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Dr. Cummings is past president of the APA, founder of American Biodyne, the nation s first Managed Behavioral Healthcare Organization (MBHO), and founder of the first professional school (the four campuses of the California school of Professional Psychology). He also founded the National Council of Professional Schools of Psychology (NCSPP) as well as the National Academies of Practice in Washington, DC. Birth Date: July 25, 1924 College: University of California, Berkeley (AB, Psychology), 1948. Graduate Programs: Claremont Graduate School (MA, Psychology), 1952; Adelphi University (PhD, Clinical Psychology), 1958. Clinical Internships: New Hope Guild Center, Brooklyn, NY; Creedmoor State Hospital, Queens, NY; Patton State Hospital, Patton, CA. Postdoctoral Fellowship: William Alanson White Institute, New York. Current Jobs: Distinguished Professor, University of Nevada, Reno; President, Cummings Foundation for Behavioral Health; Chairman, The Nicholas & Dorothy Cummings Foundation; Chairman, University Alliance for Behavioral Care (UABC). Job Locations: Reno, NV; Scottsdale, AZ.

The corresponding received power versus the scenario time is illustrated in Figs. 2.16 and 2.17. For the scenario described above, suppose each access point APi is represented by an agent Ai. Each MSj is initially associated with an APi at time tk. Each APi has a variable Xi for MSi, which represents the new association APj at time tk+1. Hence, Xi is equivalent to rij (tk+1) de ned in (2.22). At any point in time, only a subset of the agents will be involved in the resource allocation process, which means that the multiagent system is constructed dynamically. Periodically, each agent listens to handoff event triggers. The event triggers for handoffs are usually requested from MSs need to be handed off or when an AP is overloaded. They initiate agent local computations and communications with other agents in the neighborhood to handle the resource allocation problem. The DCOP is viewed as an optimization problem with the following criteria: Criterion I: Maximize the utilization of each AP in order to accommodate more users, that is, Maximize rj tk 1 Subject to PR dij tk 1 4g

Water (5 passes)

= SSE( X 3 X 1 X 3 X 2 X 3 ) SSE( X 1 X 2 X 3 X 1 X 3 X 2 X 3 ) = 488.1538 360.7500 = 127.4038 with p 1 = 2 degrees of freedom. This sum of squares is used in testing the regression null hypothesis H0: 1 = 2 = 0. Because of the correspondence between the regression and ANOVA parameters, a test of this regression null hypothesis is equivalent to testing the ANOVA null hypothesis for treatment A. The regression sum of squares corresponding to SSB in ANOVA is

of 16 measures at the 6-month follow-up These ndings indicate that in dismantling CBT for the treatment of GAD, behavior therapy was not as causative of change overall as was a more complete package incorporating cognitive therapy, and this was particularly evident at the 6-month follow-up assessment Notice how this nding invites pursuit of what it is in CBT (eg, challenging automatic thoughts or decatastrophizing feared outcomes) that causes greater change, and what it is about GAD such that these elements increment its amelioration Such an implementation of the strong inference approach would allow us to eventually establish increasingly speci c cause-and-effect conclusions regarding the treatment of GAD In addition to offering considerable basic knowledge about therapeutic change, the dismantling design also potentially yields signi cant applied implications Clinicians can discard the elements of the original package found to be inactive or super uous.

CONNECTIONISM A related set of questions about the role of central control processes is raised by the literature of connectionism. Connectionist models simulate thought processes in networks of arti cial units in which processing consists of passing activation among the units. As in neurons, the signal can be excitatory or inhibitory. Energy passes in only one direction (though some networks have feedback links). Processing proceeds entirely by the spread of activation there is no higher order executive to direct traf c. In a distributed connectionist network, knowledge is not represented centrally, as nodes of information. Rather, knowledge is represented in terms of the pattern of activation of the network as a whole (Smith, 1996). In networks with feedback relations, once the system receives input, the pattern of weights and activations is updated repeatedly across many cycles. Thus, modi cations or updates are made iteratively throughout the network, both with respect to activation in each node and the weighting functions. Gradually, the various values asymptote, and the system settles into a con guration. The settling re ects the least amount of overall error the system has been able to create, given its starting inputs and weights. Multiple Constraint Satisfaction A useful way to think about this process is that the system simultaneously satis es multiple constraints that the elements create on each other (Thagard, 1989; see also Kelso, 1995).

LOCK TABLE table_name IN SHARE ROW EXCLUSIVE MODE; LOCK TABLE table_name IN EXCLUSIVE MODE; LOCK TABLE table_name IN ROW EXCLUSIVE MODE;