### Table 10: Esterase count data of Example 5. x y x y x y x y

"... In PAGE 25: ...Table10 and it is seen, that there are few replications. In the original treatment of the example as a calibration problem, the objective is to take observed new counts and infer the corresponding concentration of esterase, using the data in the table.... ..."

### Table 10: Esterase count data of Example 5. x y x y x y x y

"... In PAGE 25: ... In an assay for the concentration of an enzyme esterase, the observed concentration of esterase was recorded, and then in a binding experiment the number of bindings were counted. The dataset is presented in Table10 and it is seen that there are few replications. In the original treatment of the example as a calibration problem, the objective is to take observed new counts and infer the corresponding concentration of esterase, using the data in the table.... ..."

### Table 10: Esterase count data of Example 5. x y x y x y x y

"... In PAGE 25: ....3, pp. 46-47). In an assay for the concentration of an enzyme esterase, the observed concentration of esterase was recorded, and then in a binding experiment the number of bindings were counted. The dataset is presented in Table10 and it is seen that there are few replications. In the original treatment of the example as a calibration problem, the objective is to take observed new counts and infer the corresponding concentration of esterase, using the data in the table.... ..."

### Table 1: The LCT-QoS Service Model and the Simulated Traffic Patterns X, Y, Z

in Multi-Class Measurement Based Admission Control for a QoS Framework with Dynamic Resource Management

2006

"... In PAGE 11: ...etwork, respectively, a core router. Admission Control is performed at the ingress router. Traffic sources are evenly distributed among source nodes and applications are chosen in compliance with the LCT-QoS service model. Table1 contains the class, delay and loss sensitivity and the traffic patterns, X, Y and Z, we use for the simulations. We choose four different source models for the applications to evaluate the performance of the algorithm under disparate conditions.... ..."

### Table 1 ACTUATOR/SENSOR COMBINATIONS EVALUATED

in On-Orbit Application of H-Infinity to the Middeck Active Controls Experiment: Overview of Results

"... In PAGE 3: ... Several sensor and actuator combinations were of interest for each of the two hardware configurations. These combinations are summarized in Table1 . The first case investigated was single axis control in the X and Z directions using single-input, single-output (SISO) control.... In PAGE 8: ... RESULTS A variety of control configurations were tested. A summary of the actuator and sensor combinations evaluated for both hardware configurations was given in Table1 . SISO X and Z axis control designs were successfully tested on-orbit for both hardware Configurations 1 and 2.... ..."

### Table 1 ACTUATOR/SENSOR COMBINATIONS EVALUATED

in ON-ORBIT APPLICATION OF H-INFINITY TO THE MIDDECK ACTIVE CONTROLS EXPERIMENT: OVERVIEW OF RESULTS

"... In PAGE 3: ... Several sensor and actuator combinations were of interest for each of the two hardware configurations. These combinations are summarized in Table1 . The first case investigated was single axis control in the X and Z directions using single-input, single-output (SISO) control.... In PAGE 8: ... RESULTS A variety of control configurations were tested. A summary of the actuator and sensor combinations evaluated for both hardware configurations was given in Table1 . SISO X and Z axis control designs were successfully tested on-orbit for both hardware Configurations 1 and 2.... ..."

### Table 3. Values of (x; y) x

1997

"... In PAGE 7: ...n O(1) operations. (This approximation breaks down for small u.) 4. A comparison of approximations We began by computing (x; y) for x = 2i, i = 10; 11; : : : ; 33 and y = 2j, j = 1; 2; : : : ; 15 (see Table3 ). We then ran our ve algorithms on the same x; y values to obtain estimates to compare to the actual values.... In PAGE 7: ... On the following pages we present tables giving the ratios between the estimates and the actual data, so that a perfect estimate will yield 1:00 entries. In the interest of space, we present the data only for Algorithm HT and Algorithm A, as the data for the other algorithms can be quickly computing from Table3 . Each table is indexed by x across the top and y down the left.... In PAGE 7: ... Our program to compute the exact values of (x; y) uses sieving, and it factors roughly a billion numbers over the primes up to 215 in one CPU day on an HP 9000 series 715/75 workstation. The values in Table3 for smaller y were checked using the recursive algorithm based on Buchstab apos;s identity mentioned in x1. All... ..."

Cited by 5

### Table 3: Errors in X and Y for Example 1

in Projective methods for stiff differential equations: problems with gaps in their eigenvalue spectrum

"... In PAGE 17: ... #28Integration using a version of LSODE with tolerance of 10 ,10 gives the answers X = 0:487424;Y = 2:724937;B = 2:999854.#29 From these we can estimate the errors in X and Y in Table 1 to be as shown in Table3 . In that table, the #0Cnal two columns show the estimated errors divided by the error coe#0Ecients shown in Table 2.... ..."

Cited by 1

### Table 1. n a (X; Y )

"... In PAGE 4: ... Since X := x0(a 1) dan and Y := x0(a 1) dan + d(a 1), if a solution of (3) leads to an integral solution of equation (1) with coordinates forming an arithmetic progression, then we also have a 1 j Y X. Using Table1 we can verify that this condition is ful lled only if (n; a; X; Y ) = (3; 93; 118; 26). However, in this case we see that x0 = 118 3 93 92 , which is not an integer.... ..."

### Table 3: Dynamical Automaton 2: transitions.

"... In PAGE 25: ... Actual dynamical implementation of the correction mechanism is a focus of current research. The Input Map for the Dynamical Automaton we used to model transitive sentences with relative clause modi ers is shown in Table3 . The automaton uses 9 partition states and moves around on a 3-dimensional fractal.... In PAGE 30: ...De nition Start (1/2, 1/2, 1/2) Comp1 (0, 0, 0) + opencube Comp2 (0, 1/2, 0) + opencube V1 (1/2, 0, 0) + opencube V2 (1/2, 1/2, 0) + opencube NObj1 (0, 0, 1/2) + opencube NObj2 (0, 1/2, 1/2) + opencube NSubj1 (1/2, 0, 1/2) + opencube NSubj2 (1/2, 1/2, 1/2) + opencube Note: opencube is the set f(x; y; z) : 0 lt; x lt; 1=2; 0 lt; y lt; 1=2; 0 lt; z lt; 1=2g. Note: Compartment A as labelled in Table3 is the union of the compartments A1 and A2 shown above for A 2 fComp; V; NObj; NSubjg. Table 4: Dynamical Automaton 2: compartment de nitions.... ..."