### Table 2: Information of the forget node of X3. vertices labels

### Table 2: Characteristics of Erasure Correcting Codes Discussed.

1994

"... In PAGE 32: ... This paper has explored the choice and implementation of redundancy codes for the practi- cal constraints of disk arrays. In Table2 , we summarize the characteristics of the main codes dis- cussed in this paper. Our codes all minimize the number of check disks that must be updated whenever an information disk is updated.... ..."

Cited by 78

### Table 3. Implicit Semantic Constraint Enforcement

"... In PAGE 18: ... 18 combinations fully preserve constraint checking on deletions we find there are a total of 8 cases (see Table3 below). Table 3.... In PAGE 18: ... Table3 below (which extends Table 2) provides an exhaustive analysis of all ternary ... In PAGE 21: ... 21 theoretical underpinnings for this position and provides a decompositional framework ( Table3 ) to assist practitioners who may have to deal with these issues. 4 REFERENCES Armstrong, W.... ..."

### Table 1: Law Enforcement Information Systems Classification

"... In PAGE 5: ... Of course, this greatly limits the available amount of information that can be accessed and analyzed. Table1 summarizes the deployment of these technologies in LEIS. Of the systems reviewed, different combinations of these technologies were found.... In PAGE 5: ...Coverage Geographic Use Areas ViCAP ES/AI homicide, abduction, missing person, sexual assault wide US provided to state and local agencies COPLINK ES/AI, DM person, incident, vehicle, location narrow Arizona major metropolitan areas SHERPA ES/AI, DM drug narrow State of Wisconsin MATRIX DM person, vehicle narrow 5 states ViCLAS DM homicide, missing person, sexual assault wide Canada, Belgium, Austria, Australia, Holland, United Kingdom, 2 US states As shown in Table1 , there is a mix of both wide and narrow geographic coverage. Narrow coverage indicates only state or local deployment of the LEIS.... ..."

### Table 4: The recovery accuracy of different codes for the erasure pattern in Example 1

"... In PAGE 4: ... Table 3 shows how each of our generator matrices is generated. Table4 compares the re- covery accuracy of our codes for the example erasure in section IV with that of the existing codes. Table 4 shows our codes are able to reconstruct the origi- nal information a6... In PAGE 4: ... Table 4 compares the re- covery accuracy of our codes for the example erasure in section IV with that of the existing codes. Table4 shows our codes are able to reconstruct the origi- nal information a6... ..."

### Table 2. Burst erasure recovery accuracy of di erent codes

2004

"... In PAGE 6: ... The machine precision is 16 digits. Table2 shows our codes are able to reconstruct the original information x with much higher accuracy than the existing codes. The reconstructed x from all existing codes lost all of their 16 e ective digits.... ..."

Cited by 1

### Table 2: An algorithm for iteratively quantizing a source with erasures.

"... In PAGE 7: ...4 Iterative Decoding/Quantization and Duality In the following we rst review the intuition behind iterative erasure decoding algorithms and describe the particular decoding algorithm we consider in Table 1. Next we outline the intuition behind a similar approach for iterative quantization and precisely describe our quantization algorithm in Table2 . Finally, we show that these algorithms are duals.... In PAGE 9: ... Essentially, the requirement of consistent tie-breaking can be interpreted as a constraint on the message-passing schedule: tie-breaking information for a given tie should be propagated through the graph before other ties are broken. In order to provide a precise algorithm for the purpose of proving theorems, we consider the ERASURE-QUANTIZE in Table2 based on applying the rules in (6) with a sequential schedule and all tie-breaking collected into step 8. Table 2: An algorithm for iteratively quantizing a source with erasures.... ..."

### Table 2: An algorithm for iteratively quantizing a source with erasures.

"... In PAGE 5: ...4 Iterative Decoding/Quantization and Duality In the following we rst review the intuition behind iterative erasure decoding algorithms and describe the particular decoding algorithm we consider in Table 1. Next we outline the intuition behind a similar approach for iterative quantization and precisely describe our quantization algorithm in Table2 . Finally, we show that these algorithms are duals.... In PAGE 7: ... Essentially, the requirement of consistent tie-breaking can be interpreted as a constraint on the message-passing schedule: tie-breaking information for a given tie should be propagated through the graph before other ties are broken. In order to provide a precise algorithm for the purpose of proving theorems, we consider the ERASURE-QUANTIZE in Table2 based on applying the rules in (6) with a sequential schedule and all tie-breaking collected into step 8. Table 2: An algorithm for iteratively quantizing a source with erasures.... ..."

### Table 1. The basic FLORA algorithm: Functions learn from(X) and forget(X)

1996

"... In PAGE 5: ... The extent of these transitions for the case of n arrivals and m deletions is quanti ed by Kubat (1991).The complete basic FLORA algorithm for maintaining the hypotheses when a positive example is processed is sketched in Table1 (if the example is negative, the algorithms work analogously | just substitute NDES for ADES). Note that there are two procedures, one for the case when a new example is added and one for the case when the oldest example is deleted from the window.... In PAGE 13: ... Therefore, all examples in the current window must be regeneralized. The counters associated with the items of the retrieved hypothesis are set to zero, and then the regular FLORA learning algorithm ( Table1 ) is invoked for each example in the window. All description items that have counters equal to zero after re-generalization are removed as irrelevant.... ..."

Cited by 93