scholarly journals Traits of Interval Tree in Solving Blind Search Problems of Finding a Term in an Ordered Data Set

2020 ◽  
Vol 10 (7) ◽  
pp. 516-522
Author(s):  
Xingbo Wang ◽  
◽  
Jicong Wu
Keyword(s):  
Search Problems ◽  
Data Set ◽  
Interval Tree ◽  
Blind Search ◽  
Ordered Data

scholarly journals Detection of gamma-ray transients with wild binary segmentation

2020 ◽  
Vol 493 (3) ◽  
pp. 4428-4441
Author(s):  
S Antier ◽  
K Barynova ◽  
P Fryzlewicz ◽  
C Lachaud ◽  
G Marchal-Duval
Keyword(s):  
Gamma Ray ◽  
Change Point Detection ◽  
Archival Data ◽  
Data Set ◽  
Temporal Properties ◽  
Background Removal ◽  
Segmentation Analysis ◽  
Binary Segmentation ◽  
Blind Search ◽  
Detection Search

ABSTRACT In the context of time domain astronomy, we present an offline detection search of gamma-ray transients using a wild binary segmentation analysis called F-WBSB targeting both short and long gamma-ray bursts (GRBs) and covering the soft and hard gamma-ray bands. We use NASA Fermi/GBM archival data as a training and testing data set. This paper describes the analysis applied to the 12 NaI detectors of the Fermi/GBM instrument. This includes background removal, change-point detection that brackets the peaks of gamma-ray flares, the evaluation of significance for each individual GBM detector, and the combination of the results among the detectors. We also explain the calibration of the ∼ 10 parameters present in the method using one week of archival data. Finally, we present our detection performance result for 60 d of a blind search analysis with F-WBSB by comparing to both the onboard and offline GBM search as well as external events found by others surveys such as Swift-BAT. We detect 42/44 onboard GBM events but also other gamma-ray flares at a rate of 1 per hour in the 4–50 keV band. Our results show that F-WBSB is capable of recovering gamma-ray flares, including the detection of soft X-ray long transients. FWBSB offers an independent identification of GRBs in combination with methods for determining spectral and temporal properties of the transient as well as localization. This is particularly useful for increasing the GRB rate and that will help the joint detection with gravitational-wave events.

Route to Higher Fidelity FT-IR Imaging

2000 ◽  
Vol 54 (4) ◽  
pp. 486-495 ◽  
Author(s):  
Rohit Bhargava ◽  
Shi-Qing Wang ◽  
Jack L. Koenig
Keyword(s):  
Signal To Noise Ratio ◽  
Original Data ◽  
Data Set ◽  
Ir Imaging ◽  
Polymer Dispersed ◽  
Data Points ◽  
Ft Ir ◽  
Ordered Data ◽  
Noise Fraction ◽  
Collection Time

FT-IR imaging employing a focal plane array (FPA) detector is often plagued by low signal-to-noise ratio (SNR) data. A mathematical transform that re-orders spectral data points into decreasing order of SNR is employed to reduce noise by retransforming the ordered data set using only a few relevant data points. This approach is shown to result in significant gains in terms of image fidelity by examining microscopically phase-separated composites termed polymer dispersed liquid crystals (PDLCs). The actual gains depend on the SNR characteristics of the original data. Noise is reduced by a factor greater than 5 if the noise in the initial data is sufficiently low. For a moderate absorbance level of 0.5 a.u., the achievable SNR by reducing noise is greater than 100 for a collection time of less than 4 min. The criteria for optimal application of a noise-reducing procedure employing the minimum noise fraction (MNF) transform are discussed and various variables in the process quantified. This noise reduction is shown to provide high-quality images for accurate morphological analysis. The coupling of mathematical transformation techniques with spectroscopic Fourier transform infrared (FT-IR) imaging is shown to result in high-fidelity images without increasing collection time or drastically modifying hardware.

A novel algorithm for searching frequent gradual patterns from an ordered data set

2020 ◽  
Vol 24 (5) ◽  
pp. 1029-1042
Author(s):  
Jerry Lonlac ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Temporal Order ◽  
Efficient Algorithms ◽  
Experimental Results ◽  
Temporal Constraint ◽  
Data Set ◽  
Order Relations ◽  
Ordered Data ◽  
Novel Algorithm ◽  
Pattern Problem

Mining frequent simultaneous attribute co-variations in numerical databases is also called frequent gradual pattern problem. Few efficient algorithms for automatically extracting such patterns have been reported in the literature. Their main difference resides in the variation semantics used. However in applications with temporal order relations, those algorithms fail to generate correct frequent gradual patterns as they do not take this temporal constraint into account in the mining process. In this paper, we propose an approach for extracting frequent gradual patterns for which the ordering of supporting objects matches the temporal order. This approach considerably reduces the number of gradual patterns within an ordered data set. The experimental results show the benefits of our approach.

scholarly journals Generating an Ordered Data Set from an OCR Text File

2014 ◽  
Author(s):  
Jon Crump
Keyword(s):  
Essential Elements ◽  
Text File ◽  
Data Set ◽  
Ordered Data

This tutorial illustrates strategies for taking raw OCR output from a scanned text, parsing it to isolate and correct essential elements of metadata, and generating an ordered data set (a python dictionary) from it.

Constrained Data Mining

2011 ◽  
pp. 301-306
Author(s):  
Brad Morantz
Keyword(s):  
Data Mining ◽  
Processing Time ◽  
Large Data ◽  
Computational Time ◽  
Data Sets ◽  
Data Set ◽  
Second Stage ◽  
Output Constraints ◽  
Ordered Data

Mining a large data set can be time consuming, and without constraints, the process could generate sets or rules that are invalid or redundant. Some methods, for example clustering, are effective, but can be extremely time consuming for large data sets. As the set grows in size, the processing time grows exponentially. In other situations, without guidance via constraints, the data mining process might find morsels that have no relevance to the topic or are trivial and hence worthless. The knowledge extracted must be comprehensible to experts in the field. (Pazzani, 1997) With time-ordered data, finding things that are in reverse chronological order might produce an impossible rule. Certain actions always precede others. Some things happen together while others are mutually exclusive. Sometimes there are maximum or minimum values that can not be violated. Must the observation fit all of the requirements or just most. And how many is “most?” Constraints attenuate the amount of output (Hipp & Guntzer, 2002). By doing a first-stage constrained mining, that is, going through the data and finding records that fulfill certain requirements before the next processing stage, time can be saved and the quality of the results improved. The second stage also might contain constraints to further refine the output. Constraints help to focus the search or mining process and attenuate the computational time. This has been empirically proven to improve cluster purity. (Wagstaff & Cardie, 2000)(Hipp & Guntzer, 2002) The theory behind these results is that the constraints help guide the clustering, showing where to connect, and which ones to avoid. The application of user-provided knowledge, in the form of constraints, reduces the hypothesis space and can reduce the processing time and improve the learning quality.

scholarly journals The MUSE Hubble Ultra Deep Field Survey

2017 ◽  
Vol 608 ◽  
pp. A1 ◽  
Author(s):  
Roland Bacon ◽  
Simon Conseil ◽  
David Mary ◽  
Jarle Brinchmann ◽  
Martin Shepherd ◽  
...  
Keyword(s):  
Emission Line ◽  
Original Method ◽  
Integration Time ◽  
Data Set ◽  
Order Of Magnitude ◽  
Emission Line Galaxies ◽  
Hubble Ultra Deep Field ◽  
Blind Search ◽  
Galaxy Population ◽  
The Impact

We present the MUSE Hubble Ultra Deep Survey, a mosaic of nine MUSE fields covering 90% of the entire HUDF region with a 10-h deep exposure time, plus a deeper 31-h exposure in a single 1.15 arcmin2 field. The improved observing strategy and advanced data reduction results in datacubes with sub-arcsecond spatial resolution (0.̋65 at 7000 Å) and accurate astrometry (0.̋07 rms). We compare the broadband photometric properties of the datacubes to HST photometry, finding a good agreement in zeropoint up to mAB = 28 but with an increasing scatter for faint objects. We have investigated the noise properties and developed an empirical way to account for the impact of the correlation introduced by the 3D drizzle interpolation. The achieved 3σ emission line detection limit for a point source is 1.5 and 3.1 × 10-19 erg s-1 cm-2 for the single ultra-deep datacube and the mosaic, respectively. We extracted 6288 sources using an optimal extraction scheme that takes the published HST source locations as prior. In parallel, we performed a blind search of emission line galaxies using an original method based on advanced test statistics and filter matching. The blind search results in 1251 emission line galaxy candidates in the mosaic and 306 in the ultradeep datacube, including 72 sources without HST counterparts (mAB > 31). In addition 88 sources missed in the HST catalog but with clear HST counterparts were identified. This data set is the deepest spectroscopic survey ever performed. In just over 100 h of integration time, it provides nearly an order of magnitude more spectroscopic redshifts compared to the data that has been accumulated on the UDF over the past decade. The depth and high quality of these datacubes enables new and detailed studies of the physical properties of the galaxy population and their environments over a large redshift range.

scholarly journals Reconnection with the Ideal Tree: A New Approach to Real-Time Search

2014 ◽  
Vol 50 ◽  
pp. 235-264 ◽  
Author(s):  
N. Rivera ◽  
L. Illanes ◽  
J. A. Baier ◽  
C. Hernandez
Keyword(s):  
Real Time ◽  
Search Algorithm ◽  
Single Agent ◽  
Time Constraints ◽  
Search Problems ◽  
Planning Time ◽  
Current State ◽  
Actual Environment ◽  
Order Of Magnitude ◽  
Blind Search

Many applications, ranging from video games to dynamic robotics, require solving single-agent, deterministic search problems in partially known environments under very tight time constraints. Real-Time Heuristic Search (RTHS) algorithms are specifically designed for those applications. As a subroutine, most of them invoke a standard, but bounded, search algorithm that searches for the goal. In this paper we present FRIT, a simple approach for single-agent deterministic search problems under tight constraints and partially known environments that unlike traditional RTHS does not search for the goal but rather searches for a path that connects the current state with a so-called ideal tree T . When the agent observes that an arc in the tree cannot be traversed in the actual environment, it removes such an arc from T and then carries out a reconnection search whose objective is to find a path between the current state and any node in T . The reconnection search is done using an algorithm that is passed as a parameter to FRIT. If such a parameter is an RTHS algorithm, then the resulting algorithm can be an RTHS algorithm. We show, in addition, that FRIT may be fed with a (bounded) complete blind-search algorithm. We evaluate our approach over grid pathfinding benchmarks including game maps and mazes. Our results show that FRIT, used with RTAA*, a standard RTHS algorithm, outperforms RTAA* significantly; by one order of magnitude under tight time constraints. In addition, FRIT(daRTAA*) substantially outperforms daRTAA*, a state-of-the-art RTHS algorithm, usually obtaining solutions 50% cheaper on average when performing the same search effort. Finally, FRIT(BFS), i.e., FRIT using breadth-first-search, obtains best-quality solutions when time is limited compared to Adaptive A* and Repeated A*. Finally we show that Bug2, a pathfinding-specific navigation algorithm, outperforms FRIT(BFS) when planning time is extremely limited, but when given more time, the situation reverses.

Compression Schemes with Data Reordering for Ordered Data

2014 ◽  
Vol 25 (1) ◽  
pp. 1-28
Author(s):  
Chun-Hee Lee ◽  
Chin-Wan Chung
Keyword(s):  
Compression Ratio ◽  
Optimization Techniques ◽  
Original Position ◽  
Data Set ◽  
Run Length ◽  
Original Order ◽  
Ordered Data ◽  
The Impact ◽  
Data Reordering ◽  
Better Than

Although there have been many compression schemes for reducing data effectively, most schemes do not consider the reordering of data. In the case of unordered data, if the users change the data order in a given data set, the compression ratio may be improved compared to the original compression before reordering data. However, in the case of ordered data, the users need a mapping table that maps the original position to the changed position in order to recover the original order. Therefore, reordering ordered data may be disadvantageous in terms of space. In this paper, the authors consider two compression schemes, run-length encoding and bucketing scheme as bases for showing the impact of data reordering in compression schemes. Also, the authors propose various optimization techniques related to data reordering. Finally, the authors show that the compression schemes with data reordering are better than the original compression schemes in terms of the compression ratio.

scholarly journals A Case of Pathology in Multiobjective Heuristic Search

2013 ◽  
Vol 48 ◽  
pp. 717-732 ◽  
Author(s):  
J.L. Pérez de la Cruz ◽  
L. Mandow ◽  
E. Machuca
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Simple Graph ◽  
Graph Search ◽  
Problem Size ◽  
Search Problems ◽  
Heuristic Information ◽  
Blind Search

This article considers the performance of the MOA* multiobjective search algorithm with heuristic information. It is shown that in certain cases blind search can be more efficient than perfectly informed search, in terms of both node and label expansions. A class of simple graph search problems is defined for which the number of nodes grows linearly with problem size and the number of nondominated labels grows quadratically. It is proved that for these problems the number of node expansions performed by blind MOA* grows linearly with problem size, while the number of such expansions performed with a perfectly informed heuristic grows quadratically. It is also proved that the number of label expansions grows quadratically in the blind case and cubically in the informed case.

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