theoretical models and algorithms in optical diffusion tomography
báo cáo khoa học: "Infrared imaging and spectral-domain optical coherence tomography findings correlate with microperimetry in acute macular neuroretinopathy: a case report" potx
... associations include OCP use and the intravenous administration of epinephrine (ranging from 0.5mL in a 1:1000 solution to 10mg) and ephedrine (25mg) [2] Our patient was not taking OCP and reports ... Infrared imaging and spectral-domain optical coherence tomography findings correlate with microperimetry in acute macular neuroretinopathy: a case report Sandeep Grover*, Vikram ... pathology is located in the outer retina [3] We present findings of infrared (IR) imaging and spectral-domain OCT (SD-OCT; Spectralis, Heidelberg, Germany) and correlate these with retinal function...
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Algorithms for quality of service provisioning and enhancement in optical burst switched networks
... variable and bursty Internet traffic Optical Packet Switching (OPS) [21,25,37,57] is an optical networking paradigm that performs packet switching in the optical domain In this approach, optical ... Network Min-SV Minimum Starting Void MPLS Multi-Protocol Label Switching OADM Optical Add/Drop Multiplexer OBS Optical Burst Switching ix O-E-O opto-electronic-opto OLT Optical Line Terminal OPS Optical ... entirely in optical domain between a pair of source and destination Based on this optical routing capability, the next-generation optical Internet architecture is envisioned to have two main functional...
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Data Streams Models and Algorithms- P1
... stamps Ti, .Tin maintained in is CF2t The sum of the time stamps Ti, Tin maintained in CFlt is The number of data points is maintained in n E7L=1 e; We note that the above definition of micro-cluster ... characteristics, and they can also be used to develop efficient and effectivemining algorithms Moreover, data streams require online mining, in which we wish to mine the data in a continuous fashion ... STREAMS: MODELS AND ALGORITHMS The Micro-clustering Based Stream Mining Framework In order to apply our technique to a variety of data mining algorithms, we utilize a micro-clustering based stream mining...
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Data Streams Models and Algorithms- P2
... the buffersize at 1600 points, which means upon receiving 1600 points (including both training and test stream points) we'll use a small set of the training data points (In this case kfit =80) ... to incorporate user-interaction rn The integration of data stream management systems and data stream mining approaches: The integration among storage, querying, mining and reasoning of the incoming ... only maintain the summary statistics for the data points in the clusters as opposed to the individual data points themselves In addition to the first and second order moments we also maintain the...
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Data Streams Models and Algorithms- P3
... 64 DATA STREAMS: MODELS AND ALGORITHMS Sliding window: Given the length of the sliding window w and current timepoint t, we are interested in the frequent pattern time in the window W [t - w 1,t] ... for Mining High-speed Data Streams, Proceedings o the Ninth International f Conference on Knowledge Discovery and Data Mining [16] Garofalakis M., Gehrke J., Rastogi R (2002) Querying and mining ... [9] Domingos P and Hulten G (2000) Mining High-speed Data Streams In Proceedings of the Association for Computing Machinery Sixth International Conference on Knowledge Discovery and Data Mining...
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Tài liệu Data Streams Models and Algorithms- P4 doc
... work in [13,14] is focussed on the effects of evolution on data mining models and algorithms While these results show some interesting results in terms of generalizing existing data mining algorithms, ... R Jin and G Agrawal An algorithm for in- core frequent itemset mining on streaming data In ICDM, November 2005 [23] Ruoming Jin and Gagan Agrawal An algorithm for in- core frequent itemset mining ... fiequent pattern mining over the entire data stream, and indeed most of the mining algorithms discussed in this chapter are derived from these work Algorithms in mining frequent items in data streams...
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Tài liệu Data Streams Models and Algorithms- P5 docx
... alternatives in a few aspects, including (1) tilted timeframe vs full nontilted timeframe, (2) using minimal interesting layer vs examiningstreamdata at the raw data layer, and (3) computing the cube ... on-line response, and both space and time are critical in processing, we examine both time and space consumption In our study, besides presenting the total time and memory taken to compute and ... method for computing multi-dimensional, multi-level stream cubes Related Work Our work is related to on-line analytical processing and mining in data cubes, and management and mining o stream data...
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Tài liệu Data Streams Models and Algorithms- P6 pdf
... load shedding decisions on the streams being joined Joins between data streams are typically sliding window joins A sliding window join with window size w introduces an implicit join predicate ... processing, database systems maintain "join statistics" for the relations participating in the join Similarly, in order to efficiently process sliding-windowjoins, we would like to maintain statistics ... problems in the sliding-window model, that not rely on the EH technique These problems include maintaining a uniform random sample(See also [3]), maintaining the min/max of real numbers, estimating...
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Tài liệu Data Streams Models and Algorithms- P7 ppt
... Another kind of inequality often used in stream mining is the Hoeffding inequality In this inequality, we bound the sum of k independent bounded random variables For example, for a set of k independent ... of finding distinct values [31,431, mining inverse distributions [23], or determining the cardinality of set expressions [35] The method in [43] uses a technique similar to that discussed in [311 ... unknown in advance, and the sampling must be performed dynamically as data points arrive Therefore, in order to maintain an unbiased representation of the underlying data, the probability of including...
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Tài liệu Data Streams Models and Algorithms- P8 doc
... sliding-window joins, focusing on developing scheduling strategies aimed at reducing response times across queries More broadly speaking, work on non-blockingjoin algorithms, e g , XJoin and ... sorting-based algorithms over hashing-bashed algorithms, including in particular the ability to handle non-equality joins A more thorough performance comparison between PMJ and XJoin for equijoins ... must be updated accordingly Since relational join is monotonic, insertions into S 1and S2can result only in possible insertions into the view The sequence ofresulting insertions into the view constitutes...
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Tài liệu Data Streams Models and Algorithms- P9 doc
... affecting the maintainability of warehouse views [21] For a streamjoin Sl w .w Sn, processing an incoming tuple from stream Si is analogous to maintaining a join view incrementally by evaluating ... STREAMS: MODELS AND ALGORITHMS [22] Golab, L., Garg, S., and ~ z s uT (2004) On indexing sliding windows over , on-line data streams In Proceedings o the 2004 International Conference f on Extending ... maintaining all the stream values within a time window equal to the size of the largest sliding window, i.e., in our running example The per-item processing cost and the space required is linear...
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Tài liệu Data Streams Models and Algorithms- P10 docx
... approach In Section we elaborate on the efficiency and accuracy of SPIRIT Finally, in Section we conclude Related work Much of the work on stream mining has focused on finding interesting patterns in ... online, by passing over the data only once Recently, 12 and 22 address the problem of finding patterns over concept drifting streams 19 proposed a method to find patterns in a single stream, using ... of retained energy be If E(*) < fEE, then we start estimating wk+l (initialising as in step of TRACKW), initialise E ~ ~ and increase k + k If E ( ~ ) > FEE, + then we discard wk and ~k and decrease...
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Tài liệu Data Streams Models and Algorithms- P11 doc
... Conference on Data Mining (SDM), 2005 [13] M Ester, H Kriegel, J Sander, M Wirnmer, and X Xu Incremental clustering for mining in a data warehousing environment In Proceedings o the International Conference ... directions in distributed stream mining Outlier and Anomaly Detection The goal in outlier or anomaly detection is to find data points that are most different from the remaining points in the data ... STREAMS: MODELS AND ALGORITHMS card (NIC) [37] with the idea of enhancing reliability and reducing the constraints imposed on the host processing environment Initial results in this domain convey...
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Tài liệu Data Streams Models and Algorithms- P12 ppt
... time and the routing decision is made based on information gain and resource cost Considering the problem in a different setting, [2] proposed a model for tracking a moving object with binary ... networks In Proceedings of the 6th ACM International Conference on Mobile Computing and Networking [29] Krishnamachari B., Estrin D and Wicker S (2002) Modelling DataCentric Routing in Wireless ... guarantees In Proceedings of the 19th International Conference on Data Engineering [3 11 Lin S., Kalogeraki V ,Gunopulos D and Lonardi S (2006) Online Infor mation Compression in Sensor Networks In Proceedings...
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Tài liệu Data Streams Models and Algorithms- P13 docx
... p-stable distributions, 190 Sliding Window Joins, 11 Sliding Window Joins and Loadshedding, 144 Sliding Window Model, 149 Sliding Window Model for Frequent Pattern Mining, 63 Spatial Velocity Profiles, ... Iceberg Cubing for Stream Data, 112 Index Maintenance in Data Streams, 244 Indexing Data Streams, 238 Join Estimation with Sketches, 187 Join Queries in Sensor Networks, 340 Joins in Data Streams, ... Frequency Based Model for Stream Joins, 218 Frequent Itemset Mining in Distributed Streams, 296 Frequent Pattern Mining in Distributed Streams, 314 Frequent Pattern Mining in streams, 61 Freauent Temvoral...
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Báo cáo khoa học: "Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank" ppt
... illuminate the issues at hand Figures (a) and (b) show the traversal curves corresponding to the times in figure The interesting cause of the varying exponents comes from the “constant” terms in ... edges and traversals of using top-down and bottom-up strategies There are some extremely minimal savings in traversals due to top-down filtering effects, but there is a corresponding penalty in edges ... rules into FSAs in three ways: L ISTs, T RIEs, and M INimized FSAs An example of each representation is given in figure For L IST encodings, each local tree type was encoded in its own, linearly...
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Godrich, tamassia, mount data structures and algorithms in c++
... objects and pointers We discuss casting with fundamental types here, and we consider casting with objects in Section 2.2.4 We begin by introducing the traditional way of casting in C++, and later ... for input and output The initial entry point for C++ programs is the function main The statement “int main( )” on line declares main to be a function that takes no arguments and returns an integer ... other using lexicographic (or dictionary) order, and they may be input and output using the >> and
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data structure and algorithms in java - mitchel waite
... structures and the class interface Searching, insertion, and deletion in arrays and ordered arrays are covered Linear searching and binary searching are explained Workshop applets demonstrate these algorithms ... coding, testing, production, and maintenance It's not clear that mixing software engineering on one hand, and data structures and algorithms on the other, actually helps the student understand ... available for input and output They're quite different from the workhorse cout and cin approach in C++ and printf() and scanf() in C All the input/output routines we show here require the line import...
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