Search results for " set"
showing 10 items of 2095 documents
Background subtraction and peak search from threefold gamma event data
1990
Abstract A method for subtracting background from triple-coincidence γ events is presented. In our data set it was used to remove 40% of the noise without affecting photopeaks with intensity of >18 counts. An example of performance of Ward's clustering algorithm applied to three-dimensional photopeak searching is also presented. Several standard clustering algorithms were found to be applicable only to background-subtracted data.
High-statistics study of f0(1500) decay into π0π0
1995
Abstract A partial-wave analysis of the reaction p p →π 0 π 0 π 0 has been performed using a high-quality high-statistics data set of 712 000 events. In addition to the f0(975) and f0(1300), the scalar resonance with mass m = (1500 ± 15) MeV and width Γ = (120 ± 25) MeV is necessary to describe the data.
Polar Classification of Nominal Data
2013
Many modern systems record various types of parameter values. Numerical values are relatively convenient for data analysis tools because there are many methods to measure distances and similarities between them. The application of dimensionality reduction techniques for data sets with such values is also a well known practice. Nominal (i.e., categorical) values, on the other hand, encompass some problems for current methods. Most of all, there is no meaningful distance between possible nominal values, which are either equal or unequal to each other. Since many dimensionality reduction methods rely on preserving some form of similarity or distance measure, their application to such data sets…
Streamlining distributed Deep Learning I/O with ad hoc file systems
2021
With evolving techniques to parallelize Deep Learning (DL) and the growing amount of training data and model complexity, High-Performance Computing (HPC) has become increasingly important for machine learning engineers. Although many compute clusters already use learning accelerators or GPUs, HPC storage systems are not suitable for the I/O requirements of DL workflows. Therefore, users typically copy the whole training data to the worker nodes or distribute partitions. Because DL depends on randomized input data, prior work stated that partitioning impacts DL accuracy. Their solutions focused mainly on training I/O performance on a high-speed network but did not cover the data stage-in pro…
Algorithms for Image Reconstruction
2010
Three-dimensional (3D) imaging is becoming one of the most important applications of radioactive materials in medicine. It offers good spatial resolution, a 3D insight into the human body, and a high sensitivity in the picomolar range because markers for biological processes can be detected well when labeled with radioactive materials. In addition, the technical equipment has undergone many technological achievements. This is true for single-photon emission computed tomography (SPECT), positron emission tomography (PET), and X-ray computed tomography (CT), which is often used in connection with the nuclear medical imaging systems, as also described in chapter 5 about sources in nuclear medi…
FABC: Retinal Vessel Segmentation Using AdaBoost
2010
This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as we…
Reference point approach for multiple decision makers
2005
We consider multiple criteria decision-making problems where a group of decision-makers wants to find the most preferred solution from a discrete set of alternatives. We develop a method that uses achievement functions for charting subsets of reference points that would support a certain alternative to be the most preferred one. The resulting descriptive information is provided to the decision-makers in the form of reference acceptability indices and central reference points for each decision alternative. Then, the decision-makers can compare this information with their own preferences. We demonstrate the use of the method using a strategic multiple criteria decision model for an electricit…
Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources
2017
Browsing digital library (DL) collections seems to pose a challenge for a user owning to the number of factors like for instance, operability of the system, interface readability or clarity, and retrieval efficiency directly related to it, or the number of digital items within the user’s domain. However, when it comes to searching for an item in a foreign language to the user, the number of the factors arises even more which translates proportionally to the growing number of clicks aimed to retrieve the target item. Such a procedure usually leads to disheartening the user from browsing the digital collections. Our study into the user’s behavior interacting with multilingual DL system is set…
Upper bounds for the tightness of the $$G_\delta $$-topology
2021
We prove that if X is a regular space with no uncountable free sequences, then the tightness of its $$G_\delta $$ topology is at most the continuum and if X is, in addition, assumed to be Lindelof then its $$G_\delta $$ topology contains no free sequences of length larger then the continuum. We also show that, surprisingly, the higher cardinal generalization of our theorem does not hold, by constructing a regular space with no free sequences of length larger than $$\omega _1$$ , but whose $$G_\delta $$ topology can have arbitrarily large tightness.
Fast noniterative orbital localization for large molecules
2006
We use Cholesky decomposition of the density matrix in atomic orbital basis to define a new set of occupied molecular orbital coefficients. Analysis of the resulting orbitals ("Cholesky molecular orbitals") demonstrates their localized character inherited from the sparsity of the density matrix. Comparison with the results of traditional iterative localization schemes shows minor differences with respect to a number of suitable measures of locality, particularly the scaling with system size of orbital pair domains used in local correlation methods. The Cholesky procedure for generating orthonormal localized orbitals is noniterative and may be made linear scaling. Although our present implem…