This document summarizes an analysis of assembly algorithms and implementation of a De Bruijn graph approach to genome assembly. It discusses how De Bruijn graphs have become a common approach for assembly, representing reads as nodes and connecting nodes based on overlap of k-mers. The document outlines challenges in assembly including repeats and errors. It also summarizes two efficient data structures for representing De Bruijn graphs and describes implementing these to assemble microbial genomes and compare to the ABySS assembler.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
In this video from the 2015 HPC User Forum in Broomfield, Barry Bolding from Cray presents: HPC + D + A = HPDA?
"The flexible, multi-use Cray Urika-XA extreme analytics platform addresses perhaps the most critical obstacle in data analytics today — limitation. Analytics problems are getting more varied and complex but the available solution technologies have significant constraints. Traditional analytics appliances lock you into a single approach and building a custom solution in-house is so difficult and time consuming that the business value derived from analytics fails to materialize. In contrast, the Urika-XA platform is open, high performing and cost effective, serving a wide range of analytics tools with varying computing demands in a single environment. Pre-integrated with the Hadoop and Spark frameworks, the Urika-XA system combines the benefits of a turnkey analytics appliance with a flexible, open platform that you can modify for future analytics workloads. This single-platform consolidation of workloads reduces your analytics footprint and total cost of ownership."
Learn more: http://www.cray.com/products/analytics/urika-xa
Watch the video presentation: http://wp.me/p3RLEV-3yR
Sign up for our insideBIGDATA Newsletter: http://insidebigdata.com/newsletter
A novel scheme for reliable multipath routing through node independent direct...eSAT Journals
Abstract Multipath routing is essential in the wake of voice over IP, multimedia streaming for efficient data transmission. The growing usage of such network requirements also demands fast recovery from network failures. Multipath routing is one of the promising routing schemes to accommodate the diverse requirements of the network with provision such as load balancing and improved bandwidth. Cho et al. introduced a resilient multipath routing scheme known as directed acyclic graphs. These graphs enable multipath routing with all possible edges while ensuring guaranteed recovery from single point of failures. We also built a prototype application that demonstrates the efficiency of the scheme. The simulation results revealed that the scheme is useful and can be used in real world network applications.
Index Terms – Multipath routing, failure recovery, directed acyclic graphs
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Penalized and Compensated Constraints Based Modified Fuzzy Possibilis...ijsrd.com
A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them.The major objective of clustering is to discover collection of comparable objects based on similarity metric. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints based FPCM (PCFPCM). The proposed PCFPCM approach differ from the conventional clustering techniques by imposing the possibilistic reasoning strategy on fuzzy clustering with penalized and compensated constraints for updating the grades of membership and typicality. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior.
In recent machine learning community, there is a trend of constructing a linear logarithm version of
nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel
fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering
algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a
nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then
clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides
in increased features specs of dimensions and therefore lack intuitive and clear descriptions without
utilizing added approximation of projection from the specs to the data as executed in the literature
presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the
conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm
(KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange
the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space
of data so that the results of clustering could be interpreted and reformulated in the spaces which are
original. This property is used for clustering incomplete data. Execution on supposed data illustrate that
KFCM has improved performance of clustering and stout as compare to other transformations of FCM for
clustering incomplete data.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
In this video from the 2015 HPC User Forum in Broomfield, Barry Bolding from Cray presents: HPC + D + A = HPDA?
"The flexible, multi-use Cray Urika-XA extreme analytics platform addresses perhaps the most critical obstacle in data analytics today — limitation. Analytics problems are getting more varied and complex but the available solution technologies have significant constraints. Traditional analytics appliances lock you into a single approach and building a custom solution in-house is so difficult and time consuming that the business value derived from analytics fails to materialize. In contrast, the Urika-XA platform is open, high performing and cost effective, serving a wide range of analytics tools with varying computing demands in a single environment. Pre-integrated with the Hadoop and Spark frameworks, the Urika-XA system combines the benefits of a turnkey analytics appliance with a flexible, open platform that you can modify for future analytics workloads. This single-platform consolidation of workloads reduces your analytics footprint and total cost of ownership."
Learn more: http://www.cray.com/products/analytics/urika-xa
Watch the video presentation: http://wp.me/p3RLEV-3yR
Sign up for our insideBIGDATA Newsletter: http://insidebigdata.com/newsletter
A novel scheme for reliable multipath routing through node independent direct...eSAT Journals
Abstract Multipath routing is essential in the wake of voice over IP, multimedia streaming for efficient data transmission. The growing usage of such network requirements also demands fast recovery from network failures. Multipath routing is one of the promising routing schemes to accommodate the diverse requirements of the network with provision such as load balancing and improved bandwidth. Cho et al. introduced a resilient multipath routing scheme known as directed acyclic graphs. These graphs enable multipath routing with all possible edges while ensuring guaranteed recovery from single point of failures. We also built a prototype application that demonstrates the efficiency of the scheme. The simulation results revealed that the scheme is useful and can be used in real world network applications.
Index Terms – Multipath routing, failure recovery, directed acyclic graphs
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Novel Penalized and Compensated Constraints Based Modified Fuzzy Possibilis...ijsrd.com
A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them.The major objective of clustering is to discover collection of comparable objects based on similarity metric. Fuzzy Possibilistic C-Means (FPCM) is the effective clustering algorithm available to cluster unlabeled data that produces both membership and typicality values during clustering process. In this approach, the efficiency of the Fuzzy Possibilistic C-means clustering approach is enhanced by using the penalized and compensated constraints based FPCM (PCFPCM). The proposed PCFPCM approach differ from the conventional clustering techniques by imposing the possibilistic reasoning strategy on fuzzy clustering with penalized and compensated constraints for updating the grades of membership and typicality. The performance of the proposed approaches is evaluated on the University of California, Irvine (UCI) machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior.
In recent machine learning community, there is a trend of constructing a linear logarithm version of
nonlinear version through the ‘kernel method’ for example kernel principal component analysis, kernel
fisher discriminant analysis, support Vector Machines (SVMs), and the current kernel clustering
algorithms. Typically, in unsupervised methods of clustering algorithms utilizing kernel method, a
nonlinear mapping is operated initially in order to map the data into a much higher space feature, and then
clustering is executed. A hitch of these kernel clustering algorithms is that the clustering prototype resides
in increased features specs of dimensions and therefore lack intuitive and clear descriptions without
utilizing added approximation of projection from the specs to the data as executed in the literature
presented. This paper aims to utilize the ‘kernel method’, a novel clustering algorithm, founded on the
conventional fuzzy clustering algorithm (FCM) is anticipated and known as kernel fuzzy c-means algorithm
(KFCM). This method embraces a novel kernel-induced metric in the space of data in order to interchange
the novel Euclidean matric norm in cluster prototype and fuzzy clustering algorithm still reside in the space
of data so that the results of clustering could be interpreted and reformulated in the spaces which are
original. This property is used for clustering incomplete data. Execution on supposed data illustrate that
KFCM has improved performance of clustering and stout as compare to other transformations of FCM for
clustering incomplete data.
On the Tree Construction of Multi hop Wireless Mesh Networks with Evolutionar...CSCJournals
Abstract — in this paper, we study the structure of WiMAX mesh networks and the influence of tree’s structure on the performance of the network. From a given network’s graph, we search for trees, which fulfill some network, QoS requirements. Since the searching space is very huge, we use genetic algorithm in order to find solution in acceptable time. We use NetKey representation which is an unbiased representation with high locality, and due to high locality we expect standard genetic operators like n-point cross over and mutation work properly and there is no need for problem specific operators. This encoding belongs to class of weighted encoding family. In contrast to other representation such as characteristics vector encoding which can only indicate whether a link is established or not, weighted encodings use weights for genotype and can thus encode the importance of links. Moreover, by using proper fitness function we can search for any desired QOS constraint in the network.
A Novel Framework for Short Tandem Repeats (STRs) Using Parallel String MatchingIJERA Editor
Short tandem repeats (STRs) have become important molecular markers for a broad range of applications, such
as genome mapping and characterization, phenotype mapping, marker assisted selection of crop plants and a
range of molecular ecology and diversity studies. These repeated DNA sequences are found in both Plants and
bacteria. Most of the computer programs that find STRs failed to report its number of occurrences of the
repeated pattern, exact position and it is difficult task to obtain accurate results from the larger datasets. So we
need high performance computing models to extract certain repeats. One of the solution is STRs using parallel
string matching, it gives number of occurrences with corresponding line number and exact location or position
of each STR in the genome of any length. In this, we implemented parallel string matching using JAVA Multithreading
with multi core processing, for this we implemented a basic algorithm and made a comparison with
previous algorithms like Knuth Morris Pratt, Boyer Moore and Brute force string matching algorithms and from
the results our new basic algorithm gives better results than the previous algorithms. We apply this algorithm in
parallel string matching using multi-threading concept to reduce the time by running on multicore processors.
From the test results it is shown that the multicore processing is a remarkably efficient and powerful compared
to lower versions and finally this proposed STR using parallel string matching algorithm is better than the
sequential approaches.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
A NEW PARALLEL MATRIX MULTIPLICATION ALGORITHM ON HEX-CELL NETWORK (PMMHC) US...ijcsit
A widespread attention has been paid in parallelizing algorithms for computationally intensive applications. In this paper, we propose a new parallel Matrix multiplication on the Hex-cell interconnection network. The proposed algorithm has been evaluated and compared with sequential algorithm in terms of speedup, and efficiency using IMAN1, where a set of simulation runs, carried out on different input data distributions with different sizes. Thus, simulation results supported the theoretical analysis and m
Paper Explained: Understanding the wiring evolution in differentiable neural ...Devansh16
Read my Explanation of the Paper here: https://medium.com/@devanshverma425/why-and-how-is-neural-architecture-search-is-biased-778763d03f38?sk=e16a3e54d6c26090a6b28f7420d3f6f7
Abstract: Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. To understand how wiring topology evolves, we study the underlying mechanism of several existing differentiable NAS frameworks. Our investigation is motivated by three observed searching patterns of differentiable NAS: 1) they search by growing instead of pruning; 2) wider networks are more preferred than deeper ones; 3) no edges are selected in bi-level optimization. To anatomize these phenomena, we propose a unified view on searching algorithms of existing frameworks, transferring the global optimization to local cost minimization. Based on this reformulation, we conduct empirical and theoretical analyses, revealing implicit inductive biases in the cost's assignment mechanism and evolution dynamics that cause the observed phenomena. These biases indicate strong discrimination towards certain topologies. To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
A general frame for building optimal multiple SVM kernelsinfopapers
Dana Simian, Florin Stoica, A General Frame for Building Optimal Multiple SVM Kernels, Large-Scale Scientific Computing, Lecture Notes in Computer Science, 2012, Volume 7116/2012, 256-263, DOI: 10.1007/978-3-642-29843-1_29
Analysis of image storage and retrieval in graded memoryeSAT Journals
Abstract An approach to storing and retrieving static images using multilayer Hopfield neural network is analyzed. Here, the Hopfield network is used as a memory, which stores images in predefined resolution. During the image retrieval, down sampled version of the stored image is provided as the query mage, The memory initially gives out a coarse image. The finer details of the image are synthesized later by using this coarse output image. This coarse output image is fed as the input to the memory again. The output this time will be better than the output that was got initially. The output of the memory becomes better and better as the time progresses. We call this memory a graded memory. Here the work proposes various models of the graded memory using multilayer Hopfield neural network, analyses the effectiveness of this memory with parameters like MSE, RMSE and PSNR. Keywords: Hopfield network, graded memory, image storage, image retrieval.
A Study of BFLOAT16 for Deep Learning TrainingSubhajit Sahu
Highlighted notes of:
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for DeepLearning training across image classification, speech recognition, language model-ing, generative networks, and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence; e.g., IEEE 754compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed-precision training and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16tensors achieves the same state-of-the-art (SOTA) results across domains as FP32tensors in the same number of iterations and with no changes to hyper-parameters.
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
On the Tree Construction of Multi hop Wireless Mesh Networks with Evolutionar...CSCJournals
Abstract — in this paper, we study the structure of WiMAX mesh networks and the influence of tree’s structure on the performance of the network. From a given network’s graph, we search for trees, which fulfill some network, QoS requirements. Since the searching space is very huge, we use genetic algorithm in order to find solution in acceptable time. We use NetKey representation which is an unbiased representation with high locality, and due to high locality we expect standard genetic operators like n-point cross over and mutation work properly and there is no need for problem specific operators. This encoding belongs to class of weighted encoding family. In contrast to other representation such as characteristics vector encoding which can only indicate whether a link is established or not, weighted encodings use weights for genotype and can thus encode the importance of links. Moreover, by using proper fitness function we can search for any desired QOS constraint in the network.
A Novel Framework for Short Tandem Repeats (STRs) Using Parallel String MatchingIJERA Editor
Short tandem repeats (STRs) have become important molecular markers for a broad range of applications, such
as genome mapping and characterization, phenotype mapping, marker assisted selection of crop plants and a
range of molecular ecology and diversity studies. These repeated DNA sequences are found in both Plants and
bacteria. Most of the computer programs that find STRs failed to report its number of occurrences of the
repeated pattern, exact position and it is difficult task to obtain accurate results from the larger datasets. So we
need high performance computing models to extract certain repeats. One of the solution is STRs using parallel
string matching, it gives number of occurrences with corresponding line number and exact location or position
of each STR in the genome of any length. In this, we implemented parallel string matching using JAVA Multithreading
with multi core processing, for this we implemented a basic algorithm and made a comparison with
previous algorithms like Knuth Morris Pratt, Boyer Moore and Brute force string matching algorithms and from
the results our new basic algorithm gives better results than the previous algorithms. We apply this algorithm in
parallel string matching using multi-threading concept to reduce the time by running on multicore processors.
From the test results it is shown that the multicore processing is a remarkably efficient and powerful compared
to lower versions and finally this proposed STR using parallel string matching algorithm is better than the
sequential approaches.
In this paper, a fruit image data set is used to compare the efficiency and accuracy of two widely used Convolutional Neural Network, namely the ResNet and the DenseNet, for the recognition of 50 different kinds of fruits. In the experiment, the structure of ResNet-34 and DenseNet_BC-121 (with bottleneck layer) are used. The mathematic principle, experiment detail and the experiment result will be explained through comparison.
A NEW PARALLEL MATRIX MULTIPLICATION ALGORITHM ON HEX-CELL NETWORK (PMMHC) US...ijcsit
A widespread attention has been paid in parallelizing algorithms for computationally intensive applications. In this paper, we propose a new parallel Matrix multiplication on the Hex-cell interconnection network. The proposed algorithm has been evaluated and compared with sequential algorithm in terms of speedup, and efficiency using IMAN1, where a set of simulation runs, carried out on different input data distributions with different sizes. Thus, simulation results supported the theoretical analysis and m
Paper Explained: Understanding the wiring evolution in differentiable neural ...Devansh16
Read my Explanation of the Paper here: https://medium.com/@devanshverma425/why-and-how-is-neural-architecture-search-is-biased-778763d03f38?sk=e16a3e54d6c26090a6b28f7420d3f6f7
Abstract: Controversy exists on whether differentiable neural architecture search methods discover wiring topology effectively. To understand how wiring topology evolves, we study the underlying mechanism of several existing differentiable NAS frameworks. Our investigation is motivated by three observed searching patterns of differentiable NAS: 1) they search by growing instead of pruning; 2) wider networks are more preferred than deeper ones; 3) no edges are selected in bi-level optimization. To anatomize these phenomena, we propose a unified view on searching algorithms of existing frameworks, transferring the global optimization to local cost minimization. Based on this reformulation, we conduct empirical and theoretical analyses, revealing implicit inductive biases in the cost's assignment mechanism and evolution dynamics that cause the observed phenomena. These biases indicate strong discrimination towards certain topologies. To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
A general frame for building optimal multiple SVM kernelsinfopapers
Dana Simian, Florin Stoica, A General Frame for Building Optimal Multiple SVM Kernels, Large-Scale Scientific Computing, Lecture Notes in Computer Science, 2012, Volume 7116/2012, 256-263, DOI: 10.1007/978-3-642-29843-1_29
Analysis of image storage and retrieval in graded memoryeSAT Journals
Abstract An approach to storing and retrieving static images using multilayer Hopfield neural network is analyzed. Here, the Hopfield network is used as a memory, which stores images in predefined resolution. During the image retrieval, down sampled version of the stored image is provided as the query mage, The memory initially gives out a coarse image. The finer details of the image are synthesized later by using this coarse output image. This coarse output image is fed as the input to the memory again. The output this time will be better than the output that was got initially. The output of the memory becomes better and better as the time progresses. We call this memory a graded memory. Here the work proposes various models of the graded memory using multilayer Hopfield neural network, analyses the effectiveness of this memory with parameters like MSE, RMSE and PSNR. Keywords: Hopfield network, graded memory, image storage, image retrieval.
A Study of BFLOAT16 for Deep Learning TrainingSubhajit Sahu
Highlighted notes of:
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for DeepLearning training across image classification, speech recognition, language model-ing, generative networks, and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence; e.g., IEEE 754compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed-precision training and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16tensors achieves the same state-of-the-art (SOTA) results across domains as FP32tensors in the same number of iterations and with no changes to hyper-parameters.
PSO-based Training, Pruning, and Ensembling of Extreme Learning Machine RBF N...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
Deep neural networks have accomplished enormous progress in tackling many problems. More specifically, convolutional neural network (CNN) is a category of deep networks that have been a dominant technique in computer vision tasks. Despite that these deep neural networks are highly effective; the ideal structure is still an issue that needs a lot of investigation. Deep Convolutional Neural Network model is usually designed manually by trials and repeated tests which enormously constrain its application. Many hyper-parameters of the CNN can affect the model performance. These parameters are depth of the network, numbers of convolutional layers, and numbers of kernels with their sizes. Therefore, it may be a huge challenge to design an appropriate CNN model that uses optimized hyper-parameters and reduces the reliance on manual involvement and domain expertise. In this paper, a design architecture method for CNNs is proposed by utilization of particle swarm optimization (PSO) algorithm to learn the optimal CNN hyper-parameters values. In the experiment, we used Modified National Institute of Standards and Technology (MNIST) database of handwritten digit recognition. The experiments showed that our proposed approach can find an architecture that is competitive to the state-of-the-art models with a testing error of 0.87%.
Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Making effective use of graphics processing units (GPUs) in computationsOregon State University
Graphics processing units (GPUs) are specialized computer processors used in computers and video game systems to accelerate the creation and display of images. Due to their inherent parallel structure, they also have great potential to speed up computations in many scientific and engineering applications. GPUs are attractive for their ability to perform a large number of computations in parallel at an attractive price. Many of the world¹s largest supercomputers use GPUs to achieve their high performance, and personal computers and laptops use them for graphics displays and image processing. This seminar will explore the use of GPUs in general, describe examples of the use of GPUs in computations, and introduce some best practices for GPU computing.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
GPCODON ALIGNMENT: A GLOBAL PAIRWISE CODON BASED SEQUENCE ALIGNMENT APPROACHijdms
The alignment of two DNA sequences is a basic step in the analysis of biological data. Sequencing a long
DNA sequence is one of the most interesting problems in bioinformatics. Several techniques have been
developed to solve this sequence alignment problem like dynamic programming and heuristic algorithms.
In this paper, we introduce (GPCodon alignment) a pairwise DNA-DNA method for global sequence
alignment that improves the accuracy of pairwise sequence alignment. We use a new scoring matrix to
produce the final alignment called the empirical codon substitution matrix. Using this matrix in our
technique enabled the discovery of new relationships between sequences that could not be discovered using
traditional matrices. In addition, we present experimental results that show the performance of the
proposed technique over eleven datasets of average length of 2967 bps. We compared the efficiency and
accuracy of our techniques against a comparable tool called “Pairwise Align Codons” [1].
A COMPARATIVE ANALYSIS OF PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT APPROACHES ...ijcseit
Multiple sequence alignment is increasingly important to bioinformatics, with several applications rangingfrom phylogenetic analyses to domain identification. There are several ways to perform multiple sequencealignment, an important way of which is the progressive alignment approach studied in this work.Progressive alignment involves three steps: find the distance between each pair of sequences; construct a guide tree based on the distance matrix; finally based on the guide tree align sequences using the concept of aligned profiles. Our contribution is in comparing two main methods of guide tree construction in terms of both efficiency and accuracy of the overall alignment: UPGMA and Neighbor Join methods. Our experimental results indicate that the Neighbor Join method is both more efficient in terms of performance and more accurate in terms of overall cost minimization.
A COMPARATIVE ANALYSIS OF PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT APPROACHES ijcseit
Multiple sequence alignment is increasingly important to bioinformatics, with several applications ranging from phylogenetic analyses to domain identification. There are several ways to perform multiple sequence alignment, an important way of which is the progressive alignment approach studied in this work.Progressive alignment involves three steps: find the distance between each pair of sequences; construct a
guide tree based on the distance matrix; finally based on the guide tree align sequences using the concept of aligned profiles. Our contribution is in comparing two main methods of guide tree construction in terms of both efficiency and accuracy of the overall alignment: UPGMA and Neighbor Join methods. Our experimental results indicate that the Neighbor Join method is both more efficient in terms of performance
and more accurate in terms of overall cost minimization.
A COMPARATIVE ANALYSIS OF PROGRESSIVE MULTIPLE SEQUENCE ALIGNMENT APPROACHES ...ijcseit
Multiple sequence alignment is increasingly important to bioinformatics, with several applications ranging
from phylogenetic analyses to domain identification. There are several ways to perform multiple sequence
alignment, an important way of which is the progressive alignment approach studied in this work.
Progressive alignment involves three steps: find the distance between each pair of sequences; construct a
guide tree based on the distance matrix; finally based on the guide tree align sequences using the concept
of aligned profiles. Our contribution is in comparing two main methods of guide tree construction in terms
of both efficiency and accuracy of the overall alignment: UPGMA and Neighbor Join methods. Our
experimental results indicate that the Neighbor Join method is both more efficient in terms of performance
and more accurate in terms of overall cost minimization.
CONVOLUTIONAL NEURAL NETWORK BASED RETINAL VESSEL SEGMENTATIONCSEIJJournal
In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel
from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of
blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and
cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels.
In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to
extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked
Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.
Convolutional Neural Network based Retinal Vessel SegmentationCSEIJJournal
In human eye, the state of the blood vessel is a crucial diagnostic factor. The segmentation of blood vessel
from the fundus image is difficult due to the spatial complexity, adjacency, overlapping and variability of
blood vessel. The detection of ophthalmic pathologies like hypertensive disorders, diabetic retinopathy and
cardiovascular diseases are remain challenging task due to the wide-ranging distribution of blood vessels.
In this paper, Stacked Autoencoder and CNN (Convolutional Neural Network) technique is proposed to
extract the blood vessel from the fundus image. Based on the experiments conducted using the Stacked
Autoencoder and Convolutional Neural Network gives 90% & 95% accuracy for segmentation.
Modeling of neural image compression using gradient decent technologytheijes
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Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach IJECEIAES
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Study on Reconstruction Accuracy using shapiness index of morphological trans...ijcseit
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1. Analysis of Assembly Algorithm and Implementation of De Bruijn
Graph to generate the longest possible consensus sequence
Ashwani Kumar*
Department of Microbiology, Miami University, Ohio
ABSTRACT
De novo genome assembly has become one of the fundamental
and computationally intensive tasks at hand. This is because of the
nature of genomic sequences ranging from microorganism to
higher organism. Thus it is very important to develop an efficient
algorithmic approach, which could give us results using less
resources and time. De Bruijn graph has proved to be a promising
approach in the constructing the correct path and hence correct
order of nucleotide. However with the ever-increasing in-flow of
data from sequencing require more efficient data structure and
algorithms. This report looks into the currently available assembly
algorithms and implements the De Bruijn graph to find the correct
order of nucleotide. Two data structure proposed by Ye et al.
(2012) and Bowe et al. (2013) will be implemented in python.
The results after implementation will be compared with ABySS,
to test memory usage and correctness of implemented data
structure.
Keywords: De novo assembly, De Bruijn Graph, nucleotide,
microorganism, assembly algorithms.
Index Terms:
Reads: Sequenced DNA fragments
Contigs: Reads assembled into contiguous sequence
1 INTRODUCTION
The first sequenced genome belongs to a phiX174 bacterial phage
(virus that infects bacteria). Frederick Sanger completed
sequencing in 1977, for which he was awarded the Nobel Prize.
Later Human genome sequencing was also done using Sanger
method of sequencing. But this took scientist around 7 years to
complete. Eventually, with technological advancements in
sequencing platforms sequencing time was greatly reduced.
Even with new technologies in market we are faced with
computational challenge in determining the order of nucleotides.
The general method followed for sequencing genome involves the
following steps (Figure 1). Experimental Biologists take DNA
samples and extract DNA from it. The extracted DNA is then fed
into sequencing machines (Illumina, 454, Pac Bio). These
machines generate millions of small sequenced fragments called
reads. Next job is to put together all of the generated reads so that
we could get sequenced order of nucleotide. This process is called
genome assembly. Major challenge while assembling genome is
that researchers have no clue of the origin coordinates of reads.
Further DNA is double stranded and there is no way of knowing a
priori which strand a read belongs to. The repeats in genome are
another source of error. In addition to this, sequencing platforms
are not perfect and are error prone as they miss lot of reads.
Most of the genome assemblers [1,2,3,4] uses De Bruijn graph
[2,3,4,5] over other methods like overlap graph. It can be defined
as a directed graph with two node connected by an edge of k-mer
length. Nodes share an exact (k-1) overlap. It also forms the basis
of different steps in genome assembly. Due to DNA sequence of
varying size, millions of nodes and edges are generated, requiring
large memory and space. Thus developing an efficient assembly
algorithm is current one of the fundamental question being
addressed. Further, from a biological point of view knowing the
correct order of nucleotide is knowing the gene sequence of an
organism which forms the basis of research in various areas like
cancer, Alzheimer’s, diseases caused by pathogens etc.
This report is an analysis of various assembly algorithms and
implementation of De Bruijn graph to find the longest consensus
sequence. A detailed analysis of Hamiltonian path in the overlap
graph and Eulerian path in De Bruijn graph has been presented.
Towards the later part, an overview of data structure [5,6] has
been described. The coding part involves the implementation of
de Bruijn graph and finding an optimal path of longest sequence.
The major aim of this project is to analyze the available efficient
algorithms for genome assembly and address the bubble problem,
which arises during the assembly. Due to complexity of genomes
of different organism small input set will be considered. Errors
due to sequencing will not be considered. The report closes with
the discussion section, where an overview of whole report has
been presented.
2 THEORY
In the last few years, research to find efficient data structure and
algorithms to assemble short DNA fragments (reads) into a
consensus sequence has taken new turns. Most of the assembler
uses either overlap graph by Hamiltonian path or de Bruijn graph
by Eulerian path to put nucleotides in correct order. Genome
assembly problem could be related to the String Reconstruction
problem, where input is an integer k and a collection of pattern of
k-mers. The expected output is a string text with k-mer
composition equal to input patterns. Hence our goal is to
reconstruct a string, given a collection of k-mer.
In below subsections overlap graph and de Bruijn graph is
described.
2.1 Overlap Graph and Hamiltonian Path
Some of the earlier assemblers used the concept of overlap graph
to assemble genomes. Given a set of patters of k-mers, the output
should be a directed graph with nodes as k-mer patterns and edges
connecting two nodes, if suffix of k-mer in one node is equal to
prefix of k-mer in other (Figure 2).
Figure 1: Michael Schatz, Cold Spring Harbor, Steps in Genome
Assembly
2. Genome assembly stitches together a genome from short
sequenced pieces of DNA. Once we have the overlap graph; we
try to find a path by visiting each of the nodes. A path visiting
each of the nodes in a graph exactly once constitutes Hamiltonian
path.
2.2 De Bruijn Graph and Eulerian Path
The concept of De Bruijn graph began to be used in most of the
assembler towards the later part. The main reason was its
efficiency when compared to overlap graph. We describe De
Bruijn graph as follows.
Given a set of k-mers patterns as input. We are expected to
output a directed graph such that edges are k-mers pattern and
nodes share (k-1)-mers occurring as a prefix or suffix in k-mers
pattern. Once we have the De Bruijn graph, a path is constructed
by visiting all the edges instead of nodes.
One of the most important feature of De Bruijn graph is that if,
the nodes are same we could glue those nodes together, thus
reducing the number of nodes. The number of edges remains
same. This is important because genomes of almost every
organism have repeat sequences. Presence of repeat sequences
makes it difficult to look ahead while constructing the path during
assembly. Gluing together repeated nodes helps in constructing
better and optimal path.
Figure 2
Assembly of large genomes using second-generation assembly
Salzberg et al. (2010), Genome Research 20, 1165-73.
2.3 Errors in Genome Assembly
This report looks into one of the errors encountered during the
genome assembly. A source of common error during sequencing
is substitution, deletion, duplication of bases in the reads being
generated. This further leads to the erroneous k-mers generation.
Hence we have a situation where two paths could be generated
and then decision has to be made as to which path to follow to get
the correct order of DNA sequence.
The case has been described below in figure.
Figure 3. Generation of bubble during assembly
3 DATA STRUCTURE
One of the fundamental tasks in creating de Bruijn graph is the
choice of an efficient data structure. There has been a lot of
research in this field. One of the first assembler [6] used an open-
addressing hash table. The idea was to store k-mers of the graph in
the keys. The edges need not be stored as it could be inferred from
nodes.
Ye et al (2012) [7] proposed sparse k-mer graph approach. This
approach greatly reduces the memory requirement by skipping
some fraction of k-mers (m). For example with overlaps (C, D)
and (D, E), D will be skipped. The number of skipped bases or
less could be stored as neighboring bases on each side of k-mer.
The k-mer storage is thus reduced to 1/g. The memory
requirement using this approach corresponds to Ω (k/g).
Bowe et al. (2013) [8] proposed a data structure, which uses
Burrows-Wheeler transformation and rank, select functions to
store the final nodes and traverse an optimum path of correct
nucleotide order.
4 IMPLEMENTATION
As stated above the aim of this project is to present a detailed
analysis of efficient algorithm present for genome assembly. The
data structure as suggested by Ye et al. (2012), and Bowe et al.
(2013) will be implemented to generate De Bruijn graph and
hence the optimal path. The path should represent correct order of
nucleotides.
The bubble error due to sequencing will be solved by Breadth
first search. All the codes will be written in python.
Algorithm:
1. Given a Text string and integer, get a collection of all
possible k-mers.
2. Construct a path PATH_OF_GRAPH, with edges as k-
mers and two nodes sharing (k-1)-mer prefix and suffix.
3. If the above condition hold true, glue all the repeating
nodes.
4. Start visiting every edge exactly once and construct the
optimal path.
5. Return path.
Less complex genomes will be used to reduce the complexity.
5 ANALYSIS AND PERFORMANCE
The correctness of algorithm will be tested with reference to
ABySS assembler. For implementing the data structure, the
genome of known microorganism will be used as an input set.
This is because; order of nucleotide generated could then be
verified against the known order of genomic sequence.
* kumara3@miamioh.edu
3. Further to measure the correctness of output sequence
generated, BLAST [9] will be performed with query as output
sequence and target as genomic databases.
6 CONCLUSION
This report aims at analyzing some of the efficient assembly
algorithms available. It has been shown by various researchers
that implementation of De Bruijn graph has reduced the memory
usage and running time. ABySS [6], Velvet [2], SOAPdenovo [3]
and ALLPATHS [4] uses the concept of De Bruijn graph.
The report is an attempt to test and analyze the behavior of
suggested data structure in implementing De Bruijn graph. The
report also considers the analysis of running time and memory
usage by comparing the results to ABySS assembler. The big
picture of this project work could be seen as an additional insight
into various assemblers, their errors and generation of ideas to
develop more efficient ones.
REFERENCES
[1] Myers, E. W. et al. A whole-genome assembly
of Drosophila. Science 287, 2196–2204 (2000)
[2] Zerbino, D. R. & Birney, E. Velvet: algorithms for de
novo short read assembly using de Bruijn graphs. Genome
Res. 18, 821–829 (2008)
[3] Li, R. et al. De novo assembly of human genomes with
massively parallel short read sequencing. Genome Res. 20,
265–272 (2010).
[4] Butler, J. et al. ALLPATHS: de novo assembly of whole-
genome shotgun microreads.Genome Res. 18, 810–820
(2008).
[5] Compeau P, Pevzner P, Tesler G (2011) How to apply de
Bruijn graphs to genome assembly. Nat Biotechnol 29:987–
991.
[6] Simpson JT, et al. (2009) ABySS: A parallel assembler for
short read sequence data. Genome Res19:1117–1123
[7] Ye C, Ma ZS, Cannon CH, Pop M, Yu DW: Exploiting
sparseness in de novo
[8] http://alexbowe.com/succinct-debruijn-graphs/#fnref:8
[9] Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and
Lipman,D.J. (1990) J. Mol. Biol., 215, 403–410.