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from the Bosque Initiative (via the U.S. Fish and Wildlife Service) and the National Science Foundation (several sources). Preparation of this manuscript was partially funded by the National Science Foundation, Award DEB-9903973.
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ANN (i.e., the number of neurons in each layer) is determined empirically beforehand. To make the centers as close as possible to many vectors from the input space, the center and weight must be adapted. This type of adaptation is particularly important because of the high dimensionality of the input layer.
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Optovar lens, 57 Ordinary ray (O ray) DIC microscopy, 157 160 polarization microscopy, 140 141, 144, 146, 148 polarized light, 124 126, 128 130, 132 Orthoscopic image, 4 Oversampling, 220 Oxyrase, 42, 199 Panning, 314 Paraboloid condenser, 114 115 Parallel register, 261 263 Parfocal optics, 9 Particle wave (P wave), phase contrast microscopy, 101 103, 107 Peltier thermoelectric device, 267 Phase contrast microscopy alignment, 106 image interpretation, 106 110 optical design, 97 99, 103 106, 168 phase immersion refractometry, 110 112 Phase gradient, 154 Phase halos, 108 110, 168 Phase object, 97 Phase plate, 105 Phase shift, 103, 108 Phosphorescence, 181 Photobleaching confocal laser scanning microscopy, 223 224 fluorescence microscopy, 181, 183 Photodiode, 261 Photomultiplier tube (PMT) confocal imaging, 207 209 Photons electromagnetic radiation, 15 16 energy, 17 light, as particles and waves, 18 20 Photon noise, 273 274, 301 302 Photon-limited signal, image processing, 301 Photopic vision, 22, 23 24 Photoreceptors cone cell, 24 defined, 15 function of, 16, 22 rod cell, 23 Phototoxicity characteristics of, 41 digital CCD microscopy, 271 fluorescence microscopy, 198 199 Photovisual pigments, 25 Pinhole aperture, confocal imaging, 208, 210 213, 215 216, 218, 224 Pinhole camera, 66 67 Pixels in confocal imaging, 210, 219 digital CCD microscopy, 261 265, 267 269, 272 273 digital image processing, 292 295, 300 304 in video microscopy, 236 237, 241
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The denominator of (3.25) is the normalizing constant. In practice, we integrate the saddlepoint density approximation numerically to calculate the normalizing constant. There is a saddlepoint CDF approximation due to Lugannani and Rice (1980); however, it is unstable near the mean. An alternative to the Lugannani and Rice approximation is to integrate the saddlepoint density approximation numerically as suggested by Daniels (1954, 1987). The saddlepoint approximation to the CDF due to Lugannani and Rice (1980) is stated as follows. Theorem 3.2.2: Saddlepoint Approximation to the CDF. Let K(s) = log [M(s)] be the cumulant generating function (CGF) of T . Let c1 and c2 be constants such that c1 < 0 < c2 . Suppose that M(s) exists for s (c1 , c2 ), an open s neighborhood of zero. Let s solve K ( ) = t, the saddlepoint equation (3.20). Let K (s) = d 2 K(s)/ds 2 and K (s) = d 3 K(s)/ds 3 . Then the saddlepoint approximation for the CDF of T is F (t) = where (w) + (w)[w 1 u 1 ], 1/2 + K (0) 6 2 K (0)3
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You may buy a lot more of the good in question because extra units bring you a lot of happiness, and you re consequently grateful to be able to purchase them for 9 instead of 10. You may barely increase your buying because, although you like being able to buy the good for 9 rather than 10, extra units just don t make you all that much happier. In such a situation, the best thing about the price cut is that it frees up money to buy more of other things. In terms of demand curves, these different reactions lead to different slopes. The person who buys a lot more when the price falls has a flat demand curve, whereas the person whose purchases barely budge when the price falls has a steep demand curve. To make this discussion more concrete, consider Figure 8-3, where we show two separate demand curves on two separate graphs. The one on the left is your demand for sherbet lemons. The one on the right is your friend s demand for sherbet lemons.
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network codes and names). Meanwhile the administrative work has been absorbed by the GSMA headquarters in Dublin. During the early days, discussion on the permissible length of network names was an issue to entertain the whole working group for some time as display space on handsets was very limited. It also happened that operators, in the rush for starting commercial operations thought of everything but of registration of the IC card identi er at the ITU via national administrations. As ITU administrations are not really business focussed, time became an issue in some cases. Security elements of the GSM standards are a strong sales argument. Two elements are best known: the SIM and the International Mobile Equipment Identity (IMEI), the latter allowing tracing of faulty handsets and prevention of network access of stolen equipment. A couple of recommendations were created in this area, starting with general instructions or suggestions 5 on IMEI checking strategy . This was very useful as we all know that standards normally contain the minimum compromise. Options often re ect that no single compromise was reached, but also contain various operational conditions. The operators active in SERG had a vital interest in harmonising the security level, e.g. checking the IMEI at similar time intervals. In addition operational speci cations were created to run the Central Equipment Identity Register (CEIR) and provide on-line interfaces with EIR operators (the local register at operators premises, SE.16 and SE.17). The CEIR was operated by GSMA HQ for some years. There was also a mandatory requirement to members to implement the EIR (originally 1 January 1995). Again, this was an issue for long disputes as the community of operators had grown a lot with a collection of diverging business ideas. After all, the requirement was compromised, permitting also off-line solutions. The number of operators connected to the CEIR is still very limited. As handset value decreases rapidly due to frequent change of models meanwhile, one can question the bene t of this security measure. However, the IMEI concept has proven to be very useful in particular when operators expand and upgrade network capabilities frequently and need to know how handsets can cope with these new functions. Service requirements and implementation
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This measure may be called conditional Hartley information gain, because it is computed from conditional relations. Note that in order to emphasize in the notation that conditional relations are considered we may write ri|j instead of rij by exploiting Definition 3.2.9 on page 70. Reconsidering the illustration of ordinary Hartley information gain in Figure 7.2 on page 172 (with attribute C replacing attribute B) we see that this measure evaluates the relation column by column : for each conditional relation the number of tuples is compared to the number of tuples in the marginal relation (this comparison is done in the argument of the logarithm). If these numbers are the same for all conditional relations, the two attributes are, obviously, independent. The more they differ, the more strongly dependent the two attributes are. The results of these comparisons are aggregated over all conditional relations containing at least one tuple. (That only nonempty relations are considered is achieved with the factor r.j .) This aggregate is normalized by dividing it by nA r.j to make it independent of the number j=1 of possible values of the attribute A. Note that, in contrast to the ordinary Hartley information gain, this measure is, in general, not symmetric. Hence we cannot obtain symmetric gain ratios as for ordinary Hartley information gain, but have to rely on I(Hartley) (C, A) = scgr Icgain
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There are two parts to dismantling this common barrier, says Phillips. The first is to create a sense of accountability among training participants. They are not passive observers in producing an ROI on training. Indeed, they are an essential part of analyzing and assessing programs, and then taking what is learned back to the job to be more efficient and productive. At the same time, if you can show managers the value of the training in business terms they will be far less reluctant to support your programs. Think of the impact if you could say to your CEO, Would you mind letting employees go to training if you knew that the results would offset the costs Managers are accustomed to asking for ROI, Phillips noted, except when it comes to training. They have systems in place to report production results, for example, and they are likely to spend 5% or more of their budgets making these measurements. Training must have the same reality. Note: Phillips estimates that 3% to 5% of your training budget will be needed for ROI measurements. 2. Curse of high results. It is not uncommon to produce an ROI of 100% to 700% on leadership, team building, or sales training initiatives, Phillips noted. For example, in 2003 IBM reported that it had achieved an ROI of 2,284% and a payback period of two weeks on its e-learning initiative. These kinds of results are so far beyond normal ROI expectations that management will refuse to accept that they are accurate. To build credibility, says Phillips, training managers must always be conservative in their estimates and show management what they need to see. That means that sometimes you leave intangibles as intangibles (increased job satisfaction, improved teamwork, reduced complaints, etc.) rather than trying to force them into some kind of absolute, Phillips stated. Tie training ROI targets to your organization s hurdle rate (the minimum required return on capital investments) or slightly higher, Phillips recommends (about 15% to 20% in the United States). In fact, your results may be higher. First-level supervisory training could have a 50% ROI in terms of its impact on turnover, absenteeism, and job satisfaction, for example. 3. Isolating the impact of training. Despite what some researchers will tell you, you do not need a control group study to isolate the impact of training on results. In fact, it is possible and plausible to use training participants estimates to assess the extent to which training has influenced performance, says Phillips. We tend to underrate participant data, but who knows best what s affected their performance For example, most sales reps know why they have increased sales. That said, it is important to be conservative in these estimates and to ask participants to rate their confidence level in their estimate of training s impact on performance. (See Exhibit 6.6.) 4. Converting data to monetary values. There are a number of conversion methods, including converting output to contribution; converting employees time; linking with other measures; and using participants , supervisors , and managers estimates. Some examples include: You can use an external database, such as industry data, to calculate the cost of turnover. You can calculate the cost of one sexual harassment complaint by tallying actual costs from historical records, including legal fees, settlements,
In the appendixes, the basic concepts, de nitions, and characteristic features of four soft computing techniques fuzzy logic, arti cial neural networks, genetic algorithms, and rough sets are described. The treatment of these topics here is both introductory and fundamental. Readers will nd this background knowledge useful for an understanding of the ideas and techniques presented in the text. In Appendix A we de ne fuzzy subsets, membership functions, basic operations, measure of fuzziness, and fuzzy classi cation. In Appendix B, the architecture, training methods, and advantages of arti cial neural networks are described. Here, four basic neural network models perceptron, multilayer perceptron, radial basis function neural network, and Kohonen neural network which are widely used, are considered. In Appendix C, the basic principle, algorithm, and merits of genetic algorithms are given. Finally, rough set theory is presented in Appendix D. This includes information system, indiscernibility relation, set approximations, rough membership, and the dependency between attributes. Note that the tools above act synergistically, not competitively, for enhancing the problem-solving ability of each other. The purpose is to provide exible information-processing systems that can exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, low solution cost, and close resemblance to human decision making.
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