### Table 1: Identifying fundamental needs for the desk lamp

1999

"... In PAGE 4: ....1.2 Design of the desk lamp requires an understanding of the mappings between design parameters and the functional requirements of the lamp. Table1 gives a brief version of the fundamental needs. Figure 1: General form for the desk lamp 4.... ..."

Cited by 1

### Table 4.7 shows several of the fundamental railway functions that have been identified and that EIRENE or GSM-R should support.

"... In PAGE 48: ... Multiple access TDMA-CDMA duplex TDD Chip Rate 4.096 Mchips Carrier spacing 5 MHz Frame structure 16 time slots per frame spreading Orthogonal,16 chips/symbol Frame duration 10 ms Variable Rate several Time slots several codes Channel coding Convolutional codes turbo codes Modulation QPSK et 16 QAM Table4... ..."

### TABLE II BOUNDS ON THE NUMBER OF CODEWORDS IN AN OPTIMAL IDENTIFYING CODE WITH 116 61 50 FOR AN 110-DIMENSIONAL BINARY CUBE

1998

Cited by 26

### Table 1: PN (the probability of necessary causation) as a function of assumptions and available data. ERR stands of the excess-risk-ratio 1 ? P (yjx0)=P (yjx) and CERR is given in Eq. (49). The non-entries (|) represent vacuous bounds, that is, 0 P N 1. Assumptions Data Available

2000

"... In PAGE 24: ...6 Summary of results We now summarize the results from Section 4 that should be of value to practicing epidemiologists and policy makers. These results are shown in Table1 , which lists the best estimand of PN under various assumptions and various types of data|the stronger the assumptions, the more informative the estimates. We see that the excess-risk-ratio (ERR), which epidemiologists commonly identify with the probability of causation, is a valid measure of PN only when two assumptions can be ascertained: exogeneity (i.... In PAGE 25: ...concerned with associations between such factors and susceptibility to expo- sure, as is often assumed in the literature [Khoury , 1989, Glymour, 1998]. The last two rows in Table1 correspond to no assumptions about exo- geneity, and they yield vacuous bounds for PN when data come from either experimental or observational study. In contrast, informative bounds (25) or point estimates (49) are obtained when data from experimental and ob- servational studies are combined.... ..."

Cited by 5

### Table 3. Fundamental IS program constraints

2007

"... In PAGE 7: ... We have elected the Prolog programming language to capture these specifications. Table3 shows a code extract describing some constraints of our IS program. Table 3.... ..."

### Table 1. Time-space tradeoffs for boolean BPs computing certain fundamental functions

2004

"... In PAGE 4: ... Specific results for certain target functions. We have applied the general method described in the foregoing paragraph to several well-studied target functions, and our results are summarized in Table1 be- low. In each case, we were able to find families of codes that encode the desired function on one hand and have a sufficiently large minimum distance on the other hand (see Section 4 for more details).... In PAGE 4: ... In each case, we were able to find families of codes that encode the desired function on one hand and have a sufficiently large minimum distance on the other hand (see Section 4 for more details). We note that the bounds1 in Table1 are based on Theorem 4 and Theorem 5, which are slightly stronger than (1). We also point out that the branching programs we consider are multi-output.... In PAGE 4: ...Table 1. Time-space tradeoffs for boolean BPs computing certain fundamental functions In all cases, except for the third row in Table1 , the underlying computation model is a deterministic boolean branching program that is not restricted in any way (not necessarily oblivious, or leveled, or read/write limited, and so forth). This makes it somewhat difficult to compare our results to the best previously known bounds, since these bounds usually apply to more restricted computation models.... In PAGE 4: ... One of these applies only to q-way BPs, where q grows as nO(1) and must be at least 2120. Since we are concerned with boolean (2-way) BPs, bounds of this kind are not directly comparable to those in Table1 . For boolean BPs, Sauerhoff and Woelfel [34] prove the following.... In PAGE 4: ... There exists a positive constant c such that for all r 6 c log n, the space of all the BPs in this set is bounded by n=r234r . There are several important differences between Theorem 1 and our bounds for IMUL in Table1 . First, Theorem 1 applies to nondeterministic BPs whereas our results do not; in this sense Theorem 1 is more 1All the logarithms in Table1, and throughout this paper, are to base 2.... In PAGE 4: ... There are several important differences between Theorem 1 and our bounds for IMUL in Table 1. First, Theorem 1 applies to nondeterministic BPs whereas our results do not; in this sense Theorem 1 is more 1All the logarithms in Table1 , and throughout this paper, are to base 2.... In PAGE 5: ... This difference does not seem to be significant, since it is known [12,38] that the middle bit is the hardest one to compute. A third difference is that the number of reads r in Theorem 1 is restricted to O(log n), whereas our bounds in the second and third rows of Table1 hold without this restriction. Note that when the number of reads is limited to r, the computation time T is also limited, since T 6 rn.... In PAGE 5: ...ondeterministic BPs and read-r vs. unrestricted BPs. However, ignoring these differences, we can try to make a comparison as follows. If r is constant and m = (n), then Theorem 2 reduces to S = (n), which is exactly the same result we get from the first row of Table1 for the case T = O(n). On the other hand, if r is allowed to grow, say r = 1=4 log n, then the bound on S in Table 1 becomes stronger than Theorem 2.... In PAGE 5: ... If r is constant and m = (n), then Theorem 2 reduces to S = (n), which is exactly the same result we get from the first row of Table 1 for the case T = O(n). On the other hand, if r is allowed to grow, say r = 1=4 log n, then the bound on S in Table1 becomes stronger than Theorem 2. With regard to DFT, the best known (to us) lower bound on the time-space tradeoff of boolean BPs, due to [1, 40], establishes TS = (n2).... In PAGE 5: ... Here, if time is superlinear in n, then the resulting bound on the space is sublinear. In contrast, the bound in the fourth row of Table1 makes it possible to provide superlinear bounds on space when time is also superlinear. For example, for2 T = !(n log1 T n) our results imply that S = !(n log1 S n), where T, S are arbitrary positive constants.... In PAGE 5: ... We then explain how these results lead to lower bounds on the time-space tradeoff of branching programs. In Section 4, we deal with specific target functions and prove the bounds compiled in Table1 . In particular, in Section 4.... In PAGE 5: ... From this, we infer the lower bound for the DFT operation. Finally, in Section 5, we describe several typical functional forms for the gen- eral lower bounds in Table1 . We also compare these results with the complexity of known algorithms [19].... In PAGE 11: ... Operation Time Space Model n-bit FMUL, CONV, MVMUL ! (n) (n) General BP2 n-bit FMUL, CONV, MVMUL ! nlog n log1+ T logn , 8 T gt; 0 ! n1 S , 8 S gt; 0 General BP2 n-bit IMUL ! n log1+ T logn , 8 T gt; 0 ! n1 S , 8 S gt; 0 General BP2 n-bit IMUL t = ! log n log1+ T log n , 8 T gt; 0 ! n1 S , 8 S gt; 0 read-r/write-w BP2 t = max fr, wg n-point DFT ! n log1 T n , 8 T gt; 0 ! n log1 S n , 8 S gt; 0 General BP2 n-point DFT ! nlog2 n log2 logn ! n1 S , 8 S gt; 0 General BP2 Table 2. Typical functional forms for the lower bounds in Table 1 It can be easily shown (using MATHEMATICATM for instance) that if we substitute the lower bounds in Table 2 for T and S in the corresponding time-space tradeoff expressions given in Table1 , they vanish asymptotically as n ! 1, thereby verifying the results in Table 2. Upper bounds from known efficient algorithms.... ..."

### Table 15. Code page identifier

"... In PAGE 40: ...Table15 . Code page identifier (continued) BG T1B00278 CP T1GI0388 BH T1B00280 CQ T1GI0389 BI T1B00281 CR T1GI0390 BJ T1B00282 CS T1GI0391 BK T1B00284 CT T1G10392 BL T1B00285 CU T1GI0393 BM T1B00297 CV T1GI0394 BN T1B00361 CW T1GI0395 BO T1B00382 CX T1GPI363 BP T1B00383 CY T1L000GN BQ T1B00384 CZ T1L000RN BR T1B00385 D0 T1L000SN BS T1B00386 D1 T1L000XN BT T1000387 D2 T1L000YN BU T1B00389 D3 T1L00A11 BV T1B00390 D4 T1L00APL BW T1B00391 D5 T1L00FMT BX T1B00392 D6 T1L00KN1 BY T1B00393 D7 T1L00QNC BZ T1B00394 D8 T1L02773 C0 T1B00395 D9 T1L02774 C1 T1B00500 DA T1L038BA C2 T1B00871 DB T1L038TE C3 T1B00BGS DC T1L0AD10 C4 T1D0BASE DD T1L0AG10 C5 T1D0GP12 DE T1L0AG12 C6 T1DABASE DF T1LOAG15 C7 T1DBBASE DG T1L0AI10 C8 T1DCDCFS DH T1L0AT10 C9 T1DDBASE DI T1L0DUMP CA T1DEBASE DJ T1L0FOLD CB T1DFBASE DK T1L0OCR1 CC T1D1BASE DL T1L0OCR2 CD T1DNBASE DM T1L0OCR3 CE T1DSBASE DN T1L0OCRB CF T1DUBASE DO T1L0PCAN CG T1GE0200 DP T1L0PCHN CH T1GE0300 DQ T1M00829 Point size The seventh and eighth characters (SM) of the coded font name indicate point size.... ..."

### Table 2 The bitrates of single-rate mesh connectivity coding algorithms Category Algorithm Bitrate (bpv) Comment

2005

"... In PAGE 16: ....1.7. Summary Table2 summarizes the bitrates of various connectivity coding methods reviewed above. The bitrates marked by C212*C213 are the theoretical upper bounds obtained by the worst-case analysis, while the others are experimental bitrates.... ..."

### Table 5-2: Message Codes ============================================================================== Code Description Type

1986

### Table II. I/O Bounds for the Four Fundamental Operations. The PDM Parameters Are Defined in Section 2.1

2001

Cited by 229