263,248 research outputs found
Diamantine allerbester Schuhputz; Diamantine mit Sparsieb bevorzugte Qualität; Rud. Starke in Melle i/H.
No full textDIAMANTINE ALLERBESTER SCHUHPUTZ; DIAMANTINE MIT SPARSIEB BEVORZUGTE QUALITÄT; RUD. STARKE IN MELLE I/H.
Diamantine allerbester Schuhputz; Diamantine mit Sparsieb bevorzugte Qualität; Rud. Starke in Melle i/H. ( -
Diamantine allerbester Schuhputz; Diamantine mit Sparsieb bevorzugte Qualität; Rud. Starke in Melle i/H.
No full textDIAMANTINE ALLERBESTER SCHUHPUTZ; DIAMANTINE MIT SPARSIEB BEVORZUGTE QUALITÄT; RUD. STARKE IN MELLE I/H.
Diamantine allerbester Schuhputz; Diamantine mit Sparsieb bevorzugte Qualität; Rud. Starke in Melle i/H. ( -
The <i>SU</i>(3)<i><sub>C</sub></i> × <i>SU</i>(3)<i><sub>L</sub></i> × <i>U</i>(1)<i><sub>X</sub></i> (331) Model: Addressing the Fermion Families Problem within Horizontal Anomalies Cancellation
No full textOne of the most important and unanswered problems in particle physics is the origin of the three generations of quarks and leptons. The Standard Model does not provide any hint regarding its sequential charge assignments, which remain a fundamental mystery of Nature. One possible solution of the puzzle is to look for charge assignments, in a given gauge theory, that are inter-generational, by employing the cancellation of the gravitational and gauge anomalies horizontally. The 331 model, based on an SU(3)C×SU(3)L×U(1)X does this in an economical way and defines a possible extension of the Standard Model, where the number of families has necessarily to be three. We review the model in Pisano, Pleitez, and Frampton’s formulation, which predicts the existence of bileptons. Another characteristics of the model is to unify the SU(3)C×SU(2)L×U(1)X into the 331 symmetry at a scale that is in the TeV range. Expressions of the scalar mass eigenstates and of the renormalization group equations of the model are also presented
Diamantine allerbester Schuhputz, Diamantine mit Sparsieb bevorzugte Qualität, Rud. Starcke in Melle i./H.
No full textDIAMANTINE ALLERBESTER SCHUHPUTZ, DIAMANTINE MIT SPARSIEB BEVORZUGTE QUALITÄT, RUD. STARCKE IN MELLE I./H.
Diamantine allerbester Schuhputz, Diamantine mit Sparsieb bevorzugte Qualität, Rud. Starcke in Melle i./H. ( -
Diamantine allerbester Schuhputz, Diamantine mit Sparsieb bevorzugte Qualität, Rud. Starcke in Melle i./H.
No full textDIAMANTINE ALLERBESTER SCHUHPUTZ, DIAMANTINE MIT SPARSIEB BEVORZUGTE QUALITÄT, RUD. STARCKE IN MELLE I./H.
Diamantine allerbester Schuhputz, Diamantine mit Sparsieb bevorzugte Qualität, Rud. Starcke in Melle i./H. ( -
AFQN: approximate Qn estimation in data streams
No full textWe present afqn (Approximate Fast Qn), a novel algorithm for approximate computation of the Qn scale estimator in a streaming setting, in the sliding window model. It is well-known that computing the Qn estimator exactly may be too costly for some applications, and the problem is a fortiori exacerbated in the streaming setting, in which the time available to process incoming data stream items is short. In this paper we show how to efficiently and accurately approximate the Qn estimator. As an application, we show the use of afqn for fast detection of outliers in data streams. In particular, the outliers are detected in the sliding window model, with a simple check based on the Qn scale estimator. Extensive experimental results on synthetic and real datasets confirm the validity of our approach by showing up to three times faster updates per second. Our contributions are the following ones: (i) to the best of our knowledge, we present the first approximation algorithm for online computation of the Qn scale estimator in a streaming setting and in the sliding window model; (ii) we show how to take advantage of our UDDSketch algorithm for quantile estimation in order to quickly compute the Qn scale estimator; (iii) as an example of a possible application of the Qn scale estimator, we discuss how to detect outliers in an input data stream
Fast online computation of the Qn estimator with applications to the detection of outliers in data streams
No full textWe present FQN (Fast Qn), a novel algorithm for online computation of the Qn scale estimator. The algorithm works in the sliding window model, cleverly computing the Qn scale estimator in the current window. We thoroughly compare our algorithm for online Qn with the state of the art competing algorithm by Nunkesser et al., and show that FQN (i) is faster, requiring only O(s) time in the worst case where s is the length of the window (ii) its computational complexity does not depend on the input distribution and (iii) it requires less space. To the best of our knowledge, our algorithm is the first that allows online computation of the Qn scale estimator in worst case time linear in the size of the window. As an example of a possible application, besides its use as a robust measure of statistical dispersion, we show how to use the Qn estimator for fast detection of outliers in data streams. Extensive experimental results on both synthetic and real datasets confirm the validity of our approach
Exponential steepest ascent from valued constraint graphs of pathwidth four
No full textMelle van Marle from the University of Utrecht, Holland, tells us about the complexity of maximising fitness via local search on valued constraint satisfaction problems (VCSPs). We consider two kinds of local ascents: (1) steepest ascents, where each step changes the domain that produces a maximal increase in fitness; and(2) -ordered ascents, where -- of the domains with available fitness increasing changes -- each step changes the -minimal domain. We provide a general padding argument to simulate any ordered ascent by a steepest ascent. We construct a VCSP that is a path of binary constraints between alternating 2-state and 3-state domains with exponentially long ordered ascents. We apply our padding argument to this VCSP to obtain a Boolean VCSP that has a constraint (hyper)graph of arity 5 and pathwidth 4 with exponential steepest ascents. This is an improvement on the previous best known construction for long steepest ascents, which had arity 8 and pathwidth 77779.mp4
7779.mp
Parallel Mining of Correlated Heavy Hitters on Distributed and Shared-Memory Architectures
No full textWe present parallel algorithms for mining Correlated Heavy Hitters from a two-dimensional data stream. In particular, we design and implement a message-passing, a shared-memory and a hybrid algorithm. To the best of our knowledge, these are the first parallel algorithms solving the problem. We show, through experimental results, that our algorithms provide very good scalability, whilst retaining the accuracy of their sequential counterpart
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