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association typically picks up the costs for fraud. However, the merchant will still have some associated costs that are not covered by this. These include the costs to process an order and to handle the chargeback, the shipping costs, and so on. The merchant will also have to pay a chargeback fee. In the CNP world, the merchant is typically the one paying for the fraud. They already paid and lost the goods, as well as all of the overhead costs they spent on the order, and they will still have to pay a chargeback fee. For customer service chargebacks, merchants pay a chargeback fee, and unless they can resolve the customer service issue, they may have physical loss of goods and services and associated overhead costs. Some merchants will go out of their way to resolve customer service complaints up front to prevent a chargeback fee. They create policies of refunds on disputes, they encourage customers to call the business if they aren t happy, and they allow for refunds without returns. Why would a merchant do this Consider what is at stake. Merchants risk the sum of the chargeback fees; the goods themselves, either lost or not returned; and the potential for increased basis points on their sales if their fraud losses get too high. Chargeback Liability Example: Total Sale $100:00 Margin 22% $22:00 Credit Card Issuer Interchange & Acquirer MDR 3:5% $3:50

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Soon after Boulder, substantial federal dollars went to supporting the establishment of doctoral training programs in clinical and counseling psychology at universities across America. The Boulder vision of the professional psychologist was most closely associated with clinical psychology, and clinical training programs would be the rst recognized by the USPHS and VA. Counseling psychologists eager to make their contribution and get their share of the funding windfall received support for training conferences of their own with funds supplied by the USPHS.

Deciphering the in uence of organic species on the hygroscopic growth of aerosols is dif cult. Ambient measurements are useful for purposes of closure, provided the appropriate modelling tools are available, and can be used to infer the in uence of the organic fraction as a whole. Laboratory measurements on the other hand are more re ned and can provide quantitative results in controlled environments, though these often include simpli ed mixtures using only one representative organic species. In either case the need for accurate modelling tools is clear, both from a validation and also from a predictive perspective.

Intel, 63

where PT is the transpose of the parity check submatrix P. The matrix H is constructed so that the inner product between any row vector gi of G and any row vector h j of H is zero: gi h j = pi j pi j = 0 This condition can be summarized in the matrix expression G HT = 0 (25) (24)

Sub DisplayVideoInfo() Const SM_CXSCREEN = 0 Const SM_CYSCREEN = 1 If Val(Application.Version) > 5 Then 32-bit Excel vidWidth = GetSystemMetrics32(SM_CXSCREEN) vidHeight = GetSystemMetrics32(SM_CYSCREEN) Else 16-bit Excel vidWidth = GetSystemMetrics16(SM_CXSCREEN) vidHeight = GetSystemMetrics16(SM_CYSCREEN) End If

trained classi ers further with new incoming images in order to extend classi ers to new operating conditions (e.g., new types of images) and, hence, to prevent extrapolation and a low classi cation performance. Please note that initially of ine-trained classi ers (from prelabeled training data) are mandatory as giving the experts a rst hint as to whether the classi cation task can be reasonably trained at all up to a certain (prede ned) level of accuracy, yielding a prediction about the success or failure of the classi er [55]. After preprocessing the images by comparing them with a master to obtain deviation images, where each pixel represents a potential candidate for indicating a fault, regions of interest are automatically recognized and for each region a large set of object features are extracted. Examples of object features describing the characteristics of a region of interest are ratio of the principal axis to the secondary axis of a circumscribing ellipse (roundness), perimeter, area, compactness, and statistical values such as curtosis, skew, or different quantiles out of the corresponding gray-level histograms. These are completed with a set of aggregated features characterizing images as a whole, and so, characterizing all pixels and objects joined together. Examples of aggregated features are the number of objects, the maximal density of objects, or the average brightness in an image. Classi cation of the images is done into good or bad ones, indicating fault-free or