What Are Polymorphically-Typed Ambients?

Abstract: The Ambient Calculus was developed by Cardelli and Gordon as a formal framework to study issues of mobility and migrant code. We consider an Ambient Calculus where ambients transport and exchange programs rather that just inert data. We propose different senses in which such a calculus can be said to be polymorphically typed, and design accordingly a polymorphic type system for it. Our type system assigns types to embedded programs and what we call behaviors to processes; a denotational semantics of behaviors is then proposed, here called trace semantics, underlying much of the remaining analysis. We state and prove a Subject Reduction property for our polymorphically typed calculus. Based on techniques borrowed from finite automata theory, type-checking of fully type-annotated processes is shown to be decidable; the time complexity of our decision procedure is exponential (this is a worst-case in theory, arguably not encountered in practice). Our polymorphically-typed calculus is a conservative extension of the typed Ambient Calculus originally proposed by Cardelli and Gordon.

Detecting Instances of Shape Classes That Exhibit Variable Structure

This paper proposes a method for detecting shapes of variable structure in images with clutter. The term "variable structure" means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance.

Type Systems for a Network Specification Language With Multiple-Choice Let

When analysing the behavior of complex networked systems, it is often the case that some components within that network are only known to the extent that they belong to one of a set of possible "implementations" – e.g., versions of a specific protocol, class of schedulers, etc. In this report we augment the specification language considered in BUCSTR-2004-021, BUCS-TR-2005-014, BUCS-TR-2005-015, and BUCS-TR-2005-033, to include a non-deterministic multiple-choice let-binding, which allows us to consider compositions of networking subsystems that allow for looser component specifications.

The Graph Analysis Toolbox: Image Processing on Arbitrary Graphs

Office of Naval Research (N00014-01-1-0624)

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.

Brightness Perception, Illusory Contours and Corticogeniculate Feedback

A neural network model of early visual processing offers an explanation of brightness effects often associated with illusory contours. Top-down feedback from the model's analog of visual cortical complex cells to model lateral geniculate nucleus (LGN) cells are used to enhance contrast at line ends and other areas of boundary discontinuity. The result is an increase in perceived brightness outside a dark line end, akin to what Kennedy (1979) termed "brightness buttons" in his analysis of visual illusions. When several lines form a suitable configuration, as in an Ehrenstein pattern, the perceptual effect of enhanced brightness can be quite strong. Model simulations show the generation of brightness buttons. With the LGN model circuitry embedded in a larger model of preattentive vision, simulations using complex inputs show the interaction of the brightness buttons with real and illusory contours.

A Neural Network Realization of Fuzzy ART

A neural network realization of the fuzzy Adaptive Resonance Theory (ART) algorithm is described. Fuzzy ART is capable of rapid stable learning of recognition categories in response to arbitrary sequences of analog or binary input patterns. Fuzzy ART incorporates computations from fuzzy set theory into the ART 1 neural network, which learns to categorize only binary input patterns, thus enabling the network to learn both analog and binary input patterns. In the neural network realization of fuzzy ART, signal transduction obeys a path capacity rule. Category choice is determined by a combination of bottom-up signals and learned category biases. Top-down signals impose upper bounds on feature node activations.

Normal and Amnesis Learning, Recognition, and Memory by a Neural Model of Cortico-Hippocampal Interactions

The processes by which humans and other primates learn to recognize objects have been the subject of many models. Processes such as learning, categorization, attention, memory search, expectation, and novelty detection work together at different stages to realize object recognition. In this article, Gail Carpenter and Stephen Grossberg describe one such model class (Adaptive Resonance Theory, ART) and discuss how its structure and function might relate to known neurological learning and memory processes, such as how inferotemporal cortex can recognize both specialized and abstract information, and how medial temporal amnesia may be caused by lesions in the hippocampal formation. The model also suggests how hippocampal and inferotemporal processing may be linked during recognition learning.

CB1 cannabinoid receptor deficiency promotes cardiac remodeling induced by pressure overload in mice

Background: The endocannabinoid system is known to play a role in regulating myocardial contractility, but the influence of cannabinoid receptor 1 (CB1) deficiency on chronic heart failure (CHF) remains unclear. In this study we attempted to investigate the effect of CB1 deficiency on CHF induced by pressure overload and the possible mechanisms involved. Methods and results: A CHF model was created by transverse aortic constriction (TAC) in both CB1 knockout mice and wild-type mice. CB1 knockout mice showed a marked increase of mortality due to CHF from 4 to 8 weeks after TAC (p = 0.021). Five weeks after TAC, in contrast to wild-type mice, CB1 knockout mice had a higher left ventricular (LV) end-diastolic pressure, lower rate of LV pressure change (± dp/dt max), lower LV contractility index, and a larger heart weight to body weight ratio and lung weight to body weight ratio compared with wild-type mice (all p < 0.05-0.001). Phosphorylation of the epidermal growth factor receptor (EGFR) and mitogen-activated protein kinases (P38 and ERK) was higher in CB1 knockout mice than that in wild-type mice. In cultured neonatal rat cardiomyocytes, a CB1 agonist reduced cAMP production stimulated by isoproterenol or forskolin, and suppressed phosphorylation of the EGFR, P38, and ERK, while the inhibitory effect of a CB1 agonist on EGFR phosphorylation was abrogated by CB1 knockdown. Conclusion: These findings indicate that cannabinoid receptor 1 inactivation promotes cardiac remodeling by enhancing the activity of the epidermal growth factor receptor and mitogen-activated protein kinases. © 2012 Elsevier Ireland Ltd.

Production of lightweight aggregate from industrial waste and carbon dioxide

The concomitant recycling of waste and carbon dioxide emissions is the subject of developing technology designed to close the industrial process loop and facilitate the bulk-re-use of waste in, for example, construction. The present work discusses a treatment step that employs accelerated carbonation to convert gaseous carbon dioxide into solid calcium carbonate through a reaction with industrial thermal residues. Treatment by accelerated carbonation enabled a synthetic aggregate to be made from thermal residues and waste quarry fines. The aggregates produced had a bulk density below 1000 kg/m3 and a high water absorption capacity. Aggregate crushing strengths were between 30% and 90% stronger than the proprietary lightweight expanded clay aggregate available in the UK. Cast concrete blocks containing the carbonated aggregate achieve compressive strengths of 24 MPa, making them suitable for use with concrete exposed to non-aggressive service environments. The energy intensive firing and sintering processes traditionally required to produce lightweight aggregates can now be augmented by a cold-bonding, low energy method that contributes to the reduction of green house gases to the atmosphere.