on the melting temperature of the metal and the energy density and total energy in the cascade. Results of TEM are presented in support of this mechanism. 14 refs., 4 figs., 1 tab
Neutrinos from gravitational collapse
International Nuclear Information System (INIS)
Mayle, R.; Wilson, J.R.; Schramm, D.N.
1986-05-01
Detailed calculations are made of the neutrino spectra emitted during gravitational collapse events (Type II supernovae). Those aspects of the neutrino signal which are relatively independent of the collapse model and those aspects which are sensitive to model details are discussed. The easier-to-detect high energy tail of the emitted neutrinos has been calculated using the Boltzmann equation which is compared with the result of the traditional multi-group flux limited diffusion calculations. 8 figs., 28 refs
Adaptive Graph Convolutional Neural Networks
Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou
2018-01-01
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...
Energy Technology Data Exchange (ETDEWEB)
Lask, Kathleen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Gadgil, Ashok [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2016-10-24
A lighting cone is a simple metal cone placed on the fuel bed of a stove during ignition to act as a chimney, increasing the draft through the fuel bed. Many stoves tend to be difficult to light due to poor draft through the fuel bed, so lighting cones are used in various parts of the world as an inexpensive accessory to help with ignition.
Collapsed Dark Matter Structures.
Buckley, Matthew R; DiFranzo, Anthony
2018-02-02
The distributions of dark matter and baryons in the Universe are known to be very different: The dark matter resides in extended halos, while a significant fraction of the baryons have radiated away much of their initial energy and fallen deep into the potential wells. This difference in morphology leads to the widely held conclusion that dark matter cannot cool and collapse on any scale. We revisit this assumption and show that a simple model where dark matter is charged under a "dark electromagnetism" can allow dark matter to form gravitationally collapsed objects with characteristic mass scales much smaller than that of a Milky-Way-type galaxy. Though the majority of the dark matter in spiral galaxies would remain in the halo, such a model opens the possibility that galaxies and their associated dark matter play host to a significant number of collapsed substructures. The observational signatures of such structures are not well explored but potentially interesting.
Collapsed Dark Matter Structures
Buckley, Matthew R.; DiFranzo, Anthony
2018-02-01
The distributions of dark matter and baryons in the Universe are known to be very different: The dark matter resides in extended halos, while a significant fraction of the baryons have radiated away much of their initial energy and fallen deep into the potential wells. This difference in morphology leads to the widely held conclusion that dark matter cannot cool and collapse on any scale. We revisit this assumption and show that a simple model where dark matter is charged under a "dark electromagnetism" can allow dark matter to form gravitationally collapsed objects with characteristic mass scales much smaller than that of a Milky-Way-type galaxy. Though the majority of the dark matter in spiral galaxies would remain in the halo, such a model opens the possibility that galaxies and their associated dark matter play host to a significant number of collapsed substructures. The observational signatures of such structures are not well explored but potentially interesting.
Convolution of Distribution-Valued Functions. Applications.
BARGETZ, CHRISTIAN
2011-01-01
In this article we examine products and convolutions of vector-valued functions. For nuclear normal spaces of distributions Proposition 25 in [31,p. 120] yields a vector-valued product or convolution if there is a continuous product or convolution mapping in the range of the vector-valued functions. For specific spaces, we generalize this result to hypocontinuous bilinear maps at the expense of generality with respect to the function space. We consider holomorphic, meromorphic and differentia...
Directory of Open Access Journals (Sweden)
Borbon Martin de
2017-02-01
Full Text Available The goal of this article is to provide a construction and classification, in the case of two complex dimensions, of the possible tangent cones at points of limit spaces of non-collapsed sequences of Kähler-Einstein metrics with cone singularities. The proofs and constructions are completely elementary, nevertheless they have an intrinsic beauty. In a few words; tangent cones correspond to spherical metrics with cone singularities in the projective line by means of the Kähler quotient construction with respect to the S1-action generated by the Reeb vector field, except in the irregular case ℂβ₁×ℂβ₂ with β₂/ β₁ ∉ Q.
Gravitational collapse and supernovae
International Nuclear Information System (INIS)
Lattimer, J.M.
1989-01-01
The collapse of the core of a massive star and the subsequent birth of a neutron star in a supernova explosion are discussed, and a model of the supernova mechanism is developed. The basic theory is then compared with the particular case of SN1987A, whose emitted neutrinos permitted the first direct test of the model. (author)
Neutrinos and supernova collapse
International Nuclear Information System (INIS)
Colgate, S.A.; Petschek, A.G.
1980-01-01
The neutrino emission resulting from stellar collapse and supernova formation is reviewed. The electron capture and consequent neutronization of the collapsing stellar matter at the end of evolution determines both the initial adiabat of core collapse as well as the trapped lepton fraction. The initial lepton fraction, Y/sub l/ = .48 supplies the pressure for neutral support of the star at the Chandrasekhar limit. High trapping values, Y/sub l/ = .4, lead to soft core collapses; low values to harder collapses. The value of Y/sub l/ is presently in dispute. The neutrino emission from initial electron capture is relatively small. A strong core-bounce shock releases both electron neutrino as well as thermal muon and tau neutrinos. Subsequent neutrino emission and cooling can sometimes lead to an unstable buoyancy gradient in the core in which case unstable core overturn is expected. Calculations have already shown the importance of the largest possible eddy or equivalently the lowest mode of overturn. Present models of low lepton trapping ratio lead to high entropy creation by the reflected shock and the stabilization of the core matter against overturn. In such cases the exterior matter must cool below an entropy of approximately s/k approx. = 2 to become unstable. This may require too long a time approximately one second for neutrino cooling from a neutrinosphere at rho approx. = 2 x 10 12 g cm -3 . On the other hand, high values of Y/sub l/ such as .4 lead to softer bounces at lower density and values of the critical stabilizing entropy of 3 or higher. Under such circumstances, core overturn can still occur
International Nuclear Information System (INIS)
Lu Weiguo; Olivera, Gustavo H; Chen Mingli; Reckwerdt, Paul J; Mackie, Thomas R
2005-01-01
Convolution/superposition (C/S) is regarded as the standard dose calculation method in most modern radiotherapy treatment planning systems. Different implementations of C/S could result in significantly different dose distributions. This paper addresses two major implementation issues associated with collapsed cone C/S: one is how to utilize the tabulated kernels instead of analytical parametrizations and the other is how to deal with voxel size effects. Three methods that utilize the tabulated kernels are presented in this paper. These methods differ in the effective kernels used: the differential kernel (DK), the cumulative kernel (CK) or the cumulative-cumulative kernel (CCK). They result in slightly different computation times but significantly different voxel size effects. Both simulated and real multi-resolution dose calculations are presented. For simulation tests, we use arbitrary kernels and various voxel sizes with a homogeneous phantom, and assume forward energy transportation only. Simulations with voxel size up to 1 cm show that the CCK algorithm has errors within 0.1% of the maximum gold standard dose. Real dose calculations use a heterogeneous slab phantom, both the 'broad' (5 x 5 cm 2 ) and the 'narrow' (1.2 x 1.2 cm 2 ) tomotherapy beams. Various voxel sizes (0.5 mm, 1 mm, 2 mm, 4 mm and 8 mm) are used for dose calculations. The results show that all three algorithms have negligible difference (0.1%) for the dose calculation in the fine resolution (0.5 mm voxels). But differences become significant when the voxel size increases. As for the DK or CK algorithm in the broad (narrow) beam dose calculation, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 10% (7%) of the maximum dose. As for the broad (narrow) beam dose calculation using the CCK algorithm, the dose differences between the 0.5 mm voxels and the voxels up to 8 mm (4 mm) are around 1% of the maximum dose. Among all three methods, the CCK algorithm
Incomplete convolutions in production and inventory models
Houtum, van G.J.J.A.N.; Zijm, W.H.M.
1997-01-01
In this paper, we study incomplete convolutions of continuous distribution functions, as they appear in the analysis of (multi-stage) production and inventory systems. Three example systems are discussed where these incomplete convolutions naturally arise. We derive explicit, nonrecursive formulae
A convolutional approach to reflection symmetry
DEFF Research Database (Denmark)
Cicconet, Marcelo; Birodkar, Vighnesh; Lund, Mads
2017-01-01
We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages w...
Symbol synchronization in convolutionally coded systems
Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.
1979-01-01
Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.
The general theory of convolutional codes
Mceliece, R. J.; Stanley, R. P.
1993-01-01
This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.
Convolution-deconvolution in DIGES
International Nuclear Information System (INIS)
Philippacopoulos, A.J.; Simos, N.
1995-01-01
Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Spherically symmetric scalar field collapse
Indian Academy of Sciences (India)
2013-03-01
Mar 1, 2013 ... The very recent interest in scalar field collapse stems from a cosmological ... The objective of the present investigation is to explore the collapsing modes of a simple ..... The authors thank the BRNS (DAE) for financial support.
Collapse settlement in compacted soils
CSIR Research Space (South Africa)
Booth, AR
1977-01-01
Full Text Available Research into collapse settlement in compacted soils is described, with special reference to recent cases in Southern Africa where collapse settlement occurred in road embankments following wetting of the soil. The laboratory work described...
Indian Academy of Sciences (India)
President's Address to the Association of Mathematics Teachers of India, December 2011. I am expected to tell you, in 25 minutes, something that should interest you, excite you, pique your curiosity, and make you look for more. It is a tall order, but I will try. The word 'interactive' is in fashion these days. So I will leave a few ...
The f electron collapse revisited
International Nuclear Information System (INIS)
Bennett, B.I.
1987-03-01
A reexamination of the collapse of 4f and 5f electrons in the lanthanide and actinide series is presented. The calculations show the well-known collapse of the f electron density at the thresholds of these series along with an f 2 collapse between thorium and protactinium. The collapse is sensitive to the choice of model for the exchange-correlation potential and the behavior of the potential at large radius
Cardiopulmonary Collapse during Labour
Directory of Open Access Journals (Sweden)
Vasilis Sitras
2010-01-01
Full Text Available Cardiopulmonary collapse during labour is a catastrophic event caused by various medical, surgical and obstetrical conditions. It is an emergency that threatens the life of the mother and her unborn child. We present a case of a pregnant woman who suffered from preeclampsia and underwent induction of labour. Severe lung edema occurred early in labour that caused cardiopulmonary collapse. Advanced heart-lung resuscitation was established immediately and continued until an emergency cesarean section was performed few minutes later. The outcome was favourable for both mother and child. We further discuss some aspects of the pathophysiology and appropriate treatment of cardiorespiratory arrest during labour, which involves the coordinated action of the obstetric, pediatric and surgical ward personnel.
International Nuclear Information System (INIS)
Hendricks, J.C.; O'Brien, J.A.
1985-01-01
Two cats examined bronchoscopically to discover the cause of tracheal collapse were found to have tracheal obstruction cranial to the collapse. Cats with this unusual sign should be examined bronchoscopically to ascertain whether there is an obstruction, as the cause in these 2 cats was distinct from the diffuse airway abnormality that causes tracheal collapse in dogs
Collapse, environment, and society
2012-01-01
Historical collapse of ancient states poses intriguing social-ecological questions, as well as potential applications to global change and contemporary strategies for sustainability. Five Old World case studies are developed to identify interactive inputs, triggers, and feedbacks in devolution. Collapse is multicausal and rarely abrupt. Political simplification undermines traditional structures of authority to favor militarization, whereas disintegration is preconditioned or triggered by acute stress (insecurity, environmental or economic crises, famine), with breakdown accompanied or followed by demographic decline. Undue attention to stressors risks underestimating the intricate interplay of environmental, political, and sociocultural resilience in limiting the damages of collapse or in facilitating reconstruction. The conceptual model emphasizes resilience, as well as the historical roles of leaders, elites, and ideology. However, a historical model cannot simply be applied to contemporary problems of sustainability without adjustment for cumulative information and increasing possibilities for popular participation. Between the 14th and 18th centuries, Western Europe responded to environmental crises by innovation and intensification; such modernization was decentralized, protracted, flexible, and broadly based. Much of the current alarmist literature that claims to draw from historical experience is poorly focused, simplistic, and unhelpful. It fails to appreciate that resilience and readaptation depend on identified options, improved understanding, cultural solidarity, enlightened leadership, and opportunities for participation and fresh ideas. PMID:22371579
Progress in light cone physics
International Nuclear Information System (INIS)
Preparata, G.
1973-01-01
A very brief review is given of the progress made in the physics of the light cone in the past year. Included are the light cone expansion, gauge invariance and the consequences of precocious scaling near threshold, the light cone description of the muon pair experiment, light cone expansions, and the assessment and exploitation of analyticity properties in both mass and energy of light cone amplitudes. (U.S.)
Adaptive decoding of convolutional codes
Directory of Open Access Journals (Sweden)
K. Hueske
2007-06-01
Full Text Available Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
Adaptive decoding of convolutional codes
Hueske, K.; Geldmacher, J.; Götze, J.
2007-06-01
Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
Energy Technology Data Exchange (ETDEWEB)
Butler, M J [National Radiological Protection Board, Harwell (UK)
1977-04-01
Dental radiographic techniques are summarized. The advantages and disadvantages of the use of both the conventional plastic pointer cone and the open-ended cylinders or divergent cones favoured both by the ICRP (Protection against Ionizing Radiation from External Sources, Oxford, Pergamon Press, 1973, ICRP Publication 15), and in the Code of Practice for the Protection of Persons against Ionizing Radiation arising from Medical and Dental Use (1972, 3rd edition, London, HMSO) are discussed. The use of the word 'should' in these recommendations to signify a desirable requirement, not an essential one, is noted. This wording is currently of interest both nationally and internationally in relation to regulations, standards and notes for guidance. The National Radiological Protection Board (NRPB) has been reviewing the position, and has concluded that open-ended cones have disadvantages which may sometimes outweigh their advantages. Although open-ended cones are preferable under some circumstances, the recommendation that they should be used ought not to be followed without an understanding of the issues involved. The hazards associated with the use of interchangeable cones are considered. The NRPB now proposes that the requirement for the replacement of pointer cones (for both new and existing equipment) should be withdrawn.
Gravitational waves from gravitational collapse
Energy Technology Data Exchange (ETDEWEB)
Fryer, Christopher L [Los Alamos National Laboratory; New, Kimberly C [Los Alamos National Laboratory
2008-01-01
Gravitational wave emission from stellar collapse has been studied for nearly four decades. Current state-of-the-art numerical investigations of collapse include those that use progenitors with more realistic angular momentum profiles, properly treat microphysics issues, account for general relativity, and examine non-axisymmetric effects in three dimensions. Such simulations predict that gravitational waves from various phenomena associated with gravitational collapse could be detectable with ground-based and space-based interferometric observatories. This review covers the entire range of stellar collapse sources of gravitational waves: from the accretion induced collapse of a white dwarf through the collapse down to neutron stars or black holes of massive stars to the collapse of supermassive stars.
Gravitational Waves from Gravitational Collapse
Directory of Open Access Journals (Sweden)
Chris L. Fryer
2011-01-01
Full Text Available Gravitational-wave emission from stellar collapse has been studied for nearly four decades. Current state-of-the-art numerical investigations of collapse include those that use progenitors with more realistic angular momentum profiles, properly treat microphysics issues, account for general relativity, and examine non-axisymmetric effects in three dimensions. Such simulations predict that gravitational waves from various phenomena associated with gravitational collapse could be detectable with ground-based and space-based interferometric observatories. This review covers the entire range of stellar collapse sources of gravitational waves: from the accretion-induced collapse of a white dwarf through the collapse down to neutron stars or black holes of massive stars to the collapse of supermassive stars.
Gravitational Waves from Gravitational Collapse.
Fryer, Chris L; New, Kimberly C B
2011-01-01
Gravitational-wave emission from stellar collapse has been studied for nearly four decades. Current state-of-the-art numerical investigations of collapse include those that use progenitors with more realistic angular momentum profiles, properly treat microphysics issues, account for general relativity, and examine non-axisymmetric effects in three dimensions. Such simulations predict that gravitational waves from various phenomena associated with gravitational collapse could be detectable with ground-based and space-based interferometric observatories. This review covers the entire range of stellar collapse sources of gravitational waves: from the accretion-induced collapse of a white dwarf through the collapse down to neutron stars or black holes of massive stars to the collapse of supermassive stars. Supplementary material is available for this article at 10.12942/lrr-2011-1.
Convolutional Neural Network for Image Recognition
Seifnashri, Sahand
2015-01-01
The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.
Scapholunate advanced collapse
International Nuclear Information System (INIS)
Chen, C.; Haller, J.; Resnick, D.
1989-01-01
Scapholunate advanced collapse 9SLAC) is a pattern of wrist malalignment (characterized mainly by radiocarpal abnormalities) that has been attributed to osteoarthritis. In order to determine the frequency of SLAC in calcium pyrophosphate dihydrate (CPPD) disease, the authors have reviewed wrist radiographs in 190 cases of this disorder. Forty-two (22%) of these cases reveal wrist abnormalities typical of SLAC. Associated findings include bilateral alterations (63%), abnormal calcification (70%), scapholunate dissociation (70%), and additional compartmental arthropathies. The authors' results confirm that CPPD crystal deposition disease is a major cause of SLAC. They believe, therefore, that this pattern of malalignment is not specific for posttraumatic or spontaneous osteoarthritis of the wrist
Energy Technology Data Exchange (ETDEWEB)
Sharafutdinov, I.G.; Asadulin, Kh.F.; Maloiaroslavtsev, D.A.; Prokopov, O.I.; Rastorquev, M.A.
1980-08-15
A collapsible shelter is proposed which includes a foundation, a framework with reinforced elements which form a roof, tie bolt elements which are riveted to the reinforced elements, and a railing; it is characterized by an arrangement whereby in order to simplify its construction and improve its reliability, the reinforced elements are detachable and are equipped with rigid connecting rods made of separate sections which are mounted to allow for movement via the reinforced elements; the connecting rod of each reinforcement element is connected to the connecting rod of the adjacent reinforced element using horizontal rods on which the shelter is secured. The shelter is made from separate planks.
On the use of Kontorovich-Lebedev transform in electromagnetic diffraction by an impedance cone
Salem, Mohamed; Kamel, Aladin Hassan; Bagci, Hakan
2012-01-01
We consider the boundary-value problem for the Helmholtz equation connected with an infinite circular cone with an impedance boundary on its face. The scheme of solution includes applying the Kontorovich-Lebedev (KL) transform to reduce the problem to that of a KL spectral amplitude function satisfying a singular integral equation of the non-convolution type with a variable coefficient. The singularities of the spectral function are deduced and representations for the field at the tip of the cone and in the near and far field regions are given together with the conditions of validity of these representations. © 2012 IEEE.
y scaling as a probe of nuclear light-cone dynamics
International Nuclear Information System (INIS)
Ji Xiangdong; Filippone, B.W.
1990-01-01
The y scaling exhibited in quasielastic electron scattering on nuclei is shown to occur in the same kinematic limit as x scaling in deep-inelastic lepton-nucleon scattering. Using the impulse approximation in a relativistic model, we demonstrate that the scaling function F(y) can be interpreted as the nucleon light-cone momentum distribution and the scaling variable y is related to the light-cone momentum t + of the nucleon. We also derive the convolution formula for deep-inelastic lepton-nucleus scattering and show that the same F(y) can be extracted from the experimental structure functions of the nucleon and nuclei
On the use of Kontorovich-Lebedev transform in electromagnetic diffraction by an impedance cone
Salem, Mohamed
2012-08-01
We consider the boundary-value problem for the Helmholtz equation connected with an infinite circular cone with an impedance boundary on its face. The scheme of solution includes applying the Kontorovich-Lebedev (KL) transform to reduce the problem to that of a KL spectral amplitude function satisfying a singular integral equation of the non-convolution type with a variable coefficient. The singularities of the spectral function are deduced and representations for the field at the tip of the cone and in the near and far field regions are given together with the conditions of validity of these representations. © 2012 IEEE.
Shearfree cylindrical gravitational collapse
International Nuclear Information System (INIS)
Di Prisco, A.; Herrera, L.; MacCallum, M. A. H.; Santos, N. O.
2009-01-01
We consider diagonal cylindrically symmetric metrics, with an interior representing a general nonrotating fluid with anisotropic pressures. An exterior vacuum Einstein-Rosen spacetime is matched to this using Darmois matching conditions. We show that the matching conditions can be explicitly solved for the boundary values of metric components and their derivatives, either for the interior or exterior. Specializing to shearfree interiors, a static exterior can only be matched to a static interior, and the evolution in the nonstatic case is found to be given in general by an elliptic function of time. For a collapsing shearfree isotropic fluid, only a Robertson-Walker dust interior is possible, and we show that all such cases were included in Cocke's discussion. For these metrics, Nolan and Nolan have shown that the matching breaks down before collapse is complete, and Tod and Mena have shown that the spacetime is not asymptotically flat in the sense of Berger, Chrusciel, and Moncrief. The issues about energy that then arise are revisited, and it is shown that the exterior is not in an intrinsic gravitational or superenergy radiative state at the boundary.
Axisymmetric core collapse simulations using characteristic numerical relativity
International Nuclear Information System (INIS)
Siebel, Florian; Mueller, Ewald; Font, Jose A.; Papadopoulos, Philippos
2003-01-01
We present results from nonrotating axisymmetric stellar core collapse simulations in general relativity. Our hydrodynamics code has proved robust and accurate enough to allow for a detailed analysis of the global dynamics of the collapse. Contrary to traditional approaches based on the 3+1 formulation of the gravitational field equations, our framework uses a foliation based on a family of outgoing light cones, emanating from a regular center, and terminating at future null infinity. Such a coordinate system is well adapted to the study of interesting dynamical spacetimes in relativistic astrophysics such as stellar core collapse and neutron star formation. Perhaps most importantly this procedure allows for the extraction of gravitational waves at future null infinity, along with the commonly used quadrupole formalism for the gravitational wave extraction. Our results concerning the gravitational wave signals show noticeable disagreement when those are extracted by computing the Bondi news at future null infinity on the one hand and by using the quadrupole formula on the other hand. We have a strong indication that for our setup the quadrupole formula on the null cone does not lead to physical gravitational wave signals. The Bondi gravitational wave signals extracted at infinity show typical oscillation frequencies of about 0.5 kHz
PREFACE: Collapse Calderas Workshop
Gottsmann, Jo; Aguirre-Diaz, Gerardo
2008-10-01
Caldera-formation is one of the most awe-inspiring and powerful displays of nature's force. Resultant deposits may cover vast areas and significantly alter the immediate topography. Post-collapse activity may include resurgence, unrest, intra-caldera volcanism and potentially the start of a new magmatic cycle, perhaps eventually leading to renewed collapse. Since volcanoes and their eruptions are the surface manifestation of magmatic processes, calderas provide key insights into the generation and evolution of large-volume silicic magma bodies in the Earth's crust. Despite their potentially ferocious nature, calderas play a crucial role in modern society's life. Collapse calderas host essential economic deposits and supply power for many via the exploitation of geothermal reservoirs, and thus receive considerable scientific, economic and industrial attention. Calderas also attract millions of visitors world-wide with their spectacular scenic displays. To build on the outcomes of the 2005 calderas workshop in Tenerife (Spain) and to assess the most recent advances on caldera research, a follow-up meeting was proposed to be held in Mexico in 2008. This abstract volume presents contributions to the 2nd Calderas Workshop held at Hotel Misión La Muralla, Querétaro, Mexico, 19-25 October 2008. The title of the workshop `Reconstructing the evolution of collapse calderas: Magma storage, mobilisation and eruption' set the theme for five days of presentations and discussions, both at the venue as well as during visits to the surrounding calderas of Amealco, Amazcala and Huichapan. The multi-disciplinary workshop was attended by more than 40 scientist from North, Central and South America, Europe, Australia and Asia. Contributions covered five thematic topics: geology, geochemistry/petrology, structural analysis/modelling, geophysics, and hazards. The workshop was generously supported by the International Association of Volcanology and the Chemistry of The Earth's Interior
A Note on Cubic Convolution Interpolation
Meijering, E.; Unser, M.
2003-01-01
We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.
Cylindrical collapse and gravitational waves
Energy Technology Data Exchange (ETDEWEB)
Herrera, L [Escuela de FIsica, Faculdad de Ciencias, Universidad Central de Venezuela, Caracas, Venezuela (Venezuela); Santos, N O [Universite Pierre et Marie Curie, CNRS/FRE 2460 LERMA/ERGA, Tour 22-12, 4eme etage, BoIte 142, 4 place Jussieu, 75252 Paris Cedex 05 (France); Laboratorio Nacional de Computacao Cientifica, 25651-070 Petropolis RJ (Brazil); Centro Brasileiro de Pesquisas Fisicas, 22290-180 Rio de Janeiro RJ (Brazil)
2005-06-21
We study the matching conditions for a collapsing anisotropic cylindrical perfect fluid, and we show that its radial pressure is non-zero on the surface of the cylinder and proportional to the time-dependent part of the field produced by the collapsing fluid. This result resembles the one that arises for the radiation-though non-gravitational-in the spherically symmetric collapsing dissipative fluid, in the diffusion approximation.
African Journals Online (AJOL)
1 Mei 1971. S.-A. TYDSKRIF VIR OBSTETRIE EN GINEKOLOGIE. CONE BIOPSY ... of the abnormal cervix in pregnancy is also no longer in question following the .... the concept of cancer prophylaxis to the majority of women, many of whom ...
Spherical collapse in chameleon models
International Nuclear Information System (INIS)
Brax, Ph.; Rosenfeld, R.; Steer, D.A.
2010-01-01
We study the gravitational collapse of an overdensity of nonrelativistic matter under the action of gravity and a chameleon scalar field. We show that the spherical collapse model is modified by the presence of a chameleon field. In particular, we find that even though the chameleon effects can be potentially large at small scales, for a large enough initial size of the inhomogeneity the collapsing region possesses a thin shell that shields the modification of gravity induced by the chameleon field, recovering the standard gravity results. We analyse the behaviour of a collapsing shell in a cosmological setting in the presence of a thin shell and find that, in contrast to the usual case, the critical density for collapse in principle depends on the initial comoving size of the inhomogeneity
Spherical collapse in chameleon models
Energy Technology Data Exchange (ETDEWEB)
Brax, Ph. [Institut de Physique Théorique, CEA, IPhT, CNRS, URA 2306, F-91191Gif/Yvette Cedex (France); Rosenfeld, R. [Instituto de Física Teórica, Universidade Estadual Paulista, Rua Dr. Bento T. Ferraz, 271, 01140-070, São Paulo (Brazil); Steer, D.A., E-mail: brax@spht.saclay.cea.fr, E-mail: rosenfel@ift.unesp.br, E-mail: daniele.steer@apc.univ-paris7.fr [APC, UMR 7164, CNRS, Université Paris 7, 10 rue Alice Domon et Léonie Duquet, 75205 Paris Cedex 13 (France)
2010-08-01
We study the gravitational collapse of an overdensity of nonrelativistic matter under the action of gravity and a chameleon scalar field. We show that the spherical collapse model is modified by the presence of a chameleon field. In particular, we find that even though the chameleon effects can be potentially large at small scales, for a large enough initial size of the inhomogeneity the collapsing region possesses a thin shell that shields the modification of gravity induced by the chameleon field, recovering the standard gravity results. We analyse the behaviour of a collapsing shell in a cosmological setting in the presence of a thin shell and find that, in contrast to the usual case, the critical density for collapse in principle depends on the initial comoving size of the inhomogeneity.
Spherical Collapse in Chameleon Models
Brax, Ph; Steer, D A
2010-01-01
We study the gravitational collapse of an overdensity of nonrelativistic matter under the action of gravity and a chameleon scalar field. We show that the spherical collapse model is modified by the presence of a chameleon field. In particular, we find that even though the chameleon effects can be potentially large at small scales, for a large enough initial size of the inhomogeneity the collapsing region possesses a thin shell that shields the modification of gravity induced by the chameleon field, recovering the standard gravity results. We analyse the behaviour of a collapsing shell in a cosmological setting in the presence of a thin shell and find that, in contrast to the usual case, the critical density for collapse depends on the initial comoving size of the inhomogeneity.
Ejecta evolution during cone impact
Marston, Jeremy
2014-07-07
We present findings from an experimental investigation into the impact of solid cone-shaped bodies onto liquid pools. Using a variety of cone angles and liquid physical properties, we show that the ejecta formed during the impact exhibits self-similarity for all impact speeds for very low surface tension liquids, whilst for high-surface tension liquids similarity is only achieved at high impact speeds. We find that the ejecta tip can detach from the cone and that this phenomenon can be attributed to the air entrainment phenomenon. We analyse of a range of cone angles, including some ogive cones, and impact speeds in terms of the spatiotemporal evolution of the ejecta tip. Using superhydrophobic cones, we also examine the entry of cones which entrain an air layer.
Semantic segmentation of bioimages using convolutional neural networks
CSIR Research Space (South Africa)
Wiehman, S
2016-07-01
Full Text Available Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live...
One weird trick for parallelizing convolutional neural networks
Krizhevsky, Alex
2014-01-01
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
Cone Algorithm of Spinning Vehicles under Dynamic Coning Environment
Directory of Open Access Journals (Sweden)
Shuang-biao Zhang
2015-01-01
Full Text Available Due to the fact that attitude error of vehicles has an intense trend of divergence when vehicles undergo worsening coning environment, in this paper, the model of dynamic coning environment is derived firstly. Then, through investigation of the effect on Euler attitude algorithm for the equivalency of traditional attitude algorithm, it is found that attitude error is actually the roll angle error including drifting error and oscillating error, which is induced directly by dynamic coning environment and further affects the pitch angle and yaw angle through transferring. Based on definition of the cone frame and cone attitude, a cone algorithm is proposed by rotation relationship to calculate cone attitude, and the relationship between cone attitude and Euler attitude of spinning vehicle is established. Through numerical simulations with different conditions of dynamic coning environment, it is shown that the induced error of Euler attitude fluctuates by the variation of precession and nutation, especially by that of nutation, and the oscillating frequency of roll angle error is twice that of pitch angle error and yaw angle error. In addition, the rotation angle is more competent to describe the spinning process of vehicles under coning environment than Euler angle gamma, and the real pitch angle and yaw angle are calculated finally.
Gradient Flow Convolutive Blind Source Separation
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Nielsen, Chinton Møller
2004-01-01
Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use...... of a circular four microphone array with a radius of 5 mm, and applying convolutive gradient flow instead of just applying instantaneous gradient flow, experimental results show an improvement of up to around 14 dB can be achieved for simulated impulse responses and up to around 10 dB for a hearing aid...
Human Face Recognition Using Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Răzvan-Daniel Albu
2009-10-01
Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.
CMOS Compressed Imaging by Random Convolution
Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf
2009-01-01
We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...
Feedback equivalence of convolutional codes over finite rings
Directory of Open Access Journals (Sweden)
DeCastro-García Noemí
2017-12-01
Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.
Computational models of stellar collapse and core-collapse supernovae
International Nuclear Information System (INIS)
Ott, Christian D; O'Connor, Evan; Schnetter, Erik; Loeffler, Frank; Burrows, Adam; Livne, Eli
2009-01-01
Core-collapse supernovae are among Nature's most energetic events. They mark the end of massive star evolution and pollute the interstellar medium with the life-enabling ashes of thermonuclear burning. Despite their importance for the evolution of galaxies and life in the universe, the details of the core-collapse supernova explosion mechanism remain in the dark and pose a daunting computational challenge. We outline the multi-dimensional, multi-scale, and multi-physics nature of the core-collapse supernova problem and discuss computational strategies and requirements for its solution. Specifically, we highlight the axisymmetric (2D) radiation-MHD code VULCAN/2D and present results obtained from the first full-2D angle-dependent neutrino radiation-hydrodynamics simulations of the post-core-bounce supernova evolution. We then go on to discuss the new code Zelmani which is based on the open-source HPC Cactus framework and provides a scalable AMR approach for 3D fully general-relativistic modeling of stellar collapse, core-collapse supernovae and black hole formation on current and future massively-parallel HPC systems. We show Zelmani's scaling properties to more than 16,000 compute cores and discuss first 3D general-relativistic core-collapse results.
Computational models of stellar collapse and core-collapse supernovae
Energy Technology Data Exchange (ETDEWEB)
Ott, Christian D; O' Connor, Evan [TAPIR, Mailcode 350-17, California Institute of Technology, Pasadena, CA (United States); Schnetter, Erik; Loeffler, Frank [Center for Computation and Technology, Louisiana State University, Baton Rouge, LA (United States); Burrows, Adam [Department of Astrophysical Sciences, Princeton University, Princeton, NJ (United States); Livne, Eli, E-mail: cott@tapir.caltech.ed [Racah Institute of Physics, Hebrew University, Jerusalem (Israel)
2009-07-01
Core-collapse supernovae are among Nature's most energetic events. They mark the end of massive star evolution and pollute the interstellar medium with the life-enabling ashes of thermonuclear burning. Despite their importance for the evolution of galaxies and life in the universe, the details of the core-collapse supernova explosion mechanism remain in the dark and pose a daunting computational challenge. We outline the multi-dimensional, multi-scale, and multi-physics nature of the core-collapse supernova problem and discuss computational strategies and requirements for its solution. Specifically, we highlight the axisymmetric (2D) radiation-MHD code VULCAN/2D and present results obtained from the first full-2D angle-dependent neutrino radiation-hydrodynamics simulations of the post-core-bounce supernova evolution. We then go on to discuss the new code Zelmani which is based on the open-source HPC Cactus framework and provides a scalable AMR approach for 3D fully general-relativistic modeling of stellar collapse, core-collapse supernovae and black hole formation on current and future massively-parallel HPC systems. We show Zelmani's scaling properties to more than 16,000 compute cores and discuss first 3D general-relativistic core-collapse results.
Electromagnetic scattering of a vector Bessel beam in the presence of an impedance cone
Salem, Mohamed
2013-07-01
The electromagnetic field scattering of a vector Bessel beam in the presence of an infinite circular cone with an impedance boundary on its surface is considered. The impinging field is normal to the tip of the cone and is expanded in terms of vector spherical wave functions; a Kontorovich-Lebedev (KL) transform is employed to expand the scattered fields. The problem is reduced to a singular integral equation with a variable coefficient of the non-convolution type. The singularities of the spectral function are deduced and representations for the field at the tip of the cone as well as other regions are given together with the conditions of validity of these representations. © 2013 IEEE.
Discrete convolution-operators and radioactive disintegration. [Numerical solution
Energy Technology Data Exchange (ETDEWEB)
Kalla, S L; VALENTINUZZI, M E [UNIVERSIDAD NACIONAL DE TUCUMAN (ARGENTINA). FACULTAD DE CIENCIAS EXACTAS Y TECNOLOGIA
1975-08-01
The basic concepts of discrete convolution and discrete convolution-operators are briefly described. Then, using the discrete convolution - operators, the differential equations associated with the process of radioactive disintegration are numerically solved. The importance of the method is emphasized to solve numerically, differential and integral equations.
De Lorenzo, Tommaso; Perez, Alejandro
2018-02-01
We show that null surfaces defined by the outgoing and infalling wave fronts emanating from and arriving at a sphere in Minkowski spacetime have thermodynamical properties that are in strict formal correspondence with those of black hole horizons in curved spacetimes. Such null surfaces, made of pieces of light cones, are bifurcate conformal Killing horizons for suitable conformally stationary observers. They can be extremal and nonextremal depending on the radius of the shining sphere. Such conformal Killing horizons have a constant light cone (conformal) temperature, given by the standard expression in terms of the generalization of surface gravity for conformal Killing horizons. Exchanges of conformally invariant energy across the horizon are described by a first law where entropy changes are given by 1 /(4 ℓp2) of the changes of a geometric quantity with the meaning of horizon area in a suitable conformal frame. These conformal horizons satisfy the zeroth to the third laws of thermodynamics in an appropriate way. In the extremal case they become light cones associated with a single event; these have vanishing temperature as well as vanishing entropy.
The collapsed football pla yer
African Journals Online (AJOL)
Football is the most popular sport in the world, played by over 265 ... FIFA Medical Officer and Honorary Part-time Lecturer, Wits Centre for Exercise Science and Sports Medicine, Johannesburg .... Management of a collapsed player does not.
Gravity induced wave function collapse
Gasbarri, G.; Toroš, M.; Donadi, S.; Bassi, A.
2017-11-01
Starting from an idea of S. L. Adler [in Quantum Nonlocality and Reality: 50 Years of Bell's Theorem, edited by M. Bell and S. Gao (Cambridge University Press, Cambridge, England 2016)], we develop a novel model of gravity induced spontaneous wave function collapse. The collapse is driven by complex stochastic fluctuations of the spacetime metric. After deriving the fundamental equations, we prove the collapse and amplification mechanism, the two most important features of a consistent collapse model. Under reasonable simplifying assumptions, we constrain the strength ξ of the complex metric fluctuations with available experimental data. We show that ξ ≥10-26 in order for the model to guarantee classicality of macro-objects, and at the same time ξ ≤10-20 in order not to contradict experimental evidence. As a comparison, in the recent discovery of gravitational waves in the frequency range 35 to 250 Hz, the (real) metric fluctuations reach a peak of ξ ˜10-21.
Collapse of large extra dimensions
International Nuclear Information System (INIS)
Geddes, James
2002-01-01
In models of spacetime that are the product of a four-dimensional spacetime with an 'extra' dimension, there is the possibility that the extra dimension will collapse to zero size, forming a singularity. We ask whether this collapse is likely to destroy the spacetime. We argue, by an appeal to the four-dimensional cosmic censorship conjecture, that--at least in the case when the extra dimension is homogeneous--such a collapse will lead to a singularity hidden within a black string. We also construct explicit initial data for a spacetime in which such a collapse is guaranteed to occur and show how the formation of a naked singularity is likely avoided
Hamel, Christian P
2007-01-01
Cone rod dystrophies (CRDs) (prevalence 1/40,000) are inherited retinal dystrophies that belong to the group of pigmentary retinopathies. CRDs are characterized by retinal pigment deposits visible on fundus examination, predominantly localized to the macular region. In contrast to typical retinitis pigmentosa (RP), also called the rod cone dystrophies (RCDs) resulting from the primary loss in rod photoreceptors and later followed by the secondary loss in cone photoreceptors, CRDs reflect the opposite sequence of events. CRD is characterized by primary cone involvement, or, sometimes, by concomitant loss of both cones and rods that explains the predominant symptoms of CRDs: decreased visual acuity, color vision defects, photoaversion and decreased sensitivity in the central visual field, later followed by progressive loss in peripheral vision and night blindness. The clinical course of CRDs is generally more severe and rapid than that of RCDs, leading to earlier legal blindness and disability. At end stage, however, CRDs do not differ from RCDs. CRDs are most frequently non syndromic, but they may also be part of several syndromes, such as Bardet Biedl syndrome and Spinocerebellar Ataxia Type 7 (SCA7). Non syndromic CRDs are genetically heterogeneous (ten cloned genes and three loci have been identified so far). The four major causative genes involved in the pathogenesis of CRDs are ABCA4 (which causes Stargardt disease and also 30 to 60% of autosomal recessive CRDs), CRX and GUCY2D (which are responsible for many reported cases of autosomal dominant CRDs), and RPGR (which causes about 2/3 of X-linked RP and also an undetermined percentage of X-linked CRDs). It is likely that highly deleterious mutations in genes that otherwise cause RP or macular dystrophy may also lead to CRDs. The diagnosis of CRDs is based on clinical history, fundus examination and electroretinogram. Molecular diagnosis can be made for some genes, genetic counseling is always advised. Currently
Directory of Open Access Journals (Sweden)
Hamel Christian P
2007-02-01
Full Text Available Abstract Cone rod dystrophies (CRDs (prevalence 1/40,000 are inherited retinal dystrophies that belong to the group of pigmentary retinopathies. CRDs are characterized by retinal pigment deposits visible on fundus examination, predominantly localized to the macular region. In contrast to typical retinitis pigmentosa (RP, also called the rod cone dystrophies (RCDs resulting from the primary loss in rod photoreceptors and later followed by the secondary loss in cone photoreceptors, CRDs reflect the opposite sequence of events. CRD is characterized by primary cone involvement, or, sometimes, by concomitant loss of both cones and rods that explains the predominant symptoms of CRDs: decreased visual acuity, color vision defects, photoaversion and decreased sensitivity in the central visual field, later followed by progressive loss in peripheral vision and night blindness. The clinical course of CRDs is generally more severe and rapid than that of RCDs, leading to earlier legal blindness and disability. At end stage, however, CRDs do not differ from RCDs. CRDs are most frequently non syndromic, but they may also be part of several syndromes, such as Bardet Biedl syndrome and Spinocerebellar Ataxia Type 7 (SCA7. Non syndromic CRDs are genetically heterogeneous (ten cloned genes and three loci have been identified so far. The four major causative genes involved in the pathogenesis of CRDs are ABCA4 (which causes Stargardt disease and also 30 to 60% of autosomal recessive CRDs, CRX and GUCY2D (which are responsible for many reported cases of autosomal dominant CRDs, and RPGR (which causes about 2/3 of X-linked RP and also an undetermined percentage of X-linked CRDs. It is likely that highly deleterious mutations in genes that otherwise cause RP or macular dystrophy may also lead to CRDs. The diagnosis of CRDs is based on clinical history, fundus examination and electroretinogram. Molecular diagnosis can be made for some genes, genetic counseling is
Stress evolution during caldera collapse
Holohan, E. P.; Schöpfer, M. P. J.; Walsh, J. J.
2015-07-01
The mechanics of caldera collapse are subject of long-running debate. Particular uncertainties concern how stresses around a magma reservoir relate to fracturing as the reservoir roof collapses, and how roof collapse in turn impacts upon the reservoir. We used two-dimensional Distinct Element Method models to characterise the evolution of stress around a depleting sub-surface magma body during gravity-driven collapse of its roof. These models illustrate how principal stress orientations rotate during progressive deformation so that roof fracturing transitions from initial reverse faulting to later normal faulting. They also reveal four end-member stress paths to fracture, each corresponding to a particular location within the roof. Analysis of these paths indicates that fractures associated with ultimate roof failure initiate in compression (i.e. as shear fractures). We also report on how mechanical and geometric conditions in the roof affect pre-failure unloading and post-failure reloading of the reservoir. In particular, the models show how residual friction within a failed roof could, without friction reduction mechanisms or fluid-derived counter-effects, inhibit a return to a lithostatically equilibrated pressure in the magma reservoir. Many of these findings should be transferable to other gravity-driven collapse processes, such as sinkhole formation, mine collapse and subsidence above hydrocarbon reservoirs.
Deformable image registration using convolutional neural networks
Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.
2018-01-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between
Epileptiform spike detection via convolutional neural networks
DEFF Research Database (Denmark)
Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz
2016-01-01
The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...
Convolutional Neural Networks for SAR Image Segmentation
DEFF Research Database (Denmark)
Malmgren-Hansen, David; Nobel-Jørgensen, Morten
2015-01-01
Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...
Convolutional Neural Networks - Generalizability and Interpretations
DEFF Research Database (Denmark)
Malmgren-Hansen, David
from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...
Towards dropout training for convolutional neural networks.
Wu, Haibing; Gu, Xiaodong
2015-11-01
Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.
A locality aware convolutional neural networks accelerator
Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.
2015-01-01
The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time
International Nuclear Information System (INIS)
Martin-Viera Cueto, J. A.; Benitez Villegas, E. M.; Bodineau Gil, C.; Parra Osorio, V.; Garcia Pareja, S.; Casado Villalon, F. J.
2013-01-01
The objective of this study is to verify the characterization of the collapsed cone algorithm of an SP using this Protocol. In addition, given that it only offers details of dose values measured at discrete points, measures are complemented by a gamma test distributions 2D of doses in different cases using film radiochromic. (Author)
Geophysical observations at cavity collapse
Jousset, Philippe; Bazargan-Sabet, Behrooz; Lebert, François; Bernardie, Séverine; Gourry, Jean-Christophe
2010-05-01
In Lorraine region (France) salt layers at about 200 meters depth are exploited by Solvay using solution mining methodology which consists in extracting the salt by dissolution, collapsing the cavern overburden during the exploitation phase and finally reclaiming the landscape by creating a water area. In this process, one of the main challenges for the exploiting company is to control the initial 120-m diameter collapse so as to minimize possible damages. In order to detect potential precursors and understand processes associated with such collapses, a wide series of monitoring techniques including micro seismics, broad-band seismology, hydro-acoustic, electromagnetism, gas probing, automatic leveling, continuous GPS, continuous gravity and borehole extensometry was set-up in the frame of an in-situ study carried out by the "Research Group for the Impact and Safety of Underground Works" (GISOS, France). Equipments were set-up well before the final collapse, giving a unique opportunity to analyze a great deal of information prior to and during the collapse process which has been successfully achieved on February the 13th, 2009 by controlling the cavity internal pressure. In this work, we present the results of data recorded by a network of 3 broadband seismometers, 2 accelerometers, 2 tilt-meters and a continuously gravity meter. We relate the variations of the brine pumping rate with the evolutions of the induced geophysical signals and finally we propose a first mechanical model for describing the controlled collapse. Beyond the studied case, extrapolation of the results obtained might contribute to the understanding of uncontrolled cavity collapses, such as pit-craters or calderas at volcanoes.
Null cone superspace supergravity
International Nuclear Information System (INIS)
Downes-Martin, S.G.
1980-03-01
The null cone formalism is used to derive a 2(N-1) parameter family of constraints for O(N) extended superspace supergravity. The invariance groups of these constraints is analysed and is found to be [subgroup U submanifold] contains GL(4,R) for N = 1, the submanifold being eliminated for N > 1. The invariance group defines non-Weyl rotations on the superbein which combine to form Weyl transformations on the supertangent space metric. The invariance of the supergravity Lagrangian under these transformations is discussed. (Auth.)
Energy Technology Data Exchange (ETDEWEB)
Bao, Ning [Institute for Quantum Information and Matter, California Institute of Technology,Pasadena, CA 91125 (United States); Walter Burke Institute for Theoretical Physics, California Institute of Technology,452-48, Pasadena, CA 91125 (United States); Nezami, Sepehr [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States); Ooguri, Hirosi [Walter Burke Institute for Theoretical Physics, California Institute of Technology,452-48, Pasadena, CA 91125 (United States); Kavli Institute for the Physics and Mathematics of the Universe, University of Tokyo,Kashiwa 277-8583 (Japan); Stoica, Bogdan [Walter Burke Institute for Theoretical Physics, California Institute of Technology,452-48, Pasadena, CA 91125 (United States); Sully, James [Theory Group, SLAC National Accelerator Laboratory, Stanford University,Menlo Park, CA 94025 (United States); Walter, Michael [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States)
2015-09-21
We initiate a systematic enumeration and classification of entropy inequalities satisfied by the Ryu-Takayanagi formula for conformal field theory states with smooth holographic dual geometries. For 2, 3, and 4 regions, we prove that the strong subadditivity and the monogamy of mutual information give the complete set of inequalities. This is in contrast to the situation for generic quantum systems, where a complete set of entropy inequalities is not known for 4 or more regions. We also find an infinite new family of inequalities applicable to 5 or more regions. The set of all holographic entropy inequalities bounds the phase space of Ryu-Takayanagi entropies, defining the holographic entropy cone. We characterize this entropy cone by reducing geometries to minimal graph models that encode the possible cutting and gluing relations of minimal surfaces. We find that, for a fixed number of regions, there are only finitely many independent entropy inequalities. To establish new holographic entropy inequalities, we introduce a combinatorial proof technique that may also be of independent interest in Riemannian geometry and graph theory.
International Nuclear Information System (INIS)
Bao, Ning; Nezami, Sepehr; Ooguri, Hirosi; Stoica, Bogdan; Sully, James; Walter, Michael
2015-01-01
We initiate a systematic enumeration and classification of entropy inequalities satisfied by the Ryu-Takayanagi formula for conformal field theory states with smooth holographic dual geometries. For 2, 3, and 4 regions, we prove that the strong subadditivity and the monogamy of mutual information give the complete set of inequalities. This is in contrast to the situation for generic quantum systems, where a complete set of entropy inequalities is not known for 4 or more regions. We also find an infinite new family of inequalities applicable to 5 or more regions. The set of all holographic entropy inequalities bounds the phase space of Ryu-Takayanagi entropies, defining the holographic entropy cone. We characterize this entropy cone by reducing geometries to minimal graph models that encode the possible cutting and gluing relations of minimal surfaces. We find that, for a fixed number of regions, there are only finitely many independent entropy inequalities. To establish new holographic entropy inequalities, we introduce a combinatorial proof technique that may also be of independent interest in Riemannian geometry and graph theory.
Magnetic tension and gravitational collapse
International Nuclear Information System (INIS)
Tsagas, Christos G
2006-01-01
The gravitational collapse of a magnetized medium is investigated by studying qualitatively the convergence of a timelike family of non-geodesic worldlines in the presence of a magnetic field. Focusing on the field's tension, we illustrate how the winding of the magnetic forcelines due to the fluid's rotation assists the collapse, while shear-like distortions in the distribution of the field's gradients resist contraction. We also show that the relativistic coupling between magnetism and geometry, together with the tension properties of the field, lead to a magneto-curvature stress that opposes the collapse. This tension stress grows stronger with increasing curvature distortion, which means that it could potentially dominate over the gravitational pull of the matter. If this happens, a converging family of non-geodesic worldlines can be prevented from focusing without violating the standard energy conditions
Collapse of nonlinear Langmuir waves
International Nuclear Information System (INIS)
Malkin, V.M.
1986-01-01
The dispersion of sufficiently intensive Langmuir waves is determined by intrinsic (electron) nonlinearity. During Langmuir collapse the wave energy density required for the appearance of electron nonlinearity is attained, generally speaking, prior to the development of dissipative processes. Up to now, the effect of electron nonlinearity on the collapse dynamics and spectrum of strong Langmuir turbulence ( which may be very appreciable ) has not been studied extensively because of the difficulty of describing nonlinear Langmuir waves. In the present paper the positive determinacy of the electron nonlinear hamiltonian is proven, the increment of modulation instability of a nonlinear Langmuir wave cluster localized in a cavity is calculated, and the universal law of their collapse is found
Review on resonance cone fields
International Nuclear Information System (INIS)
Ohnuma, Toshiro.
1980-02-01
Resonance cone fields and lower hybrid heating are reviewed in this report. The resonance cone fields were reported by Fisher and Gould, and they proposed the use of the measurement of resonance cones and structure as a diagnostic tool to determine the plasma density and electron temperature in magnetoplasma. After the resonance cone, a wave-like disturbance persists. Ohnuma et al. have measured bending, reflection and ducting of resonance cones in detail. The thermal modes in inhomogeneous magnetoplasma were seen. The reflection of thermal mode near an electron plasma frequency layer and an insulating plate has been observed. The non-linear effects of resonance cones is reported. Monochromatic electron beam produces the noise of broad band whistler mode. Lower hybrid waves have been the subject of propagation from the edge of plasma to the lower hybrid layer. Linear lower hybrid waves were studied. The lower hybrid and ion acoustic waves radiated from a point source were observed. The parametric decay of finite-extent, cold electron plasma waves was studied. The lower hybrid cone radiated from a point source going along magnetic field lines was observed. Several experimental data on the lower hybrid heating in tokamak devices have been reported. The theories on resonance cones and lower hybrid waves are introduced in this report. (Kato, T.)
DEFF Research Database (Denmark)
Simonsen, Bo Cerup
1997-01-01
The present paper is concerned with steady-state plate tearing by a cone. This is a scenario where a cone is forced through a ductile metal plate with a constant lateral tip penetration in a motion in the plane of the plate. The considered process could be an idealisaton of the damage, which...... as for the out-of-plane reaction force....
Understanding Core-Collapse Supernovae
Hix, W. R.; Lentz, E. J.; Baird, M.; Messer, O. E. B.; Mezzacappa, A.; Lee, C.-T.; Bruenn, S. W.; Blondin, J. M.; Marronetti, P.
2010-03-01
Our understanding of core-collapse supernovae continues to improve as better microphysics is included in increasingly realistic neutrino-radiationhydrodynamic simulations. Recent multi-dimensional models with spectral neutrino transport, which slowly develop successful explosions for a range of progenitors between 12 and 25 solar mass, have motivated changes in our understanding of the neutrino reheating mechanism. In a similar fashion, improvements in nuclear physics, most notably explorations of weak interactions on nuclei and the nuclear equation of state, continue to refine our understanding of how supernovae explode. Recent progresses on both the macroscopic and microscopic effects that affect core-collapse supernovae are discussed.
Electron capture and stellar collapse
International Nuclear Information System (INIS)
Chung, K.C.
1979-01-01
In order, to investigate the function of electron capture in the phenomenon of pre-supernovae gravitacional collapse, an hydrodynamic caculation was carried out, coupling capture, decay and nuclear reaction equation system. A star simplified model (homogeneous model) was adopted using fermi ideal gas approximation for tthe sea of free electrons and neutrons. The non simplified treatment from quasi-static evolution to collapse is presented. The capture and beta decay rates, as wellas neutron delayed emission, were calculated by beta decay crude theory, while the other reaction rates were determined by usual theories. The preliminary results are presented. (M.C.K.) [pt
Moduli destabilization via gravitational collapse
Energy Technology Data Exchange (ETDEWEB)
Hwang, Dong-il [Sogang Univ., Seoul (Korea, Republic of). Center for Quantum Spacetime; Pedro, Francisco G. [Deutsches Elektronen-Synchrotron DESY, Hamburg (Germany). Theory Group; Yeom, Dong-han [Sogang Univ., Seoul (Korea, Republic of). Center for Quantum Spacetime; Kyoto Univ. (Japan). Yukawa Inst. for Theoretical Physics
2013-06-15
We examine the interplay between gravitational collapse and moduli stability in the context of black hole formation. We perform numerical simulations of the collapse using the double null formalism and show that the very dense regions one expects to find in the process of black hole formation are able to destabilize the volume modulus. We establish that the effects of the destabilization will be visible to an observer at infinity, opening up a window to a region in spacetime where standard model's couplings and masses can differ significantly from their background values.
DCMDN: Deep Convolutional Mixture Density Network
D'Isanto, Antonio; Polsterer, Kai Lars
2017-09-01
Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
Gas Classification Using Deep Convolutional Neural Networks
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-01
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723
A convolutional neural network neutrino event classifier
International Nuclear Information System (INIS)
Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.
2016-01-01
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
Gas Classification Using Deep Convolutional Neural Networks.
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-08
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).
International Nuclear Information System (INIS)
Brodsky, S.J.
1992-09-01
The quantization of gauge theory at fixed light-cone time τ = t - z/c provides new perspectives for solving non-perturbative problems in quantum chromodynamics. The light-cone Fock state expansion provides both a precise definition of the relativistic wavefunctions of hadrons as bound-states of quarks and gluons and a general calculus for predicting QCD processes at the amplitude level. Applications to exclusive processes and weak decay amplitudes are discussed. The problem of computing the hadronic spectrum and the corresponding light-cone wavefunctions of QCD in one space and one time dimension has been successfully reduced to the diagonalization of a discrete representation of the light-cone Hamiltonian. The problems confronting the solution of gauge theories in 3 + 1 dimensions in the light-cone quantization formalism,, including zero modes and non-perturbative renormalization, are reviewed
Quasi-cyclic unit memory convolutional codes
DEFF Research Database (Denmark)
Justesen, Jørn; Paaske, Erik; Ballan, Mark
1990-01-01
Unit memory convolutional codes with generator matrices, which are composed of circulant submatrices, are introduced. This structure facilitates the analysis of efficient search for good codes. Equivalences among such codes and some of the basic structural properties are discussed. In particular......, catastrophic encoders and minimal encoders are characterized and dual codes treated. Further, various distance measures are discussed, and a number of good codes, some of which result from efficient computer search and some of which result from known block codes, are presented...
Applying Gradient Descent in Convolutional Neural Networks
Cui, Nan
2018-04-01
With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.
Phylogenetic convolutional neural networks in metagenomics.
Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare
2018-03-08
Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.
Image quality assessment using deep convolutional networks
Li, Yezhou; Ye, Xiang; Li, Yong
2017-12-01
This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.
Temperature evolution during dissipative collapse
Indian Academy of Sciences (India)
Abstract. We investigate the gravitational collapse of a radiating sphere evolving into a final static configuration described by the interior Schwarzschild solution. The temperature profiles of this par- ticular model are obtained within the framework of causal thermodynamics. The overall temperature evolution is enhanced by ...
Numerical investigations of gravitational collapse
Energy Technology Data Exchange (ETDEWEB)
Csizmadia, Peter; Racz, Istvan, E-mail: iracz@rmki.kfki.h [RMKI, Budapest, Konkoly Thege Miklos ut 29-33, H-1121 (Hungary)
2010-03-01
Some properties of a new framework for simulating generic 4-dimensional spherically symmetric gravitating systems are discussed. The framework can be used to investigate spacetimes that undergo complete gravitational collapse. The analytic setup is chosen to ensure that our numerical method is capable to follow the time evolution everywhere, including the black hole region.
On the Induced Gravitational Collapse
Directory of Open Access Journals (Sweden)
M. Becerra Laura
2018-01-01
Full Text Available The induced gravitational collapse (IGC paradigm has been applied to explain the long gamma ray burst (GRB associated with type Ic supernova, and recently the Xray flashes (XRFs. The progenitor is a binary systems of a carbon-oxygen core (CO and a neutron star (NS. The CO core collapses and undergoes a supernova explosion which triggers the hypercritical accretion onto the NS companion (up to 10-2 M⊙s-1. For the binary driven hypernova (BdHNe, the binary system is enough bound, the NS reach its critical mass, and collapse to a black hole (BH with a GRB emission characterized by an isotropic energy Eiso > 1052 erg. Otherwise, for binary systems with larger binary separations, the hypercritical accretion onto the NS is not sufficient to induced its gravitational collapse, a X-ray flash is produced with Eiso < 1052 erg. We’re going to focus in identify the binary parameters that limits the BdHNe systems with the XRFs systems.
Transport in the Sawtooth Collapse
International Nuclear Information System (INIS)
Wesson, J.A.; Alper, B.; Edwards, A.W.; Gill, R.D.
1997-01-01
The rapid temperature collapse in tokamak sawtooth oscillations having incomplete magnetic reconnection is generally thought to occur through ergodization of the magnetic field. An experiment in JET using injected nickel indicates that this explanation is improbable. copyright 1997 The American Physical Society
Thermal duality and gravitational collapse
International Nuclear Information System (INIS)
Hewitt, Michael
2015-01-01
Thermal duality is a relationship between the behaviour of heterotic string models of the E(8)×E(8) or SO(32) types at inversely related temperatures, a variant of T duality in the Euclidean regime. This duality would have consequences for the nature of the Hagedorn transition in these string models. We propose that the vacuum admits a family of deformations in situations where there are closed surfaces of constant area but high radial acceleration (a string regularized version of a Penrose trapped surface), such as would be formed in situations of extreme gravitational collapse. This would allow a radical resolution of the firewall paradox by allowing quantum effects to significantly modify the spacetime geometry around a collapsed object. A string bremsstrahlung process would convert the kinetic energy of infalling matter in extreme gravitational collapse to form a region of the deformed vacuum, which would be equivalent to forming a high temperature string phase. A heuristic criterion for the conversion process is presented, relating Newtonian gravity to the string tension, suggesting an upper limit to the strength of the gravitational interaction. This conversion process might have observable consequences for charged particles falling into a rotating collapsed object by producing high energy particles via a variant of the Penrose process. (paper)
Collapse of simple harmonic universe
International Nuclear Information System (INIS)
Mithani, Audrey T.; Vilenkin, Alexander
2012-01-01
In a recent paper Graham et al constructed oscillating and static universe models which are stable with respect to all classical perturbations. Here we show that such universes are quantum-mechanically unstable and can collapse by quantum tunneling to zero radius. We also present instantons describing nucleation of oscillating and static universes from nothing
Critical Effects in Gravitational Collapse
International Nuclear Information System (INIS)
Chmaj, T.
2000-01-01
The models of gravitational collapse of a dynamical system are investigated by means of the Einstein equations. Different types conjunctions to gravitational field are analyzed and it is shown that in the case of week scalar field (low energy density) the system evaluated to flat space while in the case of strong field (high energy density) to black hole
Thermal conduction and gravitational collapse
International Nuclear Information System (INIS)
Herrera, L.; Jimenez, J.; Esculpi, M.
1987-01-01
A method used to study the evolution of radiating spheres, reported some years ago by Herrera, Jimenez, and Ruggeri, is extended to the case in which thermal conduction within the sphere is taken into account. By means of an explicit example it is shown that heat flow, if present, may play an important role, affecting the final outcome of collapse
Ordered cones and approximation
Keimel, Klaus
1992-01-01
This book presents a unified approach to Korovkin-type approximation theorems. It includes classical material on the approximation of real-valuedfunctions as well as recent and new results on set-valued functions and stochastic processes, and on weighted approximation. The results are notonly of qualitative nature, but include quantitative bounds on the order of approximation. The book is addressed to researchers in functional analysis and approximation theory as well as to those that want to applythese methods in other fields. It is largely self- contained, but the readershould have a solid background in abstract functional analysis. The unified approach is based on a new notion of locally convex ordered cones that are not embeddable in vector spaces but allow Hahn-Banach type separation and extension theorems. This concept seems to be of independent interest.
An Algorithm for the Convolution of Legendre Series
Hale, Nicholas; Townsend, Alex
2014-01-01
An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.
The Urbanik generalized convolutions in the non-commutative ...
Indian Academy of Sciences (India)
−sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.
On a Generalized Hankel Type Convolution of Generalized Functions
Indian Academy of Sciences (India)
Generalized Hankel type transformation; Parserval relation; generalized ... The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. ... Proceedings – Mathematical Sciences | News.
Enhanced online convolutional neural networks for object tracking
Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen
2018-04-01
In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.
Cone and Seed Maturation of Southern Pines
James P. Barnett
1976-01-01
If slightly reduced yields and viability are acceptable, loblolly and slash cone collections can begin 2 to 3 weeks before maturity if the cones are stored before processing. Longleaf(P. palestris Mill.) pine cones should be collected only when mature, as storage decreased germination of seeds from immature cones. Biochemical analyses to determine reducing sugar...
Modelling of cladding creep collapse
International Nuclear Information System (INIS)
Koundy, V.; Forgeron, T.; Hivroz, J.
1993-01-01
The effects of the initial ovality and pressure level on the collapse time of Zircaloy-4 tubing subjected to uniform external pressure were examined experimentally and analytically. Experiments were performed on end closed tubes with two metallurgical states: stress relieved and recrystallized. Numerical simulations were accomplished with a specific computer program based on an analytical approach and the calculated results were compared with the experimental ones. As a comparison, the finite element method is also partially examined in this analysis. Numerical collapse times are in good agreement with regard to experimental results in the case of stress relieved structure. They seem to be too conservative in the case of a recrystallized metallurgical state and the use of the anisotropic option ameliorates numerical results. Sensibility of numerical solutions to the formulation of primary creep laws are presented
DEFF Research Database (Denmark)
Simonsen, Bo Cerup
1998-01-01
The present paper is concerned with steady-state plate tearing by a cone. This is a scenario where a cone is forced through a ductile metal plate with a constant lateral tip penetration in a motion in the plane of the plate. The considered process could be an idealisation of the damage, which...... as for the out-of-plane reaction force. (C) 1998 Elsevier Science Ltd. All rights reserved....
Collapsed Thunderstorm, Southwest Pacific Ocean
1992-01-01
This collapsed thunderstorm was observed over the open ocean (9.0N, 120.0E) between the Philippine island of Mindoro and Borneo, Malaysia. The cleared area in the center is the result of the clouds being driven from there by the sudden rush of katabatic air spreading downward and outward from the dying thunderstorm. Around the edges of the downdrafted air, new though smaller storms are developing. The two small coral atolls are the Tubbataha Reefs.
Critical behavior of collapsing surfaces
DEFF Research Database (Denmark)
Olsen, Kasper; Sourdis, C.
2009-01-01
We consider the mean curvature evolution of rotationally symmetric surfaces. Using numerical methods, we detect critical behavior at the threshold of singularity formation resembling that of gravitational collapse. In particular, the mean curvature simulation of a one-parameter family of initial...... data reveals the existence of a critical initial surface that develops a degenerate neckpinch. The limiting flow of the type II singularity is accurately modeled by the rotationally symmetric translating soliton....
Soliton collapse during ionospheric heating
International Nuclear Information System (INIS)
Sheerin, J.P.; Nicholson, D.R.; Payne, G.L.; Duncan, L.M.
1984-01-01
We present analytical and numerical work which indicates that during ionospheric heating with high-powered hf radio waves, the oscillating two-stream instability may dominate the parametric decay instability. The oscillating two-stream instability saturates nonlinearly through the formation of solitons which undergo a collisionally damped collapse. Using the heater and radar facilities at Arecibo Observatory, we have investigated this phenomenon experimentally. Recent results from our theoretical and experimental investigations are presented
Convolutional neural networks and face recognition task
Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.
2017-09-01
Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Kashyap Manohar
2008-01-01
Full Text Available Abstract This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Chris Winstead
2008-04-01
Full Text Available This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Fourier transforms and convolutions for the experimentalist
Jennison, RC
1961-01-01
Fourier Transforms and Convolutions for the Experimentalist provides the experimentalist with a guide to the principles and practical uses of the Fourier transformation. It aims to bridge the gap between the more abstract account of a purely mathematical approach and the rule of thumb calculation and intuition of the practical worker. The monograph springs from a lecture course which the author has given in recent years and for which he has drawn upon a number of sources, including a set of notes compiled by the late Dr. I. C. Browne from a series of lectures given by Mr. J . A. Ratcliffe of t
Target recognition based on convolutional neural network
Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian
2017-11-01
One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.
QCDNUM: Fast QCD evolution and convolution
Botje, M.
2011-02-01
The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline
Collapse models and perceptual processes
International Nuclear Information System (INIS)
Ghirardi, Gian Carlo; Romano, Raffaele
2014-01-01
Theories including a collapse mechanism have been presented various years ago. They are based on a modification of standard quantum mechanics in which nonlinear and stochastic terms are added to the evolution equation. Their principal merits derive from the fact that they are mathematically precise schemes accounting, on the basis of a unique universal dynamical principle, both for the quantum behavior of microscopic systems as well as for the reduction associated to measurement processes and for the classical behavior of macroscopic objects. Since such theories qualify themselves not as new interpretations but as modifications of the standard theory they can be, in principle, tested against quantum mechanics. Recently, various investigations identifying possible crucial test have been discussed. In spite of the extreme difficulty to perform such tests it seems that recent technological developments allow at least to put precise limits on the parameters characterizing the modifications of the evolution equation. Here we will simply mention some of the recent investigations in this direction, while we will mainly concentrate our attention to the way in which collapse theories account for definite perceptual process. The differences between the case of reductions induced by perceptions and those related to measurement procedures by means of standard macroscopic devices will be discussed. On this basis, we suggest a precise experimental test of collapse theories involving conscious observers. We make plausible, by discussing in detail a toy model, that the modified dynamics can give rise to quite small but systematic errors in the visual perceptual process.
International Nuclear Information System (INIS)
Brodsky, Stan
1993-01-01
One of the most challenging problems in theoretical high energy physics is to compute the bound state structure of the proton and other hadrons from quantum chromodynamics (QCD), the field theory of quarks and gluons. The goal is not only to calculate the spectrum of hadrons masses from first principles, but also to derive the momentum and spin distributions of the quarks and gluons which control high energy hadron interactions. One approach to these difficult calculations is to simulate QCD on an artificial lattice. Recently, several new methods based on ''light-cone'' quantization have been proposed as alternatives to lattice theory for solving non-perturbative problems in QCD and other field theories. The basic idea is a generalization of Heisenberg's pioneer matrix formulation of quantum mechanics: if one could numerically diagonalize the matrix of the Hamiltonian representing the underlying QCD interaction, then the resulting eigenvalues would give the hadron spectrum, while the corresponding eigenstates would describe each hadron in terms of its quark and gluon degrees of freedom
Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L
2018-04-01
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
Collapse Mechanisms Of Masonry Structures
International Nuclear Information System (INIS)
Zuccaro, G.; Rauci, M.
2008-01-01
The paper outlines a possible approach to typology recognition, safety check analyses and/or damage measuring taking advantage by a multimedia tool (MEDEA), tracing a guided procedure useful for seismic safety check evaluation and post event macroseismic assessment. A list of the possible collapse mechanisms observed in the post event surveys on masonry structures and a complete abacus of the damages are provided in MEDEA. In this tool a possible combination between a set of damage typologies and each collapse mechanism is supplied in order to improve the homogeneity of the damages interpretation. On the other hand recent researches of one of the author have selected a number of possible typological vulnerability factors of masonry buildings, these are listed in the paper and combined with potential collapse mechanisms to be activated under seismic excitation. The procedure takes place from simple structural behavior models, derived from the Umbria-Marche earthquake observations, and tested after the San Giuliano di Puglia event; it provides the basis either for safety check analyses of the existing buildings or for post-event structural safety assessment and economic damage evaluation. In the paper taking advantage of MEDEA mechanisms analysis, mainly developed for the post event safety check surveyors training, a simple logic path is traced in order to approach the evaluation of the masonry building safety check. The procedure starts from the identification of the typological vulnerability factors to derive the potential collapse mechanisms and their collapse multipliers and finally addresses the simplest and cheapest strengthening techniques to reduce the original vulnerability. The procedure has been introduced in the Guide Lines of the Regione Campania for the professionals in charge of the safety check analyses and the buildings strengthening in application of the national mitigation campaign introduced by the Ordinance of the Central Government n. 3362
Convolutive ICA for Spatio-Temporal Analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
2007-01-01
in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
Directory of Open Access Journals (Sweden)
Lev B. Klebanov
2018-01-01
Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.
Nuclear norm regularized convolutional Max Pos@Top machine
Li, Qinfeng; Zhou, Xiaofeng; Gu, Aihua; Li, Zonghua; Liang, Ru-Ze
2016-01-01
, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose
HIERARCHICAL GRAVITATIONAL FRAGMENTATION. I. COLLAPSING CORES WITHIN COLLAPSING CLOUDS
Energy Technology Data Exchange (ETDEWEB)
Naranjo-Romero, Raúl; Vázquez-Semadeni, Enrique; Loughnane, Robert M. [Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México, Apdo. Postal 3-72, Morelia, Michoacán, 58089, México (Mexico)
2015-11-20
We investigate the Hierarchical Gravitational Fragmentation scenario through numerical simulations of the prestellar stages of the collapse of a marginally gravitationally unstable isothermal sphere immersed in a strongly gravitationally unstable, uniform background medium. The core developes a Bonnor–Ebert (BE)-like density profile, while at the time of singularity (the protostar) formation the envelope approaches a singular-isothermal-sphere (SIS)-like r{sup −2} density profile. However, these structures are never hydrostatic. In this case, the central flat region is characterized by an infall speed, while the envelope is characterized by a uniform speed. This implies that the hydrostatic SIS initial condition leading to Shu's classical inside-out solution is not expected to occur, and therefore neither should the inside-out solution. Instead, the solution collapses from the outside-in, naturally explaining the observation of extended infall velocities. The core, defined by the radius at which it merges with the background, has a time-variable mass, and evolves along the locus of the ensemble of observed prestellar cores in a plot of M/M{sub BE} versus M, where M is the core's mass and M{sub BE} is the critical BE mass, spanning the range from the “stable” to the “unstable” regimes, even though it is collapsing at all times. We conclude that the presence of an unstable background allows a core to evolve dynamically from the time when it first appears, even when it resembles a pressure-confined, stable BE-sphere. The core can be thought of as a ram-pressure confined BE-sphere, with an increasing mass due to the accretion from the unstable background.
Black hole formation in perfect fluid collapse
International Nuclear Information System (INIS)
Goswami, Rituparno; Joshi, Pankaj S
2004-01-01
We construct here a special class of perfect fluid collapse models which generalizes the homogeneous dust collapse solution in order to include nonzero pressures and inhomogeneities into evolution. It is shown that a black hole is necessarily generated as the end product of continued gravitational collapse, rather than a naked singularity. We examine the nature of the central singularity forming as a result of endless collapse and it is shown that no nonspacelike trajectories can escape from the central singularity. Our results provide some insights into how the dynamical collapse works and into the possible formulations of the cosmic censorship hypothesis, which is as yet a major unsolved problem in black hole physics
Spherical convolutions and their application in molecular modelling
DEFF Research Database (Denmark)
Boomsma, Wouter; Frellsen, Jes
2017-01-01
Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...
Marcos, S; Tornow, R P; Elsner, A E; Navarro, R
1997-07-01
Foveal cone spacing was measured in vivo using an objective technique: ocular speckle interferometry. Cone packing density was computed from cone spacing data. Foveal cone photopigment density difference was measured in the same subjects using retinal densitometry with a scanning laser ophthalmoscope. Both the cone packing density and cone photopigment density difference decreased sharply with increasing retinal eccentricity. From the comparison of both sets of measurements, the computed amounts of photopigment per cone increased slightly with increasing retinal eccentricity. Consistent with previous results, decreases in cone outer segment length are over-compensated by an increase in the outer segment area, at least in retinal eccentricities up to 1 deg.
Collapsing stage of 'bosonic matter'
International Nuclear Information System (INIS)
Manoukian, E.B.; Muthaporn, C.; Sirininlakul, S.
2006-01-01
We prove rigorously that for 'bosonic matter', if deflation occurs upon collapse as more and more such matter is put together, then for a non-vanishing probability of having the negatively charged particles, with Coulomb interactions, within a sphere of radius R, the latter necessarily cannot decrease faster than N -1/3 for large N, where N denotes the number of the negatively charged particles. This is in clear distinction with matter (i.e., matter with the exclusion principle) which inflates and R necessarily increases not any slower than N 1/3 for large N
PSI collapse and relativistic covariance
International Nuclear Information System (INIS)
Costa de Beauregard, Olivier
1980-01-01
We call macrorelativistic a theory invariant under the orthochronous Lorentz group and obeying the 'factlike' principle of retarded causality, and microrelativistic a theory invariant under the full Lorentz group and CPT symmetric. The Einstein correlations either direct (non-separability of measurements issuing from a common preparation) or reversed (non-separability of preparations producing a common measurement) are incompatible with the macro-, but compatible with the microrelativity. We assume that fundamental physics is fully Lorentz and CPT invariant (the transition to macrophysics introducing a 'factlike asymmetry) and consequently define the collapse-and-retrocollapse concept [fr
Geotechnical properties of Egyptian collapsible soils
Directory of Open Access Journals (Sweden)
Khaled E. Gaaver
2012-09-01
Full Text Available The risk of constructing structures on collapsible soils presents significant challenges to geotechnical engineers due to sudden reduction in volume upon wetting. Identifying collapsible soils when encountered in the field and taking the needed precautions should substantially reduce the risk of such problems usually reported in buildings and highways. Collapsible soils are those unsaturated soils that can withstand relatively high pressure without showing significant change in volume, however upon wetting; they are susceptible to a large and sudden reduction in volume. Collapsible soils cover significant areas around the world. In Egypt, collapsible soils were observed within the northern portion of the western desert including Borg El-Arab region, and around the city of Cairo in Six-of-October plateau, and Tenth-of-Ramadan city. Settlements associated with development on untreated collapsible soils usually lead to expensive repairs. One method for treating collapsible soils is to densify their structure by compaction. The ongoing study presents the effect of compaction on the geotechnical properties of the collapsible soils. Undisturbed block samples were recovered from test pits at four sites in Borg El-Arab district, located at about 20 km west of the city of Alexandria, Egypt. The samples were tested in both unsoaked and soaked conditions. Influence of water inundation on the geotechnical properties of collapsible soils was demonstrated. A comparative study between natural undisturbed and compacted samples of collapsible soils was performed. An attempt was made to relate the collapse potential to the initial moisture content. An empirical correlation between California Bearing Ratio of the compacted collapsible soils and liquid limit was adopted. The presented simple relationships should enable the geotechnical engineers to estimate the complex parameters of collapsible soils using simple laboratory tests with a reasonable accuracy.
Stellar core collapse and supernova
International Nuclear Information System (INIS)
Wilson, J.R.; Mayle, R.; Woosley, S.E.; Weaver, T.
1985-04-01
Massive stars that end their stable evolution as their iron cores collapse to a neutron star or black hole long been considered good candidates for producing Type II supernovae. For many years the outward propagation of the shock wave produced by the bounce of these iron cores has been studied as a possible mechanism for the explosion. For the most part, the results of these studies have not been particularly encouraging, except, perhaps, in the case of very low mass iron cores or very soft nuclear equations of state. The shock stalls, overwhelmed by photodisintegration and neutrino losses, and the star does not explode. More recently, slow late time heating of the envelope of the incipient neutron star has been found to be capable of rejuvenating the stalled shock and producing an explosion after all. The present paper discusses this late time heating and presents results from numerical calculations of the evolution, core collapse, and subsequent explosion of a number of recent stellar models. For the first time they all, except perhaps the most massive, explode with reasonable choices of input physics. 39 refs., 17 figs., 1 tab
Collapsing stellar cores and supernovae
Energy Technology Data Exchange (ETDEWEB)
Epstein, R J [Nordisk Inst. for Teoretisk Atomfysik, Copenhagen (Denmark); Noorgaard, H [Nordisk Inst. for Teoretisk Atomfysik, Copenhagen (Denmark); Chicago Univ., IL (USA). Enrico Fermi Inst.); Bond, J R [Niels Bohr Institutet, Copenhagen (Denmark); California Inst. of Tech., Pasadena (USA). W.K. Kellogg Radiation Lab.)
1979-05-01
The evolution of a stellar core is studied during its final quasi-hydrostatic contraction. The core structure and the (poorly known) properties of neutron rich matter are parametrized to include most plausible cases. It is found that the density-temperature trajectory of the material in the central part of the core (the core-center) is insensitive to nearly all reasonable parameter variations. The central density at the onset of the dynamic phase of the collapse (when the core-center begins to fall away from the rest of the star) and the fraction of the emitted neutrinos which are trapped in the collapsing core-center depend quite sensitively on the properties of neutron rich matter. We estimate that the amount of energy Ecm which is imparted to the core-mantle by the neutrinos which escape from the imploded core-center can span a large range of values. For plausible choices of nuclear and model parameters Ecm can be large enough to yield a supernova event.
Deformable image registration using convolutional neural networks
Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.
2018-03-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.
Codeword Structure Analysis for LDPC Convolutional Codes
Directory of Open Access Journals (Sweden)
Hua Zhou
2015-12-01
Full Text Available The codewords of a low-density parity-check (LDPC convolutional code (LDPC-CC are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D , resulting in improved distance spectrum of the mother code.
Sparice, Domenico; Scarpati, Claudio; Perrotta, Annamaria; Mazzeo, Fabio Carmine; Calvert, Andrew T.; Lanphere, Marvin A.
2017-11-01
Pre-caldera (> 22 ka) lateral activity at Somma-Vesuvius is related to scoria- and spatter-cone forming events of monogenetic or polygenetic nature. A new stratigraphic, sedimentological, textural and lithofacies investigation was performed on five parasitic cones (Pollena cones, Traianello cone, S. Maria a Castello cone and the recently found Terzigno cone) occurring below the Pomici di Base (22 ka) Plinian products emplaced during the first caldera collapse at Somma-Vesuvius. A new Ar/Ar age of 23.6 ± 0.3 ka obtained for the Traianello cone as well as the absence of a paleosol or reworked material between the S. Maria a Castello cone and the Pomici di Base deposits suggest that such cone-forming eruptions occurred near the upper limit of the pre-caldera period (22-39 ky). The stratigraphy of three of these eccentric cones (Pollena cones and Traianello cone) exhibits erosion surfaces, exotic tephras, volcaniclastic layers, paleosols, unconformity and paraconformity between superimposed eruptive units revealing their multi-phase, polygenetic evolution related to activation of separate vents and periods of quiescence. Such eccentric cones have been described as composed of scoria deposits and pure effusive lavas by previous authors. Lavas are here re-interpreted as welded horizons (lava-like) composed of coalesced spatter fragments whose pyroclastic nature is locally revealed by relicts of original fragments and remnants of clast outlines. These welded horizons show, locally, rheomorphic structures allowing to define them as emplaced as clastogenic lava flows. The lava-like facies is transitional, upward and downward, to less welded facies composed of agglutinated to unwelded spatter horizons in which clasts outlines are increasingly discernible. Such textural characteristics and facies variation are consistent with a continuous fall deposition of Hawaiian fire-fountains episodes alternated with Strombolian phases emplacing loose scoria deposits. High enrichment
Collapse models with non-white noises
International Nuclear Information System (INIS)
Adler, Stephen L; Bassi, Angelo
2007-01-01
We set up a general formalism for models of spontaneous wavefunction collapse with dynamics represented by a stochastic differential equation driven by general Gaussian noises, not necessarily white in time. In particular, we show that the non-Schroedinger terms of the equation induce the collapse of the wavefunction to one of the common eigenstates of the collapsing operators, and that the collapse occurs with the correct quantum probabilities. We also develop a perturbation expansion of the solution of the equation with respect to the parameter which sets the strength of the collapse process; such an approximation allows one to compute the leading-order terms for the deviations of the predictions of collapse models with respect to those of standard quantum mechanics. This analysis shows that to leading order, the 'imaginary noise' trick can be used for non-white Gaussian noise
An Improved Convolutional Neural Network on Crowd Density Estimation
Directory of Open Access Journals (Sweden)
Pan Shao-Yun
2016-01-01
Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.
Completely quantized collapse and consequences
International Nuclear Information System (INIS)
Pearle, Philip
2005-01-01
Promotion of quantum theory from a theory of measurement to a theory of reality requires an unambiguous specification of the ensemble of realizable states (and each state's probability of realization). Although not yet achieved within the framework of standard quantum theory, it has been achieved within the framework of the continuous spontaneous localization (CSL) wave-function collapse model. In CSL, a classical random field w(x,t) interacts with quantum particles. The state vector corresponding to each w(x,t) is a realizable state. In this paper, I consider a previously presented model, which is predictively equivalent to CSL. In this completely quantized collapse (CQC) model, the classical random field is quantized. It is represented by the operator W(x,t) which satisfies [W(x,t),W(x ' ,t ' )]=0. The ensemble of realizable states is described by a single state vector, the 'ensemble vector'. Each superposed state which comprises the ensemble vector at time t is the direct product of an eigenstate of W(x,t ' ), for all x and for 0≤t ' ≤t, and the CSL state corresponding to that eigenvalue. These states never interfere (they satisfy a superselection rule at any time), they only branch, so the ensemble vector may be considered to be, as Schroedinger put it, a 'catalog' of the realizable states. In this context, many different interpretations (e.g., many worlds, environmental decoherence, consistent histories, modal interpretation) may be satisfactorily applied. Using this description, a long-standing problem is resolved, where the energy comes from the particles gain due to the narrowing of their wave packets by the collapse mechanism. It is shown how to define the energy of the random field and its energy of interaction with particles so that total energy is conserved for the ensemble of realizable states. As a by-product, since the random-field energy spectrum is unbounded, its canonical conjugate, a self-adjoint time operator, can be discussed. Finally, CSL
Spherical dust collapse in higher dimensions
International Nuclear Information System (INIS)
Goswami, Rituparno; Joshi, Pankaj S.
2004-01-01
We consider here whether it is possible to recover cosmic censorship when a transition is made to higher-dimensional spacetimes, by studying the spherically symmetric dust collapse in an arbitrary higher spacetime dimension. It is pointed out that if only black holes are to result as the end state of a continual gravitational collapse, several conditions must be imposed on the collapsing configuration, some of which may appear to be restrictive, and we need to study carefully if these can be suitably motivated physically in a realistic collapse scenario. It would appear, that, in a generic higher-dimensional dust collapse, both black holes and naked singularities would develop as end states as indicated by the results here. The mathematical approach developed here generalizes and unifies the earlier available results on higher-dimensional dust collapse as we point out. Further, the dependence of black hole or naked singularity end states as collapse outcomes on the nature of the initial data from which the collapse develops is brought out explicitly and in a transparent manner as we show here. Our method also allows us to consider here in some detail the genericity and stability aspects related to the occurrence of naked singularities in gravitational collapse
Geophysical Processes - MO 2013 Collapse Potential (SHP)
NSGIC State | GIS Inventory — Collapse potential correlates with locations of underground mines and sinkholes. Computer-generated hazard calculations include areas in close proximity to mines and...
Spherically symmetric radiation in gravitational collapse
International Nuclear Information System (INIS)
Bridy, D.J.
1983-01-01
This paper investigates a previously neglected mode by which a star may lose energy in the late stages of gravitational collapse to the black hole state. A model consisting of a Schwarzschild exterior matched to a Friedman interior of collapsing pressureless dust is studied. The matter of the collapsing star is taken as the source of a massive vector boson field and a detailed boundary value problem is carried out. Vector mesons are strongly coupled to all nucleons and will be radiated by ordinary matter during the collapse. The time dependent coupling between interior and exterior modes matched across the moving boundary of the collapsing star and the presence of the gravitational fields and their gradients in the field equations may give rise to a parametric amplification mechanism and permit the gravitational field to pump energy into the boson field, greatly enhancing the amount of boson radiation. The significance of a radiative mechanism driven by collapse is that it can react back upon the collapsing source and deprive it of some of the very mass that drives the collapse via its self gravitation. If the mass loss is great enough, this may provide a mechanism to slow or even halt gravitational collapse in some cases
Understand rotating isothermal collapses yet
International Nuclear Information System (INIS)
Tohline, J.E.
1985-01-01
A scalar virial equation is used to describe the dynamic properties of equilibrium gas clouds, taking into account the relative effects of surface pressure, rotation, self gravity and internal isothermal pressure. Details concerning the internal structure of the clouds are ignored in order to obtain a globalized analytical expression. The obtained solution to the equation is found to agree with the surface-pressure-dominated model of Stahler (1983), and the rotation-dominated model of Hayashi, Narita, and Miyama (1982). On the basis of the analytical expression of virial equilibrium in the clouds, some of the limiting properties of isothermal clouds are described, and a realistic starting model for cloud collapse is proposed. 18 references
Collapse Analysis of Timber Structures
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard
2008-01-01
of Structures and a probabilistic modelling of the timber material proposed in the Probabilistic Model Code (PMC) of the Joint Committee on Structural Safety (JCSS). Due to the framework in the Danish Code the timber structure has to be evaluated with respect to the following criteria where at least one shall...... to criteria a) and b) the timber frame structure has one column with a reliability index a bit lower than an assumed target level. By removal three columns one by one no significant extensive failure of the entire structure or significant parts of it are obtained. Therefore the structure can be considered......A probabilistic based collapse analysis has been performed for a glulam frame structure supporting the roof over the main court in a Norwegian sports centre. The robustness analysis is based on the framework for robustness analysis introduced in the Danish Code of Practice for the Safety...
An axisymmetric gravitational collapse code
Energy Technology Data Exchange (ETDEWEB)
Choptuik, Matthew W [CIAR Cosmology and Gravity Program, Department of Physics and Astronomy, University of British Columbia, Vancouver BC, V6T 1Z1 (Canada); Hirschmann, Eric W [Department of Physics and Astronomy, Brigham Young University, Provo, UT 84604 (United States); Liebling, Steven L [Southampton College, Long Island University, Southampton, NY 11968 (United States); Pretorius, Frans [Theoretical Astrophysics, California Institute of Technology, Pasadena, CA 91125 (United States)
2003-05-07
We present a new numerical code designed to solve the Einstein field equations for axisymmetric spacetimes. The long-term goal of this project is to construct a code that will be capable of studying many problems of interest in axisymmetry, including gravitational collapse, critical phenomena, investigations of cosmic censorship and head-on black-hole collisions. Our objective here is to detail the (2+1)+1 formalism we use to arrive at the corresponding system of equations and the numerical methods we use to solve them. We are able to obtain stable evolution, despite the singular nature of the coordinate system on the axis, by enforcing appropriate regularity conditions on all variables and by adding numerical dissipation to hyperbolic equations.
An axisymmetric gravitational collapse code
International Nuclear Information System (INIS)
Choptuik, Matthew W; Hirschmann, Eric W; Liebling, Steven L; Pretorius, Frans
2003-01-01
We present a new numerical code designed to solve the Einstein field equations for axisymmetric spacetimes. The long-term goal of this project is to construct a code that will be capable of studying many problems of interest in axisymmetry, including gravitational collapse, critical phenomena, investigations of cosmic censorship and head-on black-hole collisions. Our objective here is to detail the (2+1)+1 formalism we use to arrive at the corresponding system of equations and the numerical methods we use to solve them. We are able to obtain stable evolution, despite the singular nature of the coordinate system on the axis, by enforcing appropriate regularity conditions on all variables and by adding numerical dissipation to hyperbolic equations
Kwapiński, Tomasz
2017-03-01
The electron transport properties of a linear atomic chain are studied theoretically within the tight-binding Hamiltonian and the Green’s function method. Variations of the local density of states (DOS) along the chain are investigated. They are crucial in scanning tunnelling experiments and give important insight into the electron transport mechanism and charge distribution inside chains. It is found that depending on the chain parity the local DOS at the Fermi level can form cone-like structures (DOS cones) along the chain. The general condition for the local DOS oscillations is obtained and the linear behaviour of the local density function is confirmed analytically. DOS cones are characterized by a linear decay towards the chain which is in contrast to the propagation properties of charge density waves, end states and Friedel oscillations in one-dimensional systems. We find that DOS cones can appear due to non-resonant electron transport, the spin-orbit scattering or for chains fabricated on a substrate with localized electrons. It is also shown that for imperfect chains (e.g. with a reduced coupling strength between two neighboring sites) a diamond-like structure of the local DOS along the chain appears.
International Nuclear Information System (INIS)
Kwapiński, Tomasz
2017-01-01
The electron transport properties of a linear atomic chain are studied theoretically within the tight-binding Hamiltonian and the Green’s function method. Variations of the local density of states (DOS) along the chain are investigated. They are crucial in scanning tunnelling experiments and give important insight into the electron transport mechanism and charge distribution inside chains. It is found that depending on the chain parity the local DOS at the Fermi level can form cone-like structures (DOS cones) along the chain. The general condition for the local DOS oscillations is obtained and the linear behaviour of the local density function is confirmed analytically. DOS cones are characterized by a linear decay towards the chain which is in contrast to the propagation properties of charge density waves, end states and Friedel oscillations in one-dimensional systems. We find that DOS cones can appear due to non-resonant electron transport, the spin–orbit scattering or for chains fabricated on a substrate with localized electrons. It is also shown that for imperfect chains (e.g. with a reduced coupling strength between two neighboring sites) a diamond-like structure of the local DOS along the chain appears. (paper)
Ejecta evolution during cone impact
Marston, Jeremy; Thoroddsen, Sigurdur T
2014-01-01
-similarity for all impact speeds for very low surface tension liquids, whilst for high-surface tension liquids similarity is only achieved at high impact speeds. We find that the ejecta tip can detach from the cone and that this phenomenon can be attributed
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
Adversarial training and dilated convolutions for brain MRI segmentation
Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.
2017-01-01
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to
Classification of urine sediment based on convolution neural network
Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian
2018-04-01
By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.
Convolution of second order linear recursive sequences II.
Directory of Open Access Journals (Sweden)
Szakács Tamás
2017-12-01
Full Text Available We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]. In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.
FPGA-based digital convolution for wireless applications
Guan, Lei
2017-01-01
This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. Covering these digital convolutions from basic concept to vivid simulation/illustration, the book is also supplemented with MS PowerPoint presentations to aid in comprehension. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear...
Hanford waste tank cone penetrometer
International Nuclear Information System (INIS)
Seda, R.Y.
1995-12-01
A new tool is being developed to characterize tank waste at the Hanford Reservation. This tool, known as the cone penetrometer, is capable of obtaining chemical and physical properties in situ. For the past 50 years, this tool has been used extensively in soil applications and now has been modified for usage in Hanford Underground Storage tanks. These modifications include development of new ''waste'' data models as well as hardware design changes to accommodate the hazardous and radioactive environment of the tanks. The modified cone penetrometer is scheduled to be deployed at Hanford by Fall 1996. At Hanford, the cone penetrometer will be used as an instrumented pipe which measures chemical and physical properties as it pushes through tank waste. Physical data, such as tank waste stratification and mechanical properties, is obtained through three sensors measuring tip pressure, sleeve friction and pore pressure. Chemical data, such as chemical speciation, is measured using a Raman spectroscopy sensor. The sensor package contains other instrumentation as well, including a tip and side temperature sensor, tank bottom detection and an inclinometer. Once the cone penetrometer has reached the bottom of the tank, a moisture probe will be inserted into the pipe. This probe is used to measure waste moisture content, water level, waste surface moisture and tank temperature. This paper discusses the development of this new measurement system. Data from the cone penetrometer will aid in the selection of sampling tools, waste tank retrieval process, and addressing various tank safety issues. This paper will explore various waste models as well as the challenges associated with tank environment
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao
2016-01-01
A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...
Traffic sign recognition with deep convolutional neural networks
Karamatić, Boris
2016-01-01
The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...
Convolutional Codes with Maximum Column Sum Rank for Network Streaming
Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish
2015-01-01
The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Gao, Hongyang; Ji, Shuiwang
2017-01-01
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...
Prediction of Electricity Usage Using Convolutional Neural Networks
Hansen, Martin
2017-01-01
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...
Research of convolutional neural networks for traffic sign recognition
Stadalnikas, Kasparas
2017-01-01
In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...
On the Fresnel sine integral and the convolution
Directory of Open Access Journals (Sweden)
Adem Kılıçman
2003-01-01
Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.
Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex
Directory of Open Access Journals (Sweden)
Mir Jalil Razavi
2017-08-01
Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
The Collapse of the 'Celtic Tiger' Narrative
DEFF Research Database (Denmark)
Böss, Michael
2011-01-01
An account of the factors that led to the collapse of the 'Celtic Tiger' economy in 2008 and an explanation of the political effects and implications for Irish identity.......An account of the factors that led to the collapse of the 'Celtic Tiger' economy in 2008 and an explanation of the political effects and implications for Irish identity....
Non explosive collapse of white dwarfs
International Nuclear Information System (INIS)
Canal, R.; Schatzmann, E.
1976-01-01
We show that if a sufficiently cold carbon-oxygen white dwarf, close to the critical mass, accretes matter from a companion in a binary system, the time scale of collapse is long enough to allow neutronization before the onset of pycnonuclear reactions. This can possibly lead to the formation of X-ray sources by a non explosive collapse. (orig.) [de
Homoclinic phenomena in the gravitational collapse
International Nuclear Information System (INIS)
Koiller, J.; Mello Neto, J.R.T. de; Soares, I.D.
1984-01-01
A class of Bianchi IX cosmological models is shown to have chaotic gravitational collapse, due to Poincare's homoclinic phenomena. Such models can be programmed so that for any given positive integer N (N=infinity included) the universe undergoes N non-periodic oscillations (each oscillation requiring a long time) before collapsing. For N=infinity the universe undergoes periodic oscillations. (Author) [pt
On the collapse of iron stellar cores
International Nuclear Information System (INIS)
Barkat, Z.; Rakavy, G.; Reiss, Y.; Wilson, J.R.
1975-01-01
The collapse of iron stellar cores is investigated to see whether the outward shock produced by the bounce at neutron star density is sufficient to burn appreciable amounts of the envelope around the iron core. Several models were tried, and in all cases no appreciable burn took place; hence no explosion results from the collapse of these models
Plastic collapse load of corroded steel plates
Indian Academy of Sciences (India)
Keywords. Corroded steel plate; plastic collapse; FEM; rough surface. ... The main aim of present work is to study plastic collapse load of corroded steel plates with irregular surfaces under tension. Non-linear ﬁnite element method ... Department of Ocean Engineering, AmirKabir University of Technology, 15914 Tehran, Iran ...
Collapse of Electrostatic Waves in Magnetoplasmas
DEFF Research Database (Denmark)
Shukla, P. K.; Yu, M. Y.; Juul Rasmussen, Jens
1984-01-01
The two-fluid model is employed to investigate the collapse of electrostatic waves in magnetized plasmas. It is found that nonlinear interaction of ion cyclotron, upper-, and lower-hybrid waves with adiabatic particle motion along the external magnetic field can cause wave-field collapse....
Sharper criteria for the wave collapse
DEFF Research Database (Denmark)
Kuznetsov, E.A.; Juul Rasmussen, J.; Rypdal, K.
1995-01-01
Sharper criteria for three-dimensional wave collapse described by the Nonlinear Schrodinger Equation (NLSE) are derived. The collapse threshold corresponds to the ground state soliton which is known to be unstable. Thus, for nonprefocusing distributions this represents the separatrix between...
Contagious cooperation, temptation, and ecosystem collapse
Richter, A.; van Soest, D.P.; Grasman, J.
2013-01-01
Real world observations suggest that social norms of cooperation can be effective in overcoming social dilemmas such as the joint management of a common pool resource—but also that they can be subject to slow erosion and sudden collapse. We show that these patterns of erosion and collapse emerge
Fire-induced collapses of steel structures
DEFF Research Database (Denmark)
Dondera, Alexandru; Giuliani, Luisa
Single-story steel buildings such as car parks and industrial halls are often characterised by stiff beams and flexible columns and may experience an outward (sway) collapse during a fire, endangering people and properties outside the building. It is therefore a current interest of the research...... to investigate the collapse behaviour of single-story steel frames and identify relevant structural characteristics that influence the collapse mode. In this paper, a parametric study on the collapse a steel beam-column assembly with beam hinged connection and fixed column support is carried out under...... on the beam. By means of those tables, a simple method for the assessment and the countermeasure of unsafe collapse mode of single-story steel buildings can be derived....
Granular Silo collapse: an experimental study
Clement, Eric; Gutierriez, Gustavo; Boltenhagen, Philippe; Lanuza, Jose
2008-03-01
We present an experimental work that develop some basic insight into the pre-buckling behavior and the buckling transition toward plastic collapse of a granular silo. We study different patterns of deformation generated on thin paper cylindrical shells during granular discharge. We study the collapse threshold for different bed height, flow rates and grain sizes. We compare the patterns that appear during the discharge of spherical beads, with those obtained in the axially compressed cylindrical shells. When the height of the granular column is close to the collapse threshold, we describe a ladder like pattern that rises around the cylinder surface in a spiral path of diamond shaped localizations, and develops into a plastic collapsing fold that grows around the collapsing silo.
NMNAT1 variants cause cone and cone-rod dystrophy.
Nash, Benjamin M; Symes, Richard; Goel, Himanshu; Dinger, Marcel E; Bennetts, Bruce; Grigg, John R; Jamieson, Robyn V
2018-03-01
Cone and cone-rod dystrophies (CD and CRD, respectively) are degenerative retinal diseases that predominantly affect the cone photoreceptors. The underlying disease gene is not known in approximately 75% of autosomal recessive cases. Variants in NMNAT1 cause a severe, early-onset retinal dystrophy called Leber congenital amaurosis (LCA). We report two patients where clinical phenotyping indicated diagnoses of CD and CRD, respectively. NMNAT1 variants were identified, with Case 1 showing an extremely rare homozygous variant c.[271G > A] p.(Glu91Lys) and Case 2 compound heterozygous variants c.[53 A > G];[769G > A] p.(Asn18Ser);(Glu257Lys). The detailed variant analysis, in combination with the observation of an associated macular atrophy phenotype, indicated that these variants were disease-causing. This report demonstrates that the variants in NMNAT1 may cause CD or CRD associated with macular atrophy. Genetic investigations of the patients with CD or CRD should include NMNAT1 in the genes examined.
Metaheuristic Algorithms for Convolution Neural Network.
Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
Metaheuristic Algorithms for Convolution Neural Network
Directory of Open Access Journals (Sweden)
L. M. Rasdi Rere
2016-01-01
Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.
Do Convolutional Neural Networks Learn Class Hierarchy?
Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu
2018-01-01
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
Microaneurysm detection using fully convolutional neural networks.
Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew
2018-05-01
Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.
Multiscale Convolutional Neural Networks for Hand Detection
Directory of Open Access Journals (Sweden)
Shiyang Yan
2017-01-01
Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.
Collapse analysis of toroidal shell
International Nuclear Information System (INIS)
Pomares, R.J.
1990-01-01
This paper describes a study performed to determine the collapse characteristics of a toroidal shell using finite element method (FEM) analysis. The study also included free drop testing of a quarter scale prototype to verify the analytical results. The full sized toroidal shell has a 24-inch toroidal diameter with a 24-inch tubal diameter. The shell material is type 304 strainless steel. The toroidal shell is part of the GE Model 2000 transportation packaging, and acts as an energy absorbing device. The analyses performed were on a full sized and quarter scaled models. The finite element program used in all analyses was the LIBRA code. The analytical procedure used both the elasto-plastic and large displacement options within the code. The loading applied in the analyses corresponded to an impact of an infinite rigid plane oriented normal to the drop direction vector. The application of the loading continued incrementally until the work performed by the deforming structure equalled the kinetic energy developed in the free fall. The comparison of analysis and test results showed a good correlation
Basic processes and factors determining the evolution of collapse sinkholes: a sensitivity study
Romanov, Douchko; Kaufmann, Georg
2017-04-01
Collapse sinkholes appear as closed depressions at the surface. The origin of these karst features is related to the continuous dissolution of the soluble rock caused by a focussed sub-surface flow. Water flowing along a preferential pathway through fissures and fractures within the phreatic part of a karst aquifer is able to dissolve the rock (limestone, gypsum, anhydrite). With time, the dissolved void volume increases and part of the ceiling above the stream can become unstable, collapses, and accumulates as debris in the flow path. The debris partially blocks the flow and thus activates new pathways. Because of the low compaction of the debris (high hydraulic conductivity), the flow and the dissolution rates within this crushed zone remain high. This allows a relatively fast dissolutional and erosional removal of the crushed material and the development of new empty voids. The void volume expands upwards towards the surface until a collapse sinkhole is formed. The collapse sinkholes exhibit a large variety of shapes (cylindrical, cone-, bowl-shaped), depths (from few to few hundred meters) and diameters (meters up to hundreds of meters). Two major processes are responsible for this diversity: a) the karst evolution of the aquifer - responsible for the dissolutional and erosional removal of material; b) the mechanical evolution of the host rock and the existence of structural features, faults for example, which determine the stability and the magnitude of the subsequent collapses. In this work we demonstrate the influence of the host rock type, the hydrological and geological boundary conditions, the chemical composition of the flowing water, and the geometry and the scale of the crushed zone, on the location and the evolution of the growing sinkhole. We demonstrate the ability of the karst evolution models to explain, at least qualitatively, the growth and the morphology of the collapse sinkholes and to roughly predict their shape and location. Implementing
Polyhedral combinatorics of UPGMA cones
Davidson, Ruth; Sullivant, Seth
2013-01-01
Distance-based methods such as UPGMA (Unweighted Pair Group Method with Arithmetic Mean) continue to play a significant role in phylogenetic research. We use polyhedral combinatorics to analyze the natural subdivision of the positive orthant induced by classifying the input vectors according to tree topologies returned by the algorithm. The partition lattice informs the study of UPGMA trees. We give a closed form for the extreme rays of UPGMA cones on n taxa, and compute the normalized volume...
Liouville action in cone gauge
International Nuclear Information System (INIS)
Zamolodchikov, A.B.
1989-01-01
The effective action of the conformally invariant field theory in the curved background space is considered in the light cone gauge. The effective potential in the classical background stress is defined as the Legendre transform of the Liouville action. This potential is tightly connected with the sl(2) current algebra. The series of the covariant differential operators is constructed and the anomalies of their determinants are reduced to this effective potential. 7 refs
International Nuclear Information System (INIS)
Manzke, Robert
2005-01-01
This doctoral thesis addresses imaging of the heart with retrospectively gated helical cone-beam computed tomography (CT). A thorough review of the CT reconstruction literature is presented in combination with a historic overview of cardiac CT imaging and a brief introduction to other cardiac imaging modalities. The thesis includes a comprehensive chapter about the theory of CT reconstruction, familiarizing the reader with the problem of cone-beam reconstruction. The anatomic and dynamic properties of the heart are outlined and techniques to derive the gating information are reviewed. With the extended cardiac reconstruction (ECR) framework, a new approach is presented for the heart-rate-adaptive gated helical cardiac cone-beam CT reconstruction. Reconstruction assessment criteria such as the temporal resolution, the homogeneity in terms of the cardiac phase, and the smoothness at cycle-to-cycle transitions are developed. Several reconstruction optimization approaches are described: An approach for the heart-rate-adaptive optimization of the temporal resolution is presented. Streak artifacts at cycle-to-cycle transitions can be minimized by using an improved cardiac weighting scheme. The optimal quiescent cardiac phase for the reconstruction can be determined automatically with the motion map technique. Results for all optimization procedures applied to ECR are presented and discussed based on patient and phantom data. The ECR algorithm is analyzed for larger detector arrays of future cone-beam systems throughout an extensive simulation study based on a four-dimensional cardiac CT phantom. The results of the scientific work are summarized and an outlook proposing future directions is given. The presented thesis is available for public download at www.cardiac-ct.net
STELLAR TIDAL DISRUPTION EVENTS BY DIRECT-COLLAPSE BLACK HOLES
Energy Technology Data Exchange (ETDEWEB)
Kashiyama, Kazumi [Theoretical Astrophysics Center, Department of Astronomy, University of California, Berkeley, Berkeley, CA 94720 (United States); Inayoshi, Kohei, E-mail: kashiyama@berkeley.edu, E-mail: inayoshi@astro.columbia.edu [Department of Astronomy, Columbia University, 550 West 120th Street, New York, NY 10027 (United States)
2016-07-20
We analyze the early growth stage of direct-collapse black holes (DCBHs) with ∼10{sup 5} M {sub ⊙}, which are formed by collapse of supermassive stars in atomic-cooling halos at z ≳ 10. A nuclear accretion disk around a newborn DCBH is gravitationally unstable and fragments into clumps with a few × 10 M {sub ⊙} at ∼0.01–0.1 pc from the center. Such clumps evolve into massive Population III stars with a few × 10–10{sup 2} M {sub ⊙} via successive gas accretion, and a nuclear star cluster is formed. Radiative and mechanical feedback from an inner slim disk and the star cluster will significantly reduce the gas accretion rate onto the DCBH within ∼10{sup 6} yr. Some of the nuclear stars can be scattered onto the loss cone orbits also within ≲10{sup 6} yr and tidally disrupted by the central DCBH. The jet luminosity powered by such tidal disruption events can be L {sub j} ≳ 10{sup 50} erg s{sup 1}. The prompt emission will be observed in X-ray bands with a peak duration of δt {sub obs} ∼ 10{sup 5–6}(1 + z ) s followed by a tail ∝ t {sub obs} {sup 5/3}, which can be detectable by Swift BAT and eROSITA even from z ∼ 20. Follow-up observations of the radio afterglows with, e.g., eVLA and the host halos with James Webb Space Telescope could probe the earliest active galactic nucleus feedback from DCBHs.
Prescriptionless light-cone integrals
International Nuclear Information System (INIS)
Suzuki, A.T.; Schmidt, A.G.M.
2000-01-01
Perturbative quantum gauge field theory as seen within the perspective of physical gauge choices such as the light-cone gauge entails the emergence of troublesome poles of the type (k.n) -α in the Feynman integrals. These come from the boson field propagator, where α=1,2,.. and n μ is the external arbitrary four-vector that defines the gauge properly. This becomes an additional hurdle in the computation of Feynman diagrams, since any graph containing internal boson lines will inevitably produce integrands with denominators bearing the characteristic gauge-fixing factor. How one deals with them has been the subject of research over decades, and several prescriptions have been suggested and tried in the course of time, with failures and successes. However, a more recent development at this fronteer which applies the negative dimensional technique to compute light-cone Feynman integrals shows that we can altogether dispense with prescriptions to perform the calculations. An additional bonus comes to us attached to this new technique, in that not only it renders the light-cone prescriptionless but, by the very nature of it, it can also dispense with decomposition formulas or partial fractioning tricks used in the standard approach to separate pole products of the type (k.n) -α [(k-p).n] -β (β=1,2,..). In this work we demonstrate how all this can be done. (orig.)
Light-cone quantization of quantum chromodynamics
International Nuclear Information System (INIS)
Brodsky, S.J.; Pauli, H.C.
1991-06-01
We discuss the light-cone quantization of gauge theories from two perspectives: as a calculational tool for representing hadrons as QCD bound-states of relativistic quarks and gluons, and also as a novel method for simulating quantum field theory on a computer. The light-cone Fock state expansion of wavefunctions at fixed light cone time provides a precise definition of the parton model and a general calculus for hadronic matrix elements. We present several new applications of light-cone Fock methods, including calculations of exclusive weak decays of heavy hadrons, and intrinsic heavy-quark contributions to structure functions. A general nonperturbative method for numerically solving quantum field theories, ''discretized light-cone quantization,'' is outlined and applied to several gauge theories, including QCD in one space and one time dimension, and quantum electrodynamics in physical space-time at large coupling strength. The DLCQ method is invariant under the large class of light-cone Lorentz transformations, and it can be formulated such at ultraviolet regularization is independent of the momentum space discretization. Both the bound-state spectrum and the corresponding relativistic light-cone wavefunctions can be obtained by matrix diagonalization and related techniques. We also discuss the construction of the light-cone Fock basis, the structure of the light-cone vacuum, and outline the renormalization techniques required for solving gauge theories within the light-cone Hamiltonian formalism
Light-cone quantization of quantum chromodynamics
Energy Technology Data Exchange (ETDEWEB)
Brodsky, S.J. (Stanford Linear Accelerator Center, Menlo Park, CA (USA)); Pauli, H.C. (Max-Planck-Institut fuer Kernphysik, Heidelberg (Germany, F.R.))
1991-06-01
We discuss the light-cone quantization of gauge theories from two perspectives: as a calculational tool for representing hadrons as QCD bound-states of relativistic quarks and gluons, and also as a novel method for simulating quantum field theory on a computer. The light-cone Fock state expansion of wavefunctions at fixed light cone time provides a precise definition of the parton model and a general calculus for hadronic matrix elements. We present several new applications of light-cone Fock methods, including calculations of exclusive weak decays of heavy hadrons, and intrinsic heavy-quark contributions to structure functions. A general nonperturbative method for numerically solving quantum field theories, discretized light-cone quantization,'' is outlined and applied to several gauge theories, including QCD in one space and one time dimension, and quantum electrodynamics in physical space-time at large coupling strength. The DLCQ method is invariant under the large class of light-cone Lorentz transformations, and it can be formulated such at ultraviolet regularization is independent of the momentum space discretization. Both the bound-state spectrum and the corresponding relativistic light-cone wavefunctions can be obtained by matrix diagonalization and related techniques. We also discuss the construction of the light-cone Fock basis, the structure of the light-cone vacuum, and outline the renormalization techniques required for solving gauge theories within the light-cone Hamiltonian formalism.
The covariant entropy bound in gravitational collapse
International Nuclear Information System (INIS)
Gao, Sijie; Lemos, Jose P. S.
2004-01-01
We study the covariant entropy bound in the context of gravitational collapse. First, we discuss critically the heuristic arguments advanced by Bousso. Then we solve the problem through an exact model: a Tolman-Bondi dust shell collapsing into a Schwarzschild black hole. After the collapse, a new black hole with a larger mass is formed. The horizon, L, of the old black hole then terminates at the singularity. We show that the entropy crossing L does not exceed a quarter of the area of the old horizon. Therefore, the covariant entropy bound is satisfied in this process. (author)
On the quantum corrected gravitational collapse
International Nuclear Information System (INIS)
Torres, Ramón; Fayos, Francesc
2015-01-01
Based on a previously found general class of quantum improved exact solutions composed of non-interacting (dust) particles, we model the gravitational collapse of stars. As the modeled star collapses a closed apparent 3-horizon is generated due to the consideration of quantum effects. The effect of the subsequent emission of Hawking radiation related to this horizon is taken into consideration. Our computations lead us to argue that a total evaporation could be reached. The inferred global picture of the spacetime corresponding to gravitational collapse is devoid of both event horizons and shell-focusing singularities. As a consequence, there is no information paradox and no need of firewalls
On the quantum corrected gravitational collapse
Directory of Open Access Journals (Sweden)
Ramón Torres
2015-07-01
Full Text Available Based on a previously found general class of quantum improved exact solutions composed of non-interacting (dust particles, we model the gravitational collapse of stars. As the modeled star collapses a closed apparent 3-horizon is generated due to the consideration of quantum effects. The effect of the subsequent emission of Hawking radiation related to this horizon is taken into consideration. Our computations lead us to argue that a total evaporation could be reached. The inferred global picture of the spacetime corresponding to gravitational collapse is devoid of both event horizons and shell-focusing singularities. As a consequence, there is no information paradox and no need of firewalls.
On the quantum corrected gravitational collapse
Torres, Ramón; Fayos, Francesc
2015-07-01
Based on a previously found general class of quantum improved exact solutions composed of non-interacting (dust) particles, we model the gravitational collapse of stars. As the modeled star collapses a closed apparent 3-horizon is generated due to the consideration of quantum effects. The effect of the subsequent emission of Hawking radiation related to this horizon is taken into consideration. Our computations lead us to argue that a total evaporation could be reached. The inferred global picture of the spacetime corresponding to gravitational collapse is devoid of both event horizons and shell-focusing singularities. As a consequence, there is no information paradox and no need of firewalls.
Galileon radiation from a spherical collapsing shell
Energy Technology Data Exchange (ETDEWEB)
Martín-García, Javier [Instituto de Física Teórica UAM/CSIC,C/ Nicolás Cabrera 15, E-28049 Madrid (Spain); Vázquez-Mozo, Miguel Á. [Instituto Universitario de Física Fundamental y Matemáticas (IUFFyM),Universidad de Salamanca, Plaza de la Merced s/n, E-37008 Salamanca (Spain)
2017-01-17
Galileon radiation in the collapse of a thin spherical shell of matter is analyzed. In the framework of a cubic Galileon theory, we compute the field profile produced at large distances by a short collapse, finding that the radiated field has two peaks traveling ahead of light fronts. The total energy radiated during the collapse follows a power law scaling with the shell’s physical width and results from two competing effects: a Vainshtein suppression of the emission and an enhancement due to the thinness of the shell.
Kervyn, M.; Ernst, G. G. J.; Carracedo, J.-C.; Jacobs, P.
2012-01-01
Volcanic cones are the most common volcanic constructs on Earth. Their shape can be quantified using two morphometric ratios: the crater/cone base ratio (W cr/W co) and the cone height/width ratio (H co/W co). The average values for these ratios obtained over entire cone fields have been explained by the repose angle of loose granular material (i.e. scoria) controlling cone slopes. The observed variability in these ratios between individual cones has been attributed to the effect of erosional processes or contrasting eruptive conditions on cone morphometry. Using a GIS-based approach, high spatial resolution Digital Elevation Models and airphotos, two new geomorphometry datasets for cone fields at Mauna Kea (Hawaii, USA) and Lanzarote (Canary Islands, Spain) are extracted and analyzed here. The key observation in these datasets is the great variability in morphometric ratios, even for simple-shape and well-preserved cones. Simple analog experiments are presented to analyze factors influencing the morphometric ratios. The formation of a crater is simulated within an analog cone (i.e. a sand pile) by opening a drainage conduit at the cone base. Results from experiments show that variability in the morphometric ratios can be attributed to variations in the width, height and horizontal offset of the drainage point relative to the cone symmetry axis, to the dip of the underlying slope or to the influence of a small proportion of fine cohesive material. GIS analysis and analog experiments, together with specific examples of cones documented in the field, suggest that the morphometric ratios for well-preserved volcanic cones are controlled by a combination of 1) the intrinsic cone material properties, 2) time-dependent eruption conditions, 3) the local setting, and 4) the method used to estimate the cone height. Implications for interpreting cone morphometry solely as either an age or as an eruption condition indicator are highlighted.
Convolutional Dictionary Learning: Acceleration and Convergence
Chun, Il Yong; Fessler, Jeffrey A.
2018-04-01
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.
Lidar Cloud Detection with Fully Convolutional Networks
Cromwell, E.; Flynn, D.
2017-12-01
The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.
Detecting atrial fibrillation by deep convolutional neural networks.
Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui
2018-02-01
Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.
Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.
Huang, Yan; Wang, Wei; Wang, Liang
2018-04-01
Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.
g-Weak Contraction in Ordered Cone Rectangular Metric Spaces
Directory of Open Access Journals (Sweden)
S. K. Malhotra
2013-01-01
Full Text Available We prove some common fixed-point theorems for the ordered g-weak contractions in cone rectangular metric spaces without assuming the normality of cone. Our results generalize some recent results from cone metric and cone rectangular metric spaces into ordered cone rectangular metric spaces. Examples are provided which illustrate the results.
Gravitational collapse and the vacuum energy
International Nuclear Information System (INIS)
Campos, M
2014-01-01
To explain the accelerated expansion of the universe, models with interacting dark components (dark energy and dark matter) have been considered recently in the literature. Generally, the dark energy component is physically interpreted as the vacuum energy of the all fields that fill the universe. As the other side of the same coin, the influence of the vacuum energy on the gravitational collapse is of great interest. We study such collapse adopting different parameterizations for the evolution of the vacuum energy. We discuss the homogeneous collapsing star fluid, that interacts with a vacuum energy component, using the stiff matter case as example. We conclude this work with a discussion of the Cahill-McVittie mass for the collapsed object.
Tetanus with multiple wedge vertebral collapses
African Journals Online (AJOL)
owner
2012-07-06
Jul 6, 2012 ... associated with traumatic injury, often a penetrating wound inflicted by dirty ... multiple vertebral collapses and the management chal- .... back pains and swelling as in our patient.9 There are usually no ... The cervical and.
The collapse of interstellar gas clouds
International Nuclear Information System (INIS)
McNally, D.; Settle, J.J.
1980-01-01
The stability of spherically symmetric free-fall collapse to small radial perturbations is examined for non-uniform clouds. It is concluded that fragmentation of the central region of a collapsing gas cloud is possible if: (a) the density distribution is sufficiently smooth; and (b) the collapse is nearly free fall. Generally, perturbations enjoy only finite amplification during the collapse, and the amplification tends to decrease with increasing distance from the centre of the cloud. Unlimited amplification occurs only for uniform density clouds. Fragmentation is therefore unlikely to result from dynamical instability in the outer parts of a non-uniform cloud. Isothermal clouds are also briefly considered and, while it is argued that an earlier suggestion of their instability to fragmentation is unfounded, no general conclusion on the instability of such clouds could be drawn. (author)
Lung lobe collapse: pathophysiology and radiologic significance
International Nuclear Information System (INIS)
Lord, P.F.; Gomez, J.A.
1985-01-01
The radiographic changes caused by collapse of lung lobes in pulmonary disease, pneumothorax, and pleural effusion depend on the lobar recoiling force and local pleural pressure. Differences in the tendency of normal lung lobes or regions to collapse depend on the relative surface-to-volume ratio, determined by shape and size of the region or lobe. This ratio affects the physiologic parameters of pulmonary interdependence, compliance, and collateral air flow. Pulmonary surfactant increases compliance, particularly at low volumes, maintains alveolar stability, and assists in maintaining capillary patency and preventing pulmonary edema. Its loss due to lung injury increases collapsing forces. In the presence of pneumothorax or pleural effusion, diseases that cause lobar collapse produce localized air or fluid entrapment that is a diagnostic sign of the presence of the underlying pulmonary disease
Cooperation, cheating, and collapse in biological populations
Gore, Jeff
2014-03-01
Natural populations can collapse suddenly in response to small changes in environmental conditions, and recovery from such a collapse can be difficult. We have used laboratory microbial ecosystems to directly measure theoretically proposed early warning signals of impending population collapse. Yeast cooperatively break down the sugar sucrose, meaning that below a critical size the population cannot sustain itself. We have demonstrated experimentally that changes in the fluctuations of the population size can serve as an early warning signal that the population is close to collapse. The cooperative nature of yeast growth on sucrose suggests that the population may be susceptible to ``cheater'' cells, which do not contribute to the public good and instead merely take advantage of the cooperative cells. We confirm this possibility experimentally and find that such social parasitism decreases the resilience of the population.
Simple Analytic Models of Gravitational Collapse
Energy Technology Data Exchange (ETDEWEB)
Adler, R.
2005-02-09
Most general relativity textbooks devote considerable space to the simplest example of a black hole containing a singularity, the Schwarzschild geometry. However only a few discuss the dynamical process of gravitational collapse, by which black holes and singularities form. We present here two types of analytic models for this process, which we believe are the simplest available; the first involves collapsing spherical shells of light, analyzed mainly in Eddington-Finkelstein coordinates; the second involves collapsing spheres filled with a perfect fluid, analyzed mainly in Painleve-Gullstrand coordinates. Our main goal is pedagogical simplicity and algebraic completeness, but we also present some results that we believe are new, such as the collapse of a light shell in Kruskal-Szekeres coordinates.
Collapsed Lung: MedlinePlus Health Topic
... Spanish Pneumothorax - infants (Medical Encyclopedia) Also in Spanish Topic Image MedlinePlus Email Updates Get Collapsed Lung updates ... Lung surgery Pneumothorax - slideshow Pneumothorax - infants Related Health Topics Chest Injuries and Disorders Lung Diseases Pleural Disorders ...
Creep collapse of TAPS fuel cladding
International Nuclear Information System (INIS)
Chaudhry, S.M.; Anand, A.K.
1975-01-01
Densification of UO 2 can cause axial gaps between fuel pelets and cladding in unsupported (internally) at these regions. An analysis is carried out regarding the possibility of creep collapse in these regions. The analysis is based on Timoshenko's theory of collapse. At various times during the residence of fuel in reactor following parameters are calculated : (1) inelastic collapse of perfectly circular tubes (2) plastic instability in oval tubes (3) effect of creep on ovality. Creep is considered to be a non-linear combination of the following : (a) thermal creep (b) intresenic creep (c) stress aided radiation enhanced (d) stress free growth (4) Critical pressure ratio. The results obtained are compared with G.E. predictions. The results do not predict collapse of TAPS fuel cladding for five year residence time. (author)
Directory of Open Access Journals (Sweden)
Kellermann Walter
2007-01-01
Full Text Available We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.
Four tails problems for dynamical collapse theories
McQueen, Kelvin J.
2015-02-01
The primary quantum mechanical equation of motion entails that measurements typically do not have determinate outcomes, but result in superpositions of all possible outcomes. Dynamical collapse theories (e.g. GRW) supplement this equation with a stochastic Gaussian collapse function, intended to collapse the superposition of outcomes into one outcome. But the Gaussian collapses are imperfect in a way that leaves the superpositions intact. This is the tails problem. There are several ways of making this problem more precise. But many authors dismiss the problem without considering the more severe formulations. Here I distinguish four distinct tails problems. The first (bare tails problem) and second (structured tails problem) exist in the literature. I argue that while the first is a pseudo-problem, the second has not been adequately addressed. The third (multiverse tails problem) reformulates the second to account for recently discovered dynamical consequences of collapse. Finally the fourth (tails problem dilemma) shows that solving the third by replacing the Gaussian with a non-Gaussian collapse function introduces new conflict with relativity theory.
Nonlinear wave collapse and strong turbulence
International Nuclear Information System (INIS)
Robinson, P.A.
1997-01-01
The theory and applications of wave self-focusing, collapse, and strongly nonlinear wave turbulence are reviewed. In the last decade, the theory of these phenomena and experimental realizations have progressed rapidly. Various nonlinear wave systems are discussed, but the simplest case of collapse and strong turbulence of Langmuir waves in an unmagnetized plasma is primarily used in explaining the theory and illustrating the main ideas. First, an overview of the basic physics of linear waves and nonlinear wave-wave interactions is given from an introductory perspective. Wave-wave processes are then considered in more detail. Next, an introductory overview of the physics of wave collapse and strong turbulence is provided, followed by a more detailed theoretical treatment. Later sections cover numerical simulations of Langmuir collapse and strong turbulence and experimental applications to space, ionospheric, and laboratory plasmas, including laser-plasma and beam-plasma interactions. Generalizations to self-focusing, collapse, and strong turbulence of waves in other systems are also discussed, including nonlinear optics, solid-state systems, magnetized auroral and astrophysical plasmas, and deep-water waves. The review ends with a summary of the main ideas of wave collapse and strong-turbulence theory, a collection of open questions in the field, and a brief discussion of possible future research directions. copyright 1997 The American Physical Society
Invariant moments based convolutional neural networks for image analysis
Directory of Open Access Journals (Sweden)
Vijayalakshmi G.V. Mahesh
2017-01-01
Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.
Single image super-resolution based on convolutional neural networks
Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia
2018-03-01
We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.
Energy integration in south cone
International Nuclear Information System (INIS)
Ribeiro, M.A.K.
1990-01-01
The economic development of a geo-political region is directly related to the energy resources available to its productive system. The analysis carried out in this paper focus a region limited by Paraguay, Uruguay, the Argentina north and the Brazilian south, the core of the so called South Cone. The region has a diversified energy matrix that assures strong connections between the countries. The main resources available are hydroelectric but the approach gives a strong emphasis in coal and natural gas. The outlined model of a self sustained development of the region can be used as the foundation of the independent economic development of South America. (author)
DEFF Research Database (Denmark)
Vicinanza, Diego; Margheritini, Lucia; Contestabile, Pasquale
2009-01-01
This paper discusses a new type of Wave Energy Converter (WEC) named Seawave Slot-Cone Generator (SSG). The SSG is a WEC of the overtopping type. The structure consists of a number of reservoirs one on the top of each others above the mean water level in which the water of incoming waves is store...... on sloping walls constituting the structure. The research is intended to be of direct use to engineers analyzing design and stability of this peculiar kind of coastal structure....
Dimensional Changes of Fresh Sockets With Reactive Soft Tissue Preservation: A Cone Beam CT Study.
Crespi, Roberto; Capparé, Paolo; Crespi, Giovanni; Gastaldi, Giorgio; Gherlone, Enrico Felice
2017-06-01
The aim of this study was to assess dimensional changes of the fresh sockets grafted with collagen sheets and maintenance of reactive soft tissue, using cone beam computed tomography (CBCT). Tooth extractions were performed with maximum preservation of the alveolar housing, reactive soft tissue was left into the sockets and collagen sheets filled bone defects. Cone beam computed tomography were performed before and 3 months after extractions. One hundred forty-five teeth, 60 monoradiculars and 85 molars, were extracted. In total, 269 alveoli were evaluated. In Group A, not statistically significant differences were found between monoradiculars, whereas statistically significant differences (P 0.05) for all types of teeth. This study reported an atraumatic tooth extraction, reactive soft tissue left in situ, and grafted collagen sponge may be helpful to reduce fresh socket collapse after extraction procedures.
Spacings and pair correlations for finite Bernoulli convolutions
International Nuclear Information System (INIS)
Benjamini, Itai; Solomyak, Boris
2009-01-01
We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different
A New Reverberator Based on Variable Sparsity Convolution
DEFF Research Database (Denmark)
Holm-Rasmussen, Bo; Lehtonen, Heidi-Maria; Välimäki, Vesa
2013-01-01
FIR filter coefficients are selected from a velvet noise sequence, which consists of ones, minus ones, and zeros only. In this application, it is sufficient perceptually to use very sparse velvet noise sequences having only about 0.1 to 0.2% non-zero elements, with increasing sparsity along...... the impulse response. The algorithm yields a parametric approximation of the late part of the impulse response, which is more than 100 times more efficient computationally than the direct convolution. The computational load of the proposed algorithm is comparable to that of FFT-based partitioned convolution...
Convolutional cylinder-type block-circulant cycle codes
Directory of Open Access Journals (Sweden)
Mohammad Gholami
2013-06-01
Full Text Available In this paper, we consider a class of column-weight two quasi-cyclic low-density paritycheck codes in which the girth can be large enough, as an arbitrary multiple of 8. Then we devote a convolutional form to these codes, such that their generator matrix can be obtained by elementary row and column operations on the parity-check matrix. Finally, we show that the free distance of the convolutional codes is equal to the minimum distance of their block counterparts.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
DEFF Research Database (Denmark)
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl
2018-01-01
conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....
Deep Convolutional Neural Networks: Structure, Feature Extraction and Training
Directory of Open Access Journals (Sweden)
Namatēvs Ivars
2017-12-01
Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.
Very deep recurrent convolutional neural network for object recognition
Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri
2017-03-01
In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.
Spectral interpolation - Zero fill or convolution. [image processing
Forman, M. L.
1977-01-01
Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.
Energy Technology Data Exchange (ETDEWEB)
Martin-Viera Cueto, J. A.; Benitez Villegas, E. M.; Bodineau Gil, C.; Parra Osorio, V.; Garcia Pareja, S.; Casado Villalon, F. J.
2013-07-01
The objective of this study is to verify the characterization of the collapsed cone algorithm of an SP using this Protocol. In addition, given that it only offers details of dose values measured at discrete points, measures are complemented by a gamma test distributions 2D of doses in different cases using film radiochromic. (Author)
Current status of relativistic core collapse simulations
Energy Technology Data Exchange (ETDEWEB)
Font, Jose A [Departamento de Astronomia y Astrofisica, Universidad de Valencia, Dr. Moliner 50, 46100 Burjassot (Valencia) (Spain)
2007-05-15
With the first generation of ground-based gravitational wave laser interferometers already taking data, the availability of reliable waveform templates from astrophysical sources, which may help extract the signal from the anticipated noisy data, is urgently required. Gravitational stellar core collapse supernova has traditionally been considered among the most important astrophysical sources of potentially detectable gravitational radiation. Only very recently the first multidimensional simulations of relativistic rotational core collapse have been possible (albeit for models with simplified input physics), thanks to the use of conservative formulations of the hydrodynamics equations and advanced numerical methodology, as well as stable formulations of Einstein's equations. In this paper, the current status of relativistic core collapse simulations is discussed, with the emphasis given to the modelling of the collapse dynamics and to the computation of the gravitational radiation in the existing numerical approaches. Work employing the conformally-flat approximation (CFC) of the 3+1 Einstein's equations is reported, as well as extensions of this approximation (CFC+) and investigations within the framework of the so-called BSSN formulation of the 3+1 gravitational field equations (with no approximation for the spacetime dynamics). On the other hand, the incorporation of magnetic fields and the MHD equations in numerical codes to improve the realism of core collapse simulations in general relativity, is currently an emerging field where significant progress is bound to be soon achieved. The paper also contains a brief discussion of magneto-rotational simulations of core collapse, aiming at addressing the effects of magnetic fields on the collapse dynamics and on the gravitational waveforms.
Current status of relativistic core collapse simulations
International Nuclear Information System (INIS)
Font, Jose A
2007-01-01
With the first generation of ground-based gravitational wave laser interferometers already taking data, the availability of reliable waveform templates from astrophysical sources, which may help extract the signal from the anticipated noisy data, is urgently required. Gravitational stellar core collapse supernova has traditionally been considered among the most important astrophysical sources of potentially detectable gravitational radiation. Only very recently the first multidimensional simulations of relativistic rotational core collapse have been possible (albeit for models with simplified input physics), thanks to the use of conservative formulations of the hydrodynamics equations and advanced numerical methodology, as well as stable formulations of Einstein's equations. In this paper, the current status of relativistic core collapse simulations is discussed, with the emphasis given to the modelling of the collapse dynamics and to the computation of the gravitational radiation in the existing numerical approaches. Work employing the conformally-flat approximation (CFC) of the 3+1 Einstein's equations is reported, as well as extensions of this approximation (CFC+) and investigations within the framework of the so-called BSSN formulation of the 3+1 gravitational field equations (with no approximation for the spacetime dynamics). On the other hand, the incorporation of magnetic fields and the MHD equations in numerical codes to improve the realism of core collapse simulations in general relativity, is currently an emerging field where significant progress is bound to be soon achieved. The paper also contains a brief discussion of magneto-rotational simulations of core collapse, aiming at addressing the effects of magnetic fields on the collapse dynamics and on the gravitational waveforms
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
K.L. Groenland (Koen); S.M. Bohte (Sander)
2016-01-01
textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order
Cone calorimeter tests of wood composites
Robert H. White; Kuma Sumathipala
2013-01-01
The cone calorimeter is widely used for the determination of the heat release rate (HRR) of building products and other materials. As part of an effort to increase the availability of cone calorimeter data on wood products, the U.S. Forest Products Laboratory and the American Wood Council conducted this study on composite wood products in cooperation with the Composite...
Cone Penetrometer N Factor Determination Testing Results
Energy Technology Data Exchange (ETDEWEB)
Follett, Jordan R.
2014-03-05
This document contains the results of testing activities to determine the empirical 'N Factor' for the cone penetrometer in kaolin clay simulant. The N Factor is used to releate resistance measurements taken with the cone penetrometer to shear strength.
Cone penetrometer demonstration standard startup review checklist
International Nuclear Information System (INIS)
KRIEG, S.A.
1998-01-01
Startup readiness for the Cone Penetrometer Demonstration in AX Tank Farm will be verified through the application of a Standard Startup Review Checklist. This is a listing of those items essential to demonstrating readiness to start the Cone Penetrometer Demonstration in AX Tank Farm
The Extraction of Post-Earthquake Building Damage Informatiom Based on Convolutional Neural Network
Chen, M.; Wang, X.; Dou, A.; Wu, X.
2018-04-01
The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixel-based and object-oriented methods have some shortcoming in extracting information of object. Pixel-based method can't make fully use of contextual information of objects. Object-oriented method faces problem that segmentation of image is not ideal, and the choice of feature space is difficult. In this paper, a new stratage is proposed which combines Convolution Neural Network (CNN) with imagery segmentation to extract building damage information from remote sensing imagery. the key idea of this method includes two steps. First to use CNN to predicate the probability of each pixel and then integrate the probability within each segmentation spot. The method is tested through extracting the collapsed building and uncollapsed building from the aerial image which is acquired in Longtoushan Town after Ms 6.5 Ludian County, Yunnan Province earthquake. The results show that the proposed method indicates its effectiveness in extracting damage information of buildings after earthquake.
Timescales of isotropic and anisotropic cluster collapse
Bartelmann, M.; Ehlers, J.; Schneider, P.
1993-12-01
From a simple estimate for the formation time of galaxy clusters, Richstone et al. have recently concluded that the evidence for non-virialized structures in a large fraction of observed clusters points towards a high value for the cosmological density parameter Omega0. This conclusion was based on a study of the spherical collapse of density perturbations, assumed to follow a Gaussian probability distribution. In this paper, we extend their treatment in several respects: first, we argue that the collapse does not start from a comoving motion of the perturbation, but that the continuity equation requires an initial velocity perturbation directly related to the density perturbation. This requirement modifies the initial condition for the evolution equation and has the effect that the collapse proceeds faster than in the case where the initial velocity perturbation is set to zero; the timescale is reduced by a factor of up to approximately equal 0.5. Our results thus strengthens the conclusion of Richstone et al. for a high Omega0. In addition, we study the collapse of density fluctuations in the frame of the Zel'dovich approximation, using as starting condition the analytically known probability distribution of the eigenvalues of the deformation tensor, which depends only on the (Gaussian) width of the perturbation spectrum. Finally, we consider the anisotropic collapse of density perturbations dynamically, again with initial conditions drawn from the probability distribution of the deformation tensor. We find that in both cases of anisotropic collapse, in the Zel'dovich approximation and in the dynamical calculations, the resulting distribution of collapse times agrees remarkably well with the results from spherical collapse. We discuss this agreement and conclude that it is mainly due to the properties of the probability distribution for the eigenvalues of the Zel'dovich deformation tensor. Hence, the conclusions of Richstone et al. on the value of Omega0 can be
Double Dirac cones in phononic crystals
Li, Yan
2014-07-07
A double Dirac cone is realized at the center of the Brillouin zone of a two-dimensional phononic crystal (PC) consisting of a triangular array of core-shell-structure cylinders in water. The double Dirac cone is induced by the accidental degeneracy of two double-degenerate Bloch states. Using a perturbation method, we demonstrate that the double Dirac cone is composed of two identical and overlapping Dirac cones whose linear slopes can also be accurately predicted from the method. Because the double Dirac cone occurs at a relatively low frequency, a slab of the PC can be mapped onto a slab of zero refractive index material by using a standard retrieval method. Total transmission without phase change and energy tunneling at the double Dirac point frequency are unambiguously demonstrated by two examples. Potential applications can be expected in diverse fields such as acoustic wave manipulations and energy flow control.
Double Dirac cones in phononic crystals
Li, Yan; Wu, Ying; Mei, Jun
2014-01-01
A double Dirac cone is realized at the center of the Brillouin zone of a two-dimensional phononic crystal (PC) consisting of a triangular array of core-shell-structure cylinders in water. The double Dirac cone is induced by the accidental degeneracy of two double-degenerate Bloch states. Using a perturbation method, we demonstrate that the double Dirac cone is composed of two identical and overlapping Dirac cones whose linear slopes can also be accurately predicted from the method. Because the double Dirac cone occurs at a relatively low frequency, a slab of the PC can be mapped onto a slab of zero refractive index material by using a standard retrieval method. Total transmission without phase change and energy tunneling at the double Dirac point frequency are unambiguously demonstrated by two examples. Potential applications can be expected in diverse fields such as acoustic wave manipulations and energy flow control.
Review of collapse triggering mechanism of collapsible soils due to wetting
Directory of Open Access Journals (Sweden)
Ping Li
2016-04-01
Full Text Available Loess soil deposits are widely distributed in arid and semi-arid regions and constitute about 10% of land area of the world. These soils typically have a loose honeycomb-type meta-stable structure that is susceptible to a large reduction in total volume or collapse upon wetting. Collapse characteristics contribute to various problems to infrastructures that are constructed on loess soils. For this reason, collapse triggering mechanism for loess soils has been of significant interest for researchers and practitioners all over the world. This paper aims at providing a state-of-the-art review on collapse mechanism with special reference to loess soil deposits. The collapse mechanism studies are summarized under three different categories, i.e. traditional approaches, microstructure approach, and soil mechanics-based approaches. The traditional and microstructure approaches for interpreting the collapse behavior are comprehensively summarized and critically reviewed based on the experimental results from the literature. The soil mechanics-based approaches proposed based on the experimental results of both compacted soils and natural loess soils are reviewed highlighting their strengths and limitations for estimating the collapse behavior. Simpler soil mechanics-based approaches with less parameters or parameters that are easy-to-determine from conventional tests are suggested for future research to better understand the collapse behavior of natural loess soils. Such studies would be more valuable for use in conventional geotechnical engineering practice applications.
Discrete singular convolution for the generalized variable-coefficient ...
African Journals Online (AJOL)
Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...
Down image recognition based on deep convolutional neural network
Directory of Open Access Journals (Sweden)
Wenzhu Yang
2018-06-01
Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition
Face recognition: a convolutional neural-network approach.
Lawrence, S; Giles, C L; Tsoi, A C; Back, A D
1997-01-01
We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
Training Convolutional Neural Networks for Translational Invariance on SAR ATR
DEFF Research Database (Denmark)
Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten
2016-01-01
In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...
An Interactive Graphics Program for Assistance in Learning Convolution.
Frederick, Dean K.; Waag, Gary L.
1980-01-01
A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…
Diffraction and Dirchlet problem for parameter-elliptic convolution ...
African Journals Online (AJOL)
In this paper we evaluate the difference between the inverse operators of a Dirichlet problem and of a diffraction problem for parameter-elliptic convolution operators with constant symbols. We prove that the inverse operator of a Dirichlet problem can be obtained as a limit case of such a diffraction problem. Quaestiones ...
Review of the convolution algorithm for evaluating service integrated systems
DEFF Research Database (Denmark)
Iversen, Villy Bæk
1997-01-01
In this paper we give a review of the applicability of the convolution algorithm. By this we are able to evaluate communication networks end--to--end with e.g. BPP multi-ratetraffic models insensitive to the holding time distribution. Rearrangement, minimum allocation, and maximum allocation...
A convolutional neural network to filter artifacts in spectroscopic MRI.
Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D
2018-03-09
Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.
Deep convolutional neural networks for detection of rail surface defects
Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio
2016-01-01
In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and
Symbol Stream Combining in a Convolutionally Coded System
Mceliece, R. J.; Pollara, F.; Swanson, L.
1985-01-01
Symbol stream combining has been proposed as a method for arraying signals received at different antennas. If convolutional coding and Viterbi decoding are used, it is shown that a Viterbi decoder based on the proposed weighted sum of symbol streams yields maximum likelihood decisions.
Two-level convolution formula for nuclear structure function
Ma, Boqiang
1990-05-01
A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions.
Two-level convolution formula for nuclear structure function
International Nuclear Information System (INIS)
Ma Boqiang
1990-01-01
A two-level convolution formula for the nuclear structure function is derived in considering the nucleus as a composite system of baryon-mesons which are also composite systems of quark-gluons again. The results show that the European Muon Colaboration effect can not be explained by the nuclear effects as nucleon Fermi motion and nuclear binding contributions
Plant species classification using deep convolutional neural network
DEFF Research Database (Denmark)
Dyrmann, Mads; Karstoft, Henrik; Midtiby, Henrik Skov
2016-01-01
Information on which weed species are present within agricultural fields is important for site specific weed management. This paper presents a method that is capable of recognising plant species in colour images by using a convolutional neural network. The network is built from scratch trained an...
Directory of Open Access Journals (Sweden)
Daryan Chitsaz
Full Text Available During neural circuit development, attractive or repulsive guidance cue molecules direct growth cones (GCs to their targets by eliciting cytoskeletal remodeling, which is reflected in their morphology. The experimental power of in vitro neuronal cultures to assay this process and its molecular mechanisms is well established, however, a method to rapidly find and quantify multiple morphological aspects of GCs is lacking. To this end, we have developed a free, easy to use, and fully automated Fiji macro, Conographer, which accurately identifies and measures many morphological parameters of GCs in 2D explant culture images. These measurements are then subjected to principle component analysis and k-means clustering to mathematically classify the GCs as "collapsed" or "extended". The morphological parameters measured for each GC are found to be significantly different between collapsed and extended GCs, and are sufficient to classify GCs as such with the same level of accuracy as human observers. Application of a known collapse-inducing ligand results in significant changes in all parameters, resulting in an increase in 'collapsed' GCs determined by k-means clustering, as expected. Our strategy provides a powerful tool for exploring the relationship between GC morphology and guidance cue signaling, which in particular will greatly facilitate high-throughput studies of the effects of drugs, gene silencing or overexpression, or any other experimental manipulation in the context of an in vitro axon guidance assay.
Chitsaz, Daryan; Morales, Daniel; Law, Chris; Kania, Artur
2015-01-01
During neural circuit development, attractive or repulsive guidance cue molecules direct growth cones (GCs) to their targets by eliciting cytoskeletal remodeling, which is reflected in their morphology. The experimental power of in vitro neuronal cultures to assay this process and its molecular mechanisms is well established, however, a method to rapidly find and quantify multiple morphological aspects of GCs is lacking. To this end, we have developed a free, easy to use, and fully automated Fiji macro, Conographer, which accurately identifies and measures many morphological parameters of GCs in 2D explant culture images. These measurements are then subjected to principle component analysis and k-means clustering to mathematically classify the GCs as "collapsed" or "extended". The morphological parameters measured for each GC are found to be significantly different between collapsed and extended GCs, and are sufficient to classify GCs as such with the same level of accuracy as human observers. Application of a known collapse-inducing ligand results in significant changes in all parameters, resulting in an increase in 'collapsed' GCs determined by k-means clustering, as expected. Our strategy provides a powerful tool for exploring the relationship between GC morphology and guidance cue signaling, which in particular will greatly facilitate high-throughput studies of the effects of drugs, gene silencing or overexpression, or any other experimental manipulation in the context of an in vitro axon guidance assay.
Progressive Collapse of High-Rise Buildings from Fire
Directory of Open Access Journals (Sweden)
Pershakov Valerii
2016-01-01
Full Text Available Considers ensuring the stability of structures of high-rise buildings against progressive collapse due to fire, proposed measures to ensure the stability of high-rise buildings due to progressive collapse. The analysis of large fires in high-rise buildings with progressive collapse and review of the literature on the issue of progressive collapse. The analysis of the Ukrainian normative documents on progressive collapse resistance.
Inflationary gravitational waves in collapse scheme models
Energy Technology Data Exchange (ETDEWEB)
Mariani, Mauro, E-mail: mariani@carina.fcaglp.unlp.edu.ar [Facultad de Ciencias Astronómicas y Geofísicas, Universidad Nacional de La Plata, Paseo del Bosque S/N, 1900 La Plata (Argentina); Bengochea, Gabriel R., E-mail: gabriel@iafe.uba.ar [Instituto de Astronomía y Física del Espacio (IAFE), UBA-CONICET, CC 67, Suc. 28, 1428 Buenos Aires (Argentina); León, Gabriel, E-mail: gleon@df.uba.ar [Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria – Pab. I, 1428 Buenos Aires (Argentina)
2016-01-10
The inflationary paradigm is an important cornerstone of the concordance cosmological model. However, standard inflation cannot fully address the transition from an early homogeneous and isotropic stage, to another one lacking such symmetries corresponding to our present universe. In previous works, a self-induced collapse of the wave function has been suggested as the missing ingredient of inflation. Most of the analysis regarding the collapse hypothesis has been solely focused on the characteristics of the spectrum associated to scalar perturbations, and within a semiclassical gravity framework. In this Letter, working in terms of a joint metric-matter quantization for inflation, we calculate, for the first time, the tensor power spectrum and the tensor-to-scalar ratio corresponding to the amplitude of primordial gravitational waves resulting from considering a generic self-induced collapse.
Noncrossing timelike singularities of irrotational dust collapse
International Nuclear Information System (INIS)
Liang, E.P.T.
1979-01-01
Known naked singularities in spherical dust collapse are either due to shell-crossing or localized to the central world line. They will probably be destroyed by pressure gradients or blue-shift instabilities. To violate the cosmic censorship hypothesis in a more convincing and general context, collapse solutions with naked singularities that are at least nonshell-crossing and nonlocalized need to be constructed. Some results concerning the probable structure of a class of nonshellcrossing and nonlocalized timelike singularities are reviewed. The cylindrical dust model is considered but this model is not asymptotically flat. To make these noncrossing singularities viable counter examples to the cosmic censorship hypothesis, the occurrence of such singularities in asymptotically flat collapse needs to be demonstrated. (UK)
Did mud contribute to freeway collapse?
Hough, Susan E.; Friberg, Paul A.; Busby, Robert; Field, Edward F.; Jacob, Klaus H.; Borcherdt, Roger D.
At least 41 people were killed October 17 when the upper tier of the Nimitz Freeway in Oakland, Calif., collapsed during the Ms = 7.1 Loma Prieta earthquake. Seismologists studying aftershocks concluded that soil conditions and resulting ground motion amplification were important in the failure of the structure and should be considered in the reconstruction of the highway.Structural design weaknesses in the two-tiered freeway, known as the Cypress structure, had been identified before the tragedy. The seismologists, from Lamont Doherty Geological Observatory in Palisades, N.Y., and the U.S. Geological Survey in Menlo Park, Calif., found that the collapsed section was built on fill over Bay mud. A southern section of the Cypress structure built on alluvium of Quaternary age did not collapse (see Figure 1).
The southern cone petroleum market
International Nuclear Information System (INIS)
Pisani, W.
1992-01-01
The Argentine oil sector has been moving strongly toward complete deregulation since 1989. Price controls on byproducts has been lifted, old petroleum contracts became into concessions, and the state oil company, YPF, is under process of privatization. In this context, the international companies scouting for opportunities can find an important menu of potential investments But here remain some problems connected with this deregulation, too. The lack of a reference crude and product market price is one of them. This paper focuses how to overcome this trouble with the establishment of an institutional market for crude and products, not only for Argentina but also for the entire Southern Cone Region (Argentina, Bolivia, Brazil, Chile, Paraguay and Uruguay), inquiring into the benefits of its creation
Sonographic Analysis of the Collapsed Gall Bladder
International Nuclear Information System (INIS)
Han, Sang Suk; Choi, Jae Young; Choi, Seok Jin; Eun, Chung Ki; Nam, Kyung Jin; Lee, Jeong Mi
1996-01-01
This study was done to find answers for further following questions in cases of the collapsed gallbladder (GB) : What is the probability of the presence of stone when stony echo is visible in GB area? What is the probability of the presence of stone when only acoustic shadow is visible from GB area? What are the associated GB pathologies except stone or cholecystitis in previously mentioned situations and is it possible to differentiate them? What are the underlying pathologies of GB collapse without stony echo or acoustic shadow and is it possible to differentiate them sonographic ally? What are the rate and causes of re-expansion of the collapsed GB on follow-up study? Prospective study was done in 157 cases of collapsed GB with no visible or nearly no visible bile filled lumen in recent 3 years. Sonographic analysis for GB lesions was done in 61 confirmed cases. Changing pattern of GB lumen on follow-up study and their underlying pathologies were analyzed in 28 cases. Initial sonographic examination was done with 3 or 3.5 MHz transducer. No other transducer was used in cases showing stony echo or acoustic shadow in GB area, but additional examination was done with 5 or 7-4 MHz transducer in cases without stony echo or acoustic shadow. Among 31 cases, which showed stony echo, stone was found in 30 cases and milk of calcium bile in one case. Stone was present in all of the 11 cases which showed only acoustic shadow from the collapsed GB without stony echo. GB cancer was accompanied in 2 cases among upper 42 cases, and its possibility could be suspected sonographic ally. Underlying pathologies of the 19cases without stony echo or acoustic shadow were as follows : GB stone (3), cholecystitis (6), GB cancer (1), bile plug syndrome (1), hepatitis (5), and ascites (3). And sonographic differentiation of the underlying causes for the collapse was possible in only 1 case of GB cancer. Among 28 cases of the follow-up study, 20 cases showed re-expansion of the GB lumen and
Relativistic collapse using Regge calculus: Pt. 1
International Nuclear Information System (INIS)
Dubal, M.R.; Leicester Univ.
1989-01-01
Regge calculus is used to simulate the dynamical collapse of model stars. In this paper we describe the general methodology of including a perfect fluid in dynamical Regge calculus spacetimes. The Regge-Einstein equations for spherical collapse are obtained and are then specialised to mimic a particular continuum gauge. The equivalent continuum problem is also set up. This is to be solved using standard numerical techniques (i.e. the method of finite difference). A subsequent paper will consider the solution of the equations presented here and will use the continuum problem for comparison purposes in order to check the Regge calculus results. (author)
Collapse and equilibrium of rotating, adiabatic clouds
International Nuclear Information System (INIS)
Boss, A.P.
1980-01-01
A numerical hydrodynamics computer code has been used to follow the collapse and establishment of equilibrium of adiabatic gas clouds restricted to axial symmetry. The clouds are initially uniform in density and rotation, with adiabatic exponents γ=5/3 and 7/5. The numerical technique allows, for the first time, a direct comparison to be made between the dynamic collapse and approach to equilibrium of unconstrained clouds on the one hand, and the results for incompressible, uniformly rotating equilibrium clouds, and the equilibrium structures of differentially rotating polytropes, on the other hand
Static axisymmetric discs and gravitational collapse
Energy Technology Data Exchange (ETDEWEB)
Chamorro, A.; Gregory, R.; Stewart, J.M.
1987-09-08
Regular static axisymmetric vacuum solutions of Einstein's field equations representing the exterior field of a finite thin disc are found. These are used to describe the slow collapse of a disc-like object. If no conditions are placed on the matter, a naked singularity is formed and the cosmic censorship hypothesis would be violated. Imposition of the weak energy condition, however, prevents slow collapse to a singularity and preserves the validity of this hypothesis. The validity of the hoop conjecture is also discussed.
An Implementation of Error Minimization Data Transmission in OFDM using Modified Convolutional Code
Directory of Open Access Journals (Sweden)
Hendy Briantoro
2016-04-01
Full Text Available This paper presents about error minimization in OFDM system. In conventional system, usually using channel coding such as BCH Code or Convolutional Code. But, performance BCH Code or Convolutional Code is not good in implementation of OFDM System. Error bits of OFDM system without channel coding is 5.77%. Then, we used convolutional code with code rate 1/2, it can reduce error bitsonly up to 3.85%. So, we proposed OFDM system with Modified Convolutional Code. In this implementation, we used Software Define Radio (SDR, namely Universal Software Radio Peripheral (USRP NI 2920 as the transmitter and receiver. The result of OFDM system using Modified Convolutional Code with code rate is able recover all character received so can decrease until 0% error bit. Increasing performance of Modified Convolutional Code is about 1 dB in BER of 10-4 from BCH Code and Convolutional Code. So, performance of Modified Convolutional better than BCH Code or Convolutional Code. Keywords: OFDM, BCH Code, Convolutional Code, Modified Convolutional Code, SDR, USRP
Dirac cones in isogonal hexagonal metallic structures
Wang, Kang
2018-03-01
A honeycomb hexagonal metallic lattice is equivalent to a triangular atomic one and cannot create Dirac cones in its electromagnetic wave spectrum. We study in this work the low-frequency electromagnetic band structures in isogonal hexagonal metallic lattices that are directly related to the honeycomb one and show that such structures can create Dirac cones. The band formation can be described by a tight-binding model that allows investigating, in terms of correlations between local resonance modes, the condition for the Dirac cones and the consequence of the third structure tile sustaining an extra resonance mode in the unit cell that induces band shifts and thus nonlinear deformation of the Dirac cones following the wave vectors departing from the Dirac points. We show further that, under structure deformation, the deformations of the Dirac cones result from two different correlation mechanisms, both reinforced by the lattice's metallic nature, which directly affects the resonance mode correlations. The isogonal structures provide new degrees of freedom for tuning the Dirac cones, allowing adjustment of the cone shape by modulating the structure tiles at the local scale without modifying the lattice periodicity and symmetry.
Cone penetrometer moisture probe acceptance test report
International Nuclear Information System (INIS)
Barnes, G.A.
1996-01-01
This Acceptance Test Report (ATR) documents the results of WHC-SD-WM-ATP-146 (Prototype Cone Penetrometer Moisture Probe Acceptance Test Procedure) and WHC-SD-WM-ATP-145 (Cone Penetrometer Moisture Probe Acceptance Test Procedure). The master copy of WHC-SD-WM-ATP-145 can be found in Appendix A and the master copy of WHC-SD-WM-ATP-146 can be found in Appendix B. Also included with this report is a matrix showing design criteria of the cone penetrometer moisture probe and the verification method used (Appendix C)
Demise of light cone field theory
International Nuclear Information System (INIS)
Hagen, C.R.
1977-01-01
It is shown that the massive spin one-half field is noncovariant in two dimensional light cone coordinates. It is shown that spin one-half is noncovariant in four dimensions as well. It is concluded that since the case of the spin one-half field is an absolute necessity if one is to build a world containing fermions. It seems safe to infer that light cone quantization cannot be useful in the quark binding problem as currently conceived. It is suggested that further work on light cone quantization be focused solely upon the questions of consistency as discussed rather than on applications to model building. 9 references
State-of-the-Art-Review of Collapsible Soils
Directory of Open Access Journals (Sweden)
A. A. AL-Rawas
2000-12-01
Full Text Available Collapsible soils are encountered in arid and semi-arid regions. Such soils cause potential construction problems due to their collapse upon wetting. The collapse phenomenon is primarily related to the open structure of the soil. Several soil collapse classifications based on parameters such as moisture content, dry density, Atterberg limits and clay content have been proposed in the literature as indicators of the soil collapse potential. Direct measurement of the magnitude of collapse, using laboratory and/or field tests, is essential once a soil showed indications of collapse potential. Treatment methods such as soil replacement, compaction control and chemical stabilization showed significant reduction in the settlement of collapsible soils. The design of foundations on collapsible soils depends on the depth of the soil, magnitude of collapse and economics of the design. Strip foundations are commonly used when collapsing soil extends to a shallow depth while piles and drilled piers are recommended in cases where the soil extends to several meters. This paper provides a comprehensive review of collapsible soils. These include the different types of collapsible soils, mechanisms of collapse, identification and classification methods, laboratory and field testing, treatment methods and guidelines for foundation design.
Nonlinear Progressive Collapse Analysis Including Distributed Plasticity
Directory of Open Access Journals (Sweden)
Mohamed Osama Ahmed
2016-01-01
Full Text Available This paper demonstrates the effect of incorporating distributed plasticity in nonlinear analytical models used to assess the potential for progressive collapse of steel framed regular building structures. Emphasis on this paper is on the deformation response under the notionally removed column, in a typical Alternate Path (AP method. The AP method employed in this paper is based on the provisions of the Unified Facilities Criteria – Design of Buildings to Resist Progressive Collapse, developed and updated by the U.S. Department of Defense [1]. The AP method is often used for to assess the potential for progressive collapse of building structures that fall under Occupancy Category III or IV. A case study steel building is used to examine the effect of incorporating distributed plasticity, where moment frames were used on perimeter as well as the interior of the three dimensional structural system. It is concluded that the use of moment resisting frames within the structural system will enhance resistance to progressive collapse through ductile deformation response and that it is conserative to ignore the effects of distributed plasticity in determining peak displacement response under the notionally removed column.
General relativistic collapse of rotating stars
International Nuclear Information System (INIS)
Nakamura, T.
1984-01-01
When a rotating star begins to collapse, the gravity becomes so strong that there appears a region from which even a photon cannot escape. After the distortion of space-time is radiated as gravitational waves, a Kerr black hole is formed finally. One of the main goals for numerical relativity is to simulate the collapse of a rotating star under realistic conditions. However, to know both the dynamics of matter and the propagation of gravitational radiation seems to be very difficult. Therefore, in this paper the problem is divided into 4 stages. They are: (1) The time evolution of pure gravitational waves is calculated in a 2-D code. (2) In this stage, the author tries to understand the dynamics of a collapsing, rotating star in 2D code. (3) Combining the techniques from stages 1, 2, the author tries to know both the dynamics of matter and the propagation of gravitational waves generated by the nonspherical motion of matter. (4) The author simulates the gravitational collapse of a rotating star to a black hole in 3D. 25 references, 12 figures, 1 table
Langmuir field structures favored in wave collapse
International Nuclear Information System (INIS)
Robinson, P.A.; Wouters, M.J.; Broderick, N.G.
1996-01-01
Study of Langmuir collapse thresholds shows that they have little polarization dependence and that moving packets have the lowest thresholds in the undamped case. However, incorporation of damping into the density response inhibits collapse of packets moving at more than a small fraction of the sound speed. Investigation of energy transfer to packets localized in density wells emdash the nucleation process emdash shows that at most a few trapped states can exist and that energy transfer is most effective when there is a single barely-trapped state. Coupled with an argument that closely packed wave packets have lower collapse thresholds, this argument yields an estimate of the number density of localized nucleating states in a turbulent plasma. It also leads to a simple and direct semiquantitative estimate of the collapse threshold. All these results are in accord with previous numerical simulations incorporating ion-sound damping, which show a preponderance of slow-moving or stationary packets with little or no intrinsic polarization dependence of thresholds. Likewise, the number densities obtained are in good agreement with simulation values, and the simple estimate of the threshold is semiquantitatively correct. The extent of the agreement supports the nucleation scenario with close-packed nucleation sites in the turbulent state. copyright 1996 American Institute of Physics
Identification and behavior of collapsible soils.
2011-01-01
Loess is a soil that can exhibit large deformations upon wetting. Cases of wetting induced collapse in loess have : been documented for natural deposits and man-made fills. These issues are of concern to the Indiana DOT due to the growth : of the sta...
The collapse of turbulence in the evening
Wiel, van de B.J.H.; Moene, A.F.; Jonker, H.J.J.; Baas, P.; Basu, S.; Sun, J.; Holtslag, A.A.M.
2012-01-01
A common experience in everyday weather is the fact that near-surface wind speeds tend to weaken in the evening, particularly in fair weather conditions. This cessation of wind usually coincides with the collapse of turbulence which leads to a quiet flow near the ground. As the absence of turbulent
Collapsible structure for an antenna reflector
Trubert, M. R. (Inventor)
1973-01-01
A collapsible support for an antenna reflector for use in supporting spacecraft antennas is described. The support has a regid base and a number of struts which are pivoted at the base. The deployment of the struts and their final configuration for supporting the antenna are illustrated.
Hydrogen-Poor Core-Collapse Supernovae
Pian, Elena; Mazzali, Paolo A.
Hydrogen-poor core-collapse supernovae (SNe) signal the explosive death of stars more massive than the progenitors of hydrogen-rich core-collapse supernovae, i.e., approximately in the range 15-50 M⊙ in main sequence. Since hydrogen-poor core-collapse supernovae include those that accompany gamma-ray bursts (GRBs), which were all rigorously identified with type Ic supernovae, their explosion energies cover almost two decades. The light curves and spectra are consequently very heterogeneous and often bear the signature of an asymmetric, i.e., aspherical, explosion. Asphericity is best traced by early-time (within days of the explosion) optical spectropolarimetry and by late-epoch (more than ˜ 100 days after explosion) low-resolution spectroscopy. While the relationship between hydrogen-poor core-collapse supernovae to hydrogen-poor super-luminous supernovae is not understood, a known case of association between an ultra-long gamma-ray burst and a very luminous hydrogen-poor supernova may help unraveling the connection. This is tantalizingly pointing to a magnetar powering source for both phenomena, although this scenario is still highly speculative. Host galaxies of hydrogen-poor supernovae are always star forming; in those of completely stripped supernovae and gamma-ray burst supernovae, the spatial distribution of the explosions follows the blue/ultraviolet light, with a correlation that is more than linear.
Gravitational collapse with decaying vacuum energy
Indian Academy of Sciences (India)
Abstract. The effect of dark energy on the end state of spherical radiation collapse is considered within the context of the cosmic censorship hypothesis. It is found that it is possible to have both black holes as well as naked singularities.
Schuster's law, black holes and gravitational collapse
International Nuclear Information System (INIS)
Massa, C.
1988-01-01
Consequences of the application of Schuster's law to black holes are investigated. It is shown that Schuster's law can reduce the intrinsic angular momentum of a collapsing body. The possibility is supposed that Schuster's law provides the general mechanism required by the cosmic censorship hypothesis which is taken seriously as a fundamental law of nature
A spherical collapse solution with neutrino outflow
International Nuclear Information System (INIS)
Glass, E.N.
1990-01-01
A three-parameter family of solutions of Einstein's field equations is given that represents a collapsing perfect fluid with outgoing neutrino flux. Solutions with ''naked'' singularities are exhibited. They can be forbidden by requiring pressure less than or equal to the density as a condition of cosmic censorship
Gravitational wave generation by stellar core collapse
International Nuclear Information System (INIS)
Moore, T.A.
1981-01-01
Stars which have masses greater than 5 to 8 solar masses are thought to undergo a stage of catastrophic core collapse and subsequent supernova explosion at the end of their lives. If the core is not spherically symmetric, the bounce which halts its collapse at transnuclear densities will generate a pulse of gravitational waves. This thesis presents a fully relativistic model of core collapse which treats deviations from spherical symmetry as small perturbations on a spherical background. This model may be used to predict qualitative and quantitative features of the gravitational radiation emitted by stellar cores with odd-parity, axisymmetric fluid perturbations, and represents a first step in the application of perturbative methods to more general asymmetries. The first chapter reviews the present consensus on the physics of core collapse and outlines the important features, assumptions, and limitations of the model. A series of model runs are presented and discussed. Finally, several proposals for future research are presented. Subsequent chapters explore in detail the mathematical features of the present model and its realization on the computer
The heterogeneity of world trade collapses
P.A.G. van Bergeijk (Peter)
2015-01-01
textabstractThis paper analyses drivers of imports during the major world trade collapses of the Great Depression (1930s; 34 countries) and the Great Recession (1930s; 173 countries). The analysis deals with the first year of these episodes and develops a small empirical model that shows a
Correlation Between Cone Penetration Rate And Measured Cone Penetration Parameters In Silty Soils
DEFF Research Database (Denmark)
Poulsen, Rikke; Nielsen, Benjaminn Nordahl; Ibsen, Lars Bo
2013-01-01
This paper shows, how a change in cone penetration rate affects the cone penetration measurements, hence the cone resistance, pore pressure, and sleeve friction in silty soil. The standard rate of penetration is 20 mm/s, and it is generally accepted that undrained penetration occurs in clay while...... drained penetration occurs in sand. When lowering the penetration rate, the soil pore water starts to dissipate and a change in the drainage condition is seen. In intermediate soils such as silty soils, the standard cone penetration rate may result in a drainage condition that could be undrained......, partially or fully drained. However, lowering the penetration rate in silty soils has a great significance because of the soil permeability, and only a small change in penetration rate will result in changed cone penetration measurements. In this paper, analyses will be done on data from 15 field cone...
Mach cones in space and laboratory dusty magnetoplasmas
International Nuclear Information System (INIS)
Mamun, A.A.; Shukla, P.K
2004-07-01
We present a rigorous theoretical investigation on the possibility for the formation of Mach cones in both space and laboratory dusty magnetoplasmas. We find the parametric regimes for which different types of Mach cones, such as dust acoustic Mach cones, dust magneto-acoustic Mach cones, oscillonic Mach cones, etc. are formed in space and laboratory dusty magnetoplasmas. We also identify the basic features of such different classes of Mach cones (viz. dust- acoustic, dust magneto-acoustic, oscillonic Mach cones, etc.), and clearly explain how they are relevant to space and laboratory dusty manetoplasmas. (author)
Genetics Home Reference: cone-rod dystrophy
... common cause of autosomal recessive cone-rod dystrophy , accounting for 30 to 60 percent of cases. At ... dystrophy play essential roles in the structure and function of specialized light receptor cells (photoreceptors) in the ...
Perturbation theory in light-cone gauge
International Nuclear Information System (INIS)
Vianello, Eliana
2000-01-01
Perturbation calculations are presented for the light-cone gauge Schwinger model. Eigenstates can be calculated perturbatively but the perturbation theory is nonstandard. We hope to extend the work to QCD 2 to resolve some outstanding issues in those theories
Modified superstring in light cone gauge
International Nuclear Information System (INIS)
Kamimura, Kiyoshi; Tatewaki, Machiko.
1988-01-01
We analyze the covariant superstring theory proposed by Siegel in light cone gauge. The physical states are the direct product of those of Green-Schwarz Superstring and the additional internal space spanned by light cone spinors. At clasical level, there is no difference among observables in Siegel's modified Superstring theory (SMST) and Green-Schwarz's one (GSST). However SMST can not be quantized with additional constraints as the physical state conditions. (author)
Combining morphometric features and convolutional networks fusion for glaucoma diagnosis
Perdomo, Oscar; Arevalo, John; González, Fabio A.
2017-11-01
Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.
Improving deep convolutional neural networks with mixed maxout units.
Directory of Open Access Journals (Sweden)
Hui-Zhen Zhao
Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.
Convolutional over Recurrent Encoder for Neural Machine Translation
Directory of Open Access Journals (Sweden)
Dakwale Praveen
2017-06-01
Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.
Infimal Convolution Regularisation Functionals of BV and Lp Spaces
Burger, Martin
2016-02-03
We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and Lp norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case p=2. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case p=2 this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.
3D Medical Image Interpolation Based on Parametric Cubic Convolution
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In the process of display, manipulation and analysis of biomedical image data, they usually need to be converted to data of isotropic discretization through the process of interpolation, while the cubic convolution interpolation is widely used due to its good tradeoff between computational cost and accuracy. In this paper, we present a whole concept for the 3D medical image interpolation based on cubic convolution, and the six methods, with the different sharp control parameter, which are formulated in details. Furthermore, we also give an objective comparison for these methods using data sets with the different slice spacing. Each slice in these data sets is estimated by each interpolation method and compared with the original slice using three measures: mean-squared difference, number of sites of disagreement, and largest difference. According to the experimental results, we present a recommendation for 3D medical images under the different situations in the end.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.
Yu, Haiyang; Wu, Zhihai; Wang, Shuqin; Wang, Yunpeng; Ma, Xiaolei
2017-06-26
Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.
Deep learning for steganalysis via convolutional neural networks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
2015-03-01
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
ID card number detection algorithm based on convolutional neural network
Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan
2018-04-01
In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.
Trajectory Generation Method with Convolution Operation on Velocity Profile
Energy Technology Data Exchange (ETDEWEB)
Lee, Geon [Hanyang Univ., Seoul (Korea, Republic of); Kim, Doik [Korea Institute of Science and Technology, Daejeon (Korea, Republic of)
2014-03-15
The use of robots is no longer limited to the field of industrial robots and is now expanding into the fields of service and medical robots. In this light, a trajectory generation method that can respond instantaneously to the external environment is strongly required. Toward this end, this study proposes a method that enables a robot to change its trajectory in real-time using a convolution operation. The proposed method generates a trajectory in real time and satisfies the physical limits of the robot system such as acceleration and velocity limit. Moreover, a new way to improve the previous method, which generates inefficient trajectories in some cases owing to the characteristics of the trapezoidal shape of trajectories, is proposed by introducing a triangle shape. The validity and effectiveness of the proposed method is shown through a numerical simulation and a comparison with the previous convolution method.
Airplane detection in remote sensing images using convolutional neural networks
Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei
2018-03-01
Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.
Rock images classification by using deep convolution neural network
Cheng, Guojian; Guo, Wenhui
2017-08-01
Granularity analysis is one of the most essential issues in authenticate under microscope. To improve the efficiency and accuracy of traditional manual work, an convolutional neural network based method is proposed for granularity analysis from thin section image, which chooses and extracts features from image samples while build classifier to recognize granularity of input image samples. 4800 samples from Ordos basin are used for experiments under colour spaces of HSV, YCbCr and RGB respectively. On the test dataset, the correct rate in RGB colour space is 98.5%, and it is believable in HSV and YCbCr colour space. The results show that the convolution neural network can classify the rock images with high reliability.
Design of a trichromatic cone array.
Directory of Open Access Journals (Sweden)
Patrick Garrigan
2010-02-01
Full Text Available Cones with peak sensitivity to light at long (L, medium (M and short (S wavelengths are unequal in number on the human retina: S cones are rare (<10% while increasing in fraction from center to periphery, and the L/M cone proportions are highly variable between individuals. What optical properties of the eye, and statistical properties of natural scenes, might drive this organization? We found that the spatial-chromatic structure of natural scenes was largely symmetric between the L, M and S sensitivity bands. Given this symmetry, short wavelength attenuation by ocular media gave L/M cones a modest signal-to-noise advantage, which was amplified, especially in the denser central retina, by long-wavelength accommodation of the lens. Meanwhile, total information represented by the cone mosaic remained relatively insensitive to L/M proportions. Thus, the observed cone array design along with a long-wavelength accommodated lens provides a selective advantage: it is maximally informative.
User-generated content curation with deep convolutional neural networks
Tous Liesa, Rubén; Wust, Otto; Gómez, Mauro; Poveda, Jonatan; Elena, Marc; Torres Viñals, Jordi; Makni, Mouna; Ayguadé Parra, Eduard
2016-01-01
In this paper, we report a work consisting in using deep convolutional neural networks (CNNs) for curating and filtering photos posted by social media users (Instagram and Twitter). The final goal is to facilitate searching and discovering user-generated content (UGC) with potential value for digital marketing tasks. The images are captured in real time and automatically annotated with multiple CNNs. Some of the CNNs perform generic object recognition tasks while others perform what we call v...
A quantum algorithm for Viterbi decoding of classical convolutional codes
Grice, Jon R.; Meyer, David A.
2014-01-01
We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper the proposed algorithm is applied to decoding classical convolutional codes, for instance; large constraint length $Q$ and short decode frames $N$. Other applications of the classical Viterbi algorithm where $Q$ is large (e.g. speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butter...
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Xi, Pengcheng; Shu, Chang; Goubran, Rafik
2018-01-01
Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...
Quantifying Translation-Invariance in Convolutional Neural Networks
Kauderer-Abrams, Eric
2017-01-01
A fundamental problem in object recognition is the development of image representations that are invariant to common transformations such as translation, rotation, and small deformations. There are multiple hypotheses regarding the source of translation invariance in CNNs. One idea is that translation invariance is due to the increasing receptive field size of neurons in successive convolution layers. Another possibility is that invariance is due to the pooling operation. We develop a simple ...
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
Learning Convolutional Text Representations for Visual Question Answering
Wang, Zhengyang; Ji, Shuiwang
2017-01-01
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual...
Shallow and deep convolutional networks for saliency prediction
Pan, Junting; Sayrol Clols, Elisa; Giró Nieto, Xavier; McGuinness, Kevin; O'Connor, Noel
2016-01-01
The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency p...
Production and reception of meaningful sound in Foville's 'encompassing convolution'.
Schiller, F
1999-04-01
In the history of neurology. Achille Louis Foville (1799-1879) is a name deserving to be remembered. In the course of time, his circonvolution d'enceinte of 1844 (surrounding the Sylvian fissure) became the 'convolution encompassing' every aspect of aphasiology, including amusia, ie., the localization in a coherent semicircle of semicircle of cerebral cortext serving the production and perception of language, song and instrumental music in health and disease.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
Shen, Li; Lin, Zhouchen; Huang, Qingming
2015-01-01
Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015...
Maximum likelihood convolutional decoding (MCD) performance due to system losses
Webster, L.
1976-01-01
A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.
General Dirichlet Series, Arithmetic Convolution Equations and Laplace Transforms
Czech Academy of Sciences Publication Activity Database
Glöckner, H.; Lucht, L.G.; Porubský, Štefan
2009-01-01
Roč. 193, č. 2 (2009), s. 109-129 ISSN 0039-3223 R&D Projects: GA ČR GA201/07/0191 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic function * Dirichlet convolution * polynomial equation * analytic equation * topological algebra * holomorphic functional calculus * implicit function theorem * Laplace transform * semigroup * complex measure Subject RIV: BA - General Mathematics Impact factor: 0.645, year: 2009 http://arxiv.org/abs/0712.3172
Solving singular convolution equations using the inverse fast Fourier transform
Czech Academy of Sciences Publication Activity Database
Krajník, E.; Montesinos, V.; Zizler, P.; Zizler, Václav
2012-01-01
Roč. 57, č. 5 (2012), s. 543-550 ISSN 0862-7940 R&D Projects: GA AV ČR IAA100190901 Institutional research plan: CEZ:AV0Z10190503 Keywords : singular convolution equations * fast Fourier transform * tempered distribution Subject RIV: BA - General Mathematics Impact factor: 0.222, year: 2012 http://www.springerlink.com/content/m8437t3563214048/
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
AFM tip-sample convolution effects for cylinder protrusions
Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang
2017-11-01
A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.
Edgeworth Expansion Based Model for the Convolutional Noise pdf
Directory of Open Access Journals (Sweden)
Yonatan Rivlin
2014-01-01
Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.
Traffic sign recognition based on deep convolutional neural network
Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan
2017-11-01
Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.
Face recognition via Gabor and convolutional neural network
Lu, Tongwei; Wu, Menglu; Lu, Tao
2018-04-01
In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.
Nuclear norm regularized convolutional Max Pos@Top machine
Li, Qinfeng
2016-11-18
In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.
Chloride currents in cones modify feedback from horizontal cells to cones in goldfish retina
Endeman, Duco; Fahrenfort, Iris; Sjoerdsma, Trijntje; Steijaert, Marvin; ten Eikelder, Huub; Kamermans, Maarten
2012-01-01
In neuronal systems, excitation and inhibition must be well balanced to ensure reliable information transfer. The cone/horizontal cell (HC) interaction in the retina is an example of this. Because natural scenes encompass an enormous intensity range both in temporal and spatial domains, the balance between excitation and inhibition in the outer retina needs to be adaptable. How this is achieved is unknown. Using electrophysiological techniques in the isolated retina of the goldfish, it was found that opening Ca2+-dependent Cl− channels in recorded cones reduced the size of feedback responses measured in both cones and HCs. Furthermore, we show that cones express Cl− channels that are gated by GABA released from HCs. Similar to activation of ICl(Ca), opening of these GABA-gated Cl− channels reduced the size of light-induced feedback responses both in cones and HCs. Conversely, application of picrotoxin, a blocker of GABAA and GABAC receptors, had the opposite effect. In addition, reducing GABA release from HCs by blocking GABA transporters also led to an increase in the size of feedback. Because the independent manipulation of Ca2+-dependent Cl− currents in individual cones yielded results comparable to bath-applied GABA, it was concluded that activation of either Cl− current by itself is sufficient to reduce the size of HC feedback. However, additional effects of GABA on outer retinal processing cannot be excluded. These results can be accounted for by an ephaptic feedback model in which a cone Cl− current shunts the current flow in the synaptic cleft. The Ca2+-dependent Cl− current might be essential to set the initial balance between the feedforward and the feedback signals active in the cone HC synapse. It prevents that strong feedback from HCs to cones flood the cone with Ca2+. Modulation of the feedback strength by GABA might play a role during light/dark adaptation, adjusting the amount of negative feedback to the signal to noise ratio of the
Identification and behavior of collapsible soils : [technical summary].
2011-01-01
Collapsible soils are susceptible to large volumetric strains when they become saturated. Numerous soil types : fall in the general category of collapsible soils, including : loess, a well-known aeolian deposit, present throughout : most of Indiana. ...
Dynamic Control of Collapse in a Vortex Airy Beam
Chen, Rui-Pin; Chew, Khian-Hooi; He, Sailing
2013-01-01
Here we study systematically the self-focusing dynamics and collapse of vortex Airy optical beams in a Kerr medium. The collapse is suppressed compared to a non-vortex Airy beam in a Kerr medium due to the existence of vortex fields. The locations of collapse depend sensitively on the initial power, vortex order, and modulation parameters. The collapse may occur in a position where the initial field is nearly zero, while no collapse appears in the region where the initial field is mainly distributed. Compared with a non-vortex Airy beam, the collapse of a vortex Airy beam can occur at a position away from the area of the initial field distribution. Our study shows the possibility of controlling and manipulating the collapse, especially the precise position of collapse, by purposely choosing appropriate initial power, vortex order or modulation parameters of a vortex Airy beam. PMID:23518858
Unifying Research on Social-Ecological Resilience and Collapse.
Cumming, Graeme S; Peterson, Garry D
2017-09-01
Ecosystems influence human societies, leading people to manage ecosystems for human benefit. Poor environmental management can lead to reduced ecological resilience and social-ecological collapse. We review research on resilience and collapse across different systems and propose a unifying social-ecological framework based on (i) a clear definition of system identity; (ii) the use of quantitative thresholds to define collapse; (iii) relating collapse processes to system structure; and (iv) explicit comparison of alternative hypotheses and models of collapse. Analysis of 17 representative cases identified 14 mechanisms, in five classes, that explain social-ecological collapse. System structure influences the kind of collapse a system may experience. Mechanistic theories of collapse that unite structure and process can make fundamental contributions to solving global environmental problems. Copyright © 2017. Published by Elsevier Ltd.
Spectral characteristics of light sources for S-cone stimulation.
Schlegelmilch, F; Nolte, R; Schellhorn, K; Husar, P; Henning, G; Tornow, R P
2002-11-01
Electrophysiological investigations of the short-wavelength sensitive pathway of the human eye require the use of a suitable light source as a S-cone stimulator. Different light sources with their spectral distribution properties were investigated and compared with the ideal S-cone stimulator. First, the theoretical background of the calculation of relative cone energy absorption from the spectral distribution function of the light source is summarized. From the results of the calculation, the photometric properties of the ideal S-cone stimulator will be derived. The calculation procedure was applied to virtual light sources (computer generated spectral distribution functions with different medium wavelengths and spectrum widths) and to real light sources (blue and green light emitting diodes, blue phosphor of CRT-monitor, multimedia projector, LCD monitor and notebook display). The calculated relative cone absorbencies are compared to the conditions of an ideal S-cone stimulator. Monochromatic light sources with wavelengths of less than 456 nm are close to the conditions of an ideal S-cone stimulator. Spectrum widths up to 21 nm do not affect the S-cone activation significantly (S-cone activation change < 0.2%). Blue light emitting diodes with peak wavelength at 448 nm and spectrum bandwidth of 25 nm are very useful for S-cone stimulation (S-cone activation approximately 95%). A suitable display for S-cone stimulation is the Trinitron computer monitor (S-cone activation approximately 87%). The multimedia projector has a S-cone activation up to 91%, but their spectral distribution properties depends on the selected intensity. LCD monitor and notebook displays have a lower S-cone activation (< or = 74%). Carefully selecting the blue light source for S-cone stimulation can reduce the unwanted L-and M-cone activation down to 4% for M-cones and 1.5% for L-cones.
mode of collapse of square single panel reinforced concrete space
African Journals Online (AJOL)
The models were loaded directly till collapse. The estimated and actual collapse loads of the five models were compared. The estimated collapse load for the slab was 35 kN/m2. Also, the numerical estimate of the collapse load for the beam was 10.2kN/m (with an equivalent slab load of 40.8kN/m2), while the shear capacity ...
Collapse and revival in holographic quenches
International Nuclear Information System (INIS)
Silva, Emilia da; Lopez, Esperanza; Mas, Javier; Serantes, Alexandre
2015-01-01
We study holographic models related to global quantum quenches in finite size systems. The holographic set up describes naturally a CFT, which we consider on a circle and a sphere. The enhanced symmetry of the conformal group on the circle motivates us to compare the evolution in both cases. Depending on the initial conditions, the dual geometry exhibits oscillations that we holographically interpret as revivals of the initial field theory state. On the sphere, this only happens when the energy density created by the quench is small compared to the system size. However on the circle considerably larger energy densities are compatible with revivals. Two different timescales emerge in this latter case. A collapse time, when the system appears to have dephased, and the revival time, when after rephasing the initial state is partially recovered. The ratio of these two times depends upon the initial conditions in a similar way to what is observed in some experimental setups exhibiting collapse and revivals.
HII regions in collapsing massive molecular clouds
International Nuclear Information System (INIS)
Yorke, H.W.; Bodenheimer, P.; Tenorio-Tagle, G.
1982-01-01
Results of two-dimensional numerical calculations of the evolution of HII regions associated with self-gravitating, massive molecular clouds are presented. Depending on the location of the exciting star, a champagne flow can occur concurrently with the central collapse of a nonrotating cloud. Partial evaporation of the cloud at a rate of about 0.005 solar masses/yr results. When 100 O-stars are placed at the center of a freely falling cloud of 3x10 5 solar masses no evaporation takes place. Rotating clouds collapse to disks and the champagne flow can evaporate the cloud at a higher rate (0.01 solar masses/yr). It is concluded that massive clouds containing OB-stars have lifetimes of no more than 10 7 yr. (Auth.)
Collapse and bounce of null fluids
Creelman, Bradley; Booth, Ivan
2016-01-01
Exact solutions describing the spherical collapse of null fluids can contain regions which violate the energy conditions. Physically the violations occur when the infalling matter continues to move inwards even when non-gravitational repulsive forces become stronger than gravity. In 1991 Ori proposed a resolution for these violations: spacetime surgery should be used to replace the energy condition violating region with an outgoing solution. The matter bounces. We revisit and implement this p...
Analysis of power system collapse risk
International Nuclear Information System (INIS)
Eleschova, Z.; Belan, A.; Cintula, B.; Smitkova, M.
2012-01-01
In this paper are analysed the initialization events with considering different scenarios and their impact on the power system transient stability. As an initialization event is considered a short circuit at various places of power line. In each scenario are considered protection failures (backup protection), circuit-breaker failures (breaker failure relay activation). The individual states are analysed and the power system collapse risk assessed based on the simulation experiments results (Authors)
Distributed Monitoring of Voltage Collapse Sensitivity Indices
Simpson-Porco, John W.; Bullo, Francesco
2016-01-01
The assessment of voltage stability margins is a promising direction for wide-area monitoring systems. Accurate monitoring architectures for long-term voltage instability are typically centralized and lack scalability, while completely decentralized approaches relying on local measurements tend towards inaccuracy. Here we present distributed linear algorithms for the online computation of voltage collapse sensitivity indices. The computations are collectively performed by processors embedded ...
Rate of stellar collapses in the Galaxy
International Nuclear Information System (INIS)
Lande, K.; Stephens, W.E.
1977-01-01
From an analysis of pulsar spatial and luminosity distributions, the number density of observed pulsars in the local region is determined to be 1.1+-0.4x10 -7 pulsar pc -3 . Multiplication by the detection factor and by the ratio of Galaxy mass to local matter density and division by a mean lifetime of pulsars of 3x10 6 yr suggests a pulsar birth every 4 yr. A stellar collapse might occur even more often. (Auth.)
Asymmetric explosion of core-collapse supernovae
International Nuclear Information System (INIS)
Kazeroni, Remi
2016-01-01
A core-collapse supernova represents the ultimate stage of the evolution of massive stars.The iron core contraction may be followed by a gigantic explosion which gives birth to a neutron star.The multidimensional dynamics of the innermost region, during the first hundreds milliseconds, plays a decisive role on the explosion success because hydrodynamical instabilities are able to break the spherical symmetry of the collapse. Large scale transverse motions generated by two instabilities, the neutrino-driven convection and the Standing Accretion Shock Instability (SASI),increase the heating efficiency up to the point of launching an asymmetric explosion and influencing the birth properties of the neutron star. In this thesis, hydrodynamical instabilities are studied using numerical simulations of simplified models. These models enable a wide exploration of the parameter space and a better physical understanding of the instabilities, generally inaccessible to realistic models.The non-linear regime of SASI is analysed to characterize the conditions under which a spiral mode prevails and to assess its ability to redistribute angular momentum radially.The influence of rotation on the shock dynamics is also addressed. For fast enough rotation rates, a corotation instability overlaps with SASI and greatly impacts the dynamics. The simulations enable to better constrain the effect of non-axisymmetric modes on the angular momentum budget of the iron core collapsing into a neutron star. SASI may under specific conditions spin up or down the pulsar born during the explosion. Finally, an idealised model of the heating region is studied to characterize the non-linear onset of convection by perturbations such as those produced by SASI or pre-collapse combustion inhomogeneities. The dimensionality issue is examined to stress the beneficial consequences of the three-dimensional dynamics on the onset of the explosion. (author) [fr
Clinical Course, Genetic Etiology, and Visual Outcome in Cone and Cone-Rod Dystrophy
Thiadens, Alberta A. H. J.; Phan, T. My Lan; Zekveld-Vroon, Renate C.; Leroy, Bart P.; van den Born, L. Ingeborgh; Hoyng, Carel B.; Klaver, Caroline C. W.; Roosing, Susanne; Pott, Jan-Willem R.; van Schooneveld, Mary J.; van Moll-Ramirez, Norka; van Genderen, Maria M.; Boon, Camiel J. F.; den Hollander, Anneke I.; Bergen, Arthur A. B.; De Baere, Elfride; Cremers, Frans P. M.; Lotery, Andrew J.
Objective: To evaluate the clinical course, genetic etiology, and visual prognosis in patients with cone dystrophy (CD) and cone-rod dystrophy (CRD). Design: Clinic-based, longitudinal, multicenter study. Participants: Consecutive probands with CD (N = 98), CRD (N = 83), and affected relatives (N =
Cooperation, cheating, and collapse in microbial populations
Gore, Jeff
2012-02-01
Natural populations can suffer catastrophic collapse in response to small changes in environmental conditions, and recovery after such a collapse can be exceedingly difficult. We have used laboratory yeast populations to study proposed early warning signals of impending extinction. Yeast cooperatively breakdown the sugar sucrose, meaning that there is a minimum number of cells required to sustain the population. We have demonstrated experimentally that the fluctuations in the population size increase in magnitude and become slower as the population approaches collapse. The cooperative nature of yeast growth on sucrose suggests that the population may be susceptible to cheater cells, which do not contribute to the public good and instead merely take advantage of the cooperative cells. We have confirmed this possibility experimentally by using a cheater yeast strain that lacks the gene encoding the cooperative behavior [1]. However, recent results in the lab demonstrate that the presence of a bacterial competitor may drive cooperation within the yeast population.[4pt] [1] Gore et al, Nature 459, 253 -- 256 (2009)
Collapse of experimental capsules under external pressure
International Nuclear Information System (INIS)
Simonen, F.A.; Shippell, R.J. Jr.
1980-01-01
Stress analyses and developmental tests of capsules fabricated from thick-walled tubing were performed for an external pressure design condition. In the design procedure no credit was taken for the expected margin in pressure between yielding of the capsule wall and catastrophic collapse or flattening. In tests of AISI-1018 low carbon steel capsules, a significant margin was seen between yield and collapse pressure. However, the experimental yield pressures were significantly below predictions, essentially eliminating the safety margin present in the conservative design approach. The differences between predictions and test results are attributed to deficiencies in the plasticity theories commonly in use for engineering stress analyses. The results of this study show that the von Mises yield condition does not accurately describe the yield behavior of the AISI-1018 steel tubing material for the triaxial stress conditions of interest. Finite element stress analyses successfully predicted the transition between uniform inward plastic deformation and ovalization that leads to catastrophic collapse. After adjustments to correct for the unexpected yield behavior of the tube material, the predicted pressure-deflection trends were found to follow the experimental data
Collapse of tall granular columns in fluid
Kumar, Krishna; Soga, Kenichi; Delenne, Jean-Yves
2017-06-01
Avalanches, landslides, and debris flows are geophysical hazards, which involve rapid mass movement of granular solids, water, and air as a multi-phase system. In order to describe the mechanism of immersed granular flows, it is important to consider both the dynamics of the solid phase and the role of the ambient fluid. In the present study, the collapse of a granular column in fluid is studied using 2D LBM - DEM. The flow kinematics are compared with the dry and buoyant granular collapse to understand the influence of hydrodynamic forces and lubrication on the run-out. In the case of tall columns, the amount of material destabilised above the failure plane is larger than that of short columns. Therefore, the surface area of the mobilised mass that interacts with the surrounding fluid in tall columns is significantly higher than the short columns. This increase in the area of soil - fluid interaction results in an increase in the formation of turbulent vortices thereby altering the deposit morphology. It is observed that the vortices result in the formation of heaps that significantly affects the distribution of mass in the flow. In order to understand the behaviour of tall columns, the run-out behaviour of a dense granular column with an initial aspect ratio of 6 is studied. The collapse behaviour is analysed for different slope angles: 0°, 2.5°, 5° and 7.5°.
Collapse postulate for observables with continuous area
International Nuclear Information System (INIS)
Srinivas, M.D.
1979-03-01
In order to provide a mathematical framework for discussing the statistical correlations between the outcomes, when an arbitrary sequence of observables are measured, it is necessary to generalize the conventional von Neumann-Lueders collapse postulate to observables with a continuous spectrum. It is shown that the standard prescription in conventional quantum theory for the joint probabilities of compatible observables is sufficient to characterize, more or less completely, the appropriate ''generalized collapse postulate'' which associates with each observable a unique ''finitely additive expectation valued measure''. An interesting feature of the collapse associated with observables with continuous spectra, which again follows from the basic principles of conventional quantum theory, is that it must be formulated in terms of the so-called non-normal conditional expectations, which implies that the joint probabilities associated with successive observations of such observables are not in general σ-additive. The implications of this non-σ-additivity on the determination of expectation values, correlation functions etc., are also investigated. It is demonstrated that the basic prescriptions introduced in this paper constitute a natural completion of the framework of conventional quantum theory for discussing the statistics of an arbitrary sequence of observations
The Collapse of Ecosystem Engineer Populations
Directory of Open Access Journals (Sweden)
José F. Fontanari
2018-01-01
Full Text Available Humans are the ultimate ecosystem engineers who have profoundly transformed the world’s landscapes in order to enhance their survival. Somewhat paradoxically, however, sometimes the unforeseen effect of this ecosystem engineering is the very collapse of the population it intended to protect. Here we use a spatial version of a standard population dynamics model of ecosystem engineers to study the colonization of unexplored virgin territories by a small settlement of engineers. We find that during the expansion phase the population density reaches values much higher than those the environment can support in the equilibrium situation. When the colonization front reaches the boundary of the available space, the population density plunges sharply and attains its equilibrium value. The collapse takes place without warning and happens just after the population reaches its peak number. We conclude that overpopulation and the consequent collapse of an expanding population of ecosystem engineers is a natural consequence of the nonlinear feedback between the population and environment variables.
Matter and gravitons in the gravitational collapse
Directory of Open Access Journals (Sweden)
Roberto Casadio
2016-12-01
Full Text Available We consider the effects of gravitons in the collapse of baryonic matter that forms a black hole. We first note that the effective number of (soft off-shell gravitons that account for the (negative Newtonian potential energy generated by the baryons is conserved and always in agreement with Bekenstein's area law of black holes. Moreover, their (positive interaction energy reproduces the expected post-Newtonian correction and becomes of the order of the total ADM mass of the system when the size of the collapsing object approaches its gravitational radius. This result supports a scenario in which the gravitational collapse of regular baryonic matter produces a corpuscular black hole without central singularity, in which both gravitons and baryons are marginally bound and form a Bose–Einstein condensate at the critical point. The Hawking emission of baryons and gravitons is then described by the quantum depletion of the condensate and we show the two energy fluxes are comparable, albeit negligibly small on astrophysical scales.
Matter and gravitons in the gravitational collapse
Energy Technology Data Exchange (ETDEWEB)
Casadio, Roberto, E-mail: casadio@bo.infn.it [Dipartimento di Fisica e Astronomia, Alma Mater Universià di Bologna, via Irnerio 46, 40126 Bologna (Italy); I.N.F.N., Sezione di Bologna, IS FLAG, viale B. Pichat 6/2, I-40127 Bologna (Italy); Giugno, Andrea, E-mail: A.Giugno@physik.uni-muenchen.de [Arnold Sommerfeld Center, Ludwig-Maximilians-Universität, Theresienstraße 37, 80333 München (Germany); Giusti, Andrea, E-mail: andrea.giusti@bo.infn.it [Dipartimento di Fisica e Astronomia, Alma Mater Universià di Bologna, via Irnerio 46, 40126 Bologna (Italy); I.N.F.N., Sezione di Bologna, IS FLAG, viale B. Pichat 6/2, I-40127 Bologna (Italy)
2016-12-10
We consider the effects of gravitons in the collapse of baryonic matter that forms a black hole. We first note that the effective number of (soft off-shell) gravitons that account for the (negative) Newtonian potential energy generated by the baryons is conserved and always in agreement with Bekenstein's area law of black holes. Moreover, their (positive) interaction energy reproduces the expected post-Newtonian correction and becomes of the order of the total ADM mass of the system when the size of the collapsing object approaches its gravitational radius. This result supports a scenario in which the gravitational collapse of regular baryonic matter produces a corpuscular black hole without central singularity, in which both gravitons and baryons are marginally bound and form a Bose–Einstein condensate at the critical point. The Hawking emission of baryons and gravitons is then described by the quantum depletion of the condensate and we show the two energy fluxes are comparable, albeit negligibly small on astrophysical scales.
Precombination Cloud Collapse and Baryonic Dark Matter
Hogan, Craig J.
1993-01-01
A simple spherical model of dense baryon clouds in the hot big bang 'strongly nonlinear primordial isocurvature baryon fluctuations' is reviewed and used to describe the dependence of cloud behavior on the model parameters, baryon mass, and initial over-density. Gravitational collapse of clouds before and during recombination is considered including radiation diffusion and trapping, remnant type and mass, and effects on linear large-scale fluctuation modes. Sufficiently dense clouds collapse early into black holes with a minimum mass of approx. 1 solar mass, which behave dynamically like collisionless cold dark matter. Clouds below a critical over-density, however, delay collapse until recombination, remaining until then dynamically coupled to the radiation like ordinary diffuse baryons, and possibly producing remnants of other kinds and lower mass. The mean density in either type of baryonic remnant is unconstrained by observed element abundances. However, mixed or unmixed spatial variations in abundance may survive in the diffuse baryon and produce observable departures from standard predictions.
Collapse postulate for observables with continuous spectra
International Nuclear Information System (INIS)
Srinivas, M.D.; Madras Univ.
1980-01-01
In order to provide a mathematical framework for discussing the statistical correlations between the outcomes, when an arbitrary sequence of observables are measured, it is necessary to generalize the conventional von Neumann-Lueders collapse postulate to observables with a continuous spectrum. It is shown that the standard prescription in conventional quantum theory for the joint probabilities of compatible observables is sufficient to characterize, more or less completely, the appropriate 'generalized collapse postulate' which associates with each observable a unique 'finitely additive expectation valued measure'. An interesting feature of the collapse associated with observables with continuous spectra, which again follows from the basic principles of conventional quantum theory, is that it must be formulated in terms of the so-called non-normal conditional expectations, which implies that the joint probabilities associated with successive observations of such observables are not in general sigma-additive. The implications of this non-sigma-additivity on the determination of expectation values, correlation functions etc., are also investigated. It is demonstrated that the basic prescriptions introduced in this paper constitute a natural completion of the framework of conventional quantum theory for discussing the statistics of an arbitrary sequence of observations. (orig.) 891 HJ/orig. 892 CKA
Convolution equations on lattices: periodic solutions with values in a prime characteristic field
Zaidenberg, Mikhail
2006-01-01
These notes are inspired by the theory of cellular automata. A linear cellular automaton on a lattice of finite rank or on a toric grid is a discrete dinamical system generated by a convolution operator with kernel concentrated in the nearest neighborhood of the origin. In the present paper we deal with general convolution operators. We propose an approach via harmonic analysis which works over a field of positive characteristic. It occurs that a standard spectral problem for a convolution op...
National Research Council Canada - National Science Library
Ong, Choon
1998-01-01
The performance analysis of a differential phase shift keyed (DPSK) communications system, operating in a Rayleigh fading environment, employing convolutional coding and diversity processing is presented...
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2007-01-01
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
Collapse dynamics of ultrasound contrast agent microbubbles
King, Daniel Alan
Ultrasound contrast agents (UCAs) are micron-sized gas bubbles encapsulated with thin shells on the order of nanometers thick. The damping effects of these viscoelastic coatings are widely known to significantly alter the bubble dynamics for linear and low-amplitude behavior; however, their effects on strongly nonlinear and destruction responses are much less studied. This dissertation examines the behaviors of single collapsing shelled microbubbles using experimental and theoretical methods. The study of their dynamics is particularly relevant for emerging experimental uses of UCAs which seek to leverage localized mechanical forces to create or avoid specialized biomedical effects. The central component in this work is the study of postexcitation rebound and collapse, observed acoustically to identify shell rupture and transient inertial cavitation of single UCA microbubbles. This time-domain analysis of the acoustic response provides a unique method for characterization of UCA destruction dynamics. The research contains a systematic documentation of single bubble postexcitation collapse through experimental measurement with the double passive cavitation detection (PCD) system at frequencies ranging from 0.9 to 7.1 MHz and peak rarefactional pressure amplitudes (PRPA) ranging from 230 kPa to 6.37 MPa. The double PCD setup is shown to improve the quality of collected data over previous setups by allowing symmetric responses from a localized confocal region to be identified. Postexcitation signal percentages are shown to generally follow trends consistent with other similar cavitation metrics such as inertial cavitation, with greater destruction observed at both increased PRPA and lower frequency over the tested ranges. Two different types of commercially available UCAs are characterized and found to have very different collapse thresholds; lipid-shelled Definity exhibits greater postexcitation at lower PRPAs than albumin-shelled Optison. Furthermore, by altering
Strain engineering of Dirac cones in graphyne
Energy Technology Data Exchange (ETDEWEB)
Wang, Gaoxue; Kumar, Ashok; Pandey, Ravindra, E-mail: pandey@mtu.edu [Department of Physics, Michigan Technological University, Houghton, Michigan 49931 (United States); Si, Mingsu [Key Laboratory for Magnetism and Magnetic Materials of the Ministry of Education, Lanzhou University, Lanzhou 730000 (China)
2014-05-26
6,6,12-graphyne, one of the two-dimensional carbon allotropes with the rectangular lattice structure, has two kinds of non-equivalent anisotropic Dirac cones in the first Brillouin zone. We show that Dirac cones can be tuned independently by the uniaxial compressive strain applied to graphyne, which induces n-type and p-type self-doping effect, by shifting the energy of the Dirac cones in the opposite directions. On the other hand, application of the tensile strain results into a transition from gapless to finite gap system for the monolayer. For the AB-stacked bilayer, the results predict tunability of Dirac-cones by in-plane strains as well as the strain applied perpendicular to the plane. The group velocities of the Dirac cones show enhancement in the resistance anisotropy for bilayer relative to the case of monolayer. Such tunable and direction-dependent electronic properties predicted for 6,6,12-graphyne make it to be competitive for the next-generation electronic devices at nanoscale.
Collapse of Incoherent Light Beams in Inertial Bulk Kerr Media
DEFF Research Database (Denmark)
Bang, Ole; Edmundson, Darran; Królikowski, Wieslaw
1999-01-01
We use the coherent density function theory to show that partially coherent beams are unstable and may collapse in inertial bulk Kerr media. The threshold power for collapse, and its dependence on the degree of coherence, is found analytically and checked-numerically. The internal dynamics of the...... of the walk-off modes is illustrated for collapsing and diffracting partially coherent beams.......We use the coherent density function theory to show that partially coherent beams are unstable and may collapse in inertial bulk Kerr media. The threshold power for collapse, and its dependence on the degree of coherence, is found analytically and checked-numerically. The internal dynamics...
Gravitational collapse from smooth initial data with vanishing radial pressure
International Nuclear Information System (INIS)
Mahajan, Ashutosh; Goswami, Rituparno; Joshi, Pankaj S
2005-01-01
We study here the spherical gravitational collapse assuming initial data to be necessarily smooth, as motivated by requirements based on physical reasonableness. A tangential pressure model is constructed and analysed in order to understand the final fate of collapse explicitly in terms of the density and pressure parameters at the initial epoch from which the collapse develops. It is seen that both black holes and naked singularities are produced as collapse end states even when the initial data are smooth. We show that the outcome is decided entirely in terms of the initial data, as given by density, pressure and velocity profiles at the initial epoch, from which the collapse evolves
CRALBP supports the mammalian retinal visual cycle and cone vision
Xue, Yunlu; Shen, Susan Q.; Jui, Jonathan; Rupp, Alan C.; Byrne, Leah C.; Hattar, Samer; Flannery, John G.; Corbo, Joseph C.; Kefalov, Vladimir J.
2015-01-01
Mutations in the cellular retinaldehyde-binding protein (CRALBP, encoded by RLBP1) can lead to severe cone photoreceptor-mediated vision loss in patients. It is not known how CRALBP supports cone function or how altered CRALBP leads to cone dysfunction. Here, we determined that deletion of Rlbp1 in mice impairs the retinal visual cycle. Mice lacking CRALBP exhibited M-opsin mislocalization, M-cone loss, and impaired cone-driven visual behavior and light responses. Additionally, M-cone dark ad...
Respiratory correlated cone beam CT
International Nuclear Information System (INIS)
Sonke, Jan-Jakob; Zijp, Lambert; Remeijer, Peter; Herk, Marcel van
2005-01-01
A cone beam computed tomography (CBCT) scanner integrated with a linear accelerator is a powerful tool for image guided radiotherapy. Respiratory motion, however, induces artifacts in CBCT, while the respiratory correlated procedures, developed to reduce motion artifacts in axial and helical CT are not suitable for such CBCT scanners. We have developed an alternative respiratory correlated procedure for CBCT and evaluated its performance. This respiratory correlated CBCT procedure consists of retrospective sorting in projection space, yielding subsets of projections that each corresponds to a certain breathing phase. Subsequently, these subsets are reconstructed into a four-dimensional (4D) CBCT dataset. The breathing signal, required for respiratory correlation, was directly extracted from the 2D projection data, removing the need for an additional respiratory monitor system. Due to the reduced number of projections per phase, the contrast-to-noise ratio in a 4D scan reduced by a factor 2.6-3.7 compared to a 3D scan based on all projections. Projection data of a spherical phantom moving with a 3 and 5 s period with and without simulated breathing irregularities were acquired and reconstructed into 3D and 4D CBCT datasets. The positional deviations of the phantoms center of gravity between 4D CBCT and fluoroscopy were small: 0.13±0.09 mm for the regular motion and 0.39±0.24 mm for the irregular motion. Motion artifacts, clearly present in the 3D CBCT datasets, were substantially reduced in the 4D datasets, even in the presence of breathing irregularities, such that the shape of the moving structures could be identified more accurately. Moreover, the 4D CBCT dataset provided information on the 3D trajectory of the moving structures, absent in the 3D data. Considerable breathing irregularities, however, substantially reduces the image quality. Data presented for three different lung cancer patients were in line with the results obtained from the phantom study. In
Collapse of the wave function models, ontology, origin, and implications
2018-01-01
This is the first single volume about the collapse theories of quantum mechanics, which is becoming a very active field of research in both physics and philosophy. In standard quantum mechanics, it is postulated that when the wave function of a quantum system is measured, it no longer follows the Schrödinger equation, but instantaneously and randomly collapses to one of the wave functions that correspond to definite measurement results. However, why and how a definite measurement result appears is unknown. A promising solution to this problem are collapse theories in which the collapse of the wave function is spontaneous and dynamical. Chapters written by distinguished physicists and philosophers of physics discuss the origin and implications of wave-function collapse, the controversies around collapse models and their ontologies, and new arguments for the reality of wave function collapse. This is an invaluable resource for students and researchers interested in the philosophy of physics and foundations of ...
Jordan, Nina J.; Rotolo, Silvio G.; Williams, Rebecca; Speranza, Fabio; McIntosh, William C.; Branney, Michael J.; Scaillet, Stéphane
2018-01-01
A new, pre-Green Tuff (46 ka) volcanic stratigraphy is presented for the peralkaline Pantelleria Volcano, Italy. New 40Ar/39Ar and paleomagnetic data are combined with detailed field studies to develop a comprehensive stratigraphic reconstruction of the island. We find that the pre-46 ka succession is characterised by eight silica-rich peralkaline (trachyte to pantellerite) ignimbrites, many of which blanketed the entire island. The ignimbrites are typically welded to rheomorphic, and are commonly associated with lithic breccias and/or pumice deposits. They record sustained radial pyroclastic density currents fed by low pyroclastic fountains. The onset of ignimbrite emplacement is typically preceded (more rarely followed) by pumice fallout with limited dispersal, and some eruptions lack any associated pumice fall deposit, suggesting the absence of tall eruption columns. Particular attention is given to the correlation of well-developed lithic breccias in the ignimbrites, interpreted as probable tracers of caldera collapses. They record as many as five caldera collapse events, in contrast to the two events reported to date. Inter-ignimbrite periods are characterised by explosive and effusive eruptions with limited dispersal, such as small pumice cones, as well as pedogenesis. These periods have similar characteristics as the current post-Green Tuff activity on the island, and, while not imminent, it is reasonable to postulate the occurrence of another ignimbrite-forming eruption sometime in the future.
Combining slope stability and groundwater flow models to assess stratovolcano collapse hazard
Ball, J. L.; Taron, J.; Reid, M. E.; Hurwitz, S.; Finn, C.; Bedrosian, P.
2016-12-01
Flank collapses are a well-documented hazard at volcanoes. Elevated pore-fluid pressures and hydrothermal alteration are invoked as potential causes for the instability in many of these collapses. Because pore pressure is linked to water saturation and permeability of volcanic deposits, hydrothermal alteration is often suggested as a means of creating low-permeability zones in volcanoes. Here, we seek to address the question: What alteration geometries will produce elevated pore pressures in a stratovolcano, and what are the effects of these elevated pressures on slope stability? We initially use a finite element groundwater flow model (a modified version of OpenGeoSys) to simulate `generic' stratovolcano geometries that produce elevated pore pressures. We then input these results into the USGS slope-stability code Scoops3D to investigate the effects of alteration and magmatic intrusion on potential flank failure. This approach integrates geophysical data about subsurface alteration, water saturation and rock mechanical properties with data about precipitation and heat influx at Cascade stratovolcanoes. Our simulations show that it is possible to maintain high-elevation water tables in stratovolcanoes given specific ranges of edifice permeability (ideally between 10-15 and 10-16 m2). Low-permeability layers (10-17 m2, representing altered pyroclastic deposits or altered breccias) in the volcanoes can localize saturated regions close to the surface, but they may actually reduce saturation, pore pressures, and water table levels in the core of the volcano. These conditions produce universally lower factor-of-safety (F) values than at an equivalent dry edifice with the same material properties (lower values of F indicate a higher likelihood of collapse). When magmatic intrusions into the base of the cone are added, near-surface pore pressures increase and F decreases exponentially with time ( 7-8% in the first year). However, while near-surface impermeable layers
Finding strong lenses in CFHTLS using convolutional neural networks
Jacobs, C.; Glazebrook, K.; Collett, T.; More, A.; McCarthy, C.
2017-10-01
We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62 406 simulated lenses and 64 673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 deg2 of the CFHTLS wide field image data, identifying 18 861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early-type galaxies selected from the survey catalogue as potential deflectors, identified 2465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21-28 per cent with respect to detectable lenses and a purity of 15 per cent, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.
Wei, Jianing; Bouman, Charles A; Allebach, Jan P
2014-05-01
Many imaging applications require the implementation of space-varying convolution for accurate restoration and reconstruction of images. Here, we use the term space-varying convolution to refer to linear operators whose impulse response has slow spatial variation. In addition, these space-varying convolution operators are often dense, so direct implementation of the convolution operator is typically computationally impractical. One such example is the problem of stray light reduction in digital cameras, which requires the implementation of a dense space-varying deconvolution operator. However, other inverse problems, such as iterative tomographic reconstruction, can also depend on the implementation of dense space-varying convolution. While space-invariant convolution can be efficiently implemented with the fast Fourier transform, this approach does not work for space-varying operators. So direct convolution is often the only option for implementing space-varying convolution. In this paper, we develop a general approach to the efficient implementation of space-varying convolution, and demonstrate its use in the application of stray light reduction. Our approach, which we call matrix source coding, is based on lossy source coding of the dense space-varying convolution matrix. Importantly, by coding the transformation matrix, we not only reduce the memory required to store it; we also dramatically reduce the computation required to implement matrix-vector products. Our algorithm is able to reduce computation by approximately factoring the dense space-varying convolution operator into a product of sparse transforms. Experimental results show that our method can dramatically reduce the computation required for stray light reduction while maintaining high accuracy.
Thermal and Chemical Evolution of Collapsing Filaments
Energy Technology Data Exchange (ETDEWEB)
Gray, William J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Scannapieco, Evan [Arizona State Univ., Mesa, AZ (United States). School of Earth and Space Exploration
2013-01-15
Intergalactic filaments form the foundation of the cosmic web that connect galaxies together, and provide an important reservoir of gas for galaxy growth and accretion. Here we present very high resolution two-dimensional simulations of the thermal and chemical evolution of such filaments, making use of a 32 species chemistry network that tracks the evolution of key molecules formed from hydrogen, oxygen, and carbon. We study the evolution of filaments over a wide range of parameters including the initial density, initial temperature, strength of the dissociating UV background, and metallicity. In low-redshift, Z ≈ 0.1Z_{⊙} filaments, the evolution is determined completely by the initial cooling time. If this is sufficiently short, the center of the filament always collapses to form dense, cold core containing a substantial fraction of molecules. In high-redshift, Z = 10^{-3}Z_{⊙} filaments, the collapse proceeds much more slowly. This is due mostly to the lower initial temperatures, which leads to a much more modest increase in density before the atomic cooling limit is reached, making subsequent molecular cooling much less efficient. Finally, we study how the gravitational potential from a nearby dwarf galaxy affects the collapse of the filament and compare this to NGC 5253, a nearby starbusting dwarf galaxy thought to be fueled by the accretion of filament gas. In contrast to our fiducial case, a substantial density peak forms at the center of the potential. This peak evolves faster than the rest of the filament due to the increased rate at which chemical species form and cooling occur. We find that we achieve similar accretion rates as NGC 5253, but our two-dimensional simulations do not recover the formation of the giant molecular clouds that are seen in radio observations.
Holographic probes of collapsing black holes
International Nuclear Information System (INIS)
Hubeny, Veronika E.; Maxfield, Henry
2014-01-01
We continue the programme of exploring the means of holographically decoding the geometry of spacetime inside a black hole using the gauge/gravity correspondence. To this end, we study the behaviour of certain extremal surfaces (focusing on those relevant for equal-time correlators and entanglement entropy in the dual CFT) in a dynamically evolving asymptotically AdS spacetime, specifically examining how deep such probes reach. To highlight the novel effects of putting the system far out of equilibrium and at finite volume, we consider spherically symmetric Vaidya-AdS, describing black hole formation by gravitational collapse of a null shell, which provides a convenient toy model of a quantum quench in the field theory. Extremal surfaces anchored on the boundary exhibit rather rich behaviour, whose features depend on dimension of both the spacetime and the surface, as well as on the anchoring region. The main common feature is that they reach inside the horizon even in the post-collapse part of the geometry. In 3-dimensional spacetime, we find that for sub-AdS-sized black holes, the entire spacetime is accessible by the restricted class of geodesics whereas in larger black holes a small region near the imploding shell cannot be reached by any boundary-anchored geodesic. In higher dimensions, the deepest reach is attained by geodesics which (despite being asymmetric) connect equal time and antipodal boundary points soon after the collapse; these can attain spacetime regions of arbitrarily high curvature and simultaneously have smallest length. Higher-dimensional surfaces can penetrate the horizon while anchored on the boundary at arbitrarily late times, but are bounded away from the singularity. We also study the details of length or area growth during thermalization. While the area of extremal surfaces increases monotonically, geodesic length is neither monotonic nor continuous
Gravitational collapse of conventional polytropic cylinder
Lou, Yu-Qing; Hu, Xu-Yao
2017-07-01
In reference to general polytropic and conventional polytropic hydrodynamic cylinders of infinite length with axial uniformity and axisymmetry under self-gravity, the dynamic evolution of central collapsing mass string in free-fall dynamic accretion phase is re-examined in details. We compare the central mass accretion rate and the envelope mass infall rate at small radii. Among others, we correct mistakes and typos of Kawachi & Hanawa (KH hereafter) and in particular prove that their key asymptotic free-fall solution involving polytropic index γ in the two power exponents is erroneous by analytical analyses and numerical tests. The correct free-fall asymptotic solutions at sufficiently small \\hat{r} (the dimensionless independent self-similar variable) scale as {˜ } -|ln \\hat{r}|^{1/2} in contrast to KH's ˜ -|ln \\hat{r}|^{(2-γ )/2} for the reduced bulk radial flow velocity and as {˜ } \\hat{r}^{-1}|ln \\hat{r}|^{-1/2} in contrast to KH's {˜ } \\hat{r}^{-1} |ln \\hat{r}|^{-(2-γ )/2} for the reduced mass density. We offer consistent scenarios for numerical simulation code testing and theoretical study on dynamic filamentary structure formation and evolution as well as pertinent stability properties. Due to unavoidable Jeans instabilities along the cylinder, such collapsing massive filaments or strings can further break up into clumps and segments of various lengths as well as clumps embedded within segments and evolve into chains of gravitationally collapsed objects (such as gaseous planets, brown dwarfs, protostars, white dwarfs, neutron stars, black holes in a wide mass range, globular clusters, dwarf spheroidals, galaxies, galaxy clusters and even larger mass reservoirs etc.) in various astrophysical and cosmological contexts as articulated by Lou & Hu recently. As an example, we present a model scheme for comparing with observations of molecular filaments for forming protostars, brown dwarfs and gaseous planets and so forth.
Dörken, Veit Martin; Rudall, Paula J
2018-01-01
Both wild-type and teratological seed cones are described in the monoecious conifer Glyptostrobus pensilis and compared with those of other Cupressaceae sensu lato and other conifers. Some Cupressaceae apparently possess a proliferation of axillary structures in their cone scales. In our interpretation, in Glyptostrobus each bract of both typical and atypical seed cones bears two descending accessory shoots, interpreted here as seed scales (ovuliferous scales). The primary seed scale is fertile and forms the ovules, the second is sterile and forms characteristic tooth-like structures. The bract and the two axillary seed scales are each supplied with a single distinct vascular bundle that enters the cone axis as a separate strand; this vasculature also characterises the descending accessory short shoots in the vegetative parts of the crown. In wild-type seed cones, the fertile seed scale is reduced to its ovules, and the ovules are always axillary. In contrast, the ovules of some of the teratological seed cones examined were located at the centre of the cone scale. An additional tissue found on the upper surface of the sterile lower seed scale is here interpreted as the axis of the fertile seed scale. Thus, the central position of the ovules can be explained by recaulescent fusion of the upper fertile and lower sterile seed scales. In several teratological cone scales, the ovules were enveloped by an additional sterile tissue that is uniseriate and represents an epidermal outgrowth of the fertile seed scale. Close to the ovules, the epidermis was detached from lower tissue and surrounded the ovule completely, except at the micropyle. These teratological features are potentially significant in understanding seed-cone homologies among extant conifers.
Directory of Open Access Journals (Sweden)
Veit Martin Dörken
2018-06-01
Full Text Available Both wild-type and teratological seed cones are described in the monoecious conifer Glyptostrobus pensilis and compared with those of other Cupressaceae sensu lato and other conifers. Some Cupressaceae apparently possess a proliferation of axillary structures in their cone scales. In our interpretation, in Glyptostrobus each bract of both typical and atypical seed cones bears two descending accessory shoots, interpreted here as seed scales (ovuliferous scales. The primary seed scale is fertile and forms the ovules, the second is sterile and forms characteristic tooth-like structures. The bract and the two axillary seed scales are each supplied with a single distinct vascular bundle that enters the cone axis as a separate strand; this vasculature also characterises the descending accessory short shoots in the vegetative parts of the crown. In wild-type seed cones, the fertile seed scale is reduced to its ovules, and the ovules are always axillary. In contrast, the ovules of some of the teratological seed cones examined were located at the centre of the cone scale. An additional tissue found on the upper surface of the sterile lower seed scale is here interpreted as the axis of the fertile seed scale. Thus, the central position of the ovules can be explained by recaulescent fusion of the upper fertile and lower sterile seed scales. In several teratological cone scales, the ovules were enveloped by an additional sterile tissue that is uniseriate and represents an epidermal outgrowth of the fertile seed scale. Close to the ovules, the epidermis was detached from lower tissue and surrounded the ovule completely, except at the micropyle. These teratological features are potentially significant in understanding seed-cone homologies among extant conifers.
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.
Phase transitions in glassy systems via convolutional neural networks
Fang, Chao
Machine learning is a powerful approach commonplace in industry to tackle large data sets. Most recently, it has found its way into condensed matter physics, allowing for the first time the study of, e.g., topological phase transitions and strongly-correlated electron systems. The study of spin glasses is plagued by finite-size effects due to the long thermalization times needed. Here we use convolutional neural networks in an attempt to detect a phase transition in three-dimensional Ising spin glasses. Our results are compared to traditional approaches.
Visualizing Vector Fields Using Line Integral Convolution and Dye Advection
Shen, Han-Wei; Johnson, Christopher R.; Ma, Kwan-Liu
1996-01-01
We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.
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.
Salient regions detection using convolutional neural networks and color volume
Liu, Guang-Hai; Hou, Yingkun
2018-03-01
Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.
Traffic sign classification with dataset augmentation and convolutional neural network
Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun
2018-04-01
This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.
Tandem mass spectrometry data quality assessment by self-convolution
Directory of Open Access Journals (Sweden)
Tham Wai
2007-09-01
Full Text Available Abstract Background Many algorithms have been developed for deciphering the tandem mass spectrometry (MS data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. Results The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. Conclusion We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the
The Use of Finite Fields and Rings to Compute Convolutions
1975-06-06
showed in Ref. 1 that the convolution of two finite sequences of integers (a, ) and (b, ) for k = 1, 2, . . ., d can be obtained as the inverse transform of...since the T.’S are all distinct. Thus T~ exists and (7) can be solved as a = T A the inverse " transform . Next let us impose on (7) the...the inverse transform d-1 Cn= (d) I Cka k=0 If an a can be found so that multiplications by powers of a are simple in hardware, the
Tandem mass spectrometry data quality assessment by self-convolution.
Choo, Keng Wah; Tham, Wai Mun
2007-09-20
Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified. The proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores. We have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well
Classifying medical relations in clinical text via convolutional neural networks.
He, Bin; Guan, Yi; Dai, Rui
2018-05-16
Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
DEFF Research Database (Denmark)
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl
2018-01-01
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditi...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....
Rai, Nirmal Kumar; Schmidt, Martin J.; Udaykumar, H. S.
2017-04-01
Void collapse in energetic materials leads to hot spot formation and enhanced sensitivity. Much recent work has been directed towards simulation of collapse-generated reactive hot spots. The resolution of voids in calculations to date has varied as have the resulting predictions of hot spot intensity. Here we determine the required resolution for reliable cylindrical void collapse calculations leading to initiation of chemical reactions. High-resolution simulations of collapse provide new insights into the mechanism of hot spot generation. It is found that initiation can occur in two different modes depending on the loading intensity: Either the initiation occurs due to jet impact at the first collapse instant or it can occur at secondary lobes at the periphery of the collapsed void. A key observation is that secondary lobe collapse leads to large local temperatures that initiate reactions. This is due to a combination of a strong blast wave from the site of primary void collapse and strong colliding jets and vortical flows generated during the collapse of the secondary lobes. The secondary lobe collapse results in a significant lowering of the predicted threshold for ignition of the energetic material. The results suggest that mesoscale simulations of void fields may suffer from significant uncertainty in threshold predictions because unresolved calculations cannot capture the secondary lobe collapse phenomenon. The implications of this uncertainty for mesoscale simulations are discussed in this paper.
Resonance in a Cone-Topped Tube
Directory of Open Access Journals (Sweden)
Angus Cheng-Huan Chia
2011-06-01
Full Text Available The relationship between ratio of the upper opening diameter of a cone-topped cylinder to the cylinder diameter,and the ratio of the length of the air column to resonant period was examined. Plastic cones with upper openings ranging from 1.3 cm to 3.6 cm and tuning forks with frequencies ranging from 261.6 Hz to 523.3 Hz were used. The transition from a standing wave in a cylindrical column to a Helmholtz-type resonance in a resonant cavity with a narrow opening was observed.
Cone-based Electrical Resistivity Tomography
Pidlisecky, A.; Knight, R.; Haber, E.
2005-05-01
Determining the 3D spatial distribution of subsurface properties is a critical part of managing the clean-up of contaminated sites. Most standard hydrologic methods sample small regions immediately adjacent to wells or testing devices. This provides data which are not representative of the entire region of interest. Furthermore, at many contaminated sites invasive methods are not acceptable, due to the risks associated with contacting and spreading the contaminants. To address these issues, we have developed a minimally invasive technology that provides information about the 3D distribution of electrical conductivity. This new technique, cone-based electrical resistivity tomography (C-bert), involves placing several permanent current electrodes in the subsurface and using electrodes mounted on a cone penetrometer to measure the resultant potential field while advancing the cone into the subsurface. In addition to potential field measurements, we obtain the standard suite of cone-penetration measurements, including high resolution resistivity logs; these data can then be used to constrain the inversion of the potential field data. A major challenge of working with these data is that the cone penetrometer is highly conductive, and thus presents a large local perturbation around the measurement location. As the cone is very small (approximately 30mm in diameter) with respect to the total model space, explicitly modeling the cone is computationally demanding. We developed a method for solving the forward model that reduces computational time by an order of magnitude. This solution method, iteratively determined boundary conditions, makes it possible to correct for the cone effect before inversion of the data. Results from synthetic experiments suggest that the C-bert method of data acquisition can result in high quality electrical conductivity images of the subsurface. We tested the practicality of this technique by performing a field test of the C-bert system to image
Electromagnetic wave collapse in a radiation background
International Nuclear Information System (INIS)
Marklund, Mattias; Brodin, Gert; Stenflo, Lennart
2003-01-01
The nonlinear interaction, due to quantum electrodynamical (QED) effects between an electromagnetic pulse and a radiation background, is investigated by combining the methods of radiation hydrodynamics with the QED theory for photon-photon scattering. For the case of a single coherent electromagnetic pulse, we obtain a Zakharov-like system, where the radiation pressure of the pulse acts as a driver of acoustic waves in the photon gas. For a sufficiently intense pulse and/or background energy density, there is focusing and the subsequent collapse of the pulse. The relevance of our results for various astrophysical applications are discussed
Formation and collapse of internal transport barrier
International Nuclear Information System (INIS)
Fukuyama, A.; Itoh, K.; Itoh, S.I.; Yagi, M.
1999-01-01
A theoretical model of internal transport barrier (ITB) is developed. The transport model based on the self-sustained turbulence theory of the current-diffusive ballooning mode is extended to include the effects of ExB rotation shear. Delayed formation of ITB is observed in transport simulations. The influence of finite gyroradius is also discussed. Simulation of the current ramp-up experiment successfully described the radial profile of density, temperature and safety factor. A model of ITB collapse due to magnetic braiding is proposed. Sudden enhancement of transport triggered by overlapping of magnetic islands terminates ITB. The possibility of destabilizing global low-n modes is also discussed. (author)
Formation and collapse of internal transport barrier
International Nuclear Information System (INIS)
Fukuyama, A.; Itoh, K.; Itoh, S.-I.; Yagi, M.
2001-01-01
A theoretical model of internal transport barrier (ITB) is developed. The transport model based on the self-sustained turbulence theory of the current-diffusive ballooning mode is extended to include the effects of ExB rotation shear. Delayed formation of ITB is observed in transport simulations. The influence of finite gyroradius is also discussed. Simulation of the current ramp-up experiment successfully described the radial profile of density, temperature and safety factor. A model of ITB collapse due to magnetic braiding is proposed. Sudden enhancement of transport triggered by overlaping of magnetic islands terminates ITB. The possibility of destabilizing global low-n modes is also discussed. (author)
Shinde, R. B.; Mali, K. D.
2018-04-01
Collapsible impact energy absorbers play an important role of protecting automotive components from damage during collision. Collision of the two objects results into the damage to one or both of them. Damage may be in the form of crack, fracture and scratch. Designers must know about how the material and object behave under impact event. Owing to above reasons different types of collapsible impact energy absorbers are developed. In the past different studies were undertaken to improve such collapsible impact energy absorbers. This article highlights such studies on common shapes of collapsible impact energy absorber and their impact behaviour under the axial compression. The literature based on studies and analyses of effects of different geometrical parameters on the crushing behaviour of impact energy absorbers is presented in detail. The energy absorber can be of different shape such as circular tube, square tube, and frustums of cone and pyramids. The crushing behaviour of energy absorbers includes studies on crushing mechanics, modes of deformation, energy absorbing capacity, effect on peak and mean crushing load. In this work efforts are made to cover major outcomes from past studies on such behavioural parameters. Even though the major literature reviewed is related to metallic energy absorbers, emphasis is also laid on covering literature on use of composite tube, fiber metal lamination (FML) member, honeycomb plate and functionally graded thickness (FGT) tube as a collapsible impact energy absorber.
Critical condition for the transformation from Taylor cone to cone-jet
International Nuclear Information System (INIS)
Wei Cheng; Zhao Yang; Gang Tie-Qiang; Chen Li-Jie
2014-01-01
An energy method is proposed to investigate the critical transformation condition from a Taylor cone to a cone-jet. Based on the kinetic theorem, the system power allocation and the electrohydrodynamics stability are discussed. The numerical results indicate that the energy of the liquid cone tip experiences a maximum value during the transformation. With the proposed jetting energy, we give the critical transformation condition under which the derivative of jetting energy with respect to the surface area is greater than or equal to the energy required to form a unit of new liquid surface
Jordan's algebra of a facially homogeneous autopolar cone
International Nuclear Information System (INIS)
Bellissard, Jean; Iochum, Bruno
1979-01-01
It is shown that a Jordan-Banach algebra with predual may be canonically associated with a facially homogeneous autopolar cone. This construction generalizes the case where a trace vector exists in the cone [fr
Karst collapse in cities and mining areas, China
International Nuclear Information System (INIS)
Jian Chen
1988-01-01
Karst collapse is a dynamic geological phenomenon, in which the rock mass or deposits overlying the karstified zone subsides down along the karst cavity, resulting in a collapse pit or sinkhole. After discussing the typical examples of collapse emerging in the karst cities and mines in provinces and regions of South China, such as Guangdong, Guangxi, Hunan, Hubei, Zhejiang, Yunnan, Guizhou, and Jiangxi, it is considered that human activities of economy and production have become a major effect in causing karst collapse. Man-made collapses make 66.4 percent of the total, whereas natural ones 33.6 percent. Most of the collapses occurred to the area with soil overburden (96.7 percent), only a few in areas of bedrock overburden (3.3 percent). The karst collapses have a close relationship with the extent of karst development, the character and the thickness of overburden, and the dynamic condition of underground water. Collapse usually occurs in those parts of an area that are more intensely karstified, with soil thickness less than 5 m and a high amplitude of water table fluctuation. Many kinds of mechanical effects are caused by pumping or draining on the over-burden and destroying its equilibrium, leading to the collapse. These effects included the support loss and load-added effect, penetrating suffusion, gas explosion, water-hammer, suction pressure erosion, and liquefaction effects. The collapses are the result of varied comprehensive effects, particularly the support loss and load-added, and penetrating suffusion
Integrity of the cone photoreceptor mosaic in oligocone trichromacy
DEFF Research Database (Denmark)
Michaelides, Michel; Rha, Jungtae; Dees, Elise W
2011-01-01
Oligocone trichromacy (OT) is an unusual cone dysfunction syndrome characterized by reduced visual acuity, mild photophobia, reduced amplitude of the cone electroretinogram with normal rod responses, normal fundus appearance, and normal or near-normal color vision. It has been proposed that these...... that these patients have a reduced number of normal functioning cones (oligocone). This paper has sought to evaluate the integrity of the cone photoreceptor mosaic in four patients previously described as having OT....
A MacWilliams Identity for Convolutional Codes: The General Case
Gluesing-Luerssen, Heide; Schneider, Gert
2008-01-01
A MacWilliams Identity for convolutional codes will be established. It makes use of the weight adjacency matrices of the code and its dual, based on state space realizations (the controller canonical form) of the codes in question. The MacWilliams Identity applies to various notions of duality appearing in the literature on convolutional coding theory.
Isointense infant brain MRI segmentation with a dilated convolutional neural network
Moeskops, P.; Pluim, J.P.W.
2017-01-01
Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation
An upper bound on the number of errors corrected by a convolutional code
DEFF Research Database (Denmark)
Justesen, Jørn
2000-01-01
The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length.......The number of errors that a convolutional codes can correct in a segment of the encoded sequence is upper bounded by the number of distinct syndrome sequences of the relevant length....
Linear diffusion-wave channel routing using a discrete Hayami convolution method
Li Wang; Joan Q. Wu; William J. Elliot; Fritz R. Feidler; Sergey. Lapin
2014-01-01
The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces...
Using convolutional decoding to improve time delay and phase estimation in digital communications
Ormesher, Richard C [Albuquerque, NM; Mason, John J [Albuquerque, NM
2010-01-26
The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.
Anomalous polymer collapse winding angle distributions
Narros, A.; Owczarek, A. L.; Prellberg, T.
2018-03-01
In two dimensions polymer collapse has been shown to be complex with multiple low temperature states and multi-critical points. Recently, strong numerical evidence has been provided for a long-standing prediction of universal scaling of winding angle distributions, where simulations of interacting self-avoiding walks show that the winding angle distribution for N-step walks is compatible with the theoretical prediction of a Gaussian with a variance growing asymptotically as Clog N . Here we extend this work by considering interacting self-avoiding trails which are believed to be a model representative of some of the more complex behaviour. We provide robust evidence that, while the high temperature swollen state of this model has a winding angle distribution that is also Gaussian, this breaks down at the polymer collapse point and at low temperatures. Moreover, we provide some evidence that the distributions are well modelled by stretched/compressed exponentials, in contradistinction to the behaviour found in interacting self-avoiding walks. Dedicated to Professor Stu Whittington on the occasion of his 75th birthday.
Design and Analysis of Collapsible Scissor Bridge
Directory of Open Access Journals (Sweden)
Biro Mohamad Nabil Aklif
2018-01-01
Full Text Available Collapsible scissor bridge is a portable bridge that can be deployed during emergency state to access remote areas that are affected by disaster such as flood. The objective of this research is to design a collapsible scissor bridge which is able to be transported by a 4x4 vehicle and to be deployed to connect remote areas. The design is done by using Solidworks and numerical analysis for structural strength is conducted via ANSYS. The research starts with parameters setting and modelling. Finite element analysis is conducted to analyze the strength by determining the safety factor of the bridge. Kutzbach equation is also analyzed to ensure that the mechanism is able to meet the targeted degree of motion. There are five major components of the scissor structure; pin, deck, cross shaft and deck shaft. The structure is controlled by hydraulic pump driven by a motor for the motions. Material used in simulation is A36 structural steel due to limited library in ANSYS. However, the proposed material is Fiber Reinforced Polymer (FRP composites as they have a high strength to weight ratio. FRP also tends to be corrosion resistance and this characteristic is useful in flooded area.
Classifying images using restricted Boltzmann machines and convolutional neural networks
Zhao, Zhijun; Xu, Tongde; Dai, Chenyu
2017-07-01
To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.
Cloud Detection by Fusing Multi-Scale Convolutional Features
Li, Zhiwei; Shen, Huanfeng; Wei, Yancong; Cheng, Qing; Yuan, Qiangqiang
2018-04-01
Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy of cloud detection for multispectral imagery, especially for those that contain only visible and near infrared bands, in this paper, we proposed a deep learning based cloud detection method termed MSCN (multi-scale cloud net), which segments cloud by fusing multi-scale convolutional features. MSCN was trained on a global cloud cover validation collection, and was tested in more than ten types of optical images with different resolution. Experiment results show that MSCN has obvious advantages over the traditional multi-feature combined cloud detection method in accuracy, especially when in snow and other areas covered by bright non-cloud objects. Besides, MSCN produced more detailed cloud masks than the compared deep cloud detection convolution network. The effectiveness of MSCN make it promising for practical application in multiple kinds of optical imagery.
Real-Time Video Convolutional Face Finder on Embedded Platforms
Directory of Open Access Journals (Sweden)
Mamalet Franck
2007-01-01
Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.
Enhancing neutron beam production with a convoluted moderator
Energy Technology Data Exchange (ETDEWEB)
Iverson, E.B., E-mail: iversoneb@ornl.gov [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Baxter, D.V. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Muhrer, G. [Lujan Neutron Scattering Center, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM 87545 (United States); Ansell, S.; Dalgliesh, R. [ISIS Facility, Rutherford Appleton Laboratory, Chilton (United Kingdom); Gallmeier, F.X. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Kaiser, H. [Center for the Exploration of Energy and Matter, Indiana University, Bloomington, IN 47408 (United States); Lu, W. [Spallation Neutron Source, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)
2014-10-21
We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally enhanced neutron beam source, improving beam emission over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.
Photon beam convolution using polyenergetic energy deposition kernels
International Nuclear Information System (INIS)
Hoban, P.W.; Murray, D.C.; Round, W.H.
1994-01-01
In photon beam convolution calculations where polyenergetic energy deposition kernels (EDKs) are used, the primary photon energy spectrum should be correctly accounted for in Monte Carlo generation of EDKs. This requires the probability of interaction, determined by the linear attenuation coefficient, μ, to be taken into account when primary photon interactions are forced to occur at the EDK origin. The use of primary and scattered EDKs generated with a fixed photon spectrum can give rise to an error in the dose calculation due to neglecting the effects of beam hardening with depth. The proportion of primary photon energy that is transferred to secondary electrons increases with depth of interaction, due to the increase in the ratio μ ab /μ as the beam hardens. Convolution depth-dose curves calculated using polyenergetic EDKs generated for the primary photon spectra which exist at depths of 0, 20 and 40 cm in water, show a fall-off which is too steep when compared with EGS4 Monte Carlo results. A beam hardening correction factor applied to primary and scattered 0 cm EDKs, based on the ratio of kerma to terma at each depth, gives primary, scattered and total dose in good agreement with Monte Carlo results. (Author)
Multi-Branch Fully Convolutional Network for Face Detection
Bai, Yancheng
2017-07-20
Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Directory of Open Access Journals (Sweden)
Haiyang Yu
2017-06-01
Full Text Available Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs, for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs and long short-term memory (LSTM neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.
Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.
Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita
2018-03-01
Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.
Multi-Input Convolutional Neural Network for Flower Grading
Directory of Open Access Journals (Sweden)
Yu Sun
2017-01-01
Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.
Convolutional neural network architectures for predicting DNA–protein binding
Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.
2016-01-01
Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608
Transforming Musical Signals through a Genre Classifying Convolutional Neural Network
Geng, S.; Ren, G.; Ogihara, M.
2017-05-01
Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.
Siamese convolutional networks for tracking the spine motion
Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong
2017-09-01
Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.
Real-Time Video Convolutional Face Finder on Embedded Platforms
Directory of Open Access Journals (Sweden)
Franck Mamalet
2007-03-01
Full Text Available A high-level optimization methodology is applied for implementing the well-known convolutional face finder (CFF algorithm for real-time applications on mobile phones, such as teleconferencing, advanced user interfaces, image indexing, and security access control. CFF is based on a feature extraction and classification technique which consists of a pipeline of convolutions and subsampling operations. The design of embedded systems requires a good trade-off between performance and code size due to the limited amount of available resources. The followed methodology copes with the main drawbacks of the original implementation of CFF such as floating-point computation and memory allocation, in order to allow parallelism exploitation and perform algorithm optimizations. Experimental results show that our embedded face detection system can accurately locate faces with less computational load and memory cost. It runs on a 275 MHz Starcore DSP at 35 QCIF images/s with state-of-the-art detection rates and very low false alarm rates.
Digital image correlation based on a fast convolution strategy
Yuan, Yuan; Zhan, Qin; Xiong, Chunyang; Huang, Jianyong
2017-10-01
In recent years, the efficiency of digital image correlation (DIC) methods has attracted increasing attention because of its increasing importance for many engineering applications. Based on the classical affine optical flow (AOF) algorithm and the well-established inverse compositional Gauss-Newton algorithm, which is essentially a natural extension of the AOF algorithm under a nonlinear iterative framework, this paper develops a set of fast convolution-based DIC algorithms for high-efficiency subpixel image registration. Using a well-developed fast convolution technique, the set of algorithms establishes a series of global data tables (GDTs) over the digital images, which allows the reduction of the computational complexity of DIC significantly. Using the pre-calculated GDTs, the subpixel registration calculations can be implemented efficiently in a look-up-table fashion. Both numerical simulation and experimental verification indicate that the set of algorithms significantly enhances the computational efficiency of DIC, especially in the case of a dense data sampling for the digital images. Because the GDTs need to be computed only once, the algorithms are also suitable for efficiently coping with image sequences that record the time-varying dynamics of specimen deformations.
Convolutional neural network features based change detection in satellite images
Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong
2016-07-01
With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.
Classification of stroke disease using convolutional neural network
Marbun, J. T.; Seniman; Andayani, U.
2018-03-01
Stroke is a condition that occurs when the blood supply stop flowing to the brain because of a blockage or a broken blood vessel. A symptoms that happen when experiencing stroke, some of them is a dropped consciousness, disrupted vision and paralyzed body. The general examination is being done to get a picture of the brain part that have stroke using Computerized Tomography (CT) Scan. The image produced from CT will be manually checked and need a proper lighting by doctor to get a type of stroke. That is why it needs a method to classify stroke from CT image automatically. A method proposed in this research is Convolutional Neural Network. CT image of the brain is used as the input for image processing. The stage before classification are image processing (Grayscaling, Scaling, Contrast Limited Adaptive Histogram Equalization, then the image being classified with Convolutional Neural Network. The result then showed that the method significantly conducted was able to be used as a tool to classify stroke disease in order to distinguish the type of stroke from CT image.
Image Classification Based on Convolutional Denoising Sparse Autoencoder
Directory of Open Access Journals (Sweden)
Shuangshuang Chen
2017-01-01
Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm
2018-05-16
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.
Convolutional neural networks for vibrational spectroscopic data analysis.
Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena
2017-02-15
In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.
sEMG-Based Gesture Recognition with Convolution Neural Networks
Directory of Open Access Journals (Sweden)
Zhen Ding
2018-06-01
Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.
Development of a morphological convolution operator for bearing fault detection
Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan
2018-05-01
This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.
Deep multi-scale convolutional neural network for hyperspectral image classification
Zhang, Feng-zhe; Yang, Xia
2018-04-01
In this paper, we proposed a multi-scale convolutional neural network for hyperspectral image classification task. Firstly, compared with conventional convolution, we utilize multi-scale convolutions, which possess larger respective fields, to extract spectral features of hyperspectral image. We design a deep neural network with a multi-scale convolution layer which contains 3 different convolution kernel sizes. Secondly, to avoid overfitting of deep neural network, dropout is utilized, which randomly sleeps neurons, contributing to improve the classification accuracy a bit. In addition, new skills like ReLU in deep learning is utilized in this paper. We conduct experiments on University of Pavia and Salinas datasets, and obtained better classification accuracy compared with other methods.
Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures
Directory of Open Access Journals (Sweden)
Yun Ren
2018-01-01
Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.
Analog Experiment for rootless cone eruption
Noguchi, R.; Hamada, A.; Suzuki, A.; Kurita, K.
2017-09-01
Rootless cone is a unique geomorphological landmark to specify igneous origin of investigated terrane, which is formed by magma-water interaction. To understand its formation mechanism we conducted analog experiment for heat-induced vesiculation by using hot syrup and sodium bicarbonate solution.
Chloride equilibrium potential in salamander cones
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Bryson Eric J
2004-12-01
Full Text Available Abstract Background GABAergic inhibition and effects of intracellular chloride ions on calcium channel activity have been proposed to regulate neurotransmission from photoreceptors. To assess the impact of these and other chloride-dependent mechanisms on release from cones, the chloride equilibrium potential (ECl was determined in red-sensitive, large single cones from the tiger salamander retinal slice. Results Whole cell recordings were done using gramicidin perforated patch techniques to maintain endogenous Cl- levels. Membrane potentials were corrected for liquid junction potentials. Cone resting potentials were found to average -46 mV. To measure ECl, we applied long depolarizing steps to activate the calcium-activated chloride current (ICl(Ca and then determined the reversal potential for the current component that was inhibited by the Cl- channel blocker, niflumic acid. With this method, ECl was found to average -46 mV. In a complementary approach, we used a Cl-sensitive dye, MEQ, to measure the Cl- flux produced by depolarization with elevated concentrations of K+. The membrane potentials produced by the various high K+ solutions were measured in separate current clamp experiments. Consistent with electrophysiological experiments, MEQ fluorescence measurements indicated that ECl was below -36 mV. Conclusions The results of this study indicate that ECl is close to the dark resting potential. This will minimize the impact of chloride-dependent presynaptic mechanisms in cone terminals involving GABAa receptors, glutamate transporters and ICl(Ca.
Cone beam computed tomography in veterinary dentistry
van Thielen, B.; Siguenza, F.; Hassan, B.
2012-01-01
The purpose of this study was to assess the feasibility of cone beam computed tomography (CBCT) in imaging dogs and cats for diagnostic dental veterinary applications. CBCT scans of heads of six dogs and two cats were made. Dental panoramic and multi-planar reformatted (MPR) para-sagittal
Particle Identification in Cherenkov Detectors using Convolutional Neural Networks
Theodore, Tomalty
2016-01-01
Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.
Case of Unilateral Peripheral Cone Dysfunction
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Yujin Mochizuki
2012-05-01
Full Text Available Purpose: Peripheral cone dystrophy is a subgroup of cone dystrophy, and only 4 cases have been reported. We present a patient with unilateral peripheral cone dysfunction and report the functional changes determined by electrophysiological tests and ultrastructural changes determined by spectral domain optical coherence tomography (SD-OCT. Case: A 34-year-old woman complained of blurred vision in both eyes. Our examination showed that her visual acuity was 0.05 OD and 0.2 OS. A relative afferent pupillary defect was present in her right eye. The results of slit-lamp examination, ophthalmoscopy, and fluorescein angiography were normal except for pallor of the right optic disc. SD-OCT showed a diffuse thinning of the retina in the posterior pole of the right eye. A severe constriction of the visual fields was found in both eyes but more in the right eye. The photopic full-field electroretinograms (ERGs were reduced in the right eye but normal in the left eye. The multifocal ERGs were severely reduced throughout the visual field except in the central area of the right eye. The multifocal ERGs from the left eye were normal. The pattern visual evoked responses were within the normal range in both eyes. She had a 5-year history of sniffing paint thinner. Results: Although the visual dysfunction was initially suspected to be due to psychological problems from the results of subjective tests, objective tests indicated a peripheral cone dysfunction in the right eye. The pathophysiological mechanism and the relationship with thinner sniffing were not determined. Conclusions: Our findings indicate that peripheral cone dysfunction can occur unilaterally. Electrophysiology and SD-OCT are valuable tests to perform to determine the pathogenesis of unusual ocular findings objectively.
Non-Spherical Gravitational Collapse of Strange Quark Matter
Institute of Scientific and Technical Information of China (English)
Zade S S; Patil K D; Mulkalwar P N
2008-01-01
We study the non-spherical gravitational collapse of the strange quark null fluid.The interesting feature which emerges is that the non-spherical collapse of charged strange quark matter leads to a naked singularity whereas the gravitational collapse of neutral quark matter proceeds to form a black hole.We extend the earlier work of Harko and Cheng[Phys.Lett.A 266 (2000) 249]to the non-spherical case.
Collapsing dynamics of attractive Bose-Einstein condensates
DEFF Research Database (Denmark)
Bergé, L.; Juul Rasmussen, J.
2002-01-01
The self-similar collapse of 3D and quasi-2D atom condensates with negative scattering length is examined. 3D condensates are shown to blow up following the scenario of weak collapse, for which 3-body recombination weakly dissipates the atoms. In contrast, 2D condensates undergo a strong collapse......, that absorbs a significant amount of particles. (C) 2002 Elsevier Science B.V. All rights reserved....
Simulation of weak and strong Langmuir collapse regimes
International Nuclear Information System (INIS)
Hadzievski, L.R.; Skoric, M.M.; Kono, M.; Sato, T.
1998-01-01
In order to check the validity of the self-similar solutions and the existence of weak and strong collapse regimes, direct two dimensional simulation of the time evolution of a Langmuir soliton instability is performed. Simulation is based on the Zakharov model of strong Langmuir turbulence in a weakly magnetized plasma accounting for the full ion dynamics. For parameters considered, agreement with self-similar dynamics of the weak collapse type is found with no evidence of the strong Langmuir collapse. (author)
The collapse of acoustic waves in dispersive media
International Nuclear Information System (INIS)
Kuznetsov, E.A.; Musher, S.L.; Shafarenko, A.V.
1983-01-01
The existence of the collapse of acoustic waves with a positive dispersion is demonstrated. A qualitative description of wave collapse, based on the analysis of invariants, is proposed. Through the use of a numerical simulation, it is established that, in the Kadomtsev-Petviashvili three-dimensional equation, collapse is accompanied by the formation of a weakly turbulent background by the wave radiation from the cavity
Radiologic evaluation of right middle lobe collapse
International Nuclear Information System (INIS)
Kwun, Dae Young; Kim, Jong Deok; Kim, Jong Chul
1989-01-01
There are many pathogenetic factors for collapse of right middle lobe; profuse peribronchial clustering of lymph nodes about the right middle lobe bronchus, poor drainage of the bronchus because of its acute angle of take-off from the intermediate bronchus, and the isolation of this small lobe from the right upper and lower lobes, and thus from the aerating effects of collateral ventilation. Retrospectively we reviewed 36 cases of right of right middle lobe collapse of which causes were confirmed by histopathologic or bronchographic findings during the recent 6 years from March 1983 to February 1988 at Inje College Pusan Paik Hospital, and obtained the following results: 1. Male to female ratio was 1:1:4,and peak incidence (64%) was in the fifth and sixth decades with the mean age of 51.1 years. 2. Bronchiectasis was the most common cause (30.6%), and the others were chronic bronchitis (25.0%), pulmonary tuberculosis (19.4%), lung cancer (16.7%), and non-specific inflammatory disease (8.3%). This suggests benign disease is 5 times more common cause of right middle lobe collapse than lung cancer. 3. Among the plain chest radiolograph findings, obliteration of right cardiac border and triangular radiopaque density were the most frequent findings(77.8% in each) and the next was downward and anterior displacement of minor and major fissures (55.6%) 4. Bronchography was done in 11 cases; bronchiectasis was found in 8 cases and chronic bronchitis in 3 cases. Right middle lobe bronchus was obstructed in 2 cases of chronic bronchitis. 5. Chest CT scan was performed in 4 cases of lung cancer, 2 of non-specific inflammatory disease, and 1 of pulmonary tuberculosis: all of lung cancer revealed hilar mass, budged or lobulated fissures, in homogenous density, and mediastinal lymph node enlargement, and all benign disease showed homogenous density and flat to concave fissures. Right middle lobar bronchus narrowing was seen in 5 cases and its obstruction in 2 cases
The onset of coherence collapse in DBR lasers
International Nuclear Information System (INIS)
Woodward, S.L.; Koch, T.L.; Koren, U.
1990-01-01
The authors investigate how the onset of coherence collapse depends on laser output power. The lasers were three-section multiquantum-well distributed-Bragg-reflector (MQW-DBR) lasers. The fraction of light reflected back into the lasing mode was varied, and the point at which the transition to coherence collapse occurred was measured. This feedback level varies approximately linearly with laser output power. For these lasers, when the output power is 1 mW, the transition to coherence collapse beings when the optical feedback into the lasing mode is below - 40 dBm; when the feedback power is - 35 dBm the laser line is completely collapsed
Developing empirical collapse fragility functions for global building types
Jaiswal, K.; Wald, D.; D'Ayala, D.
2011-01-01
Building collapse is the dominant cause of casualties during earthquakes. In order to better predict human fatalities, the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) program requires collapse fragility functions for global building types. The collapse fragility is expressed as the probability of collapse at discrete levels of the input hazard defined in terms of macroseismic intensity. This article provides a simple procedure for quantifying collapse fragility using vulnerability criteria based on the European Macroseismic Scale (1998) for selected European building types. In addition, the collapse fragility functions are developed for global building types by fitting the beta distribution to the multiple experts’ estimates for the same building type (obtained from EERI’s World Housing Encyclopedia (WHE)-PAGER survey). Finally, using the collapse probability distributions at each shaking intensity level as a prior and field-based collapse-rate observations as likelihood, it is possible to update the collapse fragility functions for global building types using the Bayesian procedure.
Improvement of group collapsing in TRANSX code
International Nuclear Information System (INIS)
Jeong, Hyun Tae; Kim, Young Cheol; Kim, Young In; Kim, Young Kyun
1996-07-01
A cross section generating and processing computer code TRANSX version 2.15 in the K-CORE system, being developed by the KAERI LMR core design technology development team produces various cross section input files appropriated for flux calculation options from the cross section library MATXS. In this report, a group collapsing function of TRANSX has been improved to utilize the zone averaged flux file RZFLUX written in double precision as flux weighting functions. As a result, an iterative calculation system using double precision RZFLUX consisting of the cross section data library file MATXS, the effective cross section producing and processing code TRANSX, and the transport theory calculation code TWODANT has been set up and verified through a sample model calculation. 4 refs. (Author)
Magnetorotational Explosions of Core-Collapse Supernovae
Directory of Open Access Journals (Sweden)
Gennady S. Bisnovatyi-Kogan
2014-12-01
Full Text Available Core-collapse supernovae are accompanied by formation of neutron stars. The gravitation energy is transformed into the energy of the explosion, observed as SN II, SN Ib,c type supernovae. We present results of 2-D MHD simulations, where the source of energy is rotation, and magnetic eld serves as a "transition belt" for the transformation of the rotation energy into the energy of the explosion. The toroidal part of the magnetic energy initially grows linearly with time due to dierential rotation. When the twisted toroidal component strongly exceeds the poloidal eld, magneto-rotational instability develops, leading to a drastic acceleration in the growth of magnetic energy. Finally, a fast MHD shock is formed, producing a supernova explosion. Mildly collimated jet is produced for dipole-like type of the initial field. At very high initial magnetic field no MRI development was found.
Inhomogeneities from quantum collapse scheme without inflation
Energy Technology Data Exchange (ETDEWEB)
Bengochea, Gabriel R., E-mail: gabriel@iafe.uba.ar [Instituto de Astronomía y Física del Espacio (IAFE), UBA-CONICET, CC 67, Suc. 28, 1428 Buenos Aires (Argentina); Cañate, Pedro, E-mail: pedro.canate@nucleares.unam.mx [Instituto de Ciencias Nucleares, UNAM, México D.F. 04510, México (Mexico); Sudarsky, Daniel, E-mail: sudarsky@nucleares.unam.mx [Instituto de Ciencias Nucleares, UNAM, México D.F. 04510, México (Mexico)
2015-04-09
In this work, we consider the problem of the emergence of seeds of cosmic structure in the framework of the non-inflationary model proposed by Hollands and Wald. In particular, we consider a modification to that proposal designed to account for breaking the symmetries of the initial quantum state, leading to the generation of the primordial inhomogeneities. This new ingredient is described in terms of a spontaneous reduction of the wave function. We investigate under which conditions one can recover an essentially scale free spectrum of primordial inhomogeneities, and which are the dominant deviations that arise in the model as a consequence of the introduction of the collapse of the quantum state into that scenario.
Asymptotic safety, singularities, and gravitational collapse
International Nuclear Information System (INIS)
Casadio, Roberto; Hsu, Stephen D.H.; Mirza, Behrouz
2011-01-01
Asymptotic safety (an ultraviolet fixed point with finite-dimensional critical surface) offers the possibility that a predictive theory of quantum gravity can be obtained from the quantization of classical general relativity. However, it is unclear what becomes of the singularities of classical general relativity, which, it is hoped, might be resolved by quantum effects. We study dust collapse with a running gravitational coupling and find that a future singularity can be avoided if the coupling becomes exactly zero at some finite energy scale. The singularity can also be avoided (pushed off to infinite proper time) if the coupling approaches zero sufficiently rapidly at high energies. However, the evolution deduced from perturbation theory still implies a singularity at finite proper time.
On spontaneous photon emission in collapse models
International Nuclear Information System (INIS)
Adler, Stephen L; Bassi, Angelo; Donadi, Sandro
2013-01-01
We reanalyze the problem of spontaneous photon emission in collapse models. We show that the extra term found by Bassi and Dürr is present for non-white (colored) noise, but its coefficient is proportional to the zero frequency Fourier component of the noise. This leads one to suspect that the extra term is an artifact. When the calculation is repeated with the final electron in a wave packet and with the noise confined to a bounded region, the extra term vanishes in the limit of continuum state normalization. The result obtained by Fu and by Adler and Ramazanoğlu from application of the Golden Rule is then recovered. (paper)
Gas and vapor bubble growth and collapse
International Nuclear Information System (INIS)
Bonnin, J.; Reali, M.; Sardella, L.
1976-01-01
The rate of growth or collapse of a spherical bubble of gas or vapor under the effect of a nonequilibrium with the ambient liquid can be expressed in terms of generalized parameters taking into account either mass or heat diffusion. Diffusion equations have been solved either by numerical computation or under the form of a asymptotical solution, for a growing bubble only and with a constant nonequilibrium. Solutions are compared between them and with already published ones. Experimental results obtained match with a unique nonequilibrium parameter, analogous to a Jacob number. Discrepancies with asymptotical solutions can require in some cases complete numerical computation. But taking into account convection due to bubble lift will require a more sophisticated numerical computation [fr
CRALBP supports the mammalian retinal visual cycle and cone vision.
Xue, Yunlu; Shen, Susan Q; Jui, Jonathan; Rupp, Alan C; Byrne, Leah C; Hattar, Samer; Flannery, John G; Corbo, Joseph C; Kefalov, Vladimir J
2015-02-01
Mutations in the cellular retinaldehyde-binding protein (CRALBP, encoded by RLBP1) can lead to severe cone photoreceptor-mediated vision loss in patients. It is not known how CRALBP supports cone function or how altered CRALBP leads to cone dysfunction. Here, we determined that deletion of Rlbp1 in mice impairs the retinal visual cycle. Mice lacking CRALBP exhibited M-opsin mislocalization, M-cone loss, and impaired cone-driven visual behavior and light responses. Additionally, M-cone dark adaptation was largely suppressed in CRALBP-deficient animals. While rearing CRALBP-deficient mice in the dark prevented the deterioration of cone function, it did not rescue cone dark adaptation. Adeno-associated virus-mediated restoration of CRALBP expression specifically in Müller cells, but not retinal pigment epithelial (RPE) cells, rescued the retinal visual cycle and M-cone sensitivity in knockout mice. Our results identify Müller cell CRALBP as a key component of the retinal visual cycle and demonstrate that this pathway is important for maintaining normal cone-driven vision and accelerating cone dark adaptation.
Use of RI-cone penetrometer in clay foundations
International Nuclear Information System (INIS)
Mimura, Mamoru; Shibata, Toru; Shrivastava, A.K.
1993-01-01
RI cone penetrometer tests are carried out at four different sites. The foundation grounds discussed here mainly consist of clayey materials. The measured results by RI cone penetrometers are shown for Kyobashi, Hachirougata, Kurihama and Kinkai Bay site. According to comparison of water content and density profiles by RI cone measurement with the conventional testing results, RI cone penetrometers are proved to be versatile tools for site investigation. Settlement assessment by RI cone penetrometer is also discussed by exemplifying the embankment at Kinkai Bay site. Elasto-vis-coplastic finite element analysis correspondingly performed strongly supports the RI cone based assessment. Repeated use of RI cone penetrometer with the advance of construction enables us to assess the consolidation process of the clay foundation. (author)
Aksenov, A. G.; Chechetkin, V. M.
2018-04-01
Most of the energy released in the gravitational collapse of the cores of massive stars is carried away by neutrinos. Neutrinos play a pivotal role in explaining core-collape supernovae. Currently, mathematical models of the gravitational collapse are based on multi-dimensional gas dynamics and thermonuclear reactions, while neutrino transport is considered in a simplified way. Multidimensional gas dynamics is used with neutrino transport in the flux-limited diffusion approximation to study the role of multi-dimensional effects. The possibility of large-scale convection is discussed, which is interesting both for explaining SN II and for setting up observations to register possible high-energy (≳10MeV) neutrinos from the supernova. A new multi-dimensional, multi-temperature gas dynamics method with neutrino transport is presented.
Training strategy for convolutional neural networks in pedestrian gender classification
Ng, Choon-Boon; Tay, Yong-Haur; Goi, Bok-Min
2017-06-01
In this work, we studied a strategy for training a convolutional neural network in pedestrian gender classification with limited amount of labeled training data. Unsupervised learning by k-means clustering on pedestrian images was used to learn the filters to initialize the first layer of the network. As a form of pre-training, supervised learning for the related task of pedestrian classification was performed. Finally, the network was fine-tuned for gender classification. We found that this strategy improved the network's generalization ability in gender classification, achieving better test results when compared to random weights initialization and slightly more beneficial than merely initializing the first layer filters by unsupervised learning. This shows that unsupervised learning followed by pre-training with pedestrian images is an effective strategy to learn useful features for pedestrian gender classification.
Accurate lithography simulation model based on convolutional neural networks
Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki
2017-07-01
Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.
An effective convolutional neural network model for Chinese sentiment analysis
Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong
2017-06-01
Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.
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.
Classification of decays involving variable decay chains with convolutional architectures
CERN. Geneva
2018-01-01
Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...
CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking
Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.
2017-12-01
We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.
Computational optical tomography using 3-D deep convolutional neural networks
Nguyen, Thanh; Bui, Vy; Nehmetallah, George
2018-04-01
Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.
Drug-Drug Interaction Extraction via Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Shengyu Liu
2016-01-01
Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
Truncation Depth Rule-of-Thumb for Convolutional Codes
Moision, Bruce
2009-01-01
In this innovation, it is shown that a commonly used rule of thumb (that the truncation depth of a convolutional code should be five times the memory length, m, of the code) is accurate only for rate 1/2 codes. In fact, the truncation depth should be 2.5 m/(1 - r), where r is the code rate. The accuracy of this new rule is demonstrated by tabulating the distance properties of a large set of known codes. This new rule was derived by bounding the losses due to truncation as a function of the code rate. With regard to particular codes, a good indicator of the required truncation depth is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code. It is shown that the new rule of thumb provides an accurate prediction of this depth for codes of varying rates.
Finding Neutrinos in LArTPCs using Convolutional Neural Networks
Wongjirad, Taritree
2017-09-01
Deep learning algorithms, which have emerged over the last decade, are opening up new ways to analyze data for many particle physics experiments. MicroBooNE, which is a neutrino experiment at Fermilab, has been exploring the use of such algorithms, in particular, convolutional neural networks (CNNS). CNNs are the state-of-the-art method for a large class of problems involving the analysis of images. This makes CNNs an attractive approach for MicroBooNE, whose detector, a liquid argon time projection chamber (LArTPC), produces high-resolution images of particle interactions. In this talk, I will discuss the ways CNNs can be applied to tasks like neutrino interaction detection and particle identification in MicroBooNE and LArTPCs.
Radio frequency interference mitigation using deep convolutional neural networks
Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.
2017-01-01
We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.
Forecasting Flare Activity Using Deep Convolutional Neural Networks
Hernandez, T.
2017-12-01
Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.
Convolutional neural networks with balanced batches for facial expressions recognition
Battini Sönmez, Elena; Cangelosi, Angelo
2017-03-01
This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.
Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders
Directory of Open Access Journals (Sweden)
Yang Yu
2017-01-01
Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.
Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery
Li, Z.; Cai, G.; Ren, H.
2018-04-01
There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.
Finger vein recognition based on convolutional neural network
Directory of Open Access Journals (Sweden)
Meng Gesi
2017-01-01
Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.
Fully convolutional network with cluster for semantic segmentation
Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin
2018-04-01
At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.
Real Time Eye Detector with Cascaded Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Bin Li
2018-01-01
Full Text Available An accurate and efficient eye detector is essential for many computer vision applications. In this paper, we present an efficient method to evaluate the eye location from facial images. First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. In the experiments using GI4E, BioID, and our datasets, our method attained a detection accuracy which is comparable to existing state-of-the-art methods; meanwhile, our method was faster and adaptable to variations of the images, including external light changes, facial occlusion, and changes in image modality.
Convolution product construction of interactions in probabilistic physical models
International Nuclear Information System (INIS)
Ratsimbarison, H.M.; Raboanary, R.
2007-01-01
This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.
Plane-wave decomposition by spherical-convolution microphone array
Rafaely, Boaz; Park, Munhum
2004-05-01
Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.
Deep learning with convolutional neural network in radiology.
Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu
2018-04-01
Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.
Static facial expression recognition with convolution neural networks
Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei
2018-03-01
Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.
Wachinger, Christian; Reuter, Martin; Klein, Tassilo
2018-04-15
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.
FULLY CONVOLUTIONAL NETWORKS FOR GROUND CLASSIFICATION FROM LIDAR POINT CLOUDS
Directory of Open Access Journals (Sweden)
A. Rizaldy
2018-05-01
Full Text Available Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs. In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN, a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher. The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.
Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds
Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.
2018-05-01
Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.
Color encoding in biologically-inspired convolutional neural networks.
Rafegas, Ivet; Vanrell, Maria
2018-05-11
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.
Fully convolutional neural networks improve abdominal organ segmentation
Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.
2018-03-01
Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1
International Nuclear Information System (INIS)
Craig, Tim; Battista, Jerry; Van Dyk, Jake
2003-01-01
Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less