A 128 K×8 70-MHz multiport video RAM with auto registerreload and 8×4 block WRITE feature

An 80-ns 1-Mb multiport video random-access memory (VRAM) can be organized as 128 K×8 or 256 K×4. Uninterrupted serial data streams of 70 MHz are achieved by combining pipelining and interleaving techniques with an internally triggered automatic memory-to-register transfer mechanism. DRAM bandwidth is enhanced by a block WRITE feature which can write as many as four column address locations in every CAS cycle. The write-per-bit feature has been expanded by including an on-chip write-per-bit latch and an extended mode of operation to simplify its use in a wider range of systems. The VRAM is fabricated in a 1 μm CMOS technology using double-level poly/polycide, single level metal, and trench DRAM storage capacitors for high noise immunity     

The face in the crowd effect: Anger superiority when using real faces and multiple identities.

The “face in the crowd effect” refers to the finding that threatening or angry faces are detected more efficiently among a crowd of distractor faces than happy or nonthreatening faces. Work establishing this effect has primarily utilized schematic stimuli and efforts to extend the effect to real faces have yielded inconsistent results. The failure to consistently translate the effect from schematic to human faces raises questions about its ecological validity. The present study assessed the face in the crowd effect using a visual search paradigm that placed veridical faces, verified to exemplify prototypical emotional expressions, within heterogeneous crowds. Results confirmed that angry faces were found more quickly and accurately than happy expressions in crowds of both neutral and emotional distractors. These results are the first to extend the face in the crowd effect beyond homogenous crowds to more ecologically valid conditions and thus provide compelling evidence for its legitimacy as a naturalistic phenomenon. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Maximum Likelihood Estimation of the Parameters of the Beta Distribution from Smallest Order Statistics

Numerical methods, useful with high-speed computers, are described for obtaining the maximum likelihood estimat.es of the two (shape) parameters of a beta distribution using the smallest M order statistics, 0 < u ₁ ≤ … ≤ ≤ … ≤ u _M , in a random sample of size K(≥M). The maximum likelihood estimates are functions only of the ratio, n = M/K, the Mth ordered observation, u _M , and the two statistics, G ₁ = [II ^M _i=1 u _i ]^1/M , and G ₁ = [II ^M _i=1 (1 – u _i )]^1/M . For the case of the complete sample (i.e., R = 1), however, the estimates are functions only of G ₁ and G ₂, and hence, for this case, explicit tables of the estimates are provided. When R < 1, the methods described depend crucially for their usefulness on the availability of a high-speed computer. Some esamples are given of the use of the procedures described for fitting beta distributions to sets of data. In one example, the fit is studied by using beta probability plots.     

Intelligent network feature transaction interface

The feature transaction interface and associated software structures required to support an intelligent network are addressed. A layer service logic execution environment is used to describe the operation of the feature transaction interface for provisioning intelligent network services. Included in the discussion are: the need for service creation; unbundled service logic for functional flexibility; remote operation using the SS7 network; and reduced service-provisioning intervals. Service examples using the feature transaction interface are given