Comparative Experimental Studies on Spatial Memory and Learning in Rats and Robots

The study of behavioral and neurophysiological mechanisms involved in rat spatial cognition provides a basis for the development of computational models and robotic experimentation of goal-oriented learning tasks. These models and robotics architectures offer neurobiologists and neuroethologists alternative platforms to study, analyze and predict spatial cognition based behaviors. In this paper we present a comparative analysis of spatial cognition in rats and robots by contrasting similar goal-oriented tasks in a cyclical maze, where studies in rat spatial cognition are used to develop computational system-level models of hippocampus and striatum integrating kinesthetic and visual information to produce a cognitive map of the environment and drive robot experimentation. During training, Hebbian learning and reinforcement learning, in the form of Actor-Critic architecture, enable robots to learn the optimal route leading to a goal from a designated fixed location in the maze. During testing, robots exploit maximum expectations of reward stored within the previously acquired cognitive map to reach the goal from different starting positions. A detailed discussion of comparative experiments in rats and robots is presented contrasting learning latency while characterizing behavioral procedures during navigation such as errors associated with the selection of a non-optimal route, body rotations, normalized length of the traveled path, and hesitations. Additionally, we present results from evaluating neural activity in rats through detection of the immediate early gene Arc to verify the engagement of hippocampus and striatum in information processing while solving the cyclical maze task, such as robots use our corresponding models of those neural structures.     

Multiscale computation for bioartificial soft tissues with complex geometries

The mechanical function of soft collagenous tissues is inherently multiscale, with the tissue dimension being in the centimeter length scale and the underlying collagen network being in the micrometer length scale. This paper uses a volume averaging multiscale model to predict the collagen gel mechanics. The model is simulated using a multiscale component toolkit that is capable of dealing with any 3D geometries. Each scale in the multiscale model is treated as an independent component that exchanges the deformation and average stress information through a scale-linking operator. An arterial bifurcation was simulated using the multiscale model, and the results demonstrated that the mechanical response of the soft tissues is strongly sensitive to the network orientation and fiber-to-fiber interactions.     

Home-based communication system for older adults and their remote family

As in many Latin–American countries, in Mexico many older adults live alone as a result of the migration of one or more of their relatives, mostly to the USA. Thus, not only do they live alone, but they might seldom see these relatives for long periods, even though they often depend on them financially. With the goal of designing appropriate communication technology for seniors and their relatives experiencing this situation, we conducted interviews and evaluated scenarios and prototypes to reveal the practical ways they maintain emotional ties despite the distance. Based on those findings, we envisioned a communication system through which seniors and their relatives can maintain close social ties by sharing information, personal reminiscences and stories. We found that older adults perceived the system as a richer, natural form of communication with their relatives that could facilitate their integration into the networks that currently connect members of their families.     

Two complementary operations inspired by the DNA hairpin formation: Completion and reduction

We consider two complementary operations: Hairpin completion introduced in [D. Cheptea, C. Martin-Vide, V. Mitrana, A new operation on words suggested by DNA biochemistry: Hairpin completion, in: Proc. Transgressive Computing, 2006, pp. 216–228] with motivations coming from DNA biochemistry and hairpin reduction as the inverse operation of the hairpin completion. Both operations are viewed here as formal operations on words and languages. We settle the closure properties of the classes of regular and linear context-free languages under hairpin completion in comparison with hairpin reduction. While the class of linear context-free languages is exactly the weak-code image of the class of the hairpin completion of regular languages, rather surprisingly, the weak-code image of the class of the hairpin completion of linear context-free languages is a class of mildly context-sensitive languages. The closure properties with respect to the hairpin reduction of some time and space complexity classes are also studied. We show that the factors found in the general cases are not necessary for regular and context-free languages. This part of the paper completes the results given in the earlier paper, where a similar investigation was made for hairpin completion. Finally, we briefly discuss the iterated variants of these operations.     

Positional solutions of Hamilton-Jacobi equations in control problems for discrete-continuous systems

We develop a canonical global optimality theory based on operating with the set of solutions for the Hamilton-Jacobi inequalities that parametrically depend on the initial (or final) position. These solutions, called positional L-functions (of Lyapunov type), naturally arise in the studies of control problems for discrete-continuous (hybrid, impulse) systems; an important prototype of such problems are classical optimal control problems with general end constraints on the trajectory. We analyze sufficient optimality conditions with this new class of L-functions and invert the maximum principle into a sufficient condition for nonlinear problems of optimal impulse control.     

Controlled linear perturbation

We present an algorithmic solution to the robustness problem in computational geometry, called controlled linear perturbation, and demonstrate it on Minkowski sums of polyhedra. The robustness problem is how to implement real RAM algorithms accurately and efficiently using computer arithmetic. Approximate computation in floating point arithmetic is efficient but can assign incorrect signs to geometric predicates, which can cause combinatorial errors in the algorithm output. We make approximate computation accurate by performing small input perturbations, which we compute using differential calculus. This strategy supports fast, accurate Minkowski sum computation. The only prior robust implementation uses a less efficient algorithm, requires exact algebraic computation, and is far slower based on our extensive testing.     

Designing a Controller for a Multi-Train Multi-Track System

The use of formal methods for analyzing and synthesizing a controller for a multi-train multi-track railway system is discussed. The research was motivated by a case study involving the Bay Area Rapid Transit (BART) system. The overall goal is to design a train acceleration control function that enables trains to be safely placed but also increases system throughput. The use of a modeling language for specifying safety properties and a control function is illustrated. The program transformation methodology supported in the HATS system is employed to generate an efficient implementation from a high-level specification of a controller. This implementation can then be used to simulate the controller behavior, thus further enhancing confidence in the design. Properties of optimization transformations can be verified using an rewrite-rule based induction theorem prover Rewrite Rule Laboratory (RRL).     

Filtered Jitter

Jitter is one popular way of generating samples for stochastic sampling in computer graphics. The Poisson disk distribution better approximates that of the human photomosaic. In this paper we examine the spatial and frequency space behaviour of a number of existing algorithms for generating stochastic samples and propose a new algorithm based on low pass filtering a jittered set of displacements. The distribution is at least as much like that of the human photomosaic as any existing algorithm, while being fast to compute.