The optimal coding scheme leverages the link between the encoders to optimally handle the correlation between the sources . Moreover , the scheme incorporates several source coding techniques , such as Wyner-Ziv coding , binning and superposition coding . Furthermore , a dual semi-deterministic broadcast channel ( BC ) with one-sided cooperative decoders is considered . Finally , when the source motion is unknown or there are too many sources to track , the time domain must be included to reconstruct radiation movies instead of radiation images . Simply adding time binning works well when the source is strong and there are many counts reconstructed in each time bin . However , when there are only a few counts per time bin , the maximum-likelihood reconstruction method is modified to enforce smoothness in the time domain which produces much improved results over the standard maximum-likelihood reconstruction . The prediction performances for T2D usually reveal poor results which are around 65 % in accuracy in state-of-the-art . In this study , we propose a method com-bining K-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction . We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by K-means allows to increase the performance in prediction accuracy reaching approximately or more than 70 % . Using a specific display format is usually a better idea than rounding to multiples of a fraction . Quantile binning is popular in several fields . I give tips and tricks on how to produce such bins and also on how to show their limitations . We utilize the NILS tagging capabilities of Calibre TCL scripting to identify fragments with low NILS . We then perform NILS binning to assign different magnitude of retargeting to different NILS bins . NILS is determined both for width , to identify regions of pinching , and space , to locate regions of potential bridging . Data visualization is invaluable for explaining the significance of data to people who are visually oriented . The central task of automatic data visualization is , given a dataset , to visualize its compelling stories by transforming the data ( e.g. , selecting attributes , grouping and binning values ) and deciding the right type of visualization ( e.g. , bar or line charts ) . We present DEEPEYE , a novel system for automatic data visualization that tackles three problems : ( 1 ) Visualization recognition : given a visualization , is it " good or " bad " ? One area in which conservative simplifications are often made is modeling the physical interactions that occur during the progression of an accident sequence . This paper demonstrates and compares different approaches for incorporating deterministic models of physical parameters into probabilistic models ; parameter range binning , response curves , and integral deterministic models . An example that combines all three approaches in a probabilistic model for the handling of an energetic material ( i.e. high explosive , rocket propellant , ... ) is then presented using a directed graph model . Abstract Background Sequence-binning techniques enable the recovery of an increasing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly , incurring the computational cost and drawbacks of the latter , e.g. , biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets . Results We present here a scalable pre-assembly binning scheme ( i.e. , operating on unassembled short reads ) enabling latent genome recovery by leveraging sparse dictionary learning and elastic-net regularization , and its use to recover hundreds of metagenome-assembled genomes , including very low-abundance genomes , from a joint analysis of microbiomes from the LifeLines DEEP population cohort ( n = 1,135 , > 1010 reads ) . Conclusion We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale and that , despite lower genome reconstruction yields compared to assembly-based approaches , bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles . Recently , changes in SMOS Level 2 ( L2 ) SM have led to the reprocessing of the BEC SMOS L3 SM . As in previous versions , a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index ( DQX ) was applied for the L3 production . However , the DQX was modified in the latest L2 release ( v650 ) , which could possibly have an influence in the performance of the derived products . We describe a new binning technic for informed data hiding problem . In information theoretical point of view , the blind watermarking problem can be seen as transmitting a secret message M through a noisy channel on top of an interfered host signal S that is available only at the encoder . The new technique then selects the bin number which minimizes the total error . The results showed that the new binning approach is well suited for binning small datasets and can be used in geoscience applications if needed . Then we quantitatively assess the improvement by comparing the point source sizes ( § 2 ) and detections ( § 3 ) before and after the procedure . Sub-pixel binning : Chandra coordinates contain positional accuracy finer than one ACIS pixel ( 0.492 arcsec ) through dither and aspect correction . Imaging data binned by ACIS subpixel can already provide an improved resolution . The traditional binning method , which records line-segment frequency , only considers data in a two-dimensional subspace , as a result , the multi-dimensional features are not taken into account for trend and outlier analysis . Obtaining a coherent binned representation in higher dimensions is challenging because multidimensional binning can suffer from the curse of dimensionality . In this paper , we utilize the Markov Chain model to compute an n-dimensional joint probability for each data tuple based on a two-dimensional binning method . This method also acts as an effective noise filter , discarding unwanted noise from the data set . Peak picking and separation are evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set . A fast HOTLink ( 400Mbit/s , Cypress Semiconductor inc . ) serial bus is used to transfer the coincidence data to networked data acquisition and image processing computers , and the sinogram binning and image reconstruction can be processed in parallel . This camera also has an ECG/respiratory gated imaging capability . One of the senders transmits information to its intended receiver , while the other sender transmits information to both receivers . We establish a new achievable rate region for the ZC , using Marton 's binning technique . At the receivers , we use joint decoding strategy . These histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter , where they are referred to as radial distribution functions , and in the analysis of the spatial distributions of galaxies and galaxy clusters . Although conceptually simple , the calculation of radial distribution functions via distance binning requires the evaluation of O ( N 2 ) particle-pair distances where N is the number of particles under consideration . CADISHI provides fast parallel implementations of the distance histogram algorithm for the CPU and the GPU , written in templated C++ and CUDA . DBSCAN is an outstanding representative of clustering algorithms for its good performance in clustering spatial data . To improve its efficiency this paper proposes GbDBSCAN(Grid based DBSCAN).GbDBSCAN adopts gird and data binning technique to query density reachable objects for all objects in dataset . It can also identify and handle border points . As the proposed reconstruction algorithm takes only a number of operations comparable to a modest amount of FFT 's it is very worthwhile to implement the algorithm within the Radon transform , instead of collecting the traces within bins and stack them to a regular output grid to make it suitable for applying Fast Fourier Transforms . The method shows significant improvement over the binning and stacking method when applied to both synthetic as well as real data . Hadoop , a cloud computing project using the MapReduce framework in Java , has become of significant interest in distributed data mining . A feasible distributed data indexing algorithm is proposed for Hadoop data mining , based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format . A data mining framework on Hadoop using the Java Persistence API ( JPA ) and MySQL Cluster is proposed . However , a serious drawback of these approaches is the common misalignments of 1 H NMR spectra . To overcome this problem , bucketing or binning techniques have been used . In this work , an algorithm is proposed to perform an optimized bucketing that yields better results than the conventional bucketing implemented in some commercial software . Any attacker can not learn more information than the mutual information , and the discussion of this approach gives a certain upperbound on the risk of timing attacks . Köpf et al. adopted this approach to evaluate the contribution of a bucketing technique that was proposed by themselves . However , the mathematical discussion therein needs more improvement . In both cases the number of processors used is optimal for the given running time , i.e , O(n/log*n ) for the Tolerant PRAM and O(n ) for the OR PRAM , and the probability that the running time exceeds its expected value by more than a constant factor is exponentially small . The algorithm is based on the bucketing approach . Using similar techniques , other closely related proximity problems are shown to be solvable with the same resource bounds.<<ETX > > Our approach uses ( a ) patch templates and ( b ) semantic feedback from the program to automatically generate and apply approximate fixes for general bug classes . Our evaluation shows that approximate fixes are competitive with using true fixes for crash bucketing , and significantly outperforms built-in deduplication techniques for three state of the art fuzzers . A new stochastic sampling scheme is proposed and defended . We also try to answer several questions , which are generally -and perhaps voluntarily-bypassed : " does the bucketing strategy influence the regression process ? " ; " how should the data be split into buckets to get the best fits both numerically and physically ? " ... In the convex-hull problem , in two-dimensional space , the task is to find , for a given sequence S of n points , the smallest convex polygon for which each point of S is either in its interior or on its boundary . In this paper , we propose a variant of the classical bucketing algorithm that ( 1 ) solves the convex-hull problem for any multiset of points , ( 2 ) uses O ( √ n ) words of extra space , ( 3 ) runs in O(n ) expected time on points drawn independently and uniformly from a rectangle , and ( 4 ) requires O(n lgn ) time in the worst case . Also , we perform experiments to compare bucketing to other alternatives that are known to work in linear expected time . In this paper , such an algorithm is proposed . The new algorithm works under the dynamic programming framework and runs in provably linear time for multiple buffer types due to two novel techniques : restrictive cost bucketing and efficient delay update . Experiment results on industrial netlists demonstrate that the new algorithm consistently outperforms van Ginneken/Lillis algorithm ( van Ginneken , 1990 ) and ( Lillis , 1996 ) for RC buffering and all known RLC buffering algorithms . The third technique combines the Fast Fourier Transform with bucketing-based schemes for pricing basket options . This technique is extremely fast , polynomial in the number of days and stocks , and does not add any errors to those already incurred in the companion bucketing scheme . We investigate the behavior of limit order books on the meso-scale motivated by order execution scheduling algorithms . To do so we carry out empirical analysis of the order flows from market and limit order submissions , aggregated from tick-by-tick data via volume-based bucketing , as well as various LOB depth and shape metrics . We document a nonlinear relationship between trade imbalance and price change , which however can be converted into a linear link by considering a weighted average of market and limit order flows . We present a highly flexible and efficient software pipeline for programmable triangle voxelization . The pipeline , entirely written in CUDA , supports both fully conservative and thin voxelizations , multiple boolean , floating point , vector-typed render targets , user-defined vertex and fragment shaders , and a bucketing mode which can be used to generate 3D A-buffers containing the entire list of fragments belonging to each voxel . For maximum efficiency , voxelization is implemented as a sort-middle tile-based rasterizer , while the A-buffer mode , essentially performing 3D binning of triangles over uniform grids , uses a sort-last pipeline .
The optimal coding scheme leverages the link between the encoders to optimally handle the correlation between the sources . Moreover , the scheme incorporates several source coding techniques , such as Wyner-Ziv coding , binning and superposition coding . Furthermore , a dual semi-deterministic broadcast channel ( BC ) with one-sided cooperative decoders is considered .
Finally , when the source motion is unknown or there are too many sources to track , the time domain must be included to reconstruct radiation movies instead of radiation images . Simply adding time binning works well when the source is strong and there are many counts reconstructed in each time bin . However , when there are only a few counts per time bin , the maximum-likelihood reconstruction method is modified to enforce smoothness in the time domain which produces much improved results over the standard maximum-likelihood reconstruction .
The prediction performances for T2D usually reveal poor results which are around 65 % in accuracy in state-of-the-art . In this study , we propose a method com-bining K-means clustering algorithm and unsupervised binning approaches to improve the performance in metagenome-based disease prediction . We illustrate by experiments on metagenomic datasets related to Type 2 Diabetes that the proposed method embedded clusters generated by K-means allows to increase the performance in prediction accuracy reaching approximately or more than 70 % .
Using a specific display format is usually a better idea than rounding to multiples of a fraction . Quantile binning is popular in several fields . I give tips and tricks on how to produce such bins and also on how to show their limitations .
We utilize the NILS tagging capabilities of Calibre TCL scripting to identify fragments with low NILS . We then perform NILS binning to assign different magnitude of retargeting to different NILS bins . NILS is determined both for width , to identify regions of pinching , and space , to locate regions of potential bridging .
Data visualization is invaluable for explaining the significance of data to people who are visually oriented . The central task of automatic data visualization is , given a dataset , to visualize its compelling stories by transforming the data ( e.g. , selecting attributes , grouping and binning values ) and deciding the right type of visualization ( e.g. , bar or line charts ) . We present DEEPEYE , a novel system for automatic data visualization that tackles three problems : ( 1 ) Visualization recognition : given a visualization , is it " good or " bad " ?
One area in which conservative simplifications are often made is modeling the physical interactions that occur during the progression of an accident sequence . This paper demonstrates and compares different approaches for incorporating deterministic models of physical parameters into probabilistic models ; parameter range binning , response curves , and integral deterministic models . An example that combines all three approaches in a probabilistic model for the handling of an energetic material ( i.e. high explosive , rocket propellant , ... ) is then presented using a directed graph model .
Abstract Background Sequence-binning techniques enable the recovery of an increasing number of genomes from complex microbial metagenomes and typically require prior metagenome assembly , incurring the computational cost and drawbacks of the latter , e.g. , biases against low-abundance genomes and inability to conveniently assemble multi-terabyte datasets . Results We present here a scalable pre-assembly binning scheme ( i.e. , operating on unassembled short reads ) enabling latent genome recovery by leveraging sparse dictionary learning and elastic-net regularization , and its use to recover hundreds of metagenome-assembled genomes , including very low-abundance genomes , from a joint analysis of microbiomes from the LifeLines DEEP population cohort ( n = 1,135 , > 1010 reads ) . Conclusion We showed that sparse coding techniques can be leveraged to carry out read-level binning at large scale and that , despite lower genome reconstruction yields compared to assembly-based approaches , bin-first strategies can complement the more widely used assembly-first protocols by targeting distinct genome segregation profiles .
Recently , changes in SMOS Level 2 ( L2 ) SM have led to the reprocessing of the BEC SMOS L3 SM . As in previous versions , a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index ( DQX ) was applied for the L3 production . However , the DQX was modified in the latest L2 release ( v650 ) , which could possibly have an influence in the performance of the derived products .
We describe a new binning technic for informed data hiding problem . In information theoretical point of view , the blind watermarking problem can be seen as transmitting a secret message M through a noisy channel on top of an interfered host signal S that is available only at the encoder .
The new technique then selects the bin number which minimizes the total error . The results showed that the new binning approach is well suited for binning small datasets and can be used in geoscience applications if needed .
Then we quantitatively assess the improvement by comparing the point source sizes ( § 2 ) and detections ( § 3 ) before and after the procedure . Sub-pixel binning : Chandra coordinates contain positional accuracy finer than one ACIS pixel ( 0.492 arcsec ) through dither and aspect correction . Imaging data binned by ACIS subpixel can already provide an improved resolution .
The traditional binning method , which records line-segment frequency , only considers data in a two-dimensional subspace , as a result , the multi-dimensional features are not taken into account for trend and outlier analysis . Obtaining a coherent binned representation in higher dimensions is challenging because multidimensional binning can suffer from the curse of dimensionality . In this paper , we utilize the Markov Chain model to compute an n-dimensional joint probability for each data tuple based on a two-dimensional binning method .
This method also acts as an effective noise filter , discarding unwanted noise from the data set . Peak picking and separation are evaluated against the conventional methods of mass binning and manually selecting regions of a peak to image on a model data set .
A fast HOTLink ( 400Mbit/s , Cypress Semiconductor inc . ) serial bus is used to transfer the coincidence data to networked data acquisition and image processing computers , and the sinogram binning and image reconstruction can be processed in parallel . This camera also has an ECG/respiratory gated imaging capability .
One of the senders transmits information to its intended receiver , while the other sender transmits information to both receivers . We establish a new achievable rate region for the ZC , using Marton 's binning technique . At the receivers , we use joint decoding strategy .
These histograms represent 2-point spatial correlation functions and are routinely calculated from simulations of soft and condensed matter , where they are referred to as radial distribution functions , and in the analysis of the spatial distributions of galaxies and galaxy clusters . Although conceptually simple , the calculation of radial distribution functions via distance binning requires the evaluation of O ( N 2 ) particle-pair distances where N is the number of particles under consideration . CADISHI provides fast parallel implementations of the distance histogram algorithm for the CPU and the GPU , written in templated C++ and CUDA .
DBSCAN is an outstanding representative of clustering algorithms for its good performance in clustering spatial data . To improve its efficiency this paper proposes GbDBSCAN(Grid based DBSCAN).GbDBSCAN adopts gird and data binning technique to query density reachable objects for all objects in dataset . It can also identify and handle border points .
As the proposed reconstruction algorithm takes only a number of operations comparable to a modest amount of FFT 's it is very worthwhile to implement the algorithm within the Radon transform , instead of collecting the traces within bins and stack them to a regular output grid to make it suitable for applying Fast Fourier Transforms . The method shows significant improvement over the binning and stacking method when applied to both synthetic as well as real data .
Hadoop , a cloud computing project using the MapReduce framework in Java , has become of significant interest in distributed data mining . A feasible distributed data indexing algorithm is proposed for Hadoop data mining , based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format . A data mining framework on Hadoop using the Java Persistence API ( JPA ) and MySQL Cluster is proposed .
However , a serious drawback of these approaches is the common misalignments of 1 H NMR spectra . To overcome this problem , bucketing or binning techniques have been used . In this work , an algorithm is proposed to perform an optimized bucketing that yields better results than the conventional bucketing implemented in some commercial software .
Any attacker can not learn more information than the mutual information , and the discussion of this approach gives a certain upperbound on the risk of timing attacks . Köpf et al. adopted this approach to evaluate the contribution of a bucketing technique that was proposed by themselves . However , the mathematical discussion therein needs more improvement .
In both cases the number of processors used is optimal for the given running time , i.e , O(n/log*n ) for the Tolerant PRAM and O(n ) for the OR PRAM , and the probability that the running time exceeds its expected value by more than a constant factor is exponentially small . The algorithm is based on the bucketing approach . Using similar techniques , other closely related proximity problems are shown to be solvable with the same resource bounds.<<ETX > >
Our approach uses ( a ) patch templates and ( b ) semantic feedback from the program to automatically generate and apply approximate fixes for general bug classes . Our evaluation shows that approximate fixes are competitive with using true fixes for crash bucketing , and significantly outperforms built-in deduplication techniques for three state of the art fuzzers .
A new stochastic sampling scheme is proposed and defended . We also try to answer several questions , which are generally -and perhaps voluntarily-bypassed : " does the bucketing strategy influence the regression process ? " ; " how should the data be split into buckets to get the best fits both numerically and physically ? " ...
In the convex-hull problem , in two-dimensional space , the task is to find , for a given sequence S of n points , the smallest convex polygon for which each point of S is either in its interior or on its boundary . In this paper , we propose a variant of the classical bucketing algorithm that ( 1 ) solves the convex-hull problem for any multiset of points , ( 2 ) uses O ( √ n ) words of extra space , ( 3 ) runs in O(n ) expected time on points drawn independently and uniformly from a rectangle , and ( 4 ) requires O(n lgn ) time in the worst case . Also , we perform experiments to compare bucketing to other alternatives that are known to work in linear expected time .
In this paper , such an algorithm is proposed . The new algorithm works under the dynamic programming framework and runs in provably linear time for multiple buffer types due to two novel techniques : restrictive cost bucketing and efficient delay update . Experiment results on industrial netlists demonstrate that the new algorithm consistently outperforms van Ginneken/Lillis algorithm ( van Ginneken , 1990 ) and ( Lillis , 1996 ) for RC buffering and all known RLC buffering algorithms .
The third technique combines the Fast Fourier Transform with bucketing-based schemes for pricing basket options . This technique is extremely fast , polynomial in the number of days and stocks , and does not add any errors to those already incurred in the companion bucketing scheme .
We investigate the behavior of limit order books on the meso-scale motivated by order execution scheduling algorithms . To do so we carry out empirical analysis of the order flows from market and limit order submissions , aggregated from tick-by-tick data via volume-based bucketing , as well as various LOB depth and shape metrics . We document a nonlinear relationship between trade imbalance and price change , which however can be converted into a linear link by considering a weighted average of market and limit order flows .
We present a highly flexible and efficient software pipeline for programmable triangle voxelization . The pipeline , entirely written in CUDA , supports both fully conservative and thin voxelizations , multiple boolean , floating point , vector-typed render targets , user-defined vertex and fragment shaders , and a bucketing mode which can be used to generate 3D A-buffers containing the entire list of fragments belonging to each voxel . For maximum efficiency , voxelization is implemented as a sort-middle tile-based rasterizer , while the A-buffer mode , essentially performing 3D binning of triangles over uniform grids , uses a sort-last pipeline .
The Ahlswede-Körner coordination problem with one-sided encoder cooperation Compton Imaging Algorithms for Position-Sensitive Gamma-Ray Detectors in the Presence of Motion. Binning Approach based on Classical Clustering for Type 2 Diabetes Diagnosis Speaking Stata: From Rounding to Binning Integrated model-based retargeting and optical proximity correction DeepEye: Towards Automatic Data Visualization Approaches to implementing deterministic models in a probabilistic framework Binning unassembled short reads based on k-mer abundance covariance using sparse coding Influence of Quality Filtering Approaches in BEC SMOS L3 Soil Moisture Products Dirty-Paper Writing Based on LDPC Codes for Data Hiding Creation of Histograms for Data in Various Mineral Resource and Engineering Problems: A Review of Existing Methods and a Proposed New Method to Define Bin Number ACIS Sub-Pixel Resolution: Improvement in Point Source Detection Markov Chain Driven Multi-Dimensional Visual Pattern Analysis with Parallel Coordinates Peak picking as a pre‐processing technique for imaging time of flight secondary ion mass spectrometry Front-end electronics based on high-yield-pileup-event-recovery method for a high resolution PET camera with PMT-quadrant-sharing detector modules On the capacity region of the degraded Z channel CADISHI: Fast parallel calculation of particle-pair distance histograms on CPUs and GPUs Efficient DBSCAN algorithm based on grid Radon domain reconstruction of 3D irregularly sampled VSP data An Efficient Data Mining Framework on Hadoop using Java Persistence API Optimized bucketing for NMR spectra: Three case studies Information theoretical evaluation of the bucketing technique to mitigate timing attacks Improved parallel bucketing algorithms for proximity problems Semantic Crash Bucketing Bucketing Techniques in Robust Regression for Computer Vision A Faster Convex-Hull Algorithm via Bucketing An Efficient Algorithm for RLC Buffer Insertion Fast Pricing of European Asian Options with Provable Accuracy: Single-Stock and Basket Options Order Flows and Limit Order Book Resiliency on the Meso-Scale VoxelPipe: a programmable pipeline for 3D voxelization
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