WorldWideScience

Sample records for mouse distal convoluted

  1. Cystogenesis and elongated primary cilia in Tsc1-deficient distal convoluted tubules.

    Science.gov (United States)

    Armour, Eric A; Carson, Robert P; Ess, Kevin C

    2012-08-15

    Tuberous sclerosis complex (TSC) is a multiorgan hamartomatous disease caused by loss of function mutations of either the TSC1 or TSC2 genes. Neurological symptoms of TSC predominate in younger patients, but renal pathologies are a serious aspect of the disease in older children and adults. To study TSC pathogenesis in the kidney, we inactivated the mouse Tsc1 gene in the distal convoluted tubules (DCT). At young ages, Tsc1 conditional knockout (CKO) mice have enlarged kidneys and mild cystogenesis with increased mammalian target of rapamycin complex (mTORC)1 but decreased mTORC2 signaling. Treatment with the mTORC1 inhibitor rapamycin reduces kidney size and cystogenesis. Rapamycin withdrawal led to massive cystogenesis involving both distal as well as proximal tubules. To assess the contribution of decreased mTORC2 signaling in kidney pathogenesis, we also generated Rictor CKO mice. These animals did not have any detectable kidney pathology. Finally, we examined primary cilia in the DCT. Cilia were longer in Tsc1 CKO mice, and rapamycin treatment returned cilia length to normal. Rictor CKO mice had normal cilia in the DCT. Overall, our findings suggest that loss of the Tsc1 gene in the DCT is sufficient for renal cystogenesis. This cytogenesis appears to be mTORC1 but not mTORC2 dependent. Intriguingly, the mechanism may be cell autonomous as well as non-cell autonomous and possibly involves the length and function of primary cilia.

  2. Cystogenesis and elongated primary cilia in Tsc1-deficient distal convoluted tubules

    Science.gov (United States)

    Armour, Eric A.; Carson, Robert P.

    2012-01-01

    Tuberous sclerosis complex (TSC) is a multiorgan hamartomatous disease caused by loss of function mutations of either the TSC1 or TSC2 genes. Neurological symptoms of TSC predominate in younger patients, but renal pathologies are a serious aspect of the disease in older children and adults. To study TSC pathogenesis in the kidney, we inactivated the mouse Tsc1 gene in the distal convoluted tubules (DCT). At young ages, Tsc1 conditional knockout (CKO) mice have enlarged kidneys and mild cystogenesis with increased mammalian target of rapamycin complex (mTORC)1 but decreased mTORC2 signaling. Treatment with the mTORC1 inhibitor rapamycin reduces kidney size and cystogenesis. Rapamycin withdrawal led to massive cystogenesis involving both distal as well as proximal tubules. To assess the contribution of decreased mTORC2 signaling in kidney pathogenesis, we also generated Rictor CKO mice. These animals did not have any detectable kidney pathology. Finally, we examined primary cilia in the DCT. Cilia were longer in Tsc1 CKO mice, and rapamycin treatment returned cilia length to normal. Rictor CKO mice had normal cilia in the DCT. Overall, our findings suggest that loss of the Tsc1 gene in the DCT is sufficient for renal cystogenesis. This cytogenesis appears to be mTORC1 but not mTORC2 dependent. Intriguingly, the mechanism may be cell autonomous as well as non-cell autonomous and possibly involves the length and function of primary cilia. PMID:22674026

  3. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.

    Science.gov (United States)

    Zeng, Tao; Li, Rongjian; Mukkamala, Ravi; Ye, Jieping; Ji, Shuiwang

    2015-05-07

    Profiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development. We applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach. Deep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.

  4. Direct physical contact between intercalated cells in the distal convoluted tubule and the afferent arteriole in mouse kidneys.

    Directory of Open Access Journals (Sweden)

    Hao Ren

    Full Text Available Recent physiological studies in the kidney proposed the existence of a secondary feedback mechanism termed 'crosstalk' localized after the macula densa. This newly discovered crosstalk contact between the nephron tubule and its own afferent arteriole may potentially revolutionize our understanding of renal vascular resistance and electrolyte regulation. However, the nature of such a crosstalk mechanism is still debated due to a lack of direct and comprehensive morphological evidence. Its exact location along the nephron, its prevalence among the different types of nephrons, and the type of cells involved are yet unknown. To address these issues, computer assisted 3-dimensional nephron tracing was applied in combination with direct immunohistochemistry on plastic sections and electron microscopy. 'Random' contacts in the cortex were identified by the tracing and excluded. We investigated a total of 168 nephrons from all cortical regions. The results demonstrated that the crosstalk contact existed, and that it was only present in certain nephrons (90% of the short-looped and 75% of the long-looped nephrons. The crosstalk contacts always occurred at a specific position--the last 10% of the distal convoluted tubule. Importantly, we demonstrated, for the first time, that the cells found in the tubule wall at the contact site were always type nonA-nonB intercalated cells. In conclusion, the present work confirmed the existence of a post macula densa physical crosstalk contact.

  5. Direct physical contact between intercalated cells in the distal convoluted tubule and the afferent arteriole in mouse kidneys.

    Science.gov (United States)

    Ren, Hao; Liu, Ning-Yu; Andreasen, Arne; Thomsen, Jesper S; Cao, Liu; Christensen, Erik I; Zhai, Xiao-Yue

    2013-01-01

    Recent physiological studies in the kidney proposed the existence of a secondary feedback mechanism termed 'crosstalk' localized after the macula densa. This newly discovered crosstalk contact between the nephron tubule and its own afferent arteriole may potentially revolutionize our understanding of renal vascular resistance and electrolyte regulation. However, the nature of such a crosstalk mechanism is still debated due to a lack of direct and comprehensive morphological evidence. Its exact location along the nephron, its prevalence among the different types of nephrons, and the type of cells involved are yet unknown. To address these issues, computer assisted 3-dimensional nephron tracing was applied in combination with direct immunohistochemistry on plastic sections and electron microscopy. 'Random' contacts in the cortex were identified by the tracing and excluded. We investigated a total of 168 nephrons from all cortical regions. The results demonstrated that the crosstalk contact existed, and that it was only present in certain nephrons (90% of the short-looped and 75% of the long-looped nephrons). The crosstalk contacts always occurred at a specific position--the last 10% of the distal convoluted tubule. Importantly, we demonstrated, for the first time, that the cells found in the tubule wall at the contact site were always type nonA-nonB intercalated cells. In conclusion, the present work confirmed the existence of a post macula densa physical crosstalk contact.

  6. Comparative proteomic analysis of kidney distal convoluted tubule and cortical collecting duct cells following long-term hormonal stimulation

    DEFF Research Database (Denmark)

    Wu, Qi; Moller, Hanne; Rosenbaek, Lena Lindtoft

    2017-01-01

    The distal convoluted tubule (DCT) and the cortical collecting ducts (CCD) are portions of renal tubule that are partly responsible for maintaining the systemic concentrations of potassium, sodium, calcium and magnesium. Despite being structurally similar, DCT and CCD cells have different transpo...... FDR threshold in one cell type plus the unique proteins in this cell type. These 1025 mpkDCT specific proteins and 1211 mpkCCD specific proteins under the three conditions were subjected to further bioinformatics analyses including Panther and DAVID gene ontology analyses, E3 ligase...

  7. Differential roles of stretch-sensitive pelvic nerve afferents innervating mouse distal colon and rectum

    OpenAIRE

    Feng, Bin; Brumovsky, Pablo R.; Gebhart, Gerald F.

    2010-01-01

    Information about colorectal distension (i.e., colorectal dilation by increased intraluminal pressure) is primarily encoded by stretch-sensitive colorectal afferents in the pelvic nerve (PN). Despite anatomic differences between rectum and distal colon, little is known about the functional roles of colonic vs. rectal afferents in the PN pathway or the quantitative nature of mechanosensory encoding. We utilized an in vitro mouse colorectum-PN preparation to investigate pressure-encoding charac...

  8. RNA sequencing of kidney distal tubule cells reveals multiple mediators of chronic aldosterone action

    DEFF Research Database (Denmark)

    Poulsen, Søren Brandt; Limbutara, Kavee; Fenton, Robert Andrew

    2018-01-01

    The renal aldosterone-sensitive distal tubule (ASDT) is crucial for sodium reabsorption and blood pressure regulation. The ASDT consists of the late distal convoluted tubule (DCT2), connecting tubule (CNT) and collecting duct. Due to difficulties in isolating epithelial cells from the ASDT in lar...

  9. Developmental immunolocalization of the Klotho protein in mouse kidney epithelial cells

    Directory of Open Access Journals (Sweden)

    J.H. Song

    2014-01-01

    Full Text Available A defect in Klotho gene expression in the mouse results in a syndrome that resembles rapid human aging. In this study, we investigated the detailed distribution and the time of the first appearance of Klotho in developing and adult mouse kidney. Kidneys from 16-(F16, 18-(F18 and 20-day-old (F20 fetuses, 1- (P1, 4- (P4, 7- (P7, 14- (P14, and 21-day-old (P21 pups and adults were processed for immunohistochemistry and immunoblot analyses. In the developing mouse kidney, Klotho immunoreactivity was initially observed in a few cells of the connecting tubules (CNT of 18-day-old fetus (F and in the medullary collecting duct (MCD and distal nephron of the F16 developing kidney. In F20, Klotho immunoreactivity was increased in CNT and additionally observed in the outer portion of MCD and tip of the renal papilla. During the first 3 weeks after birth, Klotho-positive cells gradually disappeared from the MCD due to apoptosis, but remained in the CNT and cortical collecting ducts (CCD. In the adult mouse, the Klotho protein was expressed only in a few cells of the CNT and CCD in cortical area. Also, Klotho immunoreactivity was observed in the aquaporin 2-positive CNT, CCD, and NaCl co-transporter-positive distal convoluted tubule (DCT cells and type B and nonA-nonB intercalated cells of CNT, DCT, and CCD. Collectively, our data indicate that immunolocalization of Klotho is closely correlated with proliferation in the intercalated cells of CNT and CCD from aging, and may be involved in the regulation of tubular proliferation.

  10. 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...

  11. Potassium secretion in mammalian distal colon

    DEFF Research Database (Denmark)

    Sørensen, Mads Vaarby

    2009-01-01

    Epithelial organs adjust the „inner milieu“ of the body and are crucial for all homeostatic processes. Epithelial transport of different solutes and water is regulated phenomena. The regulation processes include both long term hormonal regulation and short term local agonist mediated regulation....... This research project is the summary of 3 original papers addressing the functional role of different regulating factors on ion transport in mouse distal colon. The first paper addresses the effect of luminal nucleotides on electrogenic Na+ absorption. The distal colon, like the distal nephron is an aldosterone......-sensitive tissue and participates in the regulation of Na+ excretion. In the distal nephron it was found that luminal nucleotides inhibit ENaC-mediated Na+ absorption. Here it was addressed whether luminal nucleotides regulate Na+ absorption and if so, which of the known luminal P2 receptors are involved. Using...

  12. Fundamentals of convolutional coding

    CERN Document Server

    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

  13. Distinct distribution of specific members of protein 4.1 genefamily in the mouse nephron

    Energy Technology Data Exchange (ETDEWEB)

    Ramez, Mohamed; Blot-Chabaud, Marcel; Cluzeaud, Francoise; Chanan, Sumita; Patterson, Michael; Walensky, Loren D.; Marfatia, Shirin; Baines, Anthony J.; Chasis, Joel A.; Conboy, John G.; Mohandas, Narla; Gascard, Philippe

    2002-12-11

    Background: Protein 4.1 is an adapter protein which linksthe actin cytoskeleton to various transmembrane proteins. 4.1 proteinsare encoded by four homologous genes, 4.1R, 4.1G, 4.1N, and 4.1B, whichundergo complex alternative splicing. Here we performed a detailedcharacterization of the expression of specific 4.1 proteins in the mousenephron. Methods: Distribution of renal 4.1 proteins was investigated bystaining of paraformaldehyde fixed mouse kidney sections with antibodieshighly specific for each 4.1 protein. Major 4.1 splice forms, amplifiedfrom mouse kidney marathon cDNA, were expressed in transfected COS-7cells in order to assign species of known exon composition to proteinsdetected in kidney. Results: A 105kDa4.1R splice form, initiating atATG-2 translation initiation site and lacking exon 16, but including exon17B, was restricted to thick ascending limb of Henle's loop. A 95kDa 4.1Nspliceform,lacking exons 15 and 17D, was expressed in either descendingor ascending thin limb of Henle'sloop, distal convoluted tubule and allregions of the collecting duct system. A major 108kDa 4.1B spliceform,initiating at a newly characterized ATG translation initiation site, andlacking exons 15, 17B, and 21, was present only in Bowman's capsule andproximal convoluted tubule (PCT). There was no expression of 4.1G inkidney. Conclusion: Distinct distribution of 4.1 proteins along thenephron suggests their involvement in targeting of selected transmembraneproteins in kidney epithelium andtherefore in regulation of specifickidney functions.

  14. Establishment of a molecular genetic map of distal mouse chromosome 1: further definition of a conserved linkage group syntenic with human chromosome 1q.

    Science.gov (United States)

    Seldin, M F; Morse, H C; LeBoeuf, R C; Steinberg, A D

    1988-01-01

    A linkage map of distal mouse chromosome 1 was constructed by restriction fragment length polymorphism analysis of DNAs from seven sets of recombinant inbred (RI) strains. The data obtained with seven probes on Southern hybridization combined with data from previous studies suggest the gene order Cfh, Pep-3/Ren-1,2, Ly-5, Lamb-2, At-3, Apoa-2/Ly-17,Spna-1. These results confirm and extend analyses of a large linkage group which includes genes present on a 20-30 cM span of mouse chromosome 1 and those localized to human chromosome 1q21-32. Moreover, the data indicate similar relative positions of human and mouse complement receptor-related genes REN, CD45, LAMB2, AT3, APOA2, and SPTA. These results suggest that mouse gene analyses may help in detailed mapping of human genes within such a syntenic group.

  15. 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

  16. Genome-wide analysis of murine renal distal convoluted tubular cells for the target genes of mineralocorticoid receptor

    Energy Technology Data Exchange (ETDEWEB)

    Ueda, Kohei [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Fujiki, Katsunori; Shirahige, Katsuhiko [Research Center for Epigenetic Disease, Institute of Molecular and Cellular Biosciences, The University of Tokyo, Tokyo (Japan); Gomez-Sanchez, Celso E. [Endocrine Section, G.V. (Sonny) Montgomery VA Medical Center, MS (United States); Endocrinology, University of Mississippi Medical Center, MS (United States); Fujita, Toshiro [Division of Clinical Epigenetics, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo (Japan); Nangaku, Masaomi [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Nagase, Miki, E-mail: mnagase-tky@umin.ac.jp [Department of Nephrology and Endocrinology, The University of Tokyo, Tokyo (Japan); Department of Anatomy and Life Structure, School of Medicine Juntendo University, Tokyo (Japan)

    2014-02-28

    Highlights: • We define a target gene of MR as that with MR-binding to the adjacent region of DNA. • We use ChIP-seq analysis in combination with microarray. • We, for the first time, explore the genome-wide binding profile of MR. • We reveal 5 genes as the direct target genes of MR in the renal epithelial cell-line. - Abstract: Background and objective: Mineralocorticoid receptor (MR) is a member of nuclear receptor family proteins and contributes to fluid homeostasis in the kidney. Although aldosterone-MR pathway induces several gene expressions in the kidney, it is often unclear whether the gene expressions are accompanied by direct regulations of MR through its binding to the regulatory region of each gene. The purpose of this study is to identify the direct target genes of MR in a murine distal convoluted tubular epithelial cell-line (mDCT). Methods: We analyzed the DNA samples of mDCT cells overexpressing 3xFLAG-hMR after treatment with 10{sup −7} M aldosterone for 1 h by chromatin immunoprecipitation with deep-sequence (ChIP-seq) and mRNA of the cell-line with treatment of 10{sup −7} M aldosterone for 3 h by microarray. Results: 3xFLAG-hMR overexpressed in mDCT cells accumulated in the nucleus in response to 10{sup −9} M aldosterone. Twenty-five genes were indicated as the candidate target genes of MR by ChIP-seq and microarray analyses. Five genes, Sgk1, Fkbp5, Rasl12, Tns1 and Tsc22d3 (Gilz), were validated as the direct target genes of MR by quantitative RT-qPCR and ChIP-qPCR. MR binding regions adjacent to Ctgf and Serpine1 were also validated. Conclusions: We, for the first time, captured the genome-wide distribution of MR in mDCT cells and, furthermore, identified five MR target genes in the cell-line. These results will contribute to further studies on the mechanisms of kidney diseases.

  17. TRPV3, a thermosensitive channel is expressed in mouse distal colon epithelium

    International Nuclear Information System (INIS)

    Ueda, Takashi; Yamada, Takahiro; Ugawa, Shinya; Ishida, Yusuke; Shimada, Shoichi

    2009-01-01

    The thermo-transient receptor potential (thermoTRP) subfamily is composed of channels that are important in nociception and thermo-sensing. Here, we show a selective expression of TRPV3 channel in the distal colon throughout the gastrointestinal tract. Expression analyses clearly revealed that TRPV3 mRNA and proteins were expressed in the superficial epithelial cells of the distal colon, but not in those of the stomach, duodenum or proximal colon. In a subset of primary epithelial cells cultured from the distal colon, carvacrol, an agonist for TRPV3, elevated cytosolic Ca 2+ concentration in a concentration-dependent manner. This response was inhibited by ruthenium red, a TRPV channel antagonist. Organotypic culture supported that the carvacrol-responsive cells were present in superficial epithelial cells. Moreover, application of carvacrol evoked ATP release in primary colonic epithelial cells. We conclude that TRPV3 is present in absorptive cells in the distal colon and may be involved in a variety of cellular functions.

  18. The ClC-K2 Chloride Channel Is Critical for Salt Handling in the Distal Nephron.

    Science.gov (United States)

    Hennings, J Christopher; Andrini, Olga; Picard, Nicolas; Paulais, Marc; Huebner, Antje K; Cayuqueo, Irma Karen Lopez; Bignon, Yohan; Keck, Mathilde; Cornière, Nicolas; Böhm, David; Jentsch, Thomas J; Chambrey, Régine; Teulon, Jacques; Hübner, Christian A; Eladari, Dominique

    2017-01-01

    Chloride transport by the renal tubule is critical for blood pressure (BP), acid-base, and potassium homeostasis. Chloride uptake from the urinary fluid is mediated by various apical transporters, whereas basolateral chloride exit is thought to be mediated by ClC-Ka/K1 and ClC-Kb/K2, two chloride channels from the ClC family, or by KCl cotransporters from the SLC12 gene family. Nevertheless, the localization and role of ClC-K channels is not fully resolved. Because inactivating mutations in ClC-Kb/K2 cause Bartter syndrome, a disease that mimics the effects of the loop diuretic furosemide, ClC-Kb/K2 is assumed to have a critical role in salt handling by the thick ascending limb. To dissect the role of this channel in detail, we generated a mouse model with a targeted disruption of the murine ortholog ClC-K2. Mutant mice developed a Bartter syndrome phenotype, characterized by renal salt loss, marked hypokalemia, and metabolic alkalosis. Patch-clamp analysis of tubules isolated from knockout (KO) mice suggested that ClC-K2 is the main basolateral chloride channel in the thick ascending limb and in the aldosterone-sensitive distal nephron. Accordingly, ClC-K2 KO mice did not exhibit the natriuretic response to furosemide and exhibited a severely blunted response to thiazide. We conclude that ClC-Kb/K2 is critical for salt absorption not only by the thick ascending limb, but also by the distal convoluted tubule. Copyright © 2016 by the American Society of Nephrology.

  19. 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.

  20. Convolutional coding techniques for data protection

    Science.gov (United States)

    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.

  1. Convolution copula econometrics

    CERN Document Server

    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.

  2. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

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

  3. Strongly-MDS convolutional codes

    NARCIS (Netherlands)

    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

  4. 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....

  5. Cerebral vessels segmentation for light-sheet microscopy image using convolutional neural networks

    Science.gov (United States)

    Hu, Chaoen; Hui, Hui; Wang, Shuo; Dong, Di; Liu, Xia; Yang, Xin; Tian, Jie

    2017-03-01

    Cerebral vessel segmentation is an important step in image analysis for brain function and brain disease studies. To extract all the cerebrovascular patterns, including arteries and capillaries, some filter-based methods are used to segment vessels. However, the design of accurate and robust vessel segmentation algorithms is still challenging, due to the variety and complexity of images, especially in cerebral blood vessel segmentation. In this work, we addressed a problem of automatic and robust segmentation of cerebral micro-vessels structures in cerebrovascular images acquired by light-sheet microscope for mouse. To segment micro-vessels in large-scale image data, we proposed a convolutional neural networks (CNNs) architecture trained by 1.58 million pixels with manual label. Three convolutional layers and one fully connected layer were used in the CNNs model. We extracted a patch of size 32x32 pixels in each acquired brain vessel image as training data set to feed into CNNs for classification. This network was trained to output the probability that the center pixel of input patch belongs to vessel structures. To build the CNNs architecture, a series of mouse brain vascular images acquired from a commercial light sheet fluorescence microscopy (LSFM) system were used for training the model. The experimental results demonstrated that our approach is a promising method for effectively segmenting micro-vessels structures in cerebrovascular images with vessel-dense, nonuniform gray-level and long-scale contrast regions.

  6. 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...

  7. Convolution of Distribution-Valued Functions. Applications.

    OpenAIRE

    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...

  8. 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.

  9. Efficient convolutional sparse coding

    Science.gov (United States)

    Wohlberg, Brendt

    2017-06-20

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

  10. Multithreaded implicitly dealiased convolutions

    Science.gov (United States)

    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.

  11. 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.

  12. DISTAL MYOPATHIES

    Science.gov (United States)

    Dimachkie, Mazen M.; Barohn, Richard J.

    2014-01-01

    Over a century ago, Gowers described two young patients in whom distal muscles weakness involved the hand, foot, sternocleidomastoid, and facial muscles in the other case the shoulder and distal leg musculature. Soon after, , similar distal myopathy cases were reported whereby the absence of sensory symptoms and of pathologic changes in the peripheral nerves and spinal cord at postmortem examination allowed differentiation from Charcot-Marie-Tooth disease. In 1951, Welander described autosomal dominant (AD) distal arm myopathy in a large Scandanavian cohort. Since then the number of well-characterized distal myopathies has continued to grow such that the distal myopathies have formed a clinically and genetically heterogeneous group of disorders. Affected kindred commonly manifest weakness that is limited to foot and toe muscles even in advanced stages of the disease, with variable mild proximal leg, distal arm, neck and laryngeal muscle involvement in selected individuals. An interesting consequence of the molecular characterization of the distal myopathies has been the recognition that mutation in a single gene can lead to more than one clinical disorder. For example, Myoshi myopathy (MM) and limb girdle muscular dystrophy (LGMD) type 2B are allelic disorders due to defects in the gene that encodes dysferlin. The six well described distal myopathy syndromes are shown in Table 1. Table 2 lists advances in our understanding of the myofibrillar myopathy group and Table 3 includes more recently delineated and less common distal myopathies. In the same manner, the first section of this review pertains to the more traditional six distal myopathies followed by discussion of the myofibrillar myopathies. In the third section, we review other clinically and genetically distinctive distal myopathy syndromes usually based upon single or smaller family cohorts. The fourth section considers other neuromuscular disorders that are important to recognize as they display prominent

  13. 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...

  14. Does computer use affect the incidence of distal arm pain? A one-year prospective study using objective measures of computer use

    DEFF Research Database (Denmark)

    Mikkelsen, S.; Lassen, C. F.; Vilstrup, Imogen

    2012-01-01

    PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded with a soft......PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded...... with a software program installed on the participants' computers. Participants reported weekly pain scores via the software program for elbow, forearm and wrist/hand as well as in a questionnaire at baseline and 1-year follow up. Associations between pain development and computer work were examined for three pain...... were not risk factors for acute pain, nor did they modify the effects of mouse or keyboard time. Computer usage parameters were not associated with prolonged or chronic pain. A major limitation of the study was low keyboard times. CONCLUSION: Computer work was not related to the development...

  15. 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...

  16. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

    Science.gov (United States)

    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.

  17. Enhanced online convolutional neural networks for object tracking

    Science.gov (United States)

    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.

  18. Convolutional Neural Network for Image Recognition

    CERN Document Server

    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.

  19. Symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    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.

  20. FPGA-based digital convolution for wireless applications

    CERN Document Server

    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...

  1. Incomplete convolutions in production and inventory models

    NARCIS (Netherlands)

    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

  2. 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) =.

  3. Does computer use affect the incidence of distal arm pain? A one-year prospective study using objective measures of computer use

    DEFF Research Database (Denmark)

    Mikkelsen, Sigurd; Lassen, Christina Funch; Vilstrup, Imogen

    2012-01-01

    PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded with a soft......PURPOSE: To study how objectively recorded mouse and keyboard activity affects distal arm pain among computer workers. METHODS: Computer activities were recorded among 2,146 computer workers. For 52 weeks mouse and keyboard time, sustained activity, speed and micropauses were recorded...... with a software program installed on the participants' computers. Participants reported weekly pain scores via the software program for elbow, forearm and wrist/hand as well as in a questionnaire at baseline and 1-year follow up. Associations between pain development and computer work were examined for three pain...... were not risk factors for acute pain, nor did they modify the effects of mouse or keyboard time. Computer usage parameters were not associated with prolonged or chronic pain. A major limitation of the study was low keyboard times. CONCLUSION: Computer work was not related to the development...

  4. Tissue-specific expression of the human laminin alpha5-chain, and mapping of the gene to human chromosome 20q13.2-13.3 and to distal mouse chromosome 2 near the locus for the ragged (Ra) mutation

    DEFF Research Database (Denmark)

    Durkin, M E; Loechel, F; Mattei, M G

    1997-01-01

    , heart, lung, skeletal muscle, kidney, and pancreas. The human laminin alpha5-chain gene (LAMA5) was assigned to chromosome 20q13.2-q13.3 by in situ hybridization, and the mouse gene (Lama5) was mapped by linkage analysis to a syntonic region of distal chromosome 2, close to the locus for the ragged (Ra...

  5. An Algorithm for the Convolution of Legendre Series

    KAUST Repository

    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.

  6. A Note on Cubic Convolution Interpolation

    OpenAIRE

    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.

  7. The general theory of convolutional codes

    Science.gov (United States)

    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.

  8. Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.

    Science.gov (United States)

    Zhang, Yan; An, Lin; Xu, Jie; Zhang, Bo; Zheng, W Jim; Hu, Ming; Tang, Jijun; Yue, Feng

    2018-02-21

    Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. We show that the models learned from one cell type can be applied to make predictions in other cell or tissue types. Our work not only provides a computational framework to enhance Hi-C data resolution but also reveals features underlying the formation of 3D chromatin interactions.

  9. One weird trick for parallelizing convolutional neural networks

    OpenAIRE

    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.

  10. Deep multi-scale convolutional neural network for hyperspectral image classification

    Science.gov (United States)

    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.

  11. Development and Characterization of a Human and Mouse Intestinal Epithelial Cell Monolayer Platform

    Directory of Open Access Journals (Sweden)

    Kenji Kozuka

    2017-12-01

    Full Text Available Summary: We describe the development and characterization of a mouse and human epithelial cell monolayer platform of the small and large intestines, with a broad range of potential applications including the discovery and development of minimally systemic drug candidates. Culture conditions for each intestinal segment were optimized by correlating monolayer global gene expression with the corresponding tissue segment. The monolayers polarized, formed tight junctions, and contained a diversity of intestinal epithelial cell lineages. Ion transport phenotypes of monolayers from the proximal and distal colon and small intestine matched the known and unique physiology of these intestinal segments. The cultures secreted serotonin, GLP-1, and FGF19 and upregulated the epithelial sodium channel in response to known biologically active agents, suggesting intact secretory and absorptive functions. A screen of over 2,000 pharmacologically active compounds for inhibition of potassium ion transport in the mouse distal colon cultures led to the identification of a tool compound. : Siegel and colleagues describe their development of a human and mouse intestinal epithelial cell monolayer platform that maintains the cellular, molecular, and functional characteristics of tissue for each intestinal segment. They demonstrate the platform's application to drug discovery by screening a library of over 2,000 compounds to identify an inhibitor of potassium ion transport in the mouse distal colon. Keywords: intestinal epithelium, organoids, monolayer, colon, small intestine, phenotype screening assays, enteroid, colonoid

  12. 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.

  13. Design of convolutional tornado code

    Science.gov (United States)

    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.

  14. 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

  15. A knock-in/knock-out mouse model of HSPB8-associated distal hereditary motor neuropathy and myopathy reveals toxic gain-of-function of mutant Hspb8.

    Science.gov (United States)

    Bouhy, Delphine; Juneja, Manisha; Katona, Istvan; Holmgren, Anne; Asselbergh, Bob; De Winter, Vicky; Hochepied, Tino; Goossens, Steven; Haigh, Jody J; Libert, Claude; Ceuterick-de Groote, Chantal; Irobi, Joy; Weis, Joachim; Timmerman, Vincent

    2018-01-01

    Mutations in the small heat shock protein B8 gene (HSPB8/HSP22) have been associated with distal hereditary motor neuropathy, Charcot-Marie-Tooth disease, and recently distal myopathy. It is so far not clear how mutant HSPB8 induces the neuronal and muscular phenotypes and if a common pathogenesis lies behind these diseases. Growing evidence points towards a role of HSPB8 in chaperone-associated autophagy, which has been shown to be a determinant for the clearance of poly-glutamine aggregates in neurodegenerative diseases but also for the maintenance of skeletal muscle myofibrils. To test this hypothesis and better dissect the pathomechanism of mutant HSPB8, we generated a new transgenic mouse model leading to the expression of the mutant protein (knock-in lines) or the loss-of-function (functional knock-out lines) of the endogenous protein Hspb8. While the homozygous knock-in mice developed motor deficits associated with degeneration of peripheral nerves and severe muscle atrophy corroborating patient data, homozygous knock-out mice had locomotor performances equivalent to those of wild-type animals. The distal skeletal muscles of the post-symptomatic homozygous knock-in displayed Z-disk disorganisation, granulofilamentous material accumulation along with Hspb8, αB-crystallin (HSPB5/CRYAB), and desmin aggregates. The presence of the aggregates correlated with reduced markers of effective autophagy. The sciatic nerve of the homozygous knock-in mice was characterized by low autophagy potential in pre-symptomatic and Hspb8 aggregates in post-symptomatic animals. On the other hand, the sciatic nerve of the homozygous knock-out mice presented a normal morphology and their distal muscle displayed accumulation of abnormal mitochondria but intact myofiber and Z-line organisation. Our data, therefore, suggest that toxic gain-of-function of mutant Hspb8 aggregates is a major contributor to the peripheral neuropathy and the myopathy. In addition, mutant Hspb8 induces

  16. 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...

  17. Face recognition: a convolutional neural-network approach.

    Science.gov (United States)

    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.

  18. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    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

  19. 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....

  20. Transepithelial SCFA fluxes link intracellular and extracellular pH regulation of mouse colonocytes.

    Science.gov (United States)

    Chu, S; Montrose, M H

    1997-10-01

    We have studied pH regulation in both intracellular and extracellular compartments of mouse colonic crypts, using distal colonic mucosa with intact epithelial architecture. In this work, we question how transepithelial SCFA gradients affect intracellular pH (pHi) and examine interactions between extracellular pH (pHo) and pHi regulation in crypts of distal colonic epithelium from mouse. We studied pH regulation in three adjacent compartments of distal colonic epithelium (crypt lumen, crypt epithelial cell cytosol, and lamina propria) with SNARF-1 (a pH sensitive fluorescent dye), digital imaging microscopy (for pHi), and confocal microscopy (for pHo). Combining results from the three compartments allows us to find how pHi and pHo are regulated and related under the influence of physiological transepithelial SCFA gradients, and develop a better understanding of pH regulation mechanisms in colonic crypts. Results suggest a complex interdependency between SCFA fluxes and pHo values, which can directly affect how strongly SCFAs acidify colonocytes.

  1. Characterization of the distal promoter of the human pyruvate carboxylase gene in pancreatic beta cells.

    Directory of Open Access Journals (Sweden)

    Ansaya Thonpho

    Full Text Available Pyruvate carboxylase (PC is an enzyme that plays a crucial role in many biosynthetic pathways in various tissues including glucose-stimulated insulin secretion. In the present study, we identify promoter usage of the human PC gene in pancreatic beta cells. The data show that in the human, two alternative promoters, proximal and distal, are responsible for the production of multiple mRNA isoforms as in the rat and mouse. RT-PCR analysis performed with cDNA prepared from human liver and islets showed that the distal promoter, but not the proximal promoter, of the human PC gene is active in pancreatic beta cells. A 1108 bp fragment of the human PC distal promoter was cloned and analyzed. It contains no TATA box but possesses two CCAAT boxes, and other putative transcription factor binding sites, similar to those of the distal promoter of rat PC gene. To localize the positive regulatory region in the human PC distal promoter, 5'-truncated and the 25-bp and 15-bp internal deletion mutants of the human PC distal promoter were generated and used in transient transfections in INS-1 832/13 insulinoma and HEK293T (kidney cell lines. The results indicated that positions -340 to -315 of the human PC distal promoter serve as (an activator element(s for cell-specific transcription factor, while the CCAAT box at -71/-67, a binding site for nuclear factor Y (NF-Y, as well as a GC box at -54/-39 of the human PC distal promoter act as activator sequences for basal transcription.

  2. CMOS Compressed Imaging by Random Convolution

    OpenAIRE

    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...

  3. Distal corporoplasty for distal cylinders extrusion after penile prosthesis implantation.

    Science.gov (United States)

    Carrino, Maurizio; Chiancone, Francesco; Battaglia, Gaetano; Pucci, Luigi; Fedelini, Paolo

    2017-02-03

    Distal extrusion of cylinders is a potential complication of the penile prosthesis implantation. Several methods have been proposed for repairing a distal penile erosion. We present our preliminary experience in "Distal corporoplasty" technique. We enrolled 18 consecutive patients whose underwent a distal corporoplasty with simultaneous reimplantation of an "AMS 700 inflatable penile prosthesis (LGX)" from January 2013 to November 2015 at our hospital. All procedures were performed by a single surgical team. Intraoperative and postoperative complications have been classified and reported according to Satava6 and Clavien-Dindo (CD) system.7 Mean values with standard deviations (±SD) were computed and reported for all items. Mean age of the patients was 53.61 (±11.90) years. Mean body max index (BMI) was 24.22 (±2.51). Mean operative time was 85.2 (±13.1) minutes. Blood losses were minimal. No intraoperative complications are reported according to Satava classification. Four out of 18 patients (22.22%) experienced postoperative complications according to CD system. All patients had sexual intercourse for the first time postsurgery after a mean of 59.11 ± 2.08 days. Mean follow-up was 22.11 (±9.95). Distal extrusion of cylinders is a potential complication of the penile prosthesis implantation. Distal corporoplasty was first described by Mulcahy. He reported a series of 14 patients with a follow-up of about 2 years with optimal functional outcomes. Moreover, distal corporoplasty resulted in shorter operative time, better function, less pain, and fewer recurrences than Gortex windsock repair.10 In our experience, distal corporoplasty is a simple and safe procedure in the treatment of distal cylinders extrusion when the prosthetic material is not exposed to the exterior.

  4. Towards dropout training for convolutional neural networks.

    Science.gov (United States)

    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.

  5. Distal digital replantation.

    Science.gov (United States)

    Jazayeri, Leila; Klausner, Jill Q; Chang, James

    2013-11-01

    Hand surgeons have been hesitant to perform distal digital replantation because of the technical challenges and the perception of a high cost-to-benefit ratio. Recent studies, however, have shown high survival rates and excellent functional and aesthetic results, providing renewed enthusiasm for distal replantation. The authors reviewed the literature and summarize key points regarding the surgical treatment, perioperative care, and outcomes of distal digital replantation. They describe specific techniques and considerations for surgical repair in each of four distal zones as described by Sebastin and Chung. Zone 1A replantation involves an artery-only anastomosis of a longitudinal pulp artery. Venous anastomosis first becomes possible in zone 1B. Zone 1C involves periarticular amputations where arthrodesis of the distal interphalangeal joint is usually indicated. Repair of the artery, vein, and nerve is technically optimal in zone 1D, where venous anastomosis should be performed. Overall, survival rates for distal digital replantation are similar to those reported for more proximal replantation. The literature reports good outcomes regarding nail salvage, fingertip sensibility, and range of motion, with restoration of length and aesthetic appearance. Distal replantation performed at institutions that specialize in microsurgery and specifically tailored to the level of injury is associated with good survival, function, and patient satisfaction and superior aesthetic outcome. More prospective data are needed to evaluate the cost of treatment, psychological outcomes, and functional outcomes of distal replantation compared with revision amputation.

  6. 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...

  7. 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.

  8. Relative biological effectiveness (RBE) and distal edge effects of proton radiation on early damage in vivo

    DEFF Research Database (Denmark)

    Sørensen, Brita Singers; Bassler, Niels; Nielsen, Steffen

    2017-01-01

    of the SOBP to behind the distal dose fall-off. Irradiations were performed with the same dose plan at all positions, corresponding to a dose of 31.25 Gy in the middle of the SOBP. Endpoint of the study was early skin damage of the foot, assessed by a mouse foot skin scoring system. RESULTS: The MDD50 values......, where LETd,z =1 was 3.3 keV/μm. CONCLUSIONS: Although there is a need to expand the current study to be able to calculate an exact enhancement ratio, an enhanced biological effect in vivo for early skin damage in the distal edge was demonstrated....

  9. On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Partha Maji

    2018-04-01

    Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

  10. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    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.

  11. Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.

    Science.gov (United States)

    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.

  12. Maxillary molar distalization with the dual-force distalizer supported by mini-implants: a clinical study.

    Science.gov (United States)

    Oberti, Giovanni; Villegas, Carlos; Ealo, Martha; Palacio, John Camilo; Baccetti, Tiziano

    2009-03-01

    The objective of this prospective study was to describe the clinical effects of a bone-supported molar distalizing appliance, the dual-force distalizer. The study group included 16 patients (mean age, 14.3 years) with Class II molar relationships. Study models and lateral cephalograms were taken before and after the distalizing movement to record significant dental and skeletal changes (Wilcoxon test). The average distalization time was 5 months, with a movement rate of 1.2 mm per month; the distalization amounts were 5.9 +/- 1.72 mm at the crown level and 4.4 +/- 1.41 mm at the furcation level. The average molar inclination was 5.6 degrees +/- 3.7 degrees ; this was less than the amount of inclination generated by bone-supported appliances that use single distalizing forces. The correlation between inclination and distalization was not significant, indicating predominantly bodily movement. The teeth anterior to the first molar moved distally also; the second premolars distalized an average of 4.26 mm, and the incisors retruded by 0.53 mm. The dual-force distalizer is a valid alternative distalizing appliance that generates controlled molar distalization with a good rate of movement and no loss of anchorage.

  13. 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.

  14. Classification of urine sediment based on convolution neural network

    Science.gov (United States)

    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.

  15. 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.

  16. A Revised Piecewise Linear Recursive Convolution FDTD Method for Magnetized Plasmas

    International Nuclear Information System (INIS)

    Liu Song; Zhong Shuangying; Liu Shaobin

    2005-01-01

    The piecewise linear recursive convolution (PLRC) finite-different time-domain (FDTD) method improves accuracy over the original recursive convolution (RC) FDTD approach and current density convolution (JEC) but retains their advantages in speed and efficiency. This paper describes a revised piecewise linear recursive convolution PLRC-FDTD formulation for magnetized plasma which incorporates both anisotropy and frequency dispersion at the same time, enabling the transient analysis of magnetized plasma media. The technique is illustrated by numerical simulations of the reflection and transmission coefficients through a magnetized plasma layer. The results show that the revised PLRC-FDTD method has improved the accuracy over the original RC FDTD method and JEC FDTD method

  17. 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.

  18. Convolution of large 3D images on GPU and its decomposition

    Science.gov (United States)

    Karas, Pavel; Svoboda, David

    2011-12-01

    In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.

  19. 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.

  20. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    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

  1. Prediction of Electricity Usage Using Convolutional Neural Networks

    OpenAIRE

    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 ...

  2. Single image super-resolution based on convolutional neural networks

    Science.gov (United States)

    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.

  3. The alpha-spectrin gene is on chromosome 1 in mouse and man.

    Science.gov (United States)

    Huebner, K; Palumbo, A P; Isobe, M; Kozak, C A; Monaco, S; Rovera, G; Croce, C M; Curtis, P J

    1985-06-01

    By using alpha-spectrin cDNA clones of murine and human origin and somatic cell hybrids segregating either mouse or human chromosomes, the gene for alpha-spectrin has been mapped to chromosome 1 in both species. This assignment of the mouse alpha-spectrin gene to mouse chromosome 1 by DNA hybridization strengthens the previous identification of the alpha-spectrin locus in mouse with the sph locus, which previously was mapped by linkage analysis to mouse chromosome 1, distal to the Pep-3 locus. By in situ hybridization to human metaphase chromosomes, the human alpha-spectrin gene has been localized to 1q22-1q25; interestingly, the locus for a non-Rh-linked form of elliptocytosis has been provisionally mapped to band 1q2 by family linkage studies.

  4. 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...

  5. Phylogenetic convolutional neural networks in metagenomics.

    Science.gov (United States)

    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.

  6. 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.

  7. Fast space-varying convolution using matrix source coding with applications to camera stray light reduction.

    Science.gov (United States)

    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.

  8. Consensus Convolutional Sparse Coding

    KAUST Repository

    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.

  9. Consensus Convolutional Sparse Coding

    KAUST Repository

    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.

  10. Consensus Convolutional Sparse Coding

    KAUST Repository

    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.

  11. Reverse Distal Transverse Palmar Arch in Distal Digital Replantation.

    Science.gov (United States)

    Wei, Ching-Yueh; Orozco, Oscar; Vinagre, Gustavo; Shafarenko, Mark

    2017-11-01

    Refinements in microsurgery have made distal finger replantation an established technique with high success rates and good functional and aesthetic outcomes. However, it still represents a technically demanding procedure due to the small vessel caliber and frequent lack of vessel length, requiring the use of interpositional venous grafts in some instances. We describe a new technique for anastomosis in fingertip replantation, whereby the need for venous grafts is eliminated. Applying the reverse distal transverse palmar arch technique, 11 cases of distal digital replantation were performed between January 2011 and July 2016. The described procedure was used for arterial anastomosis in 10 cases and arteriovenous shunting for venous drainage in 1 case. A retrospective case review was conducted. The technical description and clinical outcome evaluations are presented. Ten of the 11 replanted digits survived, corresponding to an overall success rate of 91%. One replant failed due to venous insufficiency. Blood transfusions were not required for any of the patients. Follow-up (range, 1.5-5 months) revealed near-normal range of motion and good aesthetic results. All of the replanted digits developed protective sensation. The average length of hospital admission was 5 days. All patients were satisfied with the results and were able to return to their previous work. The use of the reverse distal transverse palmar arch is a novel and reliable technique in distal digital replantation when an increase in vessel length is required, allowing for a tension-free arterial repair without the need for vein grafts.

  12. 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.

  13. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  14. Traffic sign recognition with deep convolutional neural networks

    OpenAIRE

    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...

  15. Research of convolutional neural networks for traffic sign recognition

    OpenAIRE

    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...

  16. [Distal clavicle fracture].

    Science.gov (United States)

    Seppel, G; Lenich, A; Imhoff, A B

    2014-06-01

    Reposition and fixation of unstable distal clavicle fractures with a low profile locking plate (Acumed, Hempshire, UK) in conjunction with a button/suture augmentation cerclage (DogBone/FibreTape, Arthrex, Naples, FL, USA). Unstable fractures of the distal clavicle (Jäger and Breitner IIA) in adults. Unstable fractures of the distal clavicle (Jäger and Breitner IV) in children. Distal clavicle fractures (Jäger and Breitner I, IIB or III) with marked dislocation, injury of nerves and vessels, or high functional demand. Patients in poor general condition. Fractures of the distal clavicle (Jäger and Breitner I, IIB or III) without marked dislocation or vertical instability. Local soft-tissue infection. Combination procedure: Initially the lateral part of the clavicle is exposed by a 4 cm skin incision. After reduction of the fracture, stabilization is performed with a low profile locking distal clavicle plate. Using a special guiding device, a transclavicular-transcoracoidal hole is drilled under arthroscopic view. Additional vertical stabilization is arthroscopically achieved by shuttling the DogBone/FibreTape cerclage from the lateral portal cranially through the clavicular plate. The two ends of the FibreTape cerclage are brought cranially via adjacent holes of the locking plate while the DogBone button is placed under the coracoid process. Thus, plate bridging is achieved. Finally reduction is performed and the cerclage is secured by surgical knotting. Use of an arm sling for 6 weeks. Due to the fact that the described technique is a relatively new procedure, long-term results are lacking. In the short term, patients postoperatively report high subjective satisfaction without persistent pain.

  17. Krypton laser-induced photothrombotic distal middle cerebral artery occlusion without craniectomy in mice.

    Science.gov (United States)

    Sugimori, Hiroshi; Yao, Hiroshi; Ooboshi, Hiroaki; Ibayashi, Setsuro; Iida, Mitsuo

    2004-08-01

    Recent advances in genetical engineering of the mouse have highlighted the importance of reproducible and less invasive models of cerebral ischemia in mice. In this paper, we developed minimally invasive and reproducible model of distal middle cerebral artery (MCA) occlusion in mice using krypton (Kr) laser-induced photothrombosis. C57BL/6 or BALB mice (n=8 each) were anesthetized with halothane. The skin was cut, the temporal muscle was retracted, and the right distal MCA was observed through the skull. A Kr laser beam of wavelength 568 nm was focused onto the MCA over the intact skull. Upon laser irradiation, intravenous administration of a rose bengal solution was begun. After 4 min of irradiation, the laser beam was refocused on the MCA just proximal to the first spot, and another 4-min irradiation was performed. Then, the right common carotid artery (CCA) was ligated. Three days later, the brain was removed, and infarct volume was determined. Infarction confined almost solely to the cortical area was produced in each mouse. Mean infarct volume in C57BL/6 mice was 25.2+/-13.7 mm3. The BALB mice group showed significantly larger and more reproducible infarction (44.1+/-5.2 mm3; the coefficient of variation was 12%) than did C57BL/6 mice (P<0.005). Our photothrombosis model of stroke in mice can be performed without craniectomy, and its reproducibility is satisfactory when using BALB mice.

  18. High Performance Implementation of 3D Convolutional Neural Networks on a GPU

    Science.gov (United States)

    Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

  19. High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

    Science.gov (United States)

    Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

    2017-01-01

    Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

  20. A MacWilliams Identity for Convolutional Codes: The General Case

    OpenAIRE

    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.

  1. 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

  2. DCMDN: Deep Convolutional Mixture Density Network

    Science.gov (United States)

    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.

  3. 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...

  4. 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

  5. Efficient and Invariant Convolutional Neural Networks for Dense Prediction

    OpenAIRE

    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...

  6. Spleen-preserving distal pancreatectomy in trauma.

    Science.gov (United States)

    Schellenberg, Morgan; Inaba, Kenji; Cheng, Vincent; Bardes, James M; Lam, Lydia; Benjamin, Elizabeth; Matsushima, Kazuhide; Demetriades, Demetrios

    2018-01-01

    Traumatic injuries to the distal pancreas are infrequent. Universally accepted recommendations about the need for routine splenectomy with distal pancreatectomy do not exist. The aims of this study were to compare outcomes after distal pancreatectomy and splenectomy versus spleen-preserving distal pancreatectomy, and to define the appropriate patient population for splenic preservation. All patients who underwent distal pancreatectomy (January 1, 2007, to December 31, 2014) were identified from the National Trauma Data Bank. Patients with concomitant splenic injury and those who underwent partial splenectomy were excluded. Demographics, clinical data, procedures, and outcomes were collected. Study groups were defined by surgical procedure: distal pancreatectomy and splenectomy versus spleen-preserving distal pancreatectomy. Baseline characteristics between groups were compared with univariate analysis. Multivariate analysis was performed with logistic and linear regression to examine differences in outcomes. Over the 8-year study period, 2,223 patients underwent distal pancreatectomy. After excluding 1,381 patients with concomitant splenic injury (62%) and 8 (pancreatectomy and splenectomy, those who underwent spleen-preserving distal pancreatectomy were younger (p pancreatectomy (p = 0.017). Complications, mortality, and intensive care unit LOS were not significantly different. In young patients after blunt trauma who are not severely injured, a spleen-preserving distal pancreatectomy should be considered to allow for conservation of splenic function and a shorter hospital LOS. In all other patients, the surgeon should not hesitate to remove the spleen with the distal pancreas. Therapy, level IV.

  7. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network.

    Science.gov (United States)

    Du, Xiaofeng; Qu, Xiaobo; He, Yifan; Guo, Di

    2018-03-06

    Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods.

  8. Linkage of genes for laminin B1 and B2 subunits on chromosome 1 in mouse.

    Science.gov (United States)

    Elliott, R W; Barlow, D; Hogan, B L

    1985-08-01

    We have used cDNA clones for the B1 and B2 subunits of laminin to find restriction fragment length DNA polymorphisms for the genes encoding these polypeptides in the mouse. Three alleles were found for LamB2 and two for LamB1 among the inbred mouse strains. The segregation of these polymorphisms among recombinant inbred strains showed that these genes are tightly linked in the central region of mouse Chromosome 1 between Sas-1 and Ly-m22, 7.4 +/- 3.2 cM distal to the Pep-3 locus. There is no evidence in the mouse for pseudogenes for these proteins.

  9. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    NARCIS (Netherlands)

    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

  10. Nuclear norm regularized convolutional Max Pos@Top machine

    KAUST Repository

    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.

  11. A digital pixel cell for address event representation image convolution processing

    Science.gov (United States)

    Camunas-Mesa, Luis; Acosta-Jimenez, Antonio; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2005-06-01

    Address Event Representation (AER) is an emergent neuromorphic interchip communication protocol that allows for real-time virtual massive connectivity between huge number of neurons located on different chips. By exploiting high speed digital communication circuits (with nano-seconds timings), synaptic neural connections can be time multiplexed, while neural activity signals (with mili-seconds timings) are sampled at low frequencies. Also, neurons generate events according to their information levels. Neurons with more information (activity, derivative of activities, contrast, motion, edges,...) generate more events per unit time, and access the interchip communication channel more frequently, while neurons with low activity consume less communication bandwidth. AER technology has been used and reported for the implementation of various type of image sensors or retinae: luminance with local agc, contrast retinae, motion retinae,... Also, there has been a proposal for realizing programmable kernel image convolution chips. Such convolution chips would contain an array of pixels that perform weighted addition of events. Once a pixel has added sufficient event contributions to reach a fixed threshold, the pixel fires an event, which is then routed out of the chip for further processing. Such convolution chips have been proposed to be implemented using pulsed current mode mixed analog and digital circuit techniques. In this paper we present a fully digital pixel implementation to perform the weighted additions and fire the events. This way, for a given technology, there is a fully digital implementation reference against which compare the mixed signal implementations. We have designed, implemented and tested a fully digital AER convolution pixel. This pixel will be used to implement a full AER convolution chip for programmable kernel image convolution processing.

  12. Comparison of standard laparoscopic distal pancreatectomy with minimally invasive distal pancreatectomy using the da Vinci S system.

    Science.gov (United States)

    Ito, Masahiro; Asano, Yukio; Shimizu, Tomohiro; Uyama, Ichiro; Horiguchi, Akihiko

    2014-01-01

    Minimally invasive procedures for pancreatic pathologies are increasingly being used, including distal pancreatectomy. This study aimed to assess the indications for and outcomes of the da Vinci distal pancreatectomy procedure. We reviewed the medical records of patients who underwent pancreatic head resection from April 2009 to September 2013. Four patients (mean age, 52.7 years) underwent da Vinci distal pancreatectomy and 10 (mean age, 68.0 +/- 12.1 years) underwent laparoscopic distal pancreatectomy. The mean surgical duration was 292 +/- 153 min and 306 +/- 29 min, the mean blood loss was 153 +/- 71 mL and 61.7 +/- 72 mL, and the mean postoperative length of stay was 24 +/- 11 days and 14 +/- 3 days in the da Vinci distal pancreatectomy and laparoscopic distal pancreatectomy groups, respectively. One patient who underwent da Vinci distal pancreatectomy developed a pancreatic fistula, while 2 patients in the laparoscopic distal pancreatectomy group developed splenic ischemia and gastric torsion, respectively. Laparoscopic and robotic pancreatic resection were both safe and feasible in selected patients with distal pancreatic pathologies. Further studies are necessary to clarify the role of robotic surgery in the advanced laparoscopic era.

  13. 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.

  14. The convolution transform

    CERN Document Server

    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

  15. An innovative technique to distalize maxillary molar using microimplant supported rapid molar distalizer

    Directory of Open Access Journals (Sweden)

    Meenu Goel

    2013-01-01

    Full Text Available Introduction: In recent years, enhancements in implants have made their use possible as a mode of absolute anchorage in orthodontic patients. In this paper, the authors have introduced an innovative technique to unilaterally distalize the upper left 1 st molar to obtain an ideal Class I molar relationship from a Class II existing molar relationship with an indigenous designed distalizer. Clinical Innovation: For effective unilateral diatalization of molar, a novel cantilever sliding jig assembly was utilized with coil spring supported by a buccally placed single micro implant. The results showed 3 mm of bodily distalization with 1 mm of intrusion and 2° of distal tipping of upper left 1 st molar in 1.5 months. Discussion: This appliance is relatively easy to insert, well-tolerated, and requires minimal patient cooperation compared to other present techniques of molar distalization. Moreover, it is particularly useful in cases that are Class II on one side and Class I on the other, with a minor midline discrepancy and nominal overjet. Patient acceptance level was reported to be within patients physiological and comfort limits.

  16. Off-resonance artifacts correction with convolution in k-space (ORACLE).

    Science.gov (United States)

    Lin, Wei; Huang, Feng; Simonotto, Enrico; Duensing, George R; Reykowski, Arne

    2012-06-01

    Off-resonance artifacts hinder the wider applicability of echo-planar imaging and non-Cartesian MRI methods such as radial and spiral. In this work, a general and rapid method is proposed for off-resonance artifacts correction based on data convolution in k-space. The acquired k-space is divided into multiple segments based on their acquisition times. Off-resonance-induced artifact within each segment is removed by applying a convolution kernel, which is the Fourier transform of an off-resonance correcting spatial phase modulation term. The field map is determined from the inverse Fourier transform of a basis kernel, which is calibrated from data fitting in k-space. The technique was demonstrated in phantom and in vivo studies for radial, spiral and echo-planar imaging datasets. For radial acquisitions, the proposed method allows the self-calibration of the field map from the imaging data, when an alternating view-angle ordering scheme is used. An additional advantage for off-resonance artifacts correction based on data convolution in k-space is the reusability of convolution kernels to images acquired with the same sequence but different contrasts. Copyright © 2011 Wiley-Liss, Inc.

  17. Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

    International Nuclear Information System (INIS)

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-01-01

    Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a ''pearl-necklace'' encoder. Despite their algorithm's theoretical significance as a neat way of representing quantum convolutional codes, it is not well suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm to find a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work is an extension of our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the noncommutative paths through the pearl necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.

  18. Applying Gradient Descent in Convolutional Neural Networks

    Science.gov (United States)

    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.

  19. Distal renal tubular acidosis

    Science.gov (United States)

    ... this disorder. Alternative Names Renal tubular acidosis - distal; Renal tubular acidosis type I; Type I RTA; RTA - distal; Classical RTA Images Kidney anatomy Kidney - blood and urine flow References Bose A, Monk RD, Bushinsky DA. Kidney ...

  20. Sex reversal in the mouse (Mus musculus) is caused by a recurrent nonreciprocal crossover involving the x and an aberrant y chromosome.

    Science.gov (United States)

    Singh, L; Jones, K W

    1982-02-01

    Satellite DNA (Bkm) from the W sex-determining chromosome of snakes, which is related to sequences on the mouse Y chromosome, has been used to analyze the DNA and chromosomes of sex-reversed (Sxr) XXSxr male mice. Such mice exhibit a male-specific Southern blot Bkm hybridization pattern, consistent with the presence of Y-chromosome DNA. In situ hybridization of Bkm to chromosomes of XXSxr mice shows an aberrant concentration of related sequences on the distal terminus of a large mouse chromosome. The XYSxr carrier male, however, shows a pair of small chromosomes, which are presumed to be aberrant Y derivatives. Meiosis in the XYSxr mouse involves transfer of chromatin rich in Bkm-related DNA from the Y-Y1 complex to the X distal terminus. We suggest that this event is responsible for the transmission of the Sxr trait.

  1. ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

    Science.gov (United States)

    Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

    2017-10-01

    Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

  2. Efficient airport detection using region-based fully convolutional neural networks

    Science.gov (United States)

    Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao

    2018-04-01

    This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.

  3. 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

  4. Distal protection in cardiovascular medicine: current status.

    Science.gov (United States)

    Ali, Onn Akbar; Bhindi, Ravinay; McMahon, Aisling C; Brieger, David; Kritharides, Leonard; Lowe, Harry C

    2006-08-01

    Iatrogenic and spontaneous downstream microembolization of atheromatous material is increasingly recognized as a source of cardiovascular morbidity and mortality. Devising ways of reducing this distal embolization using a variety of mechanical means--distal protection--is currently under intense and diverse investigation. This review therefore summarizes the present status of distal protection. It examines the problem of distal embolization, describes the available distal protection devices, reviews those areas of cardiovascular medicine where distal protection devices are being investigated, and discusses potential future developments.

  5. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    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

  6. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    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).

  7. Linear diffusion-wave channel routing using a discrete Hayami convolution method

    Science.gov (United States)

    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...

  8. Convolution equations on lattices: periodic solutions with values in a prime characteristic field

    OpenAIRE

    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...

  9. Odf2-deficient mother centrioles lack distal/subdistal appendages and the ability to generate primary cilia.

    Science.gov (United States)

    Ishikawa, Hiroaki; Kubo, Akiharu; Tsukita, Shoichiro; Tsukita, Sachiko

    2005-05-01

    Outer dense fibre 2 (Odf2; also known as cenexin) was initially identified as a main component of the sperm tail cytoskeleton, but was later shown to be a general scaffold protein that is specifically localized at the distal/subdistal appendages of mother centrioles. Here we show that Odf2 expression is suppressed in mouse F9 cells when both alleles of Odf2 genes are deleted. Unexpectedly, the cell cycle of Odf2(-/-) cells does not seem to be affected. Immunofluorescence and ultrathin-section electron microscopy reveals that in Odf2(-/-) cells, distal/subdistal appendages disappear from mother centrioles, making it difficult to distinguish mother from daughter centrioles. In Odf2(-/-) cells, however, the formation of primary cilia is completely suppressed, although approximately 25% of wild-type F9 cells are ciliated under the steady-state cell cycle. The loss of primary cilia in Odf2(-/-) F9 cells can be rescued by exogenous Odf2 expression. These findings indicate that Odf2 is indispensable for the formation of distal/subdistal appendages and the generation of primary cilia, but not for other cell-cycle-related centriolar functions.

  10. AFM tip-sample convolution effects for cylinder protrusions

    Science.gov (United States)

    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.

  11. Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. I. The effect of shift invariance

    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

  12. Spectral interpolation - Zero fill or convolution. [image processing

    Science.gov (United States)

    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.

  13. Cell dynamic morphology classification using deep convolutional neural networks.

    Science.gov (United States)

    Li, Heng; Pang, Fengqian; Shi, Yonggang; Liu, Zhiwen

    2018-05-15

    Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs. Considering the installation of deep learning, the classification problem was simplified from video data to image data, and was then solved by CNNs in a self-taught manner with the generated image data. CNNs were separately performed in three installation scenarios and compared with existing methods. Experimental results demonstrated the potential of CNNs in cell dynamic morphology classification, and validated the effectiveness of the proposed strategy. CNNs were successfully applied to the classification problem, and outperformed the existing methods in the classification accuracy. For the installation of CNNs, transfer learning was proved to be a promising scheme. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

  14. Image quality assessment using deep convolutional networks

    Science.gov (United States)

    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.

  15. Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

    OpenAIRE

    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...

  16. [New anterolateral approach of distal femur for treatment of distal femoral fractures].

    Science.gov (United States)

    Zhang, Bin; Dai, Min; Zou, Fan; Luo, Song; Li, Binhua; Qiu, Ping; Nie, Tao

    2013-11-01

    To assess the effectiveness of the new anterolateral approach of the distal femur for the treatment of distal femoral fractures. Between July 2007 and December 2009, 58 patients with distal femoral fractures were treated by new anterolateral approach of the distal femur in 28 patients (new approach group) and by conventional approach in 30 patients (conventional approach group). There was no significant difference in gender, age, cause of injury, affected side, type of fracture, disease duration, complication, or preoperative intervention (P > 0.05). The operation time, intraoperative blood loss, intraoperative fluoroscopy frequency, hospitalization days, and Hospital for Special Surgery (HSS) score of knee were recorded. Operation was successfully completed in all patients of 2 groups, and healing of incision by first intention was obtained; no vascular and nerves injuries occurred. The operation time and intraoperative fluoroscopy frequency of new approach group were significantly less than those of conventional approach group (P 0.05). All patients were followed up 12-36 months (mean, 19.8 months). Bone union was shown on X-ray films; the fracture healing time was (12.62 +/- 2.34) weeks in the new approach group and was (13.78 +/- 1.94) weeks in the conventional approach group, showing no significant difference (t=2.78, P=0.10). The knee HSS score at last follow-up was 94.4 +/- 4.2 in the new approach group, and was 89.2 +/- 6.0 in the conventional approach group, showing significant difference between 2 groups (t=3.85, P=0.00). New anterolateral approach of the distal femur for distal femoral fractures has the advantages of exposure plenitude, minimal tissue trauma, and early function rehabilitation training so as to enhance the function recovery of knee joint.

  17. 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....

  18. 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.

  19. A convolution method for predicting mean treatment dose including organ motion at imaging

    International Nuclear Information System (INIS)

    Booth, J.T.; Zavgorodni, S.F.; Royal Adelaide Hospital, SA

    2000-01-01

    Full text: The random treatment delivery errors (organ motion and set-up error) can be incorporated into the treatment planning software using a convolution method. Mean treatment dose is computed as the convolution of a static dose distribution with a variation kernel. Typically this variation kernel is Gaussian with variance equal to the sum of the organ motion and set-up error variances. We propose a novel variation kernel for the convolution technique that additionally considers the position of the mobile organ in the planning CT image. The systematic error of organ position in the planning CT image can be considered random for each patient over a population. Thus the variance of the variation kernel will equal the sum of treatment delivery variance and organ motion variance at planning for the population of treatments. The kernel is extended to deal with multiple pre-treatment CT scans to improve tumour localisation for planning. Mean treatment doses calculated with the convolution technique are compared to benchmark Monte Carlo (MC) computations. Calculations of mean treatment dose using the convolution technique agreed with MC results for all cases to better than ± 1 Gy in the planning treatment volume for a prescribed 60 Gy treatment. Convolution provides a quick method of incorporating random organ motion (captured in the planning CT image and during treatment delivery) and random set-up errors directly into the dose distribution. Copyright (2000) Australasian College of Physical Scientists and Engineers in Medicine

  20. 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...

  1. Convolutional Codes with Maximum Column Sum Rank for Network Streaming

    OpenAIRE

    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...

  2. 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

  3. A convolutional neural network to filter artifacts in spectroscopic MRI.

    Science.gov (United States)

    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.

  4. QCDNUM: Fast QCD evolution and convolution

    Science.gov (United States)

    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

  5. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Deformable image registration using convolutional neural networks

    NARCIS (Netherlands)

    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

  7. 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.

  8. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

    OpenAIRE

    Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

    2017-01-01

    In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

  9. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    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.

  10. 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...

  11. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    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.

  12. 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.

  13. A frequency bin-wise nonlinear masking algorithm in convolutive mixtures for speech segregation.

    Science.gov (United States)

    Chi, Tai-Shih; Huang, Ching-Wen; Chou, Wen-Sheng

    2012-05-01

    A frequency bin-wise nonlinear masking algorithm is proposed in the spectrogram domain for speech segregation in convolutive mixtures. The contributive weight from each speech source to a time-frequency unit of the mixture spectrogram is estimated by a nonlinear function based on location cues. For each sound source, a non-binary mask is formed from the estimated weights and is multiplied to the mixture spectrogram to extract the sound. Head-related transfer functions (HRTFs) are used to simulate convolutive sound mixtures perceived by listeners. Simulation results show our proposed method outperforms convolutive independent component analysis and degenerate unmixing and estimation technique methods in almost all test conditions.

  14. Face recognition via Gabor and convolutional neural network

    Science.gov (United States)

    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.

  15. Isointense infant brain MRI segmentation with a dilated convolutional neural network

    OpenAIRE

    Moeskops, Pim; Pluim, Josien 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 of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

  16. Learning text representation using recurrent convolutional neural network with highway layers

    OpenAIRE

    Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

    2016-01-01

    Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

  17. A locality aware convolutional neural networks accelerator

    NARCIS (Netherlands)

    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

  18. The convolution integral for the forward-backward asymmetry in e+e- annihilation

    International Nuclear Information System (INIS)

    Bardin, D.; Bilenky, M.; Chizhov, A.; Sazonov, A.; Sedykh, Yu.; Riemann, T.; Sachwitz, M.

    1989-01-01

    The complete convolution integral for the forward-backward asymmetry in A FB in e + e - annihilation is obtained in order O(α) with soft photon exponentiation. The influence of these QED corrections on A FB in the vicinity of the Z peak is discussed. The results are used to comment on a recent ad hoc ansatz using convolution weights derived for the total cross section. (orig.)

  19. A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks.

    Science.gov (United States)

    Serang, Oliver

    2015-08-01

    Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

  20. Arthroplasty of the distal ulna distal in managing patients with post-traumatic disorders of the distal radioulnar joint: measurement of quality of life

    Directory of Open Access Journals (Sweden)

    Marcio Aurélio Aita

    2015-12-01

    Full Text Available ABSTRACT OBJECTIVE: To measure the quality of life and clinical-functional results from patients diagnosed with osteoarthrosis of the distal radioulnar joint who underwent surgical treatment using the technique of total arthroplasty of the ulna, with a total or partial Ascension(r prosthesis of the distal ulna. METHODS: Ten patients were evaluated after 12 months of follow-up subsequent to total or partial arthroplasty of the distal ulna. All of them presented post-traumatic osteoarthrosis and/or chronic symptomatic instability of the distal radioulnar joint. The study was prospective. Seven patients had previously undergone wrist procedures (two cases with Darrach, three with Sauvé-Kapandji and two with ligament reconstruction of the fibrocartilage complex and three presented fractures of the distal ulna that evolved with pain, instability and osteoarthrosis of the distal radioulnar joint. The following were assessed: quality of life (DASH scale; percentage degree of palm grip strength (kgf and pronosupination range of motion in relation to the unaffected side; pain (VAS; return to work; subjective evaluation of radiography; and complications. RESULTS: The patients presented a mean range of motion of 174.5° (normal side: 180°. Quality of life was analyzed by applying the DASH questionnaire and the mean value found was 5.9. The mean pain score using the VAS was 2.3. The mean degree of palm grip strength (kgf was 50.7, which represented 90.7% of the strength on the unaffected side. The complication rate was 10%: this patient presented slight dorsal instability of the ulna and persistent pain, and did not return to work. This patient is still being followed up in the outpatient clinic and occupational therapy sector, with little improvement. He does not wish to undergo a new procedure. The mean length of follow-up was 16.8 months, with a minimum of 10 and maximum of 36 months. CONCLUSION: This concept is subject to the test of time

  1. Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.

  2. Musculoskeletal Geometry, Muscle Architecture and Functional Specialisations of the Mouse Hindlimb.

    Directory of Open Access Journals (Sweden)

    James P Charles

    Full Text Available Mice are one of the most commonly used laboratory animals, with an extensive array of disease models in existence, including for many neuromuscular diseases. The hindlimb is of particular interest due to several close muscle analogues/homologues to humans and other species. A detailed anatomical study describing the adult morphology is lacking, however. This study describes in detail the musculoskeletal geometry and skeletal muscle architecture of the mouse hindlimb and pelvis, determining the extent to which the muscles are adapted for their function, as inferred from their architecture. Using I2KI enhanced microCT scanning and digital segmentation, it was possible to identify 39 distinct muscles of the hindlimb and pelvis belonging to nine functional groups. The architecture of each of these muscles was determined through microdissections, revealing strong architectural specialisations between the functional groups. The hip extensors and hip adductors showed significantly stronger adaptations towards high contraction velocities and joint control relative to the distal functional groups, which exhibited larger physiological cross sectional areas and longer tendons, adaptations for high force output and elastic energy savings. These results suggest that a proximo-distal gradient in muscle architecture exists in the mouse hindlimb. Such a gradient has been purported to function in aiding locomotor stability and efficiency. The data presented here will be especially valuable to any research with a focus on the architecture or gross anatomy of the mouse hindlimb and pelvis musculature, but also of use to anyone interested in the functional significance of muscle design in relation to quadrupedal locomotion.

  3. Robotic distal pancreatectomy versus conventional laparoscopic distal pancreatectomy: a comparative study for short-term outcomes.

    Science.gov (United States)

    Lai, Eric C H; Tang, Chung Ngai

    2015-09-01

    Robotic system has been increasingly used in pancreatectomy. However, the effectiveness of this method remains uncertain. This study compared the surgical outcomes between robot-assisted laparoscopic distal pancreatectomy and conventional laparoscopic distal pancreatectomy. During a 15-year period, 35 patients underwent minimally invasive approach of distal pancreatectomy in our center. Seventeen of these patients had robot-assisted laparoscopic approach, and the remaining 18 had conventional laparoscopic approach. Their operative parameters and perioperative outcomes were analyzed retrospectively in a prospective database. The mean operating time in the robotic group (221.4 min) was significantly longer than that in the laparoscopic group (173.6 min) (P = 0.026). Both robotic and conventional laparoscopic groups presented no significant difference in spleen-preservation rate (52.9% vs. 38.9%) (P = 0.505), operative blood loss (100.3 ml vs. 268.3 ml) (P = 0.29), overall morbidity rate (47.1% vs. 38.9%) (P = 0.73), and post-operative hospital stay (11.4 days vs. 14.2 days) (P = 0.46). Both groups also showed no perioperative mortality. Similar outcomes were observed in robotic distal pancreatectomy and conventional laparoscopic approach. However, robotic approach tended to have the advantages of less blood loss and shorter hospital stay. Further studies are necessary to determine the clinical position of robotic distal pancreatectomy.

  4. Intraspecies Competition for Niches in the Distal Gut Dictate Transmission during Persistent Salmonella Infection

    Science.gov (United States)

    Lam, Lilian H.; Monack, Denise M.

    2014-01-01

    In order to be transmitted, a pathogen must first successfully colonize and multiply within a host. Ecological principles can be applied to study host-pathogen interactions to predict transmission dynamics. Little is known about the population biology of Salmonella during persistent infection. To define Salmonella enterica serovar Typhimurium population structure in this context, 129SvJ mice were oral gavaged with a mixture of eight wild-type isogenic tagged Salmonella (WITS) strains. Distinct subpopulations arose within intestinal and systemic tissues after 35 days, and clonal expansion of the cecal and colonic subpopulation was responsible for increases in Salmonella fecal shedding. A co-infection system utilizing differentially marked isogenic strains was developed in which each mouse received one strain orally and the other systemically by intraperitoneal (IP) injection. Co-infections demonstrated that the intestinal subpopulation exerted intraspecies priority effects by excluding systemic S. Typhimurium from colonizing an extracellular niche within the cecum and colon. Importantly, the systemic strain was excluded from these distal gut sites and was not transmitted to naïve hosts. In addition, S. Typhimurium required hydrogenase, an enzyme that mediates acquisition of hydrogen from the gut microbiota, during the first week of infection to exert priority effects in the gut. Thus, early inhibitory priority effects are facilitated by the acquisition of nutrients, which allow S. Typhimurium to successfully compete for a nutritional niche in the distal gut. We also show that intraspecies colonization resistance is maintained by Salmonella Pathogenicity Islands SPI1 and SPI2 during persistent distal gut infection. Thus, important virulence effectors not only modulate interactions with host cells, but are crucial for Salmonella colonization of an extracellular intestinal niche and thereby also shape intraspecies dynamics. We conclude that priority effects and

  5. Rock images classification by using deep convolution neural network

    Science.gov (United States)

    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.

  6. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2017-01-01

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

  7. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  8. 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.

  9. 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...

  10. Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

    Science.gov (United States)

    Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

    2017-11-01

    Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

  11. 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.

  12. Convoluted laminations in waterlain sediments:three examples from Eastern Canada and their relevance to neotectonics

    International Nuclear Information System (INIS)

    Macdougall, D.A.; Broster, B.E.

    1995-10-01

    The catastrophic disturbance of unconsolidated sediment produces a wide variety of deformation structures, particularly if the sediment is water-saturated at the time of disturbance. Layers, originally deposited as sub-horizontal, can become stretched or distended resulting in convoluted laminations. Faulted beds, slumped units, or dewatering structures may also occur in association with the disturbance. Convolutions were studied in five examples of Pleistocene glaciomarine deltas, at three locations in eastern Canada. Results from this study indicate that similar structures were produced in each of the sediment deposits, but some are especially common in specific facies (e.g. bottomset, foreset, topset). However, the particular cause of the convolutions varied within each deposit, and the origin could be better assessed when studied in relationship to other structures. None of the convolutions found could be attributed, categorically, to a seismic origin. However, neither could a seismic origin be dismissed for structures associated with convolutions occurring in deposits at: St. George, New Brunswick; Economy Point, Nova Scotia; and Lanark, Ontario. Of these deposits, the deformed structures at Economy Point are apparently post-glacial. (author). 24 refs., 58 figs

  13. Cascaded K-means convolutional feature learner and its application to face recognition

    Science.gov (United States)

    Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu

    2017-09-01

    Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.

  14. Deep learning for steganalysis via convolutional neural networks

    Science.gov (United States)

    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.

  15. 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....

  16. Using convolutional decoding to improve time delay and phase estimation in digital communications

    Science.gov (United States)

    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.

  17. Wrist Hypothermia Related to Continuous Work with a Computer Mouse: A Digital Infrared Imaging Pilot Study

    Directory of Open Access Journals (Sweden)

    Jelena Reste

    2015-08-01

    Full Text Available Computer work is characterized by sedentary static workload with low-intensity energy metabolism. The aim of our study was to evaluate the dynamics of skin surface temperature in the hand during prolonged computer mouse work under different ergonomic setups. Digital infrared imaging of the right forearm and wrist was performed during three hours of continuous computer work (measured at the start and every 15 minutes thereafter in a laboratory with controlled ambient conditions. Four people participated in the study. Three different ergonomic computer mouse setups were tested on three different days (horizontal computer mouse without mouse pad; horizontal computer mouse with mouse pad and padded wrist support; vertical computer mouse without mouse pad. The study revealed a significantly strong negative correlation between the temperature of the dorsal surface of the wrist and time spent working with a computer mouse. Hand skin temperature decreased markedly after one hour of continuous computer mouse work. Vertical computer mouse work preserved more stable and higher temperatures of the wrist (>30 °C, while continuous use of a horizontal mouse for more than two hours caused an extremely low temperature (<28 °C in distal parts of the hand. The preliminary observational findings indicate the significant effect of the duration and ergonomics of computer mouse work on the development of hand hypothermia.

  18. 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.

  19. Evolutionary image simplification for lung nodule classification with convolutional neural networks.

    Science.gov (United States)

    Lückehe, Daniel; von Voigt, Gabriele

    2018-05-29

    Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

  20. Acral melanoma detection using a convolutional neural network for dermoscopy images.

    Science.gov (United States)

    Yu, Chanki; Yang, Sejung; Kim, Wonoh; Jung, Jinwoong; Chung, Kee-Yang; Lee, Sang Wook; Oh, Byungho

    2018-01-01

    Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

  1. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    Directory of Open Access Journals (Sweden)

    S Safinaz

    2017-08-01

    Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.

  2. Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.

    Science.gov (United States)

    Wu, Meng; Yoon, Sungwon; Solomon, Edward G; Star-Lack, Josh; Pelc, Norbert; Fahrig, Rebecca

    2016-01-01

    Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

  3. Alternate symbol inversion for improved symbol synchronization in convolutionally coded systems

    Science.gov (United States)

    Simon, M. K.; Smith, J. G.

    1980-01-01

    Inverting alternate symbols of the encoder output of a convolutionally coded system provides sufficient density of symbol transitions to guarantee adequate symbol synchronizer performance, a guarantee otherwise lacking. Although alternate symbol inversion may increase or decrease the average transition density, depending on the data source model, it produces a maximum number of contiguous symbols without transition for a particular class of convolutional codes, independent of the data source model. Further, this maximum is sufficiently small to guarantee acceptable symbol synchronizer performance for typical applications. Subsequent inversion of alternate detected symbols permits proper decoding.

  4. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    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...

  5. No-reference image quality assessment based on statistics of convolution feature maps

    Science.gov (United States)

    Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

    2018-04-01

    We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

  6. Fast Automatic Airport Detection in Remote Sensing Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Fen Chen

    2018-03-01

    Full Text Available Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method.

  7. Design and Implementation of Convolutional Encoder and Viterbi Decoder Using FPGA.

    Directory of Open Access Journals (Sweden)

    Riham Ali Zbaid

    2018-01-01

    Full Text Available Keeping  the  fineness of data is the most significant thing in communication.There are many factors that affect the accuracy of the data when it is transmitted over the communication channel such as noise etc. to overcome these effects are encoding channels encryption.In this paper is used for one type of channel coding is convolutional codes. Convolution encoding is a Forward Error Correction (FEC method used in incessant one-way and real time communication links .It can offer a great development in the error bit rates so that small, low energy, and devices cheap transmission when used in applications such as satellites. In this paper highlight the design, simulation and implementation of convolution encoder and Viterbi decoder by using MATLAB- program (2011. SIMULINK HDL coder is used to convert MATLAB-SIMULINK models to VHDL using plates Altera Cyclone II code DE2-70. Simulation and evaluation of the implementation of the results coincided with the results of the design show the coinciding with the designed results.

  8. Enhancement of digital radiography image quality using a convolutional neural network.

    Science.gov (United States)

    Sun, Yuewen; Li, Litao; Cong, Peng; Wang, Zhentao; Guo, Xiaojing

    2017-01-01

    Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether the image quality acquired by the digital radiography system can be improved with a modified convolutional neural network to generate high-resolution images with reduced noise from the original low-quality images. The experiment evaluated on a test dataset, which contains 5 X-ray images, showed that the proposed method outperformed the traditional methods (i.e., bicubic interpolation and 3D block-matching approach) as measured by peak signal to noise ratio (PSNR) about 1.3 dB while kept highly efficient processing time within one second. Experimental results demonstrated that a residual to residual (RTR) convolutional neural network remarkably improved the image quality of object structural details by increasing the image resolution and reducing image noise. Thus, this study indicated that applying this RTR convolutional neural network system was useful to improve image quality acquired by the digital radiography system.

  9. 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

  10. Tandem mass spectrometry data quality assessment by self-convolution.

    Science.gov (United States)

    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

  11. 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 ...

  12. Symbol Stream Combining in a Convolutionally Coded System

    Science.gov (United States)

    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.

  13. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

    KAUST Repository

    Umarov, Ramzan

    2017-02-03

    Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

  14. DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

    Science.gov (United States)

    Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

    2017-09-01

    Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

  15. Convolutional Encoder and Viterbi Decoder Using SOPC For Variable Constraint Length

    DEFF Research Database (Denmark)

    Kulkarni, Anuradha; Dnyaneshwar, Mantri; Prasad, Neeli R.

    2013-01-01

    Convolution encoder and Viterbi decoder are the basic and important blocks in any Code Division Multiple Accesses (CDMA). They are widely used in communication system due to their error correcting capability But the performance degrades with variable constraint length. In this context to have...... detailed analysis, this paper deals with the implementation of convolution encoder and Viterbi decoder using system on programming chip (SOPC). It uses variable constraint length of 7, 8 and 9 bits for 1/2 and 1/3 code rates. By analyzing the Viterbi algorithm it is seen that our algorithm has a better...

  16. Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification

    Directory of Open Access Journals (Sweden)

    Na Liu

    2018-03-01

    Full Text Available The extraction of activation vectors (or deep features from the fully connected layers of a convolutional neural network (CNN model is widely used for remote sensing image (RSI representation. In this study, we propose to learn discriminative convolution filter (DCF based on class-specific separability criteria for linear transformation of deep features. In particular, two types of pretrained CNN called CaffeNet and VGG-VD16 are introduced to illustrate the generality of the proposed DCF. The activation vectors extracted from the fully connected layers of a CNN are rearranged into the form of an image matrix, from which a spatial arrangement of local patches is extracted using sliding window strategy. DCF learning is then performed on each local patch individually to obtain the corresponding discriminative convolution kernel through generalized eigenvalue decomposition. The proposed DCF learning characterizes that a convolutional kernel with small size (e.g., 3 × 3 pixels can be effectively learned on a small-size local patch (e.g., 8 × 8 pixels, thereby ensuring that the linear transformation of deep features can maintain low computational complexity. Experiments on two RSI datasets demonstrate the effectiveness of DCF in improving the classification performances of deep features without increasing dimensionality.

  17. 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...

  18. Cloud Detection by Fusing Multi-Scale Convolutional Features

    Science.gov (United States)

    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.

  19. Is Kinesio Taping to Generate Skin Convolutions Effective for Increasing Local Blood Circulation?

    OpenAIRE

    Yang, Jae-Man; Lee, Jung-Hoon

    2018-01-01

    Background It is unclear whether traditional application of Kinesio taping, which produces wrinkles in the skin, is effective for improving blood circulation. This study investigated local skin temperature changes after the application of an elastic therapeutic tape using convolution and non-convolution taping methods (CTM/NCTM). Material/Methods Twenty-eight pain-free men underwent CTM and NCTM randomly applied to the right and left sides of the lower back. Using infrared thermography, skin ...

  20. Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

    OpenAIRE

    Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

    2018-01-01

    Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

  1. Combined open proximal and stent-graft distal repair for distal arch aneurysms: an alternative to total debranching.

    Science.gov (United States)

    Zierer, Andreas; Sanchez, Luis A; Moon, Marc R

    2009-07-01

    We present herein a novel, combined, simultaneous open proximal and stent-graft distal repair for complex distal aortic arch aneurysms involving the descending aorta. In the first surgical step, the transverse arch is opened during selective antegrade cerebral perfusion, and a Dacron graft (DuPont, Wilmington, DE) is positioned down the descending aorta in an elephant trunk-like fashion with its proximal free margin sutured circumferentially to the aorta just distal to the left subclavian or left common carotid artery. With the graft serving as the new proximal landing zone, subsequent endovascular repair is performed antegrade during rewarming through the ascending aorta.

  2. Distal clavicular osteolysis: MR evidence for subchondral fracture

    Energy Technology Data Exchange (ETDEWEB)

    Kassarjian, Ara; Palmer, William E. [Massachusetts General Hospital, Department of Radiology, Division of Musculoskeletal Radiology, Yawkey Center, Boston, MA (United States); Llopis, Eva [Hospital de la Ribera, Department of Radiology, Valencia (Spain)

    2007-01-15

    To investigate the association between distal clavicular osteolysis and subchondral fractures of the distal clavicle at MRI. This study was approved by the hospital human research committee, which waived the need for informed consent. Three radiologists retrospectively analyzed 36 shoulder MR examinations in 36 patients with imaging findings of distal clavicular osteolysis. The presence of a subchondral fracture of the distal clavicle, abnormalities of the acromioclavicular joint, rotator cuff tears and labral tears were assessed by MRI. These cases were then compared with 36 age-matched controls. At MRI, 31 of 36 patients (86%) had a subchondral line within the distal clavicular edema, consistent with a subchondral fracture. Of the 36 patients, 32 (89%) had fluid in the acromioclavicular joint, while 27 of 36 patients (75%) had cysts or erosions in the distal clavicle. There were 13 patients (36%) with associated labral tears, while eight patients (22%) had partial-thickness rotator cuff tears. In the control group one of 36 (3%) had a subchondral line (P<0.05), while ten of 36 (28%) had rotator cuff tears and 13 of 36 (36%) had labral tears. These latter two were not statistically significant between the groups. A distal clavicular subchondral fracture is a common finding in patients with imaging evidence of distal clavicular osteolysis. These subchondral fractures may be responsible for the propensity of findings occurring on the clavicular side of the acromioclavicular joint. (orig.)

  3. Distal clavicular osteolysis: MR evidence for subchondral fracture

    International Nuclear Information System (INIS)

    Kassarjian, Ara; Palmer, William E.; Llopis, Eva

    2007-01-01

    To investigate the association between distal clavicular osteolysis and subchondral fractures of the distal clavicle at MRI. This study was approved by the hospital human research committee, which waived the need for informed consent. Three radiologists retrospectively analyzed 36 shoulder MR examinations in 36 patients with imaging findings of distal clavicular osteolysis. The presence of a subchondral fracture of the distal clavicle, abnormalities of the acromioclavicular joint, rotator cuff tears and labral tears were assessed by MRI. These cases were then compared with 36 age-matched controls. At MRI, 31 of 36 patients (86%) had a subchondral line within the distal clavicular edema, consistent with a subchondral fracture. Of the 36 patients, 32 (89%) had fluid in the acromioclavicular joint, while 27 of 36 patients (75%) had cysts or erosions in the distal clavicle. There were 13 patients (36%) with associated labral tears, while eight patients (22%) had partial-thickness rotator cuff tears. In the control group one of 36 (3%) had a subchondral line (P<0.05), while ten of 36 (28%) had rotator cuff tears and 13 of 36 (36%) had labral tears. These latter two were not statistically significant between the groups. A distal clavicular subchondral fracture is a common finding in patients with imaging evidence of distal clavicular osteolysis. These subchondral fractures may be responsible for the propensity of findings occurring on the clavicular side of the acromioclavicular joint. (orig.)

  4. The secretory KCa1.1 channel localises to crypts of distal mouse colon: functional and molecular evidence

    DEFF Research Database (Denmark)

    Sørensen, Mads Vaarby; Strandsby, Anne Bystrup; Larsen, Casper Kornbech

    2011-01-01

    The colonic epithelium absorbs and secretes electrolytes and water. Ion and water absorption occurs primarily in surface cells, whereas crypt cells perform secretion. Ion transport in distal colon is regulated by aldosterone, which stimulates both Na+ absorption and K+ secretion. The electrogenic...

  5. Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Abdullah-Al Nahid

    2018-01-01

    Full Text Available Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a Convolutional Neural Network Raw Image (CNN-I; (b Convolutional Neural Network CT Histogram (CNN-CH; (c Convolutional Neural Network CT LBP (CNN-CL; (d Convolutional

  6. Estimating the number of sources in a noisy convolutive mixture using BIC

    DEFF Research Database (Denmark)

    Olsson, Rasmus Kongsgaard; Hansen, Lars Kai

    2004-01-01

    The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the s......The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics...

  7. The Application of Real Convolution for Analytically Evaluating Fermi-Dirac-Type and Bose-Einstein-Type Integrals

    Directory of Open Access Journals (Sweden)

    Jerry P. Selvaggi

    2018-01-01

    Full Text Available The Fermi-Dirac-type or Bose-Einstein-type integrals can be transformed into two convergent real-convolution integrals. The transformation simplifies the integration process and may ultimately produce a complete analytical solution without recourse to any mathematical approximations. The real-convolution integrals can either be directly integrated or be transformed into the Laplace Transform inversion integral in which case the full power of contour integration becomes available. Which method is employed is dependent upon the complexity of the real-convolution integral. A number of examples are introduced which will illustrate the efficacy of the analytical approach.

  8. Theory on the mechanism of distal action of transcription factors: looping of DNA versus tracking along DNA

    Energy Technology Data Exchange (ETDEWEB)

    Murugan, R, E-mail: rmurugan@gmail.co [Department of Biotechnology, Indian Institute of Technology Madras, Chennai 600036 (India)

    2010-10-15

    In this paper, we develop a theory on the mechanism of distal action of the transcription factors, which are bound at their respective cis-regulatory enhancer modules on the promoter-RNA polymerase II (PR) complexes to initiate the transcription event in eukaryotes. We consider both the looping and tracking modes of their distal communication and calculate the mean first passage time that is required for the distal interactions of the complex of enhancer and transcription factor with the PR via both these modes. We further investigate how this mean first passage time is dependent on the length of the DNA segment (L, base-pairs) that connects the cis-regulatory binding site and the respective promoter. When the radius of curvature of this connecting segment of DNA is R that was induced upon binding of the transcription factor at the cis-acting element and RNAPII at the promoter in cis-positions, our calculations indicate that the looping mode of distal action will dominate when L is such that L > 2{pi}R and the tracking mode of distal action will be favored when L < 2{pi}R. The time required for the distal action will be minimum when L = 2{pi}R where the typical value of R for the binding of histones will be R {approx} 16 bps and L {approx} 10{sup 2} bps. It seems that the free energy associated with the binding of the transcription factor with its cis-acting element and the distance of this cis-acting element from the corresponding promoter of the gene of interest is negatively correlated. Our results suggest that the looping and tracking modes of distal action are concurrently operating on the transcription activation and the physics that determines the timescales associated with the looping/tracking in the mechanism of action of these transcription factors on the initiation of the transcription event must put a selection pressure on the distribution of the distances of cis-regulatory modules from their respective promoters of the genes. The computational analysis

  9. Theory on the mechanism of distal action of transcription factors: looping of DNA versus tracking along DNA

    International Nuclear Information System (INIS)

    Murugan, R

    2010-01-01

    In this paper, we develop a theory on the mechanism of distal action of the transcription factors, which are bound at their respective cis-regulatory enhancer modules on the promoter-RNA polymerase II (PR) complexes to initiate the transcription event in eukaryotes. We consider both the looping and tracking modes of their distal communication and calculate the mean first passage time that is required for the distal interactions of the complex of enhancer and transcription factor with the PR via both these modes. We further investigate how this mean first passage time is dependent on the length of the DNA segment (L, base-pairs) that connects the cis-regulatory binding site and the respective promoter. When the radius of curvature of this connecting segment of DNA is R that was induced upon binding of the transcription factor at the cis-acting element and RNAPII at the promoter in cis-positions, our calculations indicate that the looping mode of distal action will dominate when L is such that L > 2πR and the tracking mode of distal action will be favored when L 2 bps. It seems that the free energy associated with the binding of the transcription factor with its cis-acting element and the distance of this cis-acting element from the corresponding promoter of the gene of interest is negatively correlated. Our results suggest that the looping and tracking modes of distal action are concurrently operating on the transcription activation and the physics that determines the timescales associated with the looping/tracking in the mechanism of action of these transcription factors on the initiation of the transcription event must put a selection pressure on the distribution of the distances of cis-regulatory modules from their respective promoters of the genes. The computational analysis of the upstream sequences of promoters of various genes in the human and mouse genomes for the presence of putative cis-regulatory elements for a set of known transcription factors using

  10. Adversarial training and dilated convolutions for brain MRI segmentation

    NARCIS (Netherlands)

    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

  11. a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

    Science.gov (United States)

    Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

    2018-04-01

    Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

  12. Quantifying the interplay effect in prostate IMRT delivery using a convolution-based method

    International Nuclear Information System (INIS)

    Li, Haisen S.; Chetty, Indrin J.; Solberg, Timothy D.

    2008-01-01

    The authors present a segment-based convolution method to account for the interplay effect between intrafraction organ motion and the multileaf collimator position for each particular segment in intensity modulated radiation therapy (IMRT) delivered in a step-and-shoot manner. In this method, the static dose distribution attributed to each segment is convolved with the probability density function (PDF) of motion during delivery of the segment, whereas in the conventional convolution method (''average-based convolution''), the static dose distribution is convolved with the PDF averaged over an entire fraction, an entire treatment course, or even an entire patient population. In the case of IMRT delivered in a step-and-shoot manner, the average-based convolution method assumes that in each segment the target volume experiences the same motion pattern (PDF) as that of population. In the segment-based convolution method, the dose during each segment is calculated by convolving the static dose with the motion PDF specific to that segment, allowing both intrafraction motion and the interplay effect to be accounted for in the dose calculation. Intrafraction prostate motion data from a population of 35 patients tracked using the Calypso system (Calypso Medical Technologies, Inc., Seattle, WA) was used to generate motion PDFs. These were then convolved with dose distributions from clinical prostate IMRT plans. For a single segment with a small number of monitor units, the interplay effect introduced errors of up to 25.9% in the mean CTV dose compared against the planned dose evaluated by using the PDF of the entire fraction. In contrast, the interplay effect reduced the minimum CTV dose by 4.4%, and the CTV generalized equivalent uniform dose by 1.3%, in single fraction plans. For entire treatment courses delivered in either a hypofractionated (five fractions) or conventional (>30 fractions) regimen, the discrepancy in total dose due to interplay effect was negligible

  13. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    Science.gov (United States)

    Lee, L.-N.

    1977-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively modest coding complexity, it is proposed to concatenate a byte-oriented unit-memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real-time minimal-byte-error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  14. Space-Time Convolutional Codes over Finite Fields and Rings for Systems with Large Diversity Order

    Directory of Open Access Journals (Sweden)

    B. F. Uchôa-Filho

    2008-06-01

    Full Text Available We propose a convolutional encoder over the finite ring of integers modulo pk,ℤpk, where p is a prime number and k is any positive integer, to generate a space-time convolutional code (STCC. Under this structure, we prove three properties related to the generator matrix of the convolutional code that can be used to simplify the code search procedure for STCCs over ℤpk. Some STCCs of large diversity order (≥4 designed under the trace criterion for n=2,3, and 4 transmit antennas are presented for various PSK signal constellations.

  15. Combining morphometric features and convolutional networks fusion for glaucoma diagnosis

    Science.gov (United States)

    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.

  16. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    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.

  17. Convolute laminations — a theoretical analysis: example of a Pennsylvanian sandstone

    Science.gov (United States)

    Visher, Glenn S.; Cunningham, Russ D.

    1981-03-01

    Data from an outcropping laminated interval were collected and analyzed to test the applicability of a theoretical model describing instability of layered systems. Rayleigh—Taylor wave perturbations result at the interface between fluids of contrasting density, viscosity, and thickness. In the special case where reverse density and viscosity interlaminations are developed, the deformation response produces a single wave with predictable amplitudes, wavelengths, and amplification rates. Physical measurements from both the outcropping section and modern sediments suggest the usefulness of the model for the interpretation of convolute laminations. Internal characteristics of the stratigraphic interval, and the developmental sequence of convoluted beds, are used to document the developmental history of these structures.

  18. Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.

    Science.gov (United States)

    Wang, Shui-Hua; Lv, Yi-Ding; Sui, Yuxiu; Liu, Shuai; Wang, Su-Jing; Zhang, Yu-Dong

    2017-11-17

    Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU.

  19. Unilateral maxillary molar distalization with zygoma-gear appliance.

    Science.gov (United States)

    Kilkis, Dogan; Bayram, Mehmet; Celikoglu, Mevlut; Nur, Metin

    2012-08-01

    The aim of this study was to present the orthodontic treatment of a 15-year-old boy with a unilateral maxillary molar distalization system, called the zygoma-gear appliance. It consisted of a zygomatic anchorage miniplate, an inner bow, and a Sentalloy closed coil spring (GAC International, Bohemia, NY). A distalizing force of 350 g was used during the distalization period. The unilateral Class II malocclusion was corrected in 5 months with the zygoma-gear appliance. The maxillary left first molar showed distalization of 4 mm with an inclination of 3°. The maxillary premolars moved distally with the help of the transseptal fibers. In addition, there were slight decreases in overjet (-0.5 mm) and maxillary incisor inclination (-1°), indicating no anchorage loss from the zygoma-gear appliance. Preadjusted fixed appliances (0.022 × 0.028-in, MBT system; 3M Unitek, Monrovia, Calif) were placed in both arches to achieve leveling and alignment. After 14 months of unilateral distalization with the zygoma-gear appliance and fixed appliances, Class I molar and canine relationships were established with satisfactory interdigitation of the posterior teeth. Acceptable overjet and overbite were also achieved. This article shows that this new system, the zygoma-gear appliance, can be used for unilateral maxillary molar distalization without anchorage loss. Copyright © 2012 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

  20. Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

    Science.gov (United States)

    Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

    Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

  1. 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 ...

  2. Intra-Articular Osteotomy for Distal Humerus Malunion

    Directory of Open Access Journals (Sweden)

    René K. Marti

    2009-01-01

    Full Text Available Intra-articular osteotomy is considered in the rare case of malunion after a fracture of the distal humerus to restore humeral alignment and gain a functional arc of elbow motion. Traumatic and iatrogenic disruption of the limited blood flow to the distal end of the humerus resulting in avascular necrosis of capitellum or trochlea is a major pitfall of the this technically challenging procedure. Two cases are presented which illustrate the potential problems of intra-articular osteotomy for malunion of the distal humerus.

  3. Volar plating for distal radius fractures--do not trust the image intensifier when judging distal subchondral screw length.

    Science.gov (United States)

    Park, Derek H; Goldie, Boyd S

    2012-09-01

    The use of the volar plate to treat distal radius fractures is increasing but despite the theoretical advantages of a volar approach there have been reports of extensor tendon ruptures due to prominent screw tips protruding past the dorsal cortex. The valley in the intermediate column between Lister tubercle and the sigmoid notch of the distal radius makes it difficult to rely on fluoroscopy to judge screw length. Our aim was to quantify the dimensions of this valley and to demonstrate the danger of relying on intraoperative image intensification fluoroscopy to determine lengths of distal screws. We measured the depth of this valley in the intermediate column of the distal radius in 33 patients with computed tomographic (9 patients) or magnetic resonance image (24 patients) scans of the wrist. There was a consistent valley in all images examined [average 1.8 mm (95% confidence interval, 1.6-2.0 mm)]. Thirty-nine percent of wrists had a valley depth of at least 2 mm. Standard lateral views or rotation of the forearm to obtain oblique views does not identify prominent screw tips; and whatever the rotation of the forearm, screw tips protruding beyond dorsal cortex may look as if it is within the bone when in fact it is out. When drilling we suggest noting the depth at which the drill bit just penetrates dorsal cortex and routinely downsize the distal screw length by 2 mm. We caution against relying on flourosocopy when judging the length of the distal subchondral screws.

  4. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

    Science.gov (United States)

    Cheng, Phillip M; Malhi, Harshawn S

    2017-04-01

    The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p convolutional neural networks may be used to construct effective classifiers for abdominal ultrasound images.

  5. 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.

  6. A Dynamic Simulation of Musculoskeletal Function in the Mouse Hindlimb During Trotting Locomotion

    Directory of Open Access Journals (Sweden)

    James P. Charles

    2018-05-01

    Full Text Available Mice are often used as animal models of various human neuromuscular diseases, and analysis of these models often requires detailed gait analysis. However, little is known of the dynamics of the mouse musculoskeletal system during locomotion. In this study, we used computer optimization procedures to create a simulation of trotting in a mouse, using a previously developed mouse hindlimb musculoskeletal model in conjunction with new experimental data, allowing muscle forces, activation patterns, and levels of mechanical work to be estimated. Analyzing musculotendon unit (MTU mechanical work throughout the stride allowed a deeper understanding of their respective functions, with the rectus femoris MTU dominating the generation of positive and negative mechanical work during the swing and stance phases. This analysis also tested previous functional inferences of the mouse hindlimb made from anatomical data alone, such as the existence of a proximo-distal gradient of muscle function, thought to reflect adaptations for energy-efficient locomotion. The results do not strongly support the presence of this gradient within the mouse musculoskeletal system, particularly given relatively high negative net work output from the ankle plantarflexor MTUs, although more detailed simulations could test this further. This modeling analysis lays a foundation for future studies of the control of vertebrate movement through the development of neuromechanical simulations.

  7. Conceptualizing distal drivers in land use competition

    DEFF Research Database (Denmark)

    Niewhöner, Jörg; Nielsen, Jonas Ø; Gasparri, Gasparri

    2016-01-01

    This introductory chapter explores the notion of ‘distal drivers’ in land use competition. Research has moved beyond proximate causes of land cover and land use change to focus on the underlying drivers of these dynamics. We discuss the framework of telecoupling within human–environment systems...... as a first step to come to terms with the increasingly distal nature of driving forces behind land use practices. We then expand the notion of distal as mainly a measure of Euclidian space to include temporal, social, and institutional dimensions. This understanding of distal widens our analytical scope...... for the analysis of land use competition as a distributed process to consider the role of knowledge and power, technology, and different temporalities within a relational or systemic analysis of practices of land use competition. We conclude by pointing toward the historical and social contingency of land use...

  8. 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...

  9. Finding strong lenses in CFHTLS using convolutional neural networks

    Science.gov (United States)

    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.

  10. Fractures of the distal phalanx in the horse

    International Nuclear Information System (INIS)

    Yovich, J.V.

    1989-01-01

    Fractures of the distal phalanx are an important cause of lameness referable to the foot. Depending on the fracture configuration and articular involvement, conservative or surgical treatment may be required. Fractures of the distal phalanx have been divided into six categories based on fracture configuration. Discussion of clinical features, management, and prognosis for horses with distal phalangeal fractures is presented for each fracture type

  11. Classification of stroke disease using convolutional neural network

    Science.gov (United States)

    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.

  12. 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.

  13. 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.

  14. 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%.

  15. A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection

    OpenAIRE

    Kumar, Amit; Chellappa, Rama

    2017-01-01

    Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility ...

  16. Posttraumatic osteolysis of the distal clavicula end

    International Nuclear Information System (INIS)

    Hermanns, P.H.; Beeger, R.; Koetter, D.; Hamburg Univ.

    1981-01-01

    Posttraumatic osteolysis of bone is rare. Its etiology is unknown. A case of posttraumatic osteolysis of the distal clavicle end is reported. Differentialdiagnostical and ethiological relations are discussed. The literature of posttraumatic osteolysis especially of distal clavicle osteolysis is reported. (orig.) [de

  17. Two-level convolution formula for nuclear structure function

    Science.gov (United States)

    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.

  18. 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

  19. Correction of the tip convolution effects in the imaging of nanostructures studied through scanning force microscopy

    International Nuclear Information System (INIS)

    Canet-Ferrer, Josep; Coronado, Eugenio; Forment-Aliaga, Alicia; Pinilla-Cienfuegos, Elena

    2014-01-01

    AFM images are always affected by artifacts arising from tip convolution effects, resulting in a decrease in the lateral resolution of this technique. The magnitude of such effects is described by means of geometrical considerations, thereby providing better understanding of the convolution phenomenon. We demonstrate that for a constant tip radius, the convolution error is increased with the object height, mainly for the narrowest motifs. Certain influence of the object shape is observed between rectangular and elliptical objects with the same height. Such moderate differences are essentially expected among elongated objects; in contrast they are reduced as the object aspect ratio is increased. Finally, we propose an algorithm to study the influence of the size, shape and aspect ratio of different nanometric motifs on a flat substrate. Indeed, with this algorithm, convolution artifacts can be extended to any kind of motif including real surface roughness. From the simulation results we demonstrate that in most cases the real motif’s width can be estimated from AFM images without knowing its shape in detail. (paper)

  20. Localization of the panhypopituitary dwarf mutation (df) on mouse chromosome 11 in an intersubspecific backcross.

    Science.gov (United States)

    Buckwalter, M S; Katz, R W; Camper, S A

    1991-07-01

    Ames dwarf (df) is an autosomal recessive mutation characterized by severe dwarfism and infertility. This mutation provides a mouse model for panhypopituitarism. The dwarf phenotype results from failure in the differentiation of the cells which produce growth hormone, prolactin, and thyroid stimulating hormone. Using the backcross (DF/B-df/df X CASA/Rk) X DF/B-df/df, we confirmed the assignment of df to mouse chromosome 11 and demonstrated recombination between df and the growth hormone gene. This backcross is an invaluable resource for screening candidate genes for the df mutation. The df locus maps to less than 1 cM distal to Pad-1 (0.85 +/- 0.85 cM). Two new genes localized on mouse chromosome 11, Rpo2-1, and Edp-1, map to a region of conserved synteny with human chromosome 17. The localization of the alpha 1 adrenergic receptor, Adra-1, extends a known region of synteny conservation between mouse chromosome 11 and human chromosome 5, and suggests that a human counterpart to df would map to human chromosome 5.

  1. A STUDY OF SURGICAL MANAGEMENT OF DISTAL FEMORAL FRACTURES BY DISTAL FEMORAL LOCKING COMPRESSION PLATE OSTEOSYNTHESIS

    Directory of Open Access Journals (Sweden)

    Dema Rajaiah

    2016-08-01

    Full Text Available AIMS AND OBJECTIVES To study the fractures of distal end of femur and the mechanism of injury in distal end femur fractures, the advantages and disadvantages of open reduction and internal fixation of distal end femur fractures by distal femoral locking compression plate osteosynthesis and to analyse the outcome in terms of range of Knee motion, time to union, and limb shortening. RESULTS The mean age of patient is 44 years, 85% are males, road traffic accidents account for majority (80%, right side involved in 70%, Muller’s type C fracture is common, good range of movements is seen 90% of cases and union occurred in 95% in 5 months. The results were assessed using Neer’s score, seven (35% patients had excellent results, eight (40% patients had good results, four (20% patients had fair results and one (5% patient had poor result. CONCLUSION From our study, we conclude that DF-LCP is a safe and reliable implant and has shown excellent to satisfactory results in majority of intra-articular fractures (AO type C. Fixation with locking compression plate showed more effectiveness in severely osteoporotic bones, shorter operative stay, faster recovery, faster union rates and excellent functional outcome.

  2. A Wnt/beta-catenin pathway antagonist Chibby binds Cenexin at the distal end of mother centrioles and functions in primary cilia formation.

    Directory of Open Access Journals (Sweden)

    Nathan Steere

    Full Text Available The mother centriole of the centrosome is distinguished from immature daughter centrioles by the presence of accessory structures (distal and subdistal appendages, which play an important role in the organization of the primary cilium in quiescent cells. Primary cilia serve as sensory organelles, thus have been implicated in mediating intracellular signal transduction pathways. Here we report that Chibby (Cby, a highly conserved antagonist of the Wnt/β-catenin pathway, is a centriolar component specifically located at the distal end of the mother centriole and essential for assembly of the primary cilium. Cby appeared as a discrete dot in the middle of a ring-like structure revealed by staining with a distal appendage component of Cep164. Cby interacted with one of the appendage components, Cenexin (Cnx, which thereby abrogated the inhibitory effect of Cby on β-catenin-mediated transcriptional activation in a dose-dependent manner. Cby and Cnx did not precisely align, as Cby was detected at a more distal position than Cnx. Cnx emerged earlier than Cby during the cell cycle and was required for recruitment of Cby to the mother centriole. However, Cby was dispensable for Cnx localization to the centriole. During massive centriogenesis in in vitro cultured mouse tracheal epithelial cells, Cby and Cnx were expressed in a similar pattern, which was coincident with the expression of Foxj1. Our results suggest that Cby plays an important role in organization of both primary and motile cilia in collaboration with Cnx.

  3. A Wnt/beta-catenin pathway antagonist Chibby binds Cenexin at the distal end of mother centrioles and functions in primary cilia formation.

    Science.gov (United States)

    Steere, Nathan; Chae, Vanessa; Burke, Michael; Li, Feng-Qian; Takemaru, Ken-ichi; Kuriyama, Ryoko

    2012-01-01

    The mother centriole of the centrosome is distinguished from immature daughter centrioles by the presence of accessory structures (distal and subdistal appendages), which play an important role in the organization of the primary cilium in quiescent cells. Primary cilia serve as sensory organelles, thus have been implicated in mediating intracellular signal transduction pathways. Here we report that Chibby (Cby), a highly conserved antagonist of the Wnt/β-catenin pathway, is a centriolar component specifically located at the distal end of the mother centriole and essential for assembly of the primary cilium. Cby appeared as a discrete dot in the middle of a ring-like structure revealed by staining with a distal appendage component of Cep164. Cby interacted with one of the appendage components, Cenexin (Cnx), which thereby abrogated the inhibitory effect of Cby on β-catenin-mediated transcriptional activation in a dose-dependent manner. Cby and Cnx did not precisely align, as Cby was detected at a more distal position than Cnx. Cnx emerged earlier than Cby during the cell cycle and was required for recruitment of Cby to the mother centriole. However, Cby was dispensable for Cnx localization to the centriole. During massive centriogenesis in in vitro cultured mouse tracheal epithelial cells, Cby and Cnx were expressed in a similar pattern, which was coincident with the expression of Foxj1. Our results suggest that Cby plays an important role in organization of both primary and motile cilia in collaboration with Cnx.

  4. Infimal Convolution Regularisation Functionals of BV and Lp Spaces

    KAUST Repository

    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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Distal radioulnar joint injuries

    Directory of Open Access Journals (Sweden)

    Binu P Thomas

    2012-01-01

    Full Text Available Distal radioulnar joint is a trochoid joint relatively new in evolution. Along with proximal radioulnar joint , forearm bones and interosseous membrane, it allows pronosupination and load transmission across the wrist. Injuries around distal radioulnar joint are not uncommon, and are usually associated with distal radius fractures,fractures of the ulnar styloid and with the eponymous Galeazzi or Essex_Lopresti fractures. The injury can be purely involving the soft tissue especially the triangular fibrocartilage or the radioulnar ligaments.The patients usually present with ulnar sided wrist pain, features of instability, or restriction of rotation. Difficulty in carrying loads in the hand is a major constraint for these patients. Thorough clinical examination to localize point of tenderness and appropriate provocative tests help in diagnosis. Radiology and MRI are extremely useful, while arthroscopy is the gold standard for evaluation. The treatment protocols are continuously evolving and range from conservative, arthroscopic to open surgical methods. Isolated dislocation are uncommon. Basal fractures of the ulnar styloid tend to make the joint unstable and may require operative intervention. Chronic instability requires reconstruction of the stabilizing ligaments to avoid onset of arthritis. Prosthetic replacement in arthritis is gaining acceptance in the management of arthritis.

  10. [Involvement of distal fragment of chromosome 13 in the regulation of sensitivity to ethanol in mice].

    Science.gov (United States)

    Bazovkina, D V; Kulikov, A V

    2015-01-01

    The role of the fragment 57-65 cM of mouse chromosome 13 was studied in the regulation of ethanol action on locomotor activity, anxiety and sensitivity to hypnotic and hypothermic effects of ethanol. We used male mice of recombinant lines AKR/J and AKR.CBA-D13Mit76C, differing only in this fragment. After acute administration of ethanol only AKR mice showed the increase in the length of traveled distance in the open-field test (p mice demonstrated the increase the time spent in the center of open-field arena (p mice. The results suggest the involvement of the distal fragment 57-65 cM of chromosome 13 in the mechanisms of ethanol action in mice.

  11. Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Qingshan Liu

    2017-12-01

    Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.

  12. A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

    Science.gov (United States)

    Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

    2017-01-01

    In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Contemporary Management of Primary Distal Urethral Cancer

    NARCIS (Netherlands)

    Traboulsi, S.L.; Witjes, J.A.; Kassouf, W.

    2016-01-01

    Primary urethral cancer is one of the rare urologic tumors. Distal urethral tumors are usually less advanced at diagnosis compared with proximal tumors and have a good prognosis if treated appropriately. Low-stage distal tumors can be managed successfully with a surgical approach in men or radiation

  14. Distal technologies and type 1 diabetes management.

    Science.gov (United States)

    Duke, Danny C; Barry, Samantha; Wagner, David V; Speight, Jane; Choudhary, Pratik; Harris, Michael A

    2018-02-01

    Type 1 diabetes requires intensive self-management to avoid acute and long-term health complications. In the past two decades, substantial advances in technology have enabled more effective and convenient self-management of type 1 diabetes. Although proximal technologies (eg, insulin pumps, continuous glucose monitors, closed-loop and artificial pancreas systems) have been the subject of frequent systematic and narrative reviews, distal technologies have received scant attention. Distal technologies refer to electronic systems designed to provide a service remotely and include heterogeneous systems such as telehealth, mobile health applications, game-based support, social platforms, and patient portals. In this Review, we summarise the empirical literature to provide current information about the effectiveness of available distal technologies to improve type 1 diabetes management. We also discuss privacy, ethics, and regulatory considerations, issues of global adoption, knowledge gaps in distal technology, and recommendations for future directions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. An Interactive Graphics Program for Assistance in Learning Convolution.

    Science.gov (United States)

    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…

  16. Quantifying Translation-Invariance in Convolutional Neural Networks

    OpenAIRE

    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 ...

  17. Applications of deep convolutional neural networks to digitized natural history collections

    Directory of Open Access Journals (Sweden)

    Eric Schuettpelz

    2017-11-01

    Full Text Available Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools.

  18. Applications of deep convolutional neural networks to digitized natural history collections.

    Science.gov (United States)

    Schuettpelz, Eric; Frandsen, Paul B; Dikow, Rebecca B; Brown, Abel; Orli, Sylvia; Peters, Melinda; Metallo, Adam; Funk, Vicki A; Dorr, Laurence J

    2017-01-01

    Natural history collections contain data that are critical for many scientific endeavors. Recent efforts in mass digitization are generating large datasets from these collections that can provide unprecedented insight. Here, we present examples of how deep convolutional neural networks can be applied in analyses of imaged herbarium specimens. We first demonstrate that a convolutional neural network can detect mercury-stained specimens across a collection with 90% accuracy. We then show that such a network can correctly distinguish two morphologically similar plant families 96% of the time. Discarding the most challenging specimen images increases accuracy to 94% and 99%, respectively. These results highlight the importance of mass digitization and deep learning approaches and reveal how they can together deliver powerful new investigative tools.

  19. Intra-articular osteotomy for distal humerus malunion

    NARCIS (Netherlands)

    Marti, René K.; Doornberg, Job

    2009-01-01

    Intra-articular osteotomy is considered in the rare case of malunion after a fracture of the distal humerus to restore humeral alignment and gain a functional arc of elbow motion. Traumatic and iatrogenic disruption of the limited blood flow to the distal end of the humerus resulting in avascular

  20. Distal Communication by Chimpanzees (Pan troglodytes): Evidence for Common Ground?

    Science.gov (United States)

    Leavens, David A; Reamer, Lisa A; Mareno, Mary Catherine; Russell, Jamie L; Wilson, Daniel; Schapiro, Steven J; Hopkins, William D

    2015-01-01

    van der Goot et al. (2014) proposed that distal, deictic communication indexed the appreciation of the psychological state of a common ground between a signaler and a receiver. In their study, great apes did not signal distally, which they construed as evidence for the human uniqueness of a sense of common ground. This study exposed 166 chimpanzees to food and an experimenter, at an angular displacement, to ask, "Do chimpanzees display distal communication?" Apes were categorized as (a) proximal or (b) distal signalers on each of four trials. The number of chimpanzees who communicated proximally did not statistically differ from the number who signaled distally. Therefore, contrary to the claim by van der Goot et al., apes do communicate distally. © 2015 The Authors. Child Development © 2015 Society for Research in Child Development, Inc.

  1. A mixed-scale dense convolutional neural network for image analysis

    NARCIS (Netherlands)

    D.M. Pelt (Daniël); J.A. Sethian (James)

    2016-01-01

    textabstractDeep convolutional neural networks have been successfully applied to many image-processing problems in recent works. Popular network architectures often add additional operations and connections to the standard architecture to enable training deeper networks. To achieve accurate results

  2. 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

  3. Spatial organization and coordination of slow waves in the mouse anorectum

    Science.gov (United States)

    Hall, K A; Ward, S M; Cobine, C A; Keef, K D

    2014-01-01

    The internal anal sphincter (IAS) develops tone and is important for maintaining a high anal pressure while tone in the rectum is less. The mechanisms responsible for tone generation in the IAS are still uncertain. The present study addressed this question by comparing the electrical properties and morphology of the mouse IAS and distal rectum. The amplitude of tone and the frequency of phasic contractions was greater in the IAS than in rectum while membrane potential (Em) was less negative in the IAS than in rectum. Slow waves (SWs) were of greatest amplitude and frequency at the distal end of the IAS, declining in the oral direction. Dual microelectrode recordings revealed that SWs were coordinated over a much greater distance in the circumferential direction than in the oral direction. The circular muscle layer of the IAS was divided into five to eight ‘minibundles’ separated by connective tissue septa whereas few septa were present in the rectum. The limited coordination of SWs in the oral direction suggests that the activity in adjacent minibundles is not coordinated. Intramuscular interstitial cells of Cajal and platelet-derived growth factor receptor alpha-positive cells were present in each minibundle suggesting a role for one or both of these cells in SW generation. In summary, three important properties distinguish the IAS from the distal rectum: (1) a more depolarized Em; (2) larger and higher frequency SWs; and (3) the multiunit configuration of the muscle. All of these characteristics may contribute to greater tone generation in the IAS than in the distal rectum. PMID:24951622

  4. Distal displacement of the maxilla and the upper first molar.

    Science.gov (United States)

    Baumrind, S; Molthen, R; West, E E; Miller, D M

    1979-06-01

    Data from a sample of 198 Class II cases treated with various appliances which deliver distally directed forces to the maxilla were examined to determine the frequency of absolute distal displacement of the upper first molar and of the maxilla. Analysis revealed that such distal displacement is possible and that it is, in fact, a frequent finding following treatment. Long-range stability of distal displacement was not assessed.

  5. Distal splenorenal shunt with partial spleen resection

    Directory of Open Access Journals (Sweden)

    Gajin Predrag

    2007-01-01

    Full Text Available Introduction: Hypersplenism is a common complication of portal hypertension. Cytopenia in hypersplenism is predominantly caused by splenomegaly. Distal splenorenal shunt (Warren with partial spleen resection is an original surgical technique that regulates cytopenia by reduction of the enlarged spleen. Objective. The aim of our study was to present the advantages of distal splenorenal shunt (Warren with partial spleen resection comparing morbidity and mortality in a group of patients treated by distal splenorenal shunt with partial spleen resection with a group of patients treated only by a distal splenorenal shunt. Method. From 1995 to 2003, 41 patients with portal hypertension were surgically treated due to hypersplenism and oesophageal varices. The first group consisted of 20 patients (11 male, mean age 42.3 years who were treated by distal splenorenal shunt with partial spleen resection. The second group consisted of 21 patients (13 male, mean age 49.4 years that were treated by distal splenorenal shunt only. All patients underwent endoscopy and assessment of oesophageal varices. The size of the spleen was evaluated by ultrasound, CT or by scintigraphy. Angiography was performed in all patients. The platelet and white blood cell count and haemoglobin level were registered. Postoperatively, we noted blood transfusion, complications and total hospital stay. Follow-up period was 12 months, with first checkup after one month. Results In the first group, only one patient had splenomegaly postoperatively (5%, while in the second group there were 13 patients with splenomegaly (68%. Before surgery, the mean platelet count in the first group was 51.6±18.3x109/l, to 118.6±25.4x109/l postoperatively. The mean platelet count in the second group was 67.6±22.8x109/l, to 87.8±32.1x109/l postoperatively. Concerning postoperative splenomegaly, statistically significant difference was noted between the first and the second group (p<0.05. Comparing the

  6. Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network.

    Science.gov (United States)

    Yoon, Jaehong; Lee, Jungnyun; Whang, Mincheol

    2018-01-01

    Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain-computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects' ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

  7. Renormalized G-convolution of n-point functions in quantum field theory. I. The Euclidean case

    International Nuclear Information System (INIS)

    Bros, Jacques; Manolessou-Grammaticou, Marietta.

    1977-01-01

    The notion of Feynman amplitude associated with a graph G in perturbative quantum field theory admits a generalized version in which each vertex v of G is associated with a general (non-perturbative) nsub(v)-point function Hsup(nsub(v)), nsub(v) denoting the number of lines which are incident to v in G. In the case where no ultraviolet divergence occurs, this has been performed directly in complex momentum space through Bros-Lassalle's G-convolution procedure. The authors propose a generalization of G-convolution which includes the case when the functions Hsup(nsub(v)) are not integrable at infinity but belong to a suitable class of slowly increasing functions. A finite part of the G-convolution integral is then defined through an algorithm which closely follows Zimmermann's renormalization scheme. The case of Euclidean four-momentum configurations is only treated

  8. A comparison between robotic-assisted laparoscopic distal pancreatectomy versus laparoscopic distal pancreatectomy.

    Science.gov (United States)

    Goh, Brian K P; Chan, Chung Yip; Soh, Hui-Ling; Lee, Ser Yee; Cheow, Peng-Chung; Chow, Pierce K H; Ooi, London L P J; Chung, Alexander Y F

    2017-03-01

    This study aims to compare the early perioperative outcomes of robotic-assisted laparoscopic distal pancreatectomy (RDP) versus laparoscopic distal pancreatectomy (LDP). The clinicopathologic features of 45 consecutive patients who underwent minimally-invasive distal pancreatectomy from 2006 to 2015 were retrospectively reviewed. Thirty-nine patients who met our study criteria were included. Eight patients underwent RDP and 31 had LDP. There were 10 (25.6%) open conversions. Six (15.4%) patients had major (> grade 2) morbidities and there was no in-hospital mortality. There were 14 (35.9%) grade A and 9 (23.1%) grade B pancreatic fistulas. Comparison between RDP and LDP demonstrated no significant difference between the patients' baseline characteristics except there was increased frequency of spleen-preserving pancreatectomies (3 (37.5%) vs 25 (80.6%), P=0.016) and splenic-vessel preservation (5 (62.5%) vs 4 (12.9%), P=0.003) in RDP. Comparison between outcomes demonstrated that RDP was associated with a longer median operation time (452.5 (range, 300-685) vs 245 min (range, 85-430), P=0.001) and increased frequency of the procedure completed purely laparoscopically (8 (100%) vs 18 (58.1%), P=0.025). RDP can be safely adopted and is equivalent to LDP in most perioperative outcomes. It is also associated with a decreased frequency of the need for hand-assistance laparoscopic surgery or open conversion but needed a longer operation time. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  9. A New Orthodontic Appliance with a Mini Screw for Upper Molar Distalization.

    Science.gov (United States)

    Ozkalayci, Nurhat; Yetmez, Mehmet

    2016-01-01

    The aim of this study is to present a new upper molar distalization appliance called Cise distalizer designed as intraoral device supported with orthodontic mini screw for upper permanent molar distalization. The new appliance consists of eight main components. In order to understand the optimum force level, the appliance under static loading is tested by using strain gage measurement techniques. Results show that one of the open coils produces approximately 300 gr distalization force. Cise distalizer can provide totally 600 gr distalization force. This range of force level is enough for distalization of upper first and second molar teeth.

  10. Ibuprofen slows migration and inhibits bowel colonization by enteric nervous system precursors in zebrafish, chick and mouse

    Science.gov (United States)

    Schill, Ellen Merrick; Lake, Jonathan I.; Tusheva, Olga A.; Nagy, Nandor; Bery, Saya K.; Foster, Lynne; Avetisyan, Marina; Johnson, Stephen L.; Stenson, William F.; Goldstein, Allan M.; Heuckeroth, Robert O.

    2016-01-01

    Hirschsprung Disease (HSCR) is a potentially deadly birth defect characterized by the absence of the enteric nervous system (ENS) in distal bowel. Although HSCR has clear genetic causes, no HSCR-associated mutation is 100% penetrant, suggesting gene-gene and gene-environment interactions determine HSCR occurrence. To test the hypothesis that certain medicines might alter HSCR risk we treated zebrafish with medications commonly used during early human pregnancy and discovered that ibuprofen caused HSCR-like absence of enteric neurons in distal bowel. Using fetal CF-1 mouse gut slice cultures, we found that ibuprofen treated enteric neural crest-derived cells (ENCDC) had reduced migration, fewer lamellipodia and lower levels of active RAC1/CDC42. Additionally, inhibiting ROCK, a RHOA effector and known RAC1 antagonist, reversed ibuprofen effects on migrating mouse ENCDC in culture. Ibuprofen also inhibited colonization of Ret+/− mouse bowel by ENCDC in vivo and dramatically reduced bowel colonization by chick ENCDC in culture. Interestingly, ibuprofen did not affect ENCDC migration until after at least three hours of exposure. Furthermore, mice deficient in Ptgs1 (COX 1) and Ptgs2 (COX 2) had normal bowel colonization by ENCDC and normal ENCDC migration in vitro suggesting COX-independent effects. Consistent with selective and strain specific effects on ENCDC, ibuprofen did not affect migration of gut mesenchymal cells, NIH3T3, or WT C57BL/6 ENCDC, and did not affect dorsal root ganglion cell precursor migration in zebrafish. Thus, ibuprofen inhibits ENCDC migration in vitro and bowel colonization by ENCDC in vivo in zebrafish, mouse and chick, but there are cell type and strain specific responses. These data raise concern that ibuprofen may increase Hirschsprung disease risk in some genetically susceptible children. PMID:26586201

  11. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.

    Science.gov (United States)

    Sharma, Harshita; Zerbe, Norman; Klempert, Iris; Hellwich, Olaf; Hufnagl, Peter

    2017-11-01

    Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. 3D Medical Image Interpolation Based on Parametric Cubic Convolution

    Institute of Scientific and Technical Information of China (English)

    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.

  13. ID card number detection algorithm based on convolutional neural network

    Science.gov (United States)

    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.

  14. Laparoscopic versus open distal pancreatectomy for pancreatic cancer.

    Science.gov (United States)

    Riviere, Deniece; Gurusamy, Kurinchi Selvan; Kooby, David A; Vollmer, Charles M; Besselink, Marc G H; Davidson, Brian R; van Laarhoven, Cornelis J H M

    2016-04-04

    Surgical resection is currently the only treatment with the potential for long-term survival and cure of pancreatic cancer. Surgical resection is provided as distal pancreatectomy for cancers of the body and tail of the pancreas. It can be performed by laparoscopic or open surgery. In operations on other organs, laparoscopic surgery has been shown to reduce complications and length of hospital stay as compared with open surgery. However, concerns remain about the safety of laparoscopic distal pancreatectomy compared with open distal pancreatectomy in terms of postoperative complications and oncological clearance. To assess the benefits and harms of laparoscopic distal pancreatectomy versus open distal pancreatectomy for people undergoing distal pancreatectomy for pancreatic ductal adenocarcinoma of the body or tail of the pancreas, or both. We used search strategies to search the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, Science Citation Index Expanded and trials registers until June 2015 to identify randomised controlled trials (RCTs) and non-randomised studies. We also searched the reference lists of included trials to identify additional studies. We considered for inclusion in the review RCTs and non-randomised studies comparing laparoscopic versus open distal pancreatectomy in patients with resectable pancreatic cancer, irrespective of language, blinding or publication status.. Two review authors independently identified trials and independently extracted data. We calculated odds ratios (ORs), mean differences (MDs) or hazard ratios (HRs) along with 95% confidence intervals (CIs) using both fixed-effect and random-effects models with RevMan 5 on the basis of intention-to-treat analysis when possible. We found no RCTs on this topic. We included in this review 12 non-randomised studies that compared laparoscopic versus open distal pancreatectomy (1576 participants: 394 underwent laparoscopic distal pancreatectomy and 1182

  15. Convolution-based estimation of organ dose in tube current modulated CT

    Science.gov (United States)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The

  16. Electroencephalography Based Fusion Two-Dimensional (2D-Convolution Neural Networks (CNN Model for Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Yea-Hoon Kwon

    2018-04-01

    Full Text Available The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG and galvanic skin response (GSR signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

  17. A New Orthodontic Appliance with a Mini Screw for Upper Molar Distalization

    Directory of Open Access Journals (Sweden)

    Nurhat Ozkalayci

    2016-01-01

    Full Text Available The aim of this study is to present a new upper molar distalization appliance called Cise distalizer designed as intraoral device supported with orthodontic mini screw for upper permanent molar distalization. The new appliance consists of eight main components. In order to understand the optimum force level, the appliance under static loading is tested by using strain gage measurement techniques. Results show that one of the open coils produces approximately 300 gr distalization force. Cise distalizer can provide totally 600 gr distalization force. This range of force level is enough for distalization of upper first and second molar teeth.

  18. Fluence-convolution broad-beam (FCBB) dose calculation

    Energy Technology Data Exchange (ETDEWEB)

    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.

  19. 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...

  20. 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...

  1. A FUNCTIONAL EVALUATION STUDY OF DISTAL FEMORAL FRACTURES FIXED WITH DISTAL FEMORAL LOCKING PLATE

    Directory of Open Access Journals (Sweden)

    Manikumar C. J

    2017-04-01

    Full Text Available BACKGROUND Fractures of the distal femur present considerable challenges in management. Older patients especially women sustain fractures due to osteoporosis. Supracondylar fractures of femur have a bimodal distribution. They account for 6% of all femur fractures and 31% if hip fractures were excluded. Nearly, 50% of distal femur intra-articular fractures are open fractures. Before 1970, most supracondylar fractures were treated nonoperatively; however, difficulties were often encountered including persistent angulatory deformity, knee joint incongruity, loss of knee motion and delayed mobilisation. The trend of open reduction and internal fixation has become evident in recent years with good results being obtained with AO blade plate, dynamic condylar screw, intramedullary supracondylar nail and locking compression plate. Elderly patients and osteoporosis pose difficulty in treating intra-articular fractures of the lower end of femur. Loss of stable fixation is of great concern in these cases. Hence, locking compression plate use has an advantage in these patients. MATERIALS AND METHODS In this study, 20 patients with closed fracture of distal femur were studied. All the cases were treated at the Department of Orthopaedics, Rangaraya Medical College/Government General Hospital, Kakinada, Andhra Pradesh, between November 2013 and November 2015. The method used for fracture fixation was open reduction and internal fixation with distal femoral locking plate. The duration of follow up ranged from 3 months to 24 months. All the fractures in this series were posttraumatic. The patients were functionally evaluated with Neer’s scoring system. 1 RESULTS Twenty distal femoral fractures were treated with distal femoral locking plates. 15 patients were males and 5 patients were females. The median age was 47 years ranging from 28-70 years. 16 of the fractures were caused by road traffic accidents and 2 were due to fall, 2 were due to assault. 12 patients

  2. Cost-effectiveness of laparoscopic versus open distal pancreatectomy for pancreatic cancer.

    Science.gov (United States)

    Gurusamy, Kurinchi Selvan; Riviere, Deniece; van Laarhoven, C J H; Besselink, Marc; Abu-Hilal, Mohammed; Davidson, Brian R; Morris, Steve

    2017-01-01

    A recent Cochrane review compared laparoscopic versus open distal pancreatectomy for people with for cancers of the body and tail of the pancreas and found that laparoscopic distal pancreatectomy may reduce the length of hospital stay. We compared the cost-effectiveness of laparoscopic distal pancreatectomy versus open distal pancreatectomy for pancreatic cancer. Model based cost-utility analysis estimating mean costs and quality-adjusted life years (QALYs) per patient from the perspective of the UK National Health Service. A decision tree model was constructed using probabilities, outcomes and cost data from published sources. A time horizon of 5 years was used. One-way and probabilistic sensitivity analyses were undertaken. The probabilistic sensitivity analysis showed that the incremental net monetary benefit was positive (£3,708.58 (95% confidence intervals (CI) -£9,473.62 to £16,115.69) but the 95% CI includes zero, indicating that there is significant uncertainty about the cost-effectiveness of laparoscopic distal pancreatectomy versus open distal pancreatectomy. The probability laparoscopic distal pancreatectomy was cost-effective compared to open distal pancreatectomy for pancreatic cancer was between 70% and 80% at the willingness-to-pay thresholds generally used in England (£20,000 to £30,000 per QALY gained). Results were sensitive to the survival proportions and the operating time. There is considerable uncertainty about whether laparoscopic distal pancreatectomy is cost-effective compared to open distal pancreatectomy for pancreatic cancer in the NHS setting.

  3. Method for assessing the probability of accumulated doses from an intermittent source using the convolution technique

    International Nuclear Information System (INIS)

    Coleman, J.H.

    1980-10-01

    A technique is discussed for computing the probability distribution of the accumulated dose received by an arbitrary receptor resulting from several single releases from an intermittent source. The probability density of the accumulated dose is the convolution of the probability densities of doses from the intermittent releases. Emissions are not assumed to be constant over the brief release period. The fast fourier transform is used in the calculation of the convolution

  4. Identification of quiescent, stem-like cells in the distal female reproductive tract.

    Directory of Open Access Journals (Sweden)

    Yongyi Wang

    Full Text Available In fertile women, the endometrium undergoes regular cycles of tissue build-up and regression. It is likely that uterine stem cells are involved in this remarkable turn over. The main goal of our current investigations was to identify slow-cycling (quiescent endometrial stem cells by means of a pulse-chase approach to selectively earmark, prospectively isolate, and characterize label-retaining cells (LRCs. To this aim, transgenic mice expressing histone2B-GFP (H2B-GFP in a Tet-inducible fashion were administered doxycycline (pulse which was thereafter withdrawn from the drinking water (chase. Over time, dividing cells progressively loose GFP signal whereas infrequently dividing cells retain H2B-GFP expression. We evaluated H2B-GFP retaining cells at different chase time points and identified long-term (LT; >12 weeks LRCs. The LT-LRCs are negative for estrogen receptor-α and express low levels of progesterone receptors. LRCs sorted by FACS are able to form spheroids capable of self-renewal and differentiation. Upon serum stimulation spheroid cells are induced to differentiate and form glandular structures which express markers of mature műllerian epithelial cells. Overall, the results indicate that quiescent cells located in the distal oviduct have stem-like properties and can differentiate into distinct cell lineages specific of endometrium, proximal and distal oviduct. Future lineage-tracing studies will elucidate the role played by these cells in homeostasis, tissue injury and cancer of the female reproductive tract in the mouse and eventually in man.

  5. Traumatic Distal Ulnar Artery Thrombosis

    Directory of Open Access Journals (Sweden)

    Ahmet A. Karaarslan

    2014-01-01

    Full Text Available This paper is about a posttraumatic distal ulnar artery thrombosis case that has occurred after a single blunt trauma. The ulnar artery thrombosis because of chronic trauma is a frequent condition (hypothenar hammer syndrome but an ulnar artery thrombosis because of a single direct blunt trauma is rare. Our patient who has been affected by a single blunt trauma to his hand and developed ulnar artery thrombosis has been treated by resection of the thrombosed ulnar artery segment. This report shows that a single blunt trauma can cause distal ulnar artery thrombosis in the hand and it can be treated merely by thrombosed segment resection in suitable cases.

  6. Distinct subclassification of DRG neurons innervating the distal colon and glans penis/distal urethra based on the electrophysiological current signature.

    Science.gov (United States)

    Rau, Kristofer K; Petruska, Jeffrey C; Cooper, Brian Y; Johnson, Richard D

    2014-09-15

    Spinal sensory neurons innervating visceral and mucocutaneous tissues have unique microanatomic distribution, peripheral modality, and physiological, pharmacological, and biophysical characteristics compared with those neurons that innervate muscle and cutaneous tissues. In previous patch-clamp electrophysiological studies, we have demonstrated that small- and medium-diameter dorsal root ganglion (DRG) neurons can be subclassified on the basis of their patterns of voltage-activated currents (VAC). These VAC-based subclasses were highly consistent in their action potential characteristics, responses to algesic compounds, immunocytochemical expression patterns, and responses to thermal stimuli. For this study, we examined the VAC of neurons retrogradely traced from the distal colon and the glans penis/distal urethra in the adult male rat. The afferent population from the distal colon contained at least two previously characterized cell types observed in somatic tissues (types 5 and 8), as well as four novel cell types (types 15, 16, 17, and 18). In the glans penis/distal urethra, two previously described cell types (types 6 and 8) and three novel cell types (types 7, 14, and 15) were identified. Other characteristics, including action potential profiles, responses to algesic compounds (acetylcholine, capsaicin, ATP, and pH 5.0 solution), and neurochemistry (expression of substance P, CGRP, neurofilament, TRPV1, TRPV2, and isolectin B4 binding) were consistent for each VAC-defined subgroup. With identification of distinct DRG cell types that innervate the distal colon and glans penis/distal urethra, future in vitro studies related to the gastrointestinal and urogenital sensory function in normal as well as abnormal/pathological conditions may be benefitted. Copyright © 2014 the American Physiological Society.

  7. Nonunions of the distal tibia treated by reamed intramedullary nailing

    NARCIS (Netherlands)

    Richmond, Jeffrey; Colleran, Kevin; Borens, Olivier; Kloen, Peter; Helfet, David L.

    2004-01-01

    The purpose of this study is to determine the efficacy of reamed intramedullary nailing in the treatment of nonunions of the distal one-fourth of the tibia. Nonunions of the distal tibia are particularly difficult to treat given the short distal segment, the proximity to the ankle joint, and the

  8. Learning Convolutional Text Representations for Visual Question Answering

    OpenAIRE

    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...

  9. 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.

  10. Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks.

    Science.gov (United States)

    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.

  11. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    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.

  12. Validation of a dose-point kernel convolution technique for internal dosimetry

    International Nuclear Information System (INIS)

    Giap, H.B.; Macey, D.J.; Bayouth, J.E.; Boyer, A.L.

    1995-01-01

    The objective of this study was to validate a dose-point kernel convolution technique that provides a three-dimensional (3D) distribution of absorbed dose from a 3D distribution of the radionuclide 131 I. A dose-point kernel for the penetrating radiations was calculated by a Monte Carlo simulation and cast in a 3D rectangular matrix. This matrix was convolved with the 3D activity map furnished by quantitative single-photon-emission computed tomography (SPECT) to provide a 3D distribution of absorbed dose. The convolution calculation was performed using a 3D fast Fourier transform (FFT) technique, which takes less than 40 s for a 128 x 128 x 16 matrix on an Intel 486 DX2 (66 MHz) personal computer. The calculated photon absorbed dose was compared with values measured by thermoluminescent dosimeters (TLDS) inserted along the diameter of a 22 cm diameter annular source of 131 I. The mean and standard deviation of the percentage difference between the measurements and the calculations were equal to -1% and 3.6% respectively. This convolution method was also used to calculate the 3D dose distribution in an Alderson abdominal phantom containing a liver, a spleen, and a spherical tumour volume loaded with various concentrations of 131 I. By averaging the dose calculated throughout the liver, spleen, and tumour the dose-point kernel approach was compared with values derived using the MIRD formalism, and found to agree to better than 15%. (author)

  13. Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

    Directory of Open Access Journals (Sweden)

    Jaehong Yoon

    2018-01-01

    Full Text Available Feature of event-related potential (ERP has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

  14. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Energy Technology Data Exchange (ETDEWEB)

    Psihas, Fernanda [Indiana U.

    2017-11-22

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  15. Image inpainting and super-resolution using non-local recursive deep convolutional network with skip connections

    Science.gov (United States)

    Liu, Miaofeng

    2017-07-01

    In recent years, deep convolutional neural networks come into use in image inpainting and super-resolution in many fields. Distinct to most of the former methods requiring to know beforehand the local information for corrupted pixels, we propose a 20-depth fully convolutional network to learn an end-to-end mapping a dataset of damaged/ground truth subimage pairs realizing non-local blind inpainting and super-resolution. As there often exist image with huge corruptions or inpainting on a low-resolution image that the existing approaches unable to perform well, we also share parameters in local area of layers to achieve spatial recursion and enlarge the receptive field. To avoid the difficulty of training this deep neural network, skip-connections between symmetric convolutional layers are designed. Experimental results shows that the proposed method outperforms state-of-the-art methods for diverse corrupting and low-resolution conditions, it works excellently when realizing super-resolution and image inpainting simultaneously

  16. Detection and recognition of bridge crack based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Honggong LIU

    2016-10-01

    Full Text Available Aiming at the backward artificial visual detection status of bridge crack in China, which has a great danger coefficient, a digital and intelligent detection method of improving the diagnostic efficiency and reducing the risk coefficient is studied. Combing with machine vision and convolutional neural network technology, Raspberry Pi is used to acquire and pre-process image, and the crack image is analyzed; the processing algorithm which has the best effect in detecting and recognizing is selected; the convolutional neural network(CNN for crack classification is optimized; finally, a new intelligent crack detection method is put forward. The experimental result shows that the system can find all cracks beyond the maximum limit, and effectively identify the type of fracture, and the recognition rate is above 90%. The study provides reference data for engineering detection.

  17. CRISPR reveals a distal super-enhancer required for Sox2 expression in mouse embryonic stem cells.

    Directory of Open Access Journals (Sweden)

    Yan Li

    Full Text Available The pluripotency of embryonic stem cells (ESCs is maintained by a small group of master transcription factors including Oct4, Sox2 and Nanog. These core factors form a regulatory circuit controlling the transcription of a number of pluripotency factors including themselves. Although previous studies have identified transcriptional regulators of this core network, the cis-regulatory DNA sequences required for the transcription of these key pluripotency factors remain to be defined. We analyzed epigenomic data within the 1.5 Mb gene-desert regions around the Sox2 gene and identified a 13kb-long super-enhancer (SE located 100kb downstream of Sox2 in mouse ESCs. This SE is occupied by Oct4, Sox2, Nanog, and the mediator complex, and physically interacts with the Sox2 locus via DNA looping. Using a simple and highly efficient double-CRISPR genome editing strategy we deleted the entire 13-kb SE and characterized transcriptional defects in the resulting monoallelic and biallelic deletion clones with RNA-seq. We showed that the SE is responsible for over 90% of Sox2 expression, and Sox2 is the only target gene along the chromosome. Our results support the functional significance of a SE in maintaining the pluripotency transcription program in mouse ESCs.

  18. Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

    Science.gov (United States)

    Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung

    2018-04-23

    In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

  19. A MacWilliams Identity for Convolutional Codes : The General Case

    NARCIS (Netherlands)

    Gluesing-Luerssen, Heide; Schneider, Gert

    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

  20. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    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

  1. 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)

  2. Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

    Science.gov (United States)

    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.

  3. Physiologie et Physiopathologie du transport de chlore dans le canal collecteur rénal : caractérisation d’un modèle murin d’Acidose tubulaire rénale distale et Étude des mécanismes de régulation du canal ClC-Kb/Barttin

    OpenAIRE

    Serbin , Bettina

    2016-01-01

    Kidney plays a major role in several biological fonctions as sodium balance or acid-base homeostasis. Chloride transport in the distal nephron is a key element of these two processes. During my thesis, i have worked on two projects related to physiology and pathophysiology of chloride transport in distal nephron. The first study is the functionnal and molecular characterization of a mouse model bearing the most common dominant dRTA mutation in human AE1, R589H, which corresponds to R607H in t...

  4. Forecasting short-term data center network traffic load with convolutional neural networks

    Science.gov (United States)

    Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

  5. Forecasting short-term data center network traffic load with convolutional neural networks.

    Science.gov (United States)

    Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

    2018-01-01

    Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

  6. Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

    Science.gov (United States)

    Zhong, Jiandan; Lei, Tao; Yao, Guangle

    2017-11-24

    Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

  7. Shallow and deep convolutional networks for saliency prediction

    OpenAIRE

    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...

  8. Optimized parallel convolutions for non-linear fluid models of tokamak ηi turbulence

    International Nuclear Information System (INIS)

    Milovich, J.L.; Tomaschke, G.; Kerbel, G.D.

    1993-01-01

    Non-linear computational fluid models of plasma turbulence based on spectral methods typically spend a large fraction of the total computing time evaluating convolutions. Usually these convolutions arise from an explicit or semi implicit treatment of the convective non-linearities in the problem. Often the principal convective velocity is perpendicular to magnetic field lines allowing a reduction of the convolution to two dimensions in an appropriate geometry, but beyond this, different models vary widely in the particulars of which mode amplitudes are selectively evolved to get the most efficient representation of the turbulence. As the number of modes in the problem, N, increases, the amount of computation required for this part of the evolution algorithm then scales as N 2 /timestep for a direct or analytic method and N ln N/timestep for a pseudospectral method. The constants of proportionality depend on the particulars of mode selection and determine the size problem for which the method will perform equally. For large enough N, the pseudospectral method performance is always superior, though some problems do not require correspondingly high resolution. Further, the Courant condition for numerical stability requires that the timestep size must decrease proportionately as N increases, thus accentuating the need to have fast methods for larger N problems. The authors have developed a package for the Cray system which performs these convolutions for a rather arbitrary mode selection scheme using either method. The package is highly optimized using a combination of macro and microtasking techniques, as well as vectorization and in some cases assembly coded routines. Parts of the package have also been developed and optimized for the CM200 and CM5 system. Performance comparisons with respect to problem size, parallelization, selection schemes and architecture are presented

  9. The role of imaging in diagnosing diseases of the distal radioulnar joint, triangular fibrocartilage complex, and distal ulna.

    Science.gov (United States)

    Squires, Judy H; England, Eric; Mehta, Kaushal; Wissman, Robert D

    2014-07-01

    The purpose of this article is to review the anatomy, biomechanics, and multimodality imaging findings of common and uncommon distal radioulnar joint (DRUJ), triangular fibrocartilage complex, and distal ulna abnormalities. The DRUJ is a common site for acute and chronic injuries and is frequently imaged to evaluate chronic wrist pain, forearm dysfunction, and traumatic forearm injury. Given the complex anatomy of the wrist, the radiologist plays a vital role in the diagnosis of wrist pain and dysfunction.

  10. Minimally invasive percutaneous plate fixation of distal tibia fractures.

    LENUS (Irish Health Repository)

    Bahari, Syah

    2007-10-01

    We report a series of 42 patients reviewed at a mean of 19.6 months after treatment of distal tibial and pilon fractures using the AO distal tibia locking plate with a minimally invasive percutaneous plate osteosynthesis (MIPPO) technique. Mean time to union was 22.4 weeks. All fractures united with acceptable alignment and angulation. Two cases of superficial infection were noted, with one case of deep infection. Mean SF36 score was 85 and mean AOFAS score was 90 at a mean of 19 months follow-up. We report satisfactory outcomes with the use of the AO distal tibia locking plate in treatment of unstable distal tibial fractures. Eighty-nine percent of the patients felt that they were back to their pre injury status and 95% back to their previous employment.

  11. Discrete singular convolution method for the analysis of Mindlin plates on elastic foundations

    International Nuclear Information System (INIS)

    Civalek, Omer; Acar, Mustafa Hilmi

    2007-01-01

    The method of discrete singular convolution (DSC) is used for the bending analysis of Mindlin plates on two-parameter elastic foundations for the first time. Two different realizations of singular kernels, such as the regularized Shannon's delta (RSD) kernel and Lagrange delta sequence (LDS) kernel, are selected as singular convolution to illustrate the present algorithm. The methodology and procedures are presented and bending problems of thick plates on elastic foundations are studied for different boundary conditions. The influence of foundation parameters and shear deformation on the stress resultants and deflections of the plate have been investigated. Numerical studies are performed and the DSC results are compared well with other analytical solutions and some numerical results

  12. The quick convolution of galaxy profiles, with application to power-law intensity distributions

    International Nuclear Information System (INIS)

    Bailey, M.E.; Sparks, W.B.

    1983-01-01

    The two-dimensional convolution of a circularly symmetric galaxy model with a Gaussian point-spread function of dispersion σ reduces to a single integral. This is solved analytically for models with power-law intensity distributions and results are given which relate the apparent core radius to σ and the power-law index k. The convolution integral is also simplified for the case of a point-spread function corresponding to a circular aperture. Models of galactic nuclei with stellar density cusps can only be distinguished from alternatives with small core radii if both the brightness and seeing profiles are measured accurately. The results are applied to data on the light distribution at the Galactic Centre. (author)

  13. A novel mouse model carrying a human cytoplasmic dynein mutation shows motor behavior deficits consistent with Charcot-Marie-Tooth type 2O disease.

    Science.gov (United States)

    Sabblah, Thywill T; Nandini, Swaran; Ledray, Aaron P; Pasos, Julio; Calderon, Jami L Conley; Love, Rachal; King, Linda E; King, Stephen J

    2018-01-29

    Charcot-Marie-Tooth disease (CMT) is a peripheral neuromuscular disorder in which axonal degeneration causes progressive loss of motor and sensory nerve function. The loss of motor nerve function leads to distal muscle weakness and atrophy, resulting in gait problems and difficulties with walking, running, and balance. A mutation in the cytoplasmic dynein heavy chain (DHC) gene was discovered to cause an autosomal dominant form of the disease designated Charcot-Marie-Tooth type 2 O disease (CMT2O) in 2011. The mutation is a single amino acid change of histidine into arginine at amino acid 306 (H306R) in DHC. In order to understand the onset and progression of CMT2, we generated a knock-in mouse carrying the corresponding CMT2O mutation (H304R/+). We examined H304R/+ mouse cohorts in a 12-month longitudinal study of grip strength, tail suspension, and rotarod assays. H304R/+ mice displayed distal muscle weakness and loss of motor coordination phenotypes consistent with those of individuals with CMT2. Analysis of the gastrocnemius of H304R/+ male mice showed prominent defects in neuromuscular junction (NMJ) morphology including reduced size, branching, and complexity. Based on these results, the H304R/+ mouse will be an important model for uncovering functions of dynein in complex organisms, especially related to CMT onset and progression.

  14. Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

    CSIR Research Space (South Africa)

    Gerrand, Jonathan D

    2017-07-01

    Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...

  15. Convolution quotients in the production of heat in an infinite cylinder

    Energy Technology Data Exchange (ETDEWEB)

    Battig, A; Kalla, S L [Universidad Nacional de Tucuman (Argentina). Facultad de Ciencias Exactas y Tecnologia

    1974-12-01

    A solution of the problem of heat production in an infinite cylinder is considered by an appeal to the concept of convolution quotients and finite Hankel transforms. The result given by Erdelyi follows as a particular case of the result established here.

  16. 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.

  17. 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.

  18. Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

    Science.gov (United States)

    Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

    2018-01-01

    Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

  19. A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

    KAUST Repository

    Geng, Yanyan; Zhang, Guohui; Li, Weizhi; Gu, Yi; Liang, Ru-Ze; Liang, Gaoyuan; Wang, Jingbin; Wu, Yanbin; Patil, Nitin; Wang, Jing-Yan

    2017-01-01

    In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

  20. A Novel Image Tag Completion Method Based on Convolutional Neural Transformation

    KAUST Repository

    Geng, Yanyan

    2017-10-24

    In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.

  1. Digital image correlation based on a fast convolution strategy

    Science.gov (United States)

    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.

  2. Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    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.

  3. Cost-effectiveness of laparoscopic versus open distal pancreatectomy for pancreatic cancer

    NARCIS (Netherlands)

    Gurusamy, Kurinchi Selvan; Riviere, Deniece; van Laarhoven, C. J. H.; Besselink, Marc; Abu-Hilal, Mohammed; Davidson, Brian R.; Morris, Steve

    2017-01-01

    A recent Cochrane review compared laparoscopic versus open distal pancreatectomy for people with for cancers of the body and tail of the pancreas and found that laparoscopic distal pancreatectomy may reduce the length of hospital stay. We compared the cost-effectiveness of laparoscopic distal

  4. [Comparison of laparoscopic distal pancreatectomy and open distal pancreatectomy in pancreatic ductal adenocarcinoma].

    Science.gov (United States)

    Xu, K; Su, J J; Su, M; Yan, L; Feng, J; Xin, X L; Chen, Y L

    2017-10-23

    Objective: To compare and evaluate the curative effect of laparoscopic distal pancreatectomy(LDP) and traditional open distal pancreatectomy(ODP) in pancreatic ductal adenocarcinoma. Methods: The clinical data of 15 patients treated by LDP and 87 contemporaneous cases treated by ODP from January 2010 to November 2015 was collected, and the curative effect and prognosis of these patients were retrospectively analyzed. Results: The operation time of LDP group was (286.5±48.1) min, significantly longer than that of OPD group(226.6±56.8) min ( P 0.05). In both LDP group and ODP group, none occurred percutaneous drainage, re-admissions, second operation or perioperative death. Conclusions: Compared to ODP, LDP is much safer and more steady in perioperative periodand operation. Patients of pancreatic ductal adenocarcinoma received LDP can acquire more benefit and recovery sooner, and LDP is a safe and effective operative method.

  5. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

    Science.gov (United States)

    Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

    2018-05-10

    The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

  6. Laparoscopic distal pancreatectomy for adenocarcinoma: safe and reasonable?

    Science.gov (United States)

    Postlewait, Lauren M.

    2015-01-01

    As a result of technological advances during the past two decades, surgeons now use minimally invasive surgery (MIS) approaches to pancreatic resection more frequently, yet the role of these approaches for pancreatic ductal adenocarcinoma resections remains uncertain, given the aggressive nature of this malignancy. Although there are no controlled trials comparing MIS technique to open surgical technique, laparoscopic distal pancreatectomy for pancreatic adenocarcinoma is performed with increasing frequency. Data from retrospective studies suggest that perioperative complication profiles between open and laparoscopic distal pancreatectomy are similar, with perhaps lower blood loss and fewer wound infections in the MIS group. Concerning oncologic outcomes, there appear to be no differences in the rate of achieving negative margins or in the number of lymph nodes (LNs) resected when compared to open surgery. There are limited recurrence and survival data on laparoscopic compared to open distal pancreatectomy for pancreatic adenocarcinoma, but in the few studies that assess long term outcomes, recurrence rates and survival outcomes appear similar. Recent studies show that though laparoscopic distal pancreatectomy entails a greater operative cost, the associated shorter length of hospital stay leads to decreased overall cost compared to open procedures. Multiple new technologies are emerging to improve resection of pancreatic cancer. Robotic pancreatectomy is feasible, but there are limited data on robotic resection of pancreatic adenocarcinoma, and outcomes appear similar to laparoscopic approaches. Additionally fluorescence-guided surgery represents a new technology on the horizon that could improve oncologic outcomes after resection of pancreatic adenocarcinoma, though published data thus far are limited to animal models. Overall, MIS distal pancreatectomy appears to be a safe and reasonable approach to treating selected patients with pancreatic ductal

  7. Identification of distal silencing elements in the murine interferon-A11 gene promoter.

    Science.gov (United States)

    Roffet, P; Lopez, S; Navarro, S; Bandu, M T; Coulombel, C; Vignal, M; Doly, J; Vodjdani, G

    1996-08-01

    The murine interferon-A11 (Mu IFN-A11) gene is a member of the IFN-A multigenic family. In mouse L929 cells, the weak response of the gene's promoter to viral induction is due to a combination of both a point mutation in the virus responsive element (VRE) and the presence of negatively regulating sequences surrounding the VRE. In the distal part of the promoter, the negatively acting E1E2 sequence was delimited. This sequence displays an inhibitory effect in either orientation or position on the inducibility of a virus-responsive heterologous promoter. It selectively represses VRE-dependent transcription but is not able to reduce the transcriptional activity of a VRE-lacking promoter. In a transient transfection assay, an E1E2-containing DNA competitor was able to derepress the native Mu IFN-A11 promoter. Specific nuclear factors bind to this sequence; thus the binding of trans-regulators participates in the repression of the Mu IFN-A11 gene. The E1E2 sequence contains an IFN regulatory factor (IRF)-binding site. Recombinant IRF2 binds this sequence and anti-IRF2 antibodies supershift a major complex formed with nuclear extracts. The protein composing the complex is 50 kDa in size, indicating the presence of IRF2 or antigenically related proteins in the complex. The Mu IFN-A11 gene is the first example within the murine IFN-A family, in which a distal promoter element has been identified that can negatively modulate the transcriptional response to viral induction.

  8. Applicability of the Fourier convolution theorem to the analysis of late-type stellar spectra

    International Nuclear Information System (INIS)

    Bruning, D.H.

    1981-01-01

    Solar flux and intensity measurements were obtained at Sacramento Peak Observatory to test the validity of the Fourier convolution method as a means of analyzing the spectral line shapes of late-type stars. Analysis of six iron lines near 6200A shows that, in general, the convolution method is not a suitable approximation for the calculation of the flux profile. The convolution method does reasonably reproduce the line shape for some lines which appear not to vary across the disk of the sun, but does not properly calculate the central line depth of these lines. Even if a central depth correction could be found, it is difficult to predict, especially for stars other than the sun, which lines have nearly constant shapes and could be used with the convolution method. Therefore, explicit disk integrations are promoted as the only reliable method of spectral line analysis for late-type stars. Several methods of performing the disk integration are investigated. Although the Abt (1957) prescription appears suitable for the limited case studied, methods using annuli of equal area, equal flux, or equal width (Soberblom, 1980) are considered better models. The model that is the easiest to use and most efficient computationally is the equal area model. Model atmosphere calculations yield values for the microturbulence and macroturbulence similar to those derived by observers. Since the depth dependence of the microturbulence is ignored in the calculations, the intensity profiles at disk center and the limb do not match the observed intensity profiles with only one set of velocity parameters. Use of these incorrectly calculated intensity profiles in the integration procedure to obtain the flux profile leads to incorrect estimates of the solar macroturbulence

  9. Distal finger replantation.

    Science.gov (United States)

    Scheker, Luis R; Becker, Giles W

    2011-03-01

    Reconstruction of the fingertip distal to the flexor tendon insertion by replantation remains controversial and technically challenging, but the anatomy of the fingertip has been well described and provides help in surgical planning. The open-book surgical technique is described with potential complications and is illustrated with clinical cases. Copyright © 2011 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

  10. Defect detection and classification of galvanized stamping parts based on fully convolution neural network

    Science.gov (United States)

    Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

    2018-04-01

    In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

  11. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

    International Nuclear Information System (INIS)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.

    2017-01-01

    Here, we present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. Lastly, we also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

  12. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

    Energy Technology Data Exchange (ETDEWEB)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Fernandez, R. Castillo; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anad?n, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Sanchez, L. Escudero; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C. -M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Caicedo, D. A. Martinez; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; S?ldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y. -T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.

    2017-03-01

    We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

  13. Adaptive decoding of convolutional codes

    Science.gov (United States)

    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.

  14. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    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.

  15. Development of a morphological convolution operator for bearing fault detection

    Science.gov (United States)

    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.

  16. Multineuron spike train analysis with R-convolution linear combination kernel.

    Science.gov (United States)

    Tezuka, Taro

    2018-06-01

    A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

  17. Multi-focus image fusion with the all convolutional neural network

    Science.gov (United States)

    Du, Chao-ben; Gao, She-sheng

    2018-01-01

    A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

  18. Distal renal tubular acidosis and hepatic lipidosis in a cat.

    Science.gov (United States)

    Brown, S A; Spyridakis, L K; Crowell, W A

    1986-11-15

    Clinical and laboratory evidence of hepatic failure was found in a chronically anorectic cat. Simultaneous blood and urine pH determinations established a diagnosis of distal renal tubular acidosis. The cat did not respond to treatment. Necropsy revealed distal tubular nephrosis and hepatic lipidosis. The finding of distal renal tubular acidosis in a cat with hepatic lipidosis emphasizes the importance of complete evaluation of acid-base disorders in patients.

  19. Multicenter comparative study of laparoscopic and open distal pancreatectomy using propensity score-matching.

    Science.gov (United States)

    Nakamura, Masafumi; Wakabayashi, Go; Miyasaka, Yoshihiro; Tanaka, Masao; Morikawa, Takanori; Unno, Michiaki; Tajima, Hiroshi; Kumamoto, Yusuke; Satoi, Sohei; Kwon, Masanori; Toyama, Hirochika; Ku, Yonson; Yoshitomi, Hideyuki; Nara, Satoshi; Shimada, Kazuaki; Yokoyama, Takahide; Miyagawa, Shinichi; Toyama, Yoichi; Yanaga, Katsuhiko; Fujii, Tsutomu; Kodera, Yasuhiro; Tomiyama, Yasuyuki; Miyata, Hiroaki; Takahara, Takeshi; Beppu, Toru; Yamaue, Hiroki; Miyazaki, Masaru; Takada, Tadahiro

    2015-10-01

    Laparoscopic distal pancreatectomy has been shown to be associated with favorable postoperative outcomes using meta-analysis. However, there have been no randomized controlled studies yet. This study aimed to compare laparoscopic and open distal pancreatectomy using propensity score-matching. We retrospectively collected perioperative data of 2,266 patients who underwent distal pancreatectomy in 69 institutes from 2006-2013 in Japan. Among them, 2,010 patients were enrolled in this study and divided into two groups, laparoscopic distal pancreatectomy and open distal pancreatectomy. Perioperative outcomes were compared between the groups using unmatched and propensity matched analysis. After propensity score-matching, laparoscopic distal pancreatectomy was associated with favorable perioperative outcomes compared with open distal pancreatectomy, including higher rate of preservation of spleen and splenic vessels (P pancreatectomy was associated with more favorable perioperative outcomes than open distal pancreatectomy. © 2015 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

  20. Multi-Branch Fully Convolutional Network for Face Detection

    KAUST Repository

    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.

  1. Automatic segmentation of MR brain images with a convolutional neural network

    NARCIS (Netherlands)

    Moeskops, P.; Viergever, M.A.; Mendrik, A.M.; de Vries, L.S.; Benders, M.J.N.L.; Išgum, I.

    2016-01-01

    Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure

  2. Fractures of the bilateral distal radius and scaphoid: a case report

    Directory of Open Access Journals (Sweden)

    Ozkan Korhan

    2008-03-01

    Full Text Available Abstract Introduction Bilateral fractures of the distal radius and scaphoid are extremely rare injuries. Case presentation A patient with bilateral comminuted, displaced distal fractures of the radius and bilateral fractures of the scaphoid was treated via internal fixation of the scaphoid fractures with Herbert screws and internal fixation of the distal radius fractures with locked volar plating. Conclusion Rigid internal fixation of distal radius and scaphoid fractures is mandatory to start early active rehabilitation of the wrist without the need for wrist immobilization with a plaster or external skeletal fixation.

  3. 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/

  4. Distally based superficial sural artery flap for soft tissue coverage in the distal 2/3 of leg and foot

    Directory of Open Access Journals (Sweden)

    Kamath B

    2005-01-01

    Full Text Available Background: Skin coverage for defects in the lower 2/3 of leg, ankle region and posterior heel has always been a difficult challenge for reconstructive surgeon. Methods: We describe our experience with the distally based superficial sural artery flap coverage in 48 patients with moderate sized defects in these difficult areas. Results: One out of 48 flaps (in 48 patients was lost totally and 3 suffered marginal necrosis which did not require any secondary procedure. These complications could have been avoided by proper selection of cases and refining technical skills. Conclusion: This simple procedure could be an important and versatile tool for any reconstructive surgeon in providing skin coverage in the distal leg and proximal foot. Preservation of major arteries of the lower limb, minimal donor defect, relatively uninjured donor area in compound fracture or poly trauma involving distal leg are some of the advantages of the flap.

  5. The impact of splenectomy on outcomes after distal and total pancreatectomy

    Directory of Open Access Journals (Sweden)

    Bramhall Simon

    2007-06-01

    Full Text Available Abstract Background Several authors advocate spleen preserving distal pancreatectomy, because of the increased complication rate after splenectomy. Methods Postoperative complications and survival after distal and total pancreatectomy, were recorded and retrospectively analyzed according to spleen preservation. Patients, who underwent distal and total pancreatectomy without histologically proven adenocarcinoma, or extrapancreatic disease, were included in the cohort which was divided into splenectomy and no splenectomy groups. Statistical analysis was performed using Fisher's test. Results The study group consisted of 62 patients who underwent distal and total pancreatectomy between 26/11/1987 to 6/1/2006. Splenectomy was performed in 35 out of 62 patients (56.5%, distal pancreatectomy was performed in 49 out of 62 patients (79%. Morbidity rate was 28.6% in splenectomy group and 14.8% in the no splenectomy group (p = 0.235, while 30 days mortality rate was 2.9%; one patient died in the splenectomy group (p = 1. Conclusion Spleen-preservation did not influence the outcomes after distal and total pancreatectomy in our series.

  6. Abnormality Detection in Mammography using Deep Convolutional Neural Networks

    OpenAIRE

    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...

  7. 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

  8. 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....

  9. Two-wave propagation in in vitro swine distal ulna

    Science.gov (United States)

    Mano, Isao; Horii, Kaoru; Matsukawa, Mami; Otani, Takahiko

    2015-07-01

    Ultrasonic transmitted waves were obtained in an in vitro swine distal ulna specimen, which mimics a human distal radius, that consists of interconnected cortical bone and cancellous bone. The transmitted waveforms appeared similar to the fast waves, slow waves, and overlapping fast and slow waves measured in the specimen after removing the surface cortical bone (only cancellous bone). In addition, the circumferential waves in the cortical bone and water did not affect the fast and slow waves. This suggests that the fast-and-slow-wave phenomenon can be observed in an in vivo human distal radius.

  10. Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation

    NARCIS (Netherlands)

    Barth, R.; IJsselmuiden, J.; Hemming, J.; Henten, Van E.J.

    2017-01-01

    A current bottleneck of state-of-the-art machine learning methods for image segmentation in agriculture, e.g. convolutional neural networks (CNNs), is the requirement of large manually annotated datasets on a per-pixel level. In this paper, we investigated how related synthetic images can be used to

  11. Phenotypic and pathologic evaluation of the myd mouse. A candidate model for facioscapulohumeral dystrophy

    Energy Technology Data Exchange (ETDEWEB)

    Mathews, K.D.; Rapisarda, D.; Bailey, H.L. [Univ. of Iowa College of Medicine, Iowa City, IA (United States)] [and others

    1995-07-01

    Facioscapulohumeral dystrophy (FSHD) is an autosomal dominant disease of unknown pathogenesis which is characterized by weakness of the face and shoulder girdle. It is associated with a sensorineural hearing loss which may be subclinical. FSHD has been mapped to the distalmost portion of 4q35, although the gene has not yet been identified. Distal 4q has homology with a region of mouse chromosome 8 to which a mouse mutant, myodystrophy (myd), has been mapped. Muscle from homozygotes for the myd mutation appears dystrophic, showing degenerating and regenerating fibers, inflammatory infiltrates, central nuclei, and variation in fiber size. Brainstem auditory evoked potentials reveal a sensorineural hearing loss in myd homozygotes. Based on the homologous genetic map locations, and the phenotypic syndrome of dystrophic muscle with sensorineural hearing loss, we suggest that myd represents an animal model for the human disease FSHD. 28 refs., 4 figs.

  12. Target recognition based on convolutional neural network

    Science.gov (United States)

    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.

  13. Fourier transforms and convolutions for the experimentalist

    CERN Document Server

    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

  14. MRI of fractures of the distal radius: comparison with conventional radiographs

    International Nuclear Information System (INIS)

    Spence, L.D.; Eustace, S.

    1998-01-01

    Objective. To compare the evaluation of fractures of the distal radius with MRI and conventional radiographs. To demonstrate the ability of MRI to detect unsuspected soft tissue derangement accompanying this common injury. Design and patients. Twenty-one consecutive inpatients admitted following fracture of the distal radius underwent preoperative evaluation with both conventional radiographs and MRI. In each case, analysis was made of both the osseous and soft tissue injury. MRI findings were compared with those identified on conventional radiographs and at subsequent surgical fixation. Results. Of 21 patients with fractures of the distal radius, 20 had extension to the radiocarpal articulation, 14 had distal radio-ulnar joint extension and 5 had avulsion of the ulnar styloid.Occult carpal bone fractures accompanying fracture of the distal radius were identified in two patients: one of the capitate and the other of the second metacarpal base. Ten patients (48%) had associated soft tissue injury: six patients had scapholunate ligament rupture, two patients had disruption of the triangular fibrocartilage, one patient had extensor carpi ulnaris tenosynovitis and one patient had a tear of a dorsal radiocarpal ligament. Of five patients with ulnar styloid avulsions, none had evidence of triangular fibrocartilage tears. Conclusion. MRI affords better evaluation of osseous injury accompanying distal radial fractures than conventional radiographs. Intra-articular soft tissue injury accompanies distal radial fractures in almost 50% of cases. Scapholunate ligament disruption commonly accompanies intra-articular fracture through the lunate facet of the distal radius. Fracture of the ulnar styloid is infrequently associated with tear of the triangular fibrocartilage. (orig.)

  15. MRI of fractures of the distal radius: comparison with conventional radiographs

    Energy Technology Data Exchange (ETDEWEB)

    Spence, L.D.; Eustace, S. [Medical Center, Boston, MA (United States). Dept. of Radiol.; Savenor, A.; Nwachuku, I.; Tilsley, J. [Department of Orthopedics, Boston Medical Center, Boston, MA 02118 (United States)

    1998-05-01

    Objective. To compare the evaluation of fractures of the distal radius with MRI and conventional radiographs. To demonstrate the ability of MRI to detect unsuspected soft tissue derangement accompanying this common injury. Design and patients. Twenty-one consecutive inpatients admitted following fracture of the distal radius underwent preoperative evaluation with both conventional radiographs and MRI. In each case, analysis was made of both the osseous and soft tissue injury. MRI findings were compared with those identified on conventional radiographs and at subsequent surgical fixation. Results. Of 21 patients with fractures of the distal radius, 20 had extension to the radiocarpal articulation, 14 had distal radio-ulnar joint extension and 5 had avulsion of the ulnar styloid.Occult carpal bone fractures accompanying fracture of the distal radius were identified in two patients: one of the capitate and the other of the second metacarpal base. Ten patients (48%) had associated soft tissue injury: six patients had scapholunate ligament rupture, two patients had disruption of the triangular fibrocartilage, one patient had extensor carpi ulnaris tenosynovitis and one patient had a tear of a dorsal radiocarpal ligament. Of five patients with ulnar styloid avulsions, none had evidence of triangular fibrocartilage tears. Conclusion. MRI affords better evaluation of osseous injury accompanying distal radial fractures than conventional radiographs. Intra-articular soft tissue injury accompanies distal radial fractures in almost 50% of cases. Scapholunate ligament disruption commonly accompanies intra-articular fracture through the lunate facet of the distal radius. Fracture of the ulnar styloid is infrequently associated with tear of the triangular fibrocartilage. (orig.) With 5 figs., 16 refs.

  16. Multi-Scale Residual Convolutional Neural Network for Haze Removal of Remote Sensing Images

    Directory of Open Access Journals (Sweden)

    Hou Jiang

    2018-06-01

    Full Text Available Haze removal is a pre-processing step that operates on at-sensor radiance data prior to the physically based image correction step to enhance hazy imagery visually. Most current haze removal methods focus on point-to-point operations and utilize information in the spectral domain, without taking consideration of the multi-scale spatial information of haze. In this paper, we propose a multi-scale residual convolutional neural network (MRCNN for haze removal of remote sensing images. MRCNN utilizes 3D convolutional kernels to extract spatial–spectral correlation information and abstract features from surrounding neighborhoods for haze transmission estimation. It takes advantage of dilated convolution to aggregate multi-scale contextual information for the purpose of improving its prediction accuracy. Meanwhile, residual learning is utilized to avoid the loss of weak information while deepening the network. Our experiments indicate that MRCNN performs accurately, achieving an extremely low validation error and testing error. The haze removal results of several scenes of Landsat 8 Operational Land Imager (OLI data show that the visibility of the dehazed images is significantly improved, and the color of recovered surface is consistent with the actual scene. Quantitative analysis proves that the dehazed results of MRCNN are superior to the traditional methods and other networks. Additionally, a comparison to haze-free data illustrates the spectral consistency after haze removal and reveals the changes in the vegetation index.

  17. Young Children's Sibling Relationship Quality: Distal and Proximal Correlates

    Science.gov (United States)

    Kretschmer, Tina; Pike, Alison

    2009-01-01

    Background: Relationships within families are interdependent and related to distal environmental factors. Low socioeconomic status (SES) and high household chaos (distal factors) have been linked to less positive marital and parent-child relationships, but have not yet been examined with regard to young children's sibling relationships. The…

  18. Clinical relevance of distal biceps insertional and footprint anatomy

    NARCIS (Netherlands)

    van den Bekerom, Michel P J; Kodde, Izaäk F.; Aster, Asir; Bleys, Ronald L A W; Eygendaal, Denise

    2016-01-01

    Purpose: The aim of this review was to present an overview, based on a literature search, of surgical anatomy for distal biceps tendon repairs, based on the current literature. Methods: A narrative review was performed using Pubmed/Medline using key words: Search terms were distal biceps,

  19. Mapping of the mouse actin capping protein {alpha} subunit genes and pseudogenes

    Energy Technology Data Exchange (ETDEWEB)

    Hart, M.C.; Korshunova, Y.O.; Cooper, J.A. [Washington Univ. School of Medicine, St. Louis, MO (United States)

    1997-02-01

    Capping protein (CP), a heterodimer of {alpha} and {beta} subunits, is found in all eukaryotes. CP binds to the barbed ends of actin filaments in vitro and controls actin assembly and cell motility in vivo. Vertebrates have three {alpha} isoforms ({alpha}1, {alpha}2, {alpha}3) produced from different genes, whereas lower organisms have only one gene and one isoform. We isolated genomic clones corresponding to the a subunits of mouse CP and found three {alpha}1 genes, two of which are pseudogenes, and a single gene for both {alpha}2 and {alpha}3. Their chromosomal locations were identified by interspecies backcross mapping. The {alpha}1 gene (Cappa1) mapped to Chromosome 3 between D3Mit11 and D3Mit13. The {alpha}1 pseudogenes (Cappa1-ps1 and Cappa1-ps2) mapped to Chromosomes 1 and 9, respectively. The {alpha}2 gene (Cappa2) mapped to Chromosome 6 near Ptn. The {alpha}3 gene (Cappa3) also mapped to Chromosome 6, approximately 68 cM distal from Cappa2 near Kras2. One mouse mutation, de, maps in the vicinity of the {alpha}1 gene. No known mouse mutations map to regions near the {alpha}2 or {alpha}3 genes. 29 refs., 3 figs., 1 tab.

  20. Performance Analysis of DPSK Signals with Selection Combining and Convolutional Coding in Fading Channel

    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...

  1. Concave distal end of ulna metaphysis alone is not a sign of rickets

    Energy Technology Data Exchange (ETDEWEB)

    Oestreich, Alan E. [Cincinnati Children' s Hospital Medical Center, Department of Radiology, 5031, Cincinnati, OH (United States)

    2015-07-15

    Statements have been made in the literature and in legal testimony that misrepresent the radiographic finding of concave distal end of the ulnar metaphysis. To demonstrate that a concave distal end of the ossified ulna in infancy can be normal. Eighty distal wrists of randomly selected infants in the first year of life with radiographic evidence that ruled out rickets were reviewed. In 16 of the cases (20%), mild or moderate concavity of the distal end of the ulna was seen. An intact metaphyseal collar of distal radius or ulna confirmed the absence of radiographic rickets. The finding of 20% of concave distal ulnas in the first year of life confirms the widely acknowledged statements that concave distal end of the ulna alone is not indicative of rickets. Statements to the contrary are not justified. (orig.)

  2. Concave distal end of ulna metaphysis alone is not a sign of rickets

    International Nuclear Information System (INIS)

    Oestreich, Alan E.

    2015-01-01

    Statements have been made in the literature and in legal testimony that misrepresent the radiographic finding of concave distal end of the ulnar metaphysis. To demonstrate that a concave distal end of the ossified ulna in infancy can be normal. Eighty distal wrists of randomly selected infants in the first year of life with radiographic evidence that ruled out rickets were reviewed. In 16 of the cases (20%), mild or moderate concavity of the distal end of the ulna was seen. An intact metaphyseal collar of distal radius or ulna confirmed the absence of radiographic rickets. The finding of 20% of concave distal ulnas in the first year of life confirms the widely acknowledged statements that concave distal end of the ulna alone is not indicative of rickets. Statements to the contrary are not justified. (orig.)

  3. A novel model of distal colon cancer in athymic mice Novo modelo de câncer de cólon distal em camundongos atímicos

    Directory of Open Access Journals (Sweden)

    Denise Gonçalves Priolli

    2012-06-01

    Full Text Available PURPOSE: The present a novel adenocarcinoma model in athymic mice. METHODS: Seven athymic mice were used. Colon diversion and distal fistula were made. Adenocarcinoma cells were inoculated in the submucosa of fistula. Tumor growth was monitored daily. Scintigraphy with 99mTc-MIBI was performed to identify the tumor. RESULTS: The model of distal colon cancer is feasible. Tumor detection was possible by both, macroscopically and molecular imaging. All resections demonstrated poorly differentiated tumors. Colon obstruction occurred in one case, similarly to evolution in human tumors of distal colon. CONCLUSION: The proposed model of distal colon cancer is feasible, allows for easy monitoring of tumoral growth by both, macroscopically and molecular imaging, and is suitable for studying the evolution of tumor with implementation of cytotoxic therapy in vivo.OBJETIVO: Apresentar novo modelo de adenocarcinoma distal em camundongos atímicos. MÉTODOS: Foram utilizados sete camundongos atímicos. Desvio do cólon distal e fístula foram feitas. Células de adenocarcinoma foram inoculadas na submucosa da fístula. O crescimento do tumor foi monitorado diariamente. Cintilografia com 99mTc-MIBI foi realizada para identificar o tumor. RESULTADOS: O modelo de câncer de cólon distal é viável. Detecção do tumor foi possível macroscopicamente e por imagem molecular. Todas as ressecções demonstraram tumores pouco diferenciados. Obstrução do cólon ocorreu em um caso, de forma semelhante à evolução em tumores humanos do cólon distal. CONCLUSÃO: O modelo de câncer do cólon distal proposto é viável, permite a monitorização fácil do crescimento tumoral macroscopicamente e por imagem molecular, sendo adequado para o estudo da evolução de tumor com aplicação de terapia citotóxica in vivo.

  4. Fourier transform and mean quadratic variation of Bernoulli convolution on homogeneous Cantor set

    Energy Technology Data Exchange (ETDEWEB)

    Yu Zuguo E-mail: yuzg@hotmail.comz.yu

    2004-07-01

    For the Bernoulli convolution on homogeneous Cantor set, under some condition, it is proved that the mean quadratic variation and the average of Fourier transform of this measure are bounded above and below.

  5. Fishtail deformity - a delayed complication of distal humeral fractures in children

    Energy Technology Data Exchange (ETDEWEB)

    Narayanan, Srikala [Massachusetts General Hospital, Department of Radiology, Division of Pediatric Imaging, Boston, MA (United States); University of Pittsburgh Medical Center, Department of Radiology, Pittsburgh, PA (United States); Shailam, Randheer; Nimkin, Katherine [Massachusetts General Hospital, Department of Radiology, Division of Pediatric Imaging, Boston, MA (United States); Grottkau, Brian E. [Massachusetts General Hospital, Department of Orthopaedics, Pediatric Orthopaedics, Boston, MA (United States)

    2015-06-15

    Concavity in the central portion of the distal humerus is referred to as fishtail deformity. This entity is a rare complication of distal humeral fractures in children. The purpose of this study is to describe imaging features of post-traumatic fishtail deformity and discuss the pathophysiology. We conducted a retrospective analysis of seven cases of fishtail deformity after distal humeral fractures. Seven children ages 7-14 years (five boys, two girls) presented with elbow pain and history of distal humeral fracture. Four of the seven children had limited range of motion. Five children had prior grade 3 supracondylar fracture treated with closed reduction and percutaneous pinning. One child had a medial condylar fracture and another had a lateral condylar fracture; both had been treated with conservative casting. All children had radiographs, five had CT and three had MRI. All children had a concave central defect in the distal humerus. Other imaging features included joint space narrowing with osteophytes and subchondral cystic changes in four children, synovitis in one, hypertrophy or subluxation of the radial head in three and proximal migration of the ulna in two. Fishtail deformity of the distal humerus is a rare complication of distal humeral fractures in children. This entity is infrequently reported in the radiology literature. Awareness of the classic imaging features can result in earlier diagnosis and appropriate treatment. (orig.)

  6. Fishtail deformity - a delayed complication of distal humeral fractures in children

    International Nuclear Information System (INIS)

    Narayanan, Srikala; Shailam, Randheer; Nimkin, Katherine; Grottkau, Brian E.

    2015-01-01

    Concavity in the central portion of the distal humerus is referred to as fishtail deformity. This entity is a rare complication of distal humeral fractures in children. The purpose of this study is to describe imaging features of post-traumatic fishtail deformity and discuss the pathophysiology. We conducted a retrospective analysis of seven cases of fishtail deformity after distal humeral fractures. Seven children ages 7-14 years (five boys, two girls) presented with elbow pain and history of distal humeral fracture. Four of the seven children had limited range of motion. Five children had prior grade 3 supracondylar fracture treated with closed reduction and percutaneous pinning. One child had a medial condylar fracture and another had a lateral condylar fracture; both had been treated with conservative casting. All children had radiographs, five had CT and three had MRI. All children had a concave central defect in the distal humerus. Other imaging features included joint space narrowing with osteophytes and subchondral cystic changes in four children, synovitis in one, hypertrophy or subluxation of the radial head in three and proximal migration of the ulna in two. Fishtail deformity of the distal humerus is a rare complication of distal humeral fractures in children. This entity is infrequently reported in the radiology literature. Awareness of the classic imaging features can result in earlier diagnosis and appropriate treatment. (orig.)

  7. 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.

  8. Spontaneous distal rupture of the plantar fascia.

    Science.gov (United States)

    Gitto, Salvatore; Draghi, Ferdinando

    2018-07-01

    Spontaneous ruptures of the plantar fascia are uncommon injuries. They typically occur at its calcaneal insertion and usually represent a complication of plantar fasciitis and local treatment with steroid injections. In contrast, distal ruptures commonly result from traumatic injuries. We describe the case of a spontaneous distal rupture of the plantar fascia in a 48-year-old woman with a low level of physical activity and no history of direct injury to the foot, plantar fasciitis, or steroid injections. © 2017 Wiley Periodicals, Inc.

  9. Convolutional neural networks for segmentation and object detection of human semen

    DEFF Research Database (Denmark)

    Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian

    2017-01-01

    We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due...

  10. Minimally Invasive Distal Pancreatectomy: Review of the English Literature.

    Science.gov (United States)

    Wang, Kai; Fan, Ying

    2017-02-01

    Recently, the superiority of the minimally invasive approach, which results in a better cosmetic result, faster recovery, and shorter length of hospital stay, is a technique that has been progressively recognized as it has developed. And the minimally invasive approach has been applied to distal pancreatectomy (DP), which is a standard method for the treatment of benign, borderline, and part of malignant lesions of the pancreatic body and tail. This article aims to analyze the types, postoperative recovery, and outcomes of laparoscopic distal pancreatectomy (LDP). A systematic search of the scientific literature was performed using PubMed, EMBASE, online journals, and the Internet for all publications on LDP. Articles were selected if the abstract contained patients who underwent LDP for pancreatic diseases. All selected articles were reviewed and analyzed. If there were no contraindications for LDP, this operation is suitable for benign, borderline, or malignant tumors of the pancreatic body and tail, which should try to be performed with preservation of the spleen. LDP is safe and feasible under some conditions to experienced surgeon. Single-incision laparoscopic distal pancreatectomy (S-LDP) and robotic laparoscopic distal pancreatectomy (R-LDP) perioperative outcomes are similar with conventional multi-incision laparoscopic distal pancreatectomy (C-LDP). And the advantages of S-LDP and R-LDP require further exploration. With the application of enhanced recovery program (ERP), length of hospital stay and costs are reduced. LDP is safe and feasible under some conditions. Compared with open distal pancreatectomy, LDP has a lot of advantages; a trend was observed for LDP to replace traditional open surgery. LDP combined with ERP is expected to become standard in the treatment of pancreatic body and tail lesions.

  11. A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

    Directory of Open Access Journals (Sweden)

    Xiaocong Wei

    2017-07-01

    Full Text Available Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

  12. A Conditional Fourier-Feynman Transform and Conditional Convolution Product with Change of Scales on a Function Space II

    Directory of Open Access Journals (Sweden)

    Dong Hyun Cho

    2017-01-01

    Full Text Available Using a simple formula for conditional expectations over continuous paths, we will evaluate conditional expectations which are types of analytic conditional Fourier-Feynman transforms and conditional convolution products of generalized cylinder functions and the functions in a Banach algebra which is the space of generalized Fourier transforms of the measures on the Borel class of L2[0,T]. We will then investigate their relationships. Particularly, we prove that the conditional transform of the conditional convolution product can be expressed by the product of the conditional transforms of each function. Finally we will establish change of scale formulas for the conditional transforms and the conditional convolution products. In these evaluation formulas and change of scale formulas, we use multivariate normal distributions so that the conditioning function does not contain present positions of the paths.

  13. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Science.gov (United States)

    Zhu, Aichun; Wang, Tian; Snoussi, Hichem

    2018-03-01

    This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  14. Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature

    Directory of Open Access Journals (Sweden)

    Yuankun Li

    2018-02-01

    Full Text Available Although correlation filter (CF-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

  15. Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

    Directory of Open Access Journals (Sweden)

    Aichun Zhu

    2018-03-01

    Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

  16. A New Missing Values Estimation Algorithm in Wireless Sensor Networks Based on Convolution

    Directory of Open Access Journals (Sweden)

    Feng Liu

    2013-04-01

    Full Text Available Nowadays, with the rapid development of Internet of Things (IoT applications, data missing phenomenon becomes very common in wireless sensor networks. This problem can greatly and directly threaten the stability and usability of the Internet of things applications which are constructed based on wireless sensor networks. How to estimate the missing value has attracted wide interest, and some solutions have been proposed. Different with the previous works, in this paper, we proposed a new convolution based missing value estimation algorithm. The convolution theory, which is usually used in the area of signal and image processing, can also be a practical and efficient way to estimate the missing sensor data. The results show that the proposed algorithm in this paper is practical and effective, and can estimate the missing value accurately.

  17. Impact of a Nationwide Training Program in Minimally Invasive Distal Pancreatectomy (LAELAPS)

    NARCIS (Netherlands)

    de Rooij, Thijs; van Hilst, Jony; Boerma, Djamila; Bonsing, Bert A.; Daams, Freek; van Dam, Ronald M.; Dijkgraaf, Marcel G.; van Eijck, Casper H.; Festen, Sebastiaan; Gerhards, Michael F.; Koerkamp, Bas Groot; van der Harst, Erwin; de Hingh, Ignace H.; Kazemier, Geert; Klaase, Joost; de Kleine, Ruben H.; van Laarhoven, Cornelis J.; Lips, Daan J.; Luyer, Misha D.; Molenaar, I. Quintus; Patijn, Gijs A.; Roos, Daphne; Scheepers, Joris J.; van der Schelling, George P.; Steenvoorde, Pascal; Vriens, Menno R.; Wijsman, Jan H.; Gouma, Dirk J.; Busch, Olivier R.; Hilal, Mohammed Abu; Besselink, Marc G.; de Boer, Marieke T.

    2016-01-01

    Objective:To study the feasibility and impact of a nationwide training program in minimally invasive distal pancreatectomy (MIDP).Summary of Background Data:Superior outcomes of MIDP compared with open distal pancreatectomy have been reported. In the Netherlands (2005 to 2013) only 10% of distal

  18. Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

    Science.gov (United States)

    Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

    2017-01-01

    Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

  19. Joint Multi-scale Convolution Neural Network for Scene Classification of High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    ZHENG Zhuo

    2018-05-01

    Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.

  20. Efficiently GPU-accelerating long kernel convolutions in 3-D DIRECT TOF PET reconstruction via memory cache optimization

    Energy Technology Data Exchange (ETDEWEB)

    Ha, Sungsoo; Mueller, Klaus [Stony Brook Univ., NY (United States). Center for Visual Computing; Matej, Samuel [Pennsylvania Univ., Philadelphia, PA (United States). Dept. of Radiology

    2011-07-01

    The DIRECT represents a novel approach for 3-D Time-of-Flight (TOF) PET reconstruction. Its novelty stems from the fact that it performs all iterative predictor-corrector operations directly in image space. The projection operations now amount to convolutions in image space, using long TOF (resolution) kernels. While for spatially invariant kernels the computational complexity can be algorithmically overcome by replacing spatial convolution with multiplication in Fourier space, spatially variant kernels cannot use this shortcut. Therefore in this paper, we describe a GPU-accelerated approach for this task. However, the intricate parallel architecture of GPUs poses its own challenges, and careful memory and thread management is the key to obtaining optimal results. As convolution is mainly memory-bound we focus on the former, proposing two types of memory caching schemes that warrant best cache memory re-use by the parallel threads. In contrast to our previous two-stage algorithm, the schemes presented here are both single-stage which is more accurate. (orig.)

  1. Use of potassium-42 in the study of kidney functioning

    International Nuclear Information System (INIS)

    Morel, F.; Guinnebault, M.

    1959-01-01

    Following an intravenous injection of potassium-42 as indicator, an analysis of the specific activity vs. time curve in arterial plasma, in venous plasma efferent from the kidney, in urine and in various regions of the kidney of rabbits reveals that: 1) The turnover rate of potassium in the cortex cells (proximal and distal convoluted tubes) is very large, being limited only by renal blood flow. 2) The turnover rate of potassium in deep regions (Henle loops and collector tubules) is much smaller. 3) Potassium in the urine comes from cells of the convoluted tubes and not from cells of Henle loops, collector ducts, or glomerular filtrate. 4) Any potassium filtered at the level of the glomerules would be entirely reabsorbed at the level of the proximal tube, while total potassium in the urine results from a process of excretion by cells of the distal tube. These results are comparable with the assumption that the movement of potassium between interstitial medium and convoluted tube cells results from entirely passive processes. (author) [fr

  2. Torsion of wandering spleen and distal pancreas

    International Nuclear Information System (INIS)

    Sheflin, J.R.; Lee, C.M.; Kretchmar, K.A.

    1984-01-01

    Wandering spleen is the term applied to the condition in which a long pedicle allows the spleen to lie in an abnormal location. Torsion of a wandering spleen is an unusual cause of an acute abdomen and is rarely diagnosed preoperatively. Associated torsion of the distal pancreas is even more uncommon. The authors describe a patient with torsion of a wandering spleen and distal pancreas, who was correctly diagnosed, and define the merits of the imaging methods used. The initial examination should be 99 /sup m/Tc-sulfur colloid liner-spleen scanning

  3. Osteoblastoma-like osteosarcoma of the distal tibia

    Energy Technology Data Exchange (ETDEWEB)

    Abramovici, Luigia; Steiner, German C. [Department of Pathology and Laboratory Medicine, Hospital for Joint Diseases, New York, NY (United States); Kenan, Samuel [Department of Orthopaedic Oncology Surgery, Hospital for Joint Diseases, New York, NY (United States); Hytiroglou, Prodromos [Aristotle University, Thessaloniki (Greece); Rafii, Mahvash [Department of Radiology, Hospital for Joint Diseases, New York, NY (United States)

    2002-03-01

    We report a case of a 14-year-old boy with an intracompartmental lytic lesion with poorly defined margins in the right distal tibia that was originally treated with curettage and bone grafting. Histologic examination showed an osteoblastic tumor with unusual features, which was found on consultation to be an osteoblastoma-like osteosarcoma, a rare, low-grade variant of osteosarcoma. Subsequently, the patient underwent en bloc resection of the distal tibia, which was replaced with vascularized bone graft and followed by chemotherapy. Two years later, he is alive with lung metastases. (orig.)

  4. Osteoblastoma-like osteosarcoma of the distal tibia

    International Nuclear Information System (INIS)

    Abramovici, Luigia; Steiner, German C.; Kenan, Samuel; Hytiroglou, Prodromos; Rafii, Mahvash

    2002-01-01

    We report a case of a 14-year-old boy with an intracompartmental lytic lesion with poorly defined margins in the right distal tibia that was originally treated with curettage and bone grafting. Histologic examination showed an osteoblastic tumor with unusual features, which was found on consultation to be an osteoblastoma-like osteosarcoma, a rare, low-grade variant of osteosarcoma. Subsequently, the patient underwent en bloc resection of the distal tibia, which was replaced with vascularized bone graft and followed by chemotherapy. Two years later, he is alive with lung metastases. (orig.)

  5. Convolutional auto-encoder for image denoising of ultra-low-dose CT

    Directory of Open Access Journals (Sweden)

    Mizuho Nishio

    2017-08-01

    Conclusion: Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

  6. Siamese convolutional networks for tracking the spine motion

    Science.gov (United States)

    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.

  7. Convolutional neural networks for vibrational spectroscopic data analysis.

    Science.gov (United States)

    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.

  8. Use of palatally inserted mini-screw for upper molar distalization: A case series

    Directory of Open Access Journals (Sweden)

    Valliollah Arash

    2015-09-01

    Full Text Available  Abstract Reports have shown that molars can be distalized successfully with virtually no orthodontic anchorage loss with an intraosseous anchorage, even with fully erupted second molars. The purpose of this study was evaluating the effects of mini-screws as skeletal anchorage for upper molar distalization. In this case series, three patients needing maxillary first molar distalization, were selected. mini-screw was inserted in the anterior part of the palate. The screws were anchored to the first premolars by transpalatal arch and immediately loaded (150-160 g by 0.018-inch arch-wire and steel open-coil spring to distalize maxillary molars. The skeletal and dental changes were measured on cephalograms obtained before and after distalization. The amount of first molar distalization in the patients was 4 mm with 2°of tipping, 4 mm with 5°of tipping, and 3.5 mm with 2°of tipping respectively. Upper incisors and first premolars were stable during distalization.       

  9. Subsidence feature discrimination using deep convolutional neral networks in synthetic aperture radar imagery

    CSIR Research Space (South Africa)

    Schwegmann, Colin P

    2017-07-01

    Full Text Available International Geoscience and Remote Sensing Symposium (IGARSS), 23-28 July 2017, Fort Worth, TX, USA SUBSIDENCE FEATURE DISCRIMINATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS IN SYNTHETIC APERTURE RADAR IMAGERY Schwegmann, Colin P Kleynhans, Waldo...

  10. Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Zenghai Chen

    2018-01-01

    Full Text Available Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

  11. Yarn-dyed fabric defect classification based on convolutional neural network

    Science.gov (United States)

    Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

    2017-09-01

    Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

  12. Multimodal Classification of Violent Online Political Extremism Content with Graph Convolutional Networks

    NARCIS (Netherlands)

    Rudinac, S.; Gornishka, I.; Worring, M.

    2017-01-01

    In this paper we present a multimodal approach to categorizing user posts based on their discussion topic. To integrate heterogeneous information extracted from the posts, i.e. text, visual content and the information about user interactions with the online platform, we deploy graph convolutional

  13. Laparoscopic radical nephroureterectomy: dilemma of the distal ureter.

    Science.gov (United States)

    Steinberg, Jordan R; Matin, Surena F

    2004-03-01

    Laparoscopic nephroureterectomy has recently emerged as a safe, minimally invasive approach to upper tract urothelial cancers. The most controversial and challenging feature of laparoscopic nephroureterectomy is the management of the distal ureter. We review the most common methods of managing the distal ureter, with emphasis on contemporary oncologic outcomes, indications, advantages, and disadvantages. There are currently in excess of five different approaches to the lower ureter. These techniques often combine features of endoscopic, laparoscopic, or open management. They include open excision, a transvesical laparoscopic detachment and ligation technique, laparoscopic stapling of the distal ureter and bladder cuff, the "pluck" technique, and ureteral intussusception. Each technique has distinct advantages and disadvantages, differing not only in technical approach, but oncological principles as well. While the existing published data do not overwhelmingly support one approach over the others, the open approach remains one of the most reliable and oncologically sound procedures. The principles of surgical oncology dictate that a complete, en-bloc resection, with avoidance of tumor seeding, remains the preferred treatment of all urothelial cancers. The classical open technique of securing the distal ureter and bladder cuff achieves this principle and has withstood the test of time. Transvesical laparoscopic detachment and ligation is an oncologically valid approach in patients without bladder tumors, but is limited by technical considerations. The laparoscopic stapling technique maintains a closed system but risks leaving behind ureteral and bladder cuff segments. Both transurethral resection of the ureteral orifice (pluck) and intussusception techniques should be approached with caution, as the potential for tumor seeding exists. Additional long-term comparative outcomes are needed to solve the dilemma of the distal ureter.

  14. Simultaneous bilateral distal biceps tendon repair: case report

    Directory of Open Access Journals (Sweden)

    Thiago Medeiros Storti

    Full Text Available ABSTRACT Simultaneous bilateral rupture of the distal biceps tendon is a rare clinical entity, seldom reported in the literature and with unclear therapeutic setting. The authors report the case of a 39-year-old white man who suffered a simultaneous bilateral rupture while working out. When weightlifting with elbows at 90° of flexion, he suddenly felt pain on the anterior aspect of the arms, coming for evaluation after two days. He presented bulging contour of the biceps muscle belly and ecchymosis in the antecubital fossa, extending distally to the medial aspect of the forearm, as well as a marked decrease of supination strength and pain in active elbow flexion. MRI confirmed the rupture with retraction of the distal biceps bilaterally. The authors opted for performing the tendon repairs simultaneously through the double incision technique and fixation to the bicipital tuberosity with anchors. The patient progressed quite well, with full return to labor and sports activities, being satisfied with the result after two years of surgery. In the literature search, few reports of simultaneous bilateral rupture of the distal biceps were retrieved, with only one treated in the acute phase of injury. Therefore, the authors consider this procedure to be a good option to solve this complex condition.

  15. Simultaneous bilateral distal biceps tendon repair: case report.

    Science.gov (United States)

    Storti, Thiago Medeiros; Paniago, Alexandre Firmino; Faria, Rafael Salomon Silva

    2017-01-01

    Simultaneous bilateral rupture of the distal biceps tendon is a rare clinical entity, seldom reported in the literature and with unclear therapeutic setting. The authors report the case of a 39-year-old white man who suffered a simultaneous bilateral rupture while working out. When weightlifting with elbows at 90° of flexion, he suddenly felt pain on the anterior aspect of the arms, coming for evaluation after two days. He presented bulging contour of the biceps muscle belly and ecchymosis in the antecubital fossa, extending distally to the medial aspect of the forearm, as well as a marked decrease of supination strength and pain in active elbow flexion. MRI confirmed the rupture with retraction of the distal biceps bilaterally. The authors opted for performing the tendon repairs simultaneously through the double incision technique and fixation to the bicipital tuberosity with anchors. The patient progressed quite well, with full return to labor and sports activities, being satisfied with the result after two years of surgery. In the literature search, few reports of simultaneous bilateral rupture of the distal biceps were retrieved, with only one treated in the acute phase of injury. Therefore, the authors consider this procedure to be a good option to solve this complex condition.

  16. Limited distal organelles and synaptic function in extensive monoaminergic innervation.

    Science.gov (United States)

    Tao, Juan; Bulgari, Dinara; Deitcher, David L; Levitan, Edwin S

    2017-08-01

    Organelles such as neuropeptide-containing dense-core vesicles (DCVs) and mitochondria travel down axons to supply synaptic boutons. DCV distribution among en passant boutons in small axonal arbors is mediated by circulation with bidirectional capture. However, it is not known how organelles are distributed in extensive arbors associated with mammalian dopamine neuron vulnerability, and with volume transmission and neuromodulation by monoamines and neuropeptides. Therefore, we studied presynaptic organelle distribution in Drosophila octopamine neurons that innervate ∼20 muscles with ∼1500 boutons. Unlike in smaller arbors, distal boutons in these arbors contain fewer DCVs and mitochondria, although active zones are present. Absence of vesicle circulation is evident by proximal nascent DCV delivery, limited impact of retrograde transport and older distal DCVs. Traffic studies show that DCV axonal transport and synaptic capture are not scaled for extensive innervation, thus limiting distal delivery. Activity-induced synaptic endocytosis and synaptic neuropeptide release are also reduced distally. We propose that limits in organelle transport and synaptic capture compromise distal synapse maintenance and function in extensive axonal arbors, thereby affecting development, plasticity and vulnerability to neurodegenerative disease. © 2017. Published by The Company of Biologists Ltd.

  17. Fractures of the distal radius in children: A retrospective evaluation

    Directory of Open Access Journals (Sweden)

    Selma Yazıcı

    2012-06-01

    Full Text Available Objectives: This study designed to evaluate the resultsof treatment, closed reduction and percutaneous wires, ofthe distal radius fractures in children.Materials and methods: A retrospective analysis wascarried out in children aged between 5-15 years who presentedwith a displaced fracture of the distal radius to ourhospital. They were initially treated with closed reductionand cast immobilization. If the fractures redisplaced treatedby percutaneous Kirschner (K- wire with scope undera general anaesthesia.Results: Totally 104 patients, who have distal radius fractureswere treated by closed reduction and immobilizationin a plaster cast. 13 patient who have distal radiusfractures were treated by closed reduction under generalanaesthesia and fixed by percutaneous Kirschner (K-wire. Patients with impaired the alignment of the fracturein late period were usually completely displaced fractures.(n=5, 4,3%, in early period, completely displaced fractures(n=5, 4,3% are superior to partial displaced fractures(n=2, 1,7%.Conclusion: In our study, when children with distal radiusfracture first come, they were treated by closed reductionand immobilization in a plaster cast. We thought that inredisplaced fractures patients were suitable for the closedreduction with percutaneous wire treatment.

  18. Intersphincteric Resection and Coloanal Anastomosis in Treatment of Distal Rectal Cancer

    OpenAIRE

    Cipe, Gokhan; Muslumanoglu, Mahmut; Yardimci, Erkan; Memmi, Naim; Aysan, Erhan

    2012-01-01

    In the treatment of distal rectal cancer, abdominoperineal resection is traditionally performed. However, the recognition of shorter safe distal resection line, intersphincteric resection technique has given a chance of sphincter-saving surgery for patients with distal rectal cancer during last two decades and still is being performed as an alternative choice of abdominoperineal resection. The first aim of this study is to assess the morbidity, mortality, oncological, and functional outcomes ...

  19. Distal antebrachial fractures in toy-breed dogs

    International Nuclear Information System (INIS)

    Muir, P.

    1997-01-01

    Antebrachial fractures account for approximately 17% of all canine fractures, with motor vehicle trauma cited as one of the primary causes. However, antebrachial fractures in toy-breed dogs are often sustained after apparently minimal trauma, such as jumping or falling, and are usually distal. The cause of antebrachial fractures in toy breeds is not well understood. Complications after treatment of distal antebrachial fractures in toy-breed dogs, including delayed union, nonunion, and malunion, are common and are potentially serious because they may necessitate limb amputation. This article reports on distal antebrachial fractures in 26 toy-breed dogs that wee presented to the University of California, Davis, Veterinary Medical Teaching Hospital from April 1987 to March 1996. The author found that (1) these fractures typically occur in growing or adolescent dogs; (2) the presence of complications of union is typically associated with prior treatment using intramedullary pinning or external coaptation; and (3) successful healing of this type of fracture is obtained via rigid stabilization with bone plating in combination with cancellous bone autograft

  20. Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution.

    Science.gov (United States)

    Langenbucher, Frieder

    2003-11-01

    Convolution and deconvolution are the classical in-vitro-in-vivo correlation tools to describe the relationship between input and weighting/response in a linear system, where input represents the drug release in vitro, weighting/response any body response in vivo. While functional treatment, e.g. in terms of polyexponential or Weibull distribution, is more appropriate for general survey or prediction, numerical algorithms are useful for treating actual experimental data. Deconvolution is not considered an algorithm by its own, but the inversion of a corresponding convolution. MS Excel is shown to be a useful tool for all these applications.

  1. Convolutional neural networks and face recognition task

    Science.gov (United States)

    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.

  2. 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.

  3. Fine-grained vehicle type recognition based on deep convolution neural networks

    Directory of Open Access Journals (Sweden)

    Hongcai CHEN

    2017-12-01

    Full Text Available Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes. Aiming at the problems of great plice forces occupation, low retrieval efficiency, and lacking of intelligence for dealing with false license, fake plate vehicles and vehicles without plates, this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks. The filter size and numbers of convolution neural network are designed, the activation function and vehicle type classifier are optimally selected, and a new network framework is constructed for vehicle type fine-grained recognition. The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model. Moreover, the new model effectively reduces the number of training parameters, and saves computer memory. Fine-grained vehicle type recognition can be used in intelligent traffic management area, and has important theoretical research value and practical significance.

  4. Detection of bars in galaxies using a deep convolutional neural network

    Science.gov (United States)

    Abraham, Sheelu; Aniyan, A. K.; Kembhavi, Ajit K.; Philip, N. S.; Vaghmare, Kaustubh

    2018-06-01

    We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.

  5. Multi-scale Fully Convolutional Network for Face Detection in the Wild

    KAUST Repository

    Bai, Yancheng

    2017-08-24

    Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.

  6. View-invariant gait recognition method by three-dimensional convolutional neural network

    Science.gov (United States)

    Xing, Weiwei; Li, Ying; Zhang, Shunli

    2018-01-01

    Gait as an important biometric feature can identify a human at a long distance. View change is one of the most challenging factors for gait recognition. To address the cross view issues in gait recognition, we propose a view-invariant gait recognition method by three-dimensional (3-D) convolutional neural network. First, 3-D convolutional neural network (3DCNN) is introduced to learn view-invariant feature, which can capture the spatial information and temporal information simultaneously on normalized silhouette sequences. Second, a network training method based on cross-domain transfer learning is proposed to solve the problem of the limited gait training samples. We choose the C3D as the basic model, which is pretrained on the Sports-1M and then fine-tune C3D model to adapt gait recognition. In the recognition stage, we use the fine-tuned model to extract gait features and use Euclidean distance to measure the similarity of gait sequences. Sufficient experiments are carried out on the CASIA-B dataset and the experimental results demonstrate that our method outperforms many other methods.

  7. User-generated content curation with deep convolutional neural networks

    OpenAIRE

    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...

  8. Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images

    NARCIS (Netherlands)

    Persello, Claudio; Stein, Alfred

    2017-01-01

    This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for

  9. The neuro vector engine : flexibility to improve convolutional net efficiency for wearable vision

    NARCIS (Netherlands)

    Peemen, M.C.J.; Shi, R.; Lal, S.; Juurlink, B.H.H.; Mesman, B.; Corporaal, H.

    2016-01-01

    Deep Convolutional Networks (ConvNets) are currently superior in benchmark performance, but the associated demands on computation and data transfer prohibit straightforward mapping on energy constrained wearable platforms. The computational burden can be overcome by dedicated hardware accelerators,

  10. Ultrasound demonstration of distal biceps tendon bifurcation: normal and abnormal findings

    International Nuclear Information System (INIS)

    Tagliafico, Alberto; Capaccio, Enrico; Derchi, Lorenzo E.; Martinoli, Carlo; Michaud, Johan

    2010-01-01

    We demonstrate the US appearance of the distal biceps tendon bifurcation in normal cadavers and volunteers and in those affected by various disease processes. Three cadaveric specimens, 30 normal volunteers, and 75 patients were evaluated by means of US. Correlative MR imaging was obtained in normal volunteers and patients. In all cases US demonstrated the distal biceps tendon shaped by two separate tendons belonging to the short and long head of the biceps brachii muscle. Four patients had a complete rupture of the distal insertion of the biceps with retraction of the muscle belly. Four patients had partial tear of the distal biceps tendon with different US appearance. In two patients the partial tear involved the short head of the biceps brachii tendon, while in the other two patients, the long head was involved. Correlative MR imaging is also presented both in normal volunteers and patients. US changed the therapeutic management in the patients with partial tears involving the LH of the biceps. This is the first report in which ultrasound considers the distal biceps tendon bifurcation in detail. Isolated tears of one of these components can be identified by US. Knowledge of the distal biceps tendon bifurcation ultrasonographic anatomy and pathology has important diagnostic and therapeutic implications. (orig.)

  11. Initial-value problems for first-order differential recurrence equations with auto-convolution

    Directory of Open Access Journals (Sweden)

    Mircea Cirnu

    2011-01-01

    Full Text Available A differential recurrence equation consists of a sequence of differential equations, from which must be determined by recurrence a sequence of unknown functions. In this article, we solve two initial-value problems for some new types of nonlinear (quadratic first order homogeneous differential recurrence equations, namely with discrete auto-convolution and with combinatorial auto-convolution of the unknown functions. In both problems, all initial values form a geometric progression, but in the second problem the first initial value is exempted and has a prescribed form. Some preliminary results showing the importance of the initial conditions are obtained by reducing the differential recurrence equations to algebraic type. Final results about solving the considered initial value problems, are shown by mathematical induction. However, they can also be shown by changing the unknown functions, or by the generating function method. So in a remark, we give a proof of the first theorem by the generating function method.

  12. Seismic signal auto-detecing from different features by using Convolutional Neural Network

    Science.gov (United States)

    Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

    2017-12-01

    We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

  13. Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

    Science.gov (United States)

    Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

    2018-04-20

    Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

  14. Eye and sheath folds in turbidite convolute lamination: Aberystwyth Grits Group, Wales

    Science.gov (United States)

    McClelland, H. L. O.; Woodcock, N. H.; Gladstone, C.

    2011-07-01

    Eye and sheath folds are described from the turbidites of the Aberystwyth Group, in the Silurian of west Wales. They have been studied at outcrop and on high resolution optical scans of cut surfaces. The folds are not tectonic in origin. They occur as part of the convolute-laminated interval of each sand-mud turbidite bed. The thickness of this interval is most commonly between 20 and 100 mm. Lamination patterns confirm previous interpretations that convolute lamination nucleated on ripples and grew during continued sedimentation of the bed. The folds amplified vertically and were sheared horizontally by continuing turbidity flow, but only to average values of about γ = 1. The strongly curvilinear fold hinges are due not to high shear strains, but to nucleation on sinuous or linguoid ripples. The Aberystwyth Group structures provide a warning that not all eye folds in sedimentary or metasedimentary rocks should be interpreted as sections through high shear strain sheath folds.

  15. Locking plates in distal humerus fractures: study of 43 patients

    Directory of Open Access Journals (Sweden)

    Gupta Rakesh Kumar

    2013-08-01

    Full Text Available 【Abstract】Objective: The treatment of multi-fragmentary, intraarticular fractures of the distal humerus is difficult, even in young patients with bone of good quality. Small distal fragment, diminished bone mineral quality and increased trauma-associated joint destruction make stable joint reconstruction more problematic. The anatomically preshaped locking plates allow angular stable fixation for these complex fractures. We evaluated functional results of patients treated with open reduction and internal fixation with distal humerus locking plates for complex distal hu-merus fractures. Methods: Forty-three consecutive patients with ar-ticular fractures of the distal humerus were treated by open reduction and internal fixation with AO distal humerus plate system and locking reconstruction plates. Forty patients were available for the final outcome analysis. According to AO/ASIF classification, there were 2 cases of type A2, 4 cases of type A3, 1 case of type B1, 1 case of type B2, 14 cases of type C1, 7 cases of type C2 and 11 cases of type C3. Open reduction with triceps splitting technique was used in all patients. The clinical and radiographic follow-up was performed and outcome measures included pain assessment, range of motion, and Mayo elbow performance score. Results: Forty patients were available for the final outcome analysis. There were 29 males and 11 females with an average age of 38.4 years (18-73 years. Clinical and ra-diological consolidation of the fracture was observed in all cases at an average of 11.6 weeks (9-14 weeks. The average follow-up was 12 months (10-18 months. Using the Mayo elbow performance score the results obtained were graded as excellent or good results in 33 patients (82.5%. One pa-tient had superficial infection, and 4 had myositis ossificans. There were no cases of primary malposition or secondary displacement, implant failure or ulnar neuropathy. Conclusion: Anatomically preshaped distal humerus locking

  16. Cloning and analysis of the mouse Fanconi anemia group A cDNA and an overlapping penta zinc finger cDNA.

    Science.gov (United States)

    Wong, J C; Alon, N; Norga, K; Kruyt, F A; Youssoufian, H; Buchwald, M

    2000-08-01

    Despite the cloning of four disease-associated genes for Fanconi anemia (FA), the molecular pathogenesis of FA remains largely unknown. To study FA complementation group A using the mouse as a model system, we cloned and characterized the mouse homolog of the human FANCA cDNA. The mouse cDNA (Fanca) encodes a 161-kDa protein that shares 65% amino acid sequence identity with human FANCA. Fanca is located at the distal region of mouse chromosome 8 and has a ubiquitous pattern of expression in embryonic and adult tissues. Expression of the mouse cDNA in human FA-A cells restores the cellular drug sensitivity to normal levels. Thus, the expression pattern, protein structure, chromosomal location, and function of FANCA are conserved in the mouse. We also isolated a novel zinc finger protein, Zfp276, which has five C(2)H(2) domains. Interestingly, Zfp276 is situated in the Fanca locus, and the 3'UTR of its cDNA overlaps with the last four exons of Fanca in a tail-to-tail manner. Zfp276 is expressed in the same tissues as Fanca, but does not complement the mitomycin C (MMC)-sensitive phenotype of FA-A cells. The overlapping genomic organization between Zfp276 and Fanca may have relevance to the disease phenotype of FA. Copyright 2000 Academic Press.

  17. Clinical and non-clinical aspects of distal radioulnar joint instability

    NARCIS (Netherlands)

    Wijffels, M.; Brink, P.R.G.; Schipper, I.

    2012-01-01

    Untreated distal radioulnar joint (DRUJ) injuries can give rise to long lasting complaints. Although common, diagnosis and treatment of DRUJ injuries remains a challenge. The articulating anatomy of the distal radius and ulna, among others, enables an extensive range of forearm pronosupination

  18. Surgical treatment of distal biceps tendon rupture: a case report

    Directory of Open Access Journals (Sweden)

    Cristina N. Cozma

    2017-11-01

    Full Text Available Objectives. Distal biceps tendon rupture affects the functional upperextremity movement, impairing supination and flexion strength. According to age, profession and additional risks treatment might be nonoperative or surgical. Methods. We describe the case of a 43 years old male patient who sustained an injury to his right distal biceps and was diagnosed with acute right distal biceps rupture. Surgical treatment was decided and biceps tendon was reinserted to the radius tuberosity using a combination of a cortical button fixation associated with an interference screw. Results. Postoperative functional result was favorable with no complications and with no movement limitation after one month. Conclusions. When possible, distal biceps tendon repair should be realized surgically because this permits restoring of the muscle strength to near normal levels with no loss of motion. Nerve complications are common; therefore the surgery should be realized by experienced upper extremity surgeons.

  19. Distal protection filter device efficacy with carotid artery stenting: comparison between a distal protection filter and a distal protection balloon.

    Science.gov (United States)

    Iko, Minoru; Tsutsumi, Masanori; Aikawa, Hiroshi; Matsumoto, Yoshihisa; Go, Yoshinori; Nii, Kouhei; Abe, Gorou; Ye, Iwae; Nomoto, Yasuyuki; Kazekawa, Kiyoshi

    2013-01-01

    This retrospective study aimed to compare the effectiveness of the embolization prevention mechanism of two types of embolic protection device (EPD)-a distal protection balloon (DPB) and a distal protection filter (DPF). Subjects were 164 patients scheduled to undergo carotid artery stenting: a DPB was used in 82 cases (DPB group) from April 2007 until June 2010, and a DPF was used in 82 cases (DPF group) from July 2010 to July 2011. Rates of positive findings on postoperative diffusion-weighted imaging (DWI) and stroke incidence were compared. Positive postoperative DWI results were found in 34 cases in the DPB group (41.4 %), but in only 22 cases in the DPF group (26.8 %), and there was only a small significant difference within the DPF group. In the DPB group, there was one case of transient ischemic attack (TIA) (1.2 %) and four cases of brain infarction (2 minor strokes, 2 major strokes; 4.9 %), compared to the DFP group with one case of TIA (1.2 %) and no cases of minor or major strokes. In this study, significantly lower rates of occurrence of DWI ischemic lesions and intraoperative embolization were associated with use of the DPF compared to the DPB.

  20. Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

    KAUST Repository

    Krupskii, Pavel

    2017-11-02

    The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.

  1. Extreme-value limit of the convolution of exponential and multivariate normal distributions: Link to the Hüsler–Reiß distribution

    KAUST Repository

    Krupskii, Pavel; Joe, Harry; Lee, David; Genton, Marc G.

    2017-01-01

    The multivariate Hüsler–Reiß copula is obtained as a direct extreme-value limit from the convolution of a multivariate normal random vector and an exponential random variable multiplied by a vector of constants. It is shown how the set of Hüsler–Reiß parameters can be mapped to the parameters of this convolution model. Assuming there are no singular components in the Hüsler–Reiß copula, the convolution model leads to exact and approximate simulation methods. An application of simulation is to check if the Hüsler–Reiß copula with different parsimonious dependence structures provides adequate fit to some data consisting of multivariate extremes.

  2. Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

    Science.gov (United States)

    Cheng, Phillip M; Tejura, Tapas K; Tran, Khoa N; Whang, Gilbert

    2018-05-01

    The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the majority classification was used as ground truth; 74 images were found to be consistent with small bowel obstruction. Images were rescaled and randomized, with 2210 images constituting the training set (39 with small bowel obstruction) and 1453 images constituting the test set (35 with small bowel obstruction). Weight parameters for the final classification layer of the Inception v3 convolutional neural network, previously trained on the 2014 Large Scale Visual Recognition Challenge dataset, were retrained on the training set. After training, the neural network achieved an AUC of 0.84 on the test set (95% CI 0.78-0.89). At the maximum Youden index (sensitivity + specificity-1), the sensitivity of the system for small bowel obstruction is 83.8%, with a specificity of 68.1%. The results demonstrate that transfer learning with convolutional neural networks, even with limited training data, may be used to train a detector for high-grade small bowel obstruction gas patterns on supine radiographs.

  3. Experimental demonstration of nonbinary LDPC convolutional codes for DP-64QAM/256QAM

    NARCIS (Netherlands)

    Koike-Akino, T.; Sugihara, K.; Millar, D.S.; Pajovic, M.; Matsumoto, W.; Alvarado, A.; Maher, R.; Lavery, D.; Paskov, M.; Kojima, K.; Parsons, K.; Thomsen, B.C.; Savory, S.J.; Bayvel, P.

    2016-01-01

    We show the great potential of nonbinary LDPC convolutional codes (NB-LDPC-CC) with low-latency windowed decoding. It is experimentally demonstrated that NB-LDPC-CC can offer a performance improvement of up to 5 dB compared with binary coding.

  4. End-to-end unsupervised deformable image registration with a convolutional neural network

    NARCIS (Netherlands)

    de Vos, Bob D.; Berendsen, Floris; Viergever, Max A.; Staring, Marius; Išgum, Ivana

    2017-01-01

    In this work we propose a deep learning network for deformable image registration (DIRNet). The DIRNet consists of a convolutional neural network (ConvNet) regressor, a spatial transformer, and a resampler. The ConvNet analyzes a pair of fixed and moving images and outputs parameters for the spatial

  5. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    Science.gov (United States)

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Production and reception of meaningful sound in Foville's 'encompassing convolution'.

    Science.gov (United States)

    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.

  7. Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.

    Science.gov (United States)

    Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui

    2017-07-15

    Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning. We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm . tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn. Supplementary materials are available at

  8. Quantitative analysis of nucleolar chromatin distribution in the complex convoluted nucleoli of Didinium nasutum (Ciliophora).

    Science.gov (United States)

    Leonova, Olga G; Karajan, Bella P; Ivlev, Yuri F; Ivanova, Julia L; Skarlato, Sergei O; Popenko, Vladimir I

    2013-01-01

    We have earlier shown that the typical Didinium nasutum nucleolus is a complex convoluted branched domain, comprising a dense fibrillar component located at the periphery of the nucleolus and a granular component located in the central part. Here our main interest was to study quantitatively the spatial distribution of nucleolar chromatin structures in these convoluted nucleoli. There are no "classical" fibrillar centers in D.nasutum nucleoli. The spatial distribution of nucleolar chromatin bodies, which play the role of nucleolar organizers in the macronucleus of D.nasutum, was studied using 3D reconstructions based on serial ultrathin sections. The relative number of nucleolar chromatin bodies was determined in macronuclei of recently fed, starved D.nasutum cells and in resting cysts. This parameter is shown to correlate with the activity of the nucleolus. However, the relative number of nucleolar chromatin bodies in different regions of the same convoluted nucleolus is approximately the same. This finding suggests equal activity in different parts of the nucleolar domain and indicates the existence of some molecular mechanism enabling it to synchronize this activity in D. nasutum nucleoli. Our data show that D. nasutum nucleoli display bipartite structure. All nucleolar chromatin bodies are shown to be located outside of nucleoli, at the periphery of the fibrillar component.

  9. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.

    Science.gov (United States)

    Xu, Kele; Feng, Dawei; Mi, Haibo

    2017-11-23

    The automatic detection of diabetic retinopathy is of vital importance, as it is the main cause of irreversible vision loss in the working-age population in the developed world. The early detection of diabetic retinopathy occurrence can be very helpful for clinical treatment; although several different feature extraction approaches have been proposed, the classification task for retinal images is still tedious even for those trained clinicians. Recently, deep convolutional neural networks have manifested superior performance in image classification compared to previous handcrafted feature-based image classification methods. Thus, in this paper, we explored the use of deep convolutional neural network methodology for the automatic classification of diabetic retinopathy using color fundus image, and obtained an accuracy of 94.5% on our dataset, outperforming the results obtained by using classical approaches.

  11. Loss of Dendritic Complexity Precedes Neurodegeneration in a Mouse Model with Disrupted Mitochondrial Distribution in Mature Dendrites

    Directory of Open Access Journals (Sweden)

    Guillermo López-Doménech

    2016-10-01

    Full Text Available Correct mitochondrial distribution is critical for satisfying local energy demands and calcium buffering requirements and supporting key cellular processes. The mitochondrially targeted proteins Miro1 and Miro2 are important components of the mitochondrial transport machinery, but their specific roles in neuronal development, maintenance, and survival remain poorly understood. Using mouse knockout strategies, we demonstrate that Miro1, as opposed to Miro2, is the primary regulator of mitochondrial transport in both axons and dendrites. Miro1 deletion leads to depletion of mitochondria from distal dendrites but not axons, accompanied by a marked reduction in dendritic complexity. Disrupting postnatal mitochondrial distribution in vivo by deleting Miro1 in mature neurons causes a progressive loss of distal dendrites and compromises neuronal survival. Thus, the local availability of mitochondrial mass is critical for generating and sustaining dendritic arbors, and disruption of mitochondrial distribution in mature neurons is associated with neurodegeneration.

  12. Radiographic study of distal radial physeal closure in thoroughbred horses

    International Nuclear Information System (INIS)

    Vulcano, L.C.; Mamprim, M.J.; Muniz, L.M.R.; Moreira, A.F.; Luna, S.P.L.

    1997-01-01

    Monthly radiography was performed to study distal radial physeal closure in ten male and ten female Throughbred horses. The height, thoracic circumference and metacarpus circumference were also measured, Distal radial physeal closure time was sooner in females than males, and took 701 +/- 37 and 748 +/- 55 days respectively

  13. Distal Embolic Protection for Renal Arterial Interventions

    International Nuclear Information System (INIS)

    Dubel, Gregory J.; Murphy, Timothy P.

    2008-01-01

    Distal or embolic protection has intuitive appeal for its potential to prevent embolization of materials generated during interventional procedures. Distal protection devices (DPDs) have been most widely used in the coronary and carotid vascular beds, where they have demonstrated the ability to trap embolic materials and, in some cases, to reduce complications. Given the frequency of chronic kidney disease in patients with renal artery stenosis undergoing stent placement, it is reasonable to propose that these devices may play an important role in limiting distal embolization in the renal vasculature. Careful review of the literature reveals that atheroembolization does occur during renal arterial interventions, although it often goes undetected. Early experience with DPDs in the renal arteries in patients with suitable anatomy suggests retrieval of embolic materials in approximately 71% of cases and renal functional improvement/stabilization in 98% of cases. The combination of platelet inhibition and a DPD may provide even greater benefit. Given the critical importance of renal functional preservation, it follows that everything that can be done to prevent atheroembolism should be undertaken including the use of DPDs when anatomically feasible. The data available at this time support a beneficial role for these devices

  14. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  15. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    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.

  16. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Science.gov (United States)

    Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

    2017-01-01

    In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

  17. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    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.

  18. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    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.

  19. Contemporary Management of Primary Distal Urethral Cancer.

    Science.gov (United States)

    Traboulsi, Samer L; Witjes, Johannes Alfred; Kassouf, Wassim

    2016-11-01

    Primary urethral cancer is one of the rare urologic tumors. Distal urethral tumors are usually less advanced at diagnosis compared with proximal tumors and have a good prognosis if treated appropriately. Low-stage distal tumors can be managed successfully with a surgical approach in men or radiation therapy in women. There are no clear-cut indications for the choice of the most appropriate treatment modality. Organ-preserving modalities have shown effective and should be used whenever they do not compromise the oncological safety to decrease the physical and psychological trauma of dismemberment or loss of sexual/urinary function. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Upper bounds on the number of errors corrected by a convolutional code

    DEFF Research Database (Denmark)

    Justesen, Jørn

    2004-01-01

    We derive upper bounds on the weights of error patterns that can be corrected by a convolutional code with given parameters, or equivalently we give bounds on the code rate for a given set of error patterns. The bounds parallel the Hamming bound for block codes by relating the number of error...

  1. Inverse Problems for a Parabolic Integrodifferential Equation in a Convolutional Weak Form

    Directory of Open Access Journals (Sweden)

    Kairi Kasemets

    2013-01-01

    Full Text Available We deduce formulas for the Fréchet derivatives of cost functionals of several inverse problems for a parabolic integrodifferential equation in a weak formulation. The method consists in the application of an integrated convolutional form of the weak problem and all computations are implemented in regular Sobolev spaces.

  2. Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints

    NARCIS (Netherlands)

    Schreiber, Shaun; Geldenhuys, Jaco; Villiers, De Hendrik

    2017-01-01

    Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model

  3. A quantum algorithm for Viterbi decoding of classical convolutional codes

    OpenAIRE

    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...

  4. REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE

    OpenAIRE

    S Safinaz; A V Ravi Kumar

    2017-01-01

    In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames t...

  5. Maximum likelihood convolutional decoding (MCD) performance due to system losses

    Science.gov (United States)

    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.

  6. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

    OpenAIRE

    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...

  7. High-intensity exercise training increases the diversity and metabolic capacity of the mouse distal gut microbiota during diet-induced obesity.

    Science.gov (United States)

    Denou, Emmanuel; Marcinko, Katarina; Surette, Michael G; Steinberg, Gregory R; Schertzer, Jonathan D

    2016-06-01

    Diet and exercise underpin the risk of obesity-related metabolic disease. Diet alters the gut microbiota, which contributes to aspects of metabolic disease during obesity. Repeated exercise provides metabolic benefits during obesity. We assessed whether exercise could oppose changes in the taxonomic and predicted metagenomic characteristics of the gut microbiota during diet-induced obesity. We hypothesized that high-intensity interval training (HIIT) would counteract high-fat diet (HFD)-induced changes in the microbiota without altering obesity in mice. Compared with chow-fed mice, an obesity-causing HFD decreased the Bacteroidetes-to-Firmicutes ratio and decreased the genetic capacity in the fecal microbiota for metabolic pathways such as the tricarboxylic acid (TCA) cycle. After HFD-induced obesity was established, a subset of mice were HIIT for 6 wk, which increased host aerobic capacity but did not alter body or adipose tissue mass. The effects of exercise training on the microbiota were gut segment dependent and more extensive in the distal gut. HIIT increased the alpha diversity and Bacteroidetes/Firmicutes ratio of the distal gut and fecal microbiota during diet-induced obesity. Exercise training increased the predicted genetic capacity related to the TCA cycle among other aspects of metabolism. Strikingly, the same microbial metabolism indexes that were increased by exercise were all decreased in HFD-fed vs. chow diet-fed mice. Therefore, exercise training directly opposed some of the obesity-related changes in gut microbiota, including lower metagenomic indexes of metabolism. Some host and microbial pathways appeared similarly affected by exercise. These exercise- and diet-induced microbiota interactions can be captured in feces. Copyright © 2016 the American Physiological Society.

  8. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  9. Kinesio Taping to generate skin convolutions is not better than sham taping for people with chronic non-specific low back pain: a randomised trial

    NARCIS (Netherlands)

    Parreira, P.D.S.; Costa, L.D.M.; Takahashi, R.; Hespanhol, L.C.; da Luz, M.A.; da Silva, T.M.; Costa, L.O.P.

    2014-01-01

    Question: For people with chronic low back pain, does Kinesio Taping, applied according to the treatment manual to create skin convolutions, reduce pain and disability more than a simple application without convolutions? Design: Randomised trial with concealed allocation, intention-to-treat analysis

  10. One-stage lingual augmented urethroplasty in repair of distal penile ...

    African Journals Online (AJOL)

    E. Elsayed

    Abstract. Objectives: To evaluate the outcome of augmentation of shallow urethral plate by lingual graft in repair of distal penile hypospadias. Patients and methods: Between June 2008 and May 2011, the procedure was performed on 23 patients with mean age 2.3 years (range 1–3). All patients had distal penile ...

  11. Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

    Directory of Open Access Journals (Sweden)

    Shalin Savalia

    2018-05-01

    Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

  12. Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

    Science.gov (United States)

    Savalia, Shalin; Emamian, Vahid

    2018-05-04

    The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

  13. Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

    Directory of Open Access Journals (Sweden)

    Fernando Moya Rueda

    2018-05-01

    Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

  14. Identification and characterization of cell-specific enhancer elements for the mouse ETF/Tead2 gene.

    Science.gov (United States)

    Tanoue, Y; Yasunami, M; Suzuki, K; Ohkubo, H

    2001-12-21

    We have identified and characterized by transient transfection assays the cell-specific 117-bp enhancer sequence in the first intron of the mouse ETF (Embryonic TEA domain-containing factor)/Tead2 gene required for transcriptional activation in ETF/Tead2 gene-expressing cells, such as P19 cells. The 117-bp enhancer contains one GC-rich sequence (5'-GGGGCGGGG-3'), termed the GC box, and two tandemly repeated GA-rich sequences (5'-GGGGGAGGGG-3'), termed the proximal and distal GA elements. Further analyses, including transfection studies and electrophoretic mobility shift assays using a series of deletion and mutation constructs, indicated that Sp1, a putative activator, may be required to predominate over its competition with another unknown putative repressor, termed the GA element-binding factor, for binding to both the GC box, which overlapped with the proximal GA element, and the distal GA element in the 117-bp sequence in order to achieve a full enhancer activity. We also discuss a possible mechanism underlying the cell-specific enhancer activity of the 117-bp sequence.

  15. Locking plate fixation in distal metaphyseal tibial fractures: series of 79 patients

    OpenAIRE

    Gupta, Rakesh K.; Rohilla, Rajesh Kumar; Sangwan, Kapil; Singh, Vijendra; Walia, Saurav

    2009-01-01

    Open reduction and internal fixation in distal tibial fractures jeopardises fracture fragment vascularity and often results in soft tissue complications. Minimally invasive osteosynthesis, if possible, offers the best possible option as it permits adequate fixation in a biological manner. Seventy-nine consecutive adult patients with distal tibial fractures, including one patient with a bilateral fracture of the distal tibia, treated with locking plates, were retrospectively reviewed. The 4.5-...

  16. Distal vertebral artery reconstruction when managing vertebrobasilar insufficiency

    Directory of Open Access Journals (Sweden)

    D. M. Galaktionov

    2017-11-01

    Full Text Available This article presents a literature review devoted to the reconstruction of the distal vertebral artery and a clinical case of successful surgical treatment of a patient suffering from vertebrobasilar insufficiency caused by occlusion of the vertebral artery in a proximal segment. The external carotid artery-distal vertebral artery bypass was performed by using the radial artery.Received 27 February 2017. Revised 25 July 2017. Accepted 3 August 2017.Funding: The study did not have sponsorship.Conflict of interest: The authors declare no conflict of interest. 

  17. Neglected Distal Humeral Epiphyseal Injury - Two Case Reports

    Directory of Open Access Journals (Sweden)

    Dr. Pankaj Kumar

    2008-07-01

    Full Text Available Distal humeral epiphyseal separation is an uncommon injury in children, which can be missed or misdiagnosed at initial presentation. Awareness of this injury and appropriate radiological assessment helps in proper management. Neglected cases because of inappropriate diagnosis can result in cubitus varus deformity. Full range of movements of elbow can be achieved if properly diagnosed and managed. We present two cases of neglected distal humeral epiphyseal injury in children that resulted in cubitus varus deformity in one case. Full range of movements was achieved in both cases after proper management.

  18. Low-complexity object detection with deep convolutional neural network for embedded systems

    Science.gov (United States)

    Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

    2017-09-01

    We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

  19. SU-E-T-371: Evaluating the Convolution Algorithm of a Commercially Available Radiosurgery Irradiator Using a Novel Phantom

    Energy Technology Data Exchange (ETDEWEB)

    Cates, J; Drzymala, R [Washington Univ, Saint Louis, MO (United States)

    2015-06-15

    Purpose: The purpose of this study was to develop and use a novel phantom to evaluate the accuracy and usefulness of the Leskell Gamma Plan convolution-based dose calculation algorithm compared with the current TMR10 algorithm. Methods: A novel phantom was designed to fit the Leskell Gamma Knife G Frame which could accommodate various materials in the form of one inch diameter, cylindrical plugs. The plugs were split axially to allow EBT2 film placement. Film measurements were made during two experiments. The first utilized plans generated on a homogeneous acrylic phantom setup using the TMR10 algorithm, with various materials inserted into the phantom during film irradiation to assess the effect on delivered dose due to unplanned heterogeneities upstream in the beam path. The second experiment utilized plans made on CT scans of different heterogeneous setups, with one plan using the TMR10 dose calculation algorithm and the second using the convolution-based algorithm. Materials used to introduce heterogeneities included air, LDPE, polystyrene, Delrin, Teflon, and aluminum. Results: The data shows that, as would be expected, having heterogeneities in the beam path does induce dose delivery error when using the TMR10 algorithm, with the largest errors being due to the heterogeneities with electron densities most different from that of water, i.e. air, Teflon, and aluminum. Additionally, the Convolution algorithm did account for the heterogeneous material and provided a more accurate predicted dose, in extreme cases up to a 7–12% improvement over the TMR10 algorithm. The convolution algorithm expected dose was accurate to within 3% in all cases. Conclusion: This study proves that the convolution algorithm is an improvement over the TMR10 algorithm when heterogeneities are present. More work is needed to determine what the heterogeneity size/volume limits are where this improvement exists, and in what clinical and/or research cases this would be relevant.

  20. Conjugation weights and weighted convolution algebras on totally disconnected, locally compact groups

    OpenAIRE

    Willis, George

    2013-01-01

    A family of equivalent submultiplicative weights on the to- tally disconnected, locally compact group $G$ is defined in terms of the conjugation action of $G$ on itself. These weights therefore reflect the structure of $G$, and the corresponding weighted convolution algebra is intrinsic to $G$ in the same way that $L^1(G) is.

  1. A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

    Science.gov (United States)

    Camuñas-Mesa, Luis A.; Domínguez-Cordero, Yaisel L.; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2018-01-01

    Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network. PMID:29515349

  2. A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

    Directory of Open Access Journals (Sweden)

    Luis A. Camuñas-Mesa

    2018-02-01

    Full Text Available Convolutional Neural Networks (ConvNets are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

  3. A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation.

    Science.gov (United States)

    Camuñas-Mesa, Luis A; Domínguez-Cordero, Yaisel L; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2018-01-01

    Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

  4. Two-dimensional convolution subject to data-spreading algorithm. Report for August 1985-July 1986

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Y C; Wang, H C

    1986-01-01

    An algorithm for two-dimensional convolution is proposed to be highly efficient and suitable for parallel processing, and a hardware of pipeline architecture is implemented to realize the algorithm. The implemented hardware is built on an IBM personal computer and acts as an auxiliary processor of the personal computer. This makes the dream come true that high speed, low-cost image processing is feasible on personal computers. The hardware executes two operations of two-dimensional convolution concurrently on an 256 x 256 image frame in less than 500 miniseconds. Several functions are available to users, and parameters such as weighting coefficients and threshold value are programmable. Various processing results of the image frame can be obtained by changing these parameters. Besides, horizontal and vertical edge detection can also be executed concurrently, with results available at the same time.

  5. Distal pancreatectomy and splenectomy: a robotic or LESS approach.

    Science.gov (United States)

    Ryan, Carrie E; Ross, Sharona B; Sukharamwala, Prashant B; Sadowitz, Benjamin D; Wood, Thomas W; Rosemurgy, Alexander S

    2015-01-01

    The role and application of robotic surgery are debated, particularly given the expansion of laparoscopy, especially laparoendoscopic single-site (LESS) surgery. This cohort study was undertaken to delineate differences in outcomes between LESS and robotic distal pancreatectomy and splenectomy. With Institutional Review Board approval, patients undergoing LESS or robotic distal pancreatectomy and splenectomy from September 1, 2012, through December 31, 2014, were prospectively observed, and data were collected. The results are expressed as the median, with the mean ± SD. Thirty-four patients underwent a minimally invasive distal pancreatectomy and splenectomy: 18 with robotic and 16 with LESS surgery. The patients were similar in sex, age, and body mass index. Conversions to open surgery and estimated blood loss were similar. There were two intraoperative complications in the group that underwent the robotic approach. Time spent in the operating room was significantly longer with the robot (297 vs 254 minutes, P = .03), although operative duration (i.e., incision to closure) was not longer (225 vs 190 minutes; P = .15). Of the operations studied, 79% were undertaken for neoplastic processes. Tumor size was 3.5 cm for both approaches; R0 resections were achieved in all patients. Length of stay was similar in the two study groups (5 vs 4 days). There was one 30-day readmission after robotic surgery. Patient outcomes are similar with LESS or robotic distal pancreatectomy and splenectomy. Robotic operations require more time in the operating room. Both are safe and efficacious minimally invasive operations that follow similar oncologic principles for similar tumors, and both should be in the surgeon's armamentarium for distal pancreatectomy and splenectomy.

  6. Distal tibiofibular synostosis in a Nigerian: A case report | Owoeye ...

    African Journals Online (AJOL)

    X-ray of the bones showed an oblique fracture in the distal end of the shaft of fibula which is suggestive of post traumatic tibiofibular synostosis (TFS). Knowledge of distal TFS is important in resolving the puzzle of chronic shin pain of unknown origin and in accurate diagnosis of causes of ankle deformity and malformations.

  7. Dental and skeletal changes after intraoral molar distalization with sectional jig assembly.

    Science.gov (United States)

    Gulati, S; Kharbanda, O P; Parkash, H

    1998-09-01

    The present study was conducted on 10 subjects to evaluate dental and skeletal changes after intraoral molar distalization. The maxillary molars were distalized with a sectional jig assembly. Sentalloy open coil springs were used to exert 150 gm of force for a period of 12 weeks. A modified Nance appliance was the main source of anchorage. The pre- and postdistalization records included dental study casts, clinical photographs, and cephalograms. A total of 665 readings recorded from lateral cephalograms and dental casts were subjected to statistical analysis. The mean distal movement of the first molar was 2.78 mm, which was highly significant (o < 0.001). It moved distally at the rate of 0.86 mm/month. There was clinically some distal tipping (3.50 degrees) and distopalatal rotation (2.40 degrees). These changes were statistically significant (p < 0.001). The second molars accompanied the first molars and moved distally by nearly the same amount. There was 1.00 mm increase in the overjet and 2.60 degrees mesial tip of second premolar. The changes in the facial skeleton and dentition bases were minimal and statistically not significant. However, there was clockwise rotation of the mandible of 1.30 degrees that was statistically significant. This was the result of molar extrusion (1.60 mm).

  8. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    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

  9. Convolution Theorem of Fractional Fourier Transformation Derived by Representation Transformation in Quantum Mechancis

    International Nuclear Information System (INIS)

    Fan Hongyi; Hao Ren; Lu Hailiang

    2008-01-01

    Based on our previous paper (Commun. Theor. Phys. 39 (2003) 417) we derive the convolution theorem of fractional Fourier transformation in the context of quantum mechanics, which seems a convenient and neat way. Generalization of this method to the complex fractional Fourier transformation case is also possible

  10. Social Support Contributes to Outcomes following Distal Radius Fractures

    Directory of Open Access Journals (Sweden)

    Caitlin J. Symonette

    2013-01-01

    Full Text Available Background. Distal radius fractures are the most common fracture of the upper extremity and cause variable disability. This study examined the role of social support in patient-reported pain and disability at one year following distal radius fracture. Methods. The Medical Outcomes Study Social Support Survey was administered to a prospective cohort of 291 subjects with distal radius fractures at their baseline visit. Pearson correlations and stepwise linear regression models (F-to-remove 0.10 were used to identify whether social support contributes to wrist fracture outcomes. The primary outcome of pain and disability at one year was measured using the Patient Rated Wrist Evaluation. Results. Most injuries were low energy (67.5% and were treated nonoperatively (71.9%. Pearson correlation analysis revealed that higher reported social support correlated with improved Patient Rated Wrist Evaluation scores at 1 year, r(n=181=-0.22, P<0.05. Of the subscales within the Social Support Survey, emotional/informational support explained a significant proportion of the variance in 1-year Patient Rated Wrist Evaluation scores, R2=4.7%, F (1, 181 = 9.98, P<0.05. Conclusion. Lower emotional/informational social support at the time of distal radius fracture contributes a small but significant percentage to patient-reported pain and disability outcomes.

  11. Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

    Science.gov (United States)

    Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

    2018-06-01

    A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

  12. Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

    Science.gov (United States)

    Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

    2018-04-01

    A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

  13. Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

    Science.gov (United States)

    Xue, L.; Liu, C.; Wu, Y.; Li, H.

    2018-04-01

    Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

  14. Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

    Science.gov (United States)

    Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

    2018-05-29

    Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

  15. A Parallel Strategy for Convolutional Neural Network Based on Heterogeneous Cluster for Mobile Information System

    Directory of Open Access Journals (Sweden)

    Jilin Zhang

    2017-01-01

    Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.

  16. Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

    Science.gov (United States)

    Zhang, Junming; Wu, Yan

    2018-03-28

    Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

  17. Proximal and distal alignment of normal canine femurs: A morphometric analysis.

    Science.gov (United States)

    Kara, Mehmet Erkut; Sevil-Kilimci, Figen; Dilek, Ömer Gürkan; Onar, Vedat

    2018-05-01

    Many researchers are interested in femoral conformation because most orthopaedic problems of the long bones occur in the femur and its joints. The neck-shaft (NSA) and the anteversion (AVA) angles are good predictors for understanding the orientation of the proximal end of the femur. The varus (aLDFA) and procurvatum (CDFA) angles have also been used to understand the orientation of the distal end of the femur. The purposes of this study were to investigate the relationship between the proximal and distal angles of the femur and to compare the distal femoral angles in male and female dogs in order to investigate the sexual dimorphism. The measurements of normal CDFAs, which have not been previously reported, may also provide a database of canine distal femoral morphology. A total of 75 cleaned healthy femora from different breeds or mixed breed of dogs were used. The three-dimensional images were reconstructed from computed tomographic images. The AVA, NSA, aLDFA and CDFA were measured on the 3D images. The correlation coefficients were calculated among the measured angles. The distal femoral angles were also compared between male and female femora. The 95% confidence intervals of the AVA and the NSA were calculated to be 24.22°-29.50° and 144.97°-147.50°, respectively. The 95% confidence intervals of the aLDFA and the CDFA for all studied dogs were 92.62°-94.08° and 89.09°-91.94°, respectively. The NSA showed no correlation with either the aLDFA or CDFA. There was a weak inverse correlation between the AVA and CDFA and a weak positive correlation between the AVA and aLDFA. The differences in the aLDFA and CDFA measurements between male and female dog were not significant. In conclusion, femoral version, regardless of the plane, might have little influence on distal femoral morphology in normal dogs. Besides this, there is no evidence of a sexual dimorphism in the varus and procurvatum angles of the dog distal femur. The data from this study may be used in

  18. Phase transitions in glassy systems via convolutional neural networks

    Science.gov (United States)

    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.

  19. 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....

  20. Color encoding in biologically-inspired convolutional neural networks.

    Science.gov (United States)

    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.

  1. Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks

    OpenAIRE

    Laine, Samuli; Karras, Tero; Aila, Timo; Herva, Antti; Saito, Shunsuke; Yu, Ronald; Li, Hao; Lehtinen, Jaakko

    2016-01-01

    We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5-10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular v...

  2. Clinical study for pancreatic fistula after distal pancreatectomy with mesh reinforcement

    Directory of Open Access Journals (Sweden)

    Akira Hayashibe

    2018-05-01

    Full Text Available Summary: Background: The purpose of this cohort study was to determine whether distal pancreatectomy with mesh reinforcement can reduce postoperative pancreatic fistula (POPF rates compared with bare stapler. Methods: In total, 51 patients underwent stapled distal pancreatectomy. Out of these, 22 patients (no mesh group underwent distal pancreatectomy with bare stapler and 29 patients (mesh group underwent distal pancreatectomy with mesh reinforced stapler. The risk factor for clinically relevant POPF (grades B and C after distal pancreatectomy was also evaluated. Results: Clinical characteristics were almost similar in both the groups. The days of the mean hospital stay and drainage tube insertion in the mesh group were significantly fewer than those in the no mesh group. The mean level of amylase in the discharge fluid in the mesh group was also significantly lower than that the in no mesh group. The rate of clinically relevant POPF (grades B and C in the mesh group was significantly lower than that in the no mesh group (p=0.016. Univariate analyses of risk factors for POPF (grades B and C revealed that only mesh reinforcement was associated with POPF (grades B and C. Moreover, on multivariate analyses of POPF risk factors with p value<0.2 in univariate analyses by logistic regression, mesh reinforcement was regarded as a significant factor for POPF(grades B and C. Conclusions: The distal pancreatectomy with mesh reinforced stapler was thought to be favorable for the prevention of clinically relevant POPF (grades B and C. Keywords: mesh reinforcement, pancreatic fistula, pancreatic surgery

  3. Cost-effectiveness of laparoscopic versus open distal pancreatectomy for pancreatic cancer

    NARCIS (Netherlands)

    Gurusamy, K.S.; Riviere, D.M.; Laarhoven, C.J.H.M. van; Besselink, M.; Abu-Hilal, M.; Davidson, B.R.; Morris, S.

    2017-01-01

    BACKGROUND: A recent Cochrane review compared laparoscopic versus open distal pancreatectomy for people with for cancers of the body and tail of the pancreas and found that laparoscopic distal pancreatectomy may reduce the length of hospital stay. We compared the cost-effectiveness of laparoscopic

  4. Intersphincteric Resection and Coloanal Anastomosis in Treatment of Distal Rectal Cancer

    Directory of Open Access Journals (Sweden)

    Gokhan Cipe

    2012-01-01

    Full Text Available In the treatment of distal rectal cancer, abdominoperineal resection is traditionally performed. However, the recognition of shorter safe distal resection line, intersphincteric resection technique has given a chance of sphincter-saving surgery for patients with distal rectal cancer during last two decades and still is being performed as an alternative choice of abdominoperineal resection. The first aim of this study is to assess the morbidity, mortality, oncological, and functional outcomes of intersphincteric resection. The second aim is to compare outcomes of patients who underwent intersphincteric resection with the outcomes of patients who underwent abdominoperineal resection.

  5. Valgusdeformitet i anklen som følge af distal fibula-epifysefraktur

    DEFF Research Database (Denmark)

    Al-Aubaidi, Zaid

    2011-01-01

    Ankle fracture with involvement of the growth plate is the second most common paediatric fracture after the distal radius. The most common fracture type according to Salter Harris (SH) is type II of the distal tibia combined with green stick of the fibula. Isolated fracture of the distal fibular...... growth plate is not common and as a rule it does not give any growth arrest. We describe a case of isolated fibular fracture SH type II in a ten year-old girl which ended with symptomatic valgus deformity of the ankle. The patient was operated with good results....

  6. Epidemiology of distal forearm fractures in Oslo, Norway.

    Science.gov (United States)

    Lofthus, C M; Frihagen, F; Meyer, H E; Nordsletten, L; Melhuus, K; Falch, J A

    2008-06-01

    The population of Oslo has the highest incidence of hip fracture reported. The present study shows that the overall incidence of distal forearm fractures in Oslo is higher than in other countries and has not changed significantly when comparing the incidence of 1998/99 with 1979. The population of Oslo has the highest incidence of hip fracture reported. The present study reports the incidence of distal forearm fracture in Oslo and the fracture rates of immigrants. Patients aged > or = 20 years resident in Oslo sustaining a distal forearm fracture in a one-year period in 1998/99 were identified using electronic diagnosis registers, patient protocols, and/or X-ray registers of the clinics in Oslo. Medical records were obtained and the diagnosis verified. The age- and sex-specific incidence rates were calculated and compared with those for 1979. Data on immigrant category and country of origin of the patients were obtained. The age-adjusted fracture rates per 10,000 for the age group > or = 50 years were 109.8 and 25.4 in 1998/99 compared with 108.3 and 23.5 in 1979 for women and men, respectively (n.s.). The relative risk of fracture in Asians was 0.72 (95% CI 0.53-1.00) compared with ethnic Norwegians. The overall incidence of distal forearm fractures in Oslo is higher than in other countries and has not changed significantly when comparing the incidence of 1998/99 with 1979. Furthermore, the present data suggest that Asian immigrants in Oslo have a slightly lower fracture risk than ethnic Norwegians.

  7. Autosomal dominant distal myopathy: Linkage to chromosome 14

    Energy Technology Data Exchange (ETDEWEB)

    Laing, N.G.; Laing, B.A.; Wilton, S.D.; Dorosz, S.; Mastaglia, F.L.; Kakulas, B.A. [Australian Neuromuscular Research Institute, Perth (Australia); Robbins, P.; Meredith, C.; Honeyman, K.; Kozman, H.

    1995-02-01

    We have studied a family segregating a form of autosomal dominant distal myopathy (MIM 160500) and containing nine living affected individuals. The myopathy in this family is closest in clinical phenotype to that first described by Gowers in 1902. A search for linkage was conducted using microsatellite, VNTR, and RFLP markers. In total, 92 markers on all 22 autosomes were run. Positive linkage was obtained with 14 of 15 markers tested on chromosome 14, with little indication of linkage elsewhere in the genome. Maximum two-point LOD scores of 2.60 at recombination fraction .00 were obtained for the markers MYH7 and D14S64 - the family structure precludes a two-point LOD score {ge} 3. Recombinations with D14S72 and D14S49 indicate that this distal myopathy locus, MPD1, should lie between these markers. A multipoint analysis assuming 100% penetrance and using the markers D14S72, D14S50, MYH7, D14S64, D14S54, and D14S49 gave a LOD score of exactly 3 at MYH7. Analysis at a penetrance of 80% gave a LOD score of 2.8 at this marker. This probable localization of a gene for distal myopathy, MPD1, on chromosome 14 should allow other investigators studying distal myopathy families to test this region for linkage in other types of the disease, to confirm linkage or to demonstrate the likely genetic heterogeneity. 24 refs., 3 figs., 1 tab.

  8. Appropriateness of Dropout Layers and Allocation of Their 0.5 Rates across Convolutional Neural Networks for CIFAR-10, EEACL26, and NORB Datasets

    Directory of Open Access Journals (Sweden)

    Romanuke Vadim V.

    2017-12-01

    Full Text Available A technique of DropOut for preventing overfitting of convolutional neural networks for image classification is considered in the paper. The goal is to find a rule of rationally allocating DropOut layers of 0.5 rate to maximise performance. To achieve the goal, two common network architectures are used having either 4 or 5 convolutional layers. Benchmarking is fulfilled with CIFAR-10, EEACL26, and NORB datasets. Initially, series of all admissible versions for allocation of DropOut layers are generated. After the performance against the series is evaluated, normalized and averaged, the compromising rule is found. It consists in non-compactly inserting a few DropOut layers before the last convolutional layer. It is likely that the scheme with two or more DropOut layers fits networks of many convolutional layers for image classification problems with a plenty of features. Such a scheme shall also fit simple datasets prone to overfitting. In fact, the rule “prefers” a fewer number of DropOut layers. The exemplary gain of the rule application is roughly between 10 % and 50 %.

  9. Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

    Science.gov (United States)

    Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

    2017-11-01

    Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic

  10. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    Directory of Open Access Journals (Sweden)

    E. M. Waisman

    2014-12-01

    Full Text Available Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM. Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010

  11. Distal Oblique Bundle Reinforcement for Treatment of DRUJ Instability.

    Science.gov (United States)

    Brink, Peter R G; Hannemann, Pascal F W

    2015-08-01

    Background Chronic, dynamic bidirectional instability in the distal radioulnar joint (DRUJ) is diagnosed clinically, based on the patient's complaints and the finding of abnormal laxity in the vicinity of the distal ulna. In cases where malunion is ruled out or treated and there are no signs of osteoarthritis, stabilization of the DRUJ may offer relief. To this end, several different techniques have been investigated over the past 90 years. Materials and Methods In this article we outline the procedure for a new technique using a tendon graft to reinforce the distal edge of the interosseous membrane. Description of Technique A percutaneous technique is used to harvest the palmaris longus tendon and to create a tunnel, just proximal to the sigmoid notch, through the ulna and radius in an oblique direction. By overdrilling the radial cortex, the knotted tendon can be pulled through the radius and ulna and the knot blocked at the second radial cortex, creating a strong connection between the radius and ulna at the site of the distal oblique bundle (DOB). The tendon is fixed in the ulna with a small interference screw in full supination, preventing subluxation of the ulna out of the sigmoid notch during rotation. Results Fourteen patients were treated with this novel technique between 2011 and October 2013. The QuickDASH score at 25 months postoperatively (range 16-38 months) showed an improvement of 32 points. Similarly, an improvement of 33 points (67-34 months) was found on the PRWHE. Only one recurrence of chronic, dynamic bidirectional instability in the DRUJ was observed. Conclusion This simple percutaneous tenodesis technique between radius and ulna at the position of the distal edge of the interosseous membrane shows promise in terms of both restoring stability and relieving complaints related to chronic subluxation in the DRUJ.

  12. Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

    Directory of Open Access Journals (Sweden)

    Tayfun Gokmen

    2017-10-01

    Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

  13. AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

    Science.gov (United States)

    Wang, Sheng; Sun, Siqi; Xu, Jinbo

    2016-09-01

    Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

  14. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    NARCIS (Netherlands)

    Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

    2016-01-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

  15. Traumatisk distal humerus-epifysiolyse hos nyfødt

    DEFF Research Database (Denmark)

    Al-Aubaidi, Zaid; Nielsen, Keld Daubjerg

    2010-01-01

    Traumatic distal humerus epiphysiolysis (TDHE) is a rare injury in infants with an incidence of about 1:35,000 births. It is primarily a birth injury, but it is also seen in cases of battered child syndrome. Because of its rare occurrence and the diagnostic difficulties, the lesion may be overloo......Traumatic distal humerus epiphysiolysis (TDHE) is a rare injury in infants with an incidence of about 1:35,000 births. It is primarily a birth injury, but it is also seen in cases of battered child syndrome. Because of its rare occurrence and the diagnostic difficulties, the lesion may...

  16. Miyoshi-type distal muscular dystrophy. Clinical spectrum in 24 Dutch patients

    NARCIS (Netherlands)

    Linssen, W. H.; Notermans, N. C.; van der Graaf, Y.; Wokke, J. H.; van Doorn, P. A.; Höweler, C. J.; Busch, H. F.; de Jager, A. E.; de Visser, M.

    1997-01-01

    Miyoshi-type distal muscular dystrophy has now been found to be more frequent outside Japan than was previously thought. We studied 24 Dutch patients with Miyoshi-type distal muscular dystrophy and focused on its clinical expression and natural history, muscle CT-scans and muscle biopsy findings.

  17. Miyoshi-type distal muscular dystrophy. Clinical spectrum in 24 Dutch patients

    NARCIS (Netherlands)

    W.H.J.P. Linssen (Wim); N.C. Notermans (Nicolette); Y. van der Graaf (Yolanda); J.H.J. Wokke (John); P.A. van Doorn (Pieter); C.J. Höweler (Chris); H.F.M. Busch (Herman); A.E.J. de Jager (Aeiko); M. de Visser (Marianne)

    1997-01-01

    textabstractMiyoshi-type distal muscular dystrophy has now been found to be more frequent outside Japan than was previously thought. We studied 24 Dutch patients with Miyoshi-type distal muscular dystrophy and focused on its clinical expression and natural history muscle CT-scans and muscle biopsy

  18. Miyoshi-type distal muscular dystrophy - Clinical spectrum in 24 Dutch patients

    NARCIS (Netherlands)

    Linssen, WHJP; Notermans, NC; VanderGraaf, Y; Wokke, JHJ; VanDoorn, PA; Howeler, CJ; Busch, HFM; DeJager, AEJ; DeVisser, M

    1997-01-01

    Miyoshi-type distal muscular dystrophy has now been found to be more frequent outside Japan than was previously thought. We studied 24 Dutch patients with Miyoshi-type distal muscular dystrophy and focused on its clinical expression and natural history, muscle CT-scans and muscle biopsy findings.

  19. HIV Distal Neuropathic Pain Is Associated with Smaller Ventral Posterior Cingulate Cortex.

    Science.gov (United States)

    Keltner, John R; Connolly, Colm G; Vaida, Florin; Jenkinson, Mark; Fennema-Notestine, Christine; Archibald, Sarah; Akkari, Cherine; Schlein, Alexandra; Lee, Jisu; Wang, Dongzhe; Kim, Sung; Li, Han; Rennels, Austin; Miller, David J; Kesidis, George; Franklin, Donald R; Sanders, Chelsea; Corkran, Stephanie; Grant, Igor; Brown, Gregory G; Atkinson, J Hampton; Ellis, Ronald J

    2017-03-01

    . Despite modern antiretroviral therapy, HIV-associated neuropathy is one of the most prevalent, disabling and treatment-resistant complications of HIV disease. The presence and intensity of distal neuropathic pain is not fully explained by the degree of peripheral nerve damage. A better understanding of brain structure in HIV distal neuropathic pain may help explain why some patients with HIV neuropathy report pain while the majority does not. Previously, we reported that more intense distal neuropathic pain was associated with smaller total cerebral cortical gray matter volumes. The objective of this study was to determine which parts of the cortex are smaller. . HIV positive individuals with and without distal neuropathic pain enrolled in the multisite (N = 233) CNS HIV Antiretroviral Treatment Effects (CHARTER) study underwent structural brain magnetic resonance imaging. Voxel-based morphometry was used to investigate regional brain volumes in these structural brain images. . Left ventral posterior cingulate cortex was smaller for HIV positive individuals with versus without distal neuropathic pain (peak P  = 0.017; peak t = 5.15; MNI coordinates x = -6, y = -54, z = 20). Regional brain volumes within cortical gray matter structures typically associated with pain processing were also smaller for HIV positive individuals having higher intensity ratings of distal neuropathic pain. . The posterior cingulate is thought to be involved in inhibiting the perception of painful stimuli. Mechanistically a smaller posterior cingulate cortex structure may be related to reduced anti-nociception contributing to increased distal neuropathic pain. © 2016 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  20. Analytic continuation of solutions of some nonlinear convolution partial differential equations

    Directory of Open Access Journals (Sweden)

    Hidetoshi Tahara

    2015-01-01

    Full Text Available The paper considers a problem of analytic continuation of solutions of some nonlinear convolution partial differential equations which naturally appear in the summability theory of formal solutions of nonlinear partial differential equations. Under a suitable assumption it is proved that any local holomorphic solution has an analytic extension to a certain sector and its extension has exponential growth when the variable goes to infinity in the sector.