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One can create many accounts in which to store a wide array of payroll information, such as payables for garnishments, Medicare, Social Security, and state income tax reimbursements However, having a large number of accounts requires additional effort to keep track of outstanding balances There are cases where the number of payroll-related accounts can be drastically reduced For example, if payroll is being outsourced, there is no need for any payroll payable or payroll tax payable account since the supplier is taking care of these payments If there is a choice between keeping the responsibility for a payment in-house or giving the responsibility to a supplier, then give the chore to the supplier, thereby eliminating the related payroll account Also, if the amount of funds itemized in a separate payroll account is quite small, it may not be worth the effort to store it separately Instead, cancel the account and shift the funds to a related account However, there may be cases where significant legal liability is associated with funds payable, as is the case for gar-

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A major advantage of product units is an increased information capacity compared to summation units [222, 509] Durbin and Rumelhart showed that the information capacity of a single PU (as measured by its capacity for learning random Boolean patterns) is approximately 3I, compared to 2I for a single SU (I is the number of inputs to the unit) [222] The larger capacity means that functions approximated using PUs will require less processing elements than required if SUs were used This point can be illustrated further by considering the minimum number of processing units required for learning the simple polynomial functions in Table 31 The minimal number of SUs were determined using a sensitivity analysis variance analysis pruning algorithm [238, 246], while the minimal number of PUs is simply the number of di erent powers in the expression (provided a polynomial expression) While PUNNs provide the advantage of having smaller network architectures, a major drawback of PUs is an increased number of local minima, deep ravines and valleys The search space for PUs is usually extremely convoluted Gradient descent, which works best when the search space is relatively smooth, therefore frequently gets trapped in local minima or becomes paralyzed (which occurs when the gradient of the error with

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Payroll Journal Entries / 213

where Pc (0, 1) is the probability of crossover, and > 0 is a scaling factor The position of the particle is only replaced if the o spring is better That is, xi (t+1) = xi (t + 1) only if f (xi (t + 1)) < f (xi (t)), otherwise xi (t + 1) = xi (t) (assuming a minimization problem) Hendtlass applied the above DE process to the PSO by executing the DE crossover operator from time to time [360] That is, at speci ed intervals, the swarm serves as population for the DE algorithm, and the DE algorithm is executed for a number of iterations Hendtlass reported that this hybrid produces better results than the basic PSO Kannan et al [437] applies DE to each particle for a number of iterations, and replaces the particle with the best individual obtained from the DE process Zhang and Xie [954] followed a somewhat di erent approach where only the personal best positions are changed using the following operator: yij (t + 1) = yij (t) + j yij (t) if U (0, 1) < Pc and j = U (1, nx ) otherwise (1674)

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Control architecture (more contemporarily called leadership and integration architecture) Inputs (capital, material, data) Processes that are prescriptions for how work gets done (activities that de ne how work gets done to produce required and desired outputs) Outputs (outcomes, products, services, assets, data, and results) Enabling mechanisms that may be considered the technical architecture (people, organizations, and technologies)

where is the general di erence vector de ned as, j = y1j (t) y2j (t) 2 (1675)

nishments withheld, where one may be justified in separately identifying the funds in a different payroll account

with y1j (t) and y2j (t) randomly selected personal best positions Then, yij (t + 1) is set to yij (t + 1) only if the new personal best has a better tness evaluation

A number of cooperative and competitive PSO implementations that make use of multiple swarms have been developed Some of these are described below Multi-phase PSO approaches divide the main swarm of particles into subgroups, where each subgroup performs a di erent task, or exhibits a di erent behavior The behavior of a group or task performed by a group usually changes over time in response to the group s interaction with the environment It can also happen that individuals may migrate between groups The breeding between sub-swarms developed by L vberg et al [534, 536] (refer to Section 1653) is one form of cooperative PSO, where cooperation is implicit in the exchange of genetic material between parents of di erent sub-swarms Al-Kazemi and Mohan [16, 17, 18] explicitly divide the main swarm into two subswarms, of equal size Particles are randomly assigned to one of the sub-swarms Each sub-swarm can be in one of two phases: Attraction phase, where the particles of the corresponding sub-swarm are allowed to move towards the global best position Repulsion phase, where the particles of the corresponding sub-swarm move away from the global best position

Note again that data are both an input and an output Data can be an asset and can also be noise, just as outcomes can be positive or negative (ie, value or costs, revenue or expenses) This is the construct by which Dennis Wisnosky, Chief Technology

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PDF Generation, does it work now? · Issue #642 · Azure / azure ...
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