Modeling multi-rate DSP specification semantics for formal transformational design in HOL

The CATHEDRAL Silicon Compilers synthesize hardware for DSP algorithms specified in Silage, a high level applicative language. In order to optimize the results of the silicon compilation in terms of chip-area and/or throughput, the user often massages the specification applying transformations to the Silage code. To guarantee that the transformations preserve the behavior of the specified algorithm, the formal semantics of the specification language had to be defined. The semantics has been used to prove in HOL the correctness of the transformations and to prove properties of the specification. We are currently building a system where a menu of useful andcorrectness preserving transformations will be available to the user. In this system the user could choose appropriate transformations from the menu taking advantage of his creativity and expertise to interactively guide the silicon compiler, without the risk of introducing inconsistencies. This article describes the formalmulti-rate semantics of a substantial subset of Silage and illustrates some formally verified transformations.     

Growth in height of indigenous and non indigenous Chilean children

The aim of this study was to compare growth curves of stature in indigenous and non-indigenous children belonging to two levels of poverty and to establish the onset and evolution of the deficit. Children of indigenous and non-indigenous background living in communities of extreme and low poverty in Chile were studied and their height-for-age Z-score from birth until 6 year of age were compared. Mean weight at birth was within normal range, and no differences were found in ethnicity and levels of poverty. Length at birth was below the reference with the exception of the non indigenous newborn from counties of low poverty. Deficit in growth showed an early start, furthermore in indigenous children belonging to the extreme poverty, is from birth and progress through the 18 months. At 72 months the deficit reached -1.1 z scores in the indigenous of the extreme poverty versus -0.7 in the non indigenous group. Children from the low poverty had a Z-score of -0.4 z scores at 72 months without differences between ethnias. Indigenous of the extreme poverty had less accumulative growth while the indigenous of the low poverty areas growth satisfactory without differences with the non indigenous.     

The CAM-Brain Machine (CBM): An FPGA Based Tool for Evolving a 75 Million Neuron Artificial Brain to Control a Lifesized Kitten Robot

This article introduces the CAM-Brain Machine (CBM), an FPGA based piece of hardware which implements a genetic algorithm (GA) to evolve a cellular automata (CA) based neural network circuit module, of approximately 1,000 neurons, in about a second, i.e., a complete run of a GA, with 10,000s of circuit growths and performance evaluations. Up to 65,000 of these modules, each of which is evolved with a humanly specified function, can be downloaded into a large RAM space, and interconnected according to humanly specified artificial brain architectures. This RAM, containing an artificial brain with up to 75 million neurons, is then updated by the CBM at a rate of 130 billion CA cells per second. Such speeds should enable real time control of robots and hopefully the birth of a new research field that we call brain building. The first such artificial brain, to be built in 2000 and beyond, will be used to control the behaviors of a life sized robotkitten called Robokitty.     

Processing of observed pupil size modulates perception of sadness and predicts empathy.

Facial autonomic responses may contribute to emotional communication and reveal individual affective style. In this study, the authors examined how observed pupillary size modulates processing of facial expression, extending the finding that incidentally perceived pupils influence ratings of sadness but not those of happy, angry, or neutral facial expressions. Healthy subjects rated the valence and arousal of photographs depicting facial muscular expressions of sadness, surprise, fear, and disgust. Pupil sizes within the stimuli were experimentally manipulated. Subjects themselves were scored with an empathy questionnaire. Diminishing pupil size linearly enhanced intensity and valence judgments of sad expressions (but not fear, surprise, or disgust). At debriefing, subjects were unaware of differences in pupil size across stimuli. These observations complement an earlier study showing that pupil size directly influences processing of sadness but not other basic emotional facial expressions. Furthermore, across subjects, the degree to which pupil size influenced sadness processing correlated with individual differences in empathy score. Together, these data demonstrate a central role of sadness processing in empathetic emotion and highlight the salience of implicit autonomic signals in affective communication. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bilinear Control for Three-Phase Microgrids: A Proportional-Integral Passivity-Based Design

Abstract

A bilinear PI control based on passivity theory for the adequate integration of distributed energy resources (DERs) in ac microgrids is presented in this paper. DERs are integrated into the grid by voltage source converters (VSC), the most common and suitable technology for this type of application. The proposed control guarantees asymptotically stable operation for the dynamical system under closed-loop operating scenarios via Hamiltonian and Lyapunov formulations. ZP load models and π-model of the transmission lines are considered in the stability analysis of the microgrid. Conventional PI control is also implemented for comparative purposes. Simulation results in Matlab/Simulink demonstrate the effectiveness and stability of the proposed control’s performance in a radial microgrid composed of a photovoltaic generator, a supercapacitor energy storage (SCES) system and unbalanced loads.     

Joint training of non-negative Tucker decomposition and discrete density hidden Markov models

Non-negative Tucker decomposition (NTD) is applied to unsupervised training of discrete density HMMs for the discovery of sequential patterns in data, for segmenting sequential data into patterns and for recognition of the discovered patterns in unseen data. Structure constraints are imposed on the NTD such that it shares its parameters with the HMM. Two training schemes are proposed: one uses NTD as a regularizer for the Baum–Welch (BW) training of the HMM, the other alternates between initializing the NTD with the BW output and vice versa. On the task of unsupervised spoken pattern discovery from the TIDIGITS database, both training schemes are observed to improve over BW training in terms of pattern purity, accuracy of the segmentation boundaries and accuracy for speech recognition. Furthermore, we experimentally observe that the alternative training of NTD and BW outperforms the NTD regularized BW, BW training and BW training with simulated annealing.