Abstract not found

Abstract not found

The performance of a neural network that categorizes facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks make use of interpolations of facial expressions from the Pictures of Facial Affect Database [Ekman and Friesen, 1976]. The only difference in materials between those used in the human subjects experiments [Young et al., 1997] and our materials are the manner in which the interpolated images are constructed -- image-quality morphs versus pixel averages. Nevertheless, the neural network accurately captures the categorical nature of the human responses, showing sharp transitions in labeling of images along the interpolated sequence. Crucially for a demonstration of categorical perception [Harnad, 1987], the model shows the highest discrimination between transition images at the crossover point. The model also captures the shape of the reaction time curves of the human s...

Abstract. We analyze the fitness dynamics of a (1+1) mutation-only genetic algorithm (GA) operating on a family of simple time-dependent fitness functions. Resulting models of behavior are used in the prediction of GA performance on this fitness function. The accuracy of performance predictions are compared to actual GA runs, and results are discussed in relation to analyses of the stationary version of the dynamic fitness landscape and to prior work performed in the field of evolutionary optimization of dynamic fitness functions. 1.

Commercial cloud offerings, such as Amazon’s EC2, let users allocate compute resources on demand, charging based on reserved time intervals. While this gives great flexibility to elastic applications, users lack guidance for choosing between multiple offerings, in order to complete their computations within given budget constraints. In this work, we present BaTS, our budget-constrained scheduler. BaTS can schedule large bags of tasks onto multiple clouds with different CPU performance and cost, minimizing completion time while respecting an upper bound for the budget to be spent. BaTS requires no a-priori information about task completion times, and learns to estimate them at run time. We evaluate BaTS by emulating different cloud environments on the DAS-3 multi-cluster system. Our results show that BaTS is able to schedule within a user-defined budget (if such a schedule is possible at all.) At the expense of extra compute time, signifcant cost savings can be achieved when comparing to a cost-oblivious round-robin scheduler. 1

(Causton et al., 2001; Gasch et al., 2000). These studies also generalized the role of the transcriptional activators Msn2 and Msn4 in the stress response. One of the signaling pathways known to regulate the activity of Msn2/4 is the TOR pathway (Beck and Hall, 1Department of Biological Chemistry and 1999). The TOR pathway controls cell growth in re-Molecular Pharmacology sponse to nutrient availability by regulating both tran-Harvard Medical School scription and translation (reviewed in Rohde et al., 2001;

The Central Limit Theorem has been described as "one of the most remarkable results in all of mathematics " and "a dominating personality in the world of probability and statistics " (Adams, 1974, p. 2). Jt is "one of the oldest results in probability theory " (Araujo & Gine, 1980, p. v), "occupies a unique position at the heart of probabilistic limit theory " (Hall 1982, p. 1) and "plays a central role in the theory of statistical inference" (Keeping, 1962, p. 90). Much of its importance "stems fr~m its proven adaptability and utility in many areas of mathematics, pr~bability theory and statistics " (Hall, 1982, p. I), while it "account $ very largely for the importance of the normal distribution in theoretical investigations" (Keeping, 1962, p. 90). 2. The theorem The Central Limit Theorem may be stated as follows: Let Xi, X2..., Xn be a sequence of independent ran-' dom variables each having the samze distdbution with finite

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Selectivity of local field potentials in macaque inferior temporal cortex While single neurons in inferior temporal (IT) cortex show differential responses to distinct complex stimuli, little is known about the responses of populations of neurons in IT. We recorded single electrode data, including multi-unit activity (MUA) and local field potentials (LFP), from 618 sites in the inferior temporal cortex of macaque monkeys while the animals passively viewed 78 different pictures of complex stimuli. The LFPs were obtained by low-pass filtering the extracellular electrophysiological signal with a corner frequency of 300 Hz. As reported previously, we observed that spike counts from MUA showed selectivity for some of the pictures. Strikingly, the LFP data, which is thought to constitute an average over large numbers of neurons, also showed significantly selective responses. The LFP responses were less selective than the MUA responses both in terms of the proportion of selective sites as well as in the selectivity of each site. We observed that there was only little overlap between the selectivity of MUA and LFP recordings from the same electrode. To assess the spatial organization of selective responses, we compared the selectivity of nearby sites recorded along the same penetration and sites recorded from different penetrations. We observed that MUA selectivity was correlated on spatial scales up to 800 µm

Abstract. We introduce a refinement strategy to bring the parallel performance analysis closer to the user. The analysis starts with a simple high-level performance model. It is based on first-order approximations, in terms of the logical constituents of the parallel program and characteristics of the system. This model is then progressively refined with more detailed low-level performance aspects, to explain divergences from a ‘normal’, linear regime. We use a causal model to structure the relations between all variables involved. The approach intends to serve as a link between detailed performance data and the developer. It is demonstrated with a parallel matrix multiplication algorithm. 1