2,820 research outputs found
Study of pi+pi-, K+K-, ppbar and pi+pi+pi-pi- production in central exclusive processes with the STAR detector at RHIC
No full textTato práce se zabývá prvním měřením centrální exkluzivní produkce v proton-protonových srážkách při energii sqrt s = 510 GeV naměřených na experimentu STAR. Při této energie dominuje dvojitá výměna pomeronů. Z toho důvodu jsou tyto srážky velmi slibné pro hledání vázaných stavů gluonů, glueballů. Experimentální potvrzení jejich existence by silně podpořilo platnost kvantové chromodynamiky. Difrakčně rozptýlené protony pohybující se neporušeny uvnitř svazkové trubice byly změřeny pomocí detektorů Roman Pot, což umožnilo plnou kontrolu nad kinematikou interakce a tedy ověření exkluzivity. První výsledky ve formě distribuce invariantní hmotnosti centrálně produkovaných pi+pi-, K+K− a ppbar párů jsou prezentovány v této práci.This thesis is dedicated to the first measurement of the central exclusive production process in proton-proton collisions at RHIC with the STAR detector at sqrt s = 510 GeV. At this energy, this process is dominated by a Double Pomeron Exchange mechanism. Therefore, it is very promising for the search of gluon bound states, glueballs. The experimental confirmation of their existence would be yet another strong support for the validity of the quantum chromodynamics theory. The diffractively scattered protons, moving intact inside the RHIC beam pipe after the collision, were measured in the Roman Pots system allowing full control of the interaction’s kinematics, and thus verification of the exclusivity. The preliminary results on the invariant mass distributions of centrally exclusively produced pi+pi−, K+K− and ppbar pairs measured within the STAR acceptance are presented in this thesis
Computing Research Repository / SWOOP: Top-k Similarity Joins over Set Streams
No full textWe provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams of sets. A prototypical example setting is that of tweets. A tweet is a set of words, and Twitter emits about half a billion tweets per day. Our solution makes it possible to efficiently maintain the top-k most similar tweets from a pair of rapid Twitter streams, e.g., to discover similar trends in two cities if the streams concern cities. Using a sliding window model, the top-k result changes as new sets in the stream enter the window or existing ones leave the window. Maintaining the top-k result under rapid streams is challenging. First, when a set arrives, it may form a new pair for the top-k result with any set already in the window. Second, when a set leaves the window, all its pairings in the top-k are invalidated and must be replaced. It is not enough to maintain the k most similar pairs, as less similar pairs may eventually be promoted to the top-k result. A straightforward solution that pairs every new set with all sets in the window and keeps all pairs for maintaining the top-k result is memory intensive and too slow. We propose SWOOP, a highly scalable stream join algorithm that solves these issues. Novel indexing techniques and sophisticated filters efficiently prune useless pairs as new sets enter the window. SWOOP incrementally maintains a stock of similar pairs to update the top-k result at any time, and the stock is shown to be minimal. Our experiments confirm that SWOOP can deal with stream rates that are orders of magnitude faster than the rates of existing approaches
Computing Research Repository / SWOOP: Top-k Similarity Joins over Set Streams
No full textWe provide efficient support for applications that aim to continuously find pairs of similar sets in rapid streams of sets. A prototypical example setting is that of tweets. A tweet is a set of words, and Twitter emits about half a billion tweets per day. Our solution makes it possible to efficiently maintain the top-k most similar tweets from a pair of rapid Twitter streams, e.g., to discover similar trends in two cities if the streams concern cities. Using a sliding window model, the top-k result changes as new sets in the stream enter the window or existing ones leave the window. Maintaining the top-k result under rapid streams is challenging. First, when a set arrives, it may form a new pair for the top-k result with any set already in the window. Second, when a set leaves the window, all its pairings in the top-k are invalidated and must be replaced. It is not enough to maintain the k most similar pairs, as less similar pairs may eventually be promoted to the top-k result. A straightforward solution that pairs every new set with all sets in the window and keeps all pairs for maintaining the top-k result is memory intensive and too slow. We propose SWOOP, a highly scalable stream join algorithm that solves these issues. Novel indexing techniques and sophisticated filters efficiently prune useless pairs as new sets enter the window. SWOOP incrementally maintains a stock of similar pairs to update the top-k result at any time, and the stock is shown to be minimal. Our experiments confirm that SWOOP can deal with stream rates that are orders of magnitude faster than the rates of existing approaches
V. K. Vransky, Die Zellelektrophorese (Fortschritte der experimentellen und theoretischen Biophysik, Bd. 18). 97 S., 20 Abb., 3 Tab. Leipzig 1974: VEB Georg Thieme. M 25,00
No full textTASM: Top-k Approximate Subtree Matching
No full textWe consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree, e.g., a DBLP article with 15 nodes, in a large document tree, e.g., DBLP with 26M nodes, using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space complexity, and, thus, do not scale. Our solution is TASMpostorder, a memory-efficient and scalable TASM algorithm. We prove an upper-bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML documents confirms our analytic results
SIGMOD '19 Proceedings of the 2019 International Conference on Management of Data / A Scalable Index for Top-k Subtree Similarity Queries
No full textGiven a query tree Q, the top-k subtree similarity query retrieves the k subtrees in a large document tree T that are closest to Q in terms of tree edit distance. The classical solution scans the entire document, which is slow. The state-of-the-art approach precomputes an index to reduce the query time. However, the index is large (quadratic in the document size), building the index is expensive, updates are not supported, and data-specific tuning is required.
We present a scalable solution for the top-k subtree similarity problem that does not assume specific data types, nor does it require any tuning. The key idea is to process promising subtrees first. A subtree is promising if it shares many labels with the query. We develop a new technique based on inverted lists that efficiently retrieves subtrees in the required order and supports incremental updates of the document. To achieve linear space, we avoid full list materialization but build relevant parts of a list on the fly.
In an extensive empirical evaluation on synthetic and real-world data, our technique consistently outperforms the state-of-the-art index w.r.t. memory usage, indexing time, and the number of candidates that must be verified. In terms of query time, we clearly outperform the state of the art and achieve runtime improvements of up to four orders of magnitude
Efficient top-k approximate subtree matching in small memory
No full textWe consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree within a large document tree using the canonical tree edit distance as a similarity measure between subtrees. Evaluating the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space complexity, and, thus, do not scale. Our solution is TASM-postorder, a memory-efficient and scalable TASM algorithm. We prove an upper bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML documents confirms our analytic results
Calibration of Straw detector for studies of DVCS process at COMPASS experiment
No full textCOMPASS je terčíkový experiment, který používá urychlené protony z SPS urychlovače v CERNu k produkci mionového, pionového a elektronového svazku. Fyzikální program COMPASSu je zameren na výzkum struktury hadronu a hadronovou spektroskopii. Pro studium obecných partonových distribucí prostrednictvím hluboce virtuálního Comptonova rozptylu (DVCS) je zapotrebí ruzných detektoru, jež se rozprostírají na délce padesáti metru. Jedním z nich je dráhový detektor Straw 3 sestávající z mnoha driftových komor. Detektor Straw je hlavním predmetem této práce. Kalibrace jeho RT závislosti pro data z roku 2016 byla overena. Dále byla na základe dat z roku 2016 provedena analýza kinematických velicin, které hrají významnou roli pri studiu DVCS událostí a následné analýze obecných partonových distribucí.COMPASS is a fixed target experiment which uses protons from the CERN SPS accelerator for production of muon, pion or either electron beam. Its physical program is focused on hadron structure and hadron spectroscopy. To study the Generalized Parton Distributions (GPDs) via measurements of Deeply Virtual Compton Scattering (DVCS), various detectors at length of more than fifty meters are needed. One of them is tracking detector Straw 3 consisting of straw drift chambers. This thesis is mainly focused on the Straw detector. The verification of its calibration of RT relation for the 2016 data was done. Additionally, the thesis deals with analysis of kinematic variables, based on 2016 data, which are important for studies of DVCS events an subsequent GPD analysis
KOIOS: Top-k Semantic Overlap Set Search
No full textWe study the top-k set similarity search problem using semantic overlap.
While vanilla overlap requires exact matches between set elements, semantic
overlap allows elements that are syntactically different but semantically
related to increase the overlap. The semantic overlap is the maximum matching
score of a bipartite graph, where an edge weight between two set elements is
defined by a user-defined similarity function, e.g., cosine similarity between
embeddings. Common techniques like token indexes fail for semantic search since
similar elements may be unrelated at the character level. Further, verifying
candidates is expensive (cubic versus linear for syntactic overlap), calling
for highly selective filters. We propose KOIOS, the first exact and efficient
algorithm for semantic overlap search. KOIOS leverages sophisticated filters to
minimize the number of required graph-matching calculations. Our experiments
show that for medium to large sets less than 5% of the candidate sets need
verification, and more than half of those sets are further pruned without
requiring the expensive graph matching. We show the efficiency of our algorithm
on four real datasets and demonstrate the improved result quality of semantic
over vanilla set similarity search
L. Reimer und G. Pfefferkorn, Rasterelektronenmikroskopie (2. neubearb. u. erw. Auflage). 282 S., 146 Abb., 9 Tab. Berlin-Heidelberg-New York 1977. Springer-Verlag
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