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TABLE I EQUALIZATION FORWARD/BACKWARD ALGORITHM

in Turbo Equalization
by Ralf Koetter, Andrew C. Singer, Michael Tüchler 2004
Cited by 12

TABLE II DECODING FORWARD/BACKWARD ALGORITHM

in Turbo Equalization
by Ralf Koetter, Andrew C. Singer, Michael Tüchler 2004
Cited by 12

Table 7: Forward-backward reestimation

in Learning a Syntagmatic and Paradigmatic Structure From Language Data With a Bi-Multigram Model
by Sabine Deligne, Yoshinori Sagisaka

Table 7: Forward-backward reestimation

in data with a bi-multigram
by Sabine Deligne, Yoshinori Sagisaka

Table 2: Results of the forward-backward pruning method.

in High Quality Word Graphs Using Forward-Backward Pruning
by Achim Sixtus, Stefan Ortmanns 1999
"... In PAGE 3: ...he word graph was 4.44 % at a WGD of 105.08. Then we used for- ward pruning on the one hand and forward-backward pruning on the other hand to reduce the size of the word graph with different values for fLat and fLat fb. The results are shown in Table 1 for the forward pruning and Table2 for the forward-backward prun- ing (FLat and FLat fb denote the logarithm of fLat and fLat fb). Comparing the results of the two pruning methods we see, that forward-backward pruning leads to smaller word graphs.... ..."
Cited by 10

Table 2. Results: Viterbi vs. Forward-Backward

in A Comparison of Different Approaches to Automatic Speech Segmentation
by Kris Demuynck, Tom Laureys 2002
Cited by 5

Table 1: Schematic illustration of forward and backward discounting and augmentation. Note. T = target cause that is discounted or augmented, A = alternative cause that produces discounting or augmentation, + = focal outcome (e.g., winning a game), - = opposite outcome (e.g., losing a game).

in How learning about an absent cause: Discounting and augmentation of positively and independently related causes
by Frank Van Overwalle, Bert Timmermans 2001
"... In PAGE 2: ... When the alternative cause is already known and exerts its influence on a novel focal cause, this is called forward competition. For instance, when we know that someone is a hell of a good tennis player (A+, see Table1 ) and when that player wins a doubles game with a novel partner (AT+), we tend to discount the contribution of the novel partner in the win. Conversely, when we know that someone is a poor player (A-), we tend to augment the contribution of the novel partner in the win (AT+).... In PAGE 2: ... Conversely, when a novel alternative cause exerts its influence afterwards, that is, on a known focal cause, this is called backward competition [4]. For instance, when we only recently learn that one of two partners of a well-known successful doubles tennis team (AT+, see Table1 ) is now winning all his or her single games (A+), we tend to discount our initial high estimation of the other partner. Conversely, when one partner of a successful doubles team (AT+) is losing all his or her single games (A-), we are now likely to augment our initially evaluation of the... ..."
Cited by 1

Table 1: Well-known Ciphers

in unknown title
by unknown authors
"... In PAGE 7: ...llustration 17: OpenPGP Features................................................................................85 Index of Tables Table1 : Well-known Ciphers.... ..."

Table 1: Recent well-known pathogens.

in Surviving internet catastrophes
by Flavio Junqueira, Ranjita Bhagwan, Ro Hevia, Keith Marzullo, Geoffrey M. Voelker 2005
"... In PAGE 4: ... Consequently, H1 is present in both cores. To make our argument more concrete, consider the worms in Table1 , which are well-known worms unleashed in the past few years. For each worm, given two hosts with one not run- ning Windows or not running a specific server such as a Web server or a database, at least one survives the attack.... In PAGE 10: ... We do so based on information about past worms to support our choices and assumptions. Worms such as the ones in Table1 used services that have vulnerabilities as vectors for propagation. Code Red, for ex- ample, used a vulnerability in the IIS Web server to infect hosts.... In PAGE 14: ... Note that this scenario is far more catastrophic than what we have experienced with worms to date. The worms listed in Table1 , for example, exploit only particular services on Windows. The simulation proceeded as follows.... ..."
Cited by 17

Table 1: Recent well-known pathogens.

in Surviving Internet Catastrophes
by Flavio Junqueira, Ranjita Bhagwan, Alejandro Hevia, Ro Hevia, Keith Marzullo, Geoffrey M. Voelker 2005
"... In PAGE 4: ... Consequently, H1 is present in both cores. To make our argument more concrete, consider the worms summarized in Table1 , which are well-known worms unleashed in the past three years. For each worm, given two hosts with one not running Windows or not running a specific server such as a Web server or a database, at least one survives the attack.... In PAGE 10: ... We do so based on information about past worms to support our choices and assumptions. Worms such as the ones in Table1 used services that 0 2 4 6 8 10 2 3 4 5 6 7 8 9 10 Variance Load limit RandomUniform Weighted DWeighted Figure 7: Average load variance. have vulnerabilities as vectors for propagation.... In PAGE 14: ... Note that this scenario is far more catastrophic than what we have experienced with worms to date. The worms listed in Table1 , for example, exploit only particular services on Windows. The simulation proceeded as follows.... ..."
Cited by 17
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